Mirror/stable-diffusion-webui
1
0
Fork 0
mirror of https://github.com/AUTOMATIC1111/stable-diffusion-webui.git synced 2024-06-07 21:20:49 +00:00
Code Issues Packages Projects Releases Wiki Activity

Merge branch 'release_candidate'

Browse Source
This commit is contained in:
AUTOMATIC1111 2024-03-02 07:03:13 +03:00
commit bef51aed03
152 changed files with 5870 additions and 5599 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
.eslintrc.js
View File

@ -86,8 +86,6 @@ module.exports = {
// imageviewer.js
modalPrevImage: "readonly",
modalNextImage: "readonly",
// token-counters.js
setupTokenCounters: "readonly",
// localStorage.js
localSet: "readonly",
localGet: "readonly",

10
.github/workflows/run_tests.yaml vendored
View File

@ -20,6 +20,12 @@ jobs:
cache-dependency-path: |
**/requirements*txt
launch.py
- name: Cache models
id: cache-models
uses: actions/cache@v3
with:
path: models
key: "2023-12-30"
- name: Install test dependencies
run: pip install wait-for-it -r requirements-test.txt
env:
@ -33,6 +39,8 @@ jobs:
TORCH_INDEX_URL: https://download.pytorch.org/whl/cpu
WEBUI_LAUNCH_LIVE_OUTPUT: "1"
PYTHONUNBUFFERED: "1"
- name: Print installed packages
run: pip freeze
- name: Start test server
run: >
python -m coverage run
@ -49,7 +57,7 @@ jobs:
2>&1 | tee output.txt &
- name: Run tests
run: |
wait-for-it --service 127.0.0.1:7860 -t 600
wait-for-it --service 127.0.0.1:7860 -t 20
python -m pytest -vv --junitxml=test/results.xml --cov . --cov-report=xml --verify-base-url test
- name: Kill test server
if: always()

1
.gitignore vendored
View File

@ -37,3 +37,4 @@ notification.mp3
/node_modules
/package-lock.json
/.coverage*
/test/test_outputs

132
CHANGELOG.md
View File

@ -1,3 +1,134 @@
## 1.8.0-RC
### Features:
* Update torch to version 2.1.2
* Soft Inpainting ([#14208](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14208))
* FP8 support ([#14031](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14031), [#14327](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14327))
* Support for SDXL-Inpaint Model ([#14390](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14390))
* Use Spandrel for upscaling and face restoration architectures ([#14425](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14425), [#14467](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14467), [#14473](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14473), [#14474](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14474), [#14477](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14477), [#14476](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14476), [#14484](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14484), [#14500](https://github.com/AUTOMATIC1111/stable-difusion-webui/pull/14500), [#14501](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14501), [#14504](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14504), [#14524](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14524), [#14809](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14809))
* Automatic backwards version compatibility (when loading infotexts from old images with program version specified, will add compatibility settings)
* Implement zero terminal SNR noise schedule option (**[SEED BREAKING CHANGE](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Seed-breaking-changes#180-dev-170-225-2024-01-01---zero-terminal-snr-noise-schedule-option)**, [#14145](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14145), [#14979](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14979))
* Add a [✨] button to run hires fix on selected image in the gallery (with help from [#14598](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14598), [#14626](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14626), [#14728](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14728))
* [Separate assets repository](https://github.com/AUTOMATIC1111/stable-diffusion-webui-assets); serve fonts locally rather than from google's servers
* Official LCM Sampler Support ([#14583](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14583))
* Add support for DAT upscaler models ([#14690](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14690), [#15039](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15039))
* Extra Networks Tree View ([#14588](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14588), [#14900](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14900))
* NPU Support ([#14801](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14801))
* Propmpt comments support
### Minor:
* Allow pasting in WIDTHxHEIGHT strings into the width/height fields ([#14296](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14296))
* add option: Live preview in full page image viewer ([#14230](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14230), [#14307](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14307))
* Add keyboard shortcuts for generate/skip/interrupt ([#14269](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14269))
* Better TCMALLOC support on different platforms ([#14227](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14227), [#14883](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14883), [#14910](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14910))
* Lora not found warning ([#14464](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14464))
* Adding negative prompts to Loras in extra networks ([#14475](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14475))
* xyz_grid: allow varying the seed along an axis separate from axis options ([#12180](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12180))
* option to convert VAE to bfloat16 (implementation of [#9295](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9295))
* Better IPEX support ([#14229](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14229), [#14353](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14353), [#14559](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14559), [#14562](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14562), [#14597](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14597))
* Option to interrupt after current generation rather than immediately ([#13653](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13653), [#14659](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14659))
* Fullscreen Preview control fading/disable ([#14291](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14291))
* Finer settings freezing control ([#13789](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13789))
* Increase Upscaler Limits ([#14589](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14589))
* Adjust brush size with hotkeys ([#14638](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14638))
* Add checkpoint info to csv log file when saving images ([#14663](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14663))
* Make more columns resizable ([#14740](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14740), [#14884](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14884))
* Add an option to not overlay original image for inpainting for #14727
* Add Pad conds v0 option to support same generation with DDIM as before 1.6.0
* Add "Interrupting..." placeholder.
* Button for refresh extensions list ([#14857](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14857))
* Add an option to disable normalization after calculating emphasis. ([#14874](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14874))
* When counting tokens, also include enabled styles (can be disabled in settings to revert to previous behavior)
* Configuration for the [📂] button for image gallery ([#14947](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14947))
* Support inference with LyCORIS BOFT networks ([#14871](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14871), [#14973](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14973))
* support resizable columns for touch (tablets) ([#15002](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15002))
### Extensions and API:
* Removed packages from requirements: basicsr, gfpgan, realesrgan; as well as their dependencies: absl-py, addict, beautifulsoup4, future, gdown, grpcio, importlib-metadata, lmdb, lpips, Markdown, platformdirs, PySocks, soupsieve, tb-nightly, tensorboard-data-server, tomli, Werkzeug, yapf, zipp, soupsieve
* Enable task ids for API ([#14314](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14314))
* add override_settings support for infotext API
* rename generation_parameters_copypaste module to infotext_utils
* prevent crash due to Script __init__ exception ([#14407](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14407))
* Bump numpy to 1.26.2 ([#14471](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14471))
* Add utility to inspect a model's dtype/device ([#14478](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14478))
* Implement general forward method for all method in built-in lora ext ([#14547](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14547))
* Execute model_loaded_callback after moving to target device ([#14563](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14563))
* Add self to CFGDenoiserParams ([#14573](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14573))
* Allow TLS with API only mode (--nowebui) ([#14593](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14593))
* New callback: postprocess_image_after_composite ([#14657](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14657))
* modules/api/api.py: add api endpoint to refresh embeddings list ([#14715](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14715))
* set_named_arg ([#14773](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14773))
* add before_token_counter callback and use it for prompt comments
* ResizeHandleRow - allow overriden column scale parameter ([#15004](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15004))
### Performance
* Massive performance improvement for extra networks directories with a huge number of files in them in an attempt to tackle #14507 ([#14528](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14528))
* Reduce unnecessary re-indexing extra networks directory ([#14512](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14512))
* Avoid unnecessary `isfile`/`exists` calls ([#14527](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14527))
### Bug Fixes:
* fix multiple bugs related to styles multi-file support ([#14203](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14203), [#14276](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14276), [#14707](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14707))
* Lora fixes ([#14300](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14300), [#14237](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14237), [#14546](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14546), [#14726](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14726))
* Re-add setting lost as part of e294e46 ([#14266](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14266))
* fix extras caption BLIP ([#14330](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14330))
* include infotext into saved init image for img2img ([#14452](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14452))
* xyz grid handle axis_type is None ([#14394](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14394))
* Update Added (Fixed) IPV6 Functionality When there is No Webui Argument Passed webui.py ([#14354](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14354))
* fix API thread safe issues of txt2img and img2img ([#14421](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14421))
* handle selectable script_index is None ([#14487](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14487))
* handle config.json failed to load ([#14525](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14525), [#14767](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14767))
* paste infotext cast int as float ([#14523](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14523))
* Ensure GRADIO_ANALYTICS_ENABLED is set early enough ([#14537](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14537))
* Fix logging configuration again ([#14538](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14538))
* Handle CondFunc exception when resolving attributes ([#14560](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14560))
* Fix extras big batch crashes ([#14699](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14699))
* Fix using wrong model caused by alias ([#14655](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14655))
* Add # to the invalid_filename_chars list ([#14640](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14640))
* Fix extension check for requirements ([#14639](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14639))
* Fix tab indexes are reset after restart UI ([#14637](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14637))
* Fix nested manual cast ([#14689](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14689))
* Keep postprocessing upscale selected tab after restart ([#14702](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14702))
* XYZ grid: filter out blank vals when axis is int or float type (like int axis seed) ([#14754](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14754))
* fix CLIP Interrogator topN regex ([#14775](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14775))
* Fix dtype error in MHA layer/change dtype checking mechanism for manual cast ([#14791](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14791))
* catch load style.csv error ([#14814](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14814))
* fix error when editing extra networks card
* fix extra networks metadata failing to work properly when you create the .json file with metadata for the first time.
* util.walk_files extensions case insensitive ([#14879](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14879))
* if extensions page not loaded, prevent apply ([#14873](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14873))
* call the right function for token counter in img2img
* Fix the bugs that search/reload will disappear when using other ExtraNetworks extensions ([#14939](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14939))
* Gracefully handle mtime read exception from cache ([#14933](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14933))
* Only trigger interrupt on `Esc` when interrupt button visible ([#14932](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14932))
* Disable prompt token counters option actually disables token counting rather than just hiding results.
* avoid doble upscaling in inpaint ([#14966](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14966))
* Fix #14591 using translated content to do categories mapping ([#14995](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14995))
* fix: the `split_threshold` parameter does not work when running Split oversized images ([#15006](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15006))
* Fix resize-handle for mobile ([#15010](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15010), [#15065](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15065))
### Other:
* Assign id for "extra_options". Replace numeric field with slider. ([#14270](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14270))
* change state dict comparison to ref compare ([#14216](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14216))
* Bump torch-rocm to 5.6/5.7 ([#14293](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14293))
* Base output path off data path ([#14446](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14446))
* reorder training preprocessing modules in extras tab ([#14367](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14367))
* Remove `cleanup_models` code ([#14472](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14472))
* only rewrite ui-config when there is change ([#14352](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14352))
* Fix lint issue from 501993eb ([#14495](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14495))
* Update README.md ([#14548](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14548))
* hires button, fix seeds ()
* Logging: set formatter correctly for fallback logger too ([#14618](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14618))
* Read generation info from infotexts rather than json for internal needs (save, extract seed from generated pic) ([#14645](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14645))
* improve get_crop_region ([#14709](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14709))
* Bump safetensors' version to 0.4.2 ([#14782](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14782))
* add tooltip create_submit_box ([#14803](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14803))
* extensions tab table row hover highlight ([#14885](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14885))
* Always add timestamp to displayed image ([#14890](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14890))
* Added core.filemode=false so doesn't track changes in file permission… ([#14930](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14930))
* Normalize command-line argument paths ([#14934](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14934), [#15035](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15035))
* Use original App Title in progress bar ([#14916](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14916))
* register_tmp_file also for mtime ([#15012](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15012))
## 1.7.0
### Features:
@ -41,6 +172,7 @@
* 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))
* make extra network card description plaintext by default, with an option (Treat card description as HTML) to re-enable HTML as it was (originally by [#13241](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13241))
### Extensions and API:
* update gradio to 3.41.2

13
README.md
View File

@ -1,5 +1,5 @@
# Stable Diffusion web UI
A browser interface based on Gradio library for Stable Diffusion.
A web interface for Stable Diffusion, implemented using Gradio library.
![](screenshot.png)
@ -151,11 +151,12 @@ Licenses for borrowed code can be found in `Settings -> Licenses` screen, and al
- 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
- ESRGAN - https://github.com/xinntao/ESRGAN
- SwinIR - https://github.com/JingyunLiang/SwinIR
- Swin2SR - https://github.com/mv-lab/swin2sr
- Spandrel - https://github.com/chaiNNer-org/spandrel implementing
- GFPGAN - https://github.com/TencentARC/GFPGAN.git
- CodeFormer - https://github.com/sczhou/CodeFormer
- ESRGAN - https://github.com/xinntao/ESRGAN
- SwinIR - https://github.com/JingyunLiang/SwinIR
- Swin2SR - https://github.com/mv-lab/swin2sr
- LDSR - https://github.com/Hafiidz/latent-diffusion
- MiDaS - https://github.com/isl-org/MiDaS
- Ideas for optimizations - https://github.com/basujindal/stable-diffusion

98
configs/sd_xl_inpaint.yaml Normal file
View File

@ -0,0 +1,98 @@
model:
target: sgm.models.diffusion.DiffusionEngine
params:
scale_factor: 0.13025
disable_first_stage_autocast: True
denoiser_config:
target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
params:
num_idx: 1000
weighting_config:
target: sgm.modules.diffusionmodules.denoiser_weighting.EpsWeighting
scaling_config:
target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
discretization_config:
target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
network_config:
target: sgm.modules.diffusionmodules.openaimodel.UNetModel
params:
adm_in_channels: 2816
num_classes: sequential
use_checkpoint: True
in_channels: 9
out_channels: 4
model_channels: 320
attention_resolutions: [4, 2]
num_res_blocks: 2
channel_mult: [1, 2, 4]
num_head_channels: 64
use_spatial_transformer: True
use_linear_in_transformer: True
transformer_depth: [1, 2, 10] # note: the first is unused (due to attn_res starting at 2) 32, 16, 8 --> 64, 32, 16
context_dim: 2048
spatial_transformer_attn_type: softmax-xformers
legacy: False
conditioner_config:
target: sgm.modules.GeneralConditioner
params:
emb_models:
# crossattn cond
- is_trainable: False
input_key: txt
target: sgm.modules.encoders.modules.FrozenCLIPEmbedder
params:
layer: hidden
layer_idx: 11
# crossattn and vector cond
- is_trainable: False
input_key: txt
target: sgm.modules.encoders.modules.FrozenOpenCLIPEmbedder2
params:
arch: ViT-bigG-14
version: laion2b_s39b_b160k
freeze: True
layer: penultimate
always_return_pooled: True
legacy: False
# vector cond
- is_trainable: False
input_key: original_size_as_tuple
target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
params:
outdim: 256 # multiplied by two
# vector cond
- is_trainable: False
input_key: crop_coords_top_left
target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
params:
outdim: 256 # multiplied by two
# vector cond
- is_trainable: False
input_key: target_size_as_tuple
target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
params:
outdim: 256 # multiplied by two
first_stage_config:
target: sgm.models.autoencoder.AutoencoderKLInferenceWrapper
params:
embed_dim: 4
monitor: val/rec_loss
ddconfig:
attn_type: vanilla-xformers
double_z: true
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult: [1, 2, 4, 4]
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity

35
extensions-builtin/Lora/network.py
View File

@ -3,6 +3,9 @@ import os
from collections import namedtuple
import enum
import torch.nn as nn
import torch.nn.functional as F
from modules import sd_models, cache, errors, hashes, shared
NetworkWeights = namedtuple('NetworkWeights', ['network_key', 'sd_key', 'w', 'sd_module'])
@ -115,6 +118,29 @@ class NetworkModule:
if hasattr(self.sd_module, 'weight'):
self.shape = self.sd_module.weight.shape
self.ops = None
self.extra_kwargs = {}
if isinstance(self.sd_module, nn.Conv2d):
self.ops = F.conv2d
self.extra_kwargs = {
'stride': self.sd_module.stride,
'padding': self.sd_module.padding
}
elif isinstance(self.sd_module, nn.Linear):
self.ops = F.linear
elif isinstance(self.sd_module, nn.LayerNorm):
self.ops = F.layer_norm
self.extra_kwargs = {
'normalized_shape': self.sd_module.normalized_shape,
'eps': self.sd_module.eps
}
elif isinstance(self.sd_module, nn.GroupNorm):
self.ops = F.group_norm
self.extra_kwargs = {
'num_groups': self.sd_module.num_groups,
'eps': self.sd_module.eps
}
self.dim = None
self.bias = weights.w.get("bias")
self.alpha = weights.w["alpha"].item() if "alpha" in weights.w else None
@ -137,7 +163,7 @@ class NetworkModule:
def finalize_updown(self, updown, orig_weight, output_shape, ex_bias=None):
if self.bias is not None:
updown = updown.reshape(self.bias.shape)
updown += self.bias.to(orig_weight.device, dtype=orig_weight.dtype)
updown += self.bias.to(orig_weight.device, dtype=updown.dtype)
updown = updown.reshape(output_shape)
if len(output_shape) == 4:
@ -155,5 +181,10 @@ class NetworkModule:
raise NotImplementedError()
def forward(self, x, y):
raise NotImplementedError()
"""A general forward implementation for all modules"""
if self.ops is None:
raise NotImplementedError()
else:
updown, ex_bias = self.calc_updown(self.sd_module.weight)
return y + self.ops(x, weight=updown, bias=ex_bias, **self.extra_kwargs)

4
extensions-builtin/Lora/network_full.py
View File

@ -18,9 +18,9 @@ class NetworkModuleFull(network.NetworkModule):
def calc_updown(self, orig_weight):
output_shape = self.weight.shape
updown = self.weight.to(orig_weight.device, dtype=orig_weight.dtype)
updown = self.weight.to(orig_weight.device)
if self.ex_bias is not None:
ex_bias = self.ex_bias.to(orig_weight.device, dtype=orig_weight.dtype)
ex_bias = self.ex_bias.to(orig_weight.device)
else:
ex_bias = None

10
extensions-builtin/Lora/network_glora.py
View File

@ -22,12 +22,12 @@ class NetworkModuleGLora(network.NetworkModule):
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)
w1a = self.w1a.to(orig_weight.device)
w1b = self.w1b.to(orig_weight.device)
w2a = self.w2a.to(orig_weight.device)
w2b = self.w2b.to(orig_weight.device)
output_shape = [w1a.size(0), w1b.size(1)]
updown = ((w2b @ w1b) + ((orig_weight @ w2a) @ w1a))
updown = ((w2b @ w1b) + ((orig_weight.to(dtype = w1a.dtype) @ w2a) @ w1a))
return self.finalize_updown(updown, orig_weight, output_shape)

12
extensions-builtin/Lora/network_hada.py
View File

@ -27,16 +27,16 @@ class NetworkModuleHada(network.NetworkModule):
self.t2 = weights.w.get("hada_t2")
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)
w1a = self.w1a.to(orig_weight.device)
w1b = self.w1b.to(orig_weight.device)
w2a = self.w2a.to(orig_weight.device)
w2b = self.w2b.to(orig_weight.device)
output_shape = [w1a.size(0), w1b.size(1)]
if self.t1 is not None:
output_shape = [w1a.size(1), w1b.size(1)]
t1 = self.t1.to(orig_weight.device, dtype=orig_weight.dtype)
t1 = self.t1.to(orig_weight.device)
updown1 = lyco_helpers.make_weight_cp(t1, w1a, w1b)
output_shape += t1.shape[2:]
else:
@ -45,7 +45,7 @@ class NetworkModuleHada(network.NetworkModule):
updown1 = lyco_helpers.rebuild_conventional(w1a, w1b, output_shape)
if self.t2 is not None:
t2 = self.t2.to(orig_weight.device, dtype=orig_weight.dtype)
t2 = self.t2.to(orig_weight.device)
updown2 = lyco_helpers.make_weight_cp(t2, w2a, w2b)
else:
updown2 = lyco_helpers.rebuild_conventional(w2a, w2b, output_shape)

2
extensions-builtin/Lora/network_ia3.py
View File

@ -17,7 +17,7 @@ class NetworkModuleIa3(network.NetworkModule):
self.on_input = weights.w["on_input"].item()
def calc_updown(self, orig_weight):
w = self.w.to(orig_weight.device, dtype=orig_weight.dtype)
w = self.w.to(orig_weight.device)
output_shape = [w.size(0), orig_weight.size(1)]
if self.on_input:

18
extensions-builtin/Lora/network_lokr.py
View File

@ -37,22 +37,22 @@ class NetworkModuleLokr(network.NetworkModule):
def calc_updown(self, orig_weight):
if self.w1 is not None:
w1 = self.w1.to(orig_weight.device, dtype=orig_weight.dtype)
w1 = self.w1.to(orig_weight.device)
else:
w1a = self.w1a.to(orig_weight.device, dtype=orig_weight.dtype)
w1b = self.w1b.to(orig_weight.device, dtype=orig_weight.dtype)
w1a = self.w1a.to(orig_weight.device)
w1b = self.w1b.to(orig_weight.device)
w1 = w1a @ w1b
if self.w2 is not None:
w2 = self.w2.to(orig_weight.device, dtype=orig_weight.dtype)
w2 = self.w2.to(orig_weight.device)
elif self.t2 is None:
w2a = self.w2a.to(orig_weight.device, dtype=orig_weight.dtype)
w2b = self.w2b.to(orig_weight.device, dtype=orig_weight.dtype)
w2a = self.w2a.to(orig_weight.device)
w2b = self.w2b.to(orig_weight.device)
w2 = w2a @ w2b
else:
t2 = self.t2.to(orig_weight.device, 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)
t2 = self.t2.to(orig_weight.device)
w2a = self.w2a.to(orig_weight.device)
w2b = self.w2b.to(orig_weight.device)
w2 = lyco_helpers.make_weight_cp(t2, w2a, w2b)
output_shape = [w1.size(0) * w2.size(0), w1.size(1) * w2.size(1)]

6
extensions-builtin/Lora/network_lora.py
View File

@ -61,13 +61,13 @@ class NetworkModuleLora(network.NetworkModule):
return module
def calc_updown(self, orig_weight):
up = self.up_model.weight.to(orig_weight.device, dtype=orig_weight.dtype)
down = self.down_model.weight.to(orig_weight.device, dtype=orig_weight.dtype)
up = self.up_model.weight.to(orig_weight.device)
down = self.down_model.weight.to(orig_weight.device)
output_shape = [up.size(0), down.size(1)]
if self.mid_model is not None:
# cp-decomposition
mid = self.mid_model.weight.to(orig_weight.device, dtype=orig_weight.dtype)
mid = self.mid_model.weight.to(orig_weight.device)
updown = lyco_helpers.rebuild_cp_decomposition(up, down, mid)
output_shape += mid.shape[2:]
else:

4
extensions-builtin/Lora/network_norm.py
View File

@ -18,10 +18,10 @@ class NetworkModuleNorm(network.NetworkModule):
def calc_updown(self, orig_weight):
output_shape = self.w_norm.shape
updown = self.w_norm.to(orig_weight.device, dtype=orig_weight.dtype)
updown = self.w_norm.to(orig_weight.device)
if self.b_norm is not None:
ex_bias = self.b_norm.to(orig_weight.device, dtype=orig_weight.dtype)
ex_bias = self.b_norm.to(orig_weight.device)
else:
ex_bias = None

94
extensions-builtin/Lora/network_oft.py
View File

@ -1,6 +1,5 @@
import torch
import network
from lyco_helpers import factorization
from einops import rearrange
@ -22,20 +21,28 @@ class NetworkModuleOFT(network.NetworkModule):
self.org_module: list[torch.Module] = [self.sd_module]
self.scale = 1.0
self.is_R = False
self.is_boft = False
# kohya-ss
# kohya-ss/New LyCORIS OFT/BOFT
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.alpha = weights.w.get("alpha", None) # alpha is constraint
self.dim = self.oft_blocks.shape[0] # lora dim
# LyCORIS
# Old LyCORIS OFT
elif "oft_diag" in weights.w.keys():
self.is_kohya = False
self.is_R = True
self.oft_blocks = weights.w["oft_diag"]
# self.alpha is unused
self.dim = self.oft_blocks.shape[1] # (num_blocks, block_size, block_size)
# LyCORIS BOFT
if self.oft_blocks.dim() == 4:
self.is_boft = True
self.rescale = weights.w.get('rescale', None)
if self.rescale is not None:
self.rescale = self.rescale.reshape(-1, *[1]*(self.org_module[0].weight.dim() - 1))
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
@ -47,36 +54,65 @@ class NetworkModuleOFT(network.NetworkModule):
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.num_blocks = self.dim
self.block_size = self.out_dim // self.dim
self.constraint = (0 if self.alpha is None else self.alpha) * self.out_dim
if self.is_R:
self.constraint = None
self.block_size, self.num_blocks = factorization(self.out_dim, self.dim)
self.block_size = self.dim
self.num_blocks = self.out_dim // self.dim
elif self.is_boft:
self.boft_m = self.oft_blocks.shape[0]
self.num_blocks = self.oft_blocks.shape[1]
self.block_size = self.oft_blocks.shape[2]
self.boft_b = self.block_size
def calc_updown(self, orig_weight):
oft_blocks = self.oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)
eye = torch.eye(self.block_size, device=self.oft_blocks.device)
oft_blocks = self.oft_blocks.to(orig_weight.device)
eye = torch.eye(self.block_size, device=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))
if not self.is_R:
block_Q = oft_blocks - oft_blocks.transpose(-1, -2) # ensure skew-symmetric orthogonal matrix
if self.constraint != 0:
norm_Q = torch.norm(block_Q.flatten())
new_norm_Q = torch.clamp(norm_Q, max=self.constraint.to(oft_blocks.device))
block_Q = block_Q * ((new_norm_Q + 1e-8) / (norm_Q + 1e-8))
oft_blocks = torch.matmul(eye + block_Q, (eye - block_Q).float().inverse())
R = oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)
R = oft_blocks.to(orig_weight.device)
# 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) ...')
if not self.is_boft:
# This errors out for MultiheadAttention, might need to be handled up-stream
merged_weight = rearrange(orig_weight, '(k n) ... -> k n ...', k=self.num_blocks, n=self.block_size)
merged_weight = torch.einsum(
'k n m, k n ... -> k m ...',
R,
merged_weight
)
merged_weight = rearrange(merged_weight, 'k m ... -> (k m) ...')
else:
# TODO: determine correct value for scale
scale = 1.0
m = self.boft_m
b = self.boft_b
r_b = b // 2
inp = orig_weight
for i in range(m):
bi = R[i] # b_num, b_size, b_size
if i == 0:
# Apply multiplier/scale and rescale into first weight
bi = bi * scale + (1 - scale) * eye
inp = rearrange(inp, "(c g k) ... -> (c k g) ...", g=2, k=2**i * r_b)
inp = rearrange(inp, "(d b) ... -> d b ...", b=b)
inp = torch.einsum("b i j, b j ... -> b i ...", bi, inp)
inp = rearrange(inp, "d b ... -> (d b) ...")
inp = rearrange(inp, "(c k g) ... -> (c g k) ...", g=2, k=2**i * r_b)
merged_weight = inp
updown = merged_weight.to(orig_weight.device, dtype=orig_weight.dtype) - orig_weight
# Rescale mechanism
if self.rescale is not None:
merged_weight = self.rescale.to(merged_weight) * merged_weight
updown = merged_weight.to(orig_weight.device) - orig_weight.to(merged_weight.dtype)
output_shape = orig_weight.shape
return self.finalize_updown(updown, orig_weight, output_shape)

42
extensions-builtin/Lora/networks.py
View File

@ -1,3 +1,4 @@
import gradio as gr
import logging
import os
import re
@ -259,11 +260,11 @@ def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=No
loaded_networks.clear()
networks_on_disk = [available_network_aliases.get(name, None) for name in names]
networks_on_disk = [available_networks.get(name, None) if name.lower() in forbidden_network_aliases else available_network_aliases.get(name, None) for name in names]
if any(x is None for x in networks_on_disk):
list_available_networks()
networks_on_disk = [available_network_aliases.get(name, None) for name in names]
networks_on_disk = [available_networks.get(name, None) if name.lower() in forbidden_network_aliases else available_network_aliases.get(name, None) for name in names]
failed_to_load_networks = []
@ -314,7 +315,12 @@ def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=No
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))
lora_not_found_message = f'Lora not found: {", ".join(failed_to_load_networks)}'
sd_hijack.model_hijack.comments.append(lora_not_found_message)
if shared.opts.lora_not_found_warning_console:
print(f'\n{lora_not_found_message}\n')
if shared.opts.lora_not_found_gradio_warning:
gr.Warning(lora_not_found_message)
purge_networks_from_memory()
@ -389,18 +395,26 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
if module is not None and hasattr(self, 'weight'):
try:
with torch.no_grad():
updown, ex_bias = module.calc_updown(self.weight)
if getattr(self, 'fp16_weight', None) is None:
weight = self.weight
bias = self.bias
else:
weight = self.fp16_weight.clone().to(self.weight.device)
bias = getattr(self, 'fp16_bias', None)
if bias is not None:
bias = bias.clone().to(self.bias.device)
updown, ex_bias = module.calc_updown(weight)
if len(self.weight.shape) == 4 and self.weight.shape[1] == 9:
if len(weight.shape) == 4 and weight.shape[1] == 9:
# inpainting model. zero pad updown to make channel[1] 4 to 9
updown = torch.nn.functional.pad(updown, (0, 0, 0, 0, 0, 5))
self.weight += updown
self.weight.copy_((weight.to(dtype=updown.dtype) + updown).to(dtype=self.weight.dtype))
if ex_bias is not None and hasattr(self, 'bias'):
if self.bias is None:
self.bias = torch.nn.Parameter(ex_bias)
self.bias = torch.nn.Parameter(ex_bias).to(self.weight.dtype)
else:
self.bias += ex_bias
self.bias.copy_((bias + ex_bias).to(dtype=self.bias.dtype))
except RuntimeError as e:
logging.debug(f"Network {net.name} layer {network_layer_name}: {e}")
extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
@ -444,23 +458,23 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
self.network_current_names = wanted_names
def network_forward(module, input, original_forward):
def network_forward(org_module, input, original_forward):
"""
Old way of applying Lora by executing operations during layer's forward.
Stacking many loras this way results in big performance degradation.
"""
if len(loaded_networks) == 0:
return original_forward(module, input)
return original_forward(org_module, input)
input = devices.cond_cast_unet(input)
network_restore_weights_from_backup(module)
network_reset_cached_weight(module)
network_restore_weights_from_backup(org_module)
network_reset_cached_weight(org_module)
y = original_forward(module, input)
y = original_forward(org_module, input)
network_layer_name = getattr(module, 'network_layer_name', None)
network_layer_name = getattr(org_module, 'network_layer_name', None)
for lora in loaded_networks:
module = lora.modules.get(network_layer_name, None)
if module is None:

5
extensions-builtin/Lora/preload.py
View File

@ -1,7 +1,8 @@
import os
from modules import paths
from modules.paths_internal import normalized_filepath
def preload(parser):
parser.add_argument("--lora-dir", type=str, help="Path to directory with Lora networks.", default=os.path.join(paths.models_path, 'Lora'))
parser.add_argument("--lyco-dir-backcompat", type=str, help="Path to directory with LyCORIS networks (for backawards compatibility; can also use --lyco-dir).", default=os.path.join(paths.models_path, 'LyCORIS'))
parser.add_argument("--lora-dir", type=normalized_filepath, help="Path to directory with Lora networks.", default=os.path.join(paths.models_path, 'Lora'))
parser.add_argument("--lyco-dir-backcompat", type=normalized_filepath, help="Path to directory with LyCORIS networks (for backawards compatibility; can also use --lyco-dir).", default=os.path.join(paths.models_path, 'LyCORIS'))

2
extensions-builtin/Lora/scripts/lora_script.py
View File

@ -39,6 +39,8 @@ shared.options_templates.update(shared.options_section(('extra_networks', "Extra
"lora_show_all": shared.OptionInfo(False, "Always show all networks on the Lora page").info("otherwise, those detected as for incompatible version of Stable Diffusion will be hidden"),
"lora_hide_unknown_for_versions": shared.OptionInfo([], "Hide networks of unknown versions for model versions", gr.CheckboxGroup, {"choices": ["SD1", "SD2", "SDXL"]}),
"lora_in_memory_limit": shared.OptionInfo(0, "Number of Lora networks to keep cached in memory", gr.Number, {"precision": 0}),
"lora_not_found_warning_console": shared.OptionInfo(False, "Lora not found warning in console"),
"lora_not_found_gradio_warning": shared.OptionInfo(False, "Lora not found warning popup in webui"),
}))

9
extensions-builtin/Lora/ui_edit_user_metadata.py
View File

@ -54,12 +54,13 @@ class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor)
self.slider_preferred_weight = None
self.edit_notes = None
def save_lora_user_metadata(self, name, desc, sd_version, activation_text, preferred_weight, notes):
def save_lora_user_metadata(self, name, desc, sd_version, activation_text, preferred_weight, negative_text, notes):
user_metadata = self.get_user_metadata(name)
user_metadata["description"] = desc
user_metadata["sd version"] = sd_version
user_metadata["activation text"] = activation_text
user_metadata["preferred weight"] = preferred_weight
user_metadata["negative text"] = negative_text
user_metadata["notes"] = notes
self.write_user_metadata(name, user_metadata)
@ -127,6 +128,7 @@ class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor)
gr.HighlightedText.update(value=gradio_tags, visible=True if tags else False),
user_metadata.get('activation text', ''),
float(user_metadata.get('preferred weight', 0.0)),
user_metadata.get('negative text', ''),
gr.update(visible=True if tags else False),
gr.update(value=self.generate_random_prompt_from_tags(tags), visible=True if tags else False),
]
@ -162,7 +164,7 @@ class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor)
self.taginfo = gr.HighlightedText(label="Training dataset tags")
self.edit_activation_text = gr.Text(label='Activation text', info="Will be added to prompt along with Lora")
self.slider_preferred_weight = gr.Slider(label='Preferred weight', info="Set to 0 to disable", minimum=0.0, maximum=2.0, step=0.01)
self.edit_negative_text = gr.Text(label='Negative prompt', info="Will be added to negative prompts")
with gr.Row() as row_random_prompt:
with gr.Column(scale=8):
random_prompt = gr.Textbox(label='Random prompt', lines=4, max_lines=4, interactive=False)
@ -198,6 +200,7 @@ class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor)
self.taginfo,
self.edit_activation_text,
self.slider_preferred_weight,
self.edit_negative_text,
row_random_prompt,
random_prompt,
]
@ -211,7 +214,9 @@ class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor)
self.select_sd_version,
self.edit_activation_text,
self.slider_preferred_weight,
self.edit_negative_text,
self.edit_notes,
]
self.setup_save_handler(self.button_save, self.save_lora_user_metadata, edited_components)

10
extensions-builtin/Lora/ui_extra_networks_lora.py
View File

@ -24,13 +24,16 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
alias = lora_on_disk.get_alias()
search_terms = [self.search_terms_from_path(lora_on_disk.filename)]
if lora_on_disk.hash:
search_terms.append(lora_on_disk.hash)
item = {
"name": name,
"filename": lora_on_disk.filename,
"shorthash": lora_on_disk.shorthash,
"preview": self.find_preview(path),
"description": self.find_description(path),
"search_term": self.search_terms_from_path(lora_on_disk.filename) + " " + (lora_on_disk.hash or ""),
"search_terms": search_terms,
"local_preview": f"{path}.{shared.opts.samples_format}",
"metadata": lora_on_disk.metadata,
"sort_keys": {'default': index, **self.get_sort_keys(lora_on_disk.filename)},
@ -45,6 +48,11 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
if activation_text:
item["prompt"] += " + " + quote_js(" " + activation_text)
negative_prompt = item["user_metadata"].get("negative text")
item["negative_prompt"] = quote_js("")
if negative_prompt:
item["negative_prompt"] = quote_js('(' + negative_prompt + ':1)')
sd_version = item["user_metadata"].get("sd version")
if sd_version in network.SdVersion.__members__:
item["sd_version"] = sd_version

94
extensions-builtin/ScuNET/scripts/scunet_model.py
View File

@ -1,16 +1,9 @@
import sys
import PIL.Image
import numpy as np
import torch
from tqdm import tqdm
import modules.upscaler
from modules import devices, modelloader, script_callbacks, errors
from scunet_model_arch import SCUNet
from modules.modelloader import load_file_from_url
from modules.shared import opts
from modules import devices, errors, modelloader, script_callbacks, shared, upscaler_utils
class UpscalerScuNET(modules.upscaler.Upscaler):
@ -42,100 +35,37 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
scalers.append(scaler_data2)
self.scalers = scalers
@staticmethod
@torch.no_grad()
def tiled_inference(img, model):
# test the image tile by tile
h, w = img.shape[2:]
tile = opts.SCUNET_tile
tile_overlap = opts.SCUNET_tile_overlap
if tile == 0:
return model(img)
device = devices.get_device_for('scunet')
assert tile % 8 == 0, "tile size should be a multiple of window_size"
sf = 1
stride = tile - tile_overlap
h_idx_list = list(range(0, h - tile, stride)) + [h - tile]
w_idx_list = list(range(0, w - tile, stride)) + [w - tile]
E = torch.zeros(1, 3, h * sf, w * sf, dtype=img.dtype, device=device)
W = torch.zeros_like(E, dtype=devices.dtype, device=device)
with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="ScuNET tiles") as pbar:
for h_idx in h_idx_list:
for w_idx in w_idx_list:
in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
out_patch = model(in_patch)
out_patch_mask = torch.ones_like(out_patch)
E[
..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
].add_(out_patch)
W[
..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
].add_(out_patch_mask)
pbar.update(1)
output = E.div_(W)
return output
def do_upscale(self, img: PIL.Image.Image, selected_file):
devices.torch_gc()
try:
model = self.load_model(selected_file)
except Exception as e:
print(f"ScuNET: Unable to load model from {selected_file}: {e}", file=sys.stderr)
return img
device = devices.get_device_for('scunet')
tile = opts.SCUNET_tile
h, w = img.height, img.width
np_img = np.array(img)
np_img = np_img[:, :, ::-1] # RGB to BGR
np_img = np_img.transpose((2, 0, 1)) / 255 # HWC to CHW
torch_img = torch.from_numpy(np_img).float().unsqueeze(0).to(device) # type: ignore
if tile > h or tile > w:
_img = torch.zeros(1, 3, max(h, tile), max(w, tile), dtype=torch_img.dtype, device=torch_img.device)
_img[:, :, :h, :w] = torch_img # pad image
torch_img = _img
torch_output = self.tiled_inference(torch_img, model).squeeze(0)
torch_output = torch_output[:, :h * 1, :w * 1] # remove padding, if any
np_output: np.ndarray = torch_output.float().cpu().clamp_(0, 1).numpy()
del torch_img, torch_output
img = upscaler_utils.upscale_2(
img,
model,
tile_size=shared.opts.SCUNET_tile,
tile_overlap=shared.opts.SCUNET_tile_overlap,
scale=1, # ScuNET is a denoising model, not an upscaler
desc='ScuNET',
)
devices.torch_gc()
output = np_output.transpose((1, 2, 0)) # CHW to HWC
output = output[:, :, ::-1] # BGR to RGB
return PIL.Image.fromarray((output * 255).astype(np.uint8))
return img
def load_model(self, path: str):
device = devices.get_device_for('scunet')
if path.startswith("http"):
# TODO: this doesn't use `path` at all?
filename = load_file_from_url(self.model_url, model_dir=self.model_download_path, file_name=f"{self.name}.pth")
filename = modelloader.load_file_from_url(self.model_url, model_dir=self.model_download_path, file_name=f"{self.name}.pth")
else:
filename = path
model = SCUNet(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64)
model.load_state_dict(torch.load(filename), strict=True)
model.eval()
for _, v in model.named_parameters():
v.requires_grad = False
model = model.to(device)
return model
return modelloader.load_spandrel_model(filename, device=device, expected_architecture='SCUNet')
def on_ui_settings():
import gradio as gr
from modules import shared
shared.opts.add_option("SCUNET_tile", shared.OptionInfo(256, "Tile size for SCUNET upscalers.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}, section=('upscaling', "Upscaling")).info("0 = no tiling"))
shared.opts.add_option("SCUNET_tile_overlap", shared.OptionInfo(8, "Tile overlap for SCUNET upscalers.", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}, section=('upscaling', "Upscaling")).info("Low values = visible seam"))

268
extensions-builtin/ScuNET/scunet_model_arch.py
View File

@ -1,268 +0,0 @@
# -*- coding: utf-8 -*-
import numpy as np
import torch
import torch.nn as nn
from einops import rearrange
from einops.layers.torch import Rearrange
from timm.models.layers import trunc_normal_, DropPath
class WMSA(nn.Module):
""" Self-attention module in Swin Transformer
"""
def __init__(self, input_dim, output_dim, head_dim, window_size, type):
super(WMSA, self).__init__()
self.input_dim = input_dim
self.output_dim = output_dim
self.head_dim = head_dim
self.scale = self.head_dim ** -0.5
self.n_heads = input_dim // head_dim
self.window_size = window_size
self.type = type
self.embedding_layer = nn.Linear(self.input_dim, 3 * self.input_dim, bias=True)
self.relative_position_params = nn.Parameter(
torch.zeros((2 * window_size - 1) * (2 * window_size - 1), self.n_heads))
self.linear = nn.Linear(self.input_dim, self.output_dim)
trunc_normal_(self.relative_position_params, std=.02)
self.relative_position_params = torch.nn.Parameter(
self.relative_position_params.view(2 * window_size - 1, 2 * window_size - 1, self.n_heads).transpose(1,
2).transpose(
0, 1))
def generate_mask(self, h, w, p, shift):
""" generating the mask of SW-MSA
Args:
shift: shift parameters in CyclicShift.
Returns:
attn_mask: should be (1 1 w p p),
"""
# supporting square.
attn_mask = torch.zeros(h, w, p, p, p, p, dtype=torch.bool, device=self.relative_position_params.device)
if self.type == 'W':
return attn_mask
s = p - shift
attn_mask[-1, :, :s, :, s:, :] = True
attn_mask[-1, :, s:, :, :s, :] = True
attn_mask[:, -1, :, :s, :, s:] = True
attn_mask[:, -1, :, s:, :, :s] = True
attn_mask = rearrange(attn_mask, 'w1 w2 p1 p2 p3 p4 -> 1 1 (w1 w2) (p1 p2) (p3 p4)')
return attn_mask
def forward(self, x):
""" Forward pass of Window Multi-head Self-attention module.
Args:
x: input tensor with shape of [b h w c];
attn_mask: attention mask, fill -inf where the value is True;
Returns:
output: tensor shape [b h w c]
"""
if self.type != 'W':
x = torch.roll(x, shifts=(-(self.window_size // 2), -(self.window_size // 2)), dims=(1, 2))
x = rearrange(x, 'b (w1 p1) (w2 p2) c -> b w1 w2 p1 p2 c', p1=self.window_size, p2=self.window_size)
h_windows = x.size(1)
w_windows = x.size(2)
# square validation
# assert h_windows == w_windows
x = rearrange(x, 'b w1 w2 p1 p2 c -> b (w1 w2) (p1 p2) c', p1=self.window_size, p2=self.window_size)
qkv = self.embedding_layer(x)
q, k, v = rearrange(qkv, 'b nw np (threeh c) -> threeh b nw np c', c=self.head_dim).chunk(3, dim=0)
sim = torch.einsum('hbwpc,hbwqc->hbwpq', q, k) * self.scale
# Adding learnable relative embedding
sim = sim + rearrange(self.relative_embedding(), 'h p q -> h 1 1 p q')
# Using Attn Mask to distinguish different subwindows.
if self.type != 'W':
attn_mask = self.generate_mask(h_windows, w_windows, self.window_size, shift=self.window_size // 2)
sim = sim.masked_fill_(attn_mask, float("-inf"))
probs = nn.functional.softmax(sim, dim=-1)
output = torch.einsum('hbwij,hbwjc->hbwic', probs, v)
output = rearrange(output, 'h b w p c -> b w p (h c)')
output = self.linear(output)
output = rearrange(output, 'b (w1 w2) (p1 p2) c -> b (w1 p1) (w2 p2) c', w1=h_windows, p1=self.window_size)
if self.type != 'W':
output = torch.roll(output, shifts=(self.window_size // 2, self.window_size // 2), dims=(1, 2))
return output
def relative_embedding(self):
cord = torch.tensor(np.array([[i, j] for i in range(self.window_size) for j in range(self.window_size)]))
relation = cord[:, None, :] - cord[None, :, :] + self.window_size - 1
# negative is allowed
return self.relative_position_params[:, relation[:, :, 0].long(), relation[:, :, 1].long()]
class Block(nn.Module):
def __init__(self, input_dim, output_dim, head_dim, window_size, drop_path, type='W', input_resolution=None):
""" SwinTransformer Block
"""
super(Block, self).__init__()
self.input_dim = input_dim
self.output_dim = output_dim
assert type in ['W', 'SW']
self.type = type
if input_resolution <= window_size:
self.type = 'W'
self.ln1 = nn.LayerNorm(input_dim)
self.msa = WMSA(input_dim, input_dim, head_dim, window_size, self.type)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.ln2 = nn.LayerNorm(input_dim)
self.mlp = nn.Sequential(
nn.Linear(input_dim, 4 * input_dim),
nn.GELU(),
nn.Linear(4 * input_dim, output_dim),
)
def forward(self, x):
x = x + self.drop_path(self.msa(self.ln1(x)))
x = x + self.drop_path(self.mlp(self.ln2(x)))
return x
class ConvTransBlock(nn.Module):
def __init__(self, conv_dim, trans_dim, head_dim, window_size, drop_path, type='W', input_resolution=None):
""" SwinTransformer and Conv Block
"""
super(ConvTransBlock, self).__init__()
self.conv_dim = conv_dim
self.trans_dim = trans_dim
self.head_dim = head_dim
self.window_size = window_size
self.drop_path = drop_path
self.type = type
self.input_resolution = input_resolution
assert self.type in ['W', 'SW']
if self.input_resolution <= self.window_size:
self.type = 'W'
self.trans_block = Block(self.trans_dim, self.trans_dim, self.head_dim, self.window_size, self.drop_path,
self.type, self.input_resolution)
self.conv1_1 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True)
self.conv1_2 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True)
self.conv_block = nn.Sequential(
nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False),
nn.ReLU(True),
nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False)
)
def forward(self, x):
conv_x, trans_x = torch.split(self.conv1_1(x), (self.conv_dim, self.trans_dim), dim=1)
conv_x = self.conv_block(conv_x) + conv_x
trans_x = Rearrange('b c h w -> b h w c')(trans_x)
trans_x = self.trans_block(trans_x)
trans_x = Rearrange('b h w c -> b c h w')(trans_x)
res = self.conv1_2(torch.cat((conv_x, trans_x), dim=1))
x = x + res
return x
class SCUNet(nn.Module):
# def __init__(self, in_nc=3, config=[2, 2, 2, 2, 2, 2, 2], dim=64, drop_path_rate=0.0, input_resolution=256):
def __init__(self, in_nc=3, config=None, dim=64, drop_path_rate=0.0, input_resolution=256):
super(SCUNet, self).__init__()
if config is None:
config = [2, 2, 2, 2, 2, 2, 2]
self.config = config
self.dim = dim
self.head_dim = 32
self.window_size = 8
# drop path rate for each layer
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(config))]
self.m_head = [nn.Conv2d(in_nc, dim, 3, 1, 1, bias=False)]
begin = 0
self.m_down1 = [ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin],
'W' if not i % 2 else 'SW', input_resolution)
for i in range(config[0])] + \
[nn.Conv2d(dim, 2 * dim, 2, 2, 0, bias=False)]
begin += config[0]
self.m_down2 = [ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin],
'W' if not i % 2 else 'SW', input_resolution // 2)
for i in range(config[1])] + \
[nn.Conv2d(2 * dim, 4 * dim, 2, 2, 0, bias=False)]
begin += config[1]
self.m_down3 = [ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin],
'W' if not i % 2 else 'SW', input_resolution // 4)
for i in range(config[2])] + \
[nn.Conv2d(4 * dim, 8 * dim, 2, 2, 0, bias=False)]
begin += config[2]
self.m_body = [ConvTransBlock(4 * dim, 4 * dim, self.head_dim, self.window_size, dpr[i + begin],
'W' if not i % 2 else 'SW', input_resolution // 8)
for i in range(config[3])]
begin += config[3]
self.m_up3 = [nn.ConvTranspose2d(8 * dim, 4 * dim, 2, 2, 0, bias=False), ] + \
[ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin],
'W' if not i % 2 else 'SW', input_resolution // 4)
for i in range(config[4])]
begin += config[4]
self.m_up2 = [nn.ConvTranspose2d(4 * dim, 2 * dim, 2, 2, 0, bias=False), ] + \
[ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin],
'W' if not i % 2 else 'SW', input_resolution // 2)
for i in range(config[5])]
begin += config[5]
self.m_up1 = [nn.ConvTranspose2d(2 * dim, dim, 2, 2, 0, bias=False), ] + \
[ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin],
'W' if not i % 2 else 'SW', input_resolution)
for i in range(config[6])]
self.m_tail = [nn.Conv2d(dim, in_nc, 3, 1, 1, bias=False)]
self.m_head = nn.Sequential(*self.m_head)
self.m_down1 = nn.Sequential(*self.m_down1)
self.m_down2 = nn.Sequential(*self.m_down2)
self.m_down3 = nn.Sequential(*self.m_down3)
self.m_body = nn.Sequential(*self.m_body)
self.m_up3 = nn.Sequential(*self.m_up3)
self.m_up2 = nn.Sequential(*self.m_up2)
self.m_up1 = nn.Sequential(*self.m_up1)
self.m_tail = nn.Sequential(*self.m_tail)
# self.apply(self._init_weights)
def forward(self, x0):
h, w = x0.size()[-2:]
paddingBottom = int(np.ceil(h / 64) * 64 - h)
paddingRight = int(np.ceil(w / 64) * 64 - w)
x0 = nn.ReplicationPad2d((0, paddingRight, 0, paddingBottom))(x0)
x1 = self.m_head(x0)
x2 = self.m_down1(x1)
x3 = self.m_down2(x2)
x4 = self.m_down3(x3)
x = self.m_body(x4)
x = self.m_up3(x + x4)
x = self.m_up2(x + x3)
x = self.m_up1(x + x2)
x = self.m_tail(x + x1)
x = x[..., :h, :w]
return x
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)

161
extensions-builtin/SwinIR/scripts/swinir_model.py
View File

@ -1,20 +1,15 @@
import logging
import sys
import platform
import numpy as np
import torch
from PIL import Image
from tqdm import tqdm
from modules import modelloader, devices, script_callbacks, shared
from modules.shared import opts, state
from swinir_model_arch import SwinIR
from swinir_model_arch_v2 import Swin2SR
from modules import devices, modelloader, script_callbacks, shared, upscaler_utils
from modules.upscaler import Upscaler, UpscalerData
SWINIR_MODEL_URL = "https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_GAN.pth"
device_swinir = devices.get_device_for('swinir')
logger = logging.getLogger(__name__)
class UpscalerSwinIR(Upscaler):
@ -37,26 +32,28 @@ class UpscalerSwinIR(Upscaler):
scalers.append(model_data)
self.scalers = scalers
def do_upscale(self, img, model_file):
use_compile = hasattr(opts, 'SWIN_torch_compile') and opts.SWIN_torch_compile \
and int(torch.__version__.split('.')[0]) >= 2 and platform.system() != "Windows"
current_config = (model_file, opts.SWIN_tile)
def do_upscale(self, img: Image.Image, model_file: str) -> Image.Image:
current_config = (model_file, shared.opts.SWIN_tile)
if use_compile and self._cached_model_config == current_config:
if self._cached_model_config == current_config:
model = self._cached_model
else:
self._cached_model = None
try:
model = self.load_model(model_file)
except Exception as e:
print(f"Failed loading SwinIR model {model_file}: {e}", file=sys.stderr)
return img
model = model.to(device_swinir, dtype=devices.dtype)
if use_compile:
model = torch.compile(model)
self._cached_model = model
self._cached_model_config = current_config
img = upscale(img, model)
self._cached_model = model
self._cached_model_config = current_config
img = upscaler_utils.upscale_2(
img,
model,
tile_size=shared.opts.SWIN_tile,
tile_overlap=shared.opts.SWIN_tile_overlap,
scale=model.scale,
desc="SwinIR",
)
devices.torch_gc()
return img
@ -69,115 +66,22 @@ class UpscalerSwinIR(Upscaler):
)
else:
filename = path
if filename.endswith(".v2.pth"):
model = Swin2SR(
upscale=scale,
in_chans=3,
img_size=64,
window_size=8,
img_range=1.0,
depths=[6, 6, 6, 6, 6, 6],
embed_dim=180,
num_heads=[6, 6, 6, 6, 6, 6],
mlp_ratio=2,
upsampler="nearest+conv",
resi_connection="1conv",
)
params = None
else:
model = SwinIR(
upscale=scale,
in_chans=3,
img_size=64,
window_size=8,
img_range=1.0,
depths=[6, 6, 6, 6, 6, 6, 6, 6, 6],
embed_dim=240,
num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8],
mlp_ratio=2,
upsampler="nearest+conv",
resi_connection="3conv",
)
params = "params_ema"
pretrained_model = torch.load(filename)
if params is not None:
model.load_state_dict(pretrained_model[params], strict=True)
else:
model.load_state_dict(pretrained_model, strict=True)
return model
model_descriptor = modelloader.load_spandrel_model(
filename,
device=self._get_device(),
prefer_half=(devices.dtype == torch.float16),
expected_architecture="SwinIR",
)
if getattr(shared.opts, 'SWIN_torch_compile', False):
try:
model_descriptor.model.compile()
except Exception:
logger.warning("Failed to compile SwinIR model, fallback to JIT", exc_info=True)
return model_descriptor
def upscale(
img,
model,
tile=None,
tile_overlap=None,
window_size=8,
scale=4,
):
tile = tile or opts.SWIN_tile
tile_overlap = tile_overlap or opts.SWIN_tile_overlap
img = np.array(img)
img = img[:, :, ::-1]
img = np.moveaxis(img, 2, 0) / 255
img = torch.from_numpy(img).float()
img = img.unsqueeze(0).to(device_swinir, dtype=devices.dtype)
with torch.no_grad(), devices.autocast():
_, _, h_old, w_old = img.size()
h_pad = (h_old // window_size + 1) * window_size - h_old
w_pad = (w_old // window_size + 1) * window_size - w_old
img = torch.cat([img, torch.flip(img, [2])], 2)[:, :, : h_old + h_pad, :]
img = torch.cat([img, torch.flip(img, [3])], 3)[:, :, :, : w_old + w_pad]
output = inference(img, model, tile, tile_overlap, window_size, scale)
output = output[..., : h_old * scale, : w_old * scale]
output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
if output.ndim == 3:
output = np.transpose(
output[[2, 1, 0], :, :], (1, 2, 0)
) # CHW-RGB to HCW-BGR
output = (output * 255.0).round().astype(np.uint8) # float32 to uint8
return Image.fromarray(output, "RGB")
def inference(img, model, tile, tile_overlap, window_size, scale):
# test the image tile by tile
b, c, h, w = img.size()
tile = min(tile, h, w)
assert tile % window_size == 0, "tile size should be a multiple of window_size"
sf = scale
stride = tile - tile_overlap
h_idx_list = list(range(0, h - tile, stride)) + [h - tile]
w_idx_list = list(range(0, w - tile, stride)) + [w - tile]
E = torch.zeros(b, c, h * sf, w * sf, dtype=devices.dtype, device=device_swinir).type_as(img)
W = torch.zeros_like(E, dtype=devices.dtype, device=device_swinir)
with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="SwinIR tiles") as pbar:
for h_idx in h_idx_list:
if state.interrupted or state.skipped:
break
for w_idx in w_idx_list:
if state.interrupted or state.skipped:
break
in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
out_patch = model(in_patch)
out_patch_mask = torch.ones_like(out_patch)
E[
..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
].add_(out_patch)
W[
..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
].add_(out_patch_mask)
pbar.update(1)
output = E.div_(W)
return output
def _get_device(self):
return devices.get_device_for('swinir')
def on_ui_settings():
@ -185,8 +89,7 @@ def on_ui_settings():
shared.opts.add_option("SWIN_tile", shared.OptionInfo(192, "Tile size for all SwinIR.", gr.Slider, {"minimum": 16, "maximum": 512, "step": 16}, section=('upscaling', "Upscaling")))
shared.opts.add_option("SWIN_tile_overlap", shared.OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}, section=('upscaling', "Upscaling")))
if int(torch.__version__.split('.')[0]) >= 2 and platform.system() != "Windows": # torch.compile() require pytorch 2.0 or above, and not on Windows
shared.opts.add_option("SWIN_torch_compile", shared.OptionInfo(False, "Use torch.compile to accelerate SwinIR.", gr.Checkbox, {"interactive": True}, section=('upscaling', "Upscaling")).info("Takes longer on first run"))
shared.opts.add_option("SWIN_torch_compile", shared.OptionInfo(False, "Use torch.compile to accelerate SwinIR.", gr.Checkbox, {"interactive": True}, section=('upscaling', "Upscaling")).info("Takes longer on first run"))
script_callbacks.on_ui_settings(on_ui_settings)

867
extensions-builtin/SwinIR/swinir_model_arch.py
View File

@ -1,867 +0,0 @@
# -----------------------------------------------------------------------------------
# SwinIR: Image Restoration Using Swin Transformer, https://arxiv.org/abs/2108.10257
# Originally Written by Ze Liu, Modified by Jingyun Liang.
# -----------------------------------------------------------------------------------
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
class Mlp(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features)
self.act = act_layer()
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(drop)
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
return x
def window_partition(x, window_size):
"""
Args:
x: (B, H, W, C)
window_size (int): window size
Returns:
windows: (num_windows*B, window_size, window_size, C)
"""
B, H, W, C = x.shape
x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
return windows
def window_reverse(windows, window_size, H, W):
"""
Args:
windows: (num_windows*B, window_size, window_size, C)
window_size (int): Window size
H (int): Height of image
W (int): Width of image
Returns:
x: (B, H, W, C)
"""
B = int(windows.shape[0] / (H * W / window_size / window_size))
x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
return x
class WindowAttention(nn.Module):
r""" Window based multi-head self attention (W-MSA) module with relative position bias.
It supports both of shifted and non-shifted window.
Args:
dim (int): Number of input channels.
window_size (tuple[int]): The height and width of the window.
num_heads (int): Number of attention heads.
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
proj_drop (float, optional): Dropout ratio of output. Default: 0.0
"""
def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
super().__init__()
self.dim = dim
self.window_size = window_size # Wh, Ww
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = qk_scale or head_dim ** -0.5
# define a parameter table of relative position bias
self.relative_position_bias_table = nn.Parameter(
torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH
# get pair-wise relative position index for each token inside the window
coords_h = torch.arange(self.window_size[0])
coords_w = torch.arange(self.window_size[1])
coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
relative_coords[:, :, 1] += self.window_size[1] - 1
relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
self.register_buffer("relative_position_index", relative_position_index)
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
trunc_normal_(self.relative_position_bias_table, std=.02)
self.softmax = nn.Softmax(dim=-1)
def forward(self, x, mask=None):
"""
Args:
x: input features with shape of (num_windows*B, N, C)
mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
"""
B_, N, C = x.shape
qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
q = q * self.scale
attn = (q @ k.transpose(-2, -1))
relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH
relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
attn = attn + relative_position_bias.unsqueeze(0)
if mask is not None:
nW = mask.shape[0]
attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
attn = attn.view(-1, self.num_heads, N, N)
attn = self.softmax(attn)
else:
attn = self.softmax(attn)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
def extra_repr(self) -> str:
return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'
def flops(self, N):
# calculate flops for 1 window with token length of N
flops = 0
# qkv = self.qkv(x)
flops += N * self.dim * 3 * self.dim
# attn = (q @ k.transpose(-2, -1))
flops += self.num_heads * N * (self.dim // self.num_heads) * N
# x = (attn @ v)
flops += self.num_heads * N * N * (self.dim // self.num_heads)
# x = self.proj(x)
flops += N * self.dim * self.dim
return flops
class SwinTransformerBlock(nn.Module):
r""" Swin Transformer Block.
Args:
dim (int): Number of input channels.
input_resolution (tuple[int]): Input resolution.
num_heads (int): Number of attention heads.
window_size (int): Window size.
shift_size (int): Shift size for SW-MSA.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
drop (float, optional): Dropout rate. Default: 0.0
attn_drop (float, optional): Attention dropout rate. Default: 0.0
drop_path (float, optional): Stochastic depth rate. Default: 0.0
act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
"""
def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
act_layer=nn.GELU, norm_layer=nn.LayerNorm):
super().__init__()
self.dim = dim
self.input_resolution = input_resolution
self.num_heads = num_heads
self.window_size = window_size
self.shift_size = shift_size
self.mlp_ratio = mlp_ratio
if min(self.input_resolution) <= self.window_size:
# if window size is larger than input resolution, we don't partition windows
self.shift_size = 0
self.window_size = min(self.input_resolution)
assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
self.norm1 = norm_layer(dim)
self.attn = WindowAttention(
dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
if self.shift_size > 0:
attn_mask = self.calculate_mask(self.input_resolution)
else:
attn_mask = None
self.register_buffer("attn_mask", attn_mask)
def calculate_mask(self, x_size):
# calculate attention mask for SW-MSA
H, W = x_size
img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1
h_slices = (slice(0, -self.window_size),
slice(-self.window_size, -self.shift_size),
slice(-self.shift_size, None))
w_slices = (slice(0, -self.window_size),
slice(-self.window_size, -self.shift_size),
slice(-self.shift_size, None))
cnt = 0
for h in h_slices:
for w in w_slices:
img_mask[:, h, w, :] = cnt
cnt += 1
mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1
mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
return attn_mask
def forward(self, x, x_size):
H, W = x_size
B, L, C = x.shape
# assert L == H * W, "input feature has wrong size"
shortcut = x
x = self.norm1(x)
x = x.view(B, H, W, C)
# cyclic shift
if self.shift_size > 0:
shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
else:
shifted_x = x
# partition windows
x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C
x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C
# W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size
if self.input_resolution == x_size:
attn_windows = self.attn(x_windows, mask=self.attn_mask) # nW*B, window_size*window_size, C
else:
attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device))
# merge windows
attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C
# reverse cyclic shift
if self.shift_size > 0:
x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
else:
x = shifted_x
x = x.view(B, H * W, C)
# FFN
x = shortcut + self.drop_path(x)
x = x + self.drop_path(self.mlp(self.norm2(x)))
return x
def extra_repr(self) -> str:
return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
def flops(self):
flops = 0
H, W = self.input_resolution
# norm1
flops += self.dim * H * W
# W-MSA/SW-MSA
nW = H * W / self.window_size / self.window_size
flops += nW * self.attn.flops(self.window_size * self.window_size)
# mlp
flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
# norm2
flops += self.dim * H * W
return flops
class PatchMerging(nn.Module):
r""" Patch Merging Layer.
Args:
input_resolution (tuple[int]): Resolution of input feature.
dim (int): Number of input channels.
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
"""
def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
super().__init__()
self.input_resolution = input_resolution
self.dim = dim
self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
self.norm = norm_layer(4 * dim)
def forward(self, x):
"""
x: B, H*W, C
"""
H, W = self.input_resolution
B, L, C = x.shape
assert L == H * W, "input feature has wrong size"
assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
x = x.view(B, H, W, C)
x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C
x = self.norm(x)
x = self.reduction(x)
return x
def extra_repr(self) -> str:
return f"input_resolution={self.input_resolution}, dim={self.dim}"
def flops(self):
H, W = self.input_resolution
flops = H * W * self.dim
flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
return flops
class BasicLayer(nn.Module):
""" A basic Swin Transformer layer for one stage.
Args:
dim (int): Number of input channels.
input_resolution (tuple[int]): Input resolution.
depth (int): Number of blocks.
num_heads (int): Number of attention heads.
window_size (int): Local window size.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
drop (float, optional): Dropout rate. Default: 0.0
attn_drop (float, optional): Attention dropout rate. Default: 0.0
drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
"""
def __init__(self, dim, input_resolution, depth, num_heads, window_size,
mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):
super().__init__()
self.dim = dim
self.input_resolution = input_resolution
self.depth = depth
self.use_checkpoint = use_checkpoint
# build blocks
self.blocks = nn.ModuleList([
SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
num_heads=num_heads, window_size=window_size,
shift_size=0 if (i % 2 == 0) else window_size // 2,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop, attn_drop=attn_drop,
drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
norm_layer=norm_layer)
for i in range(depth)])
# patch merging layer
if downsample is not None:
self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
else:
self.downsample = None
def forward(self, x, x_size):
for blk in self.blocks:
if self.use_checkpoint:
x = checkpoint.checkpoint(blk, x, x_size)
else:
x = blk(x, x_size)
if self.downsample is not None:
x = self.downsample(x)
return x
def extra_repr(self) -> str:
return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
def flops(self):
flops = 0
for blk in self.blocks:
flops += blk.flops()
if self.downsample is not None:
flops += self.downsample.flops()
return flops
class RSTB(nn.Module):
"""Residual Swin Transformer Block (RSTB).
Args:
dim (int): Number of input channels.
input_resolution (tuple[int]): Input resolution.
depth (int): Number of blocks.
num_heads (int): Number of attention heads.
window_size (int): Local window size.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
drop (float, optional): Dropout rate. Default: 0.0
attn_drop (float, optional): Attention dropout rate. Default: 0.0
drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
img_size: Input image size.
patch_size: Patch size.
resi_connection: The convolutional block before residual connection.
"""
def __init__(self, dim, input_resolution, depth, num_heads, window_size,
mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
img_size=224, patch_size=4, resi_connection='1conv'):
super(RSTB, self).__init__()
self.dim = dim
self.input_resolution = input_resolution
self.residual_group = BasicLayer(dim=dim,
input_resolution=input_resolution,
depth=depth,
num_heads=num_heads,
window_size=window_size,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop, attn_drop=attn_drop,
drop_path=drop_path,
norm_layer=norm_layer,
downsample=downsample,
use_checkpoint=use_checkpoint)
if resi_connection == '1conv':
self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
elif resi_connection == '3conv':
# to save parameters and memory
self.conv = nn.Sequential(nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True),
nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),
nn.LeakyReLU(negative_slope=0.2, inplace=True),
nn.Conv2d(dim // 4, dim, 3, 1, 1))
self.patch_embed = PatchEmbed(
img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,
norm_layer=None)
self.patch_unembed = PatchUnEmbed(
img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,
norm_layer=None)
def forward(self, x, x_size):
return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x
def flops(self):
flops = 0
flops += self.residual_group.flops()
H, W = self.input_resolution
flops += H * W * self.dim * self.dim * 9
flops += self.patch_embed.flops()
flops += self.patch_unembed.flops()
return flops
class PatchEmbed(nn.Module):
r""" Image to Patch Embedding
Args:
img_size (int): Image size. Default: 224.
patch_size (int): Patch token size. Default: 4.
in_chans (int): Number of input image channels. Default: 3.
embed_dim (int): Number of linear projection output channels. Default: 96.
norm_layer (nn.Module, optional): Normalization layer. Default: None
"""
def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
self.img_size = img_size
self.patch_size = patch_size
self.patches_resolution = patches_resolution
self.num_patches = patches_resolution[0] * patches_resolution[1]
self.in_chans = in_chans
self.embed_dim = embed_dim
if norm_layer is not None:
self.norm = norm_layer(embed_dim)
else:
self.norm = None
def forward(self, x):
x = x.flatten(2).transpose(1, 2) # B Ph*Pw C
if self.norm is not None:
x = self.norm(x)
return x
def flops(self):
flops = 0
H, W = self.img_size
if self.norm is not None:
flops += H * W * self.embed_dim
return flops
class PatchUnEmbed(nn.Module):
r""" Image to Patch Unembedding
Args:
img_size (int): Image size. Default: 224.
patch_size (int): Patch token size. Default: 4.
in_chans (int): Number of input image channels. Default: 3.
embed_dim (int): Number of linear projection output channels. Default: 96.
norm_layer (nn.Module, optional): Normalization layer. Default: None
"""
def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
self.img_size = img_size
self.patch_size = patch_size
self.patches_resolution = patches_resolution
self.num_patches = patches_resolution[0] * patches_resolution[1]
self.in_chans = in_chans
self.embed_dim = embed_dim
def forward(self, x, x_size):
B, HW, C = x.shape
x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1]) # B Ph*Pw C
return x
def flops(self):
flops = 0
return flops
class Upsample(nn.Sequential):
"""Upsample module.
Args:
scale (int): Scale factor. Supported scales: 2^n and 3.
num_feat (int): Channel number of intermediate features.
"""
def __init__(self, scale, num_feat):
m = []
if (scale & (scale - 1)) == 0: # scale = 2^n
for _ in range(int(math.log(scale, 2))):
m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
m.append(nn.PixelShuffle(2))
elif scale == 3:
m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
m.append(nn.PixelShuffle(3))
else:
raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
super(Upsample, self).__init__(*m)
class UpsampleOneStep(nn.Sequential):
"""UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
Used in lightweight SR to save parameters.
Args:
scale (int): Scale factor. Supported scales: 2^n and 3.
num_feat (int): Channel number of intermediate features.
"""
def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):
self.num_feat = num_feat
self.input_resolution = input_resolution
m = []
m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1))
m.append(nn.PixelShuffle(scale))
super(UpsampleOneStep, self).__init__(*m)
def flops(self):
H, W = self.input_resolution
flops = H * W * self.num_feat * 3 * 9
return flops
class SwinIR(nn.Module):
r""" SwinIR
A PyTorch impl of : `SwinIR: Image Restoration Using Swin Transformer`, based on Swin Transformer.
Args:
img_size (int | tuple(int)): Input image size. Default 64
patch_size (int | tuple(int)): Patch size. Default: 1
in_chans (int): Number of input image channels. Default: 3
embed_dim (int): Patch embedding dimension. Default: 96
depths (tuple(int)): Depth of each Swin Transformer layer.
num_heads (tuple(int)): Number of attention heads in different layers.
window_size (int): Window size. Default: 7
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
drop_rate (float): Dropout rate. Default: 0
attn_drop_rate (float): Attention dropout rate. Default: 0
drop_path_rate (float): Stochastic depth rate. Default: 0.1
norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
patch_norm (bool): If True, add normalization after patch embedding. Default: True
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction
img_range: Image range. 1. or 255.
upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None
resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
"""
def __init__(self, img_size=64, patch_size=1, in_chans=3,
embed_dim=96, depths=(6, 6, 6, 6), num_heads=(6, 6, 6, 6),
window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv',
**kwargs):
super(SwinIR, self).__init__()
num_in_ch = in_chans
num_out_ch = in_chans
num_feat = 64
self.img_range = img_range
if in_chans == 3:
rgb_mean = (0.4488, 0.4371, 0.4040)
self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
else:
self.mean = torch.zeros(1, 1, 1, 1)
self.upscale = upscale
self.upsampler = upsampler
self.window_size = window_size
#####################################################################################################
################################### 1, shallow feature extraction ###################################
self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)
#####################################################################################################
################################### 2, deep feature extraction ######################################
self.num_layers = len(depths)
self.embed_dim = embed_dim
self.ape = ape
self.patch_norm = patch_norm
self.num_features = embed_dim
self.mlp_ratio = mlp_ratio
# split image into non-overlapping patches
self.patch_embed = PatchEmbed(
img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
norm_layer=norm_layer if self.patch_norm else None)
num_patches = self.patch_embed.num_patches
patches_resolution = self.patch_embed.patches_resolution
self.patches_resolution = patches_resolution
# merge non-overlapping patches into image
self.patch_unembed = PatchUnEmbed(
img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
norm_layer=norm_layer if self.patch_norm else None)
# absolute position embedding
if self.ape:
self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
trunc_normal_(self.absolute_pos_embed, std=.02)
self.pos_drop = nn.Dropout(p=drop_rate)
# stochastic depth
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
# build Residual Swin Transformer blocks (RSTB)
self.layers = nn.ModuleList()
for i_layer in range(self.num_layers):
layer = RSTB(dim=embed_dim,
input_resolution=(patches_resolution[0],
patches_resolution[1]),
depth=depths[i_layer],
num_heads=num_heads[i_layer],
window_size=window_size,
mlp_ratio=self.mlp_ratio,
qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate,
drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], # no impact on SR results
norm_layer=norm_layer,
downsample=None,
use_checkpoint=use_checkpoint,
img_size=img_size,
patch_size=patch_size,
resi_connection=resi_connection
)
self.layers.append(layer)
self.norm = norm_layer(self.num_features)
# build the last conv layer in deep feature extraction
if resi_connection == '1conv':
self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
elif resi_connection == '3conv':
# to save parameters and memory
self.conv_after_body = nn.Sequential(nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),
nn.LeakyReLU(negative_slope=0.2, inplace=True),
nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),
nn.LeakyReLU(negative_slope=0.2, inplace=True),
nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1))
#####################################################################################################
################################ 3, high quality image reconstruction ################################
if self.upsampler == 'pixelshuffle':
# for classical SR
self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
nn.LeakyReLU(inplace=True))
self.upsample = Upsample(upscale, num_feat)
self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
elif self.upsampler == 'pixelshuffledirect':
# for lightweight SR (to save parameters)
self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch,
(patches_resolution[0], patches_resolution[1]))
elif self.upsampler == 'nearest+conv':
# for real-world SR (less artifacts)
self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
nn.LeakyReLU(inplace=True))
self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
if self.upscale == 4:
self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
else:
# for image denoising and JPEG compression artifact reduction
self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
@torch.jit.ignore
def no_weight_decay(self):
return {'absolute_pos_embed'}
@torch.jit.ignore
def no_weight_decay_keywords(self):
return {'relative_position_bias_table'}
def check_image_size(self, x):
_, _, h, w = x.size()
mod_pad_h = (self.window_size - h % self.window_size) % self.window_size
mod_pad_w = (self.window_size - w % self.window_size) % self.window_size
x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
return x
def forward_features(self, x):
x_size = (x.shape[2], x.shape[3])
x = self.patch_embed(x)
if self.ape:
x = x + self.absolute_pos_embed
x = self.pos_drop(x)
for layer in self.layers:
x = layer(x, x_size)
x = self.norm(x) # B L C
x = self.patch_unembed(x, x_size)
return x
def forward(self, x):
H, W = x.shape[2:]
x = self.check_image_size(x)
self.mean = self.mean.type_as(x)
x = (x - self.mean) * self.img_range
if self.upsampler == 'pixelshuffle':
# for classical SR
x = self.conv_first(x)
x = self.conv_after_body(self.forward_features(x)) + x
x = self.conv_before_upsample(x)
x = self.conv_last(self.upsample(x))
elif self.upsampler == 'pixelshuffledirect':
# for lightweight SR
x = self.conv_first(x)
x = self.conv_after_body(self.forward_features(x)) + x
x = self.upsample(x)
elif self.upsampler == 'nearest+conv':
# for real-world SR
x = self.conv_first(x)
x = self.conv_after_body(self.forward_features(x)) + x
x = self.conv_before_upsample(x)
x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
if self.upscale == 4:
x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
x = self.conv_last(self.lrelu(self.conv_hr(x)))
else:
# for image denoising and JPEG compression artifact reduction
x_first = self.conv_first(x)
res = self.conv_after_body(self.forward_features(x_first)) + x_first
x = x + self.conv_last(res)
x = x / self.img_range + self.mean
return x[:, :, :H*self.upscale, :W*self.upscale]
def flops(self):
flops = 0
H, W = self.patches_resolution
flops += H * W * 3 * self.embed_dim * 9
flops += self.patch_embed.flops()
for layer in self.layers:
flops += layer.flops()
flops += H * W * 3 * self.embed_dim * self.embed_dim
flops += self.upsample.flops()
return flops
if __name__ == '__main__':
upscale = 4
window_size = 8
height = (1024 // upscale // window_size + 1) * window_size
width = (720 // upscale // window_size + 1) * window_size
model = SwinIR(upscale=2, img_size=(height, width),
window_size=window_size, img_range=1., depths=[6, 6, 6, 6],
embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffledirect')
print(model)
print(height, width, model.flops() / 1e9)
x = torch.randn((1, 3, height, width))
x = model(x)
print(x.shape)

1017
extensions-builtin/SwinIR/swinir_model_arch_v2.py
View File

File diff suppressed because it is too large Load Diff

8
extensions-builtin/canvas-zoom-and-pan/javascript/zoom.js
View File

@ -218,6 +218,8 @@ onUiLoaded(async() => {
canvas_hotkey_fullscreen: "KeyS",
canvas_hotkey_move: "KeyF",
canvas_hotkey_overlap: "KeyO",
canvas_hotkey_shrink_brush: "KeyQ",
canvas_hotkey_grow_brush: "KeyW",
canvas_disabled_functions: [],
canvas_show_tooltip: true,
canvas_auto_expand: true,
@ -227,6 +229,8 @@ onUiLoaded(async() => {
const functionMap = {
"Zoom": "canvas_hotkey_zoom",
"Adjust brush size": "canvas_hotkey_adjust",
"Hotkey shrink brush": "canvas_hotkey_shrink_brush",
"Hotkey enlarge brush": "canvas_hotkey_grow_brush",
"Moving canvas": "canvas_hotkey_move",
"Fullscreen": "canvas_hotkey_fullscreen",
"Reset Zoom": "canvas_hotkey_reset",
@ -686,7 +690,9 @@ onUiLoaded(async() => {
const hotkeyActions = {
[hotkeysConfig.canvas_hotkey_reset]: resetZoom,
[hotkeysConfig.canvas_hotkey_overlap]: toggleOverlap,
[hotkeysConfig.canvas_hotkey_fullscreen]: fitToScreen
[hotkeysConfig.canvas_hotkey_fullscreen]: fitToScreen,
[hotkeysConfig.canvas_hotkey_shrink_brush]: () => adjustBrushSize(elemId, 10),
[hotkeysConfig.canvas_hotkey_grow_brush]: () => adjustBrushSize(elemId, -10)
};
const action = hotkeyActions[event.code];

4
extensions-builtin/canvas-zoom-and-pan/scripts/hotkey_config.py
View File

@ -4,6 +4,8 @@ from modules import shared
shared.options_templates.update(shared.options_section(('canvas_hotkey', "Canvas Hotkeys"), {
"canvas_hotkey_zoom": shared.OptionInfo("Alt", "Zoom canvas", gr.Radio, {"choices": ["Shift","Ctrl", "Alt"]}).info("If you choose 'Shift' you cannot scroll horizontally, 'Alt' can cause a little trouble in firefox"),
"canvas_hotkey_adjust": shared.OptionInfo("Ctrl", "Adjust brush size", gr.Radio, {"choices": ["Shift","Ctrl", "Alt"]}).info("If you choose 'Shift' you cannot scroll horizontally, 'Alt' can cause a little trouble in firefox"),
"canvas_hotkey_shrink_brush": shared.OptionInfo("Q", "Shrink the brush size"),
"canvas_hotkey_grow_brush": shared.OptionInfo("W", "Enlarge the brush size"),
"canvas_hotkey_move": shared.OptionInfo("F", "Moving the canvas").info("To work correctly in firefox, turn off 'Automatically search the page text when typing' in the browser settings"),
"canvas_hotkey_fullscreen": shared.OptionInfo("S", "Fullscreen Mode, maximizes the picture so that it fits into the screen and stretches it to its full width "),
"canvas_hotkey_reset": shared.OptionInfo("R", "Reset zoom and canvas positon"),
@ -11,5 +13,5 @@ shared.options_templates.update(shared.options_section(('canvas_hotkey', "Canvas
"canvas_show_tooltip": shared.OptionInfo(True, "Enable tooltip on the canvas"),
"canvas_auto_expand": shared.OptionInfo(True, "Automatically expands an image that does not fit completely in the canvas area, similar to manually pressing the S and R buttons"),
"canvas_blur_prompt": shared.OptionInfo(False, "Take the focus off the prompt when working with a canvas"),
"canvas_disabled_functions": shared.OptionInfo(["Overlap"], "Disable function that you don't use", gr.CheckboxGroup, {"choices": ["Zoom","Adjust brush size", "Moving canvas","Fullscreen","Reset Zoom","Overlap"]}),
"canvas_disabled_functions": shared.OptionInfo(["Overlap"], "Disable function that you don't use", gr.CheckboxGroup, {"choices": ["Zoom","Adjust brush size","Hotkey enlarge brush","Hotkey shrink brush","Moving canvas","Fullscreen","Reset Zoom","Overlap"]}),
}))

4
extensions-builtin/extra-options-section/scripts/extra_options_section.py
View File

@ -1,7 +1,7 @@
import math
import gradio as gr
from modules import scripts, shared, ui_components, ui_settings, generation_parameters_copypaste
from modules import scripts, shared, ui_components, ui_settings, infotext_utils
from modules.ui_components import FormColumn
@ -25,7 +25,7 @@ class ExtraOptionsSection(scripts.Script):
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}
mapping = {k: v for v, k in infotext_utils.infotext_to_setting_name_mapping}
with gr.Blocks() as interface:
with gr.Accordion("Options", open=False, elem_id=elem_id_tabname) if shared.opts.extra_options_accordion and extra_options else gr.Group(elem_id=elem_id_tabname):

747
extensions-builtin/soft-inpainting/scripts/soft_inpainting.py Normal file
View File

@ -0,0 +1,747 @@
import numpy as np
import gradio as gr
import math
from modules.ui_components import InputAccordion
import modules.scripts as scripts
class SoftInpaintingSettings:
def __init__(self,
mask_blend_power,
mask_blend_scale,
inpaint_detail_preservation,
composite_mask_influence,
composite_difference_threshold,
composite_difference_contrast):
self.mask_blend_power = mask_blend_power
self.mask_blend_scale = mask_blend_scale
self.inpaint_detail_preservation = inpaint_detail_preservation
self.composite_mask_influence = composite_mask_influence
self.composite_difference_threshold = composite_difference_threshold
self.composite_difference_contrast = composite_difference_contrast
def add_generation_params(self, dest):
dest[enabled_gen_param_label] = True
dest[gen_param_labels.mask_blend_power] = self.mask_blend_power
dest[gen_param_labels.mask_blend_scale] = self.mask_blend_scale
dest[gen_param_labels.inpaint_detail_preservation] = self.inpaint_detail_preservation
dest[gen_param_labels.composite_mask_influence] = self.composite_mask_influence
dest[gen_param_labels.composite_difference_threshold] = self.composite_difference_threshold
dest[gen_param_labels.composite_difference_contrast] = self.composite_difference_contrast
# ------------------- Methods -------------------
def processing_uses_inpainting(p):
# TODO: Figure out a better way to determine if inpainting is being used by p
if getattr(p, "image_mask", None) is not None:
return True
if getattr(p, "mask", None) is not None:
return True
if getattr(p, "nmask", None) is not None:
return True
return False
def latent_blend(settings, a, b, t):
"""
Interpolates two latent image representations according to the parameter t,
where the interpolated vectors' magnitudes are also interpolated separately.
The "detail_preservation" factor biases the magnitude interpolation towards
the larger of the two magnitudes.
"""
import torch
# NOTE: We use inplace operations wherever possible.
# [4][w][h] to [1][4][w][h]
t2 = t.unsqueeze(0)
# [4][w][h] to [1][1][w][h] - the [4] seem redundant.
t3 = t[0].unsqueeze(0).unsqueeze(0)
one_minus_t2 = 1 - t2
one_minus_t3 = 1 - t3
# Linearly interpolate the image vectors.
a_scaled = a * one_minus_t2
b_scaled = b * t2
image_interp = a_scaled
image_interp.add_(b_scaled)
result_type = image_interp.dtype
del a_scaled, b_scaled, t2, one_minus_t2
# Calculate the magnitude of the interpolated vectors. (We will remove this magnitude.)
# 64-bit operations are used here to allow large exponents.
current_magnitude = torch.norm(image_interp, p=2, dim=1, keepdim=True).to(torch.float64).add_(0.00001)
# Interpolate the powered magnitudes, then un-power them (bring them back to a power of 1).
a_magnitude = torch.norm(a, p=2, dim=1, keepdim=True).to(torch.float64).pow_(
settings.inpaint_detail_preservation) * one_minus_t3
b_magnitude = torch.norm(b, p=2, dim=1, keepdim=True).to(torch.float64).pow_(
settings.inpaint_detail_preservation) * t3
desired_magnitude = a_magnitude
desired_magnitude.add_(b_magnitude).pow_(1 / settings.inpaint_detail_preservation)
del a_magnitude, b_magnitude, t3, one_minus_t3
# Change the linearly interpolated image vectors' magnitudes to the value we want.
# This is the last 64-bit operation.
image_interp_scaling_factor = desired_magnitude
image_interp_scaling_factor.div_(current_magnitude)
image_interp_scaling_factor = image_interp_scaling_factor.to(result_type)
image_interp_scaled = image_interp
image_interp_scaled.mul_(image_interp_scaling_factor)
del current_magnitude
del desired_magnitude
del image_interp
del image_interp_scaling_factor
del result_type
return image_interp_scaled
def get_modified_nmask(settings, nmask, sigma):
"""
Converts a negative mask representing the transparency of the original latent vectors being overlayed
to a mask that is scaled according to the denoising strength for this step.
Where:
0 = fully opaque, infinite density, fully masked
1 = fully transparent, zero density, fully unmasked
We bring this transparency to a power, as this allows one to simulate N number of blending operations
where N can be any positive real value. Using this one can control the balance of influence between
the denoiser and the original latents according to the sigma value.
NOTE: "mask" is not used
"""
import torch
return torch.pow(nmask, (sigma ** settings.mask_blend_power) * settings.mask_blend_scale)
def apply_adaptive_masks(
settings: SoftInpaintingSettings,
nmask,
latent_orig,
latent_processed,
overlay_images,
width, height,
paste_to):
import torch
import modules.processing as proc
import modules.images as images
from PIL import Image, ImageOps, ImageFilter
# TODO: Bias the blending according to the latent mask, add adjustable parameter for bias control.
latent_mask = nmask[0].float()
# convert the original mask into a form we use to scale distances for thresholding
mask_scalar = 1 - (torch.clamp(latent_mask, min=0, max=1) ** (settings.mask_blend_scale / 2))
mask_scalar = (0.5 * (1 - settings.composite_mask_influence)
+ mask_scalar * settings.composite_mask_influence)
mask_scalar = mask_scalar / (1.00001 - mask_scalar)
mask_scalar = mask_scalar.cpu().numpy()
latent_distance = torch.norm(latent_processed - latent_orig, p=2, dim=1)
kernel, kernel_center = get_gaussian_kernel(stddev_radius=1.5, max_radius=2)
masks_for_overlay = []
for i, (distance_map, overlay_image) in enumerate(zip(latent_distance, overlay_images)):
converted_mask = distance_map.float().cpu().numpy()
converted_mask = weighted_histogram_filter(converted_mask, kernel, kernel_center,
percentile_min=0.9, percentile_max=1, min_width=1)
converted_mask = weighted_histogram_filter(converted_mask, kernel, kernel_center,
percentile_min=0.25, percentile_max=0.75, min_width=1)
# The distance at which opacity of original decreases to 50%
half_weighted_distance = settings.composite_difference_threshold * mask_scalar
converted_mask = converted_mask / half_weighted_distance
converted_mask = 1 / (1 + converted_mask ** settings.composite_difference_contrast)
converted_mask = smootherstep(converted_mask)
converted_mask = 1 - converted_mask
converted_mask = 255. * converted_mask
converted_mask = converted_mask.astype(np.uint8)
converted_mask = Image.fromarray(converted_mask)
converted_mask = images.resize_image(2, converted_mask, width, height)
converted_mask = proc.create_binary_mask(converted_mask, round=False)
# Remove aliasing artifacts using a gaussian blur.
converted_mask = converted_mask.filter(ImageFilter.GaussianBlur(radius=4))
# Expand the mask to fit the whole image if needed.
if paste_to is not None:
converted_mask = proc.uncrop(converted_mask,
(overlay_image.width, overlay_image.height),
paste_to)
masks_for_overlay.append(converted_mask)
image_masked = Image.new('RGBa', (overlay_image.width, overlay_image.height))
image_masked.paste(overlay_image.convert("RGBA").convert("RGBa"),
mask=ImageOps.invert(converted_mask.convert('L')))
overlay_images[i] = image_masked.convert('RGBA')
return masks_for_overlay
def apply_masks(
settings,
nmask,
overlay_images,
width, height,
paste_to):
import torch
import modules.processing as proc
import modules.images as images
from PIL import Image, ImageOps, ImageFilter
converted_mask = nmask[0].float()
converted_mask = torch.clamp(converted_mask, min=0, max=1).pow_(settings.mask_blend_scale / 2)
converted_mask = 255. * converted_mask
converted_mask = converted_mask.cpu().numpy().astype(np.uint8)
converted_mask = Image.fromarray(converted_mask)
converted_mask = images.resize_image(2, converted_mask, width, height)
converted_mask = proc.create_binary_mask(converted_mask, round=False)
# Remove aliasing artifacts using a gaussian blur.
converted_mask = converted_mask.filter(ImageFilter.GaussianBlur(radius=4))
# Expand the mask to fit the whole image if needed.
if paste_to is not None:
converted_mask = proc.uncrop(converted_mask,
(width, height),
paste_to)
masks_for_overlay = []
for i, overlay_image in enumerate(overlay_images):
masks_for_overlay[i] = converted_mask
image_masked = Image.new('RGBa', (overlay_image.width, overlay_image.height))
image_masked.paste(overlay_image.convert("RGBA").convert("RGBa"),
mask=ImageOps.invert(converted_mask.convert('L')))
overlay_images[i] = image_masked.convert('RGBA')
return masks_for_overlay
def weighted_histogram_filter(img, kernel, kernel_center, percentile_min=0.0, percentile_max=1.0, min_width=1.0):
"""
Generalization convolution filter capable of applying
weighted mean, median, maximum, and minimum filters
parametrically using an arbitrary kernel.
Args:
img (nparray):
The image, a 2-D array of floats, to which the filter is being applied.
kernel (nparray):
The kernel, a 2-D array of floats.
kernel_center (nparray):
The kernel center coordinate, a 1-D array with two elements.
percentile_min (float):
The lower bound of the histogram window used by the filter,
from 0 to 1.
percentile_max (float):
The upper bound of the histogram window used by the filter,
from 0 to 1.
min_width (float):
The minimum size of the histogram window bounds, in weight units.
Must be greater than 0.
Returns:
(nparray): A filtered copy of the input image "img", a 2-D array of floats.
"""
# Converts an index tuple into a vector.
def vec(x):
return np.array(x)
kernel_min = -kernel_center
kernel_max = vec(kernel.shape) - kernel_center
def weighted_histogram_filter_single(idx):
idx = vec(idx)
min_index = np.maximum(0, idx + kernel_min)
max_index = np.minimum(vec(img.shape), idx + kernel_max)
window_shape = max_index - min_index
class WeightedElement:
"""
An element of the histogram, its weight
and bounds.
"""
def __init__(self, value, weight):
self.value: float = value
self.weight: float = weight
self.window_min: float = 0.0
self.window_max: float = 1.0
# Collect the values in the image as WeightedElements,
# weighted by their corresponding kernel values.
values = []
for window_tup in np.ndindex(tuple(window_shape)):
window_index = vec(window_tup)
image_index = window_index + min_index
centered_kernel_index = image_index - idx
kernel_index = centered_kernel_index + kernel_center
element = WeightedElement(img[tuple(image_index)], kernel[tuple(kernel_index)])
values.append(element)
def sort_key(x: WeightedElement):
return x.value
values.sort(key=sort_key)
# Calculate the height of the stack (sum)
# and each sample's range they occupy in the stack
sum = 0
for i in range(len(values)):
values[i].window_min = sum
sum += values[i].weight
values[i].window_max = sum
# Calculate what range of this stack ("window")
# we want to get the weighted average across.
window_min = sum * percentile_min
window_max = sum * percentile_max
window_width = window_max - window_min
# Ensure the window is within the stack and at least a certain size.
if window_width < min_width:
window_center = (window_min + window_max) / 2
window_min = window_center - min_width / 2
window_max = window_center + min_width / 2
if window_max > sum:
window_max = sum
window_min = sum - min_width
if window_min < 0:
window_min = 0
window_max = min_width
value = 0
value_weight = 0
# Get the weighted average of all the samples
# that overlap with the window, weighted
# by the size of their overlap.
for i in range(len(values)):
if window_min >= values[i].window_max:
continue
if window_max <= values[i].window_min:
break
s = max(window_min, values[i].window_min)
e = min(window_max, values[i].window_max)
w = e - s
value += values[i].value * w
value_weight += w
return value / value_weight if value_weight != 0 else 0
img_out = img.copy()
# Apply the kernel operation over each pixel.
for index in np.ndindex(img.shape):
img_out[index] = weighted_histogram_filter_single(index)
return img_out
def smoothstep(x):
"""
The smoothstep function, input should be clamped to 0-1 range.
Turns a diagonal line (f(x) = x) into a sigmoid-like curve.
"""
return x * x * (3 - 2 * x)
def smootherstep(x):
"""
The smootherstep function, input should be clamped to 0-1 range.
Turns a diagonal line (f(x) = x) into a sigmoid-like curve.
"""
return x * x * x * (x * (6 * x - 15) + 10)
def get_gaussian_kernel(stddev_radius=1.0, max_radius=2):
"""
Creates a Gaussian kernel with thresholded edges.
Args:
stddev_radius (float):
Standard deviation of the gaussian kernel, in pixels.
max_radius (int):
The size of the filter kernel. The number of pixels is (max_radius*2+1) ** 2.
The kernel is thresholded so that any values one pixel beyond this radius
is weighted at 0.
Returns:
(nparray, nparray): A kernel array (shape: (N, N)), its center coordinate (shape: (2))
"""
# Evaluates a 0-1 normalized gaussian function for a given square distance from the mean.
def gaussian(sqr_mag):
return math.exp(-sqr_mag / (stddev_radius * stddev_radius))
# Helper function for converting a tuple to an array.
def vec(x):
return np.array(x)
"""
Since a gaussian is unbounded, we need to limit ourselves
to a finite range.
We taper the ends off at the end of that range so they equal zero
while preserving the maximum value of 1 at the mean.
"""
zero_radius = max_radius + 1.0
gauss_zero = gaussian(zero_radius * zero_radius)
gauss_kernel_scale = 1 / (1 - gauss_zero)
def gaussian_kernel_func(coordinate):
x = coordinate[0] ** 2.0 + coordinate[1] ** 2.0
x = gaussian(x)
x -= gauss_zero
x *= gauss_kernel_scale
x = max(0.0, x)
return x
size = max_radius * 2 + 1
kernel_center = max_radius
kernel = np.zeros((size, size))
for index in np.ndindex(kernel.shape):
kernel[index] = gaussian_kernel_func(vec(index) - kernel_center)
return kernel, kernel_center
# ------------------- Constants -------------------
default = SoftInpaintingSettings(1, 0.5, 4, 0, 0.5, 2)
enabled_ui_label = "Soft inpainting"
enabled_gen_param_label = "Soft inpainting enabled"
enabled_el_id = "soft_inpainting_enabled"
ui_labels = SoftInpaintingSettings(
"Schedule bias",
"Preservation strength",
"Transition contrast boost",
"Mask influence",
"Difference threshold",
"Difference contrast")
ui_info = SoftInpaintingSettings(
"Shifts when preservation of original content occurs during denoising.",
"How strongly partially masked content should be preserved.",
"Amplifies the contrast that may be lost in partially masked regions.",
"How strongly the original mask should bias the difference threshold.",
"How much an image region can change before the original pixels are not blended in anymore.",
"How sharp the transition should be between blended and not blended.")
gen_param_labels = SoftInpaintingSettings(
"Soft inpainting schedule bias",
"Soft inpainting preservation strength",
"Soft inpainting transition contrast boost",
"Soft inpainting mask influence",
"Soft inpainting difference threshold",
"Soft inpainting difference contrast")
el_ids = SoftInpaintingSettings(
"mask_blend_power",
"mask_blend_scale",
"inpaint_detail_preservation",
"composite_mask_influence",
"composite_difference_threshold",
"composite_difference_contrast")
# ------------------- Script -------------------
class Script(scripts.Script):
def __init__(self):
self.section = "inpaint"
self.masks_for_overlay = None
self.overlay_images = None
def title(self):
return "Soft Inpainting"
def show(self, is_img2img):
return scripts.AlwaysVisible if is_img2img else False
def ui(self, is_img2img):
if not is_img2img:
return
with InputAccordion(False, label=enabled_ui_label, elem_id=enabled_el_id) as soft_inpainting_enabled:
with gr.Group():
gr.Markdown(
"""
Soft inpainting allows you to **seamlessly blend original content with inpainted content** according to the mask opacity.
**High _Mask blur_** values are recommended!
""")
power = \
gr.Slider(label=ui_labels.mask_blend_power,
info=ui_info.mask_blend_power,
minimum=0,
maximum=8,
step=0.1,
value=default.mask_blend_power,
elem_id=el_ids.mask_blend_power)
scale = \
gr.Slider(label=ui_labels.mask_blend_scale,
info=ui_info.mask_blend_scale,
minimum=0,
maximum=8,
step=0.05,
value=default.mask_blend_scale,
elem_id=el_ids.mask_blend_scale)
detail = \
gr.Slider(label=ui_labels.inpaint_detail_preservation,
info=ui_info.inpaint_detail_preservation,
minimum=1,
maximum=32,
step=0.5,
value=default.inpaint_detail_preservation,
elem_id=el_ids.inpaint_detail_preservation)
gr.Markdown(
"""
### Pixel Composite Settings
""")
mask_inf = \
gr.Slider(label=ui_labels.composite_mask_influence,
info=ui_info.composite_mask_influence,
minimum=0,
maximum=1,
step=0.05,
value=default.composite_mask_influence,
elem_id=el_ids.composite_mask_influence)
dif_thresh = \
gr.Slider(label=ui_labels.composite_difference_threshold,
info=ui_info.composite_difference_threshold,
minimum=0,
maximum=8,
step=0.25,
value=default.composite_difference_threshold,
elem_id=el_ids.composite_difference_threshold)
dif_contr = \
gr.Slider(label=ui_labels.composite_difference_contrast,
info=ui_info.composite_difference_contrast,
minimum=0,
maximum=8,
step=0.25,
value=default.composite_difference_contrast,
elem_id=el_ids.composite_difference_contrast)
with gr.Accordion("Help", open=False):
gr.Markdown(
f"""
### {ui_labels.mask_blend_power}
The blending strength of original content is scaled proportionally with the decreasing noise level values at each step (sigmas).
This ensures that the influence of the denoiser and original content preservation is roughly balanced at each step.
This balance can be shifted using this parameter, controlling whether earlier or later steps have stronger preservation.
- **Below 1**: Stronger preservation near the end (with low sigma)
- **1**: Balanced (proportional to sigma)
- **Above 1**: Stronger preservation in the beginning (with high sigma)
""")
gr.Markdown(
f"""
### {ui_labels.mask_blend_scale}
Skews whether partially masked image regions should be more likely to preserve the original content or favor inpainted content.
This may need to be adjusted depending on the {ui_labels.mask_blend_power}, CFG Scale, prompt and Denoising strength.
- **Low values**: Favors generated content.
- **High values**: Favors original content.
""")
gr.Markdown(
f"""
### {ui_labels.inpaint_detail_preservation}
This parameter controls how the original latent vectors and denoised latent vectors are interpolated.
With higher values, the magnitude of the resulting blended vector will be closer to the maximum of the two interpolated vectors.
This can prevent the loss of contrast that occurs with linear interpolation.
- **Low values**: Softer blending, details may fade.
- **High values**: Stronger contrast, may over-saturate colors.
""")
gr.Markdown(
"""
## Pixel Composite Settings
Masks are generated based on how much a part of the image changed after denoising.
These masks are used to blend the original and final images together.
If the difference is low, the original pixels are used instead of the pixels returned by the inpainting process.
""")
gr.Markdown(
f"""
### {ui_labels.composite_mask_influence}
This parameter controls how much the mask should bias this sensitivity to difference.
- **0**: Ignore the mask, only consider differences in image content.
- **1**: Follow the mask closely despite image content changes.
""")
gr.Markdown(
f"""
### {ui_labels.composite_difference_threshold}
This value represents the difference at which the original pixels will have less than 50% opacity.
- **Low values**: Two images patches must be almost the same in order to retain original pixels.
- **High values**: Two images patches can be very different and still retain original pixels.
""")
gr.Markdown(
f"""
### {ui_labels.composite_difference_contrast}
This value represents the contrast between the opacity of the original and inpainted content.
- **Low values**: The blend will be more gradual and have longer transitions, but may cause ghosting.
- **High values**: Ghosting will be less common, but transitions may be very sudden.
""")
self.infotext_fields = [(soft_inpainting_enabled, enabled_gen_param_label),
(power, gen_param_labels.mask_blend_power),
(scale, gen_param_labels.mask_blend_scale),
(detail, gen_param_labels.inpaint_detail_preservation),
(mask_inf, gen_param_labels.composite_mask_influence),
(dif_thresh, gen_param_labels.composite_difference_threshold),
(dif_contr, gen_param_labels.composite_difference_contrast)]
self.paste_field_names = []
for _, field_name in self.infotext_fields:
self.paste_field_names.append(field_name)
return [soft_inpainting_enabled,
power,
scale,
detail,
mask_inf,
dif_thresh,
dif_contr]
def process(self, p, enabled, power, scale, detail_preservation, mask_inf, dif_thresh, dif_contr):
if not enabled:
return
if not processing_uses_inpainting(p):
return
# Shut off the rounding it normally does.
p.mask_round = False
settings = SoftInpaintingSettings(power, scale, detail_preservation, mask_inf, dif_thresh, dif_contr)
# p.extra_generation_params["Mask rounding"] = False
settings.add_generation_params(p.extra_generation_params)
def on_mask_blend(self, p, mba: scripts.MaskBlendArgs, enabled, power, scale, detail_preservation, mask_inf,
dif_thresh, dif_contr):
if not enabled:
return
if not processing_uses_inpainting(p):
return
if mba.is_final_blend:
mba.blended_latent = mba.current_latent
return
settings = SoftInpaintingSettings(power, scale, detail_preservation, mask_inf, dif_thresh, dif_contr)
# todo: Why is sigma 2D? Both values are the same.
mba.blended_latent = latent_blend(settings,
mba.init_latent,
mba.current_latent,
get_modified_nmask(settings, mba.nmask, mba.sigma[0]))
def post_sample(self, p, ps: scripts.PostSampleArgs, enabled, power, scale, detail_preservation, mask_inf,
dif_thresh, dif_contr):
if not enabled:
return
if not processing_uses_inpainting(p):
return
nmask = getattr(p, "nmask", None)
if nmask is None:
return
from modules import images
from modules.shared import opts
settings = SoftInpaintingSettings(power, scale, detail_preservation, mask_inf, dif_thresh, dif_contr)
# since the original code puts holes in the existing overlay images,
# we have to rebuild them.
self.overlay_images = []
for img in p.init_images:
image = images.flatten(img, opts.img2img_background_color)
if p.paste_to is None and p.resize_mode != 3:
image = images.resize_image(p.resize_mode, image, p.width, p.height)
self.overlay_images.append(image.convert('RGBA'))
if len(p.init_images) == 1:
self.overlay_images = self.overlay_images * p.batch_size
if getattr(ps.samples, 'already_decoded', False):
self.masks_for_overlay = apply_masks(settings=settings,
nmask=nmask,
overlay_images=self.overlay_images,
width=p.width,
height=p.height,
paste_to=p.paste_to)
else:
self.masks_for_overlay = apply_adaptive_masks(settings=settings,
nmask=nmask,
latent_orig=p.init_latent,
latent_processed=ps.samples,
overlay_images=self.overlay_images,
width=p.width,
height=p.height,
paste_to=p.paste_to)
def postprocess_maskoverlay(self, p, ppmo: scripts.PostProcessMaskOverlayArgs, enabled, power, scale,
detail_preservation, mask_inf, dif_thresh, dif_contr):
if not enabled:
return
if not processing_uses_inpainting(p):
return
if self.masks_for_overlay is None:
return
if self.overlay_images is None:
return
ppmo.mask_for_overlay = self.masks_for_overlay[ppmo.index]
ppmo.overlay_image = self.overlay_images[ppmo.index]

17
html/extra-networks-card.html
View File

@ -1,14 +1,9 @@
<div class='card' style={style} onclick={card_clicked} data-name="{name}" {sort_keys}>
<div class="card" style="{style}" onclick="{card_clicked}" data-name="{name}" {sort_keys}>
{background_image}
<div class="button-row">
{metadata_button}
{edit_button}
</div>
<div class='actions'>
<div class='additional'>
<span style="display:none" class='search_term{search_only}'>{search_term}</span>
</div>
<span class='name'>{name}</span>
<span class='description'>{description}</span>
<div class="button-row">{copy_path_button}{metadata_button}{edit_button}</div>
<div class="actions">
<div class="additional">{search_terms}</div>
<span class="name">{name}</span>
<span class="description">{description}</span>
</div>
</div>

5
html/extra-networks-copy-path-button.html Normal file
View File

@ -0,0 +1,5 @@
<div class="copy-path-button card-button"
title="Copy path to clipboard"
onclick="extraNetworksCopyCardPath(event, '{filename}')"
data-clipboard-text="{filename}">
</div>

4
html/extra-networks-edit-item-button.html Normal file
View File

@ -0,0 +1,4 @@
<div class="edit-button card-button"
title="Edit metadata"
onclick="extraNetworksEditUserMetadata(event, '{tabname}', '{extra_networks_tabname}', '{name}')">
</div>

4
html/extra-networks-metadata-button.html Normal file
View File

@ -0,0 +1,4 @@
<div class="metadata-button card-button"
title="Show internal metadata"
onclick="extraNetworksRequestMetadata(event, '{extra_networks_tabname}', '{name}')">
</div>

55
html/extra-networks-pane.html Normal file
View File

@ -0,0 +1,55 @@
<div id='{tabname}_{extra_networks_tabname}_pane' class='extra-network-pane'>
<div class="extra-network-control" id="{tabname}_{extra_networks_tabname}_controls" style="display:none" >
<div class="extra-network-control--search">
<input
id="{tabname}_{extra_networks_tabname}_extra_search"
class="extra-network-control--search-text"
type="search"
placeholder="Filter files"
>
</div>
<div
id="{tabname}_{extra_networks_tabname}_extra_sort"
class="extra-network-control--sort"
data-sortmode="{data_sortmode}"
data-sortkey="{data_sortkey}"
title="Sort by path"
onclick="extraNetworksControlSortOnClick(event, '{tabname}', '{extra_networks_tabname}');"
>
<i class="extra-network-control--sort-icon"></i>
</div>
<div
id="{tabname}_{extra_networks_tabname}_extra_sort_dir"
class="extra-network-control--sort-dir"
data-sortdir="{data_sortdir}"
title="Sort ascending"
onclick="extraNetworksControlSortDirOnClick(event, '{tabname}', '{extra_networks_tabname}');"
>
<i class="extra-network-control--sort-dir-icon"></i>
</div>
<div
id="{tabname}_{extra_networks_tabname}_extra_tree_view"
class="extra-network-control--tree-view {tree_view_btn_extra_class}"
title="Enable Tree View"
onclick="extraNetworksControlTreeViewOnClick(event, '{tabname}', '{extra_networks_tabname}');"
>
<i class="extra-network-control--tree-view-icon"></i>
</div>
<div
id="{tabname}_{extra_networks_tabname}_extra_refresh"
class="extra-network-control--refresh"
title="Refresh page"
onclick="extraNetworksControlRefreshOnClick(event, '{tabname}', '{extra_networks_tabname}');"
>
<i class="extra-network-control--refresh-icon"></i>
</div>
</div>
<div class="extra-network-pane-content">
<div id='{tabname}_{extra_networks_tabname}_tree' class='extra-network-tree {tree_view_div_extra_class}'>
{tree_html}
</div>
<div id='{tabname}_{extra_networks_tabname}_cards' class='extra-network-cards'>
{items_html}
</div>
</div>
</div>

23
html/extra-networks-tree-button.html Normal file
View File

@ -0,0 +1,23 @@
<span data-filterable-item-text hidden>{search_terms}</span>
<div class="tree-list-content {subclass}"
type="button"
onclick="extraNetworksTreeOnClick(event, '{tabname}', '{extra_networks_tabname}');{onclick_extra}"
data-path="{data_path}"
data-hash="{data_hash}"
>
<span class='tree-list-item-action tree-list-item-action--leading'>
{action_list_item_action_leading}
</span>
<span class="tree-list-item-visual tree-list-item-visual--leading">
{action_list_item_visual_leading}
</span>
<span class="tree-list-item-label tree-list-item-label--truncate">
{action_list_item_label}
</span>
<span class="tree-list-item-visual tree-list-item-visual--trailing">
{action_list_item_visual_trailing}
</span>
<span class="tree-list-item-action tree-list-item-action--trailing">
{action_list_item_action_trailing}
</span>
</div>

310
html/licenses.html
View File

@ -4,107 +4,6 @@
#licenses pre { margin: 1em 0 2em 0;}
</style>
<h2><a href="https://github.com/sczhou/CodeFormer/blob/master/LICENSE">CodeFormer</a></h2>
<small>Parts of CodeFormer code had to be copied to be compatible with GFPGAN.</small>
<pre>
S-Lab License 1.0
Copyright 2022 S-Lab
Redistribution and use for non-commercial purpose in source and
binary forms, with or without modification, are permitted provided
that the following conditions are met:
1. Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in
the documentation and/or other materials provided with the
distribution.
3. Neither the name of the copyright holder nor the names of its
contributors may be used to endorse or promote products derived
from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
In the event that redistribution and/or use for commercial purpose in
source or binary forms, with or without modification is required,
please contact the contributor(s) of the work.
</pre>
<h2><a href="https://github.com/victorca25/iNNfer/blob/main/LICENSE">ESRGAN</a></h2>
<small>Code for architecture and reading models copied.</small>
<pre>
MIT License
Copyright (c) 2021 victorca25
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
</pre>
<h2><a href="https://github.com/xinntao/Real-ESRGAN/blob/master/LICENSE">Real-ESRGAN</a></h2>
<small>Some code is copied to support ESRGAN models.</small>
<pre>
BSD 3-Clause License
Copyright (c) 2021, Xintao Wang
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
3. Neither the name of the copyright holder nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
</pre>
<h2><a href="https://github.com/invoke-ai/InvokeAI/blob/main/LICENSE">InvokeAI</a></h2>
<small>Some code for compatibility with OSX is taken from lstein's repository.</small>
<pre>
@ -183,213 +82,6 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
</pre>
<h2><a href="https://github.com/JingyunLiang/SwinIR/blob/main/LICENSE">SwinIR</a></h2>
<small>Code added by contributors, most likely copied from this repository.</small>
<pre>
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
and distribution as defined by Sections 1 through 9 of this document.
"Licensor" shall mean the copyright owner or entity authorized by
the copyright owner that is granting the License.
"Legal Entity" shall mean the union of the acting entity and all
other entities that control, are controlled by, or are under common
control with that entity. For the purposes of this definition,
"control" means (i) the power, direct or indirect, to cause the
direction or management of such entity, whether by contract or
otherwise, or (ii) ownership of fifty percent (50%) or more of the
outstanding shares, or (iii) beneficial ownership of such entity.
"You" (or "Your") shall mean an individual or Legal Entity
exercising permissions granted by this License.
"Source" form shall mean the preferred form for making modifications,
including but not limited to software source code, documentation
source, and configuration files.
"Object" form shall mean any form resulting from mechanical
transformation or translation of a Source form, including but
not limited to compiled object code, generated documentation,
and conversions to other media types.
"Work" shall mean the work of authorship, whether in Source or
Object form, made available under the License, as indicated by a
copyright notice that is included in or attached to the work
(an example is provided in the Appendix below).
"Derivative Works" shall mean any work, whether in Source or Object
form, that is based on (or derived from) the Work and for which the
editorial revisions, annotations, elaborations, or other modifications
represent, as a whole, an original work of authorship. For the purposes
of this License, Derivative Works shall not include works that remain
separable from, or merely link (or bind by name) to the interfaces of,
the Work and Derivative Works thereof.
"Contribution" shall mean any work of authorship, including
the original version of the Work and any modifications or additions
to that Work or Derivative Works thereof, that is intentionally
submitted to Licensor for inclusion in the Work by the copyright owner
or by an individual or Legal Entity authorized to submit on behalf of
the copyright owner. For the purposes of this definition, "submitted"
means any form of electronic, verbal, or written communication sent
to the Licensor or its representatives, including but not limited to
communication on electronic mailing lists, source code control systems,
and issue tracking systems that are managed by, or on behalf of, the
Licensor for the purpose of discussing and improving the Work, but
excluding communication that is conspicuously marked or otherwise
designated in writing by the copyright owner as "Not a Contribution."
"Contributor" shall mean Licensor and any individual or Legal Entity
on behalf of whom a Contribution has been received by Licensor and
subsequently incorporated within the Work.
2. Grant of Copyright License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
copyright license to reproduce, prepare Derivative Works of,
publicly display, publicly perform, sublicense, and distribute the
Work and such Derivative Works in Source or Object form.
3. Grant of Patent License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
(except as stated in this section) patent license to make, have made,
use, offer to sell, sell, import, and otherwise transfer the Work,
where such license applies only to those patent claims licensable
by such Contributor that are necessarily infringed by their
Contribution(s) alone or by combination of their Contribution(s)
with the Work to which such Contribution(s) was submitted. If You
institute patent litigation against any entity (including a
cross-claim or counterclaim in a lawsuit) alleging that the Work
or a Contribution incorporated within the Work constitutes direct
or contributory patent infringement, then any patent licenses
granted to You under this License for that Work shall terminate
as of the date such litigation is filed.
4. Redistribution. You may reproduce and distribute copies of the
Work or Derivative Works thereof in any medium, with or without
modifications, and in Source or Object form, provided that You
meet the following conditions:
(a) You must give any other recipients of the Work or
Derivative Works a copy of this License; and
(b) You must cause any modified files to carry prominent notices
stating that You changed the files; and
(c) You must retain, in the Source form of any Derivative Works
that You distribute, all copyright, patent, trademark, and
attribution notices from the Source form of the Work,
excluding those notices that do not pertain to any part of
the Derivative Works; and
(d) If the Work includes a "NOTICE" text file as part of its
distribution, then any Derivative Works that You distribute must
include a readable copy of the attribution notices contained
within such NOTICE file, excluding those notices that do not
pertain to any part of the Derivative Works, in at least one
of the following places: within a NOTICE text file distributed
as part of the Derivative Works; within the Source form or
documentation, if provided along with the Derivative Works; or,
within a display generated by the Derivative Works, if and
wherever such third-party notices normally appear. The contents
of the NOTICE file are for informational purposes only and
do not modify the License. You may add Your own attribution
notices within Derivative Works that You distribute, alongside
or as an addendum to the NOTICE text from the Work, provided
that such additional attribution notices cannot be construed
as modifying the License.
You may add Your own copyright statement to Your modifications and
may provide additional or different license terms and conditions
for use, reproduction, or distribution of Your modifications, or
for any such Derivative Works as a whole, provided Your use,
reproduction, and distribution of the Work otherwise complies with
the conditions stated in this License.
5. Submission of Contributions. Unless You explicitly state otherwise,
any Contribution intentionally submitted for inclusion in the Work
by You to the Licensor shall be under the terms and conditions of
this License, without any additional terms or conditions.
Notwithstanding the above, nothing herein shall supersede or modify
the terms of any separate license agreement you may have executed
with Licensor regarding such Contributions.
6. Trademarks. This License does not grant permission to use the trade
names, trademarks, service marks, or product names of the Licensor,
except as required for reasonable and customary use in describing the
origin of the Work and reproducing the content of the NOTICE file.
7. Disclaimer of Warranty. Unless required by applicable law or
agreed to in writing, Licensor provides the Work (and each
Contributor provides its Contributions) on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
implied, including, without limitation, any warranties or conditions
of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
PARTICULAR PURPOSE. You are solely responsible for determining the
appropriateness of using or redistributing the Work and assume any
risks associated with Your exercise of permissions under this License.
8. Limitation of Liability. In no event and under no legal theory,
whether in tort (including negligence), contract, or otherwise,
unless required by applicable law (such as deliberate and grossly
negligent acts) or agreed to in writing, shall any Contributor be
liable to You for damages, including any direct, indirect, special,
incidental, or consequential damages of any character arising as a
result of this License or out of the use or inability to use the
Work (including but not limited to damages for loss of goodwill,
work stoppage, computer failure or malfunction, or any and all
other commercial damages or losses), even if such Contributor
has been advised of the possibility of such damages.
9. Accepting Warranty or Additional Liability. While redistributing
the Work or Derivative Works thereof, You may choose to offer,
and charge a fee for, acceptance of support, warranty, indemnity,
or other liability obligations and/or rights consistent with this
License. However, in accepting such obligations, You may act only
on Your own behalf and on Your sole responsibility, not on behalf
of any other Contributor, and only if You agree to indemnify,
defend, and hold each Contributor harmless for any liability
incurred by, or claims asserted against, such Contributor by reason
of your accepting any such warranty or additional liability.
END OF TERMS AND CONDITIONS
APPENDIX: How to apply the Apache License to your work.
To apply the Apache License to your work, attach the following
boilerplate notice, with the fields enclosed by brackets "[]"
replaced with your own identifying information. (Don't include
the brackets!) The text should be enclosed in the appropriate
comment syntax for the file format. We also recommend that a
file or class name and description of purpose be included on the
same "printed page" as the copyright notice for easier
identification within third-party archives.
Copyright [2021] [SwinIR Authors]
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
</pre>
<h2><a href="https://github.com/AminRezaei0x443/memory-efficient-attention/blob/main/LICENSE">Memory Efficient Attention</a></h2>
<small>The sub-quadratic cross attention optimization uses modified code from the Memory Efficient Attention package that Alex Birch optimized for 3D tensors. This license is updated to reflect that.</small>
<pre>
@ -687,4 +379,4 @@ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
</pre>
</pre>

7
javascript/extensions.js
View File

@ -2,8 +2,11 @@
function extensions_apply(_disabled_list, _update_list, disable_all) {
var disable = [];
var update = [];
gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x) {
const extensions_input = gradioApp().querySelectorAll('#extensions input[type="checkbox"]');
if (extensions_input.length == 0) {
throw Error("Extensions page not yet loaded.");
}
extensions_input.forEach(function(x) {
if (x.name.startsWith("enable_") && !x.checked) {
disable.push(x.name.substring(7));
}

517
javascript/extraNetworks.js
View File

@ -16,99 +16,112 @@ function toggleCss(key, css, enable) {
}
function setupExtraNetworksForTab(tabname) {
gradioApp().querySelector('#' + tabname + '_extra_tabs').classList.add('extra-networks');
function registerPrompt(tabname, id) {
var textarea = gradioApp().querySelector("#" + id + " > label > textarea");
var tabs = gradioApp().querySelector('#' + tabname + '_extra_tabs > div');
var searchDiv = gradioApp().getElementById(tabname + '_extra_search');
var search = searchDiv.querySelector('textarea');
var sort = gradioApp().getElementById(tabname + '_extra_sort');
var sortOrder = gradioApp().getElementById(tabname + '_extra_sortorder');
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');
tabs.appendChild(searchDiv);
tabs.appendChild(sort);
tabs.appendChild(sortOrder);
tabs.appendChild(refresh);
tabs.appendChild(showDirsDiv);
var applyFilter = function() {
var searchTerm = search.value.toLowerCase();
gradioApp().querySelectorAll('#' + tabname + '_extra_tabs div.card').forEach(function(elem) {
var searchOnly = elem.querySelector('.search_only');
var text = elem.querySelector('.name').textContent.toLowerCase() + " " + elem.querySelector('.search_term').textContent.toLowerCase();
var visible = text.indexOf(searchTerm) != -1;
if (searchOnly && searchTerm.length < 4) {
visible = false;
}
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() || "name";
sortKey = "sort" + sortKey.charAt(0).toUpperCase() + sortKey.slice(1);
var sortKeyStore = sortKey + "-" + (reverse ? "Descending" : "Ascending") + "-" + cards.length;
if (sortKeyStore == sort.dataset.sortkey) {
return;
if (!activePromptTextarea[tabname]) {
activePromptTextarea[tabname] = textarea;
}
sort.dataset.sortkey = sortKeyStore;
cards.forEach(function(card) {
card.originalParentElement = card.parentElement;
textarea.addEventListener("focus", function() {
activePromptTextarea[tabname] = textarea;
});
var sortedCards = Array.from(cards);
sortedCards.sort(function(cardA, cardB) {
var a = cardA.dataset[sortKey];
var b = cardB.dataset[sortKey];
if (!isNaN(a) && !isNaN(b)) {
return parseInt(a) - parseInt(b);
}
}
return (a < b ? -1 : (a > b ? 1 : 0));
});
if (reverse) {
sortedCards.reverse();
var tabnav = gradioApp().querySelector('#' + tabname + '_extra_tabs > div.tab-nav');
var controlsDiv = document.createElement('DIV');
controlsDiv.classList.add('extra-networks-controls-div');
tabnav.appendChild(controlsDiv);
tabnav.insertBefore(controlsDiv, null);
var this_tab = gradioApp().querySelector('#' + tabname + '_extra_tabs');
this_tab.querySelectorAll(":scope > [id^='" + tabname + "_']").forEach(function(elem) {
// tabname_full = {tabname}_{extra_networks_tabname}
var tabname_full = elem.id;
var search = gradioApp().querySelector("#" + tabname_full + "_extra_search");
var sort_mode = gradioApp().querySelector("#" + tabname_full + "_extra_sort");
var sort_dir = gradioApp().querySelector("#" + tabname_full + "_extra_sort_dir");
var refresh = gradioApp().querySelector("#" + tabname_full + "_extra_refresh");
// If any of the buttons above don't exist, we want to skip this iteration of the loop.
if (!search || !sort_mode || !sort_dir || !refresh) {
return; // `return` is equivalent of `continue` but for forEach loops.
}
cards.forEach(function(card) {
card.remove();
});
sortedCards.forEach(function(card) {
card.originalParentElement.appendChild(card);
});
};
search.addEventListener("input", applyFilter);
sortOrder.addEventListener("click", function() {
sortOrder.classList.toggle("sortReverse");
var applyFilter = function(force) {
var searchTerm = search.value.toLowerCase();
gradioApp().querySelectorAll('#' + tabname + '_extra_tabs div.card').forEach(function(elem) {
var searchOnly = elem.querySelector('.search_only');
var text = Array.prototype.map.call(elem.querySelectorAll('.search_terms'), function(t) {
return t.textContent.toLowerCase();
}).join(" ");
var visible = text.indexOf(searchTerm) != -1;
if (searchOnly && searchTerm.length < 4) {
visible = false;
}
if (visible) {
elem.classList.remove("hidden");
} else {
elem.classList.add("hidden");
}
});
applySort(force);
};
var applySort = function(force) {
var cards = gradioApp().querySelectorAll('#' + tabname + '_extra_tabs div.card');
var reverse = sort_dir.dataset.sortdir == "Descending";
var sortKey = sort_mode.dataset.sortmode.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_mode.dataset.sortkey && !force) {
return;
}
sort_mode.dataset.sortkey = sortKeyStore;
cards.forEach(function(card) {
card.originalParentElement = card.parentElement;
});
var sortedCards = Array.from(cards);
sortedCards.sort(function(cardA, cardB) {
var a = cardA.dataset[sortKey];
var b = cardB.dataset[sortKey];
if (!isNaN(a) && !isNaN(b)) {
return parseInt(a) - parseInt(b);
}
return (a < b ? -1 : (a > b ? 1 : 0));
});
if (reverse) {
sortedCards.reverse();
}
cards.forEach(function(card) {
card.remove();
});
sortedCards.forEach(function(card) {
card.originalParentElement.appendChild(card);
});
};
search.addEventListener("input", applyFilter);
applySort();
applyFilter();
extraNetworksApplySort[tabname_full] = applySort;
extraNetworksApplyFilter[tabname_full] = applyFilter;
var controls = gradioApp().querySelector("#" + tabname_full + "_controls");
controlsDiv.insertBefore(controls, null);
if (elem.style.display != "none") {
extraNetworksShowControlsForPage(tabname, tabname_full);
}
});
applyFilter();
extraNetworksApplySort[tabname] = applySort;
extraNetworksApplyFilter[tabname] = applyFilter;
var showDirsUpdate = function() {
var css = '#' + tabname + '_extra_tabs .extra-network-subdirs { display: none; }';
toggleCss(tabname + '_extra_show_dirs_style', css, !showDirs.checked);
localSet('extra-networks-show-dirs', showDirs.checked ? 1 : 0);
};
showDirs.checked = localGet('extra-networks-show-dirs', 1) == 1;
showDirs.addEventListener("change", showDirsUpdate);
showDirsUpdate();
registerPrompt(tabname, tabname + "_prompt");
registerPrompt(tabname, tabname + "_neg_prompt");
}
function extraNetworksMovePromptToTab(tabname, id, showPrompt, showNegativePrompt) {
@ -137,21 +150,42 @@ function extraNetworksMovePromptToTab(tabname, id, showPrompt, showNegativePromp
}
function extraNetworksUrelatedTabSelected(tabname) { // called from python when user selects an unrelated tab (generate)
extraNetworksMovePromptToTab(tabname, '', false, false);
function extraNetworksShowControlsForPage(tabname, tabname_full) {
gradioApp().querySelectorAll('#' + tabname + '_extra_tabs .extra-networks-controls-div > div').forEach(function(elem) {
var targetId = tabname_full + "_controls";
elem.style.display = elem.id == targetId ? "" : "none";
});
}
function extraNetworksTabSelected(tabname, id, showPrompt, showNegativePrompt) { // called from python when user selects an extra networks tab
function extraNetworksUnrelatedTabSelected(tabname) { // called from python when user selects an unrelated tab (generate)
extraNetworksMovePromptToTab(tabname, '', false, false);
extraNetworksShowControlsForPage(tabname, null);
}
function extraNetworksTabSelected(tabname, id, showPrompt, showNegativePrompt, tabname_full) { // called from python when user selects an extra networks tab
extraNetworksMovePromptToTab(tabname, id, showPrompt, showNegativePrompt);
extraNetworksShowControlsForPage(tabname, tabname_full);
}
function applyExtraNetworkFilter(tabname) {
setTimeout(extraNetworksApplyFilter[tabname], 1);
function applyExtraNetworkFilter(tabname_full) {
var doFilter = function() {
var applyFunction = extraNetworksApplyFilter[tabname_full];
if (applyFunction) {
applyFunction(true);
}
};
setTimeout(doFilter, 1);
}
function applyExtraNetworkSort(tabname) {
setTimeout(extraNetworksApplySort[tabname], 1);
function applyExtraNetworkSort(tabname_full) {
var doSort = function() {
extraNetworksApplySort[tabname_full](true);
};
setTimeout(doSort, 1);
}
var extraNetworksApplyFilter = {};
@ -161,41 +195,24 @@ var activePromptTextarea = {};
function setupExtraNetworks() {
setupExtraNetworksForTab('txt2img');
setupExtraNetworksForTab('img2img');
function registerPrompt(tabname, id) {
var textarea = gradioApp().querySelector("#" + id + " > label > textarea");
if (!activePromptTextarea[tabname]) {
activePromptTextarea[tabname] = textarea;
}
textarea.addEventListener("focus", function() {
activePromptTextarea[tabname] = textarea;
});
}
registerPrompt('txt2img', 'txt2img_prompt');
registerPrompt('txt2img', 'txt2img_neg_prompt');
registerPrompt('img2img', 'img2img_prompt');
registerPrompt('img2img', 'img2img_neg_prompt');
}
onUiLoaded(setupExtraNetworks);
var re_extranet = /<([^:^>]+:[^:]+):[\d.]+>(.*)/;
var re_extranet_g = /<([^:^>]+:[^:]+):[\d.]+>/g;
function tryToRemoveExtraNetworkFromPrompt(textarea, text) {
var m = text.match(re_extranet);
var re_extranet_neg = /\(([^:^>]+:[\d.]+)\)/;
var re_extranet_g_neg = /\(([^:^>]+:[\d.]+)\)/g;
function tryToRemoveExtraNetworkFromPrompt(textarea, text, isNeg) {
var m = text.match(isNeg ? re_extranet_neg : re_extranet);
var replaced = false;
var newTextareaText;
var extraTextBeforeNet = opts.extra_networks_add_text_separator;
if (m) {
var extraTextBeforeNet = opts.extra_networks_add_text_separator;
var extraTextAfterNet = m[2];
var partToSearch = m[1];
var foundAtPosition = -1;
newTextareaText = textarea.value.replaceAll(re_extranet_g, function(found, net, pos) {
m = found.match(re_extranet);
newTextareaText = textarea.value.replaceAll(isNeg ? re_extranet_g_neg : re_extranet_g, function(found, net, pos) {
m = found.match(isNeg ? re_extranet_neg : re_extranet);
if (m[1] == partToSearch) {
replaced = true;
foundAtPosition = pos;
@ -203,9 +220,8 @@ function tryToRemoveExtraNetworkFromPrompt(textarea, text) {
}
return found;
});
if (foundAtPosition >= 0) {
if (newTextareaText.substr(foundAtPosition, extraTextAfterNet.length) == extraTextAfterNet) {
if (extraTextAfterNet && newTextareaText.substr(foundAtPosition, extraTextAfterNet.length) == extraTextAfterNet) {
newTextareaText = newTextareaText.substr(0, foundAtPosition) + newTextareaText.substr(foundAtPosition + extraTextAfterNet.length);
}
if (newTextareaText.substr(foundAtPosition - extraTextBeforeNet.length, extraTextBeforeNet.length) == extraTextBeforeNet) {
@ -213,13 +229,8 @@ function tryToRemoveExtraNetworkFromPrompt(textarea, text) {
}
}
} else {
newTextareaText = textarea.value.replaceAll(new RegExp(text, "g"), function(found) {
if (found == text) {
replaced = true;
return "";
}
return found;
});
newTextareaText = textarea.value.replaceAll(new RegExp(`((?:${extraTextBeforeNet})?${text})`, "g"), "");
replaced = (newTextareaText != textarea.value);
}
if (replaced) {
@ -230,14 +241,22 @@ function tryToRemoveExtraNetworkFromPrompt(textarea, text) {
return false;
}
function cardClicked(tabname, textToAdd, allowNegativePrompt) {
var textarea = allowNegativePrompt ? activePromptTextarea[tabname] : gradioApp().querySelector("#" + tabname + "_prompt > label > textarea");
if (!tryToRemoveExtraNetworkFromPrompt(textarea, textToAdd)) {
textarea.value = textarea.value + opts.extra_networks_add_text_separator + textToAdd;
function updatePromptArea(text, textArea, isNeg) {
if (!tryToRemoveExtraNetworkFromPrompt(textArea, text, isNeg)) {
textArea.value = textArea.value + opts.extra_networks_add_text_separator + text;
}
updateInput(textarea);
updateInput(textArea);
}
function cardClicked(tabname, textToAdd, textToAddNegative, allowNegativePrompt) {
if (textToAddNegative.length > 0) {
updatePromptArea(textToAdd, gradioApp().querySelector("#" + tabname + "_prompt > label > textarea"));
updatePromptArea(textToAddNegative, gradioApp().querySelector("#" + tabname + "_neg_prompt > label > textarea"), true);
} else {
var textarea = allowNegativePrompt ? activePromptTextarea[tabname] : gradioApp().querySelector("#" + tabname + "_prompt > label > textarea");
updatePromptArea(textToAdd, textarea);
}
}
function saveCardPreview(event, tabname, filename) {
@ -253,13 +272,200 @@ function saveCardPreview(event, tabname, filename) {
event.preventDefault();
}
function extraNetworksSearchButton(tabs_id, event) {
var searchTextarea = gradioApp().querySelector("#" + tabs_id + ' > label > textarea');
var button = event.target;
var text = button.classList.contains("search-all") ? "" : button.textContent.trim();
function extraNetworksTreeProcessFileClick(event, btn, tabname, extra_networks_tabname) {
/**
* Processes `onclick` events when user clicks on files in tree.
*
* @param event The generated event.
* @param btn The clicked `tree-list-item` button.
* @param tabname The name of the active tab in the sd webui. Ex: txt2img, img2img, etc.
* @param extra_networks_tabname The id of the active extraNetworks tab. Ex: lora, checkpoints, etc.
*/
// NOTE: Currently unused.
return;
}
searchTextarea.value = text;
updateInput(searchTextarea);
function extraNetworksTreeProcessDirectoryClick(event, btn, tabname, extra_networks_tabname) {
/**
* Processes `onclick` events when user clicks on directories in tree.
*
* Here is how the tree reacts to clicks for various states:
* unselected unopened directory: Diretory is selected and expanded.
* unselected opened directory: Directory is selected.
* selected opened directory: Directory is collapsed and deselected.
* chevron is clicked: Directory is expanded or collapsed. Selected state unchanged.
*
* @param event The generated event.
* @param btn The clicked `tree-list-item` button.
* @param tabname The name of the active tab in the sd webui. Ex: txt2img, img2img, etc.
* @param extra_networks_tabname The id of the active extraNetworks tab. Ex: lora, checkpoints, etc.
*/
var ul = btn.nextElementSibling;
// This is the actual target that the user clicked on within the target button.
// We use this to detect if the chevron was clicked.
var true_targ = event.target;
function _expand_or_collapse(_ul, _btn) {
// Expands <ul> if it is collapsed, collapses otherwise. Updates button attributes.
if (_ul.hasAttribute("hidden")) {
_ul.removeAttribute("hidden");
_btn.dataset.expanded = "";
} else {
_ul.setAttribute("hidden", "");
delete _btn.dataset.expanded;
}
}
function _remove_selected_from_all() {
// Removes the `selected` attribute from all buttons.
var sels = document.querySelectorAll("div.tree-list-content");
[...sels].forEach(el => {
delete el.dataset.selected;
});
}
function _select_button(_btn) {
// Removes `data-selected` attribute from all buttons then adds to passed button.
_remove_selected_from_all();
_btn.dataset.selected = "";
}
function _update_search(_tabname, _extra_networks_tabname, _search_text) {
// Update search input with select button's path.
var search_input_elem = gradioApp().querySelector("#" + tabname + "_" + extra_networks_tabname + "_extra_search");
search_input_elem.value = _search_text;
updateInput(search_input_elem);
}
// If user clicks on the chevron, then we do not select the folder.
if (true_targ.matches(".tree-list-item-action--leading, .tree-list-item-action-chevron")) {
_expand_or_collapse(ul, btn);
} else {
// User clicked anywhere else on the button.
if ("selected" in btn.dataset && !(ul.hasAttribute("hidden"))) {
// If folder is select and open, collapse and deselect button.
_expand_or_collapse(ul, btn);
delete btn.dataset.selected;
_update_search(tabname, extra_networks_tabname, "");
} else if (!(!("selected" in btn.dataset) && !(ul.hasAttribute("hidden")))) {
// If folder is open and not selected, then we don't collapse; just select.
// NOTE: Double inversion sucks but it is the clearest way to show the branching here.
_expand_or_collapse(ul, btn);
_select_button(btn, tabname, extra_networks_tabname);
_update_search(tabname, extra_networks_tabname, btn.dataset.path);
} else {
// All other cases, just select the button.
_select_button(btn, tabname, extra_networks_tabname);
_update_search(tabname, extra_networks_tabname, btn.dataset.path);
}
}
}
function extraNetworksTreeOnClick(event, tabname, extra_networks_tabname) {
/**
* Handles `onclick` events for buttons within an `extra-network-tree .tree-list--tree`.
*
* Determines whether the clicked button in the tree is for a file entry or a directory
* then calls the appropriate function.
*
* @param event The generated event.
* @param tabname The name of the active tab in the sd webui. Ex: txt2img, img2img, etc.
* @param extra_networks_tabname The id of the active extraNetworks tab. Ex: lora, checkpoints, etc.
*/
var btn = event.currentTarget;
var par = btn.parentElement;
if (par.dataset.treeEntryType === "file") {
extraNetworksTreeProcessFileClick(event, btn, tabname, extra_networks_tabname);
} else {
extraNetworksTreeProcessDirectoryClick(event, btn, tabname, extra_networks_tabname);
}
}
function extraNetworksControlSortOnClick(event, tabname, extra_networks_tabname) {
/**
* Handles `onclick` events for the Sort Mode button.
*
* Modifies the data attributes of the Sort Mode button to cycle between
* various sorting modes.
*
* @param event The generated event.
* @param tabname The name of the active tab in the sd webui. Ex: txt2img, img2img, etc.
* @param extra_networks_tabname The id of the active extraNetworks tab. Ex: lora, checkpoints, etc.
*/
var curr_mode = event.currentTarget.dataset.sortmode;
var el_sort_dir = gradioApp().querySelector("#" + tabname + "_" + extra_networks_tabname + "_extra_sort_dir");
var sort_dir = el_sort_dir.dataset.sortdir;
if (curr_mode == "path") {
event.currentTarget.dataset.sortmode = "name";
event.currentTarget.dataset.sortkey = "sortName-" + sort_dir + "-640";
event.currentTarget.setAttribute("title", "Sort by filename");
} else if (curr_mode == "name") {
event.currentTarget.dataset.sortmode = "date_created";
event.currentTarget.dataset.sortkey = "sortDate_created-" + sort_dir + "-640";
event.currentTarget.setAttribute("title", "Sort by date created");
} else if (curr_mode == "date_created") {
event.currentTarget.dataset.sortmode = "date_modified";
event.currentTarget.dataset.sortkey = "sortDate_modified-" + sort_dir + "-640";
event.currentTarget.setAttribute("title", "Sort by date modified");
} else {
event.currentTarget.dataset.sortmode = "path";
event.currentTarget.dataset.sortkey = "sortPath-" + sort_dir + "-640";
event.currentTarget.setAttribute("title", "Sort by path");
}
applyExtraNetworkSort(tabname + "_" + extra_networks_tabname);
}
function extraNetworksControlSortDirOnClick(event, tabname, extra_networks_tabname) {
/**
* Handles `onclick` events for the Sort Direction button.
*
* Modifies the data attributes of the Sort Direction button to cycle between
* ascending and descending sort directions.
*
* @param event The generated event.
* @param tabname The name of the active tab in the sd webui. Ex: txt2img, img2img, etc.
* @param extra_networks_tabname The id of the active extraNetworks tab. Ex: lora, checkpoints, etc.
*/
if (event.currentTarget.dataset.sortdir == "Ascending") {
event.currentTarget.dataset.sortdir = "Descending";
event.currentTarget.setAttribute("title", "Sort descending");
} else {
event.currentTarget.dataset.sortdir = "Ascending";
event.currentTarget.setAttribute("title", "Sort ascending");
}
applyExtraNetworkSort(tabname + "_" + extra_networks_tabname);
}
function extraNetworksControlTreeViewOnClick(event, tabname, extra_networks_tabname) {
/**
* Handles `onclick` events for the Tree View button.
*
* Toggles the tree view in the extra networks pane.
*
* @param event The generated event.
* @param tabname The name of the active tab in the sd webui. Ex: txt2img, img2img, etc.
* @param extra_networks_tabname The id of the active extraNetworks tab. Ex: lora, checkpoints, etc.
*/
gradioApp().getElementById(tabname + "_" + extra_networks_tabname + "_tree").classList.toggle("hidden");
event.currentTarget.classList.toggle("extra-network-control--enabled");
}
function extraNetworksControlRefreshOnClick(event, tabname, extra_networks_tabname) {
/**
* Handles `onclick` events for the Refresh Page button.
*
* In order to actually call the python functions in `ui_extra_networks.py`
* to refresh the page, we created an empty gradio button in that file with an
* event handler that refreshes the page. So what this function here does
* is it manually raises a `click` event on that button.
*
* @param event The generated event.
* @param tabname The name of the active tab in the sd webui. Ex: txt2img, img2img, etc.
* @param extra_networks_tabname The id of the active extraNetworks tab. Ex: lora, checkpoints, etc.
*/
var btn_refresh_internal = gradioApp().getElementById(tabname + "_" + extra_networks_tabname + "_extra_refresh_internal");
btn_refresh_internal.dispatchEvent(new Event("click"));
}
var globalPopup = null;
@ -337,6 +543,11 @@ function requestGet(url, data, handler, errorHandler) {
xhr.send(js);
}
function extraNetworksCopyCardPath(event, path) {
navigator.clipboard.writeText(path);
event.stopPropagation();
}
function extraNetworksRequestMetadata(event, extraPage, cardName) {
var showError = function() {
extraNetworksShowMetadata("there was an error getting metadata");
@ -398,3 +609,39 @@ window.addEventListener("keydown", function(event) {
closePopup();
}
});
/**
* Setup custom loading for this script.
* We need to wait for all of our HTML to be generated in the extra networks tabs
* before we can actually run the `setupExtraNetworks` function.
* The `onUiLoaded` function actually runs before all of our extra network tabs are
* finished generating. Thus we needed this new method.
*
*/
var uiAfterScriptsCallbacks = [];
var uiAfterScriptsTimeout = null;
var executedAfterScripts = false;
function scheduleAfterScriptsCallbacks() {
clearTimeout(uiAfterScriptsTimeout);
uiAfterScriptsTimeout = setTimeout(function() {
executeCallbacks(uiAfterScriptsCallbacks);
}, 200);
}
onUiLoaded(function() {
var mutationObserver = new MutationObserver(function(m) {
let existingSearchfields = gradioApp().querySelectorAll("[id$='_extra_search']").length;
let neededSearchfields = gradioApp().querySelectorAll("[id$='_extra_tabs'] > .tab-nav > button").length - 2;
if (!executedAfterScripts && existingSearchfields >= neededSearchfields) {
mutationObserver.disconnect();
executedAfterScripts = true;
scheduleAfterScriptsCallbacks();
}
});
mutationObserver.observe(gradioApp(), {childList: true, subtree: true});
});
uiAfterScriptsCallbacks.push(setupExtraNetworks);

9
javascript/progressbar.js
View File

@ -45,8 +45,15 @@ function formatTime(secs) {
}
}
var originalAppTitle = undefined;
onUiLoaded(function() {
originalAppTitle = document.title;
});
function setTitle(progress) {
var title = 'Stable Diffusion';
var title = originalAppTitle;
if (opts.show_progress_in_title && progress) {
title = '[' + progress.trim() + '] ' + title;

127
javascript/resizeHandle.js
View File

@ -1,8 +1,8 @@
(function() {
const GRADIO_MIN_WIDTH = 320;
const GRID_TEMPLATE_COLUMNS = '1fr 16px 1fr';
const PAD = 16;
const DEBOUNCE_TIME = 100;
const DOUBLE_TAP_DELAY = 200; //ms
const R = {
tracking: false,
@ -11,6 +11,7 @@
leftCol: null,
leftColStartWidth: null,
screenX: null,
lastTapTime: null,
};
let resizeTimer;
@ -23,21 +24,17 @@
function displayResizeHandle(parent) {
if (window.innerWidth < GRADIO_MIN_WIDTH * 2 + PAD * 4) {
parent.style.display = 'flex';
if (R.handle != null) {
R.handle.style.opacity = '0';
}
parent.resizeHandle.style.display = "none";
return false;
} else {
parent.style.display = 'grid';
if (R.handle != null) {
R.handle.style.opacity = '100';
}
parent.resizeHandle.style.display = "block";
return true;
}
}
function afterResize(parent) {
if (displayResizeHandle(parent) && parent.style.gridTemplateColumns != GRID_TEMPLATE_COLUMNS) {
if (displayResizeHandle(parent) && parent.style.gridTemplateColumns != parent.style.originalGridTemplateColumns) {
const oldParentWidth = R.parentWidth;
const newParentWidth = parent.offsetWidth;
const widthL = parseInt(parent.style.gridTemplateColumns.split(' ')[0]);
@ -52,6 +49,14 @@
}
function setup(parent) {
function onDoubleClick(evt) {
evt.preventDefault();
evt.stopPropagation();
parent.style.gridTemplateColumns = parent.style.originalGridTemplateColumns;
}
const leftCol = parent.firstElementChild;
const rightCol = parent.lastElementChild;
@ -59,63 +64,97 @@
parent.style.display = 'grid';
parent.style.gap = '0';
parent.style.gridTemplateColumns = GRID_TEMPLATE_COLUMNS;
const gridTemplateColumns = `${parent.children[0].style.flexGrow}fr ${PAD}px ${parent.children[1].style.flexGrow}fr`;
parent.style.gridTemplateColumns = gridTemplateColumns;
parent.style.originalGridTemplateColumns = gridTemplateColumns;
const resizeHandle = document.createElement('div');
resizeHandle.classList.add('resize-handle');
parent.insertBefore(resizeHandle, rightCol);
parent.resizeHandle = resizeHandle;
resizeHandle.addEventListener('mousedown', (evt) => {
if (evt.button !== 0) return;
['mousedown', 'touchstart'].forEach((eventType) => {
resizeHandle.addEventListener(eventType, (evt) => {
if (eventType.startsWith('mouse')) {
if (evt.button !== 0) return;
} else {
if (evt.changedTouches.length !== 1) return;
evt.preventDefault();
evt.stopPropagation();
const currentTime = new Date().getTime();
if (R.lastTapTime && currentTime - R.lastTapTime <= DOUBLE_TAP_DELAY) {
onDoubleClick(evt);
return;
}
document.body.classList.add('resizing');
R.lastTapTime = currentTime;
}
R.tracking = true;
R.parent = parent;
R.parentWidth = parent.offsetWidth;
R.handle = resizeHandle;
R.leftCol = leftCol;
R.leftColStartWidth = leftCol.offsetWidth;
R.screenX = evt.screenX;
evt.preventDefault();
evt.stopPropagation();
document.body.classList.add('resizing');
R.tracking = true;
R.parent = parent;
R.parentWidth = parent.offsetWidth;
R.leftCol = leftCol;
R.leftColStartWidth = leftCol.offsetWidth;
if (eventType.startsWith('mouse')) {
R.screenX = evt.screenX;
} else {
R.screenX = evt.changedTouches[0].screenX;
}
});
});
resizeHandle.addEventListener('dblclick', (evt) => {
evt.preventDefault();
evt.stopPropagation();
parent.style.gridTemplateColumns = GRID_TEMPLATE_COLUMNS;
});
resizeHandle.addEventListener('dblclick', onDoubleClick);
afterResize(parent);
}
window.addEventListener('mousemove', (evt) => {
if (evt.button !== 0) return;
['mousemove', 'touchmove'].forEach((eventType) => {
window.addEventListener(eventType, (evt) => {
if (eventType.startsWith('mouse')) {
if (evt.button !== 0) return;
} else {
if (evt.changedTouches.length !== 1) return;
}
if (R.tracking) {
evt.preventDefault();
evt.stopPropagation();
if (R.tracking) {
if (eventType.startsWith('mouse')) {
evt.preventDefault();
}
evt.stopPropagation();
const delta = R.screenX - evt.screenX;
const leftColWidth = Math.max(Math.min(R.leftColStartWidth - delta, R.parent.offsetWidth - GRADIO_MIN_WIDTH - PAD), GRADIO_MIN_WIDTH);
setLeftColGridTemplate(R.parent, leftColWidth);
}
let delta = 0;
if (eventType.startsWith('mouse')) {
delta = R.screenX - evt.screenX;
} else {
delta = R.screenX - evt.changedTouches[0].screenX;
}
const leftColWidth = Math.max(Math.min(R.leftColStartWidth - delta, R.parent.offsetWidth - GRADIO_MIN_WIDTH - PAD), GRADIO_MIN_WIDTH);
setLeftColGridTemplate(R.parent, leftColWidth);
}
});
});
window.addEventListener('mouseup', (evt) => {
if (evt.button !== 0) return;
['mouseup', 'touchend'].forEach((eventType) => {
window.addEventListener(eventType, (evt) => {
if (eventType.startsWith('mouse')) {
if (evt.button !== 0) return;
} else {
if (evt.changedTouches.length !== 1) return;
}
if (R.tracking) {
evt.preventDefault();
evt.stopPropagation();
if (R.tracking) {
evt.preventDefault();
evt.stopPropagation();
R.tracking = false;
R.tracking = false;
document.body.classList.remove('resizing');
}
document.body.classList.remove('resizing');
}
});
});

4
javascript/settings.js
View File

@ -55,8 +55,8 @@ onOptionsChanged(function() {
});
opts._categories.forEach(function(x) {
var section = x[0];
var category = x[1];
var section = localization[x[0]] ?? x[0];
var category = localization[x[1]] ?? x[1];
var span = document.createElement('SPAN');
span.textContent = category;

34
javascript/token-counters.js
View File

@ -48,11 +48,6 @@ function setupTokenCounting(id, id_counter, id_button) {
var counter = gradioApp().getElementById(id_counter);
var textarea = gradioApp().querySelector(`#${id} > label > textarea`);
if (opts.disable_token_counters) {
counter.style.display = "none";
return;
}
if (counter.parentElement == prompt.parentElement) {
return;
}
@ -61,15 +56,32 @@ function setupTokenCounting(id, id_counter, id_button) {
prompt.parentElement.style.position = "relative";
var func = onEdit(id, textarea, 800, function() {
gradioApp().getElementById(id_button)?.click();
if (counter.classList.contains("token-counter-visible")) {
gradioApp().getElementById(id_button)?.click();
}
});
promptTokenCountUpdateFunctions[id] = func;
promptTokenCountUpdateFunctions[id_button] = func;
}
function setupTokenCounters() {
setupTokenCounting('txt2img_prompt', 'txt2img_token_counter', 'txt2img_token_button');
setupTokenCounting('txt2img_neg_prompt', 'txt2img_negative_token_counter', 'txt2img_negative_token_button');
setupTokenCounting('img2img_prompt', 'img2img_token_counter', 'img2img_token_button');
setupTokenCounting('img2img_neg_prompt', 'img2img_negative_token_counter', 'img2img_negative_token_button');
function toggleTokenCountingVisibility(id, id_counter, id_button) {
var counter = gradioApp().getElementById(id_counter);
counter.style.display = opts.disable_token_counters ? "none" : "block";
counter.classList.toggle("token-counter-visible", !opts.disable_token_counters);
}
function runCodeForTokenCounters(fun) {
fun('txt2img_prompt', 'txt2img_token_counter', 'txt2img_token_button');
fun('txt2img_neg_prompt', 'txt2img_negative_token_counter', 'txt2img_negative_token_button');
fun('img2img_prompt', 'img2img_token_counter', 'img2img_token_button');
fun('img2img_neg_prompt', 'img2img_negative_token_counter', 'img2img_negative_token_button');
}
onUiLoaded(function() {
runCodeForTokenCounters(setupTokenCounting);
});
onOptionsChanged(function() {
runCodeForTokenCounters(toggleTokenCountingVisibility);
});

23
javascript/ui.js
View File

@ -119,9 +119,18 @@ function create_submit_args(args) {
return res;
}
function setSubmitButtonsVisibility(tabname, showInterrupt, showSkip, showInterrupting) {
gradioApp().getElementById(tabname + '_interrupt').style.display = showInterrupt ? "block" : "none";
gradioApp().getElementById(tabname + '_skip').style.display = showSkip ? "block" : "none";
gradioApp().getElementById(tabname + '_interrupting').style.display = showInterrupting ? "block" : "none";
}
function showSubmitButtons(tabname, show) {
gradioApp().getElementById(tabname + '_interrupt').style.display = show ? "none" : "block";
gradioApp().getElementById(tabname + '_skip').style.display = show ? "none" : "block";
setSubmitButtonsVisibility(tabname, !show, !show, false);
}
function showSubmitInterruptingPlaceholder(tabname) {
setSubmitButtonsVisibility(tabname, false, true, true);
}
function showRestoreProgressButton(tabname, show) {
@ -150,6 +159,14 @@ function submit() {
return res;
}
function submit_txt2img_upscale() {
var res = submit(...arguments);
res[2] = selected_gallery_index();
return res;
}
function submit_img2img() {
showSubmitButtons('img2img', false);
@ -302,8 +319,6 @@ onAfterUiUpdate(function() {
});
json_elem.parentElement.style.display = "none";
setupTokenCounters();
});
onOptionsChanged(function() {

153
modules/api/api.py
View File

@ -17,7 +17,7 @@ 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, script_callbacks, generation_parameters_copypaste, sd_models
from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors, restart, shared_items, script_callbacks, infotext_utils, sd_models
from modules.api import models
from modules.shared import opts
from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
@ -31,7 +31,7 @@ from typing import Any
import piexif
import piexif.helper
from contextlib import closing
from modules.progress import create_task_id, add_task_to_queue, start_task, finish_task, current_task
def script_name_to_index(name, scripts):
try:
@ -230,6 +230,7 @@ class Api:
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-embeddings", self.refresh_embeddings, methods=["POST"])
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)
@ -251,6 +252,24 @@ class Api:
self.default_script_arg_txt2img = []
self.default_script_arg_img2img = []
txt2img_script_runner = scripts.scripts_txt2img
img2img_script_runner = scripts.scripts_img2img
if not txt2img_script_runner.scripts or not img2img_script_runner.scripts:
ui.create_ui()
if not txt2img_script_runner.scripts:
txt2img_script_runner.initialize_scripts(False)
if not self.default_script_arg_txt2img:
self.default_script_arg_txt2img = self.init_default_script_args(txt2img_script_runner)
if not img2img_script_runner.scripts:
img2img_script_runner.initialize_scripts(True)
if not self.default_script_arg_img2img:
self.default_script_arg_img2img = self.init_default_script_args(img2img_script_runner)
def add_api_route(self, path: str, endpoint, **kwargs):
if shared.cmd_opts.api_auth:
return self.app.add_api_route(path, endpoint, dependencies=[Depends(self.auth)], **kwargs)
@ -312,8 +331,13 @@ class Api:
script_args[script.args_from:script.args_to] = ui_default_values
return script_args
def init_script_args(self, request, default_script_args, selectable_scripts, selectable_idx, script_runner):
def init_script_args(self, request, default_script_args, selectable_scripts, selectable_idx, script_runner, *, input_script_args=None):
script_args = default_script_args.copy()
if input_script_args is not None:
for index, value in input_script_args.items():
script_args[index] = value
# position 0 in script_arg is the idx+1 of the selectable script that is going to be run when using scripts.scripts_*2img.run()
if selectable_scripts:
script_args[selectable_scripts.args_from:selectable_scripts.args_to] = request.script_args
@ -335,13 +359,83 @@ class Api:
script_args[alwayson_script.args_from + idx] = request.alwayson_scripts[alwayson_script_name]["args"][idx]
return script_args
def apply_infotext(self, request, tabname, *, script_runner=None, mentioned_script_args=None):
"""Processes `infotext` field from the `request`, and sets other fields of the `request` accoring to what's in infotext.
If request already has a field set, and that field is encountered in infotext too, the value from infotext is ignored.
Additionally, fills `mentioned_script_args` dict with index: value pairs for script arguments read from infotext.
"""
if not request.infotext:
return {}
possible_fields = infotext_utils.paste_fields[tabname]["fields"]
set_fields = request.model_dump(exclude_unset=True) if hasattr(request, "request") else request.dict(exclude_unset=True) # pydantic v1/v2 have differenrt names for this
params = infotext_utils.parse_generation_parameters(request.infotext)
def get_field_value(field, params):
value = field.function(params) if field.function else params.get(field.label)
if value is None:
return None
if field.api in request.__fields__:
target_type = request.__fields__[field.api].type_
else:
target_type = type(field.component.value)
if target_type == type(None):
return None
if isinstance(value, dict) and value.get('__type__') == 'generic_update': # this is a gradio.update rather than a value
value = value.get('value')
if value is not None and not isinstance(value, target_type):
value = target_type(value)
return value
for field in possible_fields:
if not field.api:
continue
if field.api in set_fields:
continue
value = get_field_value(field, params)
if value is not None:
setattr(request, field.api, value)
if request.override_settings is None:
request.override_settings = {}
overriden_settings = infotext_utils.get_override_settings(params)
for _, setting_name, value in overriden_settings:
if setting_name not in request.override_settings:
request.override_settings[setting_name] = value
if script_runner is not None and mentioned_script_args is not None:
indexes = {v: i for i, v in enumerate(script_runner.inputs)}
script_fields = ((field, indexes[field.component]) for field in possible_fields if field.component in indexes)
for field, index in script_fields:
value = get_field_value(field, params)
if value is None:
continue
mentioned_script_args[index] = value
return params
def text2imgapi(self, txt2imgreq: models.StableDiffusionTxt2ImgProcessingAPI):
task_id = txt2imgreq.force_task_id or create_task_id("txt2img")
script_runner = scripts.scripts_txt2img
if not script_runner.scripts:
script_runner.initialize_scripts(False)
ui.create_ui()
if not self.default_script_arg_txt2img:
self.default_script_arg_txt2img = self.init_default_script_args(script_runner)
infotext_script_args = {}
self.apply_infotext(txt2imgreq, "txt2img", script_runner=script_runner, mentioned_script_args=infotext_script_args)
selectable_scripts, selectable_script_idx = self.get_selectable_script(txt2imgreq.script_name, script_runner)
populate = txt2imgreq.copy(update={ # Override __init__ params
@ -356,12 +450,15 @@ class Api:
args.pop('script_name', None)
args.pop('script_args', None) # will refeed them to the pipeline directly after initializing them
args.pop('alwayson_scripts', None)
args.pop('infotext', None)
script_args = self.init_script_args(txt2imgreq, self.default_script_arg_txt2img, selectable_scripts, selectable_script_idx, script_runner)
script_args = self.init_script_args(txt2imgreq, self.default_script_arg_txt2img, selectable_scripts, selectable_script_idx, script_runner, input_script_args=infotext_script_args)
send_images = args.pop('send_images', True)
args.pop('save_images', None)
add_task_to_queue(task_id)
with self.queue_lock:
with closing(StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **args)) as p:
p.is_api = True
@ -371,12 +468,14 @@ class Api:
try:
shared.state.begin(job="scripts_txt2img")
start_task(task_id)
if selectable_scripts is not None:
p.script_args = script_args
processed = scripts.scripts_txt2img.run(p, *p.script_args) # Need to pass args as list here
else:
p.script_args = tuple(script_args) # Need to pass args as tuple here
processed = process_images(p)
finish_task(task_id)
finally:
shared.state.end()
shared.total_tqdm.clear()
@ -386,6 +485,8 @@ class Api:
return models.TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.js())
def img2imgapi(self, img2imgreq: models.StableDiffusionImg2ImgProcessingAPI):
task_id = img2imgreq.force_task_id or create_task_id("img2img")
init_images = img2imgreq.init_images
if init_images is None:
raise HTTPException(status_code=404, detail="Init image not found")
@ -395,11 +496,10 @@ class Api:
mask = decode_base64_to_image(mask)
script_runner = scripts.scripts_img2img
if not script_runner.scripts:
script_runner.initialize_scripts(True)
ui.create_ui()
if not self.default_script_arg_img2img:
self.default_script_arg_img2img = self.init_default_script_args(script_runner)
infotext_script_args = {}
self.apply_infotext(img2imgreq, "img2img", script_runner=script_runner, mentioned_script_args=infotext_script_args)
selectable_scripts, selectable_script_idx = self.get_selectable_script(img2imgreq.script_name, script_runner)
populate = img2imgreq.copy(update={ # Override __init__ params
@ -416,12 +516,15 @@ class Api:
args.pop('script_name', None)
args.pop('script_args', None) # will refeed them to the pipeline directly after initializing them
args.pop('alwayson_scripts', None)
args.pop('infotext', None)
script_args = self.init_script_args(img2imgreq, self.default_script_arg_img2img, selectable_scripts, selectable_script_idx, script_runner)
script_args = self.init_script_args(img2imgreq, self.default_script_arg_img2img, selectable_scripts, selectable_script_idx, script_runner, input_script_args=infotext_script_args)
send_images = args.pop('send_images', True)
args.pop('save_images', None)
add_task_to_queue(task_id)
with self.queue_lock:
with closing(StableDiffusionProcessingImg2Img(sd_model=shared.sd_model, **args)) as p:
p.init_images = [decode_base64_to_image(x) for x in init_images]
@ -432,12 +535,14 @@ class Api:
try:
shared.state.begin(job="scripts_img2img")
start_task(task_id)
if selectable_scripts is not None:
p.script_args = script_args
processed = scripts.scripts_img2img.run(p, *p.script_args) # Need to pass args as list here
else:
p.script_args = tuple(script_args) # Need to pass args as tuple here
processed = process_images(p)
finish_task(task_id)
finally:
shared.state.end()
shared.total_tqdm.clear()
@ -480,7 +585,7 @@ class Api:
if geninfo is None:
geninfo = ""
params = generation_parameters_copypaste.parse_generation_parameters(geninfo)
params = infotext_utils.parse_generation_parameters(geninfo)
script_callbacks.infotext_pasted_callback(geninfo, params)
return models.PNGInfoResponse(info=geninfo, items=items, parameters=params)
@ -511,7 +616,7 @@ class Api:
if shared.state.current_image and not req.skip_current_image:
current_image = encode_pil_to_base64(shared.state.current_image)
return models.ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict(), current_image=current_image, textinfo=shared.state.textinfo)
return models.ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict(), current_image=current_image, textinfo=shared.state.textinfo, current_task=current_task)
def interrogateapi(self, interrogatereq: models.InterrogateRequest):
image_b64 = interrogatereq.image
@ -643,6 +748,10 @@ class Api:
"skipped": convert_embeddings(db.skipped_embeddings),
}
def refresh_embeddings(self):
with self.queue_lock:
sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload=True)
def refresh_checkpoints(self):
with self.queue_lock:
shared.refresh_checkpoints()
@ -775,7 +884,15 @@ class Api:
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)
uvicorn.run(
self.app,
host=server_name,
port=port,
timeout_keep_alive=shared.cmd_opts.timeout_keep_alive,
root_path=root_path,
ssl_keyfile=shared.cmd_opts.tls_keyfile,
ssl_certfile=shared.cmd_opts.tls_certfile
)
def kill_webui(self):
restart.stop_program()

4
modules/api/models.py
View File

@ -107,6 +107,8 @@ StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator(
{"key": "send_images", "type": bool, "default": True},
{"key": "save_images", "type": bool, "default": False},
{"key": "alwayson_scripts", "type": dict, "default": {}},
{"key": "force_task_id", "type": str, "default": None},
{"key": "infotext", "type": str, "default": None},
]
).generate_model()
@ -124,6 +126,8 @@ StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
{"key": "send_images", "type": bool, "default": True},
{"key": "save_images", "type": bool, "default": False},
{"key": "alwayson_scripts", "type": dict, "default": {}},
{"key": "force_task_id", "type": str, "default": None},
{"key": "infotext", "type": str, "default": None},
]
).generate_model()

17
modules/cache.py
View File

@ -62,16 +62,15 @@ def cache(subsection):
if cache_data is None:
with cache_lock:
if cache_data is None:
if not os.path.isfile(cache_filename):
try:
with open(cache_filename, "r", encoding="utf8") as file:
cache_data = json.load(file)
except FileNotFoundError:
cache_data = {}
except Exception:
os.replace(cache_filename, os.path.join(script_path, "tmp", "cache.json"))
print('[ERROR] issue occurred while trying to read cache.json, move current cache to tmp/cache.json and create new cache')
cache_data = {}
else:
try:
with open(cache_filename, "r", encoding="utf8") as file:
cache_data = json.load(file)
except Exception:
os.replace(cache_filename, os.path.join(script_path, "tmp", "cache.json"))
print('[ERROR] issue occurred while trying to read cache.json, move current cache to tmp/cache.json and create new cache')
cache_data = {}
s = cache_data.get(subsection, {})
cache_data[subsection] = s

1
modules/call_queue.py
View File

@ -78,6 +78,7 @@ def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
shared.state.skipped = False
shared.state.interrupted = False
shared.state.stopping_generation = False
shared.state.job_count = 0
if not add_stats:

47
modules/cmd_args.py
View File

@ -1,7 +1,7 @@
import argparse
import json
import os
from modules.paths_internal import models_path, script_path, data_path, extensions_dir, extensions_builtin_dir, sd_default_config, sd_model_file # noqa: F401
from modules.paths_internal import normalized_filepath, models_path, script_path, data_path, extensions_dir, extensions_builtin_dir, sd_default_config, sd_model_file # noqa: F401
parser = argparse.ArgumentParser()
@ -19,21 +19,21 @@ parser.add_argument("--skip-install", action='store_true', help="launch.py argum
parser.add_argument("--dump-sysinfo", action='store_true', help="launch.py argument: dump limited sysinfo file (without information about extensions, options) to disk and quit")
parser.add_argument("--loglevel", type=str, help="log level; one of: CRITICAL, ERROR, WARNING, INFO, DEBUG", default=None)
parser.add_argument("--do-not-download-clip", action='store_true', help="do not download CLIP model even if it's not included in the checkpoint")
parser.add_argument("--data-dir", type=str, default=os.path.dirname(os.path.dirname(os.path.realpath(__file__))), help="base path where all user data is stored")
parser.add_argument("--config", type=str, default=sd_default_config, help="path to config which constructs model",)
parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",)
parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to directory with stable diffusion checkpoints")
parser.add_argument("--vae-dir", type=str, default=None, help="Path to directory with VAE files")
parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default=None)
parser.add_argument("--data-dir", type=normalized_filepath, default=os.path.dirname(os.path.dirname(os.path.realpath(__file__))), help="base path where all user data is stored")
parser.add_argument("--config", type=normalized_filepath, default=sd_default_config, help="path to config which constructs model",)
parser.add_argument("--ckpt", type=normalized_filepath, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",)
parser.add_argument("--ckpt-dir", type=normalized_filepath, default=None, help="Path to directory with stable diffusion checkpoints")
parser.add_argument("--vae-dir", type=normalized_filepath, default=None, help="Path to directory with VAE files")
parser.add_argument("--gfpgan-dir", type=normalized_filepath, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
parser.add_argument("--gfpgan-model", type=normalized_filepath, help="GFPGAN model file name", default=None)
parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats")
parser.add_argument("--no-half-vae", action='store_true', help="do not switch the VAE model to 16-bit floats")
parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)")
parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
parser.add_argument("--embeddings-dir", type=str, default=os.path.join(data_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
parser.add_argument("--textual-inversion-templates-dir", type=str, default=os.path.join(script_path, 'textual_inversion_templates'), help="directory with textual inversion templates")
parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory")
parser.add_argument("--localizations-dir", type=str, default=os.path.join(script_path, 'localizations'), help="localizations directory")
parser.add_argument("--embeddings-dir", type=normalized_filepath, default=os.path.join(data_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
parser.add_argument("--textual-inversion-templates-dir", type=normalized_filepath, default=os.path.join(script_path, 'textual_inversion_templates'), help="directory with textual inversion templates")
parser.add_argument("--hypernetwork-dir", type=normalized_filepath, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory")
parser.add_argument("--localizations-dir", type=normalized_filepath, default=os.path.join(script_path, 'localizations'), help="localizations directory")
parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage")
parser.add_argument("--medvram-sdxl", action='store_true', help="enable --medvram optimization just for SDXL models")
@ -48,12 +48,13 @@ parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to g
parser.add_argument("--ngrok-region", type=str, help="does not do anything.", default="")
parser.add_argument("--ngrok-options", type=json.loads, help='The options to pass to ngrok in JSON format, e.g.: \'{"authtoken_from_env":true, "basic_auth":"user:password", "oauth_provider":"google", "oauth_allow_emails":"user@asdf.com"}\'', default=dict())
parser.add_argument("--enable-insecure-extension-access", action='store_true', help="enable extensions tab regardless of other options")
parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer'))
parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN'))
parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(models_path, 'ESRGAN'))
parser.add_argument("--bsrgan-models-path", type=str, help="Path to directory with BSRGAN model file(s).", default=os.path.join(models_path, 'BSRGAN'))
parser.add_argument("--realesrgan-models-path", type=str, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(models_path, 'RealESRGAN'))
parser.add_argument("--clip-models-path", type=str, help="Path to directory with CLIP model file(s).", default=None)
parser.add_argument("--codeformer-models-path", type=normalized_filepath, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer'))
parser.add_argument("--gfpgan-models-path", type=normalized_filepath, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN'))
parser.add_argument("--esrgan-models-path", type=normalized_filepath, help="Path to directory with ESRGAN model file(s).", default=os.path.join(models_path, 'ESRGAN'))
parser.add_argument("--bsrgan-models-path", type=normalized_filepath, help="Path to directory with BSRGAN model file(s).", default=os.path.join(models_path, 'BSRGAN'))
parser.add_argument("--realesrgan-models-path", type=normalized_filepath, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(models_path, 'RealESRGAN'))
parser.add_argument("--dat-models-path", type=normalized_filepath, help="Path to directory with DAT model file(s).", default=os.path.join(models_path, 'DAT'))
parser.add_argument("--clip-models-path", type=normalized_filepath, help="Path to directory with CLIP model file(s).", default=None)
parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers")
parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
parser.add_argument("--xformers-flash-attention", action='store_true', help="enable xformers with Flash Attention to improve reproducibility (supported for SD2.x or variant only)")
@ -77,22 +78,24 @@ parser.add_argument("--port", type=int, help="launch gradio with given server po
parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False)
parser.add_argument("--ui-config-file", type=str, help="filename to use for ui configuration", default=os.path.join(data_path, 'ui-config.json'))
parser.add_argument("--hide-ui-dir-config", action='store_true', help="hide directory configuration from webui", default=False)
parser.add_argument("--freeze-settings", action='store_true', help="disable editing settings", default=False)
parser.add_argument("--freeze-settings", action='store_true', help="disable editing of all settings globally", default=False)
parser.add_argument("--freeze-settings-in-sections", type=str, help='disable editing settings in specific sections of the settings page by specifying a comma-delimited list such like "saving-images,upscaling". The list of setting names can be found in the modules/shared_options.py file', default=None)
parser.add_argument("--freeze-specific-settings", type=str, help='disable editing of individual settings by specifying a comma-delimited list like "samples_save,samples_format". The list of setting names can be found in the config.json file', default=None)
parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default=os.path.join(data_path, 'config.json'))
parser.add_argument("--gradio-debug", action='store_true', help="launch gradio with --debug option")
parser.add_argument("--gradio-auth", type=str, help='set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
parser.add_argument("--gradio-auth-path", type=str, help='set gradio authentication file path ex. "/path/to/auth/file" same auth format as --gradio-auth', default=None)
parser.add_argument("--gradio-auth-path", type=normalized_filepath, help='set gradio authentication file path ex. "/path/to/auth/file" same auth format as --gradio-auth', default=None)
parser.add_argument("--gradio-img2img-tool", type=str, help='does not do anything')
parser.add_argument("--gradio-inpaint-tool", type=str, help="does not do anything")
parser.add_argument("--gradio-allowed-path", action='append', help="add path to gradio's allowed_paths, make it possible to serve files from it", default=[data_path])
parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(data_path, 'styles.csv'))
parser.add_argument("--styles-file", type=str, action='append', help="path or wildcard path of styles files, allow multiple entries.", default=[])
parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
parser.add_argument("--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="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('--vae-path', type=normalized_filepath, 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)")
parser.add_argument("--api-auth", type=str, help='Set authentication for API like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)

276
modules/codeformer/codeformer_arch.py
View File

@ -1,276 +0,0 @@
# this file is copied from CodeFormer repository. Please see comment in modules/codeformer_model.py
import math
import torch
from torch import nn, Tensor
import torch.nn.functional as F
from typing import Optional
from modules.codeformer.vqgan_arch import VQAutoEncoder, ResBlock
from basicsr.utils.registry import ARCH_REGISTRY
def calc_mean_std(feat, eps=1e-5):
"""Calculate mean and std for adaptive_instance_normalization.
Args:
feat (Tensor): 4D tensor.
eps (float): A small value added to the variance to avoid
divide-by-zero. Default: 1e-5.
"""
size = feat.size()
assert len(size) == 4, 'The input feature should be 4D tensor.'
b, c = size[:2]
feat_var = feat.view(b, c, -1).var(dim=2) + eps
feat_std = feat_var.sqrt().view(b, c, 1, 1)
feat_mean = feat.view(b, c, -1).mean(dim=2).view(b, c, 1, 1)
return feat_mean, feat_std
def adaptive_instance_normalization(content_feat, style_feat):
"""Adaptive instance normalization.
Adjust the reference features to have the similar color and illuminations
as those in the degradate features.
Args:
content_feat (Tensor): The reference feature.
style_feat (Tensor): The degradate features.
"""
size = content_feat.size()
style_mean, style_std = calc_mean_std(style_feat)
content_mean, content_std = calc_mean_std(content_feat)
normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size)
return normalized_feat * style_std.expand(size) + style_mean.expand(size)
class PositionEmbeddingSine(nn.Module):
"""
This is a more standard version of the position embedding, very similar to the one
used by the Attention is all you need paper, generalized to work on images.
"""
def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):
super().__init__()
self.num_pos_feats = num_pos_feats
self.temperature = temperature
self.normalize = normalize
if scale is not None and normalize is False:
raise ValueError("normalize should be True if scale is passed")
if scale is None:
scale = 2 * math.pi
self.scale = scale
def forward(self, x, mask=None):
if mask is None:
mask = torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool)
not_mask = ~mask
y_embed = not_mask.cumsum(1, dtype=torch.float32)
x_embed = not_mask.cumsum(2, dtype=torch.float32)
if self.normalize:
eps = 1e-6
y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
pos_x = x_embed[:, :, :, None] / dim_t
pos_y = y_embed[:, :, :, None] / dim_t
pos_x = torch.stack(
(pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
).flatten(3)
pos_y = torch.stack(
(pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
).flatten(3)
pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
return pos
def _get_activation_fn(activation):
"""Return an activation function given a string"""
if activation == "relu":
return F.relu
if activation == "gelu":
return F.gelu
if activation == "glu":
return F.glu
raise RuntimeError(F"activation should be relu/gelu, not {activation}.")
class TransformerSALayer(nn.Module):
def __init__(self, embed_dim, nhead=8, dim_mlp=2048, dropout=0.0, activation="gelu"):
super().__init__()
self.self_attn = nn.MultiheadAttention(embed_dim, nhead, dropout=dropout)
# Implementation of Feedforward model - MLP
self.linear1 = nn.Linear(embed_dim, dim_mlp)
self.dropout = nn.Dropout(dropout)
self.linear2 = nn.Linear(dim_mlp, embed_dim)
self.norm1 = nn.LayerNorm(embed_dim)
self.norm2 = nn.LayerNorm(embed_dim)
self.dropout1 = nn.Dropout(dropout)
self.dropout2 = nn.Dropout(dropout)
self.activation = _get_activation_fn(activation)
def with_pos_embed(self, tensor, pos: Optional[Tensor]):
return tensor if pos is None else tensor + pos
def forward(self, tgt,
tgt_mask: Optional[Tensor] = None,
tgt_key_padding_mask: Optional[Tensor] = None,
query_pos: Optional[Tensor] = None):
# self attention
tgt2 = self.norm1(tgt)
q = k = self.with_pos_embed(tgt2, query_pos)
tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask,
key_padding_mask=tgt_key_padding_mask)[0]
tgt = tgt + self.dropout1(tgt2)
# ffn
tgt2 = self.norm2(tgt)
tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
tgt = tgt + self.dropout2(tgt2)
return tgt
class Fuse_sft_block(nn.Module):
def __init__(self, in_ch, out_ch):
super().__init__()
self.encode_enc = ResBlock(2*in_ch, out_ch)
self.scale = nn.Sequential(
nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1),
nn.LeakyReLU(0.2, True),
nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1))
self.shift = nn.Sequential(
nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1),
nn.LeakyReLU(0.2, True),
nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1))
def forward(self, enc_feat, dec_feat, w=1):
enc_feat = self.encode_enc(torch.cat([enc_feat, dec_feat], dim=1))
scale = self.scale(enc_feat)
shift = self.shift(enc_feat)
residual = w * (dec_feat * scale + shift)
out = dec_feat + residual
return out
@ARCH_REGISTRY.register()
class CodeFormer(VQAutoEncoder):
def __init__(self, dim_embd=512, n_head=8, n_layers=9,
codebook_size=1024, latent_size=256,
connect_list=('32', '64', '128', '256'),
fix_modules=('quantize', 'generator')):
super(CodeFormer, self).__init__(512, 64, [1, 2, 2, 4, 4, 8], 'nearest',2, [16], codebook_size)
if fix_modules is not None:
for module in fix_modules:
for param in getattr(self, module).parameters():
param.requires_grad = False
self.connect_list = connect_list
self.n_layers = n_layers
self.dim_embd = dim_embd
self.dim_mlp = dim_embd*2
self.position_emb = nn.Parameter(torch.zeros(latent_size, self.dim_embd))
self.feat_emb = nn.Linear(256, self.dim_embd)
# transformer
self.ft_layers = nn.Sequential(*[TransformerSALayer(embed_dim=dim_embd, nhead=n_head, dim_mlp=self.dim_mlp, dropout=0.0)
for _ in range(self.n_layers)])
# logits_predict head
self.idx_pred_layer = nn.Sequential(
nn.LayerNorm(dim_embd),
nn.Linear(dim_embd, codebook_size, bias=False))
self.channels = {
'16': 512,
'32': 256,
'64': 256,
'128': 128,
'256': 128,
'512': 64,
}
# after second residual block for > 16, before attn layer for ==16
self.fuse_encoder_block = {'512':2, '256':5, '128':8, '64':11, '32':14, '16':18}
# after first residual block for > 16, before attn layer for ==16
self.fuse_generator_block = {'16':6, '32': 9, '64':12, '128':15, '256':18, '512':21}
# fuse_convs_dict
self.fuse_convs_dict = nn.ModuleDict()
for f_size in self.connect_list:
in_ch = self.channels[f_size]
self.fuse_convs_dict[f_size] = Fuse_sft_block(in_ch, in_ch)
def _init_weights(self, module):
if isinstance(module, (nn.Linear, nn.Embedding)):
module.weight.data.normal_(mean=0.0, std=0.02)
if isinstance(module, nn.Linear) and module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
def forward(self, x, w=0, detach_16=True, code_only=False, adain=False):
# ################### Encoder #####################
enc_feat_dict = {}
out_list = [self.fuse_encoder_block[f_size] for f_size in self.connect_list]
for i, block in enumerate(self.encoder.blocks):
x = block(x)
if i in out_list:
enc_feat_dict[str(x.shape[-1])] = x.clone()
lq_feat = x
# ################# Transformer ###################
# quant_feat, codebook_loss, quant_stats = self.quantize(lq_feat)
pos_emb = self.position_emb.unsqueeze(1).repeat(1,x.shape[0],1)
# BCHW -> BC(HW) -> (HW)BC
feat_emb = self.feat_emb(lq_feat.flatten(2).permute(2,0,1))
query_emb = feat_emb
# Transformer encoder
for layer in self.ft_layers:
query_emb = layer(query_emb, query_pos=pos_emb)
# output logits
logits = self.idx_pred_layer(query_emb) # (hw)bn
logits = logits.permute(1,0,2) # (hw)bn -> b(hw)n
if code_only: # for training stage II
# logits doesn't need softmax before cross_entropy loss
return logits, lq_feat
# ################# Quantization ###################
# if self.training:
# quant_feat = torch.einsum('btn,nc->btc', [soft_one_hot, self.quantize.embedding.weight])
# # b(hw)c -> bc(hw) -> bchw
# quant_feat = quant_feat.permute(0,2,1).view(lq_feat.shape)
# ------------
soft_one_hot = F.softmax(logits, dim=2)
_, top_idx = torch.topk(soft_one_hot, 1, dim=2)
quant_feat = self.quantize.get_codebook_feat(top_idx, shape=[x.shape[0],16,16,256])
# preserve gradients
# quant_feat = lq_feat + (quant_feat - lq_feat).detach()
if detach_16:
quant_feat = quant_feat.detach() # for training stage III
if adain:
quant_feat = adaptive_instance_normalization(quant_feat, lq_feat)
# ################## Generator ####################
x = quant_feat
fuse_list = [self.fuse_generator_block[f_size] for f_size in self.connect_list]
for i, block in enumerate(self.generator.blocks):
x = block(x)
if i in fuse_list: # fuse after i-th block
f_size = str(x.shape[-1])
if w>0:
x = self.fuse_convs_dict[f_size](enc_feat_dict[f_size].detach(), x, w)
out = x
# logits doesn't need softmax before cross_entropy loss
return out, logits, lq_feat

435
modules/codeformer/vqgan_arch.py
View File

@ -1,435 +0,0 @@
# this file is copied from CodeFormer repository. Please see comment in modules/codeformer_model.py
'''
VQGAN code, adapted from the original created by the Unleashing Transformers authors:
https://github.com/samb-t/unleashing-transformers/blob/master/models/vqgan.py
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
from basicsr.utils import get_root_logger
from basicsr.utils.registry import ARCH_REGISTRY
def normalize(in_channels):
return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
@torch.jit.script
def swish(x):
return x*torch.sigmoid(x)
# Define VQVAE classes
class VectorQuantizer(nn.Module):
def __init__(self, codebook_size, emb_dim, beta):
super(VectorQuantizer, self).__init__()
self.codebook_size = codebook_size # number of embeddings
self.emb_dim = emb_dim # dimension of embedding
self.beta = beta # commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2
self.embedding = nn.Embedding(self.codebook_size, self.emb_dim)
self.embedding.weight.data.uniform_(-1.0 / self.codebook_size, 1.0 / self.codebook_size)
def forward(self, z):
# reshape z -> (batch, height, width, channel) and flatten
z = z.permute(0, 2, 3, 1).contiguous()
z_flattened = z.view(-1, self.emb_dim)
# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
d = (z_flattened ** 2).sum(dim=1, keepdim=True) + (self.embedding.weight**2).sum(1) - \
2 * torch.matmul(z_flattened, self.embedding.weight.t())
mean_distance = torch.mean(d)
# find closest encodings
# min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1)
min_encoding_scores, min_encoding_indices = torch.topk(d, 1, dim=1, largest=False)
# [0-1], higher score, higher confidence
min_encoding_scores = torch.exp(-min_encoding_scores/10)
min_encodings = torch.zeros(min_encoding_indices.shape[0], self.codebook_size).to(z)
min_encodings.scatter_(1, min_encoding_indices, 1)
# get quantized latent vectors
z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape)
# compute loss for embedding
loss = torch.mean((z_q.detach()-z)**2) + self.beta * torch.mean((z_q - z.detach()) ** 2)
# preserve gradients
z_q = z + (z_q - z).detach()
# perplexity
e_mean = torch.mean(min_encodings, dim=0)
perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10)))
# reshape back to match original input shape
z_q = z_q.permute(0, 3, 1, 2).contiguous()
return z_q, loss, {
"perplexity": perplexity,
"min_encodings": min_encodings,
"min_encoding_indices": min_encoding_indices,
"min_encoding_scores": min_encoding_scores,
"mean_distance": mean_distance
}
def get_codebook_feat(self, indices, shape):
# input indices: batch*token_num -> (batch*token_num)*1
# shape: batch, height, width, channel
indices = indices.view(-1,1)
min_encodings = torch.zeros(indices.shape[0], self.codebook_size).to(indices)
min_encodings.scatter_(1, indices, 1)
# get quantized latent vectors
z_q = torch.matmul(min_encodings.float(), self.embedding.weight)
if shape is not None: # reshape back to match original input shape
z_q = z_q.view(shape).permute(0, 3, 1, 2).contiguous()
return z_q
class GumbelQuantizer(nn.Module):
def __init__(self, codebook_size, emb_dim, num_hiddens, straight_through=False, kl_weight=5e-4, temp_init=1.0):
super().__init__()
self.codebook_size = codebook_size # number of embeddings
self.emb_dim = emb_dim # dimension of embedding
self.straight_through = straight_through
self.temperature = temp_init
self.kl_weight = kl_weight
self.proj = nn.Conv2d(num_hiddens, codebook_size, 1) # projects last encoder layer to quantized logits
self.embed = nn.Embedding(codebook_size, emb_dim)
def forward(self, z):
hard = self.straight_through if self.training else True
logits = self.proj(z)
soft_one_hot = F.gumbel_softmax(logits, tau=self.temperature, dim=1, hard=hard)
z_q = torch.einsum("b n h w, n d -> b d h w", soft_one_hot, self.embed.weight)
# + kl divergence to the prior loss
qy = F.softmax(logits, dim=1)
diff = self.kl_weight * torch.sum(qy * torch.log(qy * self.codebook_size + 1e-10), dim=1).mean()
min_encoding_indices = soft_one_hot.argmax(dim=1)
return z_q, diff, {
"min_encoding_indices": min_encoding_indices
}
class Downsample(nn.Module):
def __init__(self, in_channels):
super().__init__()
self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0)
def forward(self, x):
pad = (0, 1, 0, 1)
x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
x = self.conv(x)
return x
class Upsample(nn.Module):
def __init__(self, in_channels):
super().__init__()
self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
def forward(self, x):
x = F.interpolate(x, scale_factor=2.0, mode="nearest")
x = self.conv(x)
return x
class ResBlock(nn.Module):
def __init__(self, in_channels, out_channels=None):
super(ResBlock, self).__init__()
self.in_channels = in_channels
self.out_channels = in_channels if out_channels is None else out_channels
self.norm1 = normalize(in_channels)
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
self.norm2 = normalize(out_channels)
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
if self.in_channels != self.out_channels:
self.conv_out = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
def forward(self, x_in):
x = x_in
x = self.norm1(x)
x = swish(x)
x = self.conv1(x)
x = self.norm2(x)
x = swish(x)
x = self.conv2(x)
if self.in_channels != self.out_channels:
x_in = self.conv_out(x_in)
return x + x_in
class AttnBlock(nn.Module):
def __init__(self, in_channels):
super().__init__()
self.in_channels = in_channels
self.norm = normalize(in_channels)
self.q = torch.nn.Conv2d(
in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0
)
self.k = torch.nn.Conv2d(
in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0
)
self.v = torch.nn.Conv2d(
in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0
)
self.proj_out = torch.nn.Conv2d(
in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0
)
def forward(self, x):
h_ = x
h_ = self.norm(h_)
q = self.q(h_)
k = self.k(h_)
v = self.v(h_)
# compute attention
b, c, h, w = q.shape
q = q.reshape(b, c, h*w)
q = q.permute(0, 2, 1)
k = k.reshape(b, c, h*w)
w_ = torch.bmm(q, k)
w_ = w_ * (int(c)**(-0.5))
w_ = F.softmax(w_, dim=2)
# attend to values
v = v.reshape(b, c, h*w)
w_ = w_.permute(0, 2, 1)
h_ = torch.bmm(v, w_)
h_ = h_.reshape(b, c, h, w)
h_ = self.proj_out(h_)
return x+h_
class Encoder(nn.Module):
def __init__(self, in_channels, nf, emb_dim, ch_mult, num_res_blocks, resolution, attn_resolutions):
super().__init__()
self.nf = nf
self.num_resolutions = len(ch_mult)
self.num_res_blocks = num_res_blocks
self.resolution = resolution
self.attn_resolutions = attn_resolutions
curr_res = self.resolution
in_ch_mult = (1,)+tuple(ch_mult)
blocks = []
# initial convultion
blocks.append(nn.Conv2d(in_channels, nf, kernel_size=3, stride=1, padding=1))
# residual and downsampling blocks, with attention on smaller res (16x16)
for i in range(self.num_resolutions):
block_in_ch = nf * in_ch_mult[i]
block_out_ch = nf * ch_mult[i]
for _ in range(self.num_res_blocks):
blocks.append(ResBlock(block_in_ch, block_out_ch))
block_in_ch = block_out_ch
if curr_res in attn_resolutions:
blocks.append(AttnBlock(block_in_ch))
if i != self.num_resolutions - 1:
blocks.append(Downsample(block_in_ch))
curr_res = curr_res // 2
# non-local attention block
blocks.append(ResBlock(block_in_ch, block_in_ch))
blocks.append(AttnBlock(block_in_ch))
blocks.append(ResBlock(block_in_ch, block_in_ch))
# normalise and convert to latent size
blocks.append(normalize(block_in_ch))
blocks.append(nn.Conv2d(block_in_ch, emb_dim, kernel_size=3, stride=1, padding=1))
self.blocks = nn.ModuleList(blocks)
def forward(self, x):
for block in self.blocks:
x = block(x)
return x
class Generator(nn.Module):
def __init__(self, nf, emb_dim, ch_mult, res_blocks, img_size, attn_resolutions):
super().__init__()
self.nf = nf
self.ch_mult = ch_mult
self.num_resolutions = len(self.ch_mult)
self.num_res_blocks = res_blocks
self.resolution = img_size
self.attn_resolutions = attn_resolutions
self.in_channels = emb_dim
self.out_channels = 3
block_in_ch = self.nf * self.ch_mult[-1]
curr_res = self.resolution // 2 ** (self.num_resolutions-1)
blocks = []
# initial conv
blocks.append(nn.Conv2d(self.in_channels, block_in_ch, kernel_size=3, stride=1, padding=1))
# non-local attention block
blocks.append(ResBlock(block_in_ch, block_in_ch))
blocks.append(AttnBlock(block_in_ch))
blocks.append(ResBlock(block_in_ch, block_in_ch))
for i in reversed(range(self.num_resolutions)):
block_out_ch = self.nf * self.ch_mult[i]
for _ in range(self.num_res_blocks):
blocks.append(ResBlock(block_in_ch, block_out_ch))
block_in_ch = block_out_ch
if curr_res in self.attn_resolutions:
blocks.append(AttnBlock(block_in_ch))
if i != 0:
blocks.append(Upsample(block_in_ch))
curr_res = curr_res * 2
blocks.append(normalize(block_in_ch))
blocks.append(nn.Conv2d(block_in_ch, self.out_channels, kernel_size=3, stride=1, padding=1))
self.blocks = nn.ModuleList(blocks)
def forward(self, x):
for block in self.blocks:
x = block(x)
return x
@ARCH_REGISTRY.register()
class VQAutoEncoder(nn.Module):
def __init__(self, img_size, nf, ch_mult, quantizer="nearest", res_blocks=2, attn_resolutions=None, codebook_size=1024, emb_dim=256,
beta=0.25, gumbel_straight_through=False, gumbel_kl_weight=1e-8, model_path=None):
super().__init__()
logger = get_root_logger()
self.in_channels = 3
self.nf = nf
self.n_blocks = res_blocks
self.codebook_size = codebook_size
self.embed_dim = emb_dim
self.ch_mult = ch_mult
self.resolution = img_size
self.attn_resolutions = attn_resolutions or [16]
self.quantizer_type = quantizer
self.encoder = Encoder(
self.in_channels,
self.nf,
self.embed_dim,
self.ch_mult,
self.n_blocks,
self.resolution,
self.attn_resolutions
)
if self.quantizer_type == "nearest":
self.beta = beta #0.25
self.quantize = VectorQuantizer(self.codebook_size, self.embed_dim, self.beta)
elif self.quantizer_type == "gumbel":
self.gumbel_num_hiddens = emb_dim
self.straight_through = gumbel_straight_through
self.kl_weight = gumbel_kl_weight
self.quantize = GumbelQuantizer(
self.codebook_size,
self.embed_dim,
self.gumbel_num_hiddens,
self.straight_through,
self.kl_weight
)
self.generator = Generator(
self.nf,
self.embed_dim,
self.ch_mult,
self.n_blocks,
self.resolution,
self.attn_resolutions
)
if model_path is not None:
chkpt = torch.load(model_path, map_location='cpu')
if 'params_ema' in chkpt:
self.load_state_dict(torch.load(model_path, map_location='cpu')['params_ema'])
logger.info(f'vqgan is loaded from: {model_path} [params_ema]')
elif 'params' in chkpt:
self.load_state_dict(torch.load(model_path, map_location='cpu')['params'])
logger.info(f'vqgan is loaded from: {model_path} [params]')
else:
raise ValueError('Wrong params!')
def forward(self, x):
x = self.encoder(x)
quant, codebook_loss, quant_stats = self.quantize(x)
x = self.generator(quant)
return x, codebook_loss, quant_stats
# patch based discriminator
@ARCH_REGISTRY.register()
class VQGANDiscriminator(nn.Module):
def __init__(self, nc=3, ndf=64, n_layers=4, model_path=None):
super().__init__()
layers = [nn.Conv2d(nc, ndf, kernel_size=4, stride=2, padding=1), nn.LeakyReLU(0.2, True)]
ndf_mult = 1
ndf_mult_prev = 1
for n in range(1, n_layers): # gradually increase the number of filters
ndf_mult_prev = ndf_mult
ndf_mult = min(2 ** n, 8)
layers += [
nn.Conv2d(ndf * ndf_mult_prev, ndf * ndf_mult, kernel_size=4, stride=2, padding=1, bias=False),
nn.BatchNorm2d(ndf * ndf_mult),
nn.LeakyReLU(0.2, True)
]
ndf_mult_prev = ndf_mult
ndf_mult = min(2 ** n_layers, 8)
layers += [
nn.Conv2d(ndf * ndf_mult_prev, ndf * ndf_mult, kernel_size=4, stride=1, padding=1, bias=False),
nn.BatchNorm2d(ndf * ndf_mult),
nn.LeakyReLU(0.2, True)
]
layers += [
nn.Conv2d(ndf * ndf_mult, 1, kernel_size=4, stride=1, padding=1)] # output 1 channel prediction map
self.main = nn.Sequential(*layers)
if model_path is not None:
chkpt = torch.load(model_path, map_location='cpu')
if 'params_d' in chkpt:
self.load_state_dict(torch.load(model_path, map_location='cpu')['params_d'])
elif 'params' in chkpt:
self.load_state_dict(torch.load(model_path, map_location='cpu')['params'])
else:
raise ValueError('Wrong params!')
def forward(self, x):
return self.main(x)

166
modules/codeformer_model.py
View File

@ -1,132 +1,64 @@
import os
from __future__ import annotations
import logging
import cv2
import torch
import modules.face_restoration
import modules.shared
from modules import shared, devices, modelloader, errors
from modules.paths import models_path
from modules import (
devices,
errors,
face_restoration,
face_restoration_utils,
modelloader,
shared,
)
logger = logging.getLogger(__name__)
# codeformer people made a choice to include modified basicsr library to their project which makes
# it utterly impossible to use it alongside with other libraries that also use basicsr, like GFPGAN.
# I am making a choice to include some files from codeformer to work around this issue.
model_dir = "Codeformer"
model_path = os.path.join(models_path, model_dir)
model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth'
model_download_name = 'codeformer-v0.1.0.pth'
codeformer = None
# used by e.g. postprocessing_codeformer.py
codeformer: face_restoration.FaceRestoration | None = None
def setup_model(dirname):
os.makedirs(model_path, exist_ok=True)
class FaceRestorerCodeFormer(face_restoration_utils.CommonFaceRestoration):
def name(self):
return "CodeFormer"
path = modules.paths.paths.get("CodeFormer", None)
if path is None:
return
def load_net(self) -> torch.Module:
for model_path in modelloader.load_models(
model_path=self.model_path,
model_url=model_url,
command_path=self.model_path,
download_name=model_download_name,
ext_filter=['.pth'],
):
return modelloader.load_spandrel_model(
model_path,
device=devices.device_codeformer,
expected_architecture='CodeFormer',
).model
raise ValueError("No codeformer model found")
def get_device(self):
return devices.device_codeformer
def restore(self, np_image, w: float | None = None):
if w is None:
w = getattr(shared.opts, "code_former_weight", 0.5)
def restore_face(cropped_face_t):
assert self.net is not None
return self.net(cropped_face_t, w=w, adain=True)[0]
return self.restore_with_helper(np_image, restore_face)
def setup_model(dirname: str) -> None:
global codeformer
try:
from torchvision.transforms.functional import normalize
from modules.codeformer.codeformer_arch import CodeFormer
from basicsr.utils import img2tensor, tensor2img
from facelib.utils.face_restoration_helper import FaceRestoreHelper
from facelib.detection.retinaface import retinaface
net_class = CodeFormer
class FaceRestorerCodeFormer(modules.face_restoration.FaceRestoration):
def name(self):
return "CodeFormer"
def __init__(self, dirname):
self.net = None
self.face_helper = None
self.cmd_dir = dirname
def create_models(self):
if self.net is not None and self.face_helper is not None:
self.net.to(devices.device_codeformer)
return self.net, self.face_helper
model_paths = modelloader.load_models(model_path, model_url, self.cmd_dir, download_name='codeformer-v0.1.0.pth', ext_filter=['.pth'])
if len(model_paths) != 0:
ckpt_path = model_paths[0]
else:
print("Unable to load codeformer model.")
return None, None
net = net_class(dim_embd=512, codebook_size=1024, n_head=8, n_layers=9, connect_list=['32', '64', '128', '256']).to(devices.device_codeformer)
checkpoint = torch.load(ckpt_path)['params_ema']
net.load_state_dict(checkpoint)
net.eval()
if hasattr(retinaface, 'device'):
retinaface.device = devices.device_codeformer
face_helper = FaceRestoreHelper(1, face_size=512, crop_ratio=(1, 1), det_model='retinaface_resnet50', save_ext='png', use_parse=True, device=devices.device_codeformer)
self.net = net
self.face_helper = face_helper
return net, face_helper
def send_model_to(self, device):
self.net.to(device)
self.face_helper.face_det.to(device)
self.face_helper.face_parse.to(device)
def restore(self, np_image, w=None):
np_image = np_image[:, :, ::-1]
original_resolution = np_image.shape[0:2]
self.create_models()
if self.net is None or self.face_helper is None:
return np_image
self.send_model_to(devices.device_codeformer)
self.face_helper.clean_all()
self.face_helper.read_image(np_image)
self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
self.face_helper.align_warp_face()
for cropped_face in self.face_helper.cropped_faces:
cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
cropped_face_t = cropped_face_t.unsqueeze(0).to(devices.device_codeformer)
try:
with torch.no_grad():
output = self.net(cropped_face_t, w=w if w is not None else shared.opts.code_former_weight, adain=True)[0]
restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
del output
devices.torch_gc()
except Exception:
errors.report('Failed inference for CodeFormer', exc_info=True)
restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1))
restored_face = restored_face.astype('uint8')
self.face_helper.add_restored_face(restored_face)
self.face_helper.get_inverse_affine(None)
restored_img = self.face_helper.paste_faces_to_input_image()
restored_img = restored_img[:, :, ::-1]
if original_resolution != restored_img.shape[0:2]:
restored_img = cv2.resize(restored_img, (0, 0), fx=original_resolution[1]/restored_img.shape[1], fy=original_resolution[0]/restored_img.shape[0], interpolation=cv2.INTER_LINEAR)
self.face_helper.clean_all()
if shared.opts.face_restoration_unload:
self.send_model_to(devices.cpu)
return restored_img
global codeformer
codeformer = FaceRestorerCodeFormer(dirname)
shared.face_restorers.append(codeformer)
except Exception:
errors.report("Error setting up CodeFormer", exc_info=True)
# sys.path = stored_sys_path

79
modules/dat_model.py Normal file
View File

@ -0,0 +1,79 @@
import os
from modules import modelloader, errors
from modules.shared import cmd_opts, opts
from modules.upscaler import Upscaler, UpscalerData
from modules.upscaler_utils import upscale_with_model
class UpscalerDAT(Upscaler):
def __init__(self, user_path):
self.name = "DAT"
self.user_path = user_path
self.scalers = []
super().__init__()
for file in self.find_models(ext_filter=[".pt", ".pth"]):
name = modelloader.friendly_name(file)
scaler_data = UpscalerData(name, file, upscaler=self, scale=None)
self.scalers.append(scaler_data)
for model in get_dat_models(self):
if model.name in opts.dat_enabled_models:
self.scalers.append(model)
def do_upscale(self, img, path):
try:
info = self.load_model(path)
except Exception:
errors.report(f"Unable to load DAT model {path}", exc_info=True)
return img
model_descriptor = modelloader.load_spandrel_model(
info.local_data_path,
device=self.device,
prefer_half=(not cmd_opts.no_half and not cmd_opts.upcast_sampling),
expected_architecture="DAT",
)
return upscale_with_model(
model_descriptor,
img,
tile_size=opts.DAT_tile,
tile_overlap=opts.DAT_tile_overlap,
)
def load_model(self, path):
for scaler in self.scalers:
if scaler.data_path == path:
if scaler.local_data_path.startswith("http"):
scaler.local_data_path = modelloader.load_file_from_url(
scaler.data_path,
model_dir=self.model_download_path,
)
if not os.path.exists(scaler.local_data_path):
raise FileNotFoundError(f"DAT data missing: {scaler.local_data_path}")
return scaler
raise ValueError(f"Unable to find model info: {path}")
def get_dat_models(scaler):
return [
UpscalerData(
name="DAT x2",
path="https://github.com/n0kovo/dat_upscaler_models/raw/main/DAT/DAT_x2.pth",
scale=2,
upscaler=scaler,
),
UpscalerData(
name="DAT x3",
path="https://github.com/n0kovo/dat_upscaler_models/raw/main/DAT/DAT_x3.pth",
scale=3,
upscaler=scaler,
),
UpscalerData(
name="DAT x4",
path="https://github.com/n0kovo/dat_upscaler_models/raw/main/DAT/DAT_x4.pth",
scale=4,
upscaler=scaler,
),
]

114
modules/devices.py
View File

@ -3,7 +3,7 @@ import contextlib
from functools import lru_cache
import torch
from modules import errors, shared
from modules import errors, shared, npu_specific
if sys.platform == "darwin":
from modules import mac_specific
@ -23,6 +23,23 @@ def has_mps() -> bool:
return mac_specific.has_mps
def cuda_no_autocast(device_id=None) -> bool:
if device_id is None:
device_id = get_cuda_device_id()
return (
torch.cuda.get_device_capability(device_id) == (7, 5)
and torch.cuda.get_device_name(device_id).startswith("NVIDIA GeForce GTX 16")
)
def get_cuda_device_id():
return (
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()
def get_cuda_device_string():
if shared.cmd_opts.device_id is not None:
return f"cuda:{shared.cmd_opts.device_id}"
@ -40,6 +57,9 @@ def get_optimal_device_name():
if has_xpu():
return xpu_specific.get_xpu_device_string()
if npu_specific.has_npu:
return npu_specific.get_npu_device_string()
return "cpu"
@ -67,14 +87,23 @@ def torch_gc():
if has_xpu():
xpu_specific.torch_xpu_gc()
if npu_specific.has_npu:
torch_npu_set_device()
npu_specific.torch_npu_gc()
def torch_npu_set_device():
# Work around due to bug in torch_npu, revert me after fixed, @see https://gitee.com/ascend/pytorch/issues/I8KECW?from=project-issue
if npu_specific.has_npu:
torch.npu.set_device(0)
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
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"):
if cuda_no_autocast():
torch.backends.cudnn.benchmark = True
torch.backends.cuda.matmul.allow_tf32 = True
@ -84,6 +113,7 @@ def enable_tf32():
errors.run(enable_tf32, "Enabling TF32")
cpu: torch.device = torch.device("cpu")
fp8: bool = False
device: torch.device = None
device_interrogate: torch.device = None
device_gfpgan: torch.device = None
@ -92,6 +122,7 @@ device_codeformer: torch.device = None
dtype: torch.dtype = torch.float16
dtype_vae: torch.dtype = torch.float16
dtype_unet: torch.dtype = torch.float16
dtype_inference: torch.dtype = torch.float16
unet_needs_upcast = False
@ -104,15 +135,89 @@ def cond_cast_float(input):
nv_rng = None
patch_module_list = [
torch.nn.Linear,
torch.nn.Conv2d,
torch.nn.MultiheadAttention,
torch.nn.GroupNorm,
torch.nn.LayerNorm,
]
def manual_cast_forward(target_dtype):
def forward_wrapper(self, *args, **kwargs):
if any(
isinstance(arg, torch.Tensor) and arg.dtype != target_dtype
for arg in args
):
args = [arg.to(target_dtype) if isinstance(arg, torch.Tensor) else arg for arg in args]
kwargs = {k: v.to(target_dtype) if isinstance(v, torch.Tensor) else v for k, v in kwargs.items()}
org_dtype = target_dtype
for param in self.parameters():
if param.dtype != target_dtype:
org_dtype = param.dtype
break
if org_dtype != target_dtype:
self.to(target_dtype)
result = self.org_forward(*args, **kwargs)
if org_dtype != target_dtype:
self.to(org_dtype)
if target_dtype != dtype_inference:
if isinstance(result, tuple):
result = tuple(
i.to(dtype_inference)
if isinstance(i, torch.Tensor)
else i
for i in result
)
elif isinstance(result, torch.Tensor):
result = result.to(dtype_inference)
return result
return forward_wrapper
@contextlib.contextmanager
def manual_cast(target_dtype):
applied = False
for module_type in patch_module_list:
if hasattr(module_type, "org_forward"):
continue
applied = True
org_forward = module_type.forward
if module_type == torch.nn.MultiheadAttention:
module_type.forward = manual_cast_forward(torch.float32)
else:
module_type.forward = manual_cast_forward(target_dtype)
module_type.org_forward = org_forward
try:
yield None
finally:
if applied:
for module_type in patch_module_list:
if hasattr(module_type, "org_forward"):
module_type.forward = module_type.org_forward
delattr(module_type, "org_forward")
def autocast(disable=False):
if disable:
return contextlib.nullcontext()
if dtype == torch.float32 or shared.cmd_opts.precision == "full":
if fp8 and device==cpu:
return torch.autocast("cpu", dtype=torch.bfloat16, enabled=True)
if fp8 and dtype_inference == torch.float32:
return manual_cast(dtype)
if dtype == torch.float32 or dtype_inference == torch.float32:
return contextlib.nullcontext()
if has_xpu() or has_mps() or cuda_no_autocast():
return manual_cast(dtype)
return torch.autocast("cuda")
@ -164,4 +269,3 @@ def first_time_calculation():
x = torch.zeros((1, 1, 3, 3)).to(device, dtype)
conv2d = torch.nn.Conv2d(1, 1, (3, 3)).to(device, dtype)
conv2d(x)

4
modules/errors.py
View File

@ -107,8 +107,8 @@ def check_versions():
import torch
import gradio
expected_torch_version = "2.0.0"
expected_xformers_version = "0.0.20"
expected_torch_version = "2.1.2"
expected_xformers_version = "0.0.23.post1"
expected_gradio_version = "3.41.2"
if version.parse(torch.__version__) < version.parse(expected_torch_version):

199
modules/esrgan_model.py
View File

@ -1,121 +1,7 @@
import sys
import numpy as np
import torch
from PIL import Image
import modules.esrgan_model_arch as arch
from modules import modelloader, images, devices
from modules import modelloader, devices, errors
from modules.shared import opts
from modules.upscaler import Upscaler, UpscalerData
def mod2normal(state_dict):
# this code is copied from https://github.com/victorca25/iNNfer
if 'conv_first.weight' in state_dict:
crt_net = {}
items = list(state_dict)
crt_net['model.0.weight'] = state_dict['conv_first.weight']
crt_net['model.0.bias'] = state_dict['conv_first.bias']
for k in items.copy():
if 'RDB' in k:
ori_k = k.replace('RRDB_trunk.', 'model.1.sub.')
if '.weight' in k:
ori_k = ori_k.replace('.weight', '.0.weight')
elif '.bias' in k:
ori_k = ori_k.replace('.bias', '.0.bias')
crt_net[ori_k] = state_dict[k]
items.remove(k)
crt_net['model.1.sub.23.weight'] = state_dict['trunk_conv.weight']
crt_net['model.1.sub.23.bias'] = state_dict['trunk_conv.bias']
crt_net['model.3.weight'] = state_dict['upconv1.weight']
crt_net['model.3.bias'] = state_dict['upconv1.bias']
crt_net['model.6.weight'] = state_dict['upconv2.weight']
crt_net['model.6.bias'] = state_dict['upconv2.bias']
crt_net['model.8.weight'] = state_dict['HRconv.weight']
crt_net['model.8.bias'] = state_dict['HRconv.bias']
crt_net['model.10.weight'] = state_dict['conv_last.weight']
crt_net['model.10.bias'] = state_dict['conv_last.bias']
state_dict = crt_net
return state_dict
def resrgan2normal(state_dict, nb=23):
# this code is copied from https://github.com/victorca25/iNNfer
if "conv_first.weight" in state_dict and "body.0.rdb1.conv1.weight" in state_dict:
re8x = 0
crt_net = {}
items = list(state_dict)
crt_net['model.0.weight'] = state_dict['conv_first.weight']
crt_net['model.0.bias'] = state_dict['conv_first.bias']
for k in items.copy():
if "rdb" in k:
ori_k = k.replace('body.', 'model.1.sub.')
ori_k = ori_k.replace('.rdb', '.RDB')
if '.weight' in k:
ori_k = ori_k.replace('.weight', '.0.weight')
elif '.bias' in k:
ori_k = ori_k.replace('.bias', '.0.bias')
crt_net[ori_k] = state_dict[k]
items.remove(k)
crt_net[f'model.1.sub.{nb}.weight'] = state_dict['conv_body.weight']
crt_net[f'model.1.sub.{nb}.bias'] = state_dict['conv_body.bias']
crt_net['model.3.weight'] = state_dict['conv_up1.weight']
crt_net['model.3.bias'] = state_dict['conv_up1.bias']
crt_net['model.6.weight'] = state_dict['conv_up2.weight']
crt_net['model.6.bias'] = state_dict['conv_up2.bias']
if 'conv_up3.weight' in state_dict:
# modification supporting: https://github.com/ai-forever/Real-ESRGAN/blob/main/RealESRGAN/rrdbnet_arch.py
re8x = 3
crt_net['model.9.weight'] = state_dict['conv_up3.weight']
crt_net['model.9.bias'] = state_dict['conv_up3.bias']
crt_net[f'model.{8+re8x}.weight'] = state_dict['conv_hr.weight']
crt_net[f'model.{8+re8x}.bias'] = state_dict['conv_hr.bias']
crt_net[f'model.{10+re8x}.weight'] = state_dict['conv_last.weight']
crt_net[f'model.{10+re8x}.bias'] = state_dict['conv_last.bias']
state_dict = crt_net
return state_dict
def infer_params(state_dict):
# this code is copied from https://github.com/victorca25/iNNfer
scale2x = 0
scalemin = 6
n_uplayer = 0
plus = False
for block in list(state_dict):
parts = block.split(".")
n_parts = len(parts)
if n_parts == 5 and parts[2] == "sub":
nb = int(parts[3])
elif n_parts == 3:
part_num = int(parts[1])
if (part_num > scalemin
and parts[0] == "model"
and parts[2] == "weight"):
scale2x += 1
if part_num > n_uplayer:
n_uplayer = part_num
out_nc = state_dict[block].shape[0]
if not plus and "conv1x1" in block:
plus = True
nf = state_dict["model.0.weight"].shape[0]
in_nc = state_dict["model.0.weight"].shape[1]
out_nc = out_nc
scale = 2 ** scale2x
return in_nc, out_nc, nf, nb, plus, scale
from modules.upscaler_utils import upscale_with_model
class UpscalerESRGAN(Upscaler):
@ -143,12 +29,11 @@ class UpscalerESRGAN(Upscaler):
def do_upscale(self, img, selected_model):
try:
model = self.load_model(selected_model)
except Exception as e:
print(f"Unable to load ESRGAN model {selected_model}: {e}", file=sys.stderr)
except Exception:
errors.report(f"Unable to load ESRGAN model {selected_model}", exc_info=True)
return img
model.to(devices.device_esrgan)
img = esrgan_upscale(model, img)
return img
return esrgan_upscale(model, img)
def load_model(self, path: str):
if path.startswith("http"):
@ -161,69 +46,17 @@ class UpscalerESRGAN(Upscaler):
else:
filename = path
state_dict = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None)
if "params_ema" in state_dict:
state_dict = state_dict["params_ema"]
elif "params" in state_dict:
state_dict = state_dict["params"]
num_conv = 16 if "realesr-animevideov3" in filename else 32
model = arch.SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=num_conv, upscale=4, act_type='prelu')
model.load_state_dict(state_dict)
model.eval()
return model
if "body.0.rdb1.conv1.weight" in state_dict and "conv_first.weight" in state_dict:
nb = 6 if "RealESRGAN_x4plus_anime_6B" in filename else 23
state_dict = resrgan2normal(state_dict, nb)
elif "conv_first.weight" in state_dict:
state_dict = mod2normal(state_dict)
elif "model.0.weight" not in state_dict:
raise Exception("The file is not a recognized ESRGAN model.")
in_nc, out_nc, nf, nb, plus, mscale = infer_params(state_dict)
model = arch.RRDBNet(in_nc=in_nc, out_nc=out_nc, nf=nf, nb=nb, upscale=mscale, plus=plus)
model.load_state_dict(state_dict)
model.eval()
return model
def upscale_without_tiling(model, img):
img = np.array(img)
img = img[:, :, ::-1]
img = np.ascontiguousarray(np.transpose(img, (2, 0, 1))) / 255
img = torch.from_numpy(img).float()
img = img.unsqueeze(0).to(devices.device_esrgan)
with torch.no_grad():
output = model(img)
output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
output = 255. * np.moveaxis(output, 0, 2)
output = output.astype(np.uint8)
output = output[:, :, ::-1]
return Image.fromarray(output, 'RGB')
return modelloader.load_spandrel_model(
filename,
device=('cpu' if devices.device_esrgan.type == 'mps' else None),
expected_architecture='ESRGAN',
)
def esrgan_upscale(model, img):
if opts.ESRGAN_tile == 0:
return upscale_without_tiling(model, img)
grid = images.split_grid(img, opts.ESRGAN_tile, opts.ESRGAN_tile, opts.ESRGAN_tile_overlap)
newtiles = []
scale_factor = 1
for y, h, row in grid.tiles:
newrow = []
for tiledata in row:
x, w, tile = tiledata
output = upscale_without_tiling(model, tile)
scale_factor = output.width // tile.width
newrow.append([x * scale_factor, w * scale_factor, output])
newtiles.append([y * scale_factor, h * scale_factor, newrow])
newgrid = images.Grid(newtiles, grid.tile_w * scale_factor, grid.tile_h * scale_factor, grid.image_w * scale_factor, grid.image_h * scale_factor, grid.overlap * scale_factor)
output = images.combine_grid(newgrid)
return output
return upscale_with_model(
model,
img,
tile_size=opts.ESRGAN_tile,
tile_overlap=opts.ESRGAN_tile_overlap,
)

465
modules/esrgan_model_arch.py
View File

@ -1,465 +0,0 @@
# this file is adapted from https://github.com/victorca25/iNNfer
from collections import OrderedDict
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
####################
# RRDBNet Generator
####################
class RRDBNet(nn.Module):
def __init__(self, in_nc, out_nc, nf, nb, nr=3, gc=32, upscale=4, norm_type=None,
act_type='leakyrelu', mode='CNA', upsample_mode='upconv', convtype='Conv2D',
finalact=None, gaussian_noise=False, plus=False):
super(RRDBNet, self).__init__()
n_upscale = int(math.log(upscale, 2))
if upscale == 3:
n_upscale = 1
self.resrgan_scale = 0
if in_nc % 16 == 0:
self.resrgan_scale = 1
elif in_nc != 4 and in_nc % 4 == 0:
self.resrgan_scale = 2
fea_conv = conv_block(in_nc, nf, kernel_size=3, norm_type=None, act_type=None, convtype=convtype)
rb_blocks = [RRDB(nf, nr, kernel_size=3, gc=32, stride=1, bias=1, pad_type='zero',
norm_type=norm_type, act_type=act_type, mode='CNA', convtype=convtype,
gaussian_noise=gaussian_noise, plus=plus) for _ in range(nb)]
LR_conv = conv_block(nf, nf, kernel_size=3, norm_type=norm_type, act_type=None, mode=mode, convtype=convtype)
if upsample_mode == 'upconv':
upsample_block = upconv_block
elif upsample_mode == 'pixelshuffle':
upsample_block = pixelshuffle_block
else:
raise NotImplementedError(f'upsample mode [{upsample_mode}] is not found')
if upscale == 3:
upsampler = upsample_block(nf, nf, 3, act_type=act_type, convtype=convtype)
else:
upsampler = [upsample_block(nf, nf, act_type=act_type, convtype=convtype) for _ in range(n_upscale)]
HR_conv0 = conv_block(nf, nf, kernel_size=3, norm_type=None, act_type=act_type, convtype=convtype)
HR_conv1 = conv_block(nf, out_nc, kernel_size=3, norm_type=None, act_type=None, convtype=convtype)
outact = act(finalact) if finalact else None
self.model = sequential(fea_conv, ShortcutBlock(sequential(*rb_blocks, LR_conv)),
*upsampler, HR_conv0, HR_conv1, outact)
def forward(self, x, outm=None):
if self.resrgan_scale == 1:
feat = pixel_unshuffle(x, scale=4)
elif self.resrgan_scale == 2:
feat = pixel_unshuffle(x, scale=2)
else:
feat = x
return self.model(feat)
class RRDB(nn.Module):
"""
Residual in Residual Dense Block
(ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks)
"""
def __init__(self, nf, nr=3, kernel_size=3, gc=32, stride=1, bias=1, pad_type='zero',
norm_type=None, act_type='leakyrelu', mode='CNA', convtype='Conv2D',
spectral_norm=False, gaussian_noise=False, plus=False):
super(RRDB, self).__init__()
# This is for backwards compatibility with existing models
if nr == 3:
self.RDB1 = ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
gaussian_noise=gaussian_noise, plus=plus)
self.RDB2 = ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
gaussian_noise=gaussian_noise, plus=plus)
self.RDB3 = ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
gaussian_noise=gaussian_noise, plus=plus)
else:
RDB_list = [ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
gaussian_noise=gaussian_noise, plus=plus) for _ in range(nr)]
self.RDBs = nn.Sequential(*RDB_list)
def forward(self, x):
if hasattr(self, 'RDB1'):
out = self.RDB1(x)
out = self.RDB2(out)
out = self.RDB3(out)
else:
out = self.RDBs(x)
return out * 0.2 + x
class ResidualDenseBlock_5C(nn.Module):
"""
Residual Dense Block
The core module of paper: (Residual Dense Network for Image Super-Resolution, CVPR 18)
Modified options that can be used:
- "Partial Convolution based Padding" arXiv:1811.11718
- "Spectral normalization" arXiv:1802.05957
- "ICASSP 2020 - ESRGAN+ : Further Improving ESRGAN" N. C.
{Rakotonirina} and A. {Rasoanaivo}
"""
def __init__(self, nf=64, kernel_size=3, gc=32, stride=1, bias=1, pad_type='zero',
norm_type=None, act_type='leakyrelu', mode='CNA', convtype='Conv2D',
spectral_norm=False, gaussian_noise=False, plus=False):
super(ResidualDenseBlock_5C, self).__init__()
self.noise = GaussianNoise() if gaussian_noise else None
self.conv1x1 = conv1x1(nf, gc) if plus else None
self.conv1 = conv_block(nf, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
spectral_norm=spectral_norm)
self.conv2 = conv_block(nf+gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
spectral_norm=spectral_norm)
self.conv3 = conv_block(nf+2*gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
spectral_norm=spectral_norm)
self.conv4 = conv_block(nf+3*gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
spectral_norm=spectral_norm)
if mode == 'CNA':
last_act = None
else:
last_act = act_type
self.conv5 = conv_block(nf+4*gc, nf, 3, stride, bias=bias, pad_type=pad_type,
norm_type=norm_type, act_type=last_act, mode=mode, convtype=convtype,
spectral_norm=spectral_norm)
def forward(self, x):
x1 = self.conv1(x)
x2 = self.conv2(torch.cat((x, x1), 1))
if self.conv1x1:
x2 = x2 + self.conv1x1(x)
x3 = self.conv3(torch.cat((x, x1, x2), 1))
x4 = self.conv4(torch.cat((x, x1, x2, x3), 1))
if self.conv1x1:
x4 = x4 + x2
x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
if self.noise:
return self.noise(x5.mul(0.2) + x)
else:
return x5 * 0.2 + x
####################
# ESRGANplus
####################
class GaussianNoise(nn.Module):
def __init__(self, sigma=0.1, is_relative_detach=False):
super().__init__()
self.sigma = sigma
self.is_relative_detach = is_relative_detach
self.noise = torch.tensor(0, dtype=torch.float)
def forward(self, x):
if self.training and self.sigma != 0:
self.noise = self.noise.to(x.device)
scale = self.sigma * x.detach() if self.is_relative_detach else self.sigma * x
sampled_noise = self.noise.repeat(*x.size()).normal_() * scale
x = x + sampled_noise
return x
def conv1x1(in_planes, out_planes, stride=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
####################
# SRVGGNetCompact
####################
class SRVGGNetCompact(nn.Module):
"""A compact VGG-style network structure for super-resolution.
This class is copied from https://github.com/xinntao/Real-ESRGAN
"""
def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu'):
super(SRVGGNetCompact, self).__init__()
self.num_in_ch = num_in_ch
self.num_out_ch = num_out_ch
self.num_feat = num_feat
self.num_conv = num_conv
self.upscale = upscale
self.act_type = act_type
self.body = nn.ModuleList()
# the first conv
self.body.append(nn.Conv2d(num_in_ch, num_feat, 3, 1, 1))
# the first activation
if act_type == 'relu':
activation = nn.ReLU(inplace=True)
elif act_type == 'prelu':
activation = nn.PReLU(num_parameters=num_feat)
elif act_type == 'leakyrelu':
activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
self.body.append(activation)
# the body structure
for _ in range(num_conv):
self.body.append(nn.Conv2d(num_feat, num_feat, 3, 1, 1))
# activation
if act_type == 'relu':
activation = nn.ReLU(inplace=True)
elif act_type == 'prelu':
activation = nn.PReLU(num_parameters=num_feat)
elif act_type == 'leakyrelu':
activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
self.body.append(activation)
# the last conv
self.body.append(nn.Conv2d(num_feat, num_out_ch * upscale * upscale, 3, 1, 1))
# upsample
self.upsampler = nn.PixelShuffle(upscale)
def forward(self, x):
out = x
for i in range(0, len(self.body)):
out = self.body[i](out)
out = self.upsampler(out)
# add the nearest upsampled image, so that the network learns the residual
base = F.interpolate(x, scale_factor=self.upscale, mode='nearest')
out += base
return out
####################
# Upsampler
####################
class Upsample(nn.Module):
r"""Upsamples a given multi-channel 1D (temporal), 2D (spatial) or 3D (volumetric) data.
The input data is assumed to be of the form
`minibatch x channels x [optional depth] x [optional height] x width`.
"""
def __init__(self, size=None, scale_factor=None, mode="nearest", align_corners=None):
super(Upsample, self).__init__()
if isinstance(scale_factor, tuple):
self.scale_factor = tuple(float(factor) for factor in scale_factor)
else:
self.scale_factor = float(scale_factor) if scale_factor else None
self.mode = mode
self.size = size
self.align_corners = align_corners
def forward(self, x):
return nn.functional.interpolate(x, size=self.size, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners)
def extra_repr(self):
if self.scale_factor is not None:
info = f'scale_factor={self.scale_factor}'
else:
info = f'size={self.size}'
info += f', mode={self.mode}'
return info
def pixel_unshuffle(x, scale):
""" Pixel unshuffle.
Args:
x (Tensor): Input feature with shape (b, c, hh, hw).
scale (int): Downsample ratio.
Returns:
Tensor: the pixel unshuffled feature.
"""
b, c, hh, hw = x.size()
out_channel = c * (scale**2)
assert hh % scale == 0 and hw % scale == 0
h = hh // scale
w = hw // scale
x_view = x.view(b, c, h, scale, w, scale)
return x_view.permute(0, 1, 3, 5, 2, 4).reshape(b, out_channel, h, w)
def pixelshuffle_block(in_nc, out_nc, upscale_factor=2, kernel_size=3, stride=1, bias=True,
pad_type='zero', norm_type=None, act_type='relu', convtype='Conv2D'):
"""
Pixel shuffle layer
(Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional
Neural Network, CVPR17)
"""
conv = conv_block(in_nc, out_nc * (upscale_factor ** 2), kernel_size, stride, bias=bias,
pad_type=pad_type, norm_type=None, act_type=None, convtype=convtype)
pixel_shuffle = nn.PixelShuffle(upscale_factor)
n = norm(norm_type, out_nc) if norm_type else None
a = act(act_type) if act_type else None
return sequential(conv, pixel_shuffle, n, a)
def upconv_block(in_nc, out_nc, upscale_factor=2, kernel_size=3, stride=1, bias=True,
pad_type='zero', norm_type=None, act_type='relu', mode='nearest', convtype='Conv2D'):
""" Upconv layer """
upscale_factor = (1, upscale_factor, upscale_factor) if convtype == 'Conv3D' else upscale_factor
upsample = Upsample(scale_factor=upscale_factor, mode=mode)
conv = conv_block(in_nc, out_nc, kernel_size, stride, bias=bias,
pad_type=pad_type, norm_type=norm_type, act_type=act_type, convtype=convtype)
return sequential(upsample, conv)
####################
# Basic blocks
####################
def make_layer(basic_block, num_basic_block, **kwarg):
"""Make layers by stacking the same blocks.
Args:
basic_block (nn.module): nn.module class for basic block. (block)
num_basic_block (int): number of blocks. (n_layers)
Returns:
nn.Sequential: Stacked blocks in nn.Sequential.
"""
layers = []
for _ in range(num_basic_block):
layers.append(basic_block(**kwarg))
return nn.Sequential(*layers)
def act(act_type, inplace=True, neg_slope=0.2, n_prelu=1, beta=1.0):
""" activation helper """
act_type = act_type.lower()
if act_type == 'relu':
layer = nn.ReLU(inplace)
elif act_type in ('leakyrelu', 'lrelu'):
layer = nn.LeakyReLU(neg_slope, inplace)
elif act_type == 'prelu':
layer = nn.PReLU(num_parameters=n_prelu, init=neg_slope)
elif act_type == 'tanh': # [-1, 1] range output
layer = nn.Tanh()
elif act_type == 'sigmoid': # [0, 1] range output
layer = nn.Sigmoid()
else:
raise NotImplementedError(f'activation layer [{act_type}] is not found')
return layer
class Identity(nn.Module):
def __init__(self, *kwargs):
super(Identity, self).__init__()
def forward(self, x, *kwargs):
return x
def norm(norm_type, nc):
""" Return a normalization layer """
norm_type = norm_type.lower()
if norm_type == 'batch':
layer = nn.BatchNorm2d(nc, affine=True)
elif norm_type == 'instance':
layer = nn.InstanceNorm2d(nc, affine=False)
elif norm_type == 'none':
def norm_layer(x): return Identity()
else:
raise NotImplementedError(f'normalization layer [{norm_type}] is not found')
return layer
def pad(pad_type, padding):
""" padding layer helper """
pad_type = pad_type.lower()
if padding == 0:
return None
if pad_type == 'reflect':
layer = nn.ReflectionPad2d(padding)
elif pad_type == 'replicate':
layer = nn.ReplicationPad2d(padding)
elif pad_type == 'zero':
layer = nn.ZeroPad2d(padding)
else:
raise NotImplementedError(f'padding layer [{pad_type}] is not implemented')
return layer
def get_valid_padding(kernel_size, dilation):
kernel_size = kernel_size + (kernel_size - 1) * (dilation - 1)
padding = (kernel_size - 1) // 2
return padding
class ShortcutBlock(nn.Module):
""" Elementwise sum the output of a submodule to its input """
def __init__(self, submodule):
super(ShortcutBlock, self).__init__()
self.sub = submodule
def forward(self, x):
output = x + self.sub(x)
return output
def __repr__(self):
return 'Identity + \n|' + self.sub.__repr__().replace('\n', '\n|')
def sequential(*args):
""" Flatten Sequential. It unwraps nn.Sequential. """
if len(args) == 1:
if isinstance(args[0], OrderedDict):
raise NotImplementedError('sequential does not support OrderedDict input.')
return args[0] # No sequential is needed.
modules = []
for module in args:
if isinstance(module, nn.Sequential):
for submodule in module.children():
modules.append(submodule)
elif isinstance(module, nn.Module):
modules.append(module)
return nn.Sequential(*modules)
def conv_block(in_nc, out_nc, kernel_size, stride=1, dilation=1, groups=1, bias=True,
pad_type='zero', norm_type=None, act_type='relu', mode='CNA', convtype='Conv2D',
spectral_norm=False):
""" Conv layer with padding, normalization, activation """
assert mode in ['CNA', 'NAC', 'CNAC'], f'Wrong conv mode [{mode}]'
padding = get_valid_padding(kernel_size, dilation)
p = pad(pad_type, padding) if pad_type and pad_type != 'zero' else None
padding = padding if pad_type == 'zero' else 0
if convtype=='PartialConv2D':
from torchvision.ops import PartialConv2d # this is definitely not going to work, but PartialConv2d doesn't work anyway and this shuts up static analyzer
c = PartialConv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
dilation=dilation, bias=bias, groups=groups)
elif convtype=='DeformConv2D':
from torchvision.ops import DeformConv2d # not tested
c = DeformConv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
dilation=dilation, bias=bias, groups=groups)
elif convtype=='Conv3D':
c = nn.Conv3d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
dilation=dilation, bias=bias, groups=groups)
else:
c = nn.Conv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
dilation=dilation, bias=bias, groups=groups)
if spectral_norm:
c = nn.utils.spectral_norm(c)
a = act(act_type) if act_type else None
if 'CNA' in mode:
n = norm(norm_type, out_nc) if norm_type else None
return sequential(p, c, n, a)
elif mode == 'NAC':
if norm_type is None and act_type is not None:
a = act(act_type, inplace=False)
n = norm(norm_type, in_nc) if norm_type else None
return sequential(n, a, p, c)

16
modules/extensions.py
View File

@ -32,11 +32,12 @@ class ExtensionMetadata:
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.config.read()` will quietly swallow OSErrors (which FileNotFoundError is),
# so no need to check whether the file exists beforehand.
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()
@ -223,13 +224,16 @@ def list_extensions():
# check for requirements
for extension in extensions:
if not extension.enabled:
continue
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:
if not required_extension.enabled:
errors.report(f'Extension "{extension.name}" requires "{required_extension.name}" which is disabled.', exc_info=False)
continue

5
modules/extra_networks.py
View File

@ -206,7 +206,7 @@ def parse_prompts(prompts):
return res, extra_data
def get_user_metadata(filename):
def get_user_metadata(filename, lister=None):
if filename is None:
return {}
@ -215,7 +215,8 @@ def get_user_metadata(filename):
metadata = {}
try:
if os.path.isfile(metadata_filename):
exists = lister.exists(metadata_filename) if lister else os.path.exists(metadata_filename)
if exists:
with open(metadata_filename, "r", encoding="utf8") as file:
metadata = json.load(file)
except Exception as e:

180
modules/face_restoration_utils.py Normal file
View File

@ -0,0 +1,180 @@
from __future__ import annotations
import logging
import os
from functools import cached_property
from typing import TYPE_CHECKING, Callable
import cv2
import numpy as np
import torch
from modules import devices, errors, face_restoration, shared
if TYPE_CHECKING:
from facexlib.utils.face_restoration_helper import FaceRestoreHelper
logger = logging.getLogger(__name__)
def bgr_image_to_rgb_tensor(img: np.ndarray) -> torch.Tensor:
"""Convert a BGR NumPy image in [0..1] range to a PyTorch RGB float32 tensor."""
assert img.shape[2] == 3, "image must be RGB"
if img.dtype == "float64":
img = img.astype("float32")
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
return torch.from_numpy(img.transpose(2, 0, 1)).float()
def rgb_tensor_to_bgr_image(tensor: torch.Tensor, *, min_max=(0.0, 1.0)) -> np.ndarray:
"""
Convert a PyTorch RGB tensor in range `min_max` to a BGR NumPy image in [0..1] range.
"""
tensor = tensor.squeeze(0).float().detach().cpu().clamp_(*min_max)
tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0])
assert tensor.dim() == 3, "tensor must be RGB"
img_np = tensor.numpy().transpose(1, 2, 0)
if img_np.shape[2] == 1: # gray image, no RGB/BGR required
return np.squeeze(img_np, axis=2)
return cv2.cvtColor(img_np, cv2.COLOR_BGR2RGB)
def create_face_helper(device) -> FaceRestoreHelper:
from facexlib.detection import retinaface
from facexlib.utils.face_restoration_helper import FaceRestoreHelper
if hasattr(retinaface, 'device'):
retinaface.device = device
return FaceRestoreHelper(
upscale_factor=1,
face_size=512,
crop_ratio=(1, 1),
det_model='retinaface_resnet50',
save_ext='png',
use_parse=True,
device=device,
)
def restore_with_face_helper(
np_image: np.ndarray,
face_helper: FaceRestoreHelper,
restore_face: Callable[[torch.Tensor], torch.Tensor],
) -> np.ndarray:
"""
Find faces in the image using face_helper, restore them using restore_face, and paste them back into the image.
`restore_face` should take a cropped face image and return a restored face image.
"""
from torchvision.transforms.functional import normalize
np_image = np_image[:, :, ::-1]
original_resolution = np_image.shape[0:2]
try:
logger.debug("Detecting faces...")
face_helper.clean_all()
face_helper.read_image(np_image)
face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
face_helper.align_warp_face()
logger.debug("Found %d faces, restoring", len(face_helper.cropped_faces))
for cropped_face in face_helper.cropped_faces:
cropped_face_t = bgr_image_to_rgb_tensor(cropped_face / 255.0)
normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
cropped_face_t = cropped_face_t.unsqueeze(0).to(devices.device_codeformer)
try:
with torch.no_grad():
cropped_face_t = restore_face(cropped_face_t)
devices.torch_gc()
except Exception:
errors.report('Failed face-restoration inference', exc_info=True)
restored_face = rgb_tensor_to_bgr_image(cropped_face_t, min_max=(-1, 1))
restored_face = (restored_face * 255.0).astype('uint8')
face_helper.add_restored_face(restored_face)
logger.debug("Merging restored faces into image")
face_helper.get_inverse_affine(None)
img = face_helper.paste_faces_to_input_image()
img = img[:, :, ::-1]
if original_resolution != img.shape[0:2]:
img = cv2.resize(
img,
(0, 0),
fx=original_resolution[1] / img.shape[1],
fy=original_resolution[0] / img.shape[0],
interpolation=cv2.INTER_LINEAR,
)
logger.debug("Face restoration complete")
finally:
face_helper.clean_all()
return img
class CommonFaceRestoration(face_restoration.FaceRestoration):
net: torch.Module | None
model_url: str
model_download_name: str
def __init__(self, model_path: str):
super().__init__()
self.net = None
self.model_path = model_path
os.makedirs(model_path, exist_ok=True)
@cached_property
def face_helper(self) -> FaceRestoreHelper:
return create_face_helper(self.get_device())
def send_model_to(self, device):
if self.net:
logger.debug("Sending %s to %s", self.net, device)
self.net.to(device)
if self.face_helper:
logger.debug("Sending face helper to %s", device)
self.face_helper.face_det.to(device)
self.face_helper.face_parse.to(device)
def get_device(self):
raise NotImplementedError("get_device must be implemented by subclasses")
def load_net(self) -> torch.Module:
raise NotImplementedError("load_net must be implemented by subclasses")
def restore_with_helper(
self,
np_image: np.ndarray,
restore_face: Callable[[torch.Tensor], torch.Tensor],
) -> np.ndarray:
try:
if self.net is None:
self.net = self.load_net()
except Exception:
logger.warning("Unable to load face-restoration model", exc_info=True)
return np_image
try:
self.send_model_to(self.get_device())
return restore_with_face_helper(np_image, self.face_helper, restore_face)
finally:
if shared.opts.face_restoration_unload:
self.send_model_to(devices.cpu)
def patch_facexlib(dirname: str) -> None:
import facexlib.detection
import facexlib.parsing
det_facex_load_file_from_url = facexlib.detection.load_file_from_url
par_facex_load_file_from_url = facexlib.parsing.load_file_from_url
def update_kwargs(kwargs):
return dict(kwargs, save_dir=dirname, model_dir=None)
def facex_load_file_from_url(**kwargs):
return det_facex_load_file_from_url(**update_kwargs(kwargs))
def facex_load_file_from_url2(**kwargs):
return par_facex_load_file_from_url(**update_kwargs(kwargs))
facexlib.detection.load_file_from_url = facex_load_file_from_url
facexlib.parsing.load_file_from_url = facex_load_file_from_url2

154
modules/gfpgan_model.py
View File

@ -1,125 +1,71 @@
from __future__ import annotations
import logging
import os
import facexlib
import gfpgan
import torch
import modules.face_restoration
from modules import paths, shared, devices, modelloader, errors
from modules import (
devices,
errors,
face_restoration,
face_restoration_utils,
modelloader,
shared,
)
model_dir = "GFPGAN"
user_path = None
model_path = os.path.join(paths.models_path, model_dir)
model_file_path = None
logger = logging.getLogger(__name__)
model_url = "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth"
have_gfpgan = False
loaded_gfpgan_model = None
model_download_name = "GFPGANv1.4.pth"
gfpgan_face_restorer: face_restoration.FaceRestoration | None = 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
class FaceRestorerGFPGAN(face_restoration_utils.CommonFaceRestoration):
def name(self):
return "GFPGAN"
if gfpgan_constructor is None:
return None
def get_device(self):
return devices.device_gfpgan
models = modelloader.load_models(model_path, model_url, user_path, ext_filter=['.pth'])
def load_net(self) -> torch.Module:
for model_path in modelloader.load_models(
model_path=self.model_path,
model_url=model_url,
command_path=self.model_path,
download_name=model_download_name,
ext_filter=['.pth'],
):
if 'GFPGAN' in os.path.basename(model_path):
model = modelloader.load_spandrel_model(
model_path,
device=self.get_device(),
expected_architecture='GFPGAN',
).model
model.different_w = True # see https://github.com/chaiNNer-org/spandrel/pull/81
return model
raise ValueError("No GFPGAN model found")
if len(models) == 1 and models[0].startswith("http"):
model_file = models[0]
elif len(models) != 0:
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
def restore(self, np_image):
def restore_face(cropped_face_t):
assert self.net is not None
return self.net(cropped_face_t, return_rgb=False)[0]
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
return model
def send_model_to(model, device):
model.gfpgan.to(device)
model.face_helper.face_det.to(device)
model.face_helper.face_parse.to(device)
return self.restore_with_helper(np_image, restore_face)
def gfpgan_fix_faces(np_image):
model = gfpgann()
if model is None:
return np_image
send_model_to(model, devices.device_gfpgan)
np_image_bgr = np_image[:, :, ::-1]
cropped_faces, restored_faces, gfpgan_output_bgr = model.enhance(np_image_bgr, has_aligned=False, only_center_face=False, paste_back=True)
np_image = gfpgan_output_bgr[:, :, ::-1]
model.face_helper.clean_all()
if shared.opts.face_restoration_unload:
send_model_to(model, devices.cpu)
if gfpgan_face_restorer:
return gfpgan_face_restorer.restore(np_image)
logger.warning("GFPGAN face restorer not set up")
return np_image
gfpgan_constructor = None
def setup_model(dirname: str) -> None:
global gfpgan_face_restorer
def setup_model(dirname):
try:
os.makedirs(model_path, exist_ok=True)
from gfpgan import GFPGANer
from facexlib import detection, parsing # noqa: F401
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_file_path))
def facex_load_file_from_url(**kwargs):
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=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
facexlib.parsing.load_file_from_url = facex_load_file_from_url2
user_path = dirname
have_gfpgan = True
gfpgan_constructor = GFPGANer
class FaceRestorerGFPGAN(modules.face_restoration.FaceRestoration):
def name(self):
return "GFPGAN"
def restore(self, np_image):
return gfpgan_fix_faces(np_image)
shared.face_restorers.append(FaceRestorerGFPGAN())
face_restoration_utils.patch_facexlib(dirname)
gfpgan_face_restorer = FaceRestorerGFPGAN(model_path=dirname)
shared.face_restorers.append(gfpgan_face_restorer)
except Exception:
errors.report("Error setting up GFPGAN", exc_info=True)

5
modules/hashes.py
View File

@ -21,7 +21,10 @@ def calculate_sha256(filename):
def sha256_from_cache(filename, title, use_addnet_hash=False):
hashes = cache("hashes-addnet") if use_addnet_hash else cache("hashes")
ondisk_mtime = os.path.getmtime(filename)
try:
ondisk_mtime = os.path.getmtime(filename)
except FileNotFoundError:
return None
if title not in hashes:
return None

43
modules/hat_model.py Normal file
View File

@ -0,0 +1,43 @@
import os
import sys
from modules import modelloader, devices
from modules.shared import opts
from modules.upscaler import Upscaler, UpscalerData
from modules.upscaler_utils import upscale_with_model
class UpscalerHAT(Upscaler):
def __init__(self, dirname):
self.name = "HAT"
self.scalers = []
self.user_path = dirname
super().__init__()
for file in self.find_models(ext_filter=[".pt", ".pth"]):
name = modelloader.friendly_name(file)
scale = 4 # TODO: scale might not be 4, but we can't know without loading the model
scaler_data = UpscalerData(name, file, upscaler=self, scale=scale)
self.scalers.append(scaler_data)
def do_upscale(self, img, selected_model):
try:
model = self.load_model(selected_model)
except Exception as e:
print(f"Unable to load HAT model {selected_model}: {e}", file=sys.stderr)
return img
model.to(devices.device_esrgan) # TODO: should probably be device_hat
return upscale_with_model(
model,
img,
tile_size=opts.ESRGAN_tile, # TODO: should probably be HAT_tile
tile_overlap=opts.ESRGAN_tile_overlap, # TODO: should probably be HAT_tile_overlap
)
def load_model(self, path: str):
if not os.path.isfile(path):
raise FileNotFoundError(f"Model file {path} not found")
return modelloader.load_spandrel_model(
path,
device=devices.device_esrgan, # TODO: should probably be device_hat
expected_architecture='HAT',
)

16
modules/images.py
View File

@ -61,12 +61,17 @@ def image_grid(imgs, batch_size=1, rows=None):
return grid
Grid = namedtuple("Grid", ["tiles", "tile_w", "tile_h", "image_w", "image_h", "overlap"])
class Grid(namedtuple("_Grid", ["tiles", "tile_w", "tile_h", "image_w", "image_h", "overlap"])):
@property
def tile_count(self) -> int:
"""
The total number of tiles in the grid.
"""
return sum(len(row[2]) for row in self.tiles)
def split_grid(image, tile_w=512, tile_h=512, overlap=64):
w = image.width
h = image.height
def split_grid(image: Image.Image, tile_w: int = 512, tile_h: int = 512, overlap: int = 64) -> Grid:
w, h = image.size
non_overlap_width = tile_w - overlap
non_overlap_height = tile_h - overlap
@ -316,7 +321,7 @@ def resize_image(resize_mode, im, width, height, upscaler_name=None):
return res
invalid_filename_chars = '<>:"/\\|?*\n\r\t'
invalid_filename_chars = '#<>:"/\\|?*\n\r\t'
invalid_filename_prefix = ' '
invalid_filename_postfix = ' .'
re_nonletters = re.compile(r'[\s' + string.punctuation + ']+')
@ -791,3 +796,4 @@ def flatten(img, bgcolor):
img = background
return img.convert('RGB')

7
modules/img2img.py
View File

@ -7,7 +7,7 @@ from PIL import Image, ImageOps, ImageFilter, ImageEnhance, UnidentifiedImageErr
import gradio as gr
from modules import images as imgutil
from modules.generation_parameters_copypaste import create_override_settings_dict, parse_generation_parameters
from modules.infotext_utils 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
@ -51,7 +51,7 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=Fal
if state.skipped:
state.skipped = False
if state.interrupted:
if state.interrupted or state.stopping_generation:
break
try:
@ -222,9 +222,6 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
if shared.opts.enable_console_prompts:
print(f"\nimg2img: {prompt}", file=shared.progress_print_out)
if mask:
p.extra_generation_params["Mask blur"] = mask_blur
with closing(p):
if is_batch:
assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"

127
modules/generation_parameters_copypaste.py → modules/infotext_utils.py
View File

@ -4,12 +4,15 @@ import io
import json
import os
import re
import sys
import gradio as gr
from modules.paths import data_path
from modules import shared, ui_tempdir, script_callbacks, processing
from modules import shared, ui_tempdir, script_callbacks, processing, infotext_versions
from PIL import Image
sys.modules['modules.generation_parameters_copypaste'] = sys.modules[__name__] # alias for old name
re_param_code = r'\s*(\w[\w \-/]+):\s*("(?:\\.|[^\\"])+"|[^,]*)(?:,|$)'
re_param = re.compile(re_param_code)
re_imagesize = re.compile(r"^(\d+)x(\d+)$")
@ -28,6 +31,19 @@ class ParamBinding:
self.paste_field_names = paste_field_names or []
class PasteField(tuple):
def __new__(cls, component, target, *, api=None):
return super().__new__(cls, (component, target))
def __init__(self, component, target, *, api=None):
super().__init__()
self.api = api
self.component = component
self.label = target if isinstance(target, str) else None
self.function = target if callable(target) else None
paste_fields: dict[str, dict] = {}
registered_param_bindings: list[ParamBinding] = []
@ -84,6 +100,12 @@ def image_from_url_text(filedata):
def add_paste_fields(tabname, init_img, fields, override_settings_component=None):
if fields:
for i in range(len(fields)):
if not isinstance(fields[i], PasteField):
fields[i] = PasteField(*fields[i])
paste_fields[tabname] = {"init_img": init_img, "fields": fields, "override_settings_component": override_settings_component}
# backwards compatibility for existing extensions
@ -208,7 +230,7 @@ def restore_old_hires_fix_params(res):
res['Hires resize-2'] = height
def parse_generation_parameters(x: str):
def parse_generation_parameters(x: str, skip_fields: list[str] | None = None):
"""parses generation parameters string, the one you see in text field under the picture in UI:
```
girl with an artist's beret, determined, blue eyes, desert scene, computer monitors, heavy makeup, by Alphonse Mucha and Charlie Bowater, ((eyeshadow)), (coquettish), detailed, intricate
@ -218,6 +240,8 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
returns a dict with field values
"""
if skip_fields is None:
skip_fields = shared.opts.infotext_skip_pasting
res = {}
@ -290,6 +314,18 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
if "Hires negative prompt" not in res:
res["Hires negative prompt"] = ""
if "Mask mode" not in res:
res["Mask mode"] = "Inpaint masked"
if "Masked content" not in res:
res["Masked content"] = 'original'
if "Inpaint area" not in res:
res["Inpaint area"] = "Whole picture"
if "Masked area padding" not in res:
res["Masked area padding"] = 32
restore_old_hires_fix_params(res)
# Missing RNG means the default was set, which is GPU RNG
@ -314,8 +350,19 @@ 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}
if "FP8 weight" not in res:
res["FP8 weight"] = "Disable"
if "Cache FP16 weight for LoRA" not in res and res["FP8 weight"] != "Disable":
res["Cache FP16 weight for LoRA"] = False
if "Emphasis" not in res:
res["Emphasis"] = "Original"
infotext_versions.backcompat(res)
for key in skip_fields:
res.pop(key, None)
return res
@ -365,13 +412,57 @@ def create_override_settings_dict(text_pairs):
return res
def get_override_settings(params, *, skip_fields=None):
"""Returns a list of settings overrides from the infotext parameters dictionary.
This function checks the `params` dictionary for any keys that correspond to settings in `shared.opts` and returns
a list of tuples containing the parameter name, setting name, and new value cast to correct type.
It checks for conditions before adding an override:
- ignores settings that match the current value
- ignores parameter keys present in skip_fields argument.
Example input:
{"Clip skip": "2"}
Example output:
[("Clip skip", "CLIP_stop_at_last_layers", 2)]
"""
res = []
mapping = [(info.infotext, k) for k, info in shared.opts.data_labels.items() if info.infotext]
for param_name, setting_name in mapping + infotext_to_setting_name_mapping:
if param_name in (skip_fields or {}):
continue
v = params.get(param_name, None)
if v is None:
continue
if setting_name == "sd_model_checkpoint" and shared.opts.disable_weights_auto_swap:
continue
v = shared.opts.cast_value(setting_name, v)
current_value = getattr(shared.opts, setting_name, None)
if v == current_value:
continue
res.append((param_name, setting_name, v))
return res
def connect_paste(button, paste_fields, input_comp, override_settings_component, tabname):
def paste_func(prompt):
if not prompt and not shared.cmd_opts.hide_ui_dir_config:
filename = os.path.join(data_path, "params.txt")
if os.path.exists(filename):
try:
with open(filename, "r", encoding="utf8") as file:
prompt = file.read()
except OSError:
pass
params = parse_generation_parameters(prompt)
script_callbacks.infotext_pasted_callback(prompt, params)
@ -393,6 +484,8 @@ def connect_paste(button, paste_fields, input_comp, override_settings_component,
if valtype == bool and v == "False":
val = False
elif valtype == int:
val = float(v)
else:
val = valtype(v)
@ -406,29 +499,9 @@ def connect_paste(button, paste_fields, input_comp, override_settings_component,
already_handled_fields = {key: 1 for _, key in paste_fields}
def paste_settings(params):
vals = {}
vals = get_override_settings(params, skip_fields=already_handled_fields)
mapping = [(info.infotext, k) for k, info in shared.opts.data_labels.items() if info.infotext]
for param_name, setting_name in mapping + infotext_to_setting_name_mapping:
if param_name in already_handled_fields:
continue
v = params.get(param_name, None)
if v is None:
continue
if setting_name == "sd_model_checkpoint" and shared.opts.disable_weights_auto_swap:
continue
v = shared.opts.cast_value(setting_name, v)
current_value = getattr(shared.opts, setting_name, None)
if v == current_value:
continue
vals[param_name] = v
vals_pairs = [f"{k}: {v}" for k, v in vals.items()]
vals_pairs = [f"{infotext_text}: {value}" for infotext_text, setting_name, value in vals]
return gr.Dropdown.update(value=vals_pairs, choices=vals_pairs, visible=bool(vals_pairs))

42
modules/infotext_versions.py Normal file
View File

@ -0,0 +1,42 @@
from modules import shared
from packaging import version
import re
v160 = version.parse("1.6.0")
v170_tsnr = version.parse("v1.7.0-225")
def parse_version(text):
if text is None:
return None
m = re.match(r'([^-]+-[^-]+)-.*', text)
if m:
text = m.group(1)
try:
return version.parse(text)
except Exception:
return None
def backcompat(d):
"""Checks infotext Version field, and enables backwards compatibility options according to it."""
if not shared.opts.auto_backcompat:
return
ver = parse_version(d.get("Version"))
if ver is None:
return
if ver < v160 and '[' in d.get('Prompt', ''):
d["Old prompt editing timelines"] = True
if ver < v160 and d.get('Sampler', '') in ('DDIM', 'PLMS'):
d["Pad conds v0"] = True
if ver < v170_tsnr:
d["Downcast alphas_cumprod"] = True

8
modules/initialize.py
View File

@ -1,5 +1,6 @@
import importlib
import logging
import os
import sys
import warnings
from threading import Thread
@ -18,6 +19,7 @@ def imports():
warnings.filterwarnings(action="ignore", category=DeprecationWarning, module="pytorch_lightning")
warnings.filterwarnings(action="ignore", category=UserWarning, module="torchvision")
os.environ.setdefault('GRADIO_ANALYTICS_ENABLED', 'False')
import gradio # noqa: F401
startup_timer.record("import gradio")
@ -54,9 +56,6 @@ def initialize():
initialize_util.configure_sigint_handler()
initialize_util.configure_opts_onchange()
from modules import modelloader
modelloader.cleanup_models()
from modules import sd_models
sd_models.setup_model()
startup_timer.record("setup SD model")
@ -143,13 +142,14 @@ def initialize_rest(*, reload_script_modules=False):
its optimization may be None because the list of optimizaers has neet been filled
by that time, so we apply optimization again.
"""
from modules import devices
devices.torch_npu_set_device()
shared.sd_model # noqa: B018
if sd_hijack.current_optimizer is None:
sd_hijack.apply_optimizations()
from modules import devices
devices.first_time_calculation()
if not shared.cmd_opts.skip_load_model_at_start:
Thread(target=load_model).start()

2
modules/initialize_util.py
View File

@ -177,6 +177,8 @@ def configure_opts_onchange():
shared.opts.onchange("temp_dir", ui_tempdir.on_tmpdir_changed)
shared.opts.onchange("gradio_theme", shared.reload_gradio_theme)
shared.opts.onchange("cross_attention_optimization", wrap_queued_call(lambda: sd_hijack.model_hijack.redo_hijack(shared.sd_model)), call=False)
shared.opts.onchange("fp8_storage", wrap_queued_call(lambda: sd_models.reload_model_weights()), call=False)
shared.opts.onchange("cache_fp16_weight", wrap_queued_call(lambda: sd_models.reload_model_weights(forced_reload=True)), call=False)
startup_timer.record("opts onchange")

6
modules/interrogate.py
View File

@ -10,14 +10,14 @@ import torch.hub
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode
from modules import devices, paths, shared, lowvram, modelloader, errors
from modules import devices, paths, shared, lowvram, modelloader, errors, torch_utils
blip_image_eval_size = 384
clip_model_name = 'ViT-L/14'
Category = namedtuple("Category", ["name", "topn", "items"])
re_topn = re.compile(r"\.top(\d+)\.")
re_topn = re.compile(r"\.top(\d+)$")
def category_types():
return [f.stem for f in Path(shared.interrogator.content_dir).glob('*.txt')]
@ -131,7 +131,7 @@ class InterrogateModels:
self.clip_model = self.clip_model.to(devices.device_interrogate)
self.dtype = next(self.clip_model.parameters()).dtype
self.dtype = torch_utils.get_param(self.clip_model).dtype
def send_clip_to_ram(self):
if not shared.opts.interrogate_keep_models_in_memory:

39
modules/launch_utils.py
View File

@ -27,8 +27,7 @@ dir_repos = "repositories"
# Whether to default to printing command output
default_command_live = (os.environ.get('WEBUI_LAUNCH_LIVE_OUTPUT') == "1")
if 'GRADIO_ANALYTICS_ENABLED' not in os.environ:
os.environ['GRADIO_ANALYTICS_ENABLED'] = 'False'
os.environ.setdefault('GRADIO_ANALYTICS_ENABLED', 'False')
def check_python_version():
@ -189,7 +188,7 @@ def git_clone(url, dir, name, commithash=None):
return
try:
run(f'"{git}" clone "{url}" "{dir}"', f"Cloning {name} into {dir}...", f"Couldn't clone {name}", live=True)
run(f'"{git}" clone --config core.filemode=false "{url}" "{dir}"', f"Cloning {name} into {dir}...", f"Couldn't clone {name}", live=True)
except RuntimeError:
shutil.rmtree(dir, ignore_errors=True)
raise
@ -245,11 +244,13 @@ def list_extensions(settings_file):
settings = {}
try:
if os.path.isfile(settings_file):
with open(settings_file, "r", encoding="utf8") as file:
settings = json.load(file)
with open(settings_file, "r", encoding="utf8") as file:
settings = json.load(file)
except FileNotFoundError:
pass
except Exception:
errors.report("Could not load settings", exc_info=True)
errors.report(f'\nCould not load settings\nThe config file "{settings_file}" is likely corrupted\nIt has been moved to the "tmp/config.json"\nReverting config to default\n\n''', exc_info=True)
os.replace(settings_file, os.path.join(script_path, "tmp", "config.json"))
disabled_extensions = set(settings.get('disabled_extensions', []))
disable_all_extensions = settings.get('disable_all_extensions', 'none')
@ -314,8 +315,8 @@ 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}")
torch_index_url = os.environ.get('TORCH_INDEX_URL', "https://download.pytorch.org/whl/cu121")
torch_command = os.environ.get('TORCH_COMMAND', f"pip install torch==2.1.2 torchvision==0.16.2 --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
@ -337,21 +338,22 @@ def prepare_environment():
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")
requirements_file_for_npu = os.environ.get('REQS_FILE_FOR_NPU', "requirements_npu.txt")
xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.20')
xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.23.post1')
clip_package = os.environ.get('CLIP_PACKAGE', "https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip")
openclip_package = os.environ.get('OPENCLIP_PACKAGE', "https://github.com/mlfoundations/open_clip/archive/bb6e834e9c70d9c27d0dc3ecedeebeaeb1ffad6b.zip")
assets_repo = os.environ.get('ASSETS_REPO', "https://github.com/AUTOMATIC1111/stable-diffusion-webui-assets.git")
stable_diffusion_repo = os.environ.get('STABLE_DIFFUSION_REPO', "https://github.com/Stability-AI/stablediffusion.git")
stable_diffusion_xl_repo = os.environ.get('STABLE_DIFFUSION_XL_REPO', "https://github.com/Stability-AI/generative-models.git")
k_diffusion_repo = os.environ.get('K_DIFFUSION_REPO', 'https://github.com/crowsonkb/k-diffusion.git')
codeformer_repo = os.environ.get('CODEFORMER_REPO', 'https://github.com/sczhou/CodeFormer.git')
blip_repo = os.environ.get('BLIP_REPO', 'https://github.com/salesforce/BLIP.git')
assets_commit_hash = os.environ.get('ASSETS_COMMIT_HASH', "6f7db241d2f8ba7457bac5ca9753331f0c266917")
stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "cf1d67a6fd5ea1aa600c4df58e5b47da45f6bdbf")
stable_diffusion_xl_commit_hash = os.environ.get('STABLE_DIFFUSION_XL_COMMIT_HASH', "45c443b316737a4ab6e40413d7794a7f5657c19f")
k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "ab527a9a6d347f364e3d185ba6d714e22d80cb3c")
codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
try:
@ -405,18 +407,14 @@ def prepare_environment():
os.makedirs(os.path.join(script_path, dir_repos), exist_ok=True)
git_clone(assets_repo, repo_dir('stable-diffusion-webui-assets'), "assets", assets_commit_hash)
git_clone(stable_diffusion_repo, repo_dir('stable-diffusion-stability-ai'), "Stable Diffusion", stable_diffusion_commit_hash)
git_clone(stable_diffusion_xl_repo, repo_dir('generative-models'), "Stable Diffusion XL", stable_diffusion_xl_commit_hash)
git_clone(k_diffusion_repo, repo_dir('k-diffusion'), "K-diffusion", k_diffusion_commit_hash)
git_clone(codeformer_repo, repo_dir('CodeFormer'), "CodeFormer", codeformer_commit_hash)
git_clone(blip_repo, repo_dir('BLIP'), "BLIP", blip_commit_hash)
startup_timer.record("clone repositores")
if not is_installed("lpips"):
run_pip(f"install -r \"{os.path.join(repo_dir('CodeFormer'), 'requirements.txt')}\"", "requirements for CodeFormer")
startup_timer.record("install CodeFormer requirements")
if not os.path.isfile(requirements_file):
requirements_file = os.path.join(script_path, requirements_file)
@ -424,6 +422,13 @@ def prepare_environment():
run_pip(f"install -r \"{requirements_file}\"", "requirements")
startup_timer.record("install requirements")
if not os.path.isfile(requirements_file_for_npu):
requirements_file_for_npu = os.path.join(script_path, requirements_file_for_npu)
if "torch_npu" in torch_command and not requirements_met(requirements_file_for_npu):
run_pip(f"install -r \"{requirements_file_for_npu}\"", "requirements_for_npu")
startup_timer.record("install requirements_for_npu")
if not args.skip_install:
run_extensions_installers(settings_file=args.ui_settings_file)

63
modules/logging_config.py
View File

@ -1,41 +1,58 @@
import os
import logging
import os
try:
from tqdm.auto import tqdm
from tqdm import tqdm
class TqdmLoggingHandler(logging.Handler):
def __init__(self, level=logging.INFO):
super().__init__(level)
def __init__(self, fallback_handler: logging.Handler):
super().__init__()
self.fallback_handler = fallback_handler
def emit(self, record):
try:
msg = self.format(record)
tqdm.write(msg)
self.flush()
# If there are active tqdm progress bars,
# attempt to not interfere with them.
if tqdm._instances:
tqdm.write(self.format(record))
else:
self.fallback_handler.emit(record)
except Exception:
self.handleError(record)
self.fallback_handler.emit(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
TqdmLoggingHandler = None
def setup_logging(loglevel):
if loglevel is None:
loglevel = os.environ.get("SD_WEBUI_LOG_LEVEL")
loghandlers = []
if not loglevel:
return
if TQDM_IMPORTED:
loghandlers.append(TqdmLoggingHandler())
if logging.root.handlers:
# Already configured, do not interfere
return
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
)
formatter = logging.Formatter(
'%(asctime)s %(levelname)s [%(name)s] %(message)s',
'%Y-%m-%d %H:%M:%S',
)
if os.environ.get("SD_WEBUI_RICH_LOG"):
from rich.logging import RichHandler
handler = RichHandler()
else:
handler = logging.StreamHandler()
handler.setFormatter(formatter)
if TqdmLoggingHandler:
handler = TqdmLoggingHandler(handler)
handler.setFormatter(formatter)
log_level = getattr(logging, loglevel.upper(), None) or logging.INFO
logging.root.setLevel(log_level)
logging.root.addHandler(handler)

43
modules/masking.py
View File

@ -3,40 +3,15 @@ from PIL import Image, ImageFilter, ImageOps
def get_crop_region(mask, pad=0):
"""finds a rectangular region that contains all masked ares in an image. Returns (x1, y1, x2, y2) coordinates of the rectangle.
For example, if a user has painted the top-right part of a 512x512 image", the result may be (256, 0, 512, 256)"""
h, w = mask.shape
crop_left = 0
for i in range(w):
if not (mask[:, i] == 0).all():
break
crop_left += 1
crop_right = 0
for i in reversed(range(w)):
if not (mask[:, i] == 0).all():
break
crop_right += 1
crop_top = 0
for i in range(h):
if not (mask[i] == 0).all():
break
crop_top += 1
crop_bottom = 0
for i in reversed(range(h)):
if not (mask[i] == 0).all():
break
crop_bottom += 1
return (
int(max(crop_left-pad, 0)),
int(max(crop_top-pad, 0)),
int(min(w - crop_right + pad, w)),
int(min(h - crop_bottom + pad, h))
)
For example, if a user has painted the top-right part of a 512x512 image, the result may be (256, 0, 512, 256)"""
mask_img = mask if isinstance(mask, Image.Image) else Image.fromarray(mask)
box = mask_img.getbbox()
if box:
x1, y1, x2, y2 = box
else: # when no box is found
x1, y1 = mask_img.size
x2 = y2 = 0
return max(x1 - pad, 0), max(y1 - pad, 0), min(x2 + pad, mask_img.size[0]), min(y2 + pad, mask_img.size[1])
def expand_crop_region(crop_region, processing_width, processing_height, image_width, image_height):

92
modules/modelloader.py
View File

@ -1,13 +1,20 @@
from __future__ import annotations
import os
import shutil
import importlib
import logging
import os
from typing import TYPE_CHECKING
from urllib.parse import urlparse
import torch
from modules import shared
from modules.upscaler import Upscaler, UpscalerLanczos, UpscalerNearest, UpscalerNone
from modules.paths import script_path, models_path
if TYPE_CHECKING:
import spandrel
logger = logging.getLogger(__name__)
def load_file_from_url(
@ -90,54 +97,6 @@ def friendly_name(file: str):
return model_name
def cleanup_models():
# This code could probably be more efficient if we used a tuple list or something to store the src/destinations
# and then enumerate that, but this works for now. In the future, it'd be nice to just have every "model" scaler
# somehow auto-register and just do these things...
root_path = script_path
src_path = models_path
dest_path = os.path.join(models_path, "Stable-diffusion")
move_files(src_path, dest_path, ".ckpt")
move_files(src_path, dest_path, ".safetensors")
src_path = os.path.join(root_path, "ESRGAN")
dest_path = os.path.join(models_path, "ESRGAN")
move_files(src_path, dest_path)
src_path = os.path.join(models_path, "BSRGAN")
dest_path = os.path.join(models_path, "ESRGAN")
move_files(src_path, dest_path, ".pth")
src_path = os.path.join(root_path, "gfpgan")
dest_path = os.path.join(models_path, "GFPGAN")
move_files(src_path, dest_path)
src_path = os.path.join(root_path, "SwinIR")
dest_path = os.path.join(models_path, "SwinIR")
move_files(src_path, dest_path)
src_path = os.path.join(root_path, "repositories/latent-diffusion/experiments/pretrained_models/")
dest_path = os.path.join(models_path, "LDSR")
move_files(src_path, dest_path)
def move_files(src_path: str, dest_path: str, ext_filter: str = None):
try:
os.makedirs(dest_path, exist_ok=True)
if os.path.exists(src_path):
for file in os.listdir(src_path):
fullpath = os.path.join(src_path, file)
if os.path.isfile(fullpath):
if ext_filter is not None:
if ext_filter not in file:
continue
print(f"Moving {file} from {src_path} to {dest_path}.")
try:
shutil.move(fullpath, dest_path)
except Exception:
pass
if len(os.listdir(src_path)) == 0:
print(f"Removing empty folder: {src_path}")
shutil.rmtree(src_path, True)
except Exception:
pass
def load_upscalers():
# We can only do this 'magic' method to dynamically load upscalers if they are referenced,
# so we'll try to import any _model.py files before looking in __subclasses__
@ -177,3 +136,34 @@ def load_upscalers():
# Special case for UpscalerNone keeps it at the beginning of the list.
key=lambda x: x.name.lower() if not isinstance(x.scaler, (UpscalerNone, UpscalerLanczos, UpscalerNearest)) else ""
)
def load_spandrel_model(
path: str | os.PathLike,
*,
device: str | torch.device | None,
prefer_half: bool = False,
dtype: str | torch.dtype | None = None,
expected_architecture: str | None = None,
) -> spandrel.ModelDescriptor:
import spandrel
model_descriptor = spandrel.ModelLoader(device=device).load_from_file(str(path))
if expected_architecture and model_descriptor.architecture != expected_architecture:
logger.warning(
f"Model {path!r} is not a {expected_architecture!r} model (got {model_descriptor.architecture!r})",
)
half = False
if prefer_half:
if model_descriptor.supports_half:
model_descriptor.model.half()
half = True
else:
logger.info("Model %s does not support half precision, ignoring --half", path)
if dtype:
model_descriptor.model.to(dtype=dtype)
model_descriptor.model.eval()
logger.debug(
"Loaded %s from %s (device=%s, half=%s, dtype=%s)",
model_descriptor, path, device, half, dtype,
)
return model_descriptor

31
modules/npu_specific.py Normal file
View File

@ -0,0 +1,31 @@
import importlib
import torch
from modules import shared
def check_for_npu():
if importlib.util.find_spec("torch_npu") is None:
return False
import torch_npu
try:
# Will raise a RuntimeError if no NPU is found
_ = torch_npu.npu.device_count()
return torch.npu.is_available()
except RuntimeError:
return False
def get_npu_device_string():
if shared.cmd_opts.device_id is not None:
return f"npu:{shared.cmd_opts.device_id}"
return "npu:0"
def torch_npu_gc():
with torch.npu.device(get_npu_device_string()):
torch.npu.empty_cache()
has_npu = check_for_npu()

35
modules/options.py
View File

@ -1,3 +1,4 @@
import os
import json
import sys
from dataclasses import dataclass
@ -6,6 +7,7 @@ import gradio as gr
from modules import errors
from modules.shared_cmd_options import cmd_opts
from modules.paths_internal import script_path
class OptionInfo:
@ -91,18 +93,35 @@ class Options:
if self.data is not None:
if key in self.data or key in self.data_labels:
# Check that settings aren't globally frozen
assert not cmd_opts.freeze_settings, "changing settings is disabled"
# Get the info related to the setting being changed
info = self.data_labels.get(key, None)
if info.do_not_save:
return
# Restrict component arguments
comp_args = info.component_args if info else None
if isinstance(comp_args, dict) and comp_args.get('visible', True) is False:
raise RuntimeError(f"not possible to set {key} because it is restricted")
raise RuntimeError(f"not possible to set '{key}' because it is restricted")
# Check that this section isn't frozen
if cmd_opts.freeze_settings_in_sections is not None:
frozen_sections = list(map(str.strip, cmd_opts.freeze_settings_in_sections.split(','))) # Trim whitespace from section names
section_key = info.section[0]
section_name = info.section[1]
assert section_key not in frozen_sections, f"not possible to set '{key}' because settings in section '{section_name}' ({section_key}) are frozen with --freeze-settings-in-sections"
# Check that this section of the settings isn't frozen
if cmd_opts.freeze_specific_settings is not None:
frozen_keys = list(map(str.strip, cmd_opts.freeze_specific_settings.split(','))) # Trim whitespace from setting keys
assert key not in frozen_keys, f"not possible to set '{key}' because this setting is frozen with --freeze-specific-settings"
# Check shorthand option which disables editing options in "saving-paths"
if cmd_opts.hide_ui_dir_config and key in self.restricted_opts:
raise RuntimeError(f"not possible to set {key} because it is restricted")
raise RuntimeError(f"not possible to set '{key}' because it is restricted with --hide_ui_dir_config")
self.data[key] = value
return
@ -176,9 +195,15 @@ class Options:
return type_x == type_y
def load(self, filename):
with open(filename, "r", encoding="utf8") as file:
self.data = json.load(file)
try:
with open(filename, "r", encoding="utf8") as file:
self.data = json.load(file)
except FileNotFoundError:
self.data = {}
except Exception:
errors.report(f'\nCould not load settings\nThe config file "{filename}" is likely corrupted\nIt has been moved to the "tmp/config.json"\nReverting config to default\n\n''', exc_info=True)
os.replace(filename, os.path.join(script_path, "tmp", "config.json"))
self.data = {}
# 1.6.0 VAE defaults
if self.data.get('sd_vae_as_default') is not None and self.data.get('sd_vae_overrides_per_model_preferences') is None:
self.data['sd_vae_overrides_per_model_preferences'] = not self.data.get('sd_vae_as_default')

1
modules/paths.py
View File

@ -38,7 +38,6 @@ mute_sdxl_imports()
path_dirs = [
(sd_path, 'ldm', 'Stable Diffusion', []),
(os.path.join(sd_path, '../generative-models'), 'sgm', 'Stable Diffusion XL', ["sgm"]),
(os.path.join(sd_path, '../CodeFormer'), 'inference_codeformer.py', 'CodeFormer', []),
(os.path.join(sd_path, '../BLIP'), 'models/blip.py', 'BLIP', []),
(os.path.join(sd_path, '../k-diffusion'), 'k_diffusion/sampling.py', 'k_diffusion', ["atstart"]),
]

5
modules/paths_internal.py
View File

@ -4,6 +4,10 @@ import argparse
import os
import sys
import shlex
from pathlib import Path
normalized_filepath = lambda filepath: str(Path(filepath).absolute())
commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
sys.argv += shlex.split(commandline_args)
@ -28,5 +32,6 @@ models_path = os.path.join(data_path, "models")
extensions_dir = os.path.join(data_path, "extensions")
extensions_builtin_dir = os.path.join(script_path, "extensions-builtin")
config_states_dir = os.path.join(script_path, "config_states")
default_output_dir = os.path.join(data_path, "output")
roboto_ttf_file = os.path.join(modules_path, 'Roboto-Regular.ttf')

15
modules/postprocessing.py
View File

@ -2,7 +2,7 @@ import os
from PIL import Image
from modules import shared, images, devices, scripts, scripts_postprocessing, ui_common, generation_parameters_copypaste
from modules import shared, images, devices, scripts, scripts_postprocessing, ui_common, infotext_utils
from modules.shared import opts
@ -62,8 +62,6 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir,
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
@ -86,22 +84,25 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir,
basename = ''
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])
infotext = ", ".join([k if k == v else f'{k}: {infotext_utils.quote(v)}' for k, v in pp.info.items() if v is not None])
if opts.enable_pnginfo:
pp.image.info = existing_pnginfo
pp.image.info["postprocessing"] = infotext
shared.state.assign_current_image(pp.image)
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):
existing_caption = ""
try:
with open(caption_filename, encoding="utf8") as file:
existing_caption = file.read().strip()
else:
existing_caption = ""
except FileNotFoundError:
pass
action = shared.opts.postprocessing_existing_caption_action
if action == 'Prepend' and existing_caption:

239
modules/processing.py
View File

@ -16,7 +16,7 @@ from skimage import exposure
from typing import Any
import modules.sd_hijack
from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet, errors, rng
from modules import devices, prompt_parser, masking, sd_samplers, lowvram, infotext_utils, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet, errors, rng
from modules.rng import slerp # noqa: F401
from modules.sd_hijack import model_hijack
from modules.sd_samplers_common import images_tensor_to_samples, decode_first_stage, approximation_indexes
@ -62,28 +62,37 @@ def apply_color_correction(correction, original_image):
return image.convert('RGB')
def apply_overlay(image, paste_loc, index, overlays):
if overlays is None or index >= len(overlays):
return image
def uncrop(image, dest_size, paste_loc):
x, y, w, h = paste_loc
base_image = Image.new('RGBA', dest_size)
image = images.resize_image(1, image, w, h)
base_image.paste(image, (x, y))
image = base_image
overlay = overlays[index]
return image
def apply_overlay(image, paste_loc, overlay):
if overlay is None:
return image, image.copy()
if paste_loc is not None:
x, y, w, h = paste_loc
base_image = Image.new('RGBA', (overlay.width, overlay.height))
image = images.resize_image(1, image, w, h)
base_image.paste(image, (x, y))
image = base_image
image = uncrop(image, (overlay.width, overlay.height), paste_loc)
original_denoised_image = image.copy()
image = image.convert('RGBA')
image.alpha_composite(overlay)
image = image.convert('RGB')
return image
return image, original_denoised_image
def create_binary_mask(image):
def create_binary_mask(image, round=True):
if image.mode == 'RGBA' and image.getextrema()[-1] != (255, 255):
image = image.split()[-1].convert("L").point(lambda x: 255 if x > 128 else 0)
if round:
image = image.split()[-1].convert("L").point(lambda x: 255 if x > 128 else 0)
else:
image = image.split()[-1].convert("L")
else:
image = image.convert('L')
return image
@ -106,6 +115,21 @@ def txt2img_image_conditioning(sd_model, x, width, height):
return x.new_zeros(x.shape[0], 2*sd_model.noise_augmentor.time_embed.dim, dtype=x.dtype, device=x.device)
else:
sd = sd_model.model.state_dict()
diffusion_model_input = sd.get('diffusion_model.input_blocks.0.0.weight', None)
if diffusion_model_input is not None:
if diffusion_model_input.shape[1] == 9:
# The "masked-image" in this case will just be all 0.5 since the entire image is masked.
image_conditioning = torch.ones(x.shape[0], 3, height, width, device=x.device) * 0.5
image_conditioning = images_tensor_to_samples(image_conditioning,
approximation_indexes.get(opts.sd_vae_encode_method))
# Add the fake full 1s mask to the first dimension.
image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
image_conditioning = image_conditioning.to(x.dtype)
return image_conditioning
# Dummy zero conditioning if we're not using inpainting or unclip models.
# Still takes up a bit of memory, but no encoder call.
# Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
@ -157,6 +181,7 @@ class StableDiffusionProcessing:
token_merging_ratio = 0
token_merging_ratio_hr = 0
disable_extra_networks: bool = False
firstpass_image: Image = None
scripts_value: scripts.ScriptRunner = field(default=None, init=False)
script_args_value: list = field(default=None, init=False)
@ -308,7 +333,7 @@ class StableDiffusionProcessing:
c_adm = torch.cat((c_adm, noise_level_emb), 1)
return c_adm
def inpainting_image_conditioning(self, source_image, latent_image, image_mask=None):
def inpainting_image_conditioning(self, source_image, latent_image, image_mask=None, round_image_mask=True):
self.is_using_inpainting_conditioning = True
# Handle the different mask inputs
@ -320,8 +345,10 @@ class StableDiffusionProcessing:
conditioning_mask = conditioning_mask.astype(np.float32) / 255.0
conditioning_mask = torch.from_numpy(conditioning_mask[None, None])
# Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0
conditioning_mask = torch.round(conditioning_mask)
if round_image_mask:
# Caller is requesting a discretized mask as input, so we round to either 1.0 or 0.0
conditioning_mask = torch.round(conditioning_mask)
else:
conditioning_mask = source_image.new_ones(1, 1, *source_image.shape[-2:])
@ -345,7 +372,7 @@ class StableDiffusionProcessing:
return image_conditioning
def img2img_image_conditioning(self, source_image, latent_image, image_mask=None):
def img2img_image_conditioning(self, source_image, latent_image, image_mask=None, round_image_mask=True):
source_image = devices.cond_cast_float(source_image)
# HACK: Using introspection as the Depth2Image model doesn't appear to uniquely
@ -357,11 +384,17 @@ class StableDiffusionProcessing:
return self.edit_image_conditioning(source_image)
if self.sampler.conditioning_key in {'hybrid', 'concat'}:
return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask)
return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask, round_image_mask=round_image_mask)
if self.sampler.conditioning_key == "crossattn-adm":
return self.unclip_image_conditioning(source_image)
sd = self.sampler.model_wrap.inner_model.model.state_dict()
diffusion_model_input = sd.get('diffusion_model.input_blocks.0.0.weight', None)
if diffusion_model_input is not None:
if diffusion_model_input.shape[1] == 9:
return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask)
# Dummy zero conditioning if we're not using inpainting or depth model.
return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)
@ -422,6 +455,9 @@ class StableDiffusionProcessing:
opts.sdxl_crop_top,
self.width,
self.height,
opts.fp8_storage,
opts.cache_fp16_weight,
opts.emphasis,
)
def get_conds_with_caching(self, function, required_prompts, steps, caches, extra_network_data, hires_steps=None):
@ -596,20 +632,33 @@ def decode_latent_batch(model, batch, target_device=None, check_for_nans=False):
sample = decode_first_stage(model, batch[i:i + 1])[0]
if check_for_nans:
try:
devices.test_for_nans(sample, "vae")
except devices.NansException as e:
if devices.dtype_vae == torch.float32 or not shared.opts.auto_vae_precision:
if shared.opts.auto_vae_precision_bfloat16:
autofix_dtype = torch.bfloat16
autofix_dtype_text = "bfloat16"
autofix_dtype_setting = "Automatically convert VAE to bfloat16"
autofix_dtype_comment = ""
elif shared.opts.auto_vae_precision:
autofix_dtype = torch.float32
autofix_dtype_text = "32-bit float"
autofix_dtype_setting = "Automatically revert VAE to 32-bit floats"
autofix_dtype_comment = "\nTo always start with 32-bit VAE, use --no-half-vae commandline flag."
else:
raise e
if devices.dtype_vae == autofix_dtype:
raise e
errors.print_error_explanation(
"A tensor with all NaNs was produced in VAE.\n"
"Web UI will now convert VAE into 32-bit float and retry.\n"
"To disable this behavior, disable the 'Automatically revert VAE to 32-bit floats' setting.\n"
"To always start with 32-bit VAE, use --no-half-vae commandline flag."
f"Web UI will now convert VAE into {autofix_dtype_text} and retry.\n"
f"To disable this behavior, disable the '{autofix_dtype_setting}' setting.{autofix_dtype_comment}"
)
devices.dtype_vae = torch.float32
devices.dtype_vae = autofix_dtype
model.first_stage_model.to(devices.dtype_vae)
batch = batch.to(devices.dtype_vae)
@ -679,12 +728,14 @@ 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,
"FP8 weight": opts.fp8_storage if devices.fp8 else None,
"Cache FP16 weight for LoRA": opts.cache_fp16_weight if devices.fp8 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}"),
"Denoising strength": getattr(p, 'denoising_strength', None),
"Denoising strength": p.extra_generation_params.get("Denoising strength"),
"Conditional mask weight": getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None,
"Clip skip": None if clip_skip <= 1 else clip_skip,
"ENSD": opts.eta_noise_seed_delta if uses_ensd else None,
@ -699,7 +750,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
"User": p.user if opts.add_user_name_to_info else None,
}
generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None])
generation_params_text = ", ".join([k if k == v else f'{k}: {infotext_utils.quote(v)}' for k, v in generation_params.items() if v is not None])
prompt_text = p.main_prompt if use_main_prompt else all_prompts[index]
negative_prompt_text = f"\nNegative prompt: {p.main_negative_prompt if use_main_prompt else all_negative_prompts[index]}" if all_negative_prompts[index] else ""
@ -818,7 +869,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
if state.skipped:
state.skipped = False
if state.interrupted:
if state.interrupted or state.stopping_generation:
break
sd_models.reload_model_weights() # model can be changed for example by refiner
@ -864,9 +915,16 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
if p.n_iter > 1:
shared.state.job = f"Batch {n+1} out of {p.n_iter}"
sd_models.apply_alpha_schedule_override(p.sd_model, p)
with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():
samples_ddim = p.sample(conditioning=p.c, unconditional_conditioning=p.uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts)
if p.scripts is not None:
ps = scripts.PostSampleArgs(samples_ddim)
p.scripts.post_sample(p, ps)
samples_ddim = ps.samples
if getattr(samples_ddim, 'already_decoded', False):
x_samples_ddim = samples_ddim
else:
@ -922,13 +980,37 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
pp = scripts.PostprocessImageArgs(image)
p.scripts.postprocess_image(p, pp)
image = pp.image
mask_for_overlay = getattr(p, "mask_for_overlay", None)
if not shared.opts.overlay_inpaint:
overlay_image = None
elif getattr(p, "overlay_images", None) is not None and i < len(p.overlay_images):
overlay_image = p.overlay_images[i]
else:
overlay_image = None
if p.scripts is not None:
ppmo = scripts.PostProcessMaskOverlayArgs(i, mask_for_overlay, overlay_image)
p.scripts.postprocess_maskoverlay(p, ppmo)
mask_for_overlay, overlay_image = ppmo.mask_for_overlay, ppmo.overlay_image
if p.color_corrections is not None and i < len(p.color_corrections):
if save_samples and opts.save_images_before_color_correction:
image_without_cc = apply_overlay(image, p.paste_to, i, p.overlay_images)
image_without_cc, _ = apply_overlay(image, p.paste_to, overlay_image)
images.save_image(image_without_cc, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-before-color-correction")
image = apply_color_correction(p.color_corrections[i], image)
image = apply_overlay(image, p.paste_to, i, p.overlay_images)
# If the intention is to show the output from the model
# that is being composited over the original image,
# we need to keep the original image around
# and use it in the composite step.
image, original_denoised_image = apply_overlay(image, p.paste_to, overlay_image)
if p.scripts is not None:
pp = scripts.PostprocessImageArgs(image)
p.scripts.postprocess_image_after_composite(p, pp)
image = pp.image
if save_samples:
images.save_image(image, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p)
@ -938,16 +1020,17 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
if opts.enable_pnginfo:
image.info["parameters"] = text
output_images.append(image)
if hasattr(p, 'mask_for_overlay') and p.mask_for_overlay:
if mask_for_overlay is not None:
if opts.return_mask or opts.save_mask:
image_mask = p.mask_for_overlay.convert('RGB')
image_mask = 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.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')
image_mask_composite = Image.composite(original_denoised_image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, 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:
@ -1025,6 +1108,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
hr_sampler_name: str = None
hr_prompt: str = ''
hr_negative_prompt: str = ''
force_task_id: str = None
cached_hr_uc = [None, None]
cached_hr_c = [None, None]
@ -1097,7 +1181,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
def init(self, all_prompts, all_seeds, all_subseeds):
if self.enable_hr:
if self.hr_checkpoint_name:
self.extra_generation_params["Denoising strength"] = self.denoising_strength
if self.hr_checkpoint_name and self.hr_checkpoint_name != 'Use same checkpoint':
self.hr_checkpoint_info = sd_models.get_closet_checkpoint_match(self.hr_checkpoint_name)
if self.hr_checkpoint_info is None:
@ -1124,8 +1210,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
if not state.processing_has_refined_job_count:
if state.job_count == -1:
state.job_count = self.n_iter
shared.total_tqdm.updateTotal((self.steps + (self.hr_second_pass_steps or self.steps)) * state.job_count)
if getattr(self, 'txt2img_upscale', False):
total_steps = (self.hr_second_pass_steps or self.steps) * state.job_count
else:
total_steps = (self.steps + (self.hr_second_pass_steps or self.steps)) * state.job_count
shared.total_tqdm.updateTotal(total_steps)
state.job_count = state.job_count * 2
state.processing_has_refined_job_count = True
@ -1138,18 +1227,45 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
x = self.rng.next()
samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
del x
if self.firstpass_image is not None and self.enable_hr:
# here we don't need to generate image, we just take self.firstpass_image and prepare it for hires fix
if not self.enable_hr:
return samples
devices.torch_gc()
if self.latent_scale_mode is None:
image = np.array(self.firstpass_image).astype(np.float32) / 255.0 * 2.0 - 1.0
image = np.moveaxis(image, 2, 0)
samples = None
decoded_samples = torch.asarray(np.expand_dims(image, 0))
else:
image = np.array(self.firstpass_image).astype(np.float32) / 255.0
image = np.moveaxis(image, 2, 0)
image = torch.from_numpy(np.expand_dims(image, axis=0))
image = image.to(shared.device, dtype=devices.dtype_vae)
if opts.sd_vae_encode_method != 'Full':
self.extra_generation_params['VAE Encoder'] = opts.sd_vae_encode_method
samples = images_tensor_to_samples(image, approximation_indexes.get(opts.sd_vae_encode_method), self.sd_model)
decoded_samples = None
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)
else:
decoded_samples = None
# here we generate an image normally
x = self.rng.next()
samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
del x
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)
else:
decoded_samples = None
with sd_models.SkipWritingToConfig():
sd_models.reload_model_weights(info=self.hr_checkpoint_info)
@ -1351,12 +1467,14 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
mask_blur_x: int = 4
mask_blur_y: int = 4
mask_blur: int = None
mask_round: bool = True
inpainting_fill: int = 0
inpaint_full_res: bool = True
inpaint_full_res_padding: int = 0
inpainting_mask_invert: int = 0
initial_noise_multiplier: float = None
latent_mask: Image = None
force_task_id: str = None
image_mask: Any = field(default=None, init=False)
@ -1386,6 +1504,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
self.mask_blur_y = value
def init(self, all_prompts, all_seeds, all_subseeds):
self.extra_generation_params["Denoising strength"] = self.denoising_strength
self.image_cfg_scale: float = self.image_cfg_scale if shared.sd_model.cond_stage_key == "edit" else None
self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
@ -1396,10 +1516,11 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
if image_mask is not None:
# image_mask is passed in as RGBA by Gradio to support alpha masks,
# but we still want to support binary masks.
image_mask = create_binary_mask(image_mask)
image_mask = create_binary_mask(image_mask, round=self.mask_round)
if self.inpainting_mask_invert:
image_mask = ImageOps.invert(image_mask)
self.extra_generation_params["Mask mode"] = "Inpaint not masked"
if self.mask_blur_x > 0:
np_mask = np.array(image_mask)
@ -1413,16 +1534,22 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
np_mask = cv2.GaussianBlur(np_mask, (1, kernel_size), self.mask_blur_y)
image_mask = Image.fromarray(np_mask)
if self.mask_blur_x > 0 or self.mask_blur_y > 0:
self.extra_generation_params["Mask blur"] = self.mask_blur
if self.inpaint_full_res:
self.mask_for_overlay = image_mask
mask = image_mask.convert('L')
crop_region = masking.get_crop_region(np.array(mask), self.inpaint_full_res_padding)
crop_region = masking.get_crop_region(mask, self.inpaint_full_res_padding)
crop_region = masking.expand_crop_region(crop_region, self.width, self.height, mask.width, mask.height)
x1, y1, x2, y2 = crop_region
mask = mask.crop(crop_region)
image_mask = images.resize_image(2, mask, self.width, self.height)
self.paste_to = (x1, y1, x2-x1, y2-y1)
self.extra_generation_params["Inpaint area"] = "Only masked"
self.extra_generation_params["Masked area padding"] = self.inpaint_full_res_padding
else:
image_mask = images.resize_image(self.resize_mode, image_mask, self.width, self.height)
np_mask = np.array(image_mask)
@ -1442,7 +1569,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
# Save init image
if opts.save_init_img:
self.init_img_hash = hashlib.md5(img.tobytes()).hexdigest()
images.save_image(img, path=opts.outdir_init_images, basename=None, forced_filename=self.init_img_hash, save_to_dirs=False)
images.save_image(img, path=opts.outdir_init_images, basename=None, forced_filename=self.init_img_hash, save_to_dirs=False, existing_info=img.info)
image = images.flatten(img, opts.img2img_background_color)
@ -1464,6 +1591,9 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
if self.inpainting_fill != 1:
image = masking.fill(image, latent_mask)
if self.inpainting_fill == 0:
self.extra_generation_params["Masked content"] = 'fill'
if add_color_corrections:
self.color_corrections.append(setup_color_correction(image))
@ -1503,7 +1633,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
latmask = init_mask.convert('RGB').resize((self.init_latent.shape[3], self.init_latent.shape[2]))
latmask = np.moveaxis(np.array(latmask, dtype=np.float32), 2, 0) / 255
latmask = latmask[0]
latmask = np.around(latmask)
if self.mask_round:
latmask = np.around(latmask)
latmask = np.tile(latmask[None], (4, 1, 1))
self.mask = torch.asarray(1.0 - latmask).to(shared.device).type(self.sd_model.dtype)
@ -1512,10 +1643,13 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
# this needs to be fixed to be done in sample() using actual seeds for batches
if self.inpainting_fill == 2:
self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:], all_seeds[0:self.init_latent.shape[0]]) * self.nmask
self.extra_generation_params["Masked content"] = 'latent noise'
elif self.inpainting_fill == 3:
self.init_latent = self.init_latent * self.mask
self.extra_generation_params["Masked content"] = 'latent nothing'
self.image_conditioning = self.img2img_image_conditioning(image * 2 - 1, self.init_latent, image_mask)
self.image_conditioning = self.img2img_image_conditioning(image * 2 - 1, self.init_latent, image_mask, self.mask_round)
def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
x = self.rng.next()
@ -1527,7 +1661,14 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning)
if self.mask is not None:
samples = samples * self.nmask + self.init_latent * self.mask
blended_samples = samples * self.nmask + self.init_latent * self.mask
if self.scripts is not None:
mba = scripts.MaskBlendArgs(samples, self.nmask, self.init_latent, self.mask, blended_samples)
self.scripts.on_mask_blend(self, mba)
blended_samples = mba.blended_latent
samples = blended_samples
del x
devices.torch_gc()

42
modules/processing_scripts/comments.py Normal file
View File

@ -0,0 +1,42 @@
from modules import scripts, shared, script_callbacks
import re
def strip_comments(text):
text = re.sub('(^|\n)#[^\n]*(\n|$)', '\n', text) # while line comment
text = re.sub('#[^\n]*(\n|$)', '\n', text) # in the middle of the line comment
return text
class ScriptStripComments(scripts.Script):
def title(self):
return "Comments"
def show(self, is_img2img):
return scripts.AlwaysVisible
def process(self, p, *args):
if not shared.opts.enable_prompt_comments:
return
p.all_prompts = [strip_comments(x) for x in p.all_prompts]
p.all_negative_prompts = [strip_comments(x) for x in p.all_negative_prompts]
p.main_prompt = strip_comments(p.main_prompt)
p.main_negative_prompt = strip_comments(p.main_negative_prompt)
def before_token_counter(params: script_callbacks.BeforeTokenCounterParams):
if not shared.opts.enable_prompt_comments:
return
params.prompt = strip_comments(params.prompt)
script_callbacks.on_before_token_counter(before_token_counter)
shared.options_templates.update(shared.options_section(('sd', "Stable Diffusion", "sd"), {
"enable_prompt_comments": shared.OptionInfo(True, "Enable comments").info("Use # anywhere in the prompt to hide the text between # and the end of the line from the generation."),
}))

7
modules/processing_scripts/refiner.py
View File

@ -1,6 +1,7 @@
import gradio as gr
from modules import scripts, sd_models
from modules.infotext_utils import PasteField
from modules.ui_common import create_refresh_button
from modules.ui_components import InputAccordion
@ -31,9 +32,9 @@ class ScriptRefiner(scripts.ScriptBuiltinUI):
return None if info is None else info.title
self.infotext_fields = [
(enable_refiner, lambda d: 'Refiner' in d),
(refiner_checkpoint, lambda d: lookup_checkpoint(d.get('Refiner'))),
(refiner_switch_at, 'Refiner switch at'),
PasteField(enable_refiner, lambda d: 'Refiner' in d),
PasteField(refiner_checkpoint, lambda d: lookup_checkpoint(d.get('Refiner')), api="refiner_checkpoint"),
PasteField(refiner_switch_at, 'Refiner switch at', api="refiner_switch_at"),
]
return enable_refiner, refiner_checkpoint, refiner_switch_at

32
modules/processing_scripts/seed.py
View File

@ -3,8 +3,10 @@ import json
import gradio as gr
from modules import scripts, ui, errors
from modules.infotext_utils import PasteField
from modules.shared import cmd_opts
from modules.ui_components import ToolButton
from modules import infotext_utils
class ScriptSeed(scripts.ScriptBuiltinUI):
@ -51,12 +53,12 @@ class ScriptSeed(scripts.ScriptBuiltinUI):
seed_checkbox.change(lambda x: gr.update(visible=x), show_progress=False, inputs=[seed_checkbox], outputs=[seed_extras])
self.infotext_fields = [
(self.seed, "Seed"),
(seed_checkbox, lambda d: "Variation seed" in d or "Seed resize from-1" in d),
(subseed, "Variation seed"),
(subseed_strength, "Variation seed strength"),
(seed_resize_from_w, "Seed resize from-1"),
(seed_resize_from_h, "Seed resize from-2"),
PasteField(self.seed, "Seed", api="seed"),
PasteField(seed_checkbox, lambda d: "Variation seed" in d or "Seed resize from-1" in d),
PasteField(subseed, "Variation seed", api="subseed"),
PasteField(subseed_strength, "Variation seed strength", api="subseed_strength"),
PasteField(seed_resize_from_w, "Seed resize from-1", api="seed_resize_from_h"),
PasteField(seed_resize_from_h, "Seed resize from-2", api="seed_resize_from_w"),
]
self.on_after_component(lambda x: connect_reuse_seed(self.seed, reuse_seed, x.component, False), elem_id=f'generation_info_{self.tabname}')
@ -76,7 +78,6 @@ class ScriptSeed(scripts.ScriptBuiltinUI):
p.seed_resize_from_h = seed_resize_from_h
def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: gr.Textbox, is_subseed):
""" Connects a 'reuse (sub)seed' button's click event so that it copies last used
(sub)seed value from generation info the to the seed field. If copying subseed and subseed strength
@ -84,21 +85,14 @@ def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info:
def copy_seed(gen_info_string: str, index):
res = -1
try:
gen_info = json.loads(gen_info_string)
index -= gen_info.get('index_of_first_image', 0)
if is_subseed and gen_info.get('subseed_strength', 0) > 0:
all_subseeds = gen_info.get('all_subseeds', [-1])
res = all_subseeds[index if 0 <= index < len(all_subseeds) else 0]
else:
all_seeds = gen_info.get('all_seeds', [-1])
res = all_seeds[index if 0 <= index < len(all_seeds) else 0]
except json.decoder.JSONDecodeError:
infotext = gen_info.get('infotexts')[index]
gen_parameters = infotext_utils.parse_generation_parameters(infotext, [])
res = int(gen_parameters.get('Variation seed' if is_subseed else 'Seed', -1))
except Exception:
if gen_info_string:
errors.report(f"Error parsing JSON generation info: {gen_info_string}")
errors.report(f"Error retrieving seed from generation info: {gen_info_string}", exc_info=True)
return [res, gr.update()]

22
modules/progress.py
View File

@ -8,10 +8,13 @@ from pydantic import BaseModel, Field
from modules.shared import opts
import modules.shared as shared
from collections import OrderedDict
import string
import random
from typing import List
current_task = None
pending_tasks = {}
pending_tasks = OrderedDict()
finished_tasks = []
recorded_results = []
recorded_results_limit = 2
@ -34,6 +37,11 @@ def finish_task(id_task):
if len(finished_tasks) > 16:
finished_tasks.pop(0)
def create_task_id(task_type):
N = 7
res = ''.join(random.choices(string.ascii_uppercase +
string.digits, k=N))
return f"task({task_type}-{res})"
def record_results(id_task, res):
recorded_results.append((id_task, res))
@ -44,6 +52,9 @@ def record_results(id_task, res):
def add_task_to_queue(id_job):
pending_tasks[id_job] = time.time()
class PendingTasksResponse(BaseModel):
size: int = Field(title="Pending task size")
tasks: List[str] = Field(title="Pending task ids")
class ProgressRequest(BaseModel):
id_task: str = Field(default=None, title="Task ID", description="id of the task to get progress for")
@ -63,9 +74,16 @@ class ProgressResponse(BaseModel):
def setup_progress_api(app):
app.add_api_route("/internal/pending-tasks", get_pending_tasks, methods=["GET"])
return app.add_api_route("/internal/progress", progressapi, methods=["POST"], response_model=ProgressResponse)
def get_pending_tasks():
pending_tasks_ids = list(pending_tasks)
pending_len = len(pending_tasks_ids)
return PendingTasksResponse(size=pending_len, tasks=pending_tasks_ids)
def progressapi(req: ProgressRequest):
active = req.id_task == current_task
queued = req.id_task in pending_tasks

158
modules/realesrgan_model.py
View File

@ -1,12 +1,9 @@
import os
import numpy as np
from PIL import Image
from realesrgan import RealESRGANer
from modules.upscaler import Upscaler, UpscalerData
from modules.shared import cmd_opts, opts
from modules import modelloader, errors
from modules.shared import cmd_opts, opts
from modules.upscaler import Upscaler, UpscalerData
from modules.upscaler_utils import upscale_with_model
class UpscalerRealESRGAN(Upscaler):
@ -14,29 +11,20 @@ class UpscalerRealESRGAN(Upscaler):
self.name = "RealESRGAN"
self.user_path = path
super().__init__()
try:
from basicsr.archs.rrdbnet_arch import RRDBNet # noqa: F401
from realesrgan import RealESRGANer # noqa: F401
from realesrgan.archs.srvgg_arch import SRVGGNetCompact # noqa: F401
self.enable = True
self.scalers = []
scalers = self.load_models(path)
self.enable = True
self.scalers = []
scalers = get_realesrgan_models(self)
local_model_paths = self.find_models(ext_filter=[".pth"])
for scaler in scalers:
if scaler.local_data_path.startswith("http"):
filename = modelloader.friendly_name(scaler.local_data_path)
local_model_candidates = [local_model for local_model in local_model_paths if local_model.endswith(f"{filename}.pth")]
if local_model_candidates:
scaler.local_data_path = local_model_candidates[0]
local_model_paths = self.find_models(ext_filter=[".pth"])
for scaler in scalers:
if scaler.local_data_path.startswith("http"):
filename = modelloader.friendly_name(scaler.local_data_path)
local_model_candidates = [local_model for local_model in local_model_paths if local_model.endswith(f"{filename}.pth")]
if local_model_candidates:
scaler.local_data_path = local_model_candidates[0]
if scaler.name in opts.realesrgan_enabled_models:
self.scalers.append(scaler)
except Exception:
errors.report("Error importing Real-ESRGAN", exc_info=True)
self.enable = False
self.scalers = []
if scaler.name in opts.realesrgan_enabled_models:
self.scalers.append(scaler)
def do_upscale(self, img, path):
if not self.enable:
@ -48,20 +36,19 @@ class UpscalerRealESRGAN(Upscaler):
errors.report(f"Unable to load RealESRGAN model {path}", exc_info=True)
return img
upsampler = RealESRGANer(
scale=info.scale,
model_path=info.local_data_path,
model=info.model(),
half=not cmd_opts.no_half and not cmd_opts.upcast_sampling,
tile=opts.ESRGAN_tile,
tile_pad=opts.ESRGAN_tile_overlap,
model_descriptor = modelloader.load_spandrel_model(
info.local_data_path,
device=self.device,
prefer_half=(not cmd_opts.no_half and not cmd_opts.upcast_sampling),
expected_architecture="ESRGAN", # "RealESRGAN" isn't a specific thing for Spandrel
)
return upscale_with_model(
model_descriptor,
img,
tile_size=opts.ESRGAN_tile,
tile_overlap=opts.ESRGAN_tile_overlap,
# TODO: `outscale`?
)
upsampled = upsampler.enhance(np.array(img), outscale=info.scale)[0]
image = Image.fromarray(upsampled)
return image
def load_model(self, path):
for scaler in self.scalers:
@ -76,58 +63,43 @@ class UpscalerRealESRGAN(Upscaler):
return scaler
raise ValueError(f"Unable to find model info: {path}")
def load_models(self, _):
return get_realesrgan_models(self)
def get_realesrgan_models(scaler):
try:
from basicsr.archs.rrdbnet_arch import RRDBNet
from realesrgan.archs.srvgg_arch import SRVGGNetCompact
models = [
UpscalerData(
name="R-ESRGAN General 4xV3",
path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth",
scale=4,
upscaler=scaler,
model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
),
UpscalerData(
name="R-ESRGAN General WDN 4xV3",
path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth",
scale=4,
upscaler=scaler,
model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
),
UpscalerData(
name="R-ESRGAN AnimeVideo",
path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth",
scale=4,
upscaler=scaler,
model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
),
UpscalerData(
name="R-ESRGAN 4x+",
path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
scale=4,
upscaler=scaler,
model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
),
UpscalerData(
name="R-ESRGAN 4x+ Anime6B",
path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
scale=4,
upscaler=scaler,
model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
),
UpscalerData(
name="R-ESRGAN 2x+",
path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
scale=2,
upscaler=scaler,
model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
),
]
return models
except Exception:
errors.report("Error making Real-ESRGAN models list", exc_info=True)
def get_realesrgan_models(scaler: UpscalerRealESRGAN):
return [
UpscalerData(
name="R-ESRGAN General 4xV3",
path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth",
scale=4,
upscaler=scaler,
),
UpscalerData(
name="R-ESRGAN General WDN 4xV3",
path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth",
scale=4,
upscaler=scaler,
),
UpscalerData(
name="R-ESRGAN AnimeVideo",
path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth",
scale=4,
upscaler=scaler,
),
UpscalerData(
name="R-ESRGAN 4x+",
path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
scale=4,
upscaler=scaler,
),
UpscalerData(
name="R-ESRGAN 4x+ Anime6B",
path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
scale=4,
upscaler=scaler,
),
UpscalerData(
name="R-ESRGAN 2x+",
path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
scale=2,
upscaler=scaler,
),
]

31
modules/script_callbacks.py
View File

@ -1,3 +1,4 @@
import dataclasses
import inspect
import os
from collections import namedtuple
@ -41,7 +42,7 @@ class ExtraNoiseParams:
class CFGDenoiserParams:
def __init__(self, x, image_cond, sigma, sampling_step, total_sampling_steps, text_cond, text_uncond):
def __init__(self, x, image_cond, sigma, sampling_step, total_sampling_steps, text_cond, text_uncond, denoiser=None):
self.x = x
"""Latent image representation in the process of being denoised"""
@ -63,6 +64,9 @@ class CFGDenoiserParams:
self.text_uncond = text_uncond
""" Encoder hidden states of text conditioning from negative prompt"""
self.denoiser = denoiser
"""Current CFGDenoiser object with processing parameters"""
class CFGDenoisedParams:
def __init__(self, x, sampling_step, total_sampling_steps, inner_model):
@ -103,6 +107,15 @@ class ImageGridLoopParams:
self.rows = rows
@dataclasses.dataclass
class BeforeTokenCounterParams:
prompt: str
steps: int
styles: list
is_positive: bool = True
ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"])
callback_map = dict(
callbacks_app_started=[],
@ -125,6 +138,7 @@ callback_map = dict(
callbacks_on_reload=[],
callbacks_list_optimizers=[],
callbacks_list_unets=[],
callbacks_before_token_counter=[],
)
@ -306,6 +320,14 @@ def list_unets_callback():
return res
def before_token_counter_callback(params: BeforeTokenCounterParams):
for c in callback_map['callbacks_before_token_counter']:
try:
c.callback(params)
except Exception:
report_exception(c, 'before_token_counter')
def add_callback(callbacks, fun):
stack = [x for x in inspect.stack() if x.filename != __file__]
filename = stack[0].filename if stack else 'unknown file'
@ -480,3 +502,10 @@ def on_list_unets(callback):
The function will be called with one argument, a list, and shall add objects of type modules.sd_unet.SdUnetOption to it."""
add_callback(callback_map['callbacks_list_unets'], callback)
def on_before_token_counter(callback):
"""register a function to be called when UI is counting tokens for a prompt.
The function will be called with one argument of type BeforeTokenCounterParams, and should modify its fields if necessary."""
add_callback(callback_map['callbacks_before_token_counter'], callback)

131
modules/scripts.py
View File

@ -11,11 +11,31 @@ from modules import shared, paths, script_callbacks, extensions, script_loading,
AlwaysVisible = object()
class MaskBlendArgs:
def __init__(self, current_latent, nmask, init_latent, mask, blended_latent, denoiser=None, sigma=None):
self.current_latent = current_latent
self.nmask = nmask
self.init_latent = init_latent
self.mask = mask
self.blended_latent = blended_latent
self.denoiser = denoiser
self.is_final_blend = denoiser is None
self.sigma = sigma
class PostSampleArgs:
def __init__(self, samples):
self.samples = samples
class PostprocessImageArgs:
def __init__(self, image):
self.image = image
class PostProcessMaskOverlayArgs:
def __init__(self, index, mask_for_overlay, overlay_image):
self.index = index
self.mask_for_overlay = mask_for_overlay
self.overlay_image = overlay_image
class PostprocessBatchListArgs:
def __init__(self, images):
@ -71,6 +91,9 @@ class Script:
setup_for_ui_only = False
"""If true, the script setup will only be run in Gradio UI, not in API"""
controls = None
"""A list of controls retured by the ui()."""
def title(self):
"""this function should return the title of the script. This is what will be displayed in the dropdown menu."""
@ -206,6 +229,25 @@ class Script:
pass
def on_mask_blend(self, p, mba: MaskBlendArgs, *args):
"""
Called in inpainting mode when the original content is blended with the inpainted content.
This is called at every step in the denoising process and once at the end.
If is_final_blend is true, this is called for the final blending stage.
Otherwise, denoiser and sigma are defined and may be used to inform the procedure.
"""
pass
def post_sample(self, p, ps: PostSampleArgs, *args):
"""
Called after the samples have been generated,
but before they have been decoded by the VAE, if applicable.
Check getattr(samples, 'already_decoded', False) to test if the images are decoded.
"""
pass
def postprocess_image(self, p, pp: PostprocessImageArgs, *args):
"""
Called for every image after it has been generated.
@ -213,6 +255,22 @@ class Script:
pass
def postprocess_maskoverlay(self, p, ppmo: PostProcessMaskOverlayArgs, *args):
"""
Called for every image after it has been generated.
"""
pass
def postprocess_image_after_composite(self, p, pp: PostprocessImageArgs, *args):
"""
Called for every image after it has been generated.
Same as postprocess_image but after inpaint_full_res composite
So that it operates on the full image instead of the inpaint_full_res crop region.
"""
pass
def postprocess(self, p, processed, *args):
"""
This function is called after processing ends for AlwaysVisible scripts.
@ -520,7 +578,12 @@ class ScriptRunner:
auto_processing_scripts = scripts_auto_postprocessing.create_auto_preprocessing_script_data()
for script_data in auto_processing_scripts + scripts_data:
script = script_data.script_class()
try:
script = script_data.script_class()
except Exception:
errors.report(f"Error # failed to initialize Script {script_data.module}: ", exc_info=True)
continue
script.filename = script_data.path
script.is_txt2img = not is_img2img
script.is_img2img = is_img2img
@ -573,6 +636,7 @@ class ScriptRunner:
import modules.api.models as api_models
controls = wrap_call(script.ui, script.filename, "ui", script.is_img2img)
script.controls = controls
if controls is None:
return
@ -645,6 +709,8 @@ class ScriptRunner:
self.setup_ui_for_section(None, self.selectable_scripts)
def select_script(script_index):
if script_index is None:
script_index = 0
selected_script = self.selectable_scripts[script_index - 1] if script_index>0 else None
return [gr.update(visible=selected_script == s) for s in self.selectable_scripts]
@ -688,7 +754,7 @@ class ScriptRunner:
def run(self, p, *args):
script_index = args[0]
if script_index == 0:
if script_index == 0 or script_index is None:
return None
script = self.selectable_scripts[script_index-1]
@ -767,6 +833,22 @@ class ScriptRunner:
except Exception:
errors.report(f"Error running postprocess_batch_list: {script.filename}", exc_info=True)
def post_sample(self, p, ps: PostSampleArgs):
for script in self.alwayson_scripts:
try:
script_args = p.script_args[script.args_from:script.args_to]
script.post_sample(p, ps, *script_args)
except Exception:
errors.report(f"Error running post_sample: {script.filename}", exc_info=True)
def on_mask_blend(self, p, mba: MaskBlendArgs):
for script in self.alwayson_scripts:
try:
script_args = p.script_args[script.args_from:script.args_to]
script.on_mask_blend(p, mba, *script_args)
except Exception:
errors.report(f"Error running post_sample: {script.filename}", exc_info=True)
def postprocess_image(self, p, pp: PostprocessImageArgs):
for script in self.alwayson_scripts:
try:
@ -775,6 +857,22 @@ class ScriptRunner:
except Exception:
errors.report(f"Error running postprocess_image: {script.filename}", exc_info=True)
def postprocess_maskoverlay(self, p, ppmo: PostProcessMaskOverlayArgs):
for script in self.alwayson_scripts:
try:
script_args = p.script_args[script.args_from:script.args_to]
script.postprocess_maskoverlay(p, ppmo, *script_args)
except Exception:
errors.report(f"Error running postprocess_image: {script.filename}", exc_info=True)
def postprocess_image_after_composite(self, p, pp: PostprocessImageArgs):
for script in self.alwayson_scripts:
try:
script_args = p.script_args[script.args_from:script.args_to]
script.postprocess_image_after_composite(p, pp, *script_args)
except Exception:
errors.report(f"Error running postprocess_image_after_composite: {script.filename}", exc_info=True)
def before_component(self, component, **kwargs):
for callback, script in self.on_before_component_elem_id.get(kwargs.get("elem_id"), []):
try:
@ -841,6 +939,35 @@ class ScriptRunner:
except Exception:
errors.report(f"Error running setup: {script.filename}", exc_info=True)
def set_named_arg(self, args, script_name, arg_elem_id, value, fuzzy=False):
"""Locate an arg of a specific script in script_args and set its value
Args:
args: all script args of process p, p.script_args
script_name: the name target script name to
arg_elem_id: the elem_id of the target arg
value: the value to set
fuzzy: if True, arg_elem_id can be a substring of the control.elem_id else exact match
Returns:
Updated script args
when script_name in not found or arg_elem_id is not found in script controls, raise RuntimeError
"""
script = next((x for x in self.scripts if x.name == script_name), None)
if script is None:
raise RuntimeError(f"script {script_name} not found")
for i, control in enumerate(script.controls):
if arg_elem_id in control.elem_id if fuzzy else arg_elem_id == control.elem_id:
index = script.args_from + i
if isinstance(args, tuple):
return args[:index] + (value,) + args[index + 1:]
elif isinstance(args, list):
args[index] = value
return args
else:
raise RuntimeError(f"args is not a list or tuple, but {type(args)}")
raise RuntimeError(f"arg_elem_id {arg_elem_id} not found in script {script_name}")
scripts_txt2img: ScriptRunner = None
scripts_img2img: ScriptRunner = None

70
modules/sd_emphasis.py Normal file
View File

@ -0,0 +1,70 @@
from __future__ import annotations
import torch
class Emphasis:
"""Emphasis class decides how to death with (emphasized:1.1) text in prompts"""
name: str = "Base"
description: str = ""
tokens: list[list[int]]
"""tokens from the chunk of the prompt"""
multipliers: torch.Tensor
"""tensor with multipliers, once for each token"""
z: torch.Tensor
"""output of cond transformers network (CLIP)"""
def after_transformers(self):
"""Called after cond transformers network has processed the chunk of the prompt; this function should modify self.z to apply the emphasis"""
pass
class EmphasisNone(Emphasis):
name = "None"
description = "disable the mechanism entirely and treat (:.1.1) as literal characters"
class EmphasisIgnore(Emphasis):
name = "Ignore"
description = "treat all empasised words as if they have no emphasis"
class EmphasisOriginal(Emphasis):
name = "Original"
description = "the orginal emphasis implementation"
def after_transformers(self):
original_mean = self.z.mean()
self.z = self.z * self.multipliers.reshape(self.multipliers.shape + (1,)).expand(self.z.shape)
# restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
new_mean = self.z.mean()
self.z = self.z * (original_mean / new_mean)
class EmphasisOriginalNoNorm(EmphasisOriginal):
name = "No norm"
description = "same as orginal, but without normalization (seems to work better for SDXL)"
def after_transformers(self):
self.z = self.z * self.multipliers.reshape(self.multipliers.shape + (1,)).expand(self.z.shape)
def get_current_option(emphasis_option_name):
return next(iter([x for x in options if x.name == emphasis_option_name]), EmphasisOriginal)
def get_options_descriptions():
return ", ".join(f"{x.name}: {x.description}" for x in options)
options = [
EmphasisNone,
EmphasisIgnore,
EmphasisOriginal,
EmphasisOriginalNoNorm,
]

21
modules/sd_hijack_clip.py
View File

@ -3,7 +3,7 @@ from collections import namedtuple
import torch
from modules import prompt_parser, devices, sd_hijack
from modules import prompt_parser, devices, sd_hijack, sd_emphasis
from modules.shared import opts
@ -88,7 +88,7 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
Returns the list and the total number of tokens in the prompt.
"""
if opts.enable_emphasis:
if opts.emphasis != "None":
parsed = prompt_parser.parse_prompt_attention(line)
else:
parsed = [[line, 1.0]]
@ -249,6 +249,9 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
hashes.append(self.hijack.extra_generation_params.get("TI hashes"))
self.hijack.extra_generation_params["TI hashes"] = ", ".join(hashes)
if any(x for x in texts if "(" in x or "[" in x) and opts.emphasis != "Original":
self.hijack.extra_generation_params["Emphasis"] = opts.emphasis
if getattr(self.wrapped, 'return_pooled', False):
return torch.hstack(zs), zs[0].pooled
else:
@ -274,12 +277,14 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
pooled = getattr(z, 'pooled', None)
# restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
batch_multipliers = torch.asarray(batch_multipliers).to(devices.device)
original_mean = z.mean()
z = z * batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
new_mean = z.mean()
z = z * (original_mean / new_mean)
emphasis = sd_emphasis.get_current_option(opts.emphasis)()
emphasis.tokens = remade_batch_tokens
emphasis.multipliers = torch.asarray(batch_multipliers).to(devices.device)
emphasis.z = z
emphasis.after_transformers()
z = emphasis.z
if pooled is not None:
z.pooled = pooled

2
modules/sd_hijack_clip_old.py
View File

@ -32,7 +32,7 @@ def process_text_old(self: sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase,
embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
mult_change = self.token_mults.get(token) if shared.opts.enable_emphasis else None
mult_change = self.token_mults.get(token) if shared.opts.emphasis != "None" else None
if mult_change is not None:
mult *= mult_change
i += 1

12
modules/sd_hijack_utils.py
View File

@ -11,10 +11,14 @@ class CondFunc:
break
except ImportError:
pass
for attr_name in func_path[i:-1]:
resolved_obj = getattr(resolved_obj, attr_name)
orig_func = getattr(resolved_obj, func_path[-1])
setattr(resolved_obj, func_path[-1], lambda *args, **kwargs: self(*args, **kwargs))
try:
for attr_name in func_path[i:-1]:
resolved_obj = getattr(resolved_obj, attr_name)
orig_func = getattr(resolved_obj, func_path[-1])
setattr(resolved_obj, func_path[-1], lambda *args, **kwargs: self(*args, **kwargs))
except AttributeError:
print(f"Warning: Failed to resolve {orig_func} for CondFunc hijack")
pass
self.__init__(orig_func, sub_func, cond_func)
return lambda *args, **kwargs: self(*args, **kwargs)
def __init__(self, orig_func, sub_func, cond_func):

106
modules/sd_models.py
View File

@ -15,6 +15,7 @@ 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, patches
from modules.timer import Timer
from modules.shared import opts
import tomesd
import numpy as np
@ -348,10 +349,28 @@ class SkipWritingToConfig:
SkipWritingToConfig.skip = self.previous
def check_fp8(model):
if model is None:
return None
if devices.get_optimal_device_name() == "mps":
enable_fp8 = False
elif shared.opts.fp8_storage == "Enable":
enable_fp8 = True
elif getattr(model, "is_sdxl", False) and shared.opts.fp8_storage == "Enable for SDXL":
enable_fp8 = True
else:
enable_fp8 = False
return enable_fp8
def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer):
sd_model_hash = checkpoint_info.calculate_shorthash()
timer.record("calculate hash")
if devices.fp8:
# prevent model to load state dict in fp8
model.half()
if not SkipWritingToConfig.skip:
shared.opts.data["sd_model_checkpoint"] = checkpoint_info.title
@ -383,6 +402,7 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
if shared.cmd_opts.no_half:
model.float()
model.alphas_cumprod_original = model.alphas_cumprod
devices.dtype_unet = torch.float32
timer.record("apply float()")
else:
@ -396,7 +416,11 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
if shared.cmd_opts.upcast_sampling and depth_model:
model.depth_model = None
alphas_cumprod = model.alphas_cumprod
model.alphas_cumprod = None
model.half()
model.alphas_cumprod = alphas_cumprod
model.alphas_cumprod_original = alphas_cumprod
model.first_stage_model = vae
if depth_model:
model.depth_model = depth_model
@ -404,6 +428,30 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
devices.dtype_unet = torch.float16
timer.record("apply half()")
apply_alpha_schedule_override(model)
for module in model.modules():
if hasattr(module, 'fp16_weight'):
del module.fp16_weight
if hasattr(module, 'fp16_bias'):
del module.fp16_bias
if check_fp8(model):
devices.fp8 = True
first_stage = model.first_stage_model
model.first_stage_model = None
for module in model.modules():
if isinstance(module, (torch.nn.Conv2d, torch.nn.Linear)):
if shared.opts.cache_fp16_weight:
module.fp16_weight = module.weight.data.clone().cpu().half()
if module.bias is not None:
module.fp16_bias = module.bias.data.clone().cpu().half()
module.to(torch.float8_e4m3fn)
model.first_stage_model = first_stage
timer.record("apply fp8")
else:
devices.fp8 = False
devices.unet_needs_upcast = shared.cmd_opts.upcast_sampling and devices.dtype == torch.float16 and devices.dtype_unet == torch.float16
model.first_stage_model.to(devices.dtype_vae)
@ -505,6 +553,48 @@ def repair_config(sd_config):
sd_config.model.params.noise_aug_config.params.clip_stats_path = sd_config.model.params.noise_aug_config.params.clip_stats_path.replace("checkpoints/karlo_models", karlo_path)
def rescale_zero_terminal_snr_abar(alphas_cumprod):
alphas_bar_sqrt = alphas_cumprod.sqrt()
# Store old values.
alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()
alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()
# Shift so the last timestep is zero.
alphas_bar_sqrt -= (alphas_bar_sqrt_T)
# Scale so the first timestep is back to the old value.
alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)
# Convert alphas_bar_sqrt to betas
alphas_bar = alphas_bar_sqrt ** 2 # Revert sqrt
alphas_bar[-1] = 4.8973451890853435e-08
return alphas_bar
def apply_alpha_schedule_override(sd_model, p=None):
"""
Applies an override to the alpha schedule of the model according to settings.
- downcasts the alpha schedule to half precision
- rescales the alpha schedule to have zero terminal SNR
"""
if not hasattr(sd_model, 'alphas_cumprod') or not hasattr(sd_model, 'alphas_cumprod_original'):
return
sd_model.alphas_cumprod = sd_model.alphas_cumprod_original.to(shared.device)
if opts.use_downcasted_alpha_bar:
if p is not None:
p.extra_generation_params['Downcast alphas_cumprod'] = opts.use_downcasted_alpha_bar
sd_model.alphas_cumprod = sd_model.alphas_cumprod.half().to(shared.device)
if opts.sd_noise_schedule == "Zero Terminal SNR":
if p is not None:
p.extra_generation_params['Noise Schedule'] = opts.sd_noise_schedule
sd_model.alphas_cumprod = rescale_zero_terminal_snr_abar(sd_model.alphas_cumprod).to(shared.device)
sd1_clip_weight = 'cond_stage_model.transformer.text_model.embeddings.token_embedding.weight'
sd2_clip_weight = 'cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight'
sdxl_clip_weight = 'conditioner.embedders.1.model.ln_final.weight'
@ -651,6 +741,7 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None):
else:
weight_dtype_conversion = {
'first_stage_model': None,
'alphas_cumprod': None,
'': torch.float16,
}
@ -746,7 +837,7 @@ def reuse_model_from_already_loaded(sd_model, checkpoint_info, timer):
return None
def reload_model_weights(sd_model=None, info=None):
def reload_model_weights(sd_model=None, info=None, forced_reload=False):
checkpoint_info = info or select_checkpoint()
timer = Timer()
@ -758,11 +849,14 @@ def reload_model_weights(sd_model=None, info=None):
current_checkpoint_info = None
else:
current_checkpoint_info = sd_model.sd_checkpoint_info
if sd_model.sd_model_checkpoint == checkpoint_info.filename:
if check_fp8(sd_model) != devices.fp8:
# load from state dict again to prevent extra numerical errors
forced_reload = True
elif sd_model.sd_model_checkpoint == checkpoint_info.filename and not forced_reload:
return sd_model
sd_model = reuse_model_from_already_loaded(sd_model, checkpoint_info, timer)
if sd_model is not None and sd_model.sd_checkpoint_info.filename == checkpoint_info.filename:
if not forced_reload and sd_model is not None and sd_model.sd_checkpoint_info.filename == checkpoint_info.filename:
return sd_model
if sd_model is not None:
@ -793,13 +887,13 @@ def reload_model_weights(sd_model=None, info=None):
sd_hijack.model_hijack.hijack(sd_model)
timer.record("hijack")
script_callbacks.model_loaded_callback(sd_model)
timer.record("script callbacks")
if not sd_model.lowvram:
sd_model.to(devices.device)
timer.record("move model to device")
script_callbacks.model_loaded_callback(sd_model)
timer.record("script callbacks")
print(f"Weights loaded in {timer.summary()}.")
model_data.set_sd_model(sd_model)

6
modules/sd_models_config.py
View File

@ -15,6 +15,7 @@ config_sd2v = os.path.join(sd_repo_configs_path, "v2-inference-v.yaml")
config_sd2_inpainting = os.path.join(sd_repo_configs_path, "v2-inpainting-inference.yaml")
config_sdxl = os.path.join(sd_xl_repo_configs_path, "sd_xl_base.yaml")
config_sdxl_refiner = os.path.join(sd_xl_repo_configs_path, "sd_xl_refiner.yaml")
config_sdxl_inpainting = os.path.join(sd_configs_path, "sd_xl_inpaint.yaml")
config_depth_model = os.path.join(sd_repo_configs_path, "v2-midas-inference.yaml")
config_unclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-l-inference.yaml")
config_unopenclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-h-inference.yaml")
@ -71,7 +72,10 @@ def guess_model_config_from_state_dict(sd, filename):
sd2_variations_weight = sd.get('embedder.model.ln_final.weight', None)
if sd.get('conditioner.embedders.1.model.ln_final.weight', None) is not None:
return config_sdxl
if diffusion_model_input.shape[1] == 9:
return config_sdxl_inpainting
else:
return config_sdxl
if sd.get('conditioner.embedders.0.model.ln_final.weight', None) is not None:
return config_sdxl_refiner
elif sd.get('depth_model.model.pretrained.act_postprocess3.0.project.0.bias', None) is not None:

11
modules/sd_models_xl.py
View File

@ -6,6 +6,7 @@ import sgm.models.diffusion
import sgm.modules.diffusionmodules.denoiser_scaling
import sgm.modules.diffusionmodules.discretizer
from modules import devices, shared, prompt_parser
from modules import torch_utils
def get_learned_conditioning(self: sgm.models.diffusion.DiffusionEngine, batch: prompt_parser.SdConditioning | list[str]):
@ -34,6 +35,12 @@ def get_learned_conditioning(self: sgm.models.diffusion.DiffusionEngine, batch:
def apply_model(self: sgm.models.diffusion.DiffusionEngine, x, t, cond):
sd = self.model.state_dict()
diffusion_model_input = sd.get('diffusion_model.input_blocks.0.0.weight', None)
if diffusion_model_input is not None:
if diffusion_model_input.shape[1] == 9:
x = torch.cat([x] + cond['c_concat'], dim=1)
return self.model(x, t, cond)
@ -84,7 +91,7 @@ sgm.modules.GeneralConditioner.get_target_prompt_token_count = get_target_prompt
def extend_sdxl(model):
"""this adds a bunch of parameters to make SDXL model look a bit more like SD1.5 to the rest of the codebase."""
dtype = next(model.model.diffusion_model.parameters()).dtype
dtype = torch_utils.get_param(model.model.diffusion_model).dtype
model.model.diffusion_model.dtype = dtype
model.model.conditioning_key = 'crossattn'
model.cond_stage_key = 'txt'
@ -93,7 +100,7 @@ def extend_sdxl(model):
model.parameterization = "v" if isinstance(model.denoiser.scaling, sgm.modules.diffusionmodules.denoiser_scaling.VScaling) else "eps"
discretization = sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization()
model.alphas_cumprod = torch.asarray(discretization.alphas_cumprod, device=devices.device, dtype=dtype)
model.alphas_cumprod = torch.asarray(discretization.alphas_cumprod, device=devices.device, dtype=torch.float32)
model.conditioner.wrapped = torch.nn.Module()

3
modules/sd_samplers.py
View File

@ -1,4 +1,4 @@
from modules import sd_samplers_kdiffusion, sd_samplers_timesteps, shared
from modules import sd_samplers_kdiffusion, sd_samplers_timesteps, sd_samplers_lcm, shared
# imports for functions that previously were here and are used by other modules
from modules.sd_samplers_common import samples_to_image_grid, sample_to_image # noqa: F401
@ -6,6 +6,7 @@ from modules.sd_samplers_common import samples_to_image_grid, sample_to_image #
all_samplers = [
*sd_samplers_kdiffusion.samplers_data_k_diffusion,
*sd_samplers_timesteps.samplers_data_timesteps,
*sd_samplers_lcm.samplers_data_lcm,
]
all_samplers_map = {x.name: x for x in all_samplers}

95
modules/sd_samplers_cfg_denoiser.py
View File

@ -53,9 +53,13 @@ class CFGDenoiser(torch.nn.Module):
self.step = 0
self.image_cfg_scale = None
self.padded_cond_uncond = False
self.padded_cond_uncond_v0 = False
self.sampler = sampler
self.model_wrap = None
self.p = None
# NOTE: masking before denoising can cause the original latents to be oversmoothed
# as the original latents do not have noise
self.mask_before_denoising = False
@property
@ -88,6 +92,62 @@ class CFGDenoiser(torch.nn.Module):
self.sampler.sampler_extra_args['cond'] = c
self.sampler.sampler_extra_args['uncond'] = uc
def pad_cond_uncond(self, cond, uncond):
empty = shared.sd_model.cond_stage_model_empty_prompt
num_repeats = (cond.shape[1] - uncond.shape[1]) // empty.shape[1]
if num_repeats < 0:
cond = pad_cond(cond, -num_repeats, empty)
self.padded_cond_uncond = True
elif num_repeats > 0:
uncond = pad_cond(uncond, num_repeats, empty)
self.padded_cond_uncond = True
return cond, uncond
def pad_cond_uncond_v0(self, cond, uncond):
"""
Pads the 'uncond' tensor to match the shape of the 'cond' tensor.
If 'uncond' is a dictionary, it is assumed that the 'crossattn' key holds the tensor to be padded.
If 'uncond' is a tensor, it is padded directly.
If the number of columns in 'uncond' is less than the number of columns in 'cond', the last column of 'uncond'
is repeated to match the number of columns in 'cond'.
If the number of columns in 'uncond' is greater than the number of columns in 'cond', 'uncond' is truncated
to match the number of columns in 'cond'.
Args:
cond (torch.Tensor or DictWithShape): The condition tensor to match the shape of 'uncond'.
uncond (torch.Tensor or DictWithShape): The tensor to be padded, or a dictionary containing the tensor to be padded.
Returns:
tuple: A tuple containing the 'cond' tensor and the padded 'uncond' tensor.
Note:
This is the padding that was always used in DDIM before version 1.6.0
"""
is_dict_cond = isinstance(uncond, dict)
uncond_vec = uncond['crossattn'] if is_dict_cond else uncond
if uncond_vec.shape[1] < cond.shape[1]:
last_vector = uncond_vec[:, -1:]
last_vector_repeated = last_vector.repeat([1, cond.shape[1] - uncond_vec.shape[1], 1])
uncond_vec = torch.hstack([uncond_vec, last_vector_repeated])
self.padded_cond_uncond_v0 = True
elif uncond_vec.shape[1] > cond.shape[1]:
uncond_vec = uncond_vec[:, :cond.shape[1]]
self.padded_cond_uncond_v0 = True
if is_dict_cond:
uncond['crossattn'] = uncond_vec
else:
uncond = uncond_vec
return cond, uncond
def forward(self, x, sigma, uncond, cond, cond_scale, s_min_uncond, image_cond):
if state.interrupted or state.skipped:
raise sd_samplers_common.InterruptedException
@ -105,8 +165,21 @@ class CFGDenoiser(torch.nn.Module):
assert not is_edit_model or all(len(conds) == 1 for conds in conds_list), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)"
# If we use masks, blending between the denoised and original latent images occurs here.
def apply_blend(current_latent):
blended_latent = current_latent * self.nmask + self.init_latent * self.mask
if self.p.scripts is not None:
from modules import scripts
mba = scripts.MaskBlendArgs(current_latent, self.nmask, self.init_latent, self.mask, blended_latent, denoiser=self, sigma=sigma)
self.p.scripts.on_mask_blend(self.p, mba)
blended_latent = mba.blended_latent
return blended_latent
# Blend in the original latents (before)
if self.mask_before_denoising and self.mask is not None:
x = self.init_latent * self.mask + self.nmask * x
x = apply_blend(x)
batch_size = len(conds_list)
repeats = [len(conds_list[i]) for i in range(batch_size)]
@ -130,7 +203,7 @@ class CFGDenoiser(torch.nn.Module):
sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma])
image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond] + [torch.zeros_like(self.init_latent)])
denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps, tensor, uncond)
denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps, tensor, uncond, self)
cfg_denoiser_callback(denoiser_params)
x_in = denoiser_params.x
image_cond_in = denoiser_params.image_cond
@ -146,16 +219,11 @@ class CFGDenoiser(torch.nn.Module):
sigma_in = sigma_in[:-batch_size]
self.padded_cond_uncond = False
if shared.opts.pad_cond_uncond and tensor.shape[1] != uncond.shape[1]:
empty = shared.sd_model.cond_stage_model_empty_prompt
num_repeats = (tensor.shape[1] - uncond.shape[1]) // empty.shape[1]
if num_repeats < 0:
tensor = pad_cond(tensor, -num_repeats, empty)
self.padded_cond_uncond = True
elif num_repeats > 0:
uncond = pad_cond(uncond, num_repeats, empty)
self.padded_cond_uncond = True
self.padded_cond_uncond_v0 = False
if shared.opts.pad_cond_uncond_v0 and tensor.shape[1] != uncond.shape[1]:
tensor, uncond = self.pad_cond_uncond_v0(tensor, uncond)
elif shared.opts.pad_cond_uncond and tensor.shape[1] != uncond.shape[1]:
tensor, uncond = self.pad_cond_uncond(tensor, uncond)
if tensor.shape[1] == uncond.shape[1] or skip_uncond:
if is_edit_model:
@ -207,8 +275,9 @@ class CFGDenoiser(torch.nn.Module):
else:
denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
# Blend in the original latents (after)
if not self.mask_before_denoising and self.mask is not None:
denoised = self.init_latent * self.mask + self.nmask * denoised
denoised = apply_blend(denoised)
self.sampler.last_latent = self.get_pred_x0(torch.cat([x_in[i:i + 1] for i in denoised_image_indexes]), torch.cat([x_out[i:i + 1] for i in denoised_image_indexes]), sigma)

7
modules/sd_samplers_common.py
View File

@ -335,3 +335,10 @@ class Sampler:
def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
raise NotImplementedError()
def add_infotext(self, p):
if self.model_wrap_cfg.padded_cond_uncond:
p.extra_generation_params["Pad conds"] = True
if self.model_wrap_cfg.padded_cond_uncond_v0:
p.extra_generation_params["Pad conds v0"] = True

6
modules/sd_samplers_kdiffusion.py
View File

@ -187,8 +187,7 @@ class KDiffusionSampler(sd_samplers_common.Sampler):
samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
if self.model_wrap_cfg.padded_cond_uncond:
p.extra_generation_params["Pad conds"] = True
self.add_infotext(p)
return samples
@ -234,8 +233,7 @@ class KDiffusionSampler(sd_samplers_common.Sampler):
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
if self.model_wrap_cfg.padded_cond_uncond:
p.extra_generation_params["Pad conds"] = True
self.add_infotext(p)
return samples

104
modules/sd_samplers_lcm.py Normal file
View File

@ -0,0 +1,104 @@
import torch
from k_diffusion import utils, sampling
from k_diffusion.external import DiscreteEpsDDPMDenoiser
from k_diffusion.sampling import default_noise_sampler, trange
from modules import shared, sd_samplers_cfg_denoiser, sd_samplers_kdiffusion, sd_samplers_common
class LCMCompVisDenoiser(DiscreteEpsDDPMDenoiser):
def __init__(self, model):
timesteps = 1000
original_timesteps = 50 # LCM Original Timesteps (default=50, for current version of LCM)
self.skip_steps = timesteps // original_timesteps
alphas_cumprod_valid = torch.zeros((original_timesteps), dtype=torch.float32, device=model.device)
for x in range(original_timesteps):
alphas_cumprod_valid[original_timesteps - 1 - x] = model.alphas_cumprod[timesteps - 1 - x * self.skip_steps]
super().__init__(model, alphas_cumprod_valid, quantize=None)
def get_sigmas(self, n=None,):
if n is None:
return sampling.append_zero(self.sigmas.flip(0))
start = self.sigma_to_t(self.sigma_max)
end = self.sigma_to_t(self.sigma_min)
t = torch.linspace(start, end, n, device=shared.sd_model.device)
return sampling.append_zero(self.t_to_sigma(t))
def sigma_to_t(self, sigma, quantize=None):
log_sigma = sigma.log()
dists = log_sigma - self.log_sigmas[:, None]
return dists.abs().argmin(dim=0).view(sigma.shape) * self.skip_steps + (self.skip_steps - 1)
def t_to_sigma(self, timestep):
t = torch.clamp(((timestep - (self.skip_steps - 1)) / self.skip_steps).float(), min=0, max=(len(self.sigmas) - 1))
return super().t_to_sigma(t)
def get_eps(self, *args, **kwargs):
return self.inner_model.apply_model(*args, **kwargs)
def get_scaled_out(self, sigma, output, input):
sigma_data = 0.5
scaled_timestep = utils.append_dims(self.sigma_to_t(sigma), output.ndim) * 10.0
c_skip = sigma_data**2 / (scaled_timestep**2 + sigma_data**2)
c_out = scaled_timestep / (scaled_timestep**2 + sigma_data**2) ** 0.5
return c_out * output + c_skip * input
def forward(self, input, sigma, **kwargs):
c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)]
eps = self.get_eps(input * c_in, self.sigma_to_t(sigma), **kwargs)
return self.get_scaled_out(sigma, input + eps * c_out, input)
def sample_lcm(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None):
extra_args = {} if extra_args is None else extra_args
noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
s_in = x.new_ones([x.shape[0]])
for i in trange(len(sigmas) - 1, disable=disable):
denoised = model(x, sigmas[i] * s_in, **extra_args)
if callback is not None:
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
x = denoised
if sigmas[i + 1] > 0:
x += sigmas[i + 1] * noise_sampler(sigmas[i], sigmas[i + 1])
return x
class CFGDenoiserLCM(sd_samplers_cfg_denoiser.CFGDenoiser):
@property
def inner_model(self):
if self.model_wrap is None:
denoiser = LCMCompVisDenoiser
self.model_wrap = denoiser(shared.sd_model)
return self.model_wrap
class LCMSampler(sd_samplers_kdiffusion.KDiffusionSampler):
def __init__(self, funcname, sd_model, options=None):
super().__init__(funcname, sd_model, options)
self.model_wrap_cfg = CFGDenoiserLCM(self)
self.model_wrap = self.model_wrap_cfg.inner_model
samplers_lcm = [('LCM', sample_lcm, ['k_lcm'], {})]
samplers_data_lcm = [
sd_samplers_common.SamplerData(label, lambda model, funcname=funcname: LCMSampler(funcname, model), aliases, options)
for label, funcname, aliases, options in samplers_lcm
]

9
modules/sd_samplers_timesteps.py
View File

@ -36,7 +36,7 @@ class CompVisTimestepsVDenoiser(torch.nn.Module):
self.inner_model = model
def predict_eps_from_z_and_v(self, x_t, t, v):
return self.inner_model.sqrt_alphas_cumprod[t.to(torch.int), None, None, None] * v + self.inner_model.sqrt_one_minus_alphas_cumprod[t.to(torch.int), None, None, None] * x_t
return torch.sqrt(self.inner_model.alphas_cumprod)[t.to(torch.int), None, None, None] * v + torch.sqrt(1 - self.inner_model.alphas_cumprod)[t.to(torch.int), None, None, None] * x_t
def forward(self, input, timesteps, **kwargs):
model_output = self.inner_model.apply_model(input, timesteps, **kwargs)
@ -80,6 +80,7 @@ class CompVisSampler(sd_samplers_common.Sampler):
self.eta_default = 0.0
self.model_wrap_cfg = CFGDenoiserTimesteps(self)
self.model_wrap = self.model_wrap_cfg.inner_model
def get_timesteps(self, p, steps):
discard_next_to_last_sigma = self.config is not None and self.config.options.get('discard_next_to_last_sigma', False)
@ -132,8 +133,7 @@ class CompVisSampler(sd_samplers_common.Sampler):
samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
if self.model_wrap_cfg.padded_cond_uncond:
p.extra_generation_params["Pad conds"] = True
self.add_infotext(p)
return samples
@ -157,8 +157,7 @@ class CompVisSampler(sd_samplers_common.Sampler):
}
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
if self.model_wrap_cfg.padded_cond_uncond:
p.extra_generation_params["Pad conds"] = True
self.add_infotext(p)
return samples

3
modules/sd_vae.py
View File

@ -273,10 +273,11 @@ def reload_vae_weights(sd_model=None, vae_file=unspecified):
load_vae(sd_model, vae_file, vae_source)
sd_hijack.model_hijack.hijack(sd_model)
script_callbacks.model_loaded_callback(sd_model)
if not sd_model.lowvram:
sd_model.to(devices.device)
script_callbacks.model_loaded_callback(sd_model)
print("VAE weights loaded.")
return sd_model

Some files were not shown because too many files have changed in this diff Show More