AUTOMATIC1111/stable-diffusion-webui
1
0
Fork 0
mirror of https://github.com/AUTOMATIC1111/stable-diffusion-webui.git synced 2026-03-15 01:48:20 +00:00
Code Issues Packages Projects Releases Wiki Activity

Merge branch 'dev' into gradio4

Browse Source
This commit is contained in:
AUTOMATIC1111
2024-03-02 08:27:22 +03:00
parent 3ee79332b1 bb24c13ed7
commit f7a3067d2a
59 changed files with 1129 additions and 388 deletions
Download Patch File Download Diff File Expand all files Collapse all files

5
modules/api/api.py
View File

@@ -230,6 +230,7 @@ class Api:
self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=list[models.RealesrganItem])
self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=list[models.PromptStyleItem])
self.add_api_route("/sdapi/v1/embeddings", self.get_embeddings, methods=["GET"], response_model=models.EmbeddingsResponse)
self.add_api_route("/sdapi/v1/refresh-embeddings", self.refresh_embeddings, methods=["POST"])
self.add_api_route("/sdapi/v1/refresh-checkpoints", self.refresh_checkpoints, methods=["POST"])
self.add_api_route("/sdapi/v1/refresh-vae", self.refresh_vae, methods=["POST"])
self.add_api_route("/sdapi/v1/create/embedding", self.create_embedding, methods=["POST"], response_model=models.CreateResponse)
@@ -747,6 +748,10 @@ class Api:
"skipped": convert_embeddings(db.skipped_embeddings),
}
def refresh_embeddings(self):
with self.queue_lock:
sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload=True)
def refresh_checkpoints(self):
with self.queue_lock:
shared.refresh_checkpoints()

47
modules/cmd_args.py
View File

@@ -1,7 +1,7 @@
import argparse
import json
import os
from modules.paths_internal import models_path, script_path, data_path, extensions_dir, extensions_builtin_dir, sd_default_config, sd_model_file # noqa: F401
from modules.paths_internal import normalized_filepath, models_path, script_path, data_path, extensions_dir, extensions_builtin_dir, sd_default_config, sd_model_file # noqa: F401
parser = argparse.ArgumentParser()
@@ -19,21 +19,21 @@ parser.add_argument("--skip-install", action='store_true', help="launch.py argum
parser.add_argument("--dump-sysinfo", action='store_true', help="launch.py argument: dump limited sysinfo file (without information about extensions, options) to disk and quit")
parser.add_argument("--loglevel", type=str, help="log level; one of: CRITICAL, ERROR, WARNING, INFO, DEBUG", default=None)
parser.add_argument("--do-not-download-clip", action='store_true', help="do not download CLIP model even if it's not included in the checkpoint")
parser.add_argument("--data-dir", type=str, default=os.path.dirname(os.path.dirname(os.path.realpath(__file__))), help="base path where all user data is stored")
parser.add_argument("--config", type=str, default=sd_default_config, help="path to config which constructs model",)
parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",)
parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to directory with stable diffusion checkpoints")
parser.add_argument("--vae-dir", type=str, default=None, help="Path to directory with VAE files")
parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default=None)
parser.add_argument("--data-dir", type=normalized_filepath, default=os.path.dirname(os.path.dirname(os.path.realpath(__file__))), help="base path where all user data is stored")
parser.add_argument("--config", type=normalized_filepath, default=sd_default_config, help="path to config which constructs model",)
parser.add_argument("--ckpt", type=normalized_filepath, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",)
parser.add_argument("--ckpt-dir", type=normalized_filepath, default=None, help="Path to directory with stable diffusion checkpoints")
parser.add_argument("--vae-dir", type=normalized_filepath, default=None, help="Path to directory with VAE files")
parser.add_argument("--gfpgan-dir", type=normalized_filepath, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
parser.add_argument("--gfpgan-model", type=normalized_filepath, help="GFPGAN model file name", default=None)
parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats")
parser.add_argument("--no-half-vae", action='store_true', help="do not switch the VAE model to 16-bit floats")
parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)")
parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
parser.add_argument("--embeddings-dir", type=str, default=os.path.join(data_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
parser.add_argument("--textual-inversion-templates-dir", type=str, default=os.path.join(script_path, 'textual_inversion_templates'), help="directory with textual inversion templates")
parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory")
parser.add_argument("--localizations-dir", type=str, default=os.path.join(script_path, 'localizations'), help="localizations directory")
parser.add_argument("--embeddings-dir", type=normalized_filepath, default=os.path.join(data_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
parser.add_argument("--textual-inversion-templates-dir", type=normalized_filepath, default=os.path.join(script_path, 'textual_inversion_templates'), help="directory with textual inversion templates")
parser.add_argument("--hypernetwork-dir", type=normalized_filepath, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory")
parser.add_argument("--localizations-dir", type=normalized_filepath, default=os.path.join(script_path, 'localizations'), help="localizations directory")
parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage")
parser.add_argument("--medvram-sdxl", action='store_true', help="enable --medvram optimization just for SDXL models")
@@ -48,12 +48,13 @@ parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to g
parser.add_argument("--ngrok-region", type=str, help="does not do anything.", default="")
parser.add_argument("--ngrok-options", type=json.loads, help='The options to pass to ngrok in JSON format, e.g.: \'{"authtoken_from_env":true, "basic_auth":"user:password", "oauth_provider":"google", "oauth_allow_emails":"user@asdf.com"}\'', default=dict())
parser.add_argument("--enable-insecure-extension-access", action='store_true', help="enable extensions tab regardless of other options")
parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer'))
parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN'))
parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(models_path, 'ESRGAN'))
parser.add_argument("--bsrgan-models-path", type=str, help="Path to directory with BSRGAN model file(s).", default=os.path.join(models_path, 'BSRGAN'))
parser.add_argument("--realesrgan-models-path", type=str, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(models_path, 'RealESRGAN'))
parser.add_argument("--clip-models-path", type=str, help="Path to directory with CLIP model file(s).", default=None)
parser.add_argument("--codeformer-models-path", type=normalized_filepath, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer'))
parser.add_argument("--gfpgan-models-path", type=normalized_filepath, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN'))
parser.add_argument("--esrgan-models-path", type=normalized_filepath, help="Path to directory with ESRGAN model file(s).", default=os.path.join(models_path, 'ESRGAN'))
parser.add_argument("--bsrgan-models-path", type=normalized_filepath, help="Path to directory with BSRGAN model file(s).", default=os.path.join(models_path, 'BSRGAN'))
parser.add_argument("--realesrgan-models-path", type=normalized_filepath, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(models_path, 'RealESRGAN'))
parser.add_argument("--dat-models-path", type=normalized_filepath, help="Path to directory with DAT model file(s).", default=os.path.join(models_path, 'DAT'))
parser.add_argument("--clip-models-path", type=normalized_filepath, help="Path to directory with CLIP model file(s).", default=None)
parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers")
parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
parser.add_argument("--xformers-flash-attention", action='store_true', help="enable xformers with Flash Attention to improve reproducibility (supported for SD2.x or variant only)")
@@ -83,18 +84,18 @@ parser.add_argument("--freeze-specific-settings", type=str, help='disable editin
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-auth-path", type=normalized_filepath, 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("--gradio-allowed-path", action='append', help="add path to gradio's allowed_paths, make it possible to serve files from it", default=[data_path])
parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(data_path, 'styles.csv'))
parser.add_argument("--styles-file", type=str, action='append', help="path or wildcard path of styles files, allow multiple entries.", default=[])
parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
parser.add_argument("--theme", type=str, help="launches the UI with light or dark theme", default=None)
parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False)
parser.add_argument("--enable-console-prompts", action='store_true', help="does not do anything", default=False) # Legacy compatibility, use as default value shared.opts.enable_console_prompts
parser.add_argument('--vae-path', type=str, help='Checkpoint to use as VAE; setting this argument disables all settings related to VAE', default=None)
parser.add_argument('--vae-path', type=normalized_filepath, help='Checkpoint to use as VAE; setting this argument disables all settings related to VAE', default=None)
parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
parser.add_argument("--api", action='store_true', help="use api=True to launch the API together with the webui (use --nowebui instead for only the API)")
parser.add_argument("--api-auth", type=str, help='Set authentication for API like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
@@ -120,4 +121,6 @@ parser.add_argument('--api-server-stop', action='store_true', help='enable serve
parser.add_argument('--timeout-keep-alive', type=int, default=30, help='set timeout_keep_alive for uvicorn')
parser.add_argument("--disable-all-extensions", action='store_true', help="prevent all extensions from running regardless of any other settings", default=False)
parser.add_argument("--disable-extra-extensions", action='store_true', help="prevent all extensions except built-in from running regardless of any other settings", default=False)
parser.add_argument("--skip-load-model-at-start", action='store_true', help="if load a model at web start, only take effect when --nowebui", )
parser.add_argument("--skip-load-model-at-start", action='store_true', help="if load a model at web start, only take effect when --nowebui")
parser.add_argument("--unix-filenames-sanitization", action='store_true', help="allow any symbols except '/' in filenames. May conflict with your browser and file system")
parser.add_argument("--filenames-max-length", type=int, default=128, help='maximal length of filenames of saved images. If you override it, it can conflict with your file system')

26
modules/devices.py
View File

@@ -3,8 +3,7 @@ import contextlib
from functools import lru_cache
import torch
from modules import errors, shared
from modules import torch_utils
from modules import errors, shared, npu_specific
if sys.platform == "darwin":
from modules import mac_specific
@@ -58,6 +57,9 @@ def get_optimal_device_name():
if has_xpu():
return xpu_specific.get_xpu_device_string()
if npu_specific.has_npu:
return npu_specific.get_npu_device_string()
return "cpu"
@@ -85,6 +87,16 @@ def torch_gc():
if has_xpu():
xpu_specific.torch_xpu_gc()
if npu_specific.has_npu:
torch_npu_set_device()
npu_specific.torch_npu_gc()
def torch_npu_set_device():
# Work around due to bug in torch_npu, revert me after fixed, @see https://gitee.com/ascend/pytorch/issues/I8KECW?from=project-issue
if npu_specific.has_npu:
torch.npu.set_device(0)
def enable_tf32():
if torch.cuda.is_available():
@@ -141,7 +153,12 @@ def manual_cast_forward(target_dtype):
args = [arg.to(target_dtype) if isinstance(arg, torch.Tensor) else arg for arg in args]
kwargs = {k: v.to(target_dtype) if isinstance(v, torch.Tensor) else v for k, v in kwargs.items()}
org_dtype = torch_utils.get_param(self).dtype
org_dtype = target_dtype
for param in self.parameters():
if param.dtype != target_dtype:
org_dtype = param.dtype
break
if org_dtype != target_dtype:
self.to(target_dtype)
result = self.org_forward(*args, **kwargs)
@@ -170,7 +187,7 @@ def manual_cast(target_dtype):
continue
applied = True
org_forward = module_type.forward
if module_type == torch.nn.MultiheadAttention and has_xpu():
if module_type == torch.nn.MultiheadAttention:
module_type.forward = manual_cast_forward(torch.float32)
else:
module_type.forward = manual_cast_forward(target_dtype)
@@ -252,4 +269,3 @@ def first_time_calculation():
x = torch.zeros((1, 1, 3, 3)).to(device, dtype)
conv2d = torch.nn.Conv2d(1, 1, (3, 3)).to(device, dtype)
conv2d(x)

5
modules/hashes.py
View File

@@ -21,7 +21,10 @@ def calculate_sha256(filename):
def sha256_from_cache(filename, title, use_addnet_hash=False):
hashes = cache("hashes-addnet") if use_addnet_hash else cache("hashes")
ondisk_mtime = os.path.getmtime(filename)
try:
ondisk_mtime = os.path.getmtime(filename)
except FileNotFoundError:
return None
if title not in hashes:
return None

7
modules/images.py
View File

@@ -321,13 +321,16 @@ def resize_image(resize_mode, im, width, height, upscaler_name=None):
return res
invalid_filename_chars = '#<>:"/\\|?*\n\r\t'
if not shared.cmd_opts.unix_filenames_sanitization:
invalid_filename_chars = '#<>:"/\\|?*\n\r\t'
else:
invalid_filename_chars = '/'
invalid_filename_prefix = ' '
invalid_filename_postfix = ' .'
re_nonletters = re.compile(r'[\s' + string.punctuation + ']+')
re_pattern = re.compile(r"(.*?)(?:\[([^\[\]]+)\]|$)")
re_pattern_arg = re.compile(r"(.*)<([^>]*)>$")
max_filename_part_length = 128
max_filename_part_length = shared.cmd_opts.filenames_max_length
NOTHING_AND_SKIP_PREVIOUS_TEXT = object()

6
modules/infotext_utils.py
View File

@@ -365,6 +365,12 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
if "Cache FP16 weight for LoRA" not in res and res["FP8 weight"] != "Disable":
res["Cache FP16 weight for LoRA"] = False
if "Emphasis" not in res:
res["Emphasis"] = "Original"
if "Refiner switch by sampling steps" not in res:
res["Refiner switch by sampling steps"] = False
infotext_versions.backcompat(res)
for key in skip_fields:

8
modules/infotext_versions.py
View File

@@ -5,6 +5,7 @@ import re
v160 = version.parse("1.6.0")
v170_tsnr = version.parse("v1.7.0-225")
v180 = version.parse("1.8.0")
def parse_version(text):
@@ -31,9 +32,14 @@ def backcompat(d):
if ver is None:
return
if ver < v160:
if ver < v160 and '[' in d.get('Prompt', ''):
d["Old prompt editing timelines"] = True
if ver < v160 and d.get('Sampler', '') in ('DDIM', 'PLMS'):
d["Pad conds v0"] = True
if ver < v170_tsnr:
d["Downcast alphas_cumprod"] = True
if ver < v180 and d.get('Refiner'):
d["Refiner switch by sampling steps"] = True

3
modules/initialize.py
View File

@@ -142,13 +142,14 @@ def initialize_rest(*, reload_script_modules=False):
its optimization may be None because the list of optimizaers has neet been filled
by that time, so we apply optimization again.
"""
from modules import devices
devices.torch_npu_set_device()
shared.sd_model # noqa: B018
if sd_hijack.current_optimizer is None:
sd_hijack.apply_optimizations()
from modules import devices
devices.first_time_calculation()
if not shared.cmd_opts.skip_load_model_at_start:
Thread(target=load_model).start()

2
modules/interrogate.py
View File

@@ -17,7 +17,7 @@ clip_model_name = 'ViT-L/14'
Category = namedtuple("Category", ["name", "topn", "items"])
re_topn = re.compile(r"\.top(\d+)\.")
re_topn = re.compile(r"\.top(\d+)$")
def category_types():
return [f.stem for f in Path(shared.interrogator.content_dir).glob('*.txt')]

13
modules/launch_utils.py
View File

@@ -55,7 +55,7 @@ and delete current Python and "venv" folder in WebUI's directory.
You can download 3.10 Python from here: https://www.python.org/downloads/release/python-3106/
{"Alternatively, use a binary release of WebUI: https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases" if is_windows else ""}
{"Alternatively, use a binary release of WebUI: https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases/tag/v1.0.0-pre" if is_windows else ""}
Use --skip-python-version-check to suppress this warning.
""")
@@ -188,7 +188,7 @@ def git_clone(url, dir, name, commithash=None):
return
try:
run(f'"{git}" clone "{url}" "{dir}"', f"Cloning {name} into {dir}...", f"Couldn't clone {name}", live=True)
run(f'"{git}" clone --config core.filemode=false "{url}" "{dir}"', f"Cloning {name} into {dir}...", f"Couldn't clone {name}", live=True)
except RuntimeError:
shutil.rmtree(dir, ignore_errors=True)
raise
@@ -251,7 +251,6 @@ def list_extensions(settings_file):
except Exception:
errors.report(f'\nCould not load settings\nThe config file "{settings_file}" is likely corrupted\nIt has been moved to the "tmp/config.json"\nReverting config to default\n\n''', exc_info=True)
os.replace(settings_file, os.path.join(script_path, "tmp", "config.json"))
settings = {}
disabled_extensions = set(settings.get('disabled_extensions', []))
disable_all_extensions = settings.get('disable_all_extensions', 'none')
@@ -339,6 +338,7 @@ def prepare_environment():
torch_index_url = os.environ.get('TORCH_INDEX_URL', "https://pytorch-extension.intel.com/release-whl/stable/xpu/us/")
torch_command = os.environ.get('TORCH_COMMAND', f"pip install torch==2.0.0a0 intel-extension-for-pytorch==2.0.110+gitba7f6c1 --extra-index-url {torch_index_url}")
requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
requirements_file_for_npu = os.environ.get('REQS_FILE_FOR_NPU', "requirements_npu.txt")
xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.23.post1')
clip_package = os.environ.get('CLIP_PACKAGE', "https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip")
@@ -422,6 +422,13 @@ def prepare_environment():
run_pip(f"install -r \"{requirements_file}\"", "requirements")
startup_timer.record("install requirements")
if not os.path.isfile(requirements_file_for_npu):
requirements_file_for_npu = os.path.join(script_path, requirements_file_for_npu)
if "torch_npu" in torch_command and not requirements_met(requirements_file_for_npu):
run_pip(f"install -r \"{requirements_file_for_npu}\"", "requirements_for_npu")
startup_timer.record("install requirements_for_npu")
if not args.skip_install:
run_extensions_installers(settings_file=args.ui_settings_file)

31
modules/npu_specific.py Normal file
View File

@@ -0,0 +1,31 @@
import importlib
import torch
from modules import shared
def check_for_npu():
if importlib.util.find_spec("torch_npu") is None:
return False
import torch_npu
try:
# Will raise a RuntimeError if no NPU is found
_ = torch_npu.npu.device_count()
return torch.npu.is_available()
except RuntimeError:
return False
def get_npu_device_string():
if shared.cmd_opts.device_id is not None:
return f"npu:{shared.cmd_opts.device_id}"
return "npu:0"
def torch_npu_gc():
with torch.npu.device(get_npu_device_string()):
torch.npu.empty_cache()
has_npu = check_for_npu()

2
modules/options.py
View File

@@ -198,6 +198,8 @@ class Options:
try:
with open(filename, "r", encoding="utf8") as file:
self.data = json.load(file)
except FileNotFoundError:
self.data = {}
except Exception:
errors.report(f'\nCould not load settings\nThe config file "{filename}" is likely corrupted\nIt has been moved to the "tmp/config.json"\nReverting config to default\n\n''', exc_info=True)
os.replace(filename, os.path.join(script_path, "tmp", "config.json"))

4
modules/paths_internal.py
View File

@@ -4,6 +4,10 @@ import argparse
import os
import sys
import shlex
from pathlib import Path
normalized_filepath = lambda filepath: str(Path(filepath).absolute())
commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
sys.argv += shlex.split(commandline_args)

44
modules/processing.py
View File

@@ -74,16 +74,18 @@ def uncrop(image, dest_size, paste_loc):
def apply_overlay(image, paste_loc, overlay):
if overlay is None:
return image
return image, image.copy()
if paste_loc is not None:
image = uncrop(image, (overlay.width, overlay.height), paste_loc)
original_denoised_image = image.copy()
image = image.convert('RGBA')
image.alpha_composite(overlay)
image = image.convert('RGB')
return image
return image, original_denoised_image
def create_binary_mask(image, round=True):
if image.mode == 'RGBA' and image.getextrema()[-1] != (255, 255):
@@ -455,6 +457,7 @@ class StableDiffusionProcessing:
self.height,
opts.fp8_storage,
opts.cache_fp16_weight,
opts.emphasis,
)
def get_conds_with_caching(self, function, required_prompts, steps, caches, extra_network_data, hires_steps=None):
@@ -912,33 +915,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
if p.n_iter > 1:
shared.state.job = f"Batch {n+1} out of {p.n_iter}"
def rescale_zero_terminal_snr_abar(alphas_cumprod):
alphas_bar_sqrt = alphas_cumprod.sqrt()
# Store old values.
alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()
alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()
# Shift so the last timestep is zero.
alphas_bar_sqrt -= (alphas_bar_sqrt_T)
# Scale so the first timestep is back to the old value.
alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)
# Convert alphas_bar_sqrt to betas
alphas_bar = alphas_bar_sqrt**2 # Revert sqrt
alphas_bar[-1] = 4.8973451890853435e-08
return alphas_bar
if hasattr(p.sd_model, 'alphas_cumprod') and hasattr(p.sd_model, 'alphas_cumprod_original'):
p.sd_model.alphas_cumprod = p.sd_model.alphas_cumprod_original.to(shared.device)
if opts.use_downcasted_alpha_bar:
p.extra_generation_params['Downcast alphas_cumprod'] = opts.use_downcasted_alpha_bar
p.sd_model.alphas_cumprod = p.sd_model.alphas_cumprod.half().to(shared.device)
if opts.sd_noise_schedule == "Zero Terminal SNR":
p.extra_generation_params['Noise Schedule'] = opts.sd_noise_schedule
p.sd_model.alphas_cumprod = rescale_zero_terminal_snr_abar(p.sd_model.alphas_cumprod).to(shared.device)
sd_models.apply_alpha_schedule_override(p.sd_model, p)
with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():
samples_ddim = p.sample(conditioning=p.c, unconditional_conditioning=p.uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts)
@@ -1020,7 +997,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
if p.color_corrections is not None and i < len(p.color_corrections):
if save_samples and opts.save_images_before_color_correction:
image_without_cc = apply_overlay(image, p.paste_to, overlay_image)
image_without_cc, _ = apply_overlay(image, p.paste_to, overlay_image)
images.save_image(image_without_cc, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-before-color-correction")
image = apply_color_correction(p.color_corrections[i], image)
@@ -1028,12 +1005,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
# that is being composited over the original image,
# we need to keep the original image around
# and use it in the composite step.
original_denoised_image = image.copy()
if p.paste_to is not None:
original_denoised_image = uncrop(original_denoised_image, (overlay_image.width, overlay_image.height), p.paste_to)
image = apply_overlay(image, p.paste_to, overlay_image)
image, original_denoised_image = apply_overlay(image, p.paste_to, overlay_image)
if p.scripts is not None:
pp = scripts.PostprocessImageArgs(image)

42
modules/processing_scripts/comments.py Normal file
View File

@@ -0,0 +1,42 @@
from modules import scripts, shared, script_callbacks
import re
def strip_comments(text):
text = re.sub('(^|\n)#[^\n]*(\n|$)', '\n', text) # while line comment
text = re.sub('#[^\n]*(\n|$)', '\n', text) # in the middle of the line comment
return text
class ScriptStripComments(scripts.Script):
def title(self):
return "Comments"
def show(self, is_img2img):
return scripts.AlwaysVisible
def process(self, p, *args):
if not shared.opts.enable_prompt_comments:
return
p.all_prompts = [strip_comments(x) for x in p.all_prompts]
p.all_negative_prompts = [strip_comments(x) for x in p.all_negative_prompts]
p.main_prompt = strip_comments(p.main_prompt)
p.main_negative_prompt = strip_comments(p.main_negative_prompt)
def before_token_counter(params: script_callbacks.BeforeTokenCounterParams):
if not shared.opts.enable_prompt_comments:
return
params.prompt = strip_comments(params.prompt)
script_callbacks.on_before_token_counter(before_token_counter)
shared.options_templates.update(shared.options_section(('sd', "Stable Diffusion", "sd"), {
"enable_prompt_comments": shared.OptionInfo(True, "Enable comments").info("Use # anywhere in the prompt to hide the text between # and the end of the line from the generation."),
}))

26
modules/script_callbacks.py
View File

@@ -1,3 +1,4 @@
import dataclasses
import inspect
import os
from collections import namedtuple
@@ -106,6 +107,15 @@ class ImageGridLoopParams:
self.rows = rows
@dataclasses.dataclass
class BeforeTokenCounterParams:
prompt: str
steps: int
styles: list
is_positive: bool = True
ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"])
callback_map = dict(
callbacks_app_started=[],
@@ -128,6 +138,7 @@ callback_map = dict(
callbacks_on_reload=[],
callbacks_list_optimizers=[],
callbacks_list_unets=[],
callbacks_before_token_counter=[],
)
@@ -309,6 +320,14 @@ def list_unets_callback():
return res
def before_token_counter_callback(params: BeforeTokenCounterParams):
for c in callback_map['callbacks_before_token_counter']:
try:
c.callback(params)
except Exception:
report_exception(c, 'before_token_counter')
def add_callback(callbacks, fun):
stack = [x for x in inspect.stack() if x.filename != __file__]
filename = stack[0].filename if stack else 'unknown file'
@@ -483,3 +502,10 @@ def on_list_unets(callback):
The function will be called with one argument, a list, and shall add objects of type modules.sd_unet.SdUnetOption to it."""
add_callback(callback_map['callbacks_list_unets'], callback)
def on_before_token_counter(callback):
"""register a function to be called when UI is counting tokens for a prompt.
The function will be called with one argument of type BeforeTokenCounterParams, and should modify its fields if necessary."""
add_callback(callback_map['callbacks_before_token_counter'], callback)

28
modules/scripts.py
View File

@@ -939,22 +939,34 @@ class ScriptRunner:
except Exception:
errors.report(f"Error running setup: {script.filename}", exc_info=True)
def set_named_arg(self, args, script_type, arg_elem_id, value):
script = next((x for x in self.scripts if type(x).__name__ == script_type), None)
def set_named_arg(self, args, script_name, arg_elem_id, value, fuzzy=False):
"""Locate an arg of a specific script in script_args and set its value
Args:
args: all script args of process p, p.script_args
script_name: the name target script name to
arg_elem_id: the elem_id of the target arg
value: the value to set
fuzzy: if True, arg_elem_id can be a substring of the control.elem_id else exact match
Returns:
Updated script args
when script_name in not found or arg_elem_id is not found in script controls, raise RuntimeError
"""
script = next((x for x in self.scripts if x.name == script_name), None)
if script is None:
return
raise RuntimeError(f"script {script_name} not found")
for i, control in enumerate(script.controls):
if arg_elem_id in control.elem_id:
if arg_elem_id in control.elem_id if fuzzy else arg_elem_id == control.elem_id:
index = script.args_from + i
if isinstance(args, list):
if isinstance(args, tuple):
return args[:index] + (value,) + args[index + 1:]
elif isinstance(args, list):
args[index] = value
return args
elif isinstance(args, tuple):
return args[:index] + (value,) + args[index+1:]
else:
return None
raise RuntimeError(f"args is not a list or tuple, but {type(args)}")
raise RuntimeError(f"arg_elem_id {arg_elem_id} not found in script {script_name}")
scripts_txt2img: ScriptRunner = None

70
modules/sd_emphasis.py Normal file
View File

@@ -0,0 +1,70 @@
from __future__ import annotations
import torch
class Emphasis:
"""Emphasis class decides how to death with (emphasized:1.1) text in prompts"""
name: str = "Base"
description: str = ""
tokens: list[list[int]]
"""tokens from the chunk of the prompt"""
multipliers: torch.Tensor
"""tensor with multipliers, once for each token"""
z: torch.Tensor
"""output of cond transformers network (CLIP)"""
def after_transformers(self):
"""Called after cond transformers network has processed the chunk of the prompt; this function should modify self.z to apply the emphasis"""
pass
class EmphasisNone(Emphasis):
name = "None"
description = "disable the mechanism entirely and treat (:.1.1) as literal characters"
class EmphasisIgnore(Emphasis):
name = "Ignore"
description = "treat all empasised words as if they have no emphasis"
class EmphasisOriginal(Emphasis):
name = "Original"
description = "the orginal emphasis implementation"
def after_transformers(self):
original_mean = self.z.mean()
self.z = self.z * self.multipliers.reshape(self.multipliers.shape + (1,)).expand(self.z.shape)
# restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
new_mean = self.z.mean()
self.z = self.z * (original_mean / new_mean)
class EmphasisOriginalNoNorm(EmphasisOriginal):
name = "No norm"
description = "same as orginal, but without normalization (seems to work better for SDXL)"
def after_transformers(self):
self.z = self.z * self.multipliers.reshape(self.multipliers.shape + (1,)).expand(self.z.shape)
def get_current_option(emphasis_option_name):
return next(iter([x for x in options if x.name == emphasis_option_name]), EmphasisOriginal)
def get_options_descriptions():
return ", ".join(f"{x.name}: {x.description}" for x in options)
options = [
EmphasisNone,
EmphasisIgnore,
EmphasisOriginal,
EmphasisOriginalNoNorm,
]

21
modules/sd_hijack_clip.py
View File

@@ -3,7 +3,7 @@ from collections import namedtuple
import torch
from modules import prompt_parser, devices, sd_hijack
from modules import prompt_parser, devices, sd_hijack, sd_emphasis
from modules.shared import opts
@@ -88,7 +88,7 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
Returns the list and the total number of tokens in the prompt.
"""
if opts.enable_emphasis:
if opts.emphasis != "None":
parsed = prompt_parser.parse_prompt_attention(line)
else:
parsed = [[line, 1.0]]
@@ -249,6 +249,9 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
hashes.append(self.hijack.extra_generation_params.get("TI hashes"))
self.hijack.extra_generation_params["TI hashes"] = ", ".join(hashes)
if any(x for x in texts if "(" in x or "[" in x) and opts.emphasis != "Original":
self.hijack.extra_generation_params["Emphasis"] = opts.emphasis
if getattr(self.wrapped, 'return_pooled', False):
return torch.hstack(zs), zs[0].pooled
else:
@@ -274,12 +277,14 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
pooled = getattr(z, 'pooled', None)
# restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
batch_multipliers = torch.asarray(batch_multipliers).to(devices.device)
original_mean = z.mean()
z = z * batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
new_mean = z.mean()
z = z * (original_mean / new_mean)
emphasis = sd_emphasis.get_current_option(opts.emphasis)()
emphasis.tokens = remade_batch_tokens
emphasis.multipliers = torch.asarray(batch_multipliers).to(devices.device)
emphasis.z = z
emphasis.after_transformers()
z = emphasis.z
if pooled is not None:
z.pooled = pooled

2
modules/sd_hijack_clip_old.py
View File

@@ -32,7 +32,7 @@ def process_text_old(self: sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase,
embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
mult_change = self.token_mults.get(token) if shared.opts.enable_emphasis else None
mult_change = self.token_mults.get(token) if shared.opts.emphasis != "None" else None
if mult_change is not None:
mult *= mult_change
i += 1

45
modules/sd_models.py
View File

@@ -15,6 +15,7 @@ from ldm.util import instantiate_from_config
from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config, sd_unet, sd_models_xl, cache, extra_networks, processing, lowvram, sd_hijack, patches
from modules.timer import Timer
from modules.shared import opts
import tomesd
import numpy as np
@@ -427,6 +428,8 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
devices.dtype_unet = torch.float16
timer.record("apply half()")
apply_alpha_schedule_override(model)
for module in model.modules():
if hasattr(module, 'fp16_weight'):
del module.fp16_weight
@@ -550,6 +553,48 @@ def repair_config(sd_config):
sd_config.model.params.noise_aug_config.params.clip_stats_path = sd_config.model.params.noise_aug_config.params.clip_stats_path.replace("checkpoints/karlo_models", karlo_path)
def rescale_zero_terminal_snr_abar(alphas_cumprod):
alphas_bar_sqrt = alphas_cumprod.sqrt()
# Store old values.
alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()
alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()
# Shift so the last timestep is zero.
alphas_bar_sqrt -= (alphas_bar_sqrt_T)
# Scale so the first timestep is back to the old value.
alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)
# Convert alphas_bar_sqrt to betas
alphas_bar = alphas_bar_sqrt ** 2 # Revert sqrt
alphas_bar[-1] = 4.8973451890853435e-08
return alphas_bar
def apply_alpha_schedule_override(sd_model, p=None):
"""
Applies an override to the alpha schedule of the model according to settings.
- downcasts the alpha schedule to half precision
- rescales the alpha schedule to have zero terminal SNR
"""
if not hasattr(sd_model, 'alphas_cumprod') or not hasattr(sd_model, 'alphas_cumprod_original'):
return
sd_model.alphas_cumprod = sd_model.alphas_cumprod_original.to(shared.device)
if opts.use_downcasted_alpha_bar:
if p is not None:
p.extra_generation_params['Downcast alphas_cumprod'] = opts.use_downcasted_alpha_bar
sd_model.alphas_cumprod = sd_model.alphas_cumprod.half().to(shared.device)
if opts.sd_noise_schedule == "Zero Terminal SNR":
if p is not None:
p.extra_generation_params['Noise Schedule'] = opts.sd_noise_schedule
sd_model.alphas_cumprod = rescale_zero_terminal_snr_abar(sd_model.alphas_cumprod).to(shared.device)
sd1_clip_weight = 'cond_stage_model.transformer.text_model.embeddings.token_embedding.weight'
sd2_clip_weight = 'cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight'
sdxl_clip_weight = 'conditioner.embedders.1.model.ln_final.weight'

74
modules/sd_samplers_cfg_denoiser.py
View File

@@ -53,6 +53,7 @@ class CFGDenoiser(torch.nn.Module):
self.step = 0
self.image_cfg_scale = None
self.padded_cond_uncond = False
self.padded_cond_uncond_v0 = False
self.sampler = sampler
self.model_wrap = None
self.p = None
@@ -91,11 +92,67 @@ class CFGDenoiser(torch.nn.Module):
self.sampler.sampler_extra_args['cond'] = c
self.sampler.sampler_extra_args['uncond'] = uc
def pad_cond_uncond(self, cond, uncond):
empty = shared.sd_model.cond_stage_model_empty_prompt
num_repeats = (cond.shape[1] - uncond.shape[1]) // empty.shape[1]
if num_repeats < 0:
cond = pad_cond(cond, -num_repeats, empty)
self.padded_cond_uncond = True
elif num_repeats > 0:
uncond = pad_cond(uncond, num_repeats, empty)
self.padded_cond_uncond = True
return cond, uncond
def pad_cond_uncond_v0(self, cond, uncond):
"""
Pads the 'uncond' tensor to match the shape of the 'cond' tensor.
If 'uncond' is a dictionary, it is assumed that the 'crossattn' key holds the tensor to be padded.
If 'uncond' is a tensor, it is padded directly.
If the number of columns in 'uncond' is less than the number of columns in 'cond', the last column of 'uncond'
is repeated to match the number of columns in 'cond'.
If the number of columns in 'uncond' is greater than the number of columns in 'cond', 'uncond' is truncated
to match the number of columns in 'cond'.
Args:
cond (torch.Tensor or DictWithShape): The condition tensor to match the shape of 'uncond'.
uncond (torch.Tensor or DictWithShape): The tensor to be padded, or a dictionary containing the tensor to be padded.
Returns:
tuple: A tuple containing the 'cond' tensor and the padded 'uncond' tensor.
Note:
This is the padding that was always used in DDIM before version 1.6.0
"""
is_dict_cond = isinstance(uncond, dict)
uncond_vec = uncond['crossattn'] if is_dict_cond else uncond
if uncond_vec.shape[1] < cond.shape[1]:
last_vector = uncond_vec[:, -1:]
last_vector_repeated = last_vector.repeat([1, cond.shape[1] - uncond_vec.shape[1], 1])
uncond_vec = torch.hstack([uncond_vec, last_vector_repeated])
self.padded_cond_uncond_v0 = True
elif uncond_vec.shape[1] > cond.shape[1]:
uncond_vec = uncond_vec[:, :cond.shape[1]]
self.padded_cond_uncond_v0 = True
if is_dict_cond:
uncond['crossattn'] = uncond_vec
else:
uncond = uncond_vec
return cond, uncond
def forward(self, x, sigma, uncond, cond, cond_scale, s_min_uncond, image_cond):
if state.interrupted or state.skipped:
raise sd_samplers_common.InterruptedException
if sd_samplers_common.apply_refiner(self):
if sd_samplers_common.apply_refiner(self, sigma):
cond = self.sampler.sampler_extra_args['cond']
uncond = self.sampler.sampler_extra_args['uncond']
@@ -162,16 +219,11 @@ class CFGDenoiser(torch.nn.Module):
sigma_in = sigma_in[:-batch_size]
self.padded_cond_uncond = False
if shared.opts.pad_cond_uncond and tensor.shape[1] != uncond.shape[1]:
empty = shared.sd_model.cond_stage_model_empty_prompt
num_repeats = (tensor.shape[1] - uncond.shape[1]) // empty.shape[1]
if num_repeats < 0:
tensor = pad_cond(tensor, -num_repeats, empty)
self.padded_cond_uncond = True
elif num_repeats > 0:
uncond = pad_cond(uncond, num_repeats, empty)
self.padded_cond_uncond = True
self.padded_cond_uncond_v0 = False
if shared.opts.pad_cond_uncond_v0 and tensor.shape[1] != uncond.shape[1]:
tensor, uncond = self.pad_cond_uncond_v0(tensor, uncond)
elif shared.opts.pad_cond_uncond and tensor.shape[1] != uncond.shape[1]:
tensor, uncond = self.pad_cond_uncond(tensor, uncond)
if tensor.shape[1] == uncond.shape[1] or skip_uncond:
if is_edit_model:

22
modules/sd_samplers_common.py
View File

@@ -155,8 +155,19 @@ def replace_torchsde_browinan():
replace_torchsde_browinan()
def apply_refiner(cfg_denoiser):
completed_ratio = cfg_denoiser.step / cfg_denoiser.total_steps
def apply_refiner(cfg_denoiser, sigma=None):
if opts.refiner_switch_by_sample_steps or not sigma:
completed_ratio = cfg_denoiser.step / cfg_denoiser.total_steps
cfg_denoiser.p.extra_generation_params["Refiner switch by sampling steps"] = True
else:
# torch.max(sigma) only to handle rare case where we might have different sigmas in the same batch
try:
timestep = torch.argmin(torch.abs(cfg_denoiser.inner_model.sigmas - torch.max(sigma)))
except AttributeError: # for samplers that don't use sigmas (DDIM) sigma is actually the timestep
timestep = torch.max(sigma).to(dtype=int)
completed_ratio = (999 - timestep) / 1000
refiner_switch_at = cfg_denoiser.p.refiner_switch_at
refiner_checkpoint_info = cfg_denoiser.p.refiner_checkpoint_info
@@ -335,3 +346,10 @@ class Sampler:
def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
raise NotImplementedError()
def add_infotext(self, p):
if self.model_wrap_cfg.padded_cond_uncond:
p.extra_generation_params["Pad conds"] = True
if self.model_wrap_cfg.padded_cond_uncond_v0:
p.extra_generation_params["Pad conds v0"] = True

6
modules/sd_samplers_kdiffusion.py
View File

@@ -187,8 +187,7 @@ class KDiffusionSampler(sd_samplers_common.Sampler):
samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
if self.model_wrap_cfg.padded_cond_uncond:
p.extra_generation_params["Pad conds"] = True
self.add_infotext(p)
return samples
@@ -234,8 +233,7 @@ class KDiffusionSampler(sd_samplers_common.Sampler):
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
if self.model_wrap_cfg.padded_cond_uncond:
p.extra_generation_params["Pad conds"] = True
self.add_infotext(p)
return samples

6
modules/sd_samplers_timesteps.py
View File

@@ -133,8 +133,7 @@ class CompVisSampler(sd_samplers_common.Sampler):
samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
if self.model_wrap_cfg.padded_cond_uncond:
p.extra_generation_params["Pad conds"] = True
self.add_infotext(p)
return samples
@@ -158,8 +157,7 @@ class CompVisSampler(sd_samplers_common.Sampler):
}
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
if self.model_wrap_cfg.padded_cond_uncond:
p.extra_generation_params["Pad conds"] = True
self.add_infotext(p)
return samples

3
modules/shared.py
View File

@@ -1,3 +1,4 @@
import os
import sys
import gradio as gr
@@ -11,7 +12,7 @@ parser = shared_cmd_options.parser
batch_cond_uncond = True # old field, unused now in favor of shared.opts.batch_cond_uncond
parallel_processing_allowed = True
styles_filename = cmd_opts.styles_file
styles_filename = cmd_opts.styles_file = cmd_opts.styles_file if len(cmd_opts.styles_file) > 0 else [os.path.join(data_path, 'styles.csv')]
config_filename = cmd_opts.ui_settings_file
hide_dirs = {"visible": not cmd_opts.hide_ui_dir_config}

16
modules/shared_options.py
View File

@@ -1,7 +1,7 @@
import os
import gradio as gr
from modules import localization, ui_components, shared_items, shared, interrogate, shared_gradio_themes, util
from modules import localization, ui_components, shared_items, shared, interrogate, shared_gradio_themes, util, sd_emphasis
from modules.paths_internal import models_path, script_path, data_path, sd_configs_path, sd_default_config, sd_model_file, default_sd_model_file, extensions_dir, extensions_builtin_dir, default_output_dir # noqa: F401
from modules.shared_cmd_options import cmd_opts
from modules.options import options_section, OptionInfo, OptionHTML, categories
@@ -154,7 +154,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion", "sd"), {
"sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}).info("obsolete; set to 0 and use the two settings above instead"),
"sd_unet": OptionInfo("Automatic", "SD Unet", gr.Dropdown, lambda: {"choices": shared_items.sd_unet_items()}, refresh=shared_items.refresh_unet_list).info("choose Unet model: Automatic = use one with same filename as checkpoint; None = use Unet from checkpoint"),
"enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds").needs_reload_ui(),
"enable_emphasis": OptionInfo(True, "Enable emphasis").info("use (text) to make model pay more attention to text and [text] to make it pay less attention"),
"emphasis": OptionInfo("Original", "Emphasis mode", gr.Radio, lambda: {"choices": [x.name for x in sd_emphasis.options]}, infotext="Emphasis").info("makes it possible to make model to pay (more:1.1) or (less:0.9) attention to text when you use the syntax in prompt; " + sd_emphasis.get_options_descriptions()),
"enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
"comma_padding_backtrack": OptionInfo(20, "Prompt word wrap length limit", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1}).info("in tokens - for texts shorter than specified, if they don't fit into 75 token limit, move them to the next 75 token chunk"),
"CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}, infotext="Clip skip").link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#clip-skip").info("ignore last layers of CLIP network; 1 ignores none, 2 ignores one layer"),
@@ -209,7 +209,8 @@ options_templates.update(options_section(('optimizations', "Optimizations", "sd"
"token_merging_ratio": OptionInfo(0.0, "Token merging ratio", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}, infotext='Token merging ratio').link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9256").info("0=disable, higher=faster"),
"token_merging_ratio_img2img": OptionInfo(0.0, "Token merging ratio for img2img", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).info("only applies if non-zero and overrides above"),
"token_merging_ratio_hr": OptionInfo(0.0, "Token merging ratio for high-res pass", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}, infotext='Token merging ratio hr').info("only applies if non-zero and overrides above"),
"pad_cond_uncond": OptionInfo(False, "Pad prompt/negative prompt to be same length", infotext='Pad conds').info("improves performance when prompt and negative prompt have different lengths; changes seeds"),
"pad_cond_uncond": OptionInfo(False, "Pad prompt/negative prompt", infotext='Pad conds').info("improves performance when prompt and negative prompt have different lengths; changes seeds"),
"pad_cond_uncond_v0": OptionInfo(False, "Pad prompt/negative prompt (v0)", infotext='Pad conds v0').info("alternative implementation for the above; used prior to 1.6.0 for DDIM sampler; overrides the above if set; WARNING: truncates negative prompt if it's too long; changes seeds"),
"persistent_cond_cache": OptionInfo(True, "Persistent cond cache").info("do not recalculate conds from prompts if prompts have not changed since previous calculation"),
"batch_cond_uncond": OptionInfo(True, "Batch cond/uncond").info("do both conditional and unconditional denoising in one batch; uses a bit more VRAM during sampling, but improves speed; previously this was controlled by --always-batch-cond-uncond comandline argument"),
"fp8_storage": OptionInfo("Disable", "FP8 weight", gr.Radio, {"choices": ["Disable", "Enable for SDXL", "Enable"]}).info("Use FP8 to store Linear/Conv layers' weight. Require pytorch>=2.1.0."),
@@ -225,7 +226,8 @@ options_templates.update(options_section(('compatibility', "Compatibility", "sd"
"dont_fix_second_order_samplers_schedule": OptionInfo(False, "Do not fix prompt schedule for second order samplers."),
"hires_fix_use_firstpass_conds": OptionInfo(False, "For hires fix, calculate conds of second pass using extra networks of first pass."),
"use_old_scheduling": OptionInfo(False, "Use old prompt editing timelines.", infotext="Old prompt editing timelines").info("For [red:green:N]; old: If N < 1, it's a fraction of steps (and hires fix uses range from 0 to 1), if N >= 1, it's an absolute number of steps; new: If N has a decimal point in it, it's a fraction of steps (and hires fix uses range from 1 to 2), othewrwise it's an absolute number of steps"),
"use_downcasted_alpha_bar": OptionInfo(False, "Downcast model alphas_cumprod to fp16 before sampling. For reproducing old seeds.", infotext="Downcast alphas_cumprod")
"use_downcasted_alpha_bar": OptionInfo(False, "Downcast model alphas_cumprod to fp16 before sampling. For reproducing old seeds.", infotext="Downcast alphas_cumprod"),
"refiner_switch_by_sample_steps": OptionInfo(False, "Switch to refiner by sampling steps instead of model timesteps. Old behavior for refiner.", infotext="Refiner switch by sampling steps")
}))
options_templates.update(options_section(('interrogate', "Interrogate"), {
@@ -252,9 +254,11 @@ options_templates.update(options_section(('extra_networks', "Extra Networks", "s
"extra_networks_card_height": OptionInfo(0, "Card height for Extra Networks").info("in pixels"),
"extra_networks_card_text_scale": OptionInfo(1.0, "Card text scale", gr.Slider, {"minimum": 0.0, "maximum": 2.0, "step": 0.01}).info("1 = original size"),
"extra_networks_card_show_desc": OptionInfo(True, "Show description on card"),
"extra_networks_card_description_is_html": OptionInfo(False, "Treat card description as HTML"),
"extra_networks_card_order_field": OptionInfo("Path", "Default order field for Extra Networks cards", gr.Dropdown, {"choices": ['Path', 'Name', 'Date Created', 'Date Modified']}).needs_reload_ui(),
"extra_networks_card_order": OptionInfo("Ascending", "Default order for Extra Networks cards", gr.Dropdown, {"choices": ['Ascending', 'Descending']}).needs_reload_ui(),
"extra_networks_tree_view_default_enabled": OptionInfo(False, "Enables the Extra Networks directory tree view by default").needs_reload_ui(),
"extra_networks_tree_view_default_width": OptionInfo(180, "Default width for the Extra Networks directory tree view", gr.Number).needs_reload_ui(),
"extra_networks_add_text_separator": OptionInfo(" ", "Extra networks separator").info("extra text to add before <...> when adding extra network to prompt"),
"ui_extra_networks_tab_reorder": OptionInfo("", "Extra networks tab order").needs_reload_ui(),
"textual_inversion_print_at_load": OptionInfo(False, "Print a list of Textual Inversion embeddings when loading model"),
@@ -268,7 +272,8 @@ options_templates.update(options_section(('ui_prompt_editing', "Prompt editing",
"keyedit_delimiters": OptionInfo(r".,\/!?%^*;:{}=`~() ", "Word delimiters when editing the prompt with Ctrl+up/down"),
"keyedit_delimiters_whitespace": OptionInfo(["Tab", "Carriage Return", "Line Feed"], "Ctrl+up/down whitespace delimiters", gr.CheckboxGroup, lambda: {"choices": ["Tab", "Carriage Return", "Line Feed"]}),
"keyedit_move": OptionInfo(True, "Alt+left/right moves prompt elements"),
"disable_token_counters": OptionInfo(False, "Disable prompt token counters").needs_reload_ui(),
"disable_token_counters": OptionInfo(False, "Disable prompt token counters"),
"include_styles_into_token_counters": OptionInfo(True, "Count tokens of enabled styles").info("When calculating how many tokens the prompt has, also consider tokens added by enabled styles."),
}))
options_templates.update(options_section(('ui_gallery', "Gallery", "ui"), {
@@ -281,6 +286,7 @@ options_templates.update(options_section(('ui_gallery', "Gallery", "ui"), {
"sd_webui_modal_lightbox_icon_opacity": OptionInfo(1, "Full page image viewer: control icon unfocused opacity", gr.Slider, {"minimum": 0.0, "maximum": 1, "step": 0.01}, onchange=shared.reload_gradio_theme).info('for mouse only').needs_reload_ui(),
"sd_webui_modal_lightbox_toolbar_opacity": OptionInfo(0.9, "Full page image viewer: tool bar opacity", gr.Slider, {"minimum": 0.0, "maximum": 1, "step": 0.01}, onchange=shared.reload_gradio_theme).info('for mouse only').needs_reload_ui(),
"gallery_height": OptionInfo("", "Gallery height", gr.Textbox).info("can be any valid CSS value, for example 768px or 20em").needs_reload_ui(),
"open_dir_button_choice": OptionInfo("Subdirectory", "What directory the [📂] button opens", gr.Radio, {"choices": ["Output Root", "Subdirectory", "Subdirectory (even temp dir)"]}),
}))
options_templates.update(options_section(('ui_alternatives', "UI alternatives", "ui"), {

107
modules/styles.py
View File

@@ -1,16 +1,16 @@
from pathlib import Path
from modules import errors
import csv
import fnmatch
import os
import os.path
import typing
import shutil
class PromptStyle(typing.NamedTuple):
name: str
prompt: str
negative_prompt: str
path: str = None
prompt: str | None
negative_prompt: str | None
path: str | None = None
def merge_prompts(style_prompt: str, prompt: str) -> str:
@@ -79,14 +79,19 @@ def extract_original_prompts(style: PromptStyle, prompt, negative_prompt):
class StyleDatabase:
def __init__(self, path: str):
def __init__(self, paths: list[str | Path]):
self.no_style = PromptStyle("None", "", "", None)
self.styles = {}
self.path = path
self.paths = paths
self.all_styles_files: list[Path] = []
folder, file = os.path.split(self.path)
filename, _, ext = file.partition('*')
self.default_path = os.path.join(folder, filename + ext)
folder, file = os.path.split(self.paths[0])
if '*' in file or '?' in file:
# if the first path is a wildcard pattern, find the first match else use "folder/styles.csv" as the default path
self.default_path = next(Path(folder).glob(file), Path(os.path.join(folder, 'styles.csv')))
self.paths.insert(0, self.default_path)
else:
self.default_path = Path(self.paths[0])
self.prompt_fields = [field for field in PromptStyle._fields if field != "path"]
@@ -99,57 +104,58 @@ class StyleDatabase:
"""
self.styles.clear()
path, filename = os.path.split(self.path)
# scans for all styles files
all_styles_files = []
for pattern in self.paths:
folder, file = os.path.split(pattern)
if '*' in file or '?' in file:
found_files = Path(folder).glob(file)
[all_styles_files.append(file) for file in found_files]
else:
# if os.path.exists(pattern):
all_styles_files.append(Path(pattern))
if "*" in filename:
fileglob = filename.split("*")[0] + "*.csv"
filelist = []
for file in os.listdir(path):
if fnmatch.fnmatch(file, fileglob):
filelist.append(file)
# Add a visible divider to the style list
half_len = round(len(file) / 2)
divider = f"{'-' * (20 - half_len)} {file.upper()}"
divider = f"{divider} {'-' * (40 - len(divider))}"
self.styles[divider] = PromptStyle(
f"{divider}", None, None, "do_not_save"
# Remove any duplicate entries
seen = set()
self.all_styles_files = [s for s in all_styles_files if not (s in seen or seen.add(s))]
for styles_file in self.all_styles_files:
if len(all_styles_files) > 1:
# add divider when more than styles file
# '---------------- STYLES ----------------'
divider = f' {styles_file.stem.upper()} '.center(40, '-')
self.styles[divider] = PromptStyle(f"{divider}", None, None, "do_not_save")
if styles_file.is_file():
self.load_from_csv(styles_file)
def load_from_csv(self, path: str | Path):
try:
with open(path, "r", encoding="utf-8-sig", newline="") as file:
reader = csv.DictReader(file, skipinitialspace=True)
for row in reader:
# Ignore empty rows or rows starting with a comment
if not row or row["name"].startswith("#"):
continue
# Support loading old CSV format with "name, text"-columns
prompt = row["prompt"] if "prompt" in row else row["text"]
negative_prompt = row.get("negative_prompt", "")
# Add style to database
self.styles[row["name"]] = PromptStyle(
row["name"], prompt, negative_prompt, str(path)
)
# Add styles from this CSV file
self.load_from_csv(os.path.join(path, file))
if len(filelist) == 0:
print(f"No styles found in {path} matching {fileglob}")
return
elif not os.path.exists(self.path):
print(f"Style database not found: {self.path}")
return
else:
self.load_from_csv(self.path)
def load_from_csv(self, path: str):
with open(path, "r", encoding="utf-8-sig", newline="") as file:
reader = csv.DictReader(file, skipinitialspace=True)
for row in reader:
# Ignore empty rows or rows starting with a comment
if not row or row["name"].startswith("#"):
continue
# Support loading old CSV format with "name, text"-columns
prompt = row["prompt"] if "prompt" in row else row["text"]
negative_prompt = row.get("negative_prompt", "")
# Add style to database
self.styles[row["name"]] = PromptStyle(
row["name"], prompt, negative_prompt, path
)
except Exception:
errors.report(f'Error loading styles from {path}: ', exc_info=True)
def get_style_paths(self) -> set:
"""Returns a set of all distinct paths of files that styles are loaded from."""
# Update any styles without a path to the default path
for style in list(self.styles.values()):
if not style.path:
self.styles[style.name] = style._replace(path=self.default_path)
self.styles[style.name] = style._replace(path=str(self.default_path))
# Create a list of all distinct paths, including the default path
style_paths = set()
style_paths.add(self.default_path)
style_paths.add(str(self.default_path))
for _, style in self.styles.items():
if style.path:
style_paths.add(style.path)
@@ -177,7 +183,6 @@ class StyleDatabase:
def save_styles(self, path: str = None) -> None:
# The path argument is deprecated, but kept for backwards compatibility
_ = path
style_paths = self.get_style_paths()

1
modules/textual_inversion/textual_inversion.py
View File

@@ -150,6 +150,7 @@ class EmbeddingDatabase:
return embedding
def get_expected_shape(self):
devices.torch_npu_set_device()
vec = shared.sd_model.cond_stage_model.encode_embedding_init_text(",", 1)
return vec.shape[1]

4
modules/txt2img.py
View File

@@ -60,10 +60,10 @@ def txt2img_upscale(id_task: str, request: gr.Request, gallery, gallery_index, g
assert len(gallery) > 0, 'No image to upscale'
assert 0 <= gallery_index < len(gallery), f'Bad image index: {gallery_index}'
p = txt2img_create_processing(id_task, request, *args)
p.enable_hr = True
p = txt2img_create_processing(id_task, request, *args, force_enable_hr=True)
p.batch_size = 1
p.n_iter = 1
# txt2img_upscale attribute that signifies this is called by txt2img_upscale
p.txt2img_upscale = True
geninfo = json.loads(generation_info)

33
modules/ui.py
View File

@@ -152,7 +152,18 @@ def connect_clear_prompt(button):
)
def update_token_counter(text, steps, *, is_positive=True):
def update_token_counter(text, steps, styles, *, is_positive=True):
params = script_callbacks.BeforeTokenCounterParams(text, steps, styles, is_positive=is_positive)
script_callbacks.before_token_counter_callback(params)
text = params.prompt
steps = params.steps
styles = params.styles
is_positive = params.is_positive
if shared.opts.include_styles_into_token_counters:
apply_styles = shared.prompt_styles.apply_styles_to_prompt if is_positive else shared.prompt_styles.apply_negative_styles_to_prompt
text = apply_styles(text, styles)
try:
text, _ = extra_networks.parse_prompt(text)
@@ -174,8 +185,8 @@ def update_token_counter(text, steps, *, is_positive=True):
return f"<span class='gr-box gr-text-input'>{token_count}/{max_length}</span>"
def update_negative_prompt_token_counter(text, steps):
return update_token_counter(text, steps, is_positive=False)
def update_negative_prompt_token_counter(*args):
return update_token_counter(*args, is_positive=False)
def setup_progressbar(*args, **kwargs):
@@ -487,8 +498,10 @@ def create_ui():
height,
]
toprow.token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[toprow.prompt, steps], outputs=[toprow.token_counter])
toprow.negative_token_button.click(fn=wrap_queued_call(update_negative_prompt_token_counter), inputs=[toprow.negative_prompt, steps], outputs=[toprow.negative_token_counter])
toprow.ui_styles.dropdown.change(fn=wrap_queued_call(update_token_counter), inputs=[toprow.prompt, steps, toprow.ui_styles.dropdown], outputs=[toprow.token_counter])
toprow.ui_styles.dropdown.change(fn=wrap_queued_call(update_negative_prompt_token_counter), inputs=[toprow.negative_prompt, steps, toprow.ui_styles.dropdown], outputs=[toprow.negative_token_counter])
toprow.token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[toprow.prompt, steps, toprow.ui_styles.dropdown], outputs=[toprow.token_counter])
toprow.negative_token_button.click(fn=wrap_queued_call(update_negative_prompt_token_counter), inputs=[toprow.negative_prompt, steps, toprow.ui_styles.dropdown], outputs=[toprow.negative_token_counter])
extra_networks_ui = ui_extra_networks.create_ui(txt2img_interface, [txt2img_generation_tab], 'txt2img')
ui_extra_networks.setup_ui(extra_networks_ui, output_panel.gallery)
@@ -812,8 +825,10 @@ def create_ui():
**interrogate_args,
)
toprow.token_button.click(fn=update_token_counter, inputs=[toprow.prompt, steps], outputs=[toprow.token_counter])
toprow.negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[toprow.negative_prompt, steps], outputs=[toprow.negative_token_counter])
toprow.ui_styles.dropdown.change(fn=wrap_queued_call(update_token_counter), inputs=[toprow.prompt, steps, toprow.ui_styles.dropdown], outputs=[toprow.token_counter])
toprow.ui_styles.dropdown.change(fn=wrap_queued_call(update_negative_prompt_token_counter), inputs=[toprow.negative_prompt, steps, toprow.ui_styles.dropdown], outputs=[toprow.negative_token_counter])
toprow.token_button.click(fn=update_token_counter, inputs=[toprow.prompt, steps, toprow.ui_styles.dropdown], outputs=[toprow.token_counter])
toprow.negative_token_button.click(fn=wrap_queued_call(update_negative_prompt_token_counter), inputs=[toprow.negative_prompt, steps, toprow.ui_styles.dropdown], outputs=[toprow.negative_token_counter])
img2img_paste_fields = [
(toprow.prompt, "Prompt"),
@@ -851,7 +866,7 @@ def create_ui():
ui_postprocessing.create_ui()
with gr.Blocks(analytics_enabled=False) as pnginfo_interface:
with gr.Row(equal_height=False):
with ResizeHandleRow(equal_height=False):
with gr.Column(variant='panel'):
image = gr.Image(elem_id="pnginfo_image", label="Source", source="upload", interactive=True, type="pil")
@@ -879,7 +894,7 @@ def create_ui():
with gr.Row(equal_height=False):
gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
with gr.Row(variant="compact", equal_height=False):
with ResizeHandleRow(variant="compact", equal_height=False):
with gr.Tabs(elem_id="train_tabs"):
with gr.Tab(label="Create embedding", id="create_embedding"):

53
modules/ui_common.py
View File

@@ -10,7 +10,8 @@ import gradio as gr
import subprocess as sp
from PIL import Image
from modules import call_queue, shared
from modules import call_queue, shared, ui_tempdir
from modules.infotext_utils import image_from_url_text
import modules.images
from modules.ui_components import ToolButton
import modules.infotext_utils as parameters_copypaste
@@ -167,29 +168,43 @@ class OutputPanel:
def create_output_panel(tabname, outdir, toprow=None):
res = OutputPanel()
def open_folder(f):
def open_folder(f, images=None, index=None):
if shared.cmd_opts.hide_ui_dir_config:
return
try:
if 'Sub' in shared.opts.open_dir_button_choice:
image_dir = os.path.split(images[index]["name"].rsplit('?', 1)[0])[0]
if 'temp' in shared.opts.open_dir_button_choice or not ui_tempdir.is_gradio_temp_path(image_dir):
f = image_dir
except Exception:
pass
if not os.path.exists(f):
print(f'Folder "{f}" does not exist. After you create an image, the folder will be created.')
msg = f'Folder "{f}" does not exist. After you create an image, the folder will be created.'
print(msg)
gr.Info(msg)
return
elif not os.path.isdir(f):
print(f"""
msg = f"""
WARNING
An open_folder request was made with an argument that is not a folder.
This could be an error or a malicious attempt to run code on your computer.
Requested path was: {f}
""", file=sys.stderr)
"""
print(msg, file=sys.stderr)
gr.Warning(msg)
return
if not shared.cmd_opts.hide_ui_dir_config:
path = os.path.normpath(f)
if platform.system() == "Windows":
os.startfile(path)
elif platform.system() == "Darwin":
sp.Popen(["open", path])
elif "microsoft-standard-WSL2" in platform.uname().release:
sp.Popen(["wsl-open", path])
else:
sp.Popen(["xdg-open", path])
path = os.path.normpath(f)
if platform.system() == "Windows":
os.startfile(path)
elif platform.system() == "Darwin":
sp.Popen(["open", path])
elif "microsoft-standard-WSL2" in platform.uname().release:
sp.Popen(["wsl-open", path])
else:
sp.Popen(["xdg-open", path])
with gr.Column(elem_id=f"{tabname}_results"):
if toprow:
@@ -216,8 +231,12 @@ Requested path was: {f}
res.button_upscale = ToolButton('✨', elem_id=f'{tabname}_upscale', tooltip="Create an upscaled version of the current image using hires fix settings.")
open_folder_button.click(
fn=lambda: open_folder(shared.opts.outdir_samples or outdir),
inputs=[],
fn=lambda images, index: open_folder(shared.opts.outdir_samples or outdir, images, index),
_js="(y, w) => [y, selected_gallery_index()]",
inputs=[
res.gallery,
open_folder_button, # placeholder for index
],
outputs=[],
)

4
modules/ui_extensions.py
View File

@@ -548,6 +548,7 @@ def create_ui():
extensions_disable_all = gr.Radio(label="Disable all extensions", choices=["none", "extra", "all"], value=shared.opts.disable_all_extensions, elem_id="extensions_disable_all")
extensions_disabled_list = gr.Text(elem_id="extensions_disabled_list", visible=False, container=False)
extensions_update_list = gr.Text(elem_id="extensions_update_list", visible=False, container=False)
refresh = gr.Button(value='Refresh', variant="compact")
html = ""
@@ -566,7 +567,8 @@ def create_ui():
with gr.Row(elem_classes="progress-container"):
extensions_table = gr.HTML('Loading...', elem_id="extensions_installed_html")
ui.load(fn=extension_table, inputs=[], outputs=[extensions_table])
ui.load(fn=extension_table, inputs=[], outputs=[extensions_table], show_progress=False)
refresh.click(fn=extension_table, inputs=[], outputs=[extensions_table], show_progress=False)
apply.click(
fn=apply_and_restart,

61
modules/ui_extra_networks.py
View File

@@ -134,8 +134,8 @@ def get_single_card(page: str = "", tabname: str = "", name: str = ""):
errors.display(e, "creating item for extra network")
item = page.items.get(name)
page.read_user_metadata(item)
item_html = page.create_item_html(tabname, item)
page.read_user_metadata(item, use_cache=False)
item_html = page.create_item_html(tabname, item, shared.html("extra-networks-card.html"))
return JSONResponse({"html": item_html})
@@ -173,9 +173,9 @@ class ExtraNetworksPage:
def refresh(self):
pass
def read_user_metadata(self, item):
def read_user_metadata(self, item, use_cache=True):
filename = item.get("filename", None)
metadata = extra_networks.get_user_metadata(filename, lister=self.lister)
metadata = extra_networks.get_user_metadata(filename, lister=self.lister if use_cache else None)
desc = metadata.get("description", None)
if desc is not None:
@@ -289,12 +289,16 @@ class ExtraNetworksPage:
}
)
description = (item.get("description", "") or "" if shared.opts.extra_networks_card_show_desc else "")
if not shared.opts.extra_networks_card_description_is_html:
description = html.escape(description)
# Some items here might not be used depending on HTML template used.
args = {
"background_image": background_image,
"card_clicked": onclick,
"copy_path_button": btn_copy_path,
"description": (item.get("description", "") or "" if shared.opts.extra_networks_card_show_desc else ""),
"description": description,
"edit_button": btn_edit_item,
"local_preview": quote_js(item["local_preview"]),
"metadata_button": btn_metadata,
@@ -472,7 +476,7 @@ class ExtraNetworksPage:
return f"<ul class='tree-list tree-list--tree'>{res}</ul>"
def create_card_view_html(self, tabname: str) -> str:
def create_card_view_html(self, tabname: str, *, none_message) -> str:
"""Generates HTML for the network Card View section for a tab.
This HTML goes into the `extra-networks-pane.html` <div> with
@@ -480,6 +484,7 @@ class ExtraNetworksPage:
Args:
tabname: The name of the active tab.
none_message: HTML text to show when there are no cards.
Returns:
HTML formatted string.
@@ -490,24 +495,28 @@ class ExtraNetworksPage:
if res == "":
dirs = "".join([f"<li>{x}</li>" for x in self.allowed_directories_for_previews()])
res = shared.html("extra-networks-no-cards.html").format(dirs=dirs)
res = none_message or shared.html("extra-networks-no-cards.html").format(dirs=dirs)
return res
def create_html(self, tabname):
def create_html(self, tabname, *, empty=False):
"""Generates an HTML string for the current pane.
The generated HTML uses `extra-networks-pane.html` as a template.
Args:
tabname: The name of the active tab.
empty: create an empty HTML page with no items
Returns:
HTML formatted string.
"""
self.lister.reset()
self.metadata = {}
self.items = {x["name"]: x for x in self.list_items()}
items_list = [] if empty else self.list_items()
self.items = {x["name"]: x for x in items_list}
# Populate the instance metadata for each item.
for item in self.items.values():
metadata = item.get("metadata")
@@ -522,9 +531,13 @@ class ExtraNetworksPage:
data_sortkey = f"{data_sortmode}-{data_sortdir}-{len(self.items)}"
tree_view_btn_extra_class = ""
tree_view_div_extra_class = "hidden"
tree_view_div_default_display = "none"
extra_network_pane_content_default_display = "flex"
if shared.opts.extra_networks_tree_view_default_enabled:
tree_view_btn_extra_class = "extra-network-control--enabled"
tree_view_div_extra_class = ""
tree_view_div_default_display = "block"
extra_network_pane_content_default_display = "grid"
return self.pane_tpl.format(
**{
@@ -536,7 +549,10 @@ class ExtraNetworksPage:
"tree_view_btn_extra_class": tree_view_btn_extra_class,
"tree_view_div_extra_class": tree_view_div_extra_class,
"tree_html": self.create_tree_view_html(tabname),
"items_html": self.create_card_view_html(tabname),
"items_html": self.create_card_view_html(tabname, none_message="Loading..." if empty else None),
"extra_networks_tree_view_default_width": shared.opts.extra_networks_tree_view_default_width,
"tree_view_div_default_display": tree_view_div_default_display,
"extra_network_pane_content_default_display": extra_network_pane_content_default_display,
}
)
@@ -559,7 +575,7 @@ class ExtraNetworksPage:
"date_created": int(mtime),
"date_modified": int(ctime),
"name": pth.name.lower(),
"path": str(pth.parent).lower(),
"path": str(pth).lower(),
}
def find_preview(self, path):
@@ -638,6 +654,7 @@ def pages_in_preferred_order(pages):
return sorted(pages, key=lambda x: tab_scores[x.name])
def create_ui(interface: gr.Blocks, unrelated_tabs, tabname):
ui = ExtraNetworksUi()
ui.pages = []
@@ -648,15 +665,13 @@ def create_ui(interface: gr.Blocks, unrelated_tabs, tabname):
related_tabs = []
button_refresh = gr.Button("Refresh", elem_id=f"{tabname}_extra_refresh_internal", visible=False)
for page in ui.stored_extra_pages:
with gr.Tab(page.title, elem_id=f"{tabname}_{page.extra_networks_tabname}", elem_classes=["extra-page"]) as tab:
with gr.Column(elem_id=f"{tabname}_{page.extra_networks_tabname}_prompts", elem_classes=["extra-page-prompts"]):
pass
elem_id = f"{tabname}_{page.extra_networks_tabname}_cards_html"
page_elem = gr.HTML('Loading...', elem_id=elem_id)
page_elem = gr.HTML(page.create_html(tabname, empty=True), elem_id=elem_id)
ui.pages.append(page_elem)
editor = page.create_user_metadata_editor(ui, tabname)
@@ -680,6 +695,15 @@ def create_ui(interface: gr.Blocks, unrelated_tabs, tabname):
)
tab.select(fn=None, _js=jscode, inputs=[], outputs=[], show_progress=False)
def refresh():
for pg in ui.stored_extra_pages:
pg.refresh()
create_html()
return ui.pages_contents
button_refresh = gr.Button("Refresh", elem_id=f"{tabname}_{page.extra_networks_tabname}_extra_refresh_internal", visible=False)
button_refresh.click(fn=refresh, inputs=[], outputs=ui.pages).then(fn=lambda: None, _js="function(){ " + f"applyExtraNetworkFilter('{tabname}_{page.extra_networks_tabname}');" + " }").then(fn=lambda: None, _js='setupAllResizeHandles')
def create_html():
ui.pages_contents = [pg.create_html(ui.tabname) for pg in ui.stored_extra_pages]
@@ -688,14 +712,7 @@ def create_ui(interface: gr.Blocks, unrelated_tabs, tabname):
create_html()
return ui.pages_contents
def refresh():
for pg in ui.stored_extra_pages:
pg.refresh()
create_html()
return ui.pages_contents
interface.load(fn=pages_html, inputs=[], outputs=[*ui.pages]).then(fn=None, js='function(){applyExtraNetworkFilter(' + quote_js(tabname) + '); return []}')
button_refresh.click(fn=refresh, inputs=[], outputs=ui.pages)
interface.load(fn=pages_html, inputs=[], outputs=ui.pages).then(fn=lambda: None, _js='setupAllResizeHandles')
return ui

2
modules/ui_extra_networks_checkpoints.py
View File

@@ -30,7 +30,7 @@ class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage):
"preview": self.find_preview(path),
"description": self.find_description(path),
"search_terms": search_terms,
"onclick": html.escape(f"return selectCheckpoint('{name}');"),
"onclick": html.escape(f"return selectCheckpoint({ui_extra_networks.quote_js(name)})"),
"local_preview": f"{path}.{shared.opts.samples_format}",
"metadata": checkpoint.metadata,
"sort_keys": {'default': index, **self.get_sort_keys(checkpoint.filename)},

9
modules/ui_prompt_styles.py
View File

@@ -22,9 +22,12 @@ def save_style(name, prompt, negative_prompt):
if not name:
return gr.update(visible=False)
style = styles.PromptStyle(name, prompt, negative_prompt)
existing_style = shared.prompt_styles.styles.get(name)
path = existing_style.path if existing_style is not None else None
style = styles.PromptStyle(name, prompt, negative_prompt, path)
shared.prompt_styles.styles[style.name] = style
shared.prompt_styles.save_styles(shared.styles_filename)
shared.prompt_styles.save_styles()
return gr.update(visible=True)
@@ -34,7 +37,7 @@ def delete_style(name):
return
shared.prompt_styles.styles.pop(name, None)
shared.prompt_styles.save_styles(shared.styles_filename)
shared.prompt_styles.save_styles()
return '', '', ''

24
modules/ui_tempdir.py
View File

@@ -46,12 +46,9 @@ def save_pil_to_file(pil_image, cache_dir=None, format="png"):
already_saved_as = getattr(pil_image, 'already_saved_as', None)
if already_saved_as and os.path.isfile(already_saved_as):
register_tmp_file(shared.demo, already_saved_as)
filename = already_saved_as
if not shared.opts.save_images_add_number:
filename += f'?{os.path.getmtime(already_saved_as)}'
return filename
filename_with_mtime = f'{already_saved_as}?{os.path.getmtime(already_saved_as)}'
register_tmp_file(shared.demo, filename_with_mtime)
return filename_with_mtime
if shared.opts.temp_dir:
dir = shared.opts.temp_dir
@@ -179,3 +176,18 @@ def cleanup_tmpdr():
filename = os.path.join(root, name)
os.remove(filename)
def is_gradio_temp_path(path):
"""
Check if the path is a temp dir used by gradio
"""
path = Path(path)
if shared.opts.temp_dir and path.is_relative_to(shared.opts.temp_dir):
return True
if gradio_temp_dir := os.environ.get("GRADIO_TEMP_DIR"):
if path.is_relative_to(gradio_temp_dir):
return True
if path.is_relative_to(Path(tempfile.gettempdir()) / "gradio"):
return True
return False

26
modules/ui_toprow.py
View File

@@ -17,6 +17,7 @@ class Toprow:
button_deepbooru = None
interrupt = None
interrupting = None
skip = None
submit = None
@@ -96,15 +97,10 @@ class Toprow:
with gr.Row(elem_id=f"{self.id_part}_generate_box", elem_classes=["generate-box"] + (["generate-box-compact"] if self.is_compact else []), render=not self.is_compact) as submit_box:
self.submit_box = submit_box
self.interrupt = gr.Button('Interrupt', elem_id=f"{self.id_part}_interrupt", elem_classes="generate-box-interrupt")
self.skip = gr.Button('Skip', elem_id=f"{self.id_part}_skip", elem_classes="generate-box-skip")
self.submit = gr.Button('Generate', elem_id=f"{self.id_part}_generate", variant='primary')
self.skip.click(
fn=lambda: shared.state.skip(),
inputs=[],
outputs=[],
)
self.interrupt = gr.Button('Interrupt', elem_id=f"{self.id_part}_interrupt", elem_classes="generate-box-interrupt", tooltip="End generation immediately or after completing current batch")
self.skip = gr.Button('Skip', elem_id=f"{self.id_part}_skip", elem_classes="generate-box-skip", tooltip="Stop generation of current batch and continues onto next batch")
self.interrupting = gr.Button('Interrupting...', elem_id=f"{self.id_part}_interrupting", elem_classes="generate-box-interrupting", tooltip="Interrupting generation...")
self.submit = gr.Button('Generate', elem_id=f"{self.id_part}_generate", variant='primary', tooltip="Right click generate forever menu")
def interrupt_function():
if not shared.state.stopping_generation and shared.state.job_count > 1 and shared.opts.interrupt_after_current:
@@ -113,11 +109,9 @@ class Toprow:
else:
shared.state.interrupt()
self.interrupt.click(
fn=interrupt_function,
inputs=[],
outputs=[],
)
self.skip.click(fn=shared.state.skip)
self.interrupt.click(fn=interrupt_function, _js='function(){ showSubmitInterruptingPlaceholder("' + self.id_part + '"); }')
self.interrupting.click(fn=interrupt_function)
def create_tools_row(self):
with gr.Row(elem_id=f"{self.id_part}_tools"):
@@ -133,9 +127,9 @@ class Toprow:
self.restore_progress_button = ToolButton(value=restore_progress_symbol, elem_id=f"{self.id_part}_restore_progress", visible=False, tooltip="Restore progress")
self.token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{self.id_part}_token_counter", elem_classes=["token-counter"])
self.token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{self.id_part}_token_counter", elem_classes=["token-counter"], visible=False)
self.token_button = gr.Button(visible=False, elem_id=f"{self.id_part}_token_button")
self.negative_token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{self.id_part}_negative_token_counter", elem_classes=["token-counter"])
self.negative_token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{self.id_part}_negative_token_counter", elem_classes=["token-counter"], visible=False)
self.negative_token_button = gr.Button(visible=False, elem_id=f"{self.id_part}_negative_token_button")
self.clear_prompt_button.click(

5
modules/upscaler_utils.py
View File

@@ -6,7 +6,7 @@ import torch
import tqdm
from PIL import Image
from modules import images, shared, torch_utils
from modules import devices, images, shared, torch_utils
logger = logging.getLogger(__name__)
@@ -44,7 +44,8 @@ def upscale_pil_patch(model, img: Image.Image) -> Image.Image:
with torch.no_grad():
tensor = pil_image_to_torch_bgr(img).unsqueeze(0) # add batch dimension
tensor = tensor.to(device=param.device, dtype=param.dtype)
return torch_bgr_to_pil_image(model(tensor))
with devices.without_autocast():
return torch_bgr_to_pil_image(model(tensor))
def upscale_with_model(

2
modules/util.py
View File

@@ -42,7 +42,7 @@ def walk_files(path, allowed_extensions=None):
for filename in sorted(files, key=natural_sort_key):
if allowed_extensions is not None:
_, ext = os.path.splitext(filename)
if ext not in allowed_extensions:
if ext.lower() not in allowed_extensions:
continue
if not shared.opts.list_hidden_files and ("/." in root or "\\." in root):