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2
.github/workflows/on_pull_request.yaml vendored
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@ -20,7 +20,7 @@ jobs:
# not to have GHA download an (at the time of writing) 4 GB cache
# of PyTorch and other dependencies.
- name: Install Ruff
run: pip install ruff==0.0.272
run: pip install ruff==0.1.6
- name: Run Ruff
run: ruff .
lint-js:

162
CHANGELOG.md
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@ -1,3 +1,165 @@
## 1.7.0
### Features:
* settings tab rework: add search field, add categories, split UI settings page into many
* add altdiffusion-m18 support ([#13364](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13364))
* support inference with LyCORIS GLora networks ([#13610](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13610))
* add lora-embedding bundle system ([#13568](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13568))
* option to move prompt from top row into generation parameters
* add support for SSD-1B ([#13865](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13865))
* support inference with OFT networks ([#13692](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13692))
* script metadata and DAG sorting mechanism ([#13944](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13944))
* support HyperTile optimization ([#13948](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13948))
* add support for SD 2.1 Turbo ([#14170](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14170))
* remove Train->Preprocessing tab and put all its functionality into Extras tab
* initial IPEX support for Intel Arc GPU ([#14171](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14171))
### Minor:
* allow reading model hash from images in img2img batch mode ([#12767](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12767))
* add option to align with sgm repo's sampling implementation ([#12818](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12818))
* extra field for lora metadata viewer: `ss_output_name` ([#12838](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12838))
* add action in settings page to calculate all SD checkpoint hashes ([#12909](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12909))
* add button to copy prompt to style editor ([#12975](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12975))
* add --skip-load-model-at-start option ([#13253](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13253))
* write infotext to gif images
* read infotext from gif images ([#13068](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13068))
* allow configuring the initial state of InputAccordion in ui-config.json ([#13189](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13189))
* allow editing whitespace delimiters for ctrl+up/ctrl+down prompt editing ([#13444](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13444))
* prevent accidentally closing popup dialogs ([#13480](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13480))
* added option to play notification sound or not ([#13631](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13631))
* show the preview image in the full screen image viewer if available ([#13459](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13459))
* support for webui.settings.bat ([#13638](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13638))
* add an option to not print stack traces on ctrl+c
* start/restart generation by Ctrl (Alt) + Enter ([#13644](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13644))
* update prompts_from_file script to allow concatenating entries with the general prompt ([#13733](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13733))
* added a visible checkbox to input accordion
* added an option to hide all txt2img/img2img parameters in an accordion ([#13826](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13826))
* added 'Path' sorting option for Extra network cards ([#13968](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13968))
* enable prompt hotkeys in style editor ([#13931](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13931))
* option to show batch img2img results in UI ([#14009](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14009))
* infotext updates: add option to disregard certain infotext fields, add option to not include VAE in infotext, add explanation to infotext settings page, move some options to infotext settings page
* add FP32 fallback support on sd_vae_approx ([#14046](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14046))
* support XYZ scripts / split hires path from unet ([#14126](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14126))
* allow use of mutiple styles csv files ([#14125](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14125))
### Extensions and API:
* update gradio to 3.41.2
* support installed extensions list api ([#12774](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12774))
* update pnginfo API to return dict with parsed values
* add noisy latent to `ExtraNoiseParams` for callback ([#12856](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12856))
* show extension datetime in UTC ([#12864](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12864), [#12865](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12865), [#13281](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13281))
* add an option to choose how to combine hires fix and refiner
* include program version in info response. ([#13135](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13135))
* sd_unet support for SDXL
* patch DDPM.register_betas so that users can put given_betas in model yaml ([#13276](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13276))
* xyz_grid: add prepare ([#13266](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13266))
* allow multiple localization files with same language in extensions ([#13077](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13077))
* add onEdit function for js and rework token-counter.js to use it
* fix the key error exception when processing override_settings keys ([#13567](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13567))
* ability for extensions to return custom data via api in response.images ([#13463](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13463))
* call state.jobnext() before postproces*() ([#13762](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13762))
* add option to set notification sound volume ([#13884](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13884))
* update Ruff to 0.1.6 ([#14059](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14059))
* add Block component creation callback ([#14119](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14119))
* catch uncaught exception with ui creation scripts ([#14120](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14120))
* use extension name for determining an extension is installed in the index ([#14063](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14063))
* update is_installed() from launch_utils.py to fix reinstalling already installed packages ([#14192](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14192))
### Bug Fixes:
* fix pix2pix producing bad results
* fix defaults settings page breaking when any of main UI tabs are hidden
* fix error that causes some extra networks to be disabled if both <lora:> and <lyco:> are present in the prompt
* fix for Reload UI function: if you reload UI on one tab, other opened tabs will no longer stop working
* prevent duplicate resize handler ([#12795](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12795))
* small typo: vae resolve bug ([#12797](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12797))
* hide broken image crop tool ([#12792](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12792))
* don't show hidden samplers in dropdown for XYZ script ([#12780](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12780))
* fix style editing dialog breaking if it's opened in both img2img and txt2img tabs
* hide --gradio-auth and --api-auth values from /internal/sysinfo report
* add missing infotext for RNG in options ([#12819](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12819))
* fix notification not playing when built-in webui tab is inactive ([#12834](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12834))
* honor `--skip-install` for extension installers ([#12832](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12832))
* don't print blank stdout in extension installers ([#12833](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12833), [#12855](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12855))
* get progressbar to display correctly in extensions tab
* keep order in list of checkpoints when loading model that doesn't have a checksum
* fix inpainting models in txt2img creating black pictures
* fix generation params regex ([#12876](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12876))
* fix batch img2img output dir with script ([#12926](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12926))
* fix #13080 - Hypernetwork/TI preview generation ([#13084](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13084))
* fix bug with sigma min/max overrides. ([#12995](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12995))
* more accurate check for enabling cuDNN benchmark on 16XX cards ([#12924](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12924))
* don't use multicond parser for negative prompt counter ([#13118](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13118))
* fix data-sort-name containing spaces ([#13412](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13412))
* update card on correct tab when editing metadata ([#13411](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13411))
* fix viewing/editing metadata when filename contains an apostrophe ([#13395](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13395))
* fix: --sd_model in "Prompts from file or textbox" script is not working ([#13302](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13302))
* better Support for Portable Git ([#13231](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13231))
* fix issues when webui_dir is not work_dir ([#13210](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13210))
* fix: lora-bias-backup don't reset cache ([#13178](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13178))
* account for customizable extra network separators whyen removing extra network text from the prompt ([#12877](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12877))
* re fix batch img2img output dir with script ([#13170](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13170))
* fix `--ckpt-dir` path separator and option use `short name` for checkpoint dropdown ([#13139](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13139))
* consolidated allowed preview formats, Fix extra network `.gif` not woking as preview ([#13121](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13121))
* fix venv_dir=- environment variable not working as expected on linux ([#13469](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13469))
* repair unload sd checkpoint button
* edit-attention fixes ([#13533](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13533))
* fix bug when using --gfpgan-models-path ([#13718](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13718))
* properly apply sort order for extra network cards when selected from dropdown
* fixes generation restart not working for some users when 'Ctrl+Enter' is pressed ([#13962](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13962))
* thread safe extra network list_items ([#13014](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13014))
* fix not able to exit metadata popup when pop up is too big ([#14156](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14156))
* fix auto focal point crop for opencv >= 4.8 ([#14121](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14121))
* make 'use-cpu all' actually apply to 'all' ([#14131](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14131))
* extras tab batch: actually use original filename
* make webui not crash when running with --disable-all-extensions option
### Other:
* non-local condition ([#12814](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12814))
* fix minor typos ([#12827](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12827))
* remove xformers Python version check ([#12842](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12842))
* style: file-metadata word-break ([#12837](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12837))
* revert SGM noise multiplier change for img2img because it breaks hires fix
* do not change quicksettings dropdown option when value returned is `None` ([#12854](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12854))
* [RC 1.6.0 - zoom is partly hidden] Update style.css ([#12839](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12839))
* chore: change extension time format ([#12851](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12851))
* WEBUI.SH - Use torch 2.1.0 release candidate for Navi 3 ([#12929](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12929))
* add Fallback at images.read_info_from_image if exif data was invalid ([#13028](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13028))
* update cmd arg description ([#12986](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12986))
* fix: update shared.opts.data when add_option ([#12957](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12957), [#13213](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13213))
* restore missing tooltips ([#12976](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12976))
* use default dropdown padding on mobile ([#12880](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12880))
* put enable console prompts option into settings from commandline args ([#13119](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13119))
* fix some deprecated types ([#12846](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12846))
* bump to torchsde==0.2.6 ([#13418](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13418))
* update dragdrop.js ([#13372](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13372))
* use orderdict as lru cache:opt/bug ([#13313](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13313))
* XYZ if not include sub grids do not save sub grid ([#13282](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13282))
* initialize state.time_start befroe state.job_count ([#13229](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13229))
* fix fieldname regex ([#13458](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13458))
* change denoising_strength default to None. ([#13466](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13466))
* fix regression ([#13475](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13475))
* fix IndexError ([#13630](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13630))
* fix: checkpoints_loaded:{checkpoint:state_dict}, model.load_state_dict issue in dict value empty ([#13535](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13535))
* update bug_report.yml ([#12991](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12991))
* requirements_versions httpx==0.24.1 ([#13839](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13839))
* fix parenthesis auto selection ([#13829](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13829))
* fix #13796 ([#13797](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13797))
* corrected a typo in `modules/cmd_args.py` ([#13855](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13855))
* feat: fix randn found element of type float at pos 2 ([#14004](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14004))
* adds tqdm handler to logging_config.py for progress bar integration ([#13996](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13996))
* hotfix: call shared.state.end() after postprocessing done ([#13977](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13977))
* fix dependency address patch 1 ([#13929](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13929))
* save sysinfo as .json ([#14035](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14035))
* move exception_records related methods to errors.py ([#14084](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14084))
* compatibility ([#13936](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13936))
* json.dump(ensure_ascii=False) ([#14108](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14108))
* dir buttons start with / so only the correct dir will be shown and no… ([#13957](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13957))
* alternate implementation for unet forward replacement that does not depend on hijack being applied
* re-add `keyedit_delimiters_whitespace` setting lost as part of commit e294e46 ([#14178](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14178))
* fix `save_samples` being checked early when saving masked composite ([#14177](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14177))
* slight optimization for mask and mask_composite ([#14181](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14181))
* add import_hook hack to work around basicsr/torchvision incompatibility ([#14186](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14186))
## 1.6.1
### Bug Fixes:

7
README.md
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@ -121,7 +121,9 @@ Alternatively, use online services (like Google Colab):
# Debian-based:
sudo apt install wget git python3 python3-venv libgl1 libglib2.0-0
# Red Hat-based:
sudo dnf install wget git python3
sudo dnf install wget git python3 gperftools-libs libglvnd-glx
# openSUSE-based:
sudo zypper install wget git python3 libtcmalloc4 libglvnd
# Arch-based:
sudo pacman -S wget git python3
```
@ -147,7 +149,7 @@ For the purposes of getting Google and other search engines to crawl the wiki, h
## Credits
Licenses for borrowed code can be found in `Settings -> Licenses` screen, and also in `html/licenses.html` file.
- Stable Diffusion - https://github.com/CompVis/stable-diffusion, https://github.com/CompVis/taming-transformers
- Stable Diffusion - https://github.com/Stability-AI/stablediffusion, https://github.com/CompVis/taming-transformers
- k-diffusion - https://github.com/crowsonkb/k-diffusion.git
- GFPGAN - https://github.com/TencentARC/GFPGAN.git
- CodeFormer - https://github.com/sczhou/CodeFormer
@ -174,5 +176,6 @@ Licenses for borrowed code can be found in `Settings -> Licenses` screen, and al
- TAESD - Ollin Boer Bohan - https://github.com/madebyollin/taesd
- LyCORIS - KohakuBlueleaf
- Restart sampling - lambertae - https://github.com/Newbeeer/diffusion_restart_sampling
- Hypertile - tfernd - https://github.com/tfernd/HyperTile
- Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
- (You)

47
extensions-builtin/Lora/lyco_helpers.py
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@ -19,3 +19,50 @@ def rebuild_cp_decomposition(up, down, mid):
up = up.reshape(up.size(0), -1)
down = down.reshape(down.size(0), -1)
return torch.einsum('n m k l, i n, m j -> i j k l', mid, up, down)
# copied from https://github.com/KohakuBlueleaf/LyCORIS/blob/dev/lycoris/modules/lokr.py
def factorization(dimension: int, factor:int=-1) -> tuple[int, int]:
'''
return a tuple of two value of input dimension decomposed by the number closest to factor
second value is higher or equal than first value.
In LoRA with Kroneckor Product, first value is a value for weight scale.
secon value is a value for weight.
Becuase of non-commutative property, A⊗B ≠ B⊗A. Meaning of two matrices is slightly different.
examples)
factor
-1 2 4 8 16 ...
127 -> 1, 127 127 -> 1, 127 127 -> 1, 127 127 -> 1, 127 127 -> 1, 127
128 -> 8, 16 128 -> 2, 64 128 -> 4, 32 128 -> 8, 16 128 -> 8, 16
250 -> 10, 25 250 -> 2, 125 250 -> 2, 125 250 -> 5, 50 250 -> 10, 25
360 -> 8, 45 360 -> 2, 180 360 -> 4, 90 360 -> 8, 45 360 -> 12, 30
512 -> 16, 32 512 -> 2, 256 512 -> 4, 128 512 -> 8, 64 512 -> 16, 32
1024 -> 32, 32 1024 -> 2, 512 1024 -> 4, 256 1024 -> 8, 128 1024 -> 16, 64
'''
if factor > 0 and (dimension % factor) == 0:
m = factor
n = dimension // factor
if m > n:
n, m = m, n
return m, n
if factor < 0:
factor = dimension
m, n = 1, dimension
length = m + n
while m<n:
new_m = m + 1
while dimension%new_m != 0:
new_m += 1
new_n = dimension // new_m
if new_m + new_n > length or new_m>factor:
break
else:
m, n = new_m, new_n
if m > n:
n, m = m, n
return m, n

82
extensions-builtin/Lora/network_oft.py Normal file
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@ -0,0 +1,82 @@
import torch
import network
from lyco_helpers import factorization
from einops import rearrange
class ModuleTypeOFT(network.ModuleType):
def create_module(self, net: network.Network, weights: network.NetworkWeights):
if all(x in weights.w for x in ["oft_blocks"]) or all(x in weights.w for x in ["oft_diag"]):
return NetworkModuleOFT(net, weights)
return None
# Supports both kohya-ss' implementation of COFT https://github.com/kohya-ss/sd-scripts/blob/main/networks/oft.py
# and KohakuBlueleaf's implementation of OFT/COFT https://github.com/KohakuBlueleaf/LyCORIS/blob/dev/lycoris/modules/diag_oft.py
class NetworkModuleOFT(network.NetworkModule):
def __init__(self, net: network.Network, weights: network.NetworkWeights):
super().__init__(net, weights)
self.lin_module = None
self.org_module: list[torch.Module] = [self.sd_module]
self.scale = 1.0
# kohya-ss
if "oft_blocks" in weights.w.keys():
self.is_kohya = True
self.oft_blocks = weights.w["oft_blocks"] # (num_blocks, block_size, block_size)
self.alpha = weights.w["alpha"] # alpha is constraint
self.dim = self.oft_blocks.shape[0] # lora dim
# LyCORIS
elif "oft_diag" in weights.w.keys():
self.is_kohya = False
self.oft_blocks = weights.w["oft_diag"]
# self.alpha is unused
self.dim = self.oft_blocks.shape[1] # (num_blocks, block_size, block_size)
is_linear = type(self.sd_module) in [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear]
is_conv = type(self.sd_module) in [torch.nn.Conv2d]
is_other_linear = type(self.sd_module) in [torch.nn.MultiheadAttention] # unsupported
if is_linear:
self.out_dim = self.sd_module.out_features
elif is_conv:
self.out_dim = self.sd_module.out_channels
elif is_other_linear:
self.out_dim = self.sd_module.embed_dim
if self.is_kohya:
self.constraint = self.alpha * self.out_dim
self.num_blocks = self.dim
self.block_size = self.out_dim // self.dim
else:
self.constraint = None
self.block_size, self.num_blocks = factorization(self.out_dim, self.dim)
def calc_updown(self, orig_weight):
oft_blocks = self.oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)
eye = torch.eye(self.block_size, device=self.oft_blocks.device)
if self.is_kohya:
block_Q = oft_blocks - oft_blocks.transpose(1, 2) # ensure skew-symmetric orthogonal matrix
norm_Q = torch.norm(block_Q.flatten())
new_norm_Q = torch.clamp(norm_Q, max=self.constraint)
block_Q = block_Q * ((new_norm_Q + 1e-8) / (norm_Q + 1e-8))
oft_blocks = torch.matmul(eye + block_Q, (eye - block_Q).float().inverse())
R = oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)
# This errors out for MultiheadAttention, might need to be handled up-stream
merged_weight = rearrange(orig_weight, '(k n) ... -> k n ...', k=self.num_blocks, n=self.block_size)
merged_weight = torch.einsum(
'k n m, k n ... -> k m ...',
R,
merged_weight
)
merged_weight = rearrange(merged_weight, 'k m ... -> (k m) ...')
updown = merged_weight.to(orig_weight.device, dtype=orig_weight.dtype) - orig_weight
output_shape = orig_weight.shape
return self.finalize_updown(updown, orig_weight, output_shape)

16
extensions-builtin/Lora/networks.py
View File

@ -11,6 +11,7 @@ import network_ia3
import network_lokr
import network_full
import network_norm
import network_oft
import torch
from typing import Union
@ -28,6 +29,7 @@ module_types = [
network_full.ModuleTypeFull(),
network_norm.ModuleTypeNorm(),
network_glora.ModuleTypeGLora(),
network_oft.ModuleTypeOFT(),
]
@ -157,7 +159,8 @@ def load_network(name, network_on_disk):
bundle_embeddings = {}
for key_network, weight in sd.items():
key_network_without_network_parts, network_part = key_network.split(".", 1)
key_network_without_network_parts, _, network_part = key_network.partition(".")
if key_network_without_network_parts == "bundle_emb":
emb_name, vec_name = network_part.split(".", 1)
emb_dict = bundle_embeddings.get(emb_name, {})
@ -189,6 +192,17 @@ def load_network(name, network_on_disk):
key = key_network_without_network_parts.replace("lora_te1_text_model", "transformer_text_model")
sd_module = shared.sd_model.network_layer_mapping.get(key, None)
# kohya_ss OFT module
elif sd_module is None and "oft_unet" in key_network_without_network_parts:
key = key_network_without_network_parts.replace("oft_unet", "diffusion_model")
sd_module = shared.sd_model.network_layer_mapping.get(key, None)
# KohakuBlueLeaf OFT module
if sd_module is None and "oft_diag" in key:
key = key_network_without_network_parts.replace("lora_unet", "diffusion_model")
key = key_network_without_network_parts.replace("lora_te1_text_model", "0_transformer_text_model")
sd_module = shared.sd_model.network_layer_mapping.get(key, None)
if sd_module is None:
keys_failed_to_match[key_network] = key
continue

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

@ -17,6 +17,8 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
def create_item(self, name, index=None, enable_filter=True):
lora_on_disk = networks.available_networks.get(name)
if lora_on_disk is None:
return
path, ext = os.path.splitext(lora_on_disk.filename)
@ -66,9 +68,10 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
return item
def list_items(self):
for index, name in enumerate(networks.available_networks):
# instantiate a list to protect against concurrent modification
names = list(networks.available_networks)
for index, name in enumerate(names):
item = self.create_item(name, index)
if item is not None:
yield item

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

@ -23,11 +23,12 @@ class ExtraOptionsSection(scripts.Script):
self.setting_names = []
self.infotext_fields = []
extra_options = shared.opts.extra_options_img2img if is_img2img else shared.opts.extra_options_txt2img
elem_id_tabname = "extra_options_" + ("img2img" if is_img2img else "txt2img")
mapping = {k: v for v, k in generation_parameters_copypaste.infotext_to_setting_name_mapping}
with gr.Blocks() as interface:
with gr.Accordion("Options", open=False) if shared.opts.extra_options_accordion and extra_options else gr.Group():
with gr.Accordion("Options", open=False, elem_id=elem_id_tabname) if shared.opts.extra_options_accordion and extra_options else gr.Group(elem_id=elem_id_tabname):
row_count = math.ceil(len(extra_options) / shared.opts.extra_options_cols)
@ -64,11 +65,14 @@ class ExtraOptionsSection(scripts.Script):
p.override_settings[name] = value
shared.options_templates.update(shared.options_section(('ui', "User interface"), {
"extra_options_txt2img": shared.OptionInfo([], "Options in main UI - txt2img", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that also appear in txt2img interfaces").needs_reload_ui(),
"extra_options_img2img": shared.OptionInfo([], "Options in main UI - img2img", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that also appear in img2img interfaces").needs_reload_ui(),
"extra_options_cols": shared.OptionInfo(1, "Options in main UI - number of columns", gr.Number, {"precision": 0}).needs_reload_ui(),
"extra_options_accordion": shared.OptionInfo(False, "Options in main UI - place into an accordion").needs_reload_ui()
shared.options_templates.update(shared.options_section(('settings_in_ui', "Settings in UI", "ui"), {
"settings_in_ui": shared.OptionHTML("""
This page allows you to add some settings to the main interface of txt2img and img2img tabs.
"""),
"extra_options_txt2img": shared.OptionInfo([], "Settings for txt2img", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that also appear in txt2img interfaces").needs_reload_ui(),
"extra_options_img2img": shared.OptionInfo([], "Settings for img2img", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that also appear in img2img interfaces").needs_reload_ui(),
"extra_options_cols": shared.OptionInfo(1, "Number of columns for added settings", gr.Slider, {"step": 1, "minimum": 1, "maximum": 20}).info("displayed amount will depend on the actual browser window width").needs_reload_ui(),
"extra_options_accordion": shared.OptionInfo(False, "Place added settings into an accordion").needs_reload_ui()
}))

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

@ -0,0 +1,351 @@
"""
Hypertile module for splitting attention layers in SD-1.5 U-Net and SD-1.5 VAE
Warn: The patch works well only if the input image has a width and height that are multiples of 128
Original author: @tfernd Github: https://github.com/tfernd/HyperTile
"""
from __future__ import annotations
from dataclasses import dataclass
from typing import Callable
from functools import wraps, cache
import math
import torch.nn as nn
import random
from einops import rearrange
@dataclass
class HypertileParams:
depth = 0
layer_name = ""
tile_size: int = 0
swap_size: int = 0
aspect_ratio: float = 1.0
forward = None
enabled = False
# TODO add SD-XL layers
DEPTH_LAYERS = {
0: [
# SD 1.5 U-Net (diffusers)
"down_blocks.0.attentions.0.transformer_blocks.0.attn1",
"down_blocks.0.attentions.1.transformer_blocks.0.attn1",
"up_blocks.3.attentions.0.transformer_blocks.0.attn1",
"up_blocks.3.attentions.1.transformer_blocks.0.attn1",
"up_blocks.3.attentions.2.transformer_blocks.0.attn1",
# SD 1.5 U-Net (ldm)
"input_blocks.1.1.transformer_blocks.0.attn1",
"input_blocks.2.1.transformer_blocks.0.attn1",
"output_blocks.9.1.transformer_blocks.0.attn1",
"output_blocks.10.1.transformer_blocks.0.attn1",
"output_blocks.11.1.transformer_blocks.0.attn1",
# SD 1.5 VAE
"decoder.mid_block.attentions.0",
"decoder.mid.attn_1",
],
1: [
# SD 1.5 U-Net (diffusers)
"down_blocks.1.attentions.0.transformer_blocks.0.attn1",
"down_blocks.1.attentions.1.transformer_blocks.0.attn1",
"up_blocks.2.attentions.0.transformer_blocks.0.attn1",
"up_blocks.2.attentions.1.transformer_blocks.0.attn1",
"up_blocks.2.attentions.2.transformer_blocks.0.attn1",
# SD 1.5 U-Net (ldm)
"input_blocks.4.1.transformer_blocks.0.attn1",
"input_blocks.5.1.transformer_blocks.0.attn1",
"output_blocks.6.1.transformer_blocks.0.attn1",
"output_blocks.7.1.transformer_blocks.0.attn1",
"output_blocks.8.1.transformer_blocks.0.attn1",
],
2: [
# SD 1.5 U-Net (diffusers)
"down_blocks.2.attentions.0.transformer_blocks.0.attn1",
"down_blocks.2.attentions.1.transformer_blocks.0.attn1",
"up_blocks.1.attentions.0.transformer_blocks.0.attn1",
"up_blocks.1.attentions.1.transformer_blocks.0.attn1",
"up_blocks.1.attentions.2.transformer_blocks.0.attn1",
# SD 1.5 U-Net (ldm)
"input_blocks.7.1.transformer_blocks.0.attn1",
"input_blocks.8.1.transformer_blocks.0.attn1",
"output_blocks.3.1.transformer_blocks.0.attn1",
"output_blocks.4.1.transformer_blocks.0.attn1",
"output_blocks.5.1.transformer_blocks.0.attn1",
],
3: [
# SD 1.5 U-Net (diffusers)
"mid_block.attentions.0.transformer_blocks.0.attn1",
# SD 1.5 U-Net (ldm)
"middle_block.1.transformer_blocks.0.attn1",
],
}
# XL layers, thanks for GitHub@gel-crabs for the help
DEPTH_LAYERS_XL = {
0: [
# SD 1.5 U-Net (diffusers)
"down_blocks.0.attentions.0.transformer_blocks.0.attn1",
"down_blocks.0.attentions.1.transformer_blocks.0.attn1",
"up_blocks.3.attentions.0.transformer_blocks.0.attn1",
"up_blocks.3.attentions.1.transformer_blocks.0.attn1",
"up_blocks.3.attentions.2.transformer_blocks.0.attn1",
# SD 1.5 U-Net (ldm)
"input_blocks.4.1.transformer_blocks.0.attn1",
"input_blocks.5.1.transformer_blocks.0.attn1",
"output_blocks.3.1.transformer_blocks.0.attn1",
"output_blocks.4.1.transformer_blocks.0.attn1",
"output_blocks.5.1.transformer_blocks.0.attn1",
# SD 1.5 VAE
"decoder.mid_block.attentions.0",
"decoder.mid.attn_1",
],
1: [
# SD 1.5 U-Net (diffusers)
#"down_blocks.1.attentions.0.transformer_blocks.0.attn1",
#"down_blocks.1.attentions.1.transformer_blocks.0.attn1",
#"up_blocks.2.attentions.0.transformer_blocks.0.attn1",
#"up_blocks.2.attentions.1.transformer_blocks.0.attn1",
#"up_blocks.2.attentions.2.transformer_blocks.0.attn1",
# SD 1.5 U-Net (ldm)
"input_blocks.4.1.transformer_blocks.1.attn1",
"input_blocks.5.1.transformer_blocks.1.attn1",
"output_blocks.3.1.transformer_blocks.1.attn1",
"output_blocks.4.1.transformer_blocks.1.attn1",
"output_blocks.5.1.transformer_blocks.1.attn1",
"input_blocks.7.1.transformer_blocks.0.attn1",
"input_blocks.8.1.transformer_blocks.0.attn1",
"output_blocks.0.1.transformer_blocks.0.attn1",
"output_blocks.1.1.transformer_blocks.0.attn1",
"output_blocks.2.1.transformer_blocks.0.attn1",
"input_blocks.7.1.transformer_blocks.1.attn1",
"input_blocks.8.1.transformer_blocks.1.attn1",
"output_blocks.0.1.transformer_blocks.1.attn1",
"output_blocks.1.1.transformer_blocks.1.attn1",
"output_blocks.2.1.transformer_blocks.1.attn1",
"input_blocks.7.1.transformer_blocks.2.attn1",
"input_blocks.8.1.transformer_blocks.2.attn1",
"output_blocks.0.1.transformer_blocks.2.attn1",
"output_blocks.1.1.transformer_blocks.2.attn1",
"output_blocks.2.1.transformer_blocks.2.attn1",
"input_blocks.7.1.transformer_blocks.3.attn1",
"input_blocks.8.1.transformer_blocks.3.attn1",
"output_blocks.0.1.transformer_blocks.3.attn1",
"output_blocks.1.1.transformer_blocks.3.attn1",
"output_blocks.2.1.transformer_blocks.3.attn1",
"input_blocks.7.1.transformer_blocks.4.attn1",
"input_blocks.8.1.transformer_blocks.4.attn1",
"output_blocks.0.1.transformer_blocks.4.attn1",
"output_blocks.1.1.transformer_blocks.4.attn1",
"output_blocks.2.1.transformer_blocks.4.attn1",
"input_blocks.7.1.transformer_blocks.5.attn1",
"input_blocks.8.1.transformer_blocks.5.attn1",
"output_blocks.0.1.transformer_blocks.5.attn1",
"output_blocks.1.1.transformer_blocks.5.attn1",
"output_blocks.2.1.transformer_blocks.5.attn1",
"input_blocks.7.1.transformer_blocks.6.attn1",
"input_blocks.8.1.transformer_blocks.6.attn1",
"output_blocks.0.1.transformer_blocks.6.attn1",
"output_blocks.1.1.transformer_blocks.6.attn1",
"output_blocks.2.1.transformer_blocks.6.attn1",
"input_blocks.7.1.transformer_blocks.7.attn1",
"input_blocks.8.1.transformer_blocks.7.attn1",
"output_blocks.0.1.transformer_blocks.7.attn1",
"output_blocks.1.1.transformer_blocks.7.attn1",
"output_blocks.2.1.transformer_blocks.7.attn1",
"input_blocks.7.1.transformer_blocks.8.attn1",
"input_blocks.8.1.transformer_blocks.8.attn1",
"output_blocks.0.1.transformer_blocks.8.attn1",
"output_blocks.1.1.transformer_blocks.8.attn1",
"output_blocks.2.1.transformer_blocks.8.attn1",
"input_blocks.7.1.transformer_blocks.9.attn1",
"input_blocks.8.1.transformer_blocks.9.attn1",
"output_blocks.0.1.transformer_blocks.9.attn1",
"output_blocks.1.1.transformer_blocks.9.attn1",
"output_blocks.2.1.transformer_blocks.9.attn1",
],
2: [
# SD 1.5 U-Net (diffusers)
"mid_block.attentions.0.transformer_blocks.0.attn1",
# SD 1.5 U-Net (ldm)
"middle_block.1.transformer_blocks.0.attn1",
"middle_block.1.transformer_blocks.1.attn1",
"middle_block.1.transformer_blocks.2.attn1",
"middle_block.1.transformer_blocks.3.attn1",
"middle_block.1.transformer_blocks.4.attn1",
"middle_block.1.transformer_blocks.5.attn1",
"middle_block.1.transformer_blocks.6.attn1",
"middle_block.1.transformer_blocks.7.attn1",
"middle_block.1.transformer_blocks.8.attn1",
"middle_block.1.transformer_blocks.9.attn1",
],
3 : [] # TODO - separate layers for SD-XL
}
RNG_INSTANCE = random.Random()
@cache
def get_divisors(value: int, min_value: int, /, max_options: int = 1) -> list[int]:
"""
Returns divisors of value that
x * min_value <= value
in big -> small order, amount of divisors is limited by max_options
"""
max_options = max(1, max_options) # at least 1 option should be returned
min_value = min(min_value, value)
divisors = [i for i in range(min_value, value + 1) if value % i == 0] # divisors in small -> big order
ns = [value // i for i in divisors[:max_options]] # has at least 1 element # big -> small order
return ns
def random_divisor(value: int, min_value: int, /, max_options: int = 1) -> int:
"""
Returns a random divisor of value that
x * min_value <= value
if max_options is 1, the behavior is deterministic
"""
ns = get_divisors(value, min_value, max_options=max_options) # get cached divisors
idx = RNG_INSTANCE.randint(0, len(ns) - 1)
return ns[idx]
def set_hypertile_seed(seed: int) -> None:
RNG_INSTANCE.seed(seed)
@cache
def largest_tile_size_available(width: int, height: int) -> int:
"""
Calculates the largest tile size available for a given width and height
Tile size is always a power of 2
"""
gcd = math.gcd(width, height)
largest_tile_size_available = 1
while gcd % (largest_tile_size_available * 2) == 0:
largest_tile_size_available *= 2
return largest_tile_size_available
def iterative_closest_divisors(hw:int, aspect_ratio:float) -> tuple[int, int]:
"""
Finds h and w such that h*w = hw and h/w = aspect_ratio
We check all possible divisors of hw and return the closest to the aspect ratio
"""
divisors = [i for i in range(2, hw + 1) if hw % i == 0] # all divisors of hw
pairs = [(i, hw // i) for i in divisors] # all pairs of divisors of hw
ratios = [w/h for h, w in pairs] # all ratios of pairs of divisors of hw
closest_ratio = min(ratios, key=lambda x: abs(x - aspect_ratio)) # closest ratio to aspect_ratio
closest_pair = pairs[ratios.index(closest_ratio)] # closest pair of divisors to aspect_ratio
return closest_pair
@cache
def find_hw_candidates(hw:int, aspect_ratio:float) -> tuple[int, int]:
"""
Finds h and w such that h*w = hw and h/w = aspect_ratio
"""
h, w = round(math.sqrt(hw * aspect_ratio)), round(math.sqrt(hw / aspect_ratio))
# find h and w such that h*w = hw and h/w = aspect_ratio
if h * w != hw:
w_candidate = hw / h
# check if w is an integer
if not w_candidate.is_integer():
h_candidate = hw / w
# check if h is an integer
if not h_candidate.is_integer():
return iterative_closest_divisors(hw, aspect_ratio)
else:
h = int(h_candidate)
else:
w = int(w_candidate)
return h, w
def self_attn_forward(params: HypertileParams, scale_depth=True) -> Callable:
@wraps(params.forward)
def wrapper(*args, **kwargs):
if not params.enabled:
return params.forward(*args, **kwargs)
latent_tile_size = max(128, params.tile_size) // 8
x = args[0]
# VAE
if x.ndim == 4:
b, c, h, w = x.shape
nh = random_divisor(h, latent_tile_size, params.swap_size)
nw = random_divisor(w, latent_tile_size, params.swap_size)
if nh * nw > 1:
x = rearrange(x, "b c (nh h) (nw w) -> (b nh nw) c h w", nh=nh, nw=nw) # split into nh * nw tiles
out = params.forward(x, *args[1:], **kwargs)
if nh * nw > 1:
out = rearrange(out, "(b nh nw) c h w -> b c (nh h) (nw w)", nh=nh, nw=nw)
# U-Net
else:
hw: int = x.size(1)
h, w = find_hw_candidates(hw, params.aspect_ratio)
assert h * w == hw, f"Invalid aspect ratio {params.aspect_ratio} for input of shape {x.shape}, hw={hw}, h={h}, w={w}"
factor = 2 ** params.depth if scale_depth else 1
nh = random_divisor(h, latent_tile_size * factor, params.swap_size)
nw = random_divisor(w, latent_tile_size * factor, params.swap_size)
if nh * nw > 1:
x = rearrange(x, "b (nh h nw w) c -> (b nh nw) (h w) c", h=h // nh, w=w // nw, nh=nh, nw=nw)
out = params.forward(x, *args[1:], **kwargs)
if nh * nw > 1:
out = rearrange(out, "(b nh nw) hw c -> b nh nw hw c", nh=nh, nw=nw)
out = rearrange(out, "b nh nw (h w) c -> b (nh h nw w) c", h=h // nh, w=w // nw)
return out
return wrapper
def hypertile_hook_model(model: nn.Module, width, height, *, enable=False, tile_size_max=128, swap_size=1, max_depth=3, is_sdxl=False):
hypertile_layers = getattr(model, "__webui_hypertile_layers", None)
if hypertile_layers is None:
if not enable:
return
hypertile_layers = {}
layers = DEPTH_LAYERS_XL if is_sdxl else DEPTH_LAYERS
for depth in range(4):
for layer_name, module in model.named_modules():
if any(layer_name.endswith(try_name) for try_name in layers[depth]):
params = HypertileParams()
module.__webui_hypertile_params = params
params.forward = module.forward
params.depth = depth
params.layer_name = layer_name
module.forward = self_attn_forward(params)
hypertile_layers[layer_name] = 1
model.__webui_hypertile_layers = hypertile_layers
aspect_ratio = width / height
tile_size = min(largest_tile_size_available(width, height), tile_size_max)
for layer_name, module in model.named_modules():
if layer_name in hypertile_layers:
params = module.__webui_hypertile_params
params.tile_size = tile_size
params.swap_size = swap_size
params.aspect_ratio = aspect_ratio
params.enabled = enable and params.depth <= max_depth

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

@ -0,0 +1,109 @@
import hypertile
from modules import scripts, script_callbacks, shared
from scripts.hypertile_xyz import add_axis_options
class ScriptHypertile(scripts.Script):
name = "Hypertile"
def title(self):
return self.name
def show(self, is_img2img):
return scripts.AlwaysVisible
def process(self, p, *args):
hypertile.set_hypertile_seed(p.all_seeds[0])
configure_hypertile(p.width, p.height, enable_unet=shared.opts.hypertile_enable_unet)
self.add_infotext(p)
def before_hr(self, p, *args):
enable = shared.opts.hypertile_enable_unet_secondpass or shared.opts.hypertile_enable_unet
# exclusive hypertile seed for the second pass
if enable:
hypertile.set_hypertile_seed(p.all_seeds[0])
configure_hypertile(p.hr_upscale_to_x, p.hr_upscale_to_y, enable_unet=enable)
if enable and not shared.opts.hypertile_enable_unet:
p.extra_generation_params["Hypertile U-Net second pass"] = True
self.add_infotext(p, add_unet_params=True)
def add_infotext(self, p, add_unet_params=False):
def option(name):
value = getattr(shared.opts, name)
default_value = shared.opts.get_default(name)
return None if value == default_value else value
if shared.opts.hypertile_enable_unet:
p.extra_generation_params["Hypertile U-Net"] = True
if shared.opts.hypertile_enable_unet or add_unet_params:
p.extra_generation_params["Hypertile U-Net max depth"] = option('hypertile_max_depth_unet')
p.extra_generation_params["Hypertile U-Net max tile size"] = option('hypertile_max_tile_unet')
p.extra_generation_params["Hypertile U-Net swap size"] = option('hypertile_swap_size_unet')
if shared.opts.hypertile_enable_vae:
p.extra_generation_params["Hypertile VAE"] = True
p.extra_generation_params["Hypertile VAE max depth"] = option('hypertile_max_depth_vae')
p.extra_generation_params["Hypertile VAE max tile size"] = option('hypertile_max_tile_vae')
p.extra_generation_params["Hypertile VAE swap size"] = option('hypertile_swap_size_vae')
def configure_hypertile(width, height, enable_unet=True):
hypertile.hypertile_hook_model(
shared.sd_model.first_stage_model,
width,
height,
swap_size=shared.opts.hypertile_swap_size_vae,
max_depth=shared.opts.hypertile_max_depth_vae,
tile_size_max=shared.opts.hypertile_max_tile_vae,
enable=shared.opts.hypertile_enable_vae,
)
hypertile.hypertile_hook_model(
shared.sd_model.model,
width,
height,
swap_size=shared.opts.hypertile_swap_size_unet,
max_depth=shared.opts.hypertile_max_depth_unet,
tile_size_max=shared.opts.hypertile_max_tile_unet,
enable=enable_unet,
is_sdxl=shared.sd_model.is_sdxl
)
def on_ui_settings():
import gradio as gr
options = {
"hypertile_explanation": shared.OptionHTML("""
<a href='https://github.com/tfernd/HyperTile'>Hypertile</a> optimizes the self-attention layer within U-Net and VAE models,
resulting in a reduction in computation time ranging from 1 to 4 times. The larger the generated image is, the greater the
benefit.
"""),
"hypertile_enable_unet": shared.OptionInfo(False, "Enable Hypertile U-Net", infotext="Hypertile U-Net").info("enables hypertile for all modes, including hires fix second pass; noticeable change in details of the generated picture"),
"hypertile_enable_unet_secondpass": shared.OptionInfo(False, "Enable Hypertile U-Net for hires fix second pass", infotext="Hypertile U-Net second pass").info("enables hypertile just for hires fix second pass - regardless of whether the above setting is enabled"),
"hypertile_max_depth_unet": shared.OptionInfo(3, "Hypertile U-Net max depth", gr.Slider, {"minimum": 0, "maximum": 3, "step": 1}, infotext="Hypertile U-Net max depth").info("larger = more neural network layers affected; minor effect on performance"),
"hypertile_max_tile_unet": shared.OptionInfo(256, "Hypertile U-Net max tile size", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}, infotext="Hypertile U-Net max tile size").info("larger = worse performance"),
"hypertile_swap_size_unet": shared.OptionInfo(3, "Hypertile U-Net swap size", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}, infotext="Hypertile U-Net swap size"),
"hypertile_enable_vae": shared.OptionInfo(False, "Enable Hypertile VAE", infotext="Hypertile VAE").info("minimal change in the generated picture"),
"hypertile_max_depth_vae": shared.OptionInfo(3, "Hypertile VAE max depth", gr.Slider, {"minimum": 0, "maximum": 3, "step": 1}, infotext="Hypertile VAE max depth"),
"hypertile_max_tile_vae": shared.OptionInfo(128, "Hypertile VAE max tile size", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}, infotext="Hypertile VAE max tile size"),
"hypertile_swap_size_vae": shared.OptionInfo(3, "Hypertile VAE swap size ", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}, infotext="Hypertile VAE swap size"),
}
for name, opt in options.items():
opt.section = ('hypertile', "Hypertile")
shared.opts.add_option(name, opt)
script_callbacks.on_ui_settings(on_ui_settings)
script_callbacks.on_before_ui(add_axis_options)

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

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

10
javascript/extraNetworks.js
View File

@ -130,6 +130,10 @@ function extraNetworksMovePromptToTab(tabname, id, showPrompt, showNegativePromp
} else {
promptContainer.insertBefore(prompt, promptContainer.firstChild);
}
if (elem) {
elem.classList.toggle('extra-page-prompts-active', showNegativePrompt || showPrompt);
}
}
@ -388,3 +392,9 @@ function extraNetworksRefreshSingleCard(page, tabname, name) {
}
});
}
window.addEventListener("keydown", function(event) {
if (event.key == "Escape") {
closePopup();
}
});

2
javascript/imageviewer.js
View File

@ -34,7 +34,7 @@ function updateOnBackgroundChange() {
if (modalImage && modalImage.offsetParent) {
let currentButton = selected_gallery_button();
let preview = gradioApp().querySelectorAll('.livePreview > img');
if (preview.length > 0) {
if (opts.js_live_preview_in_modal_lightbox && preview.length > 0) {
// show preview image if available
modalImage.src = preview[preview.length - 1].src;
} else if (currentButton?.children?.length > 0 && modalImage.src != currentButton.children[0].src) {

25
javascript/settings.js
View File

@ -44,3 +44,28 @@ onUiLoaded(function() {
buttonShowAllPages.addEventListener("click", settingsShowAllTabs);
});
onOptionsChanged(function() {
if (gradioApp().querySelector('#settings .settings-category')) return;
var sectionMap = {};
gradioApp().querySelectorAll('#settings > div > button').forEach(function(x) {
sectionMap[x.textContent.trim()] = x;
});
opts._categories.forEach(function(x) {
var section = x[0];
var category = x[1];
var span = document.createElement('SPAN');
span.textContent = category;
span.className = 'settings-category';
var sectionElem = sectionMap[section];
if (!sectionElem) return;
sectionElem.parentElement.insertBefore(span, sectionElem);
});
});

41
javascript/ui.js
View File

@ -170,6 +170,23 @@ function submit_img2img() {
return res;
}
function submit_extras() {
showSubmitButtons('extras', false);
var id = randomId();
requestProgress(id, gradioApp().getElementById('extras_gallery_container'), gradioApp().getElementById('extras_gallery'), function() {
showSubmitButtons('extras', true);
});
var res = create_submit_args(arguments);
res[0] = id;
console.log(res);
return res;
}
function restoreProgressTxt2img() {
showRestoreProgressButton("txt2img", false);
var id = localGet("txt2img_task_id");
@ -198,9 +215,33 @@ function restoreProgressImg2img() {
}
/**
* Configure the width and height elements on `tabname` to accept
* pasting of resolutions in the form of "width x height".
*/
function setupResolutionPasting(tabname) {
var width = gradioApp().querySelector(`#${tabname}_width input[type=number]`);
var height = gradioApp().querySelector(`#${tabname}_height input[type=number]`);
for (const el of [width, height]) {
el.addEventListener('paste', function(event) {
var pasteData = event.clipboardData.getData('text/plain');
var parsed = pasteData.match(/^\s*(\d+)\D+(\d+)\s*$/);
if (parsed) {
width.value = parsed[1];
height.value = parsed[2];
updateInput(width);
updateInput(height);
event.preventDefault();
}
});
}
}
onUiLoaded(function() {
showRestoreProgressButton('txt2img', localGet("txt2img_task_id"));
showRestoreProgressButton('img2img', localGet("img2img_task_id"));
setupResolutionPasting('txt2img');
setupResolutionPasting('img2img');
});

15
modules/api/api.py
View File

@ -22,7 +22,6 @@ from modules.api import models
from modules.shared import opts
from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
from modules.textual_inversion.textual_inversion import create_embedding, train_embedding
from modules.textual_inversion.preprocess import preprocess
from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork
from PIL import PngImagePlugin, Image
from modules.sd_models_config import find_checkpoint_config_near_filename
@ -235,7 +234,6 @@ class Api:
self.add_api_route("/sdapi/v1/refresh-vae", self.refresh_vae, methods=["POST"])
self.add_api_route("/sdapi/v1/create/embedding", self.create_embedding, methods=["POST"], response_model=models.CreateResponse)
self.add_api_route("/sdapi/v1/create/hypernetwork", self.create_hypernetwork, methods=["POST"], response_model=models.CreateResponse)
self.add_api_route("/sdapi/v1/preprocess", self.preprocess, methods=["POST"], response_model=models.PreprocessResponse)
self.add_api_route("/sdapi/v1/train/embedding", self.train_embedding, methods=["POST"], response_model=models.TrainResponse)
self.add_api_route("/sdapi/v1/train/hypernetwork", self.train_hypernetwork, methods=["POST"], response_model=models.TrainResponse)
self.add_api_route("/sdapi/v1/memory", self.get_memory, methods=["GET"], response_model=models.MemoryResponse)
@ -675,19 +673,6 @@ class Api:
finally:
shared.state.end()
def preprocess(self, args: dict):
try:
shared.state.begin(job="preprocess")
preprocess(**args) # quick operation unless blip/booru interrogation is enabled
shared.state.end()
return models.PreprocessResponse(info='preprocess complete')
except KeyError as e:
return models.PreprocessResponse(info=f"preprocess error: invalid token: {e}")
except Exception as e:
return models.PreprocessResponse(info=f"preprocess error: {e}")
finally:
shared.state.end()
def train_embedding(self, args: dict):
try:
shared.state.begin(job="train_embedding")

3
modules/api/models.py
View File

@ -202,9 +202,6 @@ class TrainResponse(BaseModel):
class CreateResponse(BaseModel):
info: str = Field(title="Create info", description="Response string from create embedding or hypernetwork task.")
class PreprocessResponse(BaseModel):
info: str = Field(title="Preprocess info", description="Response string from preprocessing task.")
fields = {}
for key, metadata in opts.data_labels.items():
value = opts.data.get(key)

2
modules/cache.py
View File

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

1
modules/cmd_args.py
View File

@ -70,6 +70,7 @@ parser.add_argument("--opt-sdp-no-mem-attention", action='store_true', help="pre
parser.add_argument("--disable-opt-split-attention", action='store_true', help="prefer no cross-attention layer optimization for automatic choice of optimization")
parser.add_argument("--disable-nan-check", action='store_true', help="do not check if produced images/latent spaces have nans; useful for running without a checkpoint in CI")
parser.add_argument("--use-cpu", nargs='+', help="use CPU as torch device for specified modules", default=[], type=str.lower)
parser.add_argument("--use-ipex", action="store_true", help="use Intel XPU as torch device")
parser.add_argument("--disable-model-loading-ram-optimization", action='store_true', help="disable an optimization that reduces RAM use when loading a model")
parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)

15
modules/devices.py
View File

@ -8,6 +8,13 @@ from modules import errors, shared
if sys.platform == "darwin":
from modules import mac_specific
if shared.cmd_opts.use_ipex:
from modules import xpu_specific
def has_xpu() -> bool:
return shared.cmd_opts.use_ipex and xpu_specific.has_xpu
def has_mps() -> bool:
if sys.platform != "darwin":
@ -30,6 +37,9 @@ def get_optimal_device_name():
if has_mps():
return "mps"
if has_xpu():
return xpu_specific.get_xpu_device_string()
return "cpu"
@ -38,7 +48,7 @@ def get_optimal_device():
def get_device_for(task):
if task in shared.cmd_opts.use_cpu:
if task in shared.cmd_opts.use_cpu or "all" in shared.cmd_opts.use_cpu:
return cpu
return get_optimal_device()
@ -54,6 +64,9 @@ def torch_gc():
if has_mps():
mac_specific.torch_mps_gc()
if has_xpu():
xpu_specific.torch_xpu_gc()
def enable_tf32():
if torch.cuda.is_available():

18
modules/errors.py
View File

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

96
modules/extensions.py
View File

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

13
modules/generation_parameters_copypaste.py
View File

@ -1,3 +1,4 @@
from __future__ import annotations
import base64
import io
import json
@ -15,9 +16,6 @@ re_imagesize = re.compile(r"^(\d+)x(\d+)$")
re_hypernet_hash = re.compile("\(([0-9a-f]+)\)$")
type_of_gr_update = type(gr.update())
paste_fields = {}
registered_param_bindings = []
class ParamBinding:
def __init__(self, paste_button, tabname, source_text_component=None, source_image_component=None, source_tabname=None, override_settings_component=None, paste_field_names=None):
@ -30,6 +28,10 @@ class ParamBinding:
self.paste_field_names = paste_field_names or []
paste_fields: dict[str, dict] = {}
registered_param_bindings: list[ParamBinding] = []
def reset():
paste_fields.clear()
registered_param_bindings.clear()
@ -113,7 +115,6 @@ def register_paste_params_button(binding: ParamBinding):
def connect_paste_params_buttons():
binding: ParamBinding
for binding in registered_param_bindings:
destination_image_component = paste_fields[binding.tabname]["init_img"]
fields = paste_fields[binding.tabname]["fields"]
@ -313,6 +314,9 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
if "VAE Decoder" not in res:
res["VAE Decoder"] = "Full"
skip = set(shared.opts.infotext_skip_pasting)
res = {k: v for k, v in res.items() if k not in skip}
return res
@ -443,3 +447,4 @@ def connect_paste(button, paste_fields, input_comp, override_settings_component,
outputs=[],
show_progress=False,
)

10
modules/gradio_extensons.py
View File

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

24
modules/img2img.py
View File

@ -44,6 +44,8 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=Fal
steps = p.steps
override_settings = p.override_settings
sd_model_checkpoint_override = get_closet_checkpoint_match(override_settings.get("sd_model_checkpoint", None))
batch_results = None
discard_further_results = False
for i, image in enumerate(images):
state.job = f"{i+1} out of {len(images)}"
if state.skipped:
@ -127,7 +129,21 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=Fal
if proc is None:
p.override_settings.pop('save_images_replace_action', None)
process_images(p)
proc = process_images(p)
if not discard_further_results and proc:
if batch_results:
batch_results.images.extend(proc.images)
batch_results.infotexts.extend(proc.infotexts)
else:
batch_results = proc
if 0 <= shared.opts.img2img_batch_show_results_limit < len(batch_results.images):
discard_further_results = True
batch_results.images = batch_results.images[:int(shared.opts.img2img_batch_show_results_limit)]
batch_results.infotexts = batch_results.infotexts[:int(shared.opts.img2img_batch_show_results_limit)]
return batch_results
def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_name: str, mask_blur: int, mask_alpha: float, inpainting_fill: int, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, selected_scale_tab: int, height: int, width: int, scale_by: float, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, img2img_batch_use_png_info: bool, img2img_batch_png_info_props: list, img2img_batch_png_info_dir: str, request: gr.Request, *args):
@ -212,10 +228,10 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
with closing(p):
if is_batch:
assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
processed = process_batch(p, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, args, to_scale=selected_scale_tab == 1, scale_by=scale_by, use_png_info=img2img_batch_use_png_info, png_info_props=img2img_batch_png_info_props, png_info_dir=img2img_batch_png_info_dir)
process_batch(p, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, args, to_scale=selected_scale_tab == 1, scale_by=scale_by, use_png_info=img2img_batch_use_png_info, png_info_props=img2img_batch_png_info_props, png_info_dir=img2img_batch_png_info_dir)
processed = Processed(p, [], p.seed, "")
if processed is None:
processed = Processed(p, [], p.seed, "")
else:
processed = modules.scripts.scripts_img2img.run(p, *args)
if processed is None:

11
modules/import_hook.py
View File

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

38
modules/launch_utils.py
View File

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

27
modules/logging_config.py
View File

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

15
modules/mac_specific.py
View File

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

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

@ -24,10 +24,15 @@ from pytorch_lightning.utilities.distributed import rank_zero_only
from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
from ldm.modules.ema import LitEma
from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL
from ldm.models.autoencoder import IdentityFirstStage, AutoencoderKL
from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
from ldm.models.diffusion.ddim import DDIMSampler
try:
from ldm.models.autoencoder import VQModelInterface
except Exception:
class VQModelInterface:
pass
__conditioning_keys__ = {'concat': 'c_concat',
'crossattn': 'c_crossattn',

81
modules/options.py
View File

@ -1,5 +1,6 @@
import json
import sys
from dataclasses import dataclass
import gradio as gr
@ -8,13 +9,14 @@ from modules.shared_cmd_options import cmd_opts
class OptionInfo:
def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, section=None, refresh=None, comment_before='', comment_after='', infotext=None, restrict_api=False):
def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, section=None, refresh=None, comment_before='', comment_after='', infotext=None, restrict_api=False, category_id=None):
self.default = default
self.label = label
self.component = component
self.component_args = component_args
self.onchange = onchange
self.section = section
self.category_id = category_id
self.refresh = refresh
self.do_not_save = False
@ -63,7 +65,11 @@ class OptionHTML(OptionInfo):
def options_section(section_identifier, options_dict):
for v in options_dict.values():
v.section = section_identifier
if len(section_identifier) == 2:
v.section = section_identifier
elif len(section_identifier) == 3:
v.section = section_identifier[0:2]
v.category_id = section_identifier[2]
return options_dict
@ -76,7 +82,7 @@ class Options:
def __init__(self, data_labels: dict[str, OptionInfo], restricted_opts):
self.data_labels = data_labels
self.data = {k: v.default for k, v in self.data_labels.items()}
self.data = {k: v.default for k, v in self.data_labels.items() if not v.do_not_save}
self.restricted_opts = restricted_opts
def __setattr__(self, key, value):
@ -158,7 +164,7 @@ class Options:
assert not cmd_opts.freeze_settings, "saving settings is disabled"
with open(filename, "w", encoding="utf8") as file:
json.dump(self.data, file, indent=4)
json.dump(self.data, file, indent=4, ensure_ascii=False)
def same_type(self, x, y):
if x is None or y is None:
@ -206,23 +212,59 @@ class Options:
d = {k: self.data.get(k, v.default) for k, v in self.data_labels.items()}
d["_comments_before"] = {k: v.comment_before for k, v in self.data_labels.items() if v.comment_before is not None}
d["_comments_after"] = {k: v.comment_after for k, v in self.data_labels.items() if v.comment_after is not None}
item_categories = {}
for item in self.data_labels.values():
category = categories.mapping.get(item.category_id)
category = "Uncategorized" if category is None else category.label
if category not in item_categories:
item_categories[category] = item.section[1]
# _categories is a list of pairs: [section, category]. Each section (a setting page) will get a special heading above it with the category as text.
d["_categories"] = [[v, k] for k, v in item_categories.items()] + [["Defaults", "Other"]]
return json.dumps(d)
def add_option(self, key, info):
self.data_labels[key] = info
if key not in self.data:
if key not in self.data and not info.do_not_save:
self.data[key] = info.default
def reorder(self):
"""reorder settings so that all items related to section always go together"""
"""Reorder settings so that:
- all items related to section always go together
- all sections belonging to a category go together
- sections inside a category are ordered alphabetically
- categories are ordered by creation order
Category is a superset of sections: for category "postprocessing" there could be multiple sections: "face restoration", "upscaling".
This function also changes items' category_id so that all items belonging to a section have the same category_id.
"""
category_ids = {}
section_categories = {}
section_ids = {}
settings_items = self.data_labels.items()
for _, item in settings_items:
if item.section not in section_ids:
section_ids[item.section] = len(section_ids)
if item.section not in section_categories:
section_categories[item.section] = item.category_id
self.data_labels = dict(sorted(settings_items, key=lambda x: section_ids[x[1].section]))
for _, item in settings_items:
item.category_id = section_categories.get(item.section)
for category_id in categories.mapping:
if category_id not in category_ids:
category_ids[category_id] = len(category_ids)
def sort_key(x):
item: OptionInfo = x[1]
category_order = category_ids.get(item.category_id, len(category_ids))
section_order = item.section[1]
return category_order, section_order
self.data_labels = dict(sorted(settings_items, key=sort_key))
def cast_value(self, key, value):
"""casts an arbitrary to the same type as this setting's value with key
@ -245,3 +287,22 @@ class Options:
value = expected_type(value)
return value
@dataclass
class OptionsCategory:
id: str
label: str
class OptionsCategories:
def __init__(self):
self.mapping = {}
def register_category(self, category_id, label):
if category_id in self.mapping:
return category_id
self.mapping[category_id] = OptionsCategory(category_id, label)
categories = OptionsCategories()

94
modules/postprocessing.py
View File

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

38
modules/processing.py
View File

@ -679,8 +679,8 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
"Size": f"{p.width}x{p.height}",
"Model hash": p.sd_model_hash if opts.add_model_hash_to_info else None,
"Model": p.sd_model_name if opts.add_model_name_to_info else None,
"VAE hash": p.sd_vae_hash if opts.add_model_hash_to_info else None,
"VAE": p.sd_vae_name if opts.add_model_name_to_info else None,
"VAE hash": p.sd_vae_hash if opts.add_vae_hash_to_info else None,
"VAE": p.sd_vae_name if opts.add_vae_name_to_info else None,
"Variation seed": (None if p.subseed_strength == 0 else (p.all_subseeds[0] if use_main_prompt else all_subseeds[index])),
"Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
"Seed resize from": (None if p.seed_resize_from_w <= 0 or p.seed_resize_from_h <= 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
@ -799,7 +799,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
infotexts = []
output_images = []
with torch.no_grad(), p.sd_model.ema_scope():
with devices.autocast():
p.init(p.all_prompts, p.all_seeds, p.all_subseeds)
@ -873,7 +872,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
else:
if opts.sd_vae_decode_method != 'Full':
p.extra_generation_params['VAE Decoder'] = opts.sd_vae_decode_method
x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True)
x_samples_ddim = torch.stack(x_samples_ddim).float()
@ -940,21 +938,20 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
if opts.enable_pnginfo:
image.info["parameters"] = text
output_images.append(image)
if save_samples and hasattr(p, 'mask_for_overlay') and p.mask_for_overlay and any([opts.save_mask, opts.save_mask_composite, opts.return_mask, opts.return_mask_composite]):
image_mask = p.mask_for_overlay.convert('RGB')
image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, p.mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA')
if hasattr(p, 'mask_for_overlay') and p.mask_for_overlay:
if opts.return_mask or opts.save_mask:
image_mask = p.mask_for_overlay.convert('RGB')
if save_samples and opts.save_mask:
images.save_image(image_mask, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask")
if opts.return_mask:
output_images.append(image_mask)
if opts.save_mask:
images.save_image(image_mask, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask")
if opts.save_mask_composite:
images.save_image(image_mask_composite, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask-composite")
if opts.return_mask:
output_images.append(image_mask)
if opts.return_mask_composite:
output_images.append(image_mask_composite)
if opts.return_mask_composite or opts.save_mask_composite:
image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, p.mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA')
if save_samples and opts.save_mask_composite:
images.save_image(image_mask_composite, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask-composite")
if opts.return_mask_composite:
output_images.append(image_mask_composite)
del x_samples_ddim
@ -1147,6 +1144,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
if not self.enable_hr:
return samples
devices.torch_gc()
if self.latent_scale_mode is None:
decoded_samples = torch.stack(decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)).to(dtype=torch.float32)
@ -1156,8 +1154,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
with sd_models.SkipWritingToConfig():
sd_models.reload_model_weights(info=self.hr_checkpoint_info)
devices.torch_gc()
return self.sample_hr_pass(samples, decoded_samples, seeds, subseeds, subseed_strength, prompts)
def sample_hr_pass(self, samples, decoded_samples, seeds, subseeds, subseed_strength, prompts):
@ -1165,7 +1161,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
return samples
self.is_hr_pass = True
target_width = self.hr_upscale_to_x
target_height = self.hr_upscale_to_y
@ -1254,7 +1249,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
decoded_samples = decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)
self.is_hr_pass = False
return decoded_samples
def close(self):

2
modules/rng.py
View File

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

129
modules/scripts.py
View File

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

86
modules/scripts_postprocessing.py
View File

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

2
modules/sd_disable_initialization.py
View File

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

10
modules/sd_hijack.py
View File

@ -38,8 +38,12 @@ ldm.models.diffusion.ddpm.print = shared.ldm_print
optimizers = []
current_optimizer: sd_hijack_optimizations.SdOptimization = None
ldm_original_forward = patches.patch(__file__, ldm.modules.diffusionmodules.openaimodel.UNetModel, "forward", sd_unet.UNetModel_forward)
sgm_original_forward = patches.patch(__file__, sgm.modules.diffusionmodules.openaimodel.UNetModel, "forward", sd_unet.UNetModel_forward)
ldm_patched_forward = sd_unet.create_unet_forward(ldm.modules.diffusionmodules.openaimodel.UNetModel.forward)
ldm_original_forward = patches.patch(__file__, ldm.modules.diffusionmodules.openaimodel.UNetModel, "forward", ldm_patched_forward)
sgm_patched_forward = sd_unet.create_unet_forward(sgm.modules.diffusionmodules.openaimodel.UNetModel.forward)
sgm_original_forward = patches.patch(__file__, sgm.modules.diffusionmodules.openaimodel.UNetModel, "forward", sgm_patched_forward)
def list_optimizers():
new_optimizers = script_callbacks.list_optimizers_callback()
@ -303,8 +307,6 @@ class StableDiffusionModelHijack:
self.layers = None
self.clip = None
sd_unet.original_forward = None
def apply_circular(self, enable):
if self.circular_enabled == enable:

17
modules/sd_models.py
View File

@ -230,15 +230,19 @@ def select_checkpoint():
return checkpoint_info
checkpoint_dict_replacements = {
checkpoint_dict_replacements_sd1 = {
'cond_stage_model.transformer.embeddings.': 'cond_stage_model.transformer.text_model.embeddings.',
'cond_stage_model.transformer.encoder.': 'cond_stage_model.transformer.text_model.encoder.',
'cond_stage_model.transformer.final_layer_norm.': 'cond_stage_model.transformer.text_model.final_layer_norm.',
}
checkpoint_dict_replacements_sd2_turbo = { # Converts SD 2.1 Turbo from SGM to LDM format.
'conditioner.embedders.0.': 'cond_stage_model.',
}
def transform_checkpoint_dict_key(k):
for text, replacement in checkpoint_dict_replacements.items():
def transform_checkpoint_dict_key(k, replacements):
for text, replacement in replacements.items():
if k.startswith(text):
k = replacement + k[len(text):]
@ -249,9 +253,14 @@ def get_state_dict_from_checkpoint(pl_sd):
pl_sd = pl_sd.pop("state_dict", pl_sd)
pl_sd.pop("state_dict", None)
is_sd2_turbo = 'conditioner.embedders.0.model.ln_final.weight' in pl_sd and pl_sd['conditioner.embedders.0.model.ln_final.weight'].size()[0] == 1024
sd = {}
for k, v in pl_sd.items():
new_key = transform_checkpoint_dict_key(k)
if is_sd2_turbo:
new_key = transform_checkpoint_dict_key(k, checkpoint_dict_replacements_sd2_turbo)
else:
new_key = transform_checkpoint_dict_key(k, checkpoint_dict_replacements_sd1)
if new_key is not None:
sd[new_key] = v

2
modules/sd_samplers_extra.py
View File

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

4
modules/sd_samplers_timesteps_impl.py
View File

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

14
modules/sd_unet.py
View File

@ -5,8 +5,7 @@ from modules import script_callbacks, shared, devices
unet_options = []
current_unet_option = None
current_unet = None
original_forward = None
original_forward = None # not used, only left temporarily for compatibility
def list_unets():
new_unets = script_callbacks.list_unets_callback()
@ -84,9 +83,12 @@ class SdUnet(torch.nn.Module):
pass
def UNetModel_forward(self, x, timesteps=None, context=None, *args, **kwargs):
if current_unet is not None:
return current_unet.forward(x, timesteps, context, *args, **kwargs)
def create_unet_forward(original_forward):
def UNetModel_forward(self, x, timesteps=None, context=None, *args, **kwargs):
if current_unet is not None:
return current_unet.forward(x, timesteps, context, *args, **kwargs)
return original_forward(self, x, timesteps, context, *args, **kwargs)
return original_forward(self, x, timesteps, context, *args, **kwargs)
return UNetModel_forward

16
modules/shared_items.py
View File

@ -66,6 +66,22 @@ def reload_hypernetworks():
shared.hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
def get_infotext_names():
from modules import generation_parameters_copypaste, shared
res = {}
for info in shared.opts.data_labels.values():
if info.infotext:
res[info.infotext] = 1
for tab_data in generation_parameters_copypaste.paste_fields.values():
for _, name in tab_data.get("fields") or []:
if isinstance(name, str):
res[name] = 1
return list(res)
ui_reorder_categories_builtin_items = [
"prompt",
"image",

134
modules/shared_options.py
View File

@ -3,7 +3,7 @@ import gradio as gr
from modules import localization, ui_components, shared_items, shared, interrogate, shared_gradio_themes
from modules.paths_internal import models_path, script_path, data_path, sd_configs_path, sd_default_config, sd_model_file, default_sd_model_file, extensions_dir, extensions_builtin_dir # noqa: F401
from modules.shared_cmd_options import cmd_opts
from modules.options import options_section, OptionInfo, OptionHTML
from modules.options import options_section, OptionInfo, OptionHTML, categories
options_templates = {}
hide_dirs = shared.hide_dirs
@ -21,7 +21,14 @@ restricted_opts = {
"outdir_init_images"
}
options_templates.update(options_section(('saving-images', "Saving images/grids"), {
categories.register_category("saving", "Saving images")
categories.register_category("sd", "Stable Diffusion")
categories.register_category("ui", "User Interface")
categories.register_category("system", "System")
categories.register_category("postprocessing", "Postprocessing")
categories.register_category("training", "Training")
options_templates.update(options_section(('saving-images', "Saving images/grids", "saving"), {
"samples_save": OptionInfo(True, "Always save all generated images"),
"samples_format": OptionInfo('png', 'File format for images'),
"samples_filename_pattern": OptionInfo("", "Images filename pattern", component_args=hide_dirs).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"),
@ -39,8 +46,6 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
"grid_text_inactive_color": OptionInfo("#999999", "Inactive text color for image grids", ui_components.FormColorPicker, {}),
"grid_background_color": OptionInfo("#ffffff", "Background color for image grids", ui_components.FormColorPicker, {}),
"enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
"save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters."),
"save_images_before_face_restoration": OptionInfo(False, "Save a copy of image before doing face restoration."),
"save_images_before_highres_fix": OptionInfo(False, "Save a copy of image before applying highres fix."),
"save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
@ -67,7 +72,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
"notification_volume": OptionInfo(100, "Notification sound volume", gr.Slider, {"minimum": 0, "maximum": 100, "step": 1}).info("in %"),
}))
options_templates.update(options_section(('saving-paths', "Paths for saving"), {
options_templates.update(options_section(('saving-paths', "Paths for saving", "saving"), {
"outdir_samples": OptionInfo("", "Output directory for images; if empty, defaults to three directories below", component_args=hide_dirs),
"outdir_txt2img_samples": OptionInfo("outputs/txt2img-images", 'Output directory for txt2img images', component_args=hide_dirs),
"outdir_img2img_samples": OptionInfo("outputs/img2img-images", 'Output directory for img2img images', component_args=hide_dirs),
@ -79,7 +84,7 @@ options_templates.update(options_section(('saving-paths', "Paths for saving"), {
"outdir_init_images": OptionInfo("outputs/init-images", "Directory for saving init images when using img2img", component_args=hide_dirs),
}))
options_templates.update(options_section(('saving-to-dirs', "Saving to a directory"), {
options_templates.update(options_section(('saving-to-dirs', "Saving to a directory", "saving"), {
"save_to_dirs": OptionInfo(True, "Save images to a subdirectory"),
"grid_save_to_dirs": OptionInfo(True, "Save grids to a subdirectory"),
"use_save_to_dirs_for_ui": OptionInfo(False, "When using \"Save\" button, save images to a subdirectory"),
@ -87,21 +92,21 @@ options_templates.update(options_section(('saving-to-dirs', "Saving to a directo
"directories_max_prompt_words": OptionInfo(8, "Max prompt words for [prompt_words] pattern", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1, **hide_dirs}),
}))
options_templates.update(options_section(('upscaling', "Upscaling"), {
options_templates.update(options_section(('upscaling', "Upscaling", "postprocessing"), {
"ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}).info("0 = no tiling"),
"ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap for ESRGAN upscalers.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}).info("Low values = visible seam"),
"realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI.", gr.CheckboxGroup, lambda: {"choices": shared_items.realesrgan_models_names()}),
"upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in shared.sd_upscalers]}),
}))
options_templates.update(options_section(('face-restoration', "Face restoration"), {
options_templates.update(options_section(('face-restoration', "Face restoration", "postprocessing"), {
"face_restoration": OptionInfo(False, "Restore faces", infotext='Face restoration').info("will use a third-party model on generation result to reconstruct faces"),
"face_restoration_model": OptionInfo("CodeFormer", "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in shared.face_restorers]}),
"code_former_weight": OptionInfo(0.5, "CodeFormer weight", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}).info("0 = maximum effect; 1 = minimum effect"),
"face_restoration_unload": OptionInfo(False, "Move face restoration model from VRAM into RAM after processing"),
}))
options_templates.update(options_section(('system', "System"), {
options_templates.update(options_section(('system', "System", "system"), {
"auto_launch_browser": OptionInfo("Local", "Automatically open webui in browser on startup", gr.Radio, lambda: {"choices": ["Disable", "Local", "Remote"]}),
"enable_console_prompts": OptionInfo(shared.cmd_opts.enable_console_prompts, "Print prompts to console when generating with txt2img and img2img."),
"show_warnings": OptionInfo(False, "Show warnings in console.").needs_reload_ui(),
@ -116,13 +121,13 @@ options_templates.update(options_section(('system', "System"), {
"dump_stacks_on_signal": OptionInfo(False, "Print stack traces before exiting the program with ctrl+c."),
}))
options_templates.update(options_section(('API', "API"), {
options_templates.update(options_section(('API', "API", "system"), {
"api_enable_requests": OptionInfo(True, "Allow http:// and https:// URLs for input images in API", restrict_api=True),
"api_forbid_local_requests": OptionInfo(True, "Forbid URLs to local resources", restrict_api=True),
"api_useragent": OptionInfo("", "User agent for requests", restrict_api=True),
}))
options_templates.update(options_section(('training', "Training"), {
options_templates.update(options_section(('training', "Training", "training"), {
"unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
"pin_memory": OptionInfo(False, "Turn on pin_memory for DataLoader. Makes training slightly faster but can increase memory usage."),
"save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training of embedding or HN can be resumed with the matching optim file."),
@ -137,7 +142,7 @@ options_templates.update(options_section(('training', "Training"), {
"training_tensorboard_flush_every": OptionInfo(120, "How often, in seconds, to flush the pending tensorboard events and summaries to disk."),
}))
options_templates.update(options_section(('sd', "Stable Diffusion"), {
options_templates.update(options_section(('sd', "Stable Diffusion", "sd"), {
"sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": shared_items.list_checkpoint_tiles(shared.opts.sd_checkpoint_dropdown_use_short)}, refresh=shared_items.refresh_checkpoints, infotext='Model hash'),
"sd_checkpoints_limit": OptionInfo(1, "Maximum number of checkpoints loaded at the same time", gr.Slider, {"minimum": 1, "maximum": 10, "step": 1}),
"sd_checkpoints_keep_in_cpu": OptionInfo(True, "Only keep one model on device").info("will keep models other than the currently used one in RAM rather than VRAM"),
@ -154,14 +159,14 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
"hires_fix_refiner_pass": OptionInfo("second pass", "Hires fix: which pass to enable refiner for", gr.Radio, {"choices": ["first pass", "second pass", "both passes"]}, infotext="Hires refiner"),
}))
options_templates.update(options_section(('sdxl', "Stable Diffusion XL"), {
options_templates.update(options_section(('sdxl', "Stable Diffusion XL", "sd"), {
"sdxl_crop_top": OptionInfo(0, "crop top coordinate"),
"sdxl_crop_left": OptionInfo(0, "crop left coordinate"),
"sdxl_refiner_low_aesthetic_score": OptionInfo(2.5, "SDXL low aesthetic score", gr.Number).info("used for refiner model negative prompt"),
"sdxl_refiner_high_aesthetic_score": OptionInfo(6.0, "SDXL high aesthetic score", gr.Number).info("used for refiner model prompt"),
}))
options_templates.update(options_section(('vae', "VAE"), {
options_templates.update(options_section(('vae', "VAE", "sd"), {
"sd_vae_explanation": OptionHTML("""
<abbr title='Variational autoencoder'>VAE</abbr> is a neural network that transforms a standard <abbr title='red/green/blue'>RGB</abbr>
image into latent space representation and back. Latent space representation is what stable diffusion is working on during sampling
@ -176,7 +181,7 @@ For img2img, VAE is used to process user's input image before the sampling, and
"sd_vae_decode_method": OptionInfo("Full", "VAE type for decode", gr.Radio, {"choices": ["Full", "TAESD"]}, infotext='VAE Decoder').info("method to decode latent to image"),
}))
options_templates.update(options_section(('img2img', "img2img"), {
options_templates.update(options_section(('img2img', "img2img", "sd"), {
"inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, infotext='Conditional mask weight'),
"initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for img2img", gr.Slider, {"minimum": 0.0, "maximum": 1.5, "step": 0.001}, infotext='Noise multiplier'),
"img2img_extra_noise": OptionInfo(0.0, "Extra noise multiplier for img2img and hires fix", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, infotext='Extra noise').info("0 = disabled (default); should be lower than denoising strength"),
@ -189,9 +194,10 @@ options_templates.update(options_section(('img2img', "img2img"), {
"img2img_inpaint_sketch_default_brush_color": OptionInfo("#ffffff", "Inpaint sketch initial brush color", ui_components.FormColorPicker, {}).info("default brush color of img2img inpaint sketch").needs_reload_ui(),
"return_mask": OptionInfo(False, "For inpainting, include the greyscale mask in results for web"),
"return_mask_composite": OptionInfo(False, "For inpainting, include masked composite in results for web"),
"img2img_batch_show_results_limit": OptionInfo(32, "Show the first N batch img2img results in UI", gr.Slider, {"minimum": -1, "maximum": 1000, "step": 1}).info('0: disable, -1: show all images. Too many images can cause lag'),
}))
options_templates.update(options_section(('optimizations', "Optimizations"), {
options_templates.update(options_section(('optimizations', "Optimizations", "sd"), {
"cross_attention_optimization": OptionInfo("Automatic", "Cross attention optimization", gr.Dropdown, lambda: {"choices": shared_items.cross_attention_optimizations()}),
"s_min_uncond": OptionInfo(0.0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 15.0, "step": 0.01}).link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9177").info("skip negative prompt for some steps when the image is almost ready; 0=disable, higher=faster"),
"token_merging_ratio": OptionInfo(0.0, "Token merging ratio", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}, infotext='Token merging ratio').link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9256").info("0=disable, higher=faster"),
@ -202,7 +208,7 @@ options_templates.update(options_section(('optimizations', "Optimizations"), {
"batch_cond_uncond": OptionInfo(True, "Batch cond/uncond").info("do both conditional and unconditional denoising in one batch; uses a bit more VRAM during sampling, but improves speed; previously this was controlled by --always-batch-cond-uncond comandline argument"),
}))
options_templates.update(options_section(('compatibility', "Compatibility"), {
options_templates.update(options_section(('compatibility', "Compatibility", "sd"), {
"use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
"use_old_karras_scheduler_sigmas": OptionInfo(False, "Use old karras scheduler sigmas (0.1 to 10)."),
"no_dpmpp_sde_batch_determinism": OptionInfo(False, "Do not make DPM++ SDE deterministic across different batch sizes."),
@ -227,15 +233,16 @@ options_templates.update(options_section(('interrogate', "Interrogate"), {
"deepbooru_filter_tags": OptionInfo("", "deepbooru: filter out those tags").info("separate by comma"),
}))
options_templates.update(options_section(('extra_networks', "Extra Networks"), {
options_templates.update(options_section(('extra_networks', "Extra Networks", "sd"), {
"extra_networks_show_hidden_directories": OptionInfo(True, "Show hidden directories").info("directory is hidden if its name starts with \".\"."),
"extra_networks_dir_button_function": OptionInfo(False, "Add a '/' to the beginning of directory buttons").info("Buttons will display the contents of the selected directory without acting as a search filter."),
"extra_networks_hidden_models": OptionInfo("When searched", "Show cards for models in hidden directories", gr.Radio, {"choices": ["Always", "When searched", "Never"]}).info('"When searched" option will only show the item when the search string has 4 characters or more'),
"extra_networks_default_multiplier": OptionInfo(1.0, "Default multiplier for extra networks", gr.Slider, {"minimum": 0.0, "maximum": 2.0, "step": 0.01}),
"extra_networks_card_width": OptionInfo(0, "Card width for Extra Networks").info("in pixels"),
"extra_networks_card_height": OptionInfo(0, "Card height for Extra Networks").info("in pixels"),
"extra_networks_card_text_scale": OptionInfo(1.0, "Card text scale", gr.Slider, {"minimum": 0.0, "maximum": 2.0, "step": 0.01}).info("1 = original size"),
"extra_networks_card_show_desc": OptionInfo(True, "Show description on card"),
"extra_networks_card_order_field": OptionInfo("Name", "Default order field for Extra Networks cards", gr.Dropdown, {"choices": ['Name', 'Date Created', 'Date Modified']}).needs_reload_ui(),
"extra_networks_card_order_field": OptionInfo("Path", "Default order field for Extra Networks cards", gr.Dropdown, {"choices": ['Path', 'Name', 'Date Created', 'Date Modified']}).needs_reload_ui(),
"extra_networks_card_order": OptionInfo("Ascending", "Default order for Extra Networks cards", gr.Dropdown, {"choices": ['Ascending', 'Descending']}).needs_reload_ui(),
"extra_networks_add_text_separator": OptionInfo(" ", "Extra networks separator").info("extra text to add before <...> when adding extra network to prompt"),
"ui_extra_networks_tab_reorder": OptionInfo("", "Extra networks tab order").needs_reload_ui(),
@ -244,45 +251,66 @@ options_templates.update(options_section(('extra_networks', "Extra Networks"), {
"sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": ["None", *shared.hypernetworks]}, refresh=shared_items.reload_hypernetworks),
}))
options_templates.update(options_section(('ui', "User interface"), {
"localization": OptionInfo("None", "Localization", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)).needs_reload_ui(),
"gradio_theme": OptionInfo("Default", "Gradio theme", ui_components.DropdownEditable, lambda: {"choices": ["Default"] + shared_gradio_themes.gradio_hf_hub_themes}).info("you can also manually enter any of themes from the <a href='https://huggingface.co/spaces/gradio/theme-gallery'>gallery</a>.").needs_reload_ui(),
"gradio_themes_cache": OptionInfo(True, "Cache gradio themes locally").info("disable to update the selected Gradio theme"),
"gallery_height": OptionInfo("", "Gallery height", gr.Textbox).info("an be any valid CSS value").needs_reload_ui(),
"return_grid": OptionInfo(True, "Show grid in results for web"),
"do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
"send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"),
"send_size": OptionInfo(True, "Send size when sending prompt or image to another interface"),
"js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
"js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
"js_modal_lightbox_gamepad": OptionInfo(False, "Navigate image viewer with gamepad"),
"js_modal_lightbox_gamepad_repeat": OptionInfo(250, "Gamepad repeat period, in milliseconds"),
"show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
"samplers_in_dropdown": OptionInfo(True, "Use dropdown for sampler selection instead of radio group").needs_reload_ui(),
"dimensions_and_batch_together": OptionInfo(True, "Show Width/Height and Batch sliders in same row").needs_reload_ui(),
"keyedit_precision_attention": OptionInfo(0.1, "Ctrl+up/down precision when editing (attention:1.1)", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
"keyedit_precision_extra": OptionInfo(0.05, "Ctrl+up/down precision when editing <extra networks:0.9>", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
"keyedit_delimiters": OptionInfo(r".,\/!?%^*;:{}=`~() ", "Ctrl+up/down word delimiters"),
options_templates.update(options_section(('ui_prompt_editing', "Prompt editing", "ui"), {
"keyedit_precision_attention": OptionInfo(0.1, "Precision for (attention:1.1) when editing the prompt with Ctrl+up/down", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
"keyedit_precision_extra": OptionInfo(0.05, "Precision for <extra networks:0.9> when editing the prompt with Ctrl+up/down", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
"keyedit_delimiters": OptionInfo(r".,\/!?%^*;:{}=`~() ", "Word delimiters when editing the prompt with Ctrl+up/down"),
"keyedit_delimiters_whitespace": OptionInfo(["Tab", "Carriage Return", "Line Feed"], "Ctrl+up/down whitespace delimiters", gr.CheckboxGroup, lambda: {"choices": ["Tab", "Carriage Return", "Line Feed"]}),
"keyedit_move": OptionInfo(True, "Alt+left/right moves prompt elements"),
"quicksettings_list": OptionInfo(["sd_model_checkpoint"], "Quicksettings list", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that appear at the top of page rather than in settings tab").needs_reload_ui(),
"ui_tab_order": OptionInfo([], "UI tab order", ui_components.DropdownMulti, lambda: {"choices": list(shared.tab_names)}).needs_reload_ui(),
"hidden_tabs": OptionInfo([], "Hidden UI tabs", ui_components.DropdownMulti, lambda: {"choices": list(shared.tab_names)}).needs_reload_ui(),
"ui_reorder_list": OptionInfo([], "txt2img/img2img UI item order", ui_components.DropdownMulti, lambda: {"choices": list(shared_items.ui_reorder_categories())}).info("selected items appear first").needs_reload_ui(),
"disable_token_counters": OptionInfo(False, "Disable prompt token counters").needs_reload_ui(),
}))
options_templates.update(options_section(('ui_gallery', "Gallery", "ui"), {
"return_grid": OptionInfo(True, "Show grid in gallery"),
"do_not_show_images": OptionInfo(False, "Do not show any images in gallery"),
"js_modal_lightbox": OptionInfo(True, "Full page image viewer: enable"),
"js_modal_lightbox_initially_zoomed": OptionInfo(True, "Full page image viewer: show images zoomed in by default"),
"js_modal_lightbox_gamepad": OptionInfo(False, "Full page image viewer: navigate with gamepad"),
"js_modal_lightbox_gamepad_repeat": OptionInfo(250, "Full page image viewer: gamepad repeat period").info("in milliseconds"),
"gallery_height": OptionInfo("", "Gallery height", gr.Textbox).info("can be any valid CSS value, for example 768px or 20em").needs_reload_ui(),
}))
options_templates.update(options_section(('ui_alternatives', "UI alternatives", "ui"), {
"compact_prompt_box": OptionInfo(False, "Compact prompt layout").info("puts prompt and negative prompt inside the Generate tab, leaving more vertical space for the image on the right").needs_reload_ui(),
"samplers_in_dropdown": OptionInfo(True, "Use dropdown for sampler selection instead of radio group").needs_reload_ui(),
"dimensions_and_batch_together": OptionInfo(True, "Show Width/Height and Batch sliders in same row").needs_reload_ui(),
"sd_checkpoint_dropdown_use_short": OptionInfo(False, "Checkpoint dropdown: use filenames without paths").info("models in subdirectories like photo/sd15.ckpt will be listed as just sd15.ckpt"),
"hires_fix_show_sampler": OptionInfo(False, "Hires fix: show hires checkpoint and sampler selection").needs_reload_ui(),
"hires_fix_show_prompts": OptionInfo(False, "Hires fix: show hires prompt and negative prompt").needs_reload_ui(),
"disable_token_counters": OptionInfo(False, "Disable prompt token counters").needs_reload_ui(),
"compact_prompt_box": OptionInfo(False, "Compact prompt layout").info("puts prompt and negative prompt inside the Generate tab, leaving more vertical space for the image on the right").needs_reload_ui(),
"txt2img_settings_accordion": OptionInfo(False, "Settings in txt2img hidden under Accordion").needs_reload_ui(),
"img2img_settings_accordion": OptionInfo(False, "Settings in img2img hidden under Accordion").needs_reload_ui(),
}))
options_templates.update(options_section(('ui', "User interface", "ui"), {
"localization": OptionInfo("None", "Localization", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)).needs_reload_ui(),
"quicksettings_list": OptionInfo(["sd_model_checkpoint"], "Quicksettings list", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that appear at the top of page rather than in settings tab").needs_reload_ui(),
"ui_tab_order": OptionInfo([], "UI tab order", ui_components.DropdownMulti, lambda: {"choices": list(shared.tab_names)}).needs_reload_ui(),
"hidden_tabs": OptionInfo([], "Hidden UI tabs", ui_components.DropdownMulti, lambda: {"choices": list(shared.tab_names)}).needs_reload_ui(),
"ui_reorder_list": OptionInfo([], "UI item order for txt2img/img2img tabs", ui_components.DropdownMulti, lambda: {"choices": list(shared_items.ui_reorder_categories())}).info("selected items appear first").needs_reload_ui(),
"gradio_theme": OptionInfo("Default", "Gradio theme", ui_components.DropdownEditable, lambda: {"choices": ["Default"] + shared_gradio_themes.gradio_hf_hub_themes}).info("you can also manually enter any of themes from the <a href='https://huggingface.co/spaces/gradio/theme-gallery'>gallery</a>.").needs_reload_ui(),
"gradio_themes_cache": OptionInfo(True, "Cache gradio themes locally").info("disable to update the selected Gradio theme"),
"show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
"send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"),
"send_size": OptionInfo(True, "Send size when sending prompt or image to another interface"),
}))
options_templates.update(options_section(('infotext', "Infotext"), {
"add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
"add_model_name_to_info": OptionInfo(True, "Add model name to generation information"),
"add_user_name_to_info": OptionInfo(False, "Add user name to generation information when authenticated"),
"add_version_to_infotext": OptionInfo(True, "Add program version to generation information"),
options_templates.update(options_section(('infotext', "Infotext", "ui"), {
"infotext_explanation": OptionHTML("""
Infotext is what this software calls the text that contains generation parameters and can be used to generate the same picture again.
It is displayed in UI below the image. To use infotext, paste it into the prompt and click the ↙️ paste button.
"""),
"enable_pnginfo": OptionInfo(True, "Write infotext to metadata of the generated image"),
"save_txt": OptionInfo(False, "Create a text file with infotext next to every generated image"),
"add_model_name_to_info": OptionInfo(True, "Add model name to infotext"),
"add_model_hash_to_info": OptionInfo(True, "Add model hash to infotext"),
"add_vae_name_to_info": OptionInfo(True, "Add VAE name to infotext"),
"add_vae_hash_to_info": OptionInfo(True, "Add VAE hash to infotext"),
"add_user_name_to_info": OptionInfo(False, "Add user name to infotext when authenticated"),
"add_version_to_infotext": OptionInfo(True, "Add program version to infotext"),
"disable_weights_auto_swap": OptionInfo(True, "Disregard checkpoint information from pasted infotext").info("when reading generation parameters from text into UI"),
"infotext_skip_pasting": OptionInfo([], "Disregard fields from pasted infotext", ui_components.DropdownMulti, lambda: {"choices": shared_items.get_infotext_names()}),
"infotext_styles": OptionInfo("Apply if any", "Infer styles from prompts of pasted infotext", gr.Radio, {"choices": ["Ignore", "Apply", "Discard", "Apply if any"]}).info("when reading generation parameters from text into UI)").html("""<ul style='margin-left: 1.5em'>
<li>Ignore: keep prompt and styles dropdown as it is.</li>
<li>Apply: remove style text from prompt, always replace styles dropdown value with found styles (even if none are found).</li>
@ -292,7 +320,7 @@ options_templates.update(options_section(('infotext', "Infotext"), {
}))
options_templates.update(options_section(('ui', "Live previews"), {
options_templates.update(options_section(('ui', "Live previews", "ui"), {
"show_progressbar": OptionInfo(True, "Show progressbar"),
"live_previews_enable": OptionInfo(True, "Show live previews of the created image"),
"live_previews_image_format": OptionInfo("png", "Live preview file format", gr.Radio, {"choices": ["jpeg", "png", "webp"]}),
@ -303,9 +331,10 @@ options_templates.update(options_section(('ui', "Live previews"), {
"live_preview_content": OptionInfo("Prompt", "Live preview subject", gr.Radio, {"choices": ["Combined", "Prompt", "Negative prompt"]}),
"live_preview_refresh_period": OptionInfo(1000, "Progressbar and preview update period").info("in milliseconds"),
"live_preview_fast_interrupt": OptionInfo(False, "Return image with chosen live preview method on interrupt").info("makes interrupts faster"),
"js_live_preview_in_modal_lightbox": OptionInfo(False, "Show Live preview in full page image viewer"),
}))
options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
options_templates.update(options_section(('sampler-params', "Sampler parameters", "sd"), {
"hide_samplers": OptionInfo([], "Hide samplers in user interface", gr.CheckboxGroup, lambda: {"choices": [x.name for x in shared_items.list_samplers()]}).needs_reload_ui(),
"eta_ddim": OptionInfo(0.0, "Eta for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, infotext='Eta DDIM').info("noise multiplier; higher = more unpredictable results"),
"eta_ancestral": OptionInfo(1.0, "Eta for k-diffusion samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, infotext='Eta').info("noise multiplier; currently only applies to ancestral samplers (i.e. Euler a) and SDE samplers"),
@ -327,10 +356,11 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
'uni_pc_lower_order_final': OptionInfo(True, "UniPC lower order final", infotext='UniPC lower order final'),
}))
options_templates.update(options_section(('postprocessing', "Postprocessing"), {
options_templates.update(options_section(('postprocessing', "Postprocessing", "postprocessing"), {
'postprocessing_enable_in_main_ui': OptionInfo([], "Enable postprocessing operations in txt2img and img2img tabs", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
'postprocessing_operation_order': OptionInfo([], "Postprocessing operation order", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
'upscaling_max_images_in_cache': OptionInfo(5, "Maximum number of images in upscaling cache", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
'postprocessing_existing_caption_action': OptionInfo("Ignore", "Action for existing captions", gr.Radio, {"choices": ["Ignore", "Keep", "Prepend", "Append"]}).info("when generating captions using postprocessing; Ignore = use generated; Keep = use original; Prepend/Append = combine both"),
}))
options_templates.update(options_section((None, "Hidden options"), {

167
modules/styles.py
View File

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

18
modules/sysinfo.py
View File

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

233
modules/textual_inversion/autocrop.py
View File

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

232
modules/textual_inversion/preprocess.py
View File

@ -1,232 +0,0 @@
import os
from PIL import Image, ImageOps
import math
import tqdm
from modules import paths, shared, images, deepbooru
from modules.textual_inversion import autocrop
def preprocess(id_task, process_src, process_dst, process_width, process_height, preprocess_txt_action, process_keep_original_size, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.15, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False, process_multicrop=None, process_multicrop_mindim=None, process_multicrop_maxdim=None, process_multicrop_minarea=None, process_multicrop_maxarea=None, process_multicrop_objective=None, process_multicrop_threshold=None):
try:
if process_caption:
shared.interrogator.load()
if process_caption_deepbooru:
deepbooru.model.start()
preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_keep_original_size, process_flip, process_split, process_caption, process_caption_deepbooru, split_threshold, overlap_ratio, process_focal_crop, process_focal_crop_face_weight, process_focal_crop_entropy_weight, process_focal_crop_edges_weight, process_focal_crop_debug, process_multicrop, process_multicrop_mindim, process_multicrop_maxdim, process_multicrop_minarea, process_multicrop_maxarea, process_multicrop_objective, process_multicrop_threshold)
finally:
if process_caption:
shared.interrogator.send_blip_to_ram()
if process_caption_deepbooru:
deepbooru.model.stop()
def listfiles(dirname):
return os.listdir(dirname)
class PreprocessParams:
src = None
dstdir = None
subindex = 0
flip = False
process_caption = False
process_caption_deepbooru = False
preprocess_txt_action = None
def save_pic_with_caption(image, index, params: PreprocessParams, existing_caption=None):
caption = ""
if params.process_caption:
caption += shared.interrogator.generate_caption(image)
if params.process_caption_deepbooru:
if caption:
caption += ", "
caption += deepbooru.model.tag_multi(image)
filename_part = params.src
filename_part = os.path.splitext(filename_part)[0]
filename_part = os.path.basename(filename_part)
basename = f"{index:05}-{params.subindex}-{filename_part}"
image.save(os.path.join(params.dstdir, f"{basename}.png"))
if params.preprocess_txt_action == 'prepend' and existing_caption:
caption = f"{existing_caption} {caption}"
elif params.preprocess_txt_action == 'append' and existing_caption:
caption = f"{caption} {existing_caption}"
elif params.preprocess_txt_action == 'copy' and existing_caption:
caption = existing_caption
caption = caption.strip()
if caption:
with open(os.path.join(params.dstdir, f"{basename}.txt"), "w", encoding="utf8") as file:
file.write(caption)
params.subindex += 1
def save_pic(image, index, params, existing_caption=None):
save_pic_with_caption(image, index, params, existing_caption=existing_caption)
if params.flip:
save_pic_with_caption(ImageOps.mirror(image), index, params, existing_caption=existing_caption)
def split_pic(image, inverse_xy, width, height, overlap_ratio):
if inverse_xy:
from_w, from_h = image.height, image.width
to_w, to_h = height, width
else:
from_w, from_h = image.width, image.height
to_w, to_h = width, height
h = from_h * to_w // from_w
if inverse_xy:
image = image.resize((h, to_w))
else:
image = image.resize((to_w, h))
split_count = math.ceil((h - to_h * overlap_ratio) / (to_h * (1.0 - overlap_ratio)))
y_step = (h - to_h) / (split_count - 1)
for i in range(split_count):
y = int(y_step * i)
if inverse_xy:
splitted = image.crop((y, 0, y + to_h, to_w))
else:
splitted = image.crop((0, y, to_w, y + to_h))
yield splitted
# not using torchvision.transforms.CenterCrop because it doesn't allow float regions
def center_crop(image: Image, w: int, h: int):
iw, ih = image.size
if ih / h < iw / w:
sw = w * ih / h
box = (iw - sw) / 2, 0, iw - (iw - sw) / 2, ih
else:
sh = h * iw / w
box = 0, (ih - sh) / 2, iw, ih - (ih - sh) / 2
return image.resize((w, h), Image.Resampling.LANCZOS, box)
def multicrop_pic(image: Image, mindim, maxdim, minarea, maxarea, objective, threshold):
iw, ih = image.size
err = lambda w, h: 1-(lambda x: x if x < 1 else 1/x)(iw/ih/(w/h))
wh = max(((w, h) for w in range(mindim, maxdim+1, 64) for h in range(mindim, maxdim+1, 64)
if minarea <= w * h <= maxarea and err(w, h) <= threshold),
key= lambda wh: (wh[0]*wh[1], -err(*wh))[::1 if objective=='Maximize area' else -1],
default=None
)
return wh and center_crop(image, *wh)
def preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_keep_original_size, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False, process_multicrop=None, process_multicrop_mindim=None, process_multicrop_maxdim=None, process_multicrop_minarea=None, process_multicrop_maxarea=None, process_multicrop_objective=None, process_multicrop_threshold=None):
width = process_width
height = process_height
src = os.path.abspath(process_src)
dst = os.path.abspath(process_dst)
split_threshold = max(0.0, min(1.0, split_threshold))
overlap_ratio = max(0.0, min(0.9, overlap_ratio))
assert src != dst, 'same directory specified as source and destination'
os.makedirs(dst, exist_ok=True)
files = listfiles(src)
shared.state.job = "preprocess"
shared.state.textinfo = "Preprocessing..."
shared.state.job_count = len(files)
params = PreprocessParams()
params.dstdir = dst
params.flip = process_flip
params.process_caption = process_caption
params.process_caption_deepbooru = process_caption_deepbooru
params.preprocess_txt_action = preprocess_txt_action
pbar = tqdm.tqdm(files)
for index, imagefile in enumerate(pbar):
params.subindex = 0
filename = os.path.join(src, imagefile)
try:
img = Image.open(filename)
img = ImageOps.exif_transpose(img)
img = img.convert("RGB")
except Exception:
continue
description = f"Preprocessing [Image {index}/{len(files)}]"
pbar.set_description(description)
shared.state.textinfo = description
params.src = filename
existing_caption = None
existing_caption_filename = f"{os.path.splitext(filename)[0]}.txt"
if os.path.exists(existing_caption_filename):
with open(existing_caption_filename, 'r', encoding="utf8") as file:
existing_caption = file.read()
if shared.state.interrupted:
break
if img.height > img.width:
ratio = (img.width * height) / (img.height * width)
inverse_xy = False
else:
ratio = (img.height * width) / (img.width * height)
inverse_xy = True
process_default_resize = True
if process_split and ratio < 1.0 and ratio <= split_threshold:
for splitted in split_pic(img, inverse_xy, width, height, overlap_ratio):
save_pic(splitted, index, params, existing_caption=existing_caption)
process_default_resize = False
if process_focal_crop and img.height != img.width:
dnn_model_path = None
try:
dnn_model_path = autocrop.download_and_cache_models(os.path.join(paths.models_path, "opencv"))
except Exception as e:
print("Unable to load face detection model for auto crop selection. Falling back to lower quality haar method.", e)
autocrop_settings = autocrop.Settings(
crop_width = width,
crop_height = height,
face_points_weight = process_focal_crop_face_weight,
entropy_points_weight = process_focal_crop_entropy_weight,
corner_points_weight = process_focal_crop_edges_weight,
annotate_image = process_focal_crop_debug,
dnn_model_path = dnn_model_path,
)
for focal in autocrop.crop_image(img, autocrop_settings):
save_pic(focal, index, params, existing_caption=existing_caption)
process_default_resize = False
if process_multicrop:
cropped = multicrop_pic(img, process_multicrop_mindim, process_multicrop_maxdim, process_multicrop_minarea, process_multicrop_maxarea, process_multicrop_objective, process_multicrop_threshold)
if cropped is not None:
save_pic(cropped, index, params, existing_caption=existing_caption)
else:
print(f"skipped {img.width}x{img.height} image {filename} (can't find suitable size within error threshold)")
process_default_resize = False
if process_keep_original_size:
save_pic(img, index, params, existing_caption=existing_caption)
process_default_resize = False
if process_default_resize:
img = images.resize_image(1, img, width, height)
save_pic(img, index, params, existing_caption=existing_caption)
shared.state.nextjob()

7
modules/textual_inversion/ui.py
View File

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

134
modules/ui.py
View File

@ -4,6 +4,7 @@ import os
import sys
from functools import reduce
import warnings
from contextlib import ExitStack
import gradio as gr
import gradio.utils
@ -270,7 +271,11 @@ def create_ui():
extra_tabs.__enter__()
with gr.Tab("Generation", id="txt2img_generation") as txt2img_generation_tab, ResizeHandleRow(equal_height=False):
with gr.Column(variant='compact', elem_id="txt2img_settings"):
with ExitStack() as stack:
if shared.opts.txt2img_settings_accordion:
stack.enter_context(gr.Accordion("Open for Settings", open=False))
stack.enter_context(gr.Column(variant='compact', elem_id="txt2img_settings"))
scripts.scripts_txt2img.prepare_ui()
for category in ordered_ui_categories():
@ -489,7 +494,11 @@ def create_ui():
extra_tabs.__enter__()
with gr.Tab("Generation", id="img2img_generation") as img2img_generation_tab, ResizeHandleRow(equal_height=False):
with gr.Column(variant='compact', elem_id="img2img_settings"):
with ExitStack() as stack:
if shared.opts.img2img_settings_accordion:
stack.enter_context(gr.Accordion("Open for Settings", open=False))
stack.enter_context(gr.Column(variant='compact', elem_id="img2img_settings"))
copy_image_buttons = []
copy_image_destinations = {}
@ -626,12 +635,6 @@ def create_ui():
scale_by.release(**on_change_args)
button_update_resize_to.click(**on_change_args)
# the code below is meant to update the resolution label after the image in the image selection UI has changed.
# as it is now the event keeps firing continuously for inpaint edits, which ruins the page with constant requests.
# I assume this must be a gradio bug and for now we'll just do it for non-inpaint inputs.
for component in [init_img, sketch]:
component.change(fn=lambda: None, _js="updateImg2imgResizeToTextAfterChangingImage", inputs=[], outputs=[], show_progress=False)
tab_scale_to.select(fn=lambda: 0, inputs=[], outputs=[selected_scale_tab])
tab_scale_by.select(fn=lambda: 1, inputs=[], outputs=[selected_scale_tab])
@ -692,6 +695,12 @@ def create_ui():
if category not in {"accordions"}:
scripts.scripts_img2img.setup_ui_for_section(category)
# the code below is meant to update the resolution label after the image in the image selection UI has changed.
# as it is now the event keeps firing continuously for inpaint edits, which ruins the page with constant requests.
# I assume this must be a gradio bug and for now we'll just do it for non-inpaint inputs.
for component in [init_img, sketch]:
component.change(fn=lambda: None, _js="updateImg2imgResizeToTextAfterChangingImage", inputs=[], outputs=[], show_progress=False)
def select_img2img_tab(tab):
return gr.update(visible=tab in [2, 3, 4]), gr.update(visible=tab == 3),
@ -903,71 +912,6 @@ def create_ui():
with gr.Column():
create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary', elem_id="train_create_hypernetwork")
with gr.Tab(label="Preprocess images", id="preprocess_images"):
process_src = gr.Textbox(label='Source directory', elem_id="train_process_src")
process_dst = gr.Textbox(label='Destination directory', elem_id="train_process_dst")
process_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_process_width")
process_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_process_height")
preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"], elem_id="train_preprocess_txt_action")
with gr.Row():
process_keep_original_size = gr.Checkbox(label='Keep original size', elem_id="train_process_keep_original_size")
process_flip = gr.Checkbox(label='Create flipped copies', elem_id="train_process_flip")
process_split = gr.Checkbox(label='Split oversized images', elem_id="train_process_split")
process_focal_crop = gr.Checkbox(label='Auto focal point crop', elem_id="train_process_focal_crop")
process_multicrop = gr.Checkbox(label='Auto-sized crop', elem_id="train_process_multicrop")
process_caption = gr.Checkbox(label='Use BLIP for caption', elem_id="train_process_caption")
process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True, elem_id="train_process_caption_deepbooru")
with gr.Row(visible=False) as process_split_extra_row:
process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_split_threshold")
process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05, elem_id="train_process_overlap_ratio")
with gr.Row(visible=False) as process_focal_crop_row:
process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_face_weight")
process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_entropy_weight")
process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_edges_weight")
process_focal_crop_debug = gr.Checkbox(label='Create debug image', elem_id="train_process_focal_crop_debug")
with gr.Column(visible=False) as process_multicrop_col:
gr.Markdown('Each image is center-cropped with an automatically chosen width and height.')
with gr.Row():
process_multicrop_mindim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension lower bound", value=384, elem_id="train_process_multicrop_mindim")
process_multicrop_maxdim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension upper bound", value=768, elem_id="train_process_multicrop_maxdim")
with gr.Row():
process_multicrop_minarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area lower bound", value=64*64, elem_id="train_process_multicrop_minarea")
process_multicrop_maxarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area upper bound", value=640*640, elem_id="train_process_multicrop_maxarea")
with gr.Row():
process_multicrop_objective = gr.Radio(["Maximize area", "Minimize error"], value="Maximize area", label="Resizing objective", elem_id="train_process_multicrop_objective")
process_multicrop_threshold = gr.Slider(minimum=0, maximum=1, step=0.01, label="Error threshold", value=0.1, elem_id="train_process_multicrop_threshold")
with gr.Row():
with gr.Column(scale=3):
gr.HTML(value="")
with gr.Column():
with gr.Row():
interrupt_preprocessing = gr.Button("Interrupt", elem_id="train_interrupt_preprocessing")
run_preprocess = gr.Button(value="Preprocess", variant='primary', elem_id="train_run_preprocess")
process_split.change(
fn=lambda show: gr_show(show),
inputs=[process_split],
outputs=[process_split_extra_row],
)
process_focal_crop.change(
fn=lambda show: gr_show(show),
inputs=[process_focal_crop],
outputs=[process_focal_crop_row],
)
process_multicrop.change(
fn=lambda show: gr_show(show),
inputs=[process_multicrop],
outputs=[process_multicrop_col],
)
def get_textual_inversion_template_names():
return sorted(textual_inversion.textual_inversion_templates)
@ -1068,42 +1012,6 @@ def create_ui():
]
)
run_preprocess.click(
fn=wrap_gradio_gpu_call(textual_inversion_ui.preprocess, extra_outputs=[gr.update()]),
_js="start_training_textual_inversion",
inputs=[
dummy_component,
process_src,
process_dst,
process_width,
process_height,
preprocess_txt_action,
process_keep_original_size,
process_flip,
process_split,
process_caption,
process_caption_deepbooru,
process_split_threshold,
process_overlap_ratio,
process_focal_crop,
process_focal_crop_face_weight,
process_focal_crop_entropy_weight,
process_focal_crop_edges_weight,
process_focal_crop_debug,
process_multicrop,
process_multicrop_mindim,
process_multicrop_maxdim,
process_multicrop_minarea,
process_multicrop_maxarea,
process_multicrop_objective,
process_multicrop_threshold,
],
outputs=[
ti_output,
ti_outcome,
],
)
train_embedding.click(
fn=wrap_gradio_gpu_call(textual_inversion_ui.train_embedding, extra_outputs=[gr.update()]),
_js="start_training_textual_inversion",
@ -1177,12 +1085,6 @@ def create_ui():
outputs=[],
)
interrupt_preprocessing.click(
fn=lambda: shared.state.interrupt(),
inputs=[],
outputs=[],
)
loadsave = ui_loadsave.UiLoadsave(cmd_opts.ui_config_file)
settings = ui_settings.UiSettings()
@ -1299,7 +1201,7 @@ def setup_ui_api(app):
from fastapi.responses import PlainTextResponse
text = sysinfo.get()
filename = f"sysinfo-{datetime.datetime.utcnow().strftime('%Y-%m-%d-%H-%M')}.txt"
filename = f"sysinfo-{datetime.datetime.utcnow().strftime('%Y-%m-%d-%H-%M')}.json"
return PlainTextResponse(text, headers={'Content-Disposition': f'{"attachment" if attachment else "inline"}; filename="{filename}"'})

17
modules/ui_extensions.py
View File

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

17
modules/ui_extra_networks.py
View File

@ -151,8 +151,13 @@ class ExtraNetworksPage:
continue
subdir = os.path.abspath(x)[len(parentdir):].replace("\\", "/")
while subdir.startswith("/"):
subdir = subdir[1:]
if shared.opts.extra_networks_dir_button_function:
if not subdir.startswith("/"):
subdir = "/" + subdir
else:
while subdir.startswith("/"):
subdir = subdir[1:]
is_empty = len(os.listdir(x)) == 0
if not is_empty and not subdir.endswith("/"):
@ -279,6 +284,7 @@ class ExtraNetworksPage:
"date_created": int(stat.st_ctime or 0),
"date_modified": int(stat.st_mtime or 0),
"name": pth.name.lower(),
"path": str(pth.parent).lower(),
}
def find_preview(self, path):
@ -369,6 +375,9 @@ def create_ui(interface: gr.Blocks, unrelated_tabs, tabname):
for page in ui.stored_extra_pages:
with gr.Tab(page.title, elem_id=f"{tabname}_{page.id_page}", elem_classes=["extra-page"]) as tab:
with gr.Column(elem_id=f"{tabname}_{page.id_page}_prompts", elem_classes=["extra-page-prompts"]):
pass
elem_id = f"{tabname}_{page.id_page}_cards_html"
page_elem = gr.HTML('Loading...', elem_id=elem_id)
ui.pages.append(page_elem)
@ -382,7 +391,7 @@ def create_ui(interface: gr.Blocks, unrelated_tabs, tabname):
related_tabs.append(tab)
edit_search = gr.Textbox('', show_label=False, elem_id=tabname+"_extra_search", elem_classes="search", placeholder="Search...", visible=False, interactive=True)
dropdown_sort = gr.Dropdown(choices=['Name', 'Date Created', 'Date Modified', ], value=shared.opts.extra_networks_card_order_field, elem_id=tabname+"_extra_sort", elem_classes="sort", multiselect=False, visible=False, show_label=False, interactive=True, label=tabname+"_extra_sort_order")
dropdown_sort = gr.Dropdown(choices=['Path', 'Name', 'Date Created', 'Date Modified', ], value=shared.opts.extra_networks_card_order_field, elem_id=tabname+"_extra_sort", elem_classes="sort", multiselect=False, visible=False, show_label=False, interactive=True, label=tabname+"_extra_sort_order")
button_sortorder = ToolButton(switch_values_symbol, elem_id=tabname+"_extra_sortorder", elem_classes=["sortorder"] + ([] if shared.opts.extra_networks_card_order == "Ascending" else ["sortReverse"]), visible=False, tooltip="Invert sort order")
button_refresh = gr.Button('Refresh', elem_id=tabname+"_extra_refresh", visible=False)
checkbox_show_dirs = gr.Checkbox(True, label='Show dirs', elem_id=tabname+"_extra_show_dirs", elem_classes="show-dirs", visible=False)
@ -399,7 +408,7 @@ def create_ui(interface: gr.Blocks, unrelated_tabs, tabname):
allow_prompt = "true" if page.allow_prompt else "false"
allow_negative_prompt = "true" if page.allow_negative_prompt else "false"
jscode = 'extraNetworksTabSelected("' + tabname + '", "' + f"{tabname}_{page.id_page}" + '", ' + allow_prompt + ', ' + allow_negative_prompt + ');'
jscode = 'extraNetworksTabSelected("' + tabname + '", "' + f"{tabname}_{page.id_page}_prompts" + '", ' + allow_prompt + ', ' + allow_negative_prompt + ');'
tab.select(fn=lambda: [gr.update(visible=True) for _ in tab_controls], _js='function(){ ' + jscode + ' }', inputs=[], outputs=tab_controls, show_progress=False)

8
modules/ui_extra_networks_checkpoints.py
View File

@ -17,6 +17,9 @@ class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage):
def create_item(self, name, index=None, enable_filter=True):
checkpoint: sd_models.CheckpointInfo = sd_models.checkpoint_aliases.get(name)
if checkpoint is None:
return
path, ext = os.path.splitext(checkpoint.filename)
return {
"name": checkpoint.name_for_extra,
@ -32,9 +35,12 @@ class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage):
}
def list_items(self):
# instantiate a list to protect against concurrent modification
names = list(sd_models.checkpoints_list)
for index, name in enumerate(names):
yield self.create_item(name, index)
item = self.create_item(name, index)
if item is not None:
yield item
def allowed_directories_for_previews(self):
return [v for v in [shared.cmd_opts.ckpt_dir, sd_models.model_path] if v is not None]

13
modules/ui_extra_networks_hypernets.py
View File

@ -13,7 +13,10 @@ class ExtraNetworksPageHypernetworks(ui_extra_networks.ExtraNetworksPage):
shared.reload_hypernetworks()
def create_item(self, name, index=None, enable_filter=True):
full_path = shared.hypernetworks[name]
full_path = shared.hypernetworks.get(name)
if full_path is None:
return
path, ext = os.path.splitext(full_path)
sha256 = sha256_from_cache(full_path, f'hypernet/{name}')
shorthash = sha256[0:10] if sha256 else None
@ -31,8 +34,12 @@ class ExtraNetworksPageHypernetworks(ui_extra_networks.ExtraNetworksPage):
}
def list_items(self):
for index, name in enumerate(shared.hypernetworks):
yield self.create_item(name, index)
# instantiate a list to protect against concurrent modification
names = list(shared.hypernetworks)
for index, name in enumerate(names):
item = self.create_item(name, index)
if item is not None:
yield item
def allowed_directories_for_previews(self):
return [shared.cmd_opts.hypernetwork_dir]

10
modules/ui_extra_networks_textual_inversion.py
View File

@ -14,6 +14,8 @@ class ExtraNetworksPageTextualInversion(ui_extra_networks.ExtraNetworksPage):
def create_item(self, name, index=None, enable_filter=True):
embedding = sd_hijack.model_hijack.embedding_db.word_embeddings.get(name)
if embedding is None:
return
path, ext = os.path.splitext(embedding.filename)
return {
@ -29,8 +31,12 @@ class ExtraNetworksPageTextualInversion(ui_extra_networks.ExtraNetworksPage):
}
def list_items(self):
for index, name in enumerate(sd_hijack.model_hijack.embedding_db.word_embeddings):
yield self.create_item(name, index)
# instantiate a list to protect against concurrent modification
names = list(sd_hijack.model_hijack.embedding_db.word_embeddings)
for index, name in enumerate(names):
item = self.create_item(name, index)
if item is not None:
yield item
def allowed_directories_for_previews(self):
return list(sd_hijack.model_hijack.embedding_db.embedding_dirs)

2
modules/ui_extra_networks_user_metadata.py
View File

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

2
modules/ui_loadsave.py
View File

@ -141,7 +141,7 @@ class UiLoadsave:
def write_to_file(self, current_ui_settings):
with open(self.filename, "w", encoding="utf8") as file:
json.dump(current_ui_settings, file, indent=4)
json.dump(current_ui_settings, file, indent=4, ensure_ascii=False)
def dump_defaults(self):
"""saves default values to a file unless tjhe file is present and there was an error loading default values at start"""

18
modules/ui_postprocessing.py
View File

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

4
modules/ui_prompt_styles.py
View File

@ -68,10 +68,10 @@ class UiPromptStyles:
self.copy = ui_components.ToolButton(value=styles_copy_symbol, elem_id=f"{tabname}_style_copy", tooltip="Copy main UI prompt to style.")
with gr.Row():
self.prompt = gr.Textbox(label="Prompt", show_label=True, elem_id=f"{tabname}_edit_style_prompt", lines=3)
self.prompt = gr.Textbox(label="Prompt", show_label=True, elem_id=f"{tabname}_edit_style_prompt", lines=3, elem_classes=["prompt"])
with gr.Row():
self.neg_prompt = gr.Textbox(label="Negative prompt", show_label=True, elem_id=f"{tabname}_edit_style_neg_prompt", lines=3)
self.neg_prompt = gr.Textbox(label="Negative prompt", show_label=True, elem_id=f"{tabname}_edit_style_neg_prompt", lines=3, elem_classes=["prompt"])
with gr.Row():
self.save = gr.Button('Save', variant='primary', elem_id=f'{tabname}_edit_style_save', visible=False)

6
modules/ui_toprow.py
View File

@ -34,8 +34,10 @@ class Toprow:
submit_box = None
def __init__(self, is_img2img, is_compact=False):
id_part = "img2img" if is_img2img else "txt2img"
def __init__(self, is_img2img, is_compact=False, id_part=None):
if id_part is None:
id_part = "img2img" if is_img2img else "txt2img"
self.id_part = id_part
self.is_img2img = is_img2img
self.is_compact = is_compact

6
modules/upscaler.py
View File

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

59
modules/xpu_specific.py Normal file
View File

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

1
pyproject.toml
View File

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

15
script.js
View File

@ -133,9 +133,18 @@ document.addEventListener('keydown', function(e) {
if (isEnter && isModifierKey) {
if (interruptButton.style.display === 'block') {
interruptButton.click();
setTimeout(function() {
generateButton.click();
}, 500);
const callback = (mutationList) => {
for (const mutation of mutationList) {
if (mutation.type === 'attributes' && mutation.attributeName === 'style') {
if (interruptButton.style.display === 'none') {
generateButton.click();
observer.disconnect();
}
}
}
};
const observer = new MutationObserver(callback);
observer.observe(interruptButton, {attributes: true});
} else {
generateButton.click();
}

30
scripts/postprocessing_caption.py Normal file
View File

@ -0,0 +1,30 @@
from modules import scripts_postprocessing, ui_components, deepbooru, shared
import gradio as gr
class ScriptPostprocessingCeption(scripts_postprocessing.ScriptPostprocessing):
name = "Caption"
order = 4000
def ui(self):
with ui_components.InputAccordion(False, label="Caption") as enable:
option = gr.CheckboxGroup(value=["Deepbooru"], choices=["Deepbooru", "BLIP"], show_label=False)
return {
"enable": enable,
"option": option,
}
def process(self, pp: scripts_postprocessing.PostprocessedImage, enable, option):
if not enable:
return
captions = [pp.caption]
if "Deepbooru" in option:
captions.append(deepbooru.model.tag(pp.image))
if "BLIP" in option:
captions.append(shared.interrogator.generate_caption(pp.image))
pp.caption = ", ".join([x for x in captions if x])

16
scripts/postprocessing_codeformer.py
View File

@ -1,28 +1,28 @@
from PIL import Image
import numpy as np
from modules import scripts_postprocessing, codeformer_model
from modules import scripts_postprocessing, codeformer_model, ui_components
import gradio as gr
from modules.ui_components import FormRow
class ScriptPostprocessingCodeFormer(scripts_postprocessing.ScriptPostprocessing):
name = "CodeFormer"
order = 3000
def ui(self):
with FormRow():
codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer visibility", value=0, elem_id="extras_codeformer_visibility")
codeformer_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer weight (0 = maximum effect, 1 = minimum effect)", value=0, elem_id="extras_codeformer_weight")
with ui_components.InputAccordion(False, label="CodeFormer") as enable:
with gr.Row():
codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Visibility", value=1.0, elem_id="extras_codeformer_visibility")
codeformer_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Weight (0 = maximum effect, 1 = minimum effect)", value=0, elem_id="extras_codeformer_weight")
return {
"enable": enable,
"codeformer_visibility": codeformer_visibility,
"codeformer_weight": codeformer_weight,
}
def process(self, pp: scripts_postprocessing.PostprocessedImage, codeformer_visibility, codeformer_weight):
if codeformer_visibility == 0:
def process(self, pp: scripts_postprocessing.PostprocessedImage, enable, codeformer_visibility, codeformer_weight):
if codeformer_visibility == 0 or not enable:
return
restored_img = codeformer_model.codeformer.restore(np.array(pp.image, dtype=np.uint8), w=codeformer_weight)

32
scripts/postprocessing_create_flipped_copies.py Normal file
View File

@ -0,0 +1,32 @@
from PIL import ImageOps, Image
from modules import scripts_postprocessing, ui_components
import gradio as gr
class ScriptPostprocessingCreateFlippedCopies(scripts_postprocessing.ScriptPostprocessing):
name = "Create flipped copies"
order = 4000
def ui(self):
with ui_components.InputAccordion(False, label="Create flipped copies") as enable:
with gr.Row():
option = gr.CheckboxGroup(value=["Horizontal"], choices=["Horizontal", "Vertical", "Both"], show_label=False)
return {
"enable": enable,
"option": option,
}
def process(self, pp: scripts_postprocessing.PostprocessedImage, enable, option):
if not enable:
return
if "Horizontal" in option:
pp.extra_images.append(ImageOps.mirror(pp.image))
if "Vertical" in option:
pp.extra_images.append(pp.image.transpose(Image.Transpose.FLIP_TOP_BOTTOM))
if "Both" in option:
pp.extra_images.append(pp.image.transpose(Image.Transpose.FLIP_TOP_BOTTOM).transpose(Image.Transpose.FLIP_LEFT_RIGHT))

54
scripts/postprocessing_focal_crop.py Normal file
View File

@ -0,0 +1,54 @@
from modules import scripts_postprocessing, ui_components, errors
import gradio as gr
from modules.textual_inversion import autocrop
class ScriptPostprocessingFocalCrop(scripts_postprocessing.ScriptPostprocessing):
name = "Auto focal point crop"
order = 4000
def ui(self):
with ui_components.InputAccordion(False, label="Auto focal point crop") as enable:
face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05, elem_id="postprocess_focal_crop_face_weight")
entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05, elem_id="postprocess_focal_crop_entropy_weight")
edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="postprocess_focal_crop_edges_weight")
debug = gr.Checkbox(label='Create debug image', elem_id="train_process_focal_crop_debug")
return {
"enable": enable,
"face_weight": face_weight,
"entropy_weight": entropy_weight,
"edges_weight": edges_weight,
"debug": debug,
}
def process(self, pp: scripts_postprocessing.PostprocessedImage, enable, face_weight, entropy_weight, edges_weight, debug):
if not enable:
return
if not pp.shared.target_width or not pp.shared.target_height:
return
dnn_model_path = None
try:
dnn_model_path = autocrop.download_and_cache_models()
except Exception:
errors.report("Unable to load face detection model for auto crop selection. Falling back to lower quality haar method.", exc_info=True)
autocrop_settings = autocrop.Settings(
crop_width=pp.shared.target_width,
crop_height=pp.shared.target_height,
face_points_weight=face_weight,
entropy_points_weight=entropy_weight,
corner_points_weight=edges_weight,
annotate_image=debug,
dnn_model_path=dnn_model_path,
)
result, *others = autocrop.crop_image(pp.image, autocrop_settings)
pp.image = result
pp.extra_images = [pp.create_copy(x, nametags=["focal-crop-debug"], disable_processing=True) for x in others]

13
scripts/postprocessing_gfpgan.py
View File

@ -1,26 +1,25 @@
from PIL import Image
import numpy as np
from modules import scripts_postprocessing, gfpgan_model
from modules import scripts_postprocessing, gfpgan_model, ui_components
import gradio as gr
from modules.ui_components import FormRow
class ScriptPostprocessingGfpGan(scripts_postprocessing.ScriptPostprocessing):
name = "GFPGAN"
order = 2000
def ui(self):
with FormRow():
gfpgan_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="GFPGAN visibility", value=0, elem_id="extras_gfpgan_visibility")
with ui_components.InputAccordion(False, label="GFPGAN") as enable:
gfpgan_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Visibility", value=1.0, elem_id="extras_gfpgan_visibility")
return {
"enable": enable,
"gfpgan_visibility": gfpgan_visibility,
}
def process(self, pp: scripts_postprocessing.PostprocessedImage, gfpgan_visibility):
if gfpgan_visibility == 0:
def process(self, pp: scripts_postprocessing.PostprocessedImage, enable, gfpgan_visibility):
if gfpgan_visibility == 0 or not enable:
return
restored_img = gfpgan_model.gfpgan_fix_faces(np.array(pp.image, dtype=np.uint8))

71
scripts/postprocessing_split_oversized.py Normal file
View File

@ -0,0 +1,71 @@
import math
from modules import scripts_postprocessing, ui_components
import gradio as gr
def split_pic(image, inverse_xy, width, height, overlap_ratio):
if inverse_xy:
from_w, from_h = image.height, image.width
to_w, to_h = height, width
else:
from_w, from_h = image.width, image.height
to_w, to_h = width, height
h = from_h * to_w // from_w
if inverse_xy:
image = image.resize((h, to_w))
else:
image = image.resize((to_w, h))
split_count = math.ceil((h - to_h * overlap_ratio) / (to_h * (1.0 - overlap_ratio)))
y_step = (h - to_h) / (split_count - 1)
for i in range(split_count):
y = int(y_step * i)
if inverse_xy:
splitted = image.crop((y, 0, y + to_h, to_w))
else:
splitted = image.crop((0, y, to_w, y + to_h))
yield splitted
class ScriptPostprocessingSplitOversized(scripts_postprocessing.ScriptPostprocessing):
name = "Split oversized images"
order = 4000
def ui(self):
with ui_components.InputAccordion(False, label="Split oversized images") as enable:
with gr.Row():
split_threshold = gr.Slider(label='Threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="postprocess_split_threshold")
overlap_ratio = gr.Slider(label='Overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05, elem_id="postprocess_overlap_ratio")
return {
"enable": enable,
"split_threshold": split_threshold,
"overlap_ratio": overlap_ratio,
}
def process(self, pp: scripts_postprocessing.PostprocessedImage, enable, split_threshold, overlap_ratio):
if not enable:
return
width = pp.shared.target_width
height = pp.shared.target_height
if not width or not height:
return
if pp.image.height > pp.image.width:
ratio = (pp.image.width * height) / (pp.image.height * width)
inverse_xy = False
else:
ratio = (pp.image.height * width) / (pp.image.width * height)
inverse_xy = True
if ratio >= 1.0 and ratio > split_threshold:
return
result, *others = split_pic(pp.image, inverse_xy, width, height, overlap_ratio)
pp.image = result
pp.extra_images = [pp.create_copy(x) for x in others]

12
scripts/postprocessing_upscale.py
View File

@ -81,6 +81,14 @@ class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing):
return image
def process_firstpass(self, pp: scripts_postprocessing.PostprocessedImage, upscale_mode=1, upscale_by=2.0, upscale_to_width=None, upscale_to_height=None, upscale_crop=False, upscaler_1_name=None, upscaler_2_name=None, upscaler_2_visibility=0.0):
if upscale_mode == 1:
pp.shared.target_width = upscale_to_width
pp.shared.target_height = upscale_to_height
else:
pp.shared.target_width = int(pp.image.width * upscale_by)
pp.shared.target_height = int(pp.image.height * upscale_by)
def process(self, pp: scripts_postprocessing.PostprocessedImage, upscale_mode=1, upscale_by=2.0, upscale_to_width=None, upscale_to_height=None, upscale_crop=False, upscaler_1_name=None, upscaler_2_name=None, upscaler_2_visibility=0.0):
if upscaler_1_name == "None":
upscaler_1_name = None
@ -126,6 +134,10 @@ class ScriptPostprocessingUpscaleSimple(ScriptPostprocessingUpscale):
"upscaler_name": upscaler_name,
}
def process_firstpass(self, pp: scripts_postprocessing.PostprocessedImage, upscale_by=2.0, upscaler_name=None):
pp.shared.target_width = int(pp.image.width * upscale_by)
pp.shared.target_height = int(pp.image.height * upscale_by)
def process(self, pp: scripts_postprocessing.PostprocessedImage, upscale_by=2.0, upscaler_name=None):
if upscaler_name is None or upscaler_name == "None":
return

64
scripts/processing_autosized_crop.py Normal file
View File

@ -0,0 +1,64 @@
from PIL import Image
from modules import scripts_postprocessing, ui_components
import gradio as gr
def center_crop(image: Image, w: int, h: int):
iw, ih = image.size
if ih / h < iw / w:
sw = w * ih / h
box = (iw - sw) / 2, 0, iw - (iw - sw) / 2, ih
else:
sh = h * iw / w
box = 0, (ih - sh) / 2, iw, ih - (ih - sh) / 2
return image.resize((w, h), Image.Resampling.LANCZOS, box)
def multicrop_pic(image: Image, mindim, maxdim, minarea, maxarea, objective, threshold):
iw, ih = image.size
err = lambda w, h: 1 - (lambda x: x if x < 1 else 1 / x)(iw / ih / (w / h))
wh = max(((w, h) for w in range(mindim, maxdim + 1, 64) for h in range(mindim, maxdim + 1, 64)
if minarea <= w * h <= maxarea and err(w, h) <= threshold),
key=lambda wh: (wh[0] * wh[1], -err(*wh))[::1 if objective == 'Maximize area' else -1],
default=None
)
return wh and center_crop(image, *wh)
class ScriptPostprocessingAutosizedCrop(scripts_postprocessing.ScriptPostprocessing):
name = "Auto-sized crop"
order = 4000
def ui(self):
with ui_components.InputAccordion(False, label="Auto-sized crop") as enable:
gr.Markdown('Each image is center-cropped with an automatically chosen width and height.')
with gr.Row():
mindim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension lower bound", value=384, elem_id="postprocess_multicrop_mindim")
maxdim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension upper bound", value=768, elem_id="postprocess_multicrop_maxdim")
with gr.Row():
minarea = gr.Slider(minimum=64 * 64, maximum=2048 * 2048, step=1, label="Area lower bound", value=64 * 64, elem_id="postprocess_multicrop_minarea")
maxarea = gr.Slider(minimum=64 * 64, maximum=2048 * 2048, step=1, label="Area upper bound", value=640 * 640, elem_id="postprocess_multicrop_maxarea")
with gr.Row():
objective = gr.Radio(["Maximize area", "Minimize error"], value="Maximize area", label="Resizing objective", elem_id="postprocess_multicrop_objective")
threshold = gr.Slider(minimum=0, maximum=1, step=0.01, label="Error threshold", value=0.1, elem_id="postprocess_multicrop_threshold")
return {
"enable": enable,
"mindim": mindim,
"maxdim": maxdim,
"minarea": minarea,
"maxarea": maxarea,
"objective": objective,
"threshold": threshold,
}
def process(self, pp: scripts_postprocessing.PostprocessedImage, enable, mindim, maxdim, minarea, maxarea, objective, threshold):
if not enable:
return
cropped = multicrop_pic(pp.image, mindim, maxdim, minarea, maxarea, objective, threshold)
if cropped is not None:
pp.image = cropped
else:
print(f"skipped {pp.image.width}x{pp.image.height} image (can't find suitable size within error threshold)")

23
style.css
View File

@ -462,6 +462,15 @@ div.toprow-compact-tools{
padding: 4px;
}
#settings > div.tab-nav .settings-category{
display: block;
margin: 1em 0 0.25em 0;
font-weight: bold;
text-decoration: underline;
cursor: default;
user-select: none;
}
#settings_result{
height: 1.4em;
margin: 0 1.2em;
@ -637,6 +646,8 @@ table.popup-table .link{
margin: auto;
padding: 2em;
z-index: 1001;
max-height: 90%;
max-width: 90%;
}
/* fullpage image viewer */
@ -840,8 +851,16 @@ footer {
/* extra networks UI */
.extra-page .prompt{
margin: 0 0 0.5em 0;
.extra-page > div.gap{
gap: 0;
}
.extra-page-prompts{
margin-bottom: 0;
}
.extra-page-prompts.extra-page-prompts-active{
margin-bottom: 1em;
}
.extra-network-cards{

4
webui.sh
View File

@ -89,7 +89,7 @@ delimiter="################################################################"
printf "\n%s\n" "${delimiter}"
printf "\e[1m\e[32mInstall script for stable-diffusion + Web UI\n"
printf "\e[1m\e[34mTested on Debian 11 (Bullseye)\e[0m"
printf "\e[1m\e[34mTested on Debian 11 (Bullseye), Fedora 34+ and openSUSE Leap 15.4 or newer.\e[0m"
printf "\n%s\n" "${delimiter}"
# Do not run as root
@ -223,7 +223,7 @@ fi
# Try using TCMalloc on Linux
prepare_tcmalloc() {
if [[ "${OSTYPE}" == "linux"* ]] && [[ -z "${NO_TCMALLOC}" ]] && [[ -z "${LD_PRELOAD}" ]]; then
TCMALLOC="$(PATH=/usr/sbin:$PATH ldconfig -p | grep -Po "libtcmalloc(_minimal|)\.so\.\d" | head -n 1)"
TCMALLOC="$(PATH=/sbin:$PATH ldconfig -p | grep -Po "libtcmalloc(_minimal|)\.so\.\d" | head -n 1)"
if [[ ! -z "${TCMALLOC}" ]]; then
echo "Using TCMalloc: ${TCMALLOC}"
export LD_PRELOAD="${TCMALLOC}"