Merge branch 'master' into improved-hr-conflict-test
This commit is contained in:
InvincibleDude
GitHub
commit
e97b83bdbb
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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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@ -496,7 +496,7 @@ def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None,
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shared.reload_hypernetworks()
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def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_hypernetwork_every, template_filename, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
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def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_hypernetwork_every, template_filename, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
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# images allows training previews to have infotext. Importing it at the top causes a circular import problem.
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from modules import images
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@ -554,7 +554,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
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pin_memory = shared.opts.pin_memory
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ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method, varsize=varsize)
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ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method, varsize=varsize, use_weight=use_weight)
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if shared.opts.save_training_settings_to_txt:
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saved_params = dict(
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@ -640,13 +640,19 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
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with devices.autocast():
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x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
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if use_weight:
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w = batch.weight.to(devices.device, non_blocking=pin_memory)
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if tag_drop_out != 0 or shuffle_tags:
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shared.sd_model.cond_stage_model.to(devices.device)
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c = shared.sd_model.cond_stage_model(batch.cond_text).to(devices.device, non_blocking=pin_memory)
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shared.sd_model.cond_stage_model.to(devices.cpu)
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else:
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c = stack_conds(batch.cond).to(devices.device, non_blocking=pin_memory)
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loss = shared.sd_model(x, c)[0] / gradient_step
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if use_weight:
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loss = shared.sd_model.weighted_forward(x, c, w)[0] / gradient_step
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del w
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else:
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loss = shared.sd_model.forward(x, c)[0] / gradient_step
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del x
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del c
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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((round(opts.target_side_length), round(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((round(image.width * opts.target_side_length / image.height), round(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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|
|
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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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|
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@ -624,13 +624,13 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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break
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prompts, extra_network_data = extra_networks.parse_prompts(prompts)
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if type(p) == StableDiffusionProcessingTxt2Img:
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if p.enable_hr and hr_prompts != prompts:
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_, hr_extra_network_data = extra_networks.parse_prompts(hr_prompts)
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extra_network_data.update(hr_extra_network_data)
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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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|
|
|
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|
|
@ -46,6 +46,18 @@ class CFGDenoiserParams:
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"""Total number of sampling steps planned"""
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|
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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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|
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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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class UiTrainTabParams:
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||||
def __init__(self, txt2img_preview_params):
|
||||
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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def cfg_denoised_callback(params: CFGDenoisedParams):
|
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for c in callback_map['callbacks_cfg_denoised']:
|
||||
try:
|
||||
c.callback(params)
|
||||
except Exception:
|
||||
report_exception(c, 'cfg_denoised_callback')
|
||||
|
||||
|
||||
def before_component_callback(component, **kwargs):
|
||||
for c in callback_map['callbacks_before_component']:
|
||||
try:
|
||||
|
|
@ -283,6 +304,14 @@ def on_cfg_denoiser(callback):
|
|||
add_callback(callback_map['callbacks_cfg_denoiser'], callback)
|
||||
|
||||
|
||||
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.
|
||||
"""
|
||||
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:
|
||||
|
|
|
|||
|
|
@ -1,5 +1,6 @@
|
|||
import torch
|
||||
from torch.nn.functional import silu
|
||||
from types import MethodType
|
||||
|
||||
import modules.textual_inversion.textual_inversion
|
||||
from modules import devices, sd_hijack_optimizations, shared, sd_hijack_checkpoint
|
||||
|
|
@ -76,6 +77,54 @@ def fix_checkpoint():
|
|||
pass
|
||||
|
||||
|
||||
def weighted_loss(sd_model, pred, target, mean=True):
|
||||
#Calculate the weight normally, but ignore the mean
|
||||
loss = sd_model._old_get_loss(pred, target, mean=False)
|
||||
|
||||
#Check if we have weights available
|
||||
weight = getattr(sd_model, '_custom_loss_weight', None)
|
||||
if weight is not None:
|
||||
loss *= weight
|
||||
|
||||
#Return the loss, as mean if specified
|
||||
return loss.mean() if mean else loss
|
||||
|
||||
def weighted_forward(sd_model, x, c, w, *args, **kwargs):
|
||||
try:
|
||||
#Temporarily append weights to a place accessible during loss calc
|
||||
sd_model._custom_loss_weight = w
|
||||
|
||||
#Replace 'get_loss' with a weight-aware one. Otherwise we need to reimplement 'forward' completely
|
||||
#Keep 'get_loss', but don't overwrite the previous old_get_loss if it's already set
|
||||
if not hasattr(sd_model, '_old_get_loss'):
|
||||
sd_model._old_get_loss = sd_model.get_loss
|
||||
sd_model.get_loss = MethodType(weighted_loss, sd_model)
|
||||
|
||||
#Run the standard forward function, but with the patched 'get_loss'
|
||||
return sd_model.forward(x, c, *args, **kwargs)
|
||||
finally:
|
||||
try:
|
||||
#Delete temporary weights if appended
|
||||
del sd_model._custom_loss_weight
|
||||
except AttributeError as e:
|
||||
pass
|
||||
|
||||
#If we have an old loss function, reset the loss function to the original one
|
||||
if hasattr(sd_model, '_old_get_loss'):
|
||||
sd_model.get_loss = sd_model._old_get_loss
|
||||
del sd_model._old_get_loss
|
||||
|
||||
def apply_weighted_forward(sd_model):
|
||||
#Add new function 'weighted_forward' that can be called to calc weighted loss
|
||||
sd_model.weighted_forward = MethodType(weighted_forward, sd_model)
|
||||
|
||||
def undo_weighted_forward(sd_model):
|
||||
try:
|
||||
del sd_model.weighted_forward
|
||||
except AttributeError as e:
|
||||
pass
|
||||
|
||||
|
||||
class StableDiffusionModelHijack:
|
||||
fixes = None
|
||||
comments = []
|
||||
|
|
@ -104,6 +153,10 @@ class StableDiffusionModelHijack:
|
|||
m.cond_stage_model.model.token_embedding = EmbeddingsWithFixes(m.cond_stage_model.model.token_embedding, self)
|
||||
m.cond_stage_model = sd_hijack_open_clip.FrozenOpenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
|
||||
|
||||
apply_weighted_forward(m)
|
||||
if m.cond_stage_key == "edit":
|
||||
sd_hijack_unet.hijack_ddpm_edit()
|
||||
|
||||
self.optimization_method = apply_optimizations()
|
||||
|
||||
self.clip = m.cond_stage_model
|
||||
|
|
@ -132,6 +185,7 @@ class StableDiffusionModelHijack:
|
|||
m.cond_stage_model = m.cond_stage_model.wrapped
|
||||
|
||||
undo_optimizations()
|
||||
undo_weighted_forward(m)
|
||||
|
||||
self.apply_circular(False)
|
||||
self.layers = None
|
||||
|
|
|
|||
|
|
@ -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)
|
||||
|
|
|
|||
|
|
@ -105,9 +105,15 @@ 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"])
|
||||
|
||||
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):
|
||||
model_url = None
|
||||
else:
|
||||
model_url = "https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors"
|
||||
|
||||
model_list = modelloader.load_models(model_path=model_path, model_url=model_url, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"], download_name="v1-5-pruned-emaonly.safetensors", ext_blacklist=[".vae.ckpt", ".vae.safetensors"])
|
||||
|
||||
if os.path.exists(cmd_ckpt):
|
||||
checkpoint_info = CheckpointInfo(cmd_ckpt)
|
||||
checkpoint_info.register()
|
||||
|
|
|
|||
|
|
@ -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,
|
||||
|
|
|
|||
|
|
@ -81,6 +81,7 @@ parser.add_argument("--freeze-settings", action='store_true', help="disable edit
|
|||
parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default=os.path.join(data_path, 'config.json'))
|
||||
parser.add_argument("--gradio-debug", action='store_true', help="launch gradio with --debug option")
|
||||
parser.add_argument("--gradio-auth", type=str, help='set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
|
||||
parser.add_argument("--gradio-auth-path", type=str, help='set gradio authentication file path ex. "/path/to/auth/file" same auth format as --gradio-auth', default=None)
|
||||
parser.add_argument("--gradio-img2img-tool", type=str, help='does not do anything')
|
||||
parser.add_argument("--gradio-inpaint-tool", type=str, help="does not do anything")
|
||||
parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
|
||||
|
|
@ -107,6 +108,7 @@ parser.add_argument("--server-name", type=str, help="Sets hostname of server", d
|
|||
parser.add_argument("--gradio-queue", action='store_true', help="Uses gradio queue; experimental option; breaks restart UI button")
|
||||
parser.add_argument("--skip-version-check", action='store_true', help="Do not check versions of torch and xformers")
|
||||
parser.add_argument("--no-hashing", action='store_true', help="disable sha256 hashing of checkpoints to help loading performance", default=False)
|
||||
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)
|
||||
|
||||
|
||||
script_loading.preload_extensions(extensions.extensions_dir, parser)
|
||||
|
|
@ -325,7 +327,9 @@ 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 +368,7 @@ 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 +418,7 @@ 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()
|
||||
|
|
|
|||
|
|
@ -19,9 +19,10 @@ re_numbers_at_start = re.compile(r"^[-\d]+\s*")
|
|||
|
||||
|
||||
class DatasetEntry:
|
||||
def __init__(self, filename=None, filename_text=None, latent_dist=None, latent_sample=None, cond=None, cond_text=None, pixel_values=None):
|
||||
def __init__(self, filename=None, filename_text=None, latent_dist=None, latent_sample=None, cond=None, cond_text=None, pixel_values=None, weight=None):
|
||||
self.filename = filename
|
||||
self.filename_text = filename_text
|
||||
self.weight = weight
|
||||
self.latent_dist = latent_dist
|
||||
self.latent_sample = latent_sample
|
||||
self.cond = cond
|
||||
|
|
@ -30,7 +31,7 @@ class DatasetEntry:
|
|||
|
||||
|
||||
class PersonalizedBase(Dataset):
|
||||
def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, cond_model=None, device=None, template_file=None, include_cond=False, batch_size=1, gradient_step=1, shuffle_tags=False, tag_drop_out=0, latent_sampling_method='once', varsize=False):
|
||||
def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, cond_model=None, device=None, template_file=None, include_cond=False, batch_size=1, gradient_step=1, shuffle_tags=False, tag_drop_out=0, latent_sampling_method='once', varsize=False, use_weight=False):
|
||||
re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex) > 0 else None
|
||||
|
||||
self.placeholder_token = placeholder_token
|
||||
|
|
@ -56,10 +57,16 @@ class PersonalizedBase(Dataset):
|
|||
|
||||
print("Preparing dataset...")
|
||||
for path in tqdm.tqdm(self.image_paths):
|
||||
alpha_channel = None
|
||||
if shared.state.interrupted:
|
||||
raise Exception("interrupted")
|
||||
try:
|
||||
image = Image.open(path).convert('RGB')
|
||||
image = Image.open(path)
|
||||
#Currently does not work for single color transparency
|
||||
#We would need to read image.info['transparency'] for that
|
||||
if use_weight and 'A' in image.getbands():
|
||||
alpha_channel = image.getchannel('A')
|
||||
image = image.convert('RGB')
|
||||
if not varsize:
|
||||
image = image.resize((width, height), PIL.Image.BICUBIC)
|
||||
except Exception:
|
||||
|
|
@ -87,17 +94,35 @@ class PersonalizedBase(Dataset):
|
|||
with devices.autocast():
|
||||
latent_dist = model.encode_first_stage(torchdata.unsqueeze(dim=0))
|
||||
|
||||
if latent_sampling_method == "once" or (latent_sampling_method == "deterministic" and not isinstance(latent_dist, DiagonalGaussianDistribution)):
|
||||
latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu)
|
||||
latent_sampling_method = "once"
|
||||
entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample)
|
||||
elif latent_sampling_method == "deterministic":
|
||||
# Works only for DiagonalGaussianDistribution
|
||||
latent_dist.std = 0
|
||||
latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu)
|
||||
entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample)
|
||||
elif latent_sampling_method == "random":
|
||||
entry = DatasetEntry(filename=path, filename_text=filename_text, latent_dist=latent_dist)
|
||||
#Perform latent sampling, even for random sampling.
|
||||
#We need the sample dimensions for the weights
|
||||
if latent_sampling_method == "deterministic":
|
||||
if isinstance(latent_dist, DiagonalGaussianDistribution):
|
||||
# Works only for DiagonalGaussianDistribution
|
||||
latent_dist.std = 0
|
||||
else:
|
||||
latent_sampling_method = "once"
|
||||
latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu)
|
||||
|
||||
if use_weight and alpha_channel is not None:
|
||||
channels, *latent_size = latent_sample.shape
|
||||
weight_img = alpha_channel.resize(latent_size)
|
||||
npweight = np.array(weight_img).astype(np.float32)
|
||||
#Repeat for every channel in the latent sample
|
||||
weight = torch.tensor([npweight] * channels).reshape([channels] + latent_size)
|
||||
#Normalize the weight to a minimum of 0 and a mean of 1, that way the loss will be comparable to default.
|
||||
weight -= weight.min()
|
||||
weight /= weight.mean()
|
||||
elif use_weight:
|
||||
#If an image does not have a alpha channel, add a ones weight map anyway so we can stack it later
|
||||
weight = torch.ones(latent_sample.shape)
|
||||
else:
|
||||
weight = None
|
||||
|
||||
if latent_sampling_method == "random":
|
||||
entry = DatasetEntry(filename=path, filename_text=filename_text, latent_dist=latent_dist, weight=weight)
|
||||
else:
|
||||
entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample, weight=weight)
|
||||
|
||||
if not (self.tag_drop_out != 0 or self.shuffle_tags):
|
||||
entry.cond_text = self.create_text(filename_text)
|
||||
|
|
@ -110,6 +135,7 @@ class PersonalizedBase(Dataset):
|
|||
del torchdata
|
||||
del latent_dist
|
||||
del latent_sample
|
||||
del weight
|
||||
|
||||
self.length = len(self.dataset)
|
||||
self.groups = list(groups.values())
|
||||
|
|
@ -195,6 +221,10 @@ class BatchLoader:
|
|||
self.cond_text = [entry.cond_text for entry in data]
|
||||
self.cond = [entry.cond for entry in data]
|
||||
self.latent_sample = torch.stack([entry.latent_sample for entry in data]).squeeze(1)
|
||||
if all(entry.weight is not None for entry in data):
|
||||
self.weight = torch.stack([entry.weight for entry in data]).squeeze(1)
|
||||
else:
|
||||
self.weight = None
|
||||
#self.emb_index = [entry.emb_index for entry in data]
|
||||
#print(self.latent_sample.device)
|
||||
|
||||
|
|
|
|||
|
|
@ -351,7 +351,7 @@ def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, dat
|
|||
assert log_directory, "Log directory is empty"
|
||||
|
||||
|
||||
def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_embedding_every, template_filename, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
|
||||
def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_embedding_every, template_filename, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
|
||||
save_embedding_every = save_embedding_every or 0
|
||||
create_image_every = create_image_every or 0
|
||||
template_file = textual_inversion_templates.get(template_filename, None)
|
||||
|
|
@ -410,7 +410,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
|
|||
|
||||
pin_memory = shared.opts.pin_memory
|
||||
|
||||
ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method, varsize=varsize)
|
||||
ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method, varsize=varsize, use_weight=use_weight)
|
||||
|
||||
if shared.opts.save_training_settings_to_txt:
|
||||
save_settings_to_file(log_directory, {**dict(model_name=checkpoint.model_name, model_hash=checkpoint.shorthash, num_of_dataset_images=len(ds), num_vectors_per_token=len(embedding.vec)), **locals()})
|
||||
|
|
@ -480,6 +480,8 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
|
|||
|
||||
with devices.autocast():
|
||||
x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
|
||||
if use_weight:
|
||||
w = batch.weight.to(devices.device, non_blocking=pin_memory)
|
||||
c = shared.sd_model.cond_stage_model(batch.cond_text)
|
||||
|
||||
if is_training_inpainting_model:
|
||||
|
|
@ -490,7 +492,11 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
|
|||
else:
|
||||
cond = c
|
||||
|
||||
loss = shared.sd_model(x, cond)[0] / gradient_step
|
||||
if use_weight:
|
||||
loss = shared.sd_model.weighted_forward(x, cond, w)[0] / gradient_step
|
||||
del w
|
||||
else:
|
||||
loss = shared.sd_model.forward(x, cond)[0] / gradient_step
|
||||
del x
|
||||
|
||||
_loss_step += loss.item()
|
||||
|
|
|
|||
|
|
@ -649,9 +649,9 @@ def create_ui():
|
|||
(hr_negative_prompt, "Hires negative prompt"),
|
||||
*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 = [
|
||||
|
|
@ -981,10 +981,10 @@ 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
|
||||
|
|
@ -1209,6 +1209,8 @@ def create_ui():
|
|||
create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_create_image_every")
|
||||
save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_save_embedding_every")
|
||||
|
||||
use_weight = gr.Checkbox(label="Use PNG alpha channel as loss weight", value=False, elem_id="use_weight")
|
||||
|
||||
save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True, elem_id="train_save_image_with_stored_embedding")
|
||||
preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False, elem_id="train_preview_from_txt2img")
|
||||
|
||||
|
|
@ -1322,6 +1324,7 @@ def create_ui():
|
|||
shuffle_tags,
|
||||
tag_drop_out,
|
||||
latent_sampling_method,
|
||||
use_weight,
|
||||
create_image_every,
|
||||
save_embedding_every,
|
||||
template_file,
|
||||
|
|
@ -1355,6 +1358,7 @@ def create_ui():
|
|||
shuffle_tags,
|
||||
tag_drop_out,
|
||||
latent_sampling_method,
|
||||
use_weight,
|
||||
create_image_every,
|
||||
save_embedding_every,
|
||||
template_file,
|
||||
|
|
@ -1800,7 +1804,7 @@ 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>
|
||||
"""
|
||||
|
|
|
|||
|
|
@ -27,3 +27,4 @@ GitPython==3.1.27
|
|||
torchsde==0.2.5
|
||||
safetensors==0.2.7
|
||||
httpcore<=0.15
|
||||
fastapi==0.90.1
|
||||
|
|
|
|||
|
|
@ -8,6 +8,7 @@ from modules import processing, shared, sd_samplers, images
|
|||
from modules.processing import Processed
|
||||
from modules.sd_samplers import samplers
|
||||
from modules.shared import opts, cmd_opts, state
|
||||
from modules import deepbooru
|
||||
|
||||
|
||||
class Script(scripts.Script):
|
||||
|
|
@ -20,10 +21,11 @@ class Script(scripts.Script):
|
|||
def ui(self, is_img2img):
|
||||
loops = gr.Slider(minimum=1, maximum=32, step=1, label='Loops', value=4, elem_id=self.elem_id("loops"))
|
||||
denoising_strength_change_factor = gr.Slider(minimum=0.9, maximum=1.1, step=0.01, label='Denoising strength change factor', value=1, elem_id=self.elem_id("denoising_strength_change_factor"))
|
||||
append_interrogation = gr.Dropdown(label="Append interrogated prompt at each iteration", choices=["None", "CLIP", "DeepBooru"], value="None")
|
||||
|
||||
return [loops, denoising_strength_change_factor]
|
||||
return [loops, denoising_strength_change_factor, append_interrogation]
|
||||
|
||||
def run(self, p, loops, denoising_strength_change_factor):
|
||||
def run(self, p, loops, denoising_strength_change_factor, append_interrogation):
|
||||
processing.fix_seed(p)
|
||||
batch_count = p.n_iter
|
||||
p.extra_generation_params = {
|
||||
|
|
@ -40,6 +42,7 @@ class Script(scripts.Script):
|
|||
grids = []
|
||||
all_images = []
|
||||
original_init_image = p.init_images
|
||||
original_prompt = p.prompt
|
||||
state.job_count = loops * batch_count
|
||||
|
||||
initial_color_corrections = [processing.setup_color_correction(p.init_images[0])]
|
||||
|
|
@ -58,6 +61,13 @@ class Script(scripts.Script):
|
|||
if opts.img2img_color_correction:
|
||||
p.color_corrections = initial_color_corrections
|
||||
|
||||
if append_interrogation != "None":
|
||||
p.prompt = original_prompt + ", " if original_prompt != "" else ""
|
||||
if append_interrogation == "CLIP":
|
||||
p.prompt += shared.interrogator.interrogate(p.init_images[0])
|
||||
elif append_interrogation == "DeepBooru":
|
||||
p.prompt += deepbooru.model.tag(p.init_images[0])
|
||||
|
||||
state.job = f"Iteration {i + 1}/{loops}, batch {n + 1}/{batch_count}"
|
||||
|
||||
processed = processing.process_images(p)
|
||||
|
|
|
|||
|
|
@ -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]),
|
||||
|
|
@ -241,6 +244,9 @@ def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend
|
|||
cell_mode = processed_image.mode
|
||||
cell_size = processed_image.size
|
||||
processed_result.images = [Image.new(cell_mode, cell_size)]
|
||||
processed_result.all_prompts = [processed.prompt]
|
||||
processed_result.all_seeds = [processed.seed]
|
||||
processed_result.infotexts = [processed.infotexts[0]]
|
||||
|
||||
image_cache[index(ix, iy, iz)] = processed_image
|
||||
if include_lone_images:
|
||||
|
|
@ -358,7 +364,7 @@ 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 +526,10 @@ 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.
|
||||
|
|
|
|||
12
webui.py
12
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()
|
||||
|
|
@ -207,6 +207,14 @@ def webui():
|
|||
if cmd_opts.gradio_queue:
|
||||
shared.demo.queue(64)
|
||||
|
||||
gradio_auth_creds = []
|
||||
if cmd_opts.gradio_auth:
|
||||
gradio_auth_creds += cmd_opts.gradio_auth.strip('"').replace('\n', '').split(',')
|
||||
if cmd_opts.gradio_auth_path:
|
||||
with open(cmd_opts.gradio_auth_path, 'r', encoding="utf8") as file:
|
||||
for line in file.readlines():
|
||||
gradio_auth_creds += [x.strip() for x in line.split(',')]
|
||||
|
||||
app, local_url, share_url = shared.demo.launch(
|
||||
share=cmd_opts.share,
|
||||
server_name=server_name,
|
||||
|
|
@ -214,7 +222,7 @@ def webui():
|
|||
ssl_keyfile=cmd_opts.tls_keyfile,
|
||||
ssl_certfile=cmd_opts.tls_certfile,
|
||||
debug=cmd_opts.gradio_debug,
|
||||
auth=[tuple(cred.split(':')) for cred in cmd_opts.gradio_auth.strip('"').split(',')] if cmd_opts.gradio_auth else None,
|
||||
auth=[tuple(cred.split(':')) for cred in gradio_auth_creds] if gradio_auth_creds else None,
|
||||
inbrowser=cmd_opts.autolaunch,
|
||||
prevent_thread_lock=True
|
||||
)
|
||||
|
|
|
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Reference in New Issue