diff --git a/.github/workflows/on_pull_request.yaml b/.github/workflows/on_pull_request.yaml index 7b7219fd..8ebf5918 100644 --- a/.github/workflows/on_pull_request.yaml +++ b/.github/workflows/on_pull_request.yaml @@ -18,7 +18,7 @@ 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==0.0.265 + run: pip install ruff==0.0.272 - name: Run Ruff run: ruff . lint-js: diff --git a/.github/workflows/run_tests.yaml b/.github/workflows/run_tests.yaml index 226cf759..178c026a 100644 --- a/.github/workflows/run_tests.yaml +++ b/.github/workflows/run_tests.yaml @@ -42,7 +42,7 @@ jobs: --no-half --disable-opt-split-attention --use-cpu all - --add-stop-route + --api-server-stop 2>&1 | tee output.txt & - name: Run tests run: | @@ -50,7 +50,7 @@ jobs: python -m pytest -vv --junitxml=test/results.xml --cov . --cov-report=xml --verify-base-url test - name: Kill test server if: always() - run: curl -vv -XPOST http://127.0.0.1:7860/_stop && sleep 10 + run: curl -vv -XPOST http://127.0.0.1:7860/sdapi/v1/server-stop && sleep 10 - name: Show coverage run: | python -m coverage combine .coverage* diff --git a/README.md b/README.md index 73d94960..e6d8e4bd 100644 --- a/README.md +++ b/README.md @@ -135,8 +135,11 @@ Find the instructions [here](https://github.com/AUTOMATIC1111/stable-diffusion-w Here's how to add code to this repo: [Contributing](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing) ## Documentation + The documentation was moved from this README over to the project's [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki). +For the purposes of getting Google and other search engines to crawl the wiki, here's a link to the (not for humans) [crawlable wiki](https://github-wiki-see.page/m/AUTOMATIC1111/stable-diffusion-webui/wiki). + ## Credits Licenses for borrowed code can be found in `Settings -> Licenses` screen, and also in `html/licenses.html` file. diff --git a/extensions-builtin/LDSR/ldsr_model_arch.py b/extensions-builtin/LDSR/ldsr_model_arch.py index 7f450086..7cac36ce 100644 --- a/extensions-builtin/LDSR/ldsr_model_arch.py +++ b/extensions-builtin/LDSR/ldsr_model_arch.py @@ -12,7 +12,7 @@ import safetensors.torch from ldm.models.diffusion.ddim import DDIMSampler from ldm.util import instantiate_from_config, ismap -from modules import shared, sd_hijack +from modules import shared, sd_hijack, devices cached_ldsr_model: torch.nn.Module = None @@ -112,8 +112,7 @@ class LDSR: gc.collect() - if torch.cuda.is_available: - torch.cuda.empty_cache() + devices.torch_gc() im_og = image width_og, height_og = im_og.size @@ -150,8 +149,7 @@ class LDSR: del model gc.collect() - if torch.cuda.is_available: - torch.cuda.empty_cache() + devices.torch_gc() return a diff --git a/extensions-builtin/LDSR/scripts/ldsr_model.py b/extensions-builtin/LDSR/scripts/ldsr_model.py index dbd6d331..bd78dece 100644 --- a/extensions-builtin/LDSR/scripts/ldsr_model.py +++ b/extensions-builtin/LDSR/scripts/ldsr_model.py @@ -1,7 +1,6 @@ import os -from basicsr.utils.download_util import load_file_from_url - +from modules.modelloader import load_file_from_url from modules.upscaler import Upscaler, UpscalerData from ldsr_model_arch import LDSR from modules import shared, script_callbacks, errors @@ -43,20 +42,17 @@ class UpscalerLDSR(Upscaler): if local_safetensors_path is not None and os.path.exists(local_safetensors_path): model = local_safetensors_path else: - model = local_ckpt_path if local_ckpt_path is not None else load_file_from_url(url=self.model_url, model_dir=self.model_download_path, file_name="model.ckpt", progress=True) + model = local_ckpt_path or load_file_from_url(self.model_url, model_dir=self.model_download_path, file_name="model.ckpt") - yaml = local_yaml_path if local_yaml_path is not None else load_file_from_url(url=self.yaml_url, model_dir=self.model_download_path, file_name="project.yaml", progress=True) + yaml = local_yaml_path or load_file_from_url(self.yaml_url, model_dir=self.model_download_path, file_name="project.yaml") - try: - return LDSR(model, yaml) - except Exception: - errors.report("Error importing LDSR", exc_info=True) - return None + return LDSR(model, yaml) def do_upscale(self, img, path): - ldsr = self.load_model(path) - if ldsr is None: - print("NO LDSR!") + try: + ldsr = self.load_model(path) + except Exception: + errors.report(f"Failed loading LDSR model {path}", exc_info=True) return img ddim_steps = shared.opts.ldsr_steps return ldsr.super_resolution(img, ddim_steps, self.scale) diff --git a/extensions-builtin/Lora/lora.py b/extensions-builtin/Lora/lora.py index 34ff57dd..cd46e6c7 100644 --- a/extensions-builtin/Lora/lora.py +++ b/extensions-builtin/Lora/lora.py @@ -443,7 +443,7 @@ def list_available_loras(): os.makedirs(shared.cmd_opts.lora_dir, exist_ok=True) candidates = list(shared.walk_files(shared.cmd_opts.lora_dir, allowed_extensions=[".pt", ".ckpt", ".safetensors"])) - for filename in sorted(candidates, key=str.lower): + for filename in candidates: if os.path.isdir(filename): continue diff --git a/extensions-builtin/ScuNET/scripts/scunet_model.py b/extensions-builtin/ScuNET/scripts/scunet_model.py index 85b4505f..167d2f64 100644 --- a/extensions-builtin/ScuNET/scripts/scunet_model.py +++ b/extensions-builtin/ScuNET/scripts/scunet_model.py @@ -1,4 +1,3 @@ -import os.path import sys import PIL.Image @@ -6,12 +5,11 @@ import numpy as np import torch from tqdm import tqdm -from basicsr.utils.download_util import load_file_from_url - import modules.upscaler from modules import devices, modelloader, script_callbacks, errors -from scunet_model_arch import SCUNet as net +from scunet_model_arch import SCUNet +from modules.modelloader import load_file_from_url from modules.shared import opts @@ -28,7 +26,7 @@ class UpscalerScuNET(modules.upscaler.Upscaler): scalers = [] add_model2 = True for file in model_paths: - if "http" in file: + if file.startswith("http"): name = self.model_name else: name = modelloader.friendly_name(file) @@ -87,11 +85,12 @@ class UpscalerScuNET(modules.upscaler.Upscaler): def do_upscale(self, img: PIL.Image.Image, selected_file): - torch.cuda.empty_cache() + devices.torch_gc() - model = self.load_model(selected_file) - if model is None: - print(f"ScuNET: Unable to load model from {selected_file}", file=sys.stderr) + try: + model = self.load_model(selected_file) + except Exception as e: + print(f"ScuNET: Unable to load model from {selected_file}: {e}", file=sys.stderr) return img device = devices.get_device_for('scunet') @@ -111,7 +110,7 @@ class UpscalerScuNET(modules.upscaler.Upscaler): torch_output = torch_output[:, :h * 1, :w * 1] # remove padding, if any np_output: np.ndarray = torch_output.float().cpu().clamp_(0, 1).numpy() del torch_img, torch_output - torch.cuda.empty_cache() + devices.torch_gc() output = np_output.transpose((1, 2, 0)) # CHW to HWC output = output[:, :, ::-1] # BGR to RGB @@ -119,15 +118,12 @@ class UpscalerScuNET(modules.upscaler.Upscaler): def load_model(self, path: str): device = devices.get_device_for('scunet') - if "http" in path: - filename = load_file_from_url(url=self.model_url, model_dir=self.model_download_path, file_name="%s.pth" % self.name, progress=True) + if path.startswith("http"): + # TODO: this doesn't use `path` at all? + filename = load_file_from_url(self.model_url, model_dir=self.model_download_path, file_name=f"{self.name}.pth") else: filename = path - if not os.path.exists(os.path.join(self.model_path, filename)) or filename is None: - print(f"ScuNET: Unable to load model from {filename}", file=sys.stderr) - return None - - model = net(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64) + model = SCUNet(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64) model.load_state_dict(torch.load(filename), strict=True) model.eval() for _, v in model.named_parameters(): diff --git a/extensions-builtin/SwinIR/scripts/swinir_model.py b/extensions-builtin/SwinIR/scripts/swinir_model.py index 1c7bf325..ae0d0e6a 100644 --- a/extensions-builtin/SwinIR/scripts/swinir_model.py +++ b/extensions-builtin/SwinIR/scripts/swinir_model.py @@ -1,34 +1,35 @@ -import os +import sys +import platform import numpy as np import torch from PIL import Image -from basicsr.utils.download_util import load_file_from_url from tqdm import tqdm from modules import modelloader, devices, script_callbacks, shared from modules.shared import opts, state -from swinir_model_arch import SwinIR as net -from swinir_model_arch_v2 import Swin2SR as net2 +from swinir_model_arch import SwinIR +from swinir_model_arch_v2 import Swin2SR from modules.upscaler import Upscaler, UpscalerData +SWINIR_MODEL_URL = "https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_GAN.pth" device_swinir = devices.get_device_for('swinir') class UpscalerSwinIR(Upscaler): def __init__(self, dirname): + self._cached_model = None # keep the model when SWIN_torch_compile is on to prevent re-compile every runs + self._cached_model_config = None # to clear '_cached_model' when changing model (v1/v2) or settings self.name = "SwinIR" - self.model_url = "https://github.com/JingyunLiang/SwinIR/releases/download/v0.0" \ - "/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR" \ - "-L_x4_GAN.pth " + self.model_url = SWINIR_MODEL_URL self.model_name = "SwinIR 4x" self.user_path = dirname super().__init__() scalers = [] model_files = self.find_models(ext_filter=[".pt", ".pth"]) for model in model_files: - if "http" in model: + if model.startswith("http"): name = self.model_name else: name = modelloader.friendly_name(model) @@ -37,42 +38,54 @@ class UpscalerSwinIR(Upscaler): self.scalers = scalers def do_upscale(self, img, model_file): - model = self.load_model(model_file) - if model is None: - return img - model = model.to(device_swinir, dtype=devices.dtype) + use_compile = hasattr(opts, 'SWIN_torch_compile') and opts.SWIN_torch_compile \ + and int(torch.__version__.split('.')[0]) >= 2 and platform.system() != "Windows" + current_config = (model_file, opts.SWIN_tile) + + if use_compile and self._cached_model_config == current_config: + model = self._cached_model + else: + self._cached_model = None + try: + model = self.load_model(model_file) + except Exception as e: + print(f"Failed loading SwinIR model {model_file}: {e}", file=sys.stderr) + return img + model = model.to(device_swinir, dtype=devices.dtype) + if use_compile: + model = torch.compile(model) + self._cached_model = model + self._cached_model_config = current_config img = upscale(img, model) - try: - torch.cuda.empty_cache() - except Exception: - pass + devices.torch_gc() return img def load_model(self, path, scale=4): - if "http" in path: - dl_name = "%s%s" % (self.model_name.replace(" ", "_"), ".pth") - filename = load_file_from_url(url=path, model_dir=self.model_download_path, file_name=dl_name, progress=True) + if path.startswith("http"): + filename = modelloader.load_file_from_url( + url=path, + model_dir=self.model_download_path, + file_name=f"{self.model_name.replace(' ', '_')}.pth", + ) else: filename = path - if filename is None or not os.path.exists(filename): - return None if filename.endswith(".v2.pth"): - model = net2( - upscale=scale, - in_chans=3, - img_size=64, - window_size=8, - img_range=1.0, - depths=[6, 6, 6, 6, 6, 6], - embed_dim=180, - num_heads=[6, 6, 6, 6, 6, 6], - mlp_ratio=2, - upsampler="nearest+conv", - resi_connection="1conv", + model = Swin2SR( + upscale=scale, + in_chans=3, + img_size=64, + window_size=8, + img_range=1.0, + depths=[6, 6, 6, 6, 6, 6], + embed_dim=180, + num_heads=[6, 6, 6, 6, 6, 6], + mlp_ratio=2, + upsampler="nearest+conv", + resi_connection="1conv", ) params = None else: - model = net( + model = SwinIR( upscale=scale, in_chans=3, img_size=64, @@ -172,6 +185,8 @@ def on_ui_settings(): shared.opts.add_option("SWIN_tile", shared.OptionInfo(192, "Tile size for all SwinIR.", gr.Slider, {"minimum": 16, "maximum": 512, "step": 16}, section=('upscaling', "Upscaling"))) shared.opts.add_option("SWIN_tile_overlap", shared.OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}, section=('upscaling', "Upscaling"))) + if int(torch.__version__.split('.')[0]) >= 2 and platform.system() != "Windows": # torch.compile() require pytorch 2.0 or above, and not on Windows + shared.opts.add_option("SWIN_torch_compile", shared.OptionInfo(False, "Use torch.compile to accelerate SwinIR.", gr.Checkbox, {"interactive": True}, section=('upscaling', "Upscaling")).info("Takes longer on first run")) script_callbacks.on_ui_settings(on_ui_settings) diff --git a/extensions-builtin/canvas-zoom-and-pan/javascript/zoom.js b/extensions-builtin/canvas-zoom-and-pan/javascript/zoom.js index 5ebd2073..30199dcd 100644 --- a/extensions-builtin/canvas-zoom-and-pan/javascript/zoom.js +++ b/extensions-builtin/canvas-zoom-and-pan/javascript/zoom.js @@ -200,7 +200,8 @@ onUiLoaded(async() => { canvas_hotkey_move: "KeyF", canvas_hotkey_overlap: "KeyO", canvas_disabled_functions: [], - canvas_show_tooltip: true + canvas_show_tooltip: true, + canvas_blur_prompt: false }; const functionMap = { @@ -608,6 +609,19 @@ onUiLoaded(async() => { // Handle keydown events function handleKeyDown(event) { + // Disable key locks to make pasting from the buffer work correctly + if ((event.ctrlKey && event.code === 'KeyV') || (event.ctrlKey && event.code === 'KeyC') || event.code === "F5") { + return; + } + + // before activating shortcut, ensure user is not actively typing in an input field + if (!hotkeysConfig.canvas_blur_prompt) { + if (event.target.nodeName === 'TEXTAREA' || event.target.nodeName === 'INPUT') { + return; + } + } + + const hotkeyActions = { [hotkeysConfig.canvas_hotkey_reset]: resetZoom, [hotkeysConfig.canvas_hotkey_overlap]: toggleOverlap, @@ -686,6 +700,20 @@ onUiLoaded(async() => { // Handle the move event for pan functionality. Updates the panX and panY variables and applies the new transform to the target element. function handleMoveKeyDown(e) { + + // Disable key locks to make pasting from the buffer work correctly + if ((e.ctrlKey && e.code === 'KeyV') || (e.ctrlKey && event.code === 'KeyC') || e.code === "F5") { + return; + } + + // before activating shortcut, ensure user is not actively typing in an input field + if (!hotkeysConfig.canvas_blur_prompt) { + if (e.target.nodeName === 'TEXTAREA' || e.target.nodeName === 'INPUT') { + return; + } + } + + if (e.code === hotkeysConfig.canvas_hotkey_move) { if (!e.ctrlKey && !e.metaKey && isKeyDownHandlerAttached) { e.preventDefault(); diff --git a/extensions-builtin/canvas-zoom-and-pan/scripts/hotkey_config.py b/extensions-builtin/canvas-zoom-and-pan/scripts/hotkey_config.py index 1b6683aa..380176ce 100644 --- a/extensions-builtin/canvas-zoom-and-pan/scripts/hotkey_config.py +++ b/extensions-builtin/canvas-zoom-and-pan/scripts/hotkey_config.py @@ -9,5 +9,6 @@ shared.options_templates.update(shared.options_section(('canvas_hotkey', "Canvas "canvas_hotkey_reset": shared.OptionInfo("R", "Reset zoom and canvas positon"), "canvas_hotkey_overlap": shared.OptionInfo("O", "Toggle overlap").info("Technical button, neededs for testing"), "canvas_show_tooltip": shared.OptionInfo(True, "Enable tooltip on the canvas"), + "canvas_blur_prompt": shared.OptionInfo(False, "Take the focus off the prompt when working with a canvas"), "canvas_disabled_functions": shared.OptionInfo(["Overlap"], "Disable function that you don't use", gr.CheckboxGroup, {"choices": ["Zoom","Adjust brush size", "Moving canvas","Fullscreen","Reset Zoom","Overlap"]}), })) diff --git a/javascript/edit-attention.js b/javascript/edit-attention.js index ffa73147..8906c892 100644 --- a/javascript/edit-attention.js +++ b/javascript/edit-attention.js @@ -100,11 +100,12 @@ function keyupEditAttention(event) { if (String(weight).length == 1) weight += ".0"; if (closeCharacter == ')' && weight == 1) { - text = text.slice(0, selectionStart - 1) + text.slice(selectionStart, selectionEnd) + text.slice(selectionEnd + 5); + var endParenPos = text.substring(selectionEnd).indexOf(')'); + text = text.slice(0, selectionStart - 1) + text.slice(selectionStart, selectionEnd) + text.slice(selectionEnd + endParenPos + 1); selectionStart--; selectionEnd--; } else { - text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + 1 + end - 1); + text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + end); } target.focus(); diff --git a/javascript/edit-order.js b/javascript/edit-order.js new file mode 100644 index 00000000..ed4ef9ac --- /dev/null +++ b/javascript/edit-order.js @@ -0,0 +1,41 @@ +/* alt+left/right moves text in prompt */ + +function keyupEditOrder(event) { + if (!opts.keyedit_move) return; + + let target = event.originalTarget || event.composedPath()[0]; + if (!target.matches("*:is([id*='_toprow'] [id*='_prompt'], .prompt) textarea")) return; + if (!event.altKey) return; + + let isLeft = event.key == "ArrowLeft"; + let isRight = event.key == "ArrowRight"; + if (!isLeft && !isRight) return; + event.preventDefault(); + + let selectionStart = target.selectionStart; + let selectionEnd = target.selectionEnd; + let text = target.value; + let items = text.split(","); + let indexStart = (text.slice(0, selectionStart).match(/,/g) || []).length; + let indexEnd = (text.slice(0, selectionEnd).match(/,/g) || []).length; + let range = indexEnd - indexStart + 1; + + if (isLeft && indexStart > 0) { + items.splice(indexStart - 1, 0, ...items.splice(indexStart, range)); + target.value = items.join(); + target.selectionStart = items.slice(0, indexStart - 1).join().length + (indexStart == 1 ? 0 : 1); + target.selectionEnd = items.slice(0, indexEnd).join().length; + } else if (isRight && indexEnd < items.length - 1) { + items.splice(indexStart + 1, 0, ...items.splice(indexStart, range)); + target.value = items.join(); + target.selectionStart = items.slice(0, indexStart + 1).join().length + 1; + target.selectionEnd = items.slice(0, indexEnd + 2).join().length; + } + + event.preventDefault(); + updateInput(target); +} + +addEventListener('keydown', (event) => { + keyupEditOrder(event); +}); diff --git a/javascript/extensions.js b/javascript/extensions.js index efeaf3a5..1f7254c5 100644 --- a/javascript/extensions.js +++ b/javascript/extensions.js @@ -72,3 +72,21 @@ function config_state_confirm_restore(_, config_state_name, config_restore_type) } return [confirmed, config_state_name, config_restore_type]; } + +function toggle_all_extensions(event) { + gradioApp().querySelectorAll('#extensions .extension_toggle').forEach(function(checkbox_el) { + checkbox_el.checked = event.target.checked; + }); +} + +function toggle_extension() { + let all_extensions_toggled = true; + for (const checkbox_el of gradioApp().querySelectorAll('#extensions .extension_toggle')) { + if (!checkbox_el.checked) { + all_extensions_toggled = false; + break; + } + } + + gradioApp().querySelector('#extensions .all_extensions_toggle').checked = all_extensions_toggled; +} diff --git a/modules/api/api.py b/modules/api/api.py index 4ea5d825..11045292 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -14,7 +14,7 @@ from fastapi.encoders import jsonable_encoder from secrets import compare_digest import modules.shared as shared -from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors +from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors, restart from modules.api import models from modules.shared import opts from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images @@ -22,7 +22,7 @@ from modules.textual_inversion.textual_inversion import create_embedding, train_ from modules.textual_inversion.preprocess import preprocess from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork from PIL import PngImagePlugin,Image -from modules.sd_models import checkpoints_list, unload_model_weights, reload_model_weights +from modules.sd_models import checkpoints_list, unload_model_weights, reload_model_weights, checkpoint_aliases from modules.sd_vae import vae_dict from modules.sd_models_config import find_checkpoint_config_near_filename from modules.realesrgan_model import get_realesrgan_models @@ -30,13 +30,7 @@ from modules import devices from typing import Dict, List, Any import piexif import piexif.helper - - -def upscaler_to_index(name: str): - try: - return [x.name.lower() for x in shared.sd_upscalers].index(name.lower()) - except Exception as e: - raise HTTPException(status_code=400, detail=f"Invalid upscaler, needs to be one of these: {' , '.join([x.name for x in shared.sd_upscalers])}") from e +from contextlib import closing def script_name_to_index(name, scripts): @@ -84,6 +78,8 @@ def encode_pil_to_base64(image): image.save(output_bytes, format="PNG", pnginfo=(metadata if use_metadata else None), quality=opts.jpeg_quality) elif opts.samples_format.lower() in ("jpg", "jpeg", "webp"): + if image.mode == "RGBA": + image = image.convert("RGB") parameters = image.info.get('parameters', None) exif_bytes = piexif.dump({ "Exif": { piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(parameters or "", encoding="unicode") } @@ -209,6 +205,11 @@ class Api: self.add_api_route("/sdapi/v1/scripts", self.get_scripts_list, methods=["GET"], response_model=models.ScriptsList) self.add_api_route("/sdapi/v1/script-info", self.get_script_info, methods=["GET"], response_model=List[models.ScriptInfo]) + if shared.cmd_opts.api_server_stop: + self.add_api_route("/sdapi/v1/server-kill", self.kill_webui, methods=["POST"]) + self.add_api_route("/sdapi/v1/server-restart", self.restart_webui, methods=["POST"]) + self.add_api_route("/sdapi/v1/server-stop", self.stop_webui, methods=["POST"]) + self.default_script_arg_txt2img = [] self.default_script_arg_img2img = [] @@ -324,19 +325,19 @@ class Api: args.pop('save_images', None) with self.queue_lock: - p = StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **args) - p.scripts = script_runner - p.outpath_grids = opts.outdir_txt2img_grids - p.outpath_samples = opts.outdir_txt2img_samples + with closing(StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **args)) as p: + p.scripts = script_runner + p.outpath_grids = opts.outdir_txt2img_grids + p.outpath_samples = opts.outdir_txt2img_samples - shared.state.begin() - if selectable_scripts is not None: - p.script_args = script_args - processed = scripts.scripts_txt2img.run(p, *p.script_args) # Need to pass args as list here - else: - p.script_args = tuple(script_args) # Need to pass args as tuple here - processed = process_images(p) - shared.state.end() + shared.state.begin(job="scripts_txt2img") + if selectable_scripts is not None: + p.script_args = script_args + processed = scripts.scripts_txt2img.run(p, *p.script_args) # Need to pass args as list here + else: + p.script_args = tuple(script_args) # Need to pass args as tuple here + processed = process_images(p) + shared.state.end() b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else [] @@ -380,20 +381,20 @@ class Api: args.pop('save_images', None) with self.queue_lock: - p = StableDiffusionProcessingImg2Img(sd_model=shared.sd_model, **args) - p.init_images = [decode_base64_to_image(x) for x in init_images] - p.scripts = script_runner - p.outpath_grids = opts.outdir_img2img_grids - p.outpath_samples = opts.outdir_img2img_samples + with closing(StableDiffusionProcessingImg2Img(sd_model=shared.sd_model, **args)) as p: + p.init_images = [decode_base64_to_image(x) for x in init_images] + p.scripts = script_runner + p.outpath_grids = opts.outdir_img2img_grids + p.outpath_samples = opts.outdir_img2img_samples - shared.state.begin() - if selectable_scripts is not None: - p.script_args = script_args - processed = scripts.scripts_img2img.run(p, *p.script_args) # Need to pass args as list here - else: - p.script_args = tuple(script_args) # Need to pass args as tuple here - processed = process_images(p) - shared.state.end() + shared.state.begin(job="scripts_img2img") + if selectable_scripts is not None: + p.script_args = script_args + processed = scripts.scripts_img2img.run(p, *p.script_args) # Need to pass args as list here + else: + p.script_args = tuple(script_args) # Need to pass args as tuple here + processed = process_images(p) + shared.state.end() b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else [] @@ -517,6 +518,10 @@ class Api: return options def set_config(self, req: Dict[str, Any]): + checkpoint_name = req.get("sd_model_checkpoint", None) + if checkpoint_name is not None and checkpoint_name not in checkpoint_aliases: + raise RuntimeError(f"model {checkpoint_name!r} not found") + for k, v in req.items(): shared.opts.set(k, v) @@ -598,44 +603,42 @@ class Api: def create_embedding(self, args: dict): try: - shared.state.begin() + shared.state.begin(job="create_embedding") filename = create_embedding(**args) # create empty embedding sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings() # reload embeddings so new one can be immediately used - shared.state.end() return models.CreateResponse(info=f"create embedding filename: {filename}") except AssertionError as e: - shared.state.end() return models.TrainResponse(info=f"create embedding error: {e}") + finally: + shared.state.end() + def create_hypernetwork(self, args: dict): try: - shared.state.begin() + shared.state.begin(job="create_hypernetwork") filename = create_hypernetwork(**args) # create empty embedding - shared.state.end() return models.CreateResponse(info=f"create hypernetwork filename: {filename}") except AssertionError as e: - shared.state.end() return models.TrainResponse(info=f"create hypernetwork error: {e}") + finally: + shared.state.end() def preprocess(self, args: dict): try: - shared.state.begin() + shared.state.begin(job="preprocess") preprocess(**args) # quick operation unless blip/booru interrogation is enabled shared.state.end() - return models.PreprocessResponse(info = 'preprocess complete') + return models.PreprocessResponse(info='preprocess complete') except KeyError as e: - shared.state.end() return models.PreprocessResponse(info=f"preprocess error: invalid token: {e}") - except AssertionError as e: - shared.state.end() + except Exception as e: return models.PreprocessResponse(info=f"preprocess error: {e}") - except FileNotFoundError as e: + finally: shared.state.end() - return models.PreprocessResponse(info=f'preprocess error: {e}') def train_embedding(self, args: dict): try: - shared.state.begin() + shared.state.begin(job="train_embedding") apply_optimizations = shared.opts.training_xattention_optimizations error = None filename = '' @@ -648,15 +651,15 @@ class Api: finally: if not apply_optimizations: sd_hijack.apply_optimizations() - shared.state.end() return models.TrainResponse(info=f"train embedding complete: filename: {filename} error: {error}") - except AssertionError as msg: - shared.state.end() + except Exception as msg: return models.TrainResponse(info=f"train embedding error: {msg}") + finally: + shared.state.end() def train_hypernetwork(self, args: dict): try: - shared.state.begin() + shared.state.begin(job="train_hypernetwork") shared.loaded_hypernetworks = [] apply_optimizations = shared.opts.training_xattention_optimizations error = None @@ -674,9 +677,10 @@ class Api: sd_hijack.apply_optimizations() shared.state.end() return models.TrainResponse(info=f"train embedding complete: filename: {filename} error: {error}") - except AssertionError: + except Exception as exc: + return models.TrainResponse(info=f"train embedding error: {exc}") + finally: shared.state.end() - return models.TrainResponse(info=f"train embedding error: {error}") def get_memory(self): try: @@ -716,3 +720,16 @@ class Api: def launch(self, server_name, port): self.app.include_router(self.router) uvicorn.run(self.app, host=server_name, port=port, timeout_keep_alive=shared.cmd_opts.timeout_keep_alive) + + def kill_webui(self): + restart.stop_program() + + def restart_webui(self): + if restart.is_restartable(): + restart.restart_program() + return Response(status_code=501) + + def stop_webui(request): + shared.state.server_command = "stop" + return Response("Stopping.") + diff --git a/modules/api/models.py b/modules/api/models.py index b3a745f0..b5683071 100644 --- a/modules/api/models.py +++ b/modules/api/models.py @@ -274,10 +274,6 @@ class PromptStyleItem(BaseModel): prompt: Optional[str] = Field(title="Prompt") negative_prompt: Optional[str] = Field(title="Negative Prompt") -class ArtistItem(BaseModel): - name: str = Field(title="Name") - score: float = Field(title="Score") - category: str = Field(title="Category") class EmbeddingItem(BaseModel): step: Optional[int] = Field(title="Step", description="The number of steps that were used to train this embedding, if available") diff --git a/modules/call_queue.py b/modules/call_queue.py index 1b5e5273..3b94f8a4 100644 --- a/modules/call_queue.py +++ b/modules/call_queue.py @@ -1,3 +1,4 @@ +from functools import wraps import html import threading import time @@ -18,6 +19,7 @@ def wrap_queued_call(func): def wrap_gradio_gpu_call(func, extra_outputs=None): + @wraps(func) def f(*args, **kwargs): # if the first argument is a string that says "task(...)", it is treated as a job id @@ -28,7 +30,7 @@ def wrap_gradio_gpu_call(func, extra_outputs=None): id_task = None with queue_lock: - shared.state.begin() + shared.state.begin(job=id_task) progress.start_task(id_task) try: @@ -45,6 +47,7 @@ def wrap_gradio_gpu_call(func, extra_outputs=None): def wrap_gradio_call(func, extra_outputs=None, add_stats=False): + @wraps(func) def f(*args, extra_outputs_array=extra_outputs, **kwargs): run_memmon = shared.opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled and add_stats if run_memmon: diff --git a/modules/cmd_args.py b/modules/cmd_args.py index 982d9055..ae78f469 100644 --- a/modules/cmd_args.py +++ b/modules/cmd_args.py @@ -107,4 +107,5 @@ parser.add_argument("--no-hashing", action='store_true', help="disable sha256 ha parser.add_argument("--no-download-sd-model", action='store_true', help="don't download SD1.5 model even if no model is found in --ckpt-dir", default=False) parser.add_argument('--subpath', type=str, help='customize the subpath for gradio, use with reverse proxy') parser.add_argument('--add-stop-route', action='store_true', help='add /_stop route to stop server') +parser.add_argument('--api-server-stop', action='store_true', help='enable server stop/restart/kill via api') parser.add_argument('--timeout-keep-alive', type=int, default=30, help='set timeout_keep_alive for uvicorn') diff --git a/modules/codeformer_model.py b/modules/codeformer_model.py index d974e4b8..da42b5e9 100644 --- a/modules/codeformer_model.py +++ b/modules/codeformer_model.py @@ -15,7 +15,6 @@ model_dir = "Codeformer" model_path = os.path.join(models_path, model_dir) model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth' -have_codeformer = False codeformer = None @@ -100,7 +99,7 @@ def setup_model(dirname): output = self.net(cropped_face_t, w=w if w is not None else shared.opts.code_former_weight, adain=True)[0] restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1)) del output - torch.cuda.empty_cache() + devices.torch_gc() except Exception: errors.report('Failed inference for CodeFormer', exc_info=True) restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1)) @@ -123,9 +122,6 @@ def setup_model(dirname): return restored_img - global have_codeformer - have_codeformer = True - global codeformer codeformer = FaceRestorerCodeFormer(dirname) shared.face_restorers.append(codeformer) diff --git a/modules/devices.py b/modules/devices.py index 1ed6ffdc..57e51da3 100644 --- a/modules/devices.py +++ b/modules/devices.py @@ -15,13 +15,6 @@ def has_mps() -> bool: else: return mac_specific.has_mps -def extract_device_id(args, name): - for x in range(len(args)): - if name in args[x]: - return args[x + 1] - - return None - def get_cuda_device_string(): from modules import shared @@ -56,11 +49,15 @@ def get_device_for(task): def torch_gc(): + if torch.cuda.is_available(): with torch.cuda.device(get_cuda_device_string()): torch.cuda.empty_cache() torch.cuda.ipc_collect() + if has_mps(): + mac_specific.torch_mps_gc() + def enable_tf32(): if torch.cuda.is_available(): diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py index 2fced999..02a1727d 100644 --- a/modules/esrgan_model.py +++ b/modules/esrgan_model.py @@ -1,15 +1,13 @@ -import os +import sys import numpy as np import torch from PIL import Image -from basicsr.utils.download_util import load_file_from_url import modules.esrgan_model_arch as arch from modules import modelloader, images, devices -from modules.upscaler import Upscaler, UpscalerData from modules.shared import opts - +from modules.upscaler import Upscaler, UpscalerData def mod2normal(state_dict): @@ -134,7 +132,7 @@ class UpscalerESRGAN(Upscaler): scaler_data = UpscalerData(self.model_name, self.model_url, self, 4) scalers.append(scaler_data) for file in model_paths: - if "http" in file: + if file.startswith("http"): name = self.model_name else: name = modelloader.friendly_name(file) @@ -143,26 +141,25 @@ class UpscalerESRGAN(Upscaler): self.scalers.append(scaler_data) def do_upscale(self, img, selected_model): - model = self.load_model(selected_model) - if model is None: + try: + model = self.load_model(selected_model) + except Exception as e: + print(f"Unable to load ESRGAN model {selected_model}: {e}", file=sys.stderr) return img model.to(devices.device_esrgan) img = esrgan_upscale(model, img) return img def load_model(self, path: str): - if "http" in path: - filename = load_file_from_url( + if path.startswith("http"): + # TODO: this doesn't use `path` at all? + filename = modelloader.load_file_from_url( url=self.model_url, model_dir=self.model_download_path, file_name=f"{self.model_name}.pth", - progress=True, ) else: filename = path - if not os.path.exists(filename) or filename is None: - print(f"Unable to load {self.model_path} from {filename}") - return None state_dict = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None) diff --git a/modules/extra_networks.py b/modules/extra_networks.py index 1f093df2..41799b0a 100644 --- a/modules/extra_networks.py +++ b/modules/extra_networks.py @@ -103,6 +103,9 @@ def activate(p, extra_network_data): except Exception as e: errors.display(e, f"activating extra network {extra_network_name}") + if p.scripts is not None: + p.scripts.after_extra_networks_activate(p, batch_number=p.iteration, prompts=p.prompts, seeds=p.seeds, subseeds=p.subseeds, extra_network_data=extra_network_data) + def deactivate(p, extra_network_data): """call deactivate for extra networks in extra_network_data in specified order, then call diff --git a/modules/extras.py b/modules/extras.py index 830b53aa..e9c0263e 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -73,8 +73,7 @@ def to_half(tensor, enable): def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format, config_source, bake_in_vae, discard_weights, save_metadata): - shared.state.begin() - shared.state.job = 'model-merge' + shared.state.begin(job="model-merge") def fail(message): shared.state.textinfo = message diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py index dd30a1b5..a3448be9 100644 --- a/modules/generation_parameters_copypaste.py +++ b/modules/generation_parameters_copypaste.py @@ -174,31 +174,6 @@ def send_image_and_dimensions(x): return img, w, h - -def find_hypernetwork_key(hypernet_name, hypernet_hash=None): - """Determines the config parameter name to use for the hypernet based on the parameters in the infotext. - - Example: an infotext provides "Hypernet: ke-ta" and "Hypernet hash: 1234abcd". For the "Hypernet" config - parameter this means there should be an entry that looks like "ke-ta-10000(1234abcd)" to set it to. - - If the infotext has no hash, then a hypernet with the same name will be selected instead. - """ - hypernet_name = hypernet_name.lower() - if hypernet_hash is not None: - # Try to match the hash in the name - for hypernet_key in shared.hypernetworks.keys(): - result = re_hypernet_hash.search(hypernet_key) - if result is not None and result[1] == hypernet_hash: - return hypernet_key - else: - # Fall back to a hypernet with the same name - for hypernet_key in shared.hypernetworks.keys(): - if hypernet_key.lower().startswith(hypernet_name): - return hypernet_key - - return None - - def restore_old_hires_fix_params(res): """for infotexts that specify old First pass size parameter, convert it into width, height, and hr scale""" @@ -332,10 +307,6 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model return res -settings_map = {} - - - infotext_to_setting_name_mapping = [ ('Clip skip', 'CLIP_stop_at_last_layers', ), ('Conditional mask weight', 'inpainting_mask_weight'), diff --git a/modules/gfpgan_model.py b/modules/gfpgan_model.py index 6ecd295c..8e0f13bd 100644 --- a/modules/gfpgan_model.py +++ b/modules/gfpgan_model.py @@ -25,7 +25,7 @@ def gfpgann(): return None models = modelloader.load_models(model_path, model_url, user_path, ext_filter="GFPGAN") - if len(models) == 1 and "http" in models[0]: + if len(models) == 1 and models[0].startswith("http"): model_file = models[0] elif len(models) != 0: latest_file = max(models, key=os.path.getctime) diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 5d12b449..79670b87 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -3,6 +3,7 @@ import glob import html import os import inspect +from contextlib import closing import modules.textual_inversion.dataset import torch @@ -353,17 +354,6 @@ def load_hypernetworks(names, multipliers=None): shared.loaded_hypernetworks.append(hypernetwork) -def find_closest_hypernetwork_name(search: str): - if not search: - return None - search = search.lower() - applicable = [name for name in shared.hypernetworks if search in name.lower()] - if not applicable: - return None - applicable = sorted(applicable, key=lambda name: len(name)) - return applicable[0] - - def apply_single_hypernetwork(hypernetwork, context_k, context_v, layer=None): hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context_k.shape[2], None) @@ -446,18 +436,6 @@ def statistics(data): return total_information, recent_information -def report_statistics(loss_info:dict): - keys = sorted(loss_info.keys(), key=lambda x: sum(loss_info[x]) / len(loss_info[x])) - for key in keys: - try: - print("Loss statistics for file " + key) - info, recent = statistics(list(loss_info[key])) - print(info) - print(recent) - except Exception as e: - print(e) - - def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, dropout_structure=None): # Remove illegal characters from name. name = "".join( x for x in name if (x.isalnum() or x in "._- ")) @@ -734,8 +712,9 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi preview_text = p.prompt - processed = processing.process_images(p) - image = processed.images[0] if len(processed.images) > 0 else None + with closing(p): + processed = processing.process_images(p) + image = processed.images[0] if len(processed.images) > 0 else None if unload: shared.sd_model.cond_stage_model.to(devices.cpu) @@ -770,7 +749,6 @@ Last saved image: {html.escape(last_saved_image)}
pbar.leave = False pbar.close() hypernetwork.eval() - #report_statistics(loss_dict) sd_hijack_checkpoint.remove() diff --git a/modules/images.py b/modules/images.py index 7bbfc3e0..4bdedb7f 100644 --- a/modules/images.py +++ b/modules/images.py @@ -1,3 +1,5 @@ +from __future__ import annotations + import datetime import pytz @@ -10,7 +12,7 @@ import re import numpy as np import piexif import piexif.helper -from PIL import Image, ImageFont, ImageDraw, PngImagePlugin +from PIL import Image, ImageFont, ImageDraw, ImageColor, PngImagePlugin import string import json import hashlib @@ -139,6 +141,11 @@ class GridAnnotation: def draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin=0): + + color_active = ImageColor.getcolor(opts.grid_text_active_color, 'RGB') + color_inactive = ImageColor.getcolor(opts.grid_text_inactive_color, 'RGB') + color_background = ImageColor.getcolor(opts.grid_background_color, 'RGB') + def wrap(drawing, text, font, line_length): lines = [''] for word in text.split(): @@ -168,9 +175,6 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin=0): fnt = get_font(fontsize) - color_active = (0, 0, 0) - color_inactive = (153, 153, 153) - pad_left = 0 if sum([sum([len(line.text) for line in lines]) for lines in ver_texts]) == 0 else width * 3 // 4 cols = im.width // width @@ -179,7 +183,7 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin=0): assert cols == len(hor_texts), f'bad number of horizontal texts: {len(hor_texts)}; must be {cols}' assert rows == len(ver_texts), f'bad number of vertical texts: {len(ver_texts)}; must be {rows}' - calc_img = Image.new("RGB", (1, 1), "white") + calc_img = Image.new("RGB", (1, 1), color_background) calc_d = ImageDraw.Draw(calc_img) for texts, allowed_width in zip(hor_texts + ver_texts, [width] * len(hor_texts) + [pad_left] * len(ver_texts)): @@ -200,7 +204,7 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin=0): pad_top = 0 if sum(hor_text_heights) == 0 else max(hor_text_heights) + line_spacing * 2 - result = Image.new("RGB", (im.width + pad_left + margin * (cols-1), im.height + pad_top + margin * (rows-1)), "white") + result = Image.new("RGB", (im.width + pad_left + margin * (cols-1), im.height + pad_top + margin * (rows-1)), color_background) for row in range(rows): for col in range(cols): @@ -302,12 +306,14 @@ def resize_image(resize_mode, im, width, height, upscaler_name=None): if ratio < src_ratio: fill_height = height // 2 - src_h // 2 - res.paste(resized.resize((width, fill_height), box=(0, 0, width, 0)), box=(0, 0)) - res.paste(resized.resize((width, fill_height), box=(0, resized.height, width, resized.height)), box=(0, fill_height + src_h)) + if fill_height > 0: + res.paste(resized.resize((width, fill_height), box=(0, 0, width, 0)), box=(0, 0)) + res.paste(resized.resize((width, fill_height), box=(0, resized.height, width, resized.height)), box=(0, fill_height + src_h)) elif ratio > src_ratio: fill_width = width // 2 - src_w // 2 - res.paste(resized.resize((fill_width, height), box=(0, 0, 0, height)), box=(0, 0)) - res.paste(resized.resize((fill_width, height), box=(resized.width, 0, resized.width, height)), box=(fill_width + src_w, 0)) + if fill_width > 0: + res.paste(resized.resize((fill_width, height), box=(0, 0, 0, height)), box=(0, 0)) + res.paste(resized.resize((fill_width, height), box=(resized.width, 0, resized.width, height)), box=(fill_width + src_w, 0)) return res @@ -372,8 +378,8 @@ class FilenameGenerator: 'hasprompt': lambda self, *args: self.hasprompt(*args), # accepts formats:[hasprompt..] 'clip_skip': lambda self: opts.data["CLIP_stop_at_last_layers"], 'denoising': lambda self: self.p.denoising_strength if self.p and self.p.denoising_strength else NOTHING_AND_SKIP_PREVIOUS_TEXT, + 'user': lambda self: self.p.user, 'vae_filename': lambda self: self.get_vae_filename(), - } default_time_format = '%Y%m%d%H%M%S' @@ -497,13 +503,23 @@ def get_next_sequence_number(path, basename): return result + 1 -def save_image_with_geninfo(image, geninfo, filename, extension=None, existing_pnginfo=None): +def save_image_with_geninfo(image, geninfo, filename, extension=None, existing_pnginfo=None, pnginfo_section_name='parameters'): + """ + Saves image to filename, including geninfo as text information for generation info. + For PNG images, geninfo is added to existing pnginfo dictionary using the pnginfo_section_name argument as key. + For JPG images, there's no dictionary and geninfo just replaces the EXIF description. + """ + if extension is None: extension = os.path.splitext(filename)[1] image_format = Image.registered_extensions()[extension] if extension.lower() == '.png': + existing_pnginfo = existing_pnginfo or {} + if opts.enable_pnginfo: + existing_pnginfo[pnginfo_section_name] = geninfo + if opts.enable_pnginfo: pnginfo_data = PngImagePlugin.PngInfo() for k, v in (existing_pnginfo or {}).items(): @@ -622,7 +638,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i """ temp_file_path = f"{filename_without_extension}.tmp" - save_image_with_geninfo(image_to_save, info, temp_file_path, extension, params.pnginfo) + save_image_with_geninfo(image_to_save, info, temp_file_path, extension, existing_pnginfo=params.pnginfo, pnginfo_section_name=pnginfo_section_name) os.replace(temp_file_path, filename_without_extension + extension) @@ -639,12 +655,18 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i oversize = image.width > opts.target_side_length or image.height > opts.target_side_length if opts.export_for_4chan and (oversize or os.stat(fullfn).st_size > opts.img_downscale_threshold * 1024 * 1024): ratio = image.width / image.height - + resize_to = None if oversize and ratio > 1: - image = image.resize((round(opts.target_side_length), round(image.height * opts.target_side_length / image.width)), LANCZOS) + resize_to = round(opts.target_side_length), round(image.height * opts.target_side_length / image.width) elif oversize: - image = image.resize((round(image.width * opts.target_side_length / image.height), round(opts.target_side_length)), LANCZOS) + resize_to = round(image.width * opts.target_side_length / image.height), round(opts.target_side_length) + if resize_to is not None: + try: + # Resizing image with LANCZOS could throw an exception if e.g. image mode is I;16 + image = image.resize(resize_to, LANCZOS) + except Exception: + image = image.resize(resize_to) try: _atomically_save_image(image, fullfn_without_extension, ".jpg") except Exception as e: @@ -662,8 +684,15 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i return fullfn, txt_fullfn -def read_info_from_image(image): - items = image.info or {} +IGNORED_INFO_KEYS = { + 'jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif', + 'loop', 'background', 'timestamp', 'duration', 'progressive', 'progression', + 'icc_profile', 'chromaticity', 'photoshop', +} + + +def read_info_from_image(image: Image.Image) -> tuple[str | None, dict]: + items = (image.info or {}).copy() geninfo = items.pop('parameters', None) @@ -679,9 +708,7 @@ def read_info_from_image(image): items['exif comment'] = exif_comment geninfo = exif_comment - for field in ['jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif', - 'loop', 'background', 'timestamp', 'duration', 'progressive', 'progression', - 'icc_profile', 'chromaticity']: + for field in IGNORED_INFO_KEYS: items.pop(field, None) if items.get("Software", None) == "NovelAI": diff --git a/modules/img2img.py b/modules/img2img.py index 2c497020..664e2688 100644 --- a/modules/img2img.py +++ b/modules/img2img.py @@ -1,23 +1,26 @@ import os +from contextlib import closing from pathlib import Path import numpy as np from PIL import Image, ImageOps, ImageFilter, ImageEnhance, ImageChops, UnidentifiedImageError +import gradio as gr -from modules import sd_samplers -from modules.generation_parameters_copypaste import create_override_settings_dict +from modules import sd_samplers, images as imgutil +from modules.generation_parameters_copypaste import create_override_settings_dict, parse_generation_parameters from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images from modules.shared import opts, state +from modules.images import save_image import modules.shared as shared import modules.processing as processing from modules.ui import plaintext_to_html import modules.scripts -def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=False, scale_by=1.0): +def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=False, scale_by=1.0, use_png_info=False, png_info_props=None, png_info_dir=None): processing.fix_seed(p) - images = shared.listfiles(input_dir) + images = list(shared.walk_files(input_dir, allowed_extensions=(".png", ".jpg", ".jpeg", ".webp"))) is_inpaint_batch = False if inpaint_mask_dir: @@ -36,6 +39,14 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=Fal state.job_count = len(images) * p.n_iter + # extract "default" params to use in case getting png info fails + prompt = p.prompt + negative_prompt = p.negative_prompt + seed = p.seed + cfg_scale = p.cfg_scale + sampler_name = p.sampler_name + steps = p.steps + for i, image in enumerate(images): state.job = f"{i+1} out of {len(images)}" if state.skipped: @@ -79,25 +90,45 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=Fal mask_image = Image.open(mask_image_path) p.image_mask = mask_image + if use_png_info: + try: + info_img = img + if png_info_dir: + info_img_path = os.path.join(png_info_dir, os.path.basename(image)) + info_img = Image.open(info_img_path) + geninfo, _ = imgutil.read_info_from_image(info_img) + parsed_parameters = parse_generation_parameters(geninfo) + parsed_parameters = {k: v for k, v in parsed_parameters.items() if k in (png_info_props or {})} + except Exception: + parsed_parameters = {} + + p.prompt = prompt + (" " + parsed_parameters["Prompt"] if "Prompt" in parsed_parameters else "") + p.negative_prompt = negative_prompt + (" " + parsed_parameters["Negative prompt"] if "Negative prompt" in parsed_parameters else "") + p.seed = int(parsed_parameters.get("Seed", seed)) + p.cfg_scale = float(parsed_parameters.get("CFG scale", cfg_scale)) + p.sampler_name = parsed_parameters.get("Sampler", sampler_name) + p.steps = int(parsed_parameters.get("Steps", steps)) + proc = modules.scripts.scripts_img2img.run(p, *args) if proc is None: proc = process_images(p) for n, processed_image in enumerate(proc.images): - filename = image_path.name + filename = image_path.stem + infotext = proc.infotext(p, n) + relpath = os.path.dirname(os.path.relpath(image, input_dir)) if n > 0: - left, right = os.path.splitext(filename) - filename = f"{left}-{n}{right}" + filename += f"-{n}" if not save_normally: - os.makedirs(output_dir, exist_ok=True) + os.makedirs(os.path.join(output_dir, relpath), exist_ok=True) if processed_image.mode == 'RGBA': processed_image = processed_image.convert("RGB") - processed_image.save(os.path.join(output_dir, filename)) + save_image(processed_image, os.path.join(output_dir, relpath), None, extension=opts.samples_format, info=infotext, forced_filename=filename, save_to_dirs=False) -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_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, 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, *args): +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_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, 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): override_settings = create_override_settings_dict(override_settings_texts) is_batch = mode == 5 @@ -180,24 +211,25 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s p.scripts = modules.scripts.scripts_img2img p.script_args = args + p.user = request.username + if shared.cmd_opts.enable_console_prompts: print(f"\nimg2img: {prompt}", file=shared.progress_print_out) if mask: p.extra_generation_params["Mask blur"] = mask_blur - if is_batch: - assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled" + with closing(p): + if is_batch: + assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled" - 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) + 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, "") - else: - processed = modules.scripts.scripts_img2img.run(p, *args) - if processed is None: - processed = process_images(p) - - p.close() + processed = Processed(p, [], p.seed, "") + else: + processed = modules.scripts.scripts_img2img.run(p, *args) + if processed is None: + processed = process_images(p) shared.total_tqdm.clear() diff --git a/modules/interrogate.py b/modules/interrogate.py index 9b2c5b60..a3ae1dd5 100644 --- a/modules/interrogate.py +++ b/modules/interrogate.py @@ -184,8 +184,7 @@ class InterrogateModels: def interrogate(self, pil_image): res = "" - shared.state.begin() - shared.state.job = 'interrogate' + shared.state.begin(job="interrogate") try: if shared.cmd_opts.lowvram or shared.cmd_opts.medvram: lowvram.send_everything_to_cpu() diff --git a/modules/launch_utils.py b/modules/launch_utils.py index 609a181e..0e0dbca4 100644 --- a/modules/launch_utils.py +++ b/modules/launch_utils.py @@ -142,15 +142,15 @@ def git_clone(url, dir, name, commithash=None): if commithash is None: return - current_hash = run(f'"{git}" -C "{dir}" rev-parse HEAD', None, f"Couldn't determine {name}'s hash: {commithash}").strip() + current_hash = run(f'"{git}" -C "{dir}" rev-parse HEAD', None, f"Couldn't determine {name}'s hash: {commithash}", live=False).strip() if current_hash == commithash: return run(f'"{git}" -C "{dir}" fetch', f"Fetching updates for {name}...", f"Couldn't fetch {name}") - run(f'"{git}" -C "{dir}" checkout {commithash}', f"Checking out commit for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}") + run(f'"{git}" -C "{dir}" checkout {commithash}', f"Checking out commit for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}", live=True) return - run(f'"{git}" clone "{url}" "{dir}"', f"Cloning {name} into {dir}...", f"Couldn't clone {name}") + run(f'"{git}" clone "{url}" "{dir}"', f"Cloning {name} into {dir}...", f"Couldn't clone {name}", live=True) if commithash is not None: run(f'"{git}" -C "{dir}" checkout {commithash}', None, "Couldn't checkout {name}'s hash: {commithash}") diff --git a/modules/mac_specific.py b/modules/mac_specific.py index d74c6b95..9ceb43ba 100644 --- a/modules/mac_specific.py +++ b/modules/mac_specific.py @@ -1,22 +1,45 @@ +import logging + import torch import platform from modules.sd_hijack_utils import CondFunc from packaging import version +log = logging.getLogger(__name__) -# has_mps is only available in nightly pytorch (for now) and macOS 12.3+. -# check `getattr` and try it for compatibility + +# before torch version 1.13, has_mps is only available in nightly pytorch and macOS 12.3+, +# use check `getattr` and try it for compatibility. +# in torch version 1.13, backends.mps.is_available() and backends.mps.is_built() are introduced in to check mps availabilty, +# since torch 2.0.1+ nightly build, getattr(torch, 'has_mps', False) was deprecated, see https://github.com/pytorch/pytorch/pull/103279 def check_for_mps() -> bool: - if not getattr(torch, 'has_mps', False): - return False - try: - torch.zeros(1).to(torch.device("mps")) - return True - except Exception: - return False + if version.parse(torch.__version__) <= version.parse("2.0.1"): + if not getattr(torch, 'has_mps', False): + return False + try: + torch.zeros(1).to(torch.device("mps")) + return True + except Exception: + return False + else: + return torch.backends.mps.is_available() and torch.backends.mps.is_built() + + has_mps = check_for_mps() +def torch_mps_gc() -> None: + try: + from modules.shared import state + if state.current_latent is not None: + log.debug("`current_latent` is set, skipping MPS garbage collection") + return + from torch.mps import empty_cache + empty_cache() + except Exception: + log.warning("MPS garbage collection failed", exc_info=True) + + # MPS workaround for https://github.com/pytorch/pytorch/issues/89784 def cumsum_fix(input, cumsum_func, *args, **kwargs): if input.device.type == 'mps': diff --git a/modules/modelloader.py b/modules/modelloader.py index 75f01247..098bcb79 100644 --- a/modules/modelloader.py +++ b/modules/modelloader.py @@ -1,3 +1,5 @@ +from __future__ import annotations + import os import shutil import importlib @@ -8,6 +10,29 @@ from modules.upscaler import Upscaler, UpscalerLanczos, UpscalerNearest, Upscale from modules.paths import script_path, models_path +def load_file_from_url( + url: str, + *, + model_dir: str, + progress: bool = True, + file_name: str | None = None, +) -> str: + """Download a file from `url` into `model_dir`, using the file present if possible. + + Returns the path to the downloaded file. + """ + os.makedirs(model_dir, exist_ok=True) + if not file_name: + parts = urlparse(url) + file_name = os.path.basename(parts.path) + cached_file = os.path.abspath(os.path.join(model_dir, file_name)) + if not os.path.exists(cached_file): + print(f'Downloading: "{url}" to {cached_file}\n') + from torch.hub import download_url_to_file + download_url_to_file(url, cached_file, progress=progress) + return cached_file + + def load_models(model_path: str, model_url: str = None, command_path: str = None, ext_filter=None, download_name=None, ext_blacklist=None) -> list: """ A one-and done loader to try finding the desired models in specified directories. @@ -46,9 +71,7 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None if model_url is not None and len(output) == 0: if download_name is not None: - from basicsr.utils.download_util import load_file_from_url - dl = load_file_from_url(model_url, places[0], True, download_name) - output.append(dl) + output.append(load_file_from_url(model_url, model_dir=places[0], file_name=download_name)) else: output.append(model_url) @@ -59,7 +82,7 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None def friendly_name(file: str): - if "http" in file: + if file.startswith("http"): file = urlparse(file).path file = os.path.basename(file) diff --git a/modules/paths.py b/modules/paths.py index 5171df4f..bada804e 100644 --- a/modules/paths.py +++ b/modules/paths.py @@ -38,17 +38,3 @@ for d, must_exist, what, options in path_dirs: else: sys.path.append(d) paths[what] = d - - -class Prioritize: - def __init__(self, name): - self.name = name - self.path = None - - def __enter__(self): - self.path = sys.path.copy() - sys.path = [paths[self.name]] + sys.path - - def __exit__(self, exc_type, exc_val, exc_tb): - sys.path = self.path - self.path = None diff --git a/modules/postprocessing.py b/modules/postprocessing.py index 736315e2..136e9c88 100644 --- a/modules/postprocessing.py +++ b/modules/postprocessing.py @@ -9,8 +9,7 @@ from modules.shared import opts def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir, show_extras_results, *args, save_output: bool = True): devices.torch_gc() - shared.state.begin() - shared.state.job = 'extras' + shared.state.begin(job="extras") image_data = [] image_names = [] @@ -54,7 +53,9 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir, for image, name in zip(image_data, image_names): shared.state.textinfo = name - existing_pnginfo = image.info or {} + parameters, existing_pnginfo = images.read_info_from_image(image) + if parameters: + existing_pnginfo["parameters"] = parameters pp = scripts_postprocessing.PostprocessedImage(image.convert("RGB")) diff --git a/modules/processing.py b/modules/processing.py index 8da73884..cd568a20 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -184,6 +184,8 @@ class StableDiffusionProcessing: self.uc = None self.c = None + self.user = None + @property def sd_model(self): return shared.sd_model @@ -549,7 +551,7 @@ def program_version(): return res -def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0): +def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0, use_main_prompt=False): index = position_in_batch + iteration * p.batch_size clip_skip = getattr(p, 'clip_skip', opts.CLIP_stop_at_last_layers) @@ -573,7 +575,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')), "Variation seed": (None if p.subseed_strength == 0 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}"), + "Seed resize from": (None if p.seed_resize_from_w <= 0 or p.seed_resize_from_h <= 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"), "Denoising strength": getattr(p, 'denoising_strength', None), "Conditional mask weight": getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None, "Clip skip": None if clip_skip <= 1 else clip_skip, @@ -585,13 +587,15 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter "NGMS": None if p.s_min_uncond == 0 else p.s_min_uncond, **p.extra_generation_params, "Version": program_version() if opts.add_version_to_infotext else None, + "User": p.user if opts.add_user_name_to_info else None, } generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None]) + prompt_text = p.prompt if use_main_prompt else all_prompts[index] negative_prompt_text = f"\nNegative prompt: {p.all_negative_prompts[index]}" if p.all_negative_prompts[index] else "" - return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip() + return f"{prompt_text}{negative_prompt_text}\n{generation_params_text}".strip() def process_images(p: StableDiffusionProcessing) -> Processed: @@ -602,7 +606,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed: try: # if no checkpoint override or the override checkpoint can't be found, remove override entry and load opts checkpoint - if sd_models.checkpoint_alisases.get(p.override_settings.get('sd_model_checkpoint')) is None: + if sd_models.checkpoint_aliases.get(p.override_settings.get('sd_model_checkpoint')) is None: p.override_settings.pop('sd_model_checkpoint', None) sd_models.reload_model_weights() @@ -663,8 +667,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: else: p.all_subseeds = [int(subseed) + x for x in range(len(p.all_prompts))] - def infotext(iteration=0, position_in_batch=0): - return create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, comments, iteration, position_in_batch) + def infotext(iteration=0, position_in_batch=0, use_main_prompt=False): + return create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, comments, iteration, position_in_batch, use_main_prompt) if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings: model_hijack.embedding_db.load_textual_inversion_embeddings() @@ -824,7 +828,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: grid = images.image_grid(output_images, p.batch_size) if opts.return_grid: - text = infotext() + text = infotext(use_main_prompt=True) infotexts.insert(0, text) if opts.enable_pnginfo: grid.info["parameters"] = text @@ -832,7 +836,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: index_of_first_image = 1 if opts.grid_save: - images.save_image(grid, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True) + images.save_image(grid, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0], opts.grid_format, info=infotext(use_main_prompt=True), short_filename=not opts.grid_extended_filename, p=p, grid=True) if not p.disable_extra_networks and p.extra_network_data: extra_networks.deactivate(p, p.extra_network_data) @@ -1074,6 +1078,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio(for_hr=True)) + if self.scripts is not None: + self.scripts.before_hr(self) + samples = self.sampler.sample_img2img(self, samples, noise, self.hr_c, self.hr_uc, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning) sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio()) diff --git a/modules/realesrgan_model.py b/modules/realesrgan_model.py index 2d27b321..0700b853 100644 --- a/modules/realesrgan_model.py +++ b/modules/realesrgan_model.py @@ -2,7 +2,6 @@ import os import numpy as np from PIL import Image -from basicsr.utils.download_util import load_file_from_url from realesrgan import RealESRGANer from modules.upscaler import Upscaler, UpscalerData @@ -43,9 +42,10 @@ class UpscalerRealESRGAN(Upscaler): if not self.enable: return img - info = self.load_model(path) - if not os.path.exists(info.local_data_path): - print(f"Unable to load RealESRGAN model: {info.name}") + try: + info = self.load_model(path) + except Exception: + errors.report(f"Unable to load RealESRGAN model {path}", exc_info=True) return img upsampler = RealESRGANer( @@ -63,20 +63,17 @@ class UpscalerRealESRGAN(Upscaler): return image def load_model(self, path): - try: - info = next(iter([scaler for scaler in self.scalers if scaler.data_path == path]), None) - - if info is None: - print(f"Unable to find model info: {path}") - return None - - if info.local_data_path.startswith("http"): - info.local_data_path = load_file_from_url(url=info.data_path, model_dir=self.model_download_path, progress=True) - - return info - except Exception: - errors.report("Error making Real-ESRGAN models list", exc_info=True) - return None + for scaler in self.scalers: + if scaler.data_path == path: + if scaler.local_data_path.startswith("http"): + scaler.local_data_path = modelloader.load_file_from_url( + scaler.data_path, + model_dir=self.model_download_path, + ) + if not os.path.exists(scaler.local_data_path): + raise FileNotFoundError(f"RealESRGAN data missing: {scaler.local_data_path}") + return scaler + raise ValueError(f"Unable to find model info: {path}") def load_models(self, _): return get_realesrgan_models(self) diff --git a/modules/scripts.py b/modules/scripts.py index 99bf836a..7d9dd59f 100644 --- a/modules/scripts.py +++ b/modules/scripts.py @@ -1,6 +1,7 @@ import os import re import sys +import inspect from collections import namedtuple import gradio as gr @@ -116,6 +117,21 @@ class Script: pass + def after_extra_networks_activate(self, p, *args, **kwargs): + """ + Calledafter extra networks activation, before conds calculation + allow modification of the network after extra networks activation been applied + won't be call if p.disable_extra_networks + + **kwargs will have those items: + - batch_number - index of current batch, from 0 to number of batches-1 + - prompts - list of prompts for current batch; you can change contents of this list but changing the number of entries will likely break things + - seeds - list of seeds for current batch + - subseeds - list of subseeds for current batch + - extra_network_data - list of ExtraNetworkParams for current stage + """ + pass + def process_batch(self, p, *args, **kwargs): """ Same as process(), but called for every batch. @@ -186,6 +202,11 @@ class Script: return f'script_{tabname}{title}_{item_id}' + def before_hr(self, p, *args): + """ + This function is called before hires fix start. + """ + pass current_basedir = paths.script_path @@ -249,7 +270,7 @@ def load_scripts(): def register_scripts_from_module(module): for script_class in module.__dict__.values(): - if type(script_class) != type: + if not inspect.isclass(script_class): continue if issubclass(script_class, Script): @@ -483,6 +504,14 @@ class ScriptRunner: except Exception: errors.report(f"Error running before_process_batch: {script.filename}", exc_info=True) + def after_extra_networks_activate(self, p, **kwargs): + for script in self.alwayson_scripts: + try: + script_args = p.script_args[script.args_from:script.args_to] + script.after_extra_networks_activate(p, *script_args, **kwargs) + except Exception: + errors.report(f"Error running after_extra_networks_activate: {script.filename}", exc_info=True) + def process_batch(self, p, **kwargs): for script in self.alwayson_scripts: try: @@ -548,6 +577,15 @@ class ScriptRunner: self.scripts[si].args_to = args_to + def before_hr(self, p): + for script in self.alwayson_scripts: + try: + script_args = p.script_args[script.args_from:script.args_to] + script.before_hr(p, *script_args) + except Exception: + errors.report(f"Error running before_hr: {script.filename}", exc_info=True) + + scripts_txt2img: ScriptRunner = None scripts_img2img: ScriptRunner = None scripts_postproc: scripts_postprocessing.ScriptPostprocessingRunner = None diff --git a/modules/sd_models.py b/modules/sd_models.py index 6ff5d17d..060e0007 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -23,7 +23,8 @@ model_dir = "Stable-diffusion" model_path = os.path.abspath(os.path.join(paths.models_path, model_dir)) checkpoints_list = {} -checkpoint_alisases = {} +checkpoint_aliases = {} +checkpoint_alisases = checkpoint_aliases # for compatibility with old name checkpoints_loaded = collections.OrderedDict() @@ -66,7 +67,7 @@ class CheckpointInfo: def register(self): checkpoints_list[self.title] = self for id in self.ids: - checkpoint_alisases[id] = self + checkpoint_aliases[id] = self def calculate_shorthash(self): self.sha256 = hashes.sha256(self.filename, f"checkpoint/{self.name}") @@ -112,7 +113,7 @@ def checkpoint_tiles(): def list_models(): checkpoints_list.clear() - checkpoint_alisases.clear() + checkpoint_aliases.clear() cmd_ckpt = shared.cmd_opts.ckpt if shared.cmd_opts.no_download_sd_model or cmd_ckpt != shared.sd_model_file or os.path.exists(cmd_ckpt): @@ -136,7 +137,7 @@ def list_models(): def get_closet_checkpoint_match(search_string): - checkpoint_info = checkpoint_alisases.get(search_string, None) + checkpoint_info = checkpoint_aliases.get(search_string, None) if checkpoint_info is not None: return checkpoint_info @@ -166,7 +167,7 @@ def select_checkpoint(): """Raises `FileNotFoundError` if no checkpoints are found.""" model_checkpoint = shared.opts.sd_model_checkpoint - checkpoint_info = checkpoint_alisases.get(model_checkpoint, None) + checkpoint_info = checkpoint_aliases.get(model_checkpoint, None) if checkpoint_info is not None: return checkpoint_info @@ -247,7 +248,12 @@ def read_state_dict(checkpoint_file, print_global_state=False, map_location=None _, extension = os.path.splitext(checkpoint_file) if extension.lower() == ".safetensors": device = map_location or shared.weight_load_location or devices.get_optimal_device_name() - pl_sd = safetensors.torch.load_file(checkpoint_file, device=device) + + if not shared.opts.disable_mmap_load_safetensors: + pl_sd = safetensors.torch.load_file(checkpoint_file, device=device) + else: + pl_sd = safetensors.torch.load(open(checkpoint_file, 'rb').read()) + pl_sd = {k: v.to(device) for k, v in pl_sd.items()} else: pl_sd = torch.load(checkpoint_file, map_location=map_location or shared.weight_load_location) @@ -585,7 +591,6 @@ def unload_model_weights(sd_model=None, info=None): sd_model = None gc.collect() devices.torch_gc() - torch.cuda.empty_cache() print(f"Unloaded weights {timer.summary()}.") diff --git a/modules/shared.py b/modules/shared.py index a0862055..48478a68 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -1,9 +1,11 @@ import datetime import json import os +import re import sys import threading import time +import logging import gradio as gr import torch @@ -18,6 +20,8 @@ from modules.paths_internal import models_path, script_path, data_path, sd_confi from ldm.models.diffusion.ddpm import LatentDiffusion from typing import Optional +log = logging.getLogger(__name__) + demo = None parser = cmd_args.parser @@ -144,12 +148,15 @@ class State: def request_restart(self) -> None: self.interrupt() self.server_command = "restart" + log.info("Received restart request") def skip(self): self.skipped = True + log.info("Received skip request") def interrupt(self): self.interrupted = True + log.info("Received interrupt request") def nextjob(self): if opts.live_previews_enable and opts.show_progress_every_n_steps == -1: @@ -173,7 +180,7 @@ class State: return obj - def begin(self): + def begin(self, job: str = "(unknown)"): self.sampling_step = 0 self.job_count = -1 self.processing_has_refined_job_count = False @@ -187,10 +194,13 @@ class State: self.interrupted = False self.textinfo = None self.time_start = time.time() - + self.job = job devices.torch_gc() + log.info("Starting job %s", job) def end(self): + duration = time.time() - self.time_start + log.info("Ending job %s (%.2f seconds)", self.job, duration) self.job = "" self.job_count = 0 @@ -311,6 +321,10 @@ options_templates.update(options_section(('saving-images', "Saving images/grids" "grid_prevent_empty_spots": OptionInfo(False, "Prevent empty spots in grid (when set to autodetect)"), "grid_zip_filename_pattern": OptionInfo("", "Archive filename pattern", component_args=hide_dirs).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"), "n_rows": OptionInfo(-1, "Grid row count; use -1 for autodetect and 0 for it to be same as batch size", gr.Slider, {"minimum": -1, "maximum": 16, "step": 1}), + "font": OptionInfo("", "Font for image grids that have text"), + "grid_text_active_color": OptionInfo("#000000", "Text color for image grids", ui_components.FormColorPicker, {}), + "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."), @@ -376,6 +390,7 @@ options_templates.update(options_section(('system', "System"), { "multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job."), "print_hypernet_extra": OptionInfo(False, "Print extra hypernetwork information to console."), "list_hidden_files": OptionInfo(True, "Load models/files in hidden directories").info("directory is hidden if its name starts with \".\""), + "disable_mmap_load_safetensors": OptionInfo(False, "Disable memmapping for loading .safetensors files.").info("fixes very slow loading speed in some cases"), })) options_templates.update(options_section(('training', "Training"), { @@ -470,7 +485,6 @@ options_templates.update(options_section(('ui', "User interface"), { "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"), - "font": OptionInfo("", "Font for image grids that have text"), "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"), @@ -481,6 +495,7 @@ options_templates.update(options_section(('ui', "User interface"), { "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(".,\\/!?%^*;:{}=`~()", "Ctrl+up/down word delimiters"), + "keyedit_move": OptionInfo(True, "Alt+left/right moves prompt elements"), "quicksettings_list": OptionInfo(["sd_model_checkpoint"], "Quicksettings list", ui_components.DropdownMulti, lambda: {"choices": list(opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that appear at the top of page rather than in settings tab").needs_restart(), "ui_tab_order": OptionInfo([], "UI tab order", ui_components.DropdownMulti, lambda: {"choices": list(tab_names)}).needs_restart(), "hidden_tabs": OptionInfo([], "Hidden UI tabs", ui_components.DropdownMulti, lambda: {"choices": list(tab_names)}).needs_restart(), @@ -493,6 +508,7 @@ options_templates.update(options_section(('ui', "User 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"), "disable_weights_auto_swap": OptionInfo(True, "Disregard checkpoint information from pasted infotext").info("when reading generation parameters from text into UI"), "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("""
    @@ -817,8 +833,12 @@ mem_mon = modules.memmon.MemUsageMonitor("MemMon", device, opts) mem_mon.start() +def natural_sort_key(s, regex=re.compile('([0-9]+)')): + return [int(text) if text.isdigit() else text.lower() for text in regex.split(s)] + + def listfiles(dirname): - filenames = [os.path.join(dirname, x) for x in sorted(os.listdir(dirname), key=str.lower) if not x.startswith(".")] + filenames = [os.path.join(dirname, x) for x in sorted(os.listdir(dirname), key=natural_sort_key) if not x.startswith(".")] return [file for file in filenames if os.path.isfile(file)] @@ -843,8 +863,11 @@ def walk_files(path, allowed_extensions=None): if allowed_extensions is not None: allowed_extensions = set(allowed_extensions) - for root, _, files in os.walk(path, followlinks=True): - for filename in files: + items = list(os.walk(path, followlinks=True)) + items = sorted(items, key=lambda x: natural_sort_key(x[0])) + + for root, _, files in items: + for filename in sorted(files, key=natural_sort_key): if allowed_extensions is not None: _, ext = os.path.splitext(filename) if ext not in allowed_extensions: diff --git a/modules/textual_inversion/logging.py b/modules/textual_inversion/logging.py index 734a4b6f..45823eb1 100644 --- a/modules/textual_inversion/logging.py +++ b/modules/textual_inversion/logging.py @@ -2,11 +2,51 @@ import datetime import json import os -saved_params_shared = {"model_name", "model_hash", "initial_step", "num_of_dataset_images", "learn_rate", "batch_size", "clip_grad_mode", "clip_grad_value", "gradient_step", "data_root", "log_directory", "training_width", "training_height", "steps", "create_image_every", "template_file", "gradient_step", "latent_sampling_method"} -saved_params_ti = {"embedding_name", "num_vectors_per_token", "save_embedding_every", "save_image_with_stored_embedding"} -saved_params_hypernet = {"hypernetwork_name", "layer_structure", "activation_func", "weight_init", "add_layer_norm", "use_dropout", "save_hypernetwork_every"} +saved_params_shared = { + "batch_size", + "clip_grad_mode", + "clip_grad_value", + "create_image_every", + "data_root", + "gradient_step", + "initial_step", + "latent_sampling_method", + "learn_rate", + "log_directory", + "model_hash", + "model_name", + "num_of_dataset_images", + "steps", + "template_file", + "training_height", + "training_width", +} +saved_params_ti = { + "embedding_name", + "num_vectors_per_token", + "save_embedding_every", + "save_image_with_stored_embedding", +} +saved_params_hypernet = { + "activation_func", + "add_layer_norm", + "hypernetwork_name", + "layer_structure", + "save_hypernetwork_every", + "use_dropout", + "weight_init", +} saved_params_all = saved_params_shared | saved_params_ti | saved_params_hypernet -saved_params_previews = {"preview_prompt", "preview_negative_prompt", "preview_steps", "preview_sampler_index", "preview_cfg_scale", "preview_seed", "preview_width", "preview_height"} +saved_params_previews = { + "preview_cfg_scale", + "preview_height", + "preview_negative_prompt", + "preview_prompt", + "preview_sampler_index", + "preview_seed", + "preview_steps", + "preview_width", +} def save_settings_to_file(log_directory, all_params): diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py index 0d4c3f84..dbd856bd 100644 --- a/modules/textual_inversion/preprocess.py +++ b/modules/textual_inversion/preprocess.py @@ -7,7 +7,7 @@ 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=0.3, 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): +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() diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index bb6f211c..cbe975b7 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -1,5 +1,6 @@ import os from collections import namedtuple +from contextlib import closing import torch import tqdm @@ -584,8 +585,9 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st preview_text = p.prompt - processed = processing.process_images(p) - image = processed.images[0] if len(processed.images) > 0 else None + with closing(p): + processed = processing.process_images(p) + image = processed.images[0] if len(processed.images) > 0 else None if unload: shared.sd_model.first_stage_model.to(devices.cpu) diff --git a/modules/txt2img.py b/modules/txt2img.py index 2e7d202d..d0be2e73 100644 --- a/modules/txt2img.py +++ b/modules/txt2img.py @@ -1,13 +1,15 @@ +from contextlib import closing + import modules.scripts from modules import sd_samplers, processing from modules.generation_parameters_copypaste import create_override_settings_dict from modules.shared import opts, cmd_opts import modules.shared as shared from modules.ui import plaintext_to_html +import gradio as gr - -def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, hr_sampler_index: int, hr_prompt: str, hr_negative_prompt, override_settings_texts, *args): +def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, hr_sampler_index: int, hr_prompt: str, hr_negative_prompt, override_settings_texts, request: gr.Request, *args): override_settings = create_override_settings_dict(override_settings_texts) p = processing.StableDiffusionProcessingTxt2Img( @@ -48,15 +50,16 @@ def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, step p.scripts = modules.scripts.scripts_txt2img p.script_args = args + p.user = request.username + if cmd_opts.enable_console_prompts: print(f"\ntxt2img: {prompt}", file=shared.progress_print_out) - processed = modules.scripts.scripts_txt2img.run(p, *args) + with closing(p): + processed = modules.scripts.scripts_txt2img.run(p, *args) - if processed is None: - processed = processing.process_images(p) - - p.close() + if processed is None: + processed = processing.process_images(p) shared.total_tqdm.clear() diff --git a/modules/ui.py b/modules/ui.py index e2e3b6da..39d226ad 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -155,7 +155,7 @@ def process_interrogate(interrogation_function, mode, ii_input_dir, ii_output_di img = Image.open(image) filename = os.path.basename(image) left, _ = os.path.splitext(filename) - print(interrogation_function(img), file=open(os.path.join(ii_output_dir, f"{left}.txt"), 'a')) + print(interrogation_function(img), file=open(os.path.join(ii_output_dir, f"{left}.txt"), 'a', encoding='utf-8')) return [gr.update(), None] @@ -733,6 +733,10 @@ def create_ui(): img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, elem_id="img2img_batch_input_dir") img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, elem_id="img2img_batch_output_dir") img2img_batch_inpaint_mask_dir = gr.Textbox(label="Inpaint batch mask directory (required for inpaint batch processing only)", **shared.hide_dirs, elem_id="img2img_batch_inpaint_mask_dir") + with gr.Accordion("PNG info", open=False): + img2img_batch_use_png_info = gr.Checkbox(label="Append png info to prompts", **shared.hide_dirs, elem_id="img2img_batch_use_png_info") + img2img_batch_png_info_dir = gr.Textbox(label="PNG info directory", **shared.hide_dirs, placeholder="Leave empty to use input directory", elem_id="img2img_batch_png_info_dir") + img2img_batch_png_info_props = gr.CheckboxGroup(["Prompt", "Negative prompt", "Seed", "CFG scale", "Sampler", "Steps"], label="Parameters to take from png info", info="Prompts from png info will be appended to prompts set in ui.") img2img_tabs = [tab_img2img, tab_sketch, tab_inpaint, tab_inpaint_color, tab_inpaint_upload, tab_batch] @@ -773,7 +777,7 @@ def create_ui(): selected_scale_tab = gr.State(value=0) with gr.Tabs(): - with gr.Tab(label="Resize to") as tab_scale_to: + with gr.Tab(label="Resize to", elem_id="img2img_tab_resize_to") as tab_scale_to: with FormRow(): with gr.Column(elem_id="img2img_column_size", scale=4): width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width") @@ -782,7 +786,7 @@ def create_ui(): res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="img2img_res_switch_btn") detect_image_size_btn = ToolButton(value=detect_image_size_symbol, elem_id="img2img_detect_image_size_btn") - with gr.Tab(label="Resize by") as tab_scale_by: + with gr.Tab(label="Resize by", elem_id="img2img_tab_resize_by") as tab_scale_by: scale_by = gr.Slider(minimum=0.05, maximum=4.0, step=0.05, label="Scale", value=1.0, elem_id="img2img_scale") with FormRow(): @@ -934,6 +938,9 @@ def create_ui(): img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, override_settings, + img2img_batch_use_png_info, + img2img_batch_png_info_props, + img2img_batch_png_info_dir, ] + custom_inputs, outputs=[ img2img_gallery, diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py index c7e0a866..dff522ef 100644 --- a/modules/ui_extensions.py +++ b/modules/ui_extensions.py @@ -138,7 +138,10 @@ def extension_table(): - + @@ -170,7 +173,7 @@ def extension_table(): code += f""" - + @@ -421,9 +424,19 @@ sort_ordering = [ (False, lambda x: x.get('name', 'z')), (True, lambda x: x.get('name', 'z')), (False, lambda x: 'z'), + (True, lambda x: x.get('commit_time', '')), + (True, lambda x: x.get('created_at', '')), + (True, lambda x: x.get('stars', 0)), ] +def get_date(info: dict, key): + try: + return datetime.strptime(info.get(key), "%Y-%m-%dT%H:%M:%SZ").strftime("%Y-%m-%d") + except (ValueError, TypeError): + return '' + + 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} @@ -448,7 +461,10 @@ def refresh_available_extensions_from_data(hide_tags, sort_column, filter_text=" for ext in sorted(extlist, key=sort_function, reverse=sort_reverse): name = ext.get("name", "noname") + stars = int(ext.get("stars", 0)) added = ext.get('added', 'unknown') + update_time = get_date(ext, 'commit_time') + create_time = get_date(ext, 'created_at') url = ext.get("url", None) description = ext.get("description", "") extension_tags = ext.get("tags", []) @@ -475,7 +491,8 @@ def refresh_available_extensions_from_data(hide_tags, sort_column, filter_text=" code += f""" - + @@ -559,7 +576,7 @@ def create_ui(): with gr.Row(): hide_tags = gr.CheckboxGroup(value=["ads", "localization", "installed"], label="Hide extensions with tags", choices=["script", "ads", "localization", "installed"]) - sort_column = gr.Radio(value="newest first", label="Order", choices=["newest first", "oldest first", "a-z", "z-a", "internal order", ], type="index") + sort_column = gr.Radio(value="newest first", label="Order", choices=["newest first", "oldest first", "a-z", "z-a", "internal order",'update time', 'create time', "stars"], type="index") with gr.Row(): search_extensions_text = gr.Text(label="Search").style(container=False) @@ -568,9 +585,9 @@ def create_ui(): available_extensions_table = gr.HTML() refresh_available_extensions_button.click( - fn=modules.ui.wrap_gradio_call(refresh_available_extensions, extra_outputs=[gr.update(), gr.update(), gr.update()]), + fn=modules.ui.wrap_gradio_call(refresh_available_extensions, extra_outputs=[gr.update(), gr.update(), gr.update(), gr.update()]), inputs=[available_extensions_index, hide_tags, sort_column], - outputs=[available_extensions_index, available_extensions_table, hide_tags, install_result, search_extensions_text], + outputs=[available_extensions_index, available_extensions_table, hide_tags, search_extensions_text, install_result], ) install_extension_button.click( diff --git a/modules/ui_extra_networks.py b/modules/ui_extra_networks.py index a7d3bc79..693cafb6 100644 --- a/modules/ui_extra_networks.py +++ b/modules/ui_extra_networks.py @@ -30,8 +30,8 @@ def fetch_file(filename: str = ""): raise ValueError(f"File cannot be fetched: {filename}. Must be in one of directories registered by extra pages.") ext = os.path.splitext(filename)[1].lower() - if ext not in (".png", ".jpg", ".jpeg", ".webp"): - raise ValueError(f"File cannot be fetched: {filename}. Only png and jpg and webp.") + if ext not in (".png", ".jpg", ".jpeg", ".webp", ".gif"): + raise ValueError(f"File cannot be fetched: {filename}. Only png, jpg, webp, and gif.") # would profit from returning 304 return FileResponse(filename, headers={"Accept-Ranges": "bytes"}) @@ -90,8 +90,8 @@ class ExtraNetworksPage: subdirs = {} for parentdir in [os.path.abspath(x) for x in self.allowed_directories_for_previews()]: - for root, dirs, _ in os.walk(parentdir, followlinks=True): - for dirname in dirs: + for root, dirs, _ in sorted(os.walk(parentdir, followlinks=True), key=lambda x: shared.natural_sort_key(x[0])): + for dirname in sorted(dirs, key=shared.natural_sort_key): x = os.path.join(root, dirname) if not os.path.isdir(x): diff --git a/modules/ui_settings.py b/modules/ui_settings.py index 0c560b30..a6076bf3 100644 --- a/modules/ui_settings.py +++ b/modules/ui_settings.py @@ -260,13 +260,20 @@ class UiSettings: component = self.component_dict[k] info = opts.data_labels[k] - change_handler = component.release if hasattr(component, 'release') else component.change - change_handler( - fn=lambda value, k=k: self.run_settings_single(value, key=k), - inputs=[component], - outputs=[component, self.text_settings], - show_progress=info.refresh is not None, - ) + if isinstance(component, gr.Textbox): + methods = [component.submit, component.blur] + elif hasattr(component, 'release'): + methods = [component.release] + else: + methods = [component.change] + + for method in methods: + method( + fn=lambda value, k=k: self.run_settings_single(value, key=k), + inputs=[component], + outputs=[component, self.text_settings], + show_progress=info.refresh is not None, + ) button_set_checkpoint = gr.Button('Change checkpoint', elem_id='change_checkpoint', visible=False) button_set_checkpoint.click( diff --git a/style.css b/style.css index e1df716f..5073f0f0 100644 --- a/style.css +++ b/style.css @@ -704,11 +704,24 @@ table.popup-table .link{ margin: 0; } -#available_extensions .date_added{ - opacity: 0.85; +#available_extensions .info{ + margin: 0.5em 0; + display: flex; + margin-top: auto; + opacity: 0.80; font-size: 90%; } +#available_extensions .date_added{ + margin-right: auto; + display: inline-block; +} + +#available_extensions .star_count{ + margin-left: auto; + display: inline-block; +} + /* replace original footer with ours */ footer { diff --git a/webui.py b/webui.py index 136d036d..34c2fd18 100644 --- a/webui.py +++ b/webui.py @@ -11,13 +11,24 @@ import json from threading import Thread from typing import Iterable -from fastapi import FastAPI, Response +from fastapi import FastAPI from fastapi.middleware.cors import CORSMiddleware from fastapi.middleware.gzip import GZipMiddleware from packaging import version import logging +# We can't use cmd_opts for this because it will not have been initialized at this point. +log_level = os.environ.get("SD_WEBUI_LOG_LEVEL") +if log_level: + log_level = getattr(logging, log_level.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', + ) + +logging.getLogger("torch.distributed.nn").setLevel(logging.ERROR) # sshh... logging.getLogger("xformers").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage()) from modules import paths, timer, import_hook, errors, devices # noqa: F401 @@ -32,7 +43,7 @@ warnings.filterwarnings(action="ignore", category=UserWarning, module="torchvisi startup_timer.record("import torch") -import gradio +import gradio # noqa: F401 startup_timer.record("import gradio") import ldm.modules.encoders.modules # noqa: F401 @@ -359,12 +370,11 @@ def api_only(): modules.script_callbacks.app_started_callback(None, app) print(f"Startup time: {startup_timer.summary()}.") - api.launch(server_name="0.0.0.0" if cmd_opts.listen else "127.0.0.1", port=cmd_opts.port if cmd_opts.port else 7861) - - -def stop_route(request): - shared.state.server_command = "stop" - return Response("Stopping.") + api.launch( + server_name="0.0.0.0" if cmd_opts.listen else "127.0.0.1", + port=cmd_opts.port if cmd_opts.port else 7861, + root_path = f"/{cmd_opts.subpath}" + ) def webui(): @@ -403,9 +413,8 @@ def webui(): "docs_url": "/docs", "redoc_url": "/redoc", }, + root_path=f"/{cmd_opts.subpath}" if cmd_opts.subpath else "", ) - if cmd_opts.add_stop_route: - app.add_route("/_stop", stop_route, methods=["POST"]) # after initial launch, disable --autolaunch for subsequent restarts cmd_opts.autolaunch = False @@ -436,11 +445,6 @@ def webui(): timer.startup_record = startup_timer.dump() print(f"Startup time: {startup_timer.summary()}.") - if cmd_opts.subpath: - redirector = FastAPI() - redirector.get("/") - gradio.mount_gradio_app(redirector, shared.demo, path=f"/{cmd_opts.subpath}") - try: while True: server_command = shared.state.wait_for_server_command(timeout=5) diff --git a/webui.sh b/webui.sh index 5c8d977c..a683d946 100755 --- a/webui.sh +++ b/webui.sh @@ -4,26 +4,28 @@ # change the variables in webui-user.sh instead # ################################################# +SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd ) + # If run from macOS, load defaults from webui-macos-env.sh if [[ "$OSTYPE" == "darwin"* ]]; then - if [[ -f webui-macos-env.sh ]] + if [[ -f "$SCRIPT_DIR"/webui-macos-env.sh ]] then - source ./webui-macos-env.sh + source "$SCRIPT_DIR"/webui-macos-env.sh fi fi # Read variables from webui-user.sh # shellcheck source=/dev/null -if [[ -f webui-user.sh ]] +if [[ -f "$SCRIPT_DIR"/webui-user.sh ]] then - source ./webui-user.sh + source "$SCRIPT_DIR"/webui-user.sh fi # Set defaults # Install directory without trailing slash if [[ -z "${install_dir}" ]] then - install_dir="$(pwd)" + install_dir="$SCRIPT_DIR" fi # Name of the subdirectory (defaults to stable-diffusion-webui) @@ -131,6 +133,10 @@ case "$gpu_info" in ;; *"Navi 2"*) export HSA_OVERRIDE_GFX_VERSION=10.3.0 ;; + *"Navi 3"*) [[ -z "${TORCH_COMMAND}" ]] && \ + export TORCH_COMMAND="pip install --pre torch==2.1.0.dev-20230614+rocm5.5 torchvision==0.16.0.dev-20230614+rocm5.5 --index-url https://download.pytorch.org/whl/nightly/rocm5.5" + # Navi 3 needs at least 5.5 which is only on the nightly chain + ;; *"Renoir"*) export HSA_OVERRIDE_GFX_VERSION=9.0.0 printf "\n%s\n" "${delimiter}" printf "Experimental support for Renoir: make sure to have at least 4GB of VRAM and 10GB of RAM or enable cpu mode: --use-cpu all --no-half"
    Extension + + Extension + URL Branch Version
    {html.escape(ext.name)}{html.escape(ext.name)} {remote} {ext.branch} {version_link}
    {html.escape(name)}
    {tags_text}    		{html.escape(description)}

    Added: {html.escape(added)}

    		{html.escape(description)}

    + Update: {html.escape(update_time)} Added: {html.escape(added)} Created: {html.escape(create_time)}stars: {stars}

    		 {install_code}