Merge branch 'master' into weighted-learning
This commit is contained in:
AUTOMATIC1111
GitHub
commit
dfb3b8f398
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@ -104,8 +104,7 @@ Alternatively, use online services (like Google Colab):
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1. Install [Python 3.10.6](https://www.python.org/downloads/windows/), checking "Add Python to PATH"
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2. Install [git](https://git-scm.com/download/win).
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3. Download the stable-diffusion-webui repository, for example by running `git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git`.
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4. Place stable diffusion checkpoint (`model.ckpt`) in the `models/Stable-diffusion` directory (see [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) for where to get it).
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5. Run `webui-user.bat` from Windows Explorer as normal, non-administrator, user.
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4. Run `webui-user.bat` from Windows Explorer as normal, non-administrator, user.
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### Automatic Installation on Linux
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1. Install the dependencies:
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@ -121,7 +120,7 @@ sudo pacman -S wget git python3
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```bash
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bash <(wget -qO- https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh)
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```
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3. Run `webui.sh`.
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### Installation on Apple Silicon
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Find the instructions [here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Installation-on-Apple-Silicon).
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@ -8,8 +8,8 @@ titles = {
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"DDIM": "Denoising Diffusion Implicit Models - best at inpainting",
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"DPM adaptive": "Ignores step count - uses a number of steps determined by the CFG and resolution",
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"Batch count": "How many batches of images to create",
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"Batch size": "How many image to create in a single batch",
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"Batch count": "How many batches of images to create (has no impact on generation performance or VRAM usage)",
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"Batch size": "How many image to create in a single batch (increases generation performance at cost of higher VRAM usage)",
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"CFG Scale": "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results",
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"Seed": "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result",
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"\u{1f3b2}\ufe0f": "Set seed to -1, which will cause a new random number to be used every time",
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@ -242,7 +242,7 @@ def prepare_environment():
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sys.argv += shlex.split(commandline_args)
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parser = argparse.ArgumentParser()
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parser = argparse.ArgumentParser(add_help=False)
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parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default='config.json')
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args, _ = parser.parse_known_args(sys.argv)
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@ -498,7 +498,7 @@ class Api:
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if not apply_optimizations:
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sd_hijack.undo_optimizations()
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try:
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hypernetwork, filename = train_hypernetwork(*args)
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hypernetwork, filename = train_hypernetwork(**args)
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except Exception as e:
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error = e
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finally:
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@ -1,5 +1,6 @@
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# this file is adapted from https://github.com/victorca25/iNNfer
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from collections import OrderedDict
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import math
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import functools
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import torch
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@ -2,6 +2,7 @@ import os
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import sys
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import traceback
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import time
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import git
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from modules import paths, shared
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@ -25,6 +26,7 @@ class Extension:
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self.status = ''
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self.can_update = False
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self.is_builtin = is_builtin
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self.version = ''
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repo = None
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try:
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@ -40,6 +42,10 @@ class Extension:
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try:
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self.remote = next(repo.remote().urls, None)
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self.status = 'unknown'
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head = repo.head.commit
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ts = time.asctime(time.gmtime(repo.head.commit.committed_date))
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self.version = f'{head.hexsha[:8]} ({ts})'
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except Exception:
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self.remote = None
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@ -74,8 +74,8 @@ def image_from_url_text(filedata):
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return image
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def add_paste_fields(tabname, init_img, fields):
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paste_fields[tabname] = {"init_img": init_img, "fields": fields}
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def add_paste_fields(tabname, init_img, fields, override_settings_component=None):
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paste_fields[tabname] = {"init_img": init_img, "fields": fields, "override_settings_component": override_settings_component}
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# backwards compatibility for existing extensions
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import modules.ui
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@ -110,6 +110,7 @@ def connect_paste_params_buttons():
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for binding in registered_param_bindings:
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destination_image_component = paste_fields[binding.tabname]["init_img"]
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fields = paste_fields[binding.tabname]["fields"]
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override_settings_component = binding.override_settings_component or paste_fields[binding.tabname]["override_settings_component"]
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destination_width_component = next(iter([field for field, name in fields if name == "Size-1"] if fields else []), None)
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destination_height_component = next(iter([field for field, name in fields if name == "Size-2"] if fields else []), None)
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@ -130,7 +131,7 @@ def connect_paste_params_buttons():
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)
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if binding.source_text_component is not None and fields is not None:
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connect_paste(binding.paste_button, fields, binding.source_text_component, binding.override_settings_component, binding.tabname)
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connect_paste(binding.paste_button, fields, binding.source_text_component, override_settings_component, binding.tabname)
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if binding.source_tabname is not None and fields is not None:
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paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration'] + (["Seed"] if shared.opts.send_seed else [])
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@ -380,8 +380,8 @@ def apply_single_hypernetwork(hypernetwork, context_k, context_v, layer=None):
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layer.hyper_k = hypernetwork_layers[0]
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layer.hyper_v = hypernetwork_layers[1]
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context_k = hypernetwork_layers[0](context_k)
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context_v = hypernetwork_layers[1](context_v)
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context_k = devices.cond_cast_unet(hypernetwork_layers[0](devices.cond_cast_float(context_k)))
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context_v = devices.cond_cast_unet(hypernetwork_layers[1](devices.cond_cast_float(context_v)))
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return context_k, context_v
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@ -18,7 +18,7 @@ import string
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import json
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import hashlib
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from modules import sd_samplers, shared, script_callbacks
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from modules import sd_samplers, shared, script_callbacks, errors
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from modules.shared import opts, cmd_opts
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LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
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@ -553,6 +553,8 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
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elif extension.lower() in (".jpg", ".jpeg", ".webp"):
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if image_to_save.mode == 'RGBA':
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image_to_save = image_to_save.convert("RGB")
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elif image_to_save.mode == 'I;16':
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image_to_save = image_to_save.point(lambda p: p * 0.0038910505836576).convert("RGB" if extension.lower() == ".webp" else "L")
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image_to_save.save(temp_file_path, format=image_format, quality=opts.jpeg_quality)
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@ -575,17 +577,19 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
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image.already_saved_as = fullfn
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target_side_length = 4000
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oversize = image.width > target_side_length or image.height > target_side_length
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if opts.export_for_4chan and (oversize or os.stat(fullfn).st_size > 4 * 1024 * 1024):
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oversize = image.width > opts.target_side_length or image.height > opts.target_side_length
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if opts.export_for_4chan and (oversize or os.stat(fullfn).st_size > opts.img_downscale_threshold * 1024 * 1024):
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ratio = image.width / image.height
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if oversize and ratio > 1:
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image = image.resize((target_side_length, image.height * target_side_length // image.width), LANCZOS)
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image = image.resize((opts.target_side_length, image.height * opts.target_side_length // image.width), LANCZOS)
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elif oversize:
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image = image.resize((image.width * target_side_length // image.height, target_side_length), LANCZOS)
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image = image.resize((image.width * opts.target_side_length // image.height, opts.target_side_length), LANCZOS)
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_atomically_save_image(image, fullfn_without_extension, ".jpg")
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try:
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_atomically_save_image(image, fullfn_without_extension, ".jpg")
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except Exception as e:
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errors.display(e, "saving image as downscaled JPG")
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if opts.save_txt and info is not None:
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txt_fullfn = f"{fullfn_without_extension}.txt"
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@ -73,6 +73,8 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args):
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if not save_normally:
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os.makedirs(output_dir, exist_ok=True)
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if processed_image.mode == 'RGBA':
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processed_image = processed_image.convert("RGB")
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processed_image.save(os.path.join(output_dir, filename))
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@ -543,8 +543,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
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model_hijack.embedding_db.load_textual_inversion_embeddings()
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_, extra_network_data = extra_networks.parse_prompts(p.all_prompts[0:1])
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if p.scripts is not None:
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p.scripts.process(p)
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@ -582,13 +580,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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if shared.opts.live_previews_enable and opts.show_progress_type == "Approx NN":
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sd_vae_approx.model()
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if not p.disable_extra_networks:
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extra_networks.activate(p, extra_network_data)
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with open(os.path.join(paths.data_path, "params.txt"), "w", encoding="utf8") as file:
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processed = Processed(p, [], p.seed, "")
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file.write(processed.infotext(p, 0))
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if state.job_count == -1:
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state.job_count = p.n_iter
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@ -609,11 +600,24 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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if len(prompts) == 0:
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break
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prompts, _ = extra_networks.parse_prompts(prompts)
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prompts, extra_network_data = extra_networks.parse_prompts(prompts)
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if not p.disable_extra_networks:
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with devices.autocast():
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extra_networks.activate(p, extra_network_data)
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if p.scripts is not None:
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p.scripts.process_batch(p, batch_number=n, prompts=prompts, seeds=seeds, subseeds=subseeds)
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# params.txt should be saved after scripts.process_batch, since the
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# infotext could be modified by that callback
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# Example: a wildcard processed by process_batch sets an extra model
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# strength, which is saved as "Model Strength: 1.0" in the infotext
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if n == 0:
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with open(os.path.join(paths.data_path, "params.txt"), "w", encoding="utf8") as file:
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processed = Processed(p, [], p.seed, "")
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file.write(processed.infotext(p, 0))
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uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps, cached_uc)
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c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps, cached_c)
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|
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@ -46,6 +46,18 @@ class CFGDenoiserParams:
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"""Total number of sampling steps planned"""
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class CFGDenoisedParams:
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def __init__(self, x, sampling_step, total_sampling_steps):
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self.x = x
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"""Latent image representation in the process of being denoised"""
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self.sampling_step = sampling_step
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"""Current Sampling step number"""
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self.total_sampling_steps = total_sampling_steps
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"""Total number of sampling steps planned"""
|
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|
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|
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class UiTrainTabParams:
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def __init__(self, txt2img_preview_params):
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self.txt2img_preview_params = txt2img_preview_params
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@ -68,6 +80,7 @@ callback_map = dict(
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callbacks_before_image_saved=[],
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callbacks_image_saved=[],
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callbacks_cfg_denoiser=[],
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callbacks_cfg_denoised=[],
|
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callbacks_before_component=[],
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callbacks_after_component=[],
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callbacks_image_grid=[],
|
||||
|
|
@ -150,6 +163,14 @@ def cfg_denoiser_callback(params: CFGDenoiserParams):
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report_exception(c, 'cfg_denoiser_callback')
|
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|
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|
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def cfg_denoised_callback(params: CFGDenoisedParams):
|
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for c in callback_map['callbacks_cfg_denoised']:
|
||||
try:
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c.callback(params)
|
||||
except Exception:
|
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report_exception(c, 'cfg_denoised_callback')
|
||||
|
||||
|
||||
def before_component_callback(component, **kwargs):
|
||||
for c in callback_map['callbacks_before_component']:
|
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try:
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|
|
@ -283,6 +304,14 @@ def on_cfg_denoiser(callback):
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add_callback(callback_map['callbacks_cfg_denoiser'], callback)
|
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|
||||
|
||||
def on_cfg_denoised(callback):
|
||||
"""register a function to be called in the kdiffussion cfg_denoiser method after building the inner model inputs.
|
||||
The callback is called with one argument:
|
||||
- params: CFGDenoisedParams - parameters to be passed to the inner model and sampling state details.
|
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"""
|
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add_callback(callback_map['callbacks_cfg_denoised'], callback)
|
||||
|
||||
|
||||
def on_before_component(callback):
|
||||
"""register a function to be called before a component is created.
|
||||
The callback is called with arguments:
|
||||
|
|
|
|||
|
|
@ -154,6 +154,8 @@ class StableDiffusionModelHijack:
|
|||
m.cond_stage_model = sd_hijack_open_clip.FrozenOpenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
|
||||
|
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apply_weighted_forward(m)
|
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if m.cond_stage_key == "edit":
|
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sd_hijack_unet.hijack_ddpm_edit()
|
||||
|
||||
self.optimization_method = apply_optimizations()
|
||||
|
||||
|
|
|
|||
|
|
@ -11,6 +11,7 @@ import ldm.models.diffusion.plms
|
|||
from ldm.models.diffusion.ddpm import LatentDiffusion
|
||||
from ldm.models.diffusion.plms import PLMSSampler
|
||||
from ldm.models.diffusion.ddim import DDIMSampler, noise_like
|
||||
from ldm.models.diffusion.sampling_util import norm_thresholding
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
|
|
|
|||
|
|
@ -44,6 +44,7 @@ def apply_model(orig_func, self, x_noisy, t, cond, **kwargs):
|
|||
with devices.autocast():
|
||||
return orig_func(self, x_noisy.to(devices.dtype_unet), t.to(devices.dtype_unet), cond, **kwargs).float()
|
||||
|
||||
|
||||
class GELUHijack(torch.nn.GELU, torch.nn.Module):
|
||||
def __init__(self, *args, **kwargs):
|
||||
torch.nn.GELU.__init__(self, *args, **kwargs)
|
||||
|
|
@ -53,6 +54,16 @@ class GELUHijack(torch.nn.GELU, torch.nn.Module):
|
|||
else:
|
||||
return torch.nn.GELU.forward(self, x)
|
||||
|
||||
|
||||
ddpm_edit_hijack = None
|
||||
def hijack_ddpm_edit():
|
||||
global ddpm_edit_hijack
|
||||
if not ddpm_edit_hijack:
|
||||
CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.decode_first_stage', first_stage_sub, first_stage_cond)
|
||||
CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.encode_first_stage', first_stage_sub, first_stage_cond)
|
||||
ddpm_edit_hijack = CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.apply_model', apply_model, unet_needs_upcast)
|
||||
|
||||
|
||||
unet_needs_upcast = lambda *args, **kwargs: devices.unet_needs_upcast
|
||||
CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.apply_model', apply_model, unet_needs_upcast)
|
||||
CondFunc('ldm.modules.diffusionmodules.openaimodel.timestep_embedding', lambda orig_func, timesteps, *args, **kwargs: orig_func(timesteps, *args, **kwargs).to(torch.float32 if timesteps.dtype == torch.int64 else devices.dtype_unet), unet_needs_upcast)
|
||||
|
|
|
|||
|
|
@ -105,7 +105,7 @@ def checkpoint_tiles():
|
|||
def list_models():
|
||||
checkpoints_list.clear()
|
||||
checkpoint_alisases.clear()
|
||||
model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"], ext_blacklist=[".vae.safetensors"])
|
||||
model_list = modelloader.load_models(model_path=model_path, model_url="https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors", command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"], download_name="v1-5-pruned-emaonly.safetensors", ext_blacklist=[".vae.ckpt", ".vae.safetensors"])
|
||||
|
||||
cmd_ckpt = shared.cmd_opts.ckpt
|
||||
if os.path.exists(cmd_ckpt):
|
||||
|
|
|
|||
|
|
@ -8,6 +8,7 @@ from modules import prompt_parser, devices, sd_samplers_common
|
|||
from modules.shared import opts, state
|
||||
import modules.shared as shared
|
||||
from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
|
||||
from modules.script_callbacks import CFGDenoisedParams, cfg_denoised_callback
|
||||
|
||||
samplers_k_diffusion = [
|
||||
('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {}),
|
||||
|
|
@ -136,6 +137,9 @@ class CFGDenoiser(torch.nn.Module):
|
|||
|
||||
x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]})
|
||||
|
||||
denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps)
|
||||
cfg_denoised_callback(denoised_params)
|
||||
|
||||
devices.test_for_nans(x_out, "unet")
|
||||
|
||||
if opts.live_preview_content == "Prompt":
|
||||
|
|
@ -269,6 +273,16 @@ class KDiffusionSampler:
|
|||
|
||||
return sigmas
|
||||
|
||||
def create_noise_sampler(self, x, sigmas, p):
|
||||
"""For DPM++ SDE: manually create noise sampler to enable deterministic results across different batch sizes"""
|
||||
if shared.opts.no_dpmpp_sde_batch_determinism:
|
||||
return None
|
||||
|
||||
from k_diffusion.sampling import BrownianTreeNoiseSampler
|
||||
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
|
||||
current_iter_seeds = p.all_seeds[p.iteration * p.batch_size:(p.iteration + 1) * p.batch_size]
|
||||
return BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=current_iter_seeds)
|
||||
|
||||
def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
|
||||
steps, t_enc = sd_samplers_common.setup_img2img_steps(p, steps)
|
||||
|
||||
|
|
@ -278,18 +292,24 @@ class KDiffusionSampler:
|
|||
xi = x + noise * sigma_sched[0]
|
||||
|
||||
extra_params_kwargs = self.initialize(p)
|
||||
if 'sigma_min' in inspect.signature(self.func).parameters:
|
||||
parameters = inspect.signature(self.func).parameters
|
||||
|
||||
if 'sigma_min' in parameters:
|
||||
## last sigma is zero which isn't allowed by DPM Fast & Adaptive so taking value before last
|
||||
extra_params_kwargs['sigma_min'] = sigma_sched[-2]
|
||||
if 'sigma_max' in inspect.signature(self.func).parameters:
|
||||
if 'sigma_max' in parameters:
|
||||
extra_params_kwargs['sigma_max'] = sigma_sched[0]
|
||||
if 'n' in inspect.signature(self.func).parameters:
|
||||
if 'n' in parameters:
|
||||
extra_params_kwargs['n'] = len(sigma_sched) - 1
|
||||
if 'sigma_sched' in inspect.signature(self.func).parameters:
|
||||
if 'sigma_sched' in parameters:
|
||||
extra_params_kwargs['sigma_sched'] = sigma_sched
|
||||
if 'sigmas' in inspect.signature(self.func).parameters:
|
||||
if 'sigmas' in parameters:
|
||||
extra_params_kwargs['sigmas'] = sigma_sched
|
||||
|
||||
if self.funcname == 'sample_dpmpp_sde':
|
||||
noise_sampler = self.create_noise_sampler(x, sigmas, p)
|
||||
extra_params_kwargs['noise_sampler'] = noise_sampler
|
||||
|
||||
self.model_wrap_cfg.init_latent = x
|
||||
self.last_latent = x
|
||||
extra_args={
|
||||
|
|
@ -303,7 +323,7 @@ class KDiffusionSampler:
|
|||
|
||||
return samples
|
||||
|
||||
def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning = None):
|
||||
def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
|
||||
steps = steps or p.steps
|
||||
|
||||
sigmas = self.get_sigmas(p, steps)
|
||||
|
|
@ -311,14 +331,20 @@ class KDiffusionSampler:
|
|||
x = x * sigmas[0]
|
||||
|
||||
extra_params_kwargs = self.initialize(p)
|
||||
if 'sigma_min' in inspect.signature(self.func).parameters:
|
||||
parameters = inspect.signature(self.func).parameters
|
||||
|
||||
if 'sigma_min' in parameters:
|
||||
extra_params_kwargs['sigma_min'] = self.model_wrap.sigmas[0].item()
|
||||
extra_params_kwargs['sigma_max'] = self.model_wrap.sigmas[-1].item()
|
||||
if 'n' in inspect.signature(self.func).parameters:
|
||||
if 'n' in parameters:
|
||||
extra_params_kwargs['n'] = steps
|
||||
else:
|
||||
extra_params_kwargs['sigmas'] = sigmas
|
||||
|
||||
if self.funcname == 'sample_dpmpp_sde':
|
||||
noise_sampler = self.create_noise_sampler(x, sigmas, p)
|
||||
extra_params_kwargs['noise_sampler'] = noise_sampler
|
||||
|
||||
self.last_latent = x
|
||||
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
|
||||
'cond': conditioning,
|
||||
|
|
|
|||
|
|
@ -325,7 +325,9 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
|
|||
"save_images_before_highres_fix": OptionInfo(False, "Save a copy of image before applying highres fix."),
|
||||
"save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
|
||||
"jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
|
||||
"export_for_4chan": OptionInfo(True, "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG"),
|
||||
"export_for_4chan": OptionInfo(True, "If the saved image file size is above the limit, or its either width or height are above the limit, save a downscaled copy as JPG"),
|
||||
"img_downscale_threshold": OptionInfo(4.0, "File size limit for the above option, MB", gr.Number),
|
||||
"target_side_length": OptionInfo(4000, "Width/height limit for the above option, in pixels", gr.Number),
|
||||
|
||||
"use_original_name_batch": OptionInfo(True, "Use original name for output filename during batch process in extras tab"),
|
||||
"use_upscaler_name_as_suffix": OptionInfo(False, "Use upscaler name as filename suffix in the extras tab"),
|
||||
|
|
@ -364,7 +366,7 @@ options_templates.update(options_section(('upscaling', "Upscaling"), {
|
|||
}))
|
||||
|
||||
options_templates.update(options_section(('face-restoration', "Face restoration"), {
|
||||
"face_restoration_model": OptionInfo(None, "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in face_restorers]}),
|
||||
"face_restoration_model": OptionInfo("CodeFormer", "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in face_restorers]}),
|
||||
"code_former_weight": OptionInfo(0.5, "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
|
||||
"face_restoration_unload": OptionInfo(False, "Move face restoration model from VRAM into RAM after processing"),
|
||||
}))
|
||||
|
|
@ -414,6 +416,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
|
|||
options_templates.update(options_section(('compatibility', "Compatibility"), {
|
||||
"use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
|
||||
"use_old_karras_scheduler_sigmas": OptionInfo(False, "Use old karras scheduler sigmas (0.1 to 10)."),
|
||||
"no_dpmpp_sde_batch_determinism": OptionInfo(False, "Do not make DPM++ SDE deterministic across different batch sizes."),
|
||||
"use_old_hires_fix_width_height": OptionInfo(False, "For hires fix, use width/height sliders to set final resolution rather than first pass (disables Upscale by, Resize width/height to)."),
|
||||
}))
|
||||
|
||||
|
|
|
|||
|
|
@ -20,4 +20,4 @@ def sd_vae_items():
|
|||
def refresh_vae_list():
|
||||
import modules.sd_vae
|
||||
|
||||
return modules.sd_vae.refresh_vae_list
|
||||
modules.sd_vae.refresh_vae_list()
|
||||
|
|
|
|||
|
|
@ -631,9 +631,9 @@ def create_ui():
|
|||
(hr_resize_y, "Hires resize-2"),
|
||||
*modules.scripts.scripts_txt2img.infotext_fields
|
||||
]
|
||||
parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields)
|
||||
parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields, override_settings)
|
||||
parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
|
||||
paste_button=txt2img_paste, tabname="txt2img", source_text_component=txt2img_prompt, source_image_component=None, override_settings_component=override_settings,
|
||||
paste_button=txt2img_paste, tabname="txt2img", source_text_component=txt2img_prompt, source_image_component=None,
|
||||
))
|
||||
|
||||
txt2img_preview_params = [
|
||||
|
|
@ -963,10 +963,10 @@ def create_ui():
|
|||
(mask_blur, "Mask blur"),
|
||||
*modules.scripts.scripts_img2img.infotext_fields
|
||||
]
|
||||
parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields)
|
||||
parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields)
|
||||
parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields, override_settings)
|
||||
parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields, override_settings)
|
||||
parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
|
||||
paste_button=img2img_paste, tabname="img2img", source_text_component=img2img_prompt, source_image_component=None, override_settings_component=override_settings,
|
||||
paste_button=img2img_paste, tabname="img2img", source_text_component=img2img_prompt, source_image_component=None,
|
||||
))
|
||||
|
||||
modules.scripts.scripts_current = None
|
||||
|
|
@ -1786,7 +1786,7 @@ def versions_html():
|
|||
return f"""
|
||||
python: <span title="{sys.version}">{python_version}</span>
|
||||
•
|
||||
torch: {torch.__version__}
|
||||
torch: {getattr(torch, '__long_version__',torch.__version__)}
|
||||
•
|
||||
xformers: {xformers_version}
|
||||
•
|
||||
|
|
|
|||
|
|
@ -80,6 +80,7 @@ def extension_table():
|
|||
<tr>
|
||||
<th><abbr title="Use checkbox to enable the extension; it will be enabled or disabled when you click apply button">Extension</abbr></th>
|
||||
<th>URL</th>
|
||||
<th><abbr title="Extension version">Version</abbr></th>
|
||||
<th><abbr title="Use checkbox to mark the extension for update; it will be updated when you click apply button">Update</abbr></th>
|
||||
</tr>
|
||||
</thead>
|
||||
|
|
@ -87,11 +88,7 @@ def extension_table():
|
|||
"""
|
||||
|
||||
for ext in extensions.extensions:
|
||||
remote = ""
|
||||
if ext.is_builtin:
|
||||
remote = "built-in"
|
||||
elif ext.remote:
|
||||
remote = f"""<a href="{html.escape(ext.remote or '')}" target="_blank">{html.escape("built-in" if ext.is_builtin else ext.remote or '')}</a>"""
|
||||
remote = f"""<a href="{html.escape(ext.remote or '')}" target="_blank">{html.escape("built-in" if ext.is_builtin else ext.remote or '')}</a>"""
|
||||
|
||||
if ext.can_update:
|
||||
ext_status = f"""<label><input class="gr-check-radio gr-checkbox" name="update_{html.escape(ext.name)}" checked="checked" type="checkbox">{html.escape(ext.status)}</label>"""
|
||||
|
|
@ -102,6 +99,7 @@ def extension_table():
|
|||
<tr>
|
||||
<td><label><input class="gr-check-radio gr-checkbox" name="enable_{html.escape(ext.name)}" type="checkbox" {'checked="checked"' if ext.enabled else ''}>{html.escape(ext.name)}</label></td>
|
||||
<td>{remote}</td>
|
||||
<td>{ext.version}</td>
|
||||
<td{' class="extension_status"' if ext.remote is not None else ''}>{ext_status}</td>
|
||||
</tr>
|
||||
"""
|
||||
|
|
|
|||
|
|
@ -76,6 +76,10 @@ class ExtraNetworksPage:
|
|||
while subdir.startswith("/"):
|
||||
subdir = subdir[1:]
|
||||
|
||||
is_empty = len(os.listdir(x)) == 0
|
||||
if not is_empty and not subdir.endswith("/"):
|
||||
subdir = subdir + "/"
|
||||
|
||||
subdirs[subdir] = 1
|
||||
|
||||
if subdirs:
|
||||
|
|
@ -94,11 +98,13 @@ class ExtraNetworksPage:
|
|||
dirs = "".join([f"<li>{x}</li>" for x in self.allowed_directories_for_previews()])
|
||||
items_html = shared.html("extra-networks-no-cards.html").format(dirs=dirs)
|
||||
|
||||
self_name_id = self.name.replace(" ", "_")
|
||||
|
||||
res = f"""
|
||||
<div id='{tabname}_{self.name}_subdirs' class='extra-network-subdirs extra-network-subdirs-{view}'>
|
||||
<div id='{tabname}_{self_name_id}_subdirs' class='extra-network-subdirs extra-network-subdirs-{view}'>
|
||||
{subdirs_html}
|
||||
</div>
|
||||
<div id='{tabname}_{self.name}_cards' class='extra-network-{view}'>
|
||||
<div id='{tabname}_{self_name_id}_cards' class='extra-network-{view}'>
|
||||
{items_html}
|
||||
</div>
|
||||
"""
|
||||
|
|
|
|||
|
|
@ -54,7 +54,7 @@ class Script(scripts.Script):
|
|||
prompt_type = gr.Radio(["positive", "negative"], label="Select prompt", elem_id=self.elem_id("prompt_type"), value="positive")
|
||||
variations_delimiter = gr.Radio(["comma", "space"], label="Select joining char", elem_id=self.elem_id("variations_delimiter"), value="comma")
|
||||
with gr.Column():
|
||||
margin_size = gr.Slider(label="Grid margins (px)", min=0, max=500, value=0, step=2, elem_id=self.elem_id("margin_size"))
|
||||
margin_size = gr.Slider(label="Grid margins (px)", minimum=0, maximum=500, value=0, step=2, elem_id=self.elem_id("margin_size"))
|
||||
|
||||
return [put_at_start, different_seeds, prompt_type, variations_delimiter, margin_size]
|
||||
|
||||
|
|
@ -99,8 +99,8 @@ class Script(scripts.Script):
|
|||
p.prompt_for_display = positive_prompt
|
||||
processed = process_images(p)
|
||||
|
||||
grid = images.image_grid(processed.images, p.batch_size, rows=1 << ((len(prompt_matrix_parts) - 1) // 2))
|
||||
grid = images.draw_prompt_matrix(grid, p.width, p.height, prompt_matrix_parts, margin_size)
|
||||
grid = images.image_grid(processed.images, p.batch_size, rows=1 << ((len(prompt_matrix_parts) - 1) // 2))
|
||||
grid = images.draw_prompt_matrix(grid, processed.images[0].width, processed.images[1].height, prompt_matrix_parts, margin_size)
|
||||
processed.images.insert(0, grid)
|
||||
processed.index_of_first_image = 1
|
||||
processed.infotexts.insert(0, processed.infotexts[0])
|
||||
|
|
|
|||
|
|
@ -25,6 +25,8 @@ from modules.ui_components import ToolButton
|
|||
|
||||
fill_values_symbol = "\U0001f4d2" # 📒
|
||||
|
||||
AxisInfo = namedtuple('AxisInfo', ['axis', 'values'])
|
||||
|
||||
|
||||
def apply_field(field):
|
||||
def fun(p, x, xs):
|
||||
|
|
@ -186,6 +188,7 @@ axis_options = [
|
|||
AxisOption("Steps", int, apply_field("steps")),
|
||||
AxisOptionTxt2Img("Hires steps", int, apply_field("hr_second_pass_steps")),
|
||||
AxisOption("CFG Scale", float, apply_field("cfg_scale")),
|
||||
AxisOptionImg2Img("Image CFG Scale", float, apply_field("image_cfg_scale")),
|
||||
AxisOption("Prompt S/R", str, apply_prompt, format_value=format_value),
|
||||
AxisOption("Prompt order", str_permutations, apply_order, format_value=format_value_join_list),
|
||||
AxisOptionTxt2Img("Sampler", str, apply_sampler, format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]),
|
||||
|
|
@ -358,7 +361,7 @@ class Script(scripts.Script):
|
|||
include_lone_images = gr.Checkbox(label='Include Sub Images', value=False, elem_id=self.elem_id("include_lone_images"))
|
||||
include_sub_grids = gr.Checkbox(label='Include Sub Grids', value=False, elem_id=self.elem_id("include_sub_grids"))
|
||||
with gr.Column():
|
||||
margin_size = gr.Slider(label="Grid margins (px)", min=0, max=500, value=0, step=2, elem_id=self.elem_id("margin_size"))
|
||||
margin_size = gr.Slider(label="Grid margins (px)", minimum=0, maximum=500, value=0, step=2, elem_id=self.elem_id("margin_size"))
|
||||
|
||||
with gr.Row(variant="compact", elem_id="swap_axes"):
|
||||
swap_xy_axes_button = gr.Button(value="Swap X/Y axes", elem_id="xy_grid_swap_axes_button")
|
||||
|
|
@ -520,6 +523,10 @@ class Script(scripts.Script):
|
|||
|
||||
grid_infotext = [None]
|
||||
|
||||
state.xyz_plot_x = AxisInfo(x_opt, xs)
|
||||
state.xyz_plot_y = AxisInfo(y_opt, ys)
|
||||
state.xyz_plot_z = AxisInfo(z_opt, zs)
|
||||
|
||||
# If one of the axes is very slow to change between (like SD model
|
||||
# checkpoint), then make sure it is in the outer iteration of the nested
|
||||
# `for` loop.
|
||||
|
|
|
|||
2
webui.py
2
webui.py
|
|
@ -20,6 +20,7 @@ import torch
|
|||
|
||||
# Truncate version number of nightly/local build of PyTorch to not cause exceptions with CodeFormer or Safetensors
|
||||
if ".dev" in torch.__version__ or "+git" in torch.__version__:
|
||||
torch.__long_version__ = torch.__version__
|
||||
torch.__version__ = re.search(r'[\d.]+[\d]', torch.__version__).group(0)
|
||||
|
||||
from modules import shared, devices, sd_samplers, upscaler, extensions, localization, ui_tempdir, ui_extra_networks
|
||||
|
|
@ -97,7 +98,6 @@ def initialize():
|
|||
modules.sd_models.setup_model()
|
||||
codeformer.setup_model(cmd_opts.codeformer_models_path)
|
||||
gfpgan.setup_model(cmd_opts.gfpgan_models_path)
|
||||
shared.face_restorers.append(modules.face_restoration.FaceRestoration())
|
||||
|
||||
modelloader.list_builtin_upscalers()
|
||||
modules.scripts.load_scripts()
|
||||
|
|
|
|||
Loading…
Reference in New Issue