pax_global_header00006660000000000000000000000064143216172260014516gustar00rootroot0000000000000052 comment=2196f026deb4e5f0bd22071e8523f1f6e4459dd0 stable-diffusion-webui/000077500000000000000000000000001432161722600154315ustar00rootroot00000000000000stable-diffusion-webui/.github/000077500000000000000000000000001432161722600167715ustar00rootroot00000000000000stable-diffusion-webui/.github/ISSUE_TEMPLATE/000077500000000000000000000000001432161722600211545ustar00rootroot00000000000000stable-diffusion-webui/.github/ISSUE_TEMPLATE/bug_report.md000066400000000000000000000015021432161722600236440ustar00rootroot00000000000000--- name: Bug report about: Create a report to help us improve title: '' labels: bug-report assignees: '' --- **Describe the bug** A clear and concise description of what the bug is. **To Reproduce** Steps to reproduce the behavior: 1. Go to '...' 2. Click on '....' 3. Scroll down to '....' 4. See error **Expected behavior** A clear and concise description of what you expected to happen. **Screenshots** If applicable, add screenshots to help explain your problem. **Desktop (please complete the following information):** - OS: [e.g. Windows, Linux] - Browser [e.g. chrome, safari] - Commit revision [looks like this: e68484500f76a33ba477d5a99340ab30451e557b; can be seen when launching webui.bat, or obtained manually by running `git rev-parse HEAD`] **Additional context** Add any other context about the problem here. stable-diffusion-webui/.github/ISSUE_TEMPLATE/feature_request.md000066400000000000000000000011351432161722600247010ustar00rootroot00000000000000--- name: Feature request about: Suggest an idea for this project title: '' labels: 'suggestion' assignees: '' --- **Is your feature request related to a problem? Please describe.** A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] **Describe the solution you'd like** A clear and concise description of what you want to happen. **Describe alternatives you've considered** A clear and concise description of any alternative solutions or features you've considered. **Additional context** Add any other context or screenshots about the feature request here. stable-diffusion-webui/.github/PULL_REQUEST_TEMPLATE/000077500000000000000000000000001432161722600222505ustar00rootroot00000000000000stable-diffusion-webui/.github/PULL_REQUEST_TEMPLATE/pull_request_template.md000066400000000000000000000032361432161722600272150ustar00rootroot00000000000000# Please read the [contributing wiki page](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing) before submitting a pull request! If you have a large change, pay special attention to this paragraph: > Before making changes, if you think that your feature will result in more than 100 lines changing, find me and talk to me about the feature you are proposing. It pains me to reject the hard work someone else did, but I won't add everything to the repo, and it's better if the rejection happens before you have to waste time working on the feature. Otherwise, after making sure you're following the rules described in wiki page, remove this section and continue on. **Describe what this pull request is trying to achieve.** A clear and concise description of what you're trying to accomplish with this, so your intent doesn't have to be extracted from your code. **Additional notes and description of your changes** More technical discussion about your changes go here, plus anything that a maintainer might have to specifically take a look at, or be wary of. **Environment this was tested in** List the environment you have developed / tested this on. As per the contributing page, changes should be able to work on Windows out of the box. - OS: [e.g. Windows, Linux] - Browser [e.g. chrome, safari] - Graphics card [e.g. NVIDIA RTX 2080 8GB, AMD RX 6600 8GB] **Screenshots or videos of your changes** If applicable, screenshots or a video showing off your changes. If it edits an existing UI, it should ideally contain a comparison of what used to be there, before your changes were made. This is **required** for anything that touches the user interface.stable-diffusion-webui/.gitignore000066400000000000000000000005251432161722600174230ustar00rootroot00000000000000__pycache__ *.ckpt *.pth /ESRGAN/* /SwinIR/* /repositories /venv /tmp /model.ckpt /models/**/* /GFPGANv1.3.pth /gfpgan/weights/*.pth /ui-config.json /outputs /config.json /log /webui.settings.bat /embeddings /styles.csv /styles.csv.bak /webui-user.bat /webui-user.sh /interrogate /user.css /.idea notification.mp3 /SwinIR /textual_inversion stable-diffusion-webui/CODEOWNERS000066400000000000000000000000301432161722600170150ustar00rootroot00000000000000* @AUTOMATIC1111 stable-diffusion-webui/README.md000066400000000000000000000204711432161722600167140ustar00rootroot00000000000000# Stable Diffusion web UI A browser interface based on Gradio library for Stable Diffusion. ![](txt2img_Screenshot.png) Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Scripts) wiki page for extra scripts developed by users. ## Features [Detailed feature showcase with images](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features): - Original txt2img and img2img modes - One click install and run script (but you still must install python and git) - Outpainting - Inpainting - Prompt Matrix - Stable Diffusion Upscale - Attention, specify parts of text that the model should pay more attention to - a man in a ((tuxedo)) - will pay more attention to tuxedo - a man in a (tuxedo:1.21) - alternative syntax - select text and press ctrl+up or ctrl+down to automatically adjust attention to selected text (code contributed by anonymous user) - Loopback, run img2img processing multiple times - X/Y plot, a way to draw a 2 dimensional plot of images with different parameters - Textual Inversion - have as many embeddings as you want and use any names you like for them - use multiple embeddings with different numbers of vectors per token - works with half precision floating point numbers - Extras tab with: - GFPGAN, neural network that fixes faces - CodeFormer, face restoration tool as an alternative to GFPGAN - RealESRGAN, neural network upscaler - ESRGAN, neural network upscaler with a lot of third party models - SwinIR and Swin2SR([see here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/2092)), neural network upscalers - LDSR, Latent diffusion super resolution upscaling - Resizing aspect ratio options - Sampling method selection - Adjust sampler eta values (noise multiplier) - More advanced noise setting options - Interrupt processing at any time - 4GB video card support (also reports of 2GB working) - Correct seeds for batches - Prompt length validation - get length of prompt in tokens as you type - get a warning after generation if some text was truncated - Generation parameters - parameters you used to generate images are saved with that image - in PNG chunks for PNG, in EXIF for JPEG - can drag the image to PNG info tab to restore generation parameters and automatically copy them into UI - can be disabled in settings - Settings page - Running arbitrary python code from UI (must run with --allow-code to enable) - Mouseover hints for most UI elements - Possible to change defaults/mix/max/step values for UI elements via text config - Random artist button - Tiling support, a checkbox to create images that can be tiled like textures - Progress bar and live image generation preview - Negative prompt, an extra text field that allows you to list what you don't want to see in generated image - Styles, a way to save part of prompt and easily apply them via dropdown later - Variations, a way to generate same image but with tiny differences - Seed resizing, a way to generate same image but at slightly different resolution - CLIP interrogator, a button that tries to guess prompt from an image - Prompt Editing, a way to change prompt mid-generation, say to start making a watermelon and switch to anime girl midway - Batch Processing, process a group of files using img2img - Img2img Alternative - Highres Fix, a convenience option to produce high resolution pictures in one click without usual distortions - Reloading checkpoints on the fly - Checkpoint Merger, a tab that allows you to merge two checkpoints into one - [Custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Scripts) with many extensions from community - [Composable-Diffusion](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/), a way to use multiple prompts at once - separate prompts using uppercase `AND` - also supports weights for prompts: `a cat :1.2 AND a dog AND a penguin :2.2` - No token limit for prompts (original stable diffusion lets you use up to 75 tokens) - DeepDanbooru integration, creates danbooru style tags for anime prompts (add --deepdanbooru to commandline args) - [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add --xformers to commandline args) ## Installation and Running Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs. Alternatively, use Google Colab: - [Colab, maintained by Akaibu](https://colab.research.google.com/drive/1kw3egmSn-KgWsikYvOMjJkVDsPLjEMzl) - [Colab, original by me, outdated](https://colab.research.google.com/drive/1Iy-xW9t1-OQWhb0hNxueGij8phCyluOh). ### Automatic Installation on Windows 1. Install [Python 3.10.6](https://www.python.org/downloads/windows/), checking "Add Python to PATH" 2. Install [git](https://git-scm.com/download/win). 3. Download the stable-diffusion-webui repository, for example by running `git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git`. 4. Place `model.ckpt` in the `models` directory (see [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) for where to get it). 5. _*(Optional)*_ Place `GFPGANv1.4.pth` in the base directory, alongside `webui.py` (see [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) for where to get it). 6. Run `webui-user.bat` from Windows Explorer as normal, non-administrator, user. ### Automatic Installation on Linux 1. Install the dependencies: ```bash # Debian-based: sudo apt install wget git python3 python3-venv # Red Hat-based: sudo dnf install wget git python3 # Arch-based: sudo pacman -S wget git python3 ``` 2. To install in `/home/$(whoami)/stable-diffusion-webui/`, run: ```bash bash <(wget -qO- https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh) ``` ### Installation on Apple Silicon Find the instructions [here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Installation-on-Apple-Silicon). ## Contributing Here's how to add code to this repo: [Contributing](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing) ## Documentation The documentation was moved from this README over to the project's [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki). ## Credits - Stable Diffusion - https://github.com/CompVis/stable-diffusion, https://github.com/CompVis/taming-transformers - k-diffusion - https://github.com/crowsonkb/k-diffusion.git - GFPGAN - https://github.com/TencentARC/GFPGAN.git - CodeFormer - https://github.com/sczhou/CodeFormer - ESRGAN - https://github.com/xinntao/ESRGAN - SwinIR - https://github.com/JingyunLiang/SwinIR - Swin2SR - https://github.com/mv-lab/swin2sr - LDSR - https://github.com/Hafiidz/latent-diffusion - Ideas for optimizations - https://github.com/basujindal/stable-diffusion - Doggettx - Cross Attention layer optimization - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing. - InvokeAI, lstein - Cross Attention layer optimization - https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion) - Rinon Gal - Textual Inversion - https://github.com/rinongal/textual_inversion (we're not using his code, but we are using his ideas). - Idea for SD upscale - https://github.com/jquesnelle/txt2imghd - Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot - CLIP interrogator idea and borrowing some code - https://github.com/pharmapsychotic/clip-interrogator - Idea for Composable Diffusion - https://github.com/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch - xformers - https://github.com/facebookresearch/xformers - DeepDanbooru - interrogator for anime diffusers https://github.com/KichangKim/DeepDanbooru - Initial Gradio script - posted on 4chan by an Anonymous user. 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Lovecraft,0.6039597,digipa-high-impact Andrée Ruellan,0.60395086,fineart Ralph McQuarrie,0.60380936,fineart Mead Schaeffer,0.6036558,fineart Henri-Julien Dumont,0.571257,fineart Kieron Gillen,0.6035093,fineart Maginel Wright Enright Barney,0.6034306,nudity Vincent Di Fate,0.6034131,fineart Briton Rivière,0.6032918,fineart Hajime Sorayama,0.60325956,nudity Béla Czóbel,0.6031023,fineart Edmund Blampied,0.603072,fineart E. Simms Campbell,0.6030443,fineart Hisui Sugiura,0.603034,fareast Alan Davis,0.6029676,fineart Glen Keane,0.60287905,cartoon Frank Holl,0.6027312,fineart Abbott Fuller Graves,0.6025608,fineart Albert Servaes,0.60250103,black-white Hovsep Pushman,0.5937487,fineart Brian M. Viveros,0.60233414,fineart Charles Fremont Conner,0.6023278,fineart Francesco Furini,0.6022654,digipa-high-impact Camille-Pierre Pambu Bodo,0.60191673,fineart Yasushi Nirasawa,0.6016714,nudity Charles Uzzell-Edwards,0.6014683,fineart Abram Efimovich Arkhipov,0.60128385,fineart Hedda Sterne,0.6011857,digipa-high-impact Ben Aronson,0.6011548,fineart Frank Frazetta,0.551121,nudity Elizabeth Durack,0.6010842,fineart Ian Miller,0.42153555,fareast Charlie Bowater,0.4410439,special Michael Carson,0.60039437,fineart Walter Langley,0.6002273,fineart Cornelis Anthonisz,0.6001956,fineart Dorothy Elizabeth Bradford,0.6001929,fineart J.C. Leyendecker,0.5791972,fineart Willem van Haecht,0.59990716,fineart Anna and Elena Balbusso,0.59955937,digipa-low-impact Harrison Fisher,0.59952044,fineart Bill Medcalf,0.59950054,fineart Edward Arthur Walton,0.59945667,fineart Alois Arnegger,0.5991994,fineart Ray Caesar,0.59902894,digipa-high-impact Karen Wallis,0.5990094,fineart Emmanuel Shiu,0.51082766,digipa-med-impact Thomas Struth,0.5988324,digipa-high-impact Barbara Longhi,0.5985706,fineart Richard Deacon,0.59851056,fineart Constantin Hansen,0.5984213,fineart Harold Shapinsky,0.5984175,fineart George Dionysus Ehret,0.5983857,fineart Doug Wildey,0.5983639,digipa-high-impact Fernand Toussaint,0.5982694,fineart Horatio Nelson Poole,0.5982614,fineart Caesar van Everdingen,0.5981566,fineart Eva Gonzalès,0.5981396,fineart Franz Vohwinkel,0.5448179,fineart Margaret Mee,0.5979592,fineart Francis Focer Brown,0.59779185,fineart Henry Moore,0.59767926,nudity Scott Listfield,0.58795893,fineart Nikolai Ge,0.5973643,fineart Jacek Yerka,0.58198756,fineart Margaret Brundage,0.5969077,fineart JC Leyendecker,0.5620243,fineart Ben Templesmith,0.5498991,digipa-high-impact Armin Hansen,0.59669334,anime Jean-Louis Prevost,0.5966897,fineart Daphne Allen,0.59666026,fineart Franz Karl Basler-Kopp,0.59663445,fineart "Henry Ives Cobb, Jr.",0.596385,fineart Michael Sowa,0.546285,fineart Anna Füssli,0.59600973,fineart György Rózsahegyi,0.59580946,fineart Luis Royo,0.59566617,fineart Émile Gallé,0.5955559,fineart Antonio Mora,0.5334297,digipa-high-impact Edward P. Beard Jr.,0.59543866,fineart Jessica Rossier,0.54958373,special André Thomkins,0.5343785,digipa-high-impact David Macbeth Sutherland,0.5949968,fineart Charles Liu,0.5949787,digipa-high-impact Edi Rama,0.5949226,digipa-high-impact Jacques Le Moyne,0.5948843,fineart Egbert van der Poel,0.59488285,fineart Georg Jensen,0.594782,digipa-high-impact Anne Sudworth,0.5947539,fineart Jan Pietersz Saenredam,0.59472525,fineart Henryk Stażewski,0.5945748,fineart André François,0.58402044,fineart Alexander Runciman,0.5944449,digipa-high-impact Thomas Kinkade,0.594391,fineart Robert Williams,0.5567989,digipa-high-impact George Gardner Symons,0.57431924,fineart D. Alexander Gregory,0.5334464,fineart Gerald Brom,0.52473724,fineart Robert Hagan,0.59406,fineart Ernest Crichlow,0.5940588,fineart Viviane Sassen,0.5939927,digipa-high-impact Enrique Simonet,0.5937546,fineart Esther Blaikie MacKinnon,0.593747,digipa-high-impact Jeff Kinney,0.59372896,scribbles Igor Morski,0.5936732,digipa-high-impact John Currin,0.5936216,fineart Bob Ringwood,0.5935273,digipa-high-impact Jordan Grimmer,0.44948143,digipa-low-impact François Barraud,0.5933471,fineart Helen Binyon,0.59331006,digipa-high-impact Brenda Chamberlain,0.5932333,fineart Candido Bido,0.59310603,fineart Abraham Storck,0.5929502,fineart Raphael,0.59278333,fineart Larry Sultan,0.59273386,digipa-high-impact Agostino Tassi,0.59265685,fineart Alexander V. Kuprin,0.5925917,fineart Frans Koppelaar,0.5658725,fineart Richard Corben,0.59251785,fineart David Gilmour Blythe,0.5924247,digipa-high-impact František Kaván,0.5924211,fineart Rob Liefeld,0.5921167,fineart Ernő Rubik,0.5920297,fineart Byeon Sang-byeok,0.59200096,fareast Johfra Bosschart,0.5919376,fineart Emil Lindenfeld,0.5761086,fineart Howard Mehring,0.5917471,fineart Gwenda Morgan,0.5915571,digipa-high-impact Henry Asencio,0.5915404,fineart "George Barret, Sr.",0.5914306,fineart Andrew Ferez,0.5911011,fineart Ed Brubaker,0.5910869,digipa-high-impact George Reid,0.59095883,digipa-high-impact Derek Gores,0.51769906,digipa-med-impact Charles Rollier,0.5539186,fineart Terry Oakes,0.590443,fineart Thomas Blackshear,0.5078616,fineart Albert Benois,0.5902705,nudity Krenz Cushart,0.59026587,special Jeff Koons,0.5902637,digipa-high-impact Akihiko Yoshida,0.5901294,special Anja Percival,0.45039332,digipa-low-impact Eduard von Steinle,0.59008586,fineart Alex Russell Flint,0.5900352,digipa-high-impact Edward Okuń,0.5897297,fineart Emma Lampert Cooper,0.5894849,fineart Stuart Haygarth,0.58132994,digipa-high-impact George French Angas,0.5434376,fineart Edmund F. Ward,0.5892848,fineart Eleanor Vere Boyle,0.58925456,digipa-high-impact Evelyn Cheston,0.58924586,fineart Edwin Dickinson,0.58921975,digipa-high-impact Christophe Vacher,0.47325426,fineart Anne Dewailly,0.58905107,fineart Gertrude Greene,0.5862596,digipa-high-impact Boris Groh,0.5888809,digipa-high-impact Douglas Smith,0.588804,digipa-high-impact Ian Hamilton Finlay,0.5887713,fineart Derek Jarman,0.5887292,digipa-high-impact Archibald Thorburn,0.5882001,fineart Gillis d'Hondecoeter,0.58813053,fineart I Ketut Soki,0.58801544,digipa-high-impact Alex Schomburg,0.46614102,digipa-low-impact Bastien L. Deharme,0.583349,special František Jakub Prokyš,0.58782333,fineart Jesper Ejsing,0.58782053,fineart Odd Nerdrum,0.53551745,digipa-high-impact Tom Lovell,0.5877577,fineart Ayami Kojima,0.5877416,fineart Peter Sculthorpe,0.5875696,fineart Bernard D’Andrea,0.5874042,fineart Denis Eden,0.58739066,digipa-high-impact Alfons Walde,0.58728385,fineart Jovana Rikalo,0.47006977,digipa-low-impact Franklin Booth,0.5870834,fineart Mat Collishaw,0.5870676,digipa-high-impact Joseph Lorusso,0.586858,fineart Helen Stevenson,0.454647,digipa-low-impact Delaunay,0.58657396,fineart H.R. Millar,0.58655745,fineart E. Charlton Fortune,0.586376,fineart Alson Skinner Clark,0.58631575,fineart Stan And Jan Berenstain,0.5862361,digipa-high-impact Howard Lyon,0.5862271,fineart John Blanche,0.586182,fineart Bernardo Cavallino,0.5858575,fineart Tomasz Alen Kopera,0.5216588,fineart Peter Gric,0.58583695,fineart Guo Pei,0.5857794,fareast James Turrell,0.5853901,digipa-high-impact Alexandr Averin,0.58533764,fineart Bertalan Székely,0.5548113,digipa-high-impact Brothers Hildebrandt,0.5850233,fineart Ed Roth,0.5849769,digipa-high-impact Enki Bilal,0.58492255,fineart Alan Lee,0.5848701,fineart Charles H. Woodbury,0.5848688,fineart André Charles Biéler,0.5847876,fineart Annie Rose Laing,0.5597829,fineart Matt Fraction,0.58463776,cartoon Charles Alston,0.58453286,fineart Frank Xavier Leyendecker,0.545465,fineart Alfred Richard Gurrey,0.584306,fineart Dan Mumford,0.5843051,cartoon Francisco Martín,0.5842005,fineart Alvaro Siza,0.58406967,digipa-high-impact Frank J. Girardin,0.5839858,fineart Henry Carr,0.58397424,digipa-high-impact Charles Furneaux,0.58394694,fineart Daniel F. Gerhartz,0.58389103,fineart Gilberto Soren Zaragoza,0.5448442,fineart Bart Sears,0.5838427,cartoon Allison Bechdel,0.58383805,digipa-high-impact Frank O'Meara,0.5837992,fineart Charles Codman,0.5836579,fineart Francisco Zúñiga,0.58359766,fineart Vladimir Kush,0.49075457,fineart Arnold Mesches,0.5834257,fineart Frank McKelvey,0.5831641,fineart Allen Butler Talcott,0.5830911,fineart Eric Zener,0.58300316,fineart Noah Bradley,0.44176096,digipa-low-impact Robert Childress,0.58289623,fineart Frances C. Fairman,0.5827239,fineart Kathryn Morris Trotter,0.465856,digipa-low-impact Everett Raymond Kinstler,0.5824819,fineart Edward Mitchell Bannister,0.5804899,fineart "George Barret, Jr.",0.5823128,fineart Greg Hildebrandt,0.4271311,fineart Anka Zhuravleva,0.5822078,digipa-high-impact Rolf Armstrong,0.58217514,fineart Eric Wallis,0.58191466,fineart Clemens Ascher,0.5480207,digipa-high-impact Hugo Kārlis Grotuss,0.5818766,fineart Albert Paris Gütersloh,0.5817827,fineart Hilda May Gordon,0.5817449,fineart Hendrik Martenszoon Sorgh,0.5817126,fineart Pipilotti Rist,0.5816868,digipa-high-impact Hiroyuki Tajima,0.5816242,fareast Igor Zenin,0.58159757,digipa-high-impact Genevieve Springston Lynch,0.4979099,digipa-med-impact Dan Witz,0.44476372,fineart David Roberts,0.5255326,fineart Frieke Janssens,0.5706969,digipa-high-impact Arnold Schoenberg,0.56520367,fineart Inoue Naohisa,0.5809933,fareast Elfriede Lohse-Wächtler,0.58097905,fineart Alex Ross,0.42460668,digipa-low-impact Robert Irwin,0.58078,c Charles Angrand,0.58077514,fineart Anne Nasmyth,0.54221964,fineart Henri Bellechose,0.5773891,fineart De Hirsh Margules,0.58059025,fineart Hiromitsu Takahashi,0.5805599,fareast Ilya Kuvshinov,0.5805521,special Cassius Marcellus Coolidge,0.5805516,c Dorothy Burroughes,0.5804835,fineart Emanuel de Witte,0.58027405,fineart George Herbert Baker,0.5799624,digipa-high-impact Cheng Zhengkui,0.57990086,fareast Bernard Fleetwood-Walker,0.57987773,digipa-high-impact Philippe Parreno,0.57985014,digipa-high-impact Thornton Oakley,0.57969713,fineart Greg Rutkowski,0.5203395,special Ike no Taiga,0.5795857,anime Eduardo Lefebvre Scovell,0.5795808,fineart Adolfo Müller-Ury,0.57944727,fineart Patrick Woodroffe,0.5228063,fineart Wim Crouwel,0.57933235,digipa-high-impact Colijn de Coter,0.5792779,fineart François Boquet,0.57924724,fineart Gerbrand van den Eeckhout,0.57897866,fineart Eugenio Granell,0.5392264,fineart Kuang Hong,0.5782304,digipa-high-impact Justin Gerard,0.46685404,fineart Tokujin Yoshioka,0.5779153,digipa-high-impact Alan Bean,0.57788515,fineart Ernest Biéler,0.5778079,fineart Martin Deschambault,0.44401115,digipa-low-impact Anna Boch,0.577735,fineart Jack Davis,0.5775291,fineart Félix Labisse,0.5775142,fineart Greg Simkins,0.5679761,fineart David Lynch,0.57751054,digipa-low-impact Eizō Katō,0.5774127,digipa-high-impact Grethe Jürgens,0.5773412,digipa-high-impact Heinrich Bichler,0.5770147,fineart Barbara Nasmyth,0.5446056,fineart Domenico Induno,0.5583946,fineart Gustave Baumann,0.5607866,fineart Mike Mayhew,0.5765857,cartoon Delmer J. Yoakum,0.576538,fineart Aykut Aydogdu,0.43111503,digipa-low-impact George Barker,0.5763551,fineart Ernő Grünbaum,0.57634187,fineart Eliseu Visconti,0.5763241,fineart Esao Andrews,0.5761547,fineart JennyBird Alcantara,0.49165845,digipa-med-impact Joan Tuset,0.5761051,fineart Angela Barrett,0.55976534,digipa-high-impact Syd Mead,0.5758396,fineart Ignacio Bazan-Lazcano,0.5757512,fineart Franciszek Kostrzewski,0.57570386,fineart Eero Järnefelt,0.57540673,fineart Loretta Lux,0.56217635,digipa-high-impact Gaudi,0.57519895,fineart Charles Gleyre,0.57490873,fineart Antoine Verney-Carron,0.56386137,fineart Albert Edelfelt,0.57466495,fineart Fabian Perez,0.57444525,fineart Kevin Sloan,0.5737548,fineart Stanislav Poltavsky,0.57434607,fineart Abraham Hondius,0.574326,fineart Tadao Ando,0.57429105,fareast Fyodor Slavyansky,0.49796474,digipa-med-impact David Brewster,0.57385933,digipa-high-impact Cliff Chiang,0.57375133,digipa-high-impact Drew Struzan,0.5317983,digipa-high-impact Henry O. Tanner,0.5736586,fineart Alberto Sughi,0.5736495,fineart Albert J. Welti,0.5736257,fineart Charles Mahoney,0.5735923,digipa-high-impact Exekias,0.5734506,fineart Felipe Seade,0.57342744,digipa-high-impact Henriette Wyeth,0.57330644,digipa-high-impact Harold Sandys Williamson,0.5443646,fineart Eddie Campbell,0.57329535,digipa-high-impact Gao Fenghan,0.5732926,fareast Cynthia Sheppard,0.51099646,fineart Henriette Grindat,0.573179,fineart Yasutomo Oka,0.5731342,fareast Celia Frances Bedford,0.57313216,fineart Les Edwards,0.42068473,fineart Edwin Deakin,0.5031717,fineart Eero Saarinen,0.5725142,digipa-high-impact Franciszek Smuglewicz,0.5722554,fineart Doris Blair,0.57221186,fineart Seb Mckinnon,0.51721895,digipa-med-impact Gregorio Lazzarini,0.57204294,fineart Gerard Sekoto,0.5719927,fineart Francis Ernest Jackson,0.5506009,fineart Simon Birch,0.57171595,digipa-high-impact Bayard Wu,0.57171166,fineart François Clouet,0.57162094,fineart Christopher Wren,0.5715372,fineart Evgeny Lushpin,0.5714827,special Art Green,0.5714495,digipa-high-impact Amy Judd,0.57142305,digipa-high-impact Art Brenner,0.42619684,digipa-low-impact Travis Louie,0.43916368,digipa-low-impact James Jean,0.5457318,digipa-high-impact Ewald Rübsamen,0.57083976,fineart Donato Giancola,0.57052535,fineart Carl Arnold Gonzenbach,0.5703996,fineart Bastien Lecouffe-Deharme,0.5201288,fineart Howard Chandler Christy,0.5702813,nudity Dean Cornwell,0.56977296,fineart Don Maitz,0.4743015,fineart James Montgomery Flagg,0.56974065,fineart Andreas Levers,0.42125136,digipa-low-impact Edgar Schofield Baum,0.56965977,fineart Alan Parry,0.5694952,digipa-high-impact An Zhengwen,0.56942475,fareast Alayna Lemmer,0.48293802,fineart Edward Marshall Boehm,0.5530143,fineart Henri Biva,0.54013556,nudity Fiona Rae,0.4646715,digipa-low-impact Elizabeth Jane Lloyd,0.5688463,digipa-high-impact Franklin Carmichael,0.5687844,digipa-high-impact Dionisius,0.56875896,fineart Edwin Georgi,0.56868523,fineart Jenny Saville,0.5686633,fineart Ernest Hébert,0.56859314,fineart Stephan Martiniere,0.56856346,digipa-high-impact Huang Binhong,0.56841767,fineart August Lemmer,0.5683548,fineart Camille Bouvagne,0.5678048,fineart Olga Skomorokhova,0.39401102,digipa-low-impact Sacha Goldberger,0.5675477,digipa-high-impact Hilda Annetta Walker,0.5675261,digipa-high-impact Harvey Pratt,0.51314723,digipa-med-impact Jean Bourdichon,0.5670543,fineart Noriyoshi Ohrai,0.56690073,fineart Kadir Nelson,0.5669006,n Ilya Ostroukhov,0.5668801,fineart Eugène Brands,0.56681967,fineart Achille Leonardi,0.56674325,fineart Franz Cižek,0.56670356,fineart George Paul Chalmers,0.5665988,digipa-high-impact Serge Marshennikov,0.5665971,digipa-high-impact Mike Worrall,0.56641084,fineart Dirck van Delen,0.5661764,fineart Peter Andrew Jones,0.5661655,fineart Rafael Albuquerque,0.56541103,fineart Daniel Buren,0.5654043,fineart Giuseppe Grisoni,0.5432699,fineart George Fiddes Watt,0.55861616,fineart Stan Lee,0.5651268,digipa-high-impact Dorning Rasbotham,0.56511617,fineart Albert Lynch,0.56497896,fineart Lorenz Hideyoshi,0.56494075,fineart Fenghua Zhong,0.56492203,fareast Caroline Lucy Scott,0.49190843,digipa-med-impact Victoria Crowe,0.5647996,digipa-high-impact Hasegawa Settan,0.5647092,fareast Dennis H. Farber,0.56453323,digipa-high-impact Dick Bickenbach,0.5644289,fineart Art Frahm,0.56439924,fineart Edith Edmonds,0.5643151,fineart Alfred Heber Hutty,0.56419206,fineart Henry Tonks,0.56410825,fineart Peter Howson,0.5640759,fineart Albert Dorne,0.56395364,fineart Arthur Adams,0.5639404,fineart Bernt Tunold,0.56383425,digipa-high-impact Gianluca Foli,0.5637317,digipa-high-impact Vittorio Matteo Corcos,0.5636767,fineart Béla Iványi-Grünwald,0.56355745,nudity Feng Zhu,0.5634973,fineart Sam Kieth,0.47251505,digipa-low-impact Charles Crodel,0.5633834,fineart Elsie Henderson,0.56310076,digipa-high-impact George Earl Ortman,0.56295705,fineart Tari Márk Dávid,0.562937,fineart Betty Merken,0.56281745,digipa-high-impact Cecile Walton,0.46672013,digipa-low-impact Bracha L. Ettinger,0.56237936,fineart Ken Fairclough,0.56230986,digipa-high-impact Phil Koch,0.56224954,digipa-high-impact George Pirie,0.56213045,digipa-high-impact Chad Knight,0.56194013,digipa-high-impact Béla Kondor,0.5427164,digipa-high-impact Barclay Shaw,0.53689134,digipa-high-impact Tim Hildebrandt,0.47194147,fineart Hermann Rüdisühli,0.56104004,digipa-high-impact Ian McQue,0.5342066,digipa-high-impact Yanjun Cheng,0.5607171,fineart Heinrich Hofmann,0.56060636,fineart Henry Raleigh,0.5605958,fineart Ernest Buckmaster,0.5605704,fineart Charles Ricketts,0.56055415,fineart Juergen Teller,0.56051147,digipa-high-impact Auguste Mambour,0.5604873,fineart Sean Yoro,0.5601486,digipa-high-impact Sheilah Beckett,0.55995446,digipa-high-impact Eugene Tertychnyi,0.5598978,fineart Dr. Seuss,0.5597466,c Adolf Wölfli,0.5372333,digipa-high-impact Enrique Tábara,0.559323,fineart Dionisio Baixeras Verdaguer,0.5590695,fineart Aleksander Gierymski,0.5590013,fineart Augustus Dunbier,0.55872476,fineart Adolf Born,0.55848217,fineart Chris Turnham,0.5584234,digipa-high-impact James C Christensen,0.55837405,fineart Daphne Fedarb,0.5582459,digipa-high-impact Andre Kohn,0.5581832,special Ron Mueck,0.5581811,nudity Glenn Fabry,0.55786383,fineart Elizabeth Polunin,0.5578102,digipa-high-impact Charles S. Kaelin,0.5577954,fineart Arthur Radebaugh,0.5577016,fineart Ai Yazawa,0.55768114,fareast Charles Roka,0.55762553,fineart Ai Weiwei,0.5576034,digipa-high-impact Dorothy Bradford,0.55760014,digipa-high-impact Alfred Leslie,0.557555,fineart Heinrich Herzig,0.5574423,fineart Eliot Hodgkin,0.55740607,digipa-high-impact Albert Kotin,0.55737317,fineart Carlo Carlone,0.55729353,fineart Chen Rong,0.5571221,fineart Ikuo Hirayama,0.5570225,digipa-high-impact Edward Corbett,0.55701995,nudity Eugeniusz Żak,0.556925,nudity Ettore Tito,0.556875,fineart Helene Knoop,0.5567731,fineart Amanda Sage,0.37731662,fareast Annick Bouvattier,0.54647046,fineart Harvey Dunn,0.55663586,fineart Hans Sandreuter,0.5562575,digipa-high-impact Ruan Jia,0.5398549,special Anton Räderscheidt,0.55618906,fineart Tyler Shields,0.4081434,digipa-low-impact Darek Zabrocki,0.49975997,digipa-med-impact Frank Montague Moore,0.5556432,fineart Greg Staples,0.5555332,fineart Endre Bálint,0.5553731,fineart Augustus Vincent Tack,0.5136602,fineart Marc Simonetti,0.48602036,fineart Carlo Randanini,0.55493265,digipa-high-impact Diego Dayer,0.5549119,fineart Kelly Freas,0.55476534,fineart Thomas Saliot,0.5139967,digipa-med-impact Gijsbert d'Hondecoeter,0.55455256,fineart Walter Kim,0.554521,digipa-high-impact Francesco Cozza,0.5155097,digipa-med-impact Bill Watterson,0.5542879,digipa-high-impact Mark Keathley,0.4824056,fineart Béni Ferenczy,0.55405354,digipa-high-impact Amadou Opa Bathily,0.5536976,n Giuseppe Antonio Petrini,0.55340284,fineart Enzo Cucchi,0.55331933,digipa-high-impact Adolf Schrödter,0.55316544,fineart George Benjamin Luks,0.548566,fineart Glenys Cour,0.55304,digipa-high-impact Andrew Robertson,0.5529603,digipa-high-impact Claude Rogers,0.55272067,digipa-high-impact Alexandre Antigna,0.5526737,fineart Aimé Barraud,0.55265915,digipa-high-impact György Vastagh,0.55258965,fineart Bruce Nauman,0.55257386,digipa-high-impact Benjamin Block,0.55251944,digipa-high-impact Gonzalo Endara Crow,0.552346,digipa-high-impact Dirck de Bray,0.55221736,fineart Gerald Kelley,0.5521059,digipa-high-impact Dave Gibbons,0.5520954,digipa-high-impact Béla Nagy Abodi,0.5520624,digipa-high-impact Faith 47,0.5517006,digipa-high-impact Anna Razumovskaya,0.5229187,digipa-med-impact Archibald Robertson,0.55129635,digipa-high-impact Louise Dahl-Wolfe,0.55120385,digipa-high-impact Simon Bisley,0.55119276,digipa-high-impact Eric Fischl,0.55107886,fineart Hu Zaobin,0.5510481,fareast Béla Pállik,0.5507963,digipa-high-impact Eugene J. Martin,0.55078864,fineart Friedrich Gauermann,0.55063415,fineart Fritz Baumann,0.5341434,fineart Michal Lisowski,0.5505639,fineart Paolo Roversi,0.5503342,digipa-high-impact Andrew Atroshenko,0.55009747,fineart Gyula Derkovits,0.5500315,fineart Hugh Adam Crawford,0.55000615,digipa-high-impact Béla Apáti Abkarovics,0.5499799,digipa-high-impact Paul Chadeisson,0.389151,digipa-low-impact Aurél Bernáth,0.54968774,fineart Albert Henry Krehbiel,0.54952574,fineart Piet Hein Eek,0.54918796,digipa-high-impact Yoshitaka Amano,0.5491855,fareast Antonio Rotta,0.54909515,fineart Józef Mehoffer,0.50760424,fineart Donald Sherwood,0.5490415,digipa-high-impact Catrin G Grosse,0.5489286,digipa-high-impact Arthur Webster Emerson,0.5478842,fineart Incarcerated Jerkfaces,0.5488423,digipa-high-impact Emanuel Büchel,0.5487217,fineart Andrew Loomis,0.54854584,fineart Charles Hopkinson,0.54853606,fineart Gabor Szikszai,0.5485203,digipa-high-impact Archibald Standish Hartrick,0.54850936,digipa-high-impact Aleksander Orłowski,0.546705,nudity Hans Hinterreiter,0.5483628,fineart Fred Williams,0.54544824,fineart Fred A. Precht,0.5481606,fineart Camille Souter,0.5213742,fineart Emil Fuchs,0.54807395,fineart Francesco Bonsignori,0.5478936,fineart H. R. (Hans Ruedi) Giger,0.547799,fineart Harriet Zeitlin,0.5477388,digipa-high-impact Christian Jane Fergusson,0.5396168,fineart Edward Kemble,0.5476892,fineart Bernard Aubertin,0.5475396,fineart Augustyn Mirys,0.5474162,fineart Alejandro Burdisio,0.47482288,special Erin Hanson,0.4343264,digipa-low-impact Amalia Lindegren,0.5471987,digipa-high-impact Alberto Seveso,0.47735062,fineart Bartholomeus Strobel,0.54703736,fineart Jim Davis,0.54703003,digipa-high-impact Antony Gormley,0.54696125,digipa-high-impact Charles Marion Russell,0.54696095,fineart George B. Sutherland,0.5467901,fineart Almada Negreiros,0.54670584,fineart Edward Armitage,0.54358315,fineart Bruno Walpoth,0.546167,digipa-high-impact Richard Hamilton,0.5461275,nudity Charles Harold Davis,0.5460415,digipa-high-impact Fernand Verhaegen,0.54601514,fineart Bernard Meninsky,0.5302034,digipa-high-impact Fede Galizia,0.5456873,digipa-high-impact Alfred Kelsner,0.5455753,nudity Fritz Puempin,0.5452847,fineart Alfred Charles Parker,0.54521024,fineart Ahmed Yacoubi,0.544767,digipa-high-impact Arthur B. Carles,0.54447794,fineart Alice Prin,0.54435575,digipa-high-impact Carl Gustaf Pilo,0.5443212,digipa-high-impact Ross Tran,0.5259248,special Hideyuki Kikuchi,0.544193,fareast Art Fitzpatrick,0.49847245,fineart Cherryl Fountain,0.5440454,fineart Skottie Young,0.5440119,cartoon NC Wyeth,0.54382974,digipa-high-impact Rudolf Freund,0.5437342,fineart Mort Kunstler,0.5433619,digipa-high-impact Ben Goossens,0.53002644,digipa-high-impact Andreas Rocha,0.49621177,special Gérard Ernest Schneider,0.5429964,fineart Francesco Filippini,0.5429598,digipa-high-impact Alejandro Jodorowsky,0.5429065,digipa-high-impact Friedrich Traffelet,0.5428817,fineart Honor C. Appleton,0.5428735,digipa-high-impact Jason A. Engle,0.542821,fineart Henry Otto Wix,0.54271996,fineart Gregory Manchess,0.54270375,fineart Ann Stookey,0.54269934,digipa-high-impact Henryk Rodakowski,0.542589,fineart Albert Welti,0.5425134,digipa-high-impact Gerard Houckgeest,0.5424413,digipa-high-impact Dorothy Hood,0.54226196,digipa-high-impact Frank Schoonover,0.51056194,fineart Erlund Hudson,0.5422107,digipa-high-impact Alexander Litovchenko,0.54210097,fineart Sakai Hōitsu,0.5420294,digipa-high-impact Benito Quinquela Martín,0.54194224,fineart David Watson Stevenson,0.54191554,fineart Ann Thetis Blacker,0.5416629,digipa-high-impact Frank DuMond,0.51004076,digipa-med-impact David Dougal Williams,0.5410126,digipa-high-impact Robert Mcginnis,0.54098356,fineart Ernest Briggs,0.5408636,fineart Ferenc Joachim,0.5408625,fineart Carlos Saenz de Tejada,0.47332364,digipa-low-impact David Burton-Richardson,0.49659324,digipa-med-impact Ernest Heber Thompson,0.54039246,digipa-high-impact Albert Bertelsen,0.54038215,nudity Giorgio Giulio Clovio,0.5403708,fineart Eugene Leroy,0.54019785,digipa-high-impact Anna Findlay,0.54018176,digipa-high-impact Roy Gjertson,0.54012,digipa-high-impact Charmion von Wiegand,0.5400893,fineart Arnold Bronckhorst,0.526247,fineart Boris Vallejo,0.487253,fineart Adélaïde Victoire Hall,0.539939,fineart Earl Norem,0.5398575,fineart Sanford Kossin,0.53977877,digipa-high-impact Aert de Gelder,0.519166,digipa-med-impact Carl Eugen Keel,0.539739,digipa-high-impact Francis Bourgeois,0.5397272,digipa-high-impact Bojan Jevtic,0.41141546,fineart Edward Avedisian,0.5393925,fineart Gao Xiang,0.5392419,fareast Charles Hinman,0.53911865,digipa-high-impact Frits Van den Berghe,0.53896487,fineart Carlo Martini,0.5384833,digipa-high-impact Elina Karimova,0.5384318,digipa-high-impact Anto Carte,0.4708289,digipa-low-impact Andrey Yefimovich Martynov,0.537721,fineart Frances Jetter,0.5376904,fineart Yuri Ivanovich Pimenov,0.5342793,fineart Gaston Anglade,0.537608,digipa-high-impact Albert Swinden,0.5375844,fineart Bob Byerley,0.5375774,fineart A.B. Frost,0.5375025,fineart Jaya Suberg,0.5372893,digipa-high-impact Josh Keyes,0.53654516,digipa-high-impact Juliana Huxtable,0.5364195,n Everett Warner,0.53641814,digipa-high-impact Hugh Kretschmer,0.45171157,digipa-low-impact Arnold Blanch,0.535774,fineart Ryan McGinley,0.53572595,digipa-high-impact Alfons Karpiński,0.53564656,fineart George Aleef,0.5355317,digipa-high-impact Hal Foster,0.5351446,fineart Stuart Immonen,0.53501946,digipa-high-impact Craig Thompson,0.5346844,digipa-high-impact Bartolomeo Vivarini,0.53465015,fineart Hermann Feierabend,0.5346168,digipa-high-impact Antonio Donghi,0.4610982,digipa-low-impact Adonna Khare,0.4858036,digipa-med-impact James Stokoe,0.5015107,digipa-med-impact Art & Language,0.5341332,digipa-high-impact Agustín Fernández,0.53403986,fineart Germán Londoño,0.5338712,fineart Emmanuelle Moureaux,0.5335641,digipa-high-impact Conrad Marca-Relli,0.5148334,digipa-med-impact Gyula Batthyány,0.5332407,fineart Francesco Raibolini,0.53314835,fineart Apelles,0.5166026,fineart Marat Latypov,0.45811993,fineart Andrei Markin,0.5328752,fineart Einar Hakonarson,0.5328311,digipa-high-impact Beatrice Huntington,0.5328165,digipa-high-impact Coppo di Marcovaldo,0.5327443,fineart Gregorio Prestopino,0.53250784,fineart A.D.M. Cooper,0.53244877,digipa-high-impact Horatio McCulloch,0.53244334,digipa-high-impact Wes Anderson,0.5318741,digipa-high-impact Moebius,0.53178746,digipa-high-impact Gerard Soest,0.53160626,fineart Charles Ellison,0.53152347,digipa-high-impact Wojciech Ostrycharz,0.5314213,fineart Doug Chiang,0.5313724,fineart Anne Savage,0.5310638,digipa-high-impact Cor Melchers,0.53099334,fineart Gordon Browne,0.5308195,digipa-high-impact Augustus Earle,0.49196815,fineart Carlos Francisco Chang Marín,0.5304734,fineart Larry Elmore,0.53032553,fineart Adolf Hölzel,0.5303149,fineart David Ligare,0.5301894,fineart Jan Luyken,0.52985555,fineart Earle Bergey,0.5298525,fineart David Ramsay Hay,0.52974963,digipa-high-impact Alfred East,0.5296565,digipa-high-impact A. R. Middleton Todd,0.50988734,fineart Giorgio De Vincenzi,0.5291678,fineart Hugh William Williams,0.5291014,digipa-high-impact Erwin Bowien,0.52895796,digipa-high-impact Victor Adame Minguez,0.5288686,fineart Yoji Shinkawa,0.5287015,anime Clara Weaver Parrish,0.5284487,digipa-high-impact Albert Eckhout,0.5284096,fineart Dorothy Coke,0.5282345,digipa-high-impact Jerzy Duda-Gracz,0.5279943,digipa-high-impact Byron Galvez,0.39178842,fareast Alson S. Clark,0.5278568,digipa-high-impact Adolf Ulric Wertmüller,0.5278296,digipa-high-impact Bruce Coville,0.5277226,digipa-high-impact Gong Kai,0.5276811,digipa-high-impact Andréi Arinouchkine,0.52763486,digipa-high-impact Florence Engelbach,0.5273161,digipa-high-impact Brian Froud,0.5270276,fineart Charles Thomson,0.5270127,digipa-high-impact Bessie Wheeler,0.5269164,digipa-high-impact Anton Lehmden,0.5268611,fineart Emilia Wilk,0.5264961,fineart Carl Eytel,0.52646196,digipa-high-impact Alfred Janes,0.5264481,digipa-high-impact Julie Bell,0.49962538,fineart Eugenio de Arriba,0.52613926,digipa-high-impact Samuel and Joseph Newsom,0.52595663,digipa-high-impact Hans Falk,0.52588874,digipa-high-impact Guillermo del Toro,0.52565175,digipa-high-impact Félix Arauz,0.52555984,digipa-high-impact Gyula Basch,0.52524436,digipa-high-impact Haroon Mirza,0.5252279,digipa-high-impact Du Jin,0.5249934,digipa-med-impact Harry Shoulberg,0.5249456,digipa-med-impact Arie Smit,0.5249027,fineart Ahmed Karahisari,0.4259451,digipa-low-impact Brian and Wendy Froud,0.5246335,fineart E. William Gollings,0.52461207,digipa-med-impact Bo Bartlett,0.51341593,digipa-med-impact Hans Burgkmair,0.52416867,digipa-med-impact David Macaulay,0.5241233,digipa-med-impact Benedetto Caliari,0.52370214,digipa-med-impact Eliott Lilly,0.5235398,digipa-med-impact Vincent Tanguay,0.48578292,digipa-med-impact Ada Hill Walker,0.52207166,fineart Christopher Wood,0.49360397,digipa-med-impact Kris Kuksi,0.43938053,digipa-low-impact Chen Yifei,0.5217867,fineart Margaux Valonia,0.5217782,digipa-med-impact Antoni Pitxot,0.40582713,digipa-low-impact Jhonen Vasquez,0.5216471,digipa-med-impact Emilio Grau Sala,0.52156484,fineart Henry B. 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Staudinger,0.4684662,digipa-low-impact Abraham Begeyn,0.5172538,digipa-med-impact Charles Mozley,0.5171356,digipa-med-impact Elias Ravanetti,0.38719344,digipa-low-impact Herman van Swanevelt,0.5168748,digipa-med-impact David Paton,0.4842217,digipa-med-impact Hans Werner Schmidt,0.51671976,digipa-med-impact Bob Ross,0.51628315,fineart Sou Fujimoto,0.5162528,fareast Balcomb Greene,0.5162045,digipa-med-impact Glen Angus,0.51609933,digipa-med-impact Buckminster Fuller,0.51607454,digipa-med-impact Andrei Ryabushkin,0.5158933,fineart Almeida Júnior,0.515856,digipa-med-impact Tim White,0.4182697,digipa-low-impact Hans Beat Wieland,0.51553553,digipa-med-impact Jakub Różalski,0.5154904,digipa-med-impact John Whitcomb,0.51523805,digipa-med-impact Dorothy King,0.5150925,digipa-med-impact Richard S. Johnson,0.51500344,fineart Aniello Falcone,0.51475304,digipa-med-impact Henning Jakob Henrik Lund,0.5147134,c Robert M Cunningham,0.5144858,digipa-med-impact Nick Knight,0.51447505,digipa-med-impact David Chipperfield,0.51424,digipa-med-impact Bartolomeo Cesi,0.5136737,digipa-med-impact Bettina Heinen-Ayech,0.51334465,digipa-med-impact Annabel Kidston,0.51327646,digipa-med-impact Charles Schridde,0.51308405,digipa-med-impact Samuel Earp,0.51305825,digipa-med-impact Eugene Montgomery,0.5128343,digipa-med-impact Alfred Parsons,0.5127445,digipa-med-impact Anton Möller,0.5127209,digipa-med-impact Craig Davison,0.499598,special Cricorps Grégoire,0.51267076,fineart Celia Fiennes,0.51266706,digipa-med-impact Raymond Swanland,0.41350424,fineart Howard Knotts,0.5122062,digipa-med-impact Helmut Federle,0.51201206,digipa-med-impact Tyler Edlin,0.44028252,digipa-high-impact Elwood H. 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here.txt000066400000000000000000000000001432161722600276370ustar00rootroot00000000000000stable-diffusion-webui/environment-wsl2.yaml000066400000000000000000000002571432161722600215520ustar00rootroot00000000000000name: automatic channels: - pytorch - defaults dependencies: - python=3.10 - pip=22.2.2 - cudatoolkit=11.3 - pytorch=1.12.1 - torchvision=0.13.1 - numpy=1.23.1stable-diffusion-webui/javascript/000077500000000000000000000000001432161722600175775ustar00rootroot00000000000000stable-diffusion-webui/javascript/aspectRatioOverlay.js000066400000000000000000000070771432161722600237700ustar00rootroot00000000000000 let currentWidth = null; let currentHeight = null; let arFrameTimeout = setTimeout(function(){},0); function dimensionChange(e,dimname){ if(dimname == 'Width'){ currentWidth = e.target.value*1.0 } if(dimname == 'Height'){ currentHeight = e.target.value*1.0 } var inImg2img = Boolean(gradioApp().querySelector("button.rounded-t-lg.border-gray-200")) if(!inImg2img){ return; } var img2imgMode = gradioApp().querySelector('#mode_img2img.tabs > div > button.rounded-t-lg.border-gray-200') if(img2imgMode){ img2imgMode=img2imgMode.innerText }else{ return; } var redrawImage = gradioApp().querySelector('div[data-testid=image] img'); var inpaintImage = gradioApp().querySelector('#img2maskimg div[data-testid=image] img') var targetElement = null; if(img2imgMode=='img2img' && redrawImage){ targetElement = redrawImage; }else if(img2imgMode=='Inpaint' && inpaintImage){ targetElement = inpaintImage; } if(targetElement){ var arPreviewRect = gradioApp().querySelector('#imageARPreview'); if(!arPreviewRect){ arPreviewRect = document.createElement('div') arPreviewRect.id = "imageARPreview"; gradioApp().getRootNode().appendChild(arPreviewRect) } var viewportOffset = targetElement.getBoundingClientRect(); viewportscale = Math.min( targetElement.clientWidth/targetElement.naturalWidth, targetElement.clientHeight/targetElement.naturalHeight ) scaledx = targetElement.naturalWidth*viewportscale scaledy = targetElement.naturalHeight*viewportscale cleintRectTop = (viewportOffset.top+window.scrollY) cleintRectLeft = (viewportOffset.left+window.scrollX) cleintRectCentreY = cleintRectTop + (targetElement.clientHeight/2) cleintRectCentreX = cleintRectLeft + (targetElement.clientWidth/2) viewRectTop = cleintRectCentreY-(scaledy/2) viewRectLeft = cleintRectCentreX-(scaledx/2) arRectWidth = scaledx arRectHeight = scaledy arscale = Math.min( arRectWidth/currentWidth, arRectHeight/currentHeight ) arscaledx = currentWidth*arscale arscaledy = currentHeight*arscale arRectTop = cleintRectCentreY-(arscaledy/2) arRectLeft = cleintRectCentreX-(arscaledx/2) arRectWidth = arscaledx arRectHeight = arscaledy arPreviewRect.style.top = arRectTop+'px'; arPreviewRect.style.left = arRectLeft+'px'; arPreviewRect.style.width = arRectWidth+'px'; arPreviewRect.style.height = arRectHeight+'px'; clearTimeout(arFrameTimeout); arFrameTimeout = setTimeout(function(){ arPreviewRect.style.display = 'none'; },2000); arPreviewRect.style.display = 'block'; } } onUiUpdate(function(){ var arPreviewRect = gradioApp().querySelector('#imageARPreview'); if(arPreviewRect){ arPreviewRect.style.display = 'none'; } var inImg2img = Boolean(gradioApp().querySelector("button.rounded-t-lg.border-gray-200")) if(inImg2img){ let inputs = gradioApp().querySelectorAll('input'); inputs.forEach(function(e){ let parentLabel = e.parentElement.querySelector('label') if(parentLabel && parentLabel.innerText){ if(!e.classList.contains('scrollwatch')){ if(parentLabel.innerText == 'Width' || parentLabel.innerText == 'Height'){ e.addEventListener('input', function(e){dimensionChange(e,parentLabel.innerText)} ) e.classList.add('scrollwatch') } if(parentLabel.innerText == 'Width'){ currentWidth = e.value*1.0 } if(parentLabel.innerText == 'Height'){ currentHeight = e.value*1.0 } } } }) } }); stable-diffusion-webui/javascript/contextMenus.js000066400000000000000000000164531432161722600226420ustar00rootroot00000000000000 contextMenuInit = function(){ let eventListenerApplied=false; let menuSpecs = new Map(); const uid = function(){ return Date.now().toString(36) + Math.random().toString(36).substr(2); } function showContextMenu(event,element,menuEntries){ let posx = event.clientX + document.body.scrollLeft + document.documentElement.scrollLeft; let posy = event.clientY + document.body.scrollTop + document.documentElement.scrollTop; let oldMenu = gradioApp().querySelector('#context-menu') if(oldMenu){ oldMenu.remove() } let tabButton = uiCurrentTab let baseStyle = window.getComputedStyle(tabButton) const contextMenu = document.createElement('nav') contextMenu.id = "context-menu" contextMenu.style.background = baseStyle.background contextMenu.style.color = baseStyle.color contextMenu.style.fontFamily = baseStyle.fontFamily contextMenu.style.top = posy+'px' contextMenu.style.left = posx+'px' contextMenu.style.width = Math.min(window.innerWidth,400)+'px' const contextMenuList = document.createElement('ul') contextMenuList.className = 'context-menu-items'; contextMenu.append(contextMenuList); menuEntries.forEach(function(entry){ if(entry['isDynamicSubmenu']){ let contextMenuEntry = document.createElement('span') contextMenuEntry.innerHTML = entry['name'] contextMenuEntry.style.paddingLeft = '5px' contextMenuEntry.style.color = 'grey' contextMenuList.append(contextMenuEntry); entry['func']().forEach(function(innerEntry){ let contextMenuEntry = document.createElement('a') contextMenuEntry.innerHTML = innerEntry['name'] contextMenuEntry.addEventListener("click", function(e) { innerEntry['func'](); }) contextMenuList.append(contextMenuEntry); }); }else{ let contextMenuEntry = document.createElement('a') contextMenuEntry.innerHTML = entry['name'] contextMenuEntry.addEventListener("click", function(e) { entry['func'](); }) contextMenuList.append(contextMenuEntry); } }) gradioApp().getRootNode().appendChild(contextMenu) let menuWidth = contextMenu.offsetWidth + 4; let menuHeight = contextMenu.offsetHeight + 4; let windowWidth = window.innerWidth; let windowHeight = window.innerHeight; if ( (windowWidth - posx) < menuWidth ) { contextMenu.style.left = windowWidth - menuWidth + "px"; } if ( (windowHeight - posy) < menuHeight ) { contextMenu.style.top = windowHeight - menuHeight + "px"; } } function appendContextMenuOption(targetEmementSelector,entryName,entryFunction,isDynamicSubmenu=false,displayToggleFunc=null){ currentItems = menuSpecs.get(targetEmementSelector) if(!currentItems){ currentItems = [] menuSpecs.set(targetEmementSelector,currentItems); } let newItem = {'id':targetEmementSelector+'_'+uid(), 'name':entryName, 'func':entryFunction, 'isDynamicSubmenu':isDynamicSubmenu, 'displayToggleFunc':displayToggleFunc, 'isNew':true} currentItems.push(newItem) return newItem['id'] } function removeContextMenuOption(uid){ menuSpecs.forEach(function(v,k) { let index = -1 v.forEach(function(e,ei){if(e['id']==uid){index=ei}}) if(index>=0){ v.splice(index, 1); } }) } function addContextMenuEventListener(){ if(eventListenerApplied){ return; } gradioApp().addEventListener("click", function(e) { let source = e.composedPath()[0] console.log(source); if( source.id && source.id.indexOf('check_progress')>-1 ){ return } let oldMenu = gradioApp().querySelector('#context-menu') if(oldMenu){ oldMenu.remove() } }); gradioApp().addEventListener("contextmenu", function(e) { let oldMenu = gradioApp().querySelector('#context-menu') if(oldMenu){ oldMenu.remove() } menuSpecs.forEach(function(v,k) { if(e.composedPath()[0].matches(k)){ showContextMenu(e,e.composedPath()[0],v) e.preventDefault() return } }) }); eventListenerApplied=true } return [appendContextMenuOption, removeContextMenuOption, addContextMenuEventListener] } initResponse = contextMenuInit(); appendContextMenuOption = initResponse[0]; removeContextMenuOption = initResponse[1]; addContextMenuEventListener = initResponse[2]; (function(){ //Start example Context Menu Items let generateOnRepeat = function(genbuttonid,interruptbuttonid){ let genbutton = gradioApp().querySelector(genbuttonid); let interruptbutton = gradioApp().querySelector(interruptbuttonid); if(!interruptbutton.offsetParent){ genbutton.click(); } clearInterval(window.generateOnRepeatInterval) window.generateOnRepeatInterval = setInterval(function(){ if(!interruptbutton.offsetParent){ genbutton.click(); } }, 500) } appendContextMenuOption('#txt2img_generate','Generate forever',function(){ generateOnRepeat('#txt2img_generate','#txt2img_interrupt'); }) appendContextMenuOption('#img2img_generate','Generate forever',function(){ generateOnRepeat('#img2img_generate','#img2img_interrupt'); }) let cancelGenerateForever = function(){ clearInterval(window.generateOnRepeatInterval) } appendContextMenuOption('#txt2img_interrupt','Cancel generate forever',cancelGenerateForever) appendContextMenuOption('#txt2img_generate', 'Cancel generate forever',cancelGenerateForever) appendContextMenuOption('#img2img_interrupt','Cancel generate forever',cancelGenerateForever) appendContextMenuOption('#img2img_generate', 'Cancel generate forever',cancelGenerateForever) appendContextMenuOption('#roll','Roll three', function(){ let rollbutton = get_uiCurrentTabContent().querySelector('#roll'); setTimeout(function(){rollbutton.click()},100) setTimeout(function(){rollbutton.click()},200) setTimeout(function(){rollbutton.click()},300) }) function truncate(input,len) { if (input.length > len) { return input.substring(0, len) + '...'; } return input; }; appendContextMenuOption('#txt2img_prompt textarea','Prior prompts', function(){ let l=[]; let n =0; (Storage['promptHistory'] || []).forEach(function(prompt){ if(n>10){ return; } l.push({'name':truncate(prompt,20),'func':function(){ get_uiCurrentTabContent().querySelector('#txt2img_prompt textarea').value = prompt }},) }) return l; }, true ) })(); //End example Context Menu Items onUiUpdate(function(){ addContextMenuEventListener() if(window.window.getLastGenerationArgs){ let priorPrompts = Storage['promptHistory'] || []; let lastPrompt = window.getLastGenerationArgs()[0]; if( priorPrompts[priorPrompts.length - 1] != lastPrompt){ priorPrompts.push(lastPrompt) } Storage['promptHistory'] = priorPrompts } }); stable-diffusion-webui/javascript/dragdrop.js000066400000000000000000000053501432161722600217420ustar00rootroot00000000000000// allows drag-dropping files into gradio image elements, and also pasting images from clipboard function isValidImageList( files ) { return files && files?.length === 1 && ['image/png', 'image/gif', 'image/jpeg'].includes(files[0].type); } function dropReplaceImage( imgWrap, files ) { if ( ! isValidImageList( files ) ) { return; } imgWrap.querySelector('.modify-upload button + button, .touch-none + div button + button')?.click(); const callback = () => { const fileInput = imgWrap.querySelector('input[type="file"]'); if ( fileInput ) { fileInput.files = files; fileInput.dispatchEvent(new Event('change')); } }; if ( imgWrap.closest('#pnginfo_image') ) { // special treatment for PNG Info tab, wait for fetch request to finish const oldFetch = window.fetch; window.fetch = async (input, options) => { const response = await oldFetch(input, options); if ( 'api/predict/' === input ) { const content = await response.text(); window.fetch = oldFetch; window.requestAnimationFrame( () => callback() ); return new Response(content, { status: response.status, statusText: response.statusText, headers: response.headers }) } return response; }; } else { window.requestAnimationFrame( () => callback() ); } } window.document.addEventListener('dragover', e => { const target = e.composedPath()[0]; const imgWrap = target.closest('[data-testid="image"]'); if ( !imgWrap ) { return; } e.stopPropagation(); e.preventDefault(); e.dataTransfer.dropEffect = 'copy'; }); window.document.addEventListener('drop', e => { const target = e.composedPath()[0]; const imgWrap = target.closest('[data-testid="image"]'); if ( !imgWrap ) { return; } e.stopPropagation(); e.preventDefault(); const files = e.dataTransfer.files; dropReplaceImage( imgWrap, files ); }); window.addEventListener('paste', e => { const files = e.clipboardData.files; if ( ! isValidImageList( files ) ) { return; } const visibleImageFields = [...gradioApp().querySelectorAll('[data-testid="image"]')] .filter(el => uiElementIsVisible(el)); if ( ! visibleImageFields.length ) { return; } const firstFreeImageField = visibleImageFields .filter(el => el.querySelector('input[type=file]'))?.[0]; dropReplaceImage( firstFreeImageField ? firstFreeImageField : visibleImageFields[visibleImageFields.length - 1] , files ); }); stable-diffusion-webui/javascript/edit-attention.js000066400000000000000000000031431432161722600230660ustar00rootroot00000000000000addEventListener('keydown', (event) => { let target = event.originalTarget || event.composedPath()[0]; if (!target.hasAttribute("placeholder")) return; if (!target.placeholder.toLowerCase().includes("prompt")) return; let plus = "ArrowUp" let minus = "ArrowDown" if (event.key != plus && event.key != minus) return; selectionStart = target.selectionStart; selectionEnd = target.selectionEnd; if(selectionStart == selectionEnd) return; event.preventDefault(); if (selectionStart == 0 || target.value[selectionStart - 1] != "(") { target.value = target.value.slice(0, selectionStart) + "(" + target.value.slice(selectionStart, selectionEnd) + ":1.0)" + target.value.slice(selectionEnd); target.focus(); target.selectionStart = selectionStart + 1; target.selectionEnd = selectionEnd + 1; } else { end = target.value.slice(selectionEnd + 1).indexOf(")") + 1; weight = parseFloat(target.value.slice(selectionEnd + 1, selectionEnd + 1 + end)); if (isNaN(weight)) return; if (event.key == minus) weight -= 0.1; if (event.key == plus) weight += 0.1; weight = parseFloat(weight.toPrecision(12)); target.value = target.value.slice(0, selectionEnd + 1) + weight + target.value.slice(selectionEnd + 1 + end - 1); target.focus(); target.selectionStart = selectionStart; target.selectionEnd = selectionEnd; } // Since we've modified a Gradio Textbox component manually, we need to simulate an `input` DOM event to ensure its // internal Svelte data binding remains in sync. target.dispatchEvent(new Event("input", { bubbles: true })); }); stable-diffusion-webui/javascript/hints.js000066400000000000000000000217311432161722600212660ustar00rootroot00000000000000// mouseover tooltips for various UI elements titles = { "Sampling steps": "How many times to improve the generated image iteratively; higher values take longer; very low values can produce bad results", "Sampling method": "Which algorithm to use to produce the image", "GFPGAN": "Restore low quality faces using GFPGAN neural network", "Euler a": "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help", "DDIM": "Denoising Diffusion Implicit Models - best at inpainting", "Batch count": "How many batches of images to create", "Batch size": "How many image to create in a single batch", "CFG Scale": "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results", "Seed": "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result", "\u{1f3b2}\ufe0f": "Set seed to -1, which will cause a new random number to be used every time", "\u267b\ufe0f": "Reuse seed from last generation, mostly useful if it was randomed", "\u{1f3a8}": "Add a random artist to the prompt.", "\u2199\ufe0f": "Read generation parameters from prompt into user interface.", "\u{1f4c2}": "Open images output directory", "Inpaint a part of image": "Draw a mask over an image, and the script will regenerate the masked area with content according to prompt", "SD upscale": "Upscale image normally, split result into tiles, improve each tile using img2img, merge whole image back", "Just resize": "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.", "Crop and resize": "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.", "Resize and fill": "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.", "Mask blur": "How much to blur the mask before processing, in pixels.", "Masked content": "What to put inside the masked area before processing it with Stable Diffusion.", "fill": "fill it with colors of the image", "original": "keep whatever was there originally", "latent noise": "fill it with latent space noise", "latent nothing": "fill it with latent space zeroes", "Inpaint at full resolution": "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image", "Denoising strength": "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.", "Denoising strength change factor": "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.", "Skip": "Stop processing current image and continue processing.", "Interrupt": "Stop processing images and return any results accumulated so far.", "Save": "Write image to a directory (default - log/images) and generation parameters into csv file.", "X values": "Separate values for X axis using commas.", "Y values": "Separate values for Y axis using commas.", "None": "Do not do anything special", "Prompt matrix": "Separate prompts into parts using vertical pipe character (|) and the script will create a picture for every combination of them (except for the first part, which will be present in all combinations)", "X/Y plot": "Create a grid where images will have different parameters. Use inputs below to specify which parameters will be shared by columns and rows", "Custom code": "Run Python code. Advanced user only. Must run program with --allow-code for this to work", "Prompt S/R": "Separate a list of words with commas, and the first word will be used as a keyword: script will search for this word in the prompt, and replace it with others", "Prompt order": "Separate a list of words with commas, and the script will make a variation of prompt with those words for their every possible order", "Tiling": "Produce an image that can be tiled.", "Tile overlap": "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.", "Variation seed": "Seed of a different picture to be mixed into the generation.", "Variation strength": "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).", "Resize seed from height": "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution", "Resize seed from width": "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution", "Interrogate": "Reconstruct prompt from existing image and put it into the prompt field.", "Images filename pattern": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.", "Directory name pattern": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.", "Max prompt words": "Set the maximum number of words to be used in the [prompt_words] option; ATTENTION: If the words are too long, they may exceed the maximum length of the file path that the system can handle", "Loopback": "Process an image, use it as an input, repeat.", "Loops": "How many times to repeat processing an image and using it as input for the next iteration", "Style 1": "Style to apply; styles have components for both positive and negative prompts and apply to both", "Style 2": "Style to apply; styles have components for both positive and negative prompts and apply to both", "Apply style": "Insert selected styles into prompt fields", "Create style": "Save current prompts as a style. If you add the token {prompt} to the text, the style use that as placeholder for your prompt when you use the style in the future.", "Checkpoint name": "Loads weights from checkpoint before making images. You can either use hash or a part of filename (as seen in settings) for checkpoint name. Recommended to use with Y axis for less switching.", "vram": "Torch active: Peak amount of VRAM used by Torch during generation, excluding cached data.\nTorch reserved: Peak amount of VRAM allocated by Torch, including all active and cached data.\nSys VRAM: Peak amount of VRAM allocation across all applications / total GPU VRAM (peak utilization%).", "Highres. fix": "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition", "Scale latent": "Uscale the image in latent space. Alternative is to produce the full image from latent representation, upscale that, and then move it back to latent space.", "Eta noise seed delta": "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.", "Do not add watermark to images": "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.", "Filename word regex": "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.", "Filename join string": "This string will be used to hoin split words into a single line if the option above is enabled.", } onUiUpdate(function(){ gradioApp().querySelectorAll('span, button, select, p').forEach(function(span){ tooltip = titles[span.textContent]; if(!tooltip){ tooltip = titles[span.value]; } if(!tooltip){ for (const c of span.classList) { if (c in titles) { tooltip = titles[c]; break; } } } if(tooltip){ span.title = tooltip; } }) gradioApp().querySelectorAll('select').forEach(function(select){ if (select.onchange != null) return; select.onchange = function(){ select.title = titles[select.value] || ""; } }) }) stable-diffusion-webui/javascript/imageMaskFix.js000066400000000000000000000026401432161722600225040ustar00rootroot00000000000000/** * temporary fix for https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/668 * @see https://github.com/gradio-app/gradio/issues/1721 */ window.addEventListener( 'resize', () => imageMaskResize()); function imageMaskResize() { const canvases = gradioApp().querySelectorAll('#img2maskimg .touch-none canvas'); if ( ! canvases.length ) { canvases_fixed = false; window.removeEventListener( 'resize', imageMaskResize ); return; } const wrapper = canvases[0].closest('.touch-none'); const previewImage = wrapper.previousElementSibling; if ( ! previewImage.complete ) { previewImage.addEventListener( 'load', () => imageMaskResize()); return; } const w = previewImage.width; const h = previewImage.height; const nw = previewImage.naturalWidth; const nh = previewImage.naturalHeight; const portrait = nh > nw; const factor = portrait; const wW = Math.min(w, portrait ? h/nh*nw : w/nw*nw); const wH = Math.min(h, portrait ? h/nh*nh : w/nw*nh); wrapper.style.width = `${wW}px`; wrapper.style.height = `${wH}px`; wrapper.style.left = `${(w-wW)/2}px`; wrapper.style.top = `${(h-wH)/2}px`; canvases.forEach( c => { c.style.width = c.style.height = ''; c.style.maxWidth = '100%'; c.style.maxHeight = '100%'; c.style.objectFit = 'contain'; }); } onUiUpdate(() => imageMaskResize());stable-diffusion-webui/javascript/imageviewer.js000066400000000000000000000171261432161722600224500ustar00rootroot00000000000000// A full size 'lightbox' preview modal shown when left clicking on gallery previews function closeModal() { gradioApp().getElementById("lightboxModal").style.display = "none"; } function showModal(event) { const source = event.target || event.srcElement; const modalImage = gradioApp().getElementById("modalImage") const lb = gradioApp().getElementById("lightboxModal") modalImage.src = source.src if (modalImage.style.display === 'none') { lb.style.setProperty('background-image', 'url(' + source.src + ')'); } lb.style.display = "block"; lb.focus() event.stopPropagation() } function negmod(n, m) { return ((n % m) + m) % m; } function updateOnBackgroundChange() { const modalImage = gradioApp().getElementById("modalImage") if (modalImage && modalImage.offsetParent) { let allcurrentButtons = gradioApp().querySelectorAll(".gallery-item.transition-all.\\!ring-2") let currentButton = null allcurrentButtons.forEach(function(elem) { if (elem.parentElement.offsetParent) { currentButton = elem; } }) if (modalImage.src != currentButton.children[0].src) { modalImage.src = currentButton.children[0].src; if (modalImage.style.display === 'none') { modal.style.setProperty('background-image', `url(${modalImage.src})`) } } } } function modalImageSwitch(offset) { var allgalleryButtons = gradioApp().querySelectorAll(".gallery-item.transition-all") var galleryButtons = [] allgalleryButtons.forEach(function(elem) { if (elem.parentElement.offsetParent) { galleryButtons.push(elem); } }) if (galleryButtons.length > 1) { var allcurrentButtons = gradioApp().querySelectorAll(".gallery-item.transition-all.\\!ring-2") var currentButton = null allcurrentButtons.forEach(function(elem) { if (elem.parentElement.offsetParent) { currentButton = elem; } }) var result = -1 galleryButtons.forEach(function(v, i) { if (v == currentButton) { result = i } }) if (result != -1) { nextButton = galleryButtons[negmod((result + offset), galleryButtons.length)] nextButton.click() const modalImage = gradioApp().getElementById("modalImage"); const modal = gradioApp().getElementById("lightboxModal"); modalImage.src = nextButton.children[0].src; if (modalImage.style.display === 'none') { modal.style.setProperty('background-image', `url(${modalImage.src})`) } setTimeout(function() { modal.focus() }, 10) } } } function modalNextImage(event) { modalImageSwitch(1) event.stopPropagation() } function modalPrevImage(event) { modalImageSwitch(-1) event.stopPropagation() } function modalKeyHandler(event) { switch (event.key) { case "ArrowLeft": modalPrevImage(event) break; case "ArrowRight": modalNextImage(event) break; case "Escape": closeModal(); break; } } function showGalleryImage() { setTimeout(function() { fullImg_preview = gradioApp().querySelectorAll('img.w-full.object-contain') if (fullImg_preview != null) { fullImg_preview.forEach(function function_name(e) { if (e.dataset.modded) return; e.dataset.modded = true; if(e && e.parentElement.tagName == 'DIV'){ e.style.cursor='pointer' e.addEventListener('click', function (evt) { if(!opts.js_modal_lightbox) return; modalZoomSet(gradioApp().getElementById('modalImage'), opts.js_modal_lightbox_initially_zoomed) showModal(evt) }, true); } }); } }, 100); } function modalZoomSet(modalImage, enable) { if (enable) { modalImage.classList.add('modalImageFullscreen'); } else { modalImage.classList.remove('modalImageFullscreen'); } } function modalZoomToggle(event) { modalImage = gradioApp().getElementById("modalImage"); modalZoomSet(modalImage, !modalImage.classList.contains('modalImageFullscreen')) event.stopPropagation() } function modalTileImageToggle(event) { const modalImage = gradioApp().getElementById("modalImage"); const modal = gradioApp().getElementById("lightboxModal"); const isTiling = modalImage.style.display === 'none'; if (isTiling) { modalImage.style.display = 'block'; modal.style.setProperty('background-image', 'none') } else { modalImage.style.display = 'none'; modal.style.setProperty('background-image', `url(${modalImage.src})`) } event.stopPropagation() } function galleryImageHandler(e) { if (e && e.parentElement.tagName == 'BUTTON') { e.onclick = showGalleryImage; } } onUiUpdate(function() { fullImg_preview = gradioApp().querySelectorAll('img.w-full') if (fullImg_preview != null) { fullImg_preview.forEach(galleryImageHandler); } updateOnBackgroundChange(); }) document.addEventListener("DOMContentLoaded", function() { const modalFragment = document.createDocumentFragment(); const modal = document.createElement('div') modal.onclick = closeModal; modal.id = "lightboxModal"; modal.tabIndex = 0 modal.addEventListener('keydown', modalKeyHandler, true) const modalControls = document.createElement('div') modalControls.className = 'modalControls gradio-container'; modal.append(modalControls); const modalZoom = document.createElement('span') modalZoom.className = 'modalZoom cursor'; modalZoom.innerHTML = '⤡' modalZoom.addEventListener('click', modalZoomToggle, true) modalZoom.title = "Toggle zoomed view"; modalControls.appendChild(modalZoom) const modalTileImage = document.createElement('span') modalTileImage.className = 'modalTileImage cursor'; modalTileImage.innerHTML = '⊞' modalTileImage.addEventListener('click', modalTileImageToggle, true) modalTileImage.title = "Preview tiling"; modalControls.appendChild(modalTileImage) const modalClose = document.createElement('span') modalClose.className = 'modalClose cursor'; modalClose.innerHTML = '×' modalClose.onclick = closeModal; modalClose.title = "Close image viewer"; modalControls.appendChild(modalClose) const modalImage = document.createElement('img') modalImage.id = 'modalImage'; modalImage.onclick = closeModal; modalImage.tabIndex = 0 modalImage.addEventListener('keydown', modalKeyHandler, true) modal.appendChild(modalImage) const modalPrev = document.createElement('a') modalPrev.className = 'modalPrev'; modalPrev.innerHTML = '❮' modalPrev.tabIndex = 0 modalPrev.addEventListener('click', modalPrevImage, true); modalPrev.addEventListener('keydown', modalKeyHandler, true) modal.appendChild(modalPrev) const modalNext = document.createElement('a') modalNext.className = 'modalNext'; modalNext.innerHTML = '❯' modalNext.tabIndex = 0 modalNext.addEventListener('click', modalNextImage, true); modalNext.addEventListener('keydown', modalKeyHandler, true) modal.appendChild(modalNext) gradioApp().getRootNode().appendChild(modal) document.body.appendChild(modalFragment); }); stable-diffusion-webui/javascript/notification.js000066400000000000000000000026641432161722600226330ustar00rootroot00000000000000// Monitors the gallery and sends a browser notification when the leading image is new. let lastHeadImg = null; notificationButton = null onUiUpdate(function(){ if(notificationButton == null){ notificationButton = gradioApp().getElementById('request_notifications') if(notificationButton != null){ notificationButton.addEventListener('click', function (evt) { Notification.requestPermission(); },true); } } const galleryPreviews = gradioApp().querySelectorAll('img.h-full.w-full.overflow-hidden'); if (galleryPreviews == null) return; const headImg = galleryPreviews[0]?.src; if (headImg == null || headImg == lastHeadImg) return; lastHeadImg = headImg; // play notification sound if available gradioApp().querySelector('#audio_notification audio')?.play(); if (document.hasFocus()) return; // Multiple copies of the images are in the DOM when one is selected. Dedup with a Set to get the real number generated. const imgs = new Set(Array.from(galleryPreviews).map(img => img.src)); const notification = new Notification( 'Stable Diffusion', { body: `Generated ${imgs.size > 1 ? imgs.size - 1 : 1} image${imgs.size > 1 ? 's' : ''}`, icon: headImg, image: headImg, } ); notification.onclick = function(_){ parent.focus(); this.close(); }; }); stable-diffusion-webui/javascript/progressbar.js000066400000000000000000000061261432161722600224730ustar00rootroot00000000000000// code related to showing and updating progressbar shown as the image is being made global_progressbars = {} function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip, id_interrupt, id_preview, id_gallery){ var progressbar = gradioApp().getElementById(id_progressbar) var skip = id_skip ? gradioApp().getElementById(id_skip) : null var interrupt = gradioApp().getElementById(id_interrupt) if(opts.show_progress_in_title && progressbar && progressbar.offsetParent){ if(progressbar.innerText){ let newtitle = 'Stable Diffusion - ' + progressbar.innerText if(document.title != newtitle){ document.title = newtitle; } }else{ let newtitle = 'Stable Diffusion' if(document.title != newtitle){ document.title = newtitle; } } } if(progressbar!= null && progressbar != global_progressbars[id_progressbar]){ global_progressbars[id_progressbar] = progressbar var mutationObserver = new MutationObserver(function(m){ preview = gradioApp().getElementById(id_preview) gallery = gradioApp().getElementById(id_gallery) if(preview != null && gallery != null){ preview.style.width = gallery.clientWidth + "px" preview.style.height = gallery.clientHeight + "px" var progressDiv = gradioApp().querySelectorAll('#' + id_progressbar_span).length > 0; if(!progressDiv){ if (skip) { skip.style.display = "none" } interrupt.style.display = "none" } } window.setTimeout(function() { requestMoreProgress(id_part, id_progressbar_span, id_skip, id_interrupt) }, 500) }); mutationObserver.observe( progressbar, { childList:true, subtree:true }) } } onUiUpdate(function(){ check_progressbar('txt2img', 'txt2img_progressbar', 'txt2img_progress_span', 'txt2img_skip', 'txt2img_interrupt', 'txt2img_preview', 'txt2img_gallery') check_progressbar('img2img', 'img2img_progressbar', 'img2img_progress_span', 'img2img_skip', 'img2img_interrupt', 'img2img_preview', 'img2img_gallery') check_progressbar('ti', 'ti_progressbar', 'ti_progress_span', '', 'ti_interrupt', 'ti_preview', 'ti_gallery') }) function requestMoreProgress(id_part, id_progressbar_span, id_skip, id_interrupt){ btn = gradioApp().getElementById(id_part+"_check_progress"); if(btn==null) return; btn.click(); var progressDiv = gradioApp().querySelectorAll('#' + id_progressbar_span).length > 0; var skip = id_skip ? gradioApp().getElementById(id_skip) : null var interrupt = gradioApp().getElementById(id_interrupt) if(progressDiv && interrupt){ if (skip) { skip.style.display = "block" } interrupt.style.display = "block" } } function requestProgress(id_part){ btn = gradioApp().getElementById(id_part+"_check_progress_initial"); if(btn==null) return; btn.click(); } stable-diffusion-webui/javascript/textualInversion.js000066400000000000000000000002601432161722600235160ustar00rootroot00000000000000 function start_training_textual_inversion(){ requestProgress('ti') gradioApp().querySelector('#ti_error').innerHTML='' return args_to_array(arguments) } stable-diffusion-webui/javascript/ui.js000066400000000000000000000147061432161722600205620ustar00rootroot00000000000000// various functions for interation with ui.py not large enough to warrant putting them in separate files function selected_gallery_index(){ var buttons = gradioApp().querySelectorAll('[style="display: block;"].tabitem .gallery-item') var button = gradioApp().querySelector('[style="display: block;"].tabitem .gallery-item.\\!ring-2') var result = -1 buttons.forEach(function(v, i){ if(v==button) { result = i } }) return result } function extract_image_from_gallery(gallery){ if(gallery.length == 1){ return gallery[0] } index = selected_gallery_index() if (index < 0 || index >= gallery.length){ return [null] } return gallery[index]; } function args_to_array(args){ res = [] for(var i=0;i label > textarea"); txt2img_textarea?.addEventListener("input", () => update_token_counter("txt2img_token_button")); txt2img_textarea?.addEventListener("keyup", (event) => submit_prompt(event, "txt2img_generate")); } if (!img2img_textarea) { img2img_textarea = gradioApp().querySelector("#img2img_prompt > label > textarea"); img2img_textarea?.addEventListener("input", () => update_token_counter("img2img_token_button")); img2img_textarea?.addEventListener("keyup", (event) => submit_prompt(event, "img2img_generate")); } }) let txt2img_textarea, img2img_textarea = undefined; let wait_time = 800 let token_timeout; function update_txt2img_tokens(...args) { update_token_counter("txt2img_token_button") if (args.length == 2) return args[0] return args; } function update_img2img_tokens(...args) { update_token_counter("img2img_token_button") if (args.length == 2) return args[0] return args; } function update_token_counter(button_id) { if (token_timeout) clearTimeout(token_timeout); token_timeout = setTimeout(() => gradioApp().getElementById(button_id)?.click(), wait_time); } function submit_prompt(event, generate_button_id) { if (event.altKey && event.keyCode === 13) { event.preventDefault(); gradioApp().getElementById(generate_button_id).click(); return; } } function restart_reload(){ document.body.innerHTML='

Reloading...

'; setTimeout(function(){location.reload()},2000) } stable-diffusion-webui/launch.py000066400000000000000000000155161432161722600172650ustar00rootroot00000000000000# this scripts installs necessary requirements and launches main program in webui.py import subprocess import os import sys import importlib.util import shlex import platform dir_repos = "repositories" python = sys.executable git = os.environ.get('GIT', "git") def extract_arg(args, name): return [x for x in args if x != name], name in args def run(command, desc=None, errdesc=None): if desc is not None: print(desc) result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True) if result.returncode != 0: message = f"""{errdesc or 'Error running command'}. Command: {command} Error code: {result.returncode} stdout: {result.stdout.decode(encoding="utf8", errors="ignore") if len(result.stdout)>0 else ''} stderr: {result.stderr.decode(encoding="utf8", errors="ignore") if len(result.stderr)>0 else ''} """ raise RuntimeError(message) return result.stdout.decode(encoding="utf8", errors="ignore") def check_run(command): result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True) return result.returncode == 0 def is_installed(package): try: spec = importlib.util.find_spec(package) except ModuleNotFoundError: return False return spec is not None def repo_dir(name): return os.path.join(dir_repos, name) def run_python(code, desc=None, errdesc=None): return run(f'"{python}" -c "{code}"', desc, errdesc) def run_pip(args, desc=None): return run(f'"{python}" -m pip {args} --prefer-binary', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}") def check_run_python(code): return check_run(f'"{python}" -c "{code}"') def git_clone(url, dir, name, commithash=None): # TODO clone into temporary dir and move if successful if os.path.exists(dir): if commithash is None: return current_hash = run(f'"{git}" -C {dir} rev-parse HEAD', None, f"Couldn't determine {name}'s hash: {commithash}").strip() if current_hash == commithash: return run(f'"{git}" -C {dir} fetch', f"Fetching updates for {name}...", f"Couldn't fetch {name}") run(f'"{git}" -C {dir} checkout {commithash}', f"Checking out commint for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}") return run(f'"{git}" clone "{url}" "{dir}"', f"Cloning {name} into {dir}...", f"Couldn't clone {name}") if commithash is not None: run(f'"{git}" -C {dir} checkout {commithash}', None, "Couldn't checkout {name}'s hash: {commithash}") def prepare_enviroment(): torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113") requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt") commandline_args = os.environ.get('COMMANDLINE_ARGS', "") gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379") clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1") stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc") taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6") k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "f4e99857772fc3a126ba886aadf795a332774878") codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af") blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9") args = shlex.split(commandline_args) args, skip_torch_cuda_test = extract_arg(args, '--skip-torch-cuda-test') xformers = '--xformers' in args deepdanbooru = '--deepdanbooru' in args ngrok = '--ngrok' in args try: commit = run(f"{git} rev-parse HEAD").strip() except Exception: commit = "" print(f"Python {sys.version}") print(f"Commit hash: {commit}") if not is_installed("torch") or not is_installed("torchvision"): run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch") if not skip_torch_cuda_test: run_python("import torch; assert torch.cuda.is_available(), 'Torch is not able to use GPU; add --skip-torch-cuda-test to COMMANDLINE_ARGS variable to disable this check'") if not is_installed("gfpgan"): run_pip(f"install {gfpgan_package}", "gfpgan") if not is_installed("clip"): run_pip(f"install {clip_package}", "clip") if not is_installed("xformers") and xformers and platform.python_version().startswith("3.10"): if platform.system() == "Windows": run_pip("install https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/c/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl", "xformers") elif platform.system() == "Linux": run_pip("install xformers", "xformers") if not is_installed("deepdanbooru") and deepdanbooru: run_pip("install git+https://github.com/KichangKim/DeepDanbooru.git@edf73df4cdaeea2cf00e9ac08bd8a9026b7a7b26#egg=deepdanbooru[tensorflow] tensorflow==2.10.0 tensorflow-io==0.27.0", "deepdanbooru") if not is_installed("pyngrok") and ngrok: run_pip("install pyngrok", "ngrok") os.makedirs(dir_repos, exist_ok=True) git_clone("https://github.com/CompVis/stable-diffusion.git", repo_dir('stable-diffusion'), "Stable Diffusion", stable_diffusion_commit_hash) git_clone("https://github.com/CompVis/taming-transformers.git", repo_dir('taming-transformers'), "Taming Transformers", taming_transformers_commit_hash) git_clone("https://github.com/crowsonkb/k-diffusion.git", repo_dir('k-diffusion'), "K-diffusion", k_diffusion_commit_hash) git_clone("https://github.com/sczhou/CodeFormer.git", repo_dir('CodeFormer'), "CodeFormer", codeformer_commit_hash) git_clone("https://github.com/salesforce/BLIP.git", repo_dir('BLIP'), "BLIP", blip_commit_hash) if not is_installed("lpips"): run_pip(f"install -r {os.path.join(repo_dir('CodeFormer'), 'requirements.txt')}", "requirements for CodeFormer") run_pip(f"install -r {requirements_file}", "requirements for Web UI") sys.argv += args if "--exit" in args: print("Exiting because of --exit argument") exit(0) def start_webui(): print(f"Launching Web UI with arguments: {' '.join(sys.argv[1:])}") import webui webui.webui() if __name__ == "__main__": prepare_enviroment() start_webui() stable-diffusion-webui/models/000077500000000000000000000000001432161722600167145ustar00rootroot00000000000000stable-diffusion-webui/models/Stable-diffusion/000077500000000000000000000000001432161722600221125ustar00rootroot00000000000000stable-diffusion-webui/models/Stable-diffusion/Put Stable Diffusion checkpoints here.txt000066400000000000000000000000001432161722600317120ustar00rootroot00000000000000stable-diffusion-webui/models/deepbooru/000077500000000000000000000000001432161722600207005ustar00rootroot00000000000000stable-diffusion-webui/models/deepbooru/Put your deepbooru release project folder here.txt000066400000000000000000000000001432161722600323130ustar00rootroot00000000000000stable-diffusion-webui/modules/000077500000000000000000000000001432161722600171015ustar00rootroot00000000000000stable-diffusion-webui/modules/artists.py000066400000000000000000000012771432161722600211530ustar00rootroot00000000000000import os.path import csv from collections import namedtuple Artist = namedtuple("Artist", ['name', 'weight', 'category']) class ArtistsDatabase: def __init__(self, filename): self.cats = set() self.artists = [] if not os.path.exists(filename): return with open(filename, "r", newline='', encoding="utf8") as file: reader = csv.DictReader(file) for row in reader: artist = Artist(row["artist"], float(row["score"]), row["category"]) self.artists.append(artist) self.cats.add(artist.category) def categories(self): return sorted(self.cats) stable-diffusion-webui/modules/bsrgan_model.py000066400000000000000000000055001432161722600221070ustar00rootroot00000000000000import os.path import sys import traceback import PIL.Image import numpy as np import torch from basicsr.utils.download_util import load_file_from_url import modules.upscaler from modules import devices, modelloader from modules.bsrgan_model_arch import RRDBNet class UpscalerBSRGAN(modules.upscaler.Upscaler): def __init__(self, dirname): self.name = "BSRGAN" self.model_name = "BSRGAN 4x" self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/BSRGAN.pth" self.user_path = dirname super().__init__() model_paths = self.find_models(ext_filter=[".pt", ".pth"]) scalers = [] if len(model_paths) == 0: scaler_data = modules.upscaler.UpscalerData(self.model_name, self.model_url, self, 4) scalers.append(scaler_data) for file in model_paths: if "http" in file: name = self.model_name else: name = modelloader.friendly_name(file) try: scaler_data = modules.upscaler.UpscalerData(name, file, self, 4) scalers.append(scaler_data) except Exception: print(f"Error loading BSRGAN model: {file}", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) self.scalers = scalers def do_upscale(self, img: PIL.Image, selected_file): torch.cuda.empty_cache() model = self.load_model(selected_file) if model is None: return img model.to(devices.device_bsrgan) torch.cuda.empty_cache() img = np.array(img) img = img[:, :, ::-1] img = np.moveaxis(img, 2, 0) / 255 img = torch.from_numpy(img).float() img = img.unsqueeze(0).to(devices.device_bsrgan) with torch.no_grad(): output = model(img) output = output.squeeze().float().cpu().clamp_(0, 1).numpy() output = 255. * np.moveaxis(output, 0, 2) output = output.astype(np.uint8) output = output[:, :, ::-1] torch.cuda.empty_cache() return PIL.Image.fromarray(output, 'RGB') def load_model(self, path: str): if "http" in path: filename = load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="%s.pth" % self.name, progress=True) else: filename = path if not os.path.exists(filename) or filename is None: print(f"BSRGAN: Unable to load model from {filename}", file=sys.stderr) return None model = RRDBNet(in_nc=3, out_nc=3, nf=64, nb=23, gc=32, sf=4) # define network model.load_state_dict(torch.load(filename), strict=True) model.eval() for k, v in model.named_parameters(): v.requires_grad = False return model stable-diffusion-webui/modules/bsrgan_model_arch.py000066400000000000000000000072171432161722600231130ustar00rootroot00000000000000import functools import torch import torch.nn as nn import torch.nn.functional as F import torch.nn.init as init def initialize_weights(net_l, scale=1): if not isinstance(net_l, list): net_l = [net_l] for net in net_l: for m in net.modules(): if isinstance(m, nn.Conv2d): init.kaiming_normal_(m.weight, a=0, mode='fan_in') m.weight.data *= scale # for residual block if m.bias is not None: m.bias.data.zero_() elif isinstance(m, nn.Linear): init.kaiming_normal_(m.weight, a=0, mode='fan_in') m.weight.data *= scale if m.bias is not None: m.bias.data.zero_() elif isinstance(m, nn.BatchNorm2d): init.constant_(m.weight, 1) init.constant_(m.bias.data, 0.0) def make_layer(block, n_layers): layers = [] for _ in range(n_layers): layers.append(block()) return nn.Sequential(*layers) class ResidualDenseBlock_5C(nn.Module): def __init__(self, nf=64, gc=32, bias=True): super(ResidualDenseBlock_5C, self).__init__() # gc: growth channel, i.e. intermediate channels self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1, bias=bias) self.conv2 = nn.Conv2d(nf + gc, gc, 3, 1, 1, bias=bias) self.conv3 = nn.Conv2d(nf + 2 * gc, gc, 3, 1, 1, bias=bias) self.conv4 = nn.Conv2d(nf + 3 * gc, gc, 3, 1, 1, bias=bias) self.conv5 = nn.Conv2d(nf + 4 * gc, nf, 3, 1, 1, bias=bias) self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) # initialization initialize_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1) def forward(self, x): x1 = self.lrelu(self.conv1(x)) x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1))) x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1))) x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1))) x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1)) return x5 * 0.2 + x class RRDB(nn.Module): '''Residual in Residual Dense Block''' def __init__(self, nf, gc=32): super(RRDB, self).__init__() self.RDB1 = ResidualDenseBlock_5C(nf, gc) self.RDB2 = ResidualDenseBlock_5C(nf, gc) self.RDB3 = ResidualDenseBlock_5C(nf, gc) def forward(self, x): out = self.RDB1(x) out = self.RDB2(out) out = self.RDB3(out) return out * 0.2 + x class RRDBNet(nn.Module): def __init__(self, in_nc=3, out_nc=3, nf=64, nb=23, gc=32, sf=4): super(RRDBNet, self).__init__() RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc) self.sf = sf self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True) self.RRDB_trunk = make_layer(RRDB_block_f, nb) self.trunk_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True) #### upsampling self.upconv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True) if self.sf==4: self.upconv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True) self.HRconv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True) self.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True) self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) def forward(self, x): fea = self.conv_first(x) trunk = self.trunk_conv(self.RRDB_trunk(fea)) fea = fea + trunk fea = self.lrelu(self.upconv1(F.interpolate(fea, scale_factor=2, mode='nearest'))) if self.sf==4: fea = self.lrelu(self.upconv2(F.interpolate(fea, scale_factor=2, mode='nearest'))) out = self.conv_last(self.lrelu(self.HRconv(fea))) return outstable-diffusion-webui/modules/codeformer/000077500000000000000000000000001432161722600212265ustar00rootroot00000000000000stable-diffusion-webui/modules/codeformer/codeformer_arch.py000066400000000000000000000254661432161722600247370ustar00rootroot00000000000000# this file is copied from CodeFormer repository. Please see comment in modules/codeformer_model.py import math import numpy as np import torch from torch import nn, Tensor import torch.nn.functional as F from typing import Optional, List from modules.codeformer.vqgan_arch import * from basicsr.utils import get_root_logger from basicsr.utils.registry import ARCH_REGISTRY def calc_mean_std(feat, eps=1e-5): """Calculate mean and std for adaptive_instance_normalization. Args: feat (Tensor): 4D tensor. eps (float): A small value added to the variance to avoid divide-by-zero. Default: 1e-5. """ size = feat.size() assert len(size) == 4, 'The input feature should be 4D tensor.' b, c = size[:2] feat_var = feat.view(b, c, -1).var(dim=2) + eps feat_std = feat_var.sqrt().view(b, c, 1, 1) feat_mean = feat.view(b, c, -1).mean(dim=2).view(b, c, 1, 1) return feat_mean, feat_std def adaptive_instance_normalization(content_feat, style_feat): """Adaptive instance normalization. Adjust the reference features to have the similar color and illuminations as those in the degradate features. Args: content_feat (Tensor): The reference feature. style_feat (Tensor): The degradate features. """ size = content_feat.size() style_mean, style_std = calc_mean_std(style_feat) content_mean, content_std = calc_mean_std(content_feat) normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size) return normalized_feat * style_std.expand(size) + style_mean.expand(size) class PositionEmbeddingSine(nn.Module): """ This is a more standard version of the position embedding, very similar to the one used by the Attention is all you need paper, generalized to work on images. """ def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None): super().__init__() self.num_pos_feats = num_pos_feats self.temperature = temperature self.normalize = normalize if scale is not None and normalize is False: raise ValueError("normalize should be True if scale is passed") if scale is None: scale = 2 * math.pi self.scale = scale def forward(self, x, mask=None): if mask is None: mask = torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool) not_mask = ~mask y_embed = not_mask.cumsum(1, dtype=torch.float32) x_embed = not_mask.cumsum(2, dtype=torch.float32) if self.normalize: eps = 1e-6 y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats) pos_x = x_embed[:, :, :, None] / dim_t pos_y = y_embed[:, :, :, None] / dim_t pos_x = torch.stack( (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4 ).flatten(3) pos_y = torch.stack( (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4 ).flatten(3) pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) return pos def _get_activation_fn(activation): """Return an activation function given a string""" if activation == "relu": return F.relu if activation == "gelu": return F.gelu if activation == "glu": return F.glu raise RuntimeError(F"activation should be relu/gelu, not {activation}.") class TransformerSALayer(nn.Module): def __init__(self, embed_dim, nhead=8, dim_mlp=2048, dropout=0.0, activation="gelu"): super().__init__() self.self_attn = nn.MultiheadAttention(embed_dim, nhead, dropout=dropout) # Implementation of Feedforward model - MLP self.linear1 = nn.Linear(embed_dim, dim_mlp) self.dropout = nn.Dropout(dropout) self.linear2 = nn.Linear(dim_mlp, embed_dim) self.norm1 = nn.LayerNorm(embed_dim) self.norm2 = nn.LayerNorm(embed_dim) self.dropout1 = nn.Dropout(dropout) self.dropout2 = nn.Dropout(dropout) self.activation = _get_activation_fn(activation) def with_pos_embed(self, tensor, pos: Optional[Tensor]): return tensor if pos is None else tensor + pos def forward(self, tgt, tgt_mask: Optional[Tensor] = None, tgt_key_padding_mask: Optional[Tensor] = None, query_pos: Optional[Tensor] = None): # self attention tgt2 = self.norm1(tgt) q = k = self.with_pos_embed(tgt2, query_pos) tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask)[0] tgt = tgt + self.dropout1(tgt2) # ffn tgt2 = self.norm2(tgt) tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2)))) tgt = tgt + self.dropout2(tgt2) return tgt class Fuse_sft_block(nn.Module): def __init__(self, in_ch, out_ch): super().__init__() self.encode_enc = ResBlock(2*in_ch, out_ch) self.scale = nn.Sequential( nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1), nn.LeakyReLU(0.2, True), nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1)) self.shift = nn.Sequential( nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1), nn.LeakyReLU(0.2, True), nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1)) def forward(self, enc_feat, dec_feat, w=1): enc_feat = self.encode_enc(torch.cat([enc_feat, dec_feat], dim=1)) scale = self.scale(enc_feat) shift = self.shift(enc_feat) residual = w * (dec_feat * scale + shift) out = dec_feat + residual return out @ARCH_REGISTRY.register() class CodeFormer(VQAutoEncoder): def __init__(self, dim_embd=512, n_head=8, n_layers=9, codebook_size=1024, latent_size=256, connect_list=['32', '64', '128', '256'], fix_modules=['quantize','generator']): super(CodeFormer, self).__init__(512, 64, [1, 2, 2, 4, 4, 8], 'nearest',2, [16], codebook_size) if fix_modules is not None: for module in fix_modules: for param in getattr(self, module).parameters(): param.requires_grad = False self.connect_list = connect_list self.n_layers = n_layers self.dim_embd = dim_embd self.dim_mlp = dim_embd*2 self.position_emb = nn.Parameter(torch.zeros(latent_size, self.dim_embd)) self.feat_emb = nn.Linear(256, self.dim_embd) # transformer self.ft_layers = nn.Sequential(*[TransformerSALayer(embed_dim=dim_embd, nhead=n_head, dim_mlp=self.dim_mlp, dropout=0.0) for _ in range(self.n_layers)]) # logits_predict head self.idx_pred_layer = nn.Sequential( nn.LayerNorm(dim_embd), nn.Linear(dim_embd, codebook_size, bias=False)) self.channels = { '16': 512, '32': 256, '64': 256, '128': 128, '256': 128, '512': 64, } # after second residual block for > 16, before attn layer for ==16 self.fuse_encoder_block = {'512':2, '256':5, '128':8, '64':11, '32':14, '16':18} # after first residual block for > 16, before attn layer for ==16 self.fuse_generator_block = {'16':6, '32': 9, '64':12, '128':15, '256':18, '512':21} # fuse_convs_dict self.fuse_convs_dict = nn.ModuleDict() for f_size in self.connect_list: in_ch = self.channels[f_size] self.fuse_convs_dict[f_size] = Fuse_sft_block(in_ch, in_ch) def _init_weights(self, module): if isinstance(module, (nn.Linear, nn.Embedding)): module.weight.data.normal_(mean=0.0, std=0.02) if isinstance(module, nn.Linear) and module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) def forward(self, x, w=0, detach_16=True, code_only=False, adain=False): # ################### Encoder ##################### enc_feat_dict = {} out_list = [self.fuse_encoder_block[f_size] for f_size in self.connect_list] for i, block in enumerate(self.encoder.blocks): x = block(x) if i in out_list: enc_feat_dict[str(x.shape[-1])] = x.clone() lq_feat = x # ################# Transformer ################### # quant_feat, codebook_loss, quant_stats = self.quantize(lq_feat) pos_emb = self.position_emb.unsqueeze(1).repeat(1,x.shape[0],1) # BCHW -> BC(HW) -> (HW)BC feat_emb = self.feat_emb(lq_feat.flatten(2).permute(2,0,1)) query_emb = feat_emb # Transformer encoder for layer in self.ft_layers: query_emb = layer(query_emb, query_pos=pos_emb) # output logits logits = self.idx_pred_layer(query_emb) # (hw)bn logits = logits.permute(1,0,2) # (hw)bn -> b(hw)n if code_only: # for training stage II # logits doesn't need softmax before cross_entropy loss return logits, lq_feat # ################# Quantization ################### # if self.training: # quant_feat = torch.einsum('btn,nc->btc', [soft_one_hot, self.quantize.embedding.weight]) # # b(hw)c -> bc(hw) -> bchw # quant_feat = quant_feat.permute(0,2,1).view(lq_feat.shape) # ------------ soft_one_hot = F.softmax(logits, dim=2) _, top_idx = torch.topk(soft_one_hot, 1, dim=2) quant_feat = self.quantize.get_codebook_feat(top_idx, shape=[x.shape[0],16,16,256]) # preserve gradients # quant_feat = lq_feat + (quant_feat - lq_feat).detach() if detach_16: quant_feat = quant_feat.detach() # for training stage III if adain: quant_feat = adaptive_instance_normalization(quant_feat, lq_feat) # ################## Generator #################### x = quant_feat fuse_list = [self.fuse_generator_block[f_size] for f_size in self.connect_list] for i, block in enumerate(self.generator.blocks): x = block(x) if i in fuse_list: # fuse after i-th block f_size = str(x.shape[-1]) if w>0: x = self.fuse_convs_dict[f_size](enc_feat_dict[f_size].detach(), x, w) out = x # logits doesn't need softmax before cross_entropy loss return out, logits, lq_featstable-diffusion-webui/modules/codeformer/vqgan_arch.py000066400000000000000000000363371432161722600237250ustar00rootroot00000000000000# this file is copied from CodeFormer repository. Please see comment in modules/codeformer_model.py ''' VQGAN code, adapted from the original created by the Unleashing Transformers authors: https://github.com/samb-t/unleashing-transformers/blob/master/models/vqgan.py ''' import numpy as np import torch import torch.nn as nn import torch.nn.functional as F import copy from basicsr.utils import get_root_logger from basicsr.utils.registry import ARCH_REGISTRY def normalize(in_channels): return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) @torch.jit.script def swish(x): return x*torch.sigmoid(x) # Define VQVAE classes class VectorQuantizer(nn.Module): def __init__(self, codebook_size, emb_dim, beta): super(VectorQuantizer, self).__init__() self.codebook_size = codebook_size # number of embeddings self.emb_dim = emb_dim # dimension of embedding self.beta = beta # commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2 self.embedding = nn.Embedding(self.codebook_size, self.emb_dim) self.embedding.weight.data.uniform_(-1.0 / self.codebook_size, 1.0 / self.codebook_size) def forward(self, z): # reshape z -> (batch, height, width, channel) and flatten z = z.permute(0, 2, 3, 1).contiguous() z_flattened = z.view(-1, self.emb_dim) # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z d = (z_flattened ** 2).sum(dim=1, keepdim=True) + (self.embedding.weight**2).sum(1) - \ 2 * torch.matmul(z_flattened, self.embedding.weight.t()) mean_distance = torch.mean(d) # find closest encodings # min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1) min_encoding_scores, min_encoding_indices = torch.topk(d, 1, dim=1, largest=False) # [0-1], higher score, higher confidence min_encoding_scores = torch.exp(-min_encoding_scores/10) min_encodings = torch.zeros(min_encoding_indices.shape[0], self.codebook_size).to(z) min_encodings.scatter_(1, min_encoding_indices, 1) # get quantized latent vectors z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape) # compute loss for embedding loss = torch.mean((z_q.detach()-z)**2) + self.beta * torch.mean((z_q - z.detach()) ** 2) # preserve gradients z_q = z + (z_q - z).detach() # perplexity e_mean = torch.mean(min_encodings, dim=0) perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10))) # reshape back to match original input shape z_q = z_q.permute(0, 3, 1, 2).contiguous() return z_q, loss, { "perplexity": perplexity, "min_encodings": min_encodings, "min_encoding_indices": min_encoding_indices, "min_encoding_scores": min_encoding_scores, "mean_distance": mean_distance } def get_codebook_feat(self, indices, shape): # input indices: batch*token_num -> (batch*token_num)*1 # shape: batch, height, width, channel indices = indices.view(-1,1) min_encodings = torch.zeros(indices.shape[0], self.codebook_size).to(indices) min_encodings.scatter_(1, indices, 1) # get quantized latent vectors z_q = torch.matmul(min_encodings.float(), self.embedding.weight) if shape is not None: # reshape back to match original input shape z_q = z_q.view(shape).permute(0, 3, 1, 2).contiguous() return z_q class GumbelQuantizer(nn.Module): def __init__(self, codebook_size, emb_dim, num_hiddens, straight_through=False, kl_weight=5e-4, temp_init=1.0): super().__init__() self.codebook_size = codebook_size # number of embeddings self.emb_dim = emb_dim # dimension of embedding self.straight_through = straight_through self.temperature = temp_init self.kl_weight = kl_weight self.proj = nn.Conv2d(num_hiddens, codebook_size, 1) # projects last encoder layer to quantized logits self.embed = nn.Embedding(codebook_size, emb_dim) def forward(self, z): hard = self.straight_through if self.training else True logits = self.proj(z) soft_one_hot = F.gumbel_softmax(logits, tau=self.temperature, dim=1, hard=hard) z_q = torch.einsum("b n h w, n d -> b d h w", soft_one_hot, self.embed.weight) # + kl divergence to the prior loss qy = F.softmax(logits, dim=1) diff = self.kl_weight * torch.sum(qy * torch.log(qy * self.codebook_size + 1e-10), dim=1).mean() min_encoding_indices = soft_one_hot.argmax(dim=1) return z_q, diff, { "min_encoding_indices": min_encoding_indices } class Downsample(nn.Module): def __init__(self, in_channels): super().__init__() self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0) def forward(self, x): pad = (0, 1, 0, 1) x = torch.nn.functional.pad(x, pad, mode="constant", value=0) x = self.conv(x) return x class Upsample(nn.Module): def __init__(self, in_channels): super().__init__() self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1) def forward(self, x): x = F.interpolate(x, scale_factor=2.0, mode="nearest") x = self.conv(x) return x class ResBlock(nn.Module): def __init__(self, in_channels, out_channels=None): super(ResBlock, self).__init__() self.in_channels = in_channels self.out_channels = in_channels if out_channels is None else out_channels self.norm1 = normalize(in_channels) self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) self.norm2 = normalize(out_channels) self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1) if self.in_channels != self.out_channels: self.conv_out = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) def forward(self, x_in): x = x_in x = self.norm1(x) x = swish(x) x = self.conv1(x) x = self.norm2(x) x = swish(x) x = self.conv2(x) if self.in_channels != self.out_channels: x_in = self.conv_out(x_in) return x + x_in class AttnBlock(nn.Module): def __init__(self, in_channels): super().__init__() self.in_channels = in_channels self.norm = normalize(in_channels) self.q = torch.nn.Conv2d( in_channels, in_channels, kernel_size=1, stride=1, padding=0 ) self.k = torch.nn.Conv2d( in_channels, in_channels, kernel_size=1, stride=1, padding=0 ) self.v = torch.nn.Conv2d( in_channels, in_channels, kernel_size=1, stride=1, padding=0 ) self.proj_out = torch.nn.Conv2d( in_channels, in_channels, kernel_size=1, stride=1, padding=0 ) def forward(self, x): h_ = x h_ = self.norm(h_) q = self.q(h_) k = self.k(h_) v = self.v(h_) # compute attention b, c, h, w = q.shape q = q.reshape(b, c, h*w) q = q.permute(0, 2, 1) k = k.reshape(b, c, h*w) w_ = torch.bmm(q, k) w_ = w_ * (int(c)**(-0.5)) w_ = F.softmax(w_, dim=2) # attend to values v = v.reshape(b, c, h*w) w_ = w_.permute(0, 2, 1) h_ = torch.bmm(v, w_) h_ = h_.reshape(b, c, h, w) h_ = self.proj_out(h_) return x+h_ class Encoder(nn.Module): def __init__(self, in_channels, nf, emb_dim, ch_mult, num_res_blocks, resolution, attn_resolutions): super().__init__() self.nf = nf self.num_resolutions = len(ch_mult) self.num_res_blocks = num_res_blocks self.resolution = resolution self.attn_resolutions = attn_resolutions curr_res = self.resolution in_ch_mult = (1,)+tuple(ch_mult) blocks = [] # initial convultion blocks.append(nn.Conv2d(in_channels, nf, kernel_size=3, stride=1, padding=1)) # residual and downsampling blocks, with attention on smaller res (16x16) for i in range(self.num_resolutions): block_in_ch = nf * in_ch_mult[i] block_out_ch = nf * ch_mult[i] for _ in range(self.num_res_blocks): blocks.append(ResBlock(block_in_ch, block_out_ch)) block_in_ch = block_out_ch if curr_res in attn_resolutions: blocks.append(AttnBlock(block_in_ch)) if i != self.num_resolutions - 1: blocks.append(Downsample(block_in_ch)) curr_res = curr_res // 2 # non-local attention block blocks.append(ResBlock(block_in_ch, block_in_ch)) blocks.append(AttnBlock(block_in_ch)) blocks.append(ResBlock(block_in_ch, block_in_ch)) # normalise and convert to latent size blocks.append(normalize(block_in_ch)) blocks.append(nn.Conv2d(block_in_ch, emb_dim, kernel_size=3, stride=1, padding=1)) self.blocks = nn.ModuleList(blocks) def forward(self, x): for block in self.blocks: x = block(x) return x class Generator(nn.Module): def __init__(self, nf, emb_dim, ch_mult, res_blocks, img_size, attn_resolutions): super().__init__() self.nf = nf self.ch_mult = ch_mult self.num_resolutions = len(self.ch_mult) self.num_res_blocks = res_blocks self.resolution = img_size self.attn_resolutions = attn_resolutions self.in_channels = emb_dim self.out_channels = 3 block_in_ch = self.nf * self.ch_mult[-1] curr_res = self.resolution // 2 ** (self.num_resolutions-1) blocks = [] # initial conv blocks.append(nn.Conv2d(self.in_channels, block_in_ch, kernel_size=3, stride=1, padding=1)) # non-local attention block blocks.append(ResBlock(block_in_ch, block_in_ch)) blocks.append(AttnBlock(block_in_ch)) blocks.append(ResBlock(block_in_ch, block_in_ch)) for i in reversed(range(self.num_resolutions)): block_out_ch = self.nf * self.ch_mult[i] for _ in range(self.num_res_blocks): blocks.append(ResBlock(block_in_ch, block_out_ch)) block_in_ch = block_out_ch if curr_res in self.attn_resolutions: blocks.append(AttnBlock(block_in_ch)) if i != 0: blocks.append(Upsample(block_in_ch)) curr_res = curr_res * 2 blocks.append(normalize(block_in_ch)) blocks.append(nn.Conv2d(block_in_ch, self.out_channels, kernel_size=3, stride=1, padding=1)) self.blocks = nn.ModuleList(blocks) def forward(self, x): for block in self.blocks: x = block(x) return x @ARCH_REGISTRY.register() class VQAutoEncoder(nn.Module): def __init__(self, img_size, nf, ch_mult, quantizer="nearest", res_blocks=2, attn_resolutions=[16], codebook_size=1024, emb_dim=256, beta=0.25, gumbel_straight_through=False, gumbel_kl_weight=1e-8, model_path=None): super().__init__() logger = get_root_logger() self.in_channels = 3 self.nf = nf self.n_blocks = res_blocks self.codebook_size = codebook_size self.embed_dim = emb_dim self.ch_mult = ch_mult self.resolution = img_size self.attn_resolutions = attn_resolutions self.quantizer_type = quantizer self.encoder = Encoder( self.in_channels, self.nf, self.embed_dim, self.ch_mult, self.n_blocks, self.resolution, self.attn_resolutions ) if self.quantizer_type == "nearest": self.beta = beta #0.25 self.quantize = VectorQuantizer(self.codebook_size, self.embed_dim, self.beta) elif self.quantizer_type == "gumbel": self.gumbel_num_hiddens = emb_dim self.straight_through = gumbel_straight_through self.kl_weight = gumbel_kl_weight self.quantize = GumbelQuantizer( self.codebook_size, self.embed_dim, self.gumbel_num_hiddens, self.straight_through, self.kl_weight ) self.generator = Generator( self.nf, self.embed_dim, self.ch_mult, self.n_blocks, self.resolution, self.attn_resolutions ) if model_path is not None: chkpt = torch.load(model_path, map_location='cpu') if 'params_ema' in chkpt: self.load_state_dict(torch.load(model_path, map_location='cpu')['params_ema']) logger.info(f'vqgan is loaded from: {model_path} [params_ema]') elif 'params' in chkpt: self.load_state_dict(torch.load(model_path, map_location='cpu')['params']) logger.info(f'vqgan is loaded from: {model_path} [params]') else: raise ValueError(f'Wrong params!') def forward(self, x): x = self.encoder(x) quant, codebook_loss, quant_stats = self.quantize(x) x = self.generator(quant) return x, codebook_loss, quant_stats # patch based discriminator @ARCH_REGISTRY.register() class VQGANDiscriminator(nn.Module): def __init__(self, nc=3, ndf=64, n_layers=4, model_path=None): super().__init__() layers = [nn.Conv2d(nc, ndf, kernel_size=4, stride=2, padding=1), nn.LeakyReLU(0.2, True)] ndf_mult = 1 ndf_mult_prev = 1 for n in range(1, n_layers): # gradually increase the number of filters ndf_mult_prev = ndf_mult ndf_mult = min(2 ** n, 8) layers += [ nn.Conv2d(ndf * ndf_mult_prev, ndf * ndf_mult, kernel_size=4, stride=2, padding=1, bias=False), nn.BatchNorm2d(ndf * ndf_mult), nn.LeakyReLU(0.2, True) ] ndf_mult_prev = ndf_mult ndf_mult = min(2 ** n_layers, 8) layers += [ nn.Conv2d(ndf * ndf_mult_prev, ndf * ndf_mult, kernel_size=4, stride=1, padding=1, bias=False), nn.BatchNorm2d(ndf * ndf_mult), nn.LeakyReLU(0.2, True) ] layers += [ nn.Conv2d(ndf * ndf_mult, 1, kernel_size=4, stride=1, padding=1)] # output 1 channel prediction map self.main = nn.Sequential(*layers) if model_path is not None: chkpt = torch.load(model_path, map_location='cpu') if 'params_d' in chkpt: self.load_state_dict(torch.load(model_path, map_location='cpu')['params_d']) elif 'params' in chkpt: self.load_state_dict(torch.load(model_path, map_location='cpu')['params']) else: raise ValueError(f'Wrong params!') def forward(self, x): return self.main(x)stable-diffusion-webui/modules/codeformer_model.py000066400000000000000000000134441432161722600227660ustar00rootroot00000000000000import os import sys import traceback import cv2 import torch import modules.face_restoration import modules.shared from modules import shared, devices, modelloader from modules.paths import script_path, models_path # codeformer people made a choice to include modified basicsr library to their project which makes # it utterly impossible to use it alongside with other libraries that also use basicsr, like GFPGAN. # I am making a choice to include some files from codeformer to work around this issue. model_dir = "Codeformer" model_path = os.path.join(models_path, model_dir) model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth' have_codeformer = False codeformer = None def setup_model(dirname): global model_path if not os.path.exists(model_path): os.makedirs(model_path) path = modules.paths.paths.get("CodeFormer", None) if path is None: return try: from torchvision.transforms.functional import normalize from modules.codeformer.codeformer_arch import CodeFormer from basicsr.utils.download_util import load_file_from_url from basicsr.utils import imwrite, img2tensor, tensor2img from facelib.utils.face_restoration_helper import FaceRestoreHelper from modules.shared import cmd_opts net_class = CodeFormer class FaceRestorerCodeFormer(modules.face_restoration.FaceRestoration): def name(self): return "CodeFormer" def __init__(self, dirname): self.net = None self.face_helper = None self.cmd_dir = dirname def create_models(self): if self.net is not None and self.face_helper is not None: self.net.to(devices.device_codeformer) return self.net, self.face_helper model_paths = modelloader.load_models(model_path, model_url, self.cmd_dir, download_name='codeformer-v0.1.0.pth') if len(model_paths) != 0: ckpt_path = model_paths[0] else: print("Unable to load codeformer model.") return None, None net = net_class(dim_embd=512, codebook_size=1024, n_head=8, n_layers=9, connect_list=['32', '64', '128', '256']).to(devices.device_codeformer) checkpoint = torch.load(ckpt_path)['params_ema'] net.load_state_dict(checkpoint) net.eval() face_helper = FaceRestoreHelper(1, face_size=512, crop_ratio=(1, 1), det_model='retinaface_resnet50', save_ext='png', use_parse=True, device=devices.device_codeformer) self.net = net self.face_helper = face_helper return net, face_helper def send_model_to(self, device): self.net.to(device) self.face_helper.face_det.to(device) self.face_helper.face_parse.to(device) def restore(self, np_image, w=None): np_image = np_image[:, :, ::-1] original_resolution = np_image.shape[0:2] self.create_models() if self.net is None or self.face_helper is None: return np_image self.send_model_to(devices.device_codeformer) self.face_helper.clean_all() self.face_helper.read_image(np_image) self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5) self.face_helper.align_warp_face() for idx, cropped_face in enumerate(self.face_helper.cropped_faces): cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True) normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True) cropped_face_t = cropped_face_t.unsqueeze(0).to(devices.device_codeformer) try: with torch.no_grad(): output = self.net(cropped_face_t, w=w if w is not None else shared.opts.code_former_weight, adain=True)[0] restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1)) del output torch.cuda.empty_cache() except Exception as error: print(f'\tFailed inference for CodeFormer: {error}', file=sys.stderr) restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1)) restored_face = restored_face.astype('uint8') self.face_helper.add_restored_face(restored_face) self.face_helper.get_inverse_affine(None) restored_img = self.face_helper.paste_faces_to_input_image() restored_img = restored_img[:, :, ::-1] if original_resolution != restored_img.shape[0:2]: restored_img = cv2.resize(restored_img, (0, 0), fx=original_resolution[1]/restored_img.shape[1], fy=original_resolution[0]/restored_img.shape[0], interpolation=cv2.INTER_LINEAR) self.face_helper.clean_all() if shared.opts.face_restoration_unload: self.send_model_to(devices.cpu) return restored_img global have_codeformer have_codeformer = True global codeformer codeformer = FaceRestorerCodeFormer(dirname) shared.face_restorers.append(codeformer) except Exception: print("Error setting up CodeFormer:", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) # sys.path = stored_sys_path stable-diffusion-webui/modules/deepbooru.py000066400000000000000000000137031432161722600214430ustar00rootroot00000000000000import os.path from concurrent.futures import ProcessPoolExecutor import multiprocessing import time import re re_special = re.compile(r'([\\()])') def get_deepbooru_tags(pil_image): """ This method is for running only one image at a time for simple use. Used to the img2img interrogate. """ from modules import shared # prevents circular reference try: create_deepbooru_process(shared.opts.interrogate_deepbooru_score_threshold, create_deepbooru_opts()) return get_tags_from_process(pil_image) finally: release_process() def create_deepbooru_opts(): from modules import shared return { "use_spaces": shared.opts.deepbooru_use_spaces, "use_escape": shared.opts.deepbooru_escape, "alpha_sort": shared.opts.deepbooru_sort_alpha, } def deepbooru_process(queue, deepbooru_process_return, threshold, deepbooru_opts): model, tags = get_deepbooru_tags_model() while True: # while process is running, keep monitoring queue for new image pil_image = queue.get() if pil_image == "QUIT": break else: deepbooru_process_return["value"] = get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_opts) def create_deepbooru_process(threshold, deepbooru_opts): """ Creates deepbooru process. A queue is created to send images into the process. This enables multiple images to be processed in a row without reloading the model or creating a new process. To return the data, a shared dictionary is created to hold the tags created. To wait for tags to be returned, a value of -1 is assigned to the dictionary and the method adding the image to the queue should wait for this value to be updated with the tags. """ from modules import shared # prevents circular reference shared.deepbooru_process_manager = multiprocessing.Manager() shared.deepbooru_process_queue = shared.deepbooru_process_manager.Queue() shared.deepbooru_process_return = shared.deepbooru_process_manager.dict() shared.deepbooru_process_return["value"] = -1 shared.deepbooru_process = multiprocessing.Process(target=deepbooru_process, args=(shared.deepbooru_process_queue, shared.deepbooru_process_return, threshold, deepbooru_opts)) shared.deepbooru_process.start() def get_tags_from_process(image): from modules import shared shared.deepbooru_process_return["value"] = -1 shared.deepbooru_process_queue.put(image) while shared.deepbooru_process_return["value"] == -1: time.sleep(0.2) caption = shared.deepbooru_process_return["value"] shared.deepbooru_process_return["value"] = -1 return caption def release_process(): """ Stops the deepbooru process to return used memory """ from modules import shared # prevents circular reference shared.deepbooru_process_queue.put("QUIT") shared.deepbooru_process.join() shared.deepbooru_process_queue = None shared.deepbooru_process = None shared.deepbooru_process_return = None shared.deepbooru_process_manager = None def get_deepbooru_tags_model(): import deepdanbooru as dd import tensorflow as tf import numpy as np this_folder = os.path.dirname(__file__) model_path = os.path.abspath(os.path.join(this_folder, '..', 'models', 'deepbooru')) if not os.path.exists(os.path.join(model_path, 'project.json')): # there is no point importing these every time import zipfile from basicsr.utils.download_util import load_file_from_url load_file_from_url( r"https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/deepdanbooru-v3-20211112-sgd-e28.zip", model_path) with zipfile.ZipFile(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"), "r") as zip_ref: zip_ref.extractall(model_path) os.remove(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip")) tags = dd.project.load_tags_from_project(model_path) model = dd.project.load_model_from_project( model_path, compile_model=True ) return model, tags def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_opts): import deepdanbooru as dd import tensorflow as tf import numpy as np alpha_sort = deepbooru_opts['alpha_sort'] use_spaces = deepbooru_opts['use_spaces'] use_escape = deepbooru_opts['use_escape'] width = model.input_shape[2] height = model.input_shape[1] image = np.array(pil_image) image = tf.image.resize( image, size=(height, width), method=tf.image.ResizeMethod.AREA, preserve_aspect_ratio=True, ) image = image.numpy() # EagerTensor to np.array image = dd.image.transform_and_pad_image(image, width, height) image = image / 255.0 image_shape = image.shape image = image.reshape((1, image_shape[0], image_shape[1], image_shape[2])) y = model.predict(image)[0] result_dict = {} for i, tag in enumerate(tags): result_dict[tag] = y[i] unsorted_tags_in_theshold = [] result_tags_print = [] for tag in tags: if result_dict[tag] >= threshold: if tag.startswith("rating:"): continue unsorted_tags_in_theshold.append((result_dict[tag], tag)) result_tags_print.append(f'{result_dict[tag]} {tag}') # sort tags result_tags_out = [] sort_ndx = 0 if alpha_sort: sort_ndx = 1 # sort by reverse by likelihood and normal for alpha unsorted_tags_in_theshold.sort(key=lambda y: y[sort_ndx], reverse=(not alpha_sort)) for weight, tag in unsorted_tags_in_theshold: result_tags_out.append(tag) print('\n'.join(sorted(result_tags_print, reverse=True))) tags_text = ', '.join(result_tags_out) if use_spaces: tags_text = tags_text.replace('_', ' ') if use_escape: tags_text = re.sub(re_special, r'\\\1', tags_text) return tags_text.replace(':', ' ') stable-diffusion-webui/modules/devices.py000066400000000000000000000035471432161722600211060ustar00rootroot00000000000000import contextlib import torch from modules import errors # has_mps is only available in nightly pytorch (for now), `getattr` for compatibility has_mps = getattr(torch, 'has_mps', False) cpu = torch.device("cpu") def get_optimal_device(): if torch.cuda.is_available(): return torch.device("cuda") if has_mps: return torch.device("mps") return cpu def torch_gc(): if torch.cuda.is_available(): torch.cuda.empty_cache() torch.cuda.ipc_collect() def enable_tf32(): if torch.cuda.is_available(): torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cudnn.allow_tf32 = True errors.run(enable_tf32, "Enabling TF32") device = device_gfpgan = device_bsrgan = device_esrgan = device_scunet = device_codeformer = get_optimal_device() dtype = torch.float16 dtype_vae = torch.float16 def randn(seed, shape): # Pytorch currently doesn't handle setting randomness correctly when the metal backend is used. if device.type == 'mps': generator = torch.Generator(device=cpu) generator.manual_seed(seed) noise = torch.randn(shape, generator=generator, device=cpu).to(device) return noise torch.manual_seed(seed) return torch.randn(shape, device=device) def randn_without_seed(shape): # Pytorch currently doesn't handle setting randomness correctly when the metal backend is used. if device.type == 'mps': generator = torch.Generator(device=cpu) noise = torch.randn(shape, generator=generator, device=cpu).to(device) return noise return torch.randn(shape, device=device) def autocast(disable=False): from modules import shared if disable: return contextlib.nullcontext() if dtype == torch.float32 or shared.cmd_opts.precision == "full": return contextlib.nullcontext() return torch.autocast("cuda") stable-diffusion-webui/modules/errors.py000066400000000000000000000003451432161722600207710ustar00rootroot00000000000000import sys import traceback def run(code, task): try: code() except Exception as e: print(f"{task}: {type(e).__name__}", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) stable-diffusion-webui/modules/esrgan_model.py000066400000000000000000000135611432161722600221200ustar00rootroot00000000000000import os import numpy as np import torch from PIL import Image from basicsr.utils.download_util import load_file_from_url import modules.esrgan_model_arch as arch from modules import shared, modelloader, images, devices from modules.upscaler import Upscaler, UpscalerData from modules.shared import opts def fix_model_layers(crt_model, pretrained_net): # this code is adapted from https://github.com/xinntao/ESRGAN if 'conv_first.weight' in pretrained_net: return pretrained_net if 'model.0.weight' not in pretrained_net: is_realesrgan = "params_ema" in pretrained_net and 'body.0.rdb1.conv1.weight' in pretrained_net["params_ema"] if is_realesrgan: raise Exception("The file is a RealESRGAN model, it can't be used as a ESRGAN model.") else: raise Exception("The file is not a ESRGAN model.") crt_net = crt_model.state_dict() load_net_clean = {} for k, v in pretrained_net.items(): if k.startswith('module.'): load_net_clean[k[7:]] = v else: load_net_clean[k] = v pretrained_net = load_net_clean tbd = [] for k, v in crt_net.items(): tbd.append(k) # directly copy for k, v in crt_net.items(): if k in pretrained_net and pretrained_net[k].size() == v.size(): crt_net[k] = pretrained_net[k] tbd.remove(k) crt_net['conv_first.weight'] = pretrained_net['model.0.weight'] crt_net['conv_first.bias'] = pretrained_net['model.0.bias'] for k in tbd.copy(): if 'RDB' in k: ori_k = k.replace('RRDB_trunk.', 'model.1.sub.') if '.weight' in k: ori_k = ori_k.replace('.weight', '.0.weight') elif '.bias' in k: ori_k = ori_k.replace('.bias', '.0.bias') crt_net[k] = pretrained_net[ori_k] tbd.remove(k) crt_net['trunk_conv.weight'] = pretrained_net['model.1.sub.23.weight'] crt_net['trunk_conv.bias'] = pretrained_net['model.1.sub.23.bias'] crt_net['upconv1.weight'] = pretrained_net['model.3.weight'] crt_net['upconv1.bias'] = pretrained_net['model.3.bias'] crt_net['upconv2.weight'] = pretrained_net['model.6.weight'] crt_net['upconv2.bias'] = pretrained_net['model.6.bias'] crt_net['HRconv.weight'] = pretrained_net['model.8.weight'] crt_net['HRconv.bias'] = pretrained_net['model.8.bias'] crt_net['conv_last.weight'] = pretrained_net['model.10.weight'] crt_net['conv_last.bias'] = pretrained_net['model.10.bias'] return crt_net class UpscalerESRGAN(Upscaler): def __init__(self, dirname): self.name = "ESRGAN" self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/ESRGAN.pth" self.model_name = "ESRGAN_4x" self.scalers = [] self.user_path = dirname super().__init__() model_paths = self.find_models(ext_filter=[".pt", ".pth"]) scalers = [] if len(model_paths) == 0: scaler_data = UpscalerData(self.model_name, self.model_url, self, 4) scalers.append(scaler_data) for file in model_paths: if "http" in file: name = self.model_name else: name = modelloader.friendly_name(file) scaler_data = UpscalerData(name, file, self, 4) self.scalers.append(scaler_data) def do_upscale(self, img, selected_model): model = self.load_model(selected_model) if model is None: return img model.to(devices.device_esrgan) img = esrgan_upscale(model, img) return img def load_model(self, path: str): if "http" in path: filename = load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="%s.pth" % self.model_name, progress=True) else: filename = path if not os.path.exists(filename) or filename is None: print("Unable to load %s from %s" % (self.model_path, filename)) return None pretrained_net = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None) crt_model = arch.RRDBNet(3, 3, 64, 23, gc=32) pretrained_net = fix_model_layers(crt_model, pretrained_net) crt_model.load_state_dict(pretrained_net) crt_model.eval() return crt_model def upscale_without_tiling(model, img): img = np.array(img) img = img[:, :, ::-1] img = np.moveaxis(img, 2, 0) / 255 img = torch.from_numpy(img).float() img = img.unsqueeze(0).to(devices.device_esrgan) with torch.no_grad(): output = model(img) output = output.squeeze().float().cpu().clamp_(0, 1).numpy() output = 255. * np.moveaxis(output, 0, 2) output = output.astype(np.uint8) output = output[:, :, ::-1] return Image.fromarray(output, 'RGB') def esrgan_upscale(model, img): if opts.ESRGAN_tile == 0: return upscale_without_tiling(model, img) grid = images.split_grid(img, opts.ESRGAN_tile, opts.ESRGAN_tile, opts.ESRGAN_tile_overlap) newtiles = [] scale_factor = 1 for y, h, row in grid.tiles: newrow = [] for tiledata in row: x, w, tile = tiledata output = upscale_without_tiling(model, tile) scale_factor = output.width // tile.width newrow.append([x * scale_factor, w * scale_factor, output]) newtiles.append([y * scale_factor, h * scale_factor, newrow]) newgrid = images.Grid(newtiles, grid.tile_w * scale_factor, grid.tile_h * scale_factor, grid.image_w * scale_factor, grid.image_h * scale_factor, grid.overlap * scale_factor) output = images.combine_grid(newgrid) return output stable-diffusion-webui/modules/esrgan_model_arch.py000066400000000000000000000056501432161722600231150ustar00rootroot00000000000000# this file is taken from https://github.com/xinntao/ESRGAN import functools import torch import torch.nn as nn import torch.nn.functional as F def make_layer(block, n_layers): layers = [] for _ in range(n_layers): layers.append(block()) return nn.Sequential(*layers) class ResidualDenseBlock_5C(nn.Module): def __init__(self, nf=64, gc=32, bias=True): super(ResidualDenseBlock_5C, self).__init__() # gc: growth channel, i.e. intermediate channels self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1, bias=bias) self.conv2 = nn.Conv2d(nf + gc, gc, 3, 1, 1, bias=bias) self.conv3 = nn.Conv2d(nf + 2 * gc, gc, 3, 1, 1, bias=bias) self.conv4 = nn.Conv2d(nf + 3 * gc, gc, 3, 1, 1, bias=bias) self.conv5 = nn.Conv2d(nf + 4 * gc, nf, 3, 1, 1, bias=bias) self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) # initialization # mutil.initialize_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1) def forward(self, x): x1 = self.lrelu(self.conv1(x)) x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1))) x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1))) x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1))) x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1)) return x5 * 0.2 + x class RRDB(nn.Module): '''Residual in Residual Dense Block''' def __init__(self, nf, gc=32): super(RRDB, self).__init__() self.RDB1 = ResidualDenseBlock_5C(nf, gc) self.RDB2 = ResidualDenseBlock_5C(nf, gc) self.RDB3 = ResidualDenseBlock_5C(nf, gc) def forward(self, x): out = self.RDB1(x) out = self.RDB2(out) out = self.RDB3(out) return out * 0.2 + x class RRDBNet(nn.Module): def __init__(self, in_nc, out_nc, nf, nb, gc=32): super(RRDBNet, self).__init__() RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc) self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True) self.RRDB_trunk = make_layer(RRDB_block_f, nb) self.trunk_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True) #### upsampling self.upconv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True) self.upconv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True) self.HRconv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True) self.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True) self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) def forward(self, x): fea = self.conv_first(x) trunk = self.trunk_conv(self.RRDB_trunk(fea)) fea = fea + trunk fea = self.lrelu(self.upconv1(F.interpolate(fea, scale_factor=2, mode='nearest'))) fea = self.lrelu(self.upconv2(F.interpolate(fea, scale_factor=2, mode='nearest'))) out = self.conv_last(self.lrelu(self.HRconv(fea))) return out stable-diffusion-webui/modules/extras.py000066400000000000000000000216361432161722600207710ustar00rootroot00000000000000import math import os import numpy as np from PIL import Image import torch import tqdm from modules import processing, shared, images, devices, sd_models from modules.shared import opts import modules.gfpgan_model from modules.ui import plaintext_to_html import modules.codeformer_model import piexif import piexif.helper import gradio as gr cached_images = {} def run_extras(extras_mode, resize_mode, image, image_folder, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility): devices.torch_gc() imageArr = [] # Also keep track of original file names imageNameArr = [] if extras_mode == 1: #convert file to pillow image for img in image_folder: image = Image.open(img) imageArr.append(image) imageNameArr.append(os.path.splitext(img.orig_name)[0]) else: imageArr.append(image) imageNameArr.append(None) outpath = opts.outdir_samples or opts.outdir_extras_samples outputs = [] for image, image_name in zip(imageArr, imageNameArr): if image is None: return outputs, "Please select an input image.", '' existing_pnginfo = image.info or {} image = image.convert("RGB") info = "" if gfpgan_visibility > 0: restored_img = modules.gfpgan_model.gfpgan_fix_faces(np.array(image, dtype=np.uint8)) res = Image.fromarray(restored_img) if gfpgan_visibility < 1.0: res = Image.blend(image, res, gfpgan_visibility) info += f"GFPGAN visibility:{round(gfpgan_visibility, 2)}\n" image = res if codeformer_visibility > 0: restored_img = modules.codeformer_model.codeformer.restore(np.array(image, dtype=np.uint8), w=codeformer_weight) res = Image.fromarray(restored_img) if codeformer_visibility < 1.0: res = Image.blend(image, res, codeformer_visibility) info += f"CodeFormer w: {round(codeformer_weight, 2)}, CodeFormer visibility:{round(codeformer_visibility, 2)}\n" image = res if resize_mode == 1: upscaling_resize = max(upscaling_resize_w/image.width, upscaling_resize_h/image.height) crop_info = " (crop)" if upscaling_crop else "" info += f"Resize to: {upscaling_resize_w:g}x{upscaling_resize_h:g}{crop_info}\n" if upscaling_resize != 1.0: def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop): small = image.crop((image.width // 2, image.height // 2, image.width // 2 + 10, image.height // 2 + 10)) pixels = tuple(np.array(small).flatten().tolist()) key = (resize, scaler_index, image.width, image.height, gfpgan_visibility, codeformer_visibility, codeformer_weight) + pixels c = cached_images.get(key) if c is None: upscaler = shared.sd_upscalers[scaler_index] c = upscaler.scaler.upscale(image, resize, upscaler.data_path) if mode == 1 and crop: cropped = Image.new("RGB", (resize_w, resize_h)) cropped.paste(c, box=(resize_w // 2 - c.width // 2, resize_h // 2 - c.height // 2)) c = cropped cached_images[key] = c return c info += f"Upscale: {round(upscaling_resize, 3)}, model:{shared.sd_upscalers[extras_upscaler_1].name}\n" res = upscale(image, extras_upscaler_1, upscaling_resize, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop) if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0: res2 = upscale(image, extras_upscaler_2, upscaling_resize, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop) info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {round(extras_upscaler_2_visibility, 3)}, model:{shared.sd_upscalers[extras_upscaler_2].name}\n" res = Image.blend(res, res2, extras_upscaler_2_visibility) image = res while len(cached_images) > 2: del cached_images[next(iter(cached_images.keys()))] images.save_image(image, path=outpath, basename="", seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True, no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=image_name if opts.use_original_name_batch else None) if opts.enable_pnginfo: image.info = existing_pnginfo image.info["extras"] = info outputs.append(image) devices.torch_gc() return outputs, plaintext_to_html(info), '' def run_pnginfo(image): if image is None: return '', '', '' items = image.info geninfo = '' if "exif" in image.info: exif = piexif.load(image.info["exif"]) exif_comment = (exif or {}).get("Exif", {}).get(piexif.ExifIFD.UserComment, b'') try: exif_comment = piexif.helper.UserComment.load(exif_comment) except ValueError: exif_comment = exif_comment.decode('utf8', errors="ignore") items['exif comment'] = exif_comment geninfo = exif_comment for field in ['jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif', 'loop', 'background', 'timestamp', 'duration']: items.pop(field, None) geninfo = items.get('parameters', geninfo) info = '' for key, text in items.items(): info += f"""

{plaintext_to_html(str(key))}

{plaintext_to_html(str(text))}

""".strip()+"\n" if len(info) == 0: message = "Nothing found in the image." info = f"

{message}

" return '', geninfo, info def run_modelmerger(primary_model_name, secondary_model_name, interp_method, interp_amount, save_as_half, custom_name): # Linear interpolation (https://en.wikipedia.org/wiki/Linear_interpolation) def weighted_sum(theta0, theta1, alpha): return ((1 - alpha) * theta0) + (alpha * theta1) # Smoothstep (https://en.wikipedia.org/wiki/Smoothstep) def sigmoid(theta0, theta1, alpha): alpha = alpha * alpha * (3 - (2 * alpha)) return theta0 + ((theta1 - theta0) * alpha) # Inverse Smoothstep (https://en.wikipedia.org/wiki/Smoothstep) def inv_sigmoid(theta0, theta1, alpha): import math alpha = 0.5 - math.sin(math.asin(1.0 - 2.0 * alpha) / 3.0) return theta0 + ((theta1 - theta0) * alpha) primary_model_info = sd_models.checkpoints_list[primary_model_name] secondary_model_info = sd_models.checkpoints_list[secondary_model_name] print(f"Loading {primary_model_info.filename}...") primary_model = torch.load(primary_model_info.filename, map_location='cpu') print(f"Loading {secondary_model_info.filename}...") secondary_model = torch.load(secondary_model_info.filename, map_location='cpu') theta_0 = sd_models.get_state_dict_from_checkpoint(primary_model) theta_1 = sd_models.get_state_dict_from_checkpoint(secondary_model) theta_funcs = { "Weighted Sum": weighted_sum, "Sigmoid": sigmoid, "Inverse Sigmoid": inv_sigmoid, } theta_func = theta_funcs[interp_method] print(f"Merging...") for key in tqdm.tqdm(theta_0.keys()): if 'model' in key and key in theta_1: theta_0[key] = theta_func(theta_0[key], theta_1[key], (float(1.0) - interp_amount)) # Need to reverse the interp_amount to match the desired mix ration in the merged checkpoint if save_as_half: theta_0[key] = theta_0[key].half() for key in theta_1.keys(): if 'model' in key and key not in theta_0: theta_0[key] = theta_1[key] if save_as_half: theta_0[key] = theta_0[key].half() ckpt_dir = shared.cmd_opts.ckpt_dir or sd_models.model_path filename = primary_model_info.model_name + '_' + str(round(interp_amount, 2)) + '-' + secondary_model_info.model_name + '_' + str(round((float(1.0) - interp_amount), 2)) + '-' + interp_method.replace(" ", "_") + '-merged.ckpt' filename = filename if custom_name == '' else (custom_name + '.ckpt') output_modelname = os.path.join(ckpt_dir, filename) print(f"Saving to {output_modelname}...") torch.save(primary_model, output_modelname) sd_models.list_models() print(f"Checkpoint saved.") return ["Checkpoint saved to " + output_modelname] + [gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(3)] stable-diffusion-webui/modules/face_restoration.py000066400000000000000000000007561432161722600230120ustar00rootroot00000000000000from modules import shared class FaceRestoration: def name(self): return "None" def restore(self, np_image): return np_image def restore_faces(np_image): face_restorers = [x for x in shared.face_restorers if x.name() == shared.opts.face_restoration_model or shared.opts.face_restoration_model is None] if len(face_restorers) == 0: return np_image face_restorer = face_restorers[0] return face_restorer.restore(np_image) stable-diffusion-webui/modules/generation_parameters_copypaste.py000066400000000000000000000054671432161722600261340ustar00rootroot00000000000000import re import gradio as gr re_param_code = r"\s*([\w ]+):\s*([^,]+)(?:,|$)" re_param = re.compile(re_param_code) re_params = re.compile(r"^(?:" + re_param_code + "){3,}$") re_imagesize = re.compile(r"^(\d+)x(\d+)$") type_of_gr_update = type(gr.update()) def parse_generation_parameters(x: str): """parses generation parameters string, the one you see in text field under the picture in UI: ``` girl with an artist's beret, determined, blue eyes, desert scene, computer monitors, heavy makeup, by Alphonse Mucha and Charlie Bowater, ((eyeshadow)), (coquettish), detailed, intricate Negative prompt: ugly, fat, obese, chubby, (((deformed))), [blurry], bad anatomy, disfigured, poorly drawn face, mutation, mutated, (extra_limb), (ugly), (poorly drawn hands), messy drawing Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model hash: 45dee52b ``` returns a dict with field values """ res = {} prompt = "" negative_prompt = "" done_with_prompt = False *lines, lastline = x.strip().split("\n") if not re_params.match(lastline): lines.append(lastline) lastline = '' for i, line in enumerate(lines): line = line.strip() if line.startswith("Negative prompt:"): done_with_prompt = True line = line[16:].strip() if done_with_prompt: negative_prompt += ("" if negative_prompt == "" else "\n") + line else: prompt += ("" if prompt == "" else "\n") + line if len(prompt) > 0: res["Prompt"] = prompt if len(negative_prompt) > 0: res["Negative prompt"] = negative_prompt for k, v in re_param.findall(lastline): m = re_imagesize.match(v) if m is not None: res[k+"-1"] = m.group(1) res[k+"-2"] = m.group(2) else: res[k] = v return res def connect_paste(button, paste_fields, input_comp, js=None): def paste_func(prompt): params = parse_generation_parameters(prompt) res = [] for output, key in paste_fields: if callable(key): v = key(params) else: v = params.get(key, None) if v is None: res.append(gr.update()) elif isinstance(v, type_of_gr_update): res.append(v) else: try: valtype = type(output.value) val = valtype(v) res.append(gr.update(value=val)) except Exception: res.append(gr.update()) return res button.click( fn=paste_func, _js=js, inputs=[input_comp], outputs=[x[0] for x in paste_fields], ) stable-diffusion-webui/modules/gfpgan_model.py000066400000000000000000000071771432161722600221110ustar00rootroot00000000000000import os import sys import traceback import facexlib import gfpgan import modules.face_restoration from modules import shared, devices, modelloader from modules.paths import models_path model_dir = "GFPGAN" user_path = None model_path = os.path.join(models_path, model_dir) model_url = "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth" have_gfpgan = False loaded_gfpgan_model = None def gfpgann(): global loaded_gfpgan_model global model_path if loaded_gfpgan_model is not None: loaded_gfpgan_model.gfpgan.to(devices.device_gfpgan) return loaded_gfpgan_model if gfpgan_constructor is None: return None models = modelloader.load_models(model_path, model_url, user_path, ext_filter="GFPGAN") if len(models) == 1 and "http" in models[0]: model_file = models[0] elif len(models) != 0: latest_file = max(models, key=os.path.getctime) model_file = latest_file else: print("Unable to load gfpgan model!") return None model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None) loaded_gfpgan_model = model return model def send_model_to(model, device): model.gfpgan.to(device) model.face_helper.face_det.to(device) model.face_helper.face_parse.to(device) def gfpgan_fix_faces(np_image): model = gfpgann() if model is None: return np_image send_model_to(model, devices.device_gfpgan) np_image_bgr = np_image[:, :, ::-1] cropped_faces, restored_faces, gfpgan_output_bgr = model.enhance(np_image_bgr, has_aligned=False, only_center_face=False, paste_back=True) np_image = gfpgan_output_bgr[:, :, ::-1] model.face_helper.clean_all() if shared.opts.face_restoration_unload: send_model_to(model, devices.cpu) return np_image gfpgan_constructor = None def setup_model(dirname): global model_path if not os.path.exists(model_path): os.makedirs(model_path) try: from gfpgan import GFPGANer from facexlib import detection, parsing global user_path global have_gfpgan global gfpgan_constructor load_file_from_url_orig = gfpgan.utils.load_file_from_url facex_load_file_from_url_orig = facexlib.detection.load_file_from_url facex_load_file_from_url_orig2 = facexlib.parsing.load_file_from_url def my_load_file_from_url(**kwargs): return load_file_from_url_orig(**dict(kwargs, model_dir=model_path)) def facex_load_file_from_url(**kwargs): return facex_load_file_from_url_orig(**dict(kwargs, save_dir=model_path, model_dir=None)) def facex_load_file_from_url2(**kwargs): return facex_load_file_from_url_orig2(**dict(kwargs, save_dir=model_path, model_dir=None)) gfpgan.utils.load_file_from_url = my_load_file_from_url facexlib.detection.load_file_from_url = facex_load_file_from_url facexlib.parsing.load_file_from_url = facex_load_file_from_url2 user_path = dirname have_gfpgan = True gfpgan_constructor = GFPGANer class FaceRestorerGFPGAN(modules.face_restoration.FaceRestoration): def name(self): return "GFPGAN" def restore(self, np_image): return gfpgan_fix_faces(np_image) shared.face_restorers.append(FaceRestorerGFPGAN()) except Exception: print("Error setting up GFPGAN:", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) stable-diffusion-webui/modules/hypernetworks/000077500000000000000000000000001432161722600220255ustar00rootroot00000000000000stable-diffusion-webui/modules/hypernetworks/hypernetwork.py000066400000000000000000000244661432161722600251540ustar00rootroot00000000000000import datetime import glob import html import os import sys import traceback import tqdm import torch from ldm.util import default from modules import devices, shared, processing, sd_models import torch from torch import einsum from einops import rearrange, repeat import modules.textual_inversion.dataset from modules.textual_inversion.learn_schedule import LearnRateScheduler class HypernetworkModule(torch.nn.Module): def __init__(self, dim, state_dict=None): super().__init__() self.linear1 = torch.nn.Linear(dim, dim * 2) self.linear2 = torch.nn.Linear(dim * 2, dim) if state_dict is not None: self.load_state_dict(state_dict, strict=True) else: self.linear1.weight.data.normal_(mean=0.0, std=0.01) self.linear1.bias.data.zero_() self.linear2.weight.data.normal_(mean=0.0, std=0.01) self.linear2.bias.data.zero_() self.to(devices.device) def forward(self, x): return x + (self.linear2(self.linear1(x))) class Hypernetwork: filename = None name = None def __init__(self, name=None, enable_sizes=None): self.filename = None self.name = name self.layers = {} self.step = 0 self.sd_checkpoint = None self.sd_checkpoint_name = None for size in enable_sizes or []: self.layers[size] = (HypernetworkModule(size), HypernetworkModule(size)) def weights(self): res = [] for k, layers in self.layers.items(): for layer in layers: layer.train() res += [layer.linear1.weight, layer.linear1.bias, layer.linear2.weight, layer.linear2.bias] return res def save(self, filename): state_dict = {} for k, v in self.layers.items(): state_dict[k] = (v[0].state_dict(), v[1].state_dict()) state_dict['step'] = self.step state_dict['name'] = self.name state_dict['sd_checkpoint'] = self.sd_checkpoint state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name torch.save(state_dict, filename) def load(self, filename): self.filename = filename if self.name is None: self.name = os.path.splitext(os.path.basename(filename))[0] state_dict = torch.load(filename, map_location='cpu') for size, sd in state_dict.items(): if type(size) == int: self.layers[size] = (HypernetworkModule(size, sd[0]), HypernetworkModule(size, sd[1])) self.name = state_dict.get('name', self.name) self.step = state_dict.get('step', 0) self.sd_checkpoint = state_dict.get('sd_checkpoint', None) self.sd_checkpoint_name = state_dict.get('sd_checkpoint_name', None) def list_hypernetworks(path): res = {} for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True): name = os.path.splitext(os.path.basename(filename))[0] res[name] = filename return res def load_hypernetwork(filename): path = shared.hypernetworks.get(filename, None) if path is not None: print(f"Loading hypernetwork {filename}") try: shared.loaded_hypernetwork = Hypernetwork() shared.loaded_hypernetwork.load(path) except Exception: print(f"Error loading hypernetwork {path}", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) else: if shared.loaded_hypernetwork is not None: print(f"Unloading hypernetwork") shared.loaded_hypernetwork = None def find_closest_hypernetwork_name(search: str): if not search: return None search = search.lower() applicable = [name for name in shared.hypernetworks if search in name.lower()] if not applicable: return None applicable = sorted(applicable, key=lambda name: len(name)) return applicable[0] def apply_hypernetwork(hypernetwork, context, layer=None): hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None) if hypernetwork_layers is None: return context, context if layer is not None: layer.hyper_k = hypernetwork_layers[0] layer.hyper_v = hypernetwork_layers[1] context_k = hypernetwork_layers[0](context) context_v = hypernetwork_layers[1](context) return context_k, context_v def attention_CrossAttention_forward(self, x, context=None, mask=None): h = self.heads q = self.to_q(x) context = default(context, x) context_k, context_v = apply_hypernetwork(shared.loaded_hypernetwork, context, self) k = self.to_k(context_k) v = self.to_v(context_v) q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v)) sim = einsum('b i d, b j d -> b i j', q, k) * self.scale if mask is not None: mask = rearrange(mask, 'b ... -> b (...)') max_neg_value = -torch.finfo(sim.dtype).max mask = repeat(mask, 'b j -> (b h) () j', h=h) sim.masked_fill_(~mask, max_neg_value) # attention, what we cannot get enough of attn = sim.softmax(dim=-1) out = einsum('b i j, b j d -> b i d', attn, v) out = rearrange(out, '(b h) n d -> b n (h d)', h=h) return self.to_out(out) def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_image_prompt): assert hypernetwork_name, 'hypernetwork not selected' path = shared.hypernetworks.get(hypernetwork_name, None) shared.loaded_hypernetwork = Hypernetwork() shared.loaded_hypernetwork.load(path) shared.state.textinfo = "Initializing hypernetwork training..." shared.state.job_count = steps filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt') log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), hypernetwork_name) unload = shared.opts.unload_models_when_training if save_hypernetwork_every > 0: hypernetwork_dir = os.path.join(log_directory, "hypernetworks") os.makedirs(hypernetwork_dir, exist_ok=True) else: hypernetwork_dir = None if create_image_every > 0: images_dir = os.path.join(log_directory, "images") os.makedirs(images_dir, exist_ok=True) else: images_dir = None shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..." with torch.autocast("cuda"): ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=1, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True) if unload: shared.sd_model.cond_stage_model.to(devices.cpu) shared.sd_model.first_stage_model.to(devices.cpu) hypernetwork = shared.loaded_hypernetwork weights = hypernetwork.weights() for weight in weights: weight.requires_grad = True losses = torch.zeros((32,)) last_saved_file = "" last_saved_image = "" ititial_step = hypernetwork.step or 0 if ititial_step > steps: return hypernetwork, filename scheduler = LearnRateScheduler(learn_rate, steps, ititial_step) optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate) pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step) for i, entry in pbar: hypernetwork.step = i + ititial_step scheduler.apply(optimizer, hypernetwork.step) if scheduler.finished: break if shared.state.interrupted: break with torch.autocast("cuda"): cond = entry.cond.to(devices.device) x = entry.latent.to(devices.device) loss = shared.sd_model(x.unsqueeze(0), cond)[0] del x del cond losses[hypernetwork.step % losses.shape[0]] = loss.item() optimizer.zero_grad() loss.backward() optimizer.step() pbar.set_description(f"loss: {losses.mean():.7f}") if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0: last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name}-{hypernetwork.step}.pt') hypernetwork.save(last_saved_file) if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0: last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png') preview_text = entry.cond_text if preview_image_prompt == "" else preview_image_prompt optimizer.zero_grad() shared.sd_model.cond_stage_model.to(devices.device) shared.sd_model.first_stage_model.to(devices.device) p = processing.StableDiffusionProcessingTxt2Img( sd_model=shared.sd_model, prompt=preview_text, steps=20, do_not_save_grid=True, do_not_save_samples=True, ) processed = processing.process_images(p) image = processed.images[0] if len(processed.images)>0 else None if unload: shared.sd_model.cond_stage_model.to(devices.cpu) shared.sd_model.first_stage_model.to(devices.cpu) if image is not None: shared.state.current_image = image image.save(last_saved_image) last_saved_image += f", prompt: {preview_text}" shared.state.job_no = hypernetwork.step shared.state.textinfo = f"""

Loss: {losses.mean():.7f}
Step: {hypernetwork.step}
Last prompt: {html.escape(entry.cond_text)}
Last saved embedding: {html.escape(last_saved_file)}
Last saved image: {html.escape(last_saved_image)}

""" checkpoint = sd_models.select_checkpoint() hypernetwork.sd_checkpoint = checkpoint.hash hypernetwork.sd_checkpoint_name = checkpoint.model_name hypernetwork.save(filename) return hypernetwork, filename stable-diffusion-webui/modules/hypernetworks/ui.py000066400000000000000000000027711432161722600230230ustar00rootroot00000000000000import html import os import gradio as gr import modules.textual_inversion.textual_inversion import modules.textual_inversion.preprocess from modules import sd_hijack, shared, devices from modules.hypernetworks import hypernetwork def create_hypernetwork(name, enable_sizes): fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt") assert not os.path.exists(fn), f"file {fn} already exists" hypernet = modules.hypernetworks.hypernetwork.Hypernetwork(name=name, enable_sizes=[int(x) for x in enable_sizes]) hypernet.save(fn) shared.reload_hypernetworks() return gr.Dropdown.update(choices=sorted([x for x in shared.hypernetworks.keys()])), f"Created: {fn}", "" def train_hypernetwork(*args): initial_hypernetwork = shared.loaded_hypernetwork assert not shared.cmd_opts.lowvram, 'Training models with lowvram is not possible' try: sd_hijack.undo_optimizations() hypernetwork, filename = modules.hypernetworks.hypernetwork.train_hypernetwork(*args) res = f""" Training {'interrupted' if shared.state.interrupted else 'finished'} at {hypernetwork.step} steps. Hypernetwork saved to {html.escape(filename)} """ return res, "" except Exception: raise finally: shared.loaded_hypernetwork = initial_hypernetwork shared.sd_model.cond_stage_model.to(devices.device) shared.sd_model.first_stage_model.to(devices.device) sd_hijack.apply_optimizations() stable-diffusion-webui/modules/images.py000066400000000000000000000433341432161722600207270ustar00rootroot00000000000000import datetime import math import os from collections import namedtuple import re import numpy as np import piexif import piexif.helper from PIL import Image, ImageFont, ImageDraw, PngImagePlugin from fonts.ttf import Roboto import string from modules import sd_samplers, shared from modules.shared import opts, cmd_opts LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS) def image_grid(imgs, batch_size=1, rows=None): if rows is None: if opts.n_rows > 0: rows = opts.n_rows elif opts.n_rows == 0: rows = batch_size else: rows = math.sqrt(len(imgs)) rows = round(rows) cols = math.ceil(len(imgs) / rows) w, h = imgs[0].size grid = Image.new('RGB', size=(cols * w, rows * h), color='black') for i, img in enumerate(imgs): grid.paste(img, box=(i % cols * w, i // cols * h)) return grid Grid = namedtuple("Grid", ["tiles", "tile_w", "tile_h", "image_w", "image_h", "overlap"]) def split_grid(image, tile_w=512, tile_h=512, overlap=64): w = image.width h = image.height non_overlap_width = tile_w - overlap non_overlap_height = tile_h - overlap cols = math.ceil((w - overlap) / non_overlap_width) rows = math.ceil((h - overlap) / non_overlap_height) dx = (w - tile_w) / (cols - 1) if cols > 1 else 0 dy = (h - tile_h) / (rows - 1) if rows > 1 else 0 grid = Grid([], tile_w, tile_h, w, h, overlap) for row in range(rows): row_images = [] y = int(row * dy) if y + tile_h >= h: y = h - tile_h for col in range(cols): x = int(col * dx) if x + tile_w >= w: x = w - tile_w tile = image.crop((x, y, x + tile_w, y + tile_h)) row_images.append([x, tile_w, tile]) grid.tiles.append([y, tile_h, row_images]) return grid def combine_grid(grid): def make_mask_image(r): r = r * 255 / grid.overlap r = r.astype(np.uint8) return Image.fromarray(r, 'L') mask_w = make_mask_image(np.arange(grid.overlap, dtype=np.float32).reshape((1, grid.overlap)).repeat(grid.tile_h, axis=0)) mask_h = make_mask_image(np.arange(grid.overlap, dtype=np.float32).reshape((grid.overlap, 1)).repeat(grid.image_w, axis=1)) combined_image = Image.new("RGB", (grid.image_w, grid.image_h)) for y, h, row in grid.tiles: combined_row = Image.new("RGB", (grid.image_w, h)) for x, w, tile in row: if x == 0: combined_row.paste(tile, (0, 0)) continue combined_row.paste(tile.crop((0, 0, grid.overlap, h)), (x, 0), mask=mask_w) combined_row.paste(tile.crop((grid.overlap, 0, w, h)), (x + grid.overlap, 0)) if y == 0: combined_image.paste(combined_row, (0, 0)) continue combined_image.paste(combined_row.crop((0, 0, combined_row.width, grid.overlap)), (0, y), mask=mask_h) combined_image.paste(combined_row.crop((0, grid.overlap, combined_row.width, h)), (0, y + grid.overlap)) return combined_image class GridAnnotation: def __init__(self, text='', is_active=True): self.text = text self.is_active = is_active self.size = None def draw_grid_annotations(im, width, height, hor_texts, ver_texts): def wrap(drawing, text, font, line_length): lines = [''] for word in text.split(): line = f'{lines[-1]} {word}'.strip() if drawing.textlength(line, font=font) <= line_length: lines[-1] = line else: lines.append(word) return lines def draw_texts(drawing, draw_x, draw_y, lines): for i, line in enumerate(lines): drawing.multiline_text((draw_x, draw_y + line.size[1] / 2), line.text, font=fnt, fill=color_active if line.is_active else color_inactive, anchor="mm", align="center") if not line.is_active: drawing.line((draw_x - line.size[0] // 2, draw_y + line.size[1] // 2, draw_x + line.size[0] // 2, draw_y + line.size[1] // 2), fill=color_inactive, width=4) draw_y += line.size[1] + line_spacing fontsize = (width + height) // 25 line_spacing = fontsize // 2 try: fnt = ImageFont.truetype(opts.font or Roboto, fontsize) except Exception: fnt = ImageFont.truetype(Roboto, fontsize) color_active = (0, 0, 0) color_inactive = (153, 153, 153) pad_left = 0 if sum([sum([len(line.text) for line in lines]) for lines in ver_texts]) == 0 else width * 3 // 4 cols = im.width // width rows = im.height // height assert cols == len(hor_texts), f'bad number of horizontal texts: {len(hor_texts)}; must be {cols}' assert rows == len(ver_texts), f'bad number of vertical texts: {len(ver_texts)}; must be {rows}' calc_img = Image.new("RGB", (1, 1), "white") calc_d = ImageDraw.Draw(calc_img) for texts, allowed_width in zip(hor_texts + ver_texts, [width] * len(hor_texts) + [pad_left] * len(ver_texts)): items = [] + texts texts.clear() for line in items: wrapped = wrap(calc_d, line.text, fnt, allowed_width) texts += [GridAnnotation(x, line.is_active) for x in wrapped] for line in texts: bbox = calc_d.multiline_textbbox((0, 0), line.text, font=fnt) line.size = (bbox[2] - bbox[0], bbox[3] - bbox[1]) hor_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing for lines in hor_texts] ver_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing * len(lines) for lines in ver_texts] pad_top = max(hor_text_heights) + line_spacing * 2 result = Image.new("RGB", (im.width + pad_left, im.height + pad_top), "white") result.paste(im, (pad_left, pad_top)) d = ImageDraw.Draw(result) for col in range(cols): x = pad_left + width * col + width / 2 y = pad_top / 2 - hor_text_heights[col] / 2 draw_texts(d, x, y, hor_texts[col]) for row in range(rows): x = pad_left / 2 y = pad_top + height * row + height / 2 - ver_text_heights[row] / 2 draw_texts(d, x, y, ver_texts[row]) return result def draw_prompt_matrix(im, width, height, all_prompts): prompts = all_prompts[1:] boundary = math.ceil(len(prompts) / 2) prompts_horiz = prompts[:boundary] prompts_vert = prompts[boundary:] hor_texts = [[GridAnnotation(x, is_active=pos & (1 << i) != 0) for i, x in enumerate(prompts_horiz)] for pos in range(1 << len(prompts_horiz))] ver_texts = [[GridAnnotation(x, is_active=pos & (1 << i) != 0) for i, x in enumerate(prompts_vert)] for pos in range(1 << len(prompts_vert))] return draw_grid_annotations(im, width, height, hor_texts, ver_texts) def resize_image(resize_mode, im, width, height): def resize(im, w, h): if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None" or im.mode == 'L': return im.resize((w, h), resample=LANCZOS) scale = max(w / im.width, h / im.height) if scale > 1.0: upscalers = [x for x in shared.sd_upscalers if x.name == opts.upscaler_for_img2img] assert len(upscalers) > 0, f"could not find upscaler named {opts.upscaler_for_img2img}" upscaler = upscalers[0] im = upscaler.scaler.upscale(im, scale, upscaler.data_path) if im.width != w or im.height != h: im = im.resize((w, h), resample=LANCZOS) return im if resize_mode == 0: res = resize(im, width, height) elif resize_mode == 1: ratio = width / height src_ratio = im.width / im.height src_w = width if ratio > src_ratio else im.width * height // im.height src_h = height if ratio <= src_ratio else im.height * width // im.width resized = resize(im, src_w, src_h) res = Image.new("RGB", (width, height)) res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2)) else: ratio = width / height src_ratio = im.width / im.height src_w = width if ratio < src_ratio else im.width * height // im.height src_h = height if ratio >= src_ratio else im.height * width // im.width resized = resize(im, src_w, src_h) res = Image.new("RGB", (width, height)) res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2)) if ratio < src_ratio: fill_height = height // 2 - src_h // 2 res.paste(resized.resize((width, fill_height), box=(0, 0, width, 0)), box=(0, 0)) res.paste(resized.resize((width, fill_height), box=(0, resized.height, width, resized.height)), box=(0, fill_height + src_h)) elif ratio > src_ratio: fill_width = width // 2 - src_w // 2 res.paste(resized.resize((fill_width, height), box=(0, 0, 0, height)), box=(0, 0)) res.paste(resized.resize((fill_width, height), box=(resized.width, 0, resized.width, height)), box=(fill_width + src_w, 0)) return res invalid_filename_chars = '<>:"/\\|?*\n' invalid_filename_prefix = ' ' invalid_filename_postfix = ' .' re_nonletters = re.compile(r'[\s' + string.punctuation + ']+') max_filename_part_length = 128 def sanitize_filename_part(text, replace_spaces=True): if replace_spaces: text = text.replace(' ', '_') text = text.translate({ord(x): '_' for x in invalid_filename_chars}) text = text.lstrip(invalid_filename_prefix)[:max_filename_part_length] text = text.rstrip(invalid_filename_postfix) return text def apply_filename_pattern(x, p, seed, prompt): max_prompt_words = opts.directories_max_prompt_words if seed is not None: x = x.replace("[seed]", str(seed)) if p is not None: x = x.replace("[steps]", str(p.steps)) x = x.replace("[cfg]", str(p.cfg_scale)) x = x.replace("[width]", str(p.width)) x = x.replace("[height]", str(p.height)) x = x.replace("[styles]", sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"]) or "None", replace_spaces=False)) x = x.replace("[sampler]", sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, replace_spaces=False)) x = x.replace("[model_hash]", getattr(p, "sd_model_hash", shared.sd_model.sd_model_hash)) x = x.replace("[date]", datetime.date.today().isoformat()) x = x.replace("[datetime]", datetime.datetime.now().strftime("%Y%m%d%H%M%S")) x = x.replace("[job_timestamp]", getattr(p, "job_timestamp", shared.state.job_timestamp)) # Apply [prompt] at last. Because it may contain any replacement word.^M if prompt is not None: x = x.replace("[prompt]", sanitize_filename_part(prompt)) if "[prompt_no_styles]" in x: prompt_no_style = prompt for style in shared.prompt_styles.get_style_prompts(p.styles): if len(style) > 0: style_parts = [y for y in style.split("{prompt}")] for part in style_parts: prompt_no_style = prompt_no_style.replace(part, "").replace(", ,", ",").strip().strip(',') prompt_no_style = prompt_no_style.replace(style, "").strip().strip(',').strip() x = x.replace("[prompt_no_styles]", sanitize_filename_part(prompt_no_style, replace_spaces=False)) x = x.replace("[prompt_spaces]", sanitize_filename_part(prompt, replace_spaces=False)) if "[prompt_words]" in x: words = [x for x in re_nonletters.split(prompt or "") if len(x) > 0] if len(words) == 0: words = ["empty"] x = x.replace("[prompt_words]", sanitize_filename_part(" ".join(words[0:max_prompt_words]), replace_spaces=False)) if cmd_opts.hide_ui_dir_config: x = re.sub(r'^[\\/]+|\.{2,}[\\/]+|[\\/]+\.{2,}', '', x) return x def get_next_sequence_number(path, basename): """ Determines and returns the next sequence number to use when saving an image in the specified directory. The sequence starts at 0. """ result = -1 if basename != '': basename = basename + "-" prefix_length = len(basename) for p in os.listdir(path): if p.startswith(basename): l = os.path.splitext(p[prefix_length:])[0].split('-') # splits the filename (removing the basename first if one is defined, so the sequence number is always the first element) try: result = max(int(l[0]), result) except ValueError: pass return result + 1 def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None, forced_filename=None, suffix="", save_to_dirs=None): '''Save an image. Args: image (`PIL.Image`): The image to be saved. path (`str`): The directory to save the image. Note, the option `save_to_dirs` will make the image to be saved into a sub directory. basename (`str`): The base filename which will be applied to `filename pattern`. seed, prompt, short_filename, extension (`str`): Image file extension, default is `png`. pngsectionname (`str`): Specify the name of the section which `info` will be saved in. info (`str` or `PngImagePlugin.iTXt`): PNG info chunks. existing_info (`dict`): Additional PNG info. `existing_info == {pngsectionname: info, ...}` no_prompt: TODO I don't know its meaning. p (`StableDiffusionProcessing`) forced_filename (`str`): If specified, `basename` and filename pattern will be ignored. save_to_dirs (bool): If true, the image will be saved into a subdirectory of `path`. Returns: (fullfn, txt_fullfn) fullfn (`str`): The full path of the saved imaged. txt_fullfn (`str` or None): If a text file is saved for this image, this will be its full path. Otherwise None. ''' if short_filename or prompt is None or seed is None: file_decoration = "" elif opts.save_to_dirs: file_decoration = opts.samples_filename_pattern or "[seed]" else: file_decoration = opts.samples_filename_pattern or "[seed]-[prompt_spaces]" if file_decoration != "": file_decoration = "-" + file_decoration.lower() file_decoration = apply_filename_pattern(file_decoration, p, seed, prompt) + suffix if extension == 'png' and opts.enable_pnginfo and info is not None: pnginfo = PngImagePlugin.PngInfo() if existing_info is not None: for k, v in existing_info.items(): pnginfo.add_text(k, str(v)) pnginfo.add_text(pnginfo_section_name, info) else: pnginfo = None if save_to_dirs is None: save_to_dirs = (grid and opts.grid_save_to_dirs) or (not grid and opts.save_to_dirs and not no_prompt) if save_to_dirs: dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, seed, prompt).strip('\\ /') path = os.path.join(path, dirname) os.makedirs(path, exist_ok=True) if forced_filename is None: basecount = get_next_sequence_number(path, basename) fullfn = "a.png" fullfn_without_extension = "a" for i in range(500): fn = f"{basecount + i:05}" if basename == '' else f"{basename}-{basecount + i:04}" fullfn = os.path.join(path, f"{fn}{file_decoration}.{extension}") fullfn_without_extension = os.path.join(path, f"{fn}{file_decoration}") if not os.path.exists(fullfn): break else: fullfn = os.path.join(path, f"{forced_filename}.{extension}") fullfn_without_extension = os.path.join(path, forced_filename) def exif_bytes(): return piexif.dump({ "Exif": { piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(info or "", encoding="unicode") }, }) if extension.lower() in ("jpg", "jpeg", "webp"): image.save(fullfn, quality=opts.jpeg_quality) if opts.enable_pnginfo and info is not None: piexif.insert(exif_bytes(), fullfn) else: image.save(fullfn, quality=opts.jpeg_quality, pnginfo=pnginfo) target_side_length = 4000 oversize = image.width > target_side_length or image.height > target_side_length if opts.export_for_4chan and (oversize or os.stat(fullfn).st_size > 4 * 1024 * 1024): ratio = image.width / image.height if oversize and ratio > 1: image = image.resize((target_side_length, image.height * target_side_length // image.width), LANCZOS) elif oversize: image = image.resize((image.width * target_side_length // image.height, target_side_length), LANCZOS) image.save(fullfn_without_extension + ".jpg", quality=opts.jpeg_quality) if opts.enable_pnginfo and info is not None: piexif.insert(exif_bytes(), fullfn_without_extension + ".jpg") if opts.save_txt and info is not None: txt_fullfn = f"{fullfn_without_extension}.txt" with open(txt_fullfn, "w", encoding="utf8") as file: file.write(info + "\n") else: txt_fullfn = None return fullfn, txt_fullfn stable-diffusion-webui/modules/img2img.py000066400000000000000000000120641432161722600210110ustar00rootroot00000000000000import math import os import sys import traceback import numpy as np from PIL import Image, ImageOps, ImageChops from modules import devices from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images from modules.shared import opts, state import modules.shared as shared import modules.processing as processing from modules.ui import plaintext_to_html import modules.images as images import modules.scripts def process_batch(p, input_dir, output_dir, args): processing.fix_seed(p) images = [file for file in [os.path.join(input_dir, x) for x in os.listdir(input_dir)] if os.path.isfile(file)] print(f"Will process {len(images)} images, creating {p.n_iter * p.batch_size} new images for each.") save_normally = output_dir == '' p.do_not_save_grid = True p.do_not_save_samples = not save_normally state.job_count = len(images) * p.n_iter for i, image in enumerate(images): state.job = f"{i+1} out of {len(images)}" if state.skipped: state.skipped = False if state.interrupted: break img = Image.open(image) p.init_images = [img] * p.batch_size proc = modules.scripts.scripts_img2img.run(p, *args) if proc is None: proc = process_images(p) for n, processed_image in enumerate(proc.images): filename = os.path.basename(image) if n > 0: left, right = os.path.splitext(filename) filename = f"{left}-{n}{right}" if not save_normally: processed_image.save(os.path.join(output_dir, filename)) def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, init_img_with_mask, init_img_inpaint, init_mask_inpaint, mask_mode, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, *args): is_inpaint = mode == 1 is_batch = mode == 2 if is_inpaint: if mask_mode == 0: image = init_img_with_mask['image'] mask = init_img_with_mask['mask'] alpha_mask = ImageOps.invert(image.split()[-1]).convert('L').point(lambda x: 255 if x > 0 else 0, mode='1') mask = ImageChops.lighter(alpha_mask, mask.convert('L')).convert('L') image = image.convert('RGB') else: image = init_img_inpaint mask = init_mask_inpaint else: image = init_img mask = None assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]' p = StableDiffusionProcessingImg2Img( sd_model=shared.sd_model, outpath_samples=opts.outdir_samples or opts.outdir_img2img_samples, outpath_grids=opts.outdir_grids or opts.outdir_img2img_grids, prompt=prompt, negative_prompt=negative_prompt, styles=[prompt_style, prompt_style2], seed=seed, subseed=subseed, subseed_strength=subseed_strength, seed_resize_from_h=seed_resize_from_h, seed_resize_from_w=seed_resize_from_w, seed_enable_extras=seed_enable_extras, sampler_index=sampler_index, batch_size=batch_size, n_iter=n_iter, steps=steps, cfg_scale=cfg_scale, width=width, height=height, restore_faces=restore_faces, tiling=tiling, init_images=[image], mask=mask, mask_blur=mask_blur, inpainting_fill=inpainting_fill, resize_mode=resize_mode, denoising_strength=denoising_strength, inpaint_full_res=inpaint_full_res, inpaint_full_res_padding=inpaint_full_res_padding, inpainting_mask_invert=inpainting_mask_invert, ) if shared.cmd_opts.enable_console_prompts: print(f"\nimg2img: {prompt}", file=shared.progress_print_out) p.extra_generation_params["Mask blur"] = mask_blur if is_batch: assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled" process_batch(p, img2img_batch_input_dir, img2img_batch_output_dir, args) processed = Processed(p, [], p.seed, "") else: processed = modules.scripts.scripts_img2img.run(p, *args) if processed is None: processed = process_images(p) shared.total_tqdm.clear() generation_info_js = processed.js() if opts.samples_log_stdout: print(generation_info_js) if opts.do_not_show_images: processed.images = [] return processed.images, generation_info_js, plaintext_to_html(processed.info) stable-diffusion-webui/modules/interrogate.py000066400000000000000000000141531432161722600220020ustar00rootroot00000000000000import contextlib import os import sys import traceback from collections import namedtuple import re import torch from torchvision import transforms from torchvision.transforms.functional import InterpolationMode import modules.shared as shared from modules import devices, paths, lowvram blip_image_eval_size = 384 blip_model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth' clip_model_name = 'ViT-L/14' Category = namedtuple("Category", ["name", "topn", "items"]) re_topn = re.compile(r"\.top(\d+)\.") class InterrogateModels: blip_model = None clip_model = None clip_preprocess = None categories = None dtype = None def __init__(self, content_dir): self.categories = [] if os.path.exists(content_dir): for filename in os.listdir(content_dir): m = re_topn.search(filename) topn = 1 if m is None else int(m.group(1)) with open(os.path.join(content_dir, filename), "r", encoding="utf8") as file: lines = [x.strip() for x in file.readlines()] self.categories.append(Category(name=filename, topn=topn, items=lines)) def load_blip_model(self): import models.blip blip_model = models.blip.blip_decoder(pretrained=blip_model_url, image_size=blip_image_eval_size, vit='base', med_config=os.path.join(paths.paths["BLIP"], "configs", "med_config.json")) blip_model.eval() return blip_model def load_clip_model(self): import clip model, preprocess = clip.load(clip_model_name) model.eval() model = model.to(shared.device) return model, preprocess def load(self): if self.blip_model is None: self.blip_model = self.load_blip_model() if not shared.cmd_opts.no_half: self.blip_model = self.blip_model.half() self.blip_model = self.blip_model.to(shared.device) if self.clip_model is None: self.clip_model, self.clip_preprocess = self.load_clip_model() if not shared.cmd_opts.no_half: self.clip_model = self.clip_model.half() self.clip_model = self.clip_model.to(shared.device) self.dtype = next(self.clip_model.parameters()).dtype def send_clip_to_ram(self): if not shared.opts.interrogate_keep_models_in_memory: if self.clip_model is not None: self.clip_model = self.clip_model.to(devices.cpu) def send_blip_to_ram(self): if not shared.opts.interrogate_keep_models_in_memory: if self.blip_model is not None: self.blip_model = self.blip_model.to(devices.cpu) def unload(self): self.send_clip_to_ram() self.send_blip_to_ram() devices.torch_gc() def rank(self, image_features, text_array, top_count=1): import clip if shared.opts.interrogate_clip_dict_limit != 0: text_array = text_array[0:int(shared.opts.interrogate_clip_dict_limit)] top_count = min(top_count, len(text_array)) text_tokens = clip.tokenize([text for text in text_array], truncate=True).to(shared.device) text_features = self.clip_model.encode_text(text_tokens).type(self.dtype) text_features /= text_features.norm(dim=-1, keepdim=True) similarity = torch.zeros((1, len(text_array))).to(shared.device) for i in range(image_features.shape[0]): similarity += (100.0 * image_features[i].unsqueeze(0) @ text_features.T).softmax(dim=-1) similarity /= image_features.shape[0] top_probs, top_labels = similarity.cpu().topk(top_count, dim=-1) return [(text_array[top_labels[0][i].numpy()], (top_probs[0][i].numpy()*100)) for i in range(top_count)] def generate_caption(self, pil_image): gpu_image = transforms.Compose([ transforms.Resize((blip_image_eval_size, blip_image_eval_size), interpolation=InterpolationMode.BICUBIC), transforms.ToTensor(), transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)) ])(pil_image).unsqueeze(0).type(self.dtype).to(shared.device) with torch.no_grad(): caption = self.blip_model.generate(gpu_image, sample=False, num_beams=shared.opts.interrogate_clip_num_beams, min_length=shared.opts.interrogate_clip_min_length, max_length=shared.opts.interrogate_clip_max_length) return caption[0] def interrogate(self, pil_image): res = None try: if shared.cmd_opts.lowvram or shared.cmd_opts.medvram: lowvram.send_everything_to_cpu() devices.torch_gc() self.load() caption = self.generate_caption(pil_image) self.send_blip_to_ram() devices.torch_gc() res = caption clip_image = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(shared.device) precision_scope = torch.autocast if shared.cmd_opts.precision == "autocast" else contextlib.nullcontext with torch.no_grad(), precision_scope("cuda"): image_features = self.clip_model.encode_image(clip_image).type(self.dtype) image_features /= image_features.norm(dim=-1, keepdim=True) if shared.opts.interrogate_use_builtin_artists: artist = self.rank(image_features, ["by " + artist.name for artist in shared.artist_db.artists])[0] res += ", " + artist[0] for name, topn, items in self.categories: matches = self.rank(image_features, items, top_count=topn) for match, score in matches: res += ", " + match except Exception: print(f"Error interrogating", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) res += "" self.unload() return res stable-diffusion-webui/modules/ldsr_model.py000066400000000000000000000041021432161722600215740ustar00rootroot00000000000000import os import sys import traceback from basicsr.utils.download_util import load_file_from_url from modules.upscaler import Upscaler, UpscalerData from modules.ldsr_model_arch import LDSR from modules import shared class UpscalerLDSR(Upscaler): def __init__(self, user_path): self.name = "LDSR" self.user_path = user_path self.model_url = "https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1" self.yaml_url = "https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1" super().__init__() scaler_data = UpscalerData("LDSR", None, self) self.scalers = [scaler_data] def load_model(self, path: str): # Remove incorrect project.yaml file if too big yaml_path = os.path.join(self.model_path, "project.yaml") old_model_path = os.path.join(self.model_path, "model.pth") new_model_path = os.path.join(self.model_path, "model.ckpt") if os.path.exists(yaml_path): statinfo = os.stat(yaml_path) if statinfo.st_size >= 10485760: print("Removing invalid LDSR YAML file.") os.remove(yaml_path) if os.path.exists(old_model_path): print("Renaming model from model.pth to model.ckpt") os.rename(old_model_path, new_model_path) model = load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="model.ckpt", progress=True) yaml = load_file_from_url(url=self.yaml_url, model_dir=self.model_path, file_name="project.yaml", progress=True) try: return LDSR(model, yaml) except Exception: print("Error importing LDSR:", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) return None def do_upscale(self, img, path): ldsr = self.load_model(path) if ldsr is None: print("NO LDSR!") return img ddim_steps = shared.opts.ldsr_steps return ldsr.super_resolution(img, ddim_steps, self.scale) stable-diffusion-webui/modules/ldsr_model_arch.py000066400000000000000000000203521432161722600225760ustar00rootroot00000000000000import gc import time import warnings import numpy as np import torch import torchvision from PIL import Image from einops import rearrange, repeat from omegaconf import OmegaConf from ldm.models.diffusion.ddim import DDIMSampler from ldm.util import instantiate_from_config, ismap warnings.filterwarnings("ignore", category=UserWarning) # Create LDSR Class class LDSR: def load_model_from_config(self, half_attention): print(f"Loading model from {self.modelPath}") pl_sd = torch.load(self.modelPath, map_location="cpu") sd = pl_sd["state_dict"] config = OmegaConf.load(self.yamlPath) model = instantiate_from_config(config.model) model.load_state_dict(sd, strict=False) model.cuda() if half_attention: model = model.half() model.eval() return {"model": model} def __init__(self, model_path, yaml_path): self.modelPath = model_path self.yamlPath = yaml_path @staticmethod def run(model, selected_path, custom_steps, eta): example = get_cond(selected_path) n_runs = 1 guider = None ckwargs = None ddim_use_x0_pred = False temperature = 1. eta = eta custom_shape = None height, width = example["image"].shape[1:3] split_input = height >= 128 and width >= 128 if split_input: ks = 128 stride = 64 vqf = 4 # model.split_input_params = {"ks": (ks, ks), "stride": (stride, stride), "vqf": vqf, "patch_distributed_vq": True, "tie_braker": False, "clip_max_weight": 0.5, "clip_min_weight": 0.01, "clip_max_tie_weight": 0.5, "clip_min_tie_weight": 0.01} else: if hasattr(model, "split_input_params"): delattr(model, "split_input_params") x_t = None logs = None for n in range(n_runs): if custom_shape is not None: x_t = torch.randn(1, custom_shape[1], custom_shape[2], custom_shape[3]).to(model.device) x_t = repeat(x_t, '1 c h w -> b c h w', b=custom_shape[0]) logs = make_convolutional_sample(example, model, custom_steps=custom_steps, eta=eta, quantize_x0=False, custom_shape=custom_shape, temperature=temperature, noise_dropout=0., corrector=guider, corrector_kwargs=ckwargs, x_T=x_t, ddim_use_x0_pred=ddim_use_x0_pred ) return logs def super_resolution(self, image, steps=100, target_scale=2, half_attention=False): model = self.load_model_from_config(half_attention) # Run settings diffusion_steps = int(steps) eta = 1.0 down_sample_method = 'Lanczos' gc.collect() torch.cuda.empty_cache() im_og = image width_og, height_og = im_og.size # If we can adjust the max upscale size, then the 4 below should be our variable down_sample_rate = target_scale / 4 wd = width_og * down_sample_rate hd = height_og * down_sample_rate width_downsampled_pre = int(wd) height_downsampled_pre = int(hd) if down_sample_rate != 1: print( f'Downsampling from [{width_og}, {height_og}] to [{width_downsampled_pre}, {height_downsampled_pre}]') im_og = im_og.resize((width_downsampled_pre, height_downsampled_pre), Image.LANCZOS) else: print(f"Down sample rate is 1 from {target_scale} / 4 (Not downsampling)") logs = self.run(model["model"], im_og, diffusion_steps, eta) sample = logs["sample"] sample = sample.detach().cpu() sample = torch.clamp(sample, -1., 1.) sample = (sample + 1.) / 2. * 255 sample = sample.numpy().astype(np.uint8) sample = np.transpose(sample, (0, 2, 3, 1)) a = Image.fromarray(sample[0]) del model gc.collect() torch.cuda.empty_cache() return a def get_cond(selected_path): example = dict() up_f = 4 c = selected_path.convert('RGB') c = torch.unsqueeze(torchvision.transforms.ToTensor()(c), 0) c_up = torchvision.transforms.functional.resize(c, size=[up_f * c.shape[2], up_f * c.shape[3]], antialias=True) c_up = rearrange(c_up, '1 c h w -> 1 h w c') c = rearrange(c, '1 c h w -> 1 h w c') c = 2. * c - 1. c = c.to(torch.device("cuda")) example["LR_image"] = c example["image"] = c_up return example @torch.no_grad() def convsample_ddim(model, cond, steps, shape, eta=1.0, callback=None, normals_sequence=None, mask=None, x0=None, quantize_x0=False, temperature=1., score_corrector=None, corrector_kwargs=None, x_t=None ): ddim = DDIMSampler(model) bs = shape[0] shape = shape[1:] print(f"Sampling with eta = {eta}; steps: {steps}") samples, intermediates = ddim.sample(steps, batch_size=bs, shape=shape, conditioning=cond, callback=callback, normals_sequence=normals_sequence, quantize_x0=quantize_x0, eta=eta, mask=mask, x0=x0, temperature=temperature, verbose=False, score_corrector=score_corrector, corrector_kwargs=corrector_kwargs, x_t=x_t) return samples, intermediates @torch.no_grad() def make_convolutional_sample(batch, model, custom_steps=None, eta=1.0, quantize_x0=False, custom_shape=None, temperature=1., noise_dropout=0., corrector=None, corrector_kwargs=None, x_T=None, ddim_use_x0_pred=False): log = dict() z, c, x, xrec, xc = model.get_input(batch, model.first_stage_key, return_first_stage_outputs=True, force_c_encode=not (hasattr(model, 'split_input_params') and model.cond_stage_key == 'coordinates_bbox'), return_original_cond=True) if custom_shape is not None: z = torch.randn(custom_shape) print(f"Generating {custom_shape[0]} samples of shape {custom_shape[1:]}") z0 = None log["input"] = x log["reconstruction"] = xrec if ismap(xc): log["original_conditioning"] = model.to_rgb(xc) if hasattr(model, 'cond_stage_key'): log[model.cond_stage_key] = model.to_rgb(xc) else: log["original_conditioning"] = xc if xc is not None else torch.zeros_like(x) if model.cond_stage_model: log[model.cond_stage_key] = xc if xc is not None else torch.zeros_like(x) if model.cond_stage_key == 'class_label': log[model.cond_stage_key] = xc[model.cond_stage_key] with model.ema_scope("Plotting"): t0 = time.time() sample, intermediates = convsample_ddim(model, c, steps=custom_steps, shape=z.shape, eta=eta, quantize_x0=quantize_x0, mask=None, x0=z0, temperature=temperature, score_corrector=corrector, corrector_kwargs=corrector_kwargs, x_t=x_T) t1 = time.time() if ddim_use_x0_pred: sample = intermediates['pred_x0'][-1] x_sample = model.decode_first_stage(sample) try: x_sample_noquant = model.decode_first_stage(sample, force_not_quantize=True) log["sample_noquant"] = x_sample_noquant log["sample_diff"] = torch.abs(x_sample_noquant - x_sample) except: pass log["sample"] = x_sample log["time"] = t1 - t0 return log stable-diffusion-webui/modules/lowvram.py000066400000000000000000000070051432161722600211440ustar00rootroot00000000000000import torch from modules.devices import get_optimal_device module_in_gpu = None cpu = torch.device("cpu") device = gpu = get_optimal_device() def send_everything_to_cpu(): global module_in_gpu if module_in_gpu is not None: module_in_gpu.to(cpu) module_in_gpu = None def setup_for_low_vram(sd_model, use_medvram): parents = {} def send_me_to_gpu(module, _): """send this module to GPU; send whatever tracked module was previous in GPU to CPU; we add this as forward_pre_hook to a lot of modules and this way all but one of them will be in CPU """ global module_in_gpu module = parents.get(module, module) if module_in_gpu == module: return if module_in_gpu is not None: module_in_gpu.to(cpu) module.to(gpu) module_in_gpu = module # see below for register_forward_pre_hook; # first_stage_model does not use forward(), it uses encode/decode, so register_forward_pre_hook is # useless here, and we just replace those methods def first_stage_model_encode_wrap(self, encoder, x): send_me_to_gpu(self, None) return encoder(x) def first_stage_model_decode_wrap(self, decoder, z): send_me_to_gpu(self, None) return decoder(z) # remove three big modules, cond, first_stage, and unet from the model and then # send the model to GPU. Then put modules back. the modules will be in CPU. stored = sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model = None, None, None sd_model.to(device) sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model = stored # register hooks for those the first two models sd_model.cond_stage_model.transformer.register_forward_pre_hook(send_me_to_gpu) sd_model.first_stage_model.register_forward_pre_hook(send_me_to_gpu) sd_model.first_stage_model.encode = lambda x, en=sd_model.first_stage_model.encode: first_stage_model_encode_wrap(sd_model.first_stage_model, en, x) sd_model.first_stage_model.decode = lambda z, de=sd_model.first_stage_model.decode: first_stage_model_decode_wrap(sd_model.first_stage_model, de, z) parents[sd_model.cond_stage_model.transformer] = sd_model.cond_stage_model if use_medvram: sd_model.model.register_forward_pre_hook(send_me_to_gpu) else: diff_model = sd_model.model.diffusion_model # the third remaining model is still too big for 4 GB, so we also do the same for its submodules # so that only one of them is in GPU at a time stored = diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed = None, None, None, None sd_model.model.to(device) diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed = stored # install hooks for bits of third model diff_model.time_embed.register_forward_pre_hook(send_me_to_gpu) for block in diff_model.input_blocks: block.register_forward_pre_hook(send_me_to_gpu) diff_model.middle_block.register_forward_pre_hook(send_me_to_gpu) for block in diff_model.output_blocks: block.register_forward_pre_hook(send_me_to_gpu) stable-diffusion-webui/modules/masking.py000066400000000000000000000063651432161722600211160ustar00rootroot00000000000000from PIL import Image, ImageFilter, ImageOps def get_crop_region(mask, pad=0): """finds a rectangular region that contains all masked ares in an image. Returns (x1, y1, x2, y2) coordinates of the rectangle. For example, if a user has painted the top-right part of a 512x512 image", the result may be (256, 0, 512, 256)""" h, w = mask.shape crop_left = 0 for i in range(w): if not (mask[:, i] == 0).all(): break crop_left += 1 crop_right = 0 for i in reversed(range(w)): if not (mask[:, i] == 0).all(): break crop_right += 1 crop_top = 0 for i in range(h): if not (mask[i] == 0).all(): break crop_top += 1 crop_bottom = 0 for i in reversed(range(h)): if not (mask[i] == 0).all(): break crop_bottom += 1 return ( int(max(crop_left-pad, 0)), int(max(crop_top-pad, 0)), int(min(w - crop_right + pad, w)), int(min(h - crop_bottom + pad, h)) ) def expand_crop_region(crop_region, processing_width, processing_height, image_width, image_height): """expands crop region get_crop_region() to match the ratio of the image the region will processed in; returns expanded region for example, if user drew mask in a 128x32 region, and the dimensions for processing are 512x512, the region will be expanded to 128x128.""" x1, y1, x2, y2 = crop_region ratio_crop_region = (x2 - x1) / (y2 - y1) ratio_processing = processing_width / processing_height if ratio_crop_region > ratio_processing: desired_height = (x2 - x1) * ratio_processing desired_height_diff = int(desired_height - (y2-y1)) y1 -= desired_height_diff//2 y2 += desired_height_diff - desired_height_diff//2 if y2 >= image_height: diff = y2 - image_height y2 -= diff y1 -= diff if y1 < 0: y2 -= y1 y1 -= y1 if y2 >= image_height: y2 = image_height else: desired_width = (y2 - y1) * ratio_processing desired_width_diff = int(desired_width - (x2-x1)) x1 -= desired_width_diff//2 x2 += desired_width_diff - desired_width_diff//2 if x2 >= image_width: diff = x2 - image_width x2 -= diff x1 -= diff if x1 < 0: x2 -= x1 x1 -= x1 if x2 >= image_width: x2 = image_width return x1, y1, x2, y2 def fill(image, mask): """fills masked regions with colors from image using blur. Not extremely effective.""" image_mod = Image.new('RGBA', (image.width, image.height)) image_masked = Image.new('RGBa', (image.width, image.height)) image_masked.paste(image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(mask.convert('L'))) image_masked = image_masked.convert('RGBa') for radius, repeats in [(256, 1), (64, 1), (16, 2), (4, 4), (2, 2), (0, 1)]: blurred = image_masked.filter(ImageFilter.GaussianBlur(radius)).convert('RGBA') for _ in range(repeats): image_mod.alpha_composite(blurred) return image_mod.convert("RGB") stable-diffusion-webui/modules/memmon.py000066400000000000000000000046131432161722600207470ustar00rootroot00000000000000import threading import time from collections import defaultdict import torch class MemUsageMonitor(threading.Thread): run_flag = None device = None disabled = False opts = None data = None def __init__(self, name, device, opts): threading.Thread.__init__(self) self.name = name self.device = device self.opts = opts self.daemon = True self.run_flag = threading.Event() self.data = defaultdict(int) try: torch.cuda.mem_get_info() torch.cuda.memory_stats(self.device) except Exception as e: # AMD or whatever print(f"Warning: caught exception '{e}', memory monitor disabled") self.disabled = True def run(self): if self.disabled: return while True: self.run_flag.wait() torch.cuda.reset_peak_memory_stats() self.data.clear() if self.opts.memmon_poll_rate <= 0: self.run_flag.clear() continue self.data["min_free"] = torch.cuda.mem_get_info()[0] while self.run_flag.is_set(): free, total = torch.cuda.mem_get_info() # calling with self.device errors, torch bug? self.data["min_free"] = min(self.data["min_free"], free) time.sleep(1 / self.opts.memmon_poll_rate) def dump_debug(self): print(self, 'recorded data:') for k, v in self.read().items(): print(k, -(v // -(1024 ** 2))) print(self, 'raw torch memory stats:') tm = torch.cuda.memory_stats(self.device) for k, v in tm.items(): if 'bytes' not in k: continue print('\t' if 'peak' in k else '', k, -(v // -(1024 ** 2))) print(torch.cuda.memory_summary()) def monitor(self): self.run_flag.set() def read(self): if not self.disabled: free, total = torch.cuda.mem_get_info() self.data["total"] = total torch_stats = torch.cuda.memory_stats(self.device) self.data["active_peak"] = torch_stats["active_bytes.all.peak"] self.data["reserved_peak"] = torch_stats["reserved_bytes.all.peak"] self.data["system_peak"] = total - self.data["min_free"] return self.data def stop(self): self.run_flag.clear() return self.read() stable-diffusion-webui/modules/modelloader.py000066400000000000000000000130621432161722600217440ustar00rootroot00000000000000import glob import os import shutil import importlib from urllib.parse import urlparse from basicsr.utils.download_util import load_file_from_url from modules import shared from modules.upscaler import Upscaler from modules.paths import script_path, models_path def load_models(model_path: str, model_url: str = None, command_path: str = None, ext_filter=None, download_name=None) -> list: """ A one-and done loader to try finding the desired models in specified directories. @param download_name: Specify to download from model_url immediately. @param model_url: If no other models are found, this will be downloaded on upscale. @param model_path: The location to store/find models in. @param command_path: A command-line argument to search for models in first. @param ext_filter: An optional list of filename extensions to filter by @return: A list of paths containing the desired model(s) """ output = [] if ext_filter is None: ext_filter = [] try: places = [] if command_path is not None and command_path != model_path: pretrained_path = os.path.join(command_path, 'experiments/pretrained_models') if os.path.exists(pretrained_path): print(f"Appending path: {pretrained_path}") places.append(pretrained_path) elif os.path.exists(command_path): places.append(command_path) places.append(model_path) for place in places: if os.path.exists(place): for file in glob.iglob(place + '**/**', recursive=True): full_path = file if os.path.isdir(full_path): continue if len(ext_filter) != 0: model_name, extension = os.path.splitext(file) if extension not in ext_filter: continue if file not in output: output.append(full_path) if model_url is not None and len(output) == 0: if download_name is not None: dl = load_file_from_url(model_url, model_path, True, download_name) output.append(dl) else: output.append(model_url) except Exception: pass return output def friendly_name(file: str): if "http" in file: file = urlparse(file).path file = os.path.basename(file) model_name, extension = os.path.splitext(file) return model_name def cleanup_models(): # This code could probably be more efficient if we used a tuple list or something to store the src/destinations # and then enumerate that, but this works for now. In the future, it'd be nice to just have every "model" scaler # somehow auto-register and just do these things... root_path = script_path src_path = models_path dest_path = os.path.join(models_path, "Stable-diffusion") move_files(src_path, dest_path, ".ckpt") src_path = os.path.join(root_path, "ESRGAN") dest_path = os.path.join(models_path, "ESRGAN") move_files(src_path, dest_path) src_path = os.path.join(root_path, "gfpgan") dest_path = os.path.join(models_path, "GFPGAN") move_files(src_path, dest_path) src_path = os.path.join(root_path, "SwinIR") dest_path = os.path.join(models_path, "SwinIR") move_files(src_path, dest_path) src_path = os.path.join(root_path, "repositories/latent-diffusion/experiments/pretrained_models/") dest_path = os.path.join(models_path, "LDSR") move_files(src_path, dest_path) def move_files(src_path: str, dest_path: str, ext_filter: str = None): try: if not os.path.exists(dest_path): os.makedirs(dest_path) if os.path.exists(src_path): for file in os.listdir(src_path): fullpath = os.path.join(src_path, file) if os.path.isfile(fullpath): if ext_filter is not None: if ext_filter not in file: continue print(f"Moving {file} from {src_path} to {dest_path}.") try: shutil.move(fullpath, dest_path) except: pass if len(os.listdir(src_path)) == 0: print(f"Removing empty folder: {src_path}") shutil.rmtree(src_path, True) except: pass def load_upscalers(): sd = shared.script_path # We can only do this 'magic' method to dynamically load upscalers if they are referenced, # so we'll try to import any _model.py files before looking in __subclasses__ modules_dir = os.path.join(sd, "modules") for file in os.listdir(modules_dir): if "_model.py" in file: model_name = file.replace("_model.py", "") full_model = f"modules.{model_name}_model" try: importlib.import_module(full_model) except: pass datas = [] c_o = vars(shared.cmd_opts) for cls in Upscaler.__subclasses__(): name = cls.__name__ module_name = cls.__module__ module = importlib.import_module(module_name) class_ = getattr(module, name) cmd_name = f"{name.lower().replace('upscaler', '')}_models_path" opt_string = None try: if cmd_name in c_o: opt_string = c_o[cmd_name] except: pass scaler = class_(opt_string) for child in scaler.scalers: datas.append(child) shared.sd_upscalers = datas stable-diffusion-webui/modules/ngrok.py000066400000000000000000000011261432161722600205730ustar00rootroot00000000000000from pyngrok import ngrok, conf, exception def connect(token, port): if token == None: token = 'None' conf.get_default().auth_token = token try: public_url = ngrok.connect(port).public_url except exception.PyngrokNgrokError: print(f'Invalid ngrok authtoken, ngrok connection aborted.\n' f'Your token: {token}, get the right one on https://dashboard.ngrok.com/get-started/your-authtoken') else: print(f'ngrok connected to localhost:{port}! URL: {public_url}\n' 'You can use this link after the launch is complete.') stable-diffusion-webui/modules/paths.py000066400000000000000000000031131432161722600205700ustar00rootroot00000000000000import argparse import os import sys import modules.safe script_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__))) models_path = os.path.join(script_path, "models") sys.path.insert(0, script_path) # search for directory of stable diffusion in following places sd_path = None possible_sd_paths = [os.path.join(script_path, 'repositories/stable-diffusion'), '.', os.path.dirname(script_path)] for possible_sd_path in possible_sd_paths: if os.path.exists(os.path.join(possible_sd_path, 'ldm/models/diffusion/ddpm.py')): sd_path = os.path.abspath(possible_sd_path) break assert sd_path is not None, "Couldn't find Stable Diffusion in any of: " + str(possible_sd_paths) path_dirs = [ (sd_path, 'ldm', 'Stable Diffusion', []), (os.path.join(sd_path, '../taming-transformers'), 'taming', 'Taming Transformers', []), (os.path.join(sd_path, '../CodeFormer'), 'inference_codeformer.py', 'CodeFormer', []), (os.path.join(sd_path, '../BLIP'), 'models/blip.py', 'BLIP', []), (os.path.join(sd_path, '../k-diffusion'), 'k_diffusion/sampling.py', 'k_diffusion', ["atstart"]), ] paths = {} for d, must_exist, what, options in path_dirs: must_exist_path = os.path.abspath(os.path.join(script_path, d, must_exist)) if not os.path.exists(must_exist_path): print(f"Warning: {what} not found at path {must_exist_path}", file=sys.stderr) else: d = os.path.abspath(d) if "atstart" in options: sys.path.insert(0, d) else: sys.path.append(d) paths[what] = d stable-diffusion-webui/modules/processing.py000066400000000000000000000775711432161722600216500ustar00rootroot00000000000000import json import math import os import sys import torch import numpy as np from PIL import Image, ImageFilter, ImageOps import random import cv2 from skimage import exposure import modules.sd_hijack from modules import devices, prompt_parser, masking, sd_samplers, lowvram from modules.sd_hijack import model_hijack from modules.shared import opts, cmd_opts, state import modules.shared as shared import modules.face_restoration import modules.images as images import modules.styles import logging # some of those options should not be changed at all because they would break the model, so I removed them from options. opt_C = 4 opt_f = 8 def setup_color_correction(image): logging.info("Calibrating color correction.") correction_target = cv2.cvtColor(np.asarray(image.copy()), cv2.COLOR_RGB2LAB) return correction_target def apply_color_correction(correction, image): logging.info("Applying color correction.") image = Image.fromarray(cv2.cvtColor(exposure.match_histograms( cv2.cvtColor( np.asarray(image), cv2.COLOR_RGB2LAB ), correction, channel_axis=2 ), cv2.COLOR_LAB2RGB).astype("uint8")) return image def get_correct_sampler(p): if isinstance(p, modules.processing.StableDiffusionProcessingTxt2Img): return sd_samplers.samplers elif isinstance(p, modules.processing.StableDiffusionProcessingImg2Img): return sd_samplers.samplers_for_img2img class StableDiffusionProcessing: def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", styles=None, seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, seed_enable_extras=True, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None, eta=None): self.sd_model = sd_model self.outpath_samples: str = outpath_samples self.outpath_grids: str = outpath_grids self.prompt: str = prompt self.prompt_for_display: str = None self.negative_prompt: str = (negative_prompt or "") self.styles: list = styles or [] self.seed: int = seed self.subseed: int = subseed self.subseed_strength: float = subseed_strength self.seed_resize_from_h: int = seed_resize_from_h self.seed_resize_from_w: int = seed_resize_from_w self.sampler_index: int = sampler_index self.batch_size: int = batch_size self.n_iter: int = n_iter self.steps: int = steps self.cfg_scale: float = cfg_scale self.width: int = width self.height: int = height self.restore_faces: bool = restore_faces self.tiling: bool = tiling self.do_not_save_samples: bool = do_not_save_samples self.do_not_save_grid: bool = do_not_save_grid self.extra_generation_params: dict = extra_generation_params or {} self.overlay_images = overlay_images self.eta = eta self.paste_to = None self.color_corrections = None self.denoising_strength: float = 0 self.sampler_noise_scheduler_override = None self.ddim_discretize = opts.ddim_discretize self.s_churn = opts.s_churn self.s_tmin = opts.s_tmin self.s_tmax = float('inf') # not representable as a standard ui option self.s_noise = opts.s_noise if not seed_enable_extras: self.subseed = -1 self.subseed_strength = 0 self.seed_resize_from_h = 0 self.seed_resize_from_w = 0 def init(self, all_prompts, all_seeds, all_subseeds): pass def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength): raise NotImplementedError() class Processed: def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None): self.images = images_list self.prompt = p.prompt self.negative_prompt = p.negative_prompt self.seed = seed self.subseed = subseed self.subseed_strength = p.subseed_strength self.info = info self.width = p.width self.height = p.height self.sampler_index = p.sampler_index self.sampler = sd_samplers.samplers[p.sampler_index].name self.cfg_scale = p.cfg_scale self.steps = p.steps self.batch_size = p.batch_size self.restore_faces = p.restore_faces self.face_restoration_model = opts.face_restoration_model if p.restore_faces else None self.sd_model_hash = shared.sd_model.sd_model_hash self.seed_resize_from_w = p.seed_resize_from_w self.seed_resize_from_h = p.seed_resize_from_h self.denoising_strength = getattr(p, 'denoising_strength', None) self.extra_generation_params = p.extra_generation_params self.index_of_first_image = index_of_first_image self.styles = p.styles self.job_timestamp = state.job_timestamp self.clip_skip = opts.CLIP_stop_at_last_layers self.eta = p.eta self.ddim_discretize = p.ddim_discretize self.s_churn = p.s_churn self.s_tmin = p.s_tmin self.s_tmax = p.s_tmax self.s_noise = p.s_noise self.sampler_noise_scheduler_override = p.sampler_noise_scheduler_override self.prompt = self.prompt if type(self.prompt) != list else self.prompt[0] self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0] self.seed = int(self.seed if type(self.seed) != list else self.seed[0]) self.subseed = int(self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1 self.all_prompts = all_prompts or [self.prompt] self.all_seeds = all_seeds or [self.seed] self.all_subseeds = all_subseeds or [self.subseed] self.infotexts = infotexts or [info] def js(self): obj = { "prompt": self.prompt, "all_prompts": self.all_prompts, "negative_prompt": self.negative_prompt, "seed": self.seed, "all_seeds": self.all_seeds, "subseed": self.subseed, "all_subseeds": self.all_subseeds, "subseed_strength": self.subseed_strength, "width": self.width, "height": self.height, "sampler_index": self.sampler_index, "sampler": self.sampler, "cfg_scale": self.cfg_scale, "steps": self.steps, "batch_size": self.batch_size, "restore_faces": self.restore_faces, "face_restoration_model": self.face_restoration_model, "sd_model_hash": self.sd_model_hash, "seed_resize_from_w": self.seed_resize_from_w, "seed_resize_from_h": self.seed_resize_from_h, "denoising_strength": self.denoising_strength, "extra_generation_params": self.extra_generation_params, "index_of_first_image": self.index_of_first_image, "infotexts": self.infotexts, "styles": self.styles, "job_timestamp": self.job_timestamp, "clip_skip": self.clip_skip, } return json.dumps(obj) def infotext(self, p: StableDiffusionProcessing, index): return create_infotext(p, self.all_prompts, self.all_seeds, self.all_subseeds, comments=[], position_in_batch=index % self.batch_size, iteration=index // self.batch_size) # from https://discuss.pytorch.org/t/help-regarding-slerp-function-for-generative-model-sampling/32475/3 def slerp(val, low, high): low_norm = low/torch.norm(low, dim=1, keepdim=True) high_norm = high/torch.norm(high, dim=1, keepdim=True) dot = (low_norm*high_norm).sum(1) if dot.mean() > 0.9995: return low * val + high * (1 - val) omega = torch.acos(dot) so = torch.sin(omega) res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high return res def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0, p=None): xs = [] # if we have multiple seeds, this means we are working with batch size>1; this then # enables the generation of additional tensors with noise that the sampler will use during its processing. # Using those pre-generated tensors instead of simple torch.randn allows a batch with seeds [100, 101] to # produce the same images as with two batches [100], [101]. if p is not None and p.sampler is not None and (len(seeds) > 1 and opts.enable_batch_seeds or opts.eta_noise_seed_delta > 0): sampler_noises = [[] for _ in range(p.sampler.number_of_needed_noises(p))] else: sampler_noises = None for i, seed in enumerate(seeds): noise_shape = shape if seed_resize_from_h <= 0 or seed_resize_from_w <= 0 else (shape[0], seed_resize_from_h//8, seed_resize_from_w//8) subnoise = None if subseeds is not None: subseed = 0 if i >= len(subseeds) else subseeds[i] subnoise = devices.randn(subseed, noise_shape) # randn results depend on device; gpu and cpu get different results for same seed; # the way I see it, it's better to do this on CPU, so that everyone gets same result; # but the original script had it like this, so I do not dare change it for now because # it will break everyone's seeds. noise = devices.randn(seed, noise_shape) if subnoise is not None: noise = slerp(subseed_strength, noise, subnoise) if noise_shape != shape: x = devices.randn(seed, shape) dx = (shape[2] - noise_shape[2]) // 2 dy = (shape[1] - noise_shape[1]) // 2 w = noise_shape[2] if dx >= 0 else noise_shape[2] + 2 * dx h = noise_shape[1] if dy >= 0 else noise_shape[1] + 2 * dy tx = 0 if dx < 0 else dx ty = 0 if dy < 0 else dy dx = max(-dx, 0) dy = max(-dy, 0) x[:, ty:ty+h, tx:tx+w] = noise[:, dy:dy+h, dx:dx+w] noise = x if sampler_noises is not None: cnt = p.sampler.number_of_needed_noises(p) if opts.eta_noise_seed_delta > 0: torch.manual_seed(seed + opts.eta_noise_seed_delta) for j in range(cnt): sampler_noises[j].append(devices.randn_without_seed(tuple(noise_shape))) xs.append(noise) if sampler_noises is not None: p.sampler.sampler_noises = [torch.stack(n).to(shared.device) for n in sampler_noises] x = torch.stack(xs).to(shared.device) return x def decode_first_stage(model, x): with devices.autocast(disable=x.dtype == devices.dtype_vae): x = model.decode_first_stage(x) return x def get_fixed_seed(seed): if seed is None or seed == '' or seed == -1: return int(random.randrange(4294967294)) return seed def fix_seed(p): p.seed = get_fixed_seed(p.seed) p.subseed = get_fixed_seed(p.subseed) def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration=0, position_in_batch=0): index = position_in_batch + iteration * p.batch_size clip_skip = getattr(p, 'clip_skip', opts.CLIP_stop_at_last_layers) generation_params = { "Steps": p.steps, "Sampler": get_correct_sampler(p)[p.sampler_index].name, "CFG scale": p.cfg_scale, "Seed": all_seeds[index], "Face restoration": (opts.face_restoration_model if p.restore_faces else None), "Size": f"{p.width}x{p.height}", "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash), "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')), "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name.replace(',', '').replace(':', '')), "Batch size": (None if p.batch_size < 2 else p.batch_size), "Batch pos": (None if p.batch_size < 2 else position_in_batch), "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]), "Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength), "Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"), "Denoising strength": getattr(p, 'denoising_strength', None), "Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta), "Clip skip": None if clip_skip <= 1 else clip_skip, "ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta, } generation_params.update(p.extra_generation_params) generation_params_text = ", ".join([k if k == v else f'{k}: {v}' for k, v in generation_params.items() if v is not None]) negative_prompt_text = "\nNegative prompt: " + p.negative_prompt if p.negative_prompt else "" return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip() def process_images(p: StableDiffusionProcessing) -> Processed: """this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch""" if type(p.prompt) == list: assert(len(p.prompt) > 0) else: assert p.prompt is not None devices.torch_gc() seed = get_fixed_seed(p.seed) subseed = get_fixed_seed(p.subseed) if p.outpath_samples is not None: os.makedirs(p.outpath_samples, exist_ok=True) if p.outpath_grids is not None: os.makedirs(p.outpath_grids, exist_ok=True) modules.sd_hijack.model_hijack.apply_circular(p.tiling) modules.sd_hijack.model_hijack.clear_comments() comments = {} shared.prompt_styles.apply_styles(p) if type(p.prompt) == list: all_prompts = p.prompt else: all_prompts = p.batch_size * p.n_iter * [p.prompt] if type(seed) == list: all_seeds = seed else: all_seeds = [int(seed) + (x if p.subseed_strength == 0 else 0) for x in range(len(all_prompts))] if type(subseed) == list: all_subseeds = subseed else: all_subseeds = [int(subseed) + x for x in range(len(all_prompts))] def infotext(iteration=0, position_in_batch=0): return create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration, position_in_batch) if os.path.exists(cmd_opts.embeddings_dir): model_hijack.embedding_db.load_textual_inversion_embeddings() infotexts = [] output_images = [] with torch.no_grad(), p.sd_model.ema_scope(): with devices.autocast(): p.init(all_prompts, all_seeds, all_subseeds) if state.job_count == -1: state.job_count = p.n_iter for n in range(p.n_iter): if state.skipped: state.skipped = False if state.interrupted: break prompts = all_prompts[n * p.batch_size:(n + 1) * p.batch_size] seeds = all_seeds[n * p.batch_size:(n + 1) * p.batch_size] subseeds = all_subseeds[n * p.batch_size:(n + 1) * p.batch_size] if (len(prompts) == 0): break #uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt]) #c = p.sd_model.get_learned_conditioning(prompts) with devices.autocast(): uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps) c = prompt_parser.get_multicond_learned_conditioning(shared.sd_model, prompts, p.steps) if len(model_hijack.comments) > 0: for comment in model_hijack.comments: comments[comment] = 1 if p.n_iter > 1: shared.state.job = f"Batch {n+1} out of {p.n_iter}" with devices.autocast(): samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength) if state.interrupted or state.skipped: # if we are interrupted, sample returns just noise # use the image collected previously in sampler loop samples_ddim = shared.state.current_latent samples_ddim = samples_ddim.to(devices.dtype_vae) x_samples_ddim = decode_first_stage(p.sd_model, samples_ddim) x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0) del samples_ddim if shared.cmd_opts.lowvram or shared.cmd_opts.medvram: lowvram.send_everything_to_cpu() devices.torch_gc() if opts.filter_nsfw: import modules.safety as safety x_samples_ddim = modules.safety.censor_batch(x_samples_ddim) for i, x_sample in enumerate(x_samples_ddim): x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2) x_sample = x_sample.astype(np.uint8) if p.restore_faces: if opts.save and not p.do_not_save_samples and opts.save_images_before_face_restoration: images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-face-restoration") devices.torch_gc() x_sample = modules.face_restoration.restore_faces(x_sample) devices.torch_gc() image = Image.fromarray(x_sample) if p.color_corrections is not None and i < len(p.color_corrections): if opts.save and not p.do_not_save_samples and opts.save_images_before_color_correction: images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-color-correction") image = apply_color_correction(p.color_corrections[i], image) if p.overlay_images is not None and i < len(p.overlay_images): overlay = p.overlay_images[i] if p.paste_to is not None: x, y, w, h = p.paste_to base_image = Image.new('RGBA', (overlay.width, overlay.height)) image = images.resize_image(1, image, w, h) base_image.paste(image, (x, y)) image = base_image image = image.convert('RGBA') image.alpha_composite(overlay) image = image.convert('RGB') if opts.samples_save and not p.do_not_save_samples: images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p) text = infotext(n, i) infotexts.append(text) if opts.enable_pnginfo: image.info["parameters"] = text output_images.append(image) del x_samples_ddim devices.torch_gc() state.nextjob() p.color_corrections = None index_of_first_image = 0 unwanted_grid_because_of_img_count = len(output_images) < 2 and opts.grid_only_if_multiple if (opts.return_grid or opts.grid_save) and not p.do_not_save_grid and not unwanted_grid_because_of_img_count: grid = images.image_grid(output_images, p.batch_size) if opts.return_grid: text = infotext() infotexts.insert(0, text) if opts.enable_pnginfo: grid.info["parameters"] = text output_images.insert(0, grid) index_of_first_image = 1 if opts.grid_save: images.save_image(grid, p.outpath_grids, "grid", all_seeds[0], all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True) devices.torch_gc() return Processed(p, output_images, all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=all_subseeds[0], all_prompts=all_prompts, all_seeds=all_seeds, all_subseeds=all_subseeds, index_of_first_image=index_of_first_image, infotexts=infotexts) class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): sampler = None firstphase_width = 0 firstphase_height = 0 firstphase_width_truncated = 0 firstphase_height_truncated = 0 def __init__(self, enable_hr=False, scale_latent=True, denoising_strength=0.75, **kwargs): super().__init__(**kwargs) self.enable_hr = enable_hr self.scale_latent = scale_latent self.denoising_strength = denoising_strength def init(self, all_prompts, all_seeds, all_subseeds): if self.enable_hr: if state.job_count == -1: state.job_count = self.n_iter * 2 else: state.job_count = state.job_count * 2 desired_pixel_count = 512 * 512 actual_pixel_count = self.width * self.height scale = math.sqrt(desired_pixel_count / actual_pixel_count) self.firstphase_width = math.ceil(scale * self.width / 64) * 64 self.firstphase_height = math.ceil(scale * self.height / 64) * 64 self.firstphase_width_truncated = int(scale * self.width) self.firstphase_height_truncated = int(scale * self.height) def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength): self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model) if not self.enable_hr: x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning) return samples x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning) truncate_x = (self.firstphase_width - self.firstphase_width_truncated) // opt_f truncate_y = (self.firstphase_height - self.firstphase_height_truncated) // opt_f samples = samples[:, :, truncate_y//2:samples.shape[2]-truncate_y//2, truncate_x//2:samples.shape[3]-truncate_x//2] if self.scale_latent: samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear") else: decoded_samples = decode_first_stage(self.sd_model, samples) if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None": decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width), mode="bilinear") else: lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0) batch_images = [] for i, x_sample in enumerate(lowres_samples): x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2) x_sample = x_sample.astype(np.uint8) image = Image.fromarray(x_sample) image = images.resize_image(0, image, self.width, self.height) image = np.array(image).astype(np.float32) / 255.0 image = np.moveaxis(image, 2, 0) batch_images.append(image) decoded_samples = torch.from_numpy(np.array(batch_images)) decoded_samples = decoded_samples.to(shared.device) decoded_samples = 2. * decoded_samples - 1. samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples)) shared.state.nextjob() self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model) noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) # GC now before running the next img2img to prevent running out of memory x = None devices.torch_gc() samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps) return samples class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): sampler = None def __init__(self, init_images=None, resize_mode=0, denoising_strength=0.75, mask=None, mask_blur=4, inpainting_fill=0, inpaint_full_res=True, inpaint_full_res_padding=0, inpainting_mask_invert=0, **kwargs): super().__init__(**kwargs) self.init_images = init_images self.resize_mode: int = resize_mode self.denoising_strength: float = denoising_strength self.init_latent = None self.image_mask = mask #self.image_unblurred_mask = None self.latent_mask = None self.mask_for_overlay = None self.mask_blur = mask_blur self.inpainting_fill = inpainting_fill self.inpaint_full_res = inpaint_full_res self.inpaint_full_res_padding = inpaint_full_res_padding self.inpainting_mask_invert = inpainting_mask_invert self.mask = None self.nmask = None def init(self, all_prompts, all_seeds, all_subseeds): self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers_for_img2img, self.sampler_index, self.sd_model) crop_region = None if self.image_mask is not None: self.image_mask = self.image_mask.convert('L') if self.inpainting_mask_invert: self.image_mask = ImageOps.invert(self.image_mask) #self.image_unblurred_mask = self.image_mask if self.mask_blur > 0: self.image_mask = self.image_mask.filter(ImageFilter.GaussianBlur(self.mask_blur)) if self.inpaint_full_res: self.mask_for_overlay = self.image_mask mask = self.image_mask.convert('L') crop_region = masking.get_crop_region(np.array(mask), self.inpaint_full_res_padding) crop_region = masking.expand_crop_region(crop_region, self.width, self.height, mask.width, mask.height) x1, y1, x2, y2 = crop_region mask = mask.crop(crop_region) self.image_mask = images.resize_image(2, mask, self.width, self.height) self.paste_to = (x1, y1, x2-x1, y2-y1) else: self.image_mask = images.resize_image(self.resize_mode, self.image_mask, self.width, self.height) np_mask = np.array(self.image_mask) np_mask = np.clip((np_mask.astype(np.float32)) * 2, 0, 255).astype(np.uint8) self.mask_for_overlay = Image.fromarray(np_mask) self.overlay_images = [] latent_mask = self.latent_mask if self.latent_mask is not None else self.image_mask add_color_corrections = opts.img2img_color_correction and self.color_corrections is None if add_color_corrections: self.color_corrections = [] imgs = [] for img in self.init_images: image = img.convert("RGB") if crop_region is None: image = images.resize_image(self.resize_mode, image, self.width, self.height) if self.image_mask is not None: image_masked = Image.new('RGBa', (image.width, image.height)) image_masked.paste(image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(self.mask_for_overlay.convert('L'))) self.overlay_images.append(image_masked.convert('RGBA')) if crop_region is not None: image = image.crop(crop_region) image = images.resize_image(2, image, self.width, self.height) if self.image_mask is not None: if self.inpainting_fill != 1: image = masking.fill(image, latent_mask) if add_color_corrections: self.color_corrections.append(setup_color_correction(image)) image = np.array(image).astype(np.float32) / 255.0 image = np.moveaxis(image, 2, 0) imgs.append(image) if len(imgs) == 1: batch_images = np.expand_dims(imgs[0], axis=0).repeat(self.batch_size, axis=0) if self.overlay_images is not None: self.overlay_images = self.overlay_images * self.batch_size elif len(imgs) <= self.batch_size: self.batch_size = len(imgs) batch_images = np.array(imgs) else: raise RuntimeError(f"bad number of images passed: {len(imgs)}; expecting {self.batch_size} or less") image = torch.from_numpy(batch_images) image = 2. * image - 1. image = image.to(shared.device) self.init_latent = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image)) if self.image_mask is not None: init_mask = latent_mask latmask = init_mask.convert('RGB').resize((self.init_latent.shape[3], self.init_latent.shape[2])) latmask = np.moveaxis(np.array(latmask, dtype=np.float32), 2, 0) / 255 latmask = latmask[0] latmask = np.around(latmask) latmask = np.tile(latmask[None], (4, 1, 1)) self.mask = torch.asarray(1.0 - latmask).to(shared.device).type(self.sd_model.dtype) self.nmask = torch.asarray(latmask).to(shared.device).type(self.sd_model.dtype) # this needs to be fixed to be done in sample() using actual seeds for batches if self.inpainting_fill == 2: self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:], all_seeds[0:self.init_latent.shape[0]]) * self.nmask elif self.inpainting_fill == 3: self.init_latent = self.init_latent * self.mask def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength): x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning) if self.mask is not None: samples = samples * self.nmask + self.init_latent * self.mask del x devices.torch_gc() return samples stable-diffusion-webui/modules/prompt_parser.py000066400000000000000000000316141432161722600223550ustar00rootroot00000000000000import re from collections import namedtuple from typing import List import lark # a prompt like this: "fantasy landscape with a [mountain:lake:0.25] and [an oak:a christmas tree:0.75][ in foreground::0.6][ in background:0.25] [shoddy:masterful:0.5]" # will be represented with prompt_schedule like this (assuming steps=100): # [25, 'fantasy landscape with a mountain and an oak in foreground shoddy'] # [50, 'fantasy landscape with a lake and an oak in foreground in background shoddy'] # [60, 'fantasy landscape with a lake and an oak in foreground in background masterful'] # [75, 'fantasy landscape with a lake and an oak in background masterful'] # [100, 'fantasy landscape with a lake and a christmas tree in background masterful'] schedule_parser = lark.Lark(r""" !start: (prompt | /[][():]/+)* prompt: (emphasized | scheduled | alternate | plain | WHITESPACE)* !emphasized: "(" prompt ")" | "(" prompt ":" prompt ")" | "[" prompt "]" scheduled: "[" [prompt ":"] prompt ":" [WHITESPACE] NUMBER "]" alternate: "[" prompt ("|" prompt)+ "]" WHITESPACE: /\s+/ plain: /([^\\\[\]():|]|\\.)+/ %import common.SIGNED_NUMBER -> NUMBER """) def get_learned_conditioning_prompt_schedules(prompts, steps): """ >>> g = lambda p: get_learned_conditioning_prompt_schedules([p], 10)[0] >>> g("test") [[10, 'test']] >>> g("a [b:3]") [[3, 'a '], [10, 'a b']] >>> g("a [b: 3]") [[3, 'a '], [10, 'a b']] >>> g("a [[[b]]:2]") [[2, 'a '], [10, 'a [[b]]']] >>> g("[(a:2):3]") [[3, ''], [10, '(a:2)']] >>> g("a [b : c : 1] d") [[1, 'a b d'], [10, 'a c d']] >>> g("a[b:[c:d:2]:1]e") [[1, 'abe'], [2, 'ace'], [10, 'ade']] >>> g("a [unbalanced") [[10, 'a [unbalanced']] >>> g("a [b:.5] c") [[5, 'a c'], [10, 'a b c']] >>> g("a [{b|d{:.5] c") # not handling this right now [[5, 'a c'], [10, 'a {b|d{ c']] >>> g("((a][:b:c [d:3]") [[3, '((a][:b:c '], [10, '((a][:b:c d']] """ def collect_steps(steps, tree): l = [steps] class CollectSteps(lark.Visitor): def scheduled(self, tree): tree.children[-1] = float(tree.children[-1]) if tree.children[-1] < 1: tree.children[-1] *= steps tree.children[-1] = min(steps, int(tree.children[-1])) l.append(tree.children[-1]) def alternate(self, tree): l.extend(range(1, steps+1)) CollectSteps().visit(tree) return sorted(set(l)) def at_step(step, tree): class AtStep(lark.Transformer): def scheduled(self, args): before, after, _, when = args yield before or () if step <= when else after def alternate(self, args): yield next(args[(step - 1)%len(args)]) def start(self, args): def flatten(x): if type(x) == str: yield x else: for gen in x: yield from flatten(gen) return ''.join(flatten(args)) def plain(self, args): yield args[0].value def __default__(self, data, children, meta): for child in children: yield from child return AtStep().transform(tree) def get_schedule(prompt): try: tree = schedule_parser.parse(prompt) except lark.exceptions.LarkError as e: if 0: import traceback traceback.print_exc() return [[steps, prompt]] return [[t, at_step(t, tree)] for t in collect_steps(steps, tree)] promptdict = {prompt: get_schedule(prompt) for prompt in set(prompts)} return [promptdict[prompt] for prompt in prompts] ScheduledPromptConditioning = namedtuple("ScheduledPromptConditioning", ["end_at_step", "cond"]) def get_learned_conditioning(model, prompts, steps): """converts a list of prompts into a list of prompt schedules - each schedule is a list of ScheduledPromptConditioning, specifying the comdition (cond), and the sampling step at which this condition is to be replaced by the next one. Input: (model, ['a red crown', 'a [blue:green:5] jeweled crown'], 20) Output: [ [ ScheduledPromptConditioning(end_at_step=20, cond=tensor([[-0.3886, 0.0229, -0.0523, ..., -0.4901, -0.3066, 0.0674], ..., [ 0.3317, -0.5102, -0.4066, ..., 0.4119, -0.7647, -1.0160]], device='cuda:0')) ], [ ScheduledPromptConditioning(end_at_step=5, cond=tensor([[-0.3886, 0.0229, -0.0522, ..., -0.4901, -0.3067, 0.0673], ..., [-0.0192, 0.3867, -0.4644, ..., 0.1135, -0.3696, -0.4625]], device='cuda:0')), ScheduledPromptConditioning(end_at_step=20, cond=tensor([[-0.3886, 0.0229, -0.0522, ..., -0.4901, -0.3067, 0.0673], ..., [-0.7352, -0.4356, -0.7888, ..., 0.6994, -0.4312, -1.2593]], device='cuda:0')) ] ] """ res = [] prompt_schedules = get_learned_conditioning_prompt_schedules(prompts, steps) cache = {} for prompt, prompt_schedule in zip(prompts, prompt_schedules): cached = cache.get(prompt, None) if cached is not None: res.append(cached) continue texts = [x[1] for x in prompt_schedule] conds = model.get_learned_conditioning(texts) cond_schedule = [] for i, (end_at_step, text) in enumerate(prompt_schedule): cond_schedule.append(ScheduledPromptConditioning(end_at_step, conds[i])) cache[prompt] = cond_schedule res.append(cond_schedule) return res re_AND = re.compile(r"\bAND\b") re_weight = re.compile(r"^(.*?)(?:\s*:\s*([-+]?(?:\d+\.?|\d*\.\d+)))?\s*$") def get_multicond_prompt_list(prompts): res_indexes = [] prompt_flat_list = [] prompt_indexes = {} for prompt in prompts: subprompts = re_AND.split(prompt) indexes = [] for subprompt in subprompts: match = re_weight.search(subprompt) text, weight = match.groups() if match is not None else (subprompt, 1.0) weight = float(weight) if weight is not None else 1.0 index = prompt_indexes.get(text, None) if index is None: index = len(prompt_flat_list) prompt_flat_list.append(text) prompt_indexes[text] = index indexes.append((index, weight)) res_indexes.append(indexes) return res_indexes, prompt_flat_list, prompt_indexes class ComposableScheduledPromptConditioning: def __init__(self, schedules, weight=1.0): self.schedules: List[ScheduledPromptConditioning] = schedules self.weight: float = weight class MulticondLearnedConditioning: def __init__(self, shape, batch): self.shape: tuple = shape # the shape field is needed to send this object to DDIM/PLMS self.batch: List[List[ComposableScheduledPromptConditioning]] = batch def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearnedConditioning: """same as get_learned_conditioning, but returns a list of ScheduledPromptConditioning along with the weight objects for each prompt. For each prompt, the list is obtained by splitting the prompt using the AND separator. https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/ """ res_indexes, prompt_flat_list, prompt_indexes = get_multicond_prompt_list(prompts) learned_conditioning = get_learned_conditioning(model, prompt_flat_list, steps) res = [] for indexes in res_indexes: res.append([ComposableScheduledPromptConditioning(learned_conditioning[i], weight) for i, weight in indexes]) return MulticondLearnedConditioning(shape=(len(prompts),), batch=res) def reconstruct_cond_batch(c: List[List[ScheduledPromptConditioning]], current_step): param = c[0][0].cond res = torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype) for i, cond_schedule in enumerate(c): target_index = 0 for current, (end_at, cond) in enumerate(cond_schedule): if current_step <= end_at: target_index = current break res[i] = cond_schedule[target_index].cond return res def reconstruct_multicond_batch(c: MulticondLearnedConditioning, current_step): param = c.batch[0][0].schedules[0].cond tensors = [] conds_list = [] for batch_no, composable_prompts in enumerate(c.batch): conds_for_batch = [] for cond_index, composable_prompt in enumerate(composable_prompts): target_index = 0 for current, (end_at, cond) in enumerate(composable_prompt.schedules): if current_step <= end_at: target_index = current break conds_for_batch.append((len(tensors), composable_prompt.weight)) tensors.append(composable_prompt.schedules[target_index].cond) conds_list.append(conds_for_batch) # if prompts have wildly different lengths above the limit we'll get tensors fo different shapes # and won't be able to torch.stack them. So this fixes that. token_count = max([x.shape[0] for x in tensors]) for i in range(len(tensors)): if tensors[i].shape[0] != token_count: last_vector = tensors[i][-1:] last_vector_repeated = last_vector.repeat([token_count - tensors[i].shape[0], 1]) tensors[i] = torch.vstack([tensors[i], last_vector_repeated]) return conds_list, torch.stack(tensors).to(device=param.device, dtype=param.dtype) re_attention = re.compile(r""" \\\(| \\\)| \\\[| \\]| \\\\| \\| \(| \[| :([+-]?[.\d]+)\)| \)| ]| [^\\()\[\]:]+| : """, re.X) def parse_prompt_attention(text): """ Parses a string with attention tokens and returns a list of pairs: text and its assoicated weight. Accepted tokens are: (abc) - increases attention to abc by a multiplier of 1.1 (abc:3.12) - increases attention to abc by a multiplier of 3.12 [abc] - decreases attention to abc by a multiplier of 1.1 \( - literal character '(' \[ - literal character '[' \) - literal character ')' \] - literal character ']' \\ - literal character '\' anything else - just text >>> parse_prompt_attention('normal text') [['normal text', 1.0]] >>> parse_prompt_attention('an (important) word') [['an ', 1.0], ['important', 1.1], [' word', 1.0]] >>> parse_prompt_attention('(unbalanced') [['unbalanced', 1.1]] >>> parse_prompt_attention('\(literal\]') [['(literal]', 1.0]] >>> parse_prompt_attention('(unnecessary)(parens)') [['unnecessaryparens', 1.1]] >>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).') [['a ', 1.0], ['house', 1.5730000000000004], [' ', 1.1], ['on', 1.0], [' a ', 1.1], ['hill', 0.55], [', sun, ', 1.1], ['sky', 1.4641000000000006], ['.', 1.1]] """ res = [] round_brackets = [] square_brackets = [] round_bracket_multiplier = 1.1 square_bracket_multiplier = 1 / 1.1 def multiply_range(start_position, multiplier): for p in range(start_position, len(res)): res[p][1] *= multiplier for m in re_attention.finditer(text): text = m.group(0) weight = m.group(1) if text.startswith('\\'): res.append([text[1:], 1.0]) elif text == '(': round_brackets.append(len(res)) elif text == '[': square_brackets.append(len(res)) elif weight is not None and len(round_brackets) > 0: multiply_range(round_brackets.pop(), float(weight)) elif text == ')' and len(round_brackets) > 0: multiply_range(round_brackets.pop(), round_bracket_multiplier) elif text == ']' and len(square_brackets) > 0: multiply_range(square_brackets.pop(), square_bracket_multiplier) else: res.append([text, 1.0]) for pos in round_brackets: multiply_range(pos, round_bracket_multiplier) for pos in square_brackets: multiply_range(pos, square_bracket_multiplier) if len(res) == 0: res = [["", 1.0]] # merge runs of identical weights i = 0 while i + 1 < len(res): if res[i][1] == res[i + 1][1]: res[i][0] += res[i + 1][0] res.pop(i + 1) else: i += 1 return res if __name__ == "__main__": import doctest doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE) else: import torch # doctest faster stable-diffusion-webui/modules/realesrgan_model.py000066400000000000000000000122451432161722600227620ustar00rootroot00000000000000import os import sys import traceback import numpy as np from PIL import Image from basicsr.utils.download_util import load_file_from_url from realesrgan import RealESRGANer from modules.upscaler import Upscaler, UpscalerData from modules.shared import cmd_opts, opts class UpscalerRealESRGAN(Upscaler): def __init__(self, path): self.name = "RealESRGAN" self.user_path = path super().__init__() try: from basicsr.archs.rrdbnet_arch import RRDBNet from realesrgan import RealESRGANer from realesrgan.archs.srvgg_arch import SRVGGNetCompact self.enable = True self.scalers = [] scalers = self.load_models(path) for scaler in scalers: if scaler.name in opts.realesrgan_enabled_models: self.scalers.append(scaler) except Exception: print("Error importing Real-ESRGAN:", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) self.enable = False self.scalers = [] def do_upscale(self, img, path): if not self.enable: return img info = self.load_model(path) if not os.path.exists(info.data_path): print("Unable to load RealESRGAN model: %s" % info.name) return img upsampler = RealESRGANer( scale=info.scale, model_path=info.data_path, model=info.model(), half=not cmd_opts.no_half, tile=opts.ESRGAN_tile, tile_pad=opts.ESRGAN_tile_overlap, ) upsampled = upsampler.enhance(np.array(img), outscale=info.scale)[0] image = Image.fromarray(upsampled) return image def load_model(self, path): try: info = None for scaler in self.scalers: if scaler.data_path == path: info = scaler if info is None: print(f"Unable to find model info: {path}") return None model_file = load_file_from_url(url=info.data_path, model_dir=self.model_path, progress=True) info.data_path = model_file return info except Exception as e: print(f"Error making Real-ESRGAN models list: {e}", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) return None def load_models(self, _): return get_realesrgan_models(self) def get_realesrgan_models(scaler): try: from basicsr.archs.rrdbnet_arch import RRDBNet from realesrgan.archs.srvgg_arch import SRVGGNetCompact models = [ UpscalerData( name="R-ESRGAN General 4xV3", path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth", scale=4, upscaler=scaler, model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu') ), UpscalerData( name="R-ESRGAN General WDN 4xV3", path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth", scale=4, upscaler=scaler, model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu') ), UpscalerData( name="R-ESRGAN AnimeVideo", path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth", scale=4, upscaler=scaler, model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu') ), UpscalerData( name="R-ESRGAN 4x+", path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth", scale=4, upscaler=scaler, model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4) ), UpscalerData( name="R-ESRGAN 4x+ Anime6B", path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth", scale=4, upscaler=scaler, model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4) ), UpscalerData( name="R-ESRGAN 2x+", path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth", scale=2, upscaler=scaler, model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2) ), ] return models except Exception as e: print("Error making Real-ESRGAN models list:", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) stable-diffusion-webui/modules/safe.py000066400000000000000000000074431432161722600204010ustar00rootroot00000000000000# this code is adapted from the script contributed by anon from /h/ import io import pickle import collections import sys import traceback import torch import numpy import _codecs import zipfile import re # PyTorch 1.13 and later have _TypedStorage renamed to TypedStorage TypedStorage = torch.storage.TypedStorage if hasattr(torch.storage, 'TypedStorage') else torch.storage._TypedStorage def encode(*args): out = _codecs.encode(*args) return out class RestrictedUnpickler(pickle.Unpickler): def persistent_load(self, saved_id): assert saved_id[0] == 'storage' return TypedStorage() def find_class(self, module, name): if module == 'collections' and name == 'OrderedDict': return getattr(collections, name) if module == 'torch._utils' and name in ['_rebuild_tensor_v2', '_rebuild_parameter']: return getattr(torch._utils, name) if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage', 'DoubleStorage']: return getattr(torch, name) if module == 'torch.nn.modules.container' and name in ['ParameterDict']: return getattr(torch.nn.modules.container, name) if module == 'numpy.core.multiarray' and name == 'scalar': return numpy.core.multiarray.scalar if module == 'numpy' and name == 'dtype': return numpy.dtype if module == '_codecs' and name == 'encode': return encode if module == "pytorch_lightning.callbacks" and name == 'model_checkpoint': import pytorch_lightning.callbacks return pytorch_lightning.callbacks.model_checkpoint if module == "pytorch_lightning.callbacks.model_checkpoint" and name == 'ModelCheckpoint': import pytorch_lightning.callbacks.model_checkpoint return pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint if module == "__builtin__" and name == 'set': return set # Forbid everything else. raise pickle.UnpicklingError(f"global '{module}/{name}' is forbidden") allowed_zip_names = ["archive/data.pkl", "archive/version"] allowed_zip_names_re = re.compile(r"^archive/data/\d+$") def check_zip_filenames(filename, names): for name in names: if name in allowed_zip_names: continue if allowed_zip_names_re.match(name): continue raise Exception(f"bad file inside {filename}: {name}") def check_pt(filename): try: # new pytorch format is a zip file with zipfile.ZipFile(filename) as z: check_zip_filenames(filename, z.namelist()) with z.open('archive/data.pkl') as file: unpickler = RestrictedUnpickler(file) unpickler.load() except zipfile.BadZipfile: # if it's not a zip file, it's an olf pytorch format, with five objects written to pickle with open(filename, "rb") as file: unpickler = RestrictedUnpickler(file) for i in range(5): unpickler.load() def load(filename, *args, **kwargs): from modules import shared try: if not shared.cmd_opts.disable_safe_unpickle: check_pt(filename) except Exception: print(f"Error verifying pickled file from {filename}:", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) print(f"\nThe file may be malicious, so the program is not going to read it.", file=sys.stderr) print(f"You can skip this check with --disable-safe-unpickle commandline argument.", file=sys.stderr) return None return unsafe_torch_load(filename, *args, **kwargs) unsafe_torch_load = torch.load torch.load = load stable-diffusion-webui/modules/safety.py000066400000000000000000000027271432161722600207560ustar00rootroot00000000000000import torch from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker from transformers import AutoFeatureExtractor from PIL import Image import modules.shared as shared safety_model_id = "CompVis/stable-diffusion-safety-checker" safety_feature_extractor = None safety_checker = None def numpy_to_pil(images): """ Convert a numpy image or a batch of images to a PIL image. """ if images.ndim == 3: images = images[None, ...] images = (images * 255).round().astype("uint8") pil_images = [Image.fromarray(image) for image in images] return pil_images # check and replace nsfw content def check_safety(x_image): global safety_feature_extractor, safety_checker if safety_feature_extractor is None: safety_feature_extractor = AutoFeatureExtractor.from_pretrained(safety_model_id) safety_checker = StableDiffusionSafetyChecker.from_pretrained(safety_model_id) safety_checker_input = safety_feature_extractor(numpy_to_pil(x_image), return_tensors="pt") x_checked_image, has_nsfw_concept = safety_checker(images=x_image, clip_input=safety_checker_input.pixel_values) return x_checked_image, has_nsfw_concept def censor_batch(x): x_samples_ddim_numpy = x.cpu().permute(0, 2, 3, 1).numpy() x_checked_image, has_nsfw_concept = check_safety(x_samples_ddim_numpy) x = torch.from_numpy(x_checked_image).permute(0, 3, 1, 2) return x stable-diffusion-webui/modules/scripts.py000066400000000000000000000150221432161722600211420ustar00rootroot00000000000000import os import sys import traceback import modules.ui as ui import gradio as gr from modules.processing import StableDiffusionProcessing from modules import shared class Script: filename = None args_from = None args_to = None # The title of the script. This is what will be displayed in the dropdown menu. def title(self): raise NotImplementedError() # How the script is displayed in the UI. See https://gradio.app/docs/#components # for the different UI components you can use and how to create them. # Most UI components can return a value, such as a boolean for a checkbox. # The returned values are passed to the run method as parameters. def ui(self, is_img2img): pass # Determines when the script should be shown in the dropdown menu via the # returned value. As an example: # is_img2img is True if the current tab is img2img, and False if it is txt2img. # Thus, return is_img2img to only show the script on the img2img tab. def show(self, is_img2img): return True # This is where the additional processing is implemented. The parameters include # self, the model object "p" (a StableDiffusionProcessing class, see # processing.py), and the parameters returned by the ui method. # Custom functions can be defined here, and additional libraries can be imported # to be used in processing. The return value should be a Processed object, which is # what is returned by the process_images method. def run(self, *args): raise NotImplementedError() # The description method is currently unused. # To add a description that appears when hovering over the title, amend the "titles" # dict in script.js to include the script title (returned by title) as a key, and # your description as the value. def describe(self): return "" scripts_data = [] def load_scripts(basedir): if not os.path.exists(basedir): return for filename in sorted(os.listdir(basedir)): path = os.path.join(basedir, filename) if not os.path.isfile(path): continue try: with open(path, "r", encoding="utf8") as file: text = file.read() from types import ModuleType compiled = compile(text, path, 'exec') module = ModuleType(filename) exec(compiled, module.__dict__) for key, script_class in module.__dict__.items(): if type(script_class) == type and issubclass(script_class, Script): scripts_data.append((script_class, path)) except Exception: print(f"Error loading script: {filename}", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) def wrap_call(func, filename, funcname, *args, default=None, **kwargs): try: res = func(*args, **kwargs) return res except Exception: print(f"Error calling: {filename}/{funcname}", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) return default class ScriptRunner: def __init__(self): self.scripts = [] def setup_ui(self, is_img2img): for script_class, path in scripts_data: script = script_class() script.filename = path if not script.show(is_img2img): continue self.scripts.append(script) titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.scripts] dropdown = gr.Dropdown(label="Script", choices=["None"] + titles, value="None", type="index") inputs = [dropdown] for script in self.scripts: script.args_from = len(inputs) script.args_to = len(inputs) controls = wrap_call(script.ui, script.filename, "ui", is_img2img) if controls is None: continue for control in controls: control.custom_script_source = os.path.basename(script.filename) control.visible = False inputs += controls script.args_to = len(inputs) def select_script(script_index): if 0 < script_index <= len(self.scripts): script = self.scripts[script_index-1] args_from = script.args_from args_to = script.args_to else: args_from = 0 args_to = 0 return [ui.gr_show(True if i == 0 else args_from <= i < args_to) for i in range(len(inputs))] dropdown.change( fn=select_script, inputs=[dropdown], outputs=inputs ) return inputs def run(self, p: StableDiffusionProcessing, *args): script_index = args[0] if script_index == 0: return None script = self.scripts[script_index-1] if script is None: return None script_args = args[script.args_from:script.args_to] processed = script.run(p, *script_args) shared.total_tqdm.clear() return processed def reload_sources(self): for si, script in list(enumerate(self.scripts)): with open(script.filename, "r", encoding="utf8") as file: args_from = script.args_from args_to = script.args_to filename = script.filename text = file.read() from types import ModuleType compiled = compile(text, filename, 'exec') module = ModuleType(script.filename) exec(compiled, module.__dict__) for key, script_class in module.__dict__.items(): if type(script_class) == type and issubclass(script_class, Script): self.scripts[si] = script_class() self.scripts[si].filename = filename self.scripts[si].args_from = args_from self.scripts[si].args_to = args_to scripts_txt2img = ScriptRunner() scripts_img2img = ScriptRunner() def reload_script_body_only(): scripts_txt2img.reload_sources() scripts_img2img.reload_sources() def reload_scripts(basedir): global scripts_txt2img, scripts_img2img scripts_data.clear() load_scripts(basedir) scripts_txt2img = ScriptRunner() scripts_img2img = ScriptRunner() stable-diffusion-webui/modules/scunet_model.py000066400000000000000000000062351432161722600221420ustar00rootroot00000000000000import os.path import sys import traceback import PIL.Image import numpy as np import torch from basicsr.utils.download_util import load_file_from_url import modules.upscaler from modules import devices, modelloader from modules.scunet_model_arch import SCUNet as net class UpscalerScuNET(modules.upscaler.Upscaler): def __init__(self, dirname): self.name = "ScuNET" self.model_name = "ScuNET GAN" self.model_name2 = "ScuNET PSNR" self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_gan.pth" self.model_url2 = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_psnr.pth" self.user_path = dirname super().__init__() model_paths = self.find_models(ext_filter=[".pth"]) scalers = [] add_model2 = True for file in model_paths: if "http" in file: name = self.model_name else: name = modelloader.friendly_name(file) if name == self.model_name2 or file == self.model_url2: add_model2 = False try: scaler_data = modules.upscaler.UpscalerData(name, file, self, 4) scalers.append(scaler_data) except Exception: print(f"Error loading ScuNET model: {file}", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) if add_model2: scaler_data2 = modules.upscaler.UpscalerData(self.model_name2, self.model_url2, self) scalers.append(scaler_data2) self.scalers = scalers def do_upscale(self, img: PIL.Image, selected_file): torch.cuda.empty_cache() model = self.load_model(selected_file) if model is None: return img device = devices.device_scunet img = np.array(img) img = img[:, :, ::-1] img = np.moveaxis(img, 2, 0) / 255 img = torch.from_numpy(img).float() img = img.unsqueeze(0).to(device) img = img.to(device) with torch.no_grad(): output = model(img) output = output.squeeze().float().cpu().clamp_(0, 1).numpy() output = 255. * np.moveaxis(output, 0, 2) output = output.astype(np.uint8) output = output[:, :, ::-1] torch.cuda.empty_cache() return PIL.Image.fromarray(output, 'RGB') def load_model(self, path: str): device = devices.device_scunet if "http" in path: filename = load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="%s.pth" % self.name, progress=True) else: filename = path if not os.path.exists(os.path.join(self.model_path, filename)) or filename is None: print(f"ScuNET: Unable to load model from {filename}", file=sys.stderr) return None model = net(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64) model.load_state_dict(torch.load(filename), strict=True) model.eval() for k, v in model.named_parameters(): v.requires_grad = False model = model.to(device) return model stable-diffusion-webui/modules/scunet_model_arch.py000066400000000000000000000263551432161722600231440ustar00rootroot00000000000000# -*- coding: utf-8 -*- import numpy as np import torch import torch.nn as nn from einops import rearrange from einops.layers.torch import Rearrange from timm.models.layers import trunc_normal_, DropPath class WMSA(nn.Module): """ Self-attention module in Swin Transformer """ def __init__(self, input_dim, output_dim, head_dim, window_size, type): super(WMSA, self).__init__() self.input_dim = input_dim self.output_dim = output_dim self.head_dim = head_dim self.scale = self.head_dim ** -0.5 self.n_heads = input_dim // head_dim self.window_size = window_size self.type = type self.embedding_layer = nn.Linear(self.input_dim, 3 * self.input_dim, bias=True) self.relative_position_params = nn.Parameter( torch.zeros((2 * window_size - 1) * (2 * window_size - 1), self.n_heads)) self.linear = nn.Linear(self.input_dim, self.output_dim) trunc_normal_(self.relative_position_params, std=.02) self.relative_position_params = torch.nn.Parameter( self.relative_position_params.view(2 * window_size - 1, 2 * window_size - 1, self.n_heads).transpose(1, 2).transpose( 0, 1)) def generate_mask(self, h, w, p, shift): """ generating the mask of SW-MSA Args: shift: shift parameters in CyclicShift. Returns: attn_mask: should be (1 1 w p p), """ # supporting square. attn_mask = torch.zeros(h, w, p, p, p, p, dtype=torch.bool, device=self.relative_position_params.device) if self.type == 'W': return attn_mask s = p - shift attn_mask[-1, :, :s, :, s:, :] = True attn_mask[-1, :, s:, :, :s, :] = True attn_mask[:, -1, :, :s, :, s:] = True attn_mask[:, -1, :, s:, :, :s] = True attn_mask = rearrange(attn_mask, 'w1 w2 p1 p2 p3 p4 -> 1 1 (w1 w2) (p1 p2) (p3 p4)') return attn_mask def forward(self, x): """ Forward pass of Window Multi-head Self-attention module. Args: x: input tensor with shape of [b h w c]; attn_mask: attention mask, fill -inf where the value is True; Returns: output: tensor shape [b h w c] """ if self.type != 'W': x = torch.roll(x, shifts=(-(self.window_size // 2), -(self.window_size // 2)), dims=(1, 2)) x = rearrange(x, 'b (w1 p1) (w2 p2) c -> b w1 w2 p1 p2 c', p1=self.window_size, p2=self.window_size) h_windows = x.size(1) w_windows = x.size(2) # square validation # assert h_windows == w_windows x = rearrange(x, 'b w1 w2 p1 p2 c -> b (w1 w2) (p1 p2) c', p1=self.window_size, p2=self.window_size) qkv = self.embedding_layer(x) q, k, v = rearrange(qkv, 'b nw np (threeh c) -> threeh b nw np c', c=self.head_dim).chunk(3, dim=0) sim = torch.einsum('hbwpc,hbwqc->hbwpq', q, k) * self.scale # Adding learnable relative embedding sim = sim + rearrange(self.relative_embedding(), 'h p q -> h 1 1 p q') # Using Attn Mask to distinguish different subwindows. if self.type != 'W': attn_mask = self.generate_mask(h_windows, w_windows, self.window_size, shift=self.window_size // 2) sim = sim.masked_fill_(attn_mask, float("-inf")) probs = nn.functional.softmax(sim, dim=-1) output = torch.einsum('hbwij,hbwjc->hbwic', probs, v) output = rearrange(output, 'h b w p c -> b w p (h c)') output = self.linear(output) output = rearrange(output, 'b (w1 w2) (p1 p2) c -> b (w1 p1) (w2 p2) c', w1=h_windows, p1=self.window_size) if self.type != 'W': output = torch.roll(output, shifts=(self.window_size // 2, self.window_size // 2), dims=(1, 2)) return output def relative_embedding(self): cord = torch.tensor(np.array([[i, j] for i in range(self.window_size) for j in range(self.window_size)])) relation = cord[:, None, :] - cord[None, :, :] + self.window_size - 1 # negative is allowed return self.relative_position_params[:, relation[:, :, 0].long(), relation[:, :, 1].long()] class Block(nn.Module): def __init__(self, input_dim, output_dim, head_dim, window_size, drop_path, type='W', input_resolution=None): """ SwinTransformer Block """ super(Block, self).__init__() self.input_dim = input_dim self.output_dim = output_dim assert type in ['W', 'SW'] self.type = type if input_resolution <= window_size: self.type = 'W' self.ln1 = nn.LayerNorm(input_dim) self.msa = WMSA(input_dim, input_dim, head_dim, window_size, self.type) self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() self.ln2 = nn.LayerNorm(input_dim) self.mlp = nn.Sequential( nn.Linear(input_dim, 4 * input_dim), nn.GELU(), nn.Linear(4 * input_dim, output_dim), ) def forward(self, x): x = x + self.drop_path(self.msa(self.ln1(x))) x = x + self.drop_path(self.mlp(self.ln2(x))) return x class ConvTransBlock(nn.Module): def __init__(self, conv_dim, trans_dim, head_dim, window_size, drop_path, type='W', input_resolution=None): """ SwinTransformer and Conv Block """ super(ConvTransBlock, self).__init__() self.conv_dim = conv_dim self.trans_dim = trans_dim self.head_dim = head_dim self.window_size = window_size self.drop_path = drop_path self.type = type self.input_resolution = input_resolution assert self.type in ['W', 'SW'] if self.input_resolution <= self.window_size: self.type = 'W' self.trans_block = Block(self.trans_dim, self.trans_dim, self.head_dim, self.window_size, self.drop_path, self.type, self.input_resolution) self.conv1_1 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True) self.conv1_2 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True) self.conv_block = nn.Sequential( nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False), nn.ReLU(True), nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False) ) def forward(self, x): conv_x, trans_x = torch.split(self.conv1_1(x), (self.conv_dim, self.trans_dim), dim=1) conv_x = self.conv_block(conv_x) + conv_x trans_x = Rearrange('b c h w -> b h w c')(trans_x) trans_x = self.trans_block(trans_x) trans_x = Rearrange('b h w c -> b c h w')(trans_x) res = self.conv1_2(torch.cat((conv_x, trans_x), dim=1)) x = x + res return x class SCUNet(nn.Module): # def __init__(self, in_nc=3, config=[2, 2, 2, 2, 2, 2, 2], dim=64, drop_path_rate=0.0, input_resolution=256): def __init__(self, in_nc=3, config=None, dim=64, drop_path_rate=0.0, input_resolution=256): super(SCUNet, self).__init__() if config is None: config = [2, 2, 2, 2, 2, 2, 2] self.config = config self.dim = dim self.head_dim = 32 self.window_size = 8 # drop path rate for each layer dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(config))] self.m_head = [nn.Conv2d(in_nc, dim, 3, 1, 1, bias=False)] begin = 0 self.m_down1 = [ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin], 'W' if not i % 2 else 'SW', input_resolution) for i in range(config[0])] + \ [nn.Conv2d(dim, 2 * dim, 2, 2, 0, bias=False)] begin += config[0] self.m_down2 = [ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin], 'W' if not i % 2 else 'SW', input_resolution // 2) for i in range(config[1])] + \ [nn.Conv2d(2 * dim, 4 * dim, 2, 2, 0, bias=False)] begin += config[1] self.m_down3 = [ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin], 'W' if not i % 2 else 'SW', input_resolution // 4) for i in range(config[2])] + \ [nn.Conv2d(4 * dim, 8 * dim, 2, 2, 0, bias=False)] begin += config[2] self.m_body = [ConvTransBlock(4 * dim, 4 * dim, self.head_dim, self.window_size, dpr[i + begin], 'W' if not i % 2 else 'SW', input_resolution // 8) for i in range(config[3])] begin += config[3] self.m_up3 = [nn.ConvTranspose2d(8 * dim, 4 * dim, 2, 2, 0, bias=False), ] + \ [ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin], 'W' if not i % 2 else 'SW', input_resolution // 4) for i in range(config[4])] begin += config[4] self.m_up2 = [nn.ConvTranspose2d(4 * dim, 2 * dim, 2, 2, 0, bias=False), ] + \ [ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin], 'W' if not i % 2 else 'SW', input_resolution // 2) for i in range(config[5])] begin += config[5] self.m_up1 = [nn.ConvTranspose2d(2 * dim, dim, 2, 2, 0, bias=False), ] + \ [ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin], 'W' if not i % 2 else 'SW', input_resolution) for i in range(config[6])] self.m_tail = [nn.Conv2d(dim, in_nc, 3, 1, 1, bias=False)] self.m_head = nn.Sequential(*self.m_head) self.m_down1 = nn.Sequential(*self.m_down1) self.m_down2 = nn.Sequential(*self.m_down2) self.m_down3 = nn.Sequential(*self.m_down3) self.m_body = nn.Sequential(*self.m_body) self.m_up3 = nn.Sequential(*self.m_up3) self.m_up2 = nn.Sequential(*self.m_up2) self.m_up1 = nn.Sequential(*self.m_up1) self.m_tail = nn.Sequential(*self.m_tail) # self.apply(self._init_weights) def forward(self, x0): h, w = x0.size()[-2:] paddingBottom = int(np.ceil(h / 64) * 64 - h) paddingRight = int(np.ceil(w / 64) * 64 - w) x0 = nn.ReplicationPad2d((0, paddingRight, 0, paddingBottom))(x0) x1 = self.m_head(x0) x2 = self.m_down1(x1) x3 = self.m_down2(x2) x4 = self.m_down3(x3) x = self.m_body(x4) x = self.m_up3(x + x4) x = self.m_up2(x + x3) x = self.m_up1(x + x2) x = self.m_tail(x + x1) x = x[..., :h, :w] return x def _init_weights(self, m): if isinstance(m, nn.Linear): trunc_normal_(m.weight, std=.02) if m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.LayerNorm): nn.init.constant_(m.bias, 0) nn.init.constant_(m.weight, 1.0)stable-diffusion-webui/modules/sd_hijack.py000066400000000000000000000425461432161722600214050ustar00rootroot00000000000000import math import os import sys import traceback import torch import numpy as np from torch import einsum from torch.nn.functional import silu import modules.textual_inversion.textual_inversion from modules import prompt_parser, devices, sd_hijack_optimizations, shared from modules.shared import opts, device, cmd_opts from modules.sd_hijack_optimizations import invokeAI_mps_available import ldm.modules.attention import ldm.modules.diffusionmodules.model attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward def apply_optimizations(): undo_optimizations() ldm.modules.diffusionmodules.model.nonlinearity = silu if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (8, 6)): print("Applying xformers cross attention optimization.") ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward elif cmd_opts.opt_split_attention_v1: print("Applying v1 cross attention optimization.") ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1 elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention_invokeai or not torch.cuda.is_available()): if not invokeAI_mps_available and shared.device.type == 'mps': print("The InvokeAI cross attention optimization for MPS requires the psutil package which is not installed.") print("Applying v1 cross attention optimization.") ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1 else: print("Applying cross attention optimization (InvokeAI).") ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_invokeAI elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()): print("Applying cross attention optimization (Doggettx).") ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward def undo_optimizations(): from modules.hypernetworks import hypernetwork ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward ldm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward def get_target_prompt_token_count(token_count): return math.ceil(max(token_count, 1) / 75) * 75 class StableDiffusionModelHijack: fixes = None comments = [] layers = None circular_enabled = False clip = None embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase(cmd_opts.embeddings_dir) def hijack(self, m): model_embeddings = m.cond_stage_model.transformer.text_model.embeddings model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.token_embedding, self) m.cond_stage_model = FrozenCLIPEmbedderWithCustomWords(m.cond_stage_model, self) self.clip = m.cond_stage_model apply_optimizations() def flatten(el): flattened = [flatten(children) for children in el.children()] res = [el] for c in flattened: res += c return res self.layers = flatten(m) def undo_hijack(self, m): if type(m.cond_stage_model) == FrozenCLIPEmbedderWithCustomWords: m.cond_stage_model = m.cond_stage_model.wrapped model_embeddings = m.cond_stage_model.transformer.text_model.embeddings if type(model_embeddings.token_embedding) == EmbeddingsWithFixes: model_embeddings.token_embedding = model_embeddings.token_embedding.wrapped def apply_circular(self, enable): if self.circular_enabled == enable: return self.circular_enabled = enable for layer in [layer for layer in self.layers if type(layer) == torch.nn.Conv2d]: layer.padding_mode = 'circular' if enable else 'zeros' def clear_comments(self): self.comments = [] def tokenize(self, text): _, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text]) return remade_batch_tokens[0], token_count, get_target_prompt_token_count(token_count) class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): def __init__(self, wrapped, hijack): super().__init__() self.wrapped = wrapped self.hijack: StableDiffusionModelHijack = hijack self.tokenizer = wrapped.tokenizer self.token_mults = {} self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ','][0] tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if '(' in k or ')' in k or '[' in k or ']' in k] for text, ident in tokens_with_parens: mult = 1.0 for c in text: if c == '[': mult /= 1.1 if c == ']': mult *= 1.1 if c == '(': mult *= 1.1 if c == ')': mult /= 1.1 if mult != 1.0: self.token_mults[ident] = mult def tokenize_line(self, line, used_custom_terms, hijack_comments): id_end = self.wrapped.tokenizer.eos_token_id if opts.enable_emphasis: parsed = prompt_parser.parse_prompt_attention(line) else: parsed = [[line, 1.0]] tokenized = self.wrapped.tokenizer([text for text, _ in parsed], truncation=False, add_special_tokens=False)["input_ids"] fixes = [] remade_tokens = [] multipliers = [] last_comma = -1 for tokens, (text, weight) in zip(tokenized, parsed): i = 0 while i < len(tokens): token = tokens[i] embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i) if token == self.comma_token: last_comma = len(remade_tokens) elif opts.comma_padding_backtrack != 0 and max(len(remade_tokens), 1) % 75 == 0 and last_comma != -1 and len(remade_tokens) - last_comma <= opts.comma_padding_backtrack: last_comma += 1 reloc_tokens = remade_tokens[last_comma:] reloc_mults = multipliers[last_comma:] remade_tokens = remade_tokens[:last_comma] length = len(remade_tokens) rem = int(math.ceil(length / 75)) * 75 - length remade_tokens += [id_end] * rem + reloc_tokens multipliers = multipliers[:last_comma] + [1.0] * rem + reloc_mults if embedding is None: remade_tokens.append(token) multipliers.append(weight) i += 1 else: emb_len = int(embedding.vec.shape[0]) iteration = len(remade_tokens) // 75 if (len(remade_tokens) + emb_len) // 75 != iteration: rem = (75 * (iteration + 1) - len(remade_tokens)) remade_tokens += [id_end] * rem multipliers += [1.0] * rem iteration += 1 fixes.append((iteration, (len(remade_tokens) % 75, embedding))) remade_tokens += [0] * emb_len multipliers += [weight] * emb_len used_custom_terms.append((embedding.name, embedding.checksum())) i += embedding_length_in_tokens token_count = len(remade_tokens) prompt_target_length = get_target_prompt_token_count(token_count) tokens_to_add = prompt_target_length - len(remade_tokens) remade_tokens = remade_tokens + [id_end] * tokens_to_add multipliers = multipliers + [1.0] * tokens_to_add return remade_tokens, fixes, multipliers, token_count def process_text(self, texts): used_custom_terms = [] remade_batch_tokens = [] hijack_comments = [] hijack_fixes = [] token_count = 0 cache = {} batch_multipliers = [] for line in texts: if line in cache: remade_tokens, fixes, multipliers = cache[line] else: remade_tokens, fixes, multipliers, current_token_count = self.tokenize_line(line, used_custom_terms, hijack_comments) token_count = max(current_token_count, token_count) cache[line] = (remade_tokens, fixes, multipliers) remade_batch_tokens.append(remade_tokens) hijack_fixes.append(fixes) batch_multipliers.append(multipliers) return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count def process_text_old(self, text): id_start = self.wrapped.tokenizer.bos_token_id id_end = self.wrapped.tokenizer.eos_token_id maxlen = self.wrapped.max_length # you get to stay at 77 used_custom_terms = [] remade_batch_tokens = [] overflowing_words = [] hijack_comments = [] hijack_fixes = [] token_count = 0 cache = {} batch_tokens = self.wrapped.tokenizer(text, truncation=False, add_special_tokens=False)["input_ids"] batch_multipliers = [] for tokens in batch_tokens: tuple_tokens = tuple(tokens) if tuple_tokens in cache: remade_tokens, fixes, multipliers = cache[tuple_tokens] else: fixes = [] remade_tokens = [] multipliers = [] mult = 1.0 i = 0 while i < len(tokens): token = tokens[i] embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i) mult_change = self.token_mults.get(token) if opts.enable_emphasis else None if mult_change is not None: mult *= mult_change i += 1 elif embedding is None: remade_tokens.append(token) multipliers.append(mult) i += 1 else: emb_len = int(embedding.vec.shape[0]) fixes.append((len(remade_tokens), embedding)) remade_tokens += [0] * emb_len multipliers += [mult] * emb_len used_custom_terms.append((embedding.name, embedding.checksum())) i += embedding_length_in_tokens if len(remade_tokens) > maxlen - 2: vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()} ovf = remade_tokens[maxlen - 2:] overflowing_words = [vocab.get(int(x), "") for x in ovf] overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words)) hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n") token_count = len(remade_tokens) remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens)) remade_tokens = [id_start] + remade_tokens[0:maxlen-2] + [id_end] cache[tuple_tokens] = (remade_tokens, fixes, multipliers) multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers)) multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0] remade_batch_tokens.append(remade_tokens) hijack_fixes.append(fixes) batch_multipliers.append(multipliers) return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count def forward(self, text): use_old = opts.use_old_emphasis_implementation if use_old: batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text_old(text) else: batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text) self.hijack.comments += hijack_comments if len(used_custom_terms) > 0: self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms])) if use_old: self.hijack.fixes = hijack_fixes return self.process_tokens(remade_batch_tokens, batch_multipliers) z = None i = 0 while max(map(len, remade_batch_tokens)) != 0: rem_tokens = [x[75:] for x in remade_batch_tokens] rem_multipliers = [x[75:] for x in batch_multipliers] self.hijack.fixes = [] for unfiltered in hijack_fixes: fixes = [] for fix in unfiltered: if fix[0] == i: fixes.append(fix[1]) self.hijack.fixes.append(fixes) tokens = [] multipliers = [] for j in range(len(remade_batch_tokens)): if len(remade_batch_tokens[j]) > 0: tokens.append(remade_batch_tokens[j][:75]) multipliers.append(batch_multipliers[j][:75]) else: tokens.append([self.wrapped.tokenizer.eos_token_id] * 75) multipliers.append([1.0] * 75) z1 = self.process_tokens(tokens, multipliers) z = z1 if z is None else torch.cat((z, z1), axis=-2) remade_batch_tokens = rem_tokens batch_multipliers = rem_multipliers i += 1 return z def process_tokens(self, remade_batch_tokens, batch_multipliers): if not opts.use_old_emphasis_implementation: remade_batch_tokens = [[self.wrapped.tokenizer.bos_token_id] + x[:75] + [self.wrapped.tokenizer.eos_token_id] for x in remade_batch_tokens] batch_multipliers = [[1.0] + x[:75] + [1.0] for x in batch_multipliers] tokens = torch.asarray(remade_batch_tokens).to(device) outputs = self.wrapped.transformer(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers) if opts.CLIP_stop_at_last_layers > 1: z = outputs.hidden_states[-opts.CLIP_stop_at_last_layers] z = self.wrapped.transformer.text_model.final_layer_norm(z) else: z = outputs.last_hidden_state # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise batch_multipliers_of_same_length = [x + [1.0] * (75 - len(x)) for x in batch_multipliers] batch_multipliers = torch.asarray(batch_multipliers_of_same_length).to(device) original_mean = z.mean() z *= batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape) new_mean = z.mean() z *= original_mean / new_mean return z class EmbeddingsWithFixes(torch.nn.Module): def __init__(self, wrapped, embeddings): super().__init__() self.wrapped = wrapped self.embeddings = embeddings def forward(self, input_ids): batch_fixes = self.embeddings.fixes self.embeddings.fixes = None inputs_embeds = self.wrapped(input_ids) if batch_fixes is None or len(batch_fixes) == 0 or max([len(x) for x in batch_fixes]) == 0: return inputs_embeds vecs = [] for fixes, tensor in zip(batch_fixes, inputs_embeds): for offset, embedding in fixes: emb = embedding.vec emb_len = min(tensor.shape[0]-offset-1, emb.shape[0]) tensor = torch.cat([tensor[0:offset+1], emb[0:emb_len], tensor[offset+1+emb_len:]]) vecs.append(tensor) return torch.stack(vecs) def add_circular_option_to_conv_2d(): conv2d_constructor = torch.nn.Conv2d.__init__ def conv2d_constructor_circular(self, *args, **kwargs): return conv2d_constructor(self, *args, padding_mode='circular', **kwargs) torch.nn.Conv2d.__init__ = conv2d_constructor_circular model_hijack = StableDiffusionModelHijack() stable-diffusion-webui/modules/sd_hijack_optimizations.py000066400000000000000000000243461432161722600243740ustar00rootroot00000000000000import math import sys import traceback import importlib import torch from torch import einsum from ldm.util import default from einops import rearrange from modules import shared from modules.hypernetworks import hypernetwork if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers: try: import xformers.ops shared.xformers_available = True except Exception: print("Cannot import xformers", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) # see https://github.com/basujindal/stable-diffusion/pull/117 for discussion def split_cross_attention_forward_v1(self, x, context=None, mask=None): h = self.heads q_in = self.to_q(x) context = default(context, x) context_k, context_v = hypernetwork.apply_hypernetwork(shared.loaded_hypernetwork, context) k_in = self.to_k(context_k) v_in = self.to_v(context_v) del context, context_k, context_v, x q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in)) del q_in, k_in, v_in r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device) for i in range(0, q.shape[0], 2): end = i + 2 s1 = einsum('b i d, b j d -> b i j', q[i:end], k[i:end]) s1 *= self.scale s2 = s1.softmax(dim=-1) del s1 r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end]) del s2 del q, k, v r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h) del r1 return self.to_out(r2) # taken from https://github.com/Doggettx/stable-diffusion and modified def split_cross_attention_forward(self, x, context=None, mask=None): h = self.heads q_in = self.to_q(x) context = default(context, x) context_k, context_v = hypernetwork.apply_hypernetwork(shared.loaded_hypernetwork, context) k_in = self.to_k(context_k) v_in = self.to_v(context_v) k_in *= self.scale del context, x q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in)) del q_in, k_in, v_in r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype) stats = torch.cuda.memory_stats(q.device) mem_active = stats['active_bytes.all.current'] mem_reserved = stats['reserved_bytes.all.current'] mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device()) mem_free_torch = mem_reserved - mem_active mem_free_total = mem_free_cuda + mem_free_torch gb = 1024 ** 3 tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size() modifier = 3 if q.element_size() == 2 else 2.5 mem_required = tensor_size * modifier steps = 1 if mem_required > mem_free_total: steps = 2 ** (math.ceil(math.log(mem_required / mem_free_total, 2))) # print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB " # f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}") if steps > 64: max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64 raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). ' f'Need: {mem_required / 64 / gb:0.1f}GB free, Have:{mem_free_total / gb:0.1f}GB free') slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1] for i in range(0, q.shape[1], slice_size): end = i + slice_size s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k) s2 = s1.softmax(dim=-1, dtype=q.dtype) del s1 r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v) del s2 del q, k, v r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h) del r1 return self.to_out(r2) def check_for_psutil(): try: spec = importlib.util.find_spec('psutil') return spec is not None except ModuleNotFoundError: return False invokeAI_mps_available = check_for_psutil() # -- Taken from https://github.com/invoke-ai/InvokeAI -- if invokeAI_mps_available: import psutil mem_total_gb = psutil.virtual_memory().total // (1 << 30) def einsum_op_compvis(q, k, v): s = einsum('b i d, b j d -> b i j', q, k) s = s.softmax(dim=-1, dtype=s.dtype) return einsum('b i j, b j d -> b i d', s, v) def einsum_op_slice_0(q, k, v, slice_size): r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype) for i in range(0, q.shape[0], slice_size): end = i + slice_size r[i:end] = einsum_op_compvis(q[i:end], k[i:end], v[i:end]) return r def einsum_op_slice_1(q, k, v, slice_size): r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype) for i in range(0, q.shape[1], slice_size): end = i + slice_size r[:, i:end] = einsum_op_compvis(q[:, i:end], k, v) return r def einsum_op_mps_v1(q, k, v): if q.shape[1] <= 4096: # (512x512) max q.shape[1]: 4096 return einsum_op_compvis(q, k, v) else: slice_size = math.floor(2**30 / (q.shape[0] * q.shape[1])) return einsum_op_slice_1(q, k, v, slice_size) def einsum_op_mps_v2(q, k, v): if mem_total_gb > 8 and q.shape[1] <= 4096: return einsum_op_compvis(q, k, v) else: return einsum_op_slice_0(q, k, v, 1) def einsum_op_tensor_mem(q, k, v, max_tensor_mb): size_mb = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size() // (1 << 20) if size_mb <= max_tensor_mb: return einsum_op_compvis(q, k, v) div = 1 << int((size_mb - 1) / max_tensor_mb).bit_length() if div <= q.shape[0]: return einsum_op_slice_0(q, k, v, q.shape[0] // div) return einsum_op_slice_1(q, k, v, max(q.shape[1] // div, 1)) def einsum_op_cuda(q, k, v): stats = torch.cuda.memory_stats(q.device) mem_active = stats['active_bytes.all.current'] mem_reserved = stats['reserved_bytes.all.current'] mem_free_cuda, _ = torch.cuda.mem_get_info(q.device) mem_free_torch = mem_reserved - mem_active mem_free_total = mem_free_cuda + mem_free_torch # Divide factor of safety as there's copying and fragmentation return self.einsum_op_tensor_mem(q, k, v, mem_free_total / 3.3 / (1 << 20)) def einsum_op(q, k, v): if q.device.type == 'cuda': return einsum_op_cuda(q, k, v) if q.device.type == 'mps': if mem_total_gb >= 32: return einsum_op_mps_v1(q, k, v) return einsum_op_mps_v2(q, k, v) # Smaller slices are faster due to L2/L3/SLC caches. # Tested on i7 with 8MB L3 cache. return einsum_op_tensor_mem(q, k, v, 32) def split_cross_attention_forward_invokeAI(self, x, context=None, mask=None): h = self.heads q = self.to_q(x) context = default(context, x) context_k, context_v = hypernetwork.apply_hypernetwork(shared.loaded_hypernetwork, context) k = self.to_k(context_k) * self.scale v = self.to_v(context_v) del context, context_k, context_v, x q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v)) r = einsum_op(q, k, v) return self.to_out(rearrange(r, '(b h) n d -> b n (h d)', h=h)) # -- End of code from https://github.com/invoke-ai/InvokeAI -- def xformers_attention_forward(self, x, context=None, mask=None): h = self.heads q_in = self.to_q(x) context = default(context, x) context_k, context_v = hypernetwork.apply_hypernetwork(shared.loaded_hypernetwork, context) k_in = self.to_k(context_k) v_in = self.to_v(context_v) q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b n h d', h=h), (q_in, k_in, v_in)) del q_in, k_in, v_in out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None) out = rearrange(out, 'b n h d -> b n (h d)', h=h) return self.to_out(out) def cross_attention_attnblock_forward(self, x): h_ = x h_ = self.norm(h_) q1 = self.q(h_) k1 = self.k(h_) v = self.v(h_) # compute attention b, c, h, w = q1.shape q2 = q1.reshape(b, c, h*w) del q1 q = q2.permute(0, 2, 1) # b,hw,c del q2 k = k1.reshape(b, c, h*w) # b,c,hw del k1 h_ = torch.zeros_like(k, device=q.device) stats = torch.cuda.memory_stats(q.device) mem_active = stats['active_bytes.all.current'] mem_reserved = stats['reserved_bytes.all.current'] mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device()) mem_free_torch = mem_reserved - mem_active mem_free_total = mem_free_cuda + mem_free_torch tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size() mem_required = tensor_size * 2.5 steps = 1 if mem_required > mem_free_total: steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2))) slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1] for i in range(0, q.shape[1], slice_size): end = i + slice_size w1 = torch.bmm(q[:, i:end], k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j] w2 = w1 * (int(c)**(-0.5)) del w1 w3 = torch.nn.functional.softmax(w2, dim=2, dtype=q.dtype) del w2 # attend to values v1 = v.reshape(b, c, h*w) w4 = w3.permute(0, 2, 1) # b,hw,hw (first hw of k, second of q) del w3 h_[:, :, i:end] = torch.bmm(v1, w4) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j] del v1, w4 h2 = h_.reshape(b, c, h, w) del h_ h3 = self.proj_out(h2) del h2 h3 += x return h3 def xformers_attnblock_forward(self, x): try: h_ = x h_ = self.norm(h_) q1 = self.q(h_).contiguous() k1 = self.k(h_).contiguous() v = self.v(h_).contiguous() out = xformers.ops.memory_efficient_attention(q1, k1, v) out = self.proj_out(out) return x + out except NotImplementedError: return cross_attention_attnblock_forward(self, x) stable-diffusion-webui/modules/sd_models.py000066400000000000000000000166421432161722600214350ustar00rootroot00000000000000import glob import os.path import sys from collections import namedtuple import torch from omegaconf import OmegaConf from ldm.util import instantiate_from_config from modules import shared, modelloader, devices from modules.paths import models_path model_dir = "Stable-diffusion" model_path = os.path.abspath(os.path.join(models_path, model_dir)) CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name', 'config']) checkpoints_list = {} try: # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start. from transformers import logging logging.set_verbosity_error() except Exception: pass def setup_model(): if not os.path.exists(model_path): os.makedirs(model_path) list_models() def checkpoint_tiles(): return sorted([x.title for x in checkpoints_list.values()]) def list_models(): checkpoints_list.clear() model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt"]) def modeltitle(path, shorthash): abspath = os.path.abspath(path) if shared.cmd_opts.ckpt_dir is not None and abspath.startswith(shared.cmd_opts.ckpt_dir): name = abspath.replace(shared.cmd_opts.ckpt_dir, '') elif abspath.startswith(model_path): name = abspath.replace(model_path, '') else: name = os.path.basename(path) if name.startswith("\\") or name.startswith("/"): name = name[1:] shortname = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0] return f'{name} [{shorthash}]', shortname cmd_ckpt = shared.cmd_opts.ckpt if os.path.exists(cmd_ckpt): h = model_hash(cmd_ckpt) title, short_model_name = modeltitle(cmd_ckpt, h) checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name, shared.cmd_opts.config) shared.opts.data['sd_model_checkpoint'] = title elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file: print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr) for filename in model_list: h = model_hash(filename) title, short_model_name = modeltitle(filename, h) basename, _ = os.path.splitext(filename) config = basename + ".yaml" if not os.path.exists(config): config = shared.cmd_opts.config checkpoints_list[title] = CheckpointInfo(filename, title, h, short_model_name, config) def get_closet_checkpoint_match(searchString): applicable = sorted([info for info in checkpoints_list.values() if searchString in info.title], key = lambda x:len(x.title)) if len(applicable) > 0: return applicable[0] return None def model_hash(filename): try: with open(filename, "rb") as file: import hashlib m = hashlib.sha256() file.seek(0x100000) m.update(file.read(0x10000)) return m.hexdigest()[0:8] except FileNotFoundError: return 'NOFILE' def select_checkpoint(): model_checkpoint = shared.opts.sd_model_checkpoint checkpoint_info = checkpoints_list.get(model_checkpoint, None) if checkpoint_info is not None: return checkpoint_info if len(checkpoints_list) == 0: print(f"No checkpoints found. When searching for checkpoints, looked at:", file=sys.stderr) if shared.cmd_opts.ckpt is not None: print(f" - file {os.path.abspath(shared.cmd_opts.ckpt)}", file=sys.stderr) print(f" - directory {model_path}", file=sys.stderr) if shared.cmd_opts.ckpt_dir is not None: print(f" - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}", file=sys.stderr) print(f"Can't run without a checkpoint. Find and place a .ckpt file into any of those locations. The program will exit.", file=sys.stderr) exit(1) checkpoint_info = next(iter(checkpoints_list.values())) if model_checkpoint is not None: print(f"Checkpoint {model_checkpoint} not found; loading fallback {checkpoint_info.title}", file=sys.stderr) return checkpoint_info def get_state_dict_from_checkpoint(pl_sd): if "state_dict" in pl_sd: return pl_sd["state_dict"] return pl_sd def load_model_weights(model, checkpoint_info): checkpoint_file = checkpoint_info.filename sd_model_hash = checkpoint_info.hash print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}") pl_sd = torch.load(checkpoint_file, map_location="cpu") if "global_step" in pl_sd: print(f"Global Step: {pl_sd['global_step']}") sd = get_state_dict_from_checkpoint(pl_sd) model.load_state_dict(sd, strict=False) if shared.cmd_opts.opt_channelslast: model.to(memory_format=torch.channels_last) if not shared.cmd_opts.no_half: model.half() devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16 devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16 vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt" if not os.path.exists(vae_file) and shared.cmd_opts.vae_path is not None: vae_file = shared.cmd_opts.vae_path if os.path.exists(vae_file): print(f"Loading VAE weights from: {vae_file}") vae_ckpt = torch.load(vae_file, map_location="cpu") vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"} model.first_stage_model.load_state_dict(vae_dict) model.first_stage_model.to(devices.dtype_vae) model.sd_model_hash = sd_model_hash model.sd_model_checkpoint = checkpoint_file model.sd_checkpoint_info = checkpoint_info def load_model(): from modules import lowvram, sd_hijack checkpoint_info = select_checkpoint() if checkpoint_info.config != shared.cmd_opts.config: print(f"Loading config from: {checkpoint_info.config}") sd_config = OmegaConf.load(checkpoint_info.config) sd_model = instantiate_from_config(sd_config.model) load_model_weights(sd_model, checkpoint_info) if shared.cmd_opts.lowvram or shared.cmd_opts.medvram: lowvram.setup_for_low_vram(sd_model, shared.cmd_opts.medvram) else: sd_model.to(shared.device) sd_hijack.model_hijack.hijack(sd_model) sd_model.eval() print(f"Model loaded.") return sd_model def reload_model_weights(sd_model, info=None): from modules import lowvram, devices, sd_hijack checkpoint_info = info or select_checkpoint() if sd_model.sd_model_checkpoint == checkpoint_info.filename: return if sd_model.sd_checkpoint_info.config != checkpoint_info.config: shared.sd_model = load_model() return shared.sd_model if shared.cmd_opts.lowvram or shared.cmd_opts.medvram: lowvram.send_everything_to_cpu() else: sd_model.to(devices.cpu) sd_hijack.model_hijack.undo_hijack(sd_model) load_model_weights(sd_model, checkpoint_info) sd_hijack.model_hijack.hijack(sd_model) if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram: sd_model.to(devices.device) print(f"Weights loaded.") return sd_model stable-diffusion-webui/modules/sd_samplers.py000066400000000000000000000413241432161722600217730ustar00rootroot00000000000000from collections import namedtuple import numpy as np import torch import tqdm from PIL import Image import inspect import k_diffusion.sampling import ldm.models.diffusion.ddim import ldm.models.diffusion.plms from modules import prompt_parser, devices, processing from modules.shared import opts, cmd_opts, state import modules.shared as shared SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options']) samplers_k_diffusion = [ ('Euler a', 'sample_euler_ancestral', ['k_euler_a'], {}), ('Euler', 'sample_euler', ['k_euler'], {}), ('LMS', 'sample_lms', ['k_lms'], {}), ('Heun', 'sample_heun', ['k_heun'], {}), ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {}), ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {}), ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {}), ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {}), ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}), ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras'}), ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras'}), ] samplers_data_k_diffusion = [ SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases, options) for label, funcname, aliases, options in samplers_k_diffusion if hasattr(k_diffusion.sampling, funcname) ] all_samplers = [ *samplers_data_k_diffusion, SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {}), SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {}), ] samplers = [] samplers_for_img2img = [] def create_sampler_with_index(list_of_configs, index, model): config = list_of_configs[index] sampler = config.constructor(model) sampler.config = config return sampler def set_samplers(): global samplers, samplers_for_img2img hidden = set(opts.hide_samplers) hidden_img2img = set(opts.hide_samplers + ['PLMS']) samplers = [x for x in all_samplers if x.name not in hidden] samplers_for_img2img = [x for x in all_samplers if x.name not in hidden_img2img] set_samplers() sampler_extra_params = { 'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'], 'sample_heun': ['s_churn', 's_tmin', 's_tmax', 's_noise'], 'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'], } def setup_img2img_steps(p, steps=None): if opts.img2img_fix_steps or steps is not None: steps = int((steps or p.steps) / min(p.denoising_strength, 0.999)) if p.denoising_strength > 0 else 0 t_enc = p.steps - 1 else: steps = p.steps t_enc = int(min(p.denoising_strength, 0.999) * steps) return steps, t_enc def sample_to_image(samples): x_sample = processing.decode_first_stage(shared.sd_model, samples[0:1])[0] x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0) x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2) x_sample = x_sample.astype(np.uint8) return Image.fromarray(x_sample) def store_latent(decoded): state.current_latent = decoded if opts.show_progress_every_n_steps > 0 and shared.state.sampling_step % opts.show_progress_every_n_steps == 0: if not shared.parallel_processing_allowed: shared.state.current_image = sample_to_image(decoded) def extended_tdqm(sequence, *args, desc=None, **kwargs): state.sampling_steps = len(sequence) state.sampling_step = 0 seq = sequence if cmd_opts.disable_console_progressbars else tqdm.tqdm(sequence, *args, desc=state.job, file=shared.progress_print_out, **kwargs) for x in seq: if state.interrupted or state.skipped: break yield x state.sampling_step += 1 shared.total_tqdm.update() ldm.models.diffusion.ddim.tqdm = lambda *args, desc=None, **kwargs: extended_tdqm(*args, desc=desc, **kwargs) ldm.models.diffusion.plms.tqdm = lambda *args, desc=None, **kwargs: extended_tdqm(*args, desc=desc, **kwargs) class VanillaStableDiffusionSampler: def __init__(self, constructor, sd_model): self.sampler = constructor(sd_model) self.orig_p_sample_ddim = self.sampler.p_sample_ddim if hasattr(self.sampler, 'p_sample_ddim') else self.sampler.p_sample_plms self.mask = None self.nmask = None self.init_latent = None self.sampler_noises = None self.step = 0 self.eta = None self.default_eta = 0.0 self.config = None def number_of_needed_noises(self, p): return 0 def p_sample_ddim_hook(self, x_dec, cond, ts, unconditional_conditioning, *args, **kwargs): conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step) unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step) assert all([len(conds) == 1 for conds in conds_list]), 'composition via AND is not supported for DDIM/PLMS samplers' cond = tensor # for DDIM, shapes must match, we can't just process cond and uncond independently; # filling unconditional_conditioning with repeats of the last vector to match length is # not 100% correct but should work well enough if unconditional_conditioning.shape[1] < cond.shape[1]: last_vector = unconditional_conditioning[:, -1:] last_vector_repeated = last_vector.repeat([1, cond.shape[1] - unconditional_conditioning.shape[1], 1]) unconditional_conditioning = torch.hstack([unconditional_conditioning, last_vector_repeated]) elif unconditional_conditioning.shape[1] > cond.shape[1]: unconditional_conditioning = unconditional_conditioning[:, :cond.shape[1]] if self.mask is not None: img_orig = self.sampler.model.q_sample(self.init_latent, ts) x_dec = img_orig * self.mask + self.nmask * x_dec res = self.orig_p_sample_ddim(x_dec, cond, ts, unconditional_conditioning=unconditional_conditioning, *args, **kwargs) if self.mask is not None: store_latent(self.init_latent * self.mask + self.nmask * res[1]) else: store_latent(res[1]) self.step += 1 return res def initialize(self, p): self.eta = p.eta if p.eta is not None else opts.eta_ddim for fieldname in ['p_sample_ddim', 'p_sample_plms']: if hasattr(self.sampler, fieldname): setattr(self.sampler, fieldname, self.p_sample_ddim_hook) self.mask = p.mask if hasattr(p, 'mask') else None self.nmask = p.nmask if hasattr(p, 'nmask') else None def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None): steps, t_enc = setup_img2img_steps(p, steps) self.initialize(p) # existing code fails with certain step counts, like 9 try: self.sampler.make_schedule(ddim_num_steps=steps, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False) except Exception: self.sampler.make_schedule(ddim_num_steps=steps+1, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False) x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise) self.init_latent = x self.step = 0 samples = self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning) return samples def sample(self, p, x, conditioning, unconditional_conditioning, steps=None): self.initialize(p) self.init_latent = None self.step = 0 steps = steps or p.steps # existing code fails with certain step counts, like 9 try: samples_ddim, _ = self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta) except Exception: samples_ddim, _ = self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta) return samples_ddim class CFGDenoiser(torch.nn.Module): def __init__(self, model): super().__init__() self.inner_model = model self.mask = None self.nmask = None self.init_latent = None self.step = 0 def forward(self, x, sigma, uncond, cond, cond_scale): conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step) uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step) batch_size = len(conds_list) repeats = [len(conds_list[i]) for i in range(batch_size)] x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x]) sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma]) if tensor.shape[1] == uncond.shape[1]: cond_in = torch.cat([tensor, uncond]) if shared.batch_cond_uncond: x_out = self.inner_model(x_in, sigma_in, cond=cond_in) else: x_out = torch.zeros_like(x_in) for batch_offset in range(0, x_out.shape[0], batch_size): a = batch_offset b = a + batch_size x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=cond_in[a:b]) else: x_out = torch.zeros_like(x_in) batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size for batch_offset in range(0, tensor.shape[0], batch_size): a = batch_offset b = min(a + batch_size, tensor.shape[0]) x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=tensor[a:b]) x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=uncond) denoised_uncond = x_out[-uncond.shape[0]:] denoised = torch.clone(denoised_uncond) for i, conds in enumerate(conds_list): for cond_index, weight in conds: denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale) if self.mask is not None: denoised = self.init_latent * self.mask + self.nmask * denoised self.step += 1 return denoised def extended_trange(sampler, count, *args, **kwargs): state.sampling_steps = count state.sampling_step = 0 seq = range(count) if cmd_opts.disable_console_progressbars else tqdm.trange(count, *args, desc=state.job, file=shared.progress_print_out, **kwargs) for x in seq: if state.interrupted or state.skipped: break if sampler.stop_at is not None and x > sampler.stop_at: break yield x state.sampling_step += 1 shared.total_tqdm.update() class TorchHijack: def __init__(self, kdiff_sampler): self.kdiff_sampler = kdiff_sampler def __getattr__(self, item): if item == 'randn_like': return self.kdiff_sampler.randn_like if hasattr(torch, item): return getattr(torch, item) raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, item)) class KDiffusionSampler: def __init__(self, funcname, sd_model): self.model_wrap = k_diffusion.external.CompVisDenoiser(sd_model, quantize=shared.opts.enable_quantization) self.funcname = funcname self.func = getattr(k_diffusion.sampling, self.funcname) self.extra_params = sampler_extra_params.get(funcname, []) self.model_wrap_cfg = CFGDenoiser(self.model_wrap) self.sampler_noises = None self.sampler_noise_index = 0 self.stop_at = None self.eta = None self.default_eta = 1.0 self.config = None def callback_state(self, d): store_latent(d["denoised"]) def number_of_needed_noises(self, p): return p.steps def randn_like(self, x): noise = self.sampler_noises[self.sampler_noise_index] if self.sampler_noises is not None and self.sampler_noise_index < len(self.sampler_noises) else None if noise is not None and x.shape == noise.shape: res = noise else: res = torch.randn_like(x) self.sampler_noise_index += 1 return res def initialize(self, p): self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None self.model_wrap.step = 0 self.sampler_noise_index = 0 self.eta = p.eta or opts.eta_ancestral if hasattr(k_diffusion.sampling, 'trange'): k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(self, *args, **kwargs) if self.sampler_noises is not None: k_diffusion.sampling.torch = TorchHijack(self) extra_params_kwargs = {} for param_name in self.extra_params: if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters: extra_params_kwargs[param_name] = getattr(p, param_name) if 'eta' in inspect.signature(self.func).parameters: extra_params_kwargs['eta'] = self.eta return extra_params_kwargs def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None): steps, t_enc = setup_img2img_steps(p, steps) if p.sampler_noise_scheduler_override: sigmas = p.sampler_noise_scheduler_override(steps) elif self.config is not None and self.config.options.get('scheduler', None) == 'karras': sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=0.1, sigma_max=10, device=shared.device) else: sigmas = self.model_wrap.get_sigmas(steps) sigma_sched = sigmas[steps - t_enc - 1:] xi = x + noise * sigma_sched[0] extra_params_kwargs = self.initialize(p) if 'sigma_min' in inspect.signature(self.func).parameters: ## last sigma is zero which isn't allowed by DPM Fast & Adaptive so taking value before last extra_params_kwargs['sigma_min'] = sigma_sched[-2] if 'sigma_max' in inspect.signature(self.func).parameters: extra_params_kwargs['sigma_max'] = sigma_sched[0] if 'n' in inspect.signature(self.func).parameters: extra_params_kwargs['n'] = len(sigma_sched) - 1 if 'sigma_sched' in inspect.signature(self.func).parameters: extra_params_kwargs['sigma_sched'] = sigma_sched if 'sigmas' in inspect.signature(self.func).parameters: extra_params_kwargs['sigmas'] = sigma_sched self.model_wrap_cfg.init_latent = x return self.func(self.model_wrap_cfg, xi, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs) def sample(self, p, x, conditioning, unconditional_conditioning, steps=None): steps = steps or p.steps if p.sampler_noise_scheduler_override: sigmas = p.sampler_noise_scheduler_override(steps) elif self.config is not None and self.config.options.get('scheduler', None) == 'karras': sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=0.1, sigma_max=10, device=shared.device) else: sigmas = self.model_wrap.get_sigmas(steps) x = x * sigmas[0] extra_params_kwargs = self.initialize(p) if 'sigma_min' in inspect.signature(self.func).parameters: extra_params_kwargs['sigma_min'] = self.model_wrap.sigmas[0].item() extra_params_kwargs['sigma_max'] = self.model_wrap.sigmas[-1].item() if 'n' in inspect.signature(self.func).parameters: extra_params_kwargs['n'] = steps else: extra_params_kwargs['sigmas'] = sigmas samples = self.func(self.model_wrap_cfg, x, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs) return samples stable-diffusion-webui/modules/shared.py000066400000000000000000000621601432161722600207260ustar00rootroot00000000000000import argparse import datetime import json import os import sys import gradio as gr import tqdm import modules.artists import modules.interrogate import modules.memmon import modules.sd_models import modules.styles import modules.devices as devices from modules import sd_samplers from modules.hypernetworks import hypernetwork from modules.paths import models_path, script_path, sd_path sd_model_file = os.path.join(script_path, 'model.ckpt') default_sd_model_file = sd_model_file parser = argparse.ArgumentParser() parser.add_argument("--config", type=str, default=os.path.join(sd_path, "configs/stable-diffusion/v1-inference.yaml"), help="path to config which constructs model",) parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",) parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to directory with stable diffusion checkpoints") parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN')) parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default=None) parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats") parser.add_argument("--no-half-vae", action='store_true', help="do not switch the VAE model to 16-bit floats") parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)") parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI") parser.add_argument("--embeddings-dir", type=str, default=os.path.join(script_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)") parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory") parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui") parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage") parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a lot of speed for very low VRM usage") parser.add_argument("--always-batch-cond-uncond", action='store_true', help="disables cond/uncond batching that is enabled to save memory with --medvram or --lowvram") parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.") parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast") parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site (doesn't work for me but you might have better luck)") parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None) parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer')) parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN')) parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(models_path, 'ESRGAN')) parser.add_argument("--bsrgan-models-path", type=str, help="Path to directory with BSRGAN model file(s).", default=os.path.join(models_path, 'BSRGAN')) parser.add_argument("--realesrgan-models-path", type=str, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(models_path, 'RealESRGAN')) parser.add_argument("--scunet-models-path", type=str, help="Path to directory with ScuNET model file(s).", default=os.path.join(models_path, 'ScuNET')) parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(models_path, 'SwinIR')) parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(models_path, 'LDSR')) parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers") parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work") parser.add_argument("--deepdanbooru", action='store_true', help="enable deepdanbooru interrogator") parser.add_argument("--opt-split-attention", action='store_true', help="force-enables Doggettx's cross-attention layer optimization. By default, it's on for torch cuda.") parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.") parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find") parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization") parser.add_argument("--use-cpu", nargs='+',choices=['SD', 'GFPGAN', 'BSRGAN', 'ESRGAN', 'SCUNet', 'CodeFormer'], help="use CPU as torch device for specified modules", default=[]) parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests") parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None) parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False) parser.add_argument("--ui-config-file", type=str, help="filename to use for ui configuration", default=os.path.join(script_path, 'ui-config.json')) parser.add_argument("--hide-ui-dir-config", action='store_true', help="hide directory configuration from webui", default=False) parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default=os.path.join(script_path, 'config.json')) parser.add_argument("--gradio-debug", action='store_true', help="launch gradio with --debug option") parser.add_argument("--gradio-auth", type=str, help='set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None) parser.add_argument("--gradio-img2img-tool", type=str, help='gradio image uploader tool: can be either editor for ctopping, or color-sketch for drawing', choices=["color-sketch", "editor"], default="editor") parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last") parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(script_path, 'styles.csv')) parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False) parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False) parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False) parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False) parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencoders model', default=None) parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False) cmd_opts = parser.parse_args() devices.device, devices.device_gfpgan, devices.device_bsrgan, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \ (devices.cpu if x in cmd_opts.use_cpu else devices.get_optimal_device() for x in ['SD', 'GFPGAN', 'BSRGAN', 'ESRGAN', 'SCUNet', 'CodeFormer']) device = devices.device batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram) parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram xformers_available = False config_filename = cmd_opts.ui_settings_file os.makedirs(cmd_opts.hypernetwork_dir, exist_ok=True) hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir) loaded_hypernetwork = None def reload_hypernetworks(): global hypernetworks hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir) hypernetwork.load_hypernetwork(opts.sd_hypernetwork) class State: skipped = False interrupted = False job = "" job_no = 0 job_count = 0 job_timestamp = '0' sampling_step = 0 sampling_steps = 0 current_latent = None current_image = None current_image_sampling_step = 0 textinfo = None def skip(self): self.skipped = True def interrupt(self): self.interrupted = True def nextjob(self): self.job_no += 1 self.sampling_step = 0 self.current_image_sampling_step = 0 def get_job_timestamp(self): return datetime.datetime.now().strftime("%Y%m%d%H%M%S") # shouldn't this return job_timestamp? state = State() artist_db = modules.artists.ArtistsDatabase(os.path.join(script_path, 'artists.csv')) styles_filename = cmd_opts.styles_file prompt_styles = modules.styles.StyleDatabase(styles_filename) interrogator = modules.interrogate.InterrogateModels("interrogate") face_restorers = [] def realesrgan_models_names(): import modules.realesrgan_model return [x.name for x in modules.realesrgan_model.get_realesrgan_models(None)] class OptionInfo: def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, show_on_main_page=False): self.default = default self.label = label self.component = component self.component_args = component_args self.onchange = onchange self.section = None self.show_on_main_page = show_on_main_page def options_section(section_identifier, options_dict): for k, v in options_dict.items(): v.section = section_identifier return options_dict hide_dirs = {"visible": not cmd_opts.hide_ui_dir_config} options_templates = {} options_templates.update(options_section(('saving-images', "Saving images/grids"), { "samples_save": OptionInfo(True, "Always save all generated images"), "samples_format": OptionInfo('png', 'File format for images'), "samples_filename_pattern": OptionInfo("", "Images filename pattern"), "grid_save": OptionInfo(True, "Always save all generated image grids"), "grid_format": OptionInfo('png', 'File format for grids'), "grid_extended_filename": OptionInfo(False, "Add extended info (seed, prompt) to filename when saving grid"), "grid_only_if_multiple": OptionInfo(True, "Do not save grids consisting of one picture"), "n_rows": OptionInfo(-1, "Grid row count; use -1 for autodetect and 0 for it to be same as batch size", gr.Slider, {"minimum": -1, "maximum": 16, "step": 1}), "enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"), "save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters."), "save_images_before_face_restoration": OptionInfo(False, "Save a copy of image before doing face restoration."), "jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}), "export_for_4chan": OptionInfo(True, "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG"), "use_original_name_batch": OptionInfo(False, "Use original name for output filename during batch process in extras tab"), "save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"), "do_not_add_watermark": OptionInfo(False, "Do not add watermark to images"), })) options_templates.update(options_section(('saving-paths', "Paths for saving"), { "outdir_samples": OptionInfo("", "Output directory for images; if empty, defaults to three directories below", component_args=hide_dirs), "outdir_txt2img_samples": OptionInfo("outputs/txt2img-images", 'Output directory for txt2img images', component_args=hide_dirs), "outdir_img2img_samples": OptionInfo("outputs/img2img-images", 'Output directory for img2img images', component_args=hide_dirs), "outdir_extras_samples": OptionInfo("outputs/extras-images", 'Output directory for images from extras tab', component_args=hide_dirs), "outdir_grids": OptionInfo("", "Output directory for grids; if empty, defaults to two directories below", component_args=hide_dirs), "outdir_txt2img_grids": OptionInfo("outputs/txt2img-grids", 'Output directory for txt2img grids', component_args=hide_dirs), "outdir_img2img_grids": OptionInfo("outputs/img2img-grids", 'Output directory for img2img grids', component_args=hide_dirs), "outdir_save": OptionInfo("log/images", "Directory for saving images using the Save button", component_args=hide_dirs), })) options_templates.update(options_section(('saving-to-dirs', "Saving to a directory"), { "save_to_dirs": OptionInfo(False, "Save images to a subdirectory"), "grid_save_to_dirs": OptionInfo(False, "Save grids to a subdirectory"), "use_save_to_dirs_for_ui": OptionInfo(False, "When using \"Save\" button, save images to a subdirectory"), "directories_filename_pattern": OptionInfo("", "Directory name pattern"), "directories_max_prompt_words": OptionInfo(8, "Max prompt words for [prompt_words] pattern", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1}), })) options_templates.update(options_section(('upscaling', "Upscaling"), { "ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}), "ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}), "realesrgan_enabled_models": OptionInfo(["R-ESRGAN x4+", "R-ESRGAN x4+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": realesrgan_models_names()}), "SWIN_tile": OptionInfo(192, "Tile size for all SwinIR.", gr.Slider, {"minimum": 16, "maximum": 512, "step": 16}), "SWIN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}), "ldsr_steps": OptionInfo(100, "LDSR processing steps. Lower = faster", gr.Slider, {"minimum": 1, "maximum": 200, "step": 1}), "upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}), })) options_templates.update(options_section(('face-restoration', "Face restoration"), { "face_restoration_model": OptionInfo(None, "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in face_restorers]}), "code_former_weight": OptionInfo(0.5, "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}), "face_restoration_unload": OptionInfo(False, "Move face restoration model from VRAM into RAM after processing"), })) options_templates.update(options_section(('system', "System"), { "memmon_poll_rate": OptionInfo(8, "VRAM usage polls per second during generation. Set to 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}), "samples_log_stdout": OptionInfo(False, "Always print all generation info to standard output"), "multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job."), })) options_templates.update(options_section(('training', "Training"), { "unload_models_when_training": OptionInfo(False, "Unload VAE and CLIP from VRAM when training"), "dataset_filename_word_regex": OptionInfo("", "Filename word regex"), "dataset_filename_join_string": OptionInfo(" ", "Filename join string"), "training_image_repeats_per_epoch": OptionInfo(100, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}), })) options_templates.update(options_section(('sd', "Stable Diffusion"), { "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, show_on_main_page=True), "sd_hypernetwork": OptionInfo("None", "Stable Diffusion finetune hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}), "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."), "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"), "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."), "enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."), "enable_emphasis": OptionInfo(True, "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"), "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."), "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"), "comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }), "filter_nsfw": OptionInfo(False, "Filter NSFW content"), 'CLIP_stop_at_last_layers': OptionInfo(1, "Stop At last layers of CLIP model", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}), "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}), })) options_templates.update(options_section(('interrogate', "Interrogate Options"), { "interrogate_keep_models_in_memory": OptionInfo(False, "Interrogate: keep models in VRAM"), "interrogate_use_builtin_artists": OptionInfo(True, "Interrogate: use artists from artists.csv"), "interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}), "interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}), "interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}), "interrogate_deepbooru_score_threshold": OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}), "deepbooru_sort_alpha": OptionInfo(True, "Interrogate: deepbooru sort alphabetically"), "deepbooru_use_spaces": OptionInfo(False, "use spaces for tags in deepbooru"), "deepbooru_escape": OptionInfo(True, "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)"), })) options_templates.update(options_section(('ui', "User interface"), { "show_progressbar": OptionInfo(True, "Show progressbar"), "show_progress_every_n_steps": OptionInfo(0, "Show image creation progress every N sampling steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}), "return_grid": OptionInfo(True, "Show grid in results for web"), "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"), "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"), "add_model_name_to_info": OptionInfo(False, "Add model name to generation information"), "font": OptionInfo("", "Font for image grids that have text"), "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"), "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"), "show_progress_in_title": OptionInfo(True, "Show generation progress in window title."), })) options_templates.update(options_section(('sampler-params', "Sampler parameters"), { "hide_samplers": OptionInfo([], "Hide samplers in user interface (requires restart)", gr.CheckboxGroup, lambda: {"choices": [x.name for x in sd_samplers.all_samplers]}), "eta_ddim": OptionInfo(0.0, "eta (noise multiplier) for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), "eta_ancestral": OptionInfo(1.0, "eta (noise multiplier) for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), "ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}), 's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), 's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), 's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), 'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}), })) class Options: data = None data_labels = options_templates typemap = {int: float} def __init__(self): self.data = {k: v.default for k, v in self.data_labels.items()} def __setattr__(self, key, value): if self.data is not None: if key in self.data: self.data[key] = value return super(Options, self).__setattr__(key, value) def __getattr__(self, item): if self.data is not None: if item in self.data: return self.data[item] if item in self.data_labels: return self.data_labels[item].default return super(Options, self).__getattribute__(item) def save(self, filename): with open(filename, "w", encoding="utf8") as file: json.dump(self.data, file) def same_type(self, x, y): if x is None or y is None: return True type_x = self.typemap.get(type(x), type(x)) type_y = self.typemap.get(type(y), type(y)) return type_x == type_y def load(self, filename): with open(filename, "r", encoding="utf8") as file: self.data = json.load(file) bad_settings = 0 for k, v in self.data.items(): info = self.data_labels.get(k, None) if info is not None and not self.same_type(info.default, v): print(f"Warning: bad setting value: {k}: {v} ({type(v).__name__}; expected {type(info.default).__name__})", file=sys.stderr) bad_settings += 1 if bad_settings > 0: print(f"The program is likely to not work with bad settings.\nSettings file: {filename}\nEither fix the file, or delete it and restart.", file=sys.stderr) def onchange(self, key, func): item = self.data_labels.get(key) item.onchange = func def dumpjson(self): d = {k: self.data.get(k, self.data_labels.get(k).default) for k in self.data_labels.keys()} return json.dumps(d) opts = Options() if os.path.exists(config_filename): opts.load(config_filename) sd_upscalers = [] sd_model = None progress_print_out = sys.stdout class TotalTQDM: def __init__(self): self._tqdm = None def reset(self): self._tqdm = tqdm.tqdm( desc="Total progress", total=state.job_count * state.sampling_steps, position=1, file=progress_print_out ) def update(self): if not opts.multiple_tqdm or cmd_opts.disable_console_progressbars: return if self._tqdm is None: self.reset() self._tqdm.update() def updateTotal(self, new_total): if not opts.multiple_tqdm or cmd_opts.disable_console_progressbars: return if self._tqdm is None: self.reset() self._tqdm.total=new_total def clear(self): if self._tqdm is not None: self._tqdm.close() self._tqdm = None total_tqdm = TotalTQDM() mem_mon = modules.memmon.MemUsageMonitor("MemMon", device, opts) mem_mon.start() stable-diffusion-webui/modules/styles.py000066400000000000000000000071511432161722600210020ustar00rootroot00000000000000# We need this so Python doesn't complain about the unknown StableDiffusionProcessing-typehint at runtime from __future__ import annotations import csv import os import os.path import typing import collections.abc as abc import tempfile import shutil if typing.TYPE_CHECKING: # Only import this when code is being type-checked, it doesn't have any effect at runtime from .processing import StableDiffusionProcessing class PromptStyle(typing.NamedTuple): name: str prompt: str negative_prompt: str def merge_prompts(style_prompt: str, prompt: str) -> str: if "{prompt}" in style_prompt: res = style_prompt.replace("{prompt}", prompt) else: parts = filter(None, (prompt.strip(), style_prompt.strip())) res = ", ".join(parts) return res def apply_styles_to_prompt(prompt, styles): for style in styles: prompt = merge_prompts(style, prompt) return prompt class StyleDatabase: def __init__(self, path: str): self.no_style = PromptStyle("None", "", "") self.styles = {"None": self.no_style} if not os.path.exists(path): return with open(path, "r", encoding="utf8", newline='') as file: reader = csv.DictReader(file) for row in reader: # Support loading old CSV format with "name, text"-columns prompt = row["prompt"] if "prompt" in row else row["text"] negative_prompt = row.get("negative_prompt", "") self.styles[row["name"]] = PromptStyle(row["name"], prompt, negative_prompt) def get_style_prompts(self, styles): return [self.styles.get(x, self.no_style).prompt for x in styles] def get_negative_style_prompts(self, styles): return [self.styles.get(x, self.no_style).negative_prompt for x in styles] def apply_styles_to_prompt(self, prompt, styles): return apply_styles_to_prompt(prompt, [self.styles.get(x, self.no_style).prompt for x in styles]) def apply_negative_styles_to_prompt(self, prompt, styles): return apply_styles_to_prompt(prompt, [self.styles.get(x, self.no_style).negative_prompt for x in styles]) def apply_styles(self, p: StableDiffusionProcessing) -> None: if isinstance(p.prompt, list): p.prompt = [self.apply_styles_to_prompt(prompt, p.styles) for prompt in p.prompt] else: p.prompt = self.apply_styles_to_prompt(p.prompt, p.styles) if isinstance(p.negative_prompt, list): p.negative_prompt = [self.apply_negative_styles_to_prompt(prompt, p.styles) for prompt in p.negative_prompt] else: p.negative_prompt = self.apply_negative_styles_to_prompt(p.negative_prompt, p.styles) def save_styles(self, path: str) -> None: # Write to temporary file first, so we don't nuke the file if something goes wrong fd, temp_path = tempfile.mkstemp(".csv") with os.fdopen(fd, "w", encoding="utf8", newline='') as file: # _fields is actually part of the public API: typing.NamedTuple is a replacement for collections.NamedTuple, # and collections.NamedTuple has explicit documentation for accessing _fields. Same goes for _asdict() writer = csv.DictWriter(file, fieldnames=PromptStyle._fields) writer.writeheader() writer.writerows(style._asdict() for k, style in self.styles.items()) # Always keep a backup file around if os.path.exists(path): shutil.move(path, path + ".bak") shutil.move(temp_path, path) stable-diffusion-webui/modules/swinir_model.py000066400000000000000000000131161432161722600221500ustar00rootroot00000000000000import contextlib import os import numpy as np import torch from PIL import Image from basicsr.utils.download_util import load_file_from_url from tqdm import tqdm from modules import modelloader from modules.shared import cmd_opts, opts, device from modules.swinir_model_arch import SwinIR as net from modules.swinir_model_arch_v2 import Swin2SR as net2 from modules.upscaler import Upscaler, UpscalerData precision_scope = ( torch.autocast if cmd_opts.precision == "autocast" else contextlib.nullcontext ) class UpscalerSwinIR(Upscaler): def __init__(self, dirname): self.name = "SwinIR" self.model_url = "https://github.com/JingyunLiang/SwinIR/releases/download/v0.0" \ "/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR" \ "-L_x4_GAN.pth " self.model_name = "SwinIR 4x" self.user_path = dirname super().__init__() scalers = [] model_files = self.find_models(ext_filter=[".pt", ".pth"]) for model in model_files: if "http" in model: name = self.model_name else: name = modelloader.friendly_name(model) model_data = UpscalerData(name, model, self) scalers.append(model_data) self.scalers = scalers def do_upscale(self, img, model_file): model = self.load_model(model_file) if model is None: return img model = model.to(device) img = upscale(img, model) try: torch.cuda.empty_cache() except: pass return img def load_model(self, path, scale=4): if "http" in path: dl_name = "%s%s" % (self.model_name.replace(" ", "_"), ".pth") filename = load_file_from_url(url=path, model_dir=self.model_path, file_name=dl_name, progress=True) else: filename = path if filename is None or not os.path.exists(filename): return None if filename.endswith(".v2.pth"): model = net2( upscale=scale, in_chans=3, img_size=64, window_size=8, img_range=1.0, depths=[6, 6, 6, 6, 6, 6], embed_dim=180, num_heads=[6, 6, 6, 6, 6, 6], mlp_ratio=2, upsampler="nearest+conv", resi_connection="1conv", ) params = None else: model = net( upscale=scale, in_chans=3, img_size=64, window_size=8, img_range=1.0, depths=[6, 6, 6, 6, 6, 6, 6, 6, 6], embed_dim=240, num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8], mlp_ratio=2, upsampler="nearest+conv", resi_connection="3conv", ) params = "params_ema" pretrained_model = torch.load(filename) if params is not None: model.load_state_dict(pretrained_model[params], strict=True) else: model.load_state_dict(pretrained_model, strict=True) if not cmd_opts.no_half: model = model.half() return model def upscale( img, model, tile=opts.SWIN_tile, tile_overlap=opts.SWIN_tile_overlap, window_size=8, scale=4, ): img = np.array(img) img = img[:, :, ::-1] img = np.moveaxis(img, 2, 0) / 255 img = torch.from_numpy(img).float() img = img.unsqueeze(0).to(device) with torch.no_grad(), precision_scope("cuda"): _, _, h_old, w_old = img.size() h_pad = (h_old // window_size + 1) * window_size - h_old w_pad = (w_old // window_size + 1) * window_size - w_old img = torch.cat([img, torch.flip(img, [2])], 2)[:, :, : h_old + h_pad, :] img = torch.cat([img, torch.flip(img, [3])], 3)[:, :, :, : w_old + w_pad] output = inference(img, model, tile, tile_overlap, window_size, scale) output = output[..., : h_old * scale, : w_old * scale] output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy() if output.ndim == 3: output = np.transpose( output[[2, 1, 0], :, :], (1, 2, 0) ) # CHW-RGB to HCW-BGR output = (output * 255.0).round().astype(np.uint8) # float32 to uint8 return Image.fromarray(output, "RGB") def inference(img, model, tile, tile_overlap, window_size, scale): # test the image tile by tile b, c, h, w = img.size() tile = min(tile, h, w) assert tile % window_size == 0, "tile size should be a multiple of window_size" sf = scale stride = tile - tile_overlap h_idx_list = list(range(0, h - tile, stride)) + [h - tile] w_idx_list = list(range(0, w - tile, stride)) + [w - tile] E = torch.zeros(b, c, h * sf, w * sf, dtype=torch.half, device=device).type_as(img) W = torch.zeros_like(E, dtype=torch.half, device=device) with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="SwinIR tiles") as pbar: for h_idx in h_idx_list: for w_idx in w_idx_list: in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile] out_patch = model(in_patch) out_patch_mask = torch.ones_like(out_patch) E[ ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf ].add_(out_patch) W[ ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf ].add_(out_patch_mask) pbar.update(1) output = E.div_(W) return output stable-diffusion-webui/modules/swinir_model_arch.py000066400000000000000000001077401432161722600231540ustar00rootroot00000000000000# ----------------------------------------------------------------------------------- # SwinIR: Image Restoration Using Swin Transformer, https://arxiv.org/abs/2108.10257 # Originally Written by Ze Liu, Modified by Jingyun Liang. # ----------------------------------------------------------------------------------- import math import torch import torch.nn as nn import torch.nn.functional as F import torch.utils.checkpoint as checkpoint from timm.models.layers import DropPath, to_2tuple, trunc_normal_ class Mlp(nn.Module): def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features self.fc1 = nn.Linear(in_features, hidden_features) self.act = act_layer() self.fc2 = nn.Linear(hidden_features, out_features) self.drop = nn.Dropout(drop) def forward(self, x): x = self.fc1(x) x = self.act(x) x = self.drop(x) x = self.fc2(x) x = self.drop(x) return x def window_partition(x, window_size): """ Args: x: (B, H, W, C) window_size (int): window size Returns: windows: (num_windows*B, window_size, window_size, C) """ B, H, W, C = x.shape x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) return windows def window_reverse(windows, window_size, H, W): """ Args: windows: (num_windows*B, window_size, window_size, C) window_size (int): Window size H (int): Height of image W (int): Width of image Returns: x: (B, H, W, C) """ B = int(windows.shape[0] / (H * W / window_size / window_size)) x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) return x class WindowAttention(nn.Module): r""" Window based multi-head self attention (W-MSA) module with relative position bias. It supports both of shifted and non-shifted window. Args: dim (int): Number of input channels. window_size (tuple[int]): The height and width of the window. num_heads (int): Number of attention heads. qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 proj_drop (float, optional): Dropout ratio of output. Default: 0.0 """ def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.): super().__init__() self.dim = dim self.window_size = window_size # Wh, Ww self.num_heads = num_heads head_dim = dim // num_heads self.scale = qk_scale or head_dim ** -0.5 # define a parameter table of relative position bias self.relative_position_bias_table = nn.Parameter( torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH # get pair-wise relative position index for each token inside the window coords_h = torch.arange(self.window_size[0]) coords_w = torch.arange(self.window_size[1]) coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 relative_coords[:, :, 1] += self.window_size[1] - 1 relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww self.register_buffer("relative_position_index", relative_position_index) self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(proj_drop) trunc_normal_(self.relative_position_bias_table, std=.02) self.softmax = nn.Softmax(dim=-1) def forward(self, x, mask=None): """ Args: x: input features with shape of (num_windows*B, N, C) mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None """ B_, N, C = x.shape qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) q = q * self.scale attn = (q @ k.transpose(-2, -1)) relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view( self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww attn = attn + relative_position_bias.unsqueeze(0) if mask is not None: nW = mask.shape[0] attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) attn = attn.view(-1, self.num_heads, N, N) attn = self.softmax(attn) else: attn = self.softmax(attn) attn = self.attn_drop(attn) x = (attn @ v).transpose(1, 2).reshape(B_, N, C) x = self.proj(x) x = self.proj_drop(x) return x def extra_repr(self) -> str: return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}' def flops(self, N): # calculate flops for 1 window with token length of N flops = 0 # qkv = self.qkv(x) flops += N * self.dim * 3 * self.dim # attn = (q @ k.transpose(-2, -1)) flops += self.num_heads * N * (self.dim // self.num_heads) * N # x = (attn @ v) flops += self.num_heads * N * N * (self.dim // self.num_heads) # x = self.proj(x) flops += N * self.dim * self.dim return flops class SwinTransformerBlock(nn.Module): r""" Swin Transformer Block. Args: dim (int): Number of input channels. input_resolution (tuple[int]): Input resolution. num_heads (int): Number of attention heads. window_size (int): Window size. shift_size (int): Shift size for SW-MSA. mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. drop (float, optional): Dropout rate. Default: 0.0 attn_drop (float, optional): Attention dropout rate. Default: 0.0 drop_path (float, optional): Stochastic depth rate. Default: 0.0 act_layer (nn.Module, optional): Activation layer. Default: nn.GELU norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm """ def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0, mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): super().__init__() self.dim = dim self.input_resolution = input_resolution self.num_heads = num_heads self.window_size = window_size self.shift_size = shift_size self.mlp_ratio = mlp_ratio if min(self.input_resolution) <= self.window_size: # if window size is larger than input resolution, we don't partition windows self.shift_size = 0 self.window_size = min(self.input_resolution) assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size" self.norm1 = norm_layer(dim) self.attn = WindowAttention( dim, window_size=to_2tuple(self.window_size), num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() self.norm2 = norm_layer(dim) mlp_hidden_dim = int(dim * mlp_ratio) self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) if self.shift_size > 0: attn_mask = self.calculate_mask(self.input_resolution) else: attn_mask = None self.register_buffer("attn_mask", attn_mask) def calculate_mask(self, x_size): # calculate attention mask for SW-MSA H, W = x_size img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1 h_slices = (slice(0, -self.window_size), slice(-self.window_size, -self.shift_size), slice(-self.shift_size, None)) w_slices = (slice(0, -self.window_size), slice(-self.window_size, -self.shift_size), slice(-self.shift_size, None)) cnt = 0 for h in h_slices: for w in w_slices: img_mask[:, h, w, :] = cnt cnt += 1 mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1 mask_windows = mask_windows.view(-1, self.window_size * self.window_size) attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) return attn_mask def forward(self, x, x_size): H, W = x_size B, L, C = x.shape # assert L == H * W, "input feature has wrong size" shortcut = x x = self.norm1(x) x = x.view(B, H, W, C) # cyclic shift if self.shift_size > 0: shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) else: shifted_x = x # partition windows x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size if self.input_resolution == x_size: attn_windows = self.attn(x_windows, mask=self.attn_mask) # nW*B, window_size*window_size, C else: attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device)) # merge windows attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C # reverse cyclic shift if self.shift_size > 0: x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) else: x = shifted_x x = x.view(B, H * W, C) # FFN x = shortcut + self.drop_path(x) x = x + self.drop_path(self.mlp(self.norm2(x))) return x def extra_repr(self) -> str: return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \ f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}" def flops(self): flops = 0 H, W = self.input_resolution # norm1 flops += self.dim * H * W # W-MSA/SW-MSA nW = H * W / self.window_size / self.window_size flops += nW * self.attn.flops(self.window_size * self.window_size) # mlp flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio # norm2 flops += self.dim * H * W return flops class PatchMerging(nn.Module): r""" Patch Merging Layer. Args: input_resolution (tuple[int]): Resolution of input feature. dim (int): Number of input channels. norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm """ def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm): super().__init__() self.input_resolution = input_resolution self.dim = dim self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) self.norm = norm_layer(4 * dim) def forward(self, x): """ x: B, H*W, C """ H, W = self.input_resolution B, L, C = x.shape assert L == H * W, "input feature has wrong size" assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even." x = x.view(B, H, W, C) x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C x = self.norm(x) x = self.reduction(x) return x def extra_repr(self) -> str: return f"input_resolution={self.input_resolution}, dim={self.dim}" def flops(self): H, W = self.input_resolution flops = H * W * self.dim flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim return flops class BasicLayer(nn.Module): """ A basic Swin Transformer layer for one stage. Args: dim (int): Number of input channels. input_resolution (tuple[int]): Input resolution. depth (int): Number of blocks. num_heads (int): Number of attention heads. window_size (int): Local window size. mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. drop (float, optional): Dropout rate. Default: 0.0 attn_drop (float, optional): Attention dropout rate. Default: 0.0 drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. """ def __init__(self, dim, input_resolution, depth, num_heads, window_size, mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False): super().__init__() self.dim = dim self.input_resolution = input_resolution self.depth = depth self.use_checkpoint = use_checkpoint # build blocks self.blocks = nn.ModuleList([ SwinTransformerBlock(dim=dim, input_resolution=input_resolution, num_heads=num_heads, window_size=window_size, shift_size=0 if (i % 2 == 0) else window_size // 2, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop, attn_drop=attn_drop, drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, norm_layer=norm_layer) for i in range(depth)]) # patch merging layer if downsample is not None: self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer) else: self.downsample = None def forward(self, x, x_size): for blk in self.blocks: if self.use_checkpoint: x = checkpoint.checkpoint(blk, x, x_size) else: x = blk(x, x_size) if self.downsample is not None: x = self.downsample(x) return x def extra_repr(self) -> str: return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}" def flops(self): flops = 0 for blk in self.blocks: flops += blk.flops() if self.downsample is not None: flops += self.downsample.flops() return flops class RSTB(nn.Module): """Residual Swin Transformer Block (RSTB). Args: dim (int): Number of input channels. input_resolution (tuple[int]): Input resolution. depth (int): Number of blocks. num_heads (int): Number of attention heads. window_size (int): Local window size. mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. drop (float, optional): Dropout rate. Default: 0.0 attn_drop (float, optional): Attention dropout rate. Default: 0.0 drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. img_size: Input image size. patch_size: Patch size. resi_connection: The convolutional block before residual connection. """ def __init__(self, dim, input_resolution, depth, num_heads, window_size, mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False, img_size=224, patch_size=4, resi_connection='1conv'): super(RSTB, self).__init__() self.dim = dim self.input_resolution = input_resolution self.residual_group = BasicLayer(dim=dim, input_resolution=input_resolution, depth=depth, num_heads=num_heads, window_size=window_size, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop, attn_drop=attn_drop, drop_path=drop_path, norm_layer=norm_layer, downsample=downsample, use_checkpoint=use_checkpoint) if resi_connection == '1conv': self.conv = nn.Conv2d(dim, dim, 3, 1, 1) elif resi_connection == '3conv': # to save parameters and memory self.conv = nn.Sequential(nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True), nn.Conv2d(dim // 4, dim // 4, 1, 1, 0), nn.LeakyReLU(negative_slope=0.2, inplace=True), nn.Conv2d(dim // 4, dim, 3, 1, 1)) self.patch_embed = PatchEmbed( img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim, norm_layer=None) self.patch_unembed = PatchUnEmbed( img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim, norm_layer=None) def forward(self, x, x_size): return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x def flops(self): flops = 0 flops += self.residual_group.flops() H, W = self.input_resolution flops += H * W * self.dim * self.dim * 9 flops += self.patch_embed.flops() flops += self.patch_unembed.flops() return flops class PatchEmbed(nn.Module): r""" Image to Patch Embedding Args: img_size (int): Image size. Default: 224. patch_size (int): Patch token size. Default: 4. in_chans (int): Number of input image channels. Default: 3. embed_dim (int): Number of linear projection output channels. Default: 96. norm_layer (nn.Module, optional): Normalization layer. Default: None """ def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): super().__init__() img_size = to_2tuple(img_size) patch_size = to_2tuple(patch_size) patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]] self.img_size = img_size self.patch_size = patch_size self.patches_resolution = patches_resolution self.num_patches = patches_resolution[0] * patches_resolution[1] self.in_chans = in_chans self.embed_dim = embed_dim if norm_layer is not None: self.norm = norm_layer(embed_dim) else: self.norm = None def forward(self, x): x = x.flatten(2).transpose(1, 2) # B Ph*Pw C if self.norm is not None: x = self.norm(x) return x def flops(self): flops = 0 H, W = self.img_size if self.norm is not None: flops += H * W * self.embed_dim return flops class PatchUnEmbed(nn.Module): r""" Image to Patch Unembedding Args: img_size (int): Image size. Default: 224. patch_size (int): Patch token size. Default: 4. in_chans (int): Number of input image channels. Default: 3. embed_dim (int): Number of linear projection output channels. Default: 96. norm_layer (nn.Module, optional): Normalization layer. Default: None """ def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): super().__init__() img_size = to_2tuple(img_size) patch_size = to_2tuple(patch_size) patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]] self.img_size = img_size self.patch_size = patch_size self.patches_resolution = patches_resolution self.num_patches = patches_resolution[0] * patches_resolution[1] self.in_chans = in_chans self.embed_dim = embed_dim def forward(self, x, x_size): B, HW, C = x.shape x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1]) # B Ph*Pw C return x def flops(self): flops = 0 return flops class Upsample(nn.Sequential): """Upsample module. Args: scale (int): Scale factor. Supported scales: 2^n and 3. num_feat (int): Channel number of intermediate features. """ def __init__(self, scale, num_feat): m = [] if (scale & (scale - 1)) == 0: # scale = 2^n for _ in range(int(math.log(scale, 2))): m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) m.append(nn.PixelShuffle(2)) elif scale == 3: m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) m.append(nn.PixelShuffle(3)) else: raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.') super(Upsample, self).__init__(*m) class UpsampleOneStep(nn.Sequential): """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle) Used in lightweight SR to save parameters. Args: scale (int): Scale factor. Supported scales: 2^n and 3. num_feat (int): Channel number of intermediate features. """ def __init__(self, scale, num_feat, num_out_ch, input_resolution=None): self.num_feat = num_feat self.input_resolution = input_resolution m = [] m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1)) m.append(nn.PixelShuffle(scale)) super(UpsampleOneStep, self).__init__(*m) def flops(self): H, W = self.input_resolution flops = H * W * self.num_feat * 3 * 9 return flops class SwinIR(nn.Module): r""" SwinIR A PyTorch impl of : `SwinIR: Image Restoration Using Swin Transformer`, based on Swin Transformer. Args: img_size (int | tuple(int)): Input image size. Default 64 patch_size (int | tuple(int)): Patch size. Default: 1 in_chans (int): Number of input image channels. Default: 3 embed_dim (int): Patch embedding dimension. Default: 96 depths (tuple(int)): Depth of each Swin Transformer layer. num_heads (tuple(int)): Number of attention heads in different layers. window_size (int): Window size. Default: 7 mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4 qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None drop_rate (float): Dropout rate. Default: 0 attn_drop_rate (float): Attention dropout rate. Default: 0 drop_path_rate (float): Stochastic depth rate. Default: 0.1 norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. ape (bool): If True, add absolute position embedding to the patch embedding. Default: False patch_norm (bool): If True, add normalization after patch embedding. Default: True use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction img_range: Image range. 1. or 255. upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None resi_connection: The convolutional block before residual connection. '1conv'/'3conv' """ def __init__(self, img_size=64, patch_size=1, in_chans=3, embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6], window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None, drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1, norm_layer=nn.LayerNorm, ape=False, patch_norm=True, use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv', **kwargs): super(SwinIR, self).__init__() num_in_ch = in_chans num_out_ch = in_chans num_feat = 64 self.img_range = img_range if in_chans == 3: rgb_mean = (0.4488, 0.4371, 0.4040) self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1) else: self.mean = torch.zeros(1, 1, 1, 1) self.upscale = upscale self.upsampler = upsampler self.window_size = window_size ##################################################################################################### ################################### 1, shallow feature extraction ################################### self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1) ##################################################################################################### ################################### 2, deep feature extraction ###################################### self.num_layers = len(depths) self.embed_dim = embed_dim self.ape = ape self.patch_norm = patch_norm self.num_features = embed_dim self.mlp_ratio = mlp_ratio # split image into non-overlapping patches self.patch_embed = PatchEmbed( img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim, norm_layer=norm_layer if self.patch_norm else None) num_patches = self.patch_embed.num_patches patches_resolution = self.patch_embed.patches_resolution self.patches_resolution = patches_resolution # merge non-overlapping patches into image self.patch_unembed = PatchUnEmbed( img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim, norm_layer=norm_layer if self.patch_norm else None) # absolute position embedding if self.ape: self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim)) trunc_normal_(self.absolute_pos_embed, std=.02) self.pos_drop = nn.Dropout(p=drop_rate) # stochastic depth dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule # build Residual Swin Transformer blocks (RSTB) self.layers = nn.ModuleList() for i_layer in range(self.num_layers): layer = RSTB(dim=embed_dim, input_resolution=(patches_resolution[0], patches_resolution[1]), depth=depths[i_layer], num_heads=num_heads[i_layer], window_size=window_size, mlp_ratio=self.mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], # no impact on SR results norm_layer=norm_layer, downsample=None, use_checkpoint=use_checkpoint, img_size=img_size, patch_size=patch_size, resi_connection=resi_connection ) self.layers.append(layer) self.norm = norm_layer(self.num_features) # build the last conv layer in deep feature extraction if resi_connection == '1conv': self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) elif resi_connection == '3conv': # to save parameters and memory self.conv_after_body = nn.Sequential(nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True), nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0), nn.LeakyReLU(negative_slope=0.2, inplace=True), nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1)) ##################################################################################################### ################################ 3, high quality image reconstruction ################################ if self.upsampler == 'pixelshuffle': # for classical SR self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)) self.upsample = Upsample(upscale, num_feat) self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) elif self.upsampler == 'pixelshuffledirect': # for lightweight SR (to save parameters) self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch, (patches_resolution[0], patches_resolution[1])) elif self.upsampler == 'nearest+conv': # for real-world SR (less artifacts) self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)) self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) if self.upscale == 4: self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1) self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) else: # for image denoising and JPEG compression artifact reduction self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1) self.apply(self._init_weights) def _init_weights(self, m): if isinstance(m, nn.Linear): trunc_normal_(m.weight, std=.02) if isinstance(m, nn.Linear) and m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.LayerNorm): nn.init.constant_(m.bias, 0) nn.init.constant_(m.weight, 1.0) @torch.jit.ignore def no_weight_decay(self): return {'absolute_pos_embed'} @torch.jit.ignore def no_weight_decay_keywords(self): return {'relative_position_bias_table'} def check_image_size(self, x): _, _, h, w = x.size() mod_pad_h = (self.window_size - h % self.window_size) % self.window_size mod_pad_w = (self.window_size - w % self.window_size) % self.window_size x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect') return x def forward_features(self, x): x_size = (x.shape[2], x.shape[3]) x = self.patch_embed(x) if self.ape: x = x + self.absolute_pos_embed x = self.pos_drop(x) for layer in self.layers: x = layer(x, x_size) x = self.norm(x) # B L C x = self.patch_unembed(x, x_size) return x def forward(self, x): H, W = x.shape[2:] x = self.check_image_size(x) self.mean = self.mean.type_as(x) x = (x - self.mean) * self.img_range if self.upsampler == 'pixelshuffle': # for classical SR x = self.conv_first(x) x = self.conv_after_body(self.forward_features(x)) + x x = self.conv_before_upsample(x) x = self.conv_last(self.upsample(x)) elif self.upsampler == 'pixelshuffledirect': # for lightweight SR x = self.conv_first(x) x = self.conv_after_body(self.forward_features(x)) + x x = self.upsample(x) elif self.upsampler == 'nearest+conv': # for real-world SR x = self.conv_first(x) x = self.conv_after_body(self.forward_features(x)) + x x = self.conv_before_upsample(x) x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest'))) if self.upscale == 4: x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest'))) x = self.conv_last(self.lrelu(self.conv_hr(x))) else: # for image denoising and JPEG compression artifact reduction x_first = self.conv_first(x) res = self.conv_after_body(self.forward_features(x_first)) + x_first x = x + self.conv_last(res) x = x / self.img_range + self.mean return x[:, :, :H*self.upscale, :W*self.upscale] def flops(self): flops = 0 H, W = self.patches_resolution flops += H * W * 3 * self.embed_dim * 9 flops += self.patch_embed.flops() for i, layer in enumerate(self.layers): flops += layer.flops() flops += H * W * 3 * self.embed_dim * self.embed_dim flops += self.upsample.flops() return flops if __name__ == '__main__': upscale = 4 window_size = 8 height = (1024 // upscale // window_size + 1) * window_size width = (720 // upscale // window_size + 1) * window_size model = SwinIR(upscale=2, img_size=(height, width), window_size=window_size, img_range=1., depths=[6, 6, 6, 6], embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffledirect') print(model) print(height, width, model.flops() / 1e9) x = torch.randn((1, 3, height, width)) x = model(x) print(x.shape) stable-diffusion-webui/modules/swinir_model_arch_v2.py000066400000000000000000001322151432161722600235560ustar00rootroot00000000000000# ----------------------------------------------------------------------------------- # Swin2SR: Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration, https://arxiv.org/abs/ # Written by Conde and Choi et al. # ----------------------------------------------------------------------------------- import math import numpy as np import torch import torch.nn as nn import torch.nn.functional as F import torch.utils.checkpoint as checkpoint from timm.models.layers import DropPath, to_2tuple, trunc_normal_ class Mlp(nn.Module): def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features self.fc1 = nn.Linear(in_features, hidden_features) self.act = act_layer() self.fc2 = nn.Linear(hidden_features, out_features) self.drop = nn.Dropout(drop) def forward(self, x): x = self.fc1(x) x = self.act(x) x = self.drop(x) x = self.fc2(x) x = self.drop(x) return x def window_partition(x, window_size): """ Args: x: (B, H, W, C) window_size (int): window size Returns: windows: (num_windows*B, window_size, window_size, C) """ B, H, W, C = x.shape x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) return windows def window_reverse(windows, window_size, H, W): """ Args: windows: (num_windows*B, window_size, window_size, C) window_size (int): Window size H (int): Height of image W (int): Width of image Returns: x: (B, H, W, C) """ B = int(windows.shape[0] / (H * W / window_size / window_size)) x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) return x class WindowAttention(nn.Module): r""" Window based multi-head self attention (W-MSA) module with relative position bias. It supports both of shifted and non-shifted window. Args: dim (int): Number of input channels. window_size (tuple[int]): The height and width of the window. num_heads (int): Number of attention heads. qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 proj_drop (float, optional): Dropout ratio of output. Default: 0.0 pretrained_window_size (tuple[int]): The height and width of the window in pre-training. """ def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0., proj_drop=0., pretrained_window_size=[0, 0]): super().__init__() self.dim = dim self.window_size = window_size # Wh, Ww self.pretrained_window_size = pretrained_window_size self.num_heads = num_heads self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True) # mlp to generate continuous relative position bias self.cpb_mlp = nn.Sequential(nn.Linear(2, 512, bias=True), nn.ReLU(inplace=True), nn.Linear(512, num_heads, bias=False)) # get relative_coords_table relative_coords_h = torch.arange(-(self.window_size[0] - 1), self.window_size[0], dtype=torch.float32) relative_coords_w = torch.arange(-(self.window_size[1] - 1), self.window_size[1], dtype=torch.float32) relative_coords_table = torch.stack( torch.meshgrid([relative_coords_h, relative_coords_w])).permute(1, 2, 0).contiguous().unsqueeze(0) # 1, 2*Wh-1, 2*Ww-1, 2 if pretrained_window_size[0] > 0: relative_coords_table[:, :, :, 0] /= (pretrained_window_size[0] - 1) relative_coords_table[:, :, :, 1] /= (pretrained_window_size[1] - 1) else: relative_coords_table[:, :, :, 0] /= (self.window_size[0] - 1) relative_coords_table[:, :, :, 1] /= (self.window_size[1] - 1) relative_coords_table *= 8 # normalize to -8, 8 relative_coords_table = torch.sign(relative_coords_table) * torch.log2( torch.abs(relative_coords_table) + 1.0) / np.log2(8) self.register_buffer("relative_coords_table", relative_coords_table) # get pair-wise relative position index for each token inside the window coords_h = torch.arange(self.window_size[0]) coords_w = torch.arange(self.window_size[1]) coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 relative_coords[:, :, 1] += self.window_size[1] - 1 relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww self.register_buffer("relative_position_index", relative_position_index) self.qkv = nn.Linear(dim, dim * 3, bias=False) if qkv_bias: self.q_bias = nn.Parameter(torch.zeros(dim)) self.v_bias = nn.Parameter(torch.zeros(dim)) else: self.q_bias = None self.v_bias = None self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(proj_drop) self.softmax = nn.Softmax(dim=-1) def forward(self, x, mask=None): """ Args: x: input features with shape of (num_windows*B, N, C) mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None """ B_, N, C = x.shape qkv_bias = None if self.q_bias is not None: qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias)) qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias) qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) # cosine attention attn = (F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1)) logit_scale = torch.clamp(self.logit_scale, max=torch.log(torch.tensor(1. / 0.01)).to(self.logit_scale.device)).exp() attn = attn * logit_scale relative_position_bias_table = self.cpb_mlp(self.relative_coords_table).view(-1, self.num_heads) relative_position_bias = relative_position_bias_table[self.relative_position_index.view(-1)].view( self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww relative_position_bias = 16 * torch.sigmoid(relative_position_bias) attn = attn + relative_position_bias.unsqueeze(0) if mask is not None: nW = mask.shape[0] attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) attn = attn.view(-1, self.num_heads, N, N) attn = self.softmax(attn) else: attn = self.softmax(attn) attn = self.attn_drop(attn) x = (attn @ v).transpose(1, 2).reshape(B_, N, C) x = self.proj(x) x = self.proj_drop(x) return x def extra_repr(self) -> str: return f'dim={self.dim}, window_size={self.window_size}, ' \ f'pretrained_window_size={self.pretrained_window_size}, num_heads={self.num_heads}' def flops(self, N): # calculate flops for 1 window with token length of N flops = 0 # qkv = self.qkv(x) flops += N * self.dim * 3 * self.dim # attn = (q @ k.transpose(-2, -1)) flops += self.num_heads * N * (self.dim // self.num_heads) * N # x = (attn @ v) flops += self.num_heads * N * N * (self.dim // self.num_heads) # x = self.proj(x) flops += N * self.dim * self.dim return flops class SwinTransformerBlock(nn.Module): r""" Swin Transformer Block. Args: dim (int): Number of input channels. input_resolution (tuple[int]): Input resulotion. num_heads (int): Number of attention heads. window_size (int): Window size. shift_size (int): Shift size for SW-MSA. mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True drop (float, optional): Dropout rate. Default: 0.0 attn_drop (float, optional): Attention dropout rate. Default: 0.0 drop_path (float, optional): Stochastic depth rate. Default: 0.0 act_layer (nn.Module, optional): Activation layer. Default: nn.GELU norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm pretrained_window_size (int): Window size in pre-training. """ def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0, mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, pretrained_window_size=0): super().__init__() self.dim = dim self.input_resolution = input_resolution self.num_heads = num_heads self.window_size = window_size self.shift_size = shift_size self.mlp_ratio = mlp_ratio if min(self.input_resolution) <= self.window_size: # if window size is larger than input resolution, we don't partition windows self.shift_size = 0 self.window_size = min(self.input_resolution) assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size" self.norm1 = norm_layer(dim) self.attn = WindowAttention( dim, window_size=to_2tuple(self.window_size), num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop, pretrained_window_size=to_2tuple(pretrained_window_size)) self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() self.norm2 = norm_layer(dim) mlp_hidden_dim = int(dim * mlp_ratio) self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) if self.shift_size > 0: attn_mask = self.calculate_mask(self.input_resolution) else: attn_mask = None self.register_buffer("attn_mask", attn_mask) def calculate_mask(self, x_size): # calculate attention mask for SW-MSA H, W = x_size img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1 h_slices = (slice(0, -self.window_size), slice(-self.window_size, -self.shift_size), slice(-self.shift_size, None)) w_slices = (slice(0, -self.window_size), slice(-self.window_size, -self.shift_size), slice(-self.shift_size, None)) cnt = 0 for h in h_slices: for w in w_slices: img_mask[:, h, w, :] = cnt cnt += 1 mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1 mask_windows = mask_windows.view(-1, self.window_size * self.window_size) attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) return attn_mask def forward(self, x, x_size): H, W = x_size B, L, C = x.shape #assert L == H * W, "input feature has wrong size" shortcut = x x = x.view(B, H, W, C) # cyclic shift if self.shift_size > 0: shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) else: shifted_x = x # partition windows x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size if self.input_resolution == x_size: attn_windows = self.attn(x_windows, mask=self.attn_mask) # nW*B, window_size*window_size, C else: attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device)) # merge windows attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C # reverse cyclic shift if self.shift_size > 0: x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) else: x = shifted_x x = x.view(B, H * W, C) x = shortcut + self.drop_path(self.norm1(x)) # FFN x = x + self.drop_path(self.norm2(self.mlp(x))) return x def extra_repr(self) -> str: return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \ f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}" def flops(self): flops = 0 H, W = self.input_resolution # norm1 flops += self.dim * H * W # W-MSA/SW-MSA nW = H * W / self.window_size / self.window_size flops += nW * self.attn.flops(self.window_size * self.window_size) # mlp flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio # norm2 flops += self.dim * H * W return flops class PatchMerging(nn.Module): r""" Patch Merging Layer. Args: input_resolution (tuple[int]): Resolution of input feature. dim (int): Number of input channels. norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm """ def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm): super().__init__() self.input_resolution = input_resolution self.dim = dim self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) self.norm = norm_layer(2 * dim) def forward(self, x): """ x: B, H*W, C """ H, W = self.input_resolution B, L, C = x.shape assert L == H * W, "input feature has wrong size" assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even." x = x.view(B, H, W, C) x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C x = self.reduction(x) x = self.norm(x) return x def extra_repr(self) -> str: return f"input_resolution={self.input_resolution}, dim={self.dim}" def flops(self): H, W = self.input_resolution flops = (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim flops += H * W * self.dim // 2 return flops class BasicLayer(nn.Module): """ A basic Swin Transformer layer for one stage. Args: dim (int): Number of input channels. input_resolution (tuple[int]): Input resolution. depth (int): Number of blocks. num_heads (int): Number of attention heads. window_size (int): Local window size. mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True drop (float, optional): Dropout rate. Default: 0.0 attn_drop (float, optional): Attention dropout rate. Default: 0.0 drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. pretrained_window_size (int): Local window size in pre-training. """ def __init__(self, dim, input_resolution, depth, num_heads, window_size, mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False, pretrained_window_size=0): super().__init__() self.dim = dim self.input_resolution = input_resolution self.depth = depth self.use_checkpoint = use_checkpoint # build blocks self.blocks = nn.ModuleList([ SwinTransformerBlock(dim=dim, input_resolution=input_resolution, num_heads=num_heads, window_size=window_size, shift_size=0 if (i % 2 == 0) else window_size // 2, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop, attn_drop=attn_drop, drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, norm_layer=norm_layer, pretrained_window_size=pretrained_window_size) for i in range(depth)]) # patch merging layer if downsample is not None: self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer) else: self.downsample = None def forward(self, x, x_size): for blk in self.blocks: if self.use_checkpoint: x = checkpoint.checkpoint(blk, x, x_size) else: x = blk(x, x_size) if self.downsample is not None: x = self.downsample(x) return x def extra_repr(self) -> str: return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}" def flops(self): flops = 0 for blk in self.blocks: flops += blk.flops() if self.downsample is not None: flops += self.downsample.flops() return flops def _init_respostnorm(self): for blk in self.blocks: nn.init.constant_(blk.norm1.bias, 0) nn.init.constant_(blk.norm1.weight, 0) nn.init.constant_(blk.norm2.bias, 0) nn.init.constant_(blk.norm2.weight, 0) class PatchEmbed(nn.Module): r""" Image to Patch Embedding Args: img_size (int): Image size. Default: 224. patch_size (int): Patch token size. Default: 4. in_chans (int): Number of input image channels. Default: 3. embed_dim (int): Number of linear projection output channels. Default: 96. norm_layer (nn.Module, optional): Normalization layer. Default: None """ def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): super().__init__() img_size = to_2tuple(img_size) patch_size = to_2tuple(patch_size) patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]] self.img_size = img_size self.patch_size = patch_size self.patches_resolution = patches_resolution self.num_patches = patches_resolution[0] * patches_resolution[1] self.in_chans = in_chans self.embed_dim = embed_dim self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) if norm_layer is not None: self.norm = norm_layer(embed_dim) else: self.norm = None def forward(self, x): B, C, H, W = x.shape # FIXME look at relaxing size constraints # assert H == self.img_size[0] and W == self.img_size[1], # f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." x = self.proj(x).flatten(2).transpose(1, 2) # B Ph*Pw C if self.norm is not None: x = self.norm(x) return x def flops(self): Ho, Wo = self.patches_resolution flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1]) if self.norm is not None: flops += Ho * Wo * self.embed_dim return flops class RSTB(nn.Module): """Residual Swin Transformer Block (RSTB). Args: dim (int): Number of input channels. input_resolution (tuple[int]): Input resolution. depth (int): Number of blocks. num_heads (int): Number of attention heads. window_size (int): Local window size. mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True drop (float, optional): Dropout rate. Default: 0.0 attn_drop (float, optional): Attention dropout rate. Default: 0.0 drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. img_size: Input image size. patch_size: Patch size. resi_connection: The convolutional block before residual connection. """ def __init__(self, dim, input_resolution, depth, num_heads, window_size, mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False, img_size=224, patch_size=4, resi_connection='1conv'): super(RSTB, self).__init__() self.dim = dim self.input_resolution = input_resolution self.residual_group = BasicLayer(dim=dim, input_resolution=input_resolution, depth=depth, num_heads=num_heads, window_size=window_size, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop, attn_drop=attn_drop, drop_path=drop_path, norm_layer=norm_layer, downsample=downsample, use_checkpoint=use_checkpoint) if resi_connection == '1conv': self.conv = nn.Conv2d(dim, dim, 3, 1, 1) elif resi_connection == '3conv': # to save parameters and memory self.conv = nn.Sequential(nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True), nn.Conv2d(dim // 4, dim // 4, 1, 1, 0), nn.LeakyReLU(negative_slope=0.2, inplace=True), nn.Conv2d(dim // 4, dim, 3, 1, 1)) self.patch_embed = PatchEmbed( img_size=img_size, patch_size=patch_size, in_chans=dim, embed_dim=dim, norm_layer=None) self.patch_unembed = PatchUnEmbed( img_size=img_size, patch_size=patch_size, in_chans=dim, embed_dim=dim, norm_layer=None) def forward(self, x, x_size): return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x def flops(self): flops = 0 flops += self.residual_group.flops() H, W = self.input_resolution flops += H * W * self.dim * self.dim * 9 flops += self.patch_embed.flops() flops += self.patch_unembed.flops() return flops class PatchUnEmbed(nn.Module): r""" Image to Patch Unembedding Args: img_size (int): Image size. Default: 224. patch_size (int): Patch token size. Default: 4. in_chans (int): Number of input image channels. Default: 3. embed_dim (int): Number of linear projection output channels. Default: 96. norm_layer (nn.Module, optional): Normalization layer. Default: None """ def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): super().__init__() img_size = to_2tuple(img_size) patch_size = to_2tuple(patch_size) patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]] self.img_size = img_size self.patch_size = patch_size self.patches_resolution = patches_resolution self.num_patches = patches_resolution[0] * patches_resolution[1] self.in_chans = in_chans self.embed_dim = embed_dim def forward(self, x, x_size): B, HW, C = x.shape x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1]) # B Ph*Pw C return x def flops(self): flops = 0 return flops class Upsample(nn.Sequential): """Upsample module. Args: scale (int): Scale factor. Supported scales: 2^n and 3. num_feat (int): Channel number of intermediate features. """ def __init__(self, scale, num_feat): m = [] if (scale & (scale - 1)) == 0: # scale = 2^n for _ in range(int(math.log(scale, 2))): m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) m.append(nn.PixelShuffle(2)) elif scale == 3: m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) m.append(nn.PixelShuffle(3)) else: raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.') super(Upsample, self).__init__(*m) class Upsample_hf(nn.Sequential): """Upsample module. Args: scale (int): Scale factor. Supported scales: 2^n and 3. num_feat (int): Channel number of intermediate features. """ def __init__(self, scale, num_feat): m = [] if (scale & (scale - 1)) == 0: # scale = 2^n for _ in range(int(math.log(scale, 2))): m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) m.append(nn.PixelShuffle(2)) elif scale == 3: m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) m.append(nn.PixelShuffle(3)) else: raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.') super(Upsample_hf, self).__init__(*m) class UpsampleOneStep(nn.Sequential): """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle) Used in lightweight SR to save parameters. Args: scale (int): Scale factor. Supported scales: 2^n and 3. num_feat (int): Channel number of intermediate features. """ def __init__(self, scale, num_feat, num_out_ch, input_resolution=None): self.num_feat = num_feat self.input_resolution = input_resolution m = [] m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1)) m.append(nn.PixelShuffle(scale)) super(UpsampleOneStep, self).__init__(*m) def flops(self): H, W = self.input_resolution flops = H * W * self.num_feat * 3 * 9 return flops class Swin2SR(nn.Module): r""" Swin2SR A PyTorch impl of : `Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration`. Args: img_size (int | tuple(int)): Input image size. Default 64 patch_size (int | tuple(int)): Patch size. Default: 1 in_chans (int): Number of input image channels. Default: 3 embed_dim (int): Patch embedding dimension. Default: 96 depths (tuple(int)): Depth of each Swin Transformer layer. num_heads (tuple(int)): Number of attention heads in different layers. window_size (int): Window size. Default: 7 mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4 qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True drop_rate (float): Dropout rate. Default: 0 attn_drop_rate (float): Attention dropout rate. Default: 0 drop_path_rate (float): Stochastic depth rate. Default: 0.1 norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. ape (bool): If True, add absolute position embedding to the patch embedding. Default: False patch_norm (bool): If True, add normalization after patch embedding. Default: True use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction img_range: Image range. 1. or 255. upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None resi_connection: The convolutional block before residual connection. '1conv'/'3conv' """ def __init__(self, img_size=64, patch_size=1, in_chans=3, embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6], window_size=7, mlp_ratio=4., qkv_bias=True, drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1, norm_layer=nn.LayerNorm, ape=False, patch_norm=True, use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv', **kwargs): super(Swin2SR, self).__init__() num_in_ch = in_chans num_out_ch = in_chans num_feat = 64 self.img_range = img_range if in_chans == 3: rgb_mean = (0.4488, 0.4371, 0.4040) self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1) else: self.mean = torch.zeros(1, 1, 1, 1) self.upscale = upscale self.upsampler = upsampler self.window_size = window_size ##################################################################################################### ################################### 1, shallow feature extraction ################################### self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1) ##################################################################################################### ################################### 2, deep feature extraction ###################################### self.num_layers = len(depths) self.embed_dim = embed_dim self.ape = ape self.patch_norm = patch_norm self.num_features = embed_dim self.mlp_ratio = mlp_ratio # split image into non-overlapping patches self.patch_embed = PatchEmbed( img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim, norm_layer=norm_layer if self.patch_norm else None) num_patches = self.patch_embed.num_patches patches_resolution = self.patch_embed.patches_resolution self.patches_resolution = patches_resolution # merge non-overlapping patches into image self.patch_unembed = PatchUnEmbed( img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim, norm_layer=norm_layer if self.patch_norm else None) # absolute position embedding if self.ape: self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim)) trunc_normal_(self.absolute_pos_embed, std=.02) self.pos_drop = nn.Dropout(p=drop_rate) # stochastic depth dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule # build Residual Swin Transformer blocks (RSTB) self.layers = nn.ModuleList() for i_layer in range(self.num_layers): layer = RSTB(dim=embed_dim, input_resolution=(patches_resolution[0], patches_resolution[1]), depth=depths[i_layer], num_heads=num_heads[i_layer], window_size=window_size, mlp_ratio=self.mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], # no impact on SR results norm_layer=norm_layer, downsample=None, use_checkpoint=use_checkpoint, img_size=img_size, patch_size=patch_size, resi_connection=resi_connection ) self.layers.append(layer) if self.upsampler == 'pixelshuffle_hf': self.layers_hf = nn.ModuleList() for i_layer in range(self.num_layers): layer = RSTB(dim=embed_dim, input_resolution=(patches_resolution[0], patches_resolution[1]), depth=depths[i_layer], num_heads=num_heads[i_layer], window_size=window_size, mlp_ratio=self.mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], # no impact on SR results norm_layer=norm_layer, downsample=None, use_checkpoint=use_checkpoint, img_size=img_size, patch_size=patch_size, resi_connection=resi_connection ) self.layers_hf.append(layer) self.norm = norm_layer(self.num_features) # build the last conv layer in deep feature extraction if resi_connection == '1conv': self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) elif resi_connection == '3conv': # to save parameters and memory self.conv_after_body = nn.Sequential(nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True), nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0), nn.LeakyReLU(negative_slope=0.2, inplace=True), nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1)) ##################################################################################################### ################################ 3, high quality image reconstruction ################################ if self.upsampler == 'pixelshuffle': # for classical SR self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)) self.upsample = Upsample(upscale, num_feat) self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) elif self.upsampler == 'pixelshuffle_aux': self.conv_bicubic = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1) self.conv_before_upsample = nn.Sequential( nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)) self.conv_aux = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) self.conv_after_aux = nn.Sequential( nn.Conv2d(3, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)) self.upsample = Upsample(upscale, num_feat) self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) elif self.upsampler == 'pixelshuffle_hf': self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)) self.upsample = Upsample(upscale, num_feat) self.upsample_hf = Upsample_hf(upscale, num_feat) self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) self.conv_first_hf = nn.Sequential(nn.Conv2d(num_feat, embed_dim, 3, 1, 1), nn.LeakyReLU(inplace=True)) self.conv_after_body_hf = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) self.conv_before_upsample_hf = nn.Sequential( nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)) self.conv_last_hf = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) elif self.upsampler == 'pixelshuffledirect': # for lightweight SR (to save parameters) self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch, (patches_resolution[0], patches_resolution[1])) elif self.upsampler == 'nearest+conv': # for real-world SR (less artifacts) assert self.upscale == 4, 'only support x4 now.' self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)) self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1) self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) else: # for image denoising and JPEG compression artifact reduction self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1) self.apply(self._init_weights) def _init_weights(self, m): if isinstance(m, nn.Linear): trunc_normal_(m.weight, std=.02) if isinstance(m, nn.Linear) and m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.LayerNorm): nn.init.constant_(m.bias, 0) nn.init.constant_(m.weight, 1.0) @torch.jit.ignore def no_weight_decay(self): return {'absolute_pos_embed'} @torch.jit.ignore def no_weight_decay_keywords(self): return {'relative_position_bias_table'} def check_image_size(self, x): _, _, h, w = x.size() mod_pad_h = (self.window_size - h % self.window_size) % self.window_size mod_pad_w = (self.window_size - w % self.window_size) % self.window_size x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect') return x def forward_features(self, x): x_size = (x.shape[2], x.shape[3]) x = self.patch_embed(x) if self.ape: x = x + self.absolute_pos_embed x = self.pos_drop(x) for layer in self.layers: x = layer(x, x_size) x = self.norm(x) # B L C x = self.patch_unembed(x, x_size) return x def forward_features_hf(self, x): x_size = (x.shape[2], x.shape[3]) x = self.patch_embed(x) if self.ape: x = x + self.absolute_pos_embed x = self.pos_drop(x) for layer in self.layers_hf: x = layer(x, x_size) x = self.norm(x) # B L C x = self.patch_unembed(x, x_size) return x def forward(self, x): H, W = x.shape[2:] x = self.check_image_size(x) self.mean = self.mean.type_as(x) x = (x - self.mean) * self.img_range if self.upsampler == 'pixelshuffle': # for classical SR x = self.conv_first(x) x = self.conv_after_body(self.forward_features(x)) + x x = self.conv_before_upsample(x) x = self.conv_last(self.upsample(x)) elif self.upsampler == 'pixelshuffle_aux': bicubic = F.interpolate(x, size=(H * self.upscale, W * self.upscale), mode='bicubic', align_corners=False) bicubic = self.conv_bicubic(bicubic) x = self.conv_first(x) x = self.conv_after_body(self.forward_features(x)) + x x = self.conv_before_upsample(x) aux = self.conv_aux(x) # b, 3, LR_H, LR_W x = self.conv_after_aux(aux) x = self.upsample(x)[:, :, :H * self.upscale, :W * self.upscale] + bicubic[:, :, :H * self.upscale, :W * self.upscale] x = self.conv_last(x) aux = aux / self.img_range + self.mean elif self.upsampler == 'pixelshuffle_hf': # for classical SR with HF x = self.conv_first(x) x = self.conv_after_body(self.forward_features(x)) + x x_before = self.conv_before_upsample(x) x_out = self.conv_last(self.upsample(x_before)) x_hf = self.conv_first_hf(x_before) x_hf = self.conv_after_body_hf(self.forward_features_hf(x_hf)) + x_hf x_hf = self.conv_before_upsample_hf(x_hf) x_hf = self.conv_last_hf(self.upsample_hf(x_hf)) x = x_out + x_hf x_hf = x_hf / self.img_range + self.mean elif self.upsampler == 'pixelshuffledirect': # for lightweight SR x = self.conv_first(x) x = self.conv_after_body(self.forward_features(x)) + x x = self.upsample(x) elif self.upsampler == 'nearest+conv': # for real-world SR x = self.conv_first(x) x = self.conv_after_body(self.forward_features(x)) + x x = self.conv_before_upsample(x) x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest'))) x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest'))) x = self.conv_last(self.lrelu(self.conv_hr(x))) else: # for image denoising and JPEG compression artifact reduction x_first = self.conv_first(x) res = self.conv_after_body(self.forward_features(x_first)) + x_first x = x + self.conv_last(res) x = x / self.img_range + self.mean if self.upsampler == "pixelshuffle_aux": return x[:, :, :H*self.upscale, :W*self.upscale], aux elif self.upsampler == "pixelshuffle_hf": x_out = x_out / self.img_range + self.mean return x_out[:, :, :H*self.upscale, :W*self.upscale], x[:, :, :H*self.upscale, :W*self.upscale], x_hf[:, :, :H*self.upscale, :W*self.upscale] else: return x[:, :, :H*self.upscale, :W*self.upscale] def flops(self): flops = 0 H, W = self.patches_resolution flops += H * W * 3 * self.embed_dim * 9 flops += self.patch_embed.flops() for i, layer in enumerate(self.layers): flops += layer.flops() flops += H * W * 3 * self.embed_dim * self.embed_dim flops += self.upsample.flops() return flops if __name__ == '__main__': upscale = 4 window_size = 8 height = (1024 // upscale // window_size + 1) * window_size width = (720 // upscale // window_size + 1) * window_size model = Swin2SR(upscale=2, img_size=(height, width), window_size=window_size, img_range=1., depths=[6, 6, 6, 6], embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffledirect') print(model) print(height, width, model.flops() / 1e9) x = torch.randn((1, 3, height, width)) x = model(x) print(x.shape)stable-diffusion-webui/modules/textual_inversion/000077500000000000000000000000001432161722600226635ustar00rootroot00000000000000stable-diffusion-webui/modules/textual_inversion/dataset.py000066400000000000000000000076751432161722600247010ustar00rootroot00000000000000import os import numpy as np import PIL import torch from PIL import Image from torch.utils.data import Dataset from torchvision import transforms import random import tqdm from modules import devices, shared import re re_numbers_at_start = re.compile(r"^[-\d]+\s*") class DatasetEntry: def __init__(self, filename=None, latent=None, filename_text=None): self.filename = filename self.latent = latent self.filename_text = filename_text self.cond = None self.cond_text = None class PersonalizedBase(Dataset): def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None, include_cond=False): re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex)>0 else None self.placeholder_token = placeholder_token self.width = width self.height = height self.flip = transforms.RandomHorizontalFlip(p=flip_p) self.dataset = [] with open(template_file, "r") as file: lines = [x.strip() for x in file.readlines()] self.lines = lines assert data_root, 'dataset directory not specified' cond_model = shared.sd_model.cond_stage_model self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)] print("Preparing dataset...") for path in tqdm.tqdm(self.image_paths): try: image = Image.open(path).convert('RGB').resize((self.width, self.height), PIL.Image.BICUBIC) except Exception: continue text_filename = os.path.splitext(path)[0] + ".txt" filename = os.path.basename(path) if os.path.exists(text_filename): with open(text_filename, "r", encoding="utf8") as file: filename_text = file.read() else: filename_text = os.path.splitext(filename)[0] filename_text = re.sub(re_numbers_at_start, '', filename_text) if re_word: tokens = re_word.findall(filename_text) filename_text = (shared.opts.dataset_filename_join_string or "").join(tokens) npimage = np.array(image).astype(np.uint8) npimage = (npimage / 127.5 - 1.0).astype(np.float32) torchdata = torch.from_numpy(npimage).to(device=device, dtype=torch.float32) torchdata = torch.moveaxis(torchdata, 2, 0) init_latent = model.get_first_stage_encoding(model.encode_first_stage(torchdata.unsqueeze(dim=0))).squeeze() init_latent = init_latent.to(devices.cpu) entry = DatasetEntry(filename=path, filename_text=filename_text, latent=init_latent) if include_cond: entry.cond_text = self.create_text(filename_text) entry.cond = cond_model([entry.cond_text]).to(devices.cpu) self.dataset.append(entry) self.length = len(self.dataset) * repeats self.initial_indexes = np.arange(self.length) % len(self.dataset) self.indexes = None self.shuffle() def shuffle(self): self.indexes = self.initial_indexes[torch.randperm(self.initial_indexes.shape[0])] def create_text(self, filename_text): text = random.choice(self.lines) text = text.replace("[name]", self.placeholder_token) text = text.replace("[filewords]", filename_text) return text def __len__(self): return self.length def __getitem__(self, i): if i % len(self.dataset) == 0: self.shuffle() index = self.indexes[i % len(self.indexes)] entry = self.dataset[index] if entry.cond is None: entry.cond_text = self.create_text(entry.filename_text) return entry stable-diffusion-webui/modules/textual_inversion/image_embedding.py000066400000000000000000000204151432161722600263170ustar00rootroot00000000000000import base64 import json import numpy as np import zlib from PIL import Image, PngImagePlugin, ImageDraw, ImageFont from fonts.ttf import Roboto import torch class EmbeddingEncoder(json.JSONEncoder): def default(self, obj): if isinstance(obj, torch.Tensor): return {'TORCHTENSOR': obj.cpu().detach().numpy().tolist()} return json.JSONEncoder.default(self, obj) class EmbeddingDecoder(json.JSONDecoder): def __init__(self, *args, **kwargs): json.JSONDecoder.__init__(self, object_hook=self.object_hook, *args, **kwargs) def object_hook(self, d): if 'TORCHTENSOR' in d: return torch.from_numpy(np.array(d['TORCHTENSOR'])) return d def embedding_to_b64(data): d = json.dumps(data, cls=EmbeddingEncoder) return base64.b64encode(d.encode()) def embedding_from_b64(data): d = base64.b64decode(data) return json.loads(d, cls=EmbeddingDecoder) def lcg(m=2**32, a=1664525, c=1013904223, seed=0): while True: seed = (a * seed + c) % m yield seed % 255 def xor_block(block): g = lcg() randblock = np.array([next(g) for _ in range(np.product(block.shape))]).astype(np.uint8).reshape(block.shape) return np.bitwise_xor(block.astype(np.uint8), randblock & 0x0F) def style_block(block, sequence): im = Image.new('RGB', (block.shape[1], block.shape[0])) draw = ImageDraw.Draw(im) i = 0 for x in range(-6, im.size[0], 8): for yi, y in enumerate(range(-6, im.size[1], 8)): offset = 0 if yi % 2 == 0: offset = 4 shade = sequence[i % len(sequence)] i += 1 draw.ellipse((x+offset, y, x+6+offset, y+6), fill=(shade, shade, shade)) fg = np.array(im).astype(np.uint8) & 0xF0 return block ^ fg def insert_image_data_embed(image, data): d = 3 data_compressed = zlib.compress(json.dumps(data, cls=EmbeddingEncoder).encode(), level=9) data_np_ = np.frombuffer(data_compressed, np.uint8).copy() data_np_high = data_np_ >> 4 data_np_low = data_np_ & 0x0F h = image.size[1] next_size = data_np_low.shape[0] + (h-(data_np_low.shape[0] % h)) next_size = next_size + ((h*d)-(next_size % (h*d))) data_np_low.resize(next_size) data_np_low = data_np_low.reshape((h, -1, d)) data_np_high.resize(next_size) data_np_high = data_np_high.reshape((h, -1, d)) edge_style = list(data['string_to_param'].values())[0].cpu().detach().numpy().tolist()[0][:1024] edge_style = (np.abs(edge_style)/np.max(np.abs(edge_style))*255).astype(np.uint8) data_np_low = style_block(data_np_low, sequence=edge_style) data_np_low = xor_block(data_np_low) data_np_high = style_block(data_np_high, sequence=edge_style[::-1]) data_np_high = xor_block(data_np_high) im_low = Image.fromarray(data_np_low, mode='RGB') im_high = Image.fromarray(data_np_high, mode='RGB') background = Image.new('RGB', (image.size[0]+im_low.size[0]+im_high.size[0]+2, image.size[1]), (0, 0, 0)) background.paste(im_low, (0, 0)) background.paste(image, (im_low.size[0]+1, 0)) background.paste(im_high, (im_low.size[0]+1+image.size[0]+1, 0)) return background def crop_black(img, tol=0): mask = (img > tol).all(2) mask0, mask1 = mask.any(0), mask.any(1) col_start, col_end = mask0.argmax(), mask.shape[1]-mask0[::-1].argmax() row_start, row_end = mask1.argmax(), mask.shape[0]-mask1[::-1].argmax() return img[row_start:row_end, col_start:col_end] def extract_image_data_embed(image): d = 3 outarr = crop_black(np.array(image.convert('RGB').getdata()).reshape(image.size[1], image.size[0], d).astype(np.uint8)) & 0x0F black_cols = np.where(np.sum(outarr, axis=(0, 2)) == 0) if black_cols[0].shape[0] < 2: print('No Image data blocks found.') return None data_block_lower = outarr[:, :black_cols[0].min(), :].astype(np.uint8) data_block_upper = outarr[:, black_cols[0].max()+1:, :].astype(np.uint8) data_block_lower = xor_block(data_block_lower) data_block_upper = xor_block(data_block_upper) data_block = (data_block_upper << 4) | (data_block_lower) data_block = data_block.flatten().tobytes() data = zlib.decompress(data_block) return json.loads(data, cls=EmbeddingDecoder) def caption_image_overlay(srcimage, title, footerLeft, footerMid, footerRight, textfont=None): from math import cos image = srcimage.copy() if textfont is None: try: textfont = ImageFont.truetype(opts.font or Roboto, fontsize) textfont = opts.font or Roboto except Exception: textfont = Roboto factor = 1.5 gradient = Image.new('RGBA', (1, image.size[1]), color=(0, 0, 0, 0)) for y in range(image.size[1]): mag = 1-cos(y/image.size[1]*factor) mag = max(mag, 1-cos((image.size[1]-y)/image.size[1]*factor*1.1)) gradient.putpixel((0, y), (0, 0, 0, int(mag*255))) image = Image.alpha_composite(image.convert('RGBA'), gradient.resize(image.size)) draw = ImageDraw.Draw(image) fontsize = 32 font = ImageFont.truetype(textfont, fontsize) padding = 10 _, _, w, h = draw.textbbox((0, 0), title, font=font) fontsize = min(int(fontsize * (((image.size[0]*0.75)-(padding*4))/w)), 72) font = ImageFont.truetype(textfont, fontsize) _, _, w, h = draw.textbbox((0, 0), title, font=font) draw.text((padding, padding), title, anchor='lt', font=font, fill=(255, 255, 255, 230)) _, _, w, h = draw.textbbox((0, 0), footerLeft, font=font) fontsize_left = min(int(fontsize * (((image.size[0]/3)-(padding))/w)), 72) _, _, w, h = draw.textbbox((0, 0), footerMid, font=font) fontsize_mid = min(int(fontsize * (((image.size[0]/3)-(padding))/w)), 72) _, _, w, h = draw.textbbox((0, 0), footerRight, font=font) fontsize_right = min(int(fontsize * (((image.size[0]/3)-(padding))/w)), 72) font = ImageFont.truetype(textfont, min(fontsize_left, fontsize_mid, fontsize_right)) draw.text((padding, image.size[1]-padding), footerLeft, anchor='ls', font=font, fill=(255, 255, 255, 230)) draw.text((image.size[0]/2, image.size[1]-padding), footerMid, anchor='ms', font=font, fill=(255, 255, 255, 230)) draw.text((image.size[0]-padding, image.size[1]-padding), footerRight, anchor='rs', font=font, fill=(255, 255, 255, 230)) return image if __name__ == '__main__': testEmbed = Image.open('test_embedding.png') data = extract_image_data_embed(testEmbed) assert data is not None data = embedding_from_b64(testEmbed.text['sd-ti-embedding']) assert data is not None image = Image.new('RGBA', (512, 512), (255, 255, 200, 255)) cap_image = caption_image_overlay(image, 'title', 'footerLeft', 'footerMid', 'footerRight') test_embed = {'string_to_param': {'*': torch.from_numpy(np.random.random((2, 4096)))}} embedded_image = insert_image_data_embed(cap_image, test_embed) retrived_embed = extract_image_data_embed(embedded_image) assert str(retrived_embed) == str(test_embed) embedded_image2 = insert_image_data_embed(cap_image, retrived_embed) assert embedded_image == embedded_image2 g = lcg() shared_random = np.array([next(g) for _ in range(100)]).astype(np.uint8).tolist() reference_random = [253, 242, 127, 44, 157, 27, 239, 133, 38, 79, 167, 4, 177, 95, 130, 79, 78, 14, 52, 215, 220, 194, 126, 28, 240, 179, 160, 153, 149, 50, 105, 14, 21, 218, 199, 18, 54, 198, 193, 38, 128, 19, 53, 195, 124, 75, 205, 12, 6, 145, 0, 28, 30, 148, 8, 45, 218, 171, 55, 249, 97, 166, 12, 35, 0, 41, 221, 122, 215, 170, 31, 113, 186, 97, 119, 31, 23, 185, 66, 140, 30, 41, 37, 63, 137, 109, 216, 55, 159, 145, 82, 204, 86, 73, 222, 44, 198, 118, 240, 97] assert shared_random == reference_random hunna_kay_random_sum = sum(np.array([next(g) for _ in range(100000)]).astype(np.uint8).tolist()) assert 12731374 == hunna_kay_random_sum stable-diffusion-webui/modules/textual_inversion/learn_schedule.py000066400000000000000000000042501432161722600262130ustar00rootroot00000000000000import tqdm class LearnScheduleIterator: def __init__(self, learn_rate, max_steps, cur_step=0): """ specify learn_rate as "0.001:100, 0.00001:1000, 1e-5:10000" to have lr of 0.001 until step 100, 0.00001 until 1000, 1e-5:10000 until 10000 """ pairs = learn_rate.split(',') self.rates = [] self.it = 0 self.maxit = 0 for i, pair in enumerate(pairs): tmp = pair.split(':') if len(tmp) == 2: step = int(tmp[1]) if step > cur_step: self.rates.append((float(tmp[0]), min(step, max_steps))) self.maxit += 1 if step > max_steps: return elif step == -1: self.rates.append((float(tmp[0]), max_steps)) self.maxit += 1 return else: self.rates.append((float(tmp[0]), max_steps)) self.maxit += 1 return def __iter__(self): return self def __next__(self): if self.it < self.maxit: self.it += 1 return self.rates[self.it - 1] else: raise StopIteration class LearnRateScheduler: def __init__(self, learn_rate, max_steps, cur_step=0, verbose=True): self.schedules = LearnScheduleIterator(learn_rate, max_steps, cur_step) (self.learn_rate, self.end_step) = next(self.schedules) self.verbose = verbose if self.verbose: print(f'Training at rate of {self.learn_rate} until step {self.end_step}') self.finished = False def apply(self, optimizer, step_number): if step_number <= self.end_step: return try: (self.learn_rate, self.end_step) = next(self.schedules) except Exception: self.finished = True return if self.verbose: tqdm.tqdm.write(f'Training at rate of {self.learn_rate} until step {self.end_step}') for pg in optimizer.param_groups: pg['lr'] = self.learn_rate stable-diffusion-webui/modules/textual_inversion/preprocess.py000066400000000000000000000071651432161722600254330ustar00rootroot00000000000000import os from PIL import Image, ImageOps import platform import sys import tqdm import time from modules import shared, images from modules.shared import opts, cmd_opts if cmd_opts.deepdanbooru: import modules.deepbooru as deepbooru def preprocess(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False): try: if process_caption: shared.interrogator.load() if process_caption_deepbooru: deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, deepbooru.create_deepbooru_opts()) preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru) finally: if process_caption: shared.interrogator.send_blip_to_ram() if process_caption_deepbooru: deepbooru.release_process() def preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False): width = process_width height = process_height src = os.path.abspath(process_src) dst = os.path.abspath(process_dst) assert src != dst, 'same directory specified as source and destination' os.makedirs(dst, exist_ok=True) files = os.listdir(src) shared.state.textinfo = "Preprocessing..." shared.state.job_count = len(files) def save_pic_with_caption(image, index): caption = "" if process_caption: caption += shared.interrogator.generate_caption(image) if process_caption_deepbooru: if len(caption) > 0: caption += ", " caption += deepbooru.get_tags_from_process(image) filename_part = filename filename_part = os.path.splitext(filename_part)[0] filename_part = os.path.basename(filename_part) basename = f"{index:05}-{subindex[0]}-{filename_part}" image.save(os.path.join(dst, f"{basename}.png")) if len(caption) > 0: with open(os.path.join(dst, f"{basename}.txt"), "w", encoding="utf8") as file: file.write(caption) subindex[0] += 1 def save_pic(image, index): save_pic_with_caption(image, index) if process_flip: save_pic_with_caption(ImageOps.mirror(image), index) for index, imagefile in enumerate(tqdm.tqdm(files)): subindex = [0] filename = os.path.join(src, imagefile) try: img = Image.open(filename).convert("RGB") except Exception: continue if shared.state.interrupted: break ratio = img.height / img.width is_tall = ratio > 1.35 is_wide = ratio < 1 / 1.35 if process_split and is_tall: img = img.resize((width, height * img.height // img.width)) top = img.crop((0, 0, width, height)) save_pic(top, index) bot = img.crop((0, img.height - height, width, img.height)) save_pic(bot, index) elif process_split and is_wide: img = img.resize((width * img.width // img.height, height)) left = img.crop((0, 0, width, height)) save_pic(left, index) right = img.crop((img.width - width, 0, img.width, height)) save_pic(right, index) else: img = images.resize_image(1, img, width, height) save_pic(img, index) shared.state.nextjob() stable-diffusion-webui/modules/textual_inversion/test_embedding.png000066400000000000000000016734041432161722600263650ustar00rootroot00000000000000PNG  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V&R!nQ ;aNXWsdYU޷{#-u+kFoQr2+N#{XCLJ)v>Zur6l I/_M9*|/N6Û EAFw3t:)Rgt`g5I}.W'7M/:= =7y}zl1#}~g骕IU_ =a&bv-IENDB`stable-diffusion-webui/modules/textual_inversion/textual_inversion.py000066400000000000000000000300221432161722600270140ustar00rootroot00000000000000import os import sys import traceback import torch import tqdm import html import datetime from PIL import Image, PngImagePlugin from modules import shared, devices, sd_hijack, processing, sd_models import modules.textual_inversion.dataset from modules.textual_inversion.learn_schedule import LearnRateScheduler from modules.textual_inversion.image_embedding import (embedding_to_b64, embedding_from_b64, insert_image_data_embed, extract_image_data_embed, caption_image_overlay) class Embedding: def __init__(self, vec, name, step=None): self.vec = vec self.name = name self.step = step self.cached_checksum = None self.sd_checkpoint = None self.sd_checkpoint_name = None def save(self, filename): embedding_data = { "string_to_token": {"*": 265}, "string_to_param": {"*": self.vec}, "name": self.name, "step": self.step, "sd_checkpoint": self.sd_checkpoint, "sd_checkpoint_name": self.sd_checkpoint_name, } torch.save(embedding_data, filename) def checksum(self): if self.cached_checksum is not None: return self.cached_checksum def const_hash(a): r = 0 for v in a: r = (r * 281 ^ int(v) * 997) & 0xFFFFFFFF return r self.cached_checksum = f'{const_hash(self.vec.reshape(-1) * 100) & 0xffff:04x}' return self.cached_checksum class EmbeddingDatabase: def __init__(self, embeddings_dir): self.ids_lookup = {} self.word_embeddings = {} self.dir_mtime = None self.embeddings_dir = embeddings_dir def register_embedding(self, embedding, model): self.word_embeddings[embedding.name] = embedding ids = model.cond_stage_model.tokenizer([embedding.name], add_special_tokens=False)['input_ids'][0] first_id = ids[0] if first_id not in self.ids_lookup: self.ids_lookup[first_id] = [] self.ids_lookup[first_id] = sorted(self.ids_lookup[first_id] + [(ids, embedding)], key=lambda x: len(x[0]), reverse=True) return embedding def load_textual_inversion_embeddings(self): mt = os.path.getmtime(self.embeddings_dir) if self.dir_mtime is not None and mt <= self.dir_mtime: return self.dir_mtime = mt self.ids_lookup.clear() self.word_embeddings.clear() def process_file(path, filename): name = os.path.splitext(filename)[0] data = [] if filename.upper().endswith('.PNG'): embed_image = Image.open(path) if 'sd-ti-embedding' in embed_image.text: data = embedding_from_b64(embed_image.text['sd-ti-embedding']) name = data.get('name', name) else: data = extract_image_data_embed(embed_image) name = data.get('name', name) else: data = torch.load(path, map_location="cpu") # textual inversion embeddings if 'string_to_param' in data: param_dict = data['string_to_param'] if hasattr(param_dict, '_parameters'): param_dict = getattr(param_dict, '_parameters') # fix for torch 1.12.1 loading saved file from torch 1.11 assert len(param_dict) == 1, 'embedding file has multiple terms in it' emb = next(iter(param_dict.items()))[1] # diffuser concepts elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor: assert len(data.keys()) == 1, 'embedding file has multiple terms in it' emb = next(iter(data.values())) if len(emb.shape) == 1: emb = emb.unsqueeze(0) else: raise Exception(f"Couldn't identify {filename} as neither textual inversion embedding nor diffuser concept.") vec = emb.detach().to(devices.device, dtype=torch.float32) embedding = Embedding(vec, name) embedding.step = data.get('step', None) embedding.sd_checkpoint = data.get('hash', None) embedding.sd_checkpoint_name = data.get('sd_checkpoint_name', None) self.register_embedding(embedding, shared.sd_model) for fn in os.listdir(self.embeddings_dir): try: fullfn = os.path.join(self.embeddings_dir, fn) if os.stat(fullfn).st_size == 0: continue process_file(fullfn, fn) except Exception: print(f"Error loading emedding {fn}:", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) continue print(f"Loaded a total of {len(self.word_embeddings)} textual inversion embeddings.") def find_embedding_at_position(self, tokens, offset): token = tokens[offset] possible_matches = self.ids_lookup.get(token, None) if possible_matches is None: return None, None for ids, embedding in possible_matches: if tokens[offset:offset + len(ids)] == ids: return embedding, len(ids) return None, None def create_embedding(name, num_vectors_per_token, init_text='*'): cond_model = shared.sd_model.cond_stage_model embedding_layer = cond_model.wrapped.transformer.text_model.embeddings ids = cond_model.tokenizer(init_text, max_length=num_vectors_per_token, return_tensors="pt", add_special_tokens=False)["input_ids"] embedded = embedding_layer.token_embedding.wrapped(ids.to(devices.device)).squeeze(0) vec = torch.zeros((num_vectors_per_token, embedded.shape[1]), device=devices.device) for i in range(num_vectors_per_token): vec[i] = embedded[i * int(embedded.shape[0]) // num_vectors_per_token] fn = os.path.join(shared.cmd_opts.embeddings_dir, f"{name}.pt") assert not os.path.exists(fn), f"file {fn} already exists" embedding = Embedding(vec, name) embedding.step = 0 embedding.save(fn) return fn def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_image_prompt): assert embedding_name, 'embedding not selected' shared.state.textinfo = "Initializing textual inversion training..." shared.state.job_count = steps filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt') log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), embedding_name) if save_embedding_every > 0: embedding_dir = os.path.join(log_directory, "embeddings") os.makedirs(embedding_dir, exist_ok=True) else: embedding_dir = None if create_image_every > 0: images_dir = os.path.join(log_directory, "images") os.makedirs(images_dir, exist_ok=True) else: images_dir = None if create_image_every > 0 and save_image_with_stored_embedding: images_embeds_dir = os.path.join(log_directory, "image_embeddings") os.makedirs(images_embeds_dir, exist_ok=True) else: images_embeds_dir = None cond_model = shared.sd_model.cond_stage_model shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..." with torch.autocast("cuda"): ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file) hijack = sd_hijack.model_hijack embedding = hijack.embedding_db.word_embeddings[embedding_name] embedding.vec.requires_grad = True losses = torch.zeros((32,)) last_saved_file = "" last_saved_image = "" ititial_step = embedding.step or 0 if ititial_step > steps: return embedding, filename scheduler = LearnRateScheduler(learn_rate, steps, ititial_step) optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate) pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step) for i, entry in pbar: embedding.step = i + ititial_step scheduler.apply(optimizer, embedding.step) if scheduler.finished: break if shared.state.interrupted: break with torch.autocast("cuda"): c = cond_model([entry.cond_text]) x = entry.latent.to(devices.device) loss = shared.sd_model(x.unsqueeze(0), c)[0] del x losses[embedding.step % losses.shape[0]] = loss.item() optimizer.zero_grad() loss.backward() optimizer.step() epoch_num = embedding.step // len(ds) epoch_step = embedding.step - (epoch_num * len(ds)) + 1 pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{len(ds)}]loss: {losses.mean():.7f}") if embedding.step > 0 and embedding_dir is not None and embedding.step % save_embedding_every == 0: last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt') embedding.save(last_saved_file) if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0: last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png') preview_text = entry.cond_text if preview_image_prompt == "" else preview_image_prompt p = processing.StableDiffusionProcessingTxt2Img( sd_model=shared.sd_model, prompt=preview_text, steps=20, height=training_height, width=training_width, do_not_save_grid=True, do_not_save_samples=True, ) processed = processing.process_images(p) image = processed.images[0] shared.state.current_image = image if save_image_with_stored_embedding and os.path.exists(last_saved_file): last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{embedding.step}.png') info = PngImagePlugin.PngInfo() data = torch.load(last_saved_file) info.add_text("sd-ti-embedding", embedding_to_b64(data)) title = "<{}>".format(data.get('name', '???')) checkpoint = sd_models.select_checkpoint() footer_left = checkpoint.model_name footer_mid = '[{}]'.format(checkpoint.hash) footer_right = '{}'.format(embedding.step) captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right) captioned_image = insert_image_data_embed(captioned_image, data) captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info) image.save(last_saved_image) last_saved_image += f", prompt: {preview_text}" shared.state.job_no = embedding.step shared.state.textinfo = f"""

Loss: {losses.mean():.7f}
Step: {embedding.step}
Last prompt: {html.escape(entry.cond_text)}
Last saved embedding: {html.escape(last_saved_file)}
Last saved image: {html.escape(last_saved_image)}

""" checkpoint = sd_models.select_checkpoint() embedding.sd_checkpoint = checkpoint.hash embedding.sd_checkpoint_name = checkpoint.model_name embedding.cached_checksum = None embedding.save(filename) return embedding, filename stable-diffusion-webui/modules/textual_inversion/ui.py000066400000000000000000000023551432161722600236570ustar00rootroot00000000000000import html import gradio as gr import modules.textual_inversion.textual_inversion import modules.textual_inversion.preprocess from modules import sd_hijack, shared def create_embedding(name, initialization_text, nvpt): filename = modules.textual_inversion.textual_inversion.create_embedding(name, nvpt, init_text=initialization_text) sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings() return gr.Dropdown.update(choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())), f"Created: {filename}", "" def preprocess(*args): modules.textual_inversion.preprocess.preprocess(*args) return "Preprocessing finished.", "" def train_embedding(*args): assert not shared.cmd_opts.lowvram, 'Training models with lowvram not possible' try: sd_hijack.undo_optimizations() embedding, filename = modules.textual_inversion.textual_inversion.train_embedding(*args) res = f""" Training {'interrupted' if shared.state.interrupted else 'finished'} at {embedding.step} steps. Embedding saved to {html.escape(filename)} """ return res, "" except Exception: raise finally: sd_hijack.apply_optimizations() stable-diffusion-webui/modules/txt2img.py000066400000000000000000000044001432161722600210470ustar00rootroot00000000000000import modules.scripts from modules.processing import StableDiffusionProcessing, Processed, StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images from modules.shared import opts, cmd_opts import modules.shared as shared import modules.processing as processing from modules.ui import plaintext_to_html def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, scale_latent: bool, denoising_strength: float, *args): p = StableDiffusionProcessingTxt2Img( sd_model=shared.sd_model, outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples, outpath_grids=opts.outdir_grids or opts.outdir_txt2img_grids, prompt=prompt, styles=[prompt_style, prompt_style2], negative_prompt=negative_prompt, seed=seed, subseed=subseed, subseed_strength=subseed_strength, seed_resize_from_h=seed_resize_from_h, seed_resize_from_w=seed_resize_from_w, seed_enable_extras=seed_enable_extras, sampler_index=sampler_index, batch_size=batch_size, n_iter=n_iter, steps=steps, cfg_scale=cfg_scale, width=width, height=height, restore_faces=restore_faces, tiling=tiling, enable_hr=enable_hr, scale_latent=scale_latent if enable_hr else None, denoising_strength=denoising_strength if enable_hr else None, ) if cmd_opts.enable_console_prompts: print(f"\ntxt2img: {prompt}", file=shared.progress_print_out) processed = modules.scripts.scripts_txt2img.run(p, *args) if processed is None: processed = process_images(p) shared.total_tqdm.clear() generation_info_js = processed.js() if opts.samples_log_stdout: print(generation_info_js) if opts.do_not_show_images: processed.images = [] return processed.images, generation_info_js, plaintext_to_html(processed.info) stable-diffusion-webui/modules/ui.py000066400000000000000000002123431432161722600200750ustar00rootroot00000000000000import base64 import html import io import json import math import mimetypes import os import random import sys import time import traceback import platform import subprocess as sp from functools import reduce import numpy as np import torch from PIL import Image, PngImagePlugin import piexif import gradio as gr import gradio.utils import gradio.routes from modules import sd_hijack from modules.paths import script_path from modules.shared import opts, cmd_opts if cmd_opts.deepdanbooru: from modules.deepbooru import get_deepbooru_tags import modules.shared as shared from modules.sd_samplers import samplers, samplers_for_img2img from modules.sd_hijack import model_hijack import modules.ldsr_model import modules.scripts import modules.gfpgan_model import modules.codeformer_model import modules.styles import modules.generation_parameters_copypaste from modules import prompt_parser from modules.images import save_image import modules.textual_inversion.ui import modules.hypernetworks.ui # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI mimetypes.init() mimetypes.add_type('application/javascript', '.js') if not cmd_opts.share and not cmd_opts.listen: # fix gradio phoning home gradio.utils.version_check = lambda: None gradio.utils.get_local_ip_address = lambda: '127.0.0.1' if cmd_opts.ngrok != None: import modules.ngrok as ngrok print('ngrok authtoken detected, trying to connect...') ngrok.connect(cmd_opts.ngrok, cmd_opts.port if cmd_opts.port != None else 7860) def gr_show(visible=True): return {"visible": visible, "__type__": "update"} sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg" sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None css_hide_progressbar = """ .wrap .m-12 svg { display:none!important; } .wrap .m-12::before { content:"Loading..." } .progress-bar { display:none!important; } .meta-text { display:none!important; } """ # Using constants for these since the variation selector isn't visible. # Important that they exactly match script.js for tooltip to work. random_symbol = '\U0001f3b2\ufe0f' # 🎲️ reuse_symbol = '\u267b\ufe0f' # ♻️ art_symbol = '\U0001f3a8' # 🎨 paste_symbol = '\u2199\ufe0f' # ↙ folder_symbol = '\U0001f4c2' # 📂 def plaintext_to_html(text): text = "

" + "
\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "

" return text def image_from_url_text(filedata): if type(filedata) == list: if len(filedata) == 0: return None filedata = filedata[0] if filedata.startswith("data:image/png;base64,"): filedata = filedata[len("data:image/png;base64,"):] filedata = base64.decodebytes(filedata.encode('utf-8')) image = Image.open(io.BytesIO(filedata)) return image def send_gradio_gallery_to_image(x): if len(x) == 0: return None return image_from_url_text(x[0]) def save_files(js_data, images, do_make_zip, index): import csv filenames = [] fullfns = [] #quick dictionary to class object conversion. Its necessary due apply_filename_pattern requiring it class MyObject: def __init__(self, d=None): if d is not None: for key, value in d.items(): setattr(self, key, value) data = json.loads(js_data) p = MyObject(data) path = opts.outdir_save save_to_dirs = opts.use_save_to_dirs_for_ui extension: str = opts.samples_format start_index = 0 if index > -1 and opts.save_selected_only and (index >= data["index_of_first_image"]): # ensures we are looking at a specific non-grid picture, and we have save_selected_only images = [images[index]] start_index = index os.makedirs(opts.outdir_save, exist_ok=True) with open(os.path.join(opts.outdir_save, "log.csv"), "a", encoding="utf8", newline='') as file: at_start = file.tell() == 0 writer = csv.writer(file) if at_start: writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"]) for image_index, filedata in enumerate(images, start_index): if filedata.startswith("data:image/png;base64,"): filedata = filedata[len("data:image/png;base64,"):] image = Image.open(io.BytesIO(base64.decodebytes(filedata.encode('utf-8')))) is_grid = image_index < p.index_of_first_image i = 0 if is_grid else (image_index - p.index_of_first_image) fullfn, txt_fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs) filename = os.path.relpath(fullfn, path) filenames.append(filename) fullfns.append(fullfn) if txt_fullfn: filenames.append(os.path.basename(txt_fullfn)) fullfns.append(txt_fullfn) writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]]) # Make Zip if do_make_zip: zip_filepath = os.path.join(path, "images.zip") from zipfile import ZipFile with ZipFile(zip_filepath, "w") as zip_file: for i in range(len(fullfns)): with open(fullfns[i], mode="rb") as f: zip_file.writestr(filenames[i], f.read()) fullfns.insert(0, zip_filepath) return gr.File.update(value=fullfns, visible=True), '', '', plaintext_to_html(f"Saved: {filenames[0]}") def wrap_gradio_call(func, extra_outputs=None): def f(*args, extra_outputs_array=extra_outputs, **kwargs): run_memmon = opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled if run_memmon: shared.mem_mon.monitor() t = time.perf_counter() try: res = list(func(*args, **kwargs)) except Exception as e: # When printing out our debug argument list, do not print out more than a MB of text max_debug_str_len = 131072 # (1024*1024)/8 print("Error completing request", file=sys.stderr) argStr = f"Arguments: {str(args)} {str(kwargs)}" print(argStr[:max_debug_str_len], file=sys.stderr) if len(argStr) > max_debug_str_len: print(f"(Argument list truncated at {max_debug_str_len}/{len(argStr)} characters)", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) shared.state.job = "" shared.state.job_count = 0 if extra_outputs_array is None: extra_outputs_array = [None, ''] res = extra_outputs_array + [f"
{plaintext_to_html(type(e).__name__+': '+str(e))}
"] elapsed = time.perf_counter() - t elapsed_m = int(elapsed // 60) elapsed_s = elapsed % 60 elapsed_text = f"{elapsed_s:.2f}s" if (elapsed_m > 0): elapsed_text = f"{elapsed_m}m "+elapsed_text if run_memmon: mem_stats = {k: -(v//-(1024*1024)) for k, v in shared.mem_mon.stop().items()} active_peak = mem_stats['active_peak'] reserved_peak = mem_stats['reserved_peak'] sys_peak = mem_stats['system_peak'] sys_total = mem_stats['total'] sys_pct = round(sys_peak/max(sys_total, 1) * 100, 2) vram_html = f"

Torch active/reserved: {active_peak}/{reserved_peak} MiB, Sys VRAM: {sys_peak}/{sys_total} MiB ({sys_pct}%)

" else: vram_html = '' # last item is always HTML res[-1] += f"

Time taken: {elapsed_text}

{vram_html}
" shared.state.skipped = False shared.state.interrupted = False shared.state.job_count = 0 return tuple(res) return f def check_progress_call(id_part): if shared.state.job_count == 0: return "", gr_show(False), gr_show(False), gr_show(False) progress = 0 if shared.state.job_count > 0: progress += shared.state.job_no / shared.state.job_count if shared.state.sampling_steps > 0: progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps progress = min(progress, 1) progressbar = "" if opts.show_progressbar: progressbar = f"""
{str(int(progress*100))+"%" if progress > 0.01 else ""}
""" image = gr_show(False) preview_visibility = gr_show(False) if opts.show_progress_every_n_steps > 0: if shared.parallel_processing_allowed: if shared.state.sampling_step - shared.state.current_image_sampling_step >= opts.show_progress_every_n_steps and shared.state.current_latent is not None: shared.state.current_image = modules.sd_samplers.sample_to_image(shared.state.current_latent) shared.state.current_image_sampling_step = shared.state.sampling_step image = shared.state.current_image if image is None: image = gr.update(value=None) else: preview_visibility = gr_show(True) if shared.state.textinfo is not None: textinfo_result = gr.HTML.update(value=shared.state.textinfo, visible=True) else: textinfo_result = gr_show(False) return f"

{progressbar}

", preview_visibility, image, textinfo_result def check_progress_call_initial(id_part): shared.state.job_count = -1 shared.state.current_latent = None shared.state.current_image = None shared.state.textinfo = None return check_progress_call(id_part) def roll_artist(prompt): allowed_cats = set([x for x in shared.artist_db.categories() if len(opts.random_artist_categories)==0 or x in opts.random_artist_categories]) artist = random.choice([x for x in shared.artist_db.artists if x.category in allowed_cats]) return prompt + ", " + artist.name if prompt != '' else artist.name def visit(x, func, path=""): if hasattr(x, 'children'): for c in x.children: visit(c, func, path) elif x.label is not None: func(path + "/" + str(x.label), x) def add_style(name: str, prompt: str, negative_prompt: str): if name is None: return [gr_show(), gr_show()] style = modules.styles.PromptStyle(name, prompt, negative_prompt) shared.prompt_styles.styles[style.name] = style # Save all loaded prompt styles: this allows us to update the storage format in the future more easily, because we # reserialize all styles every time we save them shared.prompt_styles.save_styles(shared.styles_filename) return [gr.Dropdown.update(visible=True, choices=list(shared.prompt_styles.styles)) for _ in range(4)] def apply_styles(prompt, prompt_neg, style1_name, style2_name): prompt = shared.prompt_styles.apply_styles_to_prompt(prompt, [style1_name, style2_name]) prompt_neg = shared.prompt_styles.apply_negative_styles_to_prompt(prompt_neg, [style1_name, style2_name]) return [gr.Textbox.update(value=prompt), gr.Textbox.update(value=prompt_neg), gr.Dropdown.update(value="None"), gr.Dropdown.update(value="None")] def interrogate(image): prompt = shared.interrogator.interrogate(image) return gr_show(True) if prompt is None else prompt def interrogate_deepbooru(image): prompt = get_deepbooru_tags(image) return gr_show(True) if prompt is None else prompt def create_seed_inputs(): with gr.Row(): with gr.Box(): with gr.Row(elem_id='seed_row'): seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1) seed.style(container=False) random_seed = gr.Button(random_symbol, elem_id='random_seed') reuse_seed = gr.Button(reuse_symbol, elem_id='reuse_seed') with gr.Box(elem_id='subseed_show_box'): seed_checkbox = gr.Checkbox(label='Extra', elem_id='subseed_show', value=False) # Components to show/hide based on the 'Extra' checkbox seed_extras = [] with gr.Row(visible=False) as seed_extra_row_1: seed_extras.append(seed_extra_row_1) with gr.Box(): with gr.Row(elem_id='subseed_row'): subseed = gr.Number(label='Variation seed', value=-1) subseed.style(container=False) random_subseed = gr.Button(random_symbol, elem_id='random_subseed') reuse_subseed = gr.Button(reuse_symbol, elem_id='reuse_subseed') subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01) with gr.Row(visible=False) as seed_extra_row_2: seed_extras.append(seed_extra_row_2) seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=64, label="Resize seed from width", value=0) seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=64, label="Resize seed from height", value=0) random_seed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[seed]) random_subseed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[subseed]) def change_visibility(show): return {comp: gr_show(show) for comp in seed_extras} seed_checkbox.change(change_visibility, show_progress=False, inputs=[seed_checkbox], outputs=seed_extras) return seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: gr.Textbox, dummy_component, is_subseed): """ Connects a 'reuse (sub)seed' button's click event so that it copies last used (sub)seed value from generation info the to the seed field. If copying subseed and subseed strength was 0, i.e. no variation seed was used, it copies the normal seed value instead.""" def copy_seed(gen_info_string: str, index): res = -1 try: gen_info = json.loads(gen_info_string) index -= gen_info.get('index_of_first_image', 0) if is_subseed and gen_info.get('subseed_strength', 0) > 0: all_subseeds = gen_info.get('all_subseeds', [-1]) res = all_subseeds[index if 0 <= index < len(all_subseeds) else 0] else: all_seeds = gen_info.get('all_seeds', [-1]) res = all_seeds[index if 0 <= index < len(all_seeds) else 0] except json.decoder.JSONDecodeError as e: if gen_info_string != '': print("Error parsing JSON generation info:", file=sys.stderr) print(gen_info_string, file=sys.stderr) return [res, gr_show(False)] reuse_seed.click( fn=copy_seed, _js="(x, y) => [x, selected_gallery_index()]", show_progress=False, inputs=[generation_info, dummy_component], outputs=[seed, dummy_component] ) def update_token_counter(text, steps): try: _, prompt_flat_list, _ = prompt_parser.get_multicond_prompt_list([text]) prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(prompt_flat_list, steps) except Exception: # a parsing error can happen here during typing, and we don't want to bother the user with # messages related to it in console prompt_schedules = [[[steps, text]]] flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules) prompts = [prompt_text for step, prompt_text in flat_prompts] tokens, token_count, max_length = max([model_hijack.tokenize(prompt) for prompt in prompts], key=lambda args: args[1]) style_class = ' class="red"' if (token_count > max_length) else "" return f"{token_count}/{max_length}" def create_toprow(is_img2img): id_part = "img2img" if is_img2img else "txt2img" with gr.Row(elem_id="toprow"): with gr.Column(scale=4): with gr.Row(): with gr.Column(scale=80): with gr.Row(): prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, placeholder="Prompt", lines=2) with gr.Column(scale=1, elem_id="roll_col"): roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0) paste = gr.Button(value=paste_symbol, elem_id="paste") token_counter = gr.HTML(value="", elem_id=f"{id_part}_token_counter") token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button") with gr.Column(scale=10, elem_id="style_pos_col"): prompt_style = gr.Dropdown(label="Style 1", elem_id=f"{id_part}_style_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())), visible=len(shared.prompt_styles.styles) > 1) with gr.Row(): with gr.Column(scale=8): with gr.Row(): negative_prompt = gr.Textbox(label="Negative prompt", elem_id="negative_prompt", show_label=False, placeholder="Negative prompt", lines=2) with gr.Column(scale=1, elem_id="roll_col"): sh = gr.Button(elem_id="sh", visible=True) with gr.Column(scale=1, elem_id="style_neg_col"): prompt_style2 = gr.Dropdown(label="Style 2", elem_id=f"{id_part}_style2_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())), visible=len(shared.prompt_styles.styles) > 1) with gr.Column(scale=1): with gr.Row(): skip = gr.Button('Skip', elem_id=f"{id_part}_skip") interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt") submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary') skip.click( fn=lambda: shared.state.skip(), inputs=[], outputs=[], ) interrupt.click( fn=lambda: shared.state.interrupt(), inputs=[], outputs=[], ) with gr.Row(scale=1): if is_img2img: interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate") if cmd_opts.deepdanbooru: deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru") else: deepbooru = None else: interrogate = None deepbooru = None prompt_style_apply = gr.Button('Apply style', elem_id="style_apply") save_style = gr.Button('Create style', elem_id="style_create") return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, interrogate, deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button def setup_progressbar(progressbar, preview, id_part, textinfo=None): if textinfo is None: textinfo = gr.HTML(visible=False) check_progress = gr.Button('Check progress', elem_id=f"{id_part}_check_progress", visible=False) check_progress.click( fn=lambda: check_progress_call(id_part), show_progress=False, inputs=[], outputs=[progressbar, preview, preview, textinfo], ) check_progress_initial = gr.Button('Check progress (first)', elem_id=f"{id_part}_check_progress_initial", visible=False) check_progress_initial.click( fn=lambda: check_progress_call_initial(id_part), show_progress=False, inputs=[], outputs=[progressbar, preview, preview, textinfo], ) def create_ui(wrap_gradio_gpu_call): import modules.img2img import modules.txt2img with gr.Blocks(analytics_enabled=False) as txt2img_interface: txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=False) dummy_component = gr.Label(visible=False) with gr.Row(elem_id='txt2img_progress_row'): with gr.Column(scale=1): pass with gr.Column(scale=1): progressbar = gr.HTML(elem_id="txt2img_progressbar") txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False) setup_progressbar(progressbar, txt2img_preview, 'txt2img') with gr.Row().style(equal_height=False): with gr.Column(variant='panel'): steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20) sampler_index = gr.Radio(label='Sampling method', elem_id="txt2img_sampling", choices=[x.name for x in samplers], value=samplers[0].name, type="index") with gr.Group(): width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512) height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512) with gr.Row(): restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1) tiling = gr.Checkbox(label='Tiling', value=False) enable_hr = gr.Checkbox(label='Highres. fix', value=False) with gr.Row(visible=False) as hr_options: scale_latent = gr.Checkbox(label='Scale latent', value=False) denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7) with gr.Row(): batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1) batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1) cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0) seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs() with gr.Group(): custom_inputs = modules.scripts.scripts_txt2img.setup_ui(is_img2img=False) with gr.Column(variant='panel'): with gr.Group(): txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False) txt2img_gallery = gr.Gallery(label='Output', show_label=False, elem_id='txt2img_gallery').style(grid=4) with gr.Group(): with gr.Row(): save = gr.Button('Save') send_to_img2img = gr.Button('Send to img2img') send_to_inpaint = gr.Button('Send to inpaint') send_to_extras = gr.Button('Send to extras') button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder' open_txt2img_folder = gr.Button(folder_symbol, elem_id=button_id) with gr.Row(): do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False) with gr.Row(): download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False) with gr.Group(): html_info = gr.HTML() generation_info = gr.Textbox(visible=False) connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False) connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True) txt2img_args = dict( fn=wrap_gradio_gpu_call(modules.txt2img.txt2img), _js="submit", inputs=[ txt2img_prompt, txt2img_negative_prompt, txt2img_prompt_style, txt2img_prompt_style2, steps, sampler_index, restore_faces, tiling, batch_count, batch_size, cfg_scale, seed, subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox, height, width, enable_hr, scale_latent, denoising_strength, ] + custom_inputs, outputs=[ txt2img_gallery, generation_info, html_info ], show_progress=False, ) txt2img_prompt.submit(**txt2img_args) submit.click(**txt2img_args) enable_hr.change( fn=lambda x: gr_show(x), inputs=[enable_hr], outputs=[hr_options], ) save.click( fn=wrap_gradio_call(save_files), _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]", inputs=[ generation_info, txt2img_gallery, do_make_zip, html_info, ], outputs=[ download_files, html_info, html_info, html_info, ] ) roll.click( fn=roll_artist, _js="update_txt2img_tokens", inputs=[ txt2img_prompt, ], outputs=[ txt2img_prompt, ] ) txt2img_paste_fields = [ (txt2img_prompt, "Prompt"), (txt2img_negative_prompt, "Negative prompt"), (steps, "Steps"), (sampler_index, "Sampler"), (restore_faces, "Face restoration"), (cfg_scale, "CFG scale"), (seed, "Seed"), (width, "Size-1"), (height, "Size-2"), (batch_size, "Batch size"), (subseed, "Variation seed"), (subseed_strength, "Variation seed strength"), (seed_resize_from_w, "Seed resize from-1"), (seed_resize_from_h, "Seed resize from-2"), (denoising_strength, "Denoising strength"), (enable_hr, lambda d: "Denoising strength" in d), (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)), ] modules.generation_parameters_copypaste.connect_paste(paste, txt2img_paste_fields, txt2img_prompt) token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter]) with gr.Blocks(analytics_enabled=False) as img2img_interface: img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=True) with gr.Row(elem_id='img2img_progress_row'): with gr.Column(scale=1): pass with gr.Column(scale=1): progressbar = gr.HTML(elem_id="img2img_progressbar") img2img_preview = gr.Image(elem_id='img2img_preview', visible=False) setup_progressbar(progressbar, img2img_preview, 'img2img') with gr.Row().style(equal_height=False): with gr.Column(variant='panel'): with gr.Tabs(elem_id="mode_img2img") as tabs_img2img_mode: with gr.TabItem('img2img', id='img2img'): init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_img2img_tool) with gr.TabItem('Inpaint', id='inpaint'): init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA") init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_base") init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_mask") mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4) with gr.Row(): mask_mode = gr.Radio(label="Mask mode", show_label=False, choices=["Draw mask", "Upload mask"], type="index", value="Draw mask", elem_id="mask_mode") inpainting_mask_invert = gr.Radio(label='Masking mode', show_label=False, choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index") inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index") with gr.Row(): inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=False) inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels', minimum=0, maximum=256, step=4, value=32) with gr.TabItem('Batch img2img', id='batch'): hidden = '
Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else '' gr.HTML(f"

Process images in a directory on the same machine where the server is running.
Use an empty output directory to save pictures normally instead of writing to the output directory.{hidden}

") img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs) img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs) with gr.Row(): resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", show_label=False, choices=["Just resize", "Crop and resize", "Resize and fill"], type="index", value="Just resize") steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20) sampler_index = gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index") with gr.Group(): width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512) height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512) with gr.Row(): restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1) tiling = gr.Checkbox(label='Tiling', value=False) with gr.Row(): batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1) batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1) with gr.Group(): cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0) denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75) seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs() with gr.Group(): custom_inputs = modules.scripts.scripts_img2img.setup_ui(is_img2img=True) with gr.Column(variant='panel'): with gr.Group(): img2img_preview = gr.Image(elem_id='img2img_preview', visible=False) img2img_gallery = gr.Gallery(label='Output', show_label=False, elem_id='img2img_gallery').style(grid=4) with gr.Group(): with gr.Row(): save = gr.Button('Save') img2img_send_to_img2img = gr.Button('Send to img2img') img2img_send_to_inpaint = gr.Button('Send to inpaint') img2img_send_to_extras = gr.Button('Send to extras') button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder' open_img2img_folder = gr.Button(folder_symbol, elem_id=button_id) with gr.Row(): do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False) with gr.Row(): download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False) with gr.Group(): html_info = gr.HTML() generation_info = gr.Textbox(visible=False) connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False) connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True) mask_mode.change( lambda mode, img: { init_img_with_mask: gr_show(mode == 0), init_img_inpaint: gr_show(mode == 1), init_mask_inpaint: gr_show(mode == 1), }, inputs=[mask_mode, init_img_with_mask], outputs=[ init_img_with_mask, init_img_inpaint, init_mask_inpaint, ], ) img2img_args = dict( fn=wrap_gradio_gpu_call(modules.img2img.img2img), _js="submit_img2img", inputs=[ dummy_component, img2img_prompt, img2img_negative_prompt, img2img_prompt_style, img2img_prompt_style2, init_img, init_img_with_mask, init_img_inpaint, init_mask_inpaint, mask_mode, steps, sampler_index, mask_blur, inpainting_fill, restore_faces, tiling, batch_count, batch_size, cfg_scale, denoising_strength, seed, subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox, height, width, resize_mode, inpaint_full_res, inpaint_full_res_padding, inpainting_mask_invert, img2img_batch_input_dir, img2img_batch_output_dir, ] + custom_inputs, outputs=[ img2img_gallery, generation_info, html_info ], show_progress=False, ) img2img_prompt.submit(**img2img_args) submit.click(**img2img_args) img2img_interrogate.click( fn=interrogate, inputs=[init_img], outputs=[img2img_prompt], ) if cmd_opts.deepdanbooru: img2img_deepbooru.click( fn=interrogate_deepbooru, inputs=[init_img], outputs=[img2img_prompt], ) save.click( fn=wrap_gradio_call(save_files), _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]", inputs=[ generation_info, img2img_gallery, do_make_zip, html_info, ], outputs=[ download_files, html_info, html_info, html_info, ] ) roll.click( fn=roll_artist, _js="update_img2img_tokens", inputs=[ img2img_prompt, ], outputs=[ img2img_prompt, ] ) prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)] style_dropdowns = [(txt2img_prompt_style, txt2img_prompt_style2), (img2img_prompt_style, img2img_prompt_style2)] style_js_funcs = ["update_txt2img_tokens", "update_img2img_tokens"] for button, (prompt, negative_prompt) in zip([txt2img_save_style, img2img_save_style], prompts): button.click( fn=add_style, _js="ask_for_style_name", # Have to pass empty dummy component here, because the JavaScript and Python function have to accept # the same number of parameters, but we only know the style-name after the JavaScript prompt inputs=[dummy_component, prompt, negative_prompt], outputs=[txt2img_prompt_style, img2img_prompt_style, txt2img_prompt_style2, img2img_prompt_style2], ) for button, (prompt, negative_prompt), (style1, style2), js_func in zip([txt2img_prompt_style_apply, img2img_prompt_style_apply], prompts, style_dropdowns, style_js_funcs): button.click( fn=apply_styles, _js=js_func, inputs=[prompt, negative_prompt, style1, style2], outputs=[prompt, negative_prompt, style1, style2], ) img2img_paste_fields = [ (img2img_prompt, "Prompt"), (img2img_negative_prompt, "Negative prompt"), (steps, "Steps"), (sampler_index, "Sampler"), (restore_faces, "Face restoration"), (cfg_scale, "CFG scale"), (seed, "Seed"), (width, "Size-1"), (height, "Size-2"), (batch_size, "Batch size"), (subseed, "Variation seed"), (subseed_strength, "Variation seed strength"), (seed_resize_from_w, "Seed resize from-1"), (seed_resize_from_h, "Seed resize from-2"), (denoising_strength, "Denoising strength"), ] modules.generation_parameters_copypaste.connect_paste(paste, img2img_paste_fields, img2img_prompt) token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter]) with gr.Blocks(analytics_enabled=False) as extras_interface: with gr.Row().style(equal_height=False): with gr.Column(variant='panel'): with gr.Tabs(elem_id="mode_extras"): with gr.TabItem('Single Image'): extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil") with gr.TabItem('Batch Process'): image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file") with gr.Tabs(elem_id="extras_resize_mode"): with gr.TabItem('Scale by'): upscaling_resize = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Resize", value=2) with gr.TabItem('Scale to'): with gr.Group(): with gr.Row(): upscaling_resize_w = gr.Number(label="Width", value=512, precision=0) upscaling_resize_h = gr.Number(label="Height", value=512, precision=0) upscaling_crop = gr.Checkbox(label='Crop to fit', value=True) with gr.Group(): extras_upscaler_1 = gr.Radio(label='Upscaler 1', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index") with gr.Group(): extras_upscaler_2 = gr.Radio(label='Upscaler 2', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index") extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=1) with gr.Group(): gfpgan_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="GFPGAN visibility", value=0, interactive=modules.gfpgan_model.have_gfpgan) with gr.Group(): codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer visibility", value=0, interactive=modules.codeformer_model.have_codeformer) codeformer_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer weight (0 = maximum effect, 1 = minimum effect)", value=0, interactive=modules.codeformer_model.have_codeformer) submit = gr.Button('Generate', elem_id="extras_generate", variant='primary') with gr.Column(variant='panel'): result_images = gr.Gallery(label="Result", show_label=False) html_info_x = gr.HTML() html_info = gr.HTML() extras_send_to_img2img = gr.Button('Send to img2img') extras_send_to_inpaint = gr.Button('Send to inpaint') button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else '' open_extras_folder = gr.Button('Open output directory', elem_id=button_id) submit.click( fn=wrap_gradio_gpu_call(modules.extras.run_extras), _js="get_extras_tab_index", inputs=[ dummy_component, dummy_component, extras_image, image_batch, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, ], outputs=[ result_images, html_info_x, html_info, ] ) extras_send_to_img2img.click( fn=lambda x: image_from_url_text(x), _js="extract_image_from_gallery_img2img", inputs=[result_images], outputs=[init_img], ) extras_send_to_inpaint.click( fn=lambda x: image_from_url_text(x), _js="extract_image_from_gallery_inpaint", inputs=[result_images], outputs=[init_img_with_mask], ) with gr.Blocks(analytics_enabled=False) as pnginfo_interface: with gr.Row().style(equal_height=False): with gr.Column(variant='panel'): image = gr.Image(elem_id="pnginfo_image", label="Source", source="upload", interactive=True, type="pil") with gr.Column(variant='panel'): html = gr.HTML() generation_info = gr.Textbox(visible=False) html2 = gr.HTML() with gr.Row(): pnginfo_send_to_txt2img = gr.Button('Send to txt2img') pnginfo_send_to_img2img = gr.Button('Send to img2img') image.change( fn=wrap_gradio_call(modules.extras.run_pnginfo), inputs=[image], outputs=[html, generation_info, html2], ) with gr.Blocks() as modelmerger_interface: with gr.Row().style(equal_height=False): with gr.Column(variant='panel'): gr.HTML(value="

A merger of the two checkpoints will be generated in your checkpoint directory.

") with gr.Row(): primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary Model Name") secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary Model Name") custom_name = gr.Textbox(label="Custom Name (Optional)") interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Interpolation Amount', value=0.3) interp_method = gr.Radio(choices=["Weighted Sum", "Sigmoid", "Inverse Sigmoid"], value="Weighted Sum", label="Interpolation Method") save_as_half = gr.Checkbox(value=False, label="Save as float16") modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary') with gr.Column(variant='panel'): submit_result = gr.Textbox(elem_id="modelmerger_result", show_label=False) sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings() with gr.Blocks() as train_interface: with gr.Row().style(equal_height=False): gr.HTML(value="

See wiki for detailed explanation.

") with gr.Row().style(equal_height=False): with gr.Tabs(elem_id="train_tabs"): with gr.Tab(label="Create embedding"): new_embedding_name = gr.Textbox(label="Name") initialization_text = gr.Textbox(label="Initialization text", value="*") nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1) with gr.Row(): with gr.Column(scale=3): gr.HTML(value="") with gr.Column(): create_embedding = gr.Button(value="Create embedding", variant='primary') with gr.Tab(label="Create hypernetwork"): new_hypernetwork_name = gr.Textbox(label="Name") new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"]) with gr.Row(): with gr.Column(scale=3): gr.HTML(value="") with gr.Column(): create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary') with gr.Tab(label="Preprocess images"): process_src = gr.Textbox(label='Source directory') process_dst = gr.Textbox(label='Destination directory') process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512) process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512) with gr.Row(): process_flip = gr.Checkbox(label='Create flipped copies') process_split = gr.Checkbox(label='Split oversized images into two') process_caption = gr.Checkbox(label='Use BLIP for caption') process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True if cmd_opts.deepdanbooru else False) with gr.Row(): with gr.Column(scale=3): gr.HTML(value="") with gr.Column(): run_preprocess = gr.Button(value="Preprocess", variant='primary') with gr.Tab(label="Train"): gr.HTML(value="

Train an embedding; must specify a directory with a set of 1:1 ratio images

") train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())) train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', choices=[x for x in shared.hypernetworks.keys()]) learn_rate = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.005") dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images") log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion") template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt")) training_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512) training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512) steps = gr.Number(label='Max steps', value=100000, precision=0) create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0) save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0) save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True) preview_image_prompt = gr.Textbox(label='Preview prompt', value="") with gr.Row(): interrupt_training = gr.Button(value="Interrupt") train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary') train_embedding = gr.Button(value="Train Embedding", variant='primary') with gr.Column(): progressbar = gr.HTML(elem_id="ti_progressbar") ti_output = gr.Text(elem_id="ti_output", value="", show_label=False) ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(grid=4) ti_preview = gr.Image(elem_id='ti_preview', visible=False) ti_progress = gr.HTML(elem_id="ti_progress", value="") ti_outcome = gr.HTML(elem_id="ti_error", value="") setup_progressbar(progressbar, ti_preview, 'ti', textinfo=ti_progress) create_embedding.click( fn=modules.textual_inversion.ui.create_embedding, inputs=[ new_embedding_name, initialization_text, nvpt, ], outputs=[ train_embedding_name, ti_output, ti_outcome, ] ) create_hypernetwork.click( fn=modules.hypernetworks.ui.create_hypernetwork, inputs=[ new_hypernetwork_name, new_hypernetwork_sizes, ], outputs=[ train_hypernetwork_name, ti_output, ti_outcome, ] ) run_preprocess.click( fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]), _js="start_training_textual_inversion", inputs=[ process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru ], outputs=[ ti_output, ti_outcome, ], ) train_embedding.click( fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]), _js="start_training_textual_inversion", inputs=[ train_embedding_name, learn_rate, dataset_directory, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_image_prompt, ], outputs=[ ti_output, ti_outcome, ] ) train_hypernetwork.click( fn=wrap_gradio_gpu_call(modules.hypernetworks.ui.train_hypernetwork, extra_outputs=[gr.update()]), _js="start_training_textual_inversion", inputs=[ train_hypernetwork_name, learn_rate, dataset_directory, log_directory, steps, create_image_every, save_embedding_every, template_file, preview_image_prompt, ], outputs=[ ti_output, ti_outcome, ] ) interrupt_training.click( fn=lambda: shared.state.interrupt(), inputs=[], outputs=[], ) def create_setting_component(key): def fun(): return opts.data[key] if key in opts.data else opts.data_labels[key].default info = opts.data_labels[key] t = type(info.default) args = info.component_args() if callable(info.component_args) else info.component_args if info.component is not None: comp = info.component elif t == str: comp = gr.Textbox elif t == int: comp = gr.Number elif t == bool: comp = gr.Checkbox else: raise Exception(f'bad options item type: {str(t)} for key {key}') return comp(label=info.label, value=fun, **(args or {})) components = [] component_dict = {} def open_folder(f): if not os.path.isdir(f): print(f""" WARNING An open_folder request was made with an argument that is not a folder. This could be an error or a malicious attempt to run code on your computer. Requested path was: {f} """, file=sys.stderr) return if not shared.cmd_opts.hide_ui_dir_config: path = os.path.normpath(f) if platform.system() == "Windows": os.startfile(path) elif platform.system() == "Darwin": sp.Popen(["open", path]) else: sp.Popen(["xdg-open", path]) def run_settings(*args): changed = 0 for key, value, comp in zip(opts.data_labels.keys(), args, components): if comp != dummy_component and not opts.same_type(value, opts.data_labels[key].default): return f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}", opts.dumpjson() for key, value, comp in zip(opts.data_labels.keys(), args, components): if comp == dummy_component: continue comp_args = opts.data_labels[key].component_args if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False: continue oldval = opts.data.get(key, None) opts.data[key] = value if oldval != value: if opts.data_labels[key].onchange is not None: opts.data_labels[key].onchange() changed += 1 opts.save(shared.config_filename) return f'{changed} settings changed.', opts.dumpjson() def run_settings_single(value, key): if not opts.same_type(value, opts.data_labels[key].default): return gr.update(visible=True), opts.dumpjson() oldval = opts.data.get(key, None) opts.data[key] = value if oldval != value: if opts.data_labels[key].onchange is not None: opts.data_labels[key].onchange() opts.save(shared.config_filename) return gr.update(value=value), opts.dumpjson() with gr.Blocks(analytics_enabled=False) as settings_interface: settings_submit = gr.Button(value="Apply settings", variant='primary') result = gr.HTML() settings_cols = 3 items_per_col = int(len(opts.data_labels) * 0.9 / settings_cols) quicksettings_list = [] cols_displayed = 0 items_displayed = 0 previous_section = None column = None with gr.Row(elem_id="settings").style(equal_height=False): for i, (k, item) in enumerate(opts.data_labels.items()): if previous_section != item.section: if cols_displayed < settings_cols and (items_displayed >= items_per_col or previous_section is None): if column is not None: column.__exit__() column = gr.Column(variant='panel') column.__enter__() items_displayed = 0 cols_displayed += 1 previous_section = item.section gr.HTML(elem_id="settings_header_text_{}".format(item.section[0]), value='

{}

'.format(item.section[1])) if item.show_on_main_page: quicksettings_list.append((i, k, item)) components.append(dummy_component) else: component = create_setting_component(k) component_dict[k] = component components.append(component) items_displayed += 1 request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications") request_notifications.click( fn=lambda: None, inputs=[], outputs=[], _js='function(){}' ) with gr.Row(): reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary') restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary') def reload_scripts(): modules.scripts.reload_script_body_only() reload_script_bodies.click( fn=reload_scripts, inputs=[], outputs=[], _js='function(){}' ) def request_restart(): shared.state.interrupt() settings_interface.gradio_ref.do_restart = True restart_gradio.click( fn=request_restart, inputs=[], outputs=[], _js='function(){restart_reload()}' ) if column is not None: column.__exit__() interfaces = [ (txt2img_interface, "txt2img", "txt2img"), (img2img_interface, "img2img", "img2img"), (extras_interface, "Extras", "extras"), (pnginfo_interface, "PNG Info", "pnginfo"), (modelmerger_interface, "Checkpoint Merger", "modelmerger"), (train_interface, "Train", "ti"), (settings_interface, "Settings", "settings"), ] with open(os.path.join(script_path, "style.css"), "r", encoding="utf8") as file: css = file.read() if os.path.exists(os.path.join(script_path, "user.css")): with open(os.path.join(script_path, "user.css"), "r", encoding="utf8") as file: usercss = file.read() css += usercss if not cmd_opts.no_progressbar_hiding: css += css_hide_progressbar with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo: with gr.Row(elem_id="quicksettings"): for i, k, item in quicksettings_list: component = create_setting_component(k) component_dict[k] = component settings_interface.gradio_ref = demo with gr.Tabs() as tabs: for interface, label, ifid in interfaces: with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid): interface.render() if os.path.exists(os.path.join(script_path, "notification.mp3")): audio_notification = gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False) text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False) settings_submit.click( fn=run_settings, inputs=components, outputs=[result, text_settings], ) for i, k, item in quicksettings_list: component = component_dict[k] component.change( fn=lambda value, k=k: run_settings_single(value, key=k), inputs=[component], outputs=[component, text_settings], ) def modelmerger(*args): try: results = modules.extras.run_modelmerger(*args) except Exception as e: print("Error loading/saving model file:", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) modules.sd_models.list_models() # to remove the potentially missing models from the list return ["Error loading/saving model file. It doesn't exist or the name contains illegal characters"] + [gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(3)] return results modelmerger_merge.click( fn=modelmerger, inputs=[ primary_model_name, secondary_model_name, interp_method, interp_amount, save_as_half, custom_name, ], outputs=[ submit_result, primary_model_name, secondary_model_name, component_dict['sd_model_checkpoint'], ] ) paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration', 'Seed', 'Size-1', 'Size-2'] txt2img_fields = [field for field,name in txt2img_paste_fields if name in paste_field_names] img2img_fields = [field for field,name in img2img_paste_fields if name in paste_field_names] send_to_img2img.click( fn=lambda img, *args: (image_from_url_text(img),*args), _js="(gallery, ...args) => [extract_image_from_gallery_img2img(gallery), ...args]", inputs=[txt2img_gallery] + txt2img_fields, outputs=[init_img] + img2img_fields, ) send_to_inpaint.click( fn=lambda x, *args: (image_from_url_text(x), *args), _js="(gallery, ...args) => [extract_image_from_gallery_inpaint(gallery), ...args]", inputs=[txt2img_gallery] + txt2img_fields, outputs=[init_img_with_mask] + img2img_fields, ) img2img_send_to_img2img.click( fn=lambda x: image_from_url_text(x), _js="extract_image_from_gallery_img2img", inputs=[img2img_gallery], outputs=[init_img], ) img2img_send_to_inpaint.click( fn=lambda x: image_from_url_text(x), _js="extract_image_from_gallery_inpaint", inputs=[img2img_gallery], outputs=[init_img_with_mask], ) send_to_extras.click( fn=lambda x: image_from_url_text(x), _js="extract_image_from_gallery_extras", inputs=[txt2img_gallery], outputs=[extras_image], ) open_txt2img_folder.click( fn=lambda: open_folder(opts.outdir_samples or opts.outdir_txt2img_samples), inputs=[], outputs=[], ) open_img2img_folder.click( fn=lambda: open_folder(opts.outdir_samples or opts.outdir_img2img_samples), inputs=[], outputs=[], ) open_extras_folder.click( fn=lambda: open_folder(opts.outdir_samples or opts.outdir_extras_samples), inputs=[], outputs=[], ) img2img_send_to_extras.click( fn=lambda x: image_from_url_text(x), _js="extract_image_from_gallery_extras", inputs=[img2img_gallery], outputs=[extras_image], ) modules.generation_parameters_copypaste.connect_paste(pnginfo_send_to_txt2img, txt2img_paste_fields, generation_info, 'switch_to_txt2img') modules.generation_parameters_copypaste.connect_paste(pnginfo_send_to_img2img, img2img_paste_fields, generation_info, 'switch_to_img2img_img2img') ui_config_file = cmd_opts.ui_config_file ui_settings = {} settings_count = len(ui_settings) error_loading = False try: if os.path.exists(ui_config_file): with open(ui_config_file, "r", encoding="utf8") as file: ui_settings = json.load(file) except Exception: error_loading = True print("Error loading settings:", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) def loadsave(path, x): def apply_field(obj, field, condition=None): key = path + "/" + field if getattr(obj,'custom_script_source',None) is not None: key = 'customscript/' + obj.custom_script_source + '/' + key if getattr(obj, 'do_not_save_to_config', False): return saved_value = ui_settings.get(key, None) if saved_value is None: ui_settings[key] = getattr(obj, field) elif condition is None or condition(saved_value): setattr(obj, field, saved_value) if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number] and x.visible: apply_field(x, 'visible') if type(x) == gr.Slider: apply_field(x, 'value') apply_field(x, 'minimum') apply_field(x, 'maximum') apply_field(x, 'step') if type(x) == gr.Radio: apply_field(x, 'value', lambda val: val in x.choices) if type(x) == gr.Checkbox: apply_field(x, 'value') if type(x) == gr.Textbox: apply_field(x, 'value') if type(x) == gr.Number: apply_field(x, 'value') visit(txt2img_interface, loadsave, "txt2img") visit(img2img_interface, loadsave, "img2img") visit(extras_interface, loadsave, "extras") if not error_loading and (not os.path.exists(ui_config_file) or settings_count != len(ui_settings)): with open(ui_config_file, "w", encoding="utf8") as file: json.dump(ui_settings, file, indent=4) return demo with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as jsfile: javascript = f'' jsdir = os.path.join(script_path, "javascript") for filename in sorted(os.listdir(jsdir)): with open(os.path.join(jsdir, filename), "r", encoding="utf8") as jsfile: javascript += f"\n" if 'gradio_routes_templates_response' not in globals(): def template_response(*args, **kwargs): res = gradio_routes_templates_response(*args, **kwargs) res.body = res.body.replace(b'', f'{javascript}'.encode("utf8")) res.init_headers() return res gradio_routes_templates_response = gradio.routes.templates.TemplateResponse gradio.routes.templates.TemplateResponse = template_response stable-diffusion-webui/modules/upscaler.py000066400000000000000000000063331432161722600212760ustar00rootroot00000000000000import os from abc import abstractmethod import PIL import numpy as np import torch from PIL import Image import modules.shared from modules import modelloader, shared LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS) from modules.paths import models_path class Upscaler: name = None model_path = None model_name = None model_url = None enable = True filter = None model = None user_path = None scalers: [] tile = True def __init__(self, create_dirs=False): self.mod_pad_h = None self.tile_size = modules.shared.opts.ESRGAN_tile self.tile_pad = modules.shared.opts.ESRGAN_tile_overlap self.device = modules.shared.device self.img = None self.output = None self.scale = 1 self.half = not modules.shared.cmd_opts.no_half self.pre_pad = 0 self.mod_scale = None if self.model_path is None and self.name: self.model_path = os.path.join(models_path, self.name) if self.model_path and create_dirs: os.makedirs(self.model_path, exist_ok=True) try: import cv2 self.can_tile = True except: pass @abstractmethod def do_upscale(self, img: PIL.Image, selected_model: str): return img def upscale(self, img: PIL.Image, scale: int, selected_model: str = None): self.scale = scale dest_w = img.width * scale dest_h = img.height * scale for i in range(3): if img.width >= dest_w and img.height >= dest_h: break img = self.do_upscale(img, selected_model) if img.width != dest_w or img.height != dest_h: img = img.resize((int(dest_w), int(dest_h)), resample=LANCZOS) return img @abstractmethod def load_model(self, path: str): pass def find_models(self, ext_filter=None) -> list: return modelloader.load_models(model_path=self.model_path, model_url=self.model_url, command_path=self.user_path) def update_status(self, prompt): print(f"\nextras: {prompt}", file=shared.progress_print_out) class UpscalerData: name = None data_path = None scale: int = 4 scaler: Upscaler = None model: None def __init__(self, name: str, path: str, upscaler: Upscaler = None, scale: int = 4, model=None): self.name = name self.data_path = path self.scaler = upscaler self.scale = scale self.model = model class UpscalerNone(Upscaler): name = "None" scalers = [] def load_model(self, path): pass def do_upscale(self, img, selected_model=None): return img def __init__(self, dirname=None): super().__init__(False) self.scalers = [UpscalerData("None", None, self)] class UpscalerLanczos(Upscaler): scalers = [] def do_upscale(self, img, selected_model=None): return img.resize((int(img.width * self.scale), int(img.height * self.scale)), resample=LANCZOS) def load_model(self, _): pass def __init__(self, dirname=None): super().__init__(False) self.name = "Lanczos" self.scalers = [UpscalerData("Lanczos", None, self)] stable-diffusion-webui/requirements.txt000066400000000000000000000004551432161722600207210ustar00rootroot00000000000000basicsr diffusers fairscale==0.4.4 fonts font-roboto gfpgan gradio==3.4.1 invisible-watermark numpy omegaconf piexif Pillow pytorch_lightning realesrgan scikit-image>=0.19 timm==0.4.12 transformers==4.19.2 torch einops jsonmerge clean-fid resize-right torchdiffeq kornia lark stable-diffusion-webui/requirements_versions.txt000066400000000000000000000006131432161722600226450ustar00rootroot00000000000000transformers==4.19.2 diffusers==0.3.0 basicsr==1.4.2 gfpgan==1.3.8 gradio==3.4.1 numpy==1.23.3 Pillow==9.2.0 realesrgan==0.3.0 torch omegaconf==2.2.3 pytorch_lightning==1.7.6 scikit-image==0.19.2 fonts font-roboto timm==0.6.7 fairscale==0.4.9 piexif==1.1.3 einops==0.4.1 jsonmerge==1.8.0 clean-fid==0.1.29 resize-right==0.0.2 torchdiffeq==0.2.3 kornia==0.6.7 lark==1.1.2 stable-diffusion-webui/screenshot.png000066400000000000000000020014231432161722600203160ustar00rootroot00000000000000PNG  IHDR:ksRGBgAMA a pHYsodIDATx^ չp7f3.FM PT"Q#q ⊈(.(T\{:U]=3      ,@t|;cfr~ԸU mmڍAAAAAAZ;˼p_OhB͸ Qف     viKHε#@AAAAAZ3˴pҼnPՁ     fi19Wm      eZtyMО     Hkk'     ځ     v :pafA{"     \YО     Hkk'     ځ     v :Ӆyhmi\Hbj\OAZ$sƍG_LؕHAAAAQ 9ubD?If7?a|Z~shB{;MN?N^XLSOߜ{qYL3zy~d 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B!B!B)X B!B!BH@!B!B!B :!B!B!R0A!B!B!B!B!B!(ӺOIENDB`stable-diffusion-webui/script.js000066400000000000000000000043551432161722600173020ustar00rootroot00000000000000function gradioApp(){ return document.getElementsByTagName('gradio-app')[0].shadowRoot; } function get_uiCurrentTab() { return gradioApp().querySelector('.tabs button:not(.border-transparent)') } function get_uiCurrentTabContent() { return gradioApp().querySelector('.tabitem[id^=tab_]:not([style*="display: none"])') } uiUpdateCallbacks = [] uiTabChangeCallbacks = [] let uiCurrentTab = null function onUiUpdate(callback){ uiUpdateCallbacks.push(callback) } function onUiTabChange(callback){ uiTabChangeCallbacks.push(callback) } function runCallback(x){ try { x() } catch (e) { (console.error || console.log).call(console, e.message, e); } } function executeCallbacks(queue) { queue.forEach(runCallback) } document.addEventListener("DOMContentLoaded", function() { var mutationObserver = new MutationObserver(function(m){ executeCallbacks(uiUpdateCallbacks); const newTab = get_uiCurrentTab(); if ( newTab && ( newTab !== uiCurrentTab ) ) { uiCurrentTab = newTab; executeCallbacks(uiTabChangeCallbacks); } }); mutationObserver.observe( gradioApp(), { childList:true, subtree:true }) }); /** * Add a ctrl+enter as a shortcut to start a generation */ document.addEventListener('keydown', function(e) { var handled = false; if (e.key !== undefined) { if((e.key == "Enter" && (e.metaKey || e.ctrlKey))) handled = true; } else if (e.keyCode !== undefined) { if((e.keyCode == 13 && (e.metaKey || e.ctrlKey))) handled = true; } if (handled) { button = get_uiCurrentTabContent().querySelector('button[id$=_generate]'); if (button) { button.click(); } e.preventDefault(); } }) /** * checks that a UI element is not in another hidden element or tab content */ function uiElementIsVisible(el) { let isVisible = !el.closest('.\\!hidden'); if ( ! isVisible ) { return false; } while( isVisible = el.closest('.tabitem')?.style.display !== 'none' ) { if ( ! isVisible ) { return false; } else if ( el.parentElement ) { el = el.parentElement } else { break; } } return isVisible; }stable-diffusion-webui/scripts/000077500000000000000000000000001432161722600171205ustar00rootroot00000000000000stable-diffusion-webui/scripts/custom_code.py000066400000000000000000000021531432161722600217770ustar00rootroot00000000000000import modules.scripts as scripts import gradio as gr from modules.processing import Processed from modules.shared import opts, cmd_opts, state class Script(scripts.Script): def title(self): return "Custom code" def show(self, is_img2img): return cmd_opts.allow_code def ui(self, is_img2img): code = gr.Textbox(label="Python code", visible=False, lines=1) return [code] def run(self, p, code): assert cmd_opts.allow_code, '--allow-code option must be enabled' display_result_data = [[], -1, ""] def display(imgs, s=display_result_data[1], i=display_result_data[2]): display_result_data[0] = imgs display_result_data[1] = s display_result_data[2] = i from types import ModuleType compiled = compile(code, '', 'exec') module = ModuleType("testmodule") module.__dict__.update(globals()) module.p = p module.display = display exec(compiled, module.__dict__) return Processed(p, *display_result_data) stable-diffusion-webui/scripts/img2imgalt.py000066400000000000000000000160241432161722600215310ustar00rootroot00000000000000from collections import namedtuple import numpy as np from tqdm import trange import modules.scripts as scripts import gradio as gr from modules import processing, shared, sd_samplers, prompt_parser from modules.processing import Processed from modules.shared import opts, cmd_opts, state import torch import k_diffusion as K from PIL import Image from torch import autocast from einops import rearrange, repeat def find_noise_for_image(p, cond, uncond, cfg_scale, steps): x = p.init_latent s_in = x.new_ones([x.shape[0]]) dnw = K.external.CompVisDenoiser(shared.sd_model) sigmas = dnw.get_sigmas(steps).flip(0) shared.state.sampling_steps = steps for i in trange(1, len(sigmas)): shared.state.sampling_step += 1 x_in = torch.cat([x] * 2) sigma_in = torch.cat([sigmas[i] * s_in] * 2) cond_in = torch.cat([uncond, cond]) c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)] t = dnw.sigma_to_t(sigma_in) eps = shared.sd_model.apply_model(x_in * c_in, t, cond=cond_in) denoised_uncond, denoised_cond = (x_in + eps * c_out).chunk(2) denoised = denoised_uncond + (denoised_cond - denoised_uncond) * cfg_scale d = (x - denoised) / sigmas[i] dt = sigmas[i] - sigmas[i - 1] x = x + d * dt sd_samplers.store_latent(x) # This shouldn't be necessary, but solved some VRAM issues del x_in, sigma_in, cond_in, c_out, c_in, t, del eps, denoised_uncond, denoised_cond, denoised, d, dt shared.state.nextjob() return x / x.std() Cached = namedtuple("Cached", ["noise", "cfg_scale", "steps", "latent", "original_prompt", "original_negative_prompt", "sigma_adjustment"]) # Based on changes suggested by briansemrau in https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/736 def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps): x = p.init_latent s_in = x.new_ones([x.shape[0]]) dnw = K.external.CompVisDenoiser(shared.sd_model) sigmas = dnw.get_sigmas(steps).flip(0) shared.state.sampling_steps = steps for i in trange(1, len(sigmas)): shared.state.sampling_step += 1 x_in = torch.cat([x] * 2) sigma_in = torch.cat([sigmas[i - 1] * s_in] * 2) cond_in = torch.cat([uncond, cond]) c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)] if i == 1: t = dnw.sigma_to_t(torch.cat([sigmas[i] * s_in] * 2)) else: t = dnw.sigma_to_t(sigma_in) eps = shared.sd_model.apply_model(x_in * c_in, t, cond=cond_in) denoised_uncond, denoised_cond = (x_in + eps * c_out).chunk(2) denoised = denoised_uncond + (denoised_cond - denoised_uncond) * cfg_scale if i == 1: d = (x - denoised) / (2 * sigmas[i]) else: d = (x - denoised) / sigmas[i - 1] dt = sigmas[i] - sigmas[i - 1] x = x + d * dt sd_samplers.store_latent(x) # This shouldn't be necessary, but solved some VRAM issues del x_in, sigma_in, cond_in, c_out, c_in, t, del eps, denoised_uncond, denoised_cond, denoised, d, dt shared.state.nextjob() return x / sigmas[-1] class Script(scripts.Script): def __init__(self): self.cache = None def title(self): return "img2img alternative test" def show(self, is_img2img): return is_img2img def ui(self, is_img2img): original_prompt = gr.Textbox(label="Original prompt", lines=1) original_negative_prompt = gr.Textbox(label="Original negative prompt", lines=1) cfg = gr.Slider(label="Decode CFG scale", minimum=0.0, maximum=15.0, step=0.1, value=1.0) st = gr.Slider(label="Decode steps", minimum=1, maximum=150, step=1, value=50) randomness = gr.Slider(label="Randomness", minimum=0.0, maximum=1.0, step=0.01, value=0.0) sigma_adjustment = gr.Checkbox(label="Sigma adjustment for finding noise for image", value=False) return [original_prompt, original_negative_prompt, cfg, st, randomness, sigma_adjustment] def run(self, p, original_prompt, original_negative_prompt, cfg, st, randomness, sigma_adjustment): def sample_extra(conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength): lat = (p.init_latent.cpu().numpy() * 10).astype(int) same_params = self.cache is not None and self.cache.cfg_scale == cfg and self.cache.steps == st \ and self.cache.original_prompt == original_prompt \ and self.cache.original_negative_prompt == original_negative_prompt \ and self.cache.sigma_adjustment == sigma_adjustment same_everything = same_params and self.cache.latent.shape == lat.shape and np.abs(self.cache.latent-lat).sum() < 100 if same_everything: rec_noise = self.cache.noise else: shared.state.job_count += 1 cond = p.sd_model.get_learned_conditioning(p.batch_size * [original_prompt]) uncond = p.sd_model.get_learned_conditioning(p.batch_size * [original_negative_prompt]) if sigma_adjustment: rec_noise = find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg, st) else: rec_noise = find_noise_for_image(p, cond, uncond, cfg, st) self.cache = Cached(rec_noise, cfg, st, lat, original_prompt, original_negative_prompt, sigma_adjustment) rand_noise = processing.create_random_tensors(p.init_latent.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, seed_resize_from_h=p.seed_resize_from_h, seed_resize_from_w=p.seed_resize_from_w, p=p) combined_noise = ((1 - randomness) * rec_noise + randomness * rand_noise) / ((randomness**2 + (1-randomness)**2) ** 0.5) sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, p.sampler_index, p.sd_model) sigmas = sampler.model_wrap.get_sigmas(p.steps) noise_dt = combined_noise - (p.init_latent / sigmas[0]) p.seed = p.seed + 1 return sampler.sample_img2img(p, p.init_latent, noise_dt, conditioning, unconditional_conditioning) p.sample = sample_extra p.extra_generation_params["Decode prompt"] = original_prompt p.extra_generation_params["Decode negative prompt"] = original_negative_prompt p.extra_generation_params["Decode CFG scale"] = cfg p.extra_generation_params["Decode steps"] = st p.extra_generation_params["Randomness"] = randomness p.extra_generation_params["Sigma Adjustment"] = sigma_adjustment processed = processing.process_images(p) return processed stable-diffusion-webui/scripts/loopback.py000066400000000000000000000056541432161722600212760ustar00rootroot00000000000000import numpy as np from tqdm import trange import modules.scripts as scripts import gradio as gr from modules import processing, shared, sd_samplers, images from modules.processing import Processed from modules.sd_samplers import samplers from modules.shared import opts, cmd_opts, state class Script(scripts.Script): def title(self): return "Loopback" def show(self, is_img2img): return is_img2img def ui(self, is_img2img): loops = gr.Slider(minimum=1, maximum=32, step=1, label='Loops', value=4) denoising_strength_change_factor = gr.Slider(minimum=0.9, maximum=1.1, step=0.01, label='Denoising strength change factor', value=1) return [loops, denoising_strength_change_factor] def run(self, p, loops, denoising_strength_change_factor): processing.fix_seed(p) batch_count = p.n_iter p.extra_generation_params = { "Denoising strength change factor": denoising_strength_change_factor, } p.batch_size = 1 p.n_iter = 1 output_images, info = None, None initial_seed = None initial_info = None grids = [] all_images = [] original_init_image = p.init_images state.job_count = loops * batch_count initial_color_corrections = [processing.setup_color_correction(p.init_images[0])] for n in range(batch_count): history = [] # Reset to original init image at the start of each batch p.init_images = original_init_image for i in range(loops): p.n_iter = 1 p.batch_size = 1 p.do_not_save_grid = True if opts.img2img_color_correction: p.color_corrections = initial_color_corrections state.job = f"Iteration {i + 1}/{loops}, batch {n + 1}/{batch_count}" processed = processing.process_images(p) if initial_seed is None: initial_seed = processed.seed initial_info = processed.info init_img = processed.images[0] p.init_images = [init_img] p.seed = processed.seed + 1 p.denoising_strength = min(max(p.denoising_strength * denoising_strength_change_factor, 0.1), 1) history.append(processed.images[0]) grid = images.image_grid(history, rows=1) if opts.grid_save: images.save_image(grid, p.outpath_grids, "grid", initial_seed, p.prompt, opts.grid_format, info=info, short_filename=not opts.grid_extended_filename, grid=True, p=p) grids.append(grid) all_images += history if opts.return_grid: all_images = grids + all_images processed = Processed(p, all_images, initial_seed, initial_info) return processed stable-diffusion-webui/scripts/outpainting_mk_2.py000066400000000000000000000266021432161722600227510ustar00rootroot00000000000000import math import numpy as np import skimage import modules.scripts as scripts import gradio as gr from PIL import Image, ImageDraw from modules import images, processing, devices from modules.processing import Processed, process_images from modules.shared import opts, cmd_opts, state # this function is taken from https://github.com/parlance-zz/g-diffuser-bot def get_matched_noise(_np_src_image, np_mask_rgb, noise_q=1, color_variation=0.05): # helper fft routines that keep ortho normalization and auto-shift before and after fft def _fft2(data): if data.ndim > 2: # has channels out_fft = np.zeros((data.shape[0], data.shape[1], data.shape[2]), dtype=np.complex128) for c in range(data.shape[2]): c_data = data[:, :, c] out_fft[:, :, c] = np.fft.fft2(np.fft.fftshift(c_data), norm="ortho") out_fft[:, :, c] = np.fft.ifftshift(out_fft[:, :, c]) else: # one channel out_fft = np.zeros((data.shape[0], data.shape[1]), dtype=np.complex128) out_fft[:, :] = np.fft.fft2(np.fft.fftshift(data), norm="ortho") out_fft[:, :] = np.fft.ifftshift(out_fft[:, :]) return out_fft def _ifft2(data): if data.ndim > 2: # has channels out_ifft = np.zeros((data.shape[0], data.shape[1], data.shape[2]), dtype=np.complex128) for c in range(data.shape[2]): c_data = data[:, :, c] out_ifft[:, :, c] = np.fft.ifft2(np.fft.fftshift(c_data), norm="ortho") out_ifft[:, :, c] = np.fft.ifftshift(out_ifft[:, :, c]) else: # one channel out_ifft = np.zeros((data.shape[0], data.shape[1]), dtype=np.complex128) out_ifft[:, :] = np.fft.ifft2(np.fft.fftshift(data), norm="ortho") out_ifft[:, :] = np.fft.ifftshift(out_ifft[:, :]) return out_ifft def _get_gaussian_window(width, height, std=3.14, mode=0): window_scale_x = float(width / min(width, height)) window_scale_y = float(height / min(width, height)) window = np.zeros((width, height)) x = (np.arange(width) / width * 2. - 1.) * window_scale_x for y in range(height): fy = (y / height * 2. - 1.) * window_scale_y if mode == 0: window[:, y] = np.exp(-(x ** 2 + fy ** 2) * std) else: window[:, y] = (1 / ((x ** 2 + 1.) * (fy ** 2 + 1.))) ** (std / 3.14) # hey wait a minute that's not gaussian return window def _get_masked_window_rgb(np_mask_grey, hardness=1.): np_mask_rgb = np.zeros((np_mask_grey.shape[0], np_mask_grey.shape[1], 3)) if hardness != 1.: hardened = np_mask_grey[:] ** hardness else: hardened = np_mask_grey[:] for c in range(3): np_mask_rgb[:, :, c] = hardened[:] return np_mask_rgb width = _np_src_image.shape[0] height = _np_src_image.shape[1] num_channels = _np_src_image.shape[2] np_src_image = _np_src_image[:] * (1. - np_mask_rgb) np_mask_grey = (np.sum(np_mask_rgb, axis=2) / 3.) img_mask = np_mask_grey > 1e-6 ref_mask = np_mask_grey < 1e-3 windowed_image = _np_src_image * (1. - _get_masked_window_rgb(np_mask_grey)) windowed_image /= np.max(windowed_image) windowed_image += np.average(_np_src_image) * np_mask_rgb # / (1.-np.average(np_mask_rgb)) # rather than leave the masked area black, we get better results from fft by filling the average unmasked color src_fft = _fft2(windowed_image) # get feature statistics from masked src img src_dist = np.absolute(src_fft) src_phase = src_fft / src_dist # create a generator with a static seed to make outpainting deterministic / only follow global seed rng = np.random.default_rng(0) noise_window = _get_gaussian_window(width, height, mode=1) # start with simple gaussian noise noise_rgb = rng.random((width, height, num_channels)) noise_grey = (np.sum(noise_rgb, axis=2) / 3.) noise_rgb *= color_variation # the colorfulness of the starting noise is blended to greyscale with a parameter for c in range(num_channels): noise_rgb[:, :, c] += (1. - color_variation) * noise_grey noise_fft = _fft2(noise_rgb) for c in range(num_channels): noise_fft[:, :, c] *= noise_window noise_rgb = np.real(_ifft2(noise_fft)) shaped_noise_fft = _fft2(noise_rgb) shaped_noise_fft[:, :, :] = np.absolute(shaped_noise_fft[:, :, :]) ** 2 * (src_dist ** noise_q) * src_phase # perform the actual shaping brightness_variation = 0. # color_variation # todo: temporarily tieing brightness variation to color variation for now contrast_adjusted_np_src = _np_src_image[:] * (brightness_variation + 1.) - brightness_variation * 2. # scikit-image is used for histogram matching, very convenient! shaped_noise = np.real(_ifft2(shaped_noise_fft)) shaped_noise -= np.min(shaped_noise) shaped_noise /= np.max(shaped_noise) shaped_noise[img_mask, :] = skimage.exposure.match_histograms(shaped_noise[img_mask, :] ** 1., contrast_adjusted_np_src[ref_mask, :], channel_axis=1) shaped_noise = _np_src_image[:] * (1. - np_mask_rgb) + shaped_noise * np_mask_rgb matched_noise = shaped_noise[:] return np.clip(matched_noise, 0., 1.) class Script(scripts.Script): def title(self): return "Outpainting mk2" def show(self, is_img2img): return is_img2img def ui(self, is_img2img): if not is_img2img: return None info = gr.HTML("

Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8

") pixels = gr.Slider(label="Pixels to expand", minimum=8, maximum=256, step=8, value=128) mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=8, visible=False) direction = gr.CheckboxGroup(label="Outpainting direction", choices=['left', 'right', 'up', 'down'], value=['left', 'right', 'up', 'down']) noise_q = gr.Slider(label="Fall-off exponent (lower=higher detail)", minimum=0.0, maximum=4.0, step=0.01, value=1.0) color_variation = gr.Slider(label="Color variation", minimum=0.0, maximum=1.0, step=0.01, value=0.05) return [info, pixels, mask_blur, direction, noise_q, color_variation] def run(self, p, _, pixels, mask_blur, direction, noise_q, color_variation): initial_seed_and_info = [None, None] process_width = p.width process_height = p.height p.mask_blur = mask_blur*4 p.inpaint_full_res = False p.inpainting_fill = 1 p.do_not_save_samples = True p.do_not_save_grid = True left = pixels if "left" in direction else 0 right = pixels if "right" in direction else 0 up = pixels if "up" in direction else 0 down = pixels if "down" in direction else 0 init_img = p.init_images[0] target_w = math.ceil((init_img.width + left + right) / 64) * 64 target_h = math.ceil((init_img.height + up + down) / 64) * 64 if left > 0: left = left * (target_w - init_img.width) // (left + right) if right > 0: right = target_w - init_img.width - left if up > 0: up = up * (target_h - init_img.height) // (up + down) if down > 0: down = target_h - init_img.height - up init_image = p.init_images[0] state.job_count = (1 if left > 0 else 0) + (1 if right > 0 else 0) + (1 if up > 0 else 0) + (1 if down > 0 else 0) def expand(init, expand_pixels, is_left=False, is_right=False, is_top=False, is_bottom=False): is_horiz = is_left or is_right is_vert = is_top or is_bottom pixels_horiz = expand_pixels if is_horiz else 0 pixels_vert = expand_pixels if is_vert else 0 res_w = init.width + pixels_horiz res_h = init.height + pixels_vert process_res_w = math.ceil(res_w / 64) * 64 process_res_h = math.ceil(res_h / 64) * 64 img = Image.new("RGB", (process_res_w, process_res_h)) img.paste(init, (pixels_horiz if is_left else 0, pixels_vert if is_top else 0)) mask = Image.new("RGB", (process_res_w, process_res_h), "white") draw = ImageDraw.Draw(mask) draw.rectangle(( expand_pixels + mask_blur if is_left else 0, expand_pixels + mask_blur if is_top else 0, mask.width - expand_pixels - mask_blur if is_right else res_w, mask.height - expand_pixels - mask_blur if is_bottom else res_h, ), fill="black") np_image = (np.asarray(img) / 255.0).astype(np.float64) np_mask = (np.asarray(mask) / 255.0).astype(np.float64) noised = get_matched_noise(np_image, np_mask, noise_q, color_variation) out = Image.fromarray(np.clip(noised * 255., 0., 255.).astype(np.uint8), mode="RGB") target_width = min(process_width, init.width + pixels_horiz) if is_horiz else img.width target_height = min(process_height, init.height + pixels_vert) if is_vert else img.height crop_region = ( 0 if is_left else out.width - target_width, 0 if is_top else out.height - target_height, target_width if is_left else out.width, target_height if is_top else out.height, ) image_to_process = out.crop(crop_region) mask = mask.crop(crop_region) p.width = target_width if is_horiz else img.width p.height = target_height if is_vert else img.height p.init_images = [image_to_process] p.image_mask = mask latent_mask = Image.new("RGB", (p.width, p.height), "white") draw = ImageDraw.Draw(latent_mask) draw.rectangle(( expand_pixels + mask_blur * 2 if is_left else 0, expand_pixels + mask_blur * 2 if is_top else 0, mask.width - expand_pixels - mask_blur * 2 if is_right else res_w, mask.height - expand_pixels - mask_blur * 2 if is_bottom else res_h, ), fill="black") p.latent_mask = latent_mask proc = process_images(p) proc_img = proc.images[0] if initial_seed_and_info[0] is None: initial_seed_and_info[0] = proc.seed initial_seed_and_info[1] = proc.info out.paste(proc_img, (0 if is_left else out.width - proc_img.width, 0 if is_top else out.height - proc_img.height)) out = out.crop((0, 0, res_w, res_h)) return out img = init_image if left > 0: img = expand(img, left, is_left=True) if right > 0: img = expand(img, right, is_right=True) if up > 0: img = expand(img, up, is_top=True) if down > 0: img = expand(img, down, is_bottom=True) res = Processed(p, [img], initial_seed_and_info[0], initial_seed_and_info[1]) if opts.samples_save: images.save_image(img, p.outpath_samples, "", res.seed, p.prompt, opts.grid_format, info=res.info, p=p) return res stable-diffusion-webui/scripts/poor_mans_outpainting.py000066400000000000000000000127651432161722600241230ustar00rootroot00000000000000import math import modules.scripts as scripts import gradio as gr from PIL import Image, ImageDraw from modules import images, processing, devices from modules.processing import Processed, process_images from modules.shared import opts, cmd_opts, state class Script(scripts.Script): def title(self): return "Poor man's outpainting" def show(self, is_img2img): return is_img2img def ui(self, is_img2img): if not is_img2img: return None pixels = gr.Slider(label="Pixels to expand", minimum=8, maximum=256, step=8, value=128) mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, visible=False) inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='fill', type="index", visible=False) direction = gr.CheckboxGroup(label="Outpainting direction", choices=['left', 'right', 'up', 'down'], value=['left', 'right', 'up', 'down']) return [pixels, mask_blur, inpainting_fill, direction] def run(self, p, pixels, mask_blur, inpainting_fill, direction): initial_seed = None initial_info = None p.mask_blur = mask_blur * 2 p.inpainting_fill = inpainting_fill p.inpaint_full_res = False left = pixels if "left" in direction else 0 right = pixels if "right" in direction else 0 up = pixels if "up" in direction else 0 down = pixels if "down" in direction else 0 init_img = p.init_images[0] target_w = math.ceil((init_img.width + left + right) / 64) * 64 target_h = math.ceil((init_img.height + up + down) / 64) * 64 if left > 0: left = left * (target_w - init_img.width) // (left + right) if right > 0: right = target_w - init_img.width - left if up > 0: up = up * (target_h - init_img.height) // (up + down) if down > 0: down = target_h - init_img.height - up img = Image.new("RGB", (target_w, target_h)) img.paste(init_img, (left, up)) mask = Image.new("L", (img.width, img.height), "white") draw = ImageDraw.Draw(mask) draw.rectangle(( left + (mask_blur * 2 if left > 0 else 0), up + (mask_blur * 2 if up > 0 else 0), mask.width - right - (mask_blur * 2 if right > 0 else 0), mask.height - down - (mask_blur * 2 if down > 0 else 0) ), fill="black") latent_mask = Image.new("L", (img.width, img.height), "white") latent_draw = ImageDraw.Draw(latent_mask) latent_draw.rectangle(( left + (mask_blur//2 if left > 0 else 0), up + (mask_blur//2 if up > 0 else 0), mask.width - right - (mask_blur//2 if right > 0 else 0), mask.height - down - (mask_blur//2 if down > 0 else 0) ), fill="black") devices.torch_gc() grid = images.split_grid(img, tile_w=p.width, tile_h=p.height, overlap=pixels) grid_mask = images.split_grid(mask, tile_w=p.width, tile_h=p.height, overlap=pixels) grid_latent_mask = images.split_grid(latent_mask, tile_w=p.width, tile_h=p.height, overlap=pixels) p.n_iter = 1 p.batch_size = 1 p.do_not_save_grid = True p.do_not_save_samples = True work = [] work_mask = [] work_latent_mask = [] work_results = [] for (y, h, row), (_, _, row_mask), (_, _, row_latent_mask) in zip(grid.tiles, grid_mask.tiles, grid_latent_mask.tiles): for tiledata, tiledata_mask, tiledata_latent_mask in zip(row, row_mask, row_latent_mask): x, w = tiledata[0:2] if x >= left and x+w <= img.width - right and y >= up and y+h <= img.height - down: continue work.append(tiledata[2]) work_mask.append(tiledata_mask[2]) work_latent_mask.append(tiledata_latent_mask[2]) batch_count = len(work) print(f"Poor man's outpainting will process a total of {len(work)} images tiled as {len(grid.tiles[0][2])}x{len(grid.tiles)}.") state.job_count = batch_count for i in range(batch_count): p.init_images = [work[i]] p.image_mask = work_mask[i] p.latent_mask = work_latent_mask[i] state.job = f"Batch {i + 1} out of {batch_count}" processed = process_images(p) if initial_seed is None: initial_seed = processed.seed initial_info = processed.info p.seed = processed.seed + 1 work_results += processed.images image_index = 0 for y, h, row in grid.tiles: for tiledata in row: x, w = tiledata[0:2] if x >= left and x+w <= img.width - right and y >= up and y+h <= img.height - down: continue tiledata[2] = work_results[image_index] if image_index < len(work_results) else Image.new("RGB", (p.width, p.height)) image_index += 1 combined_image = images.combine_grid(grid) if opts.samples_save: images.save_image(combined_image, p.outpath_samples, "", initial_seed, p.prompt, opts.grid_format, info=initial_info, p=p) processed = Processed(p, [combined_image], initial_seed, initial_info) return processed stable-diffusion-webui/scripts/prompt_matrix.py000066400000000000000000000056531432161722600224100ustar00rootroot00000000000000import math from collections import namedtuple from copy import copy import random import modules.scripts as scripts import gradio as gr from modules import images from modules.processing import process_images, Processed from modules.shared import opts, cmd_opts, state import modules.sd_samplers def draw_xy_grid(xs, ys, x_label, y_label, cell): res = [] ver_texts = [[images.GridAnnotation(y_label(y))] for y in ys] hor_texts = [[images.GridAnnotation(x_label(x))] for x in xs] first_pocessed = None state.job_count = len(xs) * len(ys) for iy, y in enumerate(ys): for ix, x in enumerate(xs): state.job = f"{ix + iy * len(xs) + 1} out of {len(xs) * len(ys)}" processed = cell(x, y) if first_pocessed is None: first_pocessed = processed res.append(processed.images[0]) grid = images.image_grid(res, rows=len(ys)) grid = images.draw_grid_annotations(grid, res[0].width, res[0].height, hor_texts, ver_texts) first_pocessed.images = [grid] return first_pocessed class Script(scripts.Script): def title(self): return "Prompt matrix" def ui(self, is_img2img): put_at_start = gr.Checkbox(label='Put variable parts at start of prompt', value=False) return [put_at_start] def run(self, p, put_at_start): modules.processing.fix_seed(p) original_prompt = p.prompt[0] if type(p.prompt) == list else p.prompt all_prompts = [] prompt_matrix_parts = original_prompt.split("|") combination_count = 2 ** (len(prompt_matrix_parts) - 1) for combination_num in range(combination_count): selected_prompts = [text.strip().strip(',') for n, text in enumerate(prompt_matrix_parts[1:]) if combination_num & (1 << n)] if put_at_start: selected_prompts = selected_prompts + [prompt_matrix_parts[0]] else: selected_prompts = [prompt_matrix_parts[0]] + selected_prompts all_prompts.append(", ".join(selected_prompts)) p.n_iter = math.ceil(len(all_prompts) / p.batch_size) p.do_not_save_grid = True print(f"Prompt matrix will create {len(all_prompts)} images using a total of {p.n_iter} batches.") p.prompt = all_prompts p.seed = [p.seed for _ in all_prompts] p.prompt_for_display = original_prompt processed = process_images(p) grid = images.image_grid(processed.images, p.batch_size, rows=1 << ((len(prompt_matrix_parts) - 1) // 2)) grid = images.draw_prompt_matrix(grid, p.width, p.height, prompt_matrix_parts) processed.images.insert(0, grid) if opts.grid_save: images.save_image(processed.images[0], p.outpath_grids, "prompt_matrix", prompt=original_prompt, seed=processed.seed, grid=True, p=p) return processed stable-diffusion-webui/scripts/prompts_from_file.py000066400000000000000000000047321432161722600232260ustar00rootroot00000000000000import math import os import sys import traceback import modules.scripts as scripts import gradio as gr from modules.processing import Processed, process_images from PIL import Image from modules.shared import opts, cmd_opts, state class Script(scripts.Script): def title(self): return "Prompts from file or textbox" def ui(self, is_img2img): # This checkbox would look nicer as two tabs, but there are two problems: # 1) There is a bug in Gradio 3.3 that prevents visibility from working on Tabs # 2) Even with Gradio 3.3.1, returning a control (like Tabs) that can't be used as input # causes a AttributeError: 'Tabs' object has no attribute 'preprocess' assert, # due to the way Script assumes all controls returned can be used as inputs. # Therefore, there's no good way to use grouping components right now, # so we will use a checkbox! :) checkbox_txt = gr.Checkbox(label="Show Textbox", value=False) file = gr.File(label="File with inputs", type='bytes') prompt_txt = gr.TextArea(label="Prompts") checkbox_txt.change(fn=lambda x: [gr.File.update(visible = not x), gr.TextArea.update(visible = x)], inputs=[checkbox_txt], outputs=[file, prompt_txt]) return [checkbox_txt, file, prompt_txt] def on_show(self, checkbox_txt, file, prompt_txt): return [ gr.Checkbox.update(visible = True), gr.File.update(visible = not checkbox_txt), gr.TextArea.update(visible = checkbox_txt) ] def run(self, p, checkbox_txt, data: bytes, prompt_txt: str): if (checkbox_txt): lines = [x.strip() for x in prompt_txt.splitlines()] else: lines = [x.strip() for x in data.decode('utf8', errors='ignore').split("\n")] lines = [x for x in lines if len(x) > 0] img_count = len(lines) * p.n_iter batch_count = math.ceil(img_count / p.batch_size) loop_count = math.ceil(batch_count / p.n_iter) print(f"Will process {img_count} images in {batch_count} batches.") p.do_not_save_grid = True state.job_count = batch_count images = [] for loop_no in range(loop_count): state.job = f"{loop_no + 1} out of {loop_count}" p.prompt = lines[loop_no*p.batch_size:(loop_no+1)*p.batch_size] * p.n_iter proc = process_images(p) images += proc.images return Processed(p, images, p.seed, "") stable-diffusion-webui/scripts/sd_upscale.py000066400000000000000000000067041432161722600216230ustar00rootroot00000000000000import math import modules.scripts as scripts import gradio as gr from PIL import Image from modules import processing, shared, sd_samplers, images, devices from modules.processing import Processed from modules.shared import opts, cmd_opts, state class Script(scripts.Script): def title(self): return "SD upscale" def show(self, is_img2img): return is_img2img def ui(self, is_img2img): info = gr.HTML("

Will upscale the image to twice the dimensions; use width and height sliders to set tile size

") overlap = gr.Slider(minimum=0, maximum=256, step=16, label='Tile overlap', value=64, visible=False) upscaler_index = gr.Radio(label='Upscaler', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index", visible=False) return [info, overlap, upscaler_index] def run(self, p, _, overlap, upscaler_index): processing.fix_seed(p) upscaler = shared.sd_upscalers[upscaler_index] p.extra_generation_params["SD upscale overlap"] = overlap p.extra_generation_params["SD upscale upscaler"] = upscaler.name initial_info = None seed = p.seed init_img = p.init_images[0] if(upscaler.name != "None"): img = upscaler.scaler.upscale(init_img, 2, upscaler.data_path) else: img = init_img devices.torch_gc() grid = images.split_grid(img, tile_w=p.width, tile_h=p.height, overlap=overlap) batch_size = p.batch_size upscale_count = p.n_iter p.n_iter = 1 p.do_not_save_grid = True p.do_not_save_samples = True work = [] for y, h, row in grid.tiles: for tiledata in row: work.append(tiledata[2]) batch_count = math.ceil(len(work) / batch_size) state.job_count = batch_count * upscale_count print(f"SD upscaling will process a total of {len(work)} images tiled as {len(grid.tiles[0][2])}x{len(grid.tiles)} per upscale in a total of {state.job_count} batches.") result_images = [] for n in range(upscale_count): start_seed = seed + n p.seed = start_seed work_results = [] for i in range(batch_count): p.batch_size = batch_size p.init_images = work[i*batch_size:(i+1)*batch_size] state.job = f"Batch {i + 1 + n * batch_count} out of {state.job_count}" processed = processing.process_images(p) if initial_info is None: initial_info = processed.info p.seed = processed.seed + 1 work_results += processed.images image_index = 0 for y, h, row in grid.tiles: for tiledata in row: tiledata[2] = work_results[image_index] if image_index < len(work_results) else Image.new("RGB", (p.width, p.height)) image_index += 1 combined_image = images.combine_grid(grid) result_images.append(combined_image) if opts.samples_save: images.save_image(combined_image, p.outpath_samples, "", start_seed, p.prompt, opts.samples_format, info=initial_info, p=p) processed = Processed(p, result_images, seed, initial_info) return processed stable-diffusion-webui/scripts/xy_grid.py000066400000000000000000000315311432161722600211420ustar00rootroot00000000000000from collections import namedtuple from copy import copy from itertools import permutations, chain import random import csv from io import StringIO from PIL import Image import numpy as np import modules.scripts as scripts import gradio as gr from modules import images from modules.hypernetworks import hypernetwork from modules.processing import process_images, Processed, get_correct_sampler from modules.shared import opts, cmd_opts, state import modules.shared as shared import modules.sd_samplers import modules.sd_models import re def apply_field(field): def fun(p, x, xs): setattr(p, field, x) return fun def apply_prompt(p, x, xs): if xs[0] not in p.prompt and xs[0] not in p.negative_prompt: raise RuntimeError(f"Prompt S/R did not find {xs[0]} in prompt or negative prompt.") p.prompt = p.prompt.replace(xs[0], x) p.negative_prompt = p.negative_prompt.replace(xs[0], x) def apply_order(p, x, xs): token_order = [] # Initally grab the tokens from the prompt, so they can be replaced in order of earliest seen for token in x: token_order.append((p.prompt.find(token), token)) token_order.sort(key=lambda t: t[0]) prompt_parts = [] # Split the prompt up, taking out the tokens for _, token in token_order: n = p.prompt.find(token) prompt_parts.append(p.prompt[0:n]) p.prompt = p.prompt[n + len(token):] # Rebuild the prompt with the tokens in the order we want prompt_tmp = "" for idx, part in enumerate(prompt_parts): prompt_tmp += part prompt_tmp += x[idx] p.prompt = prompt_tmp + p.prompt def build_samplers_dict(p): samplers_dict = {} for i, sampler in enumerate(get_correct_sampler(p)): samplers_dict[sampler.name.lower()] = i for alias in sampler.aliases: samplers_dict[alias.lower()] = i return samplers_dict def apply_sampler(p, x, xs): sampler_index = build_samplers_dict(p).get(x.lower(), None) if sampler_index is None: raise RuntimeError(f"Unknown sampler: {x}") p.sampler_index = sampler_index def confirm_samplers(p, xs): samplers_dict = build_samplers_dict(p) for x in xs: if x.lower() not in samplers_dict.keys(): raise RuntimeError(f"Unknown sampler: {x}") def apply_checkpoint(p, x, xs): info = modules.sd_models.get_closet_checkpoint_match(x) if info is None: raise RuntimeError(f"Unknown checkpoint: {x}") modules.sd_models.reload_model_weights(shared.sd_model, info) def confirm_checkpoints(p, xs): for x in xs: if modules.sd_models.get_closet_checkpoint_match(x) is None: raise RuntimeError(f"Unknown checkpoint: {x}") def apply_hypernetwork(p, x, xs): if x.lower() in ["", "none"]: name = None else: name = hypernetwork.find_closest_hypernetwork_name(x) if not name: raise RuntimeError(f"Unknown hypernetwork: {x}") hypernetwork.load_hypernetwork(name) def confirm_hypernetworks(p, xs): for x in xs: if x.lower() in ["", "none"]: continue if not hypernetwork.find_closest_hypernetwork_name(x): raise RuntimeError(f"Unknown hypernetwork: {x}") def apply_clip_skip(p, x, xs): opts.data["CLIP_stop_at_last_layers"] = x def format_value_add_label(p, opt, x): if type(x) == float: x = round(x, 8) return f"{opt.label}: {x}" def format_value(p, opt, x): if type(x) == float: x = round(x, 8) return x def format_value_join_list(p, opt, x): return ", ".join(x) def do_nothing(p, x, xs): pass def format_nothing(p, opt, x): return "" def str_permutations(x): """dummy function for specifying it in AxisOption's type when you want to get a list of permutations""" return x AxisOption = namedtuple("AxisOption", ["label", "type", "apply", "format_value", "confirm"]) AxisOptionImg2Img = namedtuple("AxisOptionImg2Img", ["label", "type", "apply", "format_value", "confirm"]) axis_options = [ AxisOption("Nothing", str, do_nothing, format_nothing, None), AxisOption("Seed", int, apply_field("seed"), format_value_add_label, None), AxisOption("Var. seed", int, apply_field("subseed"), format_value_add_label, None), AxisOption("Var. strength", float, apply_field("subseed_strength"), format_value_add_label, None), AxisOption("Steps", int, apply_field("steps"), format_value_add_label, None), AxisOption("CFG Scale", float, apply_field("cfg_scale"), format_value_add_label, None), AxisOption("Prompt S/R", str, apply_prompt, format_value, None), AxisOption("Prompt order", str_permutations, apply_order, format_value_join_list, None), AxisOption("Sampler", str, apply_sampler, format_value, confirm_samplers), AxisOption("Checkpoint name", str, apply_checkpoint, format_value, confirm_checkpoints), AxisOption("Hypernetwork", str, apply_hypernetwork, format_value, confirm_hypernetworks), AxisOption("Sigma Churn", float, apply_field("s_churn"), format_value_add_label, None), AxisOption("Sigma min", float, apply_field("s_tmin"), format_value_add_label, None), AxisOption("Sigma max", float, apply_field("s_tmax"), format_value_add_label, None), AxisOption("Sigma noise", float, apply_field("s_noise"), format_value_add_label, None), AxisOption("Eta", float, apply_field("eta"), format_value_add_label, None), AxisOption("Clip skip", int, apply_clip_skip, format_value_add_label, None), AxisOptionImg2Img("Denoising", float, apply_field("denoising_strength"), format_value_add_label, None), # as it is now all AxisOptionImg2Img items must go after AxisOption ones ] def draw_xy_grid(p, xs, ys, x_labels, y_labels, cell, draw_legend): res = [] ver_texts = [[images.GridAnnotation(y)] for y in y_labels] hor_texts = [[images.GridAnnotation(x)] for x in x_labels] first_processed = None state.job_count = len(xs) * len(ys) * p.n_iter for iy, y in enumerate(ys): for ix, x in enumerate(xs): state.job = f"{ix + iy * len(xs) + 1} out of {len(xs) * len(ys)}" processed = cell(x, y) if first_processed is None: first_processed = processed try: res.append(processed.images[0]) except: res.append(Image.new(res[0].mode, res[0].size)) grid = images.image_grid(res, rows=len(ys)) if draw_legend: grid = images.draw_grid_annotations(grid, res[0].width, res[0].height, hor_texts, ver_texts) first_processed.images = [grid] return first_processed re_range = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\(([+-]\d+)\s*\))?\s*") re_range_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d*)?)(?:\s*\(([+-]\d+(?:.\d*)?)\s*\))?\s*") re_range_count = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\[(\d+)\s*\])?\s*") re_range_count_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d*)?)(?:\s*\[(\d+(?:.\d*)?)\s*\])?\s*") class Script(scripts.Script): def title(self): return "X/Y plot" def ui(self, is_img2img): current_axis_options = [x for x in axis_options if type(x) == AxisOption or type(x) == AxisOptionImg2Img and is_img2img] with gr.Row(): x_type = gr.Dropdown(label="X type", choices=[x.label for x in current_axis_options], value=current_axis_options[1].label, visible=False, type="index", elem_id="x_type") x_values = gr.Textbox(label="X values", visible=False, lines=1) with gr.Row(): y_type = gr.Dropdown(label="Y type", choices=[x.label for x in current_axis_options], value=current_axis_options[0].label, visible=False, type="index", elem_id="y_type") y_values = gr.Textbox(label="Y values", visible=False, lines=1) draw_legend = gr.Checkbox(label='Draw legend', value=True) no_fixed_seeds = gr.Checkbox(label='Keep -1 for seeds', value=False) return [x_type, x_values, y_type, y_values, draw_legend, no_fixed_seeds] def run(self, p, x_type, x_values, y_type, y_values, draw_legend, no_fixed_seeds): if not no_fixed_seeds: modules.processing.fix_seed(p) if not opts.return_grid: p.batch_size = 1 CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers def process_axis(opt, vals): if opt.label == 'Nothing': return [0] valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals)))] if opt.type == int: valslist_ext = [] for val in valslist: m = re_range.fullmatch(val) mc = re_range_count.fullmatch(val) if m is not None: start = int(m.group(1)) end = int(m.group(2))+1 step = int(m.group(3)) if m.group(3) is not None else 1 valslist_ext += list(range(start, end, step)) elif mc is not None: start = int(mc.group(1)) end = int(mc.group(2)) num = int(mc.group(3)) if mc.group(3) is not None else 1 valslist_ext += [int(x) for x in np.linspace(start=start, stop=end, num=num).tolist()] else: valslist_ext.append(val) valslist = valslist_ext elif opt.type == float: valslist_ext = [] for val in valslist: m = re_range_float.fullmatch(val) mc = re_range_count_float.fullmatch(val) if m is not None: start = float(m.group(1)) end = float(m.group(2)) step = float(m.group(3)) if m.group(3) is not None else 1 valslist_ext += np.arange(start, end + step, step).tolist() elif mc is not None: start = float(mc.group(1)) end = float(mc.group(2)) num = int(mc.group(3)) if mc.group(3) is not None else 1 valslist_ext += np.linspace(start=start, stop=end, num=num).tolist() else: valslist_ext.append(val) valslist = valslist_ext elif opt.type == str_permutations: valslist = list(permutations(valslist)) valslist = [opt.type(x) for x in valslist] # Confirm options are valid before starting if opt.confirm: opt.confirm(p, valslist) return valslist x_opt = axis_options[x_type] xs = process_axis(x_opt, x_values) y_opt = axis_options[y_type] ys = process_axis(y_opt, y_values) def fix_axis_seeds(axis_opt, axis_list): if axis_opt.label == 'Seed': return [int(random.randrange(4294967294)) if val is None or val == '' or val == -1 else val for val in axis_list] else: return axis_list if not no_fixed_seeds: xs = fix_axis_seeds(x_opt, xs) ys = fix_axis_seeds(y_opt, ys) if x_opt.label == 'Steps': total_steps = sum(xs) * len(ys) elif y_opt.label == 'Steps': total_steps = sum(ys) * len(xs) else: total_steps = p.steps * len(xs) * len(ys) print(f"X/Y plot will create {len(xs) * len(ys) * p.n_iter} images on a {len(xs)}x{len(ys)} grid. (Total steps to process: {total_steps * p.n_iter})") shared.total_tqdm.updateTotal(total_steps * p.n_iter) def cell(x, y): pc = copy(p) x_opt.apply(pc, x, xs) y_opt.apply(pc, y, ys) return process_images(pc) processed = draw_xy_grid( p, xs=xs, ys=ys, x_labels=[x_opt.format_value(p, x_opt, x) for x in xs], y_labels=[y_opt.format_value(p, y_opt, y) for y in ys], cell=cell, draw_legend=draw_legend ) if opts.grid_save: images.save_image(processed.images[0], p.outpath_grids, "xy_grid", prompt=p.prompt, seed=processed.seed, grid=True, p=p) # restore checkpoint in case it was changed by axes modules.sd_models.reload_model_weights(shared.sd_model) hypernetwork.load_hypernetwork(opts.sd_hypernetwork) opts.data["CLIP_stop_at_last_layers"] = CLIP_stop_at_last_layers return processed stable-diffusion-webui/style.css000066400000000000000000000215461432161722600173130ustar00rootroot00000000000000.container { max-width: 100%; } #txt2img_token_counter { height: 0px; } #img2img_token_counter { height: 0px; } #sh{ min-width: 2em; min-height: 2em; max-width: 2em; max-height: 2em; flex-grow: 0; padding-left: 0.25em; padding-right: 0.25em; margin: 0.1em 0; opacity: 0%; cursor: default; } .output-html p {margin: 0 0.5em;} .row > *, .row > .gr-form > * { min-width: min(120px, 100%); flex: 1 1 0%; } .performance { font-size: 0.85em; color: #444; display: flex; justify-content: space-between; white-space: nowrap; } .performance .time { margin-right: 0; } .performance .vram { margin-left: 0; text-align: right; } #txt2img_generate, #img2img_generate { min-height: 4.5em; } @media screen and (min-width: 2500px) { #txt2img_gallery, #img2img_gallery { min-height: 768px; } } #txt2img_gallery img, #img2img_gallery img{ object-fit: scale-down; } .justify-center.overflow-x-scroll { justify-content: left; } .justify-center.overflow-x-scroll button:first-of-type { margin-left: auto; } .justify-center.overflow-x-scroll button:last-of-type { margin-right: auto; } #random_seed, #random_subseed, #reuse_seed, #reuse_subseed, #open_folder{ min-width: auto; flex-grow: 0; padding-left: 0.25em; padding-right: 0.25em; } #hidden_element{ display: none; } #seed_row, #subseed_row{ gap: 0.5rem; } #subseed_show_box{ min-width: auto; flex-grow: 0; } #subseed_show_box > div{ border: 0; height: 100%; } #subseed_show{ min-width: auto; flex-grow: 0; padding: 0; } #subseed_show label{ height: 100%; } #roll_col{ min-width: unset !important; flex-grow: 0 !important; padding: 0.4em 0; } #roll, #paste{ min-width: 2em; min-height: 2em; max-width: 2em; max-height: 2em; flex-grow: 0; padding-left: 0.25em; padding-right: 0.25em; margin: 0.1em 0; } #style_apply, #style_create, #interrogate{ margin: 0.75em 0.25em 0.25em 0.25em; min-width: 5em; } #style_apply, #style_create, #deepbooru{ margin: 0.75em 0.25em 0.25em 0.25em; min-width: 5em; } #style_pos_col, #style_neg_col{ min-width: 8em !important; } #txt2img_style_index, #txt2img_style2_index, #img2img_style_index, #img2img_style2_index{ margin-top: 1em; } .gr-form{ background: transparent; } .my-4{ margin-top: 0; margin-bottom: 0; } #toprow div{ border: none; gap: 0; background: transparent; } #resize_mode{ flex: 1.5; } button{ align-self: stretch !important; } #prompt, #negative_prompt{ border: none !important; } #prompt textarea, #negative_prompt textarea{ border: none !important; } #img2maskimg .h-60{ height: 30rem; } .overflow-hidden, .gr-panel{ overflow: visible !important; } #x_type, #y_type{ max-width: 10em; } #txt2img_preview, #img2img_preview, #ti_preview{ position: absolute; width: 320px; left: 0; right: 0; margin-left: auto; margin-right: auto; margin-top: 34px; z-index: 100; border: none; border-top-left-radius: 0; border-top-right-radius: 0; } @media screen and (min-width: 768px) { #txt2img_preview, #img2img_preview, #ti_preview { position: absolute; } } @media screen and (max-width: 767px) { #txt2img_preview, #img2img_preview, #ti_preview { position: relative; } } #txt2img_preview div.left-0.top-0, #img2img_preview div.left-0.top-0, #ti_preview div.left-0.top-0{ display: none; } fieldset span.text-gray-500, .gr-block.gr-box span.text-gray-500, label.block span{ position: absolute; top: -0.6em; line-height: 1.2em; padding: 0 0.5em; margin: 0; background-color: white; border-top: 1px solid #eee; border-left: 1px solid #eee; border-right: 1px solid #eee; } .dark fieldset span.text-gray-500, .dark .gr-block.gr-box span.text-gray-500, .dark label.block span{ background-color: rgb(31, 41, 55); border-top: 1px solid rgb(55 65 81); border-left: 1px solid rgb(55 65 81); border-right: 1px solid rgb(55 65 81); } #settings fieldset span.text-gray-500, #settings .gr-block.gr-box span.text-gray-500, #settings label.block span{ position: relative; border: none; margin-right: 8em; } .gr-panel div.flex-col div.justify-between label span{ margin: 0; } .gr-panel div.flex-col div.justify-between div{ position: absolute; top: -0.1em; right: 1em; padding: 0 0.5em; } #settings .gr-panel div.flex-col div.justify-between div{ position: relative; z-index: 200; } input[type="range"]{ margin: 0.5em 0 -0.3em 0; } #txt2img_sampling label{ padding-left: 0.6em; padding-right: 0.6em; } #mask_bug_info { text-align: center; display: block; margin-top: -0.75em; margin-bottom: -0.75em; } #txt2img_negative_prompt, #img2img_negative_prompt{ } #txt2img_progressbar, #img2img_progressbar, #ti_progressbar{ position: absolute; z-index: 1000; right: 0; padding-left: 5px; padding-right: 5px; display: block; } #txt2img_progress_row, #img2img_progress_row{ margin-bottom: 10px; margin-top: -18px; } .progressDiv{ width: 100%; height: 20px; background: #b4c0cc; border-radius: 8px; } .dark .progressDiv{ background: #424c5b; } .progressDiv .progress{ width: 0%; height: 20px; background: #0060df; color: white; font-weight: bold; line-height: 20px; padding: 0 8px 0 0; text-align: right; border-radius: 8px; } #lightboxModal{ display: none; position: fixed; z-index: 1001; padding-top: 100px; left: 0; top: 0; width: 100%; height: 100%; overflow: auto; background-color: rgba(20, 20, 20, 0.95); } .modalControls { display: grid; grid-template-columns: 32px auto 1fr 32px; grid-template-areas: "zoom tile space close"; position: absolute; top: 0; left: 0; right: 0; padding: 16px; gap: 16px; background-color: rgba(0,0,0,0.2); } .modalClose { grid-area: close; } .modalZoom { grid-area: zoom; } .modalTileImage { grid-area: tile; } .modalClose, .modalZoom, .modalTileImage { color: white; font-size: 35px; font-weight: bold; cursor: pointer; } .modalClose:hover, .modalClose:focus, .modalZoom:hover, .modalZoom:focus { color: #999; text-decoration: none; cursor: pointer; } #modalImage { display: block; margin-left: auto; margin-right: auto; margin-top: auto; width: auto; } .modalImageFullscreen { object-fit: contain; height: 90%; } .modalPrev, .modalNext { cursor: pointer; position: absolute; top: 50%; width: auto; padding: 16px; margin-top: -50px; color: white; font-weight: bold; font-size: 20px; transition: 0.6s ease; border-radius: 0 3px 3px 0; user-select: none; -webkit-user-select: none; } .modalNext { right: 0; border-radius: 3px 0 0 3px; } .modalPrev:hover, .modalNext:hover { background-color: rgba(0, 0, 0, 0.8); } #imageARPreview{ position:absolute; top:0px; left:0px; border:2px solid red; background:rgba(255, 0, 0, 0.3); z-index: 900; pointer-events:none; display:none } #txt2img_interrupt, #img2img_interrupt{ position: absolute; width: 50%; height: 72px; background: #b4c0cc; border-radius: 0px; display: none; } #txt2img_skip, #img2img_skip{ position: absolute; width: 50%; right: 0px; height: 72px; background: #b4c0cc; border-radius: 0px; display: none; } .red { color: red; } .gallery-item { --tw-bg-opacity: 0 !important; } #img2img_image div.h-60{ height: 480px; } #context-menu{ z-index:9999; position:absolute; display:block; padding:0px 0; border:2px solid #a55000; border-radius:8px; box-shadow:1px 1px 2px #CE6400; width: 200px; } .context-menu-items{ list-style: none; margin: 0; padding: 0; } .context-menu-items a{ display:block; padding:5px; cursor:pointer; } .context-menu-items a:hover{ background: #a55000; } #quicksettings > div{ border: none; background: none; } #quicksettings > div > div{ max-width: 32em; padding: 0; } canvas[key="mask"] { z-index: 12 !important; filter: invert(); mix-blend-mode: multiply; pointer-events: none; } /* gradio 3.4.1 stuff for editable scrollbar values */ .gr-box > div > div > input.gr-text-input{ position: absolute; right: 0.5em; top: -0.6em; z-index: 200; width: 8em; } stable-diffusion-webui/textual_inversion_templates/000077500000000000000000000000001432161722600232715ustar00rootroot00000000000000stable-diffusion-webui/textual_inversion_templates/hypernetwork.txt000066400000000000000000000015041432161722600265730ustar00rootroot00000000000000a photo of a [filewords] a rendering of a [filewords] a cropped photo of the [filewords] the photo of a [filewords] a photo of a clean [filewords] a photo of a dirty [filewords] a dark photo of the [filewords] a photo of my [filewords] a photo of the cool [filewords] a close-up photo of a [filewords] a bright photo of the [filewords] a cropped photo of a [filewords] a photo of the [filewords] a good photo of the [filewords] a photo of one [filewords] a close-up photo of the [filewords] a rendition of the [filewords] a photo of the clean [filewords] a rendition of a [filewords] a photo of a nice [filewords] a good photo of a [filewords] a photo of the nice [filewords] a photo of the small [filewords] a photo of the weird [filewords] a photo of the large [filewords] a photo of a cool [filewords] a photo of a small [filewords] stable-diffusion-webui/textual_inversion_templates/none.txt000066400000000000000000000000101432161722600247600ustar00rootroot00000000000000picture stable-diffusion-webui/textual_inversion_templates/style.txt000066400000000000000000000011141432161722600251670ustar00rootroot00000000000000a painting, art by [name] a rendering, art by [name] a cropped painting, art by [name] the painting, art by [name] a clean painting, art by [name] a dirty painting, art by [name] a dark painting, art by [name] a picture, art by [name] a cool painting, art by [name] a close-up painting, art by [name] a bright painting, art by [name] a cropped painting, art by [name] a good painting, art by [name] a close-up painting, art by [name] a rendition, art by [name] a nice painting, art by [name] a small painting, art by [name] a weird painting, art by [name] a large painting, art by [name] stable-diffusion-webui/textual_inversion_templates/style_filewords.txt000066400000000000000000000015511432161722600272520ustar00rootroot00000000000000a painting of [filewords], art by [name] a rendering of [filewords], art by [name] a cropped painting of [filewords], art by [name] the painting of [filewords], art by [name] a clean painting of [filewords], art by [name] a dirty painting of [filewords], art by [name] a dark painting of [filewords], art by [name] a picture of [filewords], art by [name] a cool painting of [filewords], art by [name] a close-up painting of [filewords], art by [name] a bright painting of [filewords], art by [name] a cropped painting of [filewords], art by [name] a good painting of [filewords], art by [name] a close-up painting of [filewords], art by [name] a rendition of [filewords], art by [name] a nice painting of [filewords], art by [name] a small painting of [filewords], art by [name] a weird painting of [filewords], art by [name] a large painting of [filewords], art by [name] stable-diffusion-webui/textual_inversion_templates/subject.txt000066400000000000000000000012751432161722600254760ustar00rootroot00000000000000a photo of a [name] a rendering of a [name] a cropped photo of the [name] the photo of a [name] a photo of a clean [name] a photo of a dirty [name] a dark photo of the [name] a photo of my [name] a photo of the cool [name] a close-up photo of a [name] a bright photo of the [name] a cropped photo of a [name] a photo of the [name] a good photo of the [name] a photo of one [name] a close-up photo of the [name] a rendition of the [name] a photo of the clean [name] a rendition of a [name] a photo of a nice [name] a good photo of a [name] a photo of the nice [name] a photo of the small [name] a photo of the weird [name] a photo of the large [name] a photo of a cool [name] a photo of a small [name] stable-diffusion-webui/textual_inversion_templates/subject_filewords.txt000066400000000000000000000020341432161722600275460ustar00rootroot00000000000000a photo of a [name], [filewords] a rendering of a [name], [filewords] a cropped photo of the [name], [filewords] the photo of a [name], [filewords] a photo of a clean [name], [filewords] a photo of a dirty [name], [filewords] a dark photo of the [name], [filewords] a photo of my [name], [filewords] a photo of the cool [name], [filewords] a close-up photo of a [name], [filewords] a bright photo of the [name], [filewords] a cropped photo of a [name], [filewords] a photo of the [name], [filewords] a good photo of the [name], [filewords] a photo of one [name], [filewords] a close-up photo of the [name], [filewords] a rendition of the [name], [filewords] a photo of the clean [name], [filewords] a rendition of a [name], [filewords] a photo of a nice [name], [filewords] a good photo of a [name], [filewords] a photo of the nice [name], [filewords] a photo of the small [name], [filewords] a photo of the weird [name], [filewords] a photo of the large [name], [filewords] a photo of a cool [name], [filewords] a photo of a small [name], [filewords] stable-diffusion-webui/txt2img_Screenshot.png000066400000000000000000012223061432161722600217400ustar00rootroot00000000000000PNG  IHDR4jsRGBgAMA a pHYsodIDATx^ygܾwl޶s=mcn鮮.:]-#*Ui+VBX"6EbXR"A,)%E %@H$ؒ$$ys#2rA zup2~zZBDƍMv!fB!B!.^&L`NdǤFJ!B!B7L^1tYåzoB!B!BP\r,.dW!B!B!1ݜ>Kbn@gZ]B!B!T3 0fI{!B!B!$]NuYD!B!BƍSUituȥMMr|JNpGB!B!tyip3^.TJCcniB!B!tiA-r3m"j7w}@F!B!uq.]nMRGtg 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OK3%/X     2h,AAAAyU6WAAAA Ҕ}fTO    zWf7Y@AAAAy'SeKX\)QAAAAAsT7#--6fuIENDB`stable-diffusion-webui/webui-user.bat000066400000000000000000000001241432161722600202050ustar00rootroot00000000000000@echo off set PYTHON= set GIT= set VENV_DIR= set COMMANDLINE_ARGS= call webui.bat stable-diffusion-webui/webui-user.sh000066400000000000000000000023361432161722600200600ustar00rootroot00000000000000#!/bin/bash ######################################################### # Uncomment and change the variables below to your need:# ######################################################### # Install directory without trailing slash #install_dir="/home/$(whoami)" # Name of the subdirectory #clone_dir="stable-diffusion-webui" # Commandline arguments for webui.py, for example: export COMMANDLINE_ARGS="--medvram --opt-split-attention" export COMMANDLINE_ARGS="" # python3 executable #python_cmd="python3" # git executable #export GIT="git" # python3 venv without trailing slash (defaults to ${install_dir}/${clone_dir}/venv) #venv_dir="venv" # script to launch to start the app #export LAUNCH_SCRIPT="launch.py" # install command for torch #export TORCH_COMMAND="pip install torch==1.12.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113" # Requirements file to use for stable-diffusion-webui #export REQS_FILE="requirements_versions.txt" # Fixed git repos #export K_DIFFUSION_PACKAGE="" #export GFPGAN_PACKAGE="" # Fixed git commits #export STABLE_DIFFUSION_COMMIT_HASH="" #export TAMING_TRANSFORMERS_COMMIT_HASH="" #export CODEFORMER_COMMIT_HASH="" #export BLIP_COMMIT_HASH="" ########################################### stable-diffusion-webui/webui.bat000066400000000000000000000026001432161722600172320ustar00rootroot00000000000000@echo off if not defined PYTHON (set PYTHON=python) if not defined VENV_DIR (set VENV_DIR=venv) set ERROR_REPORTING=FALSE mkdir tmp 2>NUL %PYTHON% -c "" >tmp/stdout.txt 2>tmp/stderr.txt if %ERRORLEVEL% == 0 goto :start_venv echo Couldn't launch python goto :show_stdout_stderr :start_venv if [%VENV_DIR%] == [-] goto :skip_venv dir %VENV_DIR%\Scripts\Python.exe >tmp/stdout.txt 2>tmp/stderr.txt if %ERRORLEVEL% == 0 goto :activate_venv for /f "delims=" %%i in ('CALL %PYTHON% -c "import sys; print(sys.executable)"') do set PYTHON_FULLNAME="%%i" echo Creating venv in directory %VENV_DIR% using python %PYTHON_FULLNAME% %PYTHON_FULLNAME% -m venv %VENV_DIR% >tmp/stdout.txt 2>tmp/stderr.txt if %ERRORLEVEL% == 0 goto :activate_venv echo Unable to create venv in directory %VENV_DIR% goto :show_stdout_stderr :activate_venv set PYTHON="%~dp0%VENV_DIR%\Scripts\Python.exe" echo venv %PYTHON% goto :launch :skip_venv :launch %PYTHON% launch.py pause exit /b :show_stdout_stderr echo. echo exit code: %errorlevel% for /f %%i in ("tmp\stdout.txt") do set size=%%~zi if %size% equ 0 goto :show_stderr echo. echo stdout: type tmp\stdout.txt :show_stderr for /f %%i in ("tmp\stderr.txt") do set size=%%~zi if %size% equ 0 goto :show_stderr echo. echo stderr: type tmp\stderr.txt :endofscript echo. echo Launch unsuccessful. Exiting. pause stable-diffusion-webui/webui.py000066400000000000000000000077741432161722600171350ustar00rootroot00000000000000import os import threading import time import importlib import signal import threading from fastapi.middleware.gzip import GZipMiddleware from modules.paths import script_path from modules import devices, sd_samplers import modules.codeformer_model as codeformer import modules.extras import modules.face_restoration import modules.gfpgan_model as gfpgan import modules.img2img import modules.lowvram import modules.paths import modules.scripts import modules.sd_hijack import modules.sd_models import modules.shared as shared import modules.txt2img import modules.ui from modules import devices from modules import modelloader from modules.paths import script_path from modules.shared import cmd_opts import modules.hypernetworks.hypernetwork queue_lock = threading.Lock() def wrap_queued_call(func): def f(*args, **kwargs): with queue_lock: res = func(*args, **kwargs) return res return f def wrap_gradio_gpu_call(func, extra_outputs=None): def f(*args, **kwargs): devices.torch_gc() shared.state.sampling_step = 0 shared.state.job_count = -1 shared.state.job_no = 0 shared.state.job_timestamp = shared.state.get_job_timestamp() shared.state.current_latent = None shared.state.current_image = None shared.state.current_image_sampling_step = 0 shared.state.skipped = False shared.state.interrupted = False shared.state.textinfo = None with queue_lock: res = func(*args, **kwargs) shared.state.job = "" shared.state.job_count = 0 devices.torch_gc() return res return modules.ui.wrap_gradio_call(f, extra_outputs=extra_outputs) def initialize(): modelloader.cleanup_models() modules.sd_models.setup_model() codeformer.setup_model(cmd_opts.codeformer_models_path) gfpgan.setup_model(cmd_opts.gfpgan_models_path) shared.face_restorers.append(modules.face_restoration.FaceRestoration()) modelloader.load_upscalers() modules.scripts.load_scripts(os.path.join(script_path, "scripts")) shared.sd_model = modules.sd_models.load_model() shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(shared.sd_model))) shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetworks.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork))) def webui(): initialize() # make the program just exit at ctrl+c without waiting for anything def sigint_handler(sig, frame): print(f'Interrupted with signal {sig} in {frame}') os._exit(0) signal.signal(signal.SIGINT, sigint_handler) while 1: demo = modules.ui.create_ui(wrap_gradio_gpu_call=wrap_gradio_gpu_call) app, local_url, share_url = demo.launch( share=cmd_opts.share, server_name="0.0.0.0" if cmd_opts.listen else None, server_port=cmd_opts.port, debug=cmd_opts.gradio_debug, auth=[tuple(cred.split(':')) for cred in cmd_opts.gradio_auth.strip('"').split(',')] if cmd_opts.gradio_auth else None, inbrowser=cmd_opts.autolaunch, prevent_thread_lock=True ) app.add_middleware(GZipMiddleware, minimum_size=1000) while 1: time.sleep(0.5) if getattr(demo, 'do_restart', False): time.sleep(0.5) demo.close() time.sleep(0.5) break sd_samplers.set_samplers() print('Reloading Custom Scripts') modules.scripts.reload_scripts(os.path.join(script_path, "scripts")) print('Reloading modules: modules.ui') importlib.reload(modules.ui) print('Refreshing Model List') modules.sd_models.list_models() print('Restarting Gradio') if __name__ == "__main__": webui() stable-diffusion-webui/webui.sh000077500000000000000000000074411432161722600171110ustar00rootroot00000000000000#!/bin/bash ################################################# # Please do not make any changes to this file, # # change the variables in webui-user.sh instead # ################################################# # Read variables from webui-user.sh # shellcheck source=/dev/null if [[ -f webui-user.sh ]] then source ./webui-user.sh fi # Set defaults # Install directory without trailing slash if [[ -z "${install_dir}" ]] then install_dir="/home/$(whoami)" fi # Name of the subdirectory (defaults to stable-diffusion-webui) if [[ -z "${clone_dir}" ]] then clone_dir="stable-diffusion-webui" fi # python3 executable if [[ -z "${python_cmd}" ]] then python_cmd="python3" fi # git executable if [[ -z "${GIT}" ]] then export GIT="git" fi # python3 venv without trailing slash (defaults to ${install_dir}/${clone_dir}/venv) if [[ -z "${venv_dir}" ]] then venv_dir="venv" fi if [[ -z "${LAUNCH_SCRIPT}" ]] then LAUNCH_SCRIPT="launch.py" fi # Disable sentry logging export ERROR_REPORTING=FALSE # Do not reinstall existing pip packages on Debian/Ubuntu export PIP_IGNORE_INSTALLED=0 # Pretty print delimiter="################################################################" printf "\n%s\n" "${delimiter}" printf "\e[1m\e[32mInstall script for stable-diffusion + Web UI\n" printf "\e[1m\e[34mTested on Debian 11 (Bullseye)\e[0m" printf "\n%s\n" "${delimiter}" # Do not run as root if [[ $(id -u) -eq 0 ]] then printf "\n%s\n" "${delimiter}" printf "\e[1m\e[31mERROR: This script must not be launched as root, aborting...\e[0m" printf "\n%s\n" "${delimiter}" exit 1 else printf "\n%s\n" "${delimiter}" printf "Running on \e[1m\e[32m%s\e[0m user" "$(whoami)" printf "\n%s\n" "${delimiter}" fi if [[ -d .git ]] then printf "\n%s\n" "${delimiter}" printf "Repo already cloned, using it as install directory" printf "\n%s\n" "${delimiter}" install_dir="${PWD}/../" clone_dir="${PWD##*/}" fi # Check prequisites for preq in git python3 do if ! hash "${preq}" &>/dev/null then printf "\n%s\n" "${delimiter}" printf "\e[1m\e[31mERROR: %s is not installed, aborting...\e[0m" "${preq}" printf "\n%s\n" "${delimiter}" exit 1 fi done if ! "${python_cmd}" -c "import venv" &>/dev/null then printf "\n%s\n" "${delimiter}" printf "\e[1m\e[31mERROR: python3-venv is not installed, aborting...\e[0m" printf "\n%s\n" "${delimiter}" exit 1 fi printf "\n%s\n" "${delimiter}" printf "Clone or update stable-diffusion-webui" printf "\n%s\n" "${delimiter}" cd "${install_dir}"/ || { printf "\e[1m\e[31mERROR: Can't cd to %s/, aborting...\e[0m" "${install_dir}"; exit 1; } if [[ -d "${clone_dir}" ]] then cd "${clone_dir}"/ || { printf "\e[1m\e[31mERROR: Can't cd to %s/%s/, aborting...\e[0m" "${install_dir}" "${clone_dir}"; exit 1; } "${GIT}" pull else "${GIT}" clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git "${clone_dir}" cd "${clone_dir}"/ || { printf "\e[1m\e[31mERROR: Can't cd to %s/%s/, aborting...\e[0m" "${install_dir}" "${clone_dir}"; exit 1; } fi printf "\n%s\n" "${delimiter}" printf "Create and activate python venv" printf "\n%s\n" "${delimiter}" cd "${install_dir}"/"${clone_dir}"/ || { printf "\e[1m\e[31mERROR: Can't cd to %s/%s/, aborting...\e[0m" "${install_dir}" "${clone_dir}"; exit 1; } if [[ ! -d "${venv_dir}" ]] then "${python_cmd}" -m venv "${venv_dir}" first_launch=1 fi # shellcheck source=/dev/null if [[ -f "${venv_dir}"/bin/activate ]] then source "${venv_dir}"/bin/activate else printf "\n%s\n" "${delimiter}" printf "\e[1m\e[31mERROR: Cannot activate python venv, aborting...\e[0m" printf "\n%s\n" "${delimiter}" exit 1 fi printf "\n%s\n" "${delimiter}" printf "Launching launch.py..." printf "\n%s\n" "${delimiter}" "${python_cmd}" "${LAUNCH_SCRIPT}"