diff --git a/.github/workflows/on_pull_request.yaml b/.github/workflows/on_pull_request.yaml index 78e608ee9..9e44c806a 100644 --- a/.github/workflows/on_pull_request.yaml +++ b/.github/workflows/on_pull_request.yaml @@ -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: diff --git a/CHANGELOG.md b/CHANGELOG.md index 2c72359fc..67429bbff 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -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 and are present in the prompt +* fix for Reload UI function: if you reload UI on one tab, other opened tabs will no longer stop working +* prevent duplicate resize handler ([#12795](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12795)) +* small typo: vae resolve bug ([#12797](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12797)) +* hide broken image crop tool ([#12792](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12792)) +* don't show hidden samplers in dropdown for XYZ script ([#12780](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12780)) +* fix style editing dialog breaking if it's opened in both img2img and txt2img tabs +* hide --gradio-auth and --api-auth values from /internal/sysinfo report +* add missing infotext for RNG in options ([#12819](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12819)) +* fix notification not playing when built-in webui tab is inactive ([#12834](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12834)) +* honor `--skip-install` for extension installers ([#12832](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12832)) +* don't print blank stdout in extension installers ([#12833](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12833), [#12855](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12855)) +* get progressbar to display correctly in extensions tab +* keep order in list of checkpoints when loading model that doesn't have a checksum +* fix inpainting models in txt2img creating black pictures +* fix generation params regex ([#12876](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12876)) +* fix batch img2img output dir with script ([#12926](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12926)) +* fix #13080 - Hypernetwork/TI preview generation ([#13084](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13084)) +* fix bug with sigma min/max overrides. ([#12995](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12995)) +* more accurate check for enabling cuDNN benchmark on 16XX cards ([#12924](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12924)) +* don't use multicond parser for negative prompt counter ([#13118](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13118)) +* fix data-sort-name containing spaces ([#13412](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13412)) +* update card on correct tab when editing metadata ([#13411](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13411)) +* fix viewing/editing metadata when filename contains an apostrophe ([#13395](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13395)) +* fix: --sd_model in "Prompts from file or textbox" script is not working ([#13302](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13302)) +* better Support for Portable Git ([#13231](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13231)) +* fix issues when webui_dir is not work_dir ([#13210](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13210)) +* fix: lora-bias-backup don't reset cache ([#13178](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13178)) +* account for customizable extra network separators whyen removing extra network text from the prompt ([#12877](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12877)) +* re fix batch img2img output dir with script ([#13170](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13170)) +* fix `--ckpt-dir` path separator and option use `short name` for checkpoint dropdown ([#13139](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13139)) +* consolidated allowed preview formats, Fix extra network `.gif` not woking as preview ([#13121](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13121)) +* fix venv_dir=- environment variable not working as expected on linux ([#13469](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13469)) +* repair unload sd checkpoint button +* edit-attention fixes ([#13533](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13533)) +* fix bug when using --gfpgan-models-path ([#13718](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13718)) +* properly apply sort order for extra network cards when selected from dropdown +* fixes generation restart not working for some users when 'Ctrl+Enter' is pressed ([#13962](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13962)) +* thread safe extra network list_items ([#13014](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13014)) +* fix not able to exit metadata popup when pop up is too big ([#14156](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14156)) +* fix auto focal point crop for opencv >= 4.8 ([#14121](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14121)) +* make 'use-cpu all' actually apply to 'all' ([#14131](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14131)) +* extras tab batch: actually use original filename +* make webui not crash when running with --disable-all-extensions option + +### Other: +* non-local condition ([#12814](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12814)) +* fix minor typos ([#12827](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12827)) +* remove xformers Python version check ([#12842](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12842)) +* style: file-metadata word-break ([#12837](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12837)) +* revert SGM noise multiplier change for img2img because it breaks hires fix +* do not change quicksettings dropdown option when value returned is `None` ([#12854](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12854)) +* [RC 1.6.0 - zoom is partly hidden] Update style.css ([#12839](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12839)) +* chore: change extension time format ([#12851](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12851)) +* WEBUI.SH - Use torch 2.1.0 release candidate for Navi 3 ([#12929](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12929)) +* add Fallback at images.read_info_from_image if exif data was invalid ([#13028](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13028)) +* update cmd arg description ([#12986](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12986)) +* fix: update shared.opts.data when add_option ([#12957](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12957), [#13213](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13213)) +* restore missing tooltips ([#12976](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12976)) +* use default dropdown padding on mobile ([#12880](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12880)) +* put enable console prompts option into settings from commandline args ([#13119](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13119)) +* fix some deprecated types ([#12846](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12846)) +* bump to torchsde==0.2.6 ([#13418](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13418)) +* update dragdrop.js ([#13372](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13372)) +* use orderdict as lru cache:opt/bug ([#13313](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13313)) +* XYZ if not include sub grids do not save sub grid ([#13282](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13282)) +* initialize state.time_start befroe state.job_count ([#13229](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13229)) +* fix fieldname regex ([#13458](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13458)) +* change denoising_strength default to None. ([#13466](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13466)) +* fix regression ([#13475](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13475)) +* fix IndexError ([#13630](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13630)) +* fix: checkpoints_loaded:{checkpoint:state_dict}, model.load_state_dict issue in dict value empty ([#13535](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13535)) +* update bug_report.yml ([#12991](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12991)) +* requirements_versions httpx==0.24.1 ([#13839](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13839)) +* fix parenthesis auto selection ([#13829](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13829)) +* fix #13796 ([#13797](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13797)) +* corrected a typo in `modules/cmd_args.py` ([#13855](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13855)) +* feat: fix randn found element of type float at pos 2 ([#14004](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14004)) +* adds tqdm handler to logging_config.py for progress bar integration ([#13996](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13996)) +* hotfix: call shared.state.end() after postprocessing done ([#13977](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13977)) +* fix dependency address patch 1 ([#13929](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13929)) +* save sysinfo as .json ([#14035](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14035)) +* move exception_records related methods to errors.py ([#14084](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14084)) +* compatibility ([#13936](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13936)) +* json.dump(ensure_ascii=False) ([#14108](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14108)) +* dir buttons start with / so only the correct dir will be shown and no… ([#13957](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13957)) +* alternate implementation for unet forward replacement that does not depend on hijack being applied +* re-add `keyedit_delimiters_whitespace` setting lost as part of commit e294e46 ([#14178](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14178)) +* fix `save_samples` being checked early when saving masked composite ([#14177](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14177)) +* slight optimization for mask and mask_composite ([#14181](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14181)) +* add import_hook hack to work around basicsr/torchvision incompatibility ([#14186](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14186)) + ## 1.6.1 ### Bug Fixes: diff --git a/README.md b/README.md index 25ba070e7..9f9f33b12 100644 --- a/README.md +++ b/README.md @@ -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) diff --git a/extensions-builtin/Lora/lyco_helpers.py b/extensions-builtin/Lora/lyco_helpers.py index 279b34bc9..1679a0ce6 100644 --- a/extensions-builtin/Lora/lyco_helpers.py +++ b/extensions-builtin/Lora/lyco_helpers.py @@ -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 length or new_m>factor: + break + else: + m, n = new_m, new_n + if m > n: + n, m = m, n + return m, n + diff --git a/extensions-builtin/Lora/network_oft.py b/extensions-builtin/Lora/network_oft.py new file mode 100644 index 000000000..fa647020f --- /dev/null +++ b/extensions-builtin/Lora/network_oft.py @@ -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) diff --git a/extensions-builtin/Lora/networks.py b/extensions-builtin/Lora/networks.py index 60d8dec4c..629bf8537 100644 --- a/extensions-builtin/Lora/networks.py +++ b/extensions-builtin/Lora/networks.py @@ -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 diff --git a/extensions-builtin/Lora/ui_extra_networks_lora.py b/extensions-builtin/Lora/ui_extra_networks_lora.py index 55409a782..df02c663b 100644 --- a/extensions-builtin/Lora/ui_extra_networks_lora.py +++ b/extensions-builtin/Lora/ui_extra_networks_lora.py @@ -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 diff --git a/extensions-builtin/extra-options-section/scripts/extra_options_section.py b/extensions-builtin/extra-options-section/scripts/extra_options_section.py index 983f87ff0..ac2c3de46 100644 --- a/extensions-builtin/extra-options-section/scripts/extra_options_section.py +++ b/extensions-builtin/extra-options-section/scripts/extra_options_section.py @@ -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() })) diff --git a/extensions-builtin/hypertile/hypertile.py b/extensions-builtin/hypertile/hypertile.py new file mode 100644 index 000000000..0f40e2d39 --- /dev/null +++ b/extensions-builtin/hypertile/hypertile.py @@ -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 diff --git a/extensions-builtin/hypertile/scripts/hypertile_script.py b/extensions-builtin/hypertile/scripts/hypertile_script.py new file mode 100644 index 000000000..395d584b6 --- /dev/null +++ b/extensions-builtin/hypertile/scripts/hypertile_script.py @@ -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(""" + Hypertile optimizes the self-attention layer within U-Net and VAE models, + resulting in a reduction in computation time ranging from 1 to 4 times. The larger the generated image is, the greater the + benefit. + """), + + "hypertile_enable_unet": shared.OptionInfo(False, "Enable Hypertile U-Net", infotext="Hypertile U-Net").info("enables hypertile for all modes, including hires fix second pass; noticeable change in details of the generated picture"), + "hypertile_enable_unet_secondpass": shared.OptionInfo(False, "Enable Hypertile U-Net for hires fix second pass", infotext="Hypertile U-Net second pass").info("enables hypertile just for hires fix second pass - regardless of whether the above setting is enabled"), + "hypertile_max_depth_unet": shared.OptionInfo(3, "Hypertile U-Net max depth", gr.Slider, {"minimum": 0, "maximum": 3, "step": 1}, infotext="Hypertile U-Net max depth").info("larger = more neural network layers affected; minor effect on performance"), + "hypertile_max_tile_unet": shared.OptionInfo(256, "Hypertile U-Net max tile size", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}, infotext="Hypertile U-Net max tile size").info("larger = worse performance"), + "hypertile_swap_size_unet": shared.OptionInfo(3, "Hypertile U-Net swap size", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}, infotext="Hypertile U-Net swap size"), + + "hypertile_enable_vae": shared.OptionInfo(False, "Enable Hypertile VAE", infotext="Hypertile VAE").info("minimal change in the generated picture"), + "hypertile_max_depth_vae": shared.OptionInfo(3, "Hypertile VAE max depth", gr.Slider, {"minimum": 0, "maximum": 3, "step": 1}, infotext="Hypertile VAE max depth"), + "hypertile_max_tile_vae": shared.OptionInfo(128, "Hypertile VAE max tile size", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}, infotext="Hypertile VAE max tile size"), + "hypertile_swap_size_vae": shared.OptionInfo(3, "Hypertile VAE swap size ", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}, infotext="Hypertile VAE swap size"), + } + + for name, opt in options.items(): + opt.section = ('hypertile', "Hypertile") + shared.opts.add_option(name, opt) + + +script_callbacks.on_ui_settings(on_ui_settings) +script_callbacks.on_before_ui(add_axis_options) diff --git a/extensions-builtin/hypertile/scripts/hypertile_xyz.py b/extensions-builtin/hypertile/scripts/hypertile_xyz.py new file mode 100644 index 000000000..9e96ae3c5 --- /dev/null +++ b/extensions-builtin/hypertile/scripts/hypertile_xyz.py @@ -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) diff --git a/javascript/extraNetworks.js b/javascript/extraNetworks.js index a1bf29a8c..98a7abb74 100644 --- a/javascript/extraNetworks.js +++ b/javascript/extraNetworks.js @@ -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(); + } +}); diff --git a/javascript/imageviewer.js b/javascript/imageviewer.js index e4dae91bc..625c5d148 100644 --- a/javascript/imageviewer.js +++ b/javascript/imageviewer.js @@ -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) { diff --git a/javascript/settings.js b/javascript/settings.js index 4e79ec003..e6009290a 100644 --- a/javascript/settings.js +++ b/javascript/settings.js @@ -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); + }); +}); + diff --git a/javascript/ui.js b/javascript/ui.js index 2e2626020..18c9f891a 100644 --- a/javascript/ui.js +++ b/javascript/ui.js @@ -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'); }); diff --git a/modules/api/api.py b/modules/api/api.py index 090838747..b3d74e513 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -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") diff --git a/modules/api/models.py b/modules/api/models.py index a0d80af8c..33894b3e6 100644 --- a/modules/api/models.py +++ b/modules/api/models.py @@ -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) diff --git a/modules/cache.py b/modules/cache.py index ff26a2132..2d37e7b99 100644 --- a/modules/cache.py +++ b/modules/cache.py @@ -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) diff --git a/modules/cmd_args.py b/modules/cmd_args.py index a9fb9bfa3..da93eb266 100644 --- a/modules/cmd_args.py +++ b/modules/cmd_args.py @@ -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) diff --git a/modules/devices.py b/modules/devices.py index 1d4eb5635..ea1f712f9 100644 --- a/modules/devices.py +++ b/modules/devices.py @@ -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(): diff --git a/modules/errors.py b/modules/errors.py index 8c339464d..eb234a838 100644 --- a/modules/errors.py +++ b/modules/errors.py @@ -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) diff --git a/modules/extensions.py b/modules/extensions.py index bf9a1878f..1899cd529 100644 --- a/modules/extensions.py +++ b/modules/extensions.py @@ -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] = [] diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py index 0a606515b..4efe53e0c 100644 --- a/modules/generation_parameters_copypaste.py +++ b/modules/generation_parameters_copypaste.py @@ -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, ) + diff --git a/modules/gradio_extensons.py b/modules/gradio_extensons.py index e6b6835ad..7d88dc984 100644 --- a/modules/gradio_extensons.py +++ b/modules/gradio_extensons.py @@ -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 diff --git a/modules/img2img.py b/modules/img2img.py index 52cb577a6..c583290a0 100644 --- a/modules/img2img.py +++ b/modules/img2img.py @@ -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: diff --git a/modules/import_hook.py b/modules/import_hook.py index 28c67dfa8..eba9a3729 100644 --- a/modules/import_hook.py +++ b/modules/import_hook.py @@ -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... diff --git a/modules/launch_utils.py b/modules/launch_utils.py index 8cdbafa50..29506f249 100644 --- a/modules/launch_utils.py +++ b/modules/launch_utils.py @@ -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) diff --git a/modules/logging_config.py b/modules/logging_config.py index 7db23d4b6..792698756 100644 --- a/modules/logging_config.py +++ b/modules/logging_config.py @@ -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 ) - diff --git a/modules/mac_specific.py b/modules/mac_specific.py index 89256c5b0..d96d86d79 100644 --- a/modules/mac_specific.py +++ b/modules/mac_specific.py @@ -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']: diff --git a/modules/models/diffusion/ddpm_edit.py b/modules/models/diffusion/ddpm_edit.py index b892d5fc7..6db340da4 100644 --- a/modules/models/diffusion/ddpm_edit.py +++ b/modules/models/diffusion/ddpm_edit.py @@ -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', diff --git a/modules/options.py b/modules/options.py index ab40aff73..4fead690c 100644 --- a/modules/options.py +++ b/modules/options.py @@ -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() diff --git a/modules/postprocessing.py b/modules/postprocessing.py index cf04d38b0..0c59fad48 100644 --- a/modules/postprocessing.py +++ b/modules/postprocessing.py @@ -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, }, diff --git a/modules/processing.py b/modules/processing.py index b0e240a46..6f01c95f5 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -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): diff --git a/modules/rng.py b/modules/rng.py index 9e8ba2ee9..8934d39bf 100644 --- a/modules/rng.py +++ b/modules/rng.py @@ -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 = [] diff --git a/modules/scripts.py b/modules/scripts.py index 5c6e0226e..7f9454eb5 100644 --- a/modules/scripts.py +++ b/modules/scripts.py @@ -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: diff --git a/modules/scripts_postprocessing.py b/modules/scripts_postprocessing.py index bac1335dc..901cad080 100644 --- a/modules/scripts_postprocessing.py +++ b/modules/scripts_postprocessing.py @@ -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: diff --git a/modules/sd_disable_initialization.py b/modules/sd_disable_initialization.py index 8863107ae..273a7edd8 100644 --- a/modules/sd_disable_initialization.py +++ b/modules/sd_disable_initialization.py @@ -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) diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 0157e19f0..e139d9964 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -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: diff --git a/modules/sd_models.py b/modules/sd_models.py index 841402e86..9355f1e16 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -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 diff --git a/modules/sd_samplers_extra.py b/modules/sd_samplers_extra.py index 1b981ca80..72fd0aa5e 100644 --- a/modules/sd_samplers_extra.py +++ b/modules/sd_samplers_extra.py @@ -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): diff --git a/modules/sd_samplers_timesteps_impl.py b/modules/sd_samplers_timesteps_impl.py index a72daafd4..930a64af5 100644 --- a/modules/sd_samplers_timesteps_impl.py +++ b/modules/sd_samplers_timesteps_impl.py @@ -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 diff --git a/modules/sd_unet.py b/modules/sd_unet.py index 6a7bc9e26..a771849c8 100644 --- a/modules/sd_unet.py +++ b/modules/sd_unet.py @@ -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 diff --git a/modules/shared_items.py b/modules/shared_items.py index 5024b4268..991971ad0 100644 --- a/modules/shared_items.py +++ b/modules/shared_items.py @@ -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", diff --git a/modules/shared_options.py b/modules/shared_options.py index d40db5306..d2e86ff10 100644 --- a/modules/shared_options.py +++ b/modules/shared_options.py @@ -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(""" VAE is a neural network that transforms a standard RGB 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 gallery.").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 ", 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 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 gallery.").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("""
  • Ignore: keep prompt and styles dropdown as it is.
  • Apply: remove style text from prompt, always replace styles dropdown value with found styles (even if none are found).
    • @@ -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"), { diff --git a/modules/styles.py b/modules/styles.py index 0740fe1b1..81d9800d1 100644 --- a/modules/styles.py +++ b/modules/styles.py @@ -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 diff --git a/modules/sysinfo.py b/modules/sysinfo.py index 2db7551dc..b669edd0c 100644 --- a/modules/sysinfo.py +++ b/modules/sysinfo.py @@ -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} diff --git a/modules/textual_inversion/autocrop.py b/modules/textual_inversion/autocrop.py index 1675e39a5..e223a2e0c 100644 --- a/modules/textual_inversion/autocrop.py +++ b/modules/textual_inversion/autocrop.py @@ -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: diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py deleted file mode 100644 index dbd856bd8..000000000 --- a/modules/textual_inversion/preprocess.py +++ /dev/null @@ -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() diff --git a/modules/textual_inversion/ui.py b/modules/textual_inversion/ui.py index 35c4feeff..f149ad1f0 100644 --- a/modules/textual_inversion/ui.py +++ b/modules/textual_inversion/ui.py @@ -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' diff --git a/modules/ui.py b/modules/ui.py index accdb4572..d80486dd4 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -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}"'}) diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py index c0a73b573..dc1e34c8a 100644 --- a/modules/ui_extensions.py +++ b/modules/ui_extensions.py @@ -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): diff --git a/modules/ui_extra_networks.py b/modules/ui_extra_networks.py index 7907cd63f..fe5d3ba33 100644 --- a/modules/ui_extra_networks.py +++ b/modules/ui_extra_networks.py @@ -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) diff --git a/modules/ui_extra_networks_checkpoints.py b/modules/ui_extra_networks_checkpoints.py index 2fc0ed43d..1693e71f1 100644 --- a/modules/ui_extra_networks_checkpoints.py +++ b/modules/ui_extra_networks_checkpoints.py @@ -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] diff --git a/modules/ui_extra_networks_hypernets.py b/modules/ui_extra_networks_hypernets.py index 4cedf0851..c96c4fa3b 100644 --- a/modules/ui_extra_networks_hypernets.py +++ b/modules/ui_extra_networks_hypernets.py @@ -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] diff --git a/modules/ui_extra_networks_textual_inversion.py b/modules/ui_extra_networks_textual_inversion.py index 55ef0ea7b..1b334fda1 100644 --- a/modules/ui_extra_networks_textual_inversion.py +++ b/modules/ui_extra_networks_textual_inversion.py @@ -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) diff --git a/modules/ui_extra_networks_user_metadata.py b/modules/ui_extra_networks_user_metadata.py index bfec140cc..36a807fcd 100644 --- a/modules/ui_extra_networks_user_metadata.py +++ b/modules/ui_extra_networks_user_metadata.py @@ -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) diff --git a/modules/ui_loadsave.py b/modules/ui_loadsave.py index eb20ff258..7826786cc 100644 --- a/modules/ui_loadsave.py +++ b/modules/ui_loadsave.py @@ -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""" diff --git a/modules/ui_postprocessing.py b/modules/ui_postprocessing.py index 802e1ce71..13d888e48 100644 --- a/modules/ui_postprocessing.py +++ b/modules/ui_postprocessing.py @@ -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) diff --git a/modules/ui_prompt_styles.py b/modules/ui_prompt_styles.py index 3bcf092fd..0d74c23fa 100644 --- a/modules/ui_prompt_styles.py +++ b/modules/ui_prompt_styles.py @@ -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) diff --git a/modules/ui_toprow.py b/modules/ui_toprow.py index 985b5a2dd..88838f977 100644 --- a/modules/ui_toprow.py +++ b/modules/ui_toprow.py @@ -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 diff --git a/modules/upscaler.py b/modules/upscaler.py index e682bbaa2..b256e085b 100644 --- a/modules/upscaler.py +++ b/modules/upscaler.py @@ -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) diff --git a/modules/xpu_specific.py b/modules/xpu_specific.py new file mode 100644 index 000000000..d8da94a0e --- /dev/null +++ b/modules/xpu_specific.py @@ -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) diff --git a/pyproject.toml b/pyproject.toml index 80541a8f3..d03036e7d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -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 diff --git a/script.js b/script.js index 5f6ee3542..c0e678ea7 100644 --- a/script.js +++ b/script.js @@ -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(); } diff --git a/scripts/postprocessing_caption.py b/scripts/postprocessing_caption.py new file mode 100644 index 000000000..243e3ad9c --- /dev/null +++ b/scripts/postprocessing_caption.py @@ -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]) diff --git a/scripts/postprocessing_codeformer.py b/scripts/postprocessing_codeformer.py index a7d80d40e..e1e156ddc 100644 --- a/scripts/postprocessing_codeformer.py +++ b/scripts/postprocessing_codeformer.py @@ -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) diff --git a/scripts/postprocessing_create_flipped_copies.py b/scripts/postprocessing_create_flipped_copies.py new file mode 100644 index 000000000..3425571dc --- /dev/null +++ b/scripts/postprocessing_create_flipped_copies.py @@ -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)) diff --git a/scripts/postprocessing_focal_crop.py b/scripts/postprocessing_focal_crop.py new file mode 100644 index 000000000..d3baf2987 --- /dev/null +++ b/scripts/postprocessing_focal_crop.py @@ -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] + diff --git a/scripts/postprocessing_gfpgan.py b/scripts/postprocessing_gfpgan.py index d854f3f77..6e7566055 100644 --- a/scripts/postprocessing_gfpgan.py +++ b/scripts/postprocessing_gfpgan.py @@ -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)) diff --git a/scripts/postprocessing_split_oversized.py b/scripts/postprocessing_split_oversized.py new file mode 100644 index 000000000..c4a03160f --- /dev/null +++ b/scripts/postprocessing_split_oversized.py @@ -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] + diff --git a/scripts/postprocessing_upscale.py b/scripts/postprocessing_upscale.py index eb42a29e5..ed709688d 100644 --- a/scripts/postprocessing_upscale.py +++ b/scripts/postprocessing_upscale.py @@ -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 diff --git a/scripts/processing_autosized_crop.py b/scripts/processing_autosized_crop.py new file mode 100644 index 000000000..c09802264 --- /dev/null +++ b/scripts/processing_autosized_crop.py @@ -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)") diff --git a/style.css b/style.css index 731620226..ee39a57b7 100644 --- a/style.css +++ b/style.css @@ -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{ diff --git a/webui.sh b/webui.sh index 089114692..cff433272 100755 --- a/webui.sh +++ b/webui.sh @@ -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}"