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Merge branch 'release_candidate'

This commit is contained in:
AUTOMATIC1111 2023-12-16 09:58:07 +03:00
parent 4afaaf8a02 0dfffe53ec
commit cf2772fab0
116 changed files with 3835 additions and 1334 deletions
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1
.eslintrc.js
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@ -74,6 +74,7 @@ module.exports = {
create_submit_args: "readonly",
restart_reload: "readonly",
updateInput: "readonly",
onEdit: "readonly",
//extraNetworks.js
requestGet: "readonly",
popup: "readonly",

73
.github/ISSUE_TEMPLATE/bug_report.yml vendored
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@ -1,25 +1,45 @@
name: Bug Report
description: You think somethings is broken in the UI
description: You think something is broken in the UI
title: "[Bug]: "
labels: ["bug-report"]
body:
- type: checkboxes
attributes:
label: Is there an existing issue for this?
description: Please search to see if an issue already exists for the bug you encountered, and that it hasn't been fixed 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, don't forget to fill "What OS..." and "What browsers" and *provide screenshots if possible**
> 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
@ -27,9 +47,9 @@ body:
attributes:
label: Steps to reproduce the problem
description: Please provide us with precise step by step instructions on how to reproduce the bug
value: |
1. Go to ....
2. Press ....
placeholder: |
1. Go to ...
2. Press ...
3. ...
validations:
required: true
@ -38,13 +58,8 @@ body:
attributes:
label: What should have happened?
description: Tell us what you think the normal behavior should be
validations:
required: true
- 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: |
WebUI should ...
validations:
required: true
- type: dropdown
@ -58,12 +73,25 @@ body:
- 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 your bug happened. If it's very long, provide a link to pastebin or similar service.
description: Please provide **full** cmd/terminal logs from the moment you started UI to the end of it, after the bug occured. If it's very long, provide a link to pastebin or similar service.
render: Shell
validations:
required: true
@ -71,4 +99,7 @@ body:
id: misc
attributes:
label: Additional information
description: Please provide us with any relevant additional info or context.
description: |
Please provide us with any relevant additional info or context.
Examples:
　I have updated my GPU driver recently.

2
.github/workflows/on_pull_request.yaml vendored
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@ -20,7 +20,7 @@ jobs:
# 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==0.0.272
run: pip install ruff==0.1.6
- name: Run Ruff
run: ruff .
lint-js:

162
CHANGELOG.md
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@ -1,3 +1,165 @@
## 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 mutiple styles csv files ([#14125](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14125))
### 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 <lora:> and <lyco:> 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:

12
README.md
View File

@ -88,9 +88,10 @@ A browser interface based on Gradio library for Stable Diffusion.
- [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 dimension must be a multiple of 8 rather than 64
- 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:
@ -103,7 +104,7 @@ 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 it's contents.
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)
@ -120,7 +121,9 @@ Alternatively, use online services (like Google Colab):
# Debian-based:
sudo apt install wget git python3 python3-venv libgl1 libglib2.0-0
# Red Hat-based:
sudo dnf install wget git python3
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
```
@ -146,7 +149,7 @@ For the purposes of getting Google and other search engines to crawl the wiki, h
## Credits
Licenses for borrowed code can be found in `Settings -> Licenses` screen, and also in `html/licenses.html` file.
- Stable Diffusion - https://github.com/CompVis/stable-diffusion, https://github.com/CompVis/taming-transformers
- Stable Diffusion - https://github.com/Stability-AI/stablediffusion, https://github.com/CompVis/taming-transformers
- k-diffusion - https://github.com/crowsonkb/k-diffusion.git
- GFPGAN - https://github.com/TencentARC/GFPGAN.git
- CodeFormer - https://github.com/sczhou/CodeFormer
@ -173,5 +176,6 @@ Licenses for borrowed code can be found in `Settings -> Licenses` screen, and al
- 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)

73
configs/alt-diffusion-m18-inference.yaml Normal file
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@ -0,0 +1,73 @@
model:
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: True
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"

33
extensions-builtin/Lora/lora_logger.py Normal file
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@ -0,0 +1,33 @@
import 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)

47
extensions-builtin/Lora/lyco_helpers.py
View File

@ -19,3 +19,50 @@ 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.
Becuase 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<n:
new_m = m + 1
while dimension%new_m != 0:
new_m += 1
new_n = dimension // new_m
if new_m + new_n > length or new_m>factor:
break
else:
m, n = new_m, new_n
if m > n:
n, m = m, n
return m, n

1
extensions-builtin/Lora/network.py
View File

@ -93,6 +93,7 @@ class Network: # LoraModule
self.unet_multiplier = 1.0
self.dyn_dim = None
self.modules = {}
self.bundle_embeddings = {}
self.mtime = None
self.mentioned_name = None

33
extensions-builtin/Lora/network_glora.py Normal file
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@ -0,0 +1,33 @@
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, dtype=orig_weight.dtype)
w1b = self.w1b.to(orig_weight.device, dtype=orig_weight.dtype)
w2a = self.w2a.to(orig_weight.device, dtype=orig_weight.dtype)
w2b = self.w2b.to(orig_weight.device, dtype=orig_weight.dtype)
output_shape = [w1a.size(0), w1b.size(1)]
updown = ((w2b @ w1b) + ((orig_weight @ w2a) @ w1a))
return self.finalize_updown(updown, orig_weight, output_shape)

82
extensions-builtin/Lora/network_oft.py Normal file
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@ -0,0 +1,82 @@
import torch
import network
from lyco_helpers import factorization
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
# kohya-ss
if "oft_blocks" in weights.w.keys():
self.is_kohya = True
self.oft_blocks = weights.w["oft_blocks"] # (num_blocks, block_size, block_size)
self.alpha = weights.w["alpha"] # alpha is constraint
self.dim = self.oft_blocks.shape[0] # lora dim
# LyCORIS
elif "oft_diag" in weights.w.keys():
self.is_kohya = False
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
if self.is_kohya:
self.constraint = self.alpha * self.out_dim
self.num_blocks = self.dim
self.block_size = self.out_dim // self.dim
else:
self.constraint = None
self.block_size, self.num_blocks = factorization(self.out_dim, self.dim)
def calc_updown(self, orig_weight):
oft_blocks = self.oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)
eye = torch.eye(self.block_size, device=self.oft_blocks.device)
if self.is_kohya:
block_Q = oft_blocks - oft_blocks.transpose(1, 2) # ensure skew-symmetric orthogonal matrix
norm_Q = torch.norm(block_Q.flatten())
new_norm_Q = torch.clamp(norm_Q, max=self.constraint)
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, dtype=orig_weight.dtype)
# 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) ...')
updown = merged_weight.to(orig_weight.device, dtype=orig_weight.dtype) - orig_weight
output_shape = orig_weight.shape
return self.finalize_updown(updown, orig_weight, output_shape)

60
extensions-builtin/Lora/networks.py
View File

@ -5,16 +5,21 @@ 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
from lora_logger import logger
module_types = [
network_lora.ModuleTypeLora(),
@ -23,6 +28,8 @@ module_types = [
network_lokr.ModuleTypeLokr(),
network_full.ModuleTypeFull(),
network_norm.ModuleTypeNorm(),
network_glora.ModuleTypeGLora(),
network_oft.ModuleTypeOFT(),
]
@ -149,9 +156,20 @@ def load_network(name, network_on_disk):
is_sd2 = 'model_transformer_resblocks' in shared.sd_model.network_layer_mapping
matched_networks = {}
bundle_embeddings = {}
for key_network, weight in sd.items():
key_network_without_network_parts, network_part = key_network.split(".", 1)
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
key = convert_diffusers_name_to_compvis(key_network_without_network_parts, is_sd2)
sd_module = shared.sd_model.network_layer_mapping.get(key, None)
@ -174,6 +192,17 @@ def load_network(name, network_on_disk):
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
@ -195,6 +224,14 @@ def load_network(name, network_on_disk):
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
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}")
@ -210,11 +247,15 @@ def purge_networks_from_memory():
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()
@ -257,6 +298,21 @@ def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=No
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:
sd_hijack.model_hijack.comments.append("Networks not found: " + ", ".join(failed_to_load_networks))
@ -418,6 +474,7 @@ def network_forward(module, input, original_forward):
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):
@ -564,6 +621,7 @@ extra_network_lora = None
available_networks = {}
available_network_aliases = {}
loaded_networks = []
loaded_bundle_embeddings = {}
networks_in_memory = {}
available_network_hash_lookup = {}
forbidden_network_aliases = {}

7
extensions-builtin/Lora/ui_extra_networks_lora.py
View File

@ -17,6 +17,8 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
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)
@ -66,9 +68,10 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
return item
def list_items(self):
for index, name in enumerate(networks.available_networks):
# 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

16
extensions-builtin/extra-options-section/scripts/extra_options_section.py
View File

@ -23,11 +23,12 @@ class ExtraOptionsSection(scripts.Script):
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 generation_parameters_copypaste.infotext_to_setting_name_mapping}
with gr.Blocks() as interface:
with gr.Accordion("Options", open=False) if shared.opts.extra_options_accordion and extra_options else gr.Group():
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)
@ -64,11 +65,14 @@ class ExtraOptionsSection(scripts.Script):
p.override_settings[name] = value
shared.options_templates.update(shared.options_section(('ui', "User interface"), {
"extra_options_txt2img": shared.OptionInfo([], "Options in main UI - 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([], "Options in main UI - 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, "Options in main UI - number of columns", gr.Number, {"precision": 0}).needs_reload_ui(),
"extra_options_accordion": shared.OptionInfo(False, "Options in main UI - place into an accordion").needs_reload_ui()
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()
}))

351
extensions-builtin/hypertile/hypertile.py Normal file
View File

@ -0,0 +1,351 @@
"""
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

109
extensions-builtin/hypertile/scripts/hypertile_script.py Normal file
View File

@ -0,0 +1,109 @@
import hypertile
from modules import scripts, script_callbacks, shared
from scripts.hypertile_xyz import add_axis_options
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("""
<a href='https://github.com/tfernd/HyperTile'>Hypertile</a> 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)
script_callbacks.on_ui_settings(on_ui_settings)
script_callbacks.on_before_ui(add_axis_options)

51
extensions-builtin/hypertile/scripts/hypertile_xyz.py Normal file
View File

@ -0,0 +1,51 @@
from modules import scripts
from modules.shared import opts
xyz_grid = [x for x in scripts.scripts_data if x.script_class.__module__ == "xyz_grid.py"][0].module
def int_applier(value_name:str, min_range:int = -1, max_range:int = -1):
"""
Returns a function that applies the given value to the given value_name in opts.data.
"""
def validate(value_name:str, value:str):
value = int(value)
# validate value
if not min_range == -1:
assert value >= min_range, f"Value {value} for {value_name} must be greater than or equal to {min_range}"
if not max_range == -1:
assert value <= max_range, f"Value {value} for {value_name} must be less than or equal to {max_range}"
def apply_int(p, x, xs):
validate(value_name, x)
opts.data[value_name] = int(x)
return apply_int
def bool_applier(value_name:str):
"""
Returns a function that applies the given value to the given value_name in opts.data.
"""
def validate(value_name:str, value:str):
assert value.lower() in ["true", "false"], f"Value {value} for {value_name} must be either true or false"
def apply_bool(p, x, xs):
validate(value_name, x)
value_boolean = x.lower() == "true"
opts.data[value_name] = value_boolean
return apply_bool
def add_axis_options():
extra_axis_options = [
xyz_grid.AxisOption("[Hypertile] Unet First pass Enabled", str, bool_applier("hypertile_enable_unet"), choices=xyz_grid.boolean_choice(reverse=True)),
xyz_grid.AxisOption("[Hypertile] Unet Second pass Enabled", str, bool_applier("hypertile_enable_unet_secondpass"), choices=xyz_grid.boolean_choice(reverse=True)),
xyz_grid.AxisOption("[Hypertile] Unet Max Depth", int, int_applier("hypertile_max_depth_unet", 0, 3), choices=lambda: [str(x) for x in range(4)]),
xyz_grid.AxisOption("[Hypertile] Unet Max Tile Size", int, int_applier("hypertile_max_tile_unet", 0, 512)),
xyz_grid.AxisOption("[Hypertile] Unet Swap Size", int, int_applier("hypertile_swap_size_unet", 0, 64)),
xyz_grid.AxisOption("[Hypertile] VAE Enabled", str, bool_applier("hypertile_enable_vae"), choices=xyz_grid.boolean_choice(reverse=True)),
xyz_grid.AxisOption("[Hypertile] VAE Max Depth", int, int_applier("hypertile_max_depth_vae", 0, 3), choices=lambda: [str(x) for x in range(4)]),
xyz_grid.AxisOption("[Hypertile] VAE Max Tile Size", int, int_applier("hypertile_max_tile_vae", 0, 512)),
xyz_grid.AxisOption("[Hypertile] VAE Swap Size", int, int_applier("hypertile_swap_size_vae", 0, 64)),
]
set_a = {opt.label for opt in xyz_grid.axis_options}
set_b = {opt.label for opt in extra_axis_options}
if set_a.intersection(set_b):
return
xyz_grid.axis_options.extend(extra_axis_options)

2
extensions-builtin/mobile/javascript/mobile.js
View File

@ -12,6 +12,8 @@ function isMobile() {
}
function reportWindowSize() {
if (gradioApp().querySelector('.toprow-compact-tools')) return; // not applicable for compact prompt layout
var currentlyMobile = isMobile();
if (currentlyMobile == isSetupForMobile) return;
isSetupForMobile = currentlyMobile;

2
javascript/dragdrop.js vendored
View File

@ -119,7 +119,7 @@ window.addEventListener('paste', e => {
}
const firstFreeImageField = visibleImageFields
.filter(el => el.querySelector('input[type=file]'))?.[0];
.filter(el => !el.querySelector('img'))?.[0];
dropReplaceImage(
firstFreeImageField ?

79
javascript/edit-attention.js
View File

@ -18,37 +18,43 @@ function keyupEditAttention(event) {
const before = text.substring(0, selectionStart);
let beforeParen = before.lastIndexOf(OPEN);
if (beforeParen == -1) return false;
let beforeParenClose = before.lastIndexOf(CLOSE);
while (beforeParenClose !== -1 && beforeParenClose > beforeParen) {
beforeParen = before.lastIndexOf(OPEN, beforeParen - 1);
beforeParenClose = before.lastIndexOf(CLOSE, beforeParenClose - 1);
}
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 afterParenOpen = after.indexOf(OPEN);
while (afterParenOpen !== -1 && afterParen > afterParenOpen) {
afterParen = after.indexOf(CLOSE, afterParen + 1);
afterParenOpen = after.indexOf(OPEN, afterParenOpen + 1);
}
if (beforeParen === -1 || 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);
const lastColon = parenContent.lastIndexOf(":");
selectionStart = beforeParen + 1;
selectionEnd = selectionStart + lastColon;
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 delimiters = opts.keyedit_delimiters + " \r\n\t";
const whitespace_delimiters = {"Tab": "\t", "Carriage Return": "\r", "Line Feed": "\n"};
let delimiters = opts.keyedit_delimiters;
// seek backward until to find beggining
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--;
}
@ -63,7 +69,7 @@ function keyupEditAttention(event) {
}
// If the user hasn't selected anything, let's select their current parenthesis block or word
if (!selectCurrentParenthesisBlock('<', '>') && !selectCurrentParenthesisBlock('(', ')')) {
if (!selectCurrentParenthesisBlock('<', '>') && !selectCurrentParenthesisBlock('(', ')') && !selectCurrentParenthesisBlock('[', ']')) {
selectCurrentWord();
}
@ -71,33 +77,54 @@ function keyupEditAttention(event) {
var closeCharacter = ')';
var delta = opts.keyedit_precision_attention;
var start = selectionStart > 0 ? text[selectionStart - 1] : "";
var end = text[selectionEnd];
if (selectionStart > 0 && text[selectionStart - 1] == '<') {
if (start == '<') {
closeCharacter = '>';
delta = opts.keyedit_precision_extra;
} else if (selectionStart == 0 || text[selectionStart - 1] != "(") {
} 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 -= 1;
selectionEnd--;
}
if (selectionStart == selectionEnd) {
return;
}
text = text.slice(0, selectionStart) + "(" + text.slice(selectionStart, selectionEnd) + ":1.0)" + text.slice(selectionEnd);
selectionStart += 1;
selectionEnd += 1;
selectionStart++;
selectionEnd++;
}
var end = text.slice(selectionEnd + 1).indexOf(closeCharacter) + 1;
var weight = parseFloat(text.slice(selectionEnd + 1, selectionEnd + 1 + end));
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 (String(weight).length == 1) weight += ".0";
if (Number.isInteger(weight)) weight += ".0";
if (closeCharacter == ')' && weight == 1) {
var endParenPos = text.substring(selectionEnd).indexOf(')');
@ -105,7 +132,7 @@ function keyupEditAttention(event) {
selectionStart--;
selectionEnd--;
} else {
text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + end);
text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + weightLength);
}
target.focus();

95
javascript/extraNetworks.js
View File

@ -26,8 +26,9 @@ function setupExtraNetworksForTab(tabname) {
var refresh = gradioApp().getElementById(tabname + '_extra_refresh');
var showDirsDiv = gradioApp().getElementById(tabname + '_extra_show_dirs');
var showDirs = gradioApp().querySelector('#' + tabname + '_extra_show_dirs input');
var promptContainer = gradioApp().querySelector('.prompt-container-compact#' + tabname + '_prompt_container');
var negativePrompt = gradioApp().querySelector('#' + tabname + '_neg_prompt');
sort.dataset.sortkey = 'sortDefault';
tabs.appendChild(searchDiv);
tabs.appendChild(sort);
tabs.appendChild(sortOrder);
@ -49,20 +50,23 @@ function setupExtraNetworksForTab(tabname) {
elem.style.display = visible ? "" : "none";
});
applySort();
};
var applySort = function() {
var cards = gradioApp().querySelectorAll('#' + tabname + '_extra_tabs div.card');
var reverse = sortOrder.classList.contains("sortReverse");
var sortKey = sort.querySelector("input").value.toLowerCase().replace("sort", "").replaceAll(" ", "_").replace(/_+$/, "").trim();
sortKey = sortKey ? "sort" + sortKey.charAt(0).toUpperCase() + sortKey.slice(1) : "";
var sortKeyStore = sortKey ? sortKey + (reverse ? "Reverse" : "") : "";
if (!sortKey || sortKeyStore == sort.dataset.sortkey) {
var sortKey = sort.querySelector("input").value.toLowerCase().replace("sort", "").replaceAll(" ", "_").replace(/_+$/, "").trim() || "name";
sortKey = "sort" + sortKey.charAt(0).toUpperCase() + sortKey.slice(1);
var sortKeyStore = sortKey + "-" + (reverse ? "Descending" : "Ascending") + "-" + cards.length;
if (sortKeyStore == sort.dataset.sortkey) {
return;
}
sort.dataset.sortkey = sortKeyStore;
var cards = gradioApp().querySelectorAll('#' + tabname + '_extra_tabs div.card');
cards.forEach(function(card) {
card.originalParentElement = card.parentElement;
});
@ -88,15 +92,13 @@ function setupExtraNetworksForTab(tabname) {
};
search.addEventListener("input", applyFilter);
applyFilter();
["change", "blur", "click"].forEach(function(evt) {
sort.querySelector("input").addEventListener(evt, applySort);
});
sortOrder.addEventListener("click", function() {
sortOrder.classList.toggle("sortReverse");
applySort();
});
applyFilter();
extraNetworksApplySort[tabname] = applySort;
extraNetworksApplyFilter[tabname] = applyFilter;
var showDirsUpdate = function() {
@ -109,11 +111,51 @@ function setupExtraNetworksForTab(tabname) {
showDirsUpdate();
}
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 extraNetworksUrelatedTabSelected(tabname) { // called from python when user selects an unrelated tab (generate)
extraNetworksMovePromptToTab(tabname, '', false, false);
}
function extraNetworksTabSelected(tabname, id, showPrompt, showNegativePrompt) { // called from python when user selects an extra networks tab
extraNetworksMovePromptToTab(tabname, id, showPrompt, showNegativePrompt);
}
function applyExtraNetworkFilter(tabname) {
setTimeout(extraNetworksApplyFilter[tabname], 1);
}
function applyExtraNetworkSort(tabname) {
setTimeout(extraNetworksApplySort[tabname], 1);
}
var extraNetworksApplyFilter = {};
var extraNetworksApplySort = {};
var activePromptTextarea = {};
function setupExtraNetworks() {
@ -140,14 +182,15 @@ function setupExtraNetworks() {
onUiLoaded(setupExtraNetworks);
var re_extranet = /<([^:]+:[^:]+):[\d.]+>(.*)/;
var re_extranet_g = /\s+<([^:]+:[^:]+):[\d.]+>/g;
var re_extranet = /<([^:^>]+:[^:]+):[\d.]+>(.*)/;
var re_extranet_g = /<([^:^>]+:[^:]+):[\d.]+>/g;
function tryToRemoveExtraNetworkFromPrompt(textarea, text) {
var m = text.match(re_extranet);
var replaced = false;
var newTextareaText;
if (m) {
var extraTextBeforeNet = opts.extra_networks_add_text_separator;
var extraTextAfterNet = m[2];
var partToSearch = m[1];
var foundAtPosition = -1;
@ -161,8 +204,13 @@ function tryToRemoveExtraNetworkFromPrompt(textarea, text) {
return found;
});
if (foundAtPosition >= 0 && newTextareaText.substr(foundAtPosition, extraTextAfterNet.length) == extraTextAfterNet) {
newTextareaText = newTextareaText.substr(0, foundAtPosition) + newTextareaText.substr(foundAtPosition + extraTextAfterNet.length);
if (foundAtPosition >= 0) {
if (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(text, "g"), function(found) {
@ -216,27 +264,24 @@ function extraNetworksSearchButton(tabs_id, event) {
var globalPopup = null;
var globalPopupInner = null;
function closePopup() {
if (!globalPopup) return;
globalPopup.style.display = "none";
}
function popup(contents) {
if (!globalPopup) {
globalPopup = document.createElement('div');
globalPopup.onclick = closePopup;
globalPopup.classList.add('global-popup');
var close = document.createElement('div');
close.classList.add('global-popup-close');
close.onclick = closePopup;
close.addEventListener("click", closePopup);
close.title = "Close";
globalPopup.appendChild(close);
globalPopupInner = document.createElement('div');
globalPopupInner.onclick = function(event) {
event.stopPropagation(); return false;
};
globalPopupInner.classList.add('global-popup-inner');
globalPopup.appendChild(globalPopupInner);
@ -335,7 +380,7 @@ function extraNetworksEditUserMetadata(event, tabname, extraPage, cardName) {
function extraNetworksRefreshSingleCard(page, tabname, name) {
requestGet("./sd_extra_networks/get-single-card", {page: page, tabname: tabname, name: name}, function(data) {
if (data && data.html) {
var card = gradioApp().querySelector('.card[data-name=' + JSON.stringify(name) + ']'); // likely using the wrong stringify function
var card = gradioApp().querySelector(`#${tabname}_${page.replace(" ", "_")}_cards > .card[data-name="${name}"]`);
var newDiv = document.createElement('DIV');
newDiv.innerHTML = data.html;
@ -347,3 +392,9 @@ function extraNetworksRefreshSingleCard(page, tabname, name) {
}
});
}
window.addEventListener("keydown", function(event) {
if (event.key == "Escape") {
closePopup();
}
});

7
javascript/imageviewer.js
View File

@ -33,8 +33,11 @@ function updateOnBackgroundChange() {
const modalImage = gradioApp().getElementById("modalImage");
if (modalImage && modalImage.offsetParent) {
let currentButton = selected_gallery_button();
if (currentButton?.children?.length > 0 && modalImage.src != currentButton.children[0].src) {
let preview = gradioApp().querySelectorAll('.livePreview > img');
if (opts.js_live_preview_in_modal_lightbox && preview.length > 0) {
// show preview image if available
modalImage.src = preview[preview.length - 1].src;
} else if (currentButton?.children?.length > 0 && modalImage.src != currentButton.children[0].src) {
modalImage.src = currentButton.children[0].src;
if (modalImage.style.display === 'none') {
const modal = gradioApp().getElementById("lightboxModal");

89
javascript/inputAccordion.js
View File

@ -1,37 +1,68 @@
var observerAccordionOpen = new MutationObserver(function(mutations) {
mutations.forEach(function(mutationRecord) {
var elem = mutationRecord.target;
var open = elem.classList.contains('open');
var accordion = elem.parentNode;
accordion.classList.toggle('input-accordion-open', open);
var checkbox = gradioApp().querySelector('#' + accordion.id + "-checkbox input");
checkbox.checked = open;
updateInput(checkbox);
var extra = gradioApp().querySelector('#' + accordion.id + "-extra");
if (extra) {
extra.style.display = open ? "" : "none";
}
});
});
function inputAccordionChecked(id, checked) {
var label = gradioApp().querySelector('#' + id + " .label-wrap");
if (label.classList.contains('open') != checked) {
label.click();
var accordion = gradioApp().getElementById(id);
accordion.visibleCheckbox.checked = checked;
accordion.onVisibleCheckboxChange();
}
function setupAccordion(accordion) {
var labelWrap = accordion.querySelector('.label-wrap');
var gradioCheckbox = gradioApp().querySelector('#' + accordion.id + "-checkbox input");
var extra = gradioApp().querySelector('#' + accordion.id + "-extra");
var span = labelWrap.querySelector('span');
var linked = true;
var isOpen = function() {
return labelWrap.classList.contains('open');
};
var observerAccordionOpen = new MutationObserver(function(mutations) {
mutations.forEach(function(mutationRecord) {
accordion.classList.toggle('input-accordion-open', isOpen());
if (linked) {
accordion.visibleCheckbox.checked = isOpen();
accordion.onVisibleCheckboxChange();
}
});
});
observerAccordionOpen.observe(labelWrap, {attributes: true, attributeFilter: ['class']});
if (extra) {
labelWrap.insertBefore(extra, labelWrap.lastElementChild);
}
accordion.onChecked = function(checked) {
if (isOpen() != checked) {
labelWrap.click();
}
};
var visibleCheckbox = document.createElement('INPUT');
visibleCheckbox.type = 'checkbox';
visibleCheckbox.checked = isOpen();
visibleCheckbox.id = accordion.id + "-visible-checkbox";
visibleCheckbox.className = gradioCheckbox.className + " input-accordion-checkbox";
span.insertBefore(visibleCheckbox, span.firstChild);
accordion.visibleCheckbox = visibleCheckbox;
accordion.onVisibleCheckboxChange = function() {
if (linked && isOpen() != visibleCheckbox.checked) {
labelWrap.click();
}
gradioCheckbox.checked = visibleCheckbox.checked;
updateInput(gradioCheckbox);
};
visibleCheckbox.addEventListener('click', function(event) {
linked = false;
event.stopPropagation();
});
visibleCheckbox.addEventListener('input', accordion.onVisibleCheckboxChange);
}
onUiLoaded(function() {
for (var accordion of gradioApp().querySelectorAll('.input-accordion')) {
var labelWrap = accordion.querySelector('.label-wrap');
observerAccordionOpen.observe(labelWrap, {attributes: true, attributeFilter: ['class']});
var extra = gradioApp().querySelector('#' + accordion.id + "-extra");
if (extra) {
labelWrap.insertBefore(extra, labelWrap.lastElementChild);
}
setupAccordion(accordion);
}
});

6
javascript/notification.js
View File

@ -26,7 +26,11 @@ onAfterUiUpdate(function() {
lastHeadImg = headImg;
// play notification sound if available
gradioApp().querySelector('#audio_notification audio')?.play();
const notificationAudio = gradioApp().querySelector('#audio_notification audio');
if (notificationAudio) {
notificationAudio.volume = opts.notification_volume / 100.0 || 1.0;
notificationAudio.play();
}
if (document.hasFocus()) return;

71
javascript/settings.js Normal file
View File

@ -0,0 +1,71 @@
let settingsExcludeTabsFromShowAll = {
settings_tab_defaults: 1,
settings_tab_sysinfo: 1,
settings_tab_actions: 1,
settings_tab_licenses: 1,
};
function settingsShowAllTabs() {
gradioApp().querySelectorAll('#settings > div').forEach(function(elem) {
if (settingsExcludeTabsFromShowAll[elem.id]) return;
elem.style.display = "block";
});
}
function settingsShowOneTab() {
gradioApp().querySelector('#settings_show_one_page').click();
}
onUiLoaded(function() {
var edit = gradioApp().querySelector('#settings_search');
var editTextarea = gradioApp().querySelector('#settings_search > label > input');
var buttonShowAllPages = gradioApp().getElementById('settings_show_all_pages');
var settings_tabs = gradioApp().querySelector('#settings div');
onEdit('settingsSearch', editTextarea, 250, function() {
var searchText = (editTextarea.value || "").trim().toLowerCase();
gradioApp().querySelectorAll('#settings > div[id^=settings_] div[id^=column_settings_] > *').forEach(function(elem) {
var visible = elem.textContent.trim().toLowerCase().indexOf(searchText) != -1;
elem.style.display = visible ? "" : "none";
});
if (searchText != "") {
settingsShowAllTabs();
} else {
settingsShowOneTab();
}
});
settings_tabs.insertBefore(edit, settings_tabs.firstChild);
settings_tabs.appendChild(buttonShowAllPages);
buttonShowAllPages.addEventListener("click", settingsShowAllTabs);
});
onOptionsChanged(function() {
if (gradioApp().querySelector('#settings .settings-category')) return;
var sectionMap = {};
gradioApp().querySelectorAll('#settings > div > button').forEach(function(x) {
sectionMap[x.textContent.trim()] = x;
});
opts._categories.forEach(function(x) {
var section = x[0];
var category = x[1];
var span = document.createElement('SPAN');
span.textContent = category;
span.className = 'settings-category';
var sectionElem = sectionMap[section];
if (!sectionElem) return;
sectionElem.parentElement.insertBefore(span, sectionElem);
});
});

26
javascript/token-counters.js
View File

@ -1,10 +1,9 @@
let promptTokenCountDebounceTime = 800;
let promptTokenCountTimeouts = {};
var promptTokenCountUpdateFunctions = {};
let promptTokenCountUpdateFunctions = {};
function update_txt2img_tokens(...args) {
// Called from Gradio
update_token_counter("txt2img_token_button");
update_token_counter("txt2img_negative_token_button");
if (args.length == 2) {
return args[0];
}
@ -14,6 +13,7 @@ function update_txt2img_tokens(...args) {
function update_img2img_tokens(...args) {
// Called from Gradio
update_token_counter("img2img_token_button");
update_token_counter("img2img_negative_token_button");
if (args.length == 2) {
return args[0];
}
@ -21,16 +21,7 @@ function update_img2img_tokens(...args) {
}
function update_token_counter(button_id) {
if (opts.disable_token_counters) {
return;
}
if (promptTokenCountTimeouts[button_id]) {
clearTimeout(promptTokenCountTimeouts[button_id]);
}
promptTokenCountTimeouts[button_id] = setTimeout(
() => gradioApp().getElementById(button_id)?.click(),
promptTokenCountDebounceTime,
);
promptTokenCountUpdateFunctions[button_id]?.();
}
@ -69,10 +60,11 @@ function setupTokenCounting(id, id_counter, id_button) {
prompt.parentElement.insertBefore(counter, prompt);
prompt.parentElement.style.position = "relative";
promptTokenCountUpdateFunctions[id] = function() {
update_token_counter(id_button);
};
textarea.addEventListener("input", promptTokenCountUpdateFunctions[id]);
var func = onEdit(id, textarea, 800, function() {
gradioApp().getElementById(id_button)?.click();
});
promptTokenCountUpdateFunctions[id] = func;
promptTokenCountUpdateFunctions[id_button] = func;
}
function setupTokenCounters() {

73
javascript/ui.js
View File

@ -170,6 +170,23 @@ function submit_img2img() {
return res;
}
function submit_extras() {
showSubmitButtons('extras', false);
var id = randomId();
requestProgress(id, gradioApp().getElementById('extras_gallery_container'), gradioApp().getElementById('extras_gallery'), function() {
showSubmitButtons('extras', true);
});
var res = create_submit_args(arguments);
res[0] = id;
console.log(res);
return res;
}
function restoreProgressTxt2img() {
showRestoreProgressButton("txt2img", false);
var id = localGet("txt2img_task_id");
@ -198,9 +215,33 @@ function restoreProgressImg2img() {
}
/**
* Configure the width and height elements on `tabname` to accept
* pasting of resolutions in the form of "width x height".
*/
function setupResolutionPasting(tabname) {
var width = gradioApp().querySelector(`#${tabname}_width input[type=number]`);
var height = gradioApp().querySelector(`#${tabname}_height input[type=number]`);
for (const el of [width, height]) {
el.addEventListener('paste', function(event) {
var pasteData = event.clipboardData.getData('text/plain');
var parsed = pasteData.match(/^\s*(\d+)\D+(\d+)\s*$/);
if (parsed) {
width.value = parsed[1];
height.value = parsed[2];
updateInput(width);
updateInput(height);
event.preventDefault();
}
});
}
}
onUiLoaded(function() {
showRestoreProgressButton('txt2img', localGet("txt2img_task_id"));
showRestoreProgressButton('img2img', localGet("img2img_task_id"));
setupResolutionPasting('txt2img');
setupResolutionPasting('img2img');
});
@ -263,21 +304,6 @@ onAfterUiUpdate(function() {
json_elem.parentElement.style.display = "none";
setupTokenCounters();
var show_all_pages = gradioApp().getElementById('settings_show_all_pages');
var settings_tabs = gradioApp().querySelector('#settings div');
if (show_all_pages && settings_tabs) {
settings_tabs.appendChild(show_all_pages);
show_all_pages.onclick = function() {
gradioApp().querySelectorAll('#settings > div').forEach(function(elem) {
if (elem.id == "settings_tab_licenses") {
return;
}
elem.style.display = "block";
});
};
}
});
onOptionsChanged(function() {
@ -366,3 +392,20 @@ function switchWidthHeight(tabname) {
updateInput(height);
return [];
}
var onEditTimers = {};
// calls func after afterMs milliseconds has passed since the input elem has beed enited by user
function onEdit(editId, elem, afterMs, func) {
var edited = function() {
var existingTimer = onEditTimers[editId];
if (existingTimer) clearTimeout(existingTimer);
onEditTimers[editId] = setTimeout(func, afterMs);
};
elem.addEventListener("input", edited);
return edited;
}

81
modules/api/api.py
View File

@ -17,19 +17,17 @@ from fastapi.encoders import jsonable_encoder
from secrets import compare_digest
import modules.shared as shared
from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors, restart, shared_items
from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors, restart, shared_items, script_callbacks, generation_parameters_copypaste, sd_models
from modules.api import models
from modules.shared import opts
from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
from modules.textual_inversion.textual_inversion import create_embedding, train_embedding
from modules.textual_inversion.preprocess import preprocess
from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork
from PIL import PngImagePlugin,Image
from modules.sd_models import unload_model_weights, reload_model_weights, checkpoint_aliases
from PIL import PngImagePlugin, Image
from modules.sd_models_config import find_checkpoint_config_near_filename
from modules.realesrgan_model import get_realesrgan_models
from modules import devices
from typing import Dict, List, Any
from typing import Any
import piexif
import piexif.helper
from contextlib import closing
@ -103,7 +101,8 @@ def decode_base64_to_image(encoding):
def encode_pil_to_base64(image):
with io.BytesIO() as output_bytes:
if isinstance(image, str):
return image
if opts.samples_format.lower() == 'png':
use_metadata = False
metadata = PngImagePlugin.PngInfo()
@ -221,28 +220,28 @@ class Api:
self.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=models.OptionsModel)
self.add_api_route("/sdapi/v1/options", self.set_config, methods=["POST"])
self.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=models.FlagsModel)
self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[models.SamplerItem])
self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[models.UpscalerItem])
self.add_api_route("/sdapi/v1/latent-upscale-modes", self.get_latent_upscale_modes, methods=["GET"], response_model=List[models.LatentUpscalerModeItem])
self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[models.SDModelItem])
self.add_api_route("/sdapi/v1/sd-vae", self.get_sd_vaes, methods=["GET"], response_model=List[models.SDVaeItem])
self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[models.HypernetworkItem])
self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[models.FaceRestorerItem])
self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[models.RealesrganItem])
self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=List[models.PromptStyleItem])
self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=list[models.SamplerItem])
self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=list[models.UpscalerItem])
self.add_api_route("/sdapi/v1/latent-upscale-modes", self.get_latent_upscale_modes, methods=["GET"], response_model=list[models.LatentUpscalerModeItem])
self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=list[models.SDModelItem])
self.add_api_route("/sdapi/v1/sd-vae", self.get_sd_vaes, methods=["GET"], response_model=list[models.SDVaeItem])
self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=list[models.HypernetworkItem])
self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=list[models.FaceRestorerItem])
self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=list[models.RealesrganItem])
self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=list[models.PromptStyleItem])
self.add_api_route("/sdapi/v1/embeddings", self.get_embeddings, methods=["GET"], response_model=models.EmbeddingsResponse)
self.add_api_route("/sdapi/v1/refresh-checkpoints", self.refresh_checkpoints, methods=["POST"])
self.add_api_route("/sdapi/v1/refresh-vae", self.refresh_vae, methods=["POST"])
self.add_api_route("/sdapi/v1/create/embedding", self.create_embedding, methods=["POST"], response_model=models.CreateResponse)
self.add_api_route("/sdapi/v1/create/hypernetwork", self.create_hypernetwork, methods=["POST"], response_model=models.CreateResponse)
self.add_api_route("/sdapi/v1/preprocess", self.preprocess, methods=["POST"], response_model=models.PreprocessResponse)
self.add_api_route("/sdapi/v1/train/embedding", self.train_embedding, methods=["POST"], response_model=models.TrainResponse)
self.add_api_route("/sdapi/v1/train/hypernetwork", self.train_hypernetwork, methods=["POST"], response_model=models.TrainResponse)
self.add_api_route("/sdapi/v1/memory", self.get_memory, methods=["GET"], response_model=models.MemoryResponse)
self.add_api_route("/sdapi/v1/unload-checkpoint", self.unloadapi, methods=["POST"])
self.add_api_route("/sdapi/v1/reload-checkpoint", self.reloadapi, methods=["POST"])
self.add_api_route("/sdapi/v1/scripts", self.get_scripts_list, methods=["GET"], response_model=models.ScriptsList)
self.add_api_route("/sdapi/v1/script-info", self.get_script_info, methods=["GET"], response_model=List[models.ScriptInfo])
self.add_api_route("/sdapi/v1/script-info", self.get_script_info, methods=["GET"], response_model=list[models.ScriptInfo])
self.add_api_route("/sdapi/v1/extensions", self.get_extensions_list, methods=["GET"], response_model=list[models.ExtensionItem])
if shared.cmd_opts.api_server_stop:
self.add_api_route("/sdapi/v1/server-kill", self.kill_webui, methods=["POST"])
@ -473,9 +472,6 @@ class Api:
return models.ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])
def pnginfoapi(self, req: models.PNGInfoRequest):
if(not req.image.strip()):
return models.PNGInfoResponse(info="")
image = decode_base64_to_image(req.image.strip())
if image is None:
return models.PNGInfoResponse(info="")
@ -484,9 +480,10 @@ class Api:
if geninfo is None:
geninfo = ""
items = {**{'parameters': geninfo}, **items}
params = generation_parameters_copypaste.parse_generation_parameters(geninfo)
script_callbacks.infotext_pasted_callback(geninfo, params)
return models.PNGInfoResponse(info=geninfo, items=items)
return models.PNGInfoResponse(info=geninfo, items=items, parameters=params)
def progressapi(self, req: models.ProgressRequest = Depends()):
# copy from check_progress_call of ui.py
@ -541,12 +538,12 @@ class Api:
return {}
def unloadapi(self):
unload_model_weights()
sd_models.unload_model_weights()
return {}
def reloadapi(self):
reload_model_weights()
sd_models.send_model_to_device(shared.sd_model)
return {}
@ -564,9 +561,9 @@ class Api:
return options
def set_config(self, req: Dict[str, Any]):
def set_config(self, req: dict[str, Any]):
checkpoint_name = req.get("sd_model_checkpoint", None)
if checkpoint_name is not None and checkpoint_name not in checkpoint_aliases:
if checkpoint_name is not None and checkpoint_name not in sd_models.checkpoint_aliases:
raise RuntimeError(f"model {checkpoint_name!r} not found")
for k, v in req.items():
@ -676,19 +673,6 @@ class Api:
finally:
shared.state.end()
def preprocess(self, args: dict):
try:
shared.state.begin(job="preprocess")
preprocess(**args) # quick operation unless blip/booru interrogation is enabled
shared.state.end()
return models.PreprocessResponse(info='preprocess complete')
except KeyError as e:
return models.PreprocessResponse(info=f"preprocess error: invalid token: {e}")
except Exception as e:
return models.PreprocessResponse(info=f"preprocess error: {e}")
finally:
shared.state.end()
def train_embedding(self, args: dict):
try:
shared.state.begin(job="train_embedding")
@ -770,6 +754,25 @@ class Api:
cuda = {'error': f'{err}'}
return models.MemoryResponse(ram=ram, cuda=cuda)
def get_extensions_list(self):
from modules import extensions
extensions.list_extensions()
ext_list = []
for ext in extensions.extensions:
ext: extensions.Extension
ext.read_info_from_repo()
if ext.remote is not None:
ext_list.append({
"name": ext.name,
"remote": ext.remote,
"branch": ext.branch,
"commit_hash":ext.commit_hash,
"commit_date":ext.commit_date,
"version":ext.version,
"enabled":ext.enabled
})
return ext_list
def launch(self, server_name, port, root_path):
self.app.include_router(self.router)
uvicorn.run(self.app, host=server_name, port=port, timeout_keep_alive=shared.cmd_opts.timeout_keep_alive, root_path=root_path)

39
modules/api/models.py
View File

@ -1,12 +1,10 @@
import inspect
from pydantic import BaseModel, Field, create_model
from typing import Any, Optional
from typing_extensions import Literal
from typing import Any, Optional, Literal
from inflection import underscore
from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
from modules.shared import sd_upscalers, opts, parser
from typing import Dict, List
API_NOT_ALLOWED = [
"self",
@ -130,12 +128,12 @@ StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
).generate_model()
class TextToImageResponse(BaseModel):
images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
parameters: dict
info: str
class ImageToImageResponse(BaseModel):
images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
parameters: dict
info: str
@ -168,17 +166,18 @@ class FileData(BaseModel):
name: str = Field(title="File name")
class ExtrasBatchImagesRequest(ExtrasBaseRequest):
imageList: List[FileData] = Field(title="Images", description="List of images to work on. Must be Base64 strings")
imageList: list[FileData] = Field(title="Images", description="List of images to work on. Must be Base64 strings")
class ExtrasBatchImagesResponse(ExtraBaseResponse):
images: List[str] = Field(title="Images", description="The generated images in base64 format.")
images: list[str] = Field(title="Images", description="The generated images in base64 format.")
class PNGInfoRequest(BaseModel):
image: str = Field(title="Image", description="The base64 encoded PNG image")
class PNGInfoResponse(BaseModel):
info: str = Field(title="Image info", description="A string with the parameters used to generate the image")
items: dict = Field(title="Items", description="An object containing all the info the image had")
items: dict = Field(title="Items", description="A dictionary containing all the other fields the image had")
parameters: dict = Field(title="Parameters", description="A dictionary with parsed generation info fields")
class ProgressRequest(BaseModel):
skip_current_image: bool = Field(default=False, title="Skip current image", description="Skip current image serialization")
@ -203,9 +202,6 @@ class TrainResponse(BaseModel):
class CreateResponse(BaseModel):
info: str = Field(title="Create info", description="Response string from create embedding or hypernetwork task.")
class PreprocessResponse(BaseModel):
info: str = Field(title="Preprocess info", description="Response string from preprocessing task.")
fields = {}
for key, metadata in opts.data_labels.items():
value = opts.data.get(key)
@ -232,8 +228,8 @@ FlagsModel = create_model("Flags", **flags)
class SamplerItem(BaseModel):
name: str = Field(title="Name")
aliases: List[str] = Field(title="Aliases")
options: Dict[str, str] = Field(title="Options")
aliases: list[str] = Field(title="Aliases")
options: dict[str, str] = Field(title="Options")
class UpscalerItem(BaseModel):
name: str = Field(title="Name")
@ -284,8 +280,8 @@ class EmbeddingItem(BaseModel):
vectors: int = Field(title="Vectors", description="The number of vectors in the embedding")
class EmbeddingsResponse(BaseModel):
loaded: Dict[str, EmbeddingItem] = Field(title="Loaded", description="Embeddings loaded for the current model")
skipped: Dict[str, EmbeddingItem] = Field(title="Skipped", description="Embeddings skipped for the current model (likely due to architecture incompatibility)")
loaded: dict[str, EmbeddingItem] = Field(title="Loaded", description="Embeddings loaded for the current model")
skipped: dict[str, EmbeddingItem] = Field(title="Skipped", description="Embeddings skipped for the current model (likely due to architecture incompatibility)")
class MemoryResponse(BaseModel):
ram: dict = Field(title="RAM", description="System memory stats")
@ -303,11 +299,20 @@ class ScriptArg(BaseModel):
minimum: Optional[Any] = Field(default=None, title="Minimum", description="Minimum allowed value for the argumentin UI")
maximum: Optional[Any] = Field(default=None, title="Minimum", description="Maximum allowed value for the argumentin UI")
step: Optional[Any] = Field(default=None, title="Minimum", description="Step for changing value of the argumentin UI")
choices: Optional[List[str]] = Field(default=None, title="Choices", description="Possible values for the argument")
choices: Optional[list[str]] = Field(default=None, title="Choices", description="Possible values for the argument")
class ScriptInfo(BaseModel):
name: str = Field(default=None, title="Name", description="Script name")
is_alwayson: bool = Field(default=None, title="IsAlwayson", description="Flag specifying whether this script is an alwayson script")
is_img2img: bool = Field(default=None, title="IsImg2img", description="Flag specifying whether this script is an img2img script")
args: List[ScriptArg] = Field(title="Arguments", description="List of script's arguments")
args: list[ScriptArg] = Field(title="Arguments", description="List of script's arguments")
class ExtensionItem(BaseModel):
name: str = Field(title="Name", description="Extension name")
remote: str = Field(title="Remote", description="Extension Repository URL")
branch: str = Field(title="Branch", description="Extension Repository Branch")
commit_hash: str = Field(title="Commit Hash", description="Extension Repository Commit Hash")
version: str = Field(title="Version", description="Extension Version")
commit_date: str = Field(title="Commit Date", description="Extension Repository Commit Date")
enabled: bool = Field(title="Enabled", description="Flag specifying whether this extension is enabled")

2
modules/cache.py
View File

@ -32,7 +32,7 @@ def dump_cache():
with cache_lock:
cache_filename_tmp = cache_filename + "-"
with open(cache_filename_tmp, "w", encoding="utf8") as file:
json.dump(cache_data, file, indent=4)
json.dump(cache_data, file, indent=4, ensure_ascii=False)
os.replace(cache_filename_tmp, cache_filename)

10
modules/cmd_args.py
View File

@ -70,6 +70,7 @@ parser.add_argument("--opt-sdp-no-mem-attention", action='store_true', help="pre
parser.add_argument("--disable-opt-split-attention", action='store_true', help="prefer no cross-attention layer optimization for automatic choice of optimization")
parser.add_argument("--disable-nan-check", action='store_true', help="do not check if produced images/latent spaces have nans; useful for running without a checkpoint in CI")
parser.add_argument("--use-cpu", nargs='+', help="use CPU as torch device for specified modules", default=[], type=str.lower)
parser.add_argument("--use-ipex", action="store_true", help="use Intel XPU as torch device")
parser.add_argument("--disable-model-loading-ram-optimization", action='store_true', help="disable an optimization that reduces RAM use when loading a model")
parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)
@ -90,7 +91,7 @@ parser.add_argument("--autolaunch", action='store_true', help="open the webui UR
parser.add_argument("--theme", type=str, help="launches the UI with light or dark theme", default=None)
parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False)
parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
parser.add_argument("--enable-console-prompts", action='store_true', help="does not do anything", default=False) # Legacy compatibility, use as default value shared.opts.enable_console_prompts
parser.add_argument('--vae-path', type=str, help='Checkpoint to use as VAE; setting this argument disables all settings related to VAE', default=None)
parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
parser.add_argument("--api", action='store_true', help="use api=True to launch the API together with the webui (use --nowebui instead for only the API)")
@ -107,13 +108,14 @@ parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, req
parser.add_argument("--disable-tls-verify", action="store_false", help="When passed, enables the use of self-signed certificates.", default=None)
parser.add_argument("--server-name", type=str, help="Sets hostname of server", default=None)
parser.add_argument("--gradio-queue", action='store_true', help="does not do anything", default=True)
parser.add_argument("--no-gradio-queue", action='store_true', help="Disables gradio queue; causes the webpage to use http requests instead of websockets; was the defaul in earlier versions")
parser.add_argument("--no-gradio-queue", action='store_true', help="Disables gradio queue; causes the webpage to use http requests instead of websockets; was the default in earlier versions")
parser.add_argument("--skip-version-check", action='store_true', help="Do not check versions of torch and xformers")
parser.add_argument("--no-hashing", action='store_true', help="disable sha256 hashing of checkpoints to help loading performance", default=False)
parser.add_argument("--no-download-sd-model", action='store_true', help="don't download SD1.5 model even if no model is found in --ckpt-dir", default=False)
parser.add_argument('--subpath', type=str, help='customize the subpath for gradio, use with reverse proxy')
parser.add_argument('--add-stop-route', action='store_true', help='add /_stop route to stop server')
parser.add_argument('--add-stop-route', action='store_true', help='does not do anything')
parser.add_argument('--api-server-stop', action='store_true', help='enable server stop/restart/kill via api')
parser.add_argument('--timeout-keep-alive', type=int, default=30, help='set timeout_keep_alive for uvicorn')
parser.add_argument("--disable-all-extensions", action='store_true', help="prevent all extensions from running regardless of any other settings", default=False)
parser.add_argument("--disable-extra-extensions", action='store_true', help=" prevent all extensions except built-in from running regardless of any other settings", default=False)
parser.add_argument("--disable-extra-extensions", action='store_true', help="prevent all extensions except built-in from running regardless of any other settings", default=False)
parser.add_argument("--skip-load-model-at-start", action='store_true', help="if load a model at web start, only take effect when --nowebui", )

3
modules/config_states.py
View File

@ -4,7 +4,6 @@ Supports saving and restoring webui and extensions from a known working set of c
import os
import json
import time
import tqdm
from datetime import datetime
@ -38,7 +37,7 @@ def list_config_states():
config_states = sorted(config_states, key=lambda cs: cs["created_at"], reverse=True)
for cs in config_states:
timestamp = time.asctime(time.gmtime(cs["created_at"]))
timestamp = datetime.fromtimestamp(cs["created_at"]).strftime('%Y-%m-%d %H:%M:%S')
name = cs.get("name", "Config")
full_name = f"{name}: {timestamp}"
all_config_states[full_name] = cs

18
modules/devices.py
View File

@ -8,6 +8,13 @@ from modules import errors, shared
if sys.platform == "darwin":
from modules import mac_specific
if shared.cmd_opts.use_ipex:
from modules import xpu_specific
def has_xpu() -> bool:
return shared.cmd_opts.use_ipex and xpu_specific.has_xpu
def has_mps() -> bool:
if sys.platform != "darwin":
@ -30,6 +37,9 @@ def get_optimal_device_name():
if has_mps():
return "mps"
if has_xpu():
return xpu_specific.get_xpu_device_string()
return "cpu"
@ -38,7 +48,7 @@ def get_optimal_device():
def get_device_for(task):
if task in shared.cmd_opts.use_cpu:
if task in shared.cmd_opts.use_cpu or "all" in shared.cmd_opts.use_cpu:
return cpu
return get_optimal_device()
@ -54,13 +64,17 @@ def torch_gc():
if has_mps():
mac_specific.torch_mps_gc()
if has_xpu():
xpu_specific.torch_xpu_gc()
def enable_tf32():
if torch.cuda.is_available():
# enabling benchmark option seems to enable a range of cards to do fp16 when they otherwise can't
# see https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/4407
if any(torch.cuda.get_device_capability(devid) == (7, 5) for devid in range(0, torch.cuda.device_count())):
device_id = (int(shared.cmd_opts.device_id) if shared.cmd_opts.device_id is not None and shared.cmd_opts.device_id.isdigit() else 0) or torch.cuda.current_device()
if torch.cuda.get_device_capability(device_id) == (7, 5) and torch.cuda.get_device_name(device_id).startswith("NVIDIA GeForce GTX 16"):
torch.backends.cudnn.benchmark = True
torch.backends.cuda.matmul.allow_tf32 = True

18
modules/errors.py
View File

@ -6,6 +6,21 @@ import traceback
exception_records = []
def format_traceback(tb):
return [[f"{x.filename}, line {x.lineno}, {x.name}", x.line] for x in traceback.extract_tb(tb)]
def format_exception(e, tb):
return {"exception": str(e), "traceback": format_traceback(tb)}
def get_exceptions():
try:
return list(reversed(exception_records))
except Exception as e:
return str(e)
def record_exception():
_, e, tb = sys.exc_info()
if e is None:
@ -14,8 +29,7 @@ def record_exception():
if exception_records and exception_records[-1] == e:
return
from modules import sysinfo
exception_records.append(sysinfo.format_exception(e, tb))
exception_records.append(format_exception(e, tb))
if len(exception_records) > 5:
exception_records.pop(0)

96
modules/extensions.py
View File

@ -1,11 +1,14 @@
from __future__ import annotations
import configparser
import os
import threading
import re
from modules import shared, errors, cache, scripts
from modules.gitpython_hack import Repo
from modules.paths_internal import extensions_dir, extensions_builtin_dir, script_path # noqa: F401
extensions = []
os.makedirs(extensions_dir, exist_ok=True)
@ -19,11 +22,55 @@ def active():
return [x for x in extensions if x.enabled]
class ExtensionMetadata:
filename = "metadata.ini"
config: configparser.ConfigParser
canonical_name: str
requires: list
def __init__(self, path, canonical_name):
self.config = configparser.ConfigParser()
filepath = os.path.join(path, self.filename)
if os.path.isfile(filepath):
try:
self.config.read(filepath)
except Exception:
errors.report(f"Error reading {self.filename} for extension {canonical_name}.", exc_info=True)
self.canonical_name = self.config.get("Extension", "Name", fallback=canonical_name)
self.canonical_name = canonical_name.lower().strip()
self.requires = self.get_script_requirements("Requires", "Extension")
def get_script_requirements(self, field, section, extra_section=None):
"""reads a list of requirements from the config; field is the name of the field in the ini file,
like Requires or Before, and section is the name of the [section] in the ini file; additionally,
reads more requirements from [extra_section] if specified."""
x = self.config.get(section, field, fallback='')
if extra_section:
x = x + ', ' + self.config.get(extra_section, field, fallback='')
return self.parse_list(x.lower())
def parse_list(self, text):
"""converts a line from config ("ext1 ext2, ext3 ") into a python list (["ext1", "ext2", "ext3"])"""
if not text:
return []
# both "," and " " are accepted as separator
return [x for x in re.split(r"[,\s]+", text.strip()) if x]
class Extension:
lock = threading.Lock()
cached_fields = ['remote', 'commit_date', 'branch', 'commit_hash', 'version']
metadata: ExtensionMetadata
def __init__(self, name, path, enabled=True, is_builtin=False):
def __init__(self, name, path, enabled=True, is_builtin=False, metadata=None):
self.name = name
self.path = path
self.enabled = enabled
@ -36,6 +83,8 @@ class Extension:
self.branch = None
self.remote = None
self.have_info_from_repo = False
self.metadata = metadata if metadata else ExtensionMetadata(self.path, name.lower())
self.canonical_name = metadata.canonical_name
def to_dict(self):
return {x: getattr(self, x) for x in self.cached_fields}
@ -56,6 +105,7 @@ class Extension:
self.do_read_info_from_repo()
return self.to_dict()
try:
d = cache.cached_data_for_file('extensions-git', self.name, os.path.join(self.path, ".git"), read_from_repo)
self.from_dict(d)
@ -136,9 +186,6 @@ class Extension:
def list_extensions():
extensions.clear()
if not os.path.isdir(extensions_dir):
return
if shared.cmd_opts.disable_all_extensions:
print("*** \"--disable-all-extensions\" arg was used, will not load any extensions ***")
elif shared.opts.disable_all_extensions == "all":
@ -148,18 +195,43 @@ def list_extensions():
elif shared.opts.disable_all_extensions == "extra":
print("*** \"Disable all extensions\" option was set, will only load built-in extensions ***")
extension_paths = []
for dirname in [extensions_dir, extensions_builtin_dir]:
loaded_extensions = {}
# scan through extensions directory and load metadata
for dirname in [extensions_builtin_dir, extensions_dir]:
if not os.path.isdir(dirname):
return
continue
for extension_dirname in sorted(os.listdir(dirname)):
path = os.path.join(dirname, extension_dirname)
if not os.path.isdir(path):
continue
extension_paths.append((extension_dirname, path, dirname == extensions_builtin_dir))
canonical_name = extension_dirname
metadata = ExtensionMetadata(path, canonical_name)
for dirname, path, is_builtin in extension_paths:
extension = Extension(name=dirname, path=path, enabled=dirname not in shared.opts.disabled_extensions, is_builtin=is_builtin)
extensions.append(extension)
# check for duplicated canonical names
already_loaded_extension = loaded_extensions.get(metadata.canonical_name)
if already_loaded_extension is not None:
errors.report(f'Duplicate canonical name "{canonical_name}" found in extensions "{extension_dirname}" and "{already_loaded_extension.name}". Former will be discarded.', exc_info=False)
continue
is_builtin = dirname == extensions_builtin_dir
extension = Extension(name=extension_dirname, path=path, enabled=extension_dirname not in shared.opts.disabled_extensions, is_builtin=is_builtin, metadata=metadata)
extensions.append(extension)
loaded_extensions[canonical_name] = extension
# check for requirements
for extension in extensions:
for req in extension.metadata.requires:
required_extension = loaded_extensions.get(req)
if required_extension is None:
errors.report(f'Extension "{extension.name}" requires "{req}" which is not installed.', exc_info=False)
continue
if not extension.enabled:
errors.report(f'Extension "{extension.name}" requires "{required_extension.name}" which is disabled.', exc_info=False)
continue
extensions: list[Extension] = []

15
modules/generation_parameters_copypaste.py
View File

@ -1,3 +1,4 @@
from __future__ import annotations
import base64
import io
import json
@ -9,15 +10,12 @@ from modules.paths import data_path
from modules import shared, ui_tempdir, script_callbacks, processing
from PIL import Image
re_param_code = r'\s*([\w ]+):\s*("(?:\\.|[^\\"])+"|[^,]*)(?:,|$)'
re_param_code = r'\s*(\w[\w \-/]+):\s*("(?:\\.|[^\\"])+"|[^,]*)(?:,|$)'
re_param = re.compile(re_param_code)
re_imagesize = re.compile(r"^(\d+)x(\d+)$")
re_hypernet_hash = re.compile("\(([0-9a-f]+)\)$")
type_of_gr_update = type(gr.update())
paste_fields = {}
registered_param_bindings = []
class ParamBinding:
def __init__(self, paste_button, tabname, source_text_component=None, source_image_component=None, source_tabname=None, override_settings_component=None, paste_field_names=None):
@ -30,6 +28,10 @@ class ParamBinding:
self.paste_field_names = paste_field_names or []
paste_fields: dict[str, dict] = {}
registered_param_bindings: list[ParamBinding] = []
def reset():
paste_fields.clear()
registered_param_bindings.clear()
@ -113,7 +115,6 @@ def register_paste_params_button(binding: ParamBinding):
def connect_paste_params_buttons():
binding: ParamBinding
for binding in registered_param_bindings:
destination_image_component = paste_fields[binding.tabname]["init_img"]
fields = paste_fields[binding.tabname]["fields"]
@ -313,6 +314,9 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
if "VAE Decoder" not in res:
res["VAE Decoder"] = "Full"
skip = set(shared.opts.infotext_skip_pasting)
res = {k: v for k, v in res.items() if k not in skip}
return res
@ -443,3 +447,4 @@ def connect_paste(button, paste_fields, input_comp, override_settings_component,
outputs=[],
show_progress=False,
)

25
modules/gfpgan_model.py
View File

@ -9,6 +9,7 @@ from modules import paths, shared, devices, modelloader, errors
model_dir = "GFPGAN"
user_path = None
model_path = os.path.join(paths.models_path, model_dir)
model_file_path = None
model_url = "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth"
have_gfpgan = False
loaded_gfpgan_model = None
@ -17,6 +18,7 @@ loaded_gfpgan_model = None
def gfpgann():
global loaded_gfpgan_model
global model_path
global model_file_path
if loaded_gfpgan_model is not None:
loaded_gfpgan_model.gfpgan.to(devices.device_gfpgan)
return loaded_gfpgan_model
@ -24,17 +26,24 @@ def gfpgann():
if gfpgan_constructor is None:
return None
models = modelloader.load_models(model_path, model_url, user_path, ext_filter="GFPGAN")
models = modelloader.load_models(model_path, model_url, user_path, ext_filter=['.pth'])
if len(models) == 1 and models[0].startswith("http"):
model_file = models[0]
elif len(models) != 0:
latest_file = max(models, key=os.path.getctime)
gfp_models = []
for item in models:
if 'GFPGAN' in os.path.basename(item):
gfp_models.append(item)
latest_file = max(gfp_models, key=os.path.getctime)
model_file = latest_file
else:
print("Unable to load gfpgan model!")
return None
if hasattr(facexlib.detection.retinaface, 'device'):
facexlib.detection.retinaface.device = devices.device_gfpgan
model_file_path = model_file
model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=devices.device_gfpgan)
loaded_gfpgan_model = model
@ -77,19 +86,25 @@ def setup_model(dirname):
global user_path
global have_gfpgan
global gfpgan_constructor
global model_file_path
facexlib_path = model_path
if dirname is not None:
facexlib_path = dirname
load_file_from_url_orig = gfpgan.utils.load_file_from_url
facex_load_file_from_url_orig = facexlib.detection.load_file_from_url
facex_load_file_from_url_orig2 = facexlib.parsing.load_file_from_url
def my_load_file_from_url(**kwargs):
return load_file_from_url_orig(**dict(kwargs, model_dir=model_path))
return load_file_from_url_orig(**dict(kwargs, model_dir=model_file_path))
def facex_load_file_from_url(**kwargs):
return facex_load_file_from_url_orig(**dict(kwargs, save_dir=model_path, model_dir=None))
return facex_load_file_from_url_orig(**dict(kwargs, save_dir=facexlib_path, model_dir=None))
def facex_load_file_from_url2(**kwargs):
return facex_load_file_from_url_orig2(**dict(kwargs, save_dir=model_path, model_dir=None))
return facex_load_file_from_url_orig2(**dict(kwargs, save_dir=facexlib_path, model_dir=None))
gfpgan.utils.load_file_from_url = my_load_file_from_url
facexlib.detection.load_file_from_url = facex_load_file_from_url

2
modules/gitpython_hack.py
View File

@ -23,7 +23,7 @@ class Git(git.Git):
)
return self._parse_object_header(ret)
def stream_object_data(self, ref: str) -> tuple[str, str, int, "Git.CatFileContentStream"]:
def stream_object_data(self, ref: str) -> tuple[str, str, int, Git.CatFileContentStream]:
# Not really streaming, per se; this buffers the entire object in memory.
# Shouldn't be a problem for our use case, since we're only using this for
# object headers (commit objects).

10
modules/gradio_extensons.py
View File

@ -47,10 +47,20 @@ def Block_get_config(self):
def BlockContext_init(self, *args, **kwargs):
if scripts.scripts_current is not None:
scripts.scripts_current.before_component(self, **kwargs)
scripts.script_callbacks.before_component_callback(self, **kwargs)
res = original_BlockContext_init(self, *args, **kwargs)
add_classes_to_gradio_component(self)
scripts.script_callbacks.after_component_callback(self, **kwargs)
if scripts.scripts_current is not None:
scripts.scripts_current.after_component(self, **kwargs)
return res

4
modules/hypernetworks/hypernetwork.py
View File

@ -468,7 +468,7 @@ def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None,
shared.reload_hypernetworks()
def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_hypernetwork_every, template_filename, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
def train_hypernetwork(id_task, hypernetwork_name: str, learn_rate: float, batch_size: int, gradient_step: int, data_root: str, log_directory: str, training_width: int, training_height: int, varsize: bool, steps: int, clip_grad_mode: str, clip_grad_value: float, shuffle_tags: bool, tag_drop_out: bool, latent_sampling_method: str, use_weight: bool, create_image_every: int, save_hypernetwork_every: int, template_filename: str, preview_from_txt2img: bool, preview_prompt: str, preview_negative_prompt: str, preview_steps: int, preview_sampler_name: str, preview_cfg_scale: float, preview_seed: int, preview_width: int, preview_height: int):
from modules import images, processing
save_hypernetwork_every = save_hypernetwork_every or 0
@ -698,7 +698,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
p.prompt = preview_prompt
p.negative_prompt = preview_negative_prompt
p.steps = preview_steps
p.sampler_name = sd_samplers.samplers[preview_sampler_index].name
p.sampler_name = sd_samplers.samplers_map[preview_sampler_name.lower()]
p.cfg_scale = preview_cfg_scale
p.seed = preview_seed
p.width = preview_width

19
modules/images.py
View File

@ -561,6 +561,8 @@ def save_image_with_geninfo(image, geninfo, filename, extension=None, existing_p
})
piexif.insert(exif_bytes, filename)
elif extension.lower() == ".gif":
image.save(filename, format=image_format, comment=geninfo)
else:
image.save(filename, format=image_format, quality=opts.jpeg_quality)
@ -661,7 +663,13 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
save_image_with_geninfo(image_to_save, info, temp_file_path, extension, existing_pnginfo=params.pnginfo, pnginfo_section_name=pnginfo_section_name)
os.replace(temp_file_path, filename_without_extension + extension)
filename = filename_without_extension + extension
if shared.opts.save_images_replace_action != "Replace":
n = 0
while os.path.exists(filename):
n += 1
filename = f"{filename_without_extension}-{n}{extension}"
os.replace(temp_file_path, filename)
fullfn_without_extension, extension = os.path.splitext(params.filename)
if hasattr(os, 'statvfs'):
@ -718,7 +726,12 @@ def read_info_from_image(image: Image.Image) -> tuple[str | None, dict]:
geninfo = items.pop('parameters', None)
if "exif" in items:
exif = piexif.load(items["exif"])
exif_data = items["exif"]
try:
exif = piexif.load(exif_data)
except OSError:
# memory / exif was not valid so piexif tried to read from a file
exif = None
exif_comment = (exif or {}).get("Exif", {}).get(piexif.ExifIFD.UserComment, b'')
try:
exif_comment = piexif.helper.UserComment.load(exif_comment)
@ -728,6 +741,8 @@ def read_info_from_image(image: Image.Image) -> tuple[str | None, dict]:
if exif_comment:
items['exif comment'] = exif_comment
geninfo = exif_comment
elif "comment" in items: # for gif
geninfo = items["comment"].decode('utf8', errors="ignore")
for field in IGNORED_INFO_KEYS:
items.pop(field, None)

56
modules/img2img.py
View File

@ -10,6 +10,7 @@ from modules import images as imgutil
from modules.generation_parameters_copypaste import create_override_settings_dict, parse_generation_parameters
from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
from modules.shared import opts, state
from modules.sd_models import get_closet_checkpoint_match
import modules.shared as shared
import modules.processing as processing
from modules.ui import plaintext_to_html
@ -41,7 +42,10 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=Fal
cfg_scale = p.cfg_scale
sampler_name = p.sampler_name
steps = p.steps
override_settings = p.override_settings
sd_model_checkpoint_override = get_closet_checkpoint_match(override_settings.get("sd_model_checkpoint", None))
batch_results = None
discard_further_results = False
for i, image in enumerate(images):
state.job = f"{i+1} out of {len(images)}"
if state.skipped:
@ -104,16 +108,42 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=Fal
p.sampler_name = parsed_parameters.get("Sampler", sampler_name)
p.steps = int(parsed_parameters.get("Steps", steps))
model_info = get_closet_checkpoint_match(parsed_parameters.get("Model hash", None))
if model_info is not None:
p.override_settings['sd_model_checkpoint'] = model_info.name
elif sd_model_checkpoint_override:
p.override_settings['sd_model_checkpoint'] = sd_model_checkpoint_override
else:
p.override_settings.pop("sd_model_checkpoint", None)
if output_dir:
p.outpath_samples = output_dir
p.override_settings['save_to_dirs'] = False
p.override_settings['save_images_replace_action'] = "Add number suffix"
if p.n_iter > 1 or p.batch_size > 1:
p.override_settings['samples_filename_pattern'] = f'{image_path.stem}-[generation_number]'
else:
p.override_settings['samples_filename_pattern'] = f'{image_path.stem}'
proc = modules.scripts.scripts_img2img.run(p, *args)
if proc is None:
if output_dir:
p.outpath_samples = output_dir
p.override_settings['save_to_dirs'] = False
if p.n_iter > 1 or p.batch_size > 1:
p.override_settings['samples_filename_pattern'] = f'{image_path.stem}-[generation_number]'
else:
p.override_settings['samples_filename_pattern'] = f'{image_path.stem}'
process_images(p)
p.override_settings.pop('save_images_replace_action', None)
proc = process_images(p)
if not discard_further_results and proc:
if batch_results:
batch_results.images.extend(proc.images)
batch_results.infotexts.extend(proc.infotexts)
else:
batch_results = proc
if 0 <= shared.opts.img2img_batch_show_results_limit < len(batch_results.images):
discard_further_results = True
batch_results.images = batch_results.images[:int(shared.opts.img2img_batch_show_results_limit)]
batch_results.infotexts = batch_results.infotexts[:int(shared.opts.img2img_batch_show_results_limit)]
return batch_results
def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_name: str, mask_blur: int, mask_alpha: float, inpainting_fill: int, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, selected_scale_tab: int, height: int, width: int, scale_by: float, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, img2img_batch_use_png_info: bool, img2img_batch_png_info_props: list, img2img_batch_png_info_dir: str, request: gr.Request, *args):
@ -189,7 +219,7 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
p.user = request.username
if shared.cmd_opts.enable_console_prompts:
if shared.opts.enable_console_prompts:
print(f"\nimg2img: {prompt}", file=shared.progress_print_out)
if mask:
@ -198,10 +228,10 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
with closing(p):
if is_batch:
assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
processed = process_batch(p, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, args, to_scale=selected_scale_tab == 1, scale_by=scale_by, use_png_info=img2img_batch_use_png_info, png_info_props=img2img_batch_png_info_props, png_info_dir=img2img_batch_png_info_dir)
process_batch(p, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, args, to_scale=selected_scale_tab == 1, scale_by=scale_by, use_png_info=img2img_batch_use_png_info, png_info_props=img2img_batch_png_info_props, png_info_dir=img2img_batch_png_info_dir)
processed = Processed(p, [], p.seed, "")
if processed is None:
processed = Processed(p, [], p.seed, "")
else:
processed = modules.scripts.scripts_img2img.run(p, *args)
if processed is None:

11
modules/import_hook.py
View File

@ -3,3 +3,14 @@ import sys
# this will break any attempt to import xformers which will prevent stability diffusion repo from trying to use it
if "--xformers" not in "".join(sys.argv):
sys.modules["xformers"] = None
# Hack to fix a changed import in torchvision 0.17+, which otherwise breaks
# basicsr; see https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/13985
try:
import torchvision.transforms.functional_tensor # noqa: F401
except ImportError:
try:
import torchvision.transforms.functional as functional
sys.modules["torchvision.transforms.functional_tensor"] = functional
except ImportError:
pass # shrug...

4
modules/initialize.py
View File

@ -151,8 +151,8 @@ def initialize_rest(*, reload_script_modules=False):
from modules import devices
devices.first_time_calculation()
Thread(target=load_model).start()
if not shared.cmd_opts.skip_load_model_at_start:
Thread(target=load_model).start()
from modules import shared_items
shared_items.reload_hypernetworks()

6
modules/initialize_util.py
View File

@ -150,10 +150,14 @@ def dumpstacks():
def configure_sigint_handler():
# make the program just exit at ctrl+c without waiting for anything
from modules import shared
def sigint_handler(sig, frame):
print(f'Interrupted with signal {sig} in {frame}')
dumpstacks()
if shared.opts.dump_stacks_on_signal:
dumpstacks()
os._exit(0)

42
modules/launch_utils.py
View File

@ -6,6 +6,7 @@ import os
import shutil
import sys
import importlib.util
import importlib.metadata
import platform
import json
from functools import lru_cache
@ -64,7 +65,7 @@ Use --skip-python-version-check to suppress this warning.
@lru_cache()
def commit_hash():
try:
return subprocess.check_output([git, "rev-parse", "HEAD"], shell=False, encoding='utf8').strip()
return subprocess.check_output([git, "-C", script_path, "rev-parse", "HEAD"], shell=False, encoding='utf8').strip()
except Exception:
return "<none>"
@ -72,7 +73,7 @@ def commit_hash():
@lru_cache()
def git_tag():
try:
return subprocess.check_output([git, "describe", "--tags"], shell=False, encoding='utf8').strip()
return subprocess.check_output([git, "-C", script_path, "describe", "--tags"], shell=False, encoding='utf8').strip()
except Exception:
try:
@ -119,11 +120,16 @@ def run(command, desc=None, errdesc=None, custom_env=None, live: bool = default_
def is_installed(package):
try:
spec = importlib.util.find_spec(package)
except ModuleNotFoundError:
return False
dist = importlib.metadata.distribution(package)
except importlib.metadata.PackageNotFoundError:
try:
spec = importlib.util.find_spec(package)
except ModuleNotFoundError:
return False
return spec is not None
return spec is not None
return dist is not None
def repo_dir(name):
@ -310,6 +316,26 @@ def requirements_met(requirements_file):
def prepare_environment():
torch_index_url = os.environ.get('TORCH_INDEX_URL', "https://download.pytorch.org/whl/cu118")
torch_command = os.environ.get('TORCH_COMMAND', f"pip install torch==2.0.1 torchvision==0.15.2 --extra-index-url {torch_index_url}")
if args.use_ipex:
if platform.system() == "Windows":
# The "Nuullll/intel-extension-for-pytorch" wheels were built from IPEX source for Intel Arc GPU: https://github.com/intel/intel-extension-for-pytorch/tree/xpu-main
# This is NOT an Intel official release so please use it at your own risk!!
# See https://github.com/Nuullll/intel-extension-for-pytorch/releases/tag/v2.0.110%2Bxpu-master%2Bdll-bundle for details.
#
# Strengths (over official IPEX 2.0.110 windows release):
# - AOT build (for Arc GPU only) to eliminate JIT compilation overhead: https://github.com/intel/intel-extension-for-pytorch/issues/399
# - Bundles minimal oneAPI 2023.2 dependencies into the python wheels, so users don't need to install oneAPI for the whole system.
# - Provides a compatible torchvision wheel: https://github.com/intel/intel-extension-for-pytorch/issues/465
# Limitation:
# - Only works for python 3.10
url_prefix = "https://github.com/Nuullll/intel-extension-for-pytorch/releases/download/v2.0.110%2Bxpu-master%2Bdll-bundle"
torch_command = os.environ.get('TORCH_COMMAND', f"pip install {url_prefix}/torch-2.0.0a0+gite9ebda2-cp310-cp310-win_amd64.whl {url_prefix}/torchvision-0.15.2a0+fa99a53-cp310-cp310-win_amd64.whl {url_prefix}/intel_extension_for_pytorch-2.0.110+gitc6ea20b-cp310-cp310-win_amd64.whl")
else:
# Using official IPEX release for linux since it's already an AOT build.
# However, users still have to install oneAPI toolkit and activate oneAPI environment manually.
# See https://intel.github.io/intel-extension-for-pytorch/index.html#installation for details.
torch_index_url = os.environ.get('TORCH_INDEX_URL', "https://pytorch-extension.intel.com/release-whl/stable/xpu/us/")
torch_command = os.environ.get('TORCH_COMMAND', f"pip install torch==2.0.0a0 intel-extension-for-pytorch==2.0.110+gitba7f6c1 --extra-index-url {torch_index_url}")
requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.20')
@ -352,6 +378,8 @@ def prepare_environment():
run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch", live=True)
startup_timer.record("install torch")
if args.use_ipex:
args.skip_torch_cuda_test = True
if not args.skip_torch_cuda_test and not check_run_python("import torch; assert torch.cuda.is_available()"):
raise RuntimeError(
'Torch is not able to use GPU; '
@ -441,7 +469,7 @@ def dump_sysinfo():
import datetime
text = sysinfo.get()
filename = f"sysinfo-{datetime.datetime.utcnow().strftime('%Y-%m-%d-%H-%M')}.txt"
filename = f"sysinfo-{datetime.datetime.utcnow().strftime('%Y-%m-%d-%H-%M')}.json"
with open(filename, "w", encoding="utf8") as file:
file.write(text)

21
modules/localization.py
View File

@ -14,21 +14,24 @@ def list_localizations(dirname):
if ext.lower() != ".json":
continue
localizations[fn] = os.path.join(dirname, file)
localizations[fn] = [os.path.join(dirname, file)]
for file in scripts.list_scripts("localizations", ".json"):
fn, ext = os.path.splitext(file.filename)
localizations[fn] = file.path
if fn not in localizations:
localizations[fn] = []
localizations[fn].append(file.path)
def localization_js(current_localization_name: str) -> str:
fn = localizations.get(current_localization_name, None)
fns = localizations.get(current_localization_name, None)
data = {}
if fn is not None:
try:
with open(fn, "r", encoding="utf8") as file:
data = json.load(file)
except Exception:
errors.report(f"Error loading localization from {fn}", exc_info=True)
if fns is not None:
for fn in fns:
try:
with open(fn, "r", encoding="utf8") as file:
data.update(json.load(file))
except Exception:
errors.report(f"Error loading localization from {fn}", exc_info=True)
return f"window.localization = {json.dumps(data)}"

27
modules/logging_config.py
View File

@ -1,16 +1,41 @@
import os
import logging
try:
from tqdm.auto import tqdm
class TqdmLoggingHandler(logging.Handler):
def __init__(self, level=logging.INFO):
super().__init__(level)
def emit(self, record):
try:
msg = self.format(record)
tqdm.write(msg)
self.flush()
except Exception:
self.handleError(record)
TQDM_IMPORTED = True
except ImportError:
# tqdm does not exist before first launch
# I will import once the UI finishes seting up the enviroment and reloads.
TQDM_IMPORTED = False
def setup_logging(loglevel):
if loglevel is None:
loglevel = os.environ.get("SD_WEBUI_LOG_LEVEL")
loghandlers = []
if TQDM_IMPORTED:
loghandlers.append(TqdmLoggingHandler())
if loglevel:
log_level = getattr(logging, loglevel.upper(), None) or logging.INFO
logging.basicConfig(
level=log_level,
format='%(asctime)s %(levelname)s [%(name)s] %(message)s',
datefmt='%Y-%m-%d %H:%M:%S',
handlers=loghandlers
)

15
modules/mac_specific.py
View File

@ -1,6 +1,7 @@
import logging
import torch
from torch import Tensor
import platform
from modules.sd_hijack_utils import CondFunc
from packaging import version
@ -51,6 +52,17 @@ def cumsum_fix(input, cumsum_func, *args, **kwargs):
return cumsum_func(input, *args, **kwargs)
# MPS workaround for https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14046
def interpolate_with_fp32_fallback(orig_func, *args, **kwargs) -> Tensor:
try:
return orig_func(*args, **kwargs)
except RuntimeError as e:
if "not implemented for" in str(e) and "Half" in str(e):
input_tensor = args[0]
return orig_func(input_tensor.to(torch.float32), *args[1:], **kwargs).to(input_tensor.dtype)
else:
print(f"An unexpected RuntimeError occurred: {str(e)}")
if has_mps:
if platform.mac_ver()[0].startswith("13.2."):
# MPS workaround for https://github.com/pytorch/pytorch/issues/95188, thanks to danieldk (https://github.com/explosion/curated-transformers/pull/124)
@ -77,6 +89,9 @@ if has_mps:
# MPS workaround for https://github.com/pytorch/pytorch/issues/96113
CondFunc('torch.nn.functional.layer_norm', lambda orig_func, x, normalized_shape, weight, bias, eps, **kwargs: orig_func(x.float(), normalized_shape, weight.float() if weight is not None else None, bias.float() if bias is not None else bias, eps).to(x.dtype), lambda _, input, *args, **kwargs: len(args) == 4 and input.device.type == 'mps')
# MPS workaround for https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14046
CondFunc('torch.nn.functional.interpolate', interpolate_with_fp32_fallback, None)
# MPS workaround for https://github.com/pytorch/pytorch/issues/92311
if platform.processor() == 'i386':
for funcName in ['torch.argmax', 'torch.Tensor.argmax']:

7
modules/models/diffusion/ddpm_edit.py
View File

@ -24,10 +24,15 @@ 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.models.autoencoder import IdentityFirstStage, AutoencoderKL
from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
from ldm.models.diffusion.ddim import DDIMSampler
try:
from ldm.models.autoencoder import VQModelInterface
except Exception:
class VQModelInterface:
pass
__conditioning_keys__ = {'concat': 'c_concat',
'crossattn': 'c_crossattn',

81
modules/options.py
View File

@ -1,5 +1,6 @@
import json
import sys
from dataclasses import dataclass
import gradio as gr
@ -8,13 +9,14 @@ from modules.shared_cmd_options import cmd_opts
class OptionInfo:
def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, section=None, refresh=None, comment_before='', comment_after='', infotext=None, restrict_api=False):
def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, section=None, refresh=None, comment_before='', comment_after='', infotext=None, restrict_api=False, category_id=None):
self.default = default
self.label = label
self.component = component
self.component_args = component_args
self.onchange = onchange
self.section = section
self.category_id = category_id
self.refresh = refresh
self.do_not_save = False
@ -63,7 +65,11 @@ class OptionHTML(OptionInfo):
def options_section(section_identifier, options_dict):
for v in options_dict.values():
v.section = section_identifier
if len(section_identifier) == 2:
v.section = section_identifier
elif len(section_identifier) == 3:
v.section = section_identifier[0:2]
v.category_id = section_identifier[2]
return options_dict
@ -76,7 +82,7 @@ class Options:
def __init__(self, data_labels: dict[str, OptionInfo], restricted_opts):
self.data_labels = data_labels
self.data = {k: v.default for k, v in self.data_labels.items()}
self.data = {k: v.default for k, v in self.data_labels.items() if not v.do_not_save}
self.restricted_opts = restricted_opts
def __setattr__(self, key, value):
@ -158,7 +164,7 @@ class Options:
assert not cmd_opts.freeze_settings, "saving settings is disabled"
with open(filename, "w", encoding="utf8") as file:
json.dump(self.data, file, indent=4)
json.dump(self.data, file, indent=4, ensure_ascii=False)
def same_type(self, x, y):
if x is None or y is None:
@ -206,21 +212,59 @@ class Options:
d = {k: self.data.get(k, v.default) for k, v in self.data_labels.items()}
d["_comments_before"] = {k: v.comment_before for k, v in self.data_labels.items() if v.comment_before is not None}
d["_comments_after"] = {k: v.comment_after for k, v in self.data_labels.items() if v.comment_after is not None}
item_categories = {}
for item in self.data_labels.values():
category = categories.mapping.get(item.category_id)
category = "Uncategorized" if category is None else category.label
if category not in item_categories:
item_categories[category] = item.section[1]
# _categories is a list of pairs: [section, category]. Each section (a setting page) will get a special heading above it with the category as text.
d["_categories"] = [[v, k] for k, v in item_categories.items()] + [["Defaults", "Other"]]
return json.dumps(d)
def add_option(self, key, info):
self.data_labels[key] = info
if key not in self.data and not info.do_not_save:
self.data[key] = info.default
def reorder(self):
"""reorder settings so that all items related to section always go together"""
"""Reorder settings so that:
- all items related to section always go together
- all sections belonging to a category go together
- sections inside a category are ordered alphabetically
- categories are ordered by creation order
Category is a superset of sections: for category "postprocessing" there could be multiple sections: "face restoration", "upscaling".
This function also changes items' category_id so that all items belonging to a section have the same category_id.
"""
category_ids = {}
section_categories = {}
section_ids = {}
settings_items = self.data_labels.items()
for _, item in settings_items:
if item.section not in section_ids:
section_ids[item.section] = len(section_ids)
if item.section not in section_categories:
section_categories[item.section] = item.category_id
self.data_labels = dict(sorted(settings_items, key=lambda x: section_ids[x[1].section]))
for _, item in settings_items:
item.category_id = section_categories.get(item.section)
for category_id in categories.mapping:
if category_id not in category_ids:
category_ids[category_id] = len(category_ids)
def sort_key(x):
item: OptionInfo = x[1]
category_order = category_ids.get(item.category_id, len(category_ids))
section_order = item.section[1]
return category_order, section_order
self.data_labels = dict(sorted(settings_items, key=sort_key))
def cast_value(self, key, value):
"""casts an arbitrary to the same type as this setting's value with key
@ -243,3 +287,22 @@ class Options:
value = expected_type(value)
return value
@dataclass
class OptionsCategory:
id: str
label: str
class OptionsCategories:
def __init__(self):
self.mapping = {}
def register_category(self, category_id, label):
if category_id in self.mapping:
return category_id
self.mapping[category_id] = OptionsCategory(category_id, label)
categories = OptionsCategories()

2
modules/paths.py
View File

@ -1,6 +1,6 @@
import os
import sys
from modules.paths_internal import models_path, script_path, data_path, extensions_dir, extensions_builtin_dir # noqa: F401
from modules.paths_internal import models_path, script_path, data_path, extensions_dir, extensions_builtin_dir, cwd # noqa: F401
import modules.safe # noqa: F401

1
modules/paths_internal.py
View File

@ -8,6 +8,7 @@ import shlex
commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
sys.argv += shlex.split(commandline_args)
cwd = os.getcwd()
modules_path = os.path.dirname(os.path.realpath(__file__))
script_path = os.path.dirname(modules_path)

94
modules/postprocessing.py
View File

@ -29,11 +29,7 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir,
image_list = shared.listfiles(input_dir)
for filename in image_list:
try:
image = Image.open(filename)
except Exception:
continue
yield image, filename
yield filename, filename
else:
assert image, 'image not selected'
yield image, None
@ -45,43 +41,97 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir,
infotext = ''
for image_data, name in get_images(extras_mode, image, image_folder, input_dir):
data_to_process = list(get_images(extras_mode, image, image_folder, input_dir))
shared.state.job_count = len(data_to_process)
for image_placeholder, name in data_to_process:
image_data: Image.Image
shared.state.nextjob()
shared.state.textinfo = name
shared.state.skipped = False
if shared.state.interrupted:
break
if isinstance(image_placeholder, str):
try:
image_data = Image.open(image_placeholder)
except Exception:
continue
else:
image_data = image_placeholder
shared.state.assign_current_image(image_data)
parameters, existing_pnginfo = images.read_info_from_image(image_data)
if parameters:
existing_pnginfo["parameters"] = parameters
pp = scripts_postprocessing.PostprocessedImage(image_data.convert("RGB"))
initial_pp = scripts_postprocessing.PostprocessedImage(image_data.convert("RGB"))
scripts.scripts_postproc.run(pp, args)
scripts.scripts_postproc.run(initial_pp, args)
if opts.use_original_name_batch and name is not None:
basename = os.path.splitext(os.path.basename(name))[0]
else:
basename = ''
if shared.state.skipped:
continue
infotext = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in pp.info.items() if v is not None])
used_suffixes = {}
for pp in [initial_pp, *initial_pp.extra_images]:
suffix = pp.get_suffix(used_suffixes)
if opts.enable_pnginfo:
pp.image.info = existing_pnginfo
pp.image.info["postprocessing"] = infotext
if opts.use_original_name_batch and name is not None:
basename = os.path.splitext(os.path.basename(name))[0]
forced_filename = basename + suffix
else:
basename = ''
forced_filename = None
if save_output:
images.save_image(pp.image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=infotext, short_filename=True, no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None)
infotext = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in pp.info.items() if v is not None])
if extras_mode != 2 or show_extras_results:
outputs.append(pp.image)
if opts.enable_pnginfo:
pp.image.info = existing_pnginfo
pp.image.info["postprocessing"] = infotext
if save_output:
fullfn, _ = images.save_image(pp.image, path=outpath, basename=basename, extension=opts.samples_format, info=infotext, short_filename=True, no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=forced_filename, suffix=suffix)
if pp.caption:
caption_filename = os.path.splitext(fullfn)[0] + ".txt"
if os.path.isfile(caption_filename):
with open(caption_filename, encoding="utf8") as file:
existing_caption = file.read().strip()
else:
existing_caption = ""
action = shared.opts.postprocessing_existing_caption_action
if action == 'Prepend' and existing_caption:
caption = f"{existing_caption} {pp.caption}"
elif action == 'Append' and existing_caption:
caption = f"{pp.caption} {existing_caption}"
elif action == 'Keep' and existing_caption:
caption = existing_caption
else:
caption = pp.caption
caption = caption.strip()
if caption:
with open(caption_filename, "w", encoding="utf8") as file:
file.write(caption)
if extras_mode != 2 or show_extras_results:
outputs.append(pp.image)
image_data.close()
devices.torch_gc()
shared.state.end()
return outputs, ui_common.plaintext_to_html(infotext), ''
def run_postprocessing_webui(id_task, *args, **kwargs):
return run_postprocessing(*args, **kwargs)
def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, upscale_first: bool, save_output: bool = True):
"""old handler for API"""
@ -97,9 +147,11 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
"upscaler_2_visibility": extras_upscaler_2_visibility,
},
"GFPGAN": {
"enable": True,
"gfpgan_visibility": gfpgan_visibility,
},
"CodeFormer": {
"enable": True,
"codeformer_visibility": codeformer_visibility,
"codeformer_weight": codeformer_weight,
},

51
modules/processing.py
View File

@ -142,7 +142,7 @@ class StableDiffusionProcessing:
overlay_images: list = None
eta: float = None
do_not_reload_embeddings: bool = False
denoising_strength: float = 0
denoising_strength: float = None
ddim_discretize: str = None
s_min_uncond: float = None
s_churn: float = None
@ -296,7 +296,7 @@ class StableDiffusionProcessing:
return conditioning
def edit_image_conditioning(self, source_image):
conditioning_image = images_tensor_to_samples(source_image*0.5+0.5, approximation_indexes.get(opts.sd_vae_encode_method))
conditioning_image = shared.sd_model.encode_first_stage(source_image).mode()
return conditioning_image
@ -533,6 +533,7 @@ class Processed:
self.all_seeds = all_seeds or p.all_seeds or [self.seed]
self.all_subseeds = all_subseeds or p.all_subseeds or [self.subseed]
self.infotexts = infotexts or [info]
self.version = program_version()
def js(self):
obj = {
@ -567,6 +568,7 @@ class Processed:
"job_timestamp": self.job_timestamp,
"clip_skip": self.clip_skip,
"is_using_inpainting_conditioning": self.is_using_inpainting_conditioning,
"version": self.version,
}
return json.dumps(obj)
@ -677,8 +679,8 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
"Size": f"{p.width}x{p.height}",
"Model hash": p.sd_model_hash if opts.add_model_hash_to_info else None,
"Model": p.sd_model_name if opts.add_model_name_to_info else None,
"VAE hash": p.sd_vae_hash if opts.add_model_hash_to_info else None,
"VAE": p.sd_vae_name if opts.add_model_name_to_info else None,
"VAE hash": p.sd_vae_hash if opts.add_vae_hash_to_info else None,
"VAE": p.sd_vae_name if opts.add_vae_name_to_info else None,
"Variation seed": (None if p.subseed_strength == 0 else (p.all_subseeds[0] if use_main_prompt else all_subseeds[index])),
"Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
"Seed resize from": (None if p.seed_resize_from_w <= 0 or p.seed_resize_from_h <= 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
@ -709,7 +711,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
if p.scripts is not None:
p.scripts.before_process(p)
stored_opts = {k: opts.data[k] for k in p.override_settings.keys()}
stored_opts = {k: opts.data[k] if k in opts.data else opts.get_default(k) for k in p.override_settings.keys() if k in opts.data}
try:
# if no checkpoint override or the override checkpoint can't be found, remove override entry and load opts checkpoint
@ -797,7 +799,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
infotexts = []
output_images = []
with torch.no_grad(), p.sd_model.ema_scope():
with devices.autocast():
p.init(p.all_prompts, p.all_seeds, p.all_subseeds)
@ -871,7 +872,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
else:
if opts.sd_vae_decode_method != 'Full':
p.extra_generation_params['VAE Decoder'] = opts.sd_vae_decode_method
x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True)
x_samples_ddim = torch.stack(x_samples_ddim).float()
@ -884,6 +884,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
devices.torch_gc()
state.nextjob()
if p.scripts is not None:
p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
@ -936,27 +938,27 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
if opts.enable_pnginfo:
image.info["parameters"] = text
output_images.append(image)
if save_samples and hasattr(p, 'mask_for_overlay') and p.mask_for_overlay and any([opts.save_mask, opts.save_mask_composite, opts.return_mask, opts.return_mask_composite]):
image_mask = p.mask_for_overlay.convert('RGB')
image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, p.mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA')
if hasattr(p, 'mask_for_overlay') and p.mask_for_overlay:
if opts.return_mask or opts.save_mask:
image_mask = p.mask_for_overlay.convert('RGB')
if save_samples and opts.save_mask:
images.save_image(image_mask, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask")
if opts.return_mask:
output_images.append(image_mask)
if opts.save_mask:
images.save_image(image_mask, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask")
if opts.save_mask_composite:
images.save_image(image_mask_composite, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask-composite")
if opts.return_mask:
output_images.append(image_mask)
if opts.return_mask_composite:
output_images.append(image_mask_composite)
if opts.return_mask_composite or opts.save_mask_composite:
image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, p.mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA')
if save_samples and opts.save_mask_composite:
images.save_image(image_mask_composite, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask-composite")
if opts.return_mask_composite:
output_images.append(image_mask_composite)
del x_samples_ddim
devices.torch_gc()
state.nextjob()
if not infotexts:
infotexts.append(Processed(p, []).infotext(p, 0))
p.color_corrections = None
@ -1142,6 +1144,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
if not self.enable_hr:
return samples
devices.torch_gc()
if self.latent_scale_mode is None:
decoded_samples = torch.stack(decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)).to(dtype=torch.float32)
@ -1151,8 +1154,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
with sd_models.SkipWritingToConfig():
sd_models.reload_model_weights(info=self.hr_checkpoint_info)
devices.torch_gc()
return self.sample_hr_pass(samples, decoded_samples, seeds, subseeds, subseed_strength, prompts)
def sample_hr_pass(self, samples, decoded_samples, seeds, subseeds, subseed_strength, prompts):
@ -1160,7 +1161,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
return samples
self.is_hr_pass = True
target_width = self.hr_upscale_to_x
target_height = self.hr_upscale_to_y
@ -1249,7 +1249,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
decoded_samples = decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)
self.is_hr_pass = False
return decoded_samples
def close(self):

4
modules/processing_scripts/seed.py
View File

@ -29,8 +29,8 @@ class ScriptSeed(scripts.ScriptBuiltinUI):
else:
self.seed = gr.Number(label='Seed', value=-1, elem_id=self.elem_id("seed"), min_width=100, precision=0)
random_seed = ToolButton(ui.random_symbol, elem_id=self.elem_id("random_seed"), label='Random seed')
reuse_seed = ToolButton(ui.reuse_symbol, elem_id=self.elem_id("reuse_seed"), label='Reuse seed')
random_seed = ToolButton(ui.random_symbol, elem_id=self.elem_id("random_seed"), tooltip="Set seed to -1, which will cause a new random number to be used every time")
reuse_seed = ToolButton(ui.reuse_symbol, elem_id=self.elem_id("reuse_seed"), tooltip="Reuse seed from last generation, mostly useful if it was randomized")
seed_checkbox = gr.Checkbox(label='Extra', elem_id=self.elem_id("subseed_show"), value=False)

9
modules/prompt_parser.py
View File

@ -2,10 +2,9 @@ from __future__ import annotations
import re
from collections import namedtuple
from typing import List
import lark
# a prompt like this: "fantasy landscape with a [mountain:lake:0.25] and [an oak:a christmas tree:0.75][ in foreground::0.6][ in background:0.25] [shoddy:masterful:0.5]"
# a prompt like this: "fantasy landscape with a [mountain:lake:0.25] and [an oak:a christmas tree:0.75][ in foreground::0.6][: in background:0.25] [shoddy:masterful:0.5]"
# will be represented with prompt_schedule like this (assuming steps=100):
# [25, 'fantasy landscape with a mountain and an oak in foreground shoddy']
# [50, 'fantasy landscape with a lake and an oak in foreground in background shoddy']
@ -240,14 +239,14 @@ def get_multicond_prompt_list(prompts: SdConditioning | list[str]):
class ComposableScheduledPromptConditioning:
def __init__(self, schedules, weight=1.0):
self.schedules: List[ScheduledPromptConditioning] = schedules
self.schedules: list[ScheduledPromptConditioning] = schedules
self.weight: float = weight
class MulticondLearnedConditioning:
def __init__(self, shape, batch):
self.shape: tuple = shape # the shape field is needed to send this object to DDIM/PLMS
self.batch: List[List[ComposableScheduledPromptConditioning]] = batch
self.batch: list[list[ComposableScheduledPromptConditioning]] = batch
def get_multicond_learned_conditioning(model, prompts, steps, hires_steps=None, use_old_scheduling=False) -> MulticondLearnedConditioning:
@ -278,7 +277,7 @@ class DictWithShape(dict):
return self["crossattn"].shape
def reconstruct_cond_batch(c: List[List[ScheduledPromptConditioning]], current_step):
def reconstruct_cond_batch(c: list[list[ScheduledPromptConditioning]], current_step):
param = c[0][0].cond
is_dict = isinstance(param, dict)

4
modules/restart.py
View File

@ -14,7 +14,9 @@ def is_restartable() -> bool:
def restart_program() -> None:
"""creates file tmp/restart and immediately stops the process, which webui.bat/webui.sh interpret as a command to start webui again"""
(Path(script_path) / "tmp" / "restart").touch()
tmpdir = Path(script_path) / "tmp"
tmpdir.mkdir(parents=True, exist_ok=True)
(tmpdir / "restart").touch()
stop_program()

2
modules/rng.py
View File

@ -110,7 +110,7 @@ class ImageRNG:
self.is_first = True
def first(self):
noise_shape = self.shape if self.seed_resize_from_h <= 0 or self.seed_resize_from_w <= 0 else (self.shape[0], self.seed_resize_from_h // 8, self.seed_resize_from_w // 8)
noise_shape = self.shape if self.seed_resize_from_h <= 0 or self.seed_resize_from_w <= 0 else (self.shape[0], int(self.seed_resize_from_h) // 8, int(self.seed_resize_from_w // 8))
xs = []

6
modules/script_callbacks.py
View File

@ -1,7 +1,7 @@
import inspect
import os
from collections import namedtuple
from typing import Optional, Dict, Any
from typing import Optional, Any
from fastapi import FastAPI
from gradio import Blocks
@ -258,7 +258,7 @@ def image_grid_callback(params: ImageGridLoopParams):
report_exception(c, 'image_grid')
def infotext_pasted_callback(infotext: str, params: Dict[str, Any]):
def infotext_pasted_callback(infotext: str, params: dict[str, Any]):
for c in callback_map['callbacks_infotext_pasted']:
try:
c.callback(infotext, params)
@ -449,7 +449,7 @@ def on_infotext_pasted(callback):
"""register a function to be called before applying an infotext.
The callback is called with two arguments:
- infotext: str - raw infotext.
- result: Dict[str, any] - parsed infotext parameters.
- result: dict[str, any] - parsed infotext parameters.
"""
add_callback(callback_map['callbacks_infotext_pasted'], callback)

135
modules/scripts.py
View File

@ -311,20 +311,113 @@ scripts_data = []
postprocessing_scripts_data = []
ScriptClassData = namedtuple("ScriptClassData", ["script_class", "path", "basedir", "module"])
def topological_sort(dependencies):
"""Accepts a dictionary mapping name to its dependencies, returns a list of names ordered according to dependencies.
Ignores errors relating to missing dependeencies or circular dependencies
"""
visited = {}
result = []
def inner(name):
visited[name] = True
for dep in dependencies.get(name, []):
if dep in dependencies and dep not in visited:
inner(dep)
result.append(name)
for depname in dependencies:
if depname not in visited:
inner(depname)
return result
@dataclass
class ScriptWithDependencies:
script_canonical_name: str
file: ScriptFile
requires: list
load_before: list
load_after: list
def list_scripts(scriptdirname, extension, *, include_extensions=True):
scripts_list = []
scripts = {}
basedir = os.path.join(paths.script_path, scriptdirname)
if os.path.exists(basedir):
for filename in sorted(os.listdir(basedir)):
scripts_list.append(ScriptFile(paths.script_path, filename, os.path.join(basedir, filename)))
loaded_extensions = {ext.canonical_name: ext for ext in extensions.active()}
loaded_extensions_scripts = {ext.canonical_name: [] for ext in extensions.active()}
# build script dependency map
root_script_basedir = os.path.join(paths.script_path, scriptdirname)
if os.path.exists(root_script_basedir):
for filename in sorted(os.listdir(root_script_basedir)):
if not os.path.isfile(os.path.join(root_script_basedir, filename)):
continue
if os.path.splitext(filename)[1].lower() != extension:
continue
script_file = ScriptFile(paths.script_path, filename, os.path.join(root_script_basedir, filename))
scripts[filename] = ScriptWithDependencies(filename, script_file, [], [], [])
if include_extensions:
for ext in extensions.active():
scripts_list += ext.list_files(scriptdirname, extension)
extension_scripts_list = ext.list_files(scriptdirname, extension)
for extension_script in extension_scripts_list:
if not os.path.isfile(extension_script.path):
continue
scripts_list = [x for x in scripts_list if os.path.splitext(x.path)[1].lower() == extension and os.path.isfile(x.path)]
script_canonical_name = ("builtin/" if ext.is_builtin else "") + ext.canonical_name + "/" + extension_script.filename
relative_path = scriptdirname + "/" + extension_script.filename
script = ScriptWithDependencies(
script_canonical_name=script_canonical_name,
file=extension_script,
requires=ext.metadata.get_script_requirements("Requires", relative_path, scriptdirname),
load_before=ext.metadata.get_script_requirements("Before", relative_path, scriptdirname),
load_after=ext.metadata.get_script_requirements("After", relative_path, scriptdirname),
)
scripts[script_canonical_name] = script
loaded_extensions_scripts[ext.canonical_name].append(script)
for script_canonical_name, script in scripts.items():
# load before requires inverse dependency
# in this case, append the script name into the load_after list of the specified script
for load_before in script.load_before:
# if this requires an individual script to be loaded before
other_script = scripts.get(load_before)
if other_script:
other_script.load_after.append(script_canonical_name)
# if this requires an extension
other_extension_scripts = loaded_extensions_scripts.get(load_before)
if other_extension_scripts:
for other_script in other_extension_scripts:
other_script.load_after.append(script_canonical_name)
# if After mentions an extension, remove it and instead add all of its scripts
for load_after in list(script.load_after):
if load_after not in scripts and load_after in loaded_extensions_scripts:
script.load_after.remove(load_after)
for other_script in loaded_extensions_scripts.get(load_after, []):
script.load_after.append(other_script.script_canonical_name)
dependencies = {}
for script_canonical_name, script in scripts.items():
for required_script in script.requires:
if required_script not in scripts and required_script not in loaded_extensions:
errors.report(f'Script "{script_canonical_name}" requires "{required_script}" to be loaded, but it is not.', exc_info=False)
dependencies[script_canonical_name] = script.load_after
ordered_scripts = topological_sort(dependencies)
scripts_list = [scripts[script_canonical_name].file for script_canonical_name in ordered_scripts]
return scripts_list
@ -365,15 +458,9 @@ def load_scripts():
elif issubclass(script_class, scripts_postprocessing.ScriptPostprocessing):
postprocessing_scripts_data.append(ScriptClassData(script_class, scriptfile.path, scriptfile.basedir, module))
def orderby(basedir):
# 1st webui, 2nd extensions-builtin, 3rd extensions
priority = {os.path.join(paths.script_path, "extensions-builtin"):1, paths.script_path:0}
for key in priority:
if basedir.startswith(key):
return priority[key]
return 9999
for scriptfile in sorted(scripts_list, key=lambda x: [orderby(x.basedir), x]):
# here the scripts_list is already ordered
# processing_script is not considered though
for scriptfile in scripts_list:
try:
if scriptfile.basedir != paths.script_path:
sys.path = [scriptfile.basedir] + sys.path
@ -473,17 +560,25 @@ class ScriptRunner:
on_after.clear()
def create_script_ui(self, script):
import modules.api.models as api_models
script.args_from = len(self.inputs)
script.args_to = len(self.inputs)
try:
self.create_script_ui_inner(script)
except Exception:
errors.report(f"Error creating UI for {script.name}: ", exc_info=True)
def create_script_ui_inner(self, script):
import modules.api.models as api_models
controls = wrap_call(script.ui, script.filename, "ui", script.is_img2img)
if controls is None:
return
script.name = wrap_call(script.title, script.filename, "title", default=script.filename).lower()
api_args = []
for control in controls:
@ -491,11 +586,15 @@ class ScriptRunner:
arg_info = api_models.ScriptArg(label=control.label or "")
for field in ("value", "minimum", "maximum", "step", "choices"):
for field in ("value", "minimum", "maximum", "step"):
v = getattr(control, field, None)
if v is not None:
setattr(arg_info, field, v)
choices = getattr(control, 'choices', None) # as of gradio 3.41, some items in choices are strings, and some are tuples where the first elem is the string
if choices is not None:
arg_info.choices = [x[0] if isinstance(x, tuple) else x for x in choices]
api_args.append(arg_info)
script.api_info = api_models.ScriptInfo(

86
modules/scripts_postprocessing.py
View File

@ -1,13 +1,56 @@
import dataclasses
import os
import gradio as gr
from modules import errors, shared
@dataclasses.dataclass
class PostprocessedImageSharedInfo:
target_width: int = None
target_height: int = None
class PostprocessedImage:
def __init__(self, image):
self.image = image
self.info = {}
self.shared = PostprocessedImageSharedInfo()
self.extra_images = []
self.nametags = []
self.disable_processing = False
self.caption = None
def get_suffix(self, used_suffixes=None):
used_suffixes = {} if used_suffixes is None else used_suffixes
suffix = "-".join(self.nametags)
if suffix:
suffix = "-" + suffix
if suffix not in used_suffixes:
used_suffixes[suffix] = 1
return suffix
for i in range(1, 100):
proposed_suffix = suffix + "-" + str(i)
if proposed_suffix not in used_suffixes:
used_suffixes[proposed_suffix] = 1
return proposed_suffix
return suffix
def create_copy(self, new_image, *, nametags=None, disable_processing=False):
pp = PostprocessedImage(new_image)
pp.shared = self.shared
pp.nametags = self.nametags.copy()
pp.info = self.info.copy()
pp.disable_processing = disable_processing
if nametags is not None:
pp.nametags += nametags
return pp
class ScriptPostprocessing:
@ -42,10 +85,17 @@ class ScriptPostprocessing:
pass
def image_changed(self):
def process_firstpass(self, pp: PostprocessedImage, **args):
"""
Called for all scripts before calling process(). Scripts can examine the image here and set fields
of the pp object to communicate things to other scripts.
args contains a dictionary with all values returned by components from ui()
"""
pass
def image_changed(self):
pass
def wrap_call(func, filename, funcname, *args, default=None, **kwargs):
@ -118,16 +168,42 @@ class ScriptPostprocessingRunner:
return inputs
def run(self, pp: PostprocessedImage, args):
for script in self.scripts_in_preferred_order():
shared.state.job = script.name
scripts = []
for script in self.scripts_in_preferred_order():
script_args = args[script.args_from:script.args_to]
process_args = {}
for (name, _component), value in zip(script.controls.items(), script_args):
process_args[name] = value
script.process(pp, **process_args)
scripts.append((script, process_args))
for script, process_args in scripts:
script.process_firstpass(pp, **process_args)
all_images = [pp]
for script, process_args in scripts:
if shared.state.skipped:
break
shared.state.job = script.name
for single_image in all_images.copy():
if not single_image.disable_processing:
script.process(single_image, **process_args)
for extra_image in single_image.extra_images:
if not isinstance(extra_image, PostprocessedImage):
extra_image = single_image.create_copy(extra_image)
all_images.append(extra_image)
single_image.extra_images.clear()
pp.extra_images = all_images[1:]
def create_args_for_run(self, scripts_args):
if not self.ui_created:

2
modules/sd_disable_initialization.py
View File

@ -215,7 +215,7 @@ class LoadStateDictOnMeta(ReplaceHelper):
would be on the meta device.
"""
if state_dict == sd:
if state_dict is sd:
state_dict = {k: v.to(device="meta", dtype=v.dtype) for k, v in state_dict.items()}
original(module, state_dict, strict=strict)

41
modules/sd_hijack.py
View File

@ -2,14 +2,15 @@ import torch
from torch.nn.functional import silu
from types import MethodType
from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors, sd_unet
from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors, sd_unet, patches
from modules.hypernetworks import hypernetwork
from modules.shared import cmd_opts
from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr
from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr, xlmr_m18
import ldm.modules.attention
import ldm.modules.diffusionmodules.model
import ldm.modules.diffusionmodules.openaimodel
import ldm.models.diffusion.ddpm
import ldm.models.diffusion.ddim
import ldm.models.diffusion.plms
import ldm.modules.encoders.modules
@ -37,6 +38,12 @@ ldm.models.diffusion.ddpm.print = shared.ldm_print
optimizers = []
current_optimizer: sd_hijack_optimizations.SdOptimization = None
ldm_patched_forward = sd_unet.create_unet_forward(ldm.modules.diffusionmodules.openaimodel.UNetModel.forward)
ldm_original_forward = patches.patch(__file__, ldm.modules.diffusionmodules.openaimodel.UNetModel, "forward", ldm_patched_forward)
sgm_patched_forward = sd_unet.create_unet_forward(sgm.modules.diffusionmodules.openaimodel.UNetModel.forward)
sgm_original_forward = patches.patch(__file__, sgm.modules.diffusionmodules.openaimodel.UNetModel, "forward", sgm_patched_forward)
def list_optimizers():
new_optimizers = script_callbacks.list_optimizers_callback()
@ -181,6 +188,20 @@ class StableDiffusionModelHijack:
errors.display(e, "applying cross attention optimization")
undo_optimizations()
def convert_sdxl_to_ssd(self, m):
"""Converts an SDXL model to a Segmind Stable Diffusion model (see https://huggingface.co/segmind/SSD-1B)"""
delattr(m.model.diffusion_model.middle_block, '1')
delattr(m.model.diffusion_model.middle_block, '2')
for i in ['9', '8', '7', '6', '5', '4']:
delattr(m.model.diffusion_model.input_blocks[7][1].transformer_blocks, i)
delattr(m.model.diffusion_model.input_blocks[8][1].transformer_blocks, i)
delattr(m.model.diffusion_model.output_blocks[0][1].transformer_blocks, i)
delattr(m.model.diffusion_model.output_blocks[1][1].transformer_blocks, i)
delattr(m.model.diffusion_model.output_blocks[4][1].transformer_blocks, '1')
delattr(m.model.diffusion_model.output_blocks[5][1].transformer_blocks, '1')
devices.torch_gc()
def hijack(self, m):
conditioner = getattr(m, 'conditioner', None)
if conditioner:
@ -208,7 +229,7 @@ class StableDiffusionModelHijack:
else:
m.cond_stage_model = conditioner
if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation or type(m.cond_stage_model) == xlmr_m18.BertSeriesModelWithTransformation:
model_embeddings = m.cond_stage_model.roberta.embeddings
model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.word_embeddings, self)
m.cond_stage_model = sd_hijack_xlmr.FrozenXLMREmbedderWithCustomWords(m.cond_stage_model, self)
@ -239,10 +260,17 @@ class StableDiffusionModelHijack:
self.layers = flatten(m)
if not hasattr(ldm.modules.diffusionmodules.openaimodel, 'copy_of_UNetModel_forward_for_webui'):
ldm.modules.diffusionmodules.openaimodel.copy_of_UNetModel_forward_for_webui = ldm.modules.diffusionmodules.openaimodel.UNetModel.forward
import modules.models.diffusion.ddpm_edit
if isinstance(m, ldm.models.diffusion.ddpm.LatentDiffusion):
sd_unet.original_forward = ldm_original_forward
elif isinstance(m, modules.models.diffusion.ddpm_edit.LatentDiffusion):
sd_unet.original_forward = ldm_original_forward
elif isinstance(m, sgm.models.diffusion.DiffusionEngine):
sd_unet.original_forward = sgm_original_forward
else:
sd_unet.original_forward = None
ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = sd_unet.UNetModel_forward
def undo_hijack(self, m):
conditioner = getattr(m, 'conditioner', None)
@ -279,7 +307,6 @@ class StableDiffusionModelHijack:
self.layers = None
self.clip = None
ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = ldm.modules.diffusionmodules.openaimodel.copy_of_UNetModel_forward_for_webui
def apply_circular(self, enable):
if self.circular_enabled == enable:

70
modules/sd_models.py
View File

@ -1,22 +1,22 @@
import collections
import os.path
import sys
import gc
import threading
import torch
import re
import safetensors.torch
from omegaconf import OmegaConf
from omegaconf import OmegaConf, ListConfig
from os import mkdir
from urllib import request
import ldm.modules.midas as midas
from ldm.util import instantiate_from_config
from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config, sd_unet, sd_models_xl, cache, extra_networks, processing, lowvram, sd_hijack
from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config, sd_unet, sd_models_xl, cache, extra_networks, processing, lowvram, sd_hijack, patches
from modules.timer import Timer
import tomesd
import numpy as np
model_dir = "Stable-diffusion"
model_path = os.path.abspath(os.path.join(paths.models_path, model_dir))
@ -49,11 +49,12 @@ class CheckpointInfo:
def __init__(self, filename):
self.filename = filename
abspath = os.path.abspath(filename)
abs_ckpt_dir = os.path.abspath(shared.cmd_opts.ckpt_dir) if shared.cmd_opts.ckpt_dir is not None else None
self.is_safetensors = os.path.splitext(filename)[1].lower() == ".safetensors"
if shared.cmd_opts.ckpt_dir is not None and abspath.startswith(shared.cmd_opts.ckpt_dir):
name = abspath.replace(shared.cmd_opts.ckpt_dir, '')
if abs_ckpt_dir and abspath.startswith(abs_ckpt_dir):
name = abspath.replace(abs_ckpt_dir, '')
elif abspath.startswith(model_path):
name = abspath.replace(model_path, '')
else:
@ -129,9 +130,12 @@ except Exception:
def setup_model():
"""called once at startup to do various one-time tasks related to SD models"""
os.makedirs(model_path, exist_ok=True)
enable_midas_autodownload()
patch_given_betas()
def checkpoint_tiles(use_short=False):
@ -226,15 +230,19 @@ def select_checkpoint():
return checkpoint_info
checkpoint_dict_replacements = {
checkpoint_dict_replacements_sd1 = {
'cond_stage_model.transformer.embeddings.': 'cond_stage_model.transformer.text_model.embeddings.',
'cond_stage_model.transformer.encoder.': 'cond_stage_model.transformer.text_model.encoder.',
'cond_stage_model.transformer.final_layer_norm.': 'cond_stage_model.transformer.text_model.final_layer_norm.',
}
checkpoint_dict_replacements_sd2_turbo = { # Converts SD 2.1 Turbo from SGM to LDM format.
'conditioner.embedders.0.': 'cond_stage_model.',
}
def transform_checkpoint_dict_key(k):
for text, replacement in checkpoint_dict_replacements.items():
def transform_checkpoint_dict_key(k, replacements):
for text, replacement in replacements.items():
if k.startswith(text):
k = replacement + k[len(text):]
@ -245,9 +253,14 @@ def get_state_dict_from_checkpoint(pl_sd):
pl_sd = pl_sd.pop("state_dict", pl_sd)
pl_sd.pop("state_dict", None)
is_sd2_turbo = 'conditioner.embedders.0.model.ln_final.weight' in pl_sd and pl_sd['conditioner.embedders.0.model.ln_final.weight'].size()[0] == 1024
sd = {}
for k, v in pl_sd.items():
new_key = transform_checkpoint_dict_key(k)
if is_sd2_turbo:
new_key = transform_checkpoint_dict_key(k, checkpoint_dict_replacements_sd2_turbo)
else:
new_key = transform_checkpoint_dict_key(k, checkpoint_dict_replacements_sd1)
if new_key is not None:
sd[new_key] = v
@ -309,6 +322,8 @@ def get_checkpoint_state_dict(checkpoint_info: CheckpointInfo, timer):
if checkpoint_info in checkpoints_loaded:
# use checkpoint cache
print(f"Loading weights [{sd_model_hash}] from cache")
# move to end as latest
checkpoints_loaded.move_to_end(checkpoint_info)
return checkpoints_loaded[checkpoint_info]
print(f"Loading weights [{sd_model_hash}] from {checkpoint_info.filename}")
@ -346,16 +361,19 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
model.is_sdxl = hasattr(model, 'conditioner')
model.is_sd2 = not model.is_sdxl and hasattr(model.cond_stage_model, 'model')
model.is_sd1 = not model.is_sdxl and not model.is_sd2
model.is_ssd = model.is_sdxl and 'model.diffusion_model.middle_block.1.transformer_blocks.0.attn1.to_q.weight' not in state_dict.keys()
if model.is_sdxl:
sd_models_xl.extend_sdxl(model)
model.load_state_dict(state_dict, strict=False)
timer.record("apply weights to model")
if model.is_ssd:
sd_hijack.model_hijack.convert_sdxl_to_ssd(model)
if shared.opts.sd_checkpoint_cache > 0:
# cache newly loaded model
checkpoints_loaded[checkpoint_info] = state_dict
checkpoints_loaded[checkpoint_info] = state_dict.copy()
model.load_state_dict(state_dict, strict=False)
timer.record("apply weights to model")
del state_dict
@ -453,6 +471,20 @@ def enable_midas_autodownload():
midas.api.load_model = load_model_wrapper
def patch_given_betas():
import ldm.models.diffusion.ddpm
def patched_register_schedule(*args, **kwargs):
"""a modified version of register_schedule function that converts plain list from Omegaconf into numpy"""
if isinstance(args[1], ListConfig):
args = (args[0], np.array(args[1]), *args[2:])
original_register_schedule(*args, **kwargs)
original_register_schedule = patches.patch(__name__, ldm.models.diffusion.ddpm.DDPM, 'register_schedule', patched_register_schedule)
def repair_config(sd_config):
if not hasattr(sd_config.model.params, "use_ema"):
@ -777,17 +809,7 @@ def reload_model_weights(sd_model=None, info=None):
def unload_model_weights(sd_model=None, info=None):
timer = Timer()
if model_data.sd_model:
model_data.sd_model.to(devices.cpu)
sd_hijack.model_hijack.undo_hijack(model_data.sd_model)
model_data.sd_model = None
sd_model = None
gc.collect()
devices.torch_gc()
print(f"Unloaded weights {timer.summary()}.")
send_model_to_cpu(sd_model or shared.sd_model)
return sd_model

5
modules/sd_models_config.py
View File

@ -21,7 +21,7 @@ config_unopenclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-h-inf
config_inpainting = os.path.join(sd_configs_path, "v1-inpainting-inference.yaml")
config_instruct_pix2pix = os.path.join(sd_configs_path, "instruct-pix2pix.yaml")
config_alt_diffusion = os.path.join(sd_configs_path, "alt-diffusion-inference.yaml")
config_alt_diffusion_m18 = os.path.join(sd_configs_path, "alt-diffusion-m18-inference.yaml")
def is_using_v_parameterization_for_sd2(state_dict):
"""
@ -95,7 +95,10 @@ def guess_model_config_from_state_dict(sd, filename):
if diffusion_model_input.shape[1] == 8:
return config_instruct_pix2pix
if sd.get('cond_stage_model.roberta.embeddings.word_embeddings.weight', None) is not None:
if sd.get('cond_stage_model.transformation.weight').size()[0] == 1024:
return config_alt_diffusion_m18
return config_alt_diffusion
return config_default

5
modules/sd_models_types.py
View File

@ -22,7 +22,10 @@ class WebuiSdModel(LatentDiffusion):
"""structure with additional information about the file with model's weights"""
is_sdxl: bool
"""True if the model's architecture is SDXL"""
"""True if the model's architecture is SDXL or SSD"""
is_ssd: bool
"""True if the model is SSD"""
is_sd2: bool
"""True if the model's architecture is SD 2.x"""

2
modules/sd_samplers_extra.py
View File

@ -60,7 +60,7 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No
sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx].item(), sigmas[max_idx].item(), device=sigmas.device)[:-1]
while restart_times > 0:
restart_times -= 1
step_list.extend([(old_sigma, new_sigma) for (old_sigma, new_sigma) in zip(sigma_restart[:-1], sigma_restart[1:])])
step_list.extend(zip(sigma_restart[:-1], sigma_restart[1:]))
last_sigma = None
for old_sigma, new_sigma in tqdm.tqdm(step_list, disable=disable):

4
modules/sd_samplers_timesteps_impl.py
View File

@ -11,7 +11,7 @@ from modules.models.diffusion.uni_pc import uni_pc
def ddim(model, x, timesteps, extra_args=None, callback=None, disable=None, eta=0.0):
alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
alphas = alphas_cumprod[timesteps]
alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64 if x.device.type != 'mps' else torch.float32)
alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64 if x.device.type != 'mps' and x.device.type != 'xpu' else torch.float32)
sqrt_one_minus_alphas = torch.sqrt(1 - alphas)
sigmas = eta * np.sqrt((1 - alphas_prev.cpu().numpy()) / (1 - alphas.cpu()) * (1 - alphas.cpu() / alphas_prev.cpu().numpy()))
@ -43,7 +43,7 @@ def ddim(model, x, timesteps, extra_args=None, callback=None, disable=None, eta=
def plms(model, x, timesteps, extra_args=None, callback=None, disable=None):
alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
alphas = alphas_cumprod[timesteps]
alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64 if x.device.type != 'mps' else torch.float32)
alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64 if x.device.type != 'mps' and x.device.type != 'xpu' else torch.float32)
sqrt_one_minus_alphas = torch.sqrt(1 - alphas)
extra_args = {} if extra_args is None else extra_args

14
modules/sd_unet.py
View File

@ -1,12 +1,11 @@
import torch.nn
import ldm.modules.diffusionmodules.openaimodel
from modules import script_callbacks, shared, devices
unet_options = []
current_unet_option = None
current_unet = None
original_forward = None # not used, only left temporarily for compatibility
def list_unets():
new_unets = script_callbacks.list_unets_callback()
@ -84,9 +83,12 @@ class SdUnet(torch.nn.Module):
pass
def UNetModel_forward(self, x, timesteps=None, context=None, *args, **kwargs):
if current_unet is not None:
return current_unet.forward(x, timesteps, context, *args, **kwargs)
def create_unet_forward(original_forward):
def UNetModel_forward(self, x, timesteps=None, context=None, *args, **kwargs):
if current_unet is not None:
return current_unet.forward(x, timesteps, context, *args, **kwargs)
return ldm.modules.diffusionmodules.openaimodel.copy_of_UNetModel_forward_for_webui(self, x, timesteps, context, *args, **kwargs)
return original_forward(self, x, timesteps, context, *args, **kwargs)
return UNetModel_forward

4
modules/shared_cmd_options.py
View File

@ -14,5 +14,5 @@ if os.environ.get('IGNORE_CMD_ARGS_ERRORS', None) is None:
else:
cmd_opts, _ = parser.parse_known_args()
cmd_opts.disable_extension_access = any([cmd_opts.share, cmd_opts.listen, cmd_opts.ngrok, cmd_opts.server_name]) and not cmd_opts.enable_insecure_extension_access
cmd_opts.webui_is_non_local = any([cmd_opts.share, cmd_opts.listen, cmd_opts.ngrok, cmd_opts.server_name])
cmd_opts.disable_extension_access = cmd_opts.webui_is_non_local and not cmd_opts.enable_insecure_extension_access

22
modules/shared_items.py
View File

@ -44,9 +44,9 @@ def refresh_unet_list():
modules.sd_unet.list_unets()
def list_checkpoint_tiles():
def list_checkpoint_tiles(use_short=False):
import modules.sd_models
return modules.sd_models.checkpoint_tiles()
return modules.sd_models.checkpoint_tiles(use_short)
def refresh_checkpoints():
@ -66,7 +66,25 @@ def reload_hypernetworks():
shared.hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
def get_infotext_names():
from modules import generation_parameters_copypaste, shared
res = {}
for info in shared.opts.data_labels.values():
if info.infotext:
res[info.infotext] = 1
for tab_data in generation_parameters_copypaste.paste_fields.values():
for _, name in tab_data.get("fields") or []:
if isinstance(name, str):
res[name] = 1
return list(res)
ui_reorder_categories_builtin_items = [
"prompt",
"image",
"inpaint",
"sampler",
"accordions",

151
modules/shared_options.py
View File

@ -3,7 +3,7 @@ import gradio as gr
from modules import localization, ui_components, shared_items, shared, interrogate, shared_gradio_themes
from modules.paths_internal import models_path, script_path, data_path, sd_configs_path, sd_default_config, sd_model_file, default_sd_model_file, extensions_dir, extensions_builtin_dir # noqa: F401
from modules.shared_cmd_options import cmd_opts
from modules.options import options_section, OptionInfo, OptionHTML
from modules.options import options_section, OptionInfo, OptionHTML, categories
options_templates = {}
hide_dirs = shared.hide_dirs
@ -21,12 +21,19 @@ restricted_opts = {
"outdir_init_images"
}
options_templates.update(options_section(('saving-images', "Saving images/grids"), {
categories.register_category("saving", "Saving images")
categories.register_category("sd", "Stable Diffusion")
categories.register_category("ui", "User Interface")
categories.register_category("system", "System")
categories.register_category("postprocessing", "Postprocessing")
categories.register_category("training", "Training")
options_templates.update(options_section(('saving-images', "Saving images/grids", "saving"), {
"samples_save": OptionInfo(True, "Always save all generated images"),
"samples_format": OptionInfo('png', 'File format for images'),
"samples_filename_pattern": OptionInfo("", "Images filename pattern", component_args=hide_dirs).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"),
"save_images_add_number": OptionInfo(True, "Add number to filename when saving", component_args=hide_dirs),
"save_images_replace_action": OptionInfo("Replace", "Saving the image to an existing file", gr.Radio, {"choices": ["Replace", "Add number suffix"], **hide_dirs}),
"grid_save": OptionInfo(True, "Always save all generated image grids"),
"grid_format": OptionInfo('png', 'File format for grids'),
"grid_extended_filename": OptionInfo(False, "Add extended info (seed, prompt) to filename when saving grid"),
@ -39,8 +46,6 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
"grid_text_inactive_color": OptionInfo("#999999", "Inactive text color for image grids", ui_components.FormColorPicker, {}),
"grid_background_color": OptionInfo("#ffffff", "Background color for image grids", ui_components.FormColorPicker, {}),
"enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
"save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters."),
"save_images_before_face_restoration": OptionInfo(False, "Save a copy of image before doing face restoration."),
"save_images_before_highres_fix": OptionInfo(False, "Save a copy of image before applying highres fix."),
"save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
@ -62,9 +67,12 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
"clean_temp_dir_at_start": OptionInfo(False, "Cleanup non-default temporary directory when starting webui"),
"save_incomplete_images": OptionInfo(False, "Save incomplete images").info("save images that has been interrupted in mid-generation; even if not saved, they will still show up in webui output."),
"notification_audio": OptionInfo(True, "Play notification sound after image generation").info("notification.mp3 should be present in the root directory").needs_reload_ui(),
"notification_volume": OptionInfo(100, "Notification sound volume", gr.Slider, {"minimum": 0, "maximum": 100, "step": 1}).info("in %"),
}))
options_templates.update(options_section(('saving-paths', "Paths for saving"), {
options_templates.update(options_section(('saving-paths', "Paths for saving", "saving"), {
"outdir_samples": OptionInfo("", "Output directory for images; if empty, defaults to three directories below", component_args=hide_dirs),
"outdir_txt2img_samples": OptionInfo("outputs/txt2img-images", 'Output directory for txt2img images', component_args=hide_dirs),
"outdir_img2img_samples": OptionInfo("outputs/img2img-images", 'Output directory for img2img images', component_args=hide_dirs),
@ -76,7 +84,7 @@ options_templates.update(options_section(('saving-paths', "Paths for saving"), {
"outdir_init_images": OptionInfo("outputs/init-images", "Directory for saving init images when using img2img", component_args=hide_dirs),
}))
options_templates.update(options_section(('saving-to-dirs', "Saving to a directory"), {
options_templates.update(options_section(('saving-to-dirs', "Saving to a directory", "saving"), {
"save_to_dirs": OptionInfo(True, "Save images to a subdirectory"),
"grid_save_to_dirs": OptionInfo(True, "Save grids to a subdirectory"),
"use_save_to_dirs_for_ui": OptionInfo(False, "When using \"Save\" button, save images to a subdirectory"),
@ -84,22 +92,23 @@ options_templates.update(options_section(('saving-to-dirs', "Saving to a directo
"directories_max_prompt_words": OptionInfo(8, "Max prompt words for [prompt_words] pattern", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1, **hide_dirs}),
}))
options_templates.update(options_section(('upscaling', "Upscaling"), {
options_templates.update(options_section(('upscaling', "Upscaling", "postprocessing"), {
"ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}).info("0 = no tiling"),
"ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap for ESRGAN upscalers.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}).info("Low values = visible seam"),
"realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI.", gr.CheckboxGroup, lambda: {"choices": shared_items.realesrgan_models_names()}),
"upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in shared.sd_upscalers]}),
}))
options_templates.update(options_section(('face-restoration', "Face restoration"), {
options_templates.update(options_section(('face-restoration', "Face restoration", "postprocessing"), {
"face_restoration": OptionInfo(False, "Restore faces", infotext='Face restoration').info("will use a third-party model on generation result to reconstruct faces"),
"face_restoration_model": OptionInfo("CodeFormer", "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in shared.face_restorers]}),
"code_former_weight": OptionInfo(0.5, "CodeFormer weight", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}).info("0 = maximum effect; 1 = minimum effect"),
"face_restoration_unload": OptionInfo(False, "Move face restoration model from VRAM into RAM after processing"),
}))
options_templates.update(options_section(('system', "System"), {
options_templates.update(options_section(('system', "System", "system"), {
"auto_launch_browser": OptionInfo("Local", "Automatically open webui in browser on startup", gr.Radio, lambda: {"choices": ["Disable", "Local", "Remote"]}),
"enable_console_prompts": OptionInfo(shared.cmd_opts.enable_console_prompts, "Print prompts to console when generating with txt2img and img2img."),
"show_warnings": OptionInfo(False, "Show warnings in console.").needs_reload_ui(),
"show_gradio_deprecation_warnings": OptionInfo(True, "Show gradio deprecation warnings in console.").needs_reload_ui(),
"memmon_poll_rate": OptionInfo(8, "VRAM usage polls per second during generation.", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}).info("0 = disable"),
@ -109,15 +118,16 @@ options_templates.update(options_section(('system', "System"), {
"list_hidden_files": OptionInfo(True, "Load models/files in hidden directories").info("directory is hidden if its name starts with \".\""),
"disable_mmap_load_safetensors": OptionInfo(False, "Disable memmapping for loading .safetensors files.").info("fixes very slow loading speed in some cases"),
"hide_ldm_prints": OptionInfo(True, "Prevent Stability-AI's ldm/sgm modules from printing noise to console."),
"dump_stacks_on_signal": OptionInfo(False, "Print stack traces before exiting the program with ctrl+c."),
}))
options_templates.update(options_section(('API', "API"), {
options_templates.update(options_section(('API', "API", "system"), {
"api_enable_requests": OptionInfo(True, "Allow http:// and https:// URLs for input images in API", restrict_api=True),
"api_forbid_local_requests": OptionInfo(True, "Forbid URLs to local resources", restrict_api=True),
"api_useragent": OptionInfo("", "User agent for requests", restrict_api=True),
}))
options_templates.update(options_section(('training', "Training"), {
options_templates.update(options_section(('training', "Training", "training"), {
"unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
"pin_memory": OptionInfo(False, "Turn on pin_memory for DataLoader. Makes training slightly faster but can increase memory usage."),
"save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training of embedding or HN can be resumed with the matching optim file."),
@ -132,8 +142,8 @@ options_templates.update(options_section(('training', "Training"), {
"training_tensorboard_flush_every": OptionInfo(120, "How often, in seconds, to flush the pending tensorboard events and summaries to disk."),
}))
options_templates.update(options_section(('sd', "Stable Diffusion"), {
"sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": shared_items.list_checkpoint_tiles()}, refresh=shared_items.refresh_checkpoints, infotext='Model hash'),
options_templates.update(options_section(('sd', "Stable Diffusion", "sd"), {
"sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": shared_items.list_checkpoint_tiles(shared.opts.sd_checkpoint_dropdown_use_short)}, refresh=shared_items.refresh_checkpoints, infotext='Model hash'),
"sd_checkpoints_limit": OptionInfo(1, "Maximum number of checkpoints loaded at the same time", gr.Slider, {"minimum": 1, "maximum": 10, "step": 1}),
"sd_checkpoints_keep_in_cpu": OptionInfo(True, "Only keep one model on device").info("will keep models other than the currently used one in RAM rather than VRAM"),
"sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}).info("obsolete; set to 0 and use the two settings above instead"),
@ -149,14 +159,14 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
"hires_fix_refiner_pass": OptionInfo("second pass", "Hires fix: which pass to enable refiner for", gr.Radio, {"choices": ["first pass", "second pass", "both passes"]}, infotext="Hires refiner"),
}))
options_templates.update(options_section(('sdxl', "Stable Diffusion XL"), {
options_templates.update(options_section(('sdxl', "Stable Diffusion XL", "sd"), {
"sdxl_crop_top": OptionInfo(0, "crop top coordinate"),
"sdxl_crop_left": OptionInfo(0, "crop left coordinate"),
"sdxl_refiner_low_aesthetic_score": OptionInfo(2.5, "SDXL low aesthetic score", gr.Number).info("used for refiner model negative prompt"),
"sdxl_refiner_high_aesthetic_score": OptionInfo(6.0, "SDXL high aesthetic score", gr.Number).info("used for refiner model prompt"),
}))
options_templates.update(options_section(('vae', "VAE"), {
options_templates.update(options_section(('vae', "VAE", "sd"), {
"sd_vae_explanation": OptionHTML("""
<abbr title='Variational autoencoder'>VAE</abbr> is a neural network that transforms a standard <abbr title='red/green/blue'>RGB</abbr>
image into latent space representation and back. Latent space representation is what stable diffusion is working on during sampling
@ -171,7 +181,7 @@ For img2img, VAE is used to process user's input image before the sampling, and
"sd_vae_decode_method": OptionInfo("Full", "VAE type for decode", gr.Radio, {"choices": ["Full", "TAESD"]}, infotext='VAE Decoder').info("method to decode latent to image"),
}))
options_templates.update(options_section(('img2img', "img2img"), {
options_templates.update(options_section(('img2img', "img2img", "sd"), {
"inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, infotext='Conditional mask weight'),
"initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for img2img", gr.Slider, {"minimum": 0.0, "maximum": 1.5, "step": 0.001}, infotext='Noise multiplier'),
"img2img_extra_noise": OptionInfo(0.0, "Extra noise multiplier for img2img and hires fix", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, infotext='Extra noise').info("0 = disabled (default); should be lower than denoising strength"),
@ -184,9 +194,10 @@ options_templates.update(options_section(('img2img', "img2img"), {
"img2img_inpaint_sketch_default_brush_color": OptionInfo("#ffffff", "Inpaint sketch initial brush color", ui_components.FormColorPicker, {}).info("default brush color of img2img inpaint sketch").needs_reload_ui(),
"return_mask": OptionInfo(False, "For inpainting, include the greyscale mask in results for web"),
"return_mask_composite": OptionInfo(False, "For inpainting, include masked composite in results for web"),
"img2img_batch_show_results_limit": OptionInfo(32, "Show the first N batch img2img results in UI", gr.Slider, {"minimum": -1, "maximum": 1000, "step": 1}).info('0: disable, -1: show all images. Too many images can cause lag'),
}))
options_templates.update(options_section(('optimizations', "Optimizations"), {
options_templates.update(options_section(('optimizations', "Optimizations", "sd"), {
"cross_attention_optimization": OptionInfo("Automatic", "Cross attention optimization", gr.Dropdown, lambda: {"choices": shared_items.cross_attention_optimizations()}),
"s_min_uncond": OptionInfo(0.0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 15.0, "step": 0.01}).link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9177").info("skip negative prompt for some steps when the image is almost ready; 0=disable, higher=faster"),
"token_merging_ratio": OptionInfo(0.0, "Token merging ratio", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}, infotext='Token merging ratio').link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9256").info("0=disable, higher=faster"),
@ -197,7 +208,7 @@ options_templates.update(options_section(('optimizations', "Optimizations"), {
"batch_cond_uncond": OptionInfo(True, "Batch cond/uncond").info("do both conditional and unconditional denoising in one batch; uses a bit more VRAM during sampling, but improves speed; previously this was controlled by --always-batch-cond-uncond comandline argument"),
}))
options_templates.update(options_section(('compatibility', "Compatibility"), {
options_templates.update(options_section(('compatibility', "Compatibility", "sd"), {
"use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
"use_old_karras_scheduler_sigmas": OptionInfo(False, "Use old karras scheduler sigmas (0.1 to 10)."),
"no_dpmpp_sde_batch_determinism": OptionInfo(False, "Do not make DPM++ SDE deterministic across different batch sizes."),
@ -222,14 +233,17 @@ options_templates.update(options_section(('interrogate', "Interrogate"), {
"deepbooru_filter_tags": OptionInfo("", "deepbooru: filter out those tags").info("separate by comma"),
}))
options_templates.update(options_section(('extra_networks', "Extra Networks"), {
options_templates.update(options_section(('extra_networks', "Extra Networks", "sd"), {
"extra_networks_show_hidden_directories": OptionInfo(True, "Show hidden directories").info("directory is hidden if its name starts with \".\"."),
"extra_networks_dir_button_function": OptionInfo(False, "Add a '/' to the beginning of directory buttons").info("Buttons will display the contents of the selected directory without acting as a search filter."),
"extra_networks_hidden_models": OptionInfo("When searched", "Show cards for models in hidden directories", gr.Radio, {"choices": ["Always", "When searched", "Never"]}).info('"When searched" option will only show the item when the search string has 4 characters or more'),
"extra_networks_default_multiplier": OptionInfo(1.0, "Default multiplier for extra networks", gr.Slider, {"minimum": 0.0, "maximum": 2.0, "step": 0.01}),
"extra_networks_card_width": OptionInfo(0, "Card width for Extra Networks").info("in pixels"),
"extra_networks_card_height": OptionInfo(0, "Card height for Extra Networks").info("in pixels"),
"extra_networks_card_text_scale": OptionInfo(1.0, "Card text scale", gr.Slider, {"minimum": 0.0, "maximum": 2.0, "step": 0.01}).info("1 = original size"),
"extra_networks_card_show_desc": OptionInfo(True, "Show description on card"),
"extra_networks_card_order_field": OptionInfo("Path", "Default order field for Extra Networks cards", gr.Dropdown, {"choices": ['Path', 'Name', 'Date Created', 'Date Modified']}).needs_reload_ui(),
"extra_networks_card_order": OptionInfo("Ascending", "Default order for Extra Networks cards", gr.Dropdown, {"choices": ['Ascending', 'Descending']}).needs_reload_ui(),
"extra_networks_add_text_separator": OptionInfo(" ", "Extra networks separator").info("extra text to add before <...> when adding extra network to prompt"),
"ui_extra_networks_tab_reorder": OptionInfo("", "Extra networks tab order").needs_reload_ui(),
"textual_inversion_print_at_load": OptionInfo(False, "Print a list of Textual Inversion embeddings when loading model"),
@ -237,42 +251,66 @@ options_templates.update(options_section(('extra_networks', "Extra Networks"), {
"sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": ["None", *shared.hypernetworks]}, refresh=shared_items.reload_hypernetworks),
}))
options_templates.update(options_section(('ui', "User interface"), {
"localization": OptionInfo("None", "Localization", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)).needs_reload_ui(),
"gradio_theme": OptionInfo("Default", "Gradio theme", ui_components.DropdownEditable, lambda: {"choices": ["Default"] + shared_gradio_themes.gradio_hf_hub_themes}).info("you can also manually enter any of themes from the <a href='https://huggingface.co/spaces/gradio/theme-gallery'>gallery</a>.").needs_reload_ui(),
"gradio_themes_cache": OptionInfo(True, "Cache gradio themes locally").info("disable to update the selected Gradio theme"),
"gallery_height": OptionInfo("", "Gallery height", gr.Textbox).info("an be any valid CSS value").needs_reload_ui(),
"return_grid": OptionInfo(True, "Show grid in results for web"),
"do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
"send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"),
"send_size": OptionInfo(True, "Send size when sending prompt or image to another interface"),
"js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
"js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
"js_modal_lightbox_gamepad": OptionInfo(False, "Navigate image viewer with gamepad"),
"js_modal_lightbox_gamepad_repeat": OptionInfo(250, "Gamepad repeat period, in milliseconds"),
"show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
"samplers_in_dropdown": OptionInfo(True, "Use dropdown for sampler selection instead of radio group").needs_reload_ui(),
"dimensions_and_batch_together": OptionInfo(True, "Show Width/Height and Batch sliders in same row").needs_reload_ui(),
"keyedit_precision_attention": OptionInfo(0.1, "Ctrl+up/down precision when editing (attention:1.1)", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
"keyedit_precision_extra": OptionInfo(0.05, "Ctrl+up/down precision when editing <extra networks:0.9>", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
"keyedit_delimiters": OptionInfo(".,\\/!?%^*;:{}=`~()", "Ctrl+up/down word delimiters"),
options_templates.update(options_section(('ui_prompt_editing', "Prompt editing", "ui"), {
"keyedit_precision_attention": OptionInfo(0.1, "Precision for (attention:1.1) when editing the prompt with Ctrl+up/down", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
"keyedit_precision_extra": OptionInfo(0.05, "Precision for <extra networks:0.9> when editing the prompt with Ctrl+up/down", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
"keyedit_delimiters": OptionInfo(r".,\/!?%^*;:{}=`~() ", "Word delimiters when editing the prompt with Ctrl+up/down"),
"keyedit_delimiters_whitespace": OptionInfo(["Tab", "Carriage Return", "Line Feed"], "Ctrl+up/down whitespace delimiters", gr.CheckboxGroup, lambda: {"choices": ["Tab", "Carriage Return", "Line Feed"]}),
"keyedit_move": OptionInfo(True, "Alt+left/right moves prompt elements"),
"quicksettings_list": OptionInfo(["sd_model_checkpoint"], "Quicksettings list", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that appear at the top of page rather than in settings tab").needs_reload_ui(),
"ui_tab_order": OptionInfo([], "UI tab order", ui_components.DropdownMulti, lambda: {"choices": list(shared.tab_names)}).needs_reload_ui(),
"hidden_tabs": OptionInfo([], "Hidden UI tabs", ui_components.DropdownMulti, lambda: {"choices": list(shared.tab_names)}).needs_reload_ui(),
"ui_reorder_list": OptionInfo([], "txt2img/img2img UI item order", ui_components.DropdownMulti, lambda: {"choices": list(shared_items.ui_reorder_categories())}).info("selected items appear first").needs_reload_ui(),
"hires_fix_show_sampler": OptionInfo(False, "Hires fix: show hires checkpoint and sampler selection").needs_reload_ui(),
"hires_fix_show_prompts": OptionInfo(False, "Hires fix: show hires prompt and negative prompt").needs_reload_ui(),
"disable_token_counters": OptionInfo(False, "Disable prompt token counters").needs_reload_ui(),
}))
options_templates.update(options_section(('ui_gallery', "Gallery", "ui"), {
"return_grid": OptionInfo(True, "Show grid in gallery"),
"do_not_show_images": OptionInfo(False, "Do not show any images in gallery"),
"js_modal_lightbox": OptionInfo(True, "Full page image viewer: enable"),
"js_modal_lightbox_initially_zoomed": OptionInfo(True, "Full page image viewer: show images zoomed in by default"),
"js_modal_lightbox_gamepad": OptionInfo(False, "Full page image viewer: navigate with gamepad"),
"js_modal_lightbox_gamepad_repeat": OptionInfo(250, "Full page image viewer: gamepad repeat period").info("in milliseconds"),
"gallery_height": OptionInfo("", "Gallery height", gr.Textbox).info("can be any valid CSS value, for example 768px or 20em").needs_reload_ui(),
}))
options_templates.update(options_section(('infotext', "Infotext"), {
"add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
"add_model_name_to_info": OptionInfo(True, "Add model name to generation information"),
"add_user_name_to_info": OptionInfo(False, "Add user name to generation information when authenticated"),
"add_version_to_infotext": OptionInfo(True, "Add program version to generation information"),
options_templates.update(options_section(('ui_alternatives', "UI alternatives", "ui"), {
"compact_prompt_box": OptionInfo(False, "Compact prompt layout").info("puts prompt and negative prompt inside the Generate tab, leaving more vertical space for the image on the right").needs_reload_ui(),
"samplers_in_dropdown": OptionInfo(True, "Use dropdown for sampler selection instead of radio group").needs_reload_ui(),
"dimensions_and_batch_together": OptionInfo(True, "Show Width/Height and Batch sliders in same row").needs_reload_ui(),
"sd_checkpoint_dropdown_use_short": OptionInfo(False, "Checkpoint dropdown: use filenames without paths").info("models in subdirectories like photo/sd15.ckpt will be listed as just sd15.ckpt"),
"hires_fix_show_sampler": OptionInfo(False, "Hires fix: show hires checkpoint and sampler selection").needs_reload_ui(),
"hires_fix_show_prompts": OptionInfo(False, "Hires fix: show hires prompt and negative prompt").needs_reload_ui(),
"txt2img_settings_accordion": OptionInfo(False, "Settings in txt2img hidden under Accordion").needs_reload_ui(),
"img2img_settings_accordion": OptionInfo(False, "Settings in img2img hidden under Accordion").needs_reload_ui(),
}))
options_templates.update(options_section(('ui', "User interface", "ui"), {
"localization": OptionInfo("None", "Localization", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)).needs_reload_ui(),
"quicksettings_list": OptionInfo(["sd_model_checkpoint"], "Quicksettings list", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that appear at the top of page rather than in settings tab").needs_reload_ui(),
"ui_tab_order": OptionInfo([], "UI tab order", ui_components.DropdownMulti, lambda: {"choices": list(shared.tab_names)}).needs_reload_ui(),
"hidden_tabs": OptionInfo([], "Hidden UI tabs", ui_components.DropdownMulti, lambda: {"choices": list(shared.tab_names)}).needs_reload_ui(),
"ui_reorder_list": OptionInfo([], "UI item order for txt2img/img2img tabs", ui_components.DropdownMulti, lambda: {"choices": list(shared_items.ui_reorder_categories())}).info("selected items appear first").needs_reload_ui(),
"gradio_theme": OptionInfo("Default", "Gradio theme", ui_components.DropdownEditable, lambda: {"choices": ["Default"] + shared_gradio_themes.gradio_hf_hub_themes}).info("you can also manually enter any of themes from the <a href='https://huggingface.co/spaces/gradio/theme-gallery'>gallery</a>.").needs_reload_ui(),
"gradio_themes_cache": OptionInfo(True, "Cache gradio themes locally").info("disable to update the selected Gradio theme"),
"show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
"send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"),
"send_size": OptionInfo(True, "Send size when sending prompt or image to another interface"),
}))
options_templates.update(options_section(('infotext', "Infotext", "ui"), {
"infotext_explanation": OptionHTML("""
Infotext is what this software calls the text that contains generation parameters and can be used to generate the same picture again.
It is displayed in UI below the image. To use infotext, paste it into the prompt and click the ↙️ paste button.
"""),
"enable_pnginfo": OptionInfo(True, "Write infotext to metadata of the generated image"),
"save_txt": OptionInfo(False, "Create a text file with infotext next to every generated image"),
"add_model_name_to_info": OptionInfo(True, "Add model name to infotext"),
"add_model_hash_to_info": OptionInfo(True, "Add model hash to infotext"),
"add_vae_name_to_info": OptionInfo(True, "Add VAE name to infotext"),
"add_vae_hash_to_info": OptionInfo(True, "Add VAE hash to infotext"),
"add_user_name_to_info": OptionInfo(False, "Add user name to infotext when authenticated"),
"add_version_to_infotext": OptionInfo(True, "Add program version to infotext"),
"disable_weights_auto_swap": OptionInfo(True, "Disregard checkpoint information from pasted infotext").info("when reading generation parameters from text into UI"),
"infotext_skip_pasting": OptionInfo([], "Disregard fields from pasted infotext", ui_components.DropdownMulti, lambda: {"choices": shared_items.get_infotext_names()}),
"infotext_styles": OptionInfo("Apply if any", "Infer styles from prompts of pasted infotext", gr.Radio, {"choices": ["Ignore", "Apply", "Discard", "Apply if any"]}).info("when reading generation parameters from text into UI)").html("""<ul style='margin-left: 1.5em'>
<li>Ignore: keep prompt and styles dropdown as it is.</li>
<li>Apply: remove style text from prompt, always replace styles dropdown value with found styles (even if none are found).</li>
@ -282,7 +320,7 @@ options_templates.update(options_section(('infotext', "Infotext"), {
}))
options_templates.update(options_section(('ui', "Live previews"), {
options_templates.update(options_section(('ui', "Live previews", "ui"), {
"show_progressbar": OptionInfo(True, "Show progressbar"),
"live_previews_enable": OptionInfo(True, "Show live previews of the created image"),
"live_previews_image_format": OptionInfo("png", "Live preview file format", gr.Radio, {"choices": ["jpeg", "png", "webp"]}),
@ -293,9 +331,10 @@ options_templates.update(options_section(('ui', "Live previews"), {
"live_preview_content": OptionInfo("Prompt", "Live preview subject", gr.Radio, {"choices": ["Combined", "Prompt", "Negative prompt"]}),
"live_preview_refresh_period": OptionInfo(1000, "Progressbar and preview update period").info("in milliseconds"),
"live_preview_fast_interrupt": OptionInfo(False, "Return image with chosen live preview method on interrupt").info("makes interrupts faster"),
"js_live_preview_in_modal_lightbox": OptionInfo(False, "Show Live preview in full page image viewer"),
}))
options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
options_templates.update(options_section(('sampler-params', "Sampler parameters", "sd"), {
"hide_samplers": OptionInfo([], "Hide samplers in user interface", gr.CheckboxGroup, lambda: {"choices": [x.name for x in shared_items.list_samplers()]}).needs_reload_ui(),
"eta_ddim": OptionInfo(0.0, "Eta for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, infotext='Eta DDIM').info("noise multiplier; higher = more unpredictable results"),
"eta_ancestral": OptionInfo(1.0, "Eta for k-diffusion samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, infotext='Eta').info("noise multiplier; currently only applies to ancestral samplers (i.e. Euler a) and SDE samplers"),
@ -305,8 +344,8 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
's_tmax': OptionInfo(0.0, "sigma tmax", gr.Slider, {"minimum": 0.0, "maximum": 999.0, "step": 0.01}, infotext='Sigma tmax').info("0 = inf; end value of the sigma range; only applies to Euler, Heun, and DPM2"),
's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.1, "step": 0.001}, infotext='Sigma noise').info('amount of additional noise to counteract loss of detail during sampling'),
'k_sched_type': OptionInfo("Automatic", "Scheduler type", gr.Dropdown, {"choices": ["Automatic", "karras", "exponential", "polyexponential"]}, infotext='Schedule type').info("lets you override the noise schedule for k-diffusion samplers; choosing Automatic disables the three parameters below"),
'sigma_min': OptionInfo(0.0, "sigma min", gr.Number, infotext='Schedule max sigma').info("0 = default (~0.03); minimum noise strength for k-diffusion noise scheduler"),
'sigma_max': OptionInfo(0.0, "sigma max", gr.Number, infotext='Schedule min sigma').info("0 = default (~14.6); maximum noise strength for k-diffusion noise scheduler"),
'sigma_min': OptionInfo(0.0, "sigma min", gr.Number, infotext='Schedule min sigma').info("0 = default (~0.03); minimum noise strength for k-diffusion noise scheduler"),
'sigma_max': OptionInfo(0.0, "sigma max", gr.Number, infotext='Schedule max sigma').info("0 = default (~14.6); maximum noise strength for k-diffusion noise scheduler"),
'rho': OptionInfo(0.0, "rho", gr.Number, infotext='Schedule rho').info("0 = default (7 for karras, 1 for polyexponential); higher values result in a steeper noise schedule (decreases faster)"),
'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}, infotext='ENSD').info("ENSD; does not improve anything, just produces different results for ancestral samplers - only useful for reproducing images"),
'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma", infotext='Discard penultimate sigma').link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/6044"),
@ -317,10 +356,11 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
'uni_pc_lower_order_final': OptionInfo(True, "UniPC lower order final", infotext='UniPC lower order final'),
}))
options_templates.update(options_section(('postprocessing', "Postprocessing"), {
options_templates.update(options_section(('postprocessing', "Postprocessing", "postprocessing"), {
'postprocessing_enable_in_main_ui': OptionInfo([], "Enable postprocessing operations in txt2img and img2img tabs", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
'postprocessing_operation_order': OptionInfo([], "Postprocessing operation order", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
'upscaling_max_images_in_cache': OptionInfo(5, "Maximum number of images in upscaling cache", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
'postprocessing_existing_caption_action': OptionInfo("Ignore", "Action for existing captions", gr.Radio, {"choices": ["Ignore", "Keep", "Prepend", "Append"]}).info("when generating captions using postprocessing; Ignore = use generated; Keep = use original; Prepend/Append = combine both"),
}))
options_templates.update(options_section((None, "Hidden options"), {
@ -329,4 +369,3 @@ options_templates.update(options_section((None, "Hidden options"), {
"restore_config_state_file": OptionInfo("", "Config state file to restore from, under 'config-states/' folder"),
"sd_checkpoint_hash": OptionInfo("", "SHA256 hash of the current checkpoint"),
}))

2
modules/shared_state.py
View File

@ -103,6 +103,7 @@ class State:
def begin(self, job: str = "(unknown)"):
self.sampling_step = 0
self.time_start = time.time()
self.job_count = -1
self.processing_has_refined_job_count = False
self.job_no = 0
@ -114,7 +115,6 @@ class State:
self.skipped = False
self.interrupted = False
self.textinfo = None
self.time_start = time.time()
self.job = job
devices.torch_gc()
log.info("Starting job %s", job)

167
modules/styles.py
View File

@ -1,7 +1,7 @@
import csv
import fnmatch
import os
import os.path
import re
import typing
import shutil
@ -10,6 +10,7 @@ class PromptStyle(typing.NamedTuple):
name: str
prompt: str
negative_prompt: str
path: str = None
def merge_prompts(style_prompt: str, prompt: str) -> str:
@ -29,38 +30,61 @@ def apply_styles_to_prompt(prompt, styles):
return prompt
re_spaces = re.compile(" +")
def unwrap_style_text_from_prompt(style_text, prompt):
"""
Checks the prompt to see if the style text is wrapped around it. If so,
returns True plus the prompt text without the style text. Otherwise, returns
False with the original prompt.
def extract_style_text_from_prompt(style_text, prompt):
stripped_prompt = re.sub(re_spaces, " ", prompt.strip())
stripped_style_text = re.sub(re_spaces, " ", style_text.strip())
Note that the "cleaned" version of the style text is only used for matching
purposes here. It isn't returned; the original style text is not modified.
"""
stripped_prompt = prompt
stripped_style_text = style_text
if "{prompt}" in stripped_style_text:
left, right = stripped_style_text.split("{prompt}", 2)
# Work out whether the prompt is wrapped in the style text. If so, we
# return True and the "inner" prompt text that isn't part of the style.
try:
left, right = stripped_style_text.split("{prompt}", 2)
except ValueError as e:
# If the style text has multple "{prompt}"s, we can't split it into
# two parts. This is an error, but we can't do anything about it.
print(f"Unable to compare style text to prompt:\n{style_text}")
print(f"Error: {e}")
return False, prompt
if stripped_prompt.startswith(left) and stripped_prompt.endswith(right):
prompt = stripped_prompt[len(left):len(stripped_prompt)-len(right)]
prompt = stripped_prompt[len(left) : len(stripped_prompt) - len(right)]
return True, prompt
else:
# Work out whether the given prompt ends with the style text. If so, we
# return True and the prompt text up to where the style text starts.
if stripped_prompt.endswith(stripped_style_text):
prompt = stripped_prompt[:len(stripped_prompt)-len(stripped_style_text)]
if prompt.endswith(', '):
prompt = stripped_prompt[: len(stripped_prompt) - len(stripped_style_text)]
if prompt.endswith(", "):
prompt = prompt[:-2]
return True, prompt
return False, prompt
def extract_style_from_prompts(style: PromptStyle, prompt, negative_prompt):
def extract_original_prompts(style: PromptStyle, prompt, negative_prompt):
"""
Takes a style and compares it to the prompt and negative prompt. If the style
matches, returns True plus the prompt and negative prompt with the style text
removed. Otherwise, returns False with the original prompt and negative prompt.
"""
if not style.prompt and not style.negative_prompt:
return False, prompt, negative_prompt
match_positive, extracted_positive = extract_style_text_from_prompt(style.prompt, prompt)
match_positive, extracted_positive = unwrap_style_text_from_prompt(
style.prompt, prompt
)
if not match_positive:
return False, prompt, negative_prompt
match_negative, extracted_negative = extract_style_text_from_prompt(style.negative_prompt, negative_prompt)
match_negative, extracted_negative = unwrap_style_text_from_prompt(
style.negative_prompt, negative_prompt
)
if not match_negative:
return False, prompt, negative_prompt
@ -69,25 +93,84 @@ def extract_style_from_prompts(style: PromptStyle, prompt, negative_prompt):
class StyleDatabase:
def __init__(self, path: str):
self.no_style = PromptStyle("None", "", "")
self.no_style = PromptStyle("None", "", "", None)
self.styles = {}
self.path = path
folder, file = os.path.split(self.path)
filename, _, ext = file.partition('*')
self.default_path = os.path.join(folder, filename + ext)
self.prompt_fields = [field for field in PromptStyle._fields if field != "path"]
self.reload()
def reload(self):
"""
Clears the style database and reloads the styles from the CSV file(s)
matching the path used to initialize the database.
"""
self.styles.clear()
if not os.path.exists(self.path):
return
path, filename = os.path.split(self.path)
with open(self.path, "r", encoding="utf-8-sig", newline='') as file:
if "*" in filename:
fileglob = filename.split("*")[0] + "*.csv"
filelist = []
for file in os.listdir(path):
if fnmatch.fnmatch(file, fileglob):
filelist.append(file)
# Add a visible divider to the style list
half_len = round(len(file) / 2)
divider = f"{'-' * (20 - half_len)} {file.upper()}"
divider = f"{divider} {'-' * (40 - len(divider))}"
self.styles[divider] = PromptStyle(
f"{divider}", None, None, "do_not_save"
)
# Add styles from this CSV file
self.load_from_csv(os.path.join(path, file))
if len(filelist) == 0:
print(f"No styles found in {path} matching {fileglob}")
return
elif not os.path.exists(self.path):
print(f"Style database not found: {self.path}")
return
else:
self.load_from_csv(self.path)
def load_from_csv(self, path: str):
with open(path, "r", encoding="utf-8-sig", newline="") as file:
reader = csv.DictReader(file, skipinitialspace=True)
for row in reader:
# Ignore empty rows or rows starting with a comment
if not row or row["name"].startswith("#"):
continue
# Support loading old CSV format with "name, text"-columns
prompt = row["prompt"] if "prompt" in row else row["text"]
negative_prompt = row.get("negative_prompt", "")
self.styles[row["name"]] = PromptStyle(row["name"], prompt, negative_prompt)
# Add style to database
self.styles[row["name"]] = PromptStyle(
row["name"], prompt, negative_prompt, path
)
def get_style_paths(self) -> set:
"""Returns a set of all distinct paths of files that styles are loaded from."""
# Update any styles without a path to the default path
for style in list(self.styles.values()):
if not style.path:
self.styles[style.name] = style._replace(path=self.default_path)
# Create a list of all distinct paths, including the default path
style_paths = set()
style_paths.add(self.default_path)
for _, style in self.styles.items():
if style.path:
style_paths.add(style.path)
# Remove any paths for styles that are just list dividers
style_paths.discard("do_not_save")
return style_paths
def get_style_prompts(self, styles):
return [self.styles.get(x, self.no_style).prompt for x in styles]
@ -96,20 +179,40 @@ class StyleDatabase:
return [self.styles.get(x, self.no_style).negative_prompt for x in styles]
def apply_styles_to_prompt(self, prompt, styles):
return apply_styles_to_prompt(prompt, [self.styles.get(x, self.no_style).prompt for x in styles])
return apply_styles_to_prompt(
prompt, [self.styles.get(x, self.no_style).prompt for x in styles]
)
def apply_negative_styles_to_prompt(self, prompt, styles):
return apply_styles_to_prompt(prompt, [self.styles.get(x, self.no_style).negative_prompt for x in styles])
return apply_styles_to_prompt(
prompt, [self.styles.get(x, self.no_style).negative_prompt for x in styles]
)
def save_styles(self, path: str) -> None:
# Always keep a backup file around
if os.path.exists(path):
shutil.copy(path, f"{path}.bak")
def save_styles(self, path: str = None) -> None:
# The path argument is deprecated, but kept for backwards compatibility
_ = path
with open(path, "w", encoding="utf-8-sig", newline='') as file:
writer = csv.DictWriter(file, fieldnames=PromptStyle._fields)
writer.writeheader()
writer.writerows(style._asdict() for k, style in self.styles.items())
style_paths = self.get_style_paths()
csv_names = [os.path.split(path)[1].lower() for path in style_paths]
for style_path in style_paths:
# Always keep a backup file around
if os.path.exists(style_path):
shutil.copy(style_path, f"{style_path}.bak")
# Write the styles to the CSV file
with open(style_path, "w", encoding="utf-8-sig", newline="") as file:
writer = csv.DictWriter(file, fieldnames=self.prompt_fields)
writer.writeheader()
for style in (s for s in self.styles.values() if s.path == style_path):
# Skip style list dividers, e.g. "STYLES.CSV"
if style.name.lower().strip("# ") in csv_names:
continue
# Write style fields, ignoring the path field
writer.writerow(
{k: v for k, v in style._asdict().items() if k != "path"}
)
def extract_styles_from_prompt(self, prompt, negative_prompt):
extracted = []
@ -120,7 +223,9 @@ class StyleDatabase:
found_style = None
for style in applicable_styles:
is_match, new_prompt, new_neg_prompt = extract_style_from_prompts(style, prompt, negative_prompt)
is_match, new_prompt, new_neg_prompt = extract_original_prompts(
style, prompt, negative_prompt
)
if is_match:
found_style = style
prompt = new_prompt

4
modules/sub_quadratic_attention.py
View File

@ -15,7 +15,7 @@ import torch
from torch import Tensor
from torch.utils.checkpoint import checkpoint
import math
from typing import Optional, NamedTuple, List
from typing import Optional, NamedTuple
def narrow_trunc(
@ -97,7 +97,7 @@ def _query_chunk_attention(
)
return summarize_chunk(query, key_chunk, value_chunk)
chunks: List[AttnChunk] = [
chunks: list[AttnChunk] = [
chunk_scanner(chunk) for chunk in torch.arange(0, k_tokens, kv_chunk_size)
]
acc_chunk = AttnChunk(*map(torch.stack, zip(*chunks)))

18
modules/sysinfo.py
View File

@ -1,7 +1,6 @@
import json
import os
import sys
import traceback
import platform
import hashlib
@ -84,7 +83,7 @@ def get_dict():
"Checksum": checksum_token,
"Commandline": get_argv(),
"Torch env info": get_torch_sysinfo(),
"Exceptions": get_exceptions(),
"Exceptions": errors.get_exceptions(),
"CPU": {
"model": platform.processor(),
"count logical": psutil.cpu_count(logical=True),
@ -104,21 +103,6 @@ def get_dict():
return res
def format_traceback(tb):
return [[f"{x.filename}, line {x.lineno}, {x.name}", x.line] for x in traceback.extract_tb(tb)]
def format_exception(e, tb):
return {"exception": str(e), "traceback": format_traceback(tb)}
def get_exceptions():
try:
return list(reversed(errors.exception_records))
except Exception as e:
return str(e)
def get_environment():
return {k: os.environ[k] for k in sorted(os.environ) if k in environment_whitelist}

233
modules/textual_inversion/autocrop.py
View File

@ -3,6 +3,8 @@ import requests
import os
import numpy as np
from PIL import ImageDraw
from modules import paths_internal
from pkg_resources import parse_version
GREEN = "#0F0"
BLUE = "#00F"
@ -25,7 +27,6 @@ def crop_image(im, settings):
elif is_portrait(settings.crop_width, settings.crop_height):
scale_by = settings.crop_height / im.height
im = im.resize((int(im.width * scale_by), int(im.height * scale_by)))
im_debug = im.copy()
@ -69,6 +70,7 @@ def crop_image(im, settings):
return results
def focal_point(im, settings):
corner_points = image_corner_points(im, settings) if settings.corner_points_weight > 0 else []
entropy_points = image_entropy_points(im, settings) if settings.entropy_points_weight > 0 else []
@ -78,118 +80,120 @@ def focal_point(im, settings):
weight_pref_total = 0
if corner_points:
weight_pref_total += settings.corner_points_weight
weight_pref_total += settings.corner_points_weight
if entropy_points:
weight_pref_total += settings.entropy_points_weight
weight_pref_total += settings.entropy_points_weight
if face_points:
weight_pref_total += settings.face_points_weight
weight_pref_total += settings.face_points_weight
corner_centroid = None
if corner_points:
corner_centroid = centroid(corner_points)
corner_centroid.weight = settings.corner_points_weight / weight_pref_total
pois.append(corner_centroid)
corner_centroid = centroid(corner_points)
corner_centroid.weight = settings.corner_points_weight / weight_pref_total
pois.append(corner_centroid)
entropy_centroid = None
if entropy_points:
entropy_centroid = centroid(entropy_points)
entropy_centroid.weight = settings.entropy_points_weight / weight_pref_total
pois.append(entropy_centroid)
entropy_centroid = centroid(entropy_points)
entropy_centroid.weight = settings.entropy_points_weight / weight_pref_total
pois.append(entropy_centroid)
face_centroid = None
if face_points:
face_centroid = centroid(face_points)
face_centroid.weight = settings.face_points_weight / weight_pref_total
pois.append(face_centroid)
face_centroid = centroid(face_points)
face_centroid.weight = settings.face_points_weight / weight_pref_total
pois.append(face_centroid)
average_point = poi_average(pois, settings)
if settings.annotate_image:
d = ImageDraw.Draw(im)
max_size = min(im.width, im.height) * 0.07
if corner_centroid is not None:
color = BLUE
box = corner_centroid.bounding(max_size * corner_centroid.weight)
d.text((box[0], box[1]-15), f"Edge: {corner_centroid.weight:.02f}", fill=color)
d.ellipse(box, outline=color)
if len(corner_points) > 1:
for f in corner_points:
d.rectangle(f.bounding(4), outline=color)
if entropy_centroid is not None:
color = "#ff0"
box = entropy_centroid.bounding(max_size * entropy_centroid.weight)
d.text((box[0], box[1]-15), f"Entropy: {entropy_centroid.weight:.02f}", fill=color)
d.ellipse(box, outline=color)
if len(entropy_points) > 1:
for f in entropy_points:
d.rectangle(f.bounding(4), outline=color)
if face_centroid is not None:
color = RED
box = face_centroid.bounding(max_size * face_centroid.weight)
d.text((box[0], box[1]-15), f"Face: {face_centroid.weight:.02f}", fill=color)
d.ellipse(box, outline=color)
if len(face_points) > 1:
for f in face_points:
d.rectangle(f.bounding(4), outline=color)
d = ImageDraw.Draw(im)
max_size = min(im.width, im.height) * 0.07
if corner_centroid is not None:
color = BLUE
box = corner_centroid.bounding(max_size * corner_centroid.weight)
d.text((box[0], box[1] - 15), f"Edge: {corner_centroid.weight:.02f}", fill=color)
d.ellipse(box, outline=color)
if len(corner_points) > 1:
for f in corner_points:
d.rectangle(f.bounding(4), outline=color)
if entropy_centroid is not None:
color = "#ff0"
box = entropy_centroid.bounding(max_size * entropy_centroid.weight)
d.text((box[0], box[1] - 15), f"Entropy: {entropy_centroid.weight:.02f}", fill=color)
d.ellipse(box, outline=color)
if len(entropy_points) > 1:
for f in entropy_points:
d.rectangle(f.bounding(4), outline=color)
if face_centroid is not None:
color = RED
box = face_centroid.bounding(max_size * face_centroid.weight)
d.text((box[0], box[1] - 15), f"Face: {face_centroid.weight:.02f}", fill=color)
d.ellipse(box, outline=color)
if len(face_points) > 1:
for f in face_points:
d.rectangle(f.bounding(4), outline=color)
d.ellipse(average_point.bounding(max_size), outline=GREEN)
d.ellipse(average_point.bounding(max_size), outline=GREEN)
return average_point
def image_face_points(im, settings):
if settings.dnn_model_path is not None:
detector = cv2.FaceDetectorYN.create(
settings.dnn_model_path,
"",
(im.width, im.height),
0.9, # score threshold
0.3, # nms threshold
5000 # keep top k before nms
)
faces = detector.detect(np.array(im))
results = []
if faces[1] is not None:
for face in faces[1]:
x = face[0]
y = face[1]
w = face[2]
h = face[3]
results.append(
PointOfInterest(
int(x + (w * 0.5)), # face focus left/right is center
int(y + (h * 0.33)), # face focus up/down is close to the top of the head
size = w,
weight = 1/len(faces[1])
)
)
return results
detector = cv2.FaceDetectorYN.create(
settings.dnn_model_path,
"",
(im.width, im.height),
0.9, # score threshold
0.3, # nms threshold
5000 # keep top k before nms
)
faces = detector.detect(np.array(im))
results = []
if faces[1] is not None:
for face in faces[1]:
x = face[0]
y = face[1]
w = face[2]
h = face[3]
results.append(
PointOfInterest(
int(x + (w * 0.5)), # face focus left/right is center
int(y + (h * 0.33)), # face focus up/down is close to the top of the head
size=w,
weight=1 / len(faces[1])
)
)
return results
else:
np_im = np.array(im)
gray = cv2.cvtColor(np_im, cv2.COLOR_BGR2GRAY)
np_im = np.array(im)
gray = cv2.cvtColor(np_im, cv2.COLOR_BGR2GRAY)
tries = [
[ f'{cv2.data.haarcascades}haarcascade_eye.xml', 0.01 ],
[ f'{cv2.data.haarcascades}haarcascade_frontalface_default.xml', 0.05 ],
[ f'{cv2.data.haarcascades}haarcascade_profileface.xml', 0.05 ],
[ f'{cv2.data.haarcascades}haarcascade_frontalface_alt.xml', 0.05 ],
[ f'{cv2.data.haarcascades}haarcascade_frontalface_alt2.xml', 0.05 ],
[ f'{cv2.data.haarcascades}haarcascade_frontalface_alt_tree.xml', 0.05 ],
[ f'{cv2.data.haarcascades}haarcascade_eye_tree_eyeglasses.xml', 0.05 ],
[ f'{cv2.data.haarcascades}haarcascade_upperbody.xml', 0.05 ]
]
for t in tries:
classifier = cv2.CascadeClassifier(t[0])
minsize = int(min(im.width, im.height) * t[1]) # at least N percent of the smallest side
try:
faces = classifier.detectMultiScale(gray, scaleFactor=1.1,
minNeighbors=7, minSize=(minsize, minsize), flags=cv2.CASCADE_SCALE_IMAGE)
except Exception:
continue
tries = [
[f'{cv2.data.haarcascades}haarcascade_eye.xml', 0.01],
[f'{cv2.data.haarcascades}haarcascade_frontalface_default.xml', 0.05],
[f'{cv2.data.haarcascades}haarcascade_profileface.xml', 0.05],
[f'{cv2.data.haarcascades}haarcascade_frontalface_alt.xml', 0.05],
[f'{cv2.data.haarcascades}haarcascade_frontalface_alt2.xml', 0.05],
[f'{cv2.data.haarcascades}haarcascade_frontalface_alt_tree.xml', 0.05],
[f'{cv2.data.haarcascades}haarcascade_eye_tree_eyeglasses.xml', 0.05],
[f'{cv2.data.haarcascades}haarcascade_upperbody.xml', 0.05]
]
for t in tries:
classifier = cv2.CascadeClassifier(t[0])
minsize = int(min(im.width, im.height) * t[1]) # at least N percent of the smallest side
try:
faces = classifier.detectMultiScale(gray, scaleFactor=1.1,
minNeighbors=7, minSize=(minsize, minsize),
flags=cv2.CASCADE_SCALE_IMAGE)
except Exception:
continue
if faces:
rects = [[f[0], f[1], f[0] + f[2], f[1] + f[3]] for f in faces]
return [PointOfInterest((r[0] +r[2]) // 2, (r[1] + r[3]) // 2, size=abs(r[0]-r[2]), weight=1/len(rects)) for r in rects]
if faces:
rects = [[f[0], f[1], f[0] + f[2], f[1] + f[3]] for f in faces]
return [PointOfInterest((r[0] + r[2]) // 2, (r[1] + r[3]) // 2, size=abs(r[0] - r[2]),
weight=1 / len(rects)) for r in rects]
return []
@ -198,7 +202,7 @@ def image_corner_points(im, settings):
# naive attempt at preventing focal points from collecting at watermarks near the bottom
gd = ImageDraw.Draw(grayscale)
gd.rectangle([0, im.height*.9, im.width, im.height], fill="#999")
gd.rectangle([0, im.height * .9, im.width, im.height], fill="#999")
np_im = np.array(grayscale)
@ -206,7 +210,7 @@ def image_corner_points(im, settings):
np_im,
maxCorners=100,
qualityLevel=0.04,
minDistance=min(grayscale.width, grayscale.height)*0.06,
minDistance=min(grayscale.width, grayscale.height) * 0.06,
useHarrisDetector=False,
)
@ -215,8 +219,8 @@ def image_corner_points(im, settings):
focal_points = []
for point in points:
x, y = point.ravel()
focal_points.append(PointOfInterest(x, y, size=4, weight=1/len(points)))
x, y = point.ravel()
focal_points.append(PointOfInterest(x, y, size=4, weight=1 / len(points)))
return focal_points
@ -225,13 +229,13 @@ def image_entropy_points(im, settings):
landscape = im.height < im.width
portrait = im.height > im.width
if landscape:
move_idx = [0, 2]
move_max = im.size[0]
move_idx = [0, 2]
move_max = im.size[0]
elif portrait:
move_idx = [1, 3]
move_max = im.size[1]
move_idx = [1, 3]
move_max = im.size[1]
else:
return []
return []
e_max = 0
crop_current = [0, 0, settings.crop_width, settings.crop_height]
@ -241,14 +245,14 @@ def image_entropy_points(im, settings):
e = image_entropy(crop)
if (e > e_max):
e_max = e
crop_best = list(crop_current)
e_max = e
crop_best = list(crop_current)
crop_current[move_idx[0]] += 4
crop_current[move_idx[1]] += 4
x_mid = int(crop_best[0] + settings.crop_width/2)
y_mid = int(crop_best[1] + settings.crop_height/2)
x_mid = int(crop_best[0] + settings.crop_width / 2)
y_mid = int(crop_best[1] + settings.crop_height / 2)
return [PointOfInterest(x_mid, y_mid, size=25, weight=1.0)]
@ -294,22 +298,23 @@ def is_square(w, h):
return w == h
def download_and_cache_models(dirname):
download_url = 'https://github.com/opencv/opencv_zoo/blob/91fb0290f50896f38a0ab1e558b74b16bc009428/models/face_detection_yunet/face_detection_yunet_2022mar.onnx?raw=true'
model_file_name = 'face_detection_yunet.onnx'
model_dir_opencv = os.path.join(paths_internal.models_path, 'opencv')
if parse_version(cv2.__version__) >= parse_version('4.8'):
model_file_path = os.path.join(model_dir_opencv, 'face_detection_yunet_2023mar.onnx')
model_url = 'https://github.com/opencv/opencv_zoo/blob/b6e370b10f641879a87890d44e42173077154a05/models/face_detection_yunet/face_detection_yunet_2023mar.onnx?raw=true'
else:
model_file_path = os.path.join(model_dir_opencv, 'face_detection_yunet.onnx')
model_url = 'https://github.com/opencv/opencv_zoo/blob/91fb0290f50896f38a0ab1e558b74b16bc009428/models/face_detection_yunet/face_detection_yunet_2022mar.onnx?raw=true'
os.makedirs(dirname, exist_ok=True)
cache_file = os.path.join(dirname, model_file_name)
if not os.path.exists(cache_file):
print(f"downloading face detection model from '{download_url}' to '{cache_file}'")
response = requests.get(download_url)
with open(cache_file, "wb") as f:
def download_and_cache_models():
if not os.path.exists(model_file_path):
os.makedirs(model_dir_opencv, exist_ok=True)
print(f"downloading face detection model from '{model_url}' to '{model_file_path}'")
response = requests.get(model_url)
with open(model_file_path, "wb") as f:
f.write(response.content)
if os.path.exists(cache_file):
return cache_file
return None
return model_file_path
class PointOfInterest:

232
modules/textual_inversion/preprocess.py
View File

@ -1,232 +0,0 @@
import os
from PIL import Image, ImageOps
import math
import tqdm
from modules import paths, shared, images, deepbooru
from modules.textual_inversion import autocrop
def preprocess(id_task, process_src, process_dst, process_width, process_height, preprocess_txt_action, process_keep_original_size, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.15, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False, process_multicrop=None, process_multicrop_mindim=None, process_multicrop_maxdim=None, process_multicrop_minarea=None, process_multicrop_maxarea=None, process_multicrop_objective=None, process_multicrop_threshold=None):
try:
if process_caption:
shared.interrogator.load()
if process_caption_deepbooru:
deepbooru.model.start()
preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_keep_original_size, process_flip, process_split, process_caption, process_caption_deepbooru, split_threshold, overlap_ratio, process_focal_crop, process_focal_crop_face_weight, process_focal_crop_entropy_weight, process_focal_crop_edges_weight, process_focal_crop_debug, process_multicrop, process_multicrop_mindim, process_multicrop_maxdim, process_multicrop_minarea, process_multicrop_maxarea, process_multicrop_objective, process_multicrop_threshold)
finally:
if process_caption:
shared.interrogator.send_blip_to_ram()
if process_caption_deepbooru:
deepbooru.model.stop()
def listfiles(dirname):
return os.listdir(dirname)
class PreprocessParams:
src = None
dstdir = None
subindex = 0
flip = False
process_caption = False
process_caption_deepbooru = False
preprocess_txt_action = None
def save_pic_with_caption(image, index, params: PreprocessParams, existing_caption=None):
caption = ""
if params.process_caption:
caption += shared.interrogator.generate_caption(image)
if params.process_caption_deepbooru:
if caption:
caption += ", "
caption += deepbooru.model.tag_multi(image)
filename_part = params.src
filename_part = os.path.splitext(filename_part)[0]
filename_part = os.path.basename(filename_part)
basename = f"{index:05}-{params.subindex}-{filename_part}"
image.save(os.path.join(params.dstdir, f"{basename}.png"))
if params.preprocess_txt_action == 'prepend' and existing_caption:
caption = f"{existing_caption} {caption}"
elif params.preprocess_txt_action == 'append' and existing_caption:
caption = f"{caption} {existing_caption}"
elif params.preprocess_txt_action == 'copy' and existing_caption:
caption = existing_caption
caption = caption.strip()
if caption:
with open(os.path.join(params.dstdir, f"{basename}.txt"), "w", encoding="utf8") as file:
file.write(caption)
params.subindex += 1
def save_pic(image, index, params, existing_caption=None):
save_pic_with_caption(image, index, params, existing_caption=existing_caption)
if params.flip:
save_pic_with_caption(ImageOps.mirror(image), index, params, existing_caption=existing_caption)
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
# not using torchvision.transforms.CenterCrop because it doesn't allow float regions
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)
def preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_keep_original_size, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False, process_multicrop=None, process_multicrop_mindim=None, process_multicrop_maxdim=None, process_multicrop_minarea=None, process_multicrop_maxarea=None, process_multicrop_objective=None, process_multicrop_threshold=None):
width = process_width
height = process_height
src = os.path.abspath(process_src)
dst = os.path.abspath(process_dst)
split_threshold = max(0.0, min(1.0, split_threshold))
overlap_ratio = max(0.0, min(0.9, overlap_ratio))
assert src != dst, 'same directory specified as source and destination'
os.makedirs(dst, exist_ok=True)
files = listfiles(src)
shared.state.job = "preprocess"
shared.state.textinfo = "Preprocessing..."
shared.state.job_count = len(files)
params = PreprocessParams()
params.dstdir = dst
params.flip = process_flip
params.process_caption = process_caption
params.process_caption_deepbooru = process_caption_deepbooru
params.preprocess_txt_action = preprocess_txt_action
pbar = tqdm.tqdm(files)
for index, imagefile in enumerate(pbar):
params.subindex = 0
filename = os.path.join(src, imagefile)
try:
img = Image.open(filename)
img = ImageOps.exif_transpose(img)
img = img.convert("RGB")
except Exception:
continue
description = f"Preprocessing [Image {index}/{len(files)}]"
pbar.set_description(description)
shared.state.textinfo = description
params.src = filename
existing_caption = None
existing_caption_filename = f"{os.path.splitext(filename)[0]}.txt"
if os.path.exists(existing_caption_filename):
with open(existing_caption_filename, 'r', encoding="utf8") as file:
existing_caption = file.read()
if shared.state.interrupted:
break
if img.height > img.width:
ratio = (img.width * height) / (img.height * width)
inverse_xy = False
else:
ratio = (img.height * width) / (img.width * height)
inverse_xy = True
process_default_resize = True
if process_split and ratio < 1.0 and ratio <= split_threshold:
for splitted in split_pic(img, inverse_xy, width, height, overlap_ratio):
save_pic(splitted, index, params, existing_caption=existing_caption)
process_default_resize = False
if process_focal_crop and img.height != img.width:
dnn_model_path = None
try:
dnn_model_path = autocrop.download_and_cache_models(os.path.join(paths.models_path, "opencv"))
except Exception as e:
print("Unable to load face detection model for auto crop selection. Falling back to lower quality haar method.", e)
autocrop_settings = autocrop.Settings(
crop_width = width,
crop_height = height,
face_points_weight = process_focal_crop_face_weight,
entropy_points_weight = process_focal_crop_entropy_weight,
corner_points_weight = process_focal_crop_edges_weight,
annotate_image = process_focal_crop_debug,
dnn_model_path = dnn_model_path,
)
for focal in autocrop.crop_image(img, autocrop_settings):
save_pic(focal, index, params, existing_caption=existing_caption)
process_default_resize = False
if process_multicrop:
cropped = multicrop_pic(img, process_multicrop_mindim, process_multicrop_maxdim, process_multicrop_minarea, process_multicrop_maxarea, process_multicrop_objective, process_multicrop_threshold)
if cropped is not None:
save_pic(cropped, index, params, existing_caption=existing_caption)
else:
print(f"skipped {img.width}x{img.height} image {filename} (can't find suitable size within error threshold)")
process_default_resize = False
if process_keep_original_size:
save_pic(img, index, params, existing_caption=existing_caption)
process_default_resize = False
if process_default_resize:
img = images.resize_image(1, img, width, height)
save_pic(img, index, params, existing_caption=existing_caption)
shared.state.nextjob()

78
modules/textual_inversion/textual_inversion.py
View File

@ -181,40 +181,7 @@ class EmbeddingDatabase:
else:
return
# textual inversion embeddings
if 'string_to_param' in data:
param_dict = data['string_to_param']
param_dict = getattr(param_dict, '_parameters', param_dict) # fix for torch 1.12.1 loading saved file from torch 1.11
assert len(param_dict) == 1, 'embedding file has multiple terms in it'
emb = next(iter(param_dict.items()))[1]
vec = emb.detach().to(devices.device, dtype=torch.float32)
shape = vec.shape[-1]
vectors = vec.shape[0]
elif type(data) == dict and 'clip_g' in data and 'clip_l' in data: # SDXL embedding
vec = {k: v.detach().to(devices.device, dtype=torch.float32) for k, v in data.items()}
shape = data['clip_g'].shape[-1] + data['clip_l'].shape[-1]
vectors = data['clip_g'].shape[0]
elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor: # diffuser concepts
assert len(data.keys()) == 1, 'embedding file has multiple terms in it'
emb = next(iter(data.values()))
if len(emb.shape) == 1:
emb = emb.unsqueeze(0)
vec = emb.detach().to(devices.device, dtype=torch.float32)
shape = vec.shape[-1]
vectors = vec.shape[0]
else:
raise Exception(f"Couldn't identify {filename} as neither textual inversion embedding nor diffuser concept.")
embedding = Embedding(vec, name)
embedding.step = data.get('step', None)
embedding.sd_checkpoint = data.get('sd_checkpoint', None)
embedding.sd_checkpoint_name = data.get('sd_checkpoint_name', None)
embedding.vectors = vectors
embedding.shape = shape
embedding.filename = path
embedding.set_hash(hashes.sha256(embedding.filename, "textual_inversion/" + name) or '')
embedding = create_embedding_from_data(data, name, filename=filename, filepath=path)
if self.expected_shape == -1 or self.expected_shape == embedding.shape:
self.register_embedding(embedding, shared.sd_model)
@ -313,6 +280,45 @@ def create_embedding(name, num_vectors_per_token, overwrite_old, init_text='*'):
return fn
def create_embedding_from_data(data, name, filename='unknown embedding file', filepath=None):
if 'string_to_param' in data: # textual inversion embeddings
param_dict = data['string_to_param']
param_dict = getattr(param_dict, '_parameters', param_dict) # fix for torch 1.12.1 loading saved file from torch 1.11
assert len(param_dict) == 1, 'embedding file has multiple terms in it'
emb = next(iter(param_dict.items()))[1]
vec = emb.detach().to(devices.device, dtype=torch.float32)
shape = vec.shape[-1]
vectors = vec.shape[0]
elif type(data) == dict and 'clip_g' in data and 'clip_l' in data: # SDXL embedding
vec = {k: v.detach().to(devices.device, dtype=torch.float32) for k, v in data.items()}
shape = data['clip_g'].shape[-1] + data['clip_l'].shape[-1]
vectors = data['clip_g'].shape[0]
elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor: # diffuser concepts
assert len(data.keys()) == 1, 'embedding file has multiple terms in it'
emb = next(iter(data.values()))
if len(emb.shape) == 1:
emb = emb.unsqueeze(0)
vec = emb.detach().to(devices.device, dtype=torch.float32)
shape = vec.shape[-1]
vectors = vec.shape[0]
else:
raise Exception(f"Couldn't identify {filename} as neither textual inversion embedding nor diffuser concept.")
embedding = Embedding(vec, name)
embedding.step = data.get('step', None)
embedding.sd_checkpoint = data.get('sd_checkpoint', None)
embedding.sd_checkpoint_name = data.get('sd_checkpoint_name', None)
embedding.vectors = vectors
embedding.shape = shape
if filepath:
embedding.filename = filepath
embedding.set_hash(hashes.sha256(filepath, "textual_inversion/" + name) or '')
return embedding
def write_loss(log_directory, filename, step, epoch_len, values):
if shared.opts.training_write_csv_every == 0:
return
@ -386,7 +392,7 @@ def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, dat
assert log_directory, "Log directory is empty"
def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_embedding_every, template_filename, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_embedding_every, template_filename, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_name, preview_cfg_scale, preview_seed, preview_width, preview_height):
from modules import processing
save_embedding_every = save_embedding_every or 0
@ -590,7 +596,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
p.prompt = preview_prompt
p.negative_prompt = preview_negative_prompt
p.steps = preview_steps
p.sampler_name = sd_samplers.samplers[preview_sampler_index].name
p.sampler_name = sd_samplers.samplers_map[preview_sampler_name.lower()]
p.cfg_scale = preview_cfg_scale
p.seed = preview_seed
p.width = preview_width

7
modules/textual_inversion/ui.py
View File

@ -3,7 +3,6 @@ import html
import gradio as gr
import modules.textual_inversion.textual_inversion
import modules.textual_inversion.preprocess
from modules import sd_hijack, shared
@ -15,12 +14,6 @@ def create_embedding(name, initialization_text, nvpt, overwrite_old):
return gr.Dropdown.update(choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())), f"Created: {filename}", ""
def preprocess(*args):
modules.textual_inversion.preprocess.preprocess(*args)
return f"Preprocessing {'interrupted' if shared.state.interrupted else 'finished'}.", ""
def train_embedding(*args):
assert not shared.cmd_opts.lowvram, 'Training models with lowvram not possible'

4
modules/txt2img.py
View File

@ -3,7 +3,7 @@ from contextlib import closing
import modules.scripts
from modules import processing
from modules.generation_parameters_copypaste import create_override_settings_dict
from modules.shared import opts, cmd_opts
from modules.shared import opts
import modules.shared as shared
from modules.ui import plaintext_to_html
import gradio as gr
@ -45,7 +45,7 @@ def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, step
p.user = request.username
if cmd_opts.enable_console_prompts:
if shared.opts.enable_console_prompts:
print(f"\ntxt2img: {prompt}", file=shared.progress_print_out)
with closing(p):

420
modules/ui.py
View File

@ -4,6 +4,7 @@ import os
import sys
from functools import reduce
import warnings
from contextlib import ExitStack
import gradio as gr
import gradio.utils
@ -12,7 +13,7 @@ from PIL import Image, PngImagePlugin # noqa: F401
from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
from modules import gradio_extensons # noqa: F401
from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, shared_items, ui_settings, timer, sysinfo, ui_checkpoint_merger, ui_prompt_styles, scripts, sd_samplers, processing, ui_extra_networks
from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, shared_items, ui_settings, timer, sysinfo, ui_checkpoint_merger, scripts, sd_samplers, processing, ui_extra_networks, ui_toprow
from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML, InputAccordion, ResizeHandleRow
from modules.paths import script_path
from modules.ui_common import create_refresh_button
@ -25,7 +26,6 @@ import modules.hypernetworks.ui as hypernetworks_ui
import modules.textual_inversion.ui as textual_inversion_ui
import modules.textual_inversion.textual_inversion as textual_inversion
import modules.shared as shared
import modules.images
from modules import prompt_parser
from modules.sd_hijack import model_hijack
from modules.generation_parameters_copypaste import image_from_url_text
@ -151,11 +151,15 @@ def connect_clear_prompt(button):
)
def update_token_counter(text, steps):
def update_token_counter(text, steps, *, is_positive=True):
try:
text, _ = extra_networks.parse_prompt(text)
_, prompt_flat_list, _ = prompt_parser.get_multicond_prompt_list([text])
if is_positive:
_, prompt_flat_list, _ = prompt_parser.get_multicond_prompt_list([text])
else:
prompt_flat_list = [text]
prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(prompt_flat_list, steps)
except Exception:
@ -169,76 +173,9 @@ def update_token_counter(text, steps):
return f"<span class='gr-box gr-text-input'>{token_count}/{max_length}</span>"
class Toprow:
"""Creates a top row UI with prompts, generate button, styles, extra little buttons for things, and enables some functionality related to their operation"""
def update_negative_prompt_token_counter(text, steps):
return update_token_counter(text, steps, is_positive=False)
def __init__(self, is_img2img):
id_part = "img2img" if is_img2img else "txt2img"
self.id_part = id_part
with gr.Row(elem_id=f"{id_part}_toprow", variant="compact"):
with gr.Column(elem_id=f"{id_part}_prompt_container", scale=6):
with gr.Row():
with gr.Column(scale=80):
with gr.Row():
self.prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=3, placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
self.prompt_img = gr.File(label="", elem_id=f"{id_part}_prompt_image", file_count="single", type="binary", visible=False)
with gr.Row():
with gr.Column(scale=80):
with gr.Row():
self.negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
self.button_interrogate = None
self.button_deepbooru = None
if is_img2img:
with gr.Column(scale=1, elem_classes="interrogate-col"):
self.button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
self.button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
with gr.Column(scale=1, elem_id=f"{id_part}_actions_column"):
with gr.Row(elem_id=f"{id_part}_generate_box", elem_classes="generate-box"):
self.interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt", elem_classes="generate-box-interrupt")
self.skip = gr.Button('Skip', elem_id=f"{id_part}_skip", elem_classes="generate-box-skip")
self.submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary')
self.skip.click(
fn=lambda: shared.state.skip(),
inputs=[],
outputs=[],
)
self.interrupt.click(
fn=lambda: shared.state.interrupt(),
inputs=[],
outputs=[],
)
with gr.Row(elem_id=f"{id_part}_tools"):
self.paste = ToolButton(value=paste_symbol, elem_id="paste")
self.clear_prompt_button = ToolButton(value=clear_prompt_symbol, elem_id=f"{id_part}_clear_prompt")
self.restore_progress_button = ToolButton(value=restore_progress_symbol, elem_id=f"{id_part}_restore_progress", visible=False)
self.token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{id_part}_token_counter", elem_classes=["token-counter"])
self.token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
self.negative_token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{id_part}_negative_token_counter", elem_classes=["token-counter"])
self.negative_token_button = gr.Button(visible=False, elem_id=f"{id_part}_negative_token_button")
self.clear_prompt_button.click(
fn=lambda *x: x,
_js="confirm_clear_prompt",
inputs=[self.prompt, self.negative_prompt],
outputs=[self.prompt, self.negative_prompt],
)
self.ui_styles = ui_prompt_styles.UiPromptStyles(id_part, self.prompt, self.negative_prompt)
self.prompt_img.change(
fn=modules.images.image_data,
inputs=[self.prompt_img],
outputs=[self.prompt, self.prompt_img],
show_progress=False,
)
def setup_progressbar(*args, **kwargs):
@ -278,8 +215,8 @@ def apply_setting(key, value):
return getattr(opts, key)
def create_output_panel(tabname, outdir):
return ui_common.create_output_panel(tabname, outdir)
def create_output_panel(tabname, outdir, toprow=None):
return ui_common.create_output_panel(tabname, outdir, toprow)
def create_sampler_and_steps_selection(choices, tabname):
@ -326,7 +263,7 @@ def create_ui():
scripts.scripts_txt2img.initialize_scripts(is_img2img=False)
with gr.Blocks(analytics_enabled=False) as txt2img_interface:
toprow = Toprow(is_img2img=False)
toprow = ui_toprow.Toprow(is_img2img=False, is_compact=shared.opts.compact_prompt_box)
dummy_component = gr.Label(visible=False)
@ -334,10 +271,17 @@ def create_ui():
extra_tabs.__enter__()
with gr.Tab("Generation", id="txt2img_generation") as txt2img_generation_tab, ResizeHandleRow(equal_height=False):
with gr.Column(variant='compact', elem_id="txt2img_settings"):
with ExitStack() as stack:
if shared.opts.txt2img_settings_accordion:
stack.enter_context(gr.Accordion("Open for Settings", open=False))
stack.enter_context(gr.Column(variant='compact', elem_id="txt2img_settings"))
scripts.scripts_txt2img.prepare_ui()
for category in ordered_ui_categories():
if category == "prompt":
toprow.create_inline_toprow_prompts()
if category == "sampler":
steps, sampler_name = create_sampler_and_steps_selection(sd_samplers.visible_sampler_names(), "txt2img")
@ -348,7 +292,7 @@ def create_ui():
height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height")
with gr.Column(elem_id="txt2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn", label="Switch dims")
res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn", tooltip="Switch width/height")
if opts.dimensions_and_batch_together:
with gr.Column(elem_id="txt2img_column_batch"):
@ -432,7 +376,7 @@ def create_ui():
show_progress=False,
)
txt2img_gallery, generation_info, html_info, html_log = create_output_panel("txt2img", opts.outdir_txt2img_samples)
txt2img_gallery, generation_info, html_info, html_log = create_output_panel("txt2img", opts.outdir_txt2img_samples, toprow)
txt2img_args = dict(
fn=wrap_gradio_gpu_call(modules.txt2img.txt2img, extra_outputs=[None, '', '']),
@ -533,7 +477,7 @@ def create_ui():
]
toprow.token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[toprow.prompt, steps], outputs=[toprow.token_counter])
toprow.negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[toprow.negative_prompt, steps], outputs=[toprow.negative_token_counter])
toprow.negative_token_button.click(fn=wrap_queued_call(update_negative_prompt_token_counter), inputs=[toprow.negative_prompt, steps], outputs=[toprow.negative_token_counter])
extra_networks_ui = ui_extra_networks.create_ui(txt2img_interface, [txt2img_generation_tab], 'txt2img')
ui_extra_networks.setup_ui(extra_networks_ui, txt2img_gallery)
@ -544,13 +488,17 @@ def create_ui():
scripts.scripts_img2img.initialize_scripts(is_img2img=True)
with gr.Blocks(analytics_enabled=False) as img2img_interface:
toprow = Toprow(is_img2img=True)
toprow = ui_toprow.Toprow(is_img2img=True, is_compact=shared.opts.compact_prompt_box)
extra_tabs = gr.Tabs(elem_id="img2img_extra_tabs")
extra_tabs.__enter__()
with gr.Tab("Generation", id="img2img_generation") as img2img_generation_tab, ResizeHandleRow(equal_height=False):
with gr.Column(variant='compact', elem_id="img2img_settings"):
with ExitStack() as stack:
if shared.opts.img2img_settings_accordion:
stack.enter_context(gr.Accordion("Open for Settings", open=False))
stack.enter_context(gr.Column(variant='compact', elem_id="img2img_settings"))
copy_image_buttons = []
copy_image_destinations = {}
@ -567,85 +515,89 @@ def create_ui():
button = gr.Button(title)
copy_image_buttons.append((button, name, elem))
with gr.Tabs(elem_id="mode_img2img"):
img2img_selected_tab = gr.State(0)
with gr.TabItem('img2img', id='img2img', elem_id="img2img_img2img_tab") as tab_img2img:
init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool="editor", image_mode="RGBA", height=opts.img2img_editor_height)
add_copy_image_controls('img2img', init_img)
with gr.TabItem('Sketch', id='img2img_sketch', elem_id="img2img_img2img_sketch_tab") as tab_sketch:
sketch = gr.Image(label="Image for img2img", elem_id="img2img_sketch", show_label=False, source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGB", height=opts.img2img_editor_height, brush_color=opts.img2img_sketch_default_brush_color)
add_copy_image_controls('sketch', sketch)
with gr.TabItem('Inpaint', id='inpaint', elem_id="img2img_inpaint_tab") as tab_inpaint:
init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA", height=opts.img2img_editor_height, brush_color=opts.img2img_inpaint_mask_brush_color)
add_copy_image_controls('inpaint', init_img_with_mask)
with gr.TabItem('Inpaint sketch', id='inpaint_sketch', elem_id="img2img_inpaint_sketch_tab") as tab_inpaint_color:
inpaint_color_sketch = gr.Image(label="Color sketch inpainting", show_label=False, elem_id="inpaint_sketch", source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGB", height=opts.img2img_editor_height, brush_color=opts.img2img_inpaint_sketch_default_brush_color)
inpaint_color_sketch_orig = gr.State(None)
add_copy_image_controls('inpaint_sketch', inpaint_color_sketch)
def update_orig(image, state):
if image is not None:
same_size = state is not None and state.size == image.size
has_exact_match = np.any(np.all(np.array(image) == np.array(state), axis=-1))
edited = same_size and has_exact_match
return image if not edited or state is None else state
inpaint_color_sketch.change(update_orig, [inpaint_color_sketch, inpaint_color_sketch_orig], inpaint_color_sketch_orig)
with gr.TabItem('Inpaint upload', id='inpaint_upload', elem_id="img2img_inpaint_upload_tab") as tab_inpaint_upload:
init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", elem_id="img_inpaint_base")
init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", image_mode="RGBA", elem_id="img_inpaint_mask")
with gr.TabItem('Batch', id='batch', elem_id="img2img_batch_tab") as tab_batch:
hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
gr.HTML(
"<p style='padding-bottom: 1em;' class=\"text-gray-500\">Process images in a directory on the same machine where the server is running." +
"<br>Use an empty output directory to save pictures normally instead of writing to the output directory." +
f"<br>Add inpaint batch mask directory to enable inpaint batch processing."
f"{hidden}</p>"
)
img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, elem_id="img2img_batch_input_dir")
img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, elem_id="img2img_batch_output_dir")
img2img_batch_inpaint_mask_dir = gr.Textbox(label="Inpaint batch mask directory (required for inpaint batch processing only)", **shared.hide_dirs, elem_id="img2img_batch_inpaint_mask_dir")
with gr.Accordion("PNG info", open=False):
img2img_batch_use_png_info = gr.Checkbox(label="Append png info to prompts", **shared.hide_dirs, elem_id="img2img_batch_use_png_info")
img2img_batch_png_info_dir = gr.Textbox(label="PNG info directory", **shared.hide_dirs, placeholder="Leave empty to use input directory", elem_id="img2img_batch_png_info_dir")
img2img_batch_png_info_props = gr.CheckboxGroup(["Prompt", "Negative prompt", "Seed", "CFG scale", "Sampler", "Steps"], label="Parameters to take from png info", info="Prompts from png info will be appended to prompts set in ui.")
img2img_tabs = [tab_img2img, tab_sketch, tab_inpaint, tab_inpaint_color, tab_inpaint_upload, tab_batch]
for i, tab in enumerate(img2img_tabs):
tab.select(fn=lambda tabnum=i: tabnum, inputs=[], outputs=[img2img_selected_tab])
def copy_image(img):
if isinstance(img, dict) and 'image' in img:
return img['image']
return img
for button, name, elem in copy_image_buttons:
button.click(
fn=copy_image,
inputs=[elem],
outputs=[copy_image_destinations[name]],
)
button.click(
fn=lambda: None,
_js=f"switch_to_{name.replace(' ', '_')}",
inputs=[],
outputs=[],
)
with FormRow():
resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize")
scripts.scripts_img2img.prepare_ui()
for category in ordered_ui_categories():
if category == "prompt":
toprow.create_inline_toprow_prompts()
if category == "image":
with gr.Tabs(elem_id="mode_img2img"):
img2img_selected_tab = gr.State(0)
with gr.TabItem('img2img', id='img2img', elem_id="img2img_img2img_tab") as tab_img2img:
init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool="editor", image_mode="RGBA", height=opts.img2img_editor_height)
add_copy_image_controls('img2img', init_img)
with gr.TabItem('Sketch', id='img2img_sketch', elem_id="img2img_img2img_sketch_tab") as tab_sketch:
sketch = gr.Image(label="Image for img2img", elem_id="img2img_sketch", show_label=False, source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGB", height=opts.img2img_editor_height, brush_color=opts.img2img_sketch_default_brush_color)
add_copy_image_controls('sketch', sketch)
with gr.TabItem('Inpaint', id='inpaint', elem_id="img2img_inpaint_tab") as tab_inpaint:
init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA", height=opts.img2img_editor_height, brush_color=opts.img2img_inpaint_mask_brush_color)
add_copy_image_controls('inpaint', init_img_with_mask)
with gr.TabItem('Inpaint sketch', id='inpaint_sketch', elem_id="img2img_inpaint_sketch_tab") as tab_inpaint_color:
inpaint_color_sketch = gr.Image(label="Color sketch inpainting", show_label=False, elem_id="inpaint_sketch", source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGB", height=opts.img2img_editor_height, brush_color=opts.img2img_inpaint_sketch_default_brush_color)
inpaint_color_sketch_orig = gr.State(None)
add_copy_image_controls('inpaint_sketch', inpaint_color_sketch)
def update_orig(image, state):
if image is not None:
same_size = state is not None and state.size == image.size
has_exact_match = np.any(np.all(np.array(image) == np.array(state), axis=-1))
edited = same_size and has_exact_match
return image if not edited or state is None else state
inpaint_color_sketch.change(update_orig, [inpaint_color_sketch, inpaint_color_sketch_orig], inpaint_color_sketch_orig)
with gr.TabItem('Inpaint upload', id='inpaint_upload', elem_id="img2img_inpaint_upload_tab") as tab_inpaint_upload:
init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", elem_id="img_inpaint_base")
init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", image_mode="RGBA", elem_id="img_inpaint_mask")
with gr.TabItem('Batch', id='batch', elem_id="img2img_batch_tab") as tab_batch:
hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
gr.HTML(
"<p style='padding-bottom: 1em;' class=\"text-gray-500\">Process images in a directory on the same machine where the server is running." +
"<br>Use an empty output directory to save pictures normally instead of writing to the output directory." +
f"<br>Add inpaint batch mask directory to enable inpaint batch processing."
f"{hidden}</p>"
)
img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, elem_id="img2img_batch_input_dir")
img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, elem_id="img2img_batch_output_dir")
img2img_batch_inpaint_mask_dir = gr.Textbox(label="Inpaint batch mask directory (required for inpaint batch processing only)", **shared.hide_dirs, elem_id="img2img_batch_inpaint_mask_dir")
with gr.Accordion("PNG info", open=False):
img2img_batch_use_png_info = gr.Checkbox(label="Append png info to prompts", **shared.hide_dirs, elem_id="img2img_batch_use_png_info")
img2img_batch_png_info_dir = gr.Textbox(label="PNG info directory", **shared.hide_dirs, placeholder="Leave empty to use input directory", elem_id="img2img_batch_png_info_dir")
img2img_batch_png_info_props = gr.CheckboxGroup(["Prompt", "Negative prompt", "Seed", "CFG scale", "Sampler", "Steps", "Model hash"], label="Parameters to take from png info", info="Prompts from png info will be appended to prompts set in ui.")
img2img_tabs = [tab_img2img, tab_sketch, tab_inpaint, tab_inpaint_color, tab_inpaint_upload, tab_batch]
for i, tab in enumerate(img2img_tabs):
tab.select(fn=lambda tabnum=i: tabnum, inputs=[], outputs=[img2img_selected_tab])
def copy_image(img):
if isinstance(img, dict) and 'image' in img:
return img['image']
return img
for button, name, elem in copy_image_buttons:
button.click(
fn=copy_image,
inputs=[elem],
outputs=[copy_image_destinations[name]],
)
button.click(
fn=lambda: None,
_js=f"switch_to_{name.replace(' ', '_')}",
inputs=[],
outputs=[],
)
with FormRow():
resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize")
if category == "sampler":
steps, sampler_name = create_sampler_and_steps_selection(sd_samplers.visible_sampler_names(), "img2img")
@ -661,8 +613,8 @@ def create_ui():
width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height")
with gr.Column(elem_id="img2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="img2img_res_switch_btn")
detect_image_size_btn = ToolButton(value=detect_image_size_symbol, elem_id="img2img_detect_image_size_btn")
res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="img2img_res_switch_btn", tooltip="Switch width/height")
detect_image_size_btn = ToolButton(value=detect_image_size_symbol, elem_id="img2img_detect_image_size_btn", tooltip="Auto detect size from img2img")
with gr.Tab(label="Resize by", elem_id="img2img_tab_resize_by") as tab_scale_by:
scale_by = gr.Slider(minimum=0.05, maximum=4.0, step=0.05, label="Scale", value=1.0, elem_id="img2img_scale")
@ -683,12 +635,6 @@ def create_ui():
scale_by.release(**on_change_args)
button_update_resize_to.click(**on_change_args)
# the code below is meant to update the resolution label after the image in the image selection UI has changed.
# as it is now the event keeps firing continuously for inpaint edits, which ruins the page with constant requests.
# I assume this must be a gradio bug and for now we'll just do it for non-inpaint inputs.
for component in [init_img, sketch]:
component.change(fn=lambda: None, _js="updateImg2imgResizeToTextAfterChangingImage", inputs=[], outputs=[], show_progress=False)
tab_scale_to.select(fn=lambda: 0, inputs=[], outputs=[selected_scale_tab])
tab_scale_by.select(fn=lambda: 1, inputs=[], outputs=[selected_scale_tab])
@ -746,20 +692,26 @@ def create_ui():
with gr.Column(scale=4):
inpaint_full_res_padding = gr.Slider(label='Only masked padding, pixels', minimum=0, maximum=256, step=4, value=32, elem_id="img2img_inpaint_full_res_padding")
def select_img2img_tab(tab):
return gr.update(visible=tab in [2, 3, 4]), gr.update(visible=tab == 3),
for i, elem in enumerate(img2img_tabs):
elem.select(
fn=lambda tab=i: select_img2img_tab(tab),
inputs=[],
outputs=[inpaint_controls, mask_alpha],
)
if category not in {"accordions"}:
scripts.scripts_img2img.setup_ui_for_section(category)
img2img_gallery, generation_info, html_info, html_log = create_output_panel("img2img", opts.outdir_img2img_samples)
# the code below is meant to update the resolution label after the image in the image selection UI has changed.
# as it is now the event keeps firing continuously for inpaint edits, which ruins the page with constant requests.
# I assume this must be a gradio bug and for now we'll just do it for non-inpaint inputs.
for component in [init_img, sketch]:
component.change(fn=lambda: None, _js="updateImg2imgResizeToTextAfterChangingImage", inputs=[], outputs=[], show_progress=False)
def select_img2img_tab(tab):
return gr.update(visible=tab in [2, 3, 4]), gr.update(visible=tab == 3),
for i, elem in enumerate(img2img_tabs):
elem.select(
fn=lambda tab=i: select_img2img_tab(tab),
inputs=[],
outputs=[inpaint_controls, mask_alpha],
)
img2img_gallery, generation_info, html_info, html_log = create_output_panel("img2img", opts.outdir_img2img_samples, toprow)
img2img_args = dict(
fn=wrap_gradio_gpu_call(modules.img2img.img2img, extra_outputs=[None, '', '']),
@ -960,71 +912,6 @@ def create_ui():
with gr.Column():
create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary', elem_id="train_create_hypernetwork")
with gr.Tab(label="Preprocess images", id="preprocess_images"):
process_src = gr.Textbox(label='Source directory', elem_id="train_process_src")
process_dst = gr.Textbox(label='Destination directory', elem_id="train_process_dst")
process_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_process_width")
process_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_process_height")
preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"], elem_id="train_preprocess_txt_action")
with gr.Row():
process_keep_original_size = gr.Checkbox(label='Keep original size', elem_id="train_process_keep_original_size")
process_flip = gr.Checkbox(label='Create flipped copies', elem_id="train_process_flip")
process_split = gr.Checkbox(label='Split oversized images', elem_id="train_process_split")
process_focal_crop = gr.Checkbox(label='Auto focal point crop', elem_id="train_process_focal_crop")
process_multicrop = gr.Checkbox(label='Auto-sized crop', elem_id="train_process_multicrop")
process_caption = gr.Checkbox(label='Use BLIP for caption', elem_id="train_process_caption")
process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True, elem_id="train_process_caption_deepbooru")
with gr.Row(visible=False) as process_split_extra_row:
process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_split_threshold")
process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05, elem_id="train_process_overlap_ratio")
with gr.Row(visible=False) as process_focal_crop_row:
process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_face_weight")
process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_entropy_weight")
process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_edges_weight")
process_focal_crop_debug = gr.Checkbox(label='Create debug image', elem_id="train_process_focal_crop_debug")
with gr.Column(visible=False) as process_multicrop_col:
gr.Markdown('Each image is center-cropped with an automatically chosen width and height.')
with gr.Row():
process_multicrop_mindim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension lower bound", value=384, elem_id="train_process_multicrop_mindim")
process_multicrop_maxdim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension upper bound", value=768, elem_id="train_process_multicrop_maxdim")
with gr.Row():
process_multicrop_minarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area lower bound", value=64*64, elem_id="train_process_multicrop_minarea")
process_multicrop_maxarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area upper bound", value=640*640, elem_id="train_process_multicrop_maxarea")
with gr.Row():
process_multicrop_objective = gr.Radio(["Maximize area", "Minimize error"], value="Maximize area", label="Resizing objective", elem_id="train_process_multicrop_objective")
process_multicrop_threshold = gr.Slider(minimum=0, maximum=1, step=0.01, label="Error threshold", value=0.1, elem_id="train_process_multicrop_threshold")
with gr.Row():
with gr.Column(scale=3):
gr.HTML(value="")
with gr.Column():
with gr.Row():
interrupt_preprocessing = gr.Button("Interrupt", elem_id="train_interrupt_preprocessing")
run_preprocess = gr.Button(value="Preprocess", variant='primary', elem_id="train_run_preprocess")
process_split.change(
fn=lambda show: gr_show(show),
inputs=[process_split],
outputs=[process_split_extra_row],
)
process_focal_crop.change(
fn=lambda show: gr_show(show),
inputs=[process_focal_crop],
outputs=[process_focal_crop_row],
)
process_multicrop.change(
fn=lambda show: gr_show(show),
inputs=[process_multicrop],
outputs=[process_multicrop_col],
)
def get_textual_inversion_template_names():
return sorted(textual_inversion.textual_inversion_templates)
@ -1125,42 +1012,6 @@ def create_ui():
]
)
run_preprocess.click(
fn=wrap_gradio_gpu_call(textual_inversion_ui.preprocess, extra_outputs=[gr.update()]),
_js="start_training_textual_inversion",
inputs=[
dummy_component,
process_src,
process_dst,
process_width,
process_height,
preprocess_txt_action,
process_keep_original_size,
process_flip,
process_split,
process_caption,
process_caption_deepbooru,
process_split_threshold,
process_overlap_ratio,
process_focal_crop,
process_focal_crop_face_weight,
process_focal_crop_entropy_weight,
process_focal_crop_edges_weight,
process_focal_crop_debug,
process_multicrop,
process_multicrop_mindim,
process_multicrop_maxdim,
process_multicrop_minarea,
process_multicrop_maxarea,
process_multicrop_objective,
process_multicrop_threshold,
],
outputs=[
ti_output,
ti_outcome,
],
)
train_embedding.click(
fn=wrap_gradio_gpu_call(textual_inversion_ui.train_embedding, extra_outputs=[gr.update()]),
_js="start_training_textual_inversion",
@ -1234,12 +1085,6 @@ def create_ui():
outputs=[],
)
interrupt_preprocessing.click(
fn=lambda: shared.state.interrupt(),
inputs=[],
outputs=[],
)
loadsave = ui_loadsave.UiLoadsave(cmd_opts.ui_config_file)
settings = ui_settings.UiSettings()
@ -1286,7 +1131,7 @@ def create_ui():
loadsave.setup_ui()
if os.path.exists(os.path.join(script_path, "notification.mp3")):
if os.path.exists(os.path.join(script_path, "notification.mp3")) and shared.opts.notification_audio:
gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
footer = shared.html("footer.html")
@ -1338,7 +1183,6 @@ checkpoint: <a id="sd_checkpoint_hash">N/A</a>
def setup_ui_api(app):
from pydantic import BaseModel, Field
from typing import List
class QuicksettingsHint(BaseModel):
name: str = Field(title="Name of the quicksettings field")
@ -1347,7 +1191,7 @@ def setup_ui_api(app):
def quicksettings_hint():
return [QuicksettingsHint(name=k, label=v.label) for k, v in opts.data_labels.items()]
app.add_api_route("/internal/quicksettings-hint", quicksettings_hint, methods=["GET"], response_model=List[QuicksettingsHint])
app.add_api_route("/internal/quicksettings-hint", quicksettings_hint, methods=["GET"], response_model=list[QuicksettingsHint])
app.add_api_route("/internal/ping", lambda: {}, methods=["GET"])
@ -1357,7 +1201,7 @@ def setup_ui_api(app):
from fastapi.responses import PlainTextResponse
text = sysinfo.get()
filename = f"sysinfo-{datetime.datetime.utcnow().strftime('%Y-%m-%d-%H-%M')}.txt"
filename = f"sysinfo-{datetime.datetime.utcnow().strftime('%Y-%m-%d-%H-%M')}.json"
return PlainTextResponse(text, headers={'Content-Disposition': f'{"attachment" if attachment else "inline"}; filename="{filename}"'})

15
modules/ui_common.py
View File

@ -104,7 +104,7 @@ def save_files(js_data, images, do_make_zip, index):
return gr.File.update(value=fullfns, visible=True), plaintext_to_html(f"Saved: {filenames[0]}")
def create_output_panel(tabname, outdir):
def create_output_panel(tabname, outdir, toprow=None):
def open_folder(f):
if not os.path.exists(f):
@ -130,12 +130,15 @@ Requested path was: {f}
else:
sp.Popen(["xdg-open", path])
with gr.Column(variant='panel', elem_id=f"{tabname}_results"):
with gr.Group(elem_id=f"{tabname}_gallery_container"):
result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery", columns=4, preview=True, height=shared.opts.gallery_height or None)
with gr.Column(elem_id=f"{tabname}_results"):
if toprow:
toprow.create_inline_toprow_image()
generation_info = None
with gr.Column():
with gr.Column(variant='panel', elem_id=f"{tabname}_results_panel"):
with gr.Group(elem_id=f"{tabname}_gallery_container"):
result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery", columns=4, preview=True, height=shared.opts.gallery_height or None)
generation_info = None
with gr.Row(elem_id=f"image_buttons_{tabname}", elem_classes="image-buttons"):
open_folder_button = ToolButton(folder_symbol, elem_id=f'{tabname}_open_folder', visible=not shared.cmd_opts.hide_ui_dir_config, tooltip="Open images output directory.")

19
modules/ui_extensions.py
View File

@ -65,7 +65,7 @@ def save_config_state(name):
filename = os.path.join(config_states_dir, f"{timestamp}_{name}.json")
print(f"Saving backup of webui/extension state to {filename}.")
with open(filename, "w", encoding="utf-8") as f:
json.dump(current_config_state, f, indent=4)
json.dump(current_config_state, f, indent=4, ensure_ascii=False)
config_states.list_config_states()
new_value = next(iter(config_states.all_config_states.keys()), "Current")
new_choices = ["Current"] + list(config_states.all_config_states.keys())
@ -197,7 +197,7 @@ def update_config_states_table(state_name):
config_state = config_states.all_config_states[state_name]
config_name = config_state.get("name", "Config")
created_date = time.asctime(time.gmtime(config_state["created_at"]))
created_date = datetime.fromtimestamp(config_state["created_at"]).strftime('%Y-%m-%d %H:%M:%S')
filepath = config_state.get("filepath", "<unknown>")
try:
@ -335,6 +335,11 @@ def normalize_git_url(url):
return url
def get_extension_dirname_from_url(url):
*parts, last_part = url.split('/')
return normalize_git_url(last_part)
def install_extension_from_url(dirname, url, branch_name=None):
check_access()
@ -346,10 +351,7 @@ def install_extension_from_url(dirname, url, branch_name=None):
assert url, 'No URL specified'
if dirname is None or dirname == "":
*parts, last_part = url.split('/')
last_part = normalize_git_url(last_part)
dirname = last_part
dirname = get_extension_dirname_from_url(url)
target_dir = os.path.join(extensions.extensions_dir, dirname)
assert not os.path.exists(target_dir), f'Extension directory already exists: {target_dir}'
@ -449,7 +451,8 @@ def get_date(info: dict, key):
def refresh_available_extensions_from_data(hide_tags, sort_column, filter_text=""):
extlist = available_extensions["extensions"]
installed_extension_urls = {normalize_git_url(extension.remote): extension.name for extension in extensions.extensions}
installed_extensions = {extension.name for extension in extensions.extensions}
installed_extension_urls = {normalize_git_url(extension.remote) for extension in extensions.extensions if extension.remote is not None}
tags = available_extensions.get("tags", {})
tags_to_hide = set(hide_tags)
@ -482,7 +485,7 @@ def refresh_available_extensions_from_data(hide_tags, sort_column, filter_text="
if url is None:
continue
existing = installed_extension_urls.get(normalize_git_url(url), None)
existing = get_extension_dirname_from_url(url) in installed_extensions or normalize_git_url(url) in installed_extension_urls
extension_tags = extension_tags + ["installed"] if existing else extension_tags
if any(x for x in extension_tags if x in tags_to_hide):

67
modules/ui_extra_networks.py
View File

@ -1,3 +1,4 @@
import functools
import os.path
import urllib.parse
from pathlib import Path
@ -15,6 +16,17 @@ from modules.ui_components import ToolButton
extra_pages = []
allowed_dirs = set()
default_allowed_preview_extensions = ["png", "jpg", "jpeg", "webp", "gif"]
@functools.cache
def allowed_preview_extensions_with_extra(extra_extensions=None):
return set(default_allowed_preview_extensions) | set(extra_extensions or [])
def allowed_preview_extensions():
return allowed_preview_extensions_with_extra((shared.opts.samples_format, ))
def register_page(page):
"""registers extra networks page for the UI; recommend doing it in on_before_ui() callback for extensions"""
@ -33,9 +45,9 @@ def fetch_file(filename: str = ""):
if not any(Path(x).absolute() in Path(filename).absolute().parents for x in allowed_dirs):
raise ValueError(f"File cannot be fetched: {filename}. Must be in one of directories registered by extra pages.")
ext = os.path.splitext(filename)[1].lower()
if ext not in (".png", ".jpg", ".jpeg", ".webp", ".gif"):
raise ValueError(f"File cannot be fetched: {filename}. Only png, jpg, webp, and gif.")
ext = os.path.splitext(filename)[1].lower()[1:]
if ext not in allowed_preview_extensions():
raise ValueError(f"File cannot be fetched: {filename}. Extensions allowed: {allowed_preview_extensions()}.")
# would profit from returning 304
return FileResponse(filename, headers={"Accept-Ranges": "bytes"})
@ -91,6 +103,7 @@ class ExtraNetworksPage:
self.name = title.lower()
self.id_page = self.name.replace(" ", "_")
self.card_page = shared.html("extra-networks-card.html")
self.allow_prompt = True
self.allow_negative_prompt = False
self.metadata = {}
self.items = {}
@ -138,8 +151,13 @@ class ExtraNetworksPage:
continue
subdir = os.path.abspath(x)[len(parentdir):].replace("\\", "/")
while subdir.startswith("/"):
subdir = subdir[1:]
if shared.opts.extra_networks_dir_button_function:
if not subdir.startswith("/"):
subdir = "/" + subdir
else:
while subdir.startswith("/"):
subdir = subdir[1:]
is_empty = len(os.listdir(x)) == 0
if not is_empty and not subdir.endswith("/"):
@ -213,9 +231,9 @@ class ExtraNetworksPage:
metadata_button = ""
metadata = item.get("metadata")
if metadata:
metadata_button = f"<div class='metadata-button card-button' title='Show internal metadata' onclick='extraNetworksRequestMetadata(event, {quote_js(self.name)}, {quote_js(item['name'])})'></div>"
metadata_button = f"<div class='metadata-button card-button' title='Show internal metadata' onclick='extraNetworksRequestMetadata(event, {quote_js(self.name)}, {quote_js(html.escape(item['name']))})'></div>"
edit_button = f"<div class='edit-button card-button' title='Edit metadata' onclick='extraNetworksEditUserMetadata(event, {quote_js(tabname)}, {quote_js(self.id_page)}, {quote_js(item['name'])})'></div>"
edit_button = f"<div class='edit-button card-button' title='Edit metadata' onclick='extraNetworksEditUserMetadata(event, {quote_js(tabname)}, {quote_js(self.id_page)}, {quote_js(html.escape(item['name']))})'></div>"
local_path = ""
filename = item.get("filename", "")
@ -235,7 +253,7 @@ class ExtraNetworksPage:
if search_only and shared.opts.extra_networks_hidden_models == "Never":
return ""
sort_keys = " ".join([html.escape(f'data-sort-{k}={v}') for k, v in item.get("sort_keys", {}).items()]).strip()
sort_keys = " ".join([f'data-sort-{k}="{html.escape(str(v))}"' for k, v in item.get("sort_keys", {}).items()]).strip()
args = {
"background_image": background_image,
@ -266,6 +284,7 @@ class ExtraNetworksPage:
"date_created": int(stat.st_ctime or 0),
"date_modified": int(stat.st_mtime or 0),
"name": pth.name.lower(),
"path": str(pth.parent).lower(),
}
def find_preview(self, path):
@ -273,11 +292,7 @@ class ExtraNetworksPage:
Find a preview PNG for a given path (without extension) and call link_preview on it.
"""
preview_extensions = ["png", "jpg", "jpeg", "webp"]
if shared.opts.samples_format not in preview_extensions:
preview_extensions.append(shared.opts.samples_format)
potential_files = sum([[path + "." + ext, path + ".preview." + ext] for ext in preview_extensions], [])
potential_files = sum([[path + "." + ext, path + ".preview." + ext] for ext in allowed_preview_extensions()], [])
for file in potential_files:
if os.path.isfile(file):
@ -359,7 +374,10 @@ def create_ui(interface: gr.Blocks, unrelated_tabs, tabname):
related_tabs = []
for page in ui.stored_extra_pages:
with gr.Tab(page.title, id=page.id_page) as tab:
with gr.Tab(page.title, elem_id=f"{tabname}_{page.id_page}", elem_classes=["extra-page"]) as tab:
with gr.Column(elem_id=f"{tabname}_{page.id_page}_prompts", elem_classes=["extra-page-prompts"]):
pass
elem_id = f"{tabname}_{page.id_page}_cards_html"
page_elem = gr.HTML('Loading...', elem_id=elem_id)
ui.pages.append(page_elem)
@ -373,19 +391,28 @@ def create_ui(interface: gr.Blocks, unrelated_tabs, tabname):
related_tabs.append(tab)
edit_search = gr.Textbox('', show_label=False, elem_id=tabname+"_extra_search", elem_classes="search", placeholder="Search...", visible=False, interactive=True)
dropdown_sort = gr.Dropdown(choices=['Default Sort', 'Date Created', 'Date Modified', 'Name'], value='Default Sort', elem_id=tabname+"_extra_sort", elem_classes="sort", multiselect=False, visible=False, show_label=False, interactive=True, label=tabname+"_extra_sort_order")
button_sortorder = ToolButton(switch_values_symbol, elem_id=tabname+"_extra_sortorder", elem_classes="sortorder", visible=False)
dropdown_sort = gr.Dropdown(choices=['Path', 'Name', 'Date Created', 'Date Modified', ], value=shared.opts.extra_networks_card_order_field, elem_id=tabname+"_extra_sort", elem_classes="sort", multiselect=False, visible=False, show_label=False, interactive=True, label=tabname+"_extra_sort_order")
button_sortorder = ToolButton(switch_values_symbol, elem_id=tabname+"_extra_sortorder", elem_classes=["sortorder"] + ([] if shared.opts.extra_networks_card_order == "Ascending" else ["sortReverse"]), visible=False, tooltip="Invert sort order")
button_refresh = gr.Button('Refresh', elem_id=tabname+"_extra_refresh", visible=False)
checkbox_show_dirs = gr.Checkbox(True, label='Show dirs', elem_id=tabname+"_extra_show_dirs", elem_classes="show-dirs", visible=False)
ui.button_save_preview = gr.Button('Save preview', elem_id=tabname+"_save_preview", visible=False)
ui.preview_target_filename = gr.Textbox('Preview save filename', elem_id=tabname+"_preview_filename", visible=False)
for tab in unrelated_tabs:
tab.select(fn=lambda: [gr.update(visible=False) for _ in range(5)], inputs=[], outputs=[edit_search, dropdown_sort, button_sortorder, button_refresh, checkbox_show_dirs], show_progress=False)
tab_controls = [edit_search, dropdown_sort, button_sortorder, button_refresh, checkbox_show_dirs]
for tab in related_tabs:
tab.select(fn=lambda: [gr.update(visible=True) for _ in range(5)], inputs=[], outputs=[edit_search, dropdown_sort, button_sortorder, button_refresh, checkbox_show_dirs], show_progress=False)
for tab in unrelated_tabs:
tab.select(fn=lambda: [gr.update(visible=False) for _ in tab_controls], _js='function(){ extraNetworksUrelatedTabSelected("' + tabname + '"); }', inputs=[], outputs=tab_controls, show_progress=False)
for page, tab in zip(ui.stored_extra_pages, related_tabs):
allow_prompt = "true" if page.allow_prompt else "false"
allow_negative_prompt = "true" if page.allow_negative_prompt else "false"
jscode = 'extraNetworksTabSelected("' + tabname + '", "' + f"{tabname}_{page.id_page}_prompts" + '", ' + allow_prompt + ', ' + allow_negative_prompt + ');'
tab.select(fn=lambda: [gr.update(visible=True) for _ in tab_controls], _js='function(){ ' + jscode + ' }', inputs=[], outputs=tab_controls, show_progress=False)
dropdown_sort.change(fn=lambda: None, _js="function(){ applyExtraNetworkSort('" + tabname + "'); }")
def pages_html():
if not ui.pages_contents:

10
modules/ui_extra_networks_checkpoints.py
View File

@ -10,11 +10,16 @@ class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage):
def __init__(self):
super().__init__('Checkpoints')
self.allow_prompt = False
def refresh(self):
shared.refresh_checkpoints()
def create_item(self, name, index=None, enable_filter=True):
checkpoint: sd_models.CheckpointInfo = sd_models.checkpoint_aliases.get(name)
if checkpoint is None:
return
path, ext = os.path.splitext(checkpoint.filename)
return {
"name": checkpoint.name_for_extra,
@ -30,9 +35,12 @@ class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage):
}
def list_items(self):
# instantiate a list to protect against concurrent modification
names = list(sd_models.checkpoints_list)
for index, name in enumerate(names):
yield self.create_item(name, index)
item = self.create_item(name, index)
if item is not None:
yield item
def allowed_directories_for_previews(self):
return [v for v in [shared.cmd_opts.ckpt_dir, sd_models.model_path] if v is not None]

13
modules/ui_extra_networks_hypernets.py
View File

@ -13,7 +13,10 @@ class ExtraNetworksPageHypernetworks(ui_extra_networks.ExtraNetworksPage):
shared.reload_hypernetworks()
def create_item(self, name, index=None, enable_filter=True):
full_path = shared.hypernetworks[name]
full_path = shared.hypernetworks.get(name)
if full_path is None:
return
path, ext = os.path.splitext(full_path)
sha256 = sha256_from_cache(full_path, f'hypernet/{name}')
shorthash = sha256[0:10] if sha256 else None
@ -31,8 +34,12 @@ class ExtraNetworksPageHypernetworks(ui_extra_networks.ExtraNetworksPage):
}
def list_items(self):
for index, name in enumerate(shared.hypernetworks):
yield self.create_item(name, index)
# instantiate a list to protect against concurrent modification
names = list(shared.hypernetworks)
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.hypernetwork_dir]

10
modules/ui_extra_networks_textual_inversion.py
View File

@ -14,6 +14,8 @@ class ExtraNetworksPageTextualInversion(ui_extra_networks.ExtraNetworksPage):
def create_item(self, name, index=None, enable_filter=True):
embedding = sd_hijack.model_hijack.embedding_db.word_embeddings.get(name)
if embedding is None:
return
path, ext = os.path.splitext(embedding.filename)
return {
@ -29,8 +31,12 @@ class ExtraNetworksPageTextualInversion(ui_extra_networks.ExtraNetworksPage):
}
def list_items(self):
for index, name in enumerate(sd_hijack.model_hijack.embedding_db.word_embeddings):
yield self.create_item(name, index)
# instantiate a list to protect against concurrent modification
names = list(sd_hijack.model_hijack.embedding_db.word_embeddings)
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 list(sd_hijack.model_hijack.embedding_db.embedding_dirs)

2
modules/ui_extra_networks_user_metadata.py
View File

@ -134,7 +134,7 @@ class UserMetadataEditor:
basename, ext = os.path.splitext(filename)
with open(basename + '.json', "w", encoding="utf8") as file:
json.dump(metadata, file, indent=4)
json.dump(metadata, file, indent=4, ensure_ascii=False)
def save_user_metadata(self, name, desc, notes):
user_metadata = self.get_user_metadata(name)

6
modules/ui_gradio_extensions.py
View File

@ -2,12 +2,12 @@ import os
import gradio as gr
from modules import localization, shared, scripts
from modules.paths import script_path, data_path
from modules.paths import script_path, data_path, cwd
def webpath(fn):
if fn.startswith(script_path):
web_path = os.path.relpath(fn, script_path).replace('\\', '/')
if fn.startswith(cwd):
web_path = os.path.relpath(fn, cwd)
else:
web_path = os.path.abspath(fn)

24
modules/ui_loadsave.py
View File

@ -4,7 +4,7 @@ import os
import gradio as gr
from modules import errors
from modules.ui_components import ToolButton
from modules.ui_components import ToolButton, InputAccordion
def radio_choices(comp): # gradio 3.41 changes choices from list of values to list of pairs
@ -32,8 +32,6 @@ class UiLoadsave:
self.error_loading = True
errors.display(e, "loading settings")
def add_component(self, path, x):
"""adds component to the registry of tracked components"""
@ -43,20 +41,24 @@ class UiLoadsave:
key = f"{path}/{field}"
if getattr(obj, 'custom_script_source', None) is not None:
key = f"customscript/{obj.custom_script_source}/{key}"
key = f"customscript/{obj.custom_script_source}/{key}"
if getattr(obj, 'do_not_save_to_config', False):
return
saved_value = self.ui_settings.get(key, None)
if isinstance(obj, gr.Accordion) and isinstance(x, InputAccordion) and field == 'value':
field = 'open'
if saved_value is None:
self.ui_settings[key] = getattr(obj, field)
elif condition and not condition(saved_value):
pass
else:
if isinstance(x, gr.Textbox) and field == 'value': # due to an undesirable behavior of gr.Textbox, if you give it an int value instead of str, everything dies
if isinstance(obj, gr.Textbox) and field == 'value': # due to an undesirable behavior of gr.Textbox, if you give it an int value instead of str, everything dies
saved_value = str(saved_value)
elif isinstance(x, gr.Number) and field == 'value':
elif isinstance(obj, gr.Number) and field == 'value':
try:
saved_value = float(saved_value)
except ValueError:
@ -67,7 +69,7 @@ class UiLoadsave:
init_field(saved_value)
if field == 'value' and key not in self.component_mapping:
self.component_mapping[key] = x
self.component_mapping[key] = obj
if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number, gr.Dropdown, ToolButton, gr.Button] and x.visible:
apply_field(x, 'visible')
@ -100,6 +102,12 @@ class UiLoadsave:
apply_field(x, 'value', check_dropdown, getattr(x, 'init_field', None))
if type(x) == InputAccordion:
if x.accordion.visible:
apply_field(x.accordion, 'visible')
apply_field(x, 'value')
apply_field(x.accordion, 'value')
def check_tab_id(tab_id):
tab_items = list(filter(lambda e: isinstance(e, gr.TabItem), x.children))
if type(tab_id) == str:
@ -133,7 +141,7 @@ class UiLoadsave:
def write_to_file(self, current_ui_settings):
with open(self.filename, "w", encoding="utf8") as file:
json.dump(current_ui_settings, file, indent=4)
json.dump(current_ui_settings, file, indent=4, ensure_ascii=False)
def dump_defaults(self):
"""saves default values to a file unless tjhe file is present and there was an error loading default values at start"""

18
modules/ui_postprocessing.py
View File

@ -1,9 +1,10 @@
import gradio as gr
from modules import scripts, shared, ui_common, postprocessing, call_queue
from modules import scripts, shared, ui_common, postprocessing, call_queue, ui_toprow
import modules.generation_parameters_copypaste as parameters_copypaste
def create_ui():
dummy_component = gr.Label(visible=False)
tab_index = gr.State(value=0)
with gr.Row(equal_height=False, variant='compact'):
@ -20,11 +21,13 @@ def create_ui():
extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.", elem_id="extras_batch_output_dir")
show_extras_results = gr.Checkbox(label='Show result images', value=True, elem_id="extras_show_extras_results")
submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
script_inputs = scripts.scripts_postproc.setup_ui()
with gr.Column():
toprow = ui_toprow.Toprow(is_compact=True, is_img2img=False, id_part="extras")
toprow.create_inline_toprow_image()
submit = toprow.submit
result_images, html_info_x, html_info, html_log = ui_common.create_output_panel("extras", shared.opts.outdir_extras_samples)
tab_single.select(fn=lambda: 0, inputs=[], outputs=[tab_index])
@ -32,8 +35,10 @@ def create_ui():
tab_batch_dir.select(fn=lambda: 2, inputs=[], outputs=[tab_index])
submit.click(
fn=call_queue.wrap_gradio_gpu_call(postprocessing.run_postprocessing, extra_outputs=[None, '']),
fn=call_queue.wrap_gradio_gpu_call(postprocessing.run_postprocessing_webui, extra_outputs=[None, '']),
_js="submit_extras",
inputs=[
dummy_component,
tab_index,
extras_image,
image_batch,
@ -45,8 +50,9 @@ def create_ui():
outputs=[
result_images,
html_info_x,
html_info,
]
html_log,
],
show_progress=False,
)
parameters_copypaste.add_paste_fields("extras", extras_image, None)

32
modules/ui_prompt_styles.py
View File

@ -4,6 +4,7 @@ from modules import shared, ui_common, ui_components, styles
styles_edit_symbol = '\U0001f58c\uFE0F' # 🖌️
styles_materialize_symbol = '\U0001f4cb' # 📋
styles_copy_symbol = '\U0001f4dd' # 📝
def select_style(name):
@ -52,6 +53,8 @@ def refresh_styles():
class UiPromptStyles:
def __init__(self, tabname, main_ui_prompt, main_ui_negative_prompt):
self.tabname = tabname
self.main_ui_prompt = main_ui_prompt
self.main_ui_negative_prompt = main_ui_negative_prompt
with gr.Row(elem_id=f"{tabname}_styles_row"):
self.dropdown = gr.Dropdown(label="Styles", show_label=False, elem_id=f"{tabname}_styles", choices=list(shared.prompt_styles.styles), value=[], multiselect=True, tooltip="Styles")
@ -61,13 +64,14 @@ class UiPromptStyles:
with gr.Row():
self.selection = gr.Dropdown(label="Styles", elem_id=f"{tabname}_styles_edit_select", choices=list(shared.prompt_styles.styles), value=[], allow_custom_value=True, info="Styles allow you to add custom text to prompt. Use the {prompt} token in style text, and it will be replaced with user's prompt when applying style. Otherwise, style's text will be added to the end of the prompt.")
ui_common.create_refresh_button([self.dropdown, self.selection], shared.prompt_styles.reload, lambda: {"choices": list(shared.prompt_styles.styles)}, f"refresh_{tabname}_styles")
self.materialize = ui_components.ToolButton(value=styles_materialize_symbol, elem_id=f"{tabname}_style_apply", tooltip="Apply all selected styles from the style selction dropdown in main UI to the prompt.")
self.materialize = ui_components.ToolButton(value=styles_materialize_symbol, elem_id=f"{tabname}_style_apply_dialog", tooltip="Apply all selected styles from the style selction dropdown in main UI to the prompt.")
self.copy = ui_components.ToolButton(value=styles_copy_symbol, elem_id=f"{tabname}_style_copy", tooltip="Copy main UI prompt to style.")
with gr.Row():
self.prompt = gr.Textbox(label="Prompt", show_label=True, elem_id=f"{tabname}_edit_style_prompt", lines=3)
self.prompt = gr.Textbox(label="Prompt", show_label=True, elem_id=f"{tabname}_edit_style_prompt", lines=3, elem_classes=["prompt"])
with gr.Row():
self.neg_prompt = gr.Textbox(label="Negative prompt", show_label=True, elem_id=f"{tabname}_edit_style_neg_prompt", lines=3)
self.neg_prompt = gr.Textbox(label="Negative prompt", show_label=True, elem_id=f"{tabname}_edit_style_neg_prompt", lines=3, elem_classes=["prompt"])
with gr.Row():
self.save = gr.Button('Save', variant='primary', elem_id=f'{tabname}_edit_style_save', visible=False)
@ -96,15 +100,21 @@ class UiPromptStyles:
show_progress=False,
).then(refresh_styles, outputs=[self.dropdown, self.selection], show_progress=False)
self.materialize.click(
fn=materialize_styles,
inputs=[main_ui_prompt, main_ui_negative_prompt, self.dropdown],
outputs=[main_ui_prompt, main_ui_negative_prompt, self.dropdown],
self.setup_apply_button(self.materialize)
self.copy.click(
fn=lambda p, n: (p, n),
inputs=[main_ui_prompt, main_ui_negative_prompt],
outputs=[self.prompt, self.neg_prompt],
show_progress=False,
).then(fn=None, _js="function(){update_"+tabname+"_tokens(); closePopup();}", show_progress=False)
)
ui_common.setup_dialog(button_show=edit_button, dialog=styles_dialog, button_close=self.close)
def setup_apply_button(self, button):
button.click(
fn=materialize_styles,
inputs=[self.main_ui_prompt, self.main_ui_negative_prompt, self.dropdown],
outputs=[self.main_ui_prompt, self.main_ui_negative_prompt, self.dropdown],
show_progress=False,
).then(fn=None, _js="function(){update_"+self.tabname+"_tokens(); closePopup();}", show_progress=False)

59
modules/ui_settings.py
View File

@ -1,10 +1,11 @@
import gradio as gr
from modules import ui_common, shared, script_callbacks, scripts, sd_models, sysinfo
from modules import ui_common, shared, script_callbacks, scripts, sd_models, sysinfo, timer
from modules.call_queue import wrap_gradio_call
from modules.shared import opts
from modules.ui_components import FormRow
from modules.ui_gradio_extensions import reload_javascript
from concurrent.futures import ThreadPoolExecutor, as_completed
def get_value_for_setting(key):
@ -63,6 +64,9 @@ class UiSettings:
quicksettings_list = None
quicksettings_names = None
text_settings = None
show_all_pages = None
show_one_page = None
search_input = None
def run_settings(self, *args):
changed = []
@ -135,7 +139,7 @@ class UiSettings:
gr.Group()
current_tab = gr.TabItem(elem_id=f"settings_{elem_id}", label=text)
current_tab.__enter__()
current_row = gr.Column(variant='compact')
current_row = gr.Column(elem_id=f"column_settings_{elem_id}", variant='compact')
current_row.__enter__()
previous_section = item.section
@ -173,26 +177,43 @@ class UiSettings:
download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary', elem_id="settings_reload_script_bodies")
with gr.Row():
unload_sd_model = gr.Button(value='Unload SD checkpoint to free VRAM', elem_id="sett_unload_sd_model")
reload_sd_model = gr.Button(value='Reload the last SD checkpoint back into VRAM', elem_id="sett_reload_sd_model")
unload_sd_model = gr.Button(value='Unload SD checkpoint to RAM', elem_id="sett_unload_sd_model")
reload_sd_model = gr.Button(value='Load SD checkpoint to VRAM from RAM', elem_id="sett_reload_sd_model")
with gr.Row():
calculate_all_checkpoint_hash = gr.Button(value='Calculate hash for all checkpoint', elem_id="calculate_all_checkpoint_hash")
calculate_all_checkpoint_hash_threads = gr.Number(value=1, label="Number of parallel calculations", elem_id="calculate_all_checkpoint_hash_threads", precision=0, minimum=1)
with gr.TabItem("Licenses", id="licenses", elem_id="settings_tab_licenses"):
gr.HTML(shared.html("licenses.html"), elem_id="licenses")
gr.Button(value="Show all pages", elem_id="settings_show_all_pages")
self.show_all_pages = gr.Button(value="Show all pages", elem_id="settings_show_all_pages")
self.show_one_page = gr.Button(value="Show only one page", elem_id="settings_show_one_page", visible=False)
self.show_one_page.click(lambda: None)
self.search_input = gr.Textbox(value="", elem_id="settings_search", max_lines=1, placeholder="Search...", show_label=False)
self.text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False)
def call_func_and_return_text(func, text):
def handler():
t = timer.Timer()
func()
t.record(text)
return f'{text} in {t.total:.1f}s'
return handler
unload_sd_model.click(
fn=sd_models.unload_model_weights,
fn=call_func_and_return_text(sd_models.unload_model_weights, 'Unloaded the checkpoint'),
inputs=[],
outputs=[]
outputs=[self.result]
)
reload_sd_model.click(
fn=sd_models.reload_model_weights,
fn=call_func_and_return_text(lambda: sd_models.send_model_to_device(shared.sd_model), 'Loaded the checkpoint'),
inputs=[],
outputs=[]
outputs=[self.result]
)
request_notifications.click(
@ -241,6 +262,21 @@ class UiSettings:
outputs=[sysinfo_check_output],
)
def calculate_all_checkpoint_hash_fn(max_thread):
checkpoints_list = sd_models.checkpoints_list.values()
with ThreadPoolExecutor(max_workers=max_thread) as executor:
futures = [executor.submit(checkpoint.calculate_shorthash) for checkpoint in checkpoints_list]
completed = 0
for _ in as_completed(futures):
completed += 1
print(f"{completed} / {len(checkpoints_list)} ")
print("Finish calculating hash for all checkpoints")
calculate_all_checkpoint_hash.click(
fn=calculate_all_checkpoint_hash_fn,
inputs=[calculate_all_checkpoint_hash_threads],
)
self.interface = settings_interface
def add_quicksettings(self):
@ -294,3 +330,8 @@ class UiSettings:
outputs=[self.component_dict[k] for k in component_keys],
queue=False,
)
def search(self, text):
print(text)
return [gr.update(visible=text in (comp.label or "")) for comp in self.components]

143
modules/ui_toprow.py Normal file
View File

@ -0,0 +1,143 @@
import gradio as gr
from modules import shared, ui_prompt_styles
import modules.images
from modules.ui_components import ToolButton
class Toprow:
"""Creates a top row UI with prompts, generate button, styles, extra little buttons for things, and enables some functionality related to their operation"""
prompt = None
prompt_img = None
negative_prompt = None
button_interrogate = None
button_deepbooru = None
interrupt = None
skip = None
submit = None
paste = None
clear_prompt_button = None
apply_styles = None
restore_progress_button = None
token_counter = None
token_button = None
negative_token_counter = None
negative_token_button = None
ui_styles = None
submit_box = None
def __init__(self, is_img2img, is_compact=False, id_part=None):
if id_part is None:
id_part = "img2img" if is_img2img else "txt2img"
self.id_part = id_part
self.is_img2img = is_img2img
self.is_compact = is_compact
if not is_compact:
with gr.Row(elem_id=f"{id_part}_toprow", variant="compact"):
self.create_classic_toprow()
else:
self.create_submit_box()
def create_classic_toprow(self):
self.create_prompts()
with gr.Column(scale=1, elem_id=f"{self.id_part}_actions_column"):
self.create_submit_box()
self.create_tools_row()
self.create_styles_ui()
def create_inline_toprow_prompts(self):
if not self.is_compact:
return
self.create_prompts()
with gr.Row(elem_classes=["toprow-compact-stylerow"]):
with gr.Column(elem_classes=["toprow-compact-tools"]):
self.create_tools_row()
with gr.Column():
self.create_styles_ui()
def create_inline_toprow_image(self):
if not self.is_compact:
return
self.submit_box.render()
def create_prompts(self):
with gr.Column(elem_id=f"{self.id_part}_prompt_container", elem_classes=["prompt-container-compact"] if self.is_compact else [], scale=6):
with gr.Row(elem_id=f"{self.id_part}_prompt_row", elem_classes=["prompt-row"]):
self.prompt = gr.Textbox(label="Prompt", elem_id=f"{self.id_part}_prompt", show_label=False, lines=3, placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
self.prompt_img = gr.File(label="", elem_id=f"{self.id_part}_prompt_image", file_count="single", type="binary", visible=False)
with gr.Row(elem_id=f"{self.id_part}_neg_prompt_row", elem_classes=["prompt-row"]):
self.negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{self.id_part}_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
self.prompt_img.change(
fn=modules.images.image_data,
inputs=[self.prompt_img],
outputs=[self.prompt, self.prompt_img],
show_progress=False,
)
def create_submit_box(self):
with gr.Row(elem_id=f"{self.id_part}_generate_box", elem_classes=["generate-box"] + (["generate-box-compact"] if self.is_compact else []), render=not self.is_compact) as submit_box:
self.submit_box = submit_box
self.interrupt = gr.Button('Interrupt', elem_id=f"{self.id_part}_interrupt", elem_classes="generate-box-interrupt")
self.skip = gr.Button('Skip', elem_id=f"{self.id_part}_skip", elem_classes="generate-box-skip")
self.submit = gr.Button('Generate', elem_id=f"{self.id_part}_generate", variant='primary')
self.skip.click(
fn=lambda: shared.state.skip(),
inputs=[],
outputs=[],
)
self.interrupt.click(
fn=lambda: shared.state.interrupt(),
inputs=[],
outputs=[],
)
def create_tools_row(self):
with gr.Row(elem_id=f"{self.id_part}_tools"):
from modules.ui import paste_symbol, clear_prompt_symbol, restore_progress_symbol
self.paste = ToolButton(value=paste_symbol, elem_id="paste", tooltip="Read generation parameters from prompt or last generation if prompt is empty into user interface.")
self.clear_prompt_button = ToolButton(value=clear_prompt_symbol, elem_id=f"{self.id_part}_clear_prompt", tooltip="Clear prompt")
self.apply_styles = ToolButton(value=ui_prompt_styles.styles_materialize_symbol, elem_id=f"{self.id_part}_style_apply", tooltip="Apply all selected styles to prompts.")
if self.is_img2img:
self.button_interrogate = ToolButton('📎', tooltip='Interrogate CLIP - use CLIP neural network to create a text describing the image, and put it into the prompt field', elem_id="interrogate")
self.button_deepbooru = ToolButton('📦', tooltip='Interrogate DeepBooru - use DeepBooru neural network to create a text describing the image, and put it into the prompt field', elem_id="deepbooru")
self.restore_progress_button = ToolButton(value=restore_progress_symbol, elem_id=f"{self.id_part}_restore_progress", visible=False, tooltip="Restore progress")
self.token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{self.id_part}_token_counter", elem_classes=["token-counter"])
self.token_button = gr.Button(visible=False, elem_id=f"{self.id_part}_token_button")
self.negative_token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{self.id_part}_negative_token_counter", elem_classes=["token-counter"])
self.negative_token_button = gr.Button(visible=False, elem_id=f"{self.id_part}_negative_token_button")
self.clear_prompt_button.click(
fn=lambda *x: x,
_js="confirm_clear_prompt",
inputs=[self.prompt, self.negative_prompt],
outputs=[self.prompt, self.negative_prompt],
)
def create_styles_ui(self):
self.ui_styles = ui_prompt_styles.UiPromptStyles(self.id_part, self.prompt, self.negative_prompt)
self.ui_styles.setup_apply_button(self.apply_styles)

6
modules/upscaler.py
View File

@ -57,6 +57,9 @@ class Upscaler:
dest_h = int((img.height * scale) // 8 * 8)
for _ in range(3):
if img.width >= dest_w and img.height >= dest_h:
break
shape = (img.width, img.height)
img = self.do_upscale(img, selected_model)
@ -64,9 +67,6 @@ class Upscaler:
if shape == (img.width, img.height):
break
if img.width >= dest_w and img.height >= dest_h:
break
if img.width != dest_w or img.height != dest_h:
img = img.resize((int(dest_w), int(dest_h)), resample=LANCZOS)

164
modules/xlmr_m18.py Normal file
View File

@ -0,0 +1,164 @@
from transformers import BertPreTrainedModel,BertConfig
import torch.nn as nn
import torch
from transformers.models.xlm_roberta.configuration_xlm_roberta import XLMRobertaConfig
from transformers import XLMRobertaModel,XLMRobertaTokenizer
from typing import Optional
class BertSeriesConfig(BertConfig):
def __init__(self, vocab_size=30522, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02, layer_norm_eps=1e-12, pad_token_id=0, position_embedding_type="absolute", use_cache=True, classifier_dropout=None,project_dim=512, pooler_fn="average",learn_encoder=False,model_type='bert',**kwargs):
super().__init__(vocab_size, hidden_size, num_hidden_layers, num_attention_heads, intermediate_size, hidden_act, hidden_dropout_prob, attention_probs_dropout_prob, max_position_embeddings, type_vocab_size, initializer_range, layer_norm_eps, pad_token_id, position_embedding_type, use_cache, classifier_dropout, **kwargs)
self.project_dim = project_dim
self.pooler_fn = pooler_fn
self.learn_encoder = learn_encoder
class RobertaSeriesConfig(XLMRobertaConfig):
def __init__(self, pad_token_id=1, bos_token_id=0, eos_token_id=2,project_dim=512,pooler_fn='cls',learn_encoder=False, **kwargs):
super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
self.project_dim = project_dim
self.pooler_fn = pooler_fn
self.learn_encoder = learn_encoder
class BertSeriesModelWithTransformation(BertPreTrainedModel):
_keys_to_ignore_on_load_unexpected = [r"pooler"]
_keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
config_class = BertSeriesConfig
def __init__(self, config=None, **kargs):
# modify initialization for autoloading
if config is None:
config = XLMRobertaConfig()
config.attention_probs_dropout_prob= 0.1
config.bos_token_id=0
config.eos_token_id=2
config.hidden_act='gelu'
config.hidden_dropout_prob=0.1
config.hidden_size=1024
config.initializer_range=0.02
config.intermediate_size=4096
config.layer_norm_eps=1e-05
config.max_position_embeddings=514
config.num_attention_heads=16
config.num_hidden_layers=24
config.output_past=True
config.pad_token_id=1
config.position_embedding_type= "absolute"
config.type_vocab_size= 1
config.use_cache=True
config.vocab_size= 250002
config.project_dim = 1024
config.learn_encoder = False
super().__init__(config)
self.roberta = XLMRobertaModel(config)
self.transformation = nn.Linear(config.hidden_size,config.project_dim)
# self.pre_LN=nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.tokenizer = XLMRobertaTokenizer.from_pretrained('xlm-roberta-large')
# self.pooler = lambda x: x[:,0]
# self.post_init()
self.has_pre_transformation = True
if self.has_pre_transformation:
self.transformation_pre = nn.Linear(config.hidden_size, config.project_dim)
self.pre_LN = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.post_init()
def encode(self,c):
device = next(self.parameters()).device
text = self.tokenizer(c,
truncation=True,
max_length=77,
return_length=False,
return_overflowing_tokens=False,
padding="max_length",
return_tensors="pt")
text["input_ids"] = torch.tensor(text["input_ids"]).to(device)
text["attention_mask"] = torch.tensor(
text['attention_mask']).to(device)
features = self(**text)
return features['projection_state']
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
return_dict: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
) :
r"""
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.roberta(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_attentions=output_attentions,
output_hidden_states=True,
return_dict=return_dict,
)
# # last module outputs
# sequence_output = outputs[0]
# # project every module
# sequence_output_ln = self.pre_LN(sequence_output)
# # pooler
# pooler_output = self.pooler(sequence_output_ln)
# pooler_output = self.transformation(pooler_output)
# projection_state = self.transformation(outputs.last_hidden_state)
if self.has_pre_transformation:
sequence_output2 = outputs["hidden_states"][-2]
sequence_output2 = self.pre_LN(sequence_output2)
projection_state2 = self.transformation_pre(sequence_output2)
return {
"projection_state": projection_state2,
"last_hidden_state": outputs.last_hidden_state,
"hidden_states": outputs.hidden_states,
"attentions": outputs.attentions,
}
else:
projection_state = self.transformation(outputs.last_hidden_state)
return {
"projection_state": projection_state,
"last_hidden_state": outputs.last_hidden_state,
"hidden_states": outputs.hidden_states,
"attentions": outputs.attentions,
}
# return {
# 'pooler_output':pooler_output,
# 'last_hidden_state':outputs.last_hidden_state,
# 'hidden_states':outputs.hidden_states,
# 'attentions':outputs.attentions,
# 'projection_state':projection_state,
# 'sequence_out': sequence_output
# }
class RobertaSeriesModelWithTransformation(BertSeriesModelWithTransformation):
base_model_prefix = 'roberta'
config_class= RobertaSeriesConfig

59
modules/xpu_specific.py Normal file
View File

@ -0,0 +1,59 @@
from modules import shared
from modules.sd_hijack_utils import CondFunc
has_ipex = False
try:
import torch
import intel_extension_for_pytorch as ipex # noqa: F401
has_ipex = True
except Exception:
pass
def check_for_xpu():
return has_ipex and hasattr(torch, 'xpu') and torch.xpu.is_available()
def get_xpu_device_string():
if shared.cmd_opts.device_id is not None:
return f"xpu:{shared.cmd_opts.device_id}"
return "xpu"
def torch_xpu_gc():
with torch.xpu.device(get_xpu_device_string()):
torch.xpu.empty_cache()
has_xpu = check_for_xpu()
if has_xpu:
# W/A for https://github.com/intel/intel-extension-for-pytorch/issues/452: torch.Generator API doesn't support XPU device
CondFunc('torch.Generator',
lambda orig_func, device=None: torch.xpu.Generator(device),
lambda orig_func, device=None: device is not None and device.type == "xpu")
# W/A for some OPs that could not handle different input dtypes
CondFunc('torch.nn.functional.layer_norm',
lambda orig_func, input, normalized_shape=None, weight=None, *args, **kwargs:
orig_func(input.to(weight.data.dtype), normalized_shape, weight, *args, **kwargs),
lambda orig_func, input, normalized_shape=None, weight=None, *args, **kwargs:
weight is not None and input.dtype != weight.data.dtype)
CondFunc('torch.nn.modules.GroupNorm.forward',
lambda orig_func, self, input: orig_func(self, input.to(self.weight.data.dtype)),
lambda orig_func, self, input: input.dtype != self.weight.data.dtype)
CondFunc('torch.nn.modules.linear.Linear.forward',
lambda orig_func, self, input: orig_func(self, input.to(self.weight.data.dtype)),
lambda orig_func, self, input: input.dtype != self.weight.data.dtype)
CondFunc('torch.nn.modules.conv.Conv2d.forward',
lambda orig_func, self, input: orig_func(self, input.to(self.weight.data.dtype)),
lambda orig_func, self, input: input.dtype != self.weight.data.dtype)
CondFunc('torch.bmm',
lambda orig_func, input, mat2, out=None: orig_func(input.to(mat2.dtype), mat2, out=out),
lambda orig_func, input, mat2, out=None: input.dtype != mat2.dtype)
CondFunc('torch.cat',
lambda orig_func, tensors, dim=0, out=None: orig_func([t.to(tensors[0].dtype) for t in tensors], dim=dim, out=out),
lambda orig_func, tensors, dim=0, out=None: not all(t.dtype == tensors[0].dtype for t in tensors))
CondFunc('torch.nn.functional.scaled_dot_product_attention',
lambda orig_func, query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False: orig_func(query, key.to(query.dtype), value.to(query.dtype), attn_mask, dropout_p, is_causal),
lambda orig_func, query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False: query.dtype != key.dtype or query.dtype != value.dtype)

1
pyproject.toml
View File

@ -16,6 +16,7 @@ exclude = [
ignore = [
"E501", # Line too long
"E721", # Do not compare types, use `isinstance`
"E731", # Do not assign a `lambda` expression, use a `def`
"I001", # Import block is un-sorted or un-formatted

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