pax_global_header00006660000000000000000000000064146511663430014523gustar00rootroot0000000000000052 comment=82a973c04367123ae98bd9abdf80d9eda9b910e2 stable-diffusion-webui/000077500000000000000000000000001465116634300154365ustar00rootroot00000000000000stable-diffusion-webui/.eslintignore000066400000000000000000000000601465116634300201350ustar00rootroot00000000000000extensions extensions-disabled repositories venvstable-diffusion-webui/.eslintrc.js000066400000000000000000000065371465116634300177100ustar00rootroot00000000000000/* global module */ module.exports = { env: { browser: true, es2021: true, }, extends: "eslint:recommended", parserOptions: { ecmaVersion: "latest", }, rules: { "arrow-spacing": "error", "block-spacing": "error", "brace-style": "error", "comma-dangle": ["error", "only-multiline"], "comma-spacing": "error", "comma-style": ["error", "last"], "curly": ["error", "multi-line", "consistent"], "eol-last": "error", "func-call-spacing": "error", "function-call-argument-newline": ["error", "consistent"], "function-paren-newline": ["error", "consistent"], "indent": ["error", 4], "key-spacing": "error", "keyword-spacing": "error", "linebreak-style": ["error", "unix"], "no-extra-semi": "error", "no-mixed-spaces-and-tabs": "error", "no-multi-spaces": "error", "no-redeclare": ["error", {builtinGlobals: false}], "no-trailing-spaces": "error", "no-unused-vars": "off", "no-whitespace-before-property": "error", "object-curly-newline": ["error", {consistent: true, multiline: true}], "object-curly-spacing": ["error", "never"], "operator-linebreak": ["error", "after"], "quote-props": ["error", "consistent-as-needed"], "semi": ["error", "always"], "semi-spacing": "error", "semi-style": ["error", "last"], "space-before-blocks": "error", "space-before-function-paren": ["error", "never"], "space-in-parens": ["error", "never"], "space-infix-ops": "error", "space-unary-ops": "error", "switch-colon-spacing": "error", "template-curly-spacing": ["error", "never"], "unicode-bom": "error", }, globals: { //script.js gradioApp: "readonly", executeCallbacks: "readonly", onAfterUiUpdate: "readonly", onOptionsChanged: "readonly", onUiLoaded: "readonly", onUiUpdate: "readonly", uiCurrentTab: "writable", uiElementInSight: "readonly", uiElementIsVisible: "readonly", //ui.js opts: "writable", all_gallery_buttons: "readonly", selected_gallery_button: "readonly", selected_gallery_index: "readonly", switch_to_txt2img: "readonly", switch_to_img2img_tab: "readonly", switch_to_img2img: "readonly", switch_to_sketch: "readonly", switch_to_inpaint: "readonly", switch_to_inpaint_sketch: "readonly", switch_to_extras: "readonly", get_tab_index: "readonly", create_submit_args: "readonly", restart_reload: "readonly", updateInput: "readonly", onEdit: "readonly", //extraNetworks.js requestGet: "readonly", popup: "readonly", // profilerVisualization.js createVisualizationTable: "readonly", // from python localization: "readonly", // progrssbar.js randomId: "readonly", requestProgress: "readonly", // imageviewer.js modalPrevImage: "readonly", modalNextImage: "readonly", // localStorage.js localSet: "readonly", localGet: "readonly", localRemove: "readonly", // resizeHandle.js setupResizeHandle: "writable" } }; stable-diffusion-webui/.git-blame-ignore-revs000066400000000000000000000000671465116634300215410ustar00rootroot00000000000000# Apply ESlint 9c54b78d9dde5601e916f308d9a9d6953ec39430stable-diffusion-webui/.github/000077500000000000000000000000001465116634300167765ustar00rootroot00000000000000stable-diffusion-webui/.github/ISSUE_TEMPLATE/000077500000000000000000000000001465116634300211615ustar00rootroot00000000000000stable-diffusion-webui/.github/ISSUE_TEMPLATE/bug_report.yml000066400000000000000000000105011465116634300240510ustar00rootroot00000000000000name: Bug Report description: You think something is broken in the UI title: "[Bug]: " labels: ["bug-report"] body: - type: markdown attributes: value: | > The title of the bug report should be short and descriptive. > Use relevant keywords for searchability. > Do not leave it blank, but also do not put an entire error log in it. - type: checkboxes attributes: label: Checklist description: | Please perform basic debugging to see if extensions or configuration is the cause of the issue. Basic debug procedure 　1. Disable all third-party extensions - check if extension is the cause 　2. Update extensions and webui - sometimes things just need to be updated 　3. Backup and remove your config.json and ui-config.json - check if the issue is caused by bad configuration 　4. Delete venv with third-party extensions disabled - sometimes extensions might cause wrong libraries to be installed 　5. Try a fresh installation webui in a different directory - see if a clean installation solves the issue Before making a issue report please, check that the issue hasn't been reported recently. options: - label: The issue exists after disabling all extensions - label: The issue exists on a clean installation of webui - label: The issue is caused by an extension, but I believe it is caused by a bug in the webui - label: The issue exists in the current version of the webui - label: The issue has not been reported before recently - label: The issue has been reported before but has not been fixed yet - type: markdown attributes: value: | > Please fill this form with as much information as possible. Don't forget to "Upload Sysinfo" and "What browsers" and provide screenshots if possible - type: textarea id: what-did attributes: label: What happened? description: Tell us what happened in a very clear and simple way placeholder: | txt2img is not working as intended. validations: required: true - type: textarea id: steps attributes: label: Steps to reproduce the problem description: Please provide us with precise step by step instructions on how to reproduce the bug placeholder: | 1. Go to ... 2. Press ... 3. ... validations: required: true - type: textarea id: what-should attributes: label: What should have happened? description: Tell us what you think the normal behavior should be placeholder: | WebUI should ... validations: required: true - type: dropdown id: browsers attributes: label: What browsers do you use to access the UI ? multiple: true options: - Mozilla Firefox - Google Chrome - Brave - Apple Safari - Microsoft Edge - Android - iOS - Other - type: textarea id: sysinfo attributes: label: Sysinfo description: System info file, generated by WebUI. You can generate it in settings, on the Sysinfo page. Drag the file into the field to upload it. If you submit your report without including the sysinfo file, the report will be closed. If needed, review the report to make sure it includes no personal information you don't want to share. If you can't start WebUI, you can use --dump-sysinfo commandline argument to generate the file. placeholder: | 1. Go to WebUI Settings -> Sysinfo -> Download system info. If WebUI fails to launch, use --dump-sysinfo commandline argument to generate the file 2. Upload the Sysinfo as a attached file, Do NOT paste it in as plain text. validations: required: true - type: textarea id: logs attributes: label: Console logs description: Please provide **full** cmd/terminal logs from the moment you started UI to the end of it, after the bug occurred. If it's very long, provide a link to pastebin or similar service. render: Shell validations: required: true - type: textarea id: misc attributes: label: Additional information description: | Please provide us with any relevant additional info or context. Examples: 　I have updated my GPU driver recently. stable-diffusion-webui/.github/ISSUE_TEMPLATE/config.yml000066400000000000000000000003131465116634300231460ustar00rootroot00000000000000blank_issues_enabled: false contact_links: - name: WebUI Community Support url: https://github.com/AUTOMATIC1111/stable-diffusion-webui/discussions about: Please ask and answer questions here. stable-diffusion-webui/.github/ISSUE_TEMPLATE/feature_request.yml000066400000000000000000000026051465116634300251120ustar00rootroot00000000000000name: Feature request description: Suggest an idea for this project title: "[Feature Request]: " labels: ["enhancement"] body: - type: checkboxes attributes: label: Is there an existing issue for this? description: Please search to see if an issue already exists for the feature you want, and that it's not implemented in a recent build/commit. options: - label: I have searched the existing issues and checked the recent builds/commits required: true - type: markdown attributes: value: | *Please fill this form with as much information as possible, provide screenshots and/or illustrations of the feature if possible* - type: textarea id: feature attributes: label: What would your feature do ? description: Tell us about your feature in a very clear and simple way, and what problem it would solve validations: required: true - type: textarea id: workflow attributes: label: Proposed workflow description: Please provide us with step by step information on how you'd like the feature to be accessed and used value: | 1. Go to .... 2. Press .... 3. ... validations: required: true - type: textarea id: misc attributes: label: Additional information description: Add any other context or screenshots about the feature request here. stable-diffusion-webui/.github/pull_request_template.md000066400000000000000000000011011465116634300237300ustar00rootroot00000000000000## Description * a simple description of what you're trying to accomplish * a summary of changes in code * which issues it fixes, if any ## Screenshots/videos: ## Checklist: - [ ] I have read [contributing wiki page](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing) - [ ] I have performed a self-review of my own code - [ ] My code follows the [style guidelines](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing#code-style) - [ ] My code passes [tests](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Tests) stable-diffusion-webui/.github/workflows/000077500000000000000000000000001465116634300210335ustar00rootroot00000000000000stable-diffusion-webui/.github/workflows/on_pull_request.yaml000066400000000000000000000023171465116634300251420ustar00rootroot00000000000000name: Linter on: - push - pull_request jobs: lint-python: name: ruff runs-on: ubuntu-latest if: github.event_name != 'pull_request' || github.event.pull_request.head.repo.full_name != github.event.pull_request.base.repo.full_name steps: - name: Checkout Code uses: actions/checkout@v4 - uses: actions/setup-python@v5 with: python-version: 3.11 # NB: there's no cache: pip here since we're not installing anything # from the requirements.txt file(s) in the repository; it's faster # not to have GHA download an (at the time of writing) 4 GB cache # of PyTorch and other dependencies. - name: Install Ruff run: pip install ruff==0.3.3 - name: Run Ruff run: ruff . lint-js: name: eslint runs-on: ubuntu-latest if: github.event_name != 'pull_request' || github.event.pull_request.head.repo.full_name != github.event.pull_request.base.repo.full_name steps: - name: Checkout Code uses: actions/checkout@v4 - name: Install Node.js uses: actions/setup-node@v4 with: node-version: 18 - run: npm i --ci - run: npm run lint stable-diffusion-webui/.github/workflows/run_tests.yaml000066400000000000000000000047411465116634300237530ustar00rootroot00000000000000name: Tests on: - push - pull_request jobs: test: name: tests on CPU with empty model runs-on: ubuntu-latest if: github.event_name != 'pull_request' || github.event.pull_request.head.repo.full_name != github.event.pull_request.base.repo.full_name steps: - name: Checkout Code uses: actions/checkout@v4 - name: Set up Python 3.10 uses: actions/setup-python@v5 with: python-version: 3.10.6 cache: pip cache-dependency-path: | **/requirements*txt launch.py - name: Cache models id: cache-models uses: actions/cache@v4 with: path: models key: "2023-12-30" - name: Install test dependencies run: pip install wait-for-it -r requirements-test.txt env: PIP_DISABLE_PIP_VERSION_CHECK: "1" PIP_PROGRESS_BAR: "off" - name: Setup environment run: python launch.py --skip-torch-cuda-test --exit env: PIP_DISABLE_PIP_VERSION_CHECK: "1" PIP_PROGRESS_BAR: "off" TORCH_INDEX_URL: https://download.pytorch.org/whl/cpu WEBUI_LAUNCH_LIVE_OUTPUT: "1" PYTHONUNBUFFERED: "1" - name: Print installed packages run: pip freeze - name: Start test server run: > python -m coverage run --data-file=.coverage.server launch.py --skip-prepare-environment --skip-torch-cuda-test --test-server --do-not-download-clip --no-half --disable-opt-split-attention --use-cpu all --api-server-stop 2>&1 | tee output.txt & - name: Run tests run: | wait-for-it --service 127.0.0.1:7860 -t 20 python -m pytest -vv --junitxml=test/results.xml --cov . --cov-report=xml --verify-base-url test - name: Kill test server if: always() run: curl -vv -XPOST http://127.0.0.1:7860/sdapi/v1/server-stop && sleep 10 - name: Show coverage run: | python -m coverage combine .coverage* python -m coverage report -i python -m coverage html -i - name: Upload main app output uses: actions/upload-artifact@v4 if: always() with: name: output path: output.txt - name: Upload coverage HTML uses: actions/upload-artifact@v4 if: always() with: name: htmlcov path: htmlcov stable-diffusion-webui/.github/workflows/warns_merge_master.yml000066400000000000000000000006501465116634300254430ustar00rootroot00000000000000name: Pull requests can't target master branch "on": pull_request: types: - opened - synchronize - reopened branches: - master jobs: check: runs-on: ubuntu-latest steps: - name: Warning marge into master run: | echo -e "::warning::This pull request directly merge into \"master\" branch, normally development happens on \"dev\" branch." exit 1 stable-diffusion-webui/.gitignore000066400000000000000000000010751465116634300174310ustar00rootroot00000000000000__pycache__ *.ckpt *.safetensors *.pth .DS_Store /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 /params.txt /styles.csv.bak /webui-user.bat /webui-user.sh /interrogate /user.css /.idea notification.mp3 /SwinIR /textual_inversion .vscode /extensions /test/stdout.txt /test/stderr.txt /cache.json* /config_states/ /node_modules /package-lock.json /.coverage* /test/test_outputs /cache trace.json /sysinfo-????-??-??-??-??.json stable-diffusion-webui/.pylintrc000066400000000000000000000001671465116634300173070ustar00rootroot00000000000000# See https://pylint.pycqa.org/en/latest/user_guide/messages/message_control.html [MESSAGES CONTROL] disable=C,R,W,E,I stable-diffusion-webui/CHANGELOG.md000066400000000000000000002754751465116634300172730ustar00rootroot00000000000000## 1.10.1 ### Bug Fixes: * fix image upscale on cpu ([#16275](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16275)) ## 1.10.0 ### Features: * A lot of performance improvements (see below in Performance section) * Stable Diffusion 3 support ([#16030](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16030), [#16164](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16164), [#16212](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16212)) * Recommended Euler sampler; DDIM and other timestamp samplers currently not supported * T5 text model is disabled by default, enable it in settings * New schedulers: * Align Your Steps ([#15751](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15751)) * KL Optimal ([#15608](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15608)) * Normal ([#16149](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16149)) * DDIM ([#16149](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16149)) * Simple ([#16142](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16142)) * Beta ([#16235](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16235)) * New sampler: DDIM CFG++ ([#16035](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16035)) ### Minor: * Option to skip CFG on early steps ([#15607](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15607)) * Add --models-dir option ([#15742](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15742)) * Allow mobile users to open context menu by using two fingers press ([#15682](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15682)) * Infotext: add Lora name as TI hashes for bundled Textual Inversion ([#15679](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15679)) * Check model's hash after downloading it to prevent corruped downloads ([#15602](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15602)) * More extension tag filtering options ([#15627](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15627)) * When saving AVIF, use JPEG's quality setting ([#15610](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15610)) * Add filename pattern: `[basename]` ([#15978](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15978)) * Add option to enable clip skip for clip L on SDXL ([#15992](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15992)) * Option to prevent screen sleep during generation ([#16001](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16001)) * ToggleLivePriview button in image viewer ([#16065](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16065)) * Remove ui flashing on reloading and fast scrollong ([#16153](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16153)) * option to disable save button log.csv ([#16242](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16242)) ### Extensions and API: * Add process_before_every_sampling hook ([#15984](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15984)) * Return HTTP 400 instead of 404 on invalid sampler error ([#16140](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16140)) ### Performance: * [Performance 1/6] use_checkpoint = False ([#15803](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15803)) * [Performance 2/6] Replace einops.rearrange with torch native ops ([#15804](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15804)) * [Performance 4/6] Precompute is_sdxl_inpaint flag ([#15806](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15806)) * [Performance 5/6] Prevent unnecessary extra networks bias backup ([#15816](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15816)) * [Performance 6/6] Add --precision half option to avoid casting during inference ([#15820](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15820)) * [Performance] LDM optimization patches ([#15824](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15824)) * [Performance] Keep sigmas on CPU ([#15823](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15823)) * Check for nans in unet only once, after all steps have been completed * Added pption to run torch profiler for image generation ### Bug Fixes: * Fix for grids without comprehensive infotexts ([#15958](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15958)) * feat: lora partial update precede full update ([#15943](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15943)) * Fix bug where file extension had an extra '.' under some circumstances ([#15893](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15893)) * Fix corrupt model initial load loop ([#15600](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15600)) * Allow old sampler names in API ([#15656](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15656)) * more old sampler scheduler compatibility ([#15681](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15681)) * Fix Hypertile xyz ([#15831](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15831)) * XYZ CSV skipinitialspace ([#15832](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15832)) * fix soft inpainting on mps and xpu, torch_utils.float64 ([#15815](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15815)) * fix extention update when not on main branch ([#15797](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15797)) * update pickle safe filenames * use relative path for webui-assets css ([#15757](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15757)) * When creating a virtual environment, upgrade pip in webui.bat/webui.sh ([#15750](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15750)) * Fix AttributeError ([#15738](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15738)) * use script_path for webui root in launch_utils ([#15705](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15705)) * fix extra batch mode P Transparency ([#15664](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15664)) * use gradio theme colors in css ([#15680](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15680)) * Fix dragging text within prompt input ([#15657](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15657)) * Add correct mimetype for .mjs files ([#15654](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15654)) * QOL Items - handle metadata issues more cleanly for SD models, Loras and embeddings ([#15632](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15632)) * replace wsl-open with wslpath and explorer.exe ([#15968](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15968)) * Fix SDXL Inpaint ([#15976](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15976)) * multi size grid ([#15988](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15988)) * fix Replace preview ([#16118](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16118)) * Possible fix of wrong scale in weight decomposition ([#16151](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16151)) * Ensure use of python from venv on Mac and Linux ([#16116](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16116)) * Prioritize python3.10 over python3 if both are available on Linux and Mac (with fallback) ([#16092](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16092)) * stoping generation extras ([#16085](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16085)) * Fix SD2 loading ([#16078](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16078), [#16079](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16079)) * fix infotext Lora hashes for hires fix different lora ([#16062](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16062)) * Fix sampler scheduler autocorrection warning ([#16054](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16054)) * fix ui flashing on reloading and fast scrollong ([#16153](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16153)) * fix upscale logic ([#16239](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16239)) * [bug] do not break progressbar on non-job actions (add wrap_gradio_call_no_job) ([#16202](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16202)) * fix OSError: cannot write mode P as JPEG ([#16194](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16194)) ### Other: * fix changelog #15883 -> #15882 ([#15907](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15907)) * ReloadUI backgroundColor --background-fill-primary ([#15864](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15864)) * Use different torch versions for Intel and ARM Macs ([#15851](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15851)) * XYZ override rework ([#15836](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15836)) * scroll extensions table on overflow ([#15830](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15830)) * img2img batch upload method ([#15817](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15817)) * chore: sync v1.8.0 packages according to changelog ([#15783](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15783)) * Add AVIF MIME type support to mimetype definitions ([#15739](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15739)) * Update imageviewer.js ([#15730](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15730)) * no-referrer ([#15641](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15641)) * .gitignore trace.json ([#15980](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15980)) * Bump spandrel to 0.3.4 ([#16144](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16144)) * Defunct --max-batch-count ([#16119](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16119)) * docs: update bug_report.yml ([#16102](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16102)) * Maintaining Project Compatibility for Python 3.9 Users Without Upgrade Requirements. ([#16088](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16088), [#16169](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16169), [#16192](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16192)) * Update torch for ARM Macs to 2.3.1 ([#16059](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16059)) * remove deprecated setting dont_fix_second_order_samplers_schedule ([#16061](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16061)) * chore: fix typos ([#16060](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16060)) * shlex.join launch args in console log ([#16170](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16170)) * activate venv .bat ([#16231](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16231)) * add ids to the resize tabs in img2img ([#16218](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16218)) * update installation guide linux ([#16178](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16178)) * Robust sysinfo ([#16173](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16173)) * do not send image size on paste inpaint ([#16180](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16180)) * Fix noisy DS_Store files for MacOS ([#16166](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16166)) ## 1.9.4 ### Bug Fixes: * pin setuptools version to fix the startup error ([#15882](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15882)) ## 1.9.3 ### Bug Fixes: * fix get_crop_region_v2 ([#15594](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15594)) ## 1.9.2 ### Extensions and API: * restore 1.8.0-style naming of scripts ## 1.9.1 ### Minor: * Add avif support ([#15582](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15582)) * Add filename patterns: `[sampler_scheduler]` and `[scheduler]` ([#15581](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15581)) ### Extensions and API: * undo adding scripts to sys.modules * Add schedulers API endpoint ([#15577](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15577)) * Remove API upscaling factor limits ([#15560](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15560)) ### Bug Fixes: * Fix images do not match / Coordinate 'right' is less than 'left' ([#15534](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15534)) * fix: remove_callbacks_for_function should also remove from the ordered map ([#15533](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15533)) * fix x1 upscalers ([#15555](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15555)) * Fix cls.__module__ value in extension script ([#15532](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15532)) * fix typo in function call (eror -> error) ([#15531](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15531)) ### Other: * Hide 'No Image data blocks found.' message ([#15567](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15567)) * Allow webui.sh to be runnable from arbitrary directories containing a .git file ([#15561](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15561)) * Compatibility with Debian 11, Fedora 34+ and openSUSE 15.4+ ([#15544](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15544)) * numpy DeprecationWarning product -> prod ([#15547](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15547)) * get_crop_region_v2 ([#15583](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15583), [#15587](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15587)) ## 1.9.0 ### Features: * Make refiner switchover based on model timesteps instead of sampling steps ([#14978](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14978)) * add an option to have old-style directory view instead of tree view; stylistic changes for extra network sorting/search controls * add UI for reordering callbacks, support for specifying callback order in extension metadata ([#15205](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15205)) * Sgm uniform scheduler for SDXL-Lightning models ([#15325](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15325)) * Scheduler selection in main UI ([#15333](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15333), [#15361](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15361), [#15394](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15394)) ### Minor: * "open images directory" button now opens the actual dir ([#14947](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14947)) * Support inference with LyCORIS BOFT networks ([#14871](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14871), [#14973](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14973)) * make extra network card description plaintext by default, with an option to re-enable HTML as it was * resize handle for extra networks ([#15041](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15041)) * cmd args: `--unix-filenames-sanitization` and `--filenames-max-length` ([#15031](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15031)) * show extra networks parameters in HTML table rather than raw JSON ([#15131](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15131)) * Add DoRA (weight-decompose) support for LoRA/LoHa/LoKr ([#15160](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15160), [#15283](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15283)) * Add '--no-prompt-history' cmd args for disable last generation prompt history ([#15189](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15189)) * update preview on Replace Preview ([#15201](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15201)) * only fetch updates for extensions' active git branches ([#15233](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15233)) * put upscale postprocessing UI into an accordion ([#15223](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15223)) * Support dragdrop for URLs to read infotext ([#15262](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15262)) * use diskcache library for caching ([#15287](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15287), [#15299](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15299)) * Allow PNG-RGBA for Extras Tab ([#15334](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15334)) * Support cover images embedded in safetensors metadata ([#15319](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15319)) * faster interrupt when using NN upscale ([#15380](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15380)) * Extras upscaler: an input field to limit maximul side length for the output image ([#15293](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15293), [#15415](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15415), [#15417](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15417), [#15425](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15425)) * add an option to hide postprocessing options in Extras tab ### Extensions and API: * ResizeHandleRow - allow overriden column scale parametr ([#15004](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15004)) * call script_callbacks.ui_settings_callback earlier; fix extra-options-section built-in extension killing the ui if using a setting that doesn't exist * make it possible to use zoom.js outside webui context ([#15286](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15286), [#15288](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15288)) * allow variants for extension name in metadata.ini ([#15290](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15290)) * make reloading UI scripts optional when doing Reload UI, and off by default * put request: gr.Request at start of img2img function similar to txt2img * open_folder as util ([#15442](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15442)) * make it possible to import extensions' script files as `import scripts.` ([#15423](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15423)) ### Performance: * performance optimization for extra networks HTML pages * optimization for extra networks filtering * optimization for extra networks sorting ### Bug Fixes: * prevent escape button causing an interrupt when no generation has been made yet * [bug] avoid doble upscaling in inpaint ([#14966](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14966)) * possible fix for reload button not appearing in some cases for extra networks. * fix: the `split_threshold` parameter does not work when running Split oversized images ([#15006](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15006)) * Fix resize-handle visability for vertical layout (mobile) ([#15010](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15010)) * register_tmp_file also for mtime ([#15012](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15012)) * Protect alphas_cumprod during refiner switchover ([#14979](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14979)) * Fix EXIF orientation in API image loading ([#15062](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15062)) * Only override emphasis if actually used in prompt ([#15141](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15141)) * Fix emphasis infotext missing from `params.txt` ([#15142](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15142)) * fix extract_style_text_from_prompt #15132 ([#15135](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15135)) * Fix Soft Inpaint for AnimateDiff ([#15148](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15148)) * edit-attention: deselect surrounding whitespace ([#15178](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15178)) * chore: fix font not loaded ([#15183](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15183)) * use natural sort in extra networks when ordering by path * Fix built-in lora system bugs caused by torch.nn.MultiheadAttention ([#15190](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15190)) * Avoid error from None in get_learned_conditioning ([#15191](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15191)) * Add entry to MassFileLister after writing metadata ([#15199](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15199)) * fix issue with Styles when Hires prompt is used ([#15269](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15269), [#15276](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15276)) * Strip comments from hires fix prompt ([#15263](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15263)) * Make imageviewer event listeners browser consistent ([#15261](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15261)) * Fix AttributeError in OFT when trying to get MultiheadAttention weight ([#15260](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15260)) * Add missing .mean() back ([#15239](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15239)) * fix "Restore progress" button ([#15221](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15221)) * fix ui-config for InputAccordion [custom_script_source] ([#15231](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15231)) * handle 0 wheel deltaY ([#15268](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15268)) * prevent alt menu for firefox ([#15267](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15267)) * fix: fix syntax errors ([#15179](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15179)) * restore outputs path ([#15307](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15307)) * Escape btn_copy_path filename ([#15316](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15316)) * Fix extra networks buttons when filename contains an apostrophe ([#15331](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15331)) * escape brackets in lora random prompt generator ([#15343](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15343)) * fix: Python version check for PyTorch installation compatibility ([#15390](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15390)) * fix typo in call_queue.py ([#15386](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15386)) * fix: when find already_loaded model, remove loaded by array index ([#15382](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15382)) * minor bug fix of sd model memory management ([#15350](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15350)) * Fix CodeFormer weight ([#15414](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15414)) * Fix: Remove script callbacks in ordered_callbacks_map ([#15428](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15428)) * fix limited file write (thanks, Sylwia) * Fix extra-single-image API not doing upscale failed ([#15465](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15465)) * error handling paste_field callables ([#15470](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15470)) ### Hardware: * Add training support and change lspci for Ascend NPU ([#14981](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14981)) * Update to ROCm5.7 and PyTorch ([#14820](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14820)) * Better workaround for Navi1, removing --pre for Navi3 ([#15224](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15224)) * Ascend NPU wiki page ([#15228](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15228)) ### Other: * Update comment for Pad prompt/negative prompt v0 to add a warning about truncation, make it override the v1 implementation * support resizable columns for touch (tablets) ([#15002](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15002)) * Fix #14591 using translated content to do categories mapping ([#14995](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14995)) * Use `absolute` path for normalized filepath ([#15035](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15035)) * resizeHandle handle double tap ([#15065](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15065)) * --dat-models-path cmd flag ([#15039](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15039)) * Add a direct link to the binary release ([#15059](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15059)) * upscaler_utils: Reduce logging ([#15084](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15084)) * Fix various typos with crate-ci/typos ([#15116](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15116)) * fix_jpeg_live_preview ([#15102](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15102)) * [alternative fix] can't load webui if selected wrong extra option in ui ([#15121](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15121)) * Error handling for unsupported transparency ([#14958](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14958)) * Add model description to searched terms ([#15198](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15198)) * bump action version ([#15272](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15272)) * PEP 604 annotations ([#15259](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15259)) * Automatically Set the Scale by value when user selects an Upscale Model ([#15244](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15244)) * move postprocessing-for-training into builtin extensions ([#15222](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15222)) * type hinting in shared.py ([#15211](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15211)) * update ruff to 0.3.3 * Update pytorch lightning utilities ([#15310](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15310)) * Add Size as an XYZ Grid option ([#15354](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15354)) * Use HF_ENDPOINT variable for HuggingFace domain with default ([#15443](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15443)) * re-add update_file_entry ([#15446](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15446)) * create_infotext allow index and callable, re-work Hires prompt infotext ([#15460](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15460)) * update restricted_opts to include more options for --hide-ui-dir-config ([#15492](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15492)) ## 1.8.0 ### Features: * Update torch to version 2.1.2 * Soft Inpainting ([#14208](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14208)) * FP8 support ([#14031](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14031), [#14327](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14327)) * Support for SDXL-Inpaint Model ([#14390](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14390)) * Use Spandrel for upscaling and face restoration architectures ([#14425](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14425), [#14467](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14467), [#14473](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14473), [#14474](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14474), [#14477](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14477), [#14476](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14476), [#14484](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14484), [#14500](https://github.com/AUTOMATIC1111/stable-difusion-webui/pull/14500), [#14501](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14501), [#14504](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14504), [#14524](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14524), [#14809](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14809)) * Automatic backwards version compatibility (when loading infotexts from old images with program version specified, will add compatibility settings) * Implement zero terminal SNR noise schedule option (**[SEED BREAKING CHANGE](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Seed-breaking-changes#180-dev-170-225-2024-01-01---zero-terminal-snr-noise-schedule-option)**, [#14145](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14145), [#14979](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14979)) * Add a [✨] button to run hires fix on selected image in the gallery (with help from [#14598](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14598), [#14626](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14626), [#14728](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14728)) * [Separate assets repository](https://github.com/AUTOMATIC1111/stable-diffusion-webui-assets); serve fonts locally rather than from google's servers * Official LCM Sampler Support ([#14583](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14583)) * Add support for DAT upscaler models ([#14690](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14690), [#15039](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15039)) * Extra Networks Tree View ([#14588](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14588), [#14900](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14900)) * NPU Support ([#14801](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14801)) * Prompt comments support ### Minor: * Allow pasting in WIDTHxHEIGHT strings into the width/height fields ([#14296](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14296)) * add option: Live preview in full page image viewer ([#14230](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14230), [#14307](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14307)) * Add keyboard shortcuts for generate/skip/interrupt ([#14269](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14269)) * Better TCMALLOC support on different platforms ([#14227](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14227), [#14883](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14883), [#14910](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14910)) * Lora not found warning ([#14464](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14464)) * Adding negative prompts to Loras in extra networks ([#14475](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14475)) * xyz_grid: allow varying the seed along an axis separate from axis options ([#12180](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12180)) * option to convert VAE to bfloat16 (implementation of [#9295](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9295)) * Better IPEX support ([#14229](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14229), [#14353](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14353), [#14559](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14559), [#14562](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14562), [#14597](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14597)) * Option to interrupt after current generation rather than immediately ([#13653](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13653), [#14659](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14659)) * Fullscreen Preview control fading/disable ([#14291](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14291)) * Finer settings freezing control ([#13789](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13789)) * Increase Upscaler Limits ([#14589](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14589)) * Adjust brush size with hotkeys ([#14638](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14638)) * Add checkpoint info to csv log file when saving images ([#14663](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14663)) * Make more columns resizable ([#14740](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14740), [#14884](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14884)) * Add an option to not overlay original image for inpainting for #14727 * Add Pad conds v0 option to support same generation with DDIM as before 1.6.0 * Add "Interrupting..." placeholder. * Button for refresh extensions list ([#14857](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14857)) * Add an option to disable normalization after calculating emphasis. ([#14874](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14874)) * When counting tokens, also include enabled styles (can be disabled in settings to revert to previous behavior) * Configuration for the [📂] button for image gallery ([#14947](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14947)) * Support inference with LyCORIS BOFT networks ([#14871](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14871), [#14973](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14973)) * support resizable columns for touch (tablets) ([#15002](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15002)) ### Extensions and API: * Removed packages from requirements: basicsr, gfpgan, realesrgan; as well as their dependencies: absl-py, addict, beautifulsoup4, future, gdown, grpcio, importlib-metadata, lmdb, lpips, Markdown, platformdirs, PySocks, soupsieve, tb-nightly, tensorboard-data-server, tomli, Werkzeug, yapf, zipp, soupsieve * Enable task ids for API ([#14314](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14314)) * add override_settings support for infotext API * rename generation_parameters_copypaste module to infotext_utils * prevent crash due to Script __init__ exception ([#14407](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14407)) * Bump numpy to 1.26.2 ([#14471](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14471)) * Add utility to inspect a model's dtype/device ([#14478](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14478)) * Implement general forward method for all method in built-in lora ext ([#14547](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14547)) * Execute model_loaded_callback after moving to target device ([#14563](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14563)) * Add self to CFGDenoiserParams ([#14573](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14573)) * Allow TLS with API only mode (--nowebui) ([#14593](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14593)) * New callback: postprocess_image_after_composite ([#14657](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14657)) * modules/api/api.py: add api endpoint to refresh embeddings list ([#14715](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14715)) * set_named_arg ([#14773](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14773)) * add before_token_counter callback and use it for prompt comments * ResizeHandleRow - allow overridden column scale parameter ([#15004](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15004)) ### Performance: * Massive performance improvement for extra networks directories with a huge number of files in them in an attempt to tackle #14507 ([#14528](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14528)) * Reduce unnecessary re-indexing extra networks directory ([#14512](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14512)) * Avoid unnecessary `isfile`/`exists` calls ([#14527](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14527)) ### Bug Fixes: * fix multiple bugs related to styles multi-file support ([#14203](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14203), [#14276](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14276), [#14707](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14707)) * Lora fixes ([#14300](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14300), [#14237](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14237), [#14546](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14546), [#14726](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14726)) * Re-add setting lost as part of e294e46 ([#14266](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14266)) * fix extras caption BLIP ([#14330](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14330)) * include infotext into saved init image for img2img ([#14452](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14452)) * xyz grid handle axis_type is None ([#14394](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14394)) * Update Added (Fixed) IPV6 Functionality When there is No Webui Argument Passed webui.py ([#14354](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14354)) * fix API thread safe issues of txt2img and img2img ([#14421](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14421)) * handle selectable script_index is None ([#14487](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14487)) * handle config.json failed to load ([#14525](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14525), [#14767](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14767)) * paste infotext cast int as float ([#14523](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14523)) * Ensure GRADIO_ANALYTICS_ENABLED is set early enough ([#14537](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14537)) * Fix logging configuration again ([#14538](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14538)) * Handle CondFunc exception when resolving attributes ([#14560](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14560)) * Fix extras big batch crashes ([#14699](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14699)) * Fix using wrong model caused by alias ([#14655](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14655)) * Add # to the invalid_filename_chars list ([#14640](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14640)) * Fix extension check for requirements ([#14639](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14639)) * Fix tab indexes are reset after restart UI ([#14637](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14637)) * Fix nested manual cast ([#14689](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14689)) * Keep postprocessing upscale selected tab after restart ([#14702](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14702)) * XYZ grid: filter out blank vals when axis is int or float type (like int axis seed) ([#14754](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14754)) * fix CLIP Interrogator topN regex ([#14775](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14775)) * Fix dtype error in MHA layer/change dtype checking mechanism for manual cast ([#14791](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14791)) * catch load style.csv error ([#14814](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14814)) * fix error when editing extra networks card * fix extra networks metadata failing to work properly when you create the .json file with metadata for the first time. * util.walk_files extensions case insensitive ([#14879](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14879)) * if extensions page not loaded, prevent apply ([#14873](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14873)) * call the right function for token counter in img2img * Fix the bugs that search/reload will disappear when using other ExtraNetworks extensions ([#14939](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14939)) * Gracefully handle mtime read exception from cache ([#14933](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14933)) * Only trigger interrupt on `Esc` when interrupt button visible ([#14932](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14932)) * Disable prompt token counters option actually disables token counting rather than just hiding results. * avoid double upscaling in inpaint ([#14966](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14966)) * Fix #14591 using translated content to do categories mapping ([#14995](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14995)) * fix: the `split_threshold` parameter does not work when running Split oversized images ([#15006](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15006)) * Fix resize-handle for mobile ([#15010](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15010), [#15065](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15065)) ### Other: * Assign id for "extra_options". Replace numeric field with slider. ([#14270](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14270)) * change state dict comparison to ref compare ([#14216](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14216)) * Bump torch-rocm to 5.6/5.7 ([#14293](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14293)) * Base output path off data path ([#14446](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14446)) * reorder training preprocessing modules in extras tab ([#14367](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14367)) * Remove `cleanup_models` code ([#14472](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14472)) * only rewrite ui-config when there is change ([#14352](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14352)) * Fix lint issue from 501993eb ([#14495](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14495)) * Update README.md ([#14548](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14548)) * hires button, fix seeds () * Logging: set formatter correctly for fallback logger too ([#14618](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14618)) * Read generation info from infotexts rather than json for internal needs (save, extract seed from generated pic) ([#14645](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14645)) * improve get_crop_region ([#14709](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14709)) * Bump safetensors' version to 0.4.2 ([#14782](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14782)) * add tooltip create_submit_box ([#14803](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14803)) * extensions tab table row hover highlight ([#14885](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14885)) * Always add timestamp to displayed image ([#14890](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14890)) * Added core.filemode=false so doesn't track changes in file permission… ([#14930](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14930)) * Normalize command-line argument paths ([#14934](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14934), [#15035](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15035)) * Use original App Title in progress bar ([#14916](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14916)) * register_tmp_file also for mtime ([#15012](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15012)) ## 1.7.0 ### Features: * settings tab rework: add search field, add categories, split UI settings page into many * add altdiffusion-m18 support ([#13364](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13364)) * support inference with LyCORIS GLora networks ([#13610](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13610)) * add lora-embedding bundle system ([#13568](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13568)) * option to move prompt from top row into generation parameters * add support for SSD-1B ([#13865](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13865)) * support inference with OFT networks ([#13692](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13692)) * script metadata and DAG sorting mechanism ([#13944](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13944)) * support HyperTile optimization ([#13948](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13948)) * add support for SD 2.1 Turbo ([#14170](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14170)) * remove Train->Preprocessing tab and put all its functionality into Extras tab * initial IPEX support for Intel Arc GPU ([#14171](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14171)) ### Minor: * allow reading model hash from images in img2img batch mode ([#12767](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12767)) * add option to align with sgm repo's sampling implementation ([#12818](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12818)) * extra field for lora metadata viewer: `ss_output_name` ([#12838](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12838)) * add action in settings page to calculate all SD checkpoint hashes ([#12909](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12909)) * add button to copy prompt to style editor ([#12975](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12975)) * add --skip-load-model-at-start option ([#13253](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13253)) * write infotext to gif images * read infotext from gif images ([#13068](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13068)) * allow configuring the initial state of InputAccordion in ui-config.json ([#13189](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13189)) * allow editing whitespace delimiters for ctrl+up/ctrl+down prompt editing ([#13444](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13444)) * prevent accidentally closing popup dialogs ([#13480](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13480)) * added option to play notification sound or not ([#13631](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13631)) * show the preview image in the full screen image viewer if available ([#13459](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13459)) * support for webui.settings.bat ([#13638](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13638)) * add an option to not print stack traces on ctrl+c * start/restart generation by Ctrl (Alt) + Enter ([#13644](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13644)) * update prompts_from_file script to allow concatenating entries with the general prompt ([#13733](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13733)) * added a visible checkbox to input accordion * added an option to hide all txt2img/img2img parameters in an accordion ([#13826](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13826)) * added 'Path' sorting option for Extra network cards ([#13968](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13968)) * enable prompt hotkeys in style editor ([#13931](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13931)) * option to show batch img2img results in UI ([#14009](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14009)) * infotext updates: add option to disregard certain infotext fields, add option to not include VAE in infotext, add explanation to infotext settings page, move some options to infotext settings page * add FP32 fallback support on sd_vae_approx ([#14046](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14046)) * support XYZ scripts / split hires path from unet ([#14126](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14126)) * allow use of multiple styles csv files ([#14125](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14125)) * make extra network card description plaintext by default, with an option (Treat card description as HTML) to re-enable HTML as it was (originally by [#13241](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13241)) ### Extensions and API: * update gradio to 3.41.2 * support installed extensions list api ([#12774](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12774)) * update pnginfo API to return dict with parsed values * add noisy latent to `ExtraNoiseParams` for callback ([#12856](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12856)) * show extension datetime in UTC ([#12864](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12864), [#12865](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12865), [#13281](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13281)) * add an option to choose how to combine hires fix and refiner * include program version in info response. ([#13135](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13135)) * sd_unet support for SDXL * patch DDPM.register_betas so that users can put given_betas in model yaml ([#13276](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13276)) * xyz_grid: add prepare ([#13266](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13266)) * allow multiple localization files with same language in extensions ([#13077](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13077)) * add onEdit function for js and rework token-counter.js to use it * fix the key error exception when processing override_settings keys ([#13567](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13567)) * ability for extensions to return custom data via api in response.images ([#13463](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13463)) * call state.jobnext() before postproces*() ([#13762](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13762)) * add option to set notification sound volume ([#13884](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13884)) * update Ruff to 0.1.6 ([#14059](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14059)) * add Block component creation callback ([#14119](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14119)) * catch uncaught exception with ui creation scripts ([#14120](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14120)) * use extension name for determining an extension is installed in the index ([#14063](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14063)) * update is_installed() from launch_utils.py to fix reinstalling already installed packages ([#14192](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14192)) ### Bug Fixes: * fix pix2pix producing bad results * fix defaults settings page breaking when any of main UI tabs are hidden * fix error that causes some extra networks to be disabled if both and are present in the prompt * fix for Reload UI function: if you reload UI on one tab, other opened tabs will no longer stop working * prevent duplicate resize handler ([#12795](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12795)) * small typo: vae resolve bug ([#12797](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12797)) * hide broken image crop tool ([#12792](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12792)) * don't show hidden samplers in dropdown for XYZ script ([#12780](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12780)) * fix style editing dialog breaking if it's opened in both img2img and txt2img tabs * hide --gradio-auth and --api-auth values from /internal/sysinfo report * add missing infotext for RNG in options ([#12819](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12819)) * fix notification not playing when built-in webui tab is inactive ([#12834](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12834)) * honor `--skip-install` for extension installers ([#12832](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12832)) * don't print blank stdout in extension installers ([#12833](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12833), [#12855](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12855)) * get progressbar to display correctly in extensions tab * keep order in list of checkpoints when loading model that doesn't have a checksum * fix inpainting models in txt2img creating black pictures * fix generation params regex ([#12876](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12876)) * fix batch img2img output dir with script ([#12926](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12926)) * fix #13080 - Hypernetwork/TI preview generation ([#13084](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13084)) * fix bug with sigma min/max overrides. ([#12995](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12995)) * more accurate check for enabling cuDNN benchmark on 16XX cards ([#12924](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12924)) * don't use multicond parser for negative prompt counter ([#13118](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13118)) * fix data-sort-name containing spaces ([#13412](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13412)) * update card on correct tab when editing metadata ([#13411](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13411)) * fix viewing/editing metadata when filename contains an apostrophe ([#13395](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13395)) * fix: --sd_model in "Prompts from file or textbox" script is not working ([#13302](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13302)) * better Support for Portable Git ([#13231](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13231)) * fix issues when webui_dir is not work_dir ([#13210](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13210)) * fix: lora-bias-backup don't reset cache ([#13178](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13178)) * account for customizable extra network separators whyen removing extra network text from the prompt ([#12877](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12877)) * re fix batch img2img output dir with script ([#13170](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13170)) * fix `--ckpt-dir` path separator and option use `short name` for checkpoint dropdown ([#13139](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13139)) * consolidated allowed preview formats, Fix extra network `.gif` not woking as preview ([#13121](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13121)) * fix venv_dir=- environment variable not working as expected on linux ([#13469](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13469)) * repair unload sd checkpoint button * edit-attention fixes ([#13533](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13533)) * fix bug when using --gfpgan-models-path ([#13718](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13718)) * properly apply sort order for extra network cards when selected from dropdown * fixes generation restart not working for some users when 'Ctrl+Enter' is pressed ([#13962](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13962)) * thread safe extra network list_items ([#13014](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13014)) * fix not able to exit metadata popup when pop up is too big ([#14156](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14156)) * fix auto focal point crop for opencv >= 4.8 ([#14121](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14121)) * make 'use-cpu all' actually apply to 'all' ([#14131](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14131)) * extras tab batch: actually use original filename * make webui not crash when running with --disable-all-extensions option ### Other: * non-local condition ([#12814](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12814)) * fix minor typos ([#12827](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12827)) * remove xformers Python version check ([#12842](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12842)) * style: file-metadata word-break ([#12837](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12837)) * revert SGM noise multiplier change for img2img because it breaks hires fix * do not change quicksettings dropdown option when value returned is `None` ([#12854](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12854)) * [RC 1.6.0 - zoom is partly hidden] Update style.css ([#12839](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12839)) * chore: change extension time format ([#12851](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12851)) * WEBUI.SH - Use torch 2.1.0 release candidate for Navi 3 ([#12929](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12929)) * add Fallback at images.read_info_from_image if exif data was invalid ([#13028](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13028)) * update cmd arg description ([#12986](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12986)) * fix: update shared.opts.data when add_option ([#12957](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12957), [#13213](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13213)) * restore missing tooltips ([#12976](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12976)) * use default dropdown padding on mobile ([#12880](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12880)) * put enable console prompts option into settings from commandline args ([#13119](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13119)) * fix some deprecated types ([#12846](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12846)) * bump to torchsde==0.2.6 ([#13418](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13418)) * update dragdrop.js ([#13372](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13372)) * use orderdict as lru cache:opt/bug ([#13313](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13313)) * XYZ if not include sub grids do not save sub grid ([#13282](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13282)) * initialize state.time_start befroe state.job_count ([#13229](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13229)) * fix fieldname regex ([#13458](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13458)) * change denoising_strength default to None. ([#13466](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13466)) * fix regression ([#13475](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13475)) * fix IndexError ([#13630](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13630)) * fix: checkpoints_loaded:{checkpoint:state_dict}, model.load_state_dict issue in dict value empty ([#13535](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13535)) * update bug_report.yml ([#12991](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12991)) * requirements_versions httpx==0.24.1 ([#13839](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13839)) * fix parenthesis auto selection ([#13829](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13829)) * fix #13796 ([#13797](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13797)) * corrected a typo in `modules/cmd_args.py` ([#13855](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13855)) * feat: fix randn found element of type float at pos 2 ([#14004](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14004)) * adds tqdm handler to logging_config.py for progress bar integration ([#13996](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13996)) * hotfix: call shared.state.end() after postprocessing done ([#13977](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13977)) * fix dependency address patch 1 ([#13929](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13929)) * save sysinfo as .json ([#14035](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14035)) * move exception_records related methods to errors.py ([#14084](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14084)) * compatibility ([#13936](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13936)) * json.dump(ensure_ascii=False) ([#14108](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14108)) * dir buttons start with / so only the correct dir will be shown and no… ([#13957](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13957)) * alternate implementation for unet forward replacement that does not depend on hijack being applied * re-add `keyedit_delimiters_whitespace` setting lost as part of commit e294e46 ([#14178](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14178)) * fix `save_samples` being checked early when saving masked composite ([#14177](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14177)) * slight optimization for mask and mask_composite ([#14181](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14181)) * add import_hook hack to work around basicsr/torchvision incompatibility ([#14186](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14186)) ## 1.6.1 ### Bug Fixes: * fix an error causing the webui to fail to start ([#13839](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13839)) ## 1.6.0 ### Features: * refiner support [#12371](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12371) * add NV option for Random number generator source setting, which allows to generate same pictures on CPU/AMD/Mac as on NVidia videocards * add style editor dialog * hires fix: add an option to use a different checkpoint for second pass ([#12181](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12181)) * option to keep multiple loaded models in memory ([#12227](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12227)) * new samplers: Restart, DPM++ 2M SDE Exponential, DPM++ 2M SDE Heun, DPM++ 2M SDE Heun Karras, DPM++ 2M SDE Heun Exponential, DPM++ 3M SDE, DPM++ 3M SDE Karras, DPM++ 3M SDE Exponential ([#12300](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12300), [#12519](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12519), [#12542](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12542)) * rework DDIM, PLMS, UniPC to use CFG denoiser same as in k-diffusion samplers: * makes all of them work with img2img * makes prompt composition possible (AND) * makes them available for SDXL * always show extra networks tabs in the UI ([#11808](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/11808)) * use less RAM when creating models ([#11958](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/11958), [#12599](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12599)) * textual inversion inference support for SDXL * extra networks UI: show metadata for SD checkpoints * checkpoint merger: add metadata support * prompt editing and attention: add support for whitespace after the number ([ red : green : 0.5 ]) (seed breaking change) ([#12177](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12177)) * VAE: allow selecting own VAE for each checkpoint (in user metadata editor) * VAE: add selected VAE to infotext * options in main UI: add own separate setting for txt2img and img2img, correctly read values from pasted infotext, add setting for column count ([#12551](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12551)) * add resize handle to txt2img and img2img tabs, allowing to change the amount of horizontable space given to generation parameters and resulting image gallery ([#12687](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12687), [#12723](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12723)) * change default behavior for batching cond/uncond -- now it's on by default, and is disabled by an UI setting (Optimizatios -> Batch cond/uncond) - if you are on lowvram/medvram and are getting OOM exceptions, you will need to enable it * show current position in queue and make it so that requests are processed in the order of arrival ([#12707](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12707)) * add `--medvram-sdxl` flag that only enables `--medvram` for SDXL models * prompt editing timeline has separate range for first pass and hires-fix pass (seed breaking change) ([#12457](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12457)) ### Minor: * img2img batch: RAM savings, VRAM savings, .tif, .tiff in img2img batch ([#12120](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12120), [#12514](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12514), [#12515](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12515)) * postprocessing/extras: RAM savings ([#12479](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12479)) * XYZ: in the axis labels, remove pathnames from model filenames * XYZ: support hires sampler ([#12298](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12298)) * XYZ: new option: use text inputs instead of dropdowns ([#12491](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12491)) * add gradio version warning * sort list of VAE checkpoints ([#12297](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12297)) * use transparent white for mask in inpainting, along with an option to select the color ([#12326](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12326)) * move some settings to their own section: img2img, VAE * add checkbox to show/hide dirs for extra networks * Add TAESD(or more) options for all the VAE encode/decode operation ([#12311](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12311)) * gradio theme cache, new gradio themes, along with explanation that the user can input his own values ([#12346](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12346), [#12355](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12355)) * sampler fixes/tweaks: s_tmax, s_churn, s_noise, s_tmax ([#12354](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12354), [#12356](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12356), [#12357](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12357), [#12358](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12358), [#12375](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12375), [#12521](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12521)) * update README.md with correct instructions for Linux installation ([#12352](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12352)) * option to not save incomplete images, on by default ([#12338](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12338)) * enable cond cache by default * git autofix for repos that are corrupted ([#12230](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12230)) * allow to open images in new browser tab by middle mouse button ([#12379](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12379)) * automatically open webui in browser when running "locally" ([#12254](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12254)) * put commonly used samplers on top, make DPM++ 2M Karras the default choice * zoom and pan: option to auto-expand a wide image, improved integration ([#12413](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12413), [#12727](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12727)) * option to cache Lora networks in memory * rework hires fix UI to use accordion * face restoration and tiling moved to settings - use "Options in main UI" setting if you want them back * change quicksettings items to have variable width * Lora: add Norm module, add support for bias ([#12503](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12503)) * Lora: output warnings in UI rather than fail for unfitting loras; switch to logging for error output in console * support search and display of hashes for all extra network items ([#12510](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12510)) * add extra noise param for img2img operations ([#12564](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12564)) * support for Lora with bias ([#12584](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12584)) * make interrupt quicker ([#12634](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12634)) * configurable gallery height ([#12648](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12648)) * make results column sticky ([#12645](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12645)) * more hash filename patterns ([#12639](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12639)) * make image viewer actually fit the whole page ([#12635](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12635)) * make progress bar work independently from live preview display which results in it being updated a lot more often * forbid Full live preview method for medvram and add a setting to undo the forbidding * make it possible to localize tooltips and placeholders * add option to align with sgm repo's sampling implementation ([#12818](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12818)) * Restore faces and Tiling generation parameters have been moved to settings out of main UI * if you want to put them back into main UI, use `Options in main UI` setting on the UI page. ### Extensions and API: * gradio 3.41.2 * also bump versions for packages: transformers, GitPython, accelerate, scikit-image, timm, tomesd * support tooltip kwarg for gradio elements: gr.Textbox(label='hello', tooltip='world') * properly clear the total console progressbar when using txt2img and img2img from API * add cmd_arg --disable-extra-extensions and --disable-all-extensions ([#12294](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12294)) * shared.py and webui.py split into many files * add --loglevel commandline argument for logging * add a custom UI element that combines accordion and checkbox * avoid importing gradio in tests because it spams warnings * put infotext label for setting into OptionInfo definition rather than in a separate list * make `StableDiffusionProcessingImg2Img.mask_blur` a property, make more inline with PIL `GaussianBlur` ([#12470](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12470)) * option to make scripts UI without gr.Group * add a way for scripts to register a callback for before/after just a single component's creation * use dataclass for StableDiffusionProcessing * store patches for Lora in a specialized module instead of inside torch * support http/https URLs in API ([#12663](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12663), [#12698](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12698)) * add extra noise callback ([#12616](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12616)) * dump current stack traces when exiting with SIGINT * add type annotations for extra fields of shared.sd_model ### Bug Fixes: * Don't crash if out of local storage quota for javascriot localStorage * XYZ plot do not fail if an exception occurs * fix missing TI hash in infotext if generation uses both negative and positive TI ([#12269](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12269)) * localization fixes ([#12307](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12307)) * fix sdxl model invalid configuration after the hijack * correctly toggle extras checkbox for infotext paste ([#12304](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12304)) * open raw sysinfo link in new page ([#12318](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12318)) * prompt parser: Account for empty field in alternating words syntax ([#12319](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12319)) * add tab and carriage return to invalid filename chars ([#12327](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12327)) * fix api only Lora not working ([#12387](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12387)) * fix options in main UI misbehaving when there's just one element * make it possible to use a sampler from infotext even if it's hidden in the dropdown * fix styles missing from the prompt in infotext when making a grid of batch of multiplie images * prevent bogus progress output in console when calculating hires fix dimensions * fix --use-textbox-seed * fix broken `Lora/Networks: use old method` option ([#12466](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12466)) * properly return `None` for VAE hash when using `--no-hashing` ([#12463](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12463)) * MPS/macOS fixes and optimizations ([#12526](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12526)) * add second_order to samplers that mistakenly didn't have it * when refreshing cards in extra networks UI, do not discard user's custom resolution * fix processing error that happens if batch_size is not a multiple of how many prompts/negative prompts there are ([#12509](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12509)) * fix inpaint upload for alpha masks ([#12588](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12588)) * fix exception when image sizes are not integers ([#12586](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12586)) * fix incorrect TAESD Latent scale ([#12596](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12596)) * auto add data-dir to gradio-allowed-path ([#12603](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12603)) * fix exception if extensuions dir is missing ([#12607](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12607)) * fix issues with api model-refresh and vae-refresh ([#12638](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12638)) * fix img2img background color for transparent images option not being used ([#12633](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12633)) * attempt to resolve NaN issue with unstable VAEs in fp32 mk2 ([#12630](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12630)) * implement missing undo hijack for SDXL * fix xyz swap axes ([#12684](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12684)) * fix errors in backup/restore tab if any of config files are broken ([#12689](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12689)) * fix SD VAE switch error after model reuse ([#12685](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12685)) * fix trying to create images too large for the chosen format ([#12667](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12667)) * create Gradio temp directory if necessary ([#12717](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12717)) * prevent possible cache loss if exiting as it's being written by using an atomic operation to replace the cache with the new version * set devices.dtype_unet correctly * run RealESRGAN on GPU for non-CUDA devices ([#12737](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12737)) * prevent extra network buttons being obscured by description for very small card sizes ([#12745](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12745)) * fix error that causes some extra networks to be disabled if both and are present in the prompt * fix defaults settings page breaking when any of main UI tabs are hidden * fix incorrect save/display of new values in Defaults page in settings * fix for Reload UI function: if you reload UI on one tab, other opened tabs will no longer stop working * fix an error that prevents VAE being reloaded after an option change if a VAE near the checkpoint exists ([#12797](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12737)) * hide broken image crop tool ([#12792](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12737)) * don't show hidden samplers in dropdown for XYZ script ([#12780](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12737)) * fix style editing dialog breaking if it's opened in both img2img and txt2img tabs * fix a bug allowing users to bypass gradio and API authentication (reported by vysecurity) * fix notification not playing when built-in webui tab is inactive ([#12834](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12834)) * honor `--skip-install` for extension installers ([#12832](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12832)) * don't print blank stdout in extension installers ([#12833](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12832), [#12855](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12855)) * do not change quicksettings dropdown option when value returned is `None` ([#12854](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12854)) * get progressbar to display correctly in extensions tab ## 1.5.2 ### Bug Fixes: * fix memory leak when generation fails * update doggettx cross attention optimization to not use an unreasonable amount of memory in some edge cases -- suggestion by MorkTheOrk ## 1.5.1 ### Minor: * support parsing text encoder blocks in some new LoRAs * delete scale checker script due to user demand ### Extensions and API: * add postprocess_batch_list script callback ### Bug Fixes: * fix TI training for SD1 * fix reload altclip model error * prepend the pythonpath instead of overriding it * fix typo in SD_WEBUI_RESTARTING * if txt2img/img2img raises an exception, finally call state.end() * fix composable diffusion weight parsing * restyle Startup profile for black users * fix webui not launching with --nowebui * catch exception for non git extensions * fix some options missing from /sdapi/v1/options * fix for extension update status always saying "unknown" * fix display of extra network cards that have `<>` in the name * update lora extension to work with python 3.8 ## 1.5.0 ### Features: * SD XL support * user metadata system for custom networks * extended Lora metadata editor: set activation text, default weight, view tags, training info * Lora extension rework to include other types of networks (all that were previously handled by LyCORIS extension) * show github stars for extensions * img2img batch mode can read extra stuff from png info * img2img batch works with subdirectories * hotkeys to move prompt elements: alt+left/right * restyle time taken/VRAM display * add textual inversion hashes to infotext * optimization: cache git extension repo information * move generate button next to the generated picture for mobile clients * hide cards for networks of incompatible Stable Diffusion version in Lora extra networks interface * skip installing packages with pip if they all are already installed - startup speedup of about 2 seconds ### Minor: * checkbox to check/uncheck all extensions in the Installed tab * add gradio user to infotext and to filename patterns * allow gif for extra network previews * add options to change colors in grid * use natural sort for items in extra networks * Mac: use empty_cache() from torch 2 to clear VRAM * added automatic support for installing the right libraries for Navi3 (AMD) * add option SWIN_torch_compile to accelerate SwinIR upscale * suppress printing TI embedding info at start to console by default * speedup extra networks listing * added `[none]` filename token. * removed thumbs extra networks view mode (use settings tab to change width/height/scale to get thumbs) * add always_discard_next_to_last_sigma option to XYZ plot * automatically switch to 32-bit float VAE if the generated picture has NaNs without the need for `--no-half-vae` commandline flag. ### Extensions and API: * api endpoints: /sdapi/v1/server-kill, /sdapi/v1/server-restart, /sdapi/v1/server-stop * allow Script to have custom metaclass * add model exists status check /sdapi/v1/options * rename --add-stop-route to --api-server-stop * add `before_hr` script callback * add callback `after_extra_networks_activate` * disable rich exception output in console for API by default, use WEBUI_RICH_EXCEPTIONS env var to enable * return http 404 when thumb file not found * allow replacing extensions index with environment variable ### Bug Fixes: * fix for catch errors when retrieving extension index #11290 * fix very slow loading speed of .safetensors files when reading from network drives * API cache cleanup * fix UnicodeEncodeError when writing to file CLIP Interrogator batch mode * fix warning of 'has_mps' deprecated from PyTorch * fix problem with extra network saving images as previews losing generation info * fix throwing exception when trying to resize image with I;16 mode * fix for #11534: canvas zoom and pan extension hijacking shortcut keys * fixed launch script to be runnable from any directory * don't add "Seed Resize: -1x-1" to API image metadata * correctly remove end parenthesis with ctrl+up/down * fixing --subpath on newer gradio version * fix: check fill size none zero when resize (fixes #11425) * use submit and blur for quick settings textbox * save img2img batch with images.save_image() * prevent running preload.py for disabled extensions * fix: previously, model name was added together with directory name to infotext and to [model_name] filename pattern; directory name is now not included ## 1.4.1 ### Bug Fixes: * add queue lock for refresh-checkpoints ## 1.4.0 ### Features: * zoom controls for inpainting * run basic torch calculation at startup in parallel to reduce the performance impact of first generation * option to pad prompt/neg prompt to be same length * remove taming_transformers dependency * custom k-diffusion scheduler settings * add an option to show selected settings in main txt2img/img2img UI * sysinfo tab in settings * infer styles from prompts when pasting params into the UI * an option to control the behavior of the above ### Minor: * bump Gradio to 3.32.0 * bump xformers to 0.0.20 * Add option to disable token counters * tooltip fixes & optimizations * make it possible to configure filename for the zip download * `[vae_filename]` pattern for filenames * Revert discarding penultimate sigma for DPM-Solver++(2M) SDE * change UI reorder setting to multiselect * read version info form CHANGELOG.md if git version info is not available * link footer API to Wiki when API is not active * persistent conds cache (opt-in optimization) ### Extensions: * After installing extensions, webui properly restarts the process rather than reloads the UI * Added VAE listing to web API. Via: /sdapi/v1/sd-vae * custom unet support * Add onAfterUiUpdate callback * refactor EmbeddingDatabase.register_embedding() to allow unregistering * add before_process callback for scripts * add ability for alwayson scripts to specify section and let user reorder those sections ### Bug Fixes: * Fix dragging text to prompt * fix incorrect quoting for infotext values with colon in them * fix "hires. fix" prompt sharing same labels with txt2img_prompt * Fix s_min_uncond default type int * Fix for #10643 (Inpainting mask sometimes not working) * fix bad styling for thumbs view in extra networks #10639 * fix for empty list of optimizations #10605 * small fixes to prepare_tcmalloc for Debian/Ubuntu compatibility * fix --ui-debug-mode exit * patch GitPython to not use leaky persistent processes * fix duplicate Cross attention optimization after UI reload * torch.cuda.is_available() check for SdOptimizationXformers * fix hires fix using wrong conds in second pass if using Loras. * handle exception when parsing generation parameters from png info * fix upcast attention dtype error * forcing Torch Version to 1.13.1 for RX 5000 series GPUs * split mask blur into X and Y components, patch Outpainting MK2 accordingly * don't die when a LoRA is a broken symlink * allow activation of Generate Forever during generation ## 1.3.2 ### Bug Fixes: * fix files served out of tmp directory even if they are saved to disk * fix postprocessing overwriting parameters ## 1.3.1 ### Features: * revert default cross attention optimization to Doggettx ### Bug Fixes: * fix bug: LoRA don't apply on dropdown list sd_lora * fix png info always added even if setting is not enabled * fix some fields not applying in xyz plot * fix "hires. fix" prompt sharing same labels with txt2img_prompt * fix lora hashes not being added properly to infotex if there is only one lora * fix --use-cpu failing to work properly at startup * make --disable-opt-split-attention command line option work again ## 1.3.0 ### Features: * add UI to edit defaults * token merging (via dbolya/tomesd) * settings tab rework: add a lot of additional explanations and links * load extensions' Git metadata in parallel to loading the main program to save a ton of time during startup * update extensions table: show branch, show date in separate column, and show version from tags if available * TAESD - another option for cheap live previews * allow choosing sampler and prompts for second pass of hires fix - hidden by default, enabled in settings * calculate hashes for Lora * add lora hashes to infotext * when pasting infotext, use infotext's lora hashes to find local loras for `` entries whose hashes match loras the user has * select cross attention optimization from UI ### Minor: * bump Gradio to 3.31.0 * bump PyTorch to 2.0.1 for macOS and Linux AMD * allow setting defaults for elements in extensions' tabs * allow selecting file type for live previews * show "Loading..." for extra networks when displaying for the first time * suppress ENSD infotext for samplers that don't use it * clientside optimizations * add options to show/hide hidden files and dirs in extra networks, and to not list models/files in hidden directories * allow whitespace in styles.csv * add option to reorder tabs * move some functionality (swap resolution and set seed to -1) to client * option to specify editor height for img2img * button to copy image resolution into img2img width/height sliders * switch from pyngrok to ngrok-py * lazy-load images in extra networks UI * set "Navigate image viewer with gamepad" option to false by default, by request * change upscalers to download models into user-specified directory (from commandline args) rather than the default models/<...> * allow hiding buttons in ui-config.json ### Extensions: * add /sdapi/v1/script-info api * use Ruff to lint Python code * use ESlint to lint Javascript code * add/modify CFG callbacks for Self-Attention Guidance extension * add command and endpoint for graceful server stopping * add some locals (prompts/seeds/etc) from processing function into the Processing class as fields * rework quoting for infotext items that have commas in them to use JSON (should be backwards compatible except for cases where it didn't work previously) * add /sdapi/v1/refresh-loras api checkpoint post request * tests overhaul ### Bug Fixes: * fix an issue preventing the program from starting if the user specifies a bad Gradio theme * fix broken prompts from file script * fix symlink scanning for extra networks * fix --data-dir ignored when launching via webui-user.bat COMMANDLINE_ARGS * allow web UI to be ran fully offline * fix inability to run with --freeze-settings * fix inability to merge checkpoint without adding metadata * fix extra networks' save preview image not adding infotext for jpeg/webm * remove blinking effect from text in hires fix and scale resolution preview * make links to `http://<...>.git` extensions work in the extension tab * fix bug with webui hanging at startup due to hanging git process ## 1.2.1 ### Features: * add an option to always refer to LoRA by filenames ### Bug Fixes: * never refer to LoRA by an alias if multiple LoRAs have same alias or the alias is called none * fix upscalers disappearing after the user reloads UI * allow bf16 in safe unpickler (resolves problems with loading some LoRAs) * allow web UI to be ran fully offline * fix localizations not working * fix error for LoRAs: `'LatentDiffusion' object has no attribute 'lora_layer_mapping'` ## 1.2.0 ### Features: * do not wait for Stable Diffusion model to load at startup * add filename patterns: `[denoising]` * directory hiding for extra networks: dirs starting with `.` will hide their cards on extra network tabs unless specifically searched for * LoRA: for the `<...>` text in prompt, use name of LoRA that is in the metadata of the file, if present, instead of filename (both can be used to activate LoRA) * LoRA: read infotext params from kohya-ss's extension parameters if they are present and if his extension is not active * LoRA: fix some LoRAs not working (ones that have 3x3 convolution layer) * LoRA: add an option to use old method of applying LoRAs (producing same results as with kohya-ss) * add version to infotext, footer and console output when starting * add links to wiki for filename pattern settings * add extended info for quicksettings setting and use multiselect input instead of a text field ### Minor: * bump Gradio to 3.29.0 * bump PyTorch to 2.0.1 * `--subpath` option for gradio for use with reverse proxy * Linux/macOS: use existing virtualenv if already active (the VIRTUAL_ENV environment variable) * do not apply localizations if there are none (possible frontend optimization) * add extra `None` option for VAE in XYZ plot * print error to console when batch processing in img2img fails * create HTML for extra network pages only on demand * allow directories starting with `.` to still list their models for LoRA, checkpoints, etc * put infotext options into their own category in settings tab * do not show licenses page when user selects Show all pages in settings ### Extensions: * tooltip localization support * add API method to get LoRA models with prompt ### Bug Fixes: * re-add `/docs` endpoint * fix gamepad navigation * make the lightbox fullscreen image function properly * fix squished thumbnails in extras tab * keep "search" filter for extra networks when user refreshes the tab (previously it showed everything after you refreshed) * fix webui showing the same image if you configure the generation to always save results into same file * fix bug with upscalers not working properly * fix MPS on PyTorch 2.0.1, Intel Macs * make it so that custom context menu from contextMenu.js only disappears after user's click, ignoring non-user click events * prevent Reload UI button/link from reloading the page when it's not yet ready * fix prompts from file script failing to read contents from a drag/drop file ## 1.1.1 ### Bug Fixes: * fix an error that prevents running webui on PyTorch<2.0 without --disable-safe-unpickle ## 1.1.0 ### Features: * switch to PyTorch 2.0.0 (except for AMD GPUs) * visual improvements to custom code scripts * add filename patterns: `[clip_skip]`, `[hasprompt<>]`, `[batch_number]`, `[generation_number]` * add support for saving init images in img2img, and record their hashes in infotext for reproducibility * automatically select current word when adjusting weight with ctrl+up/down * add dropdowns for X/Y/Z plot * add setting: Stable Diffusion/Random number generator source: makes it possible to make images generated from a given manual seed consistent across different GPUs * support Gradio's theme API * use TCMalloc on Linux by default; possible fix for memory leaks * add optimization option to remove negative conditioning at low sigma values #9177 * embed model merge metadata in .safetensors file * extension settings backup/restore feature #9169 * add "resize by" and "resize to" tabs to img2img * add option "keep original size" to textual inversion images preprocess * image viewer scrolling via analog stick * button to restore the progress from session lost / tab reload ### Minor: * bump Gradio to 3.28.1 * change "scale to" to sliders in Extras tab * add labels to tool buttons to make it possible to hide them * add tiled inference support for ScuNET * add branch support for extension installation * change Linux installation script to install into current directory rather than `/home/username` * sort textual inversion embeddings by name (case-insensitive) * allow styles.csv to be symlinked or mounted in docker * remove the "do not add watermark to images" option * make selected tab configurable with UI config * make the extra networks UI fixed height and scrollable * add `disable_tls_verify` arg for use with self-signed certs ### Extensions: * add reload callback * add `is_hr_pass` field for processing ### Bug Fixes: * fix broken batch image processing on 'Extras/Batch Process' tab * add "None" option to extra networks dropdowns * fix FileExistsError for CLIP Interrogator * fix /sdapi/v1/txt2img endpoint not working on Linux #9319 * fix disappearing live previews and progressbar during slow tasks * fix fullscreen image view not working properly in some cases * prevent alwayson_scripts args param resizing script_arg list when they are inserted in it * fix prompt schedule for second order samplers * fix image mask/composite for weird resolutions #9628 * use correct images for previews when using AND (see #9491) * one broken image in img2img batch won't stop all processing * fix image orientation bug in train/preprocess * fix Ngrok recreating tunnels every reload * fix `--realesrgan-models-path` and `--ldsr-models-path` not working * fix `--skip-install` not working * use SAMPLE file format in Outpainting Mk2 & Poorman * do not fail all LoRAs if some have failed to load when making a picture ## 1.0.0 * everything stable-diffusion-webui/CITATION.cff000066400000000000000000000003631465116634300173320ustar00rootroot00000000000000cff-version: 1.2.0 message: "If you use this software, please cite it as below." authors: - given-names: AUTOMATIC1111 title: "Stable Diffusion Web UI" date-released: 2022-08-22 url: "https://github.com/AUTOMATIC1111/stable-diffusion-webui" stable-diffusion-webui/CODEOWNERS000066400000000000000000000012211465116634300170250ustar00rootroot00000000000000* @AUTOMATIC1111 # if you were managing a localization and were removed from this file, this is because # the intended way to do localizations now is via extensions. See: # https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Developing-extensions # Make a repo with your localization and since you are still listed as a collaborator # you can add it to the wiki page yourself. This change is because some people complained # the git commit log is cluttered with things unrelated to almost everyone and # because I believe this is the best overall for the project to handle localizations almost # entirely without my oversight. stable-diffusion-webui/LICENSE.txt000066400000000000000000001046501465116634300172670ustar00rootroot00000000000000 GNU AFFERO GENERAL PUBLIC LICENSE Version 3, 19 November 2007 Copyright (c) 2023 AUTOMATIC1111 Copyright (C) 2007 Free Software Foundation, Inc. Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. 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For more information on this, and how to apply and follow the GNU AGPL, see . stable-diffusion-webui/README.md000066400000000000000000000311741465116634300167230ustar00rootroot00000000000000# Stable Diffusion web UI A web interface for Stable Diffusion, implemented using Gradio library. ![](screenshot.png) ## 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 - Color Sketch - 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` (or `Command+Up` or `Command+Down` if you're on a MacOS) to automatically adjust attention to selected text (code contributed by anonymous user) - Loopback, run img2img processing multiple times - X/Y/Z plot, a way to draw a 3 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 - train embeddings on 8GB (also reports of 6GB working) - 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 - Live prompt token length validation - 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 - drag and drop an image/text-parameters to promptbox - Read Generation Parameters Button, loads parameters in promptbox to UI - 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 - Tiling support, a checkbox to create images that can be tiled like textures - Progress bar and live image generation preview - Can use a separate neural network to produce previews with almost none VRAM or compute requirement - 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, reverse Euler method of cross attention control - 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 up to 3 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 - [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add `--xformers` to commandline args) - via extension: [History tab](https://github.com/yfszzx/stable-diffusion-webui-images-browser): view, direct and delete images conveniently within the UI - Generate forever option - Training tab - hypernetworks and embeddings options - Preprocessing images: cropping, mirroring, autotagging using BLIP or deepdanbooru (for anime) - Clip skip - Hypernetworks - Loras (same as Hypernetworks but more pretty) - A separate UI where you can choose, with preview, which embeddings, hypernetworks or Loras to add to your prompt - Can select to load a different VAE from settings screen - Estimated completion time in progress bar - API - Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML - via extension: [Aesthetic Gradients](https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients), a way to generate images with a specific aesthetic by using clip images embeds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients)) - [Stable Diffusion 2.0](https://github.com/Stability-AI/stablediffusion) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable-diffusion-20) for instructions - [Alt-Diffusion](https://arxiv.org/abs/2211.06679) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#alt-diffusion) for instructions - Now without any bad letters! - Load checkpoints in safetensors format - Eased resolution restriction: generated image's dimensions must be a multiple of 8 rather than 64 - Now with a license! - Reorder elements in the UI from settings screen - [Segmind Stable Diffusion](https://huggingface.co/segmind/SSD-1B) support ## 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: - [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) - [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs. - [Intel CPUs, Intel GPUs (both integrated and discrete)](https://github.com/openvinotoolkit/stable-diffusion-webui/wiki/Installation-on-Intel-Silicon) (external wiki page) - [Ascend NPUs](https://github.com/wangshuai09/stable-diffusion-webui/wiki/Install-and-run-on-Ascend-NPUs) (external wiki page) Alternatively, use online services (like Google Colab): - [List of Online Services](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services) ### Installation on Windows 10/11 with NVidia-GPUs using release package 1. Download `sd.webui.zip` from [v1.0.0-pre](https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases/tag/v1.0.0-pre) and extract its contents. 2. Run `update.bat`. 3. Run `run.bat`. > For more details see [Install-and-Run-on-NVidia-GPUs](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) ### Automatic Installation on Windows 1. Install [Python 3.10.6](https://www.python.org/downloads/release/python-3106/) (Newer version of Python does not support torch), 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. 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 libgl1 libglib2.0-0 # Red Hat-based: sudo dnf install wget git python3 gperftools-libs libglvnd-glx # openSUSE-based: sudo zypper install wget git python3 libtcmalloc4 libglvnd # Arch-based: sudo pacman -S wget git python3 ``` If your system is very new, you need to install python3.11 or python3.10: ```bash # Ubuntu 24.04 sudo add-apt-repository ppa:deadsnakes/ppa sudo apt update sudo apt install python3.11 # Manjaro/Arch sudo pacman -S yay yay -S python311 # do not confuse with python3.11 package # Only for 3.11 # Then set up env variable in launch script export python_cmd="python3.11" # or in webui-user.sh python_cmd="python3.11" ``` 2. Navigate to the directory you would like the webui to be installed and execute the following command: ```bash wget -q https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh ``` Or just clone the repo wherever you want: ```bash git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui ``` 3. Run `webui.sh`. 4. Check `webui-user.sh` for options. ### 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). For the purposes of getting Google and other search engines to crawl the wiki, here's a link to the (not for humans) [crawlable wiki](https://github-wiki-see.page/m/AUTOMATIC1111/stable-diffusion-webui/wiki). ## Credits Licenses for borrowed code can be found in `Settings -> Licenses` screen, and also in `html/licenses.html` file. - Stable Diffusion - https://github.com/Stability-AI/stablediffusion, https://github.com/CompVis/taming-transformers, https://github.com/mcmonkey4eva/sd3-ref - k-diffusion - https://github.com/crowsonkb/k-diffusion.git - Spandrel - https://github.com/chaiNNer-org/spandrel implementing - 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 - MiDaS - https://github.com/isl-org/MiDaS - Ideas for optimizations - https://github.com/basujindal/stable-diffusion - Cross Attention layer optimization - Doggettx - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing. - Cross Attention layer optimization - InvokeAI, lstein - https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion) - Sub-quadratic Cross Attention layer optimization - Alex Birch (https://github.com/Birch-san/diffusers/pull/1), Amin Rezaei (https://github.com/AminRezaei0x443/memory-efficient-attention) - Textual Inversion - Rinon Gal - 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 - Sampling in float32 precision from a float16 UNet - marunine for the idea, Birch-san for the example Diffusers implementation (https://github.com/Birch-san/diffusers-play/tree/92feee6) - Instruct pix2pix - Tim Brooks (star), Aleksander Holynski (star), Alexei A. Efros (no star) - https://github.com/timothybrooks/instruct-pix2pix - Security advice - RyotaK - UniPC sampler - Wenliang Zhao - https://github.com/wl-zhao/UniPC - TAESD - Ollin Boer Bohan - https://github.com/madebyollin/taesd - LyCORIS - KohakuBlueleaf - Restart sampling - lambertae - https://github.com/Newbeeer/diffusion_restart_sampling - Hypertile - tfernd - https://github.com/tfernd/HyperTile - Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user. - (You) stable-diffusion-webui/_typos.toml000066400000000000000000000002221465116634300176440ustar00rootroot00000000000000[default.extend-words] # Part of "RGBa" (Pillow's pre-multiplied alpha RGB mode) Ba = "Ba" # HSA is something AMD uses for their GPUs HSA = "HSA" stable-diffusion-webui/configs/000077500000000000000000000000001465116634300170665ustar00rootroot00000000000000stable-diffusion-webui/configs/alt-diffusion-inference.yaml000066400000000000000000000035711465116634300244600ustar00rootroot00000000000000model: base_learning_rate: 1.0e-04 target: ldm.models.diffusion.ddpm.LatentDiffusion params: linear_start: 0.00085 linear_end: 0.0120 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: "jpg" cond_stage_key: "txt" image_size: 64 channels: 4 cond_stage_trainable: false # Note: different from the one we trained before conditioning_key: crossattn monitor: val/loss_simple_ema scale_factor: 0.18215 use_ema: False scheduler_config: # 10000 warmup steps target: ldm.lr_scheduler.LambdaLinearScheduler params: warm_up_steps: [ 10000 ] cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases f_start: [ 1.e-6 ] f_max: [ 1. ] f_min: [ 1. ] unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: image_size: 32 # unused in_channels: 4 out_channels: 4 model_channels: 320 attention_resolutions: [ 4, 2, 1 ] num_res_blocks: 2 channel_mult: [ 1, 2, 4, 4 ] num_heads: 8 use_spatial_transformer: True transformer_depth: 1 context_dim: 768 use_checkpoint: False legacy: False first_stage_config: target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss ddconfig: double_z: true z_channels: 4 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: modules.xlmr.BertSeriesModelWithTransformation params: name: "XLMR-Large"stable-diffusion-webui/configs/alt-diffusion-m18-inference.yaml000066400000000000000000000036601465116634300250620ustar00rootroot00000000000000model: base_learning_rate: 1.0e-04 target: ldm.models.diffusion.ddpm.LatentDiffusion params: linear_start: 0.00085 linear_end: 0.0120 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: "jpg" cond_stage_key: "txt" image_size: 64 channels: 4 cond_stage_trainable: false # Note: different from the one we trained before conditioning_key: crossattn monitor: val/loss_simple_ema scale_factor: 0.18215 use_ema: False scheduler_config: # 10000 warmup steps target: ldm.lr_scheduler.LambdaLinearScheduler params: warm_up_steps: [ 10000 ] cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases f_start: [ 1.e-6 ] f_max: [ 1. ] f_min: [ 1. ] unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: image_size: 32 # unused in_channels: 4 out_channels: 4 model_channels: 320 attention_resolutions: [ 4, 2, 1 ] num_res_blocks: 2 channel_mult: [ 1, 2, 4, 4 ] num_head_channels: 64 use_spatial_transformer: True use_linear_in_transformer: True transformer_depth: 1 context_dim: 1024 use_checkpoint: False legacy: False first_stage_config: target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss ddconfig: double_z: true z_channels: 4 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: modules.xlmr_m18.BertSeriesModelWithTransformation params: name: "XLMR-Large" stable-diffusion-webui/configs/instruct-pix2pix.yaml000066400000000000000000000050511465116634300232270ustar00rootroot00000000000000# File modified by authors of InstructPix2Pix from original (https://github.com/CompVis/stable-diffusion). # See more details in LICENSE. model: base_learning_rate: 1.0e-04 target: modules.models.diffusion.ddpm_edit.LatentDiffusion params: linear_start: 0.00085 linear_end: 0.0120 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: edited cond_stage_key: edit # image_size: 64 # image_size: 32 image_size: 16 channels: 4 cond_stage_trainable: false # Note: different from the one we trained before conditioning_key: hybrid monitor: val/loss_simple_ema scale_factor: 0.18215 use_ema: false scheduler_config: # 10000 warmup steps target: ldm.lr_scheduler.LambdaLinearScheduler params: warm_up_steps: [ 0 ] cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases f_start: [ 1.e-6 ] f_max: [ 1. ] f_min: [ 1. ] unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: image_size: 32 # unused in_channels: 8 out_channels: 4 model_channels: 320 attention_resolutions: [ 4, 2, 1 ] num_res_blocks: 2 channel_mult: [ 1, 2, 4, 4 ] num_heads: 8 use_spatial_transformer: True transformer_depth: 1 context_dim: 768 use_checkpoint: False legacy: False first_stage_config: target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss ddconfig: double_z: true z_channels: 4 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: ldm.modules.encoders.modules.FrozenCLIPEmbedder data: target: main.DataModuleFromConfig params: batch_size: 128 num_workers: 1 wrap: false validation: target: edit_dataset.EditDataset params: path: data/clip-filtered-dataset cache_dir: data/ cache_name: data_10k split: val min_text_sim: 0.2 min_image_sim: 0.75 min_direction_sim: 0.2 max_samples_per_prompt: 1 min_resize_res: 512 max_resize_res: 512 crop_res: 512 output_as_edit: False real_input: True stable-diffusion-webui/configs/sd3-inference.yaml000066400000000000000000000001501465116634300223730ustar00rootroot00000000000000model: target: modules.models.sd3.sd3_model.SD3Inferencer params: shift: 3 state_dict: null stable-diffusion-webui/configs/sd_xl_inpaint.yaml000066400000000000000000000062601465116634300226110ustar00rootroot00000000000000model: target: sgm.models.diffusion.DiffusionEngine params: scale_factor: 0.13025 disable_first_stage_autocast: True denoiser_config: target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser params: num_idx: 1000 weighting_config: target: sgm.modules.diffusionmodules.denoiser_weighting.EpsWeighting scaling_config: target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling discretization_config: target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization network_config: target: sgm.modules.diffusionmodules.openaimodel.UNetModel params: adm_in_channels: 2816 num_classes: sequential use_checkpoint: False in_channels: 9 out_channels: 4 model_channels: 320 attention_resolutions: [4, 2] num_res_blocks: 2 channel_mult: [1, 2, 4] num_head_channels: 64 use_spatial_transformer: True use_linear_in_transformer: True transformer_depth: [1, 2, 10] # note: the first is unused (due to attn_res starting at 2) 32, 16, 8 --> 64, 32, 16 context_dim: 2048 spatial_transformer_attn_type: softmax-xformers legacy: False conditioner_config: target: sgm.modules.GeneralConditioner params: emb_models: # crossattn cond - is_trainable: False input_key: txt target: sgm.modules.encoders.modules.FrozenCLIPEmbedder params: layer: hidden layer_idx: 11 # crossattn and vector cond - is_trainable: False input_key: txt target: sgm.modules.encoders.modules.FrozenOpenCLIPEmbedder2 params: arch: ViT-bigG-14 version: laion2b_s39b_b160k freeze: True layer: penultimate always_return_pooled: True legacy: False # vector cond - is_trainable: False input_key: original_size_as_tuple target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND params: outdim: 256 # multiplied by two # vector cond - is_trainable: False input_key: crop_coords_top_left target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND params: outdim: 256 # multiplied by two # vector cond - is_trainable: False input_key: target_size_as_tuple target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND params: outdim: 256 # multiplied by two first_stage_config: target: sgm.models.autoencoder.AutoencoderKLInferenceWrapper params: embed_dim: 4 monitor: val/rec_loss ddconfig: attn_type: vanilla-xformers double_z: true z_channels: 4 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: [1, 2, 4, 4] num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: torch.nn.Identity stable-diffusion-webui/configs/v1-inference.yaml000066400000000000000000000035221465116634300222360ustar00rootroot00000000000000model: base_learning_rate: 1.0e-04 target: ldm.models.diffusion.ddpm.LatentDiffusion params: linear_start: 0.00085 linear_end: 0.0120 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: "jpg" cond_stage_key: "txt" image_size: 64 channels: 4 cond_stage_trainable: false # Note: different from the one we trained before conditioning_key: crossattn monitor: val/loss_simple_ema scale_factor: 0.18215 use_ema: False scheduler_config: # 10000 warmup steps target: ldm.lr_scheduler.LambdaLinearScheduler params: warm_up_steps: [ 10000 ] cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases f_start: [ 1.e-6 ] f_max: [ 1. ] f_min: [ 1. ] unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: image_size: 32 # unused in_channels: 4 out_channels: 4 model_channels: 320 attention_resolutions: [ 4, 2, 1 ] num_res_blocks: 2 channel_mult: [ 1, 2, 4, 4 ] num_heads: 8 use_spatial_transformer: True transformer_depth: 1 context_dim: 768 use_checkpoint: False legacy: False first_stage_config: target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss ddconfig: double_z: true z_channels: 4 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: ldm.modules.encoders.modules.FrozenCLIPEmbedder stable-diffusion-webui/configs/v1-inpainting-inference.yaml000066400000000000000000000037101465116634300243730ustar00rootroot00000000000000model: base_learning_rate: 7.5e-05 target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion params: linear_start: 0.00085 linear_end: 0.0120 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: "jpg" cond_stage_key: "txt" image_size: 64 channels: 4 cond_stage_trainable: false # Note: different from the one we trained before conditioning_key: hybrid # important monitor: val/loss_simple_ema scale_factor: 0.18215 finetune_keys: null scheduler_config: # 10000 warmup steps target: ldm.lr_scheduler.LambdaLinearScheduler params: warm_up_steps: [ 2500 ] # NOTE for resuming. use 10000 if starting from scratch cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases f_start: [ 1.e-6 ] f_max: [ 1. ] f_min: [ 1. ] unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: image_size: 32 # unused in_channels: 9 # 4 data + 4 downscaled image + 1 mask out_channels: 4 model_channels: 320 attention_resolutions: [ 4, 2, 1 ] num_res_blocks: 2 channel_mult: [ 1, 2, 4, 4 ] num_heads: 8 use_spatial_transformer: True transformer_depth: 1 context_dim: 768 use_checkpoint: False legacy: False first_stage_config: target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss ddconfig: double_z: true z_channels: 4 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: ldm.modules.encoders.modules.FrozenCLIPEmbedder stable-diffusion-webui/embeddings/000077500000000000000000000000001465116634300175375ustar00rootroot00000000000000stable-diffusion-webui/embeddings/Place Textual Inversion embeddings here.txt000066400000000000000000000000001465116634300276440ustar00rootroot00000000000000stable-diffusion-webui/environment-wsl2.yaml000066400000000000000000000002471465116634300215560ustar00rootroot00000000000000name: automatic channels: - pytorch - defaults dependencies: - python=3.10 - pip=23.0 - cudatoolkit=11.8 - pytorch=2.0 - torchvision=0.15 - numpy=1.23 stable-diffusion-webui/extensions-builtin/000077500000000000000000000000001465116634300213015ustar00rootroot00000000000000stable-diffusion-webui/extensions-builtin/LDSR/000077500000000000000000000000001465116634300220455ustar00rootroot00000000000000stable-diffusion-webui/extensions-builtin/LDSR/ldsr_model_arch.py000066400000000000000000000230371465116634300255450ustar00rootroot00000000000000import os import gc import time import numpy as np import torch import torchvision from PIL import Image from einops import rearrange, repeat from omegaconf import OmegaConf import safetensors.torch from ldm.models.diffusion.ddim import DDIMSampler from ldm.util import instantiate_from_config, ismap from modules import shared, sd_hijack, devices cached_ldsr_model: torch.nn.Module = None # Create LDSR Class class LDSR: def load_model_from_config(self, half_attention): global cached_ldsr_model if shared.opts.ldsr_cached and cached_ldsr_model is not None: print("Loading model from cache") model: torch.nn.Module = cached_ldsr_model else: print(f"Loading model from {self.modelPath}") _, extension = os.path.splitext(self.modelPath) if extension.lower() == ".safetensors": pl_sd = safetensors.torch.load_file(self.modelPath, device="cpu") else: pl_sd = torch.load(self.modelPath, map_location="cpu") sd = pl_sd["state_dict"] if "state_dict" in pl_sd else pl_sd config = OmegaConf.load(self.yamlPath) config.model.target = "ldm.models.diffusion.ddpm.LatentDiffusionV1" model: torch.nn.Module = instantiate_from_config(config.model) model.load_state_dict(sd, strict=False) model = model.to(shared.device) if half_attention: model = model.half() if shared.cmd_opts.opt_channelslast: model = model.to(memory_format=torch.channels_last) sd_hijack.model_hijack.hijack(model) # apply optimization model.eval() if shared.opts.ldsr_cached: cached_ldsr_model = model 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 _ 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 gc.collect() devices.torch_gc() 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(np.ceil(wd)) height_downsampled_pre = int(np.ceil(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)") # pad width and height to multiples of 64, pads with the edge values of image to avoid artifacts pad_w, pad_h = np.max(((2, 2), np.ceil(np.array(im_og.size) / 64).astype(int)), axis=0) * 64 - im_og.size im_padded = Image.fromarray(np.pad(np.array(im_og), ((0, pad_h), (0, pad_w), (0, 0)), mode='edge')) logs = self.run(model["model"], im_padded, 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]) # remove padding a = a.crop((0, 0) + tuple(np.array(im_og.size) * 4)) del model gc.collect() devices.torch_gc() return a def get_cond(selected_path): example = {} 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(shared.device) 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 = {} 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 Exception: pass log["sample"] = x_sample log["time"] = t1 - t0 return log stable-diffusion-webui/extensions-builtin/LDSR/preload.py000066400000000000000000000003351465116634300240460ustar00rootroot00000000000000import os from modules import paths def preload(parser): parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(paths.models_path, 'LDSR')) stable-diffusion-webui/extensions-builtin/LDSR/scripts/000077500000000000000000000000001465116634300235345ustar00rootroot00000000000000stable-diffusion-webui/extensions-builtin/LDSR/scripts/ldsr_model.py000066400000000000000000000060701465116634300262350ustar00rootroot00000000000000import os from modules.modelloader import load_file_from_url from modules.upscaler import Upscaler, UpscalerData from ldsr_model_arch import LDSR from modules import shared, script_callbacks, errors import sd_hijack_autoencoder # noqa: F401 import sd_hijack_ddpm_v1 # noqa: F401 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") local_model_paths = self.find_models(ext_filter=[".ckpt", ".safetensors"]) local_ckpt_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("model.ckpt")]), None) local_safetensors_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("model.safetensors")]), None) local_yaml_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("project.yaml")]), None) 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) if local_safetensors_path is not None and os.path.exists(local_safetensors_path): model = local_safetensors_path else: model = local_ckpt_path or load_file_from_url(self.model_url, model_dir=self.model_download_path, file_name="model.ckpt") yaml = local_yaml_path or load_file_from_url(self.yaml_url, model_dir=self.model_download_path, file_name="project.yaml") return LDSR(model, yaml) def do_upscale(self, img, path): try: ldsr = self.load_model(path) except Exception: errors.report(f"Failed loading LDSR model {path}", exc_info=True) return img ddim_steps = shared.opts.ldsr_steps return ldsr.super_resolution(img, ddim_steps, self.scale) def on_ui_settings(): import gradio as gr shared.opts.add_option("ldsr_steps", shared.OptionInfo(100, "LDSR processing steps. Lower = faster", gr.Slider, {"minimum": 1, "maximum": 200, "step": 1}, section=('upscaling', "Upscaling"))) shared.opts.add_option("ldsr_cached", shared.OptionInfo(False, "Cache LDSR model in memory", gr.Checkbox, {"interactive": True}, section=('upscaling', "Upscaling"))) script_callbacks.on_ui_settings(on_ui_settings) stable-diffusion-webui/extensions-builtin/LDSR/sd_hijack_autoencoder.py000066400000000000000000000270741465116634300267400ustar00rootroot00000000000000# The content of this file comes from the ldm/models/autoencoder.py file of the compvis/stable-diffusion repo # The VQModel & VQModelInterface were subsequently removed from ldm/models/autoencoder.py when we moved to the stability-ai/stablediffusion repo # As the LDSR upscaler relies on VQModel & VQModelInterface, the hijack aims to put them back into the ldm.models.autoencoder import numpy as np import torch import pytorch_lightning as pl import torch.nn.functional as F from contextlib import contextmanager from torch.optim.lr_scheduler import LambdaLR from ldm.modules.ema import LitEma from vqvae_quantize import VectorQuantizer2 as VectorQuantizer from ldm.modules.diffusionmodules.model import Encoder, Decoder from ldm.util import instantiate_from_config import ldm.models.autoencoder from packaging import version class VQModel(pl.LightningModule): def __init__(self, ddconfig, lossconfig, n_embed, embed_dim, ckpt_path=None, ignore_keys=None, image_key="image", colorize_nlabels=None, monitor=None, batch_resize_range=None, scheduler_config=None, lr_g_factor=1.0, remap=None, sane_index_shape=False, # tell vector quantizer to return indices as bhw use_ema=False ): super().__init__() self.embed_dim = embed_dim self.n_embed = n_embed self.image_key = image_key self.encoder = Encoder(**ddconfig) self.decoder = Decoder(**ddconfig) self.loss = instantiate_from_config(lossconfig) self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25, remap=remap, sane_index_shape=sane_index_shape) self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1) self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1) if colorize_nlabels is not None: assert type(colorize_nlabels)==int self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1)) if monitor is not None: self.monitor = monitor self.batch_resize_range = batch_resize_range if self.batch_resize_range is not None: print(f"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.") self.use_ema = use_ema if self.use_ema: self.model_ema = LitEma(self) print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") if ckpt_path is not None: self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys or []) self.scheduler_config = scheduler_config self.lr_g_factor = lr_g_factor @contextmanager def ema_scope(self, context=None): if self.use_ema: self.model_ema.store(self.parameters()) self.model_ema.copy_to(self) if context is not None: print(f"{context}: Switched to EMA weights") try: yield None finally: if self.use_ema: self.model_ema.restore(self.parameters()) if context is not None: print(f"{context}: Restored training weights") def init_from_ckpt(self, path, ignore_keys=None): sd = torch.load(path, map_location="cpu")["state_dict"] keys = list(sd.keys()) for k in keys: for ik in ignore_keys or []: if k.startswith(ik): print("Deleting key {} from state_dict.".format(k)) del sd[k] missing, unexpected = self.load_state_dict(sd, strict=False) print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") if missing: print(f"Missing Keys: {missing}") if unexpected: print(f"Unexpected Keys: {unexpected}") def on_train_batch_end(self, *args, **kwargs): if self.use_ema: self.model_ema(self) def encode(self, x): h = self.encoder(x) h = self.quant_conv(h) quant, emb_loss, info = self.quantize(h) return quant, emb_loss, info def encode_to_prequant(self, x): h = self.encoder(x) h = self.quant_conv(h) return h def decode(self, quant): quant = self.post_quant_conv(quant) dec = self.decoder(quant) return dec def decode_code(self, code_b): quant_b = self.quantize.embed_code(code_b) dec = self.decode(quant_b) return dec def forward(self, input, return_pred_indices=False): quant, diff, (_,_,ind) = self.encode(input) dec = self.decode(quant) if return_pred_indices: return dec, diff, ind return dec, diff def get_input(self, batch, k): x = batch[k] if len(x.shape) == 3: x = x[..., None] x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float() if self.batch_resize_range is not None: lower_size = self.batch_resize_range[0] upper_size = self.batch_resize_range[1] if self.global_step <= 4: # do the first few batches with max size to avoid later oom new_resize = upper_size else: new_resize = np.random.choice(np.arange(lower_size, upper_size+16, 16)) if new_resize != x.shape[2]: x = F.interpolate(x, size=new_resize, mode="bicubic") x = x.detach() return x def training_step(self, batch, batch_idx, optimizer_idx): # https://github.com/pytorch/pytorch/issues/37142 # try not to fool the heuristics x = self.get_input(batch, self.image_key) xrec, qloss, ind = self(x, return_pred_indices=True) if optimizer_idx == 0: # autoencode aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step, last_layer=self.get_last_layer(), split="train", predicted_indices=ind) self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True) return aeloss if optimizer_idx == 1: # discriminator discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step, last_layer=self.get_last_layer(), split="train") self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True) return discloss def validation_step(self, batch, batch_idx): log_dict = self._validation_step(batch, batch_idx) with self.ema_scope(): self._validation_step(batch, batch_idx, suffix="_ema") return log_dict def _validation_step(self, batch, batch_idx, suffix=""): x = self.get_input(batch, self.image_key) xrec, qloss, ind = self(x, return_pred_indices=True) aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0, self.global_step, last_layer=self.get_last_layer(), split="val"+suffix, predicted_indices=ind ) discloss, log_dict_disc = self.loss(qloss, x, xrec, 1, self.global_step, last_layer=self.get_last_layer(), split="val"+suffix, predicted_indices=ind ) rec_loss = log_dict_ae[f"val{suffix}/rec_loss"] self.log(f"val{suffix}/rec_loss", rec_loss, prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True) self.log(f"val{suffix}/aeloss", aeloss, prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True) if version.parse(pl.__version__) >= version.parse('1.4.0'): del log_dict_ae[f"val{suffix}/rec_loss"] self.log_dict(log_dict_ae) self.log_dict(log_dict_disc) return self.log_dict def configure_optimizers(self): lr_d = self.learning_rate lr_g = self.lr_g_factor*self.learning_rate print("lr_d", lr_d) print("lr_g", lr_g) opt_ae = torch.optim.Adam(list(self.encoder.parameters())+ list(self.decoder.parameters())+ list(self.quantize.parameters())+ list(self.quant_conv.parameters())+ list(self.post_quant_conv.parameters()), lr=lr_g, betas=(0.5, 0.9)) opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(), lr=lr_d, betas=(0.5, 0.9)) if self.scheduler_config is not None: scheduler = instantiate_from_config(self.scheduler_config) print("Setting up LambdaLR scheduler...") scheduler = [ { 'scheduler': LambdaLR(opt_ae, lr_lambda=scheduler.schedule), 'interval': 'step', 'frequency': 1 }, { 'scheduler': LambdaLR(opt_disc, lr_lambda=scheduler.schedule), 'interval': 'step', 'frequency': 1 }, ] return [opt_ae, opt_disc], scheduler return [opt_ae, opt_disc], [] def get_last_layer(self): return self.decoder.conv_out.weight def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs): log = {} x = self.get_input(batch, self.image_key) x = x.to(self.device) if only_inputs: log["inputs"] = x return log xrec, _ = self(x) if x.shape[1] > 3: # colorize with random projection assert xrec.shape[1] > 3 x = self.to_rgb(x) xrec = self.to_rgb(xrec) log["inputs"] = x log["reconstructions"] = xrec if plot_ema: with self.ema_scope(): xrec_ema, _ = self(x) if x.shape[1] > 3: xrec_ema = self.to_rgb(xrec_ema) log["reconstructions_ema"] = xrec_ema return log def to_rgb(self, x): assert self.image_key == "segmentation" if not hasattr(self, "colorize"): self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x)) x = F.conv2d(x, weight=self.colorize) x = 2.*(x-x.min())/(x.max()-x.min()) - 1. return x class VQModelInterface(VQModel): def __init__(self, embed_dim, *args, **kwargs): super().__init__(*args, embed_dim=embed_dim, **kwargs) self.embed_dim = embed_dim def encode(self, x): h = self.encoder(x) h = self.quant_conv(h) return h def decode(self, h, force_not_quantize=False): # also go through quantization layer if not force_not_quantize: quant, emb_loss, info = self.quantize(h) else: quant = h quant = self.post_quant_conv(quant) dec = self.decoder(quant) return dec ldm.models.autoencoder.VQModel = VQModel ldm.models.autoencoder.VQModelInterface = VQModelInterface stable-diffusion-webui/extensions-builtin/LDSR/sd_hijack_ddpm_v1.py000066400000000000000000002032661465116634300257610ustar00rootroot00000000000000# This script is copied from the compvis/stable-diffusion repo (aka the SD V1 repo) # Original filename: ldm/models/diffusion/ddpm.py # The purpose to reinstate the old DDPM logic which works with VQ, whereas the V2 one doesn't # Some models such as LDSR require VQ to work correctly # The classes are suffixed with "V1" and added back to the "ldm.models.diffusion.ddpm" module import torch import torch.nn as nn import numpy as np import pytorch_lightning as pl from torch.optim.lr_scheduler import LambdaLR from einops import rearrange, repeat from contextlib import contextmanager from functools import partial from tqdm import tqdm from torchvision.utils import make_grid from pytorch_lightning.utilities.distributed import rank_zero_only from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config from ldm.modules.ema import LitEma from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like from ldm.models.diffusion.ddim import DDIMSampler import ldm.models.diffusion.ddpm __conditioning_keys__ = {'concat': 'c_concat', 'crossattn': 'c_crossattn', 'adm': 'y'} def disabled_train(self, mode=True): """Overwrite model.train with this function to make sure train/eval mode does not change anymore.""" return self def uniform_on_device(r1, r2, shape, device): return (r1 - r2) * torch.rand(*shape, device=device) + r2 class DDPMV1(pl.LightningModule): # classic DDPM with Gaussian diffusion, in image space def __init__(self, unet_config, timesteps=1000, beta_schedule="linear", loss_type="l2", ckpt_path=None, ignore_keys=None, load_only_unet=False, monitor="val/loss", use_ema=True, first_stage_key="image", image_size=256, channels=3, log_every_t=100, clip_denoised=True, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3, given_betas=None, original_elbo_weight=0., v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta l_simple_weight=1., conditioning_key=None, parameterization="eps", # all assuming fixed variance schedules scheduler_config=None, use_positional_encodings=False, learn_logvar=False, logvar_init=0., ): super().__init__() assert parameterization in ["eps", "x0"], 'currently only supporting "eps" and "x0"' self.parameterization = parameterization print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode") self.cond_stage_model = None self.clip_denoised = clip_denoised self.log_every_t = log_every_t self.first_stage_key = first_stage_key self.image_size = image_size # try conv? self.channels = channels self.use_positional_encodings = use_positional_encodings self.model = DiffusionWrapperV1(unet_config, conditioning_key) count_params(self.model, verbose=True) self.use_ema = use_ema if self.use_ema: self.model_ema = LitEma(self.model) print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") self.use_scheduler = scheduler_config is not None if self.use_scheduler: self.scheduler_config = scheduler_config self.v_posterior = v_posterior self.original_elbo_weight = original_elbo_weight self.l_simple_weight = l_simple_weight if monitor is not None: self.monitor = monitor if ckpt_path is not None: self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys or [], only_model=load_only_unet) self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s) self.loss_type = loss_type self.learn_logvar = learn_logvar self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,)) if self.learn_logvar: self.logvar = nn.Parameter(self.logvar, requires_grad=True) def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): if exists(given_betas): betas = given_betas else: betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s) alphas = 1. - betas alphas_cumprod = np.cumprod(alphas, axis=0) alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) timesteps, = betas.shape self.num_timesteps = int(timesteps) self.linear_start = linear_start self.linear_end = linear_end assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep' to_torch = partial(torch.tensor, dtype=torch.float32) self.register_buffer('betas', to_torch(betas)) self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev)) # calculations for diffusion q(x_t | x_{t-1}) and others self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))) self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod))) self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod))) self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1))) # calculations for posterior q(x_{t-1} | x_t, x_0) posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / ( 1. - alphas_cumprod) + self.v_posterior * betas # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t) self.register_buffer('posterior_variance', to_torch(posterior_variance)) # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20)))) self.register_buffer('posterior_mean_coef1', to_torch( betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))) self.register_buffer('posterior_mean_coef2', to_torch( (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod))) if self.parameterization == "eps": lvlb_weights = self.betas ** 2 / ( 2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod)) elif self.parameterization == "x0": lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod)) else: raise NotImplementedError("mu not supported") # TODO how to choose this term lvlb_weights[0] = lvlb_weights[1] self.register_buffer('lvlb_weights', lvlb_weights, persistent=False) assert not torch.isnan(self.lvlb_weights).all() @contextmanager def ema_scope(self, context=None): if self.use_ema: self.model_ema.store(self.model.parameters()) self.model_ema.copy_to(self.model) if context is not None: print(f"{context}: Switched to EMA weights") try: yield None finally: if self.use_ema: self.model_ema.restore(self.model.parameters()) if context is not None: print(f"{context}: Restored training weights") def init_from_ckpt(self, path, ignore_keys=None, only_model=False): sd = torch.load(path, map_location="cpu") if "state_dict" in list(sd.keys()): sd = sd["state_dict"] keys = list(sd.keys()) for k in keys: for ik in ignore_keys or []: if k.startswith(ik): print("Deleting key {} from state_dict.".format(k)) del sd[k] missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict( sd, strict=False) print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") if missing: print(f"Missing Keys: {missing}") if unexpected: print(f"Unexpected Keys: {unexpected}") def q_mean_variance(self, x_start, t): """ Get the distribution q(x_t | x_0). :param x_start: the [N x C x ...] tensor of noiseless inputs. :param t: the number of diffusion steps (minus 1). Here, 0 means one step. :return: A tuple (mean, variance, log_variance), all of x_start's shape. """ mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start) variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape) log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape) return mean, variance, log_variance def predict_start_from_noise(self, x_t, t, noise): return ( extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise ) def q_posterior(self, x_start, x_t, t): posterior_mean = ( extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start + extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t ) posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape) posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape) return posterior_mean, posterior_variance, posterior_log_variance_clipped def p_mean_variance(self, x, t, clip_denoised: bool): model_out = self.model(x, t) if self.parameterization == "eps": x_recon = self.predict_start_from_noise(x, t=t, noise=model_out) elif self.parameterization == "x0": x_recon = model_out if clip_denoised: x_recon.clamp_(-1., 1.) model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) return model_mean, posterior_variance, posterior_log_variance @torch.no_grad() def p_sample(self, x, t, clip_denoised=True, repeat_noise=False): b, *_, device = *x.shape, x.device model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised) noise = noise_like(x.shape, device, repeat_noise) # no noise when t == 0 nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise @torch.no_grad() def p_sample_loop(self, shape, return_intermediates=False): device = self.betas.device b = shape[0] img = torch.randn(shape, device=device) intermediates = [img] for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps): img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long), clip_denoised=self.clip_denoised) if i % self.log_every_t == 0 or i == self.num_timesteps - 1: intermediates.append(img) if return_intermediates: return img, intermediates return img @torch.no_grad() def sample(self, batch_size=16, return_intermediates=False): image_size = self.image_size channels = self.channels return self.p_sample_loop((batch_size, channels, image_size, image_size), return_intermediates=return_intermediates) def q_sample(self, x_start, t, noise=None): noise = default(noise, lambda: torch.randn_like(x_start)) return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise) def get_loss(self, pred, target, mean=True): if self.loss_type == 'l1': loss = (target - pred).abs() if mean: loss = loss.mean() elif self.loss_type == 'l2': if mean: loss = torch.nn.functional.mse_loss(target, pred) else: loss = torch.nn.functional.mse_loss(target, pred, reduction='none') else: raise NotImplementedError("unknown loss type '{loss_type}'") return loss def p_losses(self, x_start, t, noise=None): noise = default(noise, lambda: torch.randn_like(x_start)) x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) model_out = self.model(x_noisy, t) loss_dict = {} if self.parameterization == "eps": target = noise elif self.parameterization == "x0": target = x_start else: raise NotImplementedError(f"Parameterization {self.parameterization} not yet supported") loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3]) log_prefix = 'train' if self.training else 'val' loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()}) loss_simple = loss.mean() * self.l_simple_weight loss_vlb = (self.lvlb_weights[t] * loss).mean() loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb}) loss = loss_simple + self.original_elbo_weight * loss_vlb loss_dict.update({f'{log_prefix}/loss': loss}) return loss, loss_dict def forward(self, x, *args, **kwargs): # b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size # assert h == img_size and w == img_size, f'height and width of image must be {img_size}' t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long() return self.p_losses(x, t, *args, **kwargs) def get_input(self, batch, k): x = batch[k] if len(x.shape) == 3: x = x[..., None] x = rearrange(x, 'b h w c -> b c h w') x = x.to(memory_format=torch.contiguous_format).float() return x def shared_step(self, batch): x = self.get_input(batch, self.first_stage_key) loss, loss_dict = self(x) return loss, loss_dict def training_step(self, batch, batch_idx): loss, loss_dict = self.shared_step(batch) self.log_dict(loss_dict, prog_bar=True, logger=True, on_step=True, on_epoch=True) self.log("global_step", self.global_step, prog_bar=True, logger=True, on_step=True, on_epoch=False) if self.use_scheduler: lr = self.optimizers().param_groups[0]['lr'] self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False) return loss @torch.no_grad() def validation_step(self, batch, batch_idx): _, loss_dict_no_ema = self.shared_step(batch) with self.ema_scope(): _, loss_dict_ema = self.shared_step(batch) loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema} self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True) self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True) def on_train_batch_end(self, *args, **kwargs): if self.use_ema: self.model_ema(self.model) def _get_rows_from_list(self, samples): n_imgs_per_row = len(samples) denoise_grid = rearrange(samples, 'n b c h w -> b n c h w') denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w') denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row) return denoise_grid @torch.no_grad() def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs): log = {} x = self.get_input(batch, self.first_stage_key) N = min(x.shape[0], N) n_row = min(x.shape[0], n_row) x = x.to(self.device)[:N] log["inputs"] = x # get diffusion row diffusion_row = [] x_start = x[:n_row] for t in range(self.num_timesteps): if t % self.log_every_t == 0 or t == self.num_timesteps - 1: t = repeat(torch.tensor([t]), '1 -> b', b=n_row) t = t.to(self.device).long() noise = torch.randn_like(x_start) x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) diffusion_row.append(x_noisy) log["diffusion_row"] = self._get_rows_from_list(diffusion_row) if sample: # get denoise row with self.ema_scope("Plotting"): samples, denoise_row = self.sample(batch_size=N, return_intermediates=True) log["samples"] = samples log["denoise_row"] = self._get_rows_from_list(denoise_row) if return_keys: if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0: return log else: return {key: log[key] for key in return_keys} return log def configure_optimizers(self): lr = self.learning_rate params = list(self.model.parameters()) if self.learn_logvar: params = params + [self.logvar] opt = torch.optim.AdamW(params, lr=lr) return opt class LatentDiffusionV1(DDPMV1): """main class""" def __init__(self, first_stage_config, cond_stage_config, num_timesteps_cond=None, cond_stage_key="image", cond_stage_trainable=False, concat_mode=True, cond_stage_forward=None, conditioning_key=None, scale_factor=1.0, scale_by_std=False, *args, **kwargs): self.num_timesteps_cond = default(num_timesteps_cond, 1) self.scale_by_std = scale_by_std assert self.num_timesteps_cond <= kwargs['timesteps'] # for backwards compatibility after implementation of DiffusionWrapper if conditioning_key is None: conditioning_key = 'concat' if concat_mode else 'crossattn' if cond_stage_config == '__is_unconditional__': conditioning_key = None ckpt_path = kwargs.pop("ckpt_path", None) ignore_keys = kwargs.pop("ignore_keys", []) super().__init__(*args, conditioning_key=conditioning_key, **kwargs) self.concat_mode = concat_mode self.cond_stage_trainable = cond_stage_trainable self.cond_stage_key = cond_stage_key try: self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1 except Exception: self.num_downs = 0 if not scale_by_std: self.scale_factor = scale_factor else: self.register_buffer('scale_factor', torch.tensor(scale_factor)) self.instantiate_first_stage(first_stage_config) self.instantiate_cond_stage(cond_stage_config) self.cond_stage_forward = cond_stage_forward self.clip_denoised = False self.bbox_tokenizer = None self.restarted_from_ckpt = False if ckpt_path is not None: self.init_from_ckpt(ckpt_path, ignore_keys) self.restarted_from_ckpt = True def make_cond_schedule(self, ): self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long) ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long() self.cond_ids[:self.num_timesteps_cond] = ids @rank_zero_only @torch.no_grad() def on_train_batch_start(self, batch, batch_idx, dataloader_idx): # only for very first batch if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt: assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously' # set rescale weight to 1./std of encodings print("### USING STD-RESCALING ###") x = super().get_input(batch, self.first_stage_key) x = x.to(self.device) encoder_posterior = self.encode_first_stage(x) z = self.get_first_stage_encoding(encoder_posterior).detach() del self.scale_factor self.register_buffer('scale_factor', 1. / z.flatten().std()) print(f"setting self.scale_factor to {self.scale_factor}") print("### USING STD-RESCALING ###") def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s) self.shorten_cond_schedule = self.num_timesteps_cond > 1 if self.shorten_cond_schedule: self.make_cond_schedule() def instantiate_first_stage(self, config): model = instantiate_from_config(config) self.first_stage_model = model.eval() self.first_stage_model.train = disabled_train for param in self.first_stage_model.parameters(): param.requires_grad = False def instantiate_cond_stage(self, config): if not self.cond_stage_trainable: if config == "__is_first_stage__": print("Using first stage also as cond stage.") self.cond_stage_model = self.first_stage_model elif config == "__is_unconditional__": print(f"Training {self.__class__.__name__} as an unconditional model.") self.cond_stage_model = None # self.be_unconditional = True else: model = instantiate_from_config(config) self.cond_stage_model = model.eval() self.cond_stage_model.train = disabled_train for param in self.cond_stage_model.parameters(): param.requires_grad = False else: assert config != '__is_first_stage__' assert config != '__is_unconditional__' model = instantiate_from_config(config) self.cond_stage_model = model def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False): denoise_row = [] for zd in tqdm(samples, desc=desc): denoise_row.append(self.decode_first_stage(zd.to(self.device), force_not_quantize=force_no_decoder_quantization)) n_imgs_per_row = len(denoise_row) denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w') denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w') denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row) return denoise_grid def get_first_stage_encoding(self, encoder_posterior): if isinstance(encoder_posterior, DiagonalGaussianDistribution): z = encoder_posterior.sample() elif isinstance(encoder_posterior, torch.Tensor): z = encoder_posterior else: raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented") return self.scale_factor * z def get_learned_conditioning(self, c): if self.cond_stage_forward is None: if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode): c = self.cond_stage_model.encode(c) if isinstance(c, DiagonalGaussianDistribution): c = c.mode() else: c = self.cond_stage_model(c) else: assert hasattr(self.cond_stage_model, self.cond_stage_forward) c = getattr(self.cond_stage_model, self.cond_stage_forward)(c) return c def meshgrid(self, h, w): y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1) x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1) arr = torch.cat([y, x], dim=-1) return arr def delta_border(self, h, w): """ :param h: height :param w: width :return: normalized distance to image border, with min distance = 0 at border and max dist = 0.5 at image center """ lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2) arr = self.meshgrid(h, w) / lower_right_corner dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0] dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0] edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0] return edge_dist def get_weighting(self, h, w, Ly, Lx, device): weighting = self.delta_border(h, w) weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"], self.split_input_params["clip_max_weight"], ) weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device) if self.split_input_params["tie_braker"]: L_weighting = self.delta_border(Ly, Lx) L_weighting = torch.clip(L_weighting, self.split_input_params["clip_min_tie_weight"], self.split_input_params["clip_max_tie_weight"]) L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device) weighting = weighting * L_weighting return weighting def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code """ :param x: img of size (bs, c, h, w) :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1]) """ bs, nc, h, w = x.shape # number of crops in image Ly = (h - kernel_size[0]) // stride[0] + 1 Lx = (w - kernel_size[1]) // stride[1] + 1 if uf == 1 and df == 1: fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) unfold = torch.nn.Unfold(**fold_params) fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params) weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype) normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx)) elif uf > 1 and df == 1: fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) unfold = torch.nn.Unfold(**fold_params) fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf), dilation=1, padding=0, stride=(stride[0] * uf, stride[1] * uf)) fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2) weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype) normalization = fold(weighting).view(1, 1, h * uf, w * uf) # normalizes the overlap weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx)) elif df > 1 and uf == 1: fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) unfold = torch.nn.Unfold(**fold_params) fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df), dilation=1, padding=0, stride=(stride[0] // df, stride[1] // df)) fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2) weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype) normalization = fold(weighting).view(1, 1, h // df, w // df) # normalizes the overlap weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx)) else: raise NotImplementedError return fold, unfold, normalization, weighting @torch.no_grad() def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False, cond_key=None, return_original_cond=False, bs=None): x = super().get_input(batch, k) if bs is not None: x = x[:bs] x = x.to(self.device) encoder_posterior = self.encode_first_stage(x) z = self.get_first_stage_encoding(encoder_posterior).detach() if self.model.conditioning_key is not None: if cond_key is None: cond_key = self.cond_stage_key if cond_key != self.first_stage_key: if cond_key in ['caption', 'coordinates_bbox']: xc = batch[cond_key] elif cond_key == 'class_label': xc = batch else: xc = super().get_input(batch, cond_key).to(self.device) else: xc = x if not self.cond_stage_trainable or force_c_encode: if isinstance(xc, dict) or isinstance(xc, list): # import pudb; pudb.set_trace() c = self.get_learned_conditioning(xc) else: c = self.get_learned_conditioning(xc.to(self.device)) else: c = xc if bs is not None: c = c[:bs] if self.use_positional_encodings: pos_x, pos_y = self.compute_latent_shifts(batch) ckey = __conditioning_keys__[self.model.conditioning_key] c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y} else: c = None xc = None if self.use_positional_encodings: pos_x, pos_y = self.compute_latent_shifts(batch) c = {'pos_x': pos_x, 'pos_y': pos_y} out = [z, c] if return_first_stage_outputs: xrec = self.decode_first_stage(z) out.extend([x, xrec]) if return_original_cond: out.append(xc) return out @torch.no_grad() def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False): if predict_cids: if z.dim() == 4: z = torch.argmax(z.exp(), dim=1).long() z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None) z = rearrange(z, 'b h w c -> b c h w').contiguous() z = 1. / self.scale_factor * z if hasattr(self, "split_input_params"): if self.split_input_params["patch_distributed_vq"]: ks = self.split_input_params["ks"] # eg. (128, 128) stride = self.split_input_params["stride"] # eg. (64, 64) uf = self.split_input_params["vqf"] bs, nc, h, w = z.shape if ks[0] > h or ks[1] > w: ks = (min(ks[0], h), min(ks[1], w)) print("reducing Kernel") if stride[0] > h or stride[1] > w: stride = (min(stride[0], h), min(stride[1], w)) print("reducing stride") fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf) z = unfold(z) # (bn, nc * prod(**ks), L) # 1. Reshape to img shape z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) # 2. apply model loop over last dim if isinstance(self.first_stage_model, VQModelInterface): output_list = [self.first_stage_model.decode(z[:, :, :, :, i], force_not_quantize=predict_cids or force_not_quantize) for i in range(z.shape[-1])] else: output_list = [self.first_stage_model.decode(z[:, :, :, :, i]) for i in range(z.shape[-1])] o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L) o = o * weighting # Reverse 1. reshape to img shape o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) # stitch crops together decoded = fold(o) decoded = decoded / normalization # norm is shape (1, 1, h, w) return decoded else: if isinstance(self.first_stage_model, VQModelInterface): return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) else: return self.first_stage_model.decode(z) else: if isinstance(self.first_stage_model, VQModelInterface): return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) else: return self.first_stage_model.decode(z) # same as above but without decorator def differentiable_decode_first_stage(self, z, predict_cids=False, force_not_quantize=False): if predict_cids: if z.dim() == 4: z = torch.argmax(z.exp(), dim=1).long() z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None) z = rearrange(z, 'b h w c -> b c h w').contiguous() z = 1. / self.scale_factor * z if hasattr(self, "split_input_params"): if self.split_input_params["patch_distributed_vq"]: ks = self.split_input_params["ks"] # eg. (128, 128) stride = self.split_input_params["stride"] # eg. (64, 64) uf = self.split_input_params["vqf"] bs, nc, h, w = z.shape if ks[0] > h or ks[1] > w: ks = (min(ks[0], h), min(ks[1], w)) print("reducing Kernel") if stride[0] > h or stride[1] > w: stride = (min(stride[0], h), min(stride[1], w)) print("reducing stride") fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf) z = unfold(z) # (bn, nc * prod(**ks), L) # 1. Reshape to img shape z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) # 2. apply model loop over last dim if isinstance(self.first_stage_model, VQModelInterface): output_list = [self.first_stage_model.decode(z[:, :, :, :, i], force_not_quantize=predict_cids or force_not_quantize) for i in range(z.shape[-1])] else: output_list = [self.first_stage_model.decode(z[:, :, :, :, i]) for i in range(z.shape[-1])] o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L) o = o * weighting # Reverse 1. reshape to img shape o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) # stitch crops together decoded = fold(o) decoded = decoded / normalization # norm is shape (1, 1, h, w) return decoded else: if isinstance(self.first_stage_model, VQModelInterface): return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) else: return self.first_stage_model.decode(z) else: if isinstance(self.first_stage_model, VQModelInterface): return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) else: return self.first_stage_model.decode(z) @torch.no_grad() def encode_first_stage(self, x): if hasattr(self, "split_input_params"): if self.split_input_params["patch_distributed_vq"]: ks = self.split_input_params["ks"] # eg. (128, 128) stride = self.split_input_params["stride"] # eg. (64, 64) df = self.split_input_params["vqf"] self.split_input_params['original_image_size'] = x.shape[-2:] bs, nc, h, w = x.shape if ks[0] > h or ks[1] > w: ks = (min(ks[0], h), min(ks[1], w)) print("reducing Kernel") if stride[0] > h or stride[1] > w: stride = (min(stride[0], h), min(stride[1], w)) print("reducing stride") fold, unfold, normalization, weighting = self.get_fold_unfold(x, ks, stride, df=df) z = unfold(x) # (bn, nc * prod(**ks), L) # Reshape to img shape z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) output_list = [self.first_stage_model.encode(z[:, :, :, :, i]) for i in range(z.shape[-1])] o = torch.stack(output_list, axis=-1) o = o * weighting # Reverse reshape to img shape o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) # stitch crops together decoded = fold(o) decoded = decoded / normalization return decoded else: return self.first_stage_model.encode(x) else: return self.first_stage_model.encode(x) def shared_step(self, batch, **kwargs): x, c = self.get_input(batch, self.first_stage_key) loss = self(x, c) return loss def forward(self, x, c, *args, **kwargs): t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long() if self.model.conditioning_key is not None: assert c is not None if self.cond_stage_trainable: c = self.get_learned_conditioning(c) if self.shorten_cond_schedule: # TODO: drop this option tc = self.cond_ids[t].to(self.device) c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float())) return self.p_losses(x, c, t, *args, **kwargs) def apply_model(self, x_noisy, t, cond, return_ids=False): if isinstance(cond, dict): # hybrid case, cond is expected to be a dict pass else: if not isinstance(cond, list): cond = [cond] key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn' cond = {key: cond} if hasattr(self, "split_input_params"): assert len(cond) == 1 # todo can only deal with one conditioning atm assert not return_ids ks = self.split_input_params["ks"] # eg. (128, 128) stride = self.split_input_params["stride"] # eg. (64, 64) h, w = x_noisy.shape[-2:] fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride) z = unfold(x_noisy) # (bn, nc * prod(**ks), L) # Reshape to img shape z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])] if self.cond_stage_key in ["image", "LR_image", "segmentation", 'bbox_img'] and self.model.conditioning_key: # todo check for completeness c_key = next(iter(cond.keys())) # get key c = next(iter(cond.values())) # get value assert (len(c) == 1) # todo extend to list with more than one elem c = c[0] # get element c = unfold(c) c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1])) # (bn, nc, ks[0], ks[1], L ) cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])] elif self.cond_stage_key == 'coordinates_bbox': assert 'original_image_size' in self.split_input_params, 'BoundingBoxRescaling is missing original_image_size' # assuming padding of unfold is always 0 and its dilation is always 1 n_patches_per_row = int((w - ks[0]) / stride[0] + 1) full_img_h, full_img_w = self.split_input_params['original_image_size'] # as we are operating on latents, we need the factor from the original image size to the # spatial latent size to properly rescale the crops for regenerating the bbox annotations num_downs = self.first_stage_model.encoder.num_resolutions - 1 rescale_latent = 2 ** (num_downs) # get top left positions of patches as conforming for the bbbox tokenizer, therefore we # need to rescale the tl patch coordinates to be in between (0,1) tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w, rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h) for patch_nr in range(z.shape[-1])] # patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w) patch_limits = [(x_tl, y_tl, rescale_latent * ks[0] / full_img_w, rescale_latent * ks[1] / full_img_h) for x_tl, y_tl in tl_patch_coordinates] # patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates] # tokenize crop coordinates for the bounding boxes of the respective patches patch_limits_tknzd = [torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(self.device) for bbox in patch_limits] # list of length l with tensors of shape (1, 2) print(patch_limits_tknzd[0].shape) # cut tknzd crop position from conditioning assert isinstance(cond, dict), 'cond must be dict to be fed into model' cut_cond = cond['c_crossattn'][0][..., :-2].to(self.device) print(cut_cond.shape) adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd]) adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n') print(adapted_cond.shape) adapted_cond = self.get_learned_conditioning(adapted_cond) print(adapted_cond.shape) adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1]) print(adapted_cond.shape) cond_list = [{'c_crossattn': [e]} for e in adapted_cond] else: cond_list = [cond for i in range(z.shape[-1])] # Todo make this more efficient # apply model by loop over crops output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])] assert not isinstance(output_list[0], tuple) # todo cant deal with multiple model outputs check this never happens o = torch.stack(output_list, axis=-1) o = o * weighting # Reverse reshape to img shape o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) # stitch crops together x_recon = fold(o) / normalization else: x_recon = self.model(x_noisy, t, **cond) if isinstance(x_recon, tuple) and not return_ids: return x_recon[0] else: return x_recon def _predict_eps_from_xstart(self, x_t, t, pred_xstart): return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \ extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) def _prior_bpd(self, x_start): """ Get the prior KL term for the variational lower-bound, measured in bits-per-dim. This term can't be optimized, as it only depends on the encoder. :param x_start: the [N x C x ...] tensor of inputs. :return: a batch of [N] KL values (in bits), one per batch element. """ batch_size = x_start.shape[0] t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device) qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t) kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0) return mean_flat(kl_prior) / np.log(2.0) def p_losses(self, x_start, cond, t, noise=None): noise = default(noise, lambda: torch.randn_like(x_start)) x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) model_output = self.apply_model(x_noisy, t, cond) loss_dict = {} prefix = 'train' if self.training else 'val' if self.parameterization == "x0": target = x_start elif self.parameterization == "eps": target = noise else: raise NotImplementedError() loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3]) loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()}) logvar_t = self.logvar[t].to(self.device) loss = loss_simple / torch.exp(logvar_t) + logvar_t # loss = loss_simple / torch.exp(self.logvar) + self.logvar if self.learn_logvar: loss_dict.update({f'{prefix}/loss_gamma': loss.mean()}) loss_dict.update({'logvar': self.logvar.data.mean()}) loss = self.l_simple_weight * loss.mean() loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3)) loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean() loss_dict.update({f'{prefix}/loss_vlb': loss_vlb}) loss += (self.original_elbo_weight * loss_vlb) loss_dict.update({f'{prefix}/loss': loss}) return loss, loss_dict def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False, return_x0=False, score_corrector=None, corrector_kwargs=None): t_in = t model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids) if score_corrector is not None: assert self.parameterization == "eps" model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs) if return_codebook_ids: model_out, logits = model_out if self.parameterization == "eps": x_recon = self.predict_start_from_noise(x, t=t, noise=model_out) elif self.parameterization == "x0": x_recon = model_out else: raise NotImplementedError() if clip_denoised: x_recon.clamp_(-1., 1.) if quantize_denoised: x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon) model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) if return_codebook_ids: return model_mean, posterior_variance, posterior_log_variance, logits elif return_x0: return model_mean, posterior_variance, posterior_log_variance, x_recon else: return model_mean, posterior_variance, posterior_log_variance @torch.no_grad() def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False, return_codebook_ids=False, quantize_denoised=False, return_x0=False, temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None): b, *_, device = *x.shape, x.device outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised, return_codebook_ids=return_codebook_ids, quantize_denoised=quantize_denoised, return_x0=return_x0, score_corrector=score_corrector, corrector_kwargs=corrector_kwargs) if return_codebook_ids: raise DeprecationWarning("Support dropped.") model_mean, _, model_log_variance, logits = outputs elif return_x0: model_mean, _, model_log_variance, x0 = outputs else: model_mean, _, model_log_variance = outputs noise = noise_like(x.shape, device, repeat_noise) * temperature if noise_dropout > 0.: noise = torch.nn.functional.dropout(noise, p=noise_dropout) # no noise when t == 0 nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) if return_codebook_ids: return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1) if return_x0: return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0 else: return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise @torch.no_grad() def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False, img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None, log_every_t=None): if not log_every_t: log_every_t = self.log_every_t timesteps = self.num_timesteps if batch_size is not None: b = batch_size if batch_size is not None else shape[0] shape = [batch_size] + list(shape) else: b = batch_size = shape[0] if x_T is None: img = torch.randn(shape, device=self.device) else: img = x_T intermediates = [] if cond is not None: if isinstance(cond, dict): cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else [x[:batch_size] for x in cond[key]] for key in cond} else: cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] if start_T is not None: timesteps = min(timesteps, start_T) iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation', total=timesteps) if verbose else reversed( range(0, timesteps)) if type(temperature) == float: temperature = [temperature] * timesteps for i in iterator: ts = torch.full((b,), i, device=self.device, dtype=torch.long) if self.shorten_cond_schedule: assert self.model.conditioning_key != 'hybrid' tc = self.cond_ids[ts].to(cond.device) cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond)) img, x0_partial = self.p_sample(img, cond, ts, clip_denoised=self.clip_denoised, quantize_denoised=quantize_denoised, return_x0=True, temperature=temperature[i], noise_dropout=noise_dropout, score_corrector=score_corrector, corrector_kwargs=corrector_kwargs) if mask is not None: assert x0 is not None img_orig = self.q_sample(x0, ts) img = img_orig * mask + (1. - mask) * img if i % log_every_t == 0 or i == timesteps - 1: intermediates.append(x0_partial) if callback: callback(i) if img_callback: img_callback(img, i) return img, intermediates @torch.no_grad() def p_sample_loop(self, cond, shape, return_intermediates=False, x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False, mask=None, x0=None, img_callback=None, start_T=None, log_every_t=None): if not log_every_t: log_every_t = self.log_every_t device = self.betas.device b = shape[0] if x_T is None: img = torch.randn(shape, device=device) else: img = x_T intermediates = [img] if timesteps is None: timesteps = self.num_timesteps if start_T is not None: timesteps = min(timesteps, start_T) iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed( range(0, timesteps)) if mask is not None: assert x0 is not None assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match for i in iterator: ts = torch.full((b,), i, device=device, dtype=torch.long) if self.shorten_cond_schedule: assert self.model.conditioning_key != 'hybrid' tc = self.cond_ids[ts].to(cond.device) cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond)) img = self.p_sample(img, cond, ts, clip_denoised=self.clip_denoised, quantize_denoised=quantize_denoised) if mask is not None: img_orig = self.q_sample(x0, ts) img = img_orig * mask + (1. - mask) * img if i % log_every_t == 0 or i == timesteps - 1: intermediates.append(img) if callback: callback(i) if img_callback: img_callback(img, i) if return_intermediates: return img, intermediates return img @torch.no_grad() def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None, verbose=True, timesteps=None, quantize_denoised=False, mask=None, x0=None, shape=None,**kwargs): if shape is None: shape = (batch_size, self.channels, self.image_size, self.image_size) if cond is not None: if isinstance(cond, dict): cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else [x[:batch_size] for x in cond[key]] for key in cond} else: cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] return self.p_sample_loop(cond, shape, return_intermediates=return_intermediates, x_T=x_T, verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised, mask=mask, x0=x0) @torch.no_grad() def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs): if ddim: ddim_sampler = DDIMSampler(self) shape = (self.channels, self.image_size, self.image_size) samples, intermediates =ddim_sampler.sample(ddim_steps,batch_size, shape,cond,verbose=False,**kwargs) else: samples, intermediates = self.sample(cond=cond, batch_size=batch_size, return_intermediates=True,**kwargs) return samples, intermediates @torch.no_grad() def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None, quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True, plot_diffusion_rows=True, **kwargs): use_ddim = ddim_steps is not None log = {} z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key, return_first_stage_outputs=True, force_c_encode=True, return_original_cond=True, bs=N) N = min(x.shape[0], N) n_row = min(x.shape[0], n_row) log["inputs"] = x log["reconstruction"] = xrec if self.model.conditioning_key is not None: if hasattr(self.cond_stage_model, "decode"): xc = self.cond_stage_model.decode(c) log["conditioning"] = xc elif self.cond_stage_key in ["caption"]: xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["caption"]) log["conditioning"] = xc elif self.cond_stage_key == 'class_label': xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"]) log['conditioning'] = xc elif isimage(xc): log["conditioning"] = xc if ismap(xc): log["original_conditioning"] = self.to_rgb(xc) if plot_diffusion_rows: # get diffusion row diffusion_row = [] z_start = z[:n_row] for t in range(self.num_timesteps): if t % self.log_every_t == 0 or t == self.num_timesteps - 1: t = repeat(torch.tensor([t]), '1 -> b', b=n_row) t = t.to(self.device).long() noise = torch.randn_like(z_start) z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise) diffusion_row.append(self.decode_first_stage(z_noisy)) diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w') diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w') diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0]) log["diffusion_row"] = diffusion_grid if sample: # get denoise row with self.ema_scope("Plotting"): samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, ddim_steps=ddim_steps,eta=ddim_eta) # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True) x_samples = self.decode_first_stage(samples) log["samples"] = x_samples if plot_denoise_rows: denoise_grid = self._get_denoise_row_from_list(z_denoise_row) log["denoise_row"] = denoise_grid if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance( self.first_stage_model, IdentityFirstStage): # also display when quantizing x0 while sampling with self.ema_scope("Plotting Quantized Denoised"): samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, ddim_steps=ddim_steps,eta=ddim_eta, quantize_denoised=True) # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True, # quantize_denoised=True) x_samples = self.decode_first_stage(samples.to(self.device)) log["samples_x0_quantized"] = x_samples if inpaint: # make a simple center square h, w = z.shape[2], z.shape[3] mask = torch.ones(N, h, w).to(self.device) # zeros will be filled in mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0. mask = mask[:, None, ...] with self.ema_scope("Plotting Inpaint"): samples, _ = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, eta=ddim_eta, ddim_steps=ddim_steps, x0=z[:N], mask=mask) x_samples = self.decode_first_stage(samples.to(self.device)) log["samples_inpainting"] = x_samples log["mask"] = mask # outpaint with self.ema_scope("Plotting Outpaint"): samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,eta=ddim_eta, ddim_steps=ddim_steps, x0=z[:N], mask=mask) x_samples = self.decode_first_stage(samples.to(self.device)) log["samples_outpainting"] = x_samples if plot_progressive_rows: with self.ema_scope("Plotting Progressives"): img, progressives = self.progressive_denoising(c, shape=(self.channels, self.image_size, self.image_size), batch_size=N) prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation") log["progressive_row"] = prog_row if return_keys: if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0: return log else: return {key: log[key] for key in return_keys} return log def configure_optimizers(self): lr = self.learning_rate params = list(self.model.parameters()) if self.cond_stage_trainable: print(f"{self.__class__.__name__}: Also optimizing conditioner params!") params = params + list(self.cond_stage_model.parameters()) if self.learn_logvar: print('Diffusion model optimizing logvar') params.append(self.logvar) opt = torch.optim.AdamW(params, lr=lr) if self.use_scheduler: assert 'target' in self.scheduler_config scheduler = instantiate_from_config(self.scheduler_config) print("Setting up LambdaLR scheduler...") scheduler = [ { 'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule), 'interval': 'step', 'frequency': 1 }] return [opt], scheduler return opt @torch.no_grad() def to_rgb(self, x): x = x.float() if not hasattr(self, "colorize"): self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x) x = nn.functional.conv2d(x, weight=self.colorize) x = 2. * (x - x.min()) / (x.max() - x.min()) - 1. return x class DiffusionWrapperV1(pl.LightningModule): def __init__(self, diff_model_config, conditioning_key): super().__init__() self.diffusion_model = instantiate_from_config(diff_model_config) self.conditioning_key = conditioning_key assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm'] def forward(self, x, t, c_concat: list = None, c_crossattn: list = None): if self.conditioning_key is None: out = self.diffusion_model(x, t) elif self.conditioning_key == 'concat': xc = torch.cat([x] + c_concat, dim=1) out = self.diffusion_model(xc, t) elif self.conditioning_key == 'crossattn': cc = torch.cat(c_crossattn, 1) out = self.diffusion_model(x, t, context=cc) elif self.conditioning_key == 'hybrid': xc = torch.cat([x] + c_concat, dim=1) cc = torch.cat(c_crossattn, 1) out = self.diffusion_model(xc, t, context=cc) elif self.conditioning_key == 'adm': cc = c_crossattn[0] out = self.diffusion_model(x, t, y=cc) else: raise NotImplementedError() return out class Layout2ImgDiffusionV1(LatentDiffusionV1): # TODO: move all layout-specific hacks to this class def __init__(self, cond_stage_key, *args, **kwargs): assert cond_stage_key == 'coordinates_bbox', 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"' super().__init__(*args, cond_stage_key=cond_stage_key, **kwargs) def log_images(self, batch, N=8, *args, **kwargs): logs = super().log_images(*args, batch=batch, N=N, **kwargs) key = 'train' if self.training else 'validation' dset = self.trainer.datamodule.datasets[key] mapper = dset.conditional_builders[self.cond_stage_key] bbox_imgs = [] map_fn = lambda catno: dset.get_textual_label(dset.get_category_id(catno)) for tknzd_bbox in batch[self.cond_stage_key][:N]: bboximg = mapper.plot(tknzd_bbox.detach().cpu(), map_fn, (256, 256)) bbox_imgs.append(bboximg) cond_img = torch.stack(bbox_imgs, dim=0) logs['bbox_image'] = cond_img return logs ldm.models.diffusion.ddpm.DDPMV1 = DDPMV1 ldm.models.diffusion.ddpm.LatentDiffusionV1 = LatentDiffusionV1 ldm.models.diffusion.ddpm.DiffusionWrapperV1 = DiffusionWrapperV1 ldm.models.diffusion.ddpm.Layout2ImgDiffusionV1 = Layout2ImgDiffusionV1 stable-diffusion-webui/extensions-builtin/LDSR/vqvae_quantize.py000066400000000000000000000145171465116634300254710ustar00rootroot00000000000000# Vendored from https://raw.githubusercontent.com/CompVis/taming-transformers/24268930bf1dce879235a7fddd0b2355b84d7ea6/taming/modules/vqvae/quantize.py, # where the license is as follows: # # Copyright (c) 2020 Patrick Esser and Robin Rombach and Björn Ommer # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, # EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. # IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, # DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR # OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE # OR OTHER DEALINGS IN THE SOFTWARE./ import torch import torch.nn as nn import numpy as np from einops import rearrange class VectorQuantizer2(nn.Module): """ Improved version over VectorQuantizer, can be used as a drop-in replacement. Mostly avoids costly matrix multiplications and allows for post-hoc remapping of indices. """ # NOTE: due to a bug the beta term was applied to the wrong term. for # backwards compatibility we use the buggy version by default, but you can # specify legacy=False to fix it. def __init__(self, n_e, e_dim, beta, remap=None, unknown_index="random", sane_index_shape=False, legacy=True): super().__init__() self.n_e = n_e self.e_dim = e_dim self.beta = beta self.legacy = legacy self.embedding = nn.Embedding(self.n_e, self.e_dim) self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e) self.remap = remap if self.remap is not None: self.register_buffer("used", torch.tensor(np.load(self.remap))) self.re_embed = self.used.shape[0] self.unknown_index = unknown_index # "random" or "extra" or integer if self.unknown_index == "extra": self.unknown_index = self.re_embed self.re_embed = self.re_embed + 1 print(f"Remapping {self.n_e} indices to {self.re_embed} indices. " f"Using {self.unknown_index} for unknown indices.") else: self.re_embed = n_e self.sane_index_shape = sane_index_shape def remap_to_used(self, inds): ishape = inds.shape assert len(ishape) > 1 inds = inds.reshape(ishape[0], -1) used = self.used.to(inds) match = (inds[:, :, None] == used[None, None, ...]).long() new = match.argmax(-1) unknown = match.sum(2) < 1 if self.unknown_index == "random": new[unknown] = torch.randint(0, self.re_embed, size=new[unknown].shape).to(device=new.device) else: new[unknown] = self.unknown_index return new.reshape(ishape) def unmap_to_all(self, inds): ishape = inds.shape assert len(ishape) > 1 inds = inds.reshape(ishape[0], -1) used = self.used.to(inds) if self.re_embed > self.used.shape[0]: # extra token inds[inds >= self.used.shape[0]] = 0 # simply set to zero back = torch.gather(used[None, :][inds.shape[0] * [0], :], 1, inds) return back.reshape(ishape) def forward(self, z, temp=None, rescale_logits=False, return_logits=False): assert temp is None or temp == 1.0, "Only for interface compatible with Gumbel" assert rescale_logits is False, "Only for interface compatible with Gumbel" assert return_logits is False, "Only for interface compatible with Gumbel" # reshape z -> (batch, height, width, channel) and flatten z = rearrange(z, 'b c h w -> b h w c').contiguous() z_flattened = z.view(-1, self.e_dim) # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z d = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + \ torch.sum(self.embedding.weight ** 2, dim=1) - 2 * \ torch.einsum('bd,dn->bn', z_flattened, rearrange(self.embedding.weight, 'n d -> d n')) min_encoding_indices = torch.argmin(d, dim=1) z_q = self.embedding(min_encoding_indices).view(z.shape) perplexity = None min_encodings = None # compute loss for embedding if not self.legacy: loss = self.beta * torch.mean((z_q.detach() - z) ** 2) + \ torch.mean((z_q - z.detach()) ** 2) else: 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() # reshape back to match original input shape z_q = rearrange(z_q, 'b h w c -> b c h w').contiguous() if self.remap is not None: min_encoding_indices = min_encoding_indices.reshape(z.shape[0], -1) # add batch axis min_encoding_indices = self.remap_to_used(min_encoding_indices) min_encoding_indices = min_encoding_indices.reshape(-1, 1) # flatten if self.sane_index_shape: min_encoding_indices = min_encoding_indices.reshape( z_q.shape[0], z_q.shape[2], z_q.shape[3]) return z_q, loss, (perplexity, min_encodings, min_encoding_indices) def get_codebook_entry(self, indices, shape): # shape specifying (batch, height, width, channel) if self.remap is not None: indices = indices.reshape(shape[0], -1) # add batch axis indices = self.unmap_to_all(indices) indices = indices.reshape(-1) # flatten again # get quantized latent vectors z_q = self.embedding(indices) if shape is not None: z_q = z_q.view(shape) # reshape back to match original input shape z_q = z_q.permute(0, 3, 1, 2).contiguous() return z_q stable-diffusion-webui/extensions-builtin/Lora/000077500000000000000000000000001465116634300221765ustar00rootroot00000000000000stable-diffusion-webui/extensions-builtin/Lora/extra_networks_lora.py000066400000000000000000000051271465116634300266510ustar00rootroot00000000000000from modules import extra_networks, shared import networks class ExtraNetworkLora(extra_networks.ExtraNetwork): def __init__(self): super().__init__('lora') self.errors = {} """mapping of network names to the number of errors the network had during operation""" remove_symbols = str.maketrans('', '', ":,") def activate(self, p, params_list): additional = shared.opts.sd_lora self.errors.clear() if additional != "None" and additional in networks.available_networks and not any(x for x in params_list if x.items[0] == additional): p.all_prompts = [x + f"" for x in p.all_prompts] params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier])) names = [] te_multipliers = [] unet_multipliers = [] dyn_dims = [] for params in params_list: assert params.items names.append(params.positional[0]) te_multiplier = float(params.positional[1]) if len(params.positional) > 1 else 1.0 te_multiplier = float(params.named.get("te", te_multiplier)) unet_multiplier = float(params.positional[2]) if len(params.positional) > 2 else te_multiplier unet_multiplier = float(params.named.get("unet", unet_multiplier)) dyn_dim = int(params.positional[3]) if len(params.positional) > 3 else None dyn_dim = int(params.named["dyn"]) if "dyn" in params.named else dyn_dim te_multipliers.append(te_multiplier) unet_multipliers.append(unet_multiplier) dyn_dims.append(dyn_dim) networks.load_networks(names, te_multipliers, unet_multipliers, dyn_dims) if shared.opts.lora_add_hashes_to_infotext: if not getattr(p, "is_hr_pass", False) or not hasattr(p, "lora_hashes"): p.lora_hashes = {} for item in networks.loaded_networks: if item.network_on_disk.shorthash and item.mentioned_name: p.lora_hashes[item.mentioned_name.translate(self.remove_symbols)] = item.network_on_disk.shorthash if p.lora_hashes: p.extra_generation_params["Lora hashes"] = ', '.join(f'{k}: {v}' for k, v in p.lora_hashes.items()) def deactivate(self, p): if self.errors: p.comment("Networks with errors: " + ", ".join(f"{k} ({v})" for k, v in self.errors.items())) self.errors.clear() stable-diffusion-webui/extensions-builtin/Lora/lora.py000066400000000000000000000005451465116634300235110ustar00rootroot00000000000000import networks list_available_loras = networks.list_available_networks available_loras = networks.available_networks available_lora_aliases = networks.available_network_aliases available_lora_hash_lookup = networks.available_network_hash_lookup forbidden_lora_aliases = networks.forbidden_network_aliases loaded_loras = networks.loaded_networks stable-diffusion-webui/extensions-builtin/Lora/lora_logger.py000066400000000000000000000016411465116634300250460ustar00rootroot00000000000000import sys import copy import logging class ColoredFormatter(logging.Formatter): COLORS = { "DEBUG": "\033[0;36m", # CYAN "INFO": "\033[0;32m", # GREEN "WARNING": "\033[0;33m", # YELLOW "ERROR": "\033[0;31m", # RED "CRITICAL": "\033[0;37;41m", # WHITE ON RED "RESET": "\033[0m", # RESET COLOR } def format(self, record): colored_record = copy.copy(record) levelname = colored_record.levelname seq = self.COLORS.get(levelname, self.COLORS["RESET"]) colored_record.levelname = f"{seq}{levelname}{self.COLORS['RESET']}" return super().format(colored_record) logger = logging.getLogger("lora") logger.propagate = False if not logger.handlers: handler = logging.StreamHandler(sys.stdout) handler.setFormatter( ColoredFormatter("[%(name)s]-%(levelname)s: %(message)s") ) logger.addHandler(handler) stable-diffusion-webui/extensions-builtin/Lora/lora_patches.py000066400000000000000000000047741465116634300252300ustar00rootroot00000000000000import torch import networks from modules import patches class LoraPatches: def __init__(self): self.Linear_forward = patches.patch(__name__, torch.nn.Linear, 'forward', networks.network_Linear_forward) self.Linear_load_state_dict = patches.patch(__name__, torch.nn.Linear, '_load_from_state_dict', networks.network_Linear_load_state_dict) self.Conv2d_forward = patches.patch(__name__, torch.nn.Conv2d, 'forward', networks.network_Conv2d_forward) self.Conv2d_load_state_dict = patches.patch(__name__, torch.nn.Conv2d, '_load_from_state_dict', networks.network_Conv2d_load_state_dict) self.GroupNorm_forward = patches.patch(__name__, torch.nn.GroupNorm, 'forward', networks.network_GroupNorm_forward) self.GroupNorm_load_state_dict = patches.patch(__name__, torch.nn.GroupNorm, '_load_from_state_dict', networks.network_GroupNorm_load_state_dict) self.LayerNorm_forward = patches.patch(__name__, torch.nn.LayerNorm, 'forward', networks.network_LayerNorm_forward) self.LayerNorm_load_state_dict = patches.patch(__name__, torch.nn.LayerNorm, '_load_from_state_dict', networks.network_LayerNorm_load_state_dict) self.MultiheadAttention_forward = patches.patch(__name__, torch.nn.MultiheadAttention, 'forward', networks.network_MultiheadAttention_forward) self.MultiheadAttention_load_state_dict = patches.patch(__name__, torch.nn.MultiheadAttention, '_load_from_state_dict', networks.network_MultiheadAttention_load_state_dict) def undo(self): self.Linear_forward = patches.undo(__name__, torch.nn.Linear, 'forward') self.Linear_load_state_dict = patches.undo(__name__, torch.nn.Linear, '_load_from_state_dict') self.Conv2d_forward = patches.undo(__name__, torch.nn.Conv2d, 'forward') self.Conv2d_load_state_dict = patches.undo(__name__, torch.nn.Conv2d, '_load_from_state_dict') self.GroupNorm_forward = patches.undo(__name__, torch.nn.GroupNorm, 'forward') self.GroupNorm_load_state_dict = patches.undo(__name__, torch.nn.GroupNorm, '_load_from_state_dict') self.LayerNorm_forward = patches.undo(__name__, torch.nn.LayerNorm, 'forward') self.LayerNorm_load_state_dict = patches.undo(__name__, torch.nn.LayerNorm, '_load_from_state_dict') self.MultiheadAttention_forward = patches.undo(__name__, torch.nn.MultiheadAttention, 'forward') self.MultiheadAttention_load_state_dict = patches.undo(__name__, torch.nn.MultiheadAttention, '_load_from_state_dict') stable-diffusion-webui/extensions-builtin/Lora/lyco_helpers.py000066400000000000000000000045511465116634300252450ustar00rootroot00000000000000import torch def make_weight_cp(t, wa, wb): temp = torch.einsum('i j k l, j r -> i r k l', t, wb) return torch.einsum('i j k l, i r -> r j k l', temp, wa) def rebuild_conventional(up, down, shape, dyn_dim=None): up = up.reshape(up.size(0), -1) down = down.reshape(down.size(0), -1) if dyn_dim is not None: up = up[:, :dyn_dim] down = down[:dyn_dim, :] return (up @ down).reshape(shape) def rebuild_cp_decomposition(up, down, mid): up = up.reshape(up.size(0), -1) down = down.reshape(down.size(0), -1) return torch.einsum('n m k l, i n, m j -> i j k l', mid, up, down) # copied from https://github.com/KohakuBlueleaf/LyCORIS/blob/dev/lycoris/modules/lokr.py def factorization(dimension: int, factor:int=-1) -> tuple[int, int]: ''' return a tuple of two value of input dimension decomposed by the number closest to factor second value is higher or equal than first value. In LoRA with Kroneckor Product, first value is a value for weight scale. secon value is a value for weight. Because of non-commutative property, A⊗B ≠ B⊗A. Meaning of two matrices is slightly different. examples) factor -1 2 4 8 16 ... 127 -> 1, 127 127 -> 1, 127 127 -> 1, 127 127 -> 1, 127 127 -> 1, 127 128 -> 8, 16 128 -> 2, 64 128 -> 4, 32 128 -> 8, 16 128 -> 8, 16 250 -> 10, 25 250 -> 2, 125 250 -> 2, 125 250 -> 5, 50 250 -> 10, 25 360 -> 8, 45 360 -> 2, 180 360 -> 4, 90 360 -> 8, 45 360 -> 12, 30 512 -> 16, 32 512 -> 2, 256 512 -> 4, 128 512 -> 8, 64 512 -> 16, 32 1024 -> 32, 32 1024 -> 2, 512 1024 -> 4, 256 1024 -> 8, 128 1024 -> 16, 64 ''' if factor > 0 and (dimension % factor) == 0: m = factor n = dimension // factor if m > n: n, m = m, n return m, n if factor < 0: factor = dimension m, n = 1, dimension length = m + n while m length or new_m>factor: break else: m, n = new_m, new_n if m > n: n, m = m, n return m, n stable-diffusion-webui/extensions-builtin/Lora/network.py000066400000000000000000000174101465116634300242440ustar00rootroot00000000000000from __future__ import annotations import os from collections import namedtuple import enum import torch.nn as nn import torch.nn.functional as F from modules import sd_models, cache, errors, hashes, shared import modules.models.sd3.mmdit NetworkWeights = namedtuple('NetworkWeights', ['network_key', 'sd_key', 'w', 'sd_module']) metadata_tags_order = {"ss_sd_model_name": 1, "ss_resolution": 2, "ss_clip_skip": 3, "ss_num_train_images": 10, "ss_tag_frequency": 20} class SdVersion(enum.Enum): Unknown = 1 SD1 = 2 SD2 = 3 SDXL = 4 class NetworkOnDisk: def __init__(self, name, filename): self.name = name self.filename = filename self.metadata = {} self.is_safetensors = os.path.splitext(filename)[1].lower() == ".safetensors" def read_metadata(): metadata = sd_models.read_metadata_from_safetensors(filename) return metadata if self.is_safetensors: try: self.metadata = cache.cached_data_for_file('safetensors-metadata', "lora/" + self.name, filename, read_metadata) except Exception as e: errors.display(e, f"reading lora {filename}") if self.metadata: m = {} for k, v in sorted(self.metadata.items(), key=lambda x: metadata_tags_order.get(x[0], 999)): m[k] = v self.metadata = m self.alias = self.metadata.get('ss_output_name', self.name) self.hash = None self.shorthash = None self.set_hash( self.metadata.get('sshs_model_hash') or hashes.sha256_from_cache(self.filename, "lora/" + self.name, use_addnet_hash=self.is_safetensors) or '' ) self.sd_version = self.detect_version() def detect_version(self): if str(self.metadata.get('ss_base_model_version', "")).startswith("sdxl_"): return SdVersion.SDXL elif str(self.metadata.get('ss_v2', "")) == "True": return SdVersion.SD2 elif len(self.metadata): return SdVersion.SD1 return SdVersion.Unknown def set_hash(self, v): self.hash = v self.shorthash = self.hash[0:12] if self.shorthash: import networks networks.available_network_hash_lookup[self.shorthash] = self def read_hash(self): if not self.hash: self.set_hash(hashes.sha256(self.filename, "lora/" + self.name, use_addnet_hash=self.is_safetensors) or '') def get_alias(self): import networks if shared.opts.lora_preferred_name == "Filename" or self.alias.lower() in networks.forbidden_network_aliases: return self.name else: return self.alias class Network: # LoraModule def __init__(self, name, network_on_disk: NetworkOnDisk): self.name = name self.network_on_disk = network_on_disk self.te_multiplier = 1.0 self.unet_multiplier = 1.0 self.dyn_dim = None self.modules = {} self.bundle_embeddings = {} self.mtime = None self.mentioned_name = None """the text that was used to add the network to prompt - can be either name or an alias""" class ModuleType: def create_module(self, net: Network, weights: NetworkWeights) -> Network | None: return None class NetworkModule: def __init__(self, net: Network, weights: NetworkWeights): self.network = net self.network_key = weights.network_key self.sd_key = weights.sd_key self.sd_module = weights.sd_module if isinstance(self.sd_module, modules.models.sd3.mmdit.QkvLinear): s = self.sd_module.weight.shape self.shape = (s[0] // 3, s[1]) elif hasattr(self.sd_module, 'weight'): self.shape = self.sd_module.weight.shape elif isinstance(self.sd_module, nn.MultiheadAttention): # For now, only self-attn use Pytorch's MHA # So assume all qkvo proj have same shape self.shape = self.sd_module.out_proj.weight.shape else: self.shape = None self.ops = None self.extra_kwargs = {} if isinstance(self.sd_module, nn.Conv2d): self.ops = F.conv2d self.extra_kwargs = { 'stride': self.sd_module.stride, 'padding': self.sd_module.padding } elif isinstance(self.sd_module, nn.Linear): self.ops = F.linear elif isinstance(self.sd_module, nn.LayerNorm): self.ops = F.layer_norm self.extra_kwargs = { 'normalized_shape': self.sd_module.normalized_shape, 'eps': self.sd_module.eps } elif isinstance(self.sd_module, nn.GroupNorm): self.ops = F.group_norm self.extra_kwargs = { 'num_groups': self.sd_module.num_groups, 'eps': self.sd_module.eps } self.dim = None self.bias = weights.w.get("bias") self.alpha = weights.w["alpha"].item() if "alpha" in weights.w else None self.scale = weights.w["scale"].item() if "scale" in weights.w else None self.dora_scale = weights.w.get("dora_scale", None) self.dora_norm_dims = len(self.shape) - 1 def multiplier(self): if 'transformer' in self.sd_key[:20]: return self.network.te_multiplier else: return self.network.unet_multiplier def calc_scale(self): if self.scale is not None: return self.scale if self.dim is not None and self.alpha is not None: return self.alpha / self.dim return 1.0 def apply_weight_decompose(self, updown, orig_weight): # Match the device/dtype orig_weight = orig_weight.to(updown.dtype) dora_scale = self.dora_scale.to(device=orig_weight.device, dtype=updown.dtype) updown = updown.to(orig_weight.device) merged_scale1 = updown + orig_weight merged_scale1_norm = ( merged_scale1.transpose(0, 1) .reshape(merged_scale1.shape[1], -1) .norm(dim=1, keepdim=True) .reshape(merged_scale1.shape[1], *[1] * self.dora_norm_dims) .transpose(0, 1) ) dora_merged = ( merged_scale1 * (dora_scale / merged_scale1_norm) ) final_updown = dora_merged - orig_weight return final_updown def finalize_updown(self, updown, orig_weight, output_shape, ex_bias=None): if self.bias is not None: updown = updown.reshape(self.bias.shape) updown += self.bias.to(orig_weight.device, dtype=updown.dtype) updown = updown.reshape(output_shape) if len(output_shape) == 4: updown = updown.reshape(output_shape) if orig_weight.size().numel() == updown.size().numel(): updown = updown.reshape(orig_weight.shape) if ex_bias is not None: ex_bias = ex_bias * self.multiplier() updown = updown * self.calc_scale() if self.dora_scale is not None: updown = self.apply_weight_decompose(updown, orig_weight) return updown * self.multiplier(), ex_bias def calc_updown(self, target): raise NotImplementedError() def forward(self, x, y): """A general forward implementation for all modules""" if self.ops is None: raise NotImplementedError() else: updown, ex_bias = self.calc_updown(self.sd_module.weight) return y + self.ops(x, weight=updown, bias=ex_bias, **self.extra_kwargs) stable-diffusion-webui/extensions-builtin/Lora/network_full.py000066400000000000000000000016101465116634300252610ustar00rootroot00000000000000import network class ModuleTypeFull(network.ModuleType): def create_module(self, net: network.Network, weights: network.NetworkWeights): if all(x in weights.w for x in ["diff"]): return NetworkModuleFull(net, weights) return None class NetworkModuleFull(network.NetworkModule): def __init__(self, net: network.Network, weights: network.NetworkWeights): super().__init__(net, weights) self.weight = weights.w.get("diff") self.ex_bias = weights.w.get("diff_b") def calc_updown(self, orig_weight): output_shape = self.weight.shape updown = self.weight.to(orig_weight.device) if self.ex_bias is not None: ex_bias = self.ex_bias.to(orig_weight.device) else: ex_bias = None return self.finalize_updown(updown, orig_weight, output_shape, ex_bias) stable-diffusion-webui/extensions-builtin/Lora/network_glora.py000066400000000000000000000023361465116634300254310ustar00rootroot00000000000000 import network class ModuleTypeGLora(network.ModuleType): def create_module(self, net: network.Network, weights: network.NetworkWeights): if all(x in weights.w for x in ["a1.weight", "a2.weight", "alpha", "b1.weight", "b2.weight"]): return NetworkModuleGLora(net, weights) return None # adapted from https://github.com/KohakuBlueleaf/LyCORIS class NetworkModuleGLora(network.NetworkModule): def __init__(self, net: network.Network, weights: network.NetworkWeights): super().__init__(net, weights) if hasattr(self.sd_module, 'weight'): self.shape = self.sd_module.weight.shape self.w1a = weights.w["a1.weight"] self.w1b = weights.w["b1.weight"] self.w2a = weights.w["a2.weight"] self.w2b = weights.w["b2.weight"] def calc_updown(self, orig_weight): w1a = self.w1a.to(orig_weight.device) w1b = self.w1b.to(orig_weight.device) w2a = self.w2a.to(orig_weight.device) w2b = self.w2b.to(orig_weight.device) output_shape = [w1a.size(0), w1b.size(1)] updown = ((w2b @ w1b) + ((orig_weight.to(dtype = w1a.dtype) @ w2a) @ w1a)) return self.finalize_updown(updown, orig_weight, output_shape) stable-diffusion-webui/extensions-builtin/Lora/network_hada.py000066400000000000000000000037011465116634300252170ustar00rootroot00000000000000import lyco_helpers import network class ModuleTypeHada(network.ModuleType): def create_module(self, net: network.Network, weights: network.NetworkWeights): if all(x in weights.w for x in ["hada_w1_a", "hada_w1_b", "hada_w2_a", "hada_w2_b"]): return NetworkModuleHada(net, weights) return None class NetworkModuleHada(network.NetworkModule): def __init__(self, net: network.Network, weights: network.NetworkWeights): super().__init__(net, weights) if hasattr(self.sd_module, 'weight'): self.shape = self.sd_module.weight.shape self.w1a = weights.w["hada_w1_a"] self.w1b = weights.w["hada_w1_b"] self.dim = self.w1b.shape[0] self.w2a = weights.w["hada_w2_a"] self.w2b = weights.w["hada_w2_b"] self.t1 = weights.w.get("hada_t1") self.t2 = weights.w.get("hada_t2") def calc_updown(self, orig_weight): w1a = self.w1a.to(orig_weight.device) w1b = self.w1b.to(orig_weight.device) w2a = self.w2a.to(orig_weight.device) w2b = self.w2b.to(orig_weight.device) output_shape = [w1a.size(0), w1b.size(1)] if self.t1 is not None: output_shape = [w1a.size(1), w1b.size(1)] t1 = self.t1.to(orig_weight.device) updown1 = lyco_helpers.make_weight_cp(t1, w1a, w1b) output_shape += t1.shape[2:] else: if len(w1b.shape) == 4: output_shape += w1b.shape[2:] updown1 = lyco_helpers.rebuild_conventional(w1a, w1b, output_shape) if self.t2 is not None: t2 = self.t2.to(orig_weight.device) updown2 = lyco_helpers.make_weight_cp(t2, w2a, w2b) else: updown2 = lyco_helpers.rebuild_conventional(w2a, w2b, output_shape) updown = updown1 * updown2 return self.finalize_updown(updown, orig_weight, output_shape) stable-diffusion-webui/extensions-builtin/Lora/network_ia3.py000066400000000000000000000016141465116634300247770ustar00rootroot00000000000000import network class ModuleTypeIa3(network.ModuleType): def create_module(self, net: network.Network, weights: network.NetworkWeights): if all(x in weights.w for x in ["weight"]): return NetworkModuleIa3(net, weights) return None class NetworkModuleIa3(network.NetworkModule): def __init__(self, net: network.Network, weights: network.NetworkWeights): super().__init__(net, weights) self.w = weights.w["weight"] self.on_input = weights.w["on_input"].item() def calc_updown(self, orig_weight): w = self.w.to(orig_weight.device) output_shape = [w.size(0), orig_weight.size(1)] if self.on_input: output_shape.reverse() else: w = w.reshape(-1, 1) updown = orig_weight * w return self.finalize_updown(updown, orig_weight, output_shape) stable-diffusion-webui/extensions-builtin/Lora/network_lokr.py000066400000000000000000000044621465116634300252760ustar00rootroot00000000000000import torch import lyco_helpers import network class ModuleTypeLokr(network.ModuleType): def create_module(self, net: network.Network, weights: network.NetworkWeights): has_1 = "lokr_w1" in weights.w or ("lokr_w1_a" in weights.w and "lokr_w1_b" in weights.w) has_2 = "lokr_w2" in weights.w or ("lokr_w2_a" in weights.w and "lokr_w2_b" in weights.w) if has_1 and has_2: return NetworkModuleLokr(net, weights) return None def make_kron(orig_shape, w1, w2): if len(w2.shape) == 4: w1 = w1.unsqueeze(2).unsqueeze(2) w2 = w2.contiguous() return torch.kron(w1, w2).reshape(orig_shape) class NetworkModuleLokr(network.NetworkModule): def __init__(self, net: network.Network, weights: network.NetworkWeights): super().__init__(net, weights) self.w1 = weights.w.get("lokr_w1") self.w1a = weights.w.get("lokr_w1_a") self.w1b = weights.w.get("lokr_w1_b") self.dim = self.w1b.shape[0] if self.w1b is not None else self.dim self.w2 = weights.w.get("lokr_w2") self.w2a = weights.w.get("lokr_w2_a") self.w2b = weights.w.get("lokr_w2_b") self.dim = self.w2b.shape[0] if self.w2b is not None else self.dim self.t2 = weights.w.get("lokr_t2") def calc_updown(self, orig_weight): if self.w1 is not None: w1 = self.w1.to(orig_weight.device) else: w1a = self.w1a.to(orig_weight.device) w1b = self.w1b.to(orig_weight.device) w1 = w1a @ w1b if self.w2 is not None: w2 = self.w2.to(orig_weight.device) elif self.t2 is None: w2a = self.w2a.to(orig_weight.device) w2b = self.w2b.to(orig_weight.device) w2 = w2a @ w2b else: t2 = self.t2.to(orig_weight.device) w2a = self.w2a.to(orig_weight.device) w2b = self.w2b.to(orig_weight.device) w2 = lyco_helpers.make_weight_cp(t2, w2a, w2b) output_shape = [w1.size(0) * w2.size(0), w1.size(1) * w2.size(1)] if len(orig_weight.shape) == 4: output_shape = orig_weight.shape updown = make_kron(output_shape, w1, w2) return self.finalize_updown(updown, orig_weight, output_shape) stable-diffusion-webui/extensions-builtin/Lora/network_lora.py000066400000000000000000000100241465116634300252530ustar00rootroot00000000000000import torch import lyco_helpers import modules.models.sd3.mmdit import network from modules import devices class ModuleTypeLora(network.ModuleType): def create_module(self, net: network.Network, weights: network.NetworkWeights): if all(x in weights.w for x in ["lora_up.weight", "lora_down.weight"]): return NetworkModuleLora(net, weights) if all(x in weights.w for x in ["lora_A.weight", "lora_B.weight"]): w = weights.w.copy() weights.w.clear() weights.w.update({"lora_up.weight": w["lora_B.weight"], "lora_down.weight": w["lora_A.weight"]}) return NetworkModuleLora(net, weights) return None class NetworkModuleLora(network.NetworkModule): def __init__(self, net: network.Network, weights: network.NetworkWeights): super().__init__(net, weights) self.up_model = self.create_module(weights.w, "lora_up.weight") self.down_model = self.create_module(weights.w, "lora_down.weight") self.mid_model = self.create_module(weights.w, "lora_mid.weight", none_ok=True) self.dim = weights.w["lora_down.weight"].shape[0] def create_module(self, weights, key, none_ok=False): weight = weights.get(key) if weight is None and none_ok: return None is_linear = type(self.sd_module) in [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear, torch.nn.MultiheadAttention, modules.models.sd3.mmdit.QkvLinear] is_conv = type(self.sd_module) in [torch.nn.Conv2d] if is_linear: weight = weight.reshape(weight.shape[0], -1) module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False) elif is_conv and key == "lora_down.weight" or key == "dyn_up": if len(weight.shape) == 2: weight = weight.reshape(weight.shape[0], -1, 1, 1) if weight.shape[2] != 1 or weight.shape[3] != 1: module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], self.sd_module.kernel_size, self.sd_module.stride, self.sd_module.padding, bias=False) else: module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False) elif is_conv and key == "lora_mid.weight": module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], self.sd_module.kernel_size, self.sd_module.stride, self.sd_module.padding, bias=False) elif is_conv and key == "lora_up.weight" or key == "dyn_down": module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False) else: raise AssertionError(f'Lora layer {self.network_key} matched a layer with unsupported type: {type(self.sd_module).__name__}') with torch.no_grad(): if weight.shape != module.weight.shape: weight = weight.reshape(module.weight.shape) module.weight.copy_(weight) module.to(device=devices.cpu, dtype=devices.dtype) module.weight.requires_grad_(False) return module def calc_updown(self, orig_weight): up = self.up_model.weight.to(orig_weight.device) down = self.down_model.weight.to(orig_weight.device) output_shape = [up.size(0), down.size(1)] if self.mid_model is not None: # cp-decomposition mid = self.mid_model.weight.to(orig_weight.device) updown = lyco_helpers.rebuild_cp_decomposition(up, down, mid) output_shape += mid.shape[2:] else: if len(down.shape) == 4: output_shape += down.shape[2:] updown = lyco_helpers.rebuild_conventional(up, down, output_shape, self.network.dyn_dim) return self.finalize_updown(updown, orig_weight, output_shape) def forward(self, x, y): self.up_model.to(device=devices.device) self.down_model.to(device=devices.device) return y + self.up_model(self.down_model(x)) * self.multiplier() * self.calc_scale() stable-diffusion-webui/extensions-builtin/Lora/network_norm.py000066400000000000000000000015711465116634300253000ustar00rootroot00000000000000import network class ModuleTypeNorm(network.ModuleType): def create_module(self, net: network.Network, weights: network.NetworkWeights): if all(x in weights.w for x in ["w_norm", "b_norm"]): return NetworkModuleNorm(net, weights) return None class NetworkModuleNorm(network.NetworkModule): def __init__(self, net: network.Network, weights: network.NetworkWeights): super().__init__(net, weights) self.w_norm = weights.w.get("w_norm") self.b_norm = weights.w.get("b_norm") def calc_updown(self, orig_weight): output_shape = self.w_norm.shape updown = self.w_norm.to(orig_weight.device) if self.b_norm is not None: ex_bias = self.b_norm.to(orig_weight.device) else: ex_bias = None return self.finalize_updown(updown, orig_weight, output_shape, ex_bias) stable-diffusion-webui/extensions-builtin/Lora/network_oft.py000066400000000000000000000121771465116634300251210ustar00rootroot00000000000000import torch import network from einops import rearrange class ModuleTypeOFT(network.ModuleType): def create_module(self, net: network.Network, weights: network.NetworkWeights): if all(x in weights.w for x in ["oft_blocks"]) or all(x in weights.w for x in ["oft_diag"]): return NetworkModuleOFT(net, weights) return None # Supports both kohya-ss' implementation of COFT https://github.com/kohya-ss/sd-scripts/blob/main/networks/oft.py # and KohakuBlueleaf's implementation of OFT/COFT https://github.com/KohakuBlueleaf/LyCORIS/blob/dev/lycoris/modules/diag_oft.py class NetworkModuleOFT(network.NetworkModule): def __init__(self, net: network.Network, weights: network.NetworkWeights): super().__init__(net, weights) self.lin_module = None self.org_module: list[torch.Module] = [self.sd_module] self.scale = 1.0 self.is_R = False self.is_boft = False # kohya-ss/New LyCORIS OFT/BOFT if "oft_blocks" in weights.w.keys(): self.oft_blocks = weights.w["oft_blocks"] # (num_blocks, block_size, block_size) self.alpha = weights.w.get("alpha", None) # alpha is constraint self.dim = self.oft_blocks.shape[0] # lora dim # Old LyCORIS OFT elif "oft_diag" in weights.w.keys(): self.is_R = True self.oft_blocks = weights.w["oft_diag"] # self.alpha is unused self.dim = self.oft_blocks.shape[1] # (num_blocks, block_size, block_size) is_linear = type(self.sd_module) in [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear] is_conv = type(self.sd_module) in [torch.nn.Conv2d] is_other_linear = type(self.sd_module) in [torch.nn.MultiheadAttention] # unsupported if is_linear: self.out_dim = self.sd_module.out_features elif is_conv: self.out_dim = self.sd_module.out_channels elif is_other_linear: self.out_dim = self.sd_module.embed_dim # LyCORIS BOFT if self.oft_blocks.dim() == 4: self.is_boft = True self.rescale = weights.w.get('rescale', None) if self.rescale is not None and not is_other_linear: self.rescale = self.rescale.reshape(-1, *[1]*(self.org_module[0].weight.dim() - 1)) self.num_blocks = self.dim self.block_size = self.out_dim // self.dim self.constraint = (0 if self.alpha is None else self.alpha) * self.out_dim if self.is_R: self.constraint = None self.block_size = self.dim self.num_blocks = self.out_dim // self.dim elif self.is_boft: self.boft_m = self.oft_blocks.shape[0] self.num_blocks = self.oft_blocks.shape[1] self.block_size = self.oft_blocks.shape[2] self.boft_b = self.block_size def calc_updown(self, orig_weight): oft_blocks = self.oft_blocks.to(orig_weight.device) eye = torch.eye(self.block_size, device=oft_blocks.device) if not self.is_R: block_Q = oft_blocks - oft_blocks.transpose(-1, -2) # ensure skew-symmetric orthogonal matrix if self.constraint != 0: norm_Q = torch.norm(block_Q.flatten()) new_norm_Q = torch.clamp(norm_Q, max=self.constraint.to(oft_blocks.device)) block_Q = block_Q * ((new_norm_Q + 1e-8) / (norm_Q + 1e-8)) oft_blocks = torch.matmul(eye + block_Q, (eye - block_Q).float().inverse()) R = oft_blocks.to(orig_weight.device) if not self.is_boft: # This errors out for MultiheadAttention, might need to be handled up-stream merged_weight = rearrange(orig_weight, '(k n) ... -> k n ...', k=self.num_blocks, n=self.block_size) merged_weight = torch.einsum( 'k n m, k n ... -> k m ...', R, merged_weight ) merged_weight = rearrange(merged_weight, 'k m ... -> (k m) ...') else: # TODO: determine correct value for scale scale = 1.0 m = self.boft_m b = self.boft_b r_b = b // 2 inp = orig_weight for i in range(m): bi = R[i] # b_num, b_size, b_size if i == 0: # Apply multiplier/scale and rescale into first weight bi = bi * scale + (1 - scale) * eye inp = rearrange(inp, "(c g k) ... -> (c k g) ...", g=2, k=2**i * r_b) inp = rearrange(inp, "(d b) ... -> d b ...", b=b) inp = torch.einsum("b i j, b j ... -> b i ...", bi, inp) inp = rearrange(inp, "d b ... -> (d b) ...") inp = rearrange(inp, "(c k g) ... -> (c g k) ...", g=2, k=2**i * r_b) merged_weight = inp # Rescale mechanism if self.rescale is not None: merged_weight = self.rescale.to(merged_weight) * merged_weight updown = merged_weight.to(orig_weight.device) - orig_weight.to(merged_weight.dtype) output_shape = orig_weight.shape return self.finalize_updown(updown, orig_weight, output_shape) stable-diffusion-webui/extensions-builtin/Lora/networks.py000066400000000000000000000710251465116634300244310ustar00rootroot00000000000000from __future__ import annotations import gradio as gr import logging import os import re import lora_patches import network import network_lora import network_glora import network_hada import network_ia3 import network_lokr import network_full import network_norm import network_oft import torch from typing import Union from modules import shared, devices, sd_models, errors, scripts, sd_hijack import modules.textual_inversion.textual_inversion as textual_inversion import modules.models.sd3.mmdit from lora_logger import logger module_types = [ network_lora.ModuleTypeLora(), network_hada.ModuleTypeHada(), network_ia3.ModuleTypeIa3(), network_lokr.ModuleTypeLokr(), network_full.ModuleTypeFull(), network_norm.ModuleTypeNorm(), network_glora.ModuleTypeGLora(), network_oft.ModuleTypeOFT(), ] re_digits = re.compile(r"\d+") re_x_proj = re.compile(r"(.*)_([qkv]_proj)$") re_compiled = {} suffix_conversion = { "attentions": {}, "resnets": { "conv1": "in_layers_2", "conv2": "out_layers_3", "norm1": "in_layers_0", "norm2": "out_layers_0", "time_emb_proj": "emb_layers_1", "conv_shortcut": "skip_connection", } } def convert_diffusers_name_to_compvis(key, is_sd2): def match(match_list, regex_text): regex = re_compiled.get(regex_text) if regex is None: regex = re.compile(regex_text) re_compiled[regex_text] = regex r = re.match(regex, key) if not r: return False match_list.clear() match_list.extend([int(x) if re.match(re_digits, x) else x for x in r.groups()]) return True m = [] if match(m, r"lora_unet_conv_in(.*)"): return f'diffusion_model_input_blocks_0_0{m[0]}' if match(m, r"lora_unet_conv_out(.*)"): return f'diffusion_model_out_2{m[0]}' if match(m, r"lora_unet_time_embedding_linear_(\d+)(.*)"): return f"diffusion_model_time_embed_{m[0] * 2 - 2}{m[1]}" if match(m, r"lora_unet_down_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"): suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3]) return f"diffusion_model_input_blocks_{1 + m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}" if match(m, r"lora_unet_mid_block_(attentions|resnets)_(\d+)_(.+)"): suffix = suffix_conversion.get(m[0], {}).get(m[2], m[2]) return f"diffusion_model_middle_block_{1 if m[0] == 'attentions' else m[1] * 2}_{suffix}" if match(m, r"lora_unet_up_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"): suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3]) return f"diffusion_model_output_blocks_{m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}" if match(m, r"lora_unet_down_blocks_(\d+)_downsamplers_0_conv"): return f"diffusion_model_input_blocks_{3 + m[0] * 3}_0_op" if match(m, r"lora_unet_up_blocks_(\d+)_upsamplers_0_conv"): return f"diffusion_model_output_blocks_{2 + m[0] * 3}_{2 if m[0]>0 else 1}_conv" if match(m, r"lora_te_text_model_encoder_layers_(\d+)_(.+)"): if is_sd2: if 'mlp_fc1' in m[1]: return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc1', 'mlp_c_fc')}" elif 'mlp_fc2' in m[1]: return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc2', 'mlp_c_proj')}" else: return f"model_transformer_resblocks_{m[0]}_{m[1].replace('self_attn', 'attn')}" return f"transformer_text_model_encoder_layers_{m[0]}_{m[1]}" if match(m, r"lora_te2_text_model_encoder_layers_(\d+)_(.+)"): if 'mlp_fc1' in m[1]: return f"1_model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc1', 'mlp_c_fc')}" elif 'mlp_fc2' in m[1]: return f"1_model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc2', 'mlp_c_proj')}" else: return f"1_model_transformer_resblocks_{m[0]}_{m[1].replace('self_attn', 'attn')}" return key def assign_network_names_to_compvis_modules(sd_model): network_layer_mapping = {} if shared.sd_model.is_sdxl: for i, embedder in enumerate(shared.sd_model.conditioner.embedders): if not hasattr(embedder, 'wrapped'): continue for name, module in embedder.wrapped.named_modules(): network_name = f'{i}_{name.replace(".", "_")}' network_layer_mapping[network_name] = module module.network_layer_name = network_name else: cond_stage_model = getattr(shared.sd_model.cond_stage_model, 'wrapped', shared.sd_model.cond_stage_model) for name, module in cond_stage_model.named_modules(): network_name = name.replace(".", "_") network_layer_mapping[network_name] = module module.network_layer_name = network_name for name, module in shared.sd_model.model.named_modules(): network_name = name.replace(".", "_") network_layer_mapping[network_name] = module module.network_layer_name = network_name sd_model.network_layer_mapping = network_layer_mapping class BundledTIHash(str): def __init__(self, hash_str): self.hash = hash_str def __str__(self): return self.hash if shared.opts.lora_bundled_ti_to_infotext else '' def load_network(name, network_on_disk): net = network.Network(name, network_on_disk) net.mtime = os.path.getmtime(network_on_disk.filename) sd = sd_models.read_state_dict(network_on_disk.filename) # this should not be needed but is here as an emergency fix for an unknown error people are experiencing in 1.2.0 if not hasattr(shared.sd_model, 'network_layer_mapping'): assign_network_names_to_compvis_modules(shared.sd_model) keys_failed_to_match = {} is_sd2 = 'model_transformer_resblocks' in shared.sd_model.network_layer_mapping if hasattr(shared.sd_model, 'diffusers_weight_map'): diffusers_weight_map = shared.sd_model.diffusers_weight_map elif hasattr(shared.sd_model, 'diffusers_weight_mapping'): diffusers_weight_map = {} for k, v in shared.sd_model.diffusers_weight_mapping(): diffusers_weight_map[k] = v shared.sd_model.diffusers_weight_map = diffusers_weight_map else: diffusers_weight_map = None matched_networks = {} bundle_embeddings = {} for key_network, weight in sd.items(): if diffusers_weight_map: key_network_without_network_parts, network_name, network_weight = key_network.rsplit(".", 2) network_part = network_name + '.' + network_weight else: key_network_without_network_parts, _, network_part = key_network.partition(".") if key_network_without_network_parts == "bundle_emb": emb_name, vec_name = network_part.split(".", 1) emb_dict = bundle_embeddings.get(emb_name, {}) if vec_name.split('.')[0] == 'string_to_param': _, k2 = vec_name.split('.', 1) emb_dict['string_to_param'] = {k2: weight} else: emb_dict[vec_name] = weight bundle_embeddings[emb_name] = emb_dict if diffusers_weight_map: key = diffusers_weight_map.get(key_network_without_network_parts, key_network_without_network_parts) else: key = convert_diffusers_name_to_compvis(key_network_without_network_parts, is_sd2) sd_module = shared.sd_model.network_layer_mapping.get(key, None) if sd_module is None: m = re_x_proj.match(key) if m: sd_module = shared.sd_model.network_layer_mapping.get(m.group(1), None) # SDXL loras seem to already have correct compvis keys, so only need to replace "lora_unet" with "diffusion_model" if sd_module is None and "lora_unet" in key_network_without_network_parts: key = key_network_without_network_parts.replace("lora_unet", "diffusion_model") sd_module = shared.sd_model.network_layer_mapping.get(key, None) elif sd_module is None and "lora_te1_text_model" in key_network_without_network_parts: key = key_network_without_network_parts.replace("lora_te1_text_model", "0_transformer_text_model") sd_module = shared.sd_model.network_layer_mapping.get(key, None) # some SD1 Loras also have correct compvis keys if sd_module is None: key = key_network_without_network_parts.replace("lora_te1_text_model", "transformer_text_model") sd_module = shared.sd_model.network_layer_mapping.get(key, None) # kohya_ss OFT module elif sd_module is None and "oft_unet" in key_network_without_network_parts: key = key_network_without_network_parts.replace("oft_unet", "diffusion_model") sd_module = shared.sd_model.network_layer_mapping.get(key, None) # KohakuBlueLeaf OFT module if sd_module is None and "oft_diag" in key: key = key_network_without_network_parts.replace("lora_unet", "diffusion_model") key = key_network_without_network_parts.replace("lora_te1_text_model", "0_transformer_text_model") sd_module = shared.sd_model.network_layer_mapping.get(key, None) if sd_module is None: keys_failed_to_match[key_network] = key continue if key not in matched_networks: matched_networks[key] = network.NetworkWeights(network_key=key_network, sd_key=key, w={}, sd_module=sd_module) matched_networks[key].w[network_part] = weight for key, weights in matched_networks.items(): net_module = None for nettype in module_types: net_module = nettype.create_module(net, weights) if net_module is not None: break if net_module is None: raise AssertionError(f"Could not find a module type (out of {', '.join([x.__class__.__name__ for x in module_types])}) that would accept those keys: {', '.join(weights.w)}") net.modules[key] = net_module embeddings = {} for emb_name, data in bundle_embeddings.items(): embedding = textual_inversion.create_embedding_from_data(data, emb_name, filename=network_on_disk.filename + "/" + emb_name) embedding.loaded = None embedding.shorthash = BundledTIHash(name) embeddings[emb_name] = embedding net.bundle_embeddings = embeddings if keys_failed_to_match: logging.debug(f"Network {network_on_disk.filename} didn't match keys: {keys_failed_to_match}") return net def purge_networks_from_memory(): while len(networks_in_memory) > shared.opts.lora_in_memory_limit and len(networks_in_memory) > 0: name = next(iter(networks_in_memory)) networks_in_memory.pop(name, None) devices.torch_gc() def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=None): emb_db = sd_hijack.model_hijack.embedding_db already_loaded = {} for net in loaded_networks: if net.name in names: already_loaded[net.name] = net for emb_name, embedding in net.bundle_embeddings.items(): if embedding.loaded: emb_db.register_embedding_by_name(None, shared.sd_model, emb_name) loaded_networks.clear() unavailable_networks = [] for name in names: if name.lower() in forbidden_network_aliases and available_networks.get(name) is None: unavailable_networks.append(name) elif available_network_aliases.get(name) is None: unavailable_networks.append(name) if unavailable_networks: update_available_networks_by_names(unavailable_networks) networks_on_disk = [available_networks.get(name, None) if name.lower() in forbidden_network_aliases else available_network_aliases.get(name, None) for name in names] if any(x is None for x in networks_on_disk): list_available_networks() networks_on_disk = [available_networks.get(name, None) if name.lower() in forbidden_network_aliases else available_network_aliases.get(name, None) for name in names] failed_to_load_networks = [] for i, (network_on_disk, name) in enumerate(zip(networks_on_disk, names)): net = already_loaded.get(name, None) if network_on_disk is not None: if net is None: net = networks_in_memory.get(name) if net is None or os.path.getmtime(network_on_disk.filename) > net.mtime: try: net = load_network(name, network_on_disk) networks_in_memory.pop(name, None) networks_in_memory[name] = net except Exception as e: errors.display(e, f"loading network {network_on_disk.filename}") continue net.mentioned_name = name network_on_disk.read_hash() if net is None: failed_to_load_networks.append(name) logging.info(f"Couldn't find network with name {name}") continue net.te_multiplier = te_multipliers[i] if te_multipliers else 1.0 net.unet_multiplier = unet_multipliers[i] if unet_multipliers else 1.0 net.dyn_dim = dyn_dims[i] if dyn_dims else 1.0 loaded_networks.append(net) for emb_name, embedding in net.bundle_embeddings.items(): if embedding.loaded is None and emb_name in emb_db.word_embeddings: logger.warning( f'Skip bundle embedding: "{emb_name}"' ' as it was already loaded from embeddings folder' ) continue embedding.loaded = False if emb_db.expected_shape == -1 or emb_db.expected_shape == embedding.shape: embedding.loaded = True emb_db.register_embedding(embedding, shared.sd_model) else: emb_db.skipped_embeddings[name] = embedding if failed_to_load_networks: lora_not_found_message = f'Lora not found: {", ".join(failed_to_load_networks)}' sd_hijack.model_hijack.comments.append(lora_not_found_message) if shared.opts.lora_not_found_warning_console: print(f'\n{lora_not_found_message}\n') if shared.opts.lora_not_found_gradio_warning: gr.Warning(lora_not_found_message) purge_networks_from_memory() def allowed_layer_without_weight(layer): if isinstance(layer, torch.nn.LayerNorm) and not layer.elementwise_affine: return True return False def store_weights_backup(weight): if weight is None: return None return weight.to(devices.cpu, copy=True) def restore_weights_backup(obj, field, weight): if weight is None: setattr(obj, field, None) return getattr(obj, field).copy_(weight) def network_restore_weights_from_backup(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention]): weights_backup = getattr(self, "network_weights_backup", None) bias_backup = getattr(self, "network_bias_backup", None) if weights_backup is None and bias_backup is None: return if weights_backup is not None: if isinstance(self, torch.nn.MultiheadAttention): restore_weights_backup(self, 'in_proj_weight', weights_backup[0]) restore_weights_backup(self.out_proj, 'weight', weights_backup[1]) else: restore_weights_backup(self, 'weight', weights_backup) if isinstance(self, torch.nn.MultiheadAttention): restore_weights_backup(self.out_proj, 'bias', bias_backup) else: restore_weights_backup(self, 'bias', bias_backup) def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention]): """ Applies the currently selected set of networks to the weights of torch layer self. If weights already have this particular set of networks applied, does nothing. If not, restores original weights from backup and alters weights according to networks. """ network_layer_name = getattr(self, 'network_layer_name', None) if network_layer_name is None: return current_names = getattr(self, "network_current_names", ()) wanted_names = tuple((x.name, x.te_multiplier, x.unet_multiplier, x.dyn_dim) for x in loaded_networks) weights_backup = getattr(self, "network_weights_backup", None) if weights_backup is None and wanted_names != (): if current_names != () and not allowed_layer_without_weight(self): raise RuntimeError(f"{network_layer_name} - no backup weights found and current weights are not unchanged") if isinstance(self, torch.nn.MultiheadAttention): weights_backup = (store_weights_backup(self.in_proj_weight), store_weights_backup(self.out_proj.weight)) else: weights_backup = store_weights_backup(self.weight) self.network_weights_backup = weights_backup bias_backup = getattr(self, "network_bias_backup", None) if bias_backup is None and wanted_names != (): if isinstance(self, torch.nn.MultiheadAttention) and self.out_proj.bias is not None: bias_backup = store_weights_backup(self.out_proj.bias) elif getattr(self, 'bias', None) is not None: bias_backup = store_weights_backup(self.bias) else: bias_backup = None # Unlike weight which always has value, some modules don't have bias. # Only report if bias is not None and current bias are not unchanged. if bias_backup is not None and current_names != (): raise RuntimeError("no backup bias found and current bias are not unchanged") self.network_bias_backup = bias_backup if current_names != wanted_names: network_restore_weights_from_backup(self) for net in loaded_networks: module = net.modules.get(network_layer_name, None) if module is not None and hasattr(self, 'weight') and not isinstance(module, modules.models.sd3.mmdit.QkvLinear): try: with torch.no_grad(): if getattr(self, 'fp16_weight', None) is None: weight = self.weight bias = self.bias else: weight = self.fp16_weight.clone().to(self.weight.device) bias = getattr(self, 'fp16_bias', None) if bias is not None: bias = bias.clone().to(self.bias.device) updown, ex_bias = module.calc_updown(weight) if len(weight.shape) == 4 and weight.shape[1] == 9: # inpainting model. zero pad updown to make channel[1] 4 to 9 updown = torch.nn.functional.pad(updown, (0, 0, 0, 0, 0, 5)) self.weight.copy_((weight.to(dtype=updown.dtype) + updown).to(dtype=self.weight.dtype)) if ex_bias is not None and hasattr(self, 'bias'): if self.bias is None: self.bias = torch.nn.Parameter(ex_bias).to(self.weight.dtype) else: self.bias.copy_((bias + ex_bias).to(dtype=self.bias.dtype)) except RuntimeError as e: logging.debug(f"Network {net.name} layer {network_layer_name}: {e}") extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1 continue module_q = net.modules.get(network_layer_name + "_q_proj", None) module_k = net.modules.get(network_layer_name + "_k_proj", None) module_v = net.modules.get(network_layer_name + "_v_proj", None) module_out = net.modules.get(network_layer_name + "_out_proj", None) if isinstance(self, torch.nn.MultiheadAttention) and module_q and module_k and module_v and module_out: try: with torch.no_grad(): # Send "real" orig_weight into MHA's lora module qw, kw, vw = self.in_proj_weight.chunk(3, 0) updown_q, _ = module_q.calc_updown(qw) updown_k, _ = module_k.calc_updown(kw) updown_v, _ = module_v.calc_updown(vw) del qw, kw, vw updown_qkv = torch.vstack([updown_q, updown_k, updown_v]) updown_out, ex_bias = module_out.calc_updown(self.out_proj.weight) self.in_proj_weight += updown_qkv self.out_proj.weight += updown_out if ex_bias is not None: if self.out_proj.bias is None: self.out_proj.bias = torch.nn.Parameter(ex_bias) else: self.out_proj.bias += ex_bias except RuntimeError as e: logging.debug(f"Network {net.name} layer {network_layer_name}: {e}") extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1 continue if isinstance(self, modules.models.sd3.mmdit.QkvLinear) and module_q and module_k and module_v: try: with torch.no_grad(): # Send "real" orig_weight into MHA's lora module qw, kw, vw = self.weight.chunk(3, 0) updown_q, _ = module_q.calc_updown(qw) updown_k, _ = module_k.calc_updown(kw) updown_v, _ = module_v.calc_updown(vw) del qw, kw, vw updown_qkv = torch.vstack([updown_q, updown_k, updown_v]) self.weight += updown_qkv except RuntimeError as e: logging.debug(f"Network {net.name} layer {network_layer_name}: {e}") extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1 continue if module is None: continue logging.debug(f"Network {net.name} layer {network_layer_name}: couldn't find supported operation") extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1 self.network_current_names = wanted_names def network_forward(org_module, input, original_forward): """ Old way of applying Lora by executing operations during layer's forward. Stacking many loras this way results in big performance degradation. """ if len(loaded_networks) == 0: return original_forward(org_module, input) input = devices.cond_cast_unet(input) network_restore_weights_from_backup(org_module) network_reset_cached_weight(org_module) y = original_forward(org_module, input) network_layer_name = getattr(org_module, 'network_layer_name', None) for lora in loaded_networks: module = lora.modules.get(network_layer_name, None) if module is None: continue y = module.forward(input, y) return y def network_reset_cached_weight(self: Union[torch.nn.Conv2d, torch.nn.Linear]): self.network_current_names = () self.network_weights_backup = None self.network_bias_backup = None def network_Linear_forward(self, input): if shared.opts.lora_functional: return network_forward(self, input, originals.Linear_forward) network_apply_weights(self) return originals.Linear_forward(self, input) def network_Linear_load_state_dict(self, *args, **kwargs): network_reset_cached_weight(self) return originals.Linear_load_state_dict(self, *args, **kwargs) def network_Conv2d_forward(self, input): if shared.opts.lora_functional: return network_forward(self, input, originals.Conv2d_forward) network_apply_weights(self) return originals.Conv2d_forward(self, input) def network_Conv2d_load_state_dict(self, *args, **kwargs): network_reset_cached_weight(self) return originals.Conv2d_load_state_dict(self, *args, **kwargs) def network_GroupNorm_forward(self, input): if shared.opts.lora_functional: return network_forward(self, input, originals.GroupNorm_forward) network_apply_weights(self) return originals.GroupNorm_forward(self, input) def network_GroupNorm_load_state_dict(self, *args, **kwargs): network_reset_cached_weight(self) return originals.GroupNorm_load_state_dict(self, *args, **kwargs) def network_LayerNorm_forward(self, input): if shared.opts.lora_functional: return network_forward(self, input, originals.LayerNorm_forward) network_apply_weights(self) return originals.LayerNorm_forward(self, input) def network_LayerNorm_load_state_dict(self, *args, **kwargs): network_reset_cached_weight(self) return originals.LayerNorm_load_state_dict(self, *args, **kwargs) def network_MultiheadAttention_forward(self, *args, **kwargs): network_apply_weights(self) return originals.MultiheadAttention_forward(self, *args, **kwargs) def network_MultiheadAttention_load_state_dict(self, *args, **kwargs): network_reset_cached_weight(self) return originals.MultiheadAttention_load_state_dict(self, *args, **kwargs) def process_network_files(names: list[str] | None = None): candidates = list(shared.walk_files(shared.cmd_opts.lora_dir, allowed_extensions=[".pt", ".ckpt", ".safetensors"])) candidates += list(shared.walk_files(shared.cmd_opts.lyco_dir_backcompat, allowed_extensions=[".pt", ".ckpt", ".safetensors"])) for filename in candidates: if os.path.isdir(filename): continue name = os.path.splitext(os.path.basename(filename))[0] # if names is provided, only load networks with names in the list if names and name not in names: continue try: entry = network.NetworkOnDisk(name, filename) except OSError: # should catch FileNotFoundError and PermissionError etc. errors.report(f"Failed to load network {name} from {filename}", exc_info=True) continue available_networks[name] = entry if entry.alias in available_network_aliases: forbidden_network_aliases[entry.alias.lower()] = 1 available_network_aliases[name] = entry available_network_aliases[entry.alias] = entry def update_available_networks_by_names(names: list[str]): process_network_files(names) def list_available_networks(): available_networks.clear() available_network_aliases.clear() forbidden_network_aliases.clear() available_network_hash_lookup.clear() forbidden_network_aliases.update({"none": 1, "Addams": 1}) os.makedirs(shared.cmd_opts.lora_dir, exist_ok=True) process_network_files() re_network_name = re.compile(r"(.*)\s*\([0-9a-fA-F]+\)") def infotext_pasted(infotext, params): if "AddNet Module 1" in [x[1] for x in scripts.scripts_txt2img.infotext_fields]: return # if the other extension is active, it will handle those fields, no need to do anything added = [] for k in params: if not k.startswith("AddNet Model "): continue num = k[13:] if params.get("AddNet Module " + num) != "LoRA": continue name = params.get("AddNet Model " + num) if name is None: continue m = re_network_name.match(name) if m: name = m.group(1) multiplier = params.get("AddNet Weight A " + num, "1.0") added.append(f"") if added: params["Prompt"] += "\n" + "".join(added) originals: lora_patches.LoraPatches = None extra_network_lora = None available_networks = {} available_network_aliases = {} loaded_networks = [] loaded_bundle_embeddings = {} networks_in_memory = {} available_network_hash_lookup = {} forbidden_network_aliases = {} list_available_networks() stable-diffusion-webui/extensions-builtin/Lora/preload.py000066400000000000000000000010011465116634300241660ustar00rootroot00000000000000import os from modules import paths from modules.paths_internal import normalized_filepath def preload(parser): parser.add_argument("--lora-dir", type=normalized_filepath, help="Path to directory with Lora networks.", default=os.path.join(paths.models_path, 'Lora')) parser.add_argument("--lyco-dir-backcompat", type=normalized_filepath, help="Path to directory with LyCORIS networks (for backawards compatibility; can also use --lyco-dir).", default=os.path.join(paths.models_path, 'LyCORIS')) stable-diffusion-webui/extensions-builtin/Lora/scripts/000077500000000000000000000000001465116634300236655ustar00rootroot00000000000000stable-diffusion-webui/extensions-builtin/Lora/scripts/lora_script.py000066400000000000000000000101261465116634300265600ustar00rootroot00000000000000import re import gradio as gr from fastapi import FastAPI import network import networks import lora # noqa:F401 import lora_patches import extra_networks_lora import ui_extra_networks_lora from modules import script_callbacks, ui_extra_networks, extra_networks, shared def unload(): networks.originals.undo() def before_ui(): ui_extra_networks.register_page(ui_extra_networks_lora.ExtraNetworksPageLora()) networks.extra_network_lora = extra_networks_lora.ExtraNetworkLora() extra_networks.register_extra_network(networks.extra_network_lora) extra_networks.register_extra_network_alias(networks.extra_network_lora, "lyco") networks.originals = lora_patches.LoraPatches() script_callbacks.on_model_loaded(networks.assign_network_names_to_compvis_modules) script_callbacks.on_script_unloaded(unload) script_callbacks.on_before_ui(before_ui) script_callbacks.on_infotext_pasted(networks.infotext_pasted) shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), { "sd_lora": shared.OptionInfo("None", "Add network to prompt", gr.Dropdown, lambda: {"choices": ["None", *networks.available_networks]}, refresh=networks.list_available_networks), "lora_preferred_name": shared.OptionInfo("Alias from file", "When adding to prompt, refer to Lora by", gr.Radio, {"choices": ["Alias from file", "Filename"]}), "lora_add_hashes_to_infotext": shared.OptionInfo(True, "Add Lora hashes to infotext"), "lora_bundled_ti_to_infotext": shared.OptionInfo(True, "Add Lora name as TI hashes for bundled Textual Inversion").info('"Add Textual Inversion hashes to infotext" needs to be enabled'), "lora_show_all": shared.OptionInfo(False, "Always show all networks on the Lora page").info("otherwise, those detected as for incompatible version of Stable Diffusion will be hidden"), "lora_hide_unknown_for_versions": shared.OptionInfo([], "Hide networks of unknown versions for model versions", gr.CheckboxGroup, {"choices": ["SD1", "SD2", "SDXL"]}), "lora_in_memory_limit": shared.OptionInfo(0, "Number of Lora networks to keep cached in memory", gr.Number, {"precision": 0}), "lora_not_found_warning_console": shared.OptionInfo(False, "Lora not found warning in console"), "lora_not_found_gradio_warning": shared.OptionInfo(False, "Lora not found warning popup in webui"), })) shared.options_templates.update(shared.options_section(('compatibility', "Compatibility"), { "lora_functional": shared.OptionInfo(False, "Lora/Networks: use old method that takes longer when you have multiple Loras active and produces same results as kohya-ss/sd-webui-additional-networks extension"), })) def create_lora_json(obj: network.NetworkOnDisk): return { "name": obj.name, "alias": obj.alias, "path": obj.filename, "metadata": obj.metadata, } def api_networks(_: gr.Blocks, app: FastAPI): @app.get("/sdapi/v1/loras") async def get_loras(): return [create_lora_json(obj) for obj in networks.available_networks.values()] @app.post("/sdapi/v1/refresh-loras") async def refresh_loras(): return networks.list_available_networks() script_callbacks.on_app_started(api_networks) re_lora = re.compile("= 16 re_word = re.compile(r"[-_\w']+") re_comma = re.compile(r" *, *") def build_tags(metadata): tags = {} ss_tag_frequency = metadata.get("ss_tag_frequency", {}) if ss_tag_frequency is not None and hasattr(ss_tag_frequency, 'items'): for _, tags_dict in ss_tag_frequency.items(): for tag, tag_count in tags_dict.items(): tag = tag.strip() tags[tag] = tags.get(tag, 0) + int(tag_count) if tags and is_non_comma_tagset(tags): new_tags = {} for text, text_count in tags.items(): for word in re.findall(re_word, text): if len(word) < 3: continue new_tags[word] = new_tags.get(word, 0) + text_count tags = new_tags ordered_tags = sorted(tags.keys(), key=tags.get, reverse=True) return [(tag, tags[tag]) for tag in ordered_tags] class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor): def __init__(self, ui, tabname, page): super().__init__(ui, tabname, page) self.select_sd_version = None self.taginfo = None self.edit_activation_text = None self.slider_preferred_weight = None self.edit_notes = None def save_lora_user_metadata(self, name, desc, sd_version, activation_text, preferred_weight, negative_text, notes): user_metadata = self.get_user_metadata(name) user_metadata["description"] = desc user_metadata["sd version"] = sd_version user_metadata["activation text"] = activation_text user_metadata["preferred weight"] = preferred_weight user_metadata["negative text"] = negative_text user_metadata["notes"] = notes self.write_user_metadata(name, user_metadata) def get_metadata_table(self, name): table = super().get_metadata_table(name) item = self.page.items.get(name, {}) metadata = item.get("metadata") or {} keys = { 'ss_output_name': "Output name:", 'ss_sd_model_name': "Model:", 'ss_clip_skip': "Clip skip:", 'ss_network_module': "Kohya module:", } for key, label in keys.items(): value = metadata.get(key, None) if value is not None and str(value) != "None": table.append((label, html.escape(value))) ss_training_started_at = metadata.get('ss_training_started_at') if ss_training_started_at: table.append(("Date trained:", datetime.datetime.utcfromtimestamp(float(ss_training_started_at)).strftime('%Y-%m-%d %H:%M'))) ss_bucket_info = metadata.get("ss_bucket_info") if ss_bucket_info and "buckets" in ss_bucket_info: resolutions = {} for _, bucket in ss_bucket_info["buckets"].items(): resolution = bucket["resolution"] resolution = f'{resolution[1]}x{resolution[0]}' resolutions[resolution] = resolutions.get(resolution, 0) + int(bucket["count"]) resolutions_list = sorted(resolutions.keys(), key=resolutions.get, reverse=True) resolutions_text = html.escape(", ".join(resolutions_list[0:4])) if len(resolutions) > 4: resolutions_text += ", ..." resolutions_text = f"{resolutions_text}" table.append(('Resolutions:' if len(resolutions_list) > 1 else 'Resolution:', resolutions_text)) image_count = 0 for _, params in metadata.get("ss_dataset_dirs", {}).items(): image_count += int(params.get("img_count", 0)) if image_count: table.append(("Dataset size:", image_count)) return table def put_values_into_components(self, name): user_metadata = self.get_user_metadata(name) values = super().put_values_into_components(name) item = self.page.items.get(name, {}) metadata = item.get("metadata") or {} tags = build_tags(metadata) gradio_tags = [(tag, str(count)) for tag, count in tags[0:24]] return [ *values[0:5], item.get("sd_version", "Unknown"), gr.HighlightedText.update(value=gradio_tags, visible=True if tags else False), user_metadata.get('activation text', ''), float(user_metadata.get('preferred weight', 0.0)), user_metadata.get('negative text', ''), gr.update(visible=True if tags else False), gr.update(value=self.generate_random_prompt_from_tags(tags), visible=True if tags else False), ] def generate_random_prompt(self, name): item = self.page.items.get(name, {}) metadata = item.get("metadata") or {} tags = build_tags(metadata) return self.generate_random_prompt_from_tags(tags) def generate_random_prompt_from_tags(self, tags): max_count = None res = [] for tag, count in tags: if not max_count: max_count = count v = random.random() * max_count if count > v: for x in "({[]})": tag = tag.replace(x, '\\' + x) res.append(tag) return ", ".join(sorted(res)) def create_extra_default_items_in_left_column(self): # this would be a lot better as gr.Radio but I can't make it work self.select_sd_version = gr.Dropdown(['SD1', 'SD2', 'SDXL', 'Unknown'], value='Unknown', label='Stable Diffusion version', interactive=True) def create_editor(self): self.create_default_editor_elems() self.taginfo = gr.HighlightedText(label="Training dataset tags") self.edit_activation_text = gr.Text(label='Activation text', info="Will be added to prompt along with Lora") self.slider_preferred_weight = gr.Slider(label='Preferred weight', info="Set to 0 to disable", minimum=0.0, maximum=2.0, step=0.01) self.edit_negative_text = gr.Text(label='Negative prompt', info="Will be added to negative prompts") with gr.Row() as row_random_prompt: with gr.Column(scale=8): random_prompt = gr.Textbox(label='Random prompt', lines=4, max_lines=4, interactive=False) with gr.Column(scale=1, min_width=120): generate_random_prompt = gr.Button('Generate', size="lg", scale=1) self.edit_notes = gr.TextArea(label='Notes', lines=4) generate_random_prompt.click(fn=self.generate_random_prompt, inputs=[self.edit_name_input], outputs=[random_prompt], show_progress=False) def select_tag(activation_text, evt: gr.SelectData): tag = evt.value[0] words = re.split(re_comma, activation_text) if tag in words: words = [x for x in words if x != tag and x.strip()] return ", ".join(words) return activation_text + ", " + tag if activation_text else tag self.taginfo.select(fn=select_tag, inputs=[self.edit_activation_text], outputs=[self.edit_activation_text], show_progress=False) self.create_default_buttons() viewed_components = [ self.edit_name, self.edit_description, self.html_filedata, self.html_preview, self.edit_notes, self.select_sd_version, self.taginfo, self.edit_activation_text, self.slider_preferred_weight, self.edit_negative_text, row_random_prompt, random_prompt, ] self.button_edit\ .click(fn=self.put_values_into_components, inputs=[self.edit_name_input], outputs=viewed_components)\ .then(fn=lambda: gr.update(visible=True), inputs=[], outputs=[self.box]) edited_components = [ self.edit_description, self.select_sd_version, self.edit_activation_text, self.slider_preferred_weight, self.edit_negative_text, self.edit_notes, ] self.setup_save_handler(self.button_save, self.save_lora_user_metadata, edited_components) stable-diffusion-webui/extensions-builtin/Lora/ui_extra_networks_lora.py000066400000000000000000000073241465116634300273470ustar00rootroot00000000000000import os import network import networks from modules import shared, ui_extra_networks from modules.ui_extra_networks import quote_js from ui_edit_user_metadata import LoraUserMetadataEditor class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage): def __init__(self): super().__init__('Lora') def refresh(self): networks.list_available_networks() def create_item(self, name, index=None, enable_filter=True): lora_on_disk = networks.available_networks.get(name) if lora_on_disk is None: return path, ext = os.path.splitext(lora_on_disk.filename) alias = lora_on_disk.get_alias() search_terms = [self.search_terms_from_path(lora_on_disk.filename)] if lora_on_disk.hash: search_terms.append(lora_on_disk.hash) item = { "name": name, "filename": lora_on_disk.filename, "shorthash": lora_on_disk.shorthash, "preview": self.find_preview(path) or self.find_embedded_preview(path, name, lora_on_disk.metadata), "description": self.find_description(path), "search_terms": search_terms, "local_preview": f"{path}.{shared.opts.samples_format}", "metadata": lora_on_disk.metadata, "sort_keys": {'default': index, **self.get_sort_keys(lora_on_disk.filename)}, "sd_version": lora_on_disk.sd_version.name, } self.read_user_metadata(item) activation_text = item["user_metadata"].get("activation text") preferred_weight = item["user_metadata"].get("preferred weight", 0.0) item["prompt"] = quote_js(f"") if activation_text: item["prompt"] += " + " + quote_js(" " + activation_text) negative_prompt = item["user_metadata"].get("negative text") item["negative_prompt"] = quote_js("") if negative_prompt: item["negative_prompt"] = quote_js('(' + negative_prompt + ':1)') sd_version = item["user_metadata"].get("sd version") if sd_version in network.SdVersion.__members__: item["sd_version"] = sd_version sd_version = network.SdVersion[sd_version] else: sd_version = lora_on_disk.sd_version if shared.opts.lora_show_all or not enable_filter or not shared.sd_model: pass elif sd_version == network.SdVersion.Unknown: model_version = network.SdVersion.SDXL if shared.sd_model.is_sdxl else network.SdVersion.SD2 if shared.sd_model.is_sd2 else network.SdVersion.SD1 if model_version.name in shared.opts.lora_hide_unknown_for_versions: return None elif shared.sd_model.is_sdxl and sd_version != network.SdVersion.SDXL: return None elif shared.sd_model.is_sd2 and sd_version != network.SdVersion.SD2: return None elif shared.sd_model.is_sd1 and sd_version != network.SdVersion.SD1: return None return item def list_items(self): # instantiate a list to protect against concurrent modification names = list(networks.available_networks) for index, name in enumerate(names): item = self.create_item(name, index) if item is not None: yield item def allowed_directories_for_previews(self): return [shared.cmd_opts.lora_dir, shared.cmd_opts.lyco_dir_backcompat] def create_user_metadata_editor(self, ui, tabname): return LoraUserMetadataEditor(ui, tabname, self) stable-diffusion-webui/extensions-builtin/ScuNET/000077500000000000000000000000001465116634300224025ustar00rootroot00000000000000stable-diffusion-webui/extensions-builtin/ScuNET/preload.py000066400000000000000000000003431465116634300244020ustar00rootroot00000000000000import os from modules import paths def preload(parser): parser.add_argument("--scunet-models-path", type=str, help="Path to directory with ScuNET model file(s).", default=os.path.join(paths.models_path, 'ScuNET')) stable-diffusion-webui/extensions-builtin/ScuNET/scripts/000077500000000000000000000000001465116634300240715ustar00rootroot00000000000000stable-diffusion-webui/extensions-builtin/ScuNET/scripts/scunet_model.py000066400000000000000000000060271465116634300271310ustar00rootroot00000000000000import sys import PIL.Image import modules.upscaler from modules import devices, errors, modelloader, script_callbacks, shared, upscaler_utils 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 file.startswith("http"): 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: errors.report(f"Error loading ScuNET model: {file}", exc_info=True) 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.Image, selected_file): devices.torch_gc() try: model = self.load_model(selected_file) except Exception as e: print(f"ScuNET: Unable to load model from {selected_file}: {e}", file=sys.stderr) return img img = upscaler_utils.upscale_2( img, model, tile_size=shared.opts.SCUNET_tile, tile_overlap=shared.opts.SCUNET_tile_overlap, scale=1, # ScuNET is a denoising model, not an upscaler desc='ScuNET', ) devices.torch_gc() return img def load_model(self, path: str): device = devices.get_device_for('scunet') if path.startswith("http"): # TODO: this doesn't use `path` at all? filename = modelloader.load_file_from_url(self.model_url, model_dir=self.model_download_path, file_name=f"{self.name}.pth") else: filename = path return modelloader.load_spandrel_model(filename, device=device, expected_architecture='SCUNet') def on_ui_settings(): import gradio as gr shared.opts.add_option("SCUNET_tile", shared.OptionInfo(256, "Tile size for SCUNET upscalers.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}, section=('upscaling', "Upscaling")).info("0 = no tiling")) shared.opts.add_option("SCUNET_tile_overlap", shared.OptionInfo(8, "Tile overlap for SCUNET upscalers.", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}, section=('upscaling', "Upscaling")).info("Low values = visible seam")) script_callbacks.on_ui_settings(on_ui_settings) stable-diffusion-webui/extensions-builtin/SwinIR/000077500000000000000000000000001465116634300224545ustar00rootroot00000000000000stable-diffusion-webui/extensions-builtin/SwinIR/preload.py000066400000000000000000000003431465116634300244540ustar00rootroot00000000000000import os from modules import paths def preload(parser): parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(paths.models_path, 'SwinIR')) stable-diffusion-webui/extensions-builtin/SwinIR/scripts/000077500000000000000000000000001465116634300241435ustar00rootroot00000000000000stable-diffusion-webui/extensions-builtin/SwinIR/scripts/swinir_model.py000066400000000000000000000072671465116634300272240ustar00rootroot00000000000000import logging import sys import torch from PIL import Image from modules import devices, modelloader, script_callbacks, shared, upscaler_utils from modules.upscaler import Upscaler, UpscalerData SWINIR_MODEL_URL = "https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_GAN.pth" logger = logging.getLogger(__name__) class UpscalerSwinIR(Upscaler): def __init__(self, dirname): self._cached_model = None # keep the model when SWIN_torch_compile is on to prevent re-compile every runs self._cached_model_config = None # to clear '_cached_model' when changing model (v1/v2) or settings self.name = "SwinIR" self.model_url = SWINIR_MODEL_URL 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 model.startswith("http"): 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: Image.Image, model_file: str) -> Image.Image: current_config = (model_file, shared.opts.SWIN_tile) if self._cached_model_config == current_config: model = self._cached_model else: try: model = self.load_model(model_file) except Exception as e: print(f"Failed loading SwinIR model {model_file}: {e}", file=sys.stderr) return img self._cached_model = model self._cached_model_config = current_config img = upscaler_utils.upscale_2( img, model, tile_size=shared.opts.SWIN_tile, tile_overlap=shared.opts.SWIN_tile_overlap, scale=model.scale, desc="SwinIR", ) devices.torch_gc() return img def load_model(self, path, scale=4): if path.startswith("http"): filename = modelloader.load_file_from_url( url=path, model_dir=self.model_download_path, file_name=f"{self.model_name.replace(' ', '_')}.pth", ) else: filename = path model_descriptor = modelloader.load_spandrel_model( filename, device=self._get_device(), prefer_half=(devices.dtype == torch.float16), expected_architecture="SwinIR", ) if getattr(shared.opts, 'SWIN_torch_compile', False): try: model_descriptor.model.compile() except Exception: logger.warning("Failed to compile SwinIR model, fallback to JIT", exc_info=True) return model_descriptor def _get_device(self): return devices.get_device_for('swinir') def on_ui_settings(): import gradio as gr shared.opts.add_option("SWIN_tile", shared.OptionInfo(192, "Tile size for all SwinIR.", gr.Slider, {"minimum": 16, "maximum": 512, "step": 16}, section=('upscaling', "Upscaling"))) shared.opts.add_option("SWIN_tile_overlap", shared.OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}, section=('upscaling', "Upscaling"))) shared.opts.add_option("SWIN_torch_compile", shared.OptionInfo(False, "Use torch.compile to accelerate SwinIR.", gr.Checkbox, {"interactive": True}, section=('upscaling', "Upscaling")).info("Takes longer on first run")) script_callbacks.on_ui_settings(on_ui_settings) stable-diffusion-webui/extensions-builtin/canvas-zoom-and-pan/000077500000000000000000000000001465116634300250525ustar00rootroot00000000000000stable-diffusion-webui/extensions-builtin/canvas-zoom-and-pan/javascript/000077500000000000000000000000001465116634300272205ustar00rootroot00000000000000stable-diffusion-webui/extensions-builtin/canvas-zoom-and-pan/javascript/zoom.js000066400000000000000000001077331465116634300305550ustar00rootroot00000000000000onUiLoaded(async() => { const elementIDs = { img2imgTabs: "#mode_img2img .tab-nav", inpaint: "#img2maskimg", inpaintSketch: "#inpaint_sketch", rangeGroup: "#img2img_column_size", sketch: "#img2img_sketch" }; const tabNameToElementId = { "Inpaint sketch": elementIDs.inpaintSketch, "Inpaint": elementIDs.inpaint, "Sketch": elementIDs.sketch }; // Helper functions // Get active tab /** * Waits for an element to be present in the DOM. */ const waitForElement = (id) => new Promise(resolve => { const checkForElement = () => { const element = document.querySelector(id); if (element) return resolve(element); setTimeout(checkForElement, 100); }; checkForElement(); }); function getActiveTab(elements, all = false) { if (!elements.img2imgTabs) return null; const tabs = elements.img2imgTabs.querySelectorAll("button"); if (all) return tabs; for (let tab of tabs) { if (tab.classList.contains("selected")) { return tab; } } } // Get tab ID function getTabId(elements) { const activeTab = getActiveTab(elements); if (!activeTab) return null; return tabNameToElementId[activeTab.innerText]; } // Wait until opts loaded async function waitForOpts() { for (; ;) { if (window.opts && Object.keys(window.opts).length) { return window.opts; } await new Promise(resolve => setTimeout(resolve, 100)); } } // Detect whether the element has a horizontal scroll bar function hasHorizontalScrollbar(element) { return element.scrollWidth > element.clientWidth; } // Function for defining the "Ctrl", "Shift" and "Alt" keys function isModifierKey(event, key) { switch (key) { case "Ctrl": return event.ctrlKey; case "Shift": return event.shiftKey; case "Alt": return event.altKey; default: return false; } } // Check if hotkey is valid function isValidHotkey(value) { const specialKeys = ["Ctrl", "Alt", "Shift", "Disable"]; return ( (typeof value === "string" && value.length === 1 && /[a-z]/i.test(value)) || specialKeys.includes(value) ); } // Normalize hotkey function normalizeHotkey(hotkey) { return hotkey.length === 1 ? "Key" + hotkey.toUpperCase() : hotkey; } // Format hotkey for display function formatHotkeyForDisplay(hotkey) { return hotkey.startsWith("Key") ? hotkey.slice(3) : hotkey; } // Create hotkey configuration with the provided options function createHotkeyConfig(defaultHotkeysConfig, hotkeysConfigOpts) { const result = {}; // Resulting hotkey configuration const usedKeys = new Set(); // Set of used hotkeys // Iterate through defaultHotkeysConfig keys for (const key in defaultHotkeysConfig) { const userValue = hotkeysConfigOpts[key]; // User-provided hotkey value const defaultValue = defaultHotkeysConfig[key]; // Default hotkey value // Apply appropriate value for undefined, boolean, or object userValue if ( userValue === undefined || typeof userValue === "boolean" || typeof userValue === "object" || userValue === "disable" ) { result[key] = userValue === undefined ? defaultValue : userValue; } else if (isValidHotkey(userValue)) { const normalizedUserValue = normalizeHotkey(userValue); // Check for conflicting hotkeys if (!usedKeys.has(normalizedUserValue)) { usedKeys.add(normalizedUserValue); result[key] = normalizedUserValue; } else { console.error( `Hotkey: ${formatHotkeyForDisplay( userValue )} for ${key} is repeated and conflicts with another hotkey. The default hotkey is used: ${formatHotkeyForDisplay( defaultValue )}` ); result[key] = defaultValue; } } else { console.error( `Hotkey: ${formatHotkeyForDisplay( userValue )} for ${key} is not valid. The default hotkey is used: ${formatHotkeyForDisplay( defaultValue )}` ); result[key] = defaultValue; } } return result; } // Disables functions in the config object based on the provided list of function names function disableFunctions(config, disabledFunctions) { // Bind the hasOwnProperty method to the functionMap object to avoid errors const hasOwnProperty = Object.prototype.hasOwnProperty.bind(functionMap); // Loop through the disabledFunctions array and disable the corresponding functions in the config object disabledFunctions.forEach(funcName => { if (hasOwnProperty(funcName)) { const key = functionMap[funcName]; config[key] = "disable"; } }); // Return the updated config object return config; } /** * The restoreImgRedMask function displays a red mask around an image to indicate the aspect ratio. * If the image display property is set to 'none', the mask breaks. To fix this, the function * temporarily sets the display property to 'block' and then hides the mask again after 300 milliseconds * to avoid breaking the canvas. Additionally, the function adjusts the mask to work correctly on * very long images. */ function restoreImgRedMask(elements) { const mainTabId = getTabId(elements); if (!mainTabId) return; const mainTab = gradioApp().querySelector(mainTabId); const img = mainTab.querySelector("img"); const imageARPreview = gradioApp().querySelector("#imageARPreview"); if (!img || !imageARPreview) return; imageARPreview.style.transform = ""; if (parseFloat(mainTab.style.width) > 865) { const transformString = mainTab.style.transform; const scaleMatch = transformString.match( /scale\(([-+]?[0-9]*\.?[0-9]+)\)/ ); let zoom = 1; // default zoom if (scaleMatch && scaleMatch[1]) { zoom = Number(scaleMatch[1]); } imageARPreview.style.transformOrigin = "0 0"; imageARPreview.style.transform = `scale(${zoom})`; } if (img.style.display !== "none") return; img.style.display = "block"; setTimeout(() => { img.style.display = "none"; }, 400); } const hotkeysConfigOpts = await waitForOpts(); // Default config const defaultHotkeysConfig = { canvas_hotkey_zoom: "Alt", canvas_hotkey_adjust: "Ctrl", canvas_hotkey_reset: "KeyR", canvas_hotkey_fullscreen: "KeyS", canvas_hotkey_move: "KeyF", canvas_hotkey_overlap: "KeyO", canvas_hotkey_shrink_brush: "KeyQ", canvas_hotkey_grow_brush: "KeyW", canvas_disabled_functions: [], canvas_show_tooltip: true, canvas_auto_expand: true, canvas_blur_prompt: false, }; const functionMap = { "Zoom": "canvas_hotkey_zoom", "Adjust brush size": "canvas_hotkey_adjust", "Hotkey shrink brush": "canvas_hotkey_shrink_brush", "Hotkey enlarge brush": "canvas_hotkey_grow_brush", "Moving canvas": "canvas_hotkey_move", "Fullscreen": "canvas_hotkey_fullscreen", "Reset Zoom": "canvas_hotkey_reset", "Overlap": "canvas_hotkey_overlap" }; // Loading the configuration from opts const preHotkeysConfig = createHotkeyConfig( defaultHotkeysConfig, hotkeysConfigOpts ); // Disable functions that are not needed by the user const hotkeysConfig = disableFunctions( preHotkeysConfig, preHotkeysConfig.canvas_disabled_functions ); let isMoving = false; let mouseX, mouseY; let activeElement; let interactedWithAltKey = false; const elements = Object.fromEntries( Object.keys(elementIDs).map(id => [ id, gradioApp().querySelector(elementIDs[id]) ]) ); const elemData = {}; // Apply functionality to the range inputs. Restore redmask and correct for long images. const rangeInputs = elements.rangeGroup ? Array.from(elements.rangeGroup.querySelectorAll("input")) : [ gradioApp().querySelector("#img2img_width input[type='range']"), gradioApp().querySelector("#img2img_height input[type='range']") ]; for (const input of rangeInputs) { input?.addEventListener("input", () => restoreImgRedMask(elements)); } function applyZoomAndPan(elemId, isExtension = true) { const targetElement = gradioApp().querySelector(elemId); if (!targetElement) { console.log("Element not found", elemId); return; } targetElement.style.transformOrigin = "0 0"; elemData[elemId] = { zoom: 1, panX: 0, panY: 0 }; let fullScreenMode = false; // Create tooltip function createTooltip() { const toolTipElement = targetElement.querySelector(".image-container"); const tooltip = document.createElement("div"); tooltip.className = "canvas-tooltip"; // Creating an item of information const info = document.createElement("i"); info.className = "canvas-tooltip-info"; info.textContent = ""; // Create a container for the contents of the tooltip const tooltipContent = document.createElement("div"); tooltipContent.className = "canvas-tooltip-content"; // Define an array with hotkey information and their actions const hotkeysInfo = [ { configKey: "canvas_hotkey_zoom", action: "Zoom canvas", keySuffix: " + wheel" }, { configKey: "canvas_hotkey_adjust", action: "Adjust brush size", keySuffix: " + wheel" }, {configKey: "canvas_hotkey_reset", action: "Reset zoom"}, { configKey: "canvas_hotkey_fullscreen", action: "Fullscreen mode" }, {configKey: "canvas_hotkey_move", action: "Move canvas"}, {configKey: "canvas_hotkey_overlap", action: "Overlap"} ]; // Create hotkeys array with disabled property based on the config values const hotkeys = hotkeysInfo.map(info => { const configValue = hotkeysConfig[info.configKey]; const key = info.keySuffix ? `${configValue}${info.keySuffix}` : configValue.charAt(configValue.length - 1); return { key, action: info.action, disabled: configValue === "disable" }; }); for (const hotkey of hotkeys) { if (hotkey.disabled) { continue; } const p = document.createElement("p"); p.innerHTML = `${hotkey.key} - ${hotkey.action}`; tooltipContent.appendChild(p); } // Add information and content elements to the tooltip element tooltip.appendChild(info); tooltip.appendChild(tooltipContent); // Add a hint element to the target element toolTipElement.appendChild(tooltip); } //Show tool tip if setting enable if (hotkeysConfig.canvas_show_tooltip) { createTooltip(); } // In the course of research, it was found that the tag img is very harmful when zooming and creates white canvases. This hack allows you to almost never think about this problem, it has no effect on webui. function fixCanvas() { const activeTab = getActiveTab(elements)?.textContent.trim(); if (activeTab && activeTab !== "img2img") { const img = targetElement.querySelector(`${elemId} img`); if (img && img.style.display !== "none") { img.style.display = "none"; img.style.visibility = "hidden"; } } } // Reset the zoom level and pan position of the target element to their initial values function resetZoom() { elemData[elemId] = { zoomLevel: 1, panX: 0, panY: 0 }; if (isExtension) { targetElement.style.overflow = "hidden"; } targetElement.isZoomed = false; fixCanvas(); targetElement.style.transform = `scale(${elemData[elemId].zoomLevel}) translate(${elemData[elemId].panX}px, ${elemData[elemId].panY}px)`; const canvas = gradioApp().querySelector( `${elemId} canvas[key="interface"]` ); toggleOverlap("off"); fullScreenMode = false; const closeBtn = targetElement.querySelector("button[aria-label='Remove Image']"); if (closeBtn) { closeBtn.addEventListener("click", resetZoom); } if (canvas && isExtension) { const parentElement = targetElement.closest('[id^="component-"]'); if ( canvas && parseFloat(canvas.style.width) > parentElement.offsetWidth && parseFloat(targetElement.style.width) > parentElement.offsetWidth ) { fitToElement(); return; } } if ( canvas && !isExtension && parseFloat(canvas.style.width) > 865 && parseFloat(targetElement.style.width) > 865 ) { fitToElement(); return; } targetElement.style.width = ""; } // Toggle the zIndex of the target element between two values, allowing it to overlap or be overlapped by other elements function toggleOverlap(forced = "") { const zIndex1 = "0"; const zIndex2 = "998"; targetElement.style.zIndex = targetElement.style.zIndex !== zIndex2 ? zIndex2 : zIndex1; if (forced === "off") { targetElement.style.zIndex = zIndex1; } else if (forced === "on") { targetElement.style.zIndex = zIndex2; } } // Adjust the brush size based on the deltaY value from a mouse wheel event function adjustBrushSize( elemId, deltaY, withoutValue = false, percentage = 5 ) { const input = gradioApp().querySelector( `${elemId} input[aria-label='Brush radius']` ) || gradioApp().querySelector( `${elemId} button[aria-label="Use brush"]` ); if (input) { input.click(); if (!withoutValue) { const maxValue = parseFloat(input.getAttribute("max")) || 100; const changeAmount = maxValue * (percentage / 100); const newValue = parseFloat(input.value) + (deltaY > 0 ? -changeAmount : changeAmount); input.value = Math.min(Math.max(newValue, 0), maxValue); input.dispatchEvent(new Event("change")); } } } // Reset zoom when uploading a new image const fileInput = gradioApp().querySelector( `${elemId} input[type="file"][accept="image/*"].svelte-116rqfv` ); fileInput.addEventListener("click", resetZoom); // Update the zoom level and pan position of the target element based on the values of the zoomLevel, panX and panY variables function updateZoom(newZoomLevel, mouseX, mouseY) { newZoomLevel = Math.max(0.1, Math.min(newZoomLevel, 15)); elemData[elemId].panX += mouseX - (mouseX * newZoomLevel) / elemData[elemId].zoomLevel; elemData[elemId].panY += mouseY - (mouseY * newZoomLevel) / elemData[elemId].zoomLevel; targetElement.style.transformOrigin = "0 0"; targetElement.style.transform = `translate(${elemData[elemId].panX}px, ${elemData[elemId].panY}px) scale(${newZoomLevel})`; toggleOverlap("on"); if (isExtension) { targetElement.style.overflow = "visible"; } return newZoomLevel; } // Change the zoom level based on user interaction function changeZoomLevel(operation, e) { if (isModifierKey(e, hotkeysConfig.canvas_hotkey_zoom)) { e.preventDefault(); if (hotkeysConfig.canvas_hotkey_zoom === "Alt") { interactedWithAltKey = true; } let zoomPosX, zoomPosY; let delta = 0.2; if (elemData[elemId].zoomLevel > 7) { delta = 0.9; } else if (elemData[elemId].zoomLevel > 2) { delta = 0.6; } zoomPosX = e.clientX; zoomPosY = e.clientY; fullScreenMode = false; elemData[elemId].zoomLevel = updateZoom( elemData[elemId].zoomLevel + (operation === "+" ? delta : -delta), zoomPosX - targetElement.getBoundingClientRect().left, zoomPosY - targetElement.getBoundingClientRect().top ); targetElement.isZoomed = true; } } /** * This function fits the target element to the screen by calculating * the required scale and offsets. It also updates the global variables * zoomLevel, panX, and panY to reflect the new state. */ function fitToElement() { //Reset Zoom targetElement.style.transform = `translate(${0}px, ${0}px) scale(${1})`; let parentElement; if (isExtension) { parentElement = targetElement.closest('[id^="component-"]'); } else { parentElement = targetElement.parentElement; } // Get element and screen dimensions const elementWidth = targetElement.offsetWidth; const elementHeight = targetElement.offsetHeight; const screenWidth = parentElement.clientWidth; const screenHeight = parentElement.clientHeight; // Get element's coordinates relative to the parent element const elementRect = targetElement.getBoundingClientRect(); const parentRect = parentElement.getBoundingClientRect(); const elementX = elementRect.x - parentRect.x; // Calculate scale and offsets const scaleX = screenWidth / elementWidth; const scaleY = screenHeight / elementHeight; const scale = Math.min(scaleX, scaleY); const transformOrigin = window.getComputedStyle(targetElement).transformOrigin; const [originX, originY] = transformOrigin.split(" "); const originXValue = parseFloat(originX); const originYValue = parseFloat(originY); const offsetX = (screenWidth - elementWidth * scale) / 2 - originXValue * (1 - scale); const offsetY = (screenHeight - elementHeight * scale) / 2.5 - originYValue * (1 - scale); // Apply scale and offsets to the element targetElement.style.transform = `translate(${offsetX}px, ${offsetY}px) scale(${scale})`; // Update global variables elemData[elemId].zoomLevel = scale; elemData[elemId].panX = offsetX; elemData[elemId].panY = offsetY; fullScreenMode = false; toggleOverlap("off"); } /** * This function fits the target element to the screen by calculating * the required scale and offsets. It also updates the global variables * zoomLevel, panX, and panY to reflect the new state. */ // Fullscreen mode function fitToScreen() { const canvas = gradioApp().querySelector( `${elemId} canvas[key="interface"]` ); if (!canvas) return; if (canvas.offsetWidth > 862 || isExtension) { targetElement.style.width = (canvas.offsetWidth + 2) + "px"; } if (isExtension) { targetElement.style.overflow = "visible"; } if (fullScreenMode) { resetZoom(); fullScreenMode = false; return; } //Reset Zoom targetElement.style.transform = `translate(${0}px, ${0}px) scale(${1})`; // Get scrollbar width to right-align the image const scrollbarWidth = window.innerWidth - document.documentElement.clientWidth; // Get element and screen dimensions const elementWidth = targetElement.offsetWidth; const elementHeight = targetElement.offsetHeight; const screenWidth = window.innerWidth - scrollbarWidth; const screenHeight = window.innerHeight; // Get element's coordinates relative to the page const elementRect = targetElement.getBoundingClientRect(); const elementY = elementRect.y; const elementX = elementRect.x; // Calculate scale and offsets const scaleX = screenWidth / elementWidth; const scaleY = screenHeight / elementHeight; const scale = Math.min(scaleX, scaleY); // Get the current transformOrigin const computedStyle = window.getComputedStyle(targetElement); const transformOrigin = computedStyle.transformOrigin; const [originX, originY] = transformOrigin.split(" "); const originXValue = parseFloat(originX); const originYValue = parseFloat(originY); // Calculate offsets with respect to the transformOrigin const offsetX = (screenWidth - elementWidth * scale) / 2 - elementX - originXValue * (1 - scale); const offsetY = (screenHeight - elementHeight * scale) / 2 - elementY - originYValue * (1 - scale); // Apply scale and offsets to the element targetElement.style.transform = `translate(${offsetX}px, ${offsetY}px) scale(${scale})`; // Update global variables elemData[elemId].zoomLevel = scale; elemData[elemId].panX = offsetX; elemData[elemId].panY = offsetY; fullScreenMode = true; toggleOverlap("on"); } // Handle keydown events function handleKeyDown(event) { // Disable key locks to make pasting from the buffer work correctly if ((event.ctrlKey && event.code === 'KeyV') || (event.ctrlKey && event.code === 'KeyC') || event.code === "F5") { return; } // before activating shortcut, ensure user is not actively typing in an input field if (!hotkeysConfig.canvas_blur_prompt) { if (event.target.nodeName === 'TEXTAREA' || event.target.nodeName === 'INPUT') { return; } } const hotkeyActions = { [hotkeysConfig.canvas_hotkey_reset]: resetZoom, [hotkeysConfig.canvas_hotkey_overlap]: toggleOverlap, [hotkeysConfig.canvas_hotkey_fullscreen]: fitToScreen, [hotkeysConfig.canvas_hotkey_shrink_brush]: () => adjustBrushSize(elemId, 10), [hotkeysConfig.canvas_hotkey_grow_brush]: () => adjustBrushSize(elemId, -10) }; const action = hotkeyActions[event.code]; if (action) { event.preventDefault(); action(event); } if ( isModifierKey(event, hotkeysConfig.canvas_hotkey_zoom) || isModifierKey(event, hotkeysConfig.canvas_hotkey_adjust) ) { event.preventDefault(); } } // Get Mouse position function getMousePosition(e) { mouseX = e.offsetX; mouseY = e.offsetY; } // Simulation of the function to put a long image into the screen. // We detect if an image has a scroll bar or not, make a fullscreen to reveal the image, then reduce it to fit into the element. // We hide the image and show it to the user when it is ready. targetElement.isExpanded = false; function autoExpand() { const canvas = document.querySelector(`${elemId} canvas[key="interface"]`); if (canvas) { if (hasHorizontalScrollbar(targetElement) && targetElement.isExpanded === false) { targetElement.style.visibility = "hidden"; setTimeout(() => { fitToScreen(); resetZoom(); targetElement.style.visibility = "visible"; targetElement.isExpanded = true; }, 10); } } } targetElement.addEventListener("mousemove", getMousePosition); //observers // Creating an observer with a callback function to handle DOM changes const observer = new MutationObserver((mutationsList, observer) => { for (let mutation of mutationsList) { // If the style attribute of the canvas has changed, by observation it happens only when the picture changes if (mutation.type === 'attributes' && mutation.attributeName === 'style' && mutation.target.tagName.toLowerCase() === 'canvas') { targetElement.isExpanded = false; setTimeout(resetZoom, 10); } } }); // Apply auto expand if enabled if (hotkeysConfig.canvas_auto_expand) { targetElement.addEventListener("mousemove", autoExpand); // Set up an observer to track attribute changes observer.observe(targetElement, {attributes: true, childList: true, subtree: true}); } // Handle events only inside the targetElement let isKeyDownHandlerAttached = false; function handleMouseMove() { if (!isKeyDownHandlerAttached) { document.addEventListener("keydown", handleKeyDown); isKeyDownHandlerAttached = true; activeElement = elemId; } } function handleMouseLeave() { if (isKeyDownHandlerAttached) { document.removeEventListener("keydown", handleKeyDown); isKeyDownHandlerAttached = false; activeElement = null; } } // Add mouse event handlers targetElement.addEventListener("mousemove", handleMouseMove); targetElement.addEventListener("mouseleave", handleMouseLeave); // Reset zoom when click on another tab if (elements.img2imgTabs) { elements.img2imgTabs.addEventListener("click", resetZoom); elements.img2imgTabs.addEventListener("click", () => { // targetElement.style.width = ""; if (parseInt(targetElement.style.width) > 865) { setTimeout(fitToElement, 0); } }); } targetElement.addEventListener("wheel", e => { // change zoom level const operation = (e.deltaY || -e.wheelDelta) > 0 ? "-" : "+"; changeZoomLevel(operation, e); // Handle brush size adjustment with ctrl key pressed if (isModifierKey(e, hotkeysConfig.canvas_hotkey_adjust)) { e.preventDefault(); if (hotkeysConfig.canvas_hotkey_adjust === "Alt") { interactedWithAltKey = true; } // Increase or decrease brush size based on scroll direction adjustBrushSize(elemId, e.deltaY); } }); // Handle the move event for pan functionality. Updates the panX and panY variables and applies the new transform to the target element. function handleMoveKeyDown(e) { // Disable key locks to make pasting from the buffer work correctly if ((e.ctrlKey && e.code === 'KeyV') || (e.ctrlKey && event.code === 'KeyC') || e.code === "F5") { return; } // before activating shortcut, ensure user is not actively typing in an input field if (!hotkeysConfig.canvas_blur_prompt) { if (e.target.nodeName === 'TEXTAREA' || e.target.nodeName === 'INPUT') { return; } } if (e.code === hotkeysConfig.canvas_hotkey_move) { if (!e.ctrlKey && !e.metaKey && isKeyDownHandlerAttached) { e.preventDefault(); document.activeElement.blur(); isMoving = true; } } } function handleMoveKeyUp(e) { if (e.code === hotkeysConfig.canvas_hotkey_move) { isMoving = false; } } document.addEventListener("keydown", handleMoveKeyDown); document.addEventListener("keyup", handleMoveKeyUp); // Prevent firefox from opening main menu when alt is used as a hotkey for zoom or brush size function handleAltKeyUp(e) { if (e.key !== "Alt" || !interactedWithAltKey) { return; } e.preventDefault(); interactedWithAltKey = false; } document.addEventListener("keyup", handleAltKeyUp); // Detect zoom level and update the pan speed. function updatePanPosition(movementX, movementY) { let panSpeed = 2; if (elemData[elemId].zoomLevel > 8) { panSpeed = 3.5; } elemData[elemId].panX += movementX * panSpeed; elemData[elemId].panY += movementY * panSpeed; // Delayed redraw of an element requestAnimationFrame(() => { targetElement.style.transform = `translate(${elemData[elemId].panX}px, ${elemData[elemId].panY}px) scale(${elemData[elemId].zoomLevel})`; toggleOverlap("on"); }); } function handleMoveByKey(e) { if (isMoving && elemId === activeElement) { updatePanPosition(e.movementX, e.movementY); targetElement.style.pointerEvents = "none"; if (isExtension) { targetElement.style.overflow = "visible"; } } else { targetElement.style.pointerEvents = "auto"; } } // Prevents sticking to the mouse window.onblur = function() { isMoving = false; }; // Checks for extension function checkForOutBox() { const parentElement = targetElement.closest('[id^="component-"]'); if (parentElement.offsetWidth < targetElement.offsetWidth && !targetElement.isExpanded) { resetZoom(); targetElement.isExpanded = true; } if (parentElement.offsetWidth < targetElement.offsetWidth && elemData[elemId].zoomLevel == 1) { resetZoom(); } if (parentElement.offsetWidth < targetElement.offsetWidth && targetElement.offsetWidth * elemData[elemId].zoomLevel > parentElement.offsetWidth && elemData[elemId].zoomLevel < 1 && !targetElement.isZoomed) { resetZoom(); } } if (isExtension) { targetElement.addEventListener("mousemove", checkForOutBox); } window.addEventListener('resize', (e) => { resetZoom(); if (isExtension) { targetElement.isExpanded = false; targetElement.isZoomed = false; } }); gradioApp().addEventListener("mousemove", handleMoveByKey); } applyZoomAndPan(elementIDs.sketch, false); applyZoomAndPan(elementIDs.inpaint, false); applyZoomAndPan(elementIDs.inpaintSketch, false); // Make the function global so that other extensions can take advantage of this solution const applyZoomAndPanIntegration = async(id, elementIDs) => { const mainEl = document.querySelector(id); if (id.toLocaleLowerCase() === "none") { for (const elementID of elementIDs) { const el = await waitForElement(elementID); if (!el) break; applyZoomAndPan(elementID); } return; } if (!mainEl) return; mainEl.addEventListener("click", async() => { for (const elementID of elementIDs) { const el = await waitForElement(elementID); if (!el) break; applyZoomAndPan(elementID); } }, {once: true}); }; window.applyZoomAndPan = applyZoomAndPan; // Only 1 elements, argument elementID, for example applyZoomAndPan("#txt2img_controlnet_ControlNet_input_image") window.applyZoomAndPanIntegration = applyZoomAndPanIntegration; // for any extension /* The function `applyZoomAndPanIntegration` takes two arguments: 1. `id`: A string identifier for the element to which zoom and pan functionality will be applied on click. If the `id` value is "none", the functionality will be applied to all elements specified in the second argument without a click event. 2. `elementIDs`: An array of string identifiers for elements. Zoom and pan functionality will be applied to each of these elements on click of the element specified by the first argument. If "none" is specified in the first argument, the functionality will be applied to each of these elements without a click event. Example usage: applyZoomAndPanIntegration("#txt2img_controlnet", ["#txt2img_controlnet_ControlNet_input_image"]); In this example, zoom and pan functionality will be applied to the element with the identifier "txt2img_controlnet_ControlNet_input_image" upon clicking the element with the identifier "txt2img_controlnet". */ // More examples // Add integration with ControlNet txt2img One TAB // applyZoomAndPanIntegration("#txt2img_controlnet", ["#txt2img_controlnet_ControlNet_input_image"]); // Add integration with ControlNet txt2img Tabs // applyZoomAndPanIntegration("#txt2img_controlnet",Array.from({ length: 10 }, (_, i) => `#txt2img_controlnet_ControlNet-${i}_input_image`)); // Add integration with Inpaint Anything // applyZoomAndPanIntegration("None", ["#ia_sam_image", "#ia_sel_mask"]); }); stable-diffusion-webui/extensions-builtin/canvas-zoom-and-pan/scripts/000077500000000000000000000000001465116634300265415ustar00rootroot00000000000000stable-diffusion-webui/extensions-builtin/canvas-zoom-and-pan/scripts/hotkey_config.py000066400000000000000000000036511465116634300317500ustar00rootroot00000000000000import gradio as gr from modules import shared shared.options_templates.update(shared.options_section(('canvas_hotkey', "Canvas Hotkeys"), { "canvas_hotkey_zoom": shared.OptionInfo("Alt", "Zoom canvas", gr.Radio, {"choices": ["Shift","Ctrl", "Alt"]}).info("If you choose 'Shift' you cannot scroll horizontally, 'Alt' can cause a little trouble in firefox"), "canvas_hotkey_adjust": shared.OptionInfo("Ctrl", "Adjust brush size", gr.Radio, {"choices": ["Shift","Ctrl", "Alt"]}).info("If you choose 'Shift' you cannot scroll horizontally, 'Alt' can cause a little trouble in firefox"), "canvas_hotkey_shrink_brush": shared.OptionInfo("Q", "Shrink the brush size"), "canvas_hotkey_grow_brush": shared.OptionInfo("W", "Enlarge the brush size"), "canvas_hotkey_move": shared.OptionInfo("F", "Moving the canvas").info("To work correctly in firefox, turn off 'Automatically search the page text when typing' in the browser settings"), "canvas_hotkey_fullscreen": shared.OptionInfo("S", "Fullscreen Mode, maximizes the picture so that it fits into the screen and stretches it to its full width "), "canvas_hotkey_reset": shared.OptionInfo("R", "Reset zoom and canvas position"), "canvas_hotkey_overlap": shared.OptionInfo("O", "Toggle overlap").info("Technical button, needed for testing"), "canvas_show_tooltip": shared.OptionInfo(True, "Enable tooltip on the canvas"), "canvas_auto_expand": shared.OptionInfo(True, "Automatically expands an image that does not fit completely in the canvas area, similar to manually pressing the S and R buttons"), "canvas_blur_prompt": shared.OptionInfo(False, "Take the focus off the prompt when working with a canvas"), "canvas_disabled_functions": shared.OptionInfo(["Overlap"], "Disable function that you don't use", gr.CheckboxGroup, {"choices": ["Zoom","Adjust brush size","Hotkey enlarge brush","Hotkey shrink brush","Moving canvas","Fullscreen","Reset Zoom","Overlap"]}), })) stable-diffusion-webui/extensions-builtin/canvas-zoom-and-pan/style.css000066400000000000000000000021111465116634300267170ustar00rootroot00000000000000.canvas-tooltip-info { position: absolute; top: 10px; left: 10px; cursor: help; background-color: rgba(0, 0, 0, 0.3); width: 20px; height: 20px; border-radius: 50%; display: flex; align-items: center; justify-content: center; flex-direction: column; z-index: 100; } .canvas-tooltip-info::after { content: ''; display: block; width: 2px; height: 7px; background-color: white; margin-top: 2px; } .canvas-tooltip-info::before { content: ''; display: block; width: 2px; height: 2px; background-color: white; } .canvas-tooltip-content { display: none; background-color: #f9f9f9; color: #333; border: 1px solid #ddd; padding: 15px; position: absolute; top: 40px; left: 10px; width: 250px; font-size: 16px; opacity: 0; border-radius: 8px; box-shadow: 0px 8px 16px 0px rgba(0,0,0,0.2); z-index: 100; } .canvas-tooltip:hover .canvas-tooltip-content { display: block; animation: fadeIn 0.5s; opacity: 1; } @keyframes fadeIn { from {opacity: 0;} to {opacity: 1;} } .styler { overflow:inherit !important; }stable-diffusion-webui/extensions-builtin/extra-options-section/000077500000000000000000000000001465116634300255575ustar00rootroot00000000000000stable-diffusion-webui/extensions-builtin/extra-options-section/scripts/000077500000000000000000000000001465116634300272465ustar00rootroot00000000000000stable-diffusion-webui/extensions-builtin/extra-options-section/scripts/extra_options_section.py000066400000000000000000000077411465116634300342530ustar00rootroot00000000000000import math import gradio as gr from modules import scripts, shared, ui_components, ui_settings, infotext_utils, errors from modules.ui_components import FormColumn class ExtraOptionsSection(scripts.Script): section = "extra_options" def __init__(self): self.comps = None self.setting_names = None def title(self): return "Extra options" def show(self, is_img2img): return scripts.AlwaysVisible def ui(self, is_img2img): self.comps = [] self.setting_names = [] self.infotext_fields = [] extra_options = shared.opts.extra_options_img2img if is_img2img else shared.opts.extra_options_txt2img elem_id_tabname = "extra_options_" + ("img2img" if is_img2img else "txt2img") mapping = {k: v for v, k in infotext_utils.infotext_to_setting_name_mapping} with gr.Blocks() as interface: with gr.Accordion("Options", open=False, elem_id=elem_id_tabname) if shared.opts.extra_options_accordion and extra_options else gr.Group(elem_id=elem_id_tabname): row_count = math.ceil(len(extra_options) / shared.opts.extra_options_cols) for row in range(row_count): with gr.Row(): for col in range(shared.opts.extra_options_cols): index = row * shared.opts.extra_options_cols + col if index >= len(extra_options): break setting_name = extra_options[index] with FormColumn(): try: comp = ui_settings.create_setting_component(setting_name) except KeyError: errors.report(f"Can't add extra options for {setting_name} in ui") continue self.comps.append(comp) self.setting_names.append(setting_name) setting_infotext_name = mapping.get(setting_name) if setting_infotext_name is not None: self.infotext_fields.append((comp, setting_infotext_name)) def get_settings_values(): res = [ui_settings.get_value_for_setting(key) for key in self.setting_names] return res[0] if len(res) == 1 else res interface.load(fn=get_settings_values, inputs=[], outputs=self.comps, queue=False, show_progress=False) return self.comps def before_process(self, p, *args): for name, value in zip(self.setting_names, args): if name not in p.override_settings: p.override_settings[name] = value shared.options_templates.update(shared.options_section(('settings_in_ui', "Settings in UI", "ui"), { "settings_in_ui": shared.OptionHTML(""" This page allows you to add some settings to the main interface of txt2img and img2img tabs. """), "extra_options_txt2img": shared.OptionInfo([], "Settings for txt2img", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that also appear in txt2img interfaces").needs_reload_ui(), "extra_options_img2img": shared.OptionInfo([], "Settings for img2img", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that also appear in img2img interfaces").needs_reload_ui(), "extra_options_cols": shared.OptionInfo(1, "Number of columns for added settings", gr.Slider, {"step": 1, "minimum": 1, "maximum": 20}).info("displayed amount will depend on the actual browser window width").needs_reload_ui(), "extra_options_accordion": shared.OptionInfo(False, "Place added settings into an accordion").needs_reload_ui() })) stable-diffusion-webui/extensions-builtin/hypertile/000077500000000000000000000000001465116634300233065ustar00rootroot00000000000000stable-diffusion-webui/extensions-builtin/hypertile/hypertile.py000066400000000000000000000335201465116634300256700ustar00rootroot00000000000000""" Hypertile module for splitting attention layers in SD-1.5 U-Net and SD-1.5 VAE Warn: The patch works well only if the input image has a width and height that are multiples of 128 Original author: @tfernd Github: https://github.com/tfernd/HyperTile """ from __future__ import annotations from dataclasses import dataclass from typing import Callable from functools import wraps, cache import math import torch.nn as nn import random from einops import rearrange @dataclass class HypertileParams: depth = 0 layer_name = "" tile_size: int = 0 swap_size: int = 0 aspect_ratio: float = 1.0 forward = None enabled = False # TODO add SD-XL layers DEPTH_LAYERS = { 0: [ # SD 1.5 U-Net (diffusers) "down_blocks.0.attentions.0.transformer_blocks.0.attn1", "down_blocks.0.attentions.1.transformer_blocks.0.attn1", "up_blocks.3.attentions.0.transformer_blocks.0.attn1", "up_blocks.3.attentions.1.transformer_blocks.0.attn1", "up_blocks.3.attentions.2.transformer_blocks.0.attn1", # SD 1.5 U-Net (ldm) "input_blocks.1.1.transformer_blocks.0.attn1", "input_blocks.2.1.transformer_blocks.0.attn1", "output_blocks.9.1.transformer_blocks.0.attn1", "output_blocks.10.1.transformer_blocks.0.attn1", "output_blocks.11.1.transformer_blocks.0.attn1", # SD 1.5 VAE "decoder.mid_block.attentions.0", "decoder.mid.attn_1", ], 1: [ # SD 1.5 U-Net (diffusers) "down_blocks.1.attentions.0.transformer_blocks.0.attn1", "down_blocks.1.attentions.1.transformer_blocks.0.attn1", "up_blocks.2.attentions.0.transformer_blocks.0.attn1", "up_blocks.2.attentions.1.transformer_blocks.0.attn1", "up_blocks.2.attentions.2.transformer_blocks.0.attn1", # SD 1.5 U-Net (ldm) "input_blocks.4.1.transformer_blocks.0.attn1", "input_blocks.5.1.transformer_blocks.0.attn1", "output_blocks.6.1.transformer_blocks.0.attn1", "output_blocks.7.1.transformer_blocks.0.attn1", "output_blocks.8.1.transformer_blocks.0.attn1", ], 2: [ # SD 1.5 U-Net (diffusers) "down_blocks.2.attentions.0.transformer_blocks.0.attn1", "down_blocks.2.attentions.1.transformer_blocks.0.attn1", "up_blocks.1.attentions.0.transformer_blocks.0.attn1", "up_blocks.1.attentions.1.transformer_blocks.0.attn1", "up_blocks.1.attentions.2.transformer_blocks.0.attn1", # SD 1.5 U-Net (ldm) "input_blocks.7.1.transformer_blocks.0.attn1", "input_blocks.8.1.transformer_blocks.0.attn1", "output_blocks.3.1.transformer_blocks.0.attn1", "output_blocks.4.1.transformer_blocks.0.attn1", "output_blocks.5.1.transformer_blocks.0.attn1", ], 3: [ # SD 1.5 U-Net (diffusers) "mid_block.attentions.0.transformer_blocks.0.attn1", # SD 1.5 U-Net (ldm) "middle_block.1.transformer_blocks.0.attn1", ], } # XL layers, thanks for GitHub@gel-crabs for the help DEPTH_LAYERS_XL = { 0: [ # SD 1.5 U-Net (diffusers) "down_blocks.0.attentions.0.transformer_blocks.0.attn1", "down_blocks.0.attentions.1.transformer_blocks.0.attn1", "up_blocks.3.attentions.0.transformer_blocks.0.attn1", "up_blocks.3.attentions.1.transformer_blocks.0.attn1", "up_blocks.3.attentions.2.transformer_blocks.0.attn1", # SD 1.5 U-Net (ldm) "input_blocks.4.1.transformer_blocks.0.attn1", "input_blocks.5.1.transformer_blocks.0.attn1", "output_blocks.3.1.transformer_blocks.0.attn1", "output_blocks.4.1.transformer_blocks.0.attn1", "output_blocks.5.1.transformer_blocks.0.attn1", # SD 1.5 VAE "decoder.mid_block.attentions.0", "decoder.mid.attn_1", ], 1: [ # SD 1.5 U-Net (diffusers) #"down_blocks.1.attentions.0.transformer_blocks.0.attn1", #"down_blocks.1.attentions.1.transformer_blocks.0.attn1", #"up_blocks.2.attentions.0.transformer_blocks.0.attn1", #"up_blocks.2.attentions.1.transformer_blocks.0.attn1", #"up_blocks.2.attentions.2.transformer_blocks.0.attn1", # SD 1.5 U-Net (ldm) "input_blocks.4.1.transformer_blocks.1.attn1", "input_blocks.5.1.transformer_blocks.1.attn1", "output_blocks.3.1.transformer_blocks.1.attn1", "output_blocks.4.1.transformer_blocks.1.attn1", "output_blocks.5.1.transformer_blocks.1.attn1", "input_blocks.7.1.transformer_blocks.0.attn1", "input_blocks.8.1.transformer_blocks.0.attn1", "output_blocks.0.1.transformer_blocks.0.attn1", "output_blocks.1.1.transformer_blocks.0.attn1", "output_blocks.2.1.transformer_blocks.0.attn1", "input_blocks.7.1.transformer_blocks.1.attn1", "input_blocks.8.1.transformer_blocks.1.attn1", "output_blocks.0.1.transformer_blocks.1.attn1", "output_blocks.1.1.transformer_blocks.1.attn1", "output_blocks.2.1.transformer_blocks.1.attn1", "input_blocks.7.1.transformer_blocks.2.attn1", "input_blocks.8.1.transformer_blocks.2.attn1", "output_blocks.0.1.transformer_blocks.2.attn1", "output_blocks.1.1.transformer_blocks.2.attn1", "output_blocks.2.1.transformer_blocks.2.attn1", "input_blocks.7.1.transformer_blocks.3.attn1", "input_blocks.8.1.transformer_blocks.3.attn1", "output_blocks.0.1.transformer_blocks.3.attn1", "output_blocks.1.1.transformer_blocks.3.attn1", "output_blocks.2.1.transformer_blocks.3.attn1", "input_blocks.7.1.transformer_blocks.4.attn1", "input_blocks.8.1.transformer_blocks.4.attn1", "output_blocks.0.1.transformer_blocks.4.attn1", "output_blocks.1.1.transformer_blocks.4.attn1", "output_blocks.2.1.transformer_blocks.4.attn1", "input_blocks.7.1.transformer_blocks.5.attn1", "input_blocks.8.1.transformer_blocks.5.attn1", "output_blocks.0.1.transformer_blocks.5.attn1", "output_blocks.1.1.transformer_blocks.5.attn1", "output_blocks.2.1.transformer_blocks.5.attn1", "input_blocks.7.1.transformer_blocks.6.attn1", "input_blocks.8.1.transformer_blocks.6.attn1", "output_blocks.0.1.transformer_blocks.6.attn1", "output_blocks.1.1.transformer_blocks.6.attn1", "output_blocks.2.1.transformer_blocks.6.attn1", "input_blocks.7.1.transformer_blocks.7.attn1", "input_blocks.8.1.transformer_blocks.7.attn1", "output_blocks.0.1.transformer_blocks.7.attn1", "output_blocks.1.1.transformer_blocks.7.attn1", "output_blocks.2.1.transformer_blocks.7.attn1", "input_blocks.7.1.transformer_blocks.8.attn1", "input_blocks.8.1.transformer_blocks.8.attn1", "output_blocks.0.1.transformer_blocks.8.attn1", "output_blocks.1.1.transformer_blocks.8.attn1", "output_blocks.2.1.transformer_blocks.8.attn1", "input_blocks.7.1.transformer_blocks.9.attn1", "input_blocks.8.1.transformer_blocks.9.attn1", "output_blocks.0.1.transformer_blocks.9.attn1", "output_blocks.1.1.transformer_blocks.9.attn1", "output_blocks.2.1.transformer_blocks.9.attn1", ], 2: [ # SD 1.5 U-Net (diffusers) "mid_block.attentions.0.transformer_blocks.0.attn1", # SD 1.5 U-Net (ldm) "middle_block.1.transformer_blocks.0.attn1", "middle_block.1.transformer_blocks.1.attn1", "middle_block.1.transformer_blocks.2.attn1", "middle_block.1.transformer_blocks.3.attn1", "middle_block.1.transformer_blocks.4.attn1", "middle_block.1.transformer_blocks.5.attn1", "middle_block.1.transformer_blocks.6.attn1", "middle_block.1.transformer_blocks.7.attn1", "middle_block.1.transformer_blocks.8.attn1", "middle_block.1.transformer_blocks.9.attn1", ], 3 : [] # TODO - separate layers for SD-XL } RNG_INSTANCE = random.Random() @cache def get_divisors(value: int, min_value: int, /, max_options: int = 1) -> list[int]: """ Returns divisors of value that x * min_value <= value in big -> small order, amount of divisors is limited by max_options """ max_options = max(1, max_options) # at least 1 option should be returned min_value = min(min_value, value) divisors = [i for i in range(min_value, value + 1) if value % i == 0] # divisors in small -> big order ns = [value // i for i in divisors[:max_options]] # has at least 1 element # big -> small order return ns def random_divisor(value: int, min_value: int, /, max_options: int = 1) -> int: """ Returns a random divisor of value that x * min_value <= value if max_options is 1, the behavior is deterministic """ ns = get_divisors(value, min_value, max_options=max_options) # get cached divisors idx = RNG_INSTANCE.randint(0, len(ns) - 1) return ns[idx] def set_hypertile_seed(seed: int) -> None: RNG_INSTANCE.seed(seed) @cache def largest_tile_size_available(width: int, height: int) -> int: """ Calculates the largest tile size available for a given width and height Tile size is always a power of 2 """ gcd = math.gcd(width, height) largest_tile_size_available = 1 while gcd % (largest_tile_size_available * 2) == 0: largest_tile_size_available *= 2 return largest_tile_size_available def iterative_closest_divisors(hw:int, aspect_ratio:float) -> tuple[int, int]: """ Finds h and w such that h*w = hw and h/w = aspect_ratio We check all possible divisors of hw and return the closest to the aspect ratio """ divisors = [i for i in range(2, hw + 1) if hw % i == 0] # all divisors of hw pairs = [(i, hw // i) for i in divisors] # all pairs of divisors of hw ratios = [w/h for h, w in pairs] # all ratios of pairs of divisors of hw closest_ratio = min(ratios, key=lambda x: abs(x - aspect_ratio)) # closest ratio to aspect_ratio closest_pair = pairs[ratios.index(closest_ratio)] # closest pair of divisors to aspect_ratio return closest_pair @cache def find_hw_candidates(hw:int, aspect_ratio:float) -> tuple[int, int]: """ Finds h and w such that h*w = hw and h/w = aspect_ratio """ h, w = round(math.sqrt(hw * aspect_ratio)), round(math.sqrt(hw / aspect_ratio)) # find h and w such that h*w = hw and h/w = aspect_ratio if h * w != hw: w_candidate = hw / h # check if w is an integer if not w_candidate.is_integer(): h_candidate = hw / w # check if h is an integer if not h_candidate.is_integer(): return iterative_closest_divisors(hw, aspect_ratio) else: h = int(h_candidate) else: w = int(w_candidate) return h, w def self_attn_forward(params: HypertileParams, scale_depth=True) -> Callable: @wraps(params.forward) def wrapper(*args, **kwargs): if not params.enabled: return params.forward(*args, **kwargs) latent_tile_size = max(128, params.tile_size) // 8 x = args[0] # VAE if x.ndim == 4: b, c, h, w = x.shape nh = random_divisor(h, latent_tile_size, params.swap_size) nw = random_divisor(w, latent_tile_size, params.swap_size) if nh * nw > 1: x = rearrange(x, "b c (nh h) (nw w) -> (b nh nw) c h w", nh=nh, nw=nw) # split into nh * nw tiles out = params.forward(x, *args[1:], **kwargs) if nh * nw > 1: out = rearrange(out, "(b nh nw) c h w -> b c (nh h) (nw w)", nh=nh, nw=nw) # U-Net else: hw: int = x.size(1) h, w = find_hw_candidates(hw, params.aspect_ratio) assert h * w == hw, f"Invalid aspect ratio {params.aspect_ratio} for input of shape {x.shape}, hw={hw}, h={h}, w={w}" factor = 2 ** params.depth if scale_depth else 1 nh = random_divisor(h, latent_tile_size * factor, params.swap_size) nw = random_divisor(w, latent_tile_size * factor, params.swap_size) if nh * nw > 1: x = rearrange(x, "b (nh h nw w) c -> (b nh nw) (h w) c", h=h // nh, w=w // nw, nh=nh, nw=nw) out = params.forward(x, *args[1:], **kwargs) if nh * nw > 1: out = rearrange(out, "(b nh nw) hw c -> b nh nw hw c", nh=nh, nw=nw) out = rearrange(out, "b nh nw (h w) c -> b (nh h nw w) c", h=h // nh, w=w // nw) return out return wrapper def hypertile_hook_model(model: nn.Module, width, height, *, enable=False, tile_size_max=128, swap_size=1, max_depth=3, is_sdxl=False): hypertile_layers = getattr(model, "__webui_hypertile_layers", None) if hypertile_layers is None: if not enable: return hypertile_layers = {} layers = DEPTH_LAYERS_XL if is_sdxl else DEPTH_LAYERS for depth in range(4): for layer_name, module in model.named_modules(): if any(layer_name.endswith(try_name) for try_name in layers[depth]): params = HypertileParams() module.__webui_hypertile_params = params params.forward = module.forward params.depth = depth params.layer_name = layer_name module.forward = self_attn_forward(params) hypertile_layers[layer_name] = 1 model.__webui_hypertile_layers = hypertile_layers aspect_ratio = width / height tile_size = min(largest_tile_size_available(width, height), tile_size_max) for layer_name, module in model.named_modules(): if layer_name in hypertile_layers: params = module.__webui_hypertile_params params.tile_size = tile_size params.swap_size = swap_size params.aspect_ratio = aspect_ratio params.enabled = enable and params.depth <= max_depth stable-diffusion-webui/extensions-builtin/hypertile/scripts/000077500000000000000000000000001465116634300247755ustar00rootroot00000000000000stable-diffusion-webui/extensions-builtin/hypertile/scripts/hypertile_script.py000066400000000000000000000164331465116634300307470ustar00rootroot00000000000000import hypertile from modules import scripts, script_callbacks, shared class ScriptHypertile(scripts.Script): name = "Hypertile" def title(self): return self.name def show(self, is_img2img): return scripts.AlwaysVisible def process(self, p, *args): hypertile.set_hypertile_seed(p.all_seeds[0]) configure_hypertile(p.width, p.height, enable_unet=shared.opts.hypertile_enable_unet) self.add_infotext(p) def before_hr(self, p, *args): enable = shared.opts.hypertile_enable_unet_secondpass or shared.opts.hypertile_enable_unet # exclusive hypertile seed for the second pass if enable: hypertile.set_hypertile_seed(p.all_seeds[0]) configure_hypertile(p.hr_upscale_to_x, p.hr_upscale_to_y, enable_unet=enable) if enable and not shared.opts.hypertile_enable_unet: p.extra_generation_params["Hypertile U-Net second pass"] = True self.add_infotext(p, add_unet_params=True) def add_infotext(self, p, add_unet_params=False): def option(name): value = getattr(shared.opts, name) default_value = shared.opts.get_default(name) return None if value == default_value else value if shared.opts.hypertile_enable_unet: p.extra_generation_params["Hypertile U-Net"] = True if shared.opts.hypertile_enable_unet or add_unet_params: p.extra_generation_params["Hypertile U-Net max depth"] = option('hypertile_max_depth_unet') p.extra_generation_params["Hypertile U-Net max tile size"] = option('hypertile_max_tile_unet') p.extra_generation_params["Hypertile U-Net swap size"] = option('hypertile_swap_size_unet') if shared.opts.hypertile_enable_vae: p.extra_generation_params["Hypertile VAE"] = True p.extra_generation_params["Hypertile VAE max depth"] = option('hypertile_max_depth_vae') p.extra_generation_params["Hypertile VAE max tile size"] = option('hypertile_max_tile_vae') p.extra_generation_params["Hypertile VAE swap size"] = option('hypertile_swap_size_vae') def configure_hypertile(width, height, enable_unet=True): hypertile.hypertile_hook_model( shared.sd_model.first_stage_model, width, height, swap_size=shared.opts.hypertile_swap_size_vae, max_depth=shared.opts.hypertile_max_depth_vae, tile_size_max=shared.opts.hypertile_max_tile_vae, enable=shared.opts.hypertile_enable_vae, ) hypertile.hypertile_hook_model( shared.sd_model.model, width, height, swap_size=shared.opts.hypertile_swap_size_unet, max_depth=shared.opts.hypertile_max_depth_unet, tile_size_max=shared.opts.hypertile_max_tile_unet, enable=enable_unet, is_sdxl=shared.sd_model.is_sdxl ) def on_ui_settings(): import gradio as gr options = { "hypertile_explanation": shared.OptionHTML(""" Hypertile optimizes the self-attention layer within U-Net and VAE models, resulting in a reduction in computation time ranging from 1 to 4 times. The larger the generated image is, the greater the benefit. """), "hypertile_enable_unet": shared.OptionInfo(False, "Enable Hypertile U-Net", infotext="Hypertile U-Net").info("enables hypertile for all modes, including hires fix second pass; noticeable change in details of the generated picture"), "hypertile_enable_unet_secondpass": shared.OptionInfo(False, "Enable Hypertile U-Net for hires fix second pass", infotext="Hypertile U-Net second pass").info("enables hypertile just for hires fix second pass - regardless of whether the above setting is enabled"), "hypertile_max_depth_unet": shared.OptionInfo(3, "Hypertile U-Net max depth", gr.Slider, {"minimum": 0, "maximum": 3, "step": 1}, infotext="Hypertile U-Net max depth").info("larger = more neural network layers affected; minor effect on performance"), "hypertile_max_tile_unet": shared.OptionInfo(256, "Hypertile U-Net max tile size", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}, infotext="Hypertile U-Net max tile size").info("larger = worse performance"), "hypertile_swap_size_unet": shared.OptionInfo(3, "Hypertile U-Net swap size", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}, infotext="Hypertile U-Net swap size"), "hypertile_enable_vae": shared.OptionInfo(False, "Enable Hypertile VAE", infotext="Hypertile VAE").info("minimal change in the generated picture"), "hypertile_max_depth_vae": shared.OptionInfo(3, "Hypertile VAE max depth", gr.Slider, {"minimum": 0, "maximum": 3, "step": 1}, infotext="Hypertile VAE max depth"), "hypertile_max_tile_vae": shared.OptionInfo(128, "Hypertile VAE max tile size", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}, infotext="Hypertile VAE max tile size"), "hypertile_swap_size_vae": shared.OptionInfo(3, "Hypertile VAE swap size ", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}, infotext="Hypertile VAE swap size"), } for name, opt in options.items(): opt.section = ('hypertile', "Hypertile") shared.opts.add_option(name, opt) def add_axis_options(): xyz_grid = [x for x in scripts.scripts_data if x.script_class.__module__ == "xyz_grid.py"][0].module xyz_grid.axis_options.extend([ xyz_grid.AxisOption("[Hypertile] Unet First pass Enabled", str, xyz_grid.apply_override('hypertile_enable_unet', boolean=True), choices=xyz_grid.boolean_choice(reverse=True)), xyz_grid.AxisOption("[Hypertile] Unet Second pass Enabled", str, xyz_grid.apply_override('hypertile_enable_unet_secondpass', boolean=True), choices=xyz_grid.boolean_choice(reverse=True)), xyz_grid.AxisOption("[Hypertile] Unet Max Depth", int, xyz_grid.apply_override("hypertile_max_depth_unet"), confirm=xyz_grid.confirm_range(0, 3, '[Hypertile] Unet Max Depth'), choices=lambda: [str(x) for x in range(4)]), xyz_grid.AxisOption("[Hypertile] Unet Max Tile Size", int, xyz_grid.apply_override("hypertile_max_tile_unet"), confirm=xyz_grid.confirm_range(0, 512, '[Hypertile] Unet Max Tile Size')), xyz_grid.AxisOption("[Hypertile] Unet Swap Size", int, xyz_grid.apply_override("hypertile_swap_size_unet"), confirm=xyz_grid.confirm_range(0, 64, '[Hypertile] Unet Swap Size')), xyz_grid.AxisOption("[Hypertile] VAE Enabled", str, xyz_grid.apply_override('hypertile_enable_vae', boolean=True), choices=xyz_grid.boolean_choice(reverse=True)), xyz_grid.AxisOption("[Hypertile] VAE Max Depth", int, xyz_grid.apply_override("hypertile_max_depth_vae"), confirm=xyz_grid.confirm_range(0, 3, '[Hypertile] VAE Max Depth'), choices=lambda: [str(x) for x in range(4)]), xyz_grid.AxisOption("[Hypertile] VAE Max Tile Size", int, xyz_grid.apply_override("hypertile_max_tile_vae"), confirm=xyz_grid.confirm_range(0, 512, '[Hypertile] VAE Max Tile Size')), xyz_grid.AxisOption("[Hypertile] VAE Swap Size", int, xyz_grid.apply_override("hypertile_swap_size_vae"), confirm=xyz_grid.confirm_range(0, 64, '[Hypertile] VAE Swap Size')), ]) script_callbacks.on_ui_settings(on_ui_settings) script_callbacks.on_before_ui(add_axis_options) stable-diffusion-webui/extensions-builtin/mobile/000077500000000000000000000000001465116634300225505ustar00rootroot00000000000000stable-diffusion-webui/extensions-builtin/mobile/javascript/000077500000000000000000000000001465116634300247165ustar00rootroot00000000000000stable-diffusion-webui/extensions-builtin/mobile/javascript/mobile.js000066400000000000000000000021011465116634300265150ustar00rootroot00000000000000var isSetupForMobile = false; function isMobile() { for (var tab of ["txt2img", "img2img"]) { var imageTab = gradioApp().getElementById(tab + '_results'); if (imageTab && imageTab.offsetParent && imageTab.offsetLeft == 0) { return true; } } return false; } function reportWindowSize() { if (gradioApp().querySelector('.toprow-compact-tools')) return; // not applicable for compact prompt layout var currentlyMobile = isMobile(); if (currentlyMobile == isSetupForMobile) return; isSetupForMobile = currentlyMobile; for (var tab of ["txt2img", "img2img"]) { var button = gradioApp().getElementById(tab + '_generate_box'); var target = gradioApp().getElementById(currentlyMobile ? tab + '_results' : tab + '_actions_column'); target.insertBefore(button, target.firstElementChild); gradioApp().getElementById(tab + '_results').classList.toggle('mobile', currentlyMobile); } } window.addEventListener("resize", reportWindowSize); onUiLoaded(function() { reportWindowSize(); }); stable-diffusion-webui/extensions-builtin/postprocessing-for-training/000077500000000000000000000000001465116634300267605ustar00rootroot00000000000000stable-diffusion-webui/extensions-builtin/postprocessing-for-training/scripts/000077500000000000000000000000001465116634300304475ustar00rootroot00000000000000postprocessing_autosized_crop.py000066400000000000000000000061111465116634300371350ustar00rootroot00000000000000stable-diffusion-webui/extensions-builtin/postprocessing-for-training/scriptsfrom PIL import Image from modules import scripts_postprocessing, ui_components import gradio as gr def center_crop(image: Image, w: int, h: int): iw, ih = image.size if ih / h < iw / w: sw = w * ih / h box = (iw - sw) / 2, 0, iw - (iw - sw) / 2, ih else: sh = h * iw / w box = 0, (ih - sh) / 2, iw, ih - (ih - sh) / 2 return image.resize((w, h), Image.Resampling.LANCZOS, box) def multicrop_pic(image: Image, mindim, maxdim, minarea, maxarea, objective, threshold): iw, ih = image.size err = lambda w, h: 1 - (lambda x: x if x < 1 else 1 / x)(iw / ih / (w / h)) wh = max(((w, h) for w in range(mindim, maxdim + 1, 64) for h in range(mindim, maxdim + 1, 64) if minarea <= w * h <= maxarea and err(w, h) <= threshold), key=lambda wh: (wh[0] * wh[1], -err(*wh))[::1 if objective == 'Maximize area' else -1], default=None ) return wh and center_crop(image, *wh) class ScriptPostprocessingAutosizedCrop(scripts_postprocessing.ScriptPostprocessing): name = "Auto-sized crop" order = 4020 def ui(self): with ui_components.InputAccordion(False, label="Auto-sized crop") as enable: gr.Markdown('Each image is center-cropped with an automatically chosen width and height.') with gr.Row(): mindim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension lower bound", value=384, elem_id="postprocess_multicrop_mindim") maxdim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension upper bound", value=768, elem_id="postprocess_multicrop_maxdim") with gr.Row(): minarea = gr.Slider(minimum=64 * 64, maximum=2048 * 2048, step=1, label="Area lower bound", value=64 * 64, elem_id="postprocess_multicrop_minarea") maxarea = gr.Slider(minimum=64 * 64, maximum=2048 * 2048, step=1, label="Area upper bound", value=640 * 640, elem_id="postprocess_multicrop_maxarea") with gr.Row(): objective = gr.Radio(["Maximize area", "Minimize error"], value="Maximize area", label="Resizing objective", elem_id="postprocess_multicrop_objective") threshold = gr.Slider(minimum=0, maximum=1, step=0.01, label="Error threshold", value=0.1, elem_id="postprocess_multicrop_threshold") return { "enable": enable, "mindim": mindim, "maxdim": maxdim, "minarea": minarea, "maxarea": maxarea, "objective": objective, "threshold": threshold, } def process(self, pp: scripts_postprocessing.PostprocessedImage, enable, mindim, maxdim, minarea, maxarea, objective, threshold): if not enable: return cropped = multicrop_pic(pp.image, mindim, maxdim, minarea, maxarea, objective, threshold) if cropped is not None: pp.image = cropped else: print(f"skipped {pp.image.width}x{pp.image.height} image (can't find suitable size within error threshold)") postprocessing_caption.py000066400000000000000000000017131465116634300355430ustar00rootroot00000000000000stable-diffusion-webui/extensions-builtin/postprocessing-for-training/scriptsfrom modules import scripts_postprocessing, ui_components, deepbooru, shared import gradio as gr class ScriptPostprocessingCeption(scripts_postprocessing.ScriptPostprocessing): name = "Caption" order = 4040 def ui(self): with ui_components.InputAccordion(False, label="Caption") as enable: option = gr.CheckboxGroup(value=["Deepbooru"], choices=["Deepbooru", "BLIP"], show_label=False) return { "enable": enable, "option": option, } def process(self, pp: scripts_postprocessing.PostprocessedImage, enable, option): if not enable: return captions = [pp.caption] if "Deepbooru" in option: captions.append(deepbooru.model.tag(pp.image)) if "BLIP" in option: captions.append(shared.interrogator.interrogate(pp.image.convert("RGB"))) pp.caption = ", ".join([x for x in captions if x]) postprocessing_create_flipped_copies.py000066400000000000000000000021761465116634300404220ustar00rootroot00000000000000stable-diffusion-webui/extensions-builtin/postprocessing-for-training/scriptsfrom PIL import ImageOps, Image from modules import scripts_postprocessing, ui_components import gradio as gr class ScriptPostprocessingCreateFlippedCopies(scripts_postprocessing.ScriptPostprocessing): name = "Create flipped copies" order = 4030 def ui(self): with ui_components.InputAccordion(False, label="Create flipped copies") as enable: with gr.Row(): option = gr.CheckboxGroup(value=["Horizontal"], choices=["Horizontal", "Vertical", "Both"], show_label=False) return { "enable": enable, "option": option, } def process(self, pp: scripts_postprocessing.PostprocessedImage, enable, option): if not enable: return if "Horizontal" in option: pp.extra_images.append(ImageOps.mirror(pp.image)) if "Vertical" in option: pp.extra_images.append(pp.image.transpose(Image.Transpose.FLIP_TOP_BOTTOM)) if "Both" in option: pp.extra_images.append(pp.image.transpose(Image.Transpose.FLIP_TOP_BOTTOM).transpose(Image.Transpose.FLIP_LEFT_RIGHT)) postprocessing_focal_crop.py000066400000000000000000000045301465116634300362150ustar00rootroot00000000000000stable-diffusion-webui/extensions-builtin/postprocessing-for-training/scripts from modules import scripts_postprocessing, ui_components, errors import gradio as gr from modules.textual_inversion import autocrop class ScriptPostprocessingFocalCrop(scripts_postprocessing.ScriptPostprocessing): name = "Auto focal point crop" order = 4010 def ui(self): with ui_components.InputAccordion(False, label="Auto focal point crop") as enable: face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05, elem_id="postprocess_focal_crop_face_weight") entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05, elem_id="postprocess_focal_crop_entropy_weight") edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="postprocess_focal_crop_edges_weight") debug = gr.Checkbox(label='Create debug image', elem_id="train_process_focal_crop_debug") return { "enable": enable, "face_weight": face_weight, "entropy_weight": entropy_weight, "edges_weight": edges_weight, "debug": debug, } def process(self, pp: scripts_postprocessing.PostprocessedImage, enable, face_weight, entropy_weight, edges_weight, debug): if not enable: return if not pp.shared.target_width or not pp.shared.target_height: return dnn_model_path = None try: dnn_model_path = autocrop.download_and_cache_models() except Exception: errors.report("Unable to load face detection model for auto crop selection. Falling back to lower quality haar method.", exc_info=True) autocrop_settings = autocrop.Settings( crop_width=pp.shared.target_width, crop_height=pp.shared.target_height, face_points_weight=face_weight, entropy_points_weight=entropy_weight, corner_points_weight=edges_weight, annotate_image=debug, dnn_model_path=dnn_model_path, ) result, *others = autocrop.crop_image(pp.image, autocrop_settings) pp.image = result pp.extra_images = [pp.create_copy(x, nametags=["focal-crop-debug"], disable_processing=True) for x in others] postprocessing_split_oversized.py000066400000000000000000000046611465116634300373400ustar00rootroot00000000000000stable-diffusion-webui/extensions-builtin/postprocessing-for-training/scriptsimport math from modules import scripts_postprocessing, ui_components import gradio as gr def split_pic(image, inverse_xy, width, height, overlap_ratio): if inverse_xy: from_w, from_h = image.height, image.width to_w, to_h = height, width else: from_w, from_h = image.width, image.height to_w, to_h = width, height h = from_h * to_w // from_w if inverse_xy: image = image.resize((h, to_w)) else: image = image.resize((to_w, h)) split_count = math.ceil((h - to_h * overlap_ratio) / (to_h * (1.0 - overlap_ratio))) y_step = (h - to_h) / (split_count - 1) for i in range(split_count): y = int(y_step * i) if inverse_xy: splitted = image.crop((y, 0, y + to_h, to_w)) else: splitted = image.crop((0, y, to_w, y + to_h)) yield splitted class ScriptPostprocessingSplitOversized(scripts_postprocessing.ScriptPostprocessing): name = "Split oversized images" order = 4000 def ui(self): with ui_components.InputAccordion(False, label="Split oversized images") as enable: with gr.Row(): split_threshold = gr.Slider(label='Threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="postprocess_split_threshold") overlap_ratio = gr.Slider(label='Overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05, elem_id="postprocess_overlap_ratio") return { "enable": enable, "split_threshold": split_threshold, "overlap_ratio": overlap_ratio, } def process(self, pp: scripts_postprocessing.PostprocessedImage, enable, split_threshold, overlap_ratio): if not enable: return width = pp.shared.target_width height = pp.shared.target_height if not width or not height: return if pp.image.height > pp.image.width: ratio = (pp.image.width * height) / (pp.image.height * width) inverse_xy = False else: ratio = (pp.image.height * width) / (pp.image.width * height) inverse_xy = True if ratio >= 1.0 or ratio > split_threshold: return result, *others = split_pic(pp.image, inverse_xy, width, height, overlap_ratio) pp.image = result pp.extra_images = [pp.create_copy(x) for x in others] stable-diffusion-webui/extensions-builtin/prompt-bracket-checker/000077500000000000000000000000001465116634300256355ustar00rootroot00000000000000stable-diffusion-webui/extensions-builtin/prompt-bracket-checker/javascript/000077500000000000000000000000001465116634300300035ustar00rootroot00000000000000prompt-bracket-checker.js000066400000000000000000000032621465116634300346210ustar00rootroot00000000000000stable-diffusion-webui/extensions-builtin/prompt-bracket-checker/javascript// Stable Diffusion WebUI - Bracket checker // By Hingashi no Florin/Bwin4L & @akx // Counts open and closed brackets (round, square, curly) in the prompt and negative prompt text boxes in the txt2img and img2img tabs. // If there's a mismatch, the keyword counter turns red and if you hover on it, a tooltip tells you what's wrong. function checkBrackets(textArea, counterElt) { var counts = {}; (textArea.value.match(/[(){}[\]]/g) || []).forEach(bracket => { counts[bracket] = (counts[bracket] || 0) + 1; }); var errors = []; function checkPair(open, close, kind) { if (counts[open] !== counts[close]) { errors.push( `${open}...${close} - Detected ${counts[open] || 0} opening and ${counts[close] || 0} closing ${kind}.` ); } } checkPair('(', ')', 'round brackets'); checkPair('[', ']', 'square brackets'); checkPair('{', '}', 'curly brackets'); counterElt.title = errors.join('\n'); counterElt.classList.toggle('error', errors.length !== 0); } function setupBracketChecking(id_prompt, id_counter) { var textarea = gradioApp().querySelector("#" + id_prompt + " > label > textarea"); var counter = gradioApp().getElementById(id_counter); if (textarea && counter) { textarea.addEventListener("input", () => checkBrackets(textarea, counter)); } } onUiLoaded(function() { setupBracketChecking('txt2img_prompt', 'txt2img_token_counter'); setupBracketChecking('txt2img_neg_prompt', 'txt2img_negative_token_counter'); setupBracketChecking('img2img_prompt', 'img2img_token_counter'); setupBracketChecking('img2img_neg_prompt', 'img2img_negative_token_counter'); }); stable-diffusion-webui/extensions-builtin/soft-inpainting/000077500000000000000000000000001465116634300244125ustar00rootroot00000000000000stable-diffusion-webui/extensions-builtin/soft-inpainting/scripts/000077500000000000000000000000001465116634300261015ustar00rootroot00000000000000stable-diffusion-webui/extensions-builtin/soft-inpainting/scripts/soft_inpainting.py000066400000000000000000000742351465116634300316610ustar00rootroot00000000000000import numpy as np import gradio as gr import math from modules.ui_components import InputAccordion import modules.scripts as scripts from modules.torch_utils import float64 class SoftInpaintingSettings: def __init__(self, mask_blend_power, mask_blend_scale, inpaint_detail_preservation, composite_mask_influence, composite_difference_threshold, composite_difference_contrast): self.mask_blend_power = mask_blend_power self.mask_blend_scale = mask_blend_scale self.inpaint_detail_preservation = inpaint_detail_preservation self.composite_mask_influence = composite_mask_influence self.composite_difference_threshold = composite_difference_threshold self.composite_difference_contrast = composite_difference_contrast def add_generation_params(self, dest): dest[enabled_gen_param_label] = True dest[gen_param_labels.mask_blend_power] = self.mask_blend_power dest[gen_param_labels.mask_blend_scale] = self.mask_blend_scale dest[gen_param_labels.inpaint_detail_preservation] = self.inpaint_detail_preservation dest[gen_param_labels.composite_mask_influence] = self.composite_mask_influence dest[gen_param_labels.composite_difference_threshold] = self.composite_difference_threshold dest[gen_param_labels.composite_difference_contrast] = self.composite_difference_contrast # ------------------- Methods ------------------- def processing_uses_inpainting(p): # TODO: Figure out a better way to determine if inpainting is being used by p if getattr(p, "image_mask", None) is not None: return True if getattr(p, "mask", None) is not None: return True if getattr(p, "nmask", None) is not None: return True return False def latent_blend(settings, a, b, t): """ Interpolates two latent image representations according to the parameter t, where the interpolated vectors' magnitudes are also interpolated separately. The "detail_preservation" factor biases the magnitude interpolation towards the larger of the two magnitudes. """ import torch # NOTE: We use inplace operations wherever possible. if len(t.shape) == 3: # [4][w][h] to [1][4][w][h] t2 = t.unsqueeze(0) # [4][w][h] to [1][1][w][h] - the [4] seem redundant. t3 = t[0].unsqueeze(0).unsqueeze(0) else: t2 = t t3 = t[:, 0][:, None] one_minus_t2 = 1 - t2 one_minus_t3 = 1 - t3 # Linearly interpolate the image vectors. a_scaled = a * one_minus_t2 b_scaled = b * t2 image_interp = a_scaled image_interp.add_(b_scaled) result_type = image_interp.dtype del a_scaled, b_scaled, t2, one_minus_t2 # Calculate the magnitude of the interpolated vectors. (We will remove this magnitude.) # 64-bit operations are used here to allow large exponents. current_magnitude = torch.norm(image_interp, p=2, dim=1, keepdim=True).to(float64(image_interp)).add_(0.00001) # Interpolate the powered magnitudes, then un-power them (bring them back to a power of 1). a_magnitude = torch.norm(a, p=2, dim=1, keepdim=True).to(float64(a)).pow_(settings.inpaint_detail_preservation) * one_minus_t3 b_magnitude = torch.norm(b, p=2, dim=1, keepdim=True).to(float64(b)).pow_(settings.inpaint_detail_preservation) * t3 desired_magnitude = a_magnitude desired_magnitude.add_(b_magnitude).pow_(1 / settings.inpaint_detail_preservation) del a_magnitude, b_magnitude, t3, one_minus_t3 # Change the linearly interpolated image vectors' magnitudes to the value we want. # This is the last 64-bit operation. image_interp_scaling_factor = desired_magnitude image_interp_scaling_factor.div_(current_magnitude) image_interp_scaling_factor = image_interp_scaling_factor.to(result_type) image_interp_scaled = image_interp image_interp_scaled.mul_(image_interp_scaling_factor) del current_magnitude del desired_magnitude del image_interp del image_interp_scaling_factor del result_type return image_interp_scaled def get_modified_nmask(settings, nmask, sigma): """ Converts a negative mask representing the transparency of the original latent vectors being overlaid to a mask that is scaled according to the denoising strength for this step. Where: 0 = fully opaque, infinite density, fully masked 1 = fully transparent, zero density, fully unmasked We bring this transparency to a power, as this allows one to simulate N number of blending operations where N can be any positive real value. Using this one can control the balance of influence between the denoiser and the original latents according to the sigma value. NOTE: "mask" is not used """ import torch return torch.pow(nmask, (sigma ** settings.mask_blend_power) * settings.mask_blend_scale) def apply_adaptive_masks( settings: SoftInpaintingSettings, nmask, latent_orig, latent_processed, overlay_images, width, height, paste_to): import torch import modules.processing as proc import modules.images as images from PIL import Image, ImageOps, ImageFilter # TODO: Bias the blending according to the latent mask, add adjustable parameter for bias control. if len(nmask.shape) == 3: latent_mask = nmask[0].float() else: latent_mask = nmask[:, 0].float() # convert the original mask into a form we use to scale distances for thresholding mask_scalar = 1 - (torch.clamp(latent_mask, min=0, max=1) ** (settings.mask_blend_scale / 2)) mask_scalar = (0.5 * (1 - settings.composite_mask_influence) + mask_scalar * settings.composite_mask_influence) mask_scalar = mask_scalar / (1.00001 - mask_scalar) mask_scalar = mask_scalar.cpu().numpy() latent_distance = torch.norm(latent_processed - latent_orig, p=2, dim=1) kernel, kernel_center = get_gaussian_kernel(stddev_radius=1.5, max_radius=2) masks_for_overlay = [] for i, (distance_map, overlay_image) in enumerate(zip(latent_distance, overlay_images)): converted_mask = distance_map.float().cpu().numpy() converted_mask = weighted_histogram_filter(converted_mask, kernel, kernel_center, percentile_min=0.9, percentile_max=1, min_width=1) converted_mask = weighted_histogram_filter(converted_mask, kernel, kernel_center, percentile_min=0.25, percentile_max=0.75, min_width=1) # The distance at which opacity of original decreases to 50% if len(mask_scalar.shape) == 3: if mask_scalar.shape[0] > i: half_weighted_distance = settings.composite_difference_threshold * mask_scalar[i] else: half_weighted_distance = settings.composite_difference_threshold * mask_scalar[0] else: half_weighted_distance = settings.composite_difference_threshold * mask_scalar converted_mask = converted_mask / half_weighted_distance converted_mask = 1 / (1 + converted_mask ** settings.composite_difference_contrast) converted_mask = smootherstep(converted_mask) converted_mask = 1 - converted_mask converted_mask = 255. * converted_mask converted_mask = converted_mask.astype(np.uint8) converted_mask = Image.fromarray(converted_mask) converted_mask = images.resize_image(2, converted_mask, width, height) converted_mask = proc.create_binary_mask(converted_mask, round=False) # Remove aliasing artifacts using a gaussian blur. converted_mask = converted_mask.filter(ImageFilter.GaussianBlur(radius=4)) # Expand the mask to fit the whole image if needed. if paste_to is not None: converted_mask = proc.uncrop(converted_mask, (overlay_image.width, overlay_image.height), paste_to) masks_for_overlay.append(converted_mask) image_masked = Image.new('RGBa', (overlay_image.width, overlay_image.height)) image_masked.paste(overlay_image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(converted_mask.convert('L'))) overlay_images[i] = image_masked.convert('RGBA') return masks_for_overlay def apply_masks( settings, nmask, overlay_images, width, height, paste_to): import torch import modules.processing as proc import modules.images as images from PIL import Image, ImageOps, ImageFilter converted_mask = nmask[0].float() converted_mask = torch.clamp(converted_mask, min=0, max=1).pow_(settings.mask_blend_scale / 2) converted_mask = 255. * converted_mask converted_mask = converted_mask.cpu().numpy().astype(np.uint8) converted_mask = Image.fromarray(converted_mask) converted_mask = images.resize_image(2, converted_mask, width, height) converted_mask = proc.create_binary_mask(converted_mask, round=False) # Remove aliasing artifacts using a gaussian blur. converted_mask = converted_mask.filter(ImageFilter.GaussianBlur(radius=4)) # Expand the mask to fit the whole image if needed. if paste_to is not None: converted_mask = proc.uncrop(converted_mask, (width, height), paste_to) masks_for_overlay = [] for i, overlay_image in enumerate(overlay_images): masks_for_overlay[i] = converted_mask image_masked = Image.new('RGBa', (overlay_image.width, overlay_image.height)) image_masked.paste(overlay_image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(converted_mask.convert('L'))) overlay_images[i] = image_masked.convert('RGBA') return masks_for_overlay def weighted_histogram_filter(img, kernel, kernel_center, percentile_min=0.0, percentile_max=1.0, min_width=1.0): """ Generalization convolution filter capable of applying weighted mean, median, maximum, and minimum filters parametrically using an arbitrary kernel. Args: img (nparray): The image, a 2-D array of floats, to which the filter is being applied. kernel (nparray): The kernel, a 2-D array of floats. kernel_center (nparray): The kernel center coordinate, a 1-D array with two elements. percentile_min (float): The lower bound of the histogram window used by the filter, from 0 to 1. percentile_max (float): The upper bound of the histogram window used by the filter, from 0 to 1. min_width (float): The minimum size of the histogram window bounds, in weight units. Must be greater than 0. Returns: (nparray): A filtered copy of the input image "img", a 2-D array of floats. """ # Converts an index tuple into a vector. def vec(x): return np.array(x) kernel_min = -kernel_center kernel_max = vec(kernel.shape) - kernel_center def weighted_histogram_filter_single(idx): idx = vec(idx) min_index = np.maximum(0, idx + kernel_min) max_index = np.minimum(vec(img.shape), idx + kernel_max) window_shape = max_index - min_index class WeightedElement: """ An element of the histogram, its weight and bounds. """ def __init__(self, value, weight): self.value: float = value self.weight: float = weight self.window_min: float = 0.0 self.window_max: float = 1.0 # Collect the values in the image as WeightedElements, # weighted by their corresponding kernel values. values = [] for window_tup in np.ndindex(tuple(window_shape)): window_index = vec(window_tup) image_index = window_index + min_index centered_kernel_index = image_index - idx kernel_index = centered_kernel_index + kernel_center element = WeightedElement(img[tuple(image_index)], kernel[tuple(kernel_index)]) values.append(element) def sort_key(x: WeightedElement): return x.value values.sort(key=sort_key) # Calculate the height of the stack (sum) # and each sample's range they occupy in the stack sum = 0 for i in range(len(values)): values[i].window_min = sum sum += values[i].weight values[i].window_max = sum # Calculate what range of this stack ("window") # we want to get the weighted average across. window_min = sum * percentile_min window_max = sum * percentile_max window_width = window_max - window_min # Ensure the window is within the stack and at least a certain size. if window_width < min_width: window_center = (window_min + window_max) / 2 window_min = window_center - min_width / 2 window_max = window_center + min_width / 2 if window_max > sum: window_max = sum window_min = sum - min_width if window_min < 0: window_min = 0 window_max = min_width value = 0 value_weight = 0 # Get the weighted average of all the samples # that overlap with the window, weighted # by the size of their overlap. for i in range(len(values)): if window_min >= values[i].window_max: continue if window_max <= values[i].window_min: break s = max(window_min, values[i].window_min) e = min(window_max, values[i].window_max) w = e - s value += values[i].value * w value_weight += w return value / value_weight if value_weight != 0 else 0 img_out = img.copy() # Apply the kernel operation over each pixel. for index in np.ndindex(img.shape): img_out[index] = weighted_histogram_filter_single(index) return img_out def smoothstep(x): """ The smoothstep function, input should be clamped to 0-1 range. Turns a diagonal line (f(x) = x) into a sigmoid-like curve. """ return x * x * (3 - 2 * x) def smootherstep(x): """ The smootherstep function, input should be clamped to 0-1 range. Turns a diagonal line (f(x) = x) into a sigmoid-like curve. """ return x * x * x * (x * (6 * x - 15) + 10) def get_gaussian_kernel(stddev_radius=1.0, max_radius=2): """ Creates a Gaussian kernel with thresholded edges. Args: stddev_radius (float): Standard deviation of the gaussian kernel, in pixels. max_radius (int): The size of the filter kernel. The number of pixels is (max_radius*2+1) ** 2. The kernel is thresholded so that any values one pixel beyond this radius is weighted at 0. Returns: (nparray, nparray): A kernel array (shape: (N, N)), its center coordinate (shape: (2)) """ # Evaluates a 0-1 normalized gaussian function for a given square distance from the mean. def gaussian(sqr_mag): return math.exp(-sqr_mag / (stddev_radius * stddev_radius)) # Helper function for converting a tuple to an array. def vec(x): return np.array(x) """ Since a gaussian is unbounded, we need to limit ourselves to a finite range. We taper the ends off at the end of that range so they equal zero while preserving the maximum value of 1 at the mean. """ zero_radius = max_radius + 1.0 gauss_zero = gaussian(zero_radius * zero_radius) gauss_kernel_scale = 1 / (1 - gauss_zero) def gaussian_kernel_func(coordinate): x = coordinate[0] ** 2.0 + coordinate[1] ** 2.0 x = gaussian(x) x -= gauss_zero x *= gauss_kernel_scale x = max(0.0, x) return x size = max_radius * 2 + 1 kernel_center = max_radius kernel = np.zeros((size, size)) for index in np.ndindex(kernel.shape): kernel[index] = gaussian_kernel_func(vec(index) - kernel_center) return kernel, kernel_center # ------------------- Constants ------------------- default = SoftInpaintingSettings(1, 0.5, 4, 0, 0.5, 2) enabled_ui_label = "Soft inpainting" enabled_gen_param_label = "Soft inpainting enabled" enabled_el_id = "soft_inpainting_enabled" ui_labels = SoftInpaintingSettings( "Schedule bias", "Preservation strength", "Transition contrast boost", "Mask influence", "Difference threshold", "Difference contrast") ui_info = SoftInpaintingSettings( "Shifts when preservation of original content occurs during denoising.", "How strongly partially masked content should be preserved.", "Amplifies the contrast that may be lost in partially masked regions.", "How strongly the original mask should bias the difference threshold.", "How much an image region can change before the original pixels are not blended in anymore.", "How sharp the transition should be between blended and not blended.") gen_param_labels = SoftInpaintingSettings( "Soft inpainting schedule bias", "Soft inpainting preservation strength", "Soft inpainting transition contrast boost", "Soft inpainting mask influence", "Soft inpainting difference threshold", "Soft inpainting difference contrast") el_ids = SoftInpaintingSettings( "mask_blend_power", "mask_blend_scale", "inpaint_detail_preservation", "composite_mask_influence", "composite_difference_threshold", "composite_difference_contrast") # ------------------- Script ------------------- class Script(scripts.Script): def __init__(self): self.section = "inpaint" self.masks_for_overlay = None self.overlay_images = None def title(self): return "Soft Inpainting" def show(self, is_img2img): return scripts.AlwaysVisible if is_img2img else False def ui(self, is_img2img): if not is_img2img: return with InputAccordion(False, label=enabled_ui_label, elem_id=enabled_el_id) as soft_inpainting_enabled: with gr.Group(): gr.Markdown( """ Soft inpainting allows you to **seamlessly blend original content with inpainted content** according to the mask opacity. **High _Mask blur_** values are recommended! """) power = \ gr.Slider(label=ui_labels.mask_blend_power, info=ui_info.mask_blend_power, minimum=0, maximum=8, step=0.1, value=default.mask_blend_power, elem_id=el_ids.mask_blend_power) scale = \ gr.Slider(label=ui_labels.mask_blend_scale, info=ui_info.mask_blend_scale, minimum=0, maximum=8, step=0.05, value=default.mask_blend_scale, elem_id=el_ids.mask_blend_scale) detail = \ gr.Slider(label=ui_labels.inpaint_detail_preservation, info=ui_info.inpaint_detail_preservation, minimum=1, maximum=32, step=0.5, value=default.inpaint_detail_preservation, elem_id=el_ids.inpaint_detail_preservation) gr.Markdown( """ ### Pixel Composite Settings """) mask_inf = \ gr.Slider(label=ui_labels.composite_mask_influence, info=ui_info.composite_mask_influence, minimum=0, maximum=1, step=0.05, value=default.composite_mask_influence, elem_id=el_ids.composite_mask_influence) dif_thresh = \ gr.Slider(label=ui_labels.composite_difference_threshold, info=ui_info.composite_difference_threshold, minimum=0, maximum=8, step=0.25, value=default.composite_difference_threshold, elem_id=el_ids.composite_difference_threshold) dif_contr = \ gr.Slider(label=ui_labels.composite_difference_contrast, info=ui_info.composite_difference_contrast, minimum=0, maximum=8, step=0.25, value=default.composite_difference_contrast, elem_id=el_ids.composite_difference_contrast) with gr.Accordion("Help", open=False): gr.Markdown( f""" ### {ui_labels.mask_blend_power} The blending strength of original content is scaled proportionally with the decreasing noise level values at each step (sigmas). This ensures that the influence of the denoiser and original content preservation is roughly balanced at each step. This balance can be shifted using this parameter, controlling whether earlier or later steps have stronger preservation. - **Below 1**: Stronger preservation near the end (with low sigma) - **1**: Balanced (proportional to sigma) - **Above 1**: Stronger preservation in the beginning (with high sigma) """) gr.Markdown( f""" ### {ui_labels.mask_blend_scale} Skews whether partially masked image regions should be more likely to preserve the original content or favor inpainted content. This may need to be adjusted depending on the {ui_labels.mask_blend_power}, CFG Scale, prompt and Denoising strength. - **Low values**: Favors generated content. - **High values**: Favors original content. """) gr.Markdown( f""" ### {ui_labels.inpaint_detail_preservation} This parameter controls how the original latent vectors and denoised latent vectors are interpolated. With higher values, the magnitude of the resulting blended vector will be closer to the maximum of the two interpolated vectors. This can prevent the loss of contrast that occurs with linear interpolation. - **Low values**: Softer blending, details may fade. - **High values**: Stronger contrast, may over-saturate colors. """) gr.Markdown( """ ## Pixel Composite Settings Masks are generated based on how much a part of the image changed after denoising. These masks are used to blend the original and final images together. If the difference is low, the original pixels are used instead of the pixels returned by the inpainting process. """) gr.Markdown( f""" ### {ui_labels.composite_mask_influence} This parameter controls how much the mask should bias this sensitivity to difference. - **0**: Ignore the mask, only consider differences in image content. - **1**: Follow the mask closely despite image content changes. """) gr.Markdown( f""" ### {ui_labels.composite_difference_threshold} This value represents the difference at which the original pixels will have less than 50% opacity. - **Low values**: Two images patches must be almost the same in order to retain original pixels. - **High values**: Two images patches can be very different and still retain original pixels. """) gr.Markdown( f""" ### {ui_labels.composite_difference_contrast} This value represents the contrast between the opacity of the original and inpainted content. - **Low values**: The blend will be more gradual and have longer transitions, but may cause ghosting. - **High values**: Ghosting will be less common, but transitions may be very sudden. """) self.infotext_fields = [(soft_inpainting_enabled, enabled_gen_param_label), (power, gen_param_labels.mask_blend_power), (scale, gen_param_labels.mask_blend_scale), (detail, gen_param_labels.inpaint_detail_preservation), (mask_inf, gen_param_labels.composite_mask_influence), (dif_thresh, gen_param_labels.composite_difference_threshold), (dif_contr, gen_param_labels.composite_difference_contrast)] self.paste_field_names = [] for _, field_name in self.infotext_fields: self.paste_field_names.append(field_name) return [soft_inpainting_enabled, power, scale, detail, mask_inf, dif_thresh, dif_contr] def process(self, p, enabled, power, scale, detail_preservation, mask_inf, dif_thresh, dif_contr): if not enabled: return if not processing_uses_inpainting(p): return # Shut off the rounding it normally does. p.mask_round = False settings = SoftInpaintingSettings(power, scale, detail_preservation, mask_inf, dif_thresh, dif_contr) # p.extra_generation_params["Mask rounding"] = False settings.add_generation_params(p.extra_generation_params) def on_mask_blend(self, p, mba: scripts.MaskBlendArgs, enabled, power, scale, detail_preservation, mask_inf, dif_thresh, dif_contr): if not enabled: return if not processing_uses_inpainting(p): return if mba.is_final_blend: mba.blended_latent = mba.current_latent return settings = SoftInpaintingSettings(power, scale, detail_preservation, mask_inf, dif_thresh, dif_contr) # todo: Why is sigma 2D? Both values are the same. mba.blended_latent = latent_blend(settings, mba.init_latent, mba.current_latent, get_modified_nmask(settings, mba.nmask, mba.sigma[0])) def post_sample(self, p, ps: scripts.PostSampleArgs, enabled, power, scale, detail_preservation, mask_inf, dif_thresh, dif_contr): if not enabled: return if not processing_uses_inpainting(p): return nmask = getattr(p, "nmask", None) if nmask is None: return from modules import images from modules.shared import opts settings = SoftInpaintingSettings(power, scale, detail_preservation, mask_inf, dif_thresh, dif_contr) # since the original code puts holes in the existing overlay images, # we have to rebuild them. self.overlay_images = [] for img in p.init_images: image = images.flatten(img, opts.img2img_background_color) if p.paste_to is None and p.resize_mode != 3: image = images.resize_image(p.resize_mode, image, p.width, p.height) self.overlay_images.append(image.convert('RGBA')) if len(p.init_images) == 1: self.overlay_images = self.overlay_images * p.batch_size if getattr(ps.samples, 'already_decoded', False): self.masks_for_overlay = apply_masks(settings=settings, nmask=nmask, overlay_images=self.overlay_images, width=p.width, height=p.height, paste_to=p.paste_to) else: self.masks_for_overlay = apply_adaptive_masks(settings=settings, nmask=nmask, latent_orig=p.init_latent, latent_processed=ps.samples, overlay_images=self.overlay_images, width=p.width, height=p.height, paste_to=p.paste_to) def postprocess_maskoverlay(self, p, ppmo: scripts.PostProcessMaskOverlayArgs, enabled, power, scale, detail_preservation, mask_inf, dif_thresh, dif_contr): if not enabled: return if not processing_uses_inpainting(p): return if self.masks_for_overlay is None: return if self.overlay_images is None: return ppmo.mask_for_overlay = self.masks_for_overlay[ppmo.index] ppmo.overlay_image = self.overlay_images[ppmo.index] stable-diffusion-webui/extensions/000077500000000000000000000000001465116634300176355ustar00rootroot00000000000000stable-diffusion-webui/extensions/put extensions here.txt000066400000000000000000000000001465116634300242600ustar00rootroot00000000000000stable-diffusion-webui/html/000077500000000000000000000000001465116634300164025ustar00rootroot00000000000000stable-diffusion-webui/html/card-no-preview.png000066400000000000000000002447301465116634300221240ustar00rootroot00000000000000PNG  IHDRmH tEXtparametersdreamlike neural network logo that says NO PREVIEW Negative prompt: 3d Steps: 20, Sampler: Euler a, CFG scale: 12, Seed: 2448144561, Size: 512x768, Model hash: 92970aa785, Model: dreamlike-photoreal-2.0, Denoising strength: 0.57, ENSD: 31337, Mask blur: 4wpgIDATxi$u{D%ʪʪB($A\@pkQE{{<|0P#%HQ"A"AĎ*Ծgww;g>Kčǽio݊ܖ1?߲)X[P]?AjxRb4NOamoiEN %TJ(Tn!izTJ.>zg,YΖbźa.^7\j.7J$%xuUo&:PAD Uhf䐆^|4d62 .kg]ϵ'ȥSP}.h%a21C@ =Qh@|8ʼ':\ڮK" DjKWb ]0tII E`!g@ tR;gAwSgsdQ9c+j~jG"@(|A^)y)iCʇ%Gkce4ӧ~5\ ¾~RJN=:E^mЗWc*7tFFqL+@IqZG9ylt?֡ q4,K/KQ?IsbL*RhH9Dˮ6Vd5מ)M~BQFFhwo` ơ%DTxD0[Z\FFFFh}ZµTt/&(tɂcgddd [Hg"M) v.mU?tiG!Geddd#БVhj`.:MKY8r&6Ԁ0"J+ uK_z<####c1b%W[zpFІ)wKWt;22222=-P2M tuZXEuXg|CZq-AU)###IG[^ CW=],\qRKx =*β}FFFƅAdC{5i~hfddd9լ'ԣ̈́V:x8/rнddd\L, kIc{YOJ99puUWe+tO:0.3NzH(z\p4zw6H)2Oݎf_t~1 {o[ ϫ[Ӊ%[.9R]Ӡt 2 _y?Odϝґuw*-C<4"j!ڟx(ywn}QhPs^8Z˫~k2GnSiE=V{6%.U_}x2#QhO']DIM\zxGJɳ4## Q~Б}28 L\y8\D)gddd:hB!(@&kc%#󀌌S@1ZNwЕqvѰN,}qt@-dꟑ.С!Gk43 Y+[N` B*22 w&#)-[xJ>`@P䢼kiY_o8YtԯΩ_ 0wVD1uvtp*͞nsd/^ (Rɞ hXK.RBYUtTEj#gBP'YnP٫>E|."Iis~^SAl.,=:3\w9.*s1k;x.s24M~8Al\UkhNM)Zu C.ˊa HOԵ.!b'[Ea=6MY"8%뺡8竣h48Y 4B~6\xz-<.nyE\ƹ^X .=".cIРc9r (׊y6e@i#onЀNg4h\HB}G'/L 9~ p&˅C;,yp%NXm}Kп SSA_> vR5S[HU*bJtbOptm u "iwbgȸThoPwfbE'"vb'@FFqs whA6 6u3vpb}`'^8V ]'YkŤkSˉ]=l%]Z322:tзdo=闾Q.=/ƽc`hІ0$`=;Cө2zAefdd}aڝ|FaUcR ` ywBGoe##DLfP&ԶqG^ߛqLyGC8u[zB7X#Jbq?cFa%l`S}]PdBqa83222N8tFFFF2Bdddd<4/xb֣K>&ՙgddd RM@'XTFFFF 5҅9DFFFǑ絊Ic2WX Ǡk (XHVbdddd-j,Yȸ ts|hFFFFI `|Y8iD@ы>䭣2222N4Q'zx'pFFFFƅGtƟcC22222. :c:p&l8*QPZxZ22222VNY X ! 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ҔEՆalQq~cWxܣS- 8S"l]< ev6jCpCpL' v-_}OǸΩS-z<6esOXtVǦS}WMxv{8I\q%Qm9y˸ ^;4)7ane!{^}frA5zŽU7a te"W\"WTTT|P M@@Hn0"fm,UWTl n+yȧW&ȸ#y}bO{@l}K/5& X&a y+*&pU Ѭ<='eej\ y1ɐ6Ŗs4,ENEIlmBʒ9R87ڔ%V[x^Br%:wwlS.WiаLmFFhD1 S\4%qQC?4T\_~K^x0loH-i{*4P'+:*6t\5JD,y]?<7lx|Z?=)3Ϙ].,还2w%CD_)"DGwfIENDB`stable-diffusion-webui/html/extra-networks-card.html000066400000000000000000000005501465116634300231740ustar00rootroot00000000000000
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stable-diffusion-webui/html/extra-networks-edit-item-button.html000066400000000000000000000002421465116634300254530ustar00rootroot00000000000000
stable-diffusion-webui/html/extra-networks-metadata-button.html000066400000000000000000000002411465116634300253510ustar00rootroot00000000000000
stable-diffusion-webui/html/extra-networks-no-cards.html000066400000000000000000000001671465116634300237750ustar00rootroot00000000000000

Nothing here. Add some content to the following directories:

    {dirs}
stable-diffusion-webui/html/extra-networks-pane-dirs.html000066400000000000000000000005021465116634300241420ustar00rootroot00000000000000
{dirs_html}
{items_html}
stable-diffusion-webui/html/extra-networks-pane-tree.html000066400000000000000000000006471465116634300241520ustar00rootroot00000000000000
{tree_html}
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API  •  Github  •  Gradio  •  Startup profile  •  Reload UI

{versions}
stable-diffusion-webui/html/licenses.html000066400000000000000000000502451465116634300211030ustar00rootroot00000000000000

InvokeAI

Some code for compatibility with OSX is taken from lstein's repository. 
MIT License

Copyright (c) 2022 InvokeAI Team

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

LDSR

Code added by contirubtors, most likely copied from this repository. 
MIT License

Copyright (c) 2022 Machine Vision and Learning Group, LMU Munich

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

CLIP Interrogator

Some small amounts of code borrowed and reworked. 
MIT License

Copyright (c) 2022 pharmapsychotic

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

Memory Efficient Attention

The sub-quadratic cross attention optimization uses modified code from the Memory Efficient Attention package that Alex Birch optimized for 3D tensors. This license is updated to reflect that. 
MIT License

Copyright (c) 2023 Alex Birch
Copyright (c) 2023 Amin Rezaei

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

Scaled Dot Product Attention

Some small amounts of code borrowed and reworked. 
   Copyright 2023 The HuggingFace Team. All rights reserved.

   Licensed under the Apache License, Version 2.0 (the "License");
   you may not use this file except in compliance with the License.
   You may obtain a copy of the License at

      http://www.apache.org/licenses/LICENSE-2.0

   Unless required by applicable law or agreed to in writing, software
   distributed under the License is distributed on an "AS IS" BASIS,
   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   See the License for the specific language governing permissions and
   limitations under the License.

                                 Apache License
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Curated transformers

The MPS workaround for nn.Linear on macOS 13.2.X is based on the MPS workaround for nn.Linear created by danieldk for Curated transformers 
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THE SOFTWARE.

TAESD

Tiny AutoEncoder for Stable Diffusion option for live previews 
MIT License

Copyright (c) 2023 Ollin Boer Bohan

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stable-diffusion-webui/javascript/000077500000000000000000000000001465116634300176045ustar00rootroot00000000000000stable-diffusion-webui/javascript/aspectRatioOverlay.js000066400000000000000000000076321465116634300237720ustar00rootroot00000000000000 let currentWidth = null; let currentHeight = null; let arFrameTimeout = setTimeout(function() {}, 0); function dimensionChange(e, is_width, is_height) { if (is_width) { currentWidth = e.target.value * 1.0; } if (is_height) { currentHeight = e.target.value * 1.0; } var inImg2img = gradioApp().querySelector("#tab_img2img").style.display == "block"; if (!inImg2img) { return; } var targetElement = null; var tabIndex = get_tab_index('mode_img2img'); if (tabIndex == 0) { // img2img targetElement = gradioApp().querySelector('#img2img_image div[data-testid=image] img'); } else if (tabIndex == 1) { //Sketch targetElement = gradioApp().querySelector('#img2img_sketch div[data-testid=image] img'); } else if (tabIndex == 2) { // Inpaint targetElement = gradioApp().querySelector('#img2maskimg div[data-testid=image] img'); } else if (tabIndex == 3) { // Inpaint sketch targetElement = gradioApp().querySelector('#inpaint_sketch div[data-testid=image] img'); } if (targetElement) { var arPreviewRect = gradioApp().querySelector('#imageARPreview'); if (!arPreviewRect) { arPreviewRect = document.createElement('div'); arPreviewRect.id = "imageARPreview"; gradioApp().appendChild(arPreviewRect); } var viewportOffset = targetElement.getBoundingClientRect(); var viewportscale = Math.min(targetElement.clientWidth / targetElement.naturalWidth, targetElement.clientHeight / targetElement.naturalHeight); var scaledx = targetElement.naturalWidth * viewportscale; var scaledy = targetElement.naturalHeight * viewportscale; var clientRectTop = (viewportOffset.top + window.scrollY); var clientRectLeft = (viewportOffset.left + window.scrollX); var clientRectCentreY = clientRectTop + (targetElement.clientHeight / 2); var clientRectCentreX = clientRectLeft + (targetElement.clientWidth / 2); var arscale = Math.min(scaledx / currentWidth, scaledy / currentHeight); var arscaledx = currentWidth * arscale; var arscaledy = currentHeight * arscale; var arRectTop = clientRectCentreY - (arscaledy / 2); var arRectLeft = clientRectCentreX - (arscaledx / 2); var arRectWidth = arscaledx; var 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'; } } onAfterUiUpdate(function() { var arPreviewRect = gradioApp().querySelector('#imageARPreview'); if (arPreviewRect) { arPreviewRect.style.display = 'none'; } var tabImg2img = gradioApp().querySelector("#tab_img2img"); if (tabImg2img) { var inImg2img = tabImg2img.style.display == "block"; if (inImg2img) { let inputs = gradioApp().querySelectorAll('input'); inputs.forEach(function(e) { var is_width = e.parentElement.id == "img2img_width"; var is_height = e.parentElement.id == "img2img_height"; if ((is_width || is_height) && !e.classList.contains('scrollwatch')) { e.addEventListener('input', function(e) { dimensionChange(e, is_width, is_height); }); e.classList.add('scrollwatch'); } if (is_width) { currentWidth = e.value * 1.0; } if (is_height) { currentHeight = e.value * 1.0; } }); } } }); stable-diffusion-webui/javascript/contextMenus.js000066400000000000000000000130321465116634300226350ustar00rootroot00000000000000 var contextMenuInit = function() { let eventListenerApplied = false; let menuSpecs = new Map(); const uid = function() { return Date.now().toString(36) + Math.random().toString(36).substring(2); }; function showContextMenu(event, element, menuEntries) { let oldMenu = gradioApp().querySelector('#context-menu'); if (oldMenu) { oldMenu.remove(); } let baseStyle = window.getComputedStyle(uiCurrentTab); 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 = event.pageY + 'px'; contextMenu.style.left = event.pageX + 'px'; const contextMenuList = document.createElement('ul'); contextMenuList.className = 'context-menu-items'; contextMenu.append(contextMenuList); menuEntries.forEach(function(entry) { let contextMenuEntry = document.createElement('a'); contextMenuEntry.innerHTML = entry['name']; contextMenuEntry.addEventListener("click", function() { entry['func'](); }); contextMenuList.append(contextMenuEntry); }); gradioApp().appendChild(contextMenu); } function appendContextMenuOption(targetElementSelector, entryName, entryFunction) { var currentItems = menuSpecs.get(targetElementSelector); if (!currentItems) { currentItems = []; menuSpecs.set(targetElementSelector, currentItems); } let newItem = { id: targetElementSelector + '_' + uid(), name: entryName, func: entryFunction, isNew: true }; currentItems.push(newItem); return newItem['id']; } function removeContextMenuOption(uid) { menuSpecs.forEach(function(v) { 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) { if (!e.isTrusted) { return; } let oldMenu = gradioApp().querySelector('#context-menu'); if (oldMenu) { oldMenu.remove(); } }); ['contextmenu', 'touchstart'].forEach((eventType) => { gradioApp().addEventListener(eventType, function(e) { let ev = e; if (eventType.startsWith('touch')) { if (e.touches.length !== 2) return; ev = e.touches[0]; } let oldMenu = gradioApp().querySelector('#context-menu'); if (oldMenu) { oldMenu.remove(); } menuSpecs.forEach(function(v, k) { if (e.composedPath()[0].matches(k)) { showContextMenu(ev, e.composedPath()[0], v); e.preventDefault(); } }); }); }); eventListenerApplied = true; } return [appendContextMenuOption, removeContextMenuOption, addContextMenuEventListener]; }; var initResponse = contextMenuInit(); var appendContextMenuOption = initResponse[0]; var removeContextMenuOption = initResponse[1]; var 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); }; let generateOnRepeat_txt2img = function() { generateOnRepeat('#txt2img_generate', '#txt2img_interrupt'); }; let generateOnRepeat_img2img = function() { generateOnRepeat('#img2img_generate', '#img2img_interrupt'); }; appendContextMenuOption('#txt2img_generate', 'Generate forever', generateOnRepeat_txt2img); appendContextMenuOption('#txt2img_interrupt', 'Generate forever', generateOnRepeat_txt2img); appendContextMenuOption('#img2img_generate', 'Generate forever', generateOnRepeat_img2img); appendContextMenuOption('#img2img_interrupt', 'Generate forever', generateOnRepeat_img2img); 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); })(); //End example Context Menu Items onAfterUiUpdate(addContextMenuEventListener); stable-diffusion-webui/javascript/dragdrop.js000066400000000000000000000120361465116634300217460ustar00rootroot00000000000000// 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; } const tmpFile = files[0]; imgWrap.querySelector('.modify-upload button + button, .touch-none + div button + button')?.click(); const callback = () => { const fileInput = imgWrap.querySelector('input[type="file"]'); if (fileInput) { if (files.length === 0) { files = new DataTransfer(); files.items.add(tmpFile); fileInput.files = files.files; } else { 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()); } } function eventHasFiles(e) { if (!e.dataTransfer || !e.dataTransfer.files) return false; if (e.dataTransfer.files.length > 0) return true; if (e.dataTransfer.items.length > 0 && e.dataTransfer.items[0].kind == "file") return true; return false; } function isURL(url) { try { const _ = new URL(url); return true; } catch { return false; } } function dragDropTargetIsPrompt(target) { if (target?.placeholder && target?.placeholder.indexOf("Prompt") >= 0) return true; if (target?.parentNode?.parentNode?.className?.indexOf("prompt") > 0) return true; return false; } window.document.addEventListener('dragover', e => { const target = e.composedPath()[0]; if (!eventHasFiles(e)) return; var targetImage = target.closest('[data-testid="image"]'); if (!dragDropTargetIsPrompt(target) && !targetImage) return; e.stopPropagation(); e.preventDefault(); e.dataTransfer.dropEffect = 'copy'; }); window.document.addEventListener('drop', async e => { const target = e.composedPath()[0]; const url = e.dataTransfer.getData('text/uri-list') || e.dataTransfer.getData('text/plain'); if (!eventHasFiles(e) && !isURL(url)) return; if (dragDropTargetIsPrompt(target)) { e.stopPropagation(); e.preventDefault(); const isImg2img = get_tab_index('tabs') == 1; let prompt_image_target = isImg2img ? "img2img_prompt_image" : "txt2img_prompt_image"; const imgParent = gradioApp().getElementById(prompt_image_target); const files = e.dataTransfer.files; const fileInput = imgParent.querySelector('input[type="file"]'); if (eventHasFiles(e) && fileInput) { fileInput.files = files; fileInput.dispatchEvent(new Event('change')); } else if (url) { try { const request = await fetch(url); if (!request.ok) { console.error('Error fetching URL:', url, request.status); return; } const data = new DataTransfer(); data.items.add(new File([await request.blob()], 'image.png')); fileInput.files = data.files; fileInput.dispatchEvent(new Event('change')); } catch (error) { console.error('Error fetching URL:', url, error); return; } } } var targetImage = target.closest('[data-testid="image"]'); if (targetImage) { e.stopPropagation(); e.preventDefault(); const files = e.dataTransfer.files; dropReplaceImage(targetImage, files); return; } }); window.addEventListener('paste', e => { const files = e.clipboardData.files; if (!isValidImageList(files)) { return; } const visibleImageFields = [...gradioApp().querySelectorAll('[data-testid="image"]')] .filter(el => uiElementIsVisible(el)) .sort((a, b) => uiElementInSight(b) - uiElementInSight(a)); if (!visibleImageFields.length) { return; } const firstFreeImageField = visibleImageFields .filter(el => !el.querySelector('img'))?.[0]; dropReplaceImage( firstFreeImageField ? firstFreeImageField : visibleImageFields[visibleImageFields.length - 1] , files ); }); stable-diffusion-webui/javascript/edit-attention.js000066400000000000000000000133221465116634300230730ustar00rootroot00000000000000function keyupEditAttention(event) { let target = event.originalTarget || event.composedPath()[0]; if (!target.matches("*:is([id*='_toprow'] [id*='_prompt'], .prompt) textarea")) return; if (!(event.metaKey || event.ctrlKey)) return; let isPlus = event.key == "ArrowUp"; let isMinus = event.key == "ArrowDown"; if (!isPlus && !isMinus) return; let selectionStart = target.selectionStart; let selectionEnd = target.selectionEnd; let text = target.value; function selectCurrentParenthesisBlock(OPEN, CLOSE) { if (selectionStart !== selectionEnd) return false; // Find opening parenthesis around current cursor const before = text.substring(0, selectionStart); let beforeParen = before.lastIndexOf(OPEN); if (beforeParen == -1) return false; let beforeClosingParen = before.lastIndexOf(CLOSE); if (beforeClosingParen != -1 && beforeClosingParen > beforeParen) return false; // Find closing parenthesis around current cursor const after = text.substring(selectionStart); let afterParen = after.indexOf(CLOSE); if (afterParen == -1) return false; let afterOpeningParen = after.indexOf(OPEN); if (afterOpeningParen != -1 && afterOpeningParen < afterParen) return false; // Set the selection to the text between the parenthesis const parenContent = text.substring(beforeParen + 1, selectionStart + afterParen); if (/.*:-?[\d.]+/s.test(parenContent)) { const lastColon = parenContent.lastIndexOf(":"); selectionStart = beforeParen + 1; selectionEnd = selectionStart + lastColon; } else { selectionStart = beforeParen + 1; selectionEnd = selectionStart + parenContent.length; } target.setSelectionRange(selectionStart, selectionEnd); return true; } function selectCurrentWord() { if (selectionStart !== selectionEnd) return false; const whitespace_delimiters = {"Tab": "\t", "Carriage Return": "\r", "Line Feed": "\n"}; let delimiters = opts.keyedit_delimiters; for (let i of opts.keyedit_delimiters_whitespace) { delimiters += whitespace_delimiters[i]; } // seek backward to find beginning while (!delimiters.includes(text[selectionStart - 1]) && selectionStart > 0) { selectionStart--; } // seek forward to find end while (!delimiters.includes(text[selectionEnd]) && selectionEnd < text.length) { selectionEnd++; } // deselect surrounding whitespace while (text[selectionStart] == " " && selectionStart < selectionEnd) { selectionStart++; } while (text[selectionEnd - 1] == " " && selectionEnd > selectionStart) { selectionEnd--; } target.setSelectionRange(selectionStart, selectionEnd); return true; } // If the user hasn't selected anything, let's select their current parenthesis block or word if (!selectCurrentParenthesisBlock('<', '>') && !selectCurrentParenthesisBlock('(', ')') && !selectCurrentParenthesisBlock('[', ']')) { selectCurrentWord(); } event.preventDefault(); var closeCharacter = ')'; var delta = opts.keyedit_precision_attention; var start = selectionStart > 0 ? text[selectionStart - 1] : ""; var end = text[selectionEnd]; if (start == '<') { closeCharacter = '>'; delta = opts.keyedit_precision_extra; } else if (start == '(' && end == ')' || start == '[' && end == ']') { // convert old-style (((emphasis))) let numParen = 0; while (text[selectionStart - numParen - 1] == start && text[selectionEnd + numParen] == end) { numParen++; } if (start == "[") { weight = (1 / 1.1) ** numParen; } else { weight = 1.1 ** numParen; } weight = Math.round(weight / opts.keyedit_precision_attention) * opts.keyedit_precision_attention; text = text.slice(0, selectionStart - numParen) + "(" + text.slice(selectionStart, selectionEnd) + ":" + weight + ")" + text.slice(selectionEnd + numParen); selectionStart -= numParen - 1; selectionEnd -= numParen - 1; } else if (start != '(') { // do not include spaces at the end while (selectionEnd > selectionStart && text[selectionEnd - 1] == ' ') { selectionEnd--; } if (selectionStart == selectionEnd) { return; } text = text.slice(0, selectionStart) + "(" + text.slice(selectionStart, selectionEnd) + ":1.0)" + text.slice(selectionEnd); selectionStart++; selectionEnd++; } if (text[selectionEnd] != ':') return; var weightLength = text.slice(selectionEnd + 1).indexOf(closeCharacter) + 1; var weight = parseFloat(text.slice(selectionEnd + 1, selectionEnd + weightLength)); if (isNaN(weight)) return; weight += isPlus ? delta : -delta; weight = parseFloat(weight.toPrecision(12)); if (Number.isInteger(weight)) weight += ".0"; if (closeCharacter == ')' && weight == 1) { var endParenPos = text.substring(selectionEnd).indexOf(')'); text = text.slice(0, selectionStart - 1) + text.slice(selectionStart, selectionEnd) + text.slice(selectionEnd + endParenPos + 1); selectionStart--; selectionEnd--; } else { text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + weightLength); } target.focus(); target.value = text; target.selectionStart = selectionStart; target.selectionEnd = selectionEnd; updateInput(target); } addEventListener('keydown', (event) => { keyupEditAttention(event); }); stable-diffusion-webui/javascript/edit-order.js000066400000000000000000000030751465116634300222050ustar00rootroot00000000000000/* alt+left/right moves text in prompt */ function keyupEditOrder(event) { if (!opts.keyedit_move) return; let target = event.originalTarget || event.composedPath()[0]; if (!target.matches("*:is([id*='_toprow'] [id*='_prompt'], .prompt) textarea")) return; if (!event.altKey) return; let isLeft = event.key == "ArrowLeft"; let isRight = event.key == "ArrowRight"; if (!isLeft && !isRight) return; event.preventDefault(); let selectionStart = target.selectionStart; let selectionEnd = target.selectionEnd; let text = target.value; let items = text.split(","); let indexStart = (text.slice(0, selectionStart).match(/,/g) || []).length; let indexEnd = (text.slice(0, selectionEnd).match(/,/g) || []).length; let range = indexEnd - indexStart + 1; if (isLeft && indexStart > 0) { items.splice(indexStart - 1, 0, ...items.splice(indexStart, range)); target.value = items.join(); target.selectionStart = items.slice(0, indexStart - 1).join().length + (indexStart == 1 ? 0 : 1); target.selectionEnd = items.slice(0, indexEnd).join().length; } else if (isRight && indexEnd < items.length - 1) { items.splice(indexStart + 1, 0, ...items.splice(indexStart, range)); target.value = items.join(); target.selectionStart = items.slice(0, indexStart + 1).join().length + 1; target.selectionEnd = items.slice(0, indexEnd + 2).join().length; } event.preventDefault(); updateInput(target); } addEventListener('keydown', (event) => { keyupEditOrder(event); }); stable-diffusion-webui/javascript/extensions.js000066400000000000000000000060001465116634300223350ustar00rootroot00000000000000 function extensions_apply(_disabled_list, _update_list, disable_all) { var disable = []; var update = []; const extensions_input = gradioApp().querySelectorAll('#extensions input[type="checkbox"]'); if (extensions_input.length == 0) { throw Error("Extensions page not yet loaded."); } extensions_input.forEach(function(x) { if (x.name.startsWith("enable_") && !x.checked) { disable.push(x.name.substring(7)); } if (x.name.startsWith("update_") && x.checked) { update.push(x.name.substring(7)); } }); restart_reload(); return [JSON.stringify(disable), JSON.stringify(update), disable_all]; } function extensions_check() { var disable = []; gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x) { if (x.name.startsWith("enable_") && !x.checked) { disable.push(x.name.substring(7)); } }); gradioApp().querySelectorAll('#extensions .extension_status').forEach(function(x) { x.innerHTML = "Loading..."; }); var id = randomId(); requestProgress(id, gradioApp().getElementById('extensions_installed_html'), null, function() { }); return [id, JSON.stringify(disable)]; } function install_extension_from_index(button, url) { button.disabled = "disabled"; button.value = "Installing..."; var textarea = gradioApp().querySelector('#extension_to_install textarea'); textarea.value = url; updateInput(textarea); gradioApp().querySelector('#install_extension_button').click(); } function config_state_confirm_restore(_, config_state_name, config_restore_type) { if (config_state_name == "Current") { return [false, config_state_name, config_restore_type]; } let restored = ""; if (config_restore_type == "extensions") { restored = "all saved extension versions"; } else if (config_restore_type == "webui") { restored = "the webui version"; } else { restored = "the webui version and all saved extension versions"; } let confirmed = confirm("Are you sure you want to restore from this state?\nThis will reset " + restored + "."); if (confirmed) { restart_reload(); gradioApp().querySelectorAll('#extensions .extension_status').forEach(function(x) { x.innerHTML = "Loading..."; }); } return [confirmed, config_state_name, config_restore_type]; } function toggle_all_extensions(event) { gradioApp().querySelectorAll('#extensions .extension_toggle').forEach(function(checkbox_el) { checkbox_el.checked = event.target.checked; }); } function toggle_extension() { let all_extensions_toggled = true; for (const checkbox_el of gradioApp().querySelectorAll('#extensions .extension_toggle')) { if (!checkbox_el.checked) { all_extensions_toggled = false; break; } } gradioApp().querySelector('#extensions .all_extensions_toggle').checked = all_extensions_toggled; } stable-diffusion-webui/javascript/extraNetworks.js000066400000000000000000000645121465116634300230320ustar00rootroot00000000000000function toggleCss(key, css, enable) { var style = document.getElementById(key); if (enable && !style) { style = document.createElement('style'); style.id = key; style.type = 'text/css'; document.head.appendChild(style); } if (style && !enable) { document.head.removeChild(style); } if (style) { style.innerHTML == ''; style.appendChild(document.createTextNode(css)); } } function setupExtraNetworksForTab(tabname) { function registerPrompt(tabname, id) { var textarea = gradioApp().querySelector("#" + id + " > label > textarea"); if (!activePromptTextarea[tabname]) { activePromptTextarea[tabname] = textarea; } textarea.addEventListener("focus", function() { activePromptTextarea[tabname] = textarea; }); } var tabnav = gradioApp().querySelector('#' + tabname + '_extra_tabs > div.tab-nav'); var controlsDiv = document.createElement('DIV'); controlsDiv.classList.add('extra-networks-controls-div'); tabnav.appendChild(controlsDiv); tabnav.insertBefore(controlsDiv, null); var this_tab = gradioApp().querySelector('#' + tabname + '_extra_tabs'); this_tab.querySelectorAll(":scope > [id^='" + tabname + "_']").forEach(function(elem) { // tabname_full = {tabname}_{extra_networks_tabname} var tabname_full = elem.id; var search = gradioApp().querySelector("#" + tabname_full + "_extra_search"); var sort_dir = gradioApp().querySelector("#" + tabname_full + "_extra_sort_dir"); var refresh = gradioApp().querySelector("#" + tabname_full + "_extra_refresh"); var currentSort = ''; // If any of the buttons above don't exist, we want to skip this iteration of the loop. if (!search || !sort_dir || !refresh) { return; // `return` is equivalent of `continue` but for forEach loops. } var applyFilter = function(force) { var searchTerm = search.value.toLowerCase(); gradioApp().querySelectorAll('#' + tabname + '_extra_tabs div.card').forEach(function(elem) { var searchOnly = elem.querySelector('.search_only'); var text = Array.prototype.map.call(elem.querySelectorAll('.search_terms, .description'), function(t) { return t.textContent.toLowerCase(); }).join(" "); var visible = text.indexOf(searchTerm) != -1; if (searchOnly && searchTerm.length < 4) { visible = false; } if (visible) { elem.classList.remove("hidden"); } else { elem.classList.add("hidden"); } }); applySort(force); }; var applySort = function(force) { var cards = gradioApp().querySelectorAll('#' + tabname_full + ' div.card'); var parent = gradioApp().querySelector('#' + tabname_full + "_cards"); var reverse = sort_dir.dataset.sortdir == "Descending"; var activeSearchElem = gradioApp().querySelector('#' + tabname_full + "_controls .extra-network-control--sort.extra-network-control--enabled"); var sortKey = activeSearchElem ? activeSearchElem.dataset.sortkey : "default"; var sortKeyDataField = "sort" + sortKey.charAt(0).toUpperCase() + sortKey.slice(1); var sortKeyStore = sortKey + "-" + sort_dir.dataset.sortdir + "-" + cards.length; if (sortKeyStore == currentSort && !force) { return; } currentSort = sortKeyStore; var sortedCards = Array.from(cards); sortedCards.sort(function(cardA, cardB) { var a = cardA.dataset[sortKeyDataField]; var b = cardB.dataset[sortKeyDataField]; if (!isNaN(a) && !isNaN(b)) { return parseInt(a) - parseInt(b); } return (a < b ? -1 : (a > b ? 1 : 0)); }); if (reverse) { sortedCards.reverse(); } parent.innerHTML = ''; var frag = document.createDocumentFragment(); sortedCards.forEach(function(card) { frag.appendChild(card); }); parent.appendChild(frag); }; search.addEventListener("input", function() { applyFilter(); }); applySort(); applyFilter(); extraNetworksApplySort[tabname_full] = applySort; extraNetworksApplyFilter[tabname_full] = applyFilter; var controls = gradioApp().querySelector("#" + tabname_full + "_controls"); controlsDiv.insertBefore(controls, null); if (elem.style.display != "none") { extraNetworksShowControlsForPage(tabname, tabname_full); } }); registerPrompt(tabname, tabname + "_prompt"); registerPrompt(tabname, tabname + "_neg_prompt"); } function extraNetworksMovePromptToTab(tabname, id, showPrompt, showNegativePrompt) { if (!gradioApp().querySelector('.toprow-compact-tools')) return; // only applicable for compact prompt layout var promptContainer = gradioApp().getElementById(tabname + '_prompt_container'); var prompt = gradioApp().getElementById(tabname + '_prompt_row'); var negPrompt = gradioApp().getElementById(tabname + '_neg_prompt_row'); var elem = id ? gradioApp().getElementById(id) : null; if (showNegativePrompt && elem) { elem.insertBefore(negPrompt, elem.firstChild); } else { promptContainer.insertBefore(negPrompt, promptContainer.firstChild); } if (showPrompt && elem) { elem.insertBefore(prompt, elem.firstChild); } else { promptContainer.insertBefore(prompt, promptContainer.firstChild); } if (elem) { elem.classList.toggle('extra-page-prompts-active', showNegativePrompt || showPrompt); } } function extraNetworksShowControlsForPage(tabname, tabname_full) { gradioApp().querySelectorAll('#' + tabname + '_extra_tabs .extra-networks-controls-div > div').forEach(function(elem) { var targetId = tabname_full + "_controls"; elem.style.display = elem.id == targetId ? "" : "none"; }); } function extraNetworksUnrelatedTabSelected(tabname) { // called from python when user selects an unrelated tab (generate) extraNetworksMovePromptToTab(tabname, '', false, false); extraNetworksShowControlsForPage(tabname, null); } function extraNetworksTabSelected(tabname, id, showPrompt, showNegativePrompt, tabname_full) { // called from python when user selects an extra networks tab extraNetworksMovePromptToTab(tabname, id, showPrompt, showNegativePrompt); extraNetworksShowControlsForPage(tabname, tabname_full); } function applyExtraNetworkFilter(tabname_full) { var doFilter = function() { var applyFunction = extraNetworksApplyFilter[tabname_full]; if (applyFunction) { applyFunction(true); } }; setTimeout(doFilter, 1); } function applyExtraNetworkSort(tabname_full) { var doSort = function() { extraNetworksApplySort[tabname_full](true); }; setTimeout(doSort, 1); } var extraNetworksApplyFilter = {}; var extraNetworksApplySort = {}; var activePromptTextarea = {}; function setupExtraNetworks() { setupExtraNetworksForTab('txt2img'); setupExtraNetworksForTab('img2img'); } var re_extranet = /<([^:^>]+:[^:]+):[\d.]+>(.*)/; var re_extranet_g = /<([^:^>]+:[^:]+):[\d.]+>/g; var re_extranet_neg = /\(([^:^>]+:[\d.]+)\)/; var re_extranet_g_neg = /\(([^:^>]+:[\d.]+)\)/g; function tryToRemoveExtraNetworkFromPrompt(textarea, text, isNeg) { var m = text.match(isNeg ? re_extranet_neg : re_extranet); var replaced = false; var newTextareaText; var extraTextBeforeNet = opts.extra_networks_add_text_separator; if (m) { var extraTextAfterNet = m[2]; var partToSearch = m[1]; var foundAtPosition = -1; newTextareaText = textarea.value.replaceAll(isNeg ? re_extranet_g_neg : re_extranet_g, function(found, net, pos) { m = found.match(isNeg ? re_extranet_neg : re_extranet); if (m[1] == partToSearch) { replaced = true; foundAtPosition = pos; return ""; } return found; }); if (foundAtPosition >= 0) { if (extraTextAfterNet && newTextareaText.substr(foundAtPosition, extraTextAfterNet.length) == extraTextAfterNet) { newTextareaText = newTextareaText.substr(0, foundAtPosition) + newTextareaText.substr(foundAtPosition + extraTextAfterNet.length); } if (newTextareaText.substr(foundAtPosition - extraTextBeforeNet.length, extraTextBeforeNet.length) == extraTextBeforeNet) { newTextareaText = newTextareaText.substr(0, foundAtPosition - extraTextBeforeNet.length) + newTextareaText.substr(foundAtPosition); } } } else { newTextareaText = textarea.value.replaceAll(new RegExp(`((?:${extraTextBeforeNet})?${text})`, "g"), ""); replaced = (newTextareaText != textarea.value); } if (replaced) { textarea.value = newTextareaText; return true; } return false; } function updatePromptArea(text, textArea, isNeg) { if (!tryToRemoveExtraNetworkFromPrompt(textArea, text, isNeg)) { textArea.value = textArea.value + opts.extra_networks_add_text_separator + text; } updateInput(textArea); } function cardClicked(tabname, textToAdd, textToAddNegative, allowNegativePrompt) { if (textToAddNegative.length > 0) { updatePromptArea(textToAdd, gradioApp().querySelector("#" + tabname + "_prompt > label > textarea")); updatePromptArea(textToAddNegative, gradioApp().querySelector("#" + tabname + "_neg_prompt > label > textarea"), true); } else { var textarea = allowNegativePrompt ? activePromptTextarea[tabname] : gradioApp().querySelector("#" + tabname + "_prompt > label > textarea"); updatePromptArea(textToAdd, textarea); } } function saveCardPreview(event, tabname, filename) { var textarea = gradioApp().querySelector("#" + tabname + '_preview_filename > label > textarea'); var button = gradioApp().getElementById(tabname + '_save_preview'); textarea.value = filename; updateInput(textarea); button.click(); event.stopPropagation(); event.preventDefault(); } function extraNetworksSearchButton(tabname, extra_networks_tabname, event) { var searchTextarea = gradioApp().querySelector("#" + tabname + "_" + extra_networks_tabname + "_extra_search"); var button = event.target; var text = button.classList.contains("search-all") ? "" : button.textContent.trim(); searchTextarea.value = text; updateInput(searchTextarea); } function extraNetworksTreeProcessFileClick(event, btn, tabname, extra_networks_tabname) { /** * Processes `onclick` events when user clicks on files in tree. * * @param event The generated event. * @param btn The clicked `tree-list-item` button. * @param tabname The name of the active tab in the sd webui. Ex: txt2img, img2img, etc. * @param extra_networks_tabname The id of the active extraNetworks tab. Ex: lora, checkpoints, etc. */ // NOTE: Currently unused. return; } function extraNetworksTreeProcessDirectoryClick(event, btn, tabname, extra_networks_tabname) { /** * Processes `onclick` events when user clicks on directories in tree. * * Here is how the tree reacts to clicks for various states: * unselected unopened directory: Directory is selected and expanded. * unselected opened directory: Directory is selected. * selected opened directory: Directory is collapsed and deselected. * chevron is clicked: Directory is expanded or collapsed. Selected state unchanged. * * @param event The generated event. * @param btn The clicked `tree-list-item` button. * @param tabname The name of the active tab in the sd webui. Ex: txt2img, img2img, etc. * @param extra_networks_tabname The id of the active extraNetworks tab. Ex: lora, checkpoints, etc. */ var ul = btn.nextElementSibling; // This is the actual target that the user clicked on within the target button. // We use this to detect if the chevron was clicked. var true_targ = event.target; function _expand_or_collapse(_ul, _btn) { // Expands