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Merge branch 'dev' into startup-profile

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AUTOMATIC
2023-06-01 08:42:50 +03:00
parent 0cc05fc492 36888092af
commit b3390a9840
75 changed files with 2431 additions and 1235 deletions
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15
modules/api/api.py
View File

@@ -14,7 +14,7 @@ from fastapi.encoders import jsonable_encoder
from secrets import compare_digest
import modules.shared as shared
from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing
from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors
from modules.api import models
from modules.shared import opts
from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
@@ -23,6 +23,7 @@ from modules.textual_inversion.preprocess import preprocess
from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork
from PIL import PngImagePlugin,Image
from modules.sd_models import checkpoints_list, unload_model_weights, reload_model_weights
from modules.sd_vae import vae_dict
from modules.sd_models_config import find_checkpoint_config_near_filename
from modules.realesrgan_model import get_realesrgan_models
from modules import devices
@@ -108,7 +109,6 @@ def api_middleware(app: FastAPI):
from rich.console import Console
console = Console()
except Exception:
import traceback
rich_available = False
@app.middleware("http")
@@ -139,11 +139,12 @@ def api_middleware(app: FastAPI):
"errors": str(e),
}
if not isinstance(e, HTTPException): # do not print backtrace on known httpexceptions
print(f"API error: {request.method}: {request.url} {err}")
message = f"API error: {request.method}: {request.url} {err}"
if rich_available:
print(message)
console.print_exception(show_locals=True, max_frames=2, extra_lines=1, suppress=[anyio, starlette], word_wrap=False, width=min([console.width, 200]))
else:
traceback.print_exc()
errors.report(message, exc_info=True)
return JSONResponse(status_code=vars(e).get('status_code', 500), content=jsonable_encoder(err))
@app.middleware("http")
@@ -189,6 +190,7 @@ class Api:
self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[models.SamplerItem])
self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[models.UpscalerItem])
self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[models.SDModelItem])
self.add_api_route("/sdapi/v1/sd-vae", self.get_sd_vaes, methods=["GET"], response_model=List[models.SDVaeItem])
self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[models.HypernetworkItem])
self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[models.FaceRestorerItem])
self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[models.RealesrganItem])
@@ -541,6 +543,9 @@ class Api:
def get_sd_models(self):
return [{"title": x.title, "model_name": x.model_name, "hash": x.shorthash, "sha256": x.sha256, "filename": x.filename, "config": find_checkpoint_config_near_filename(x)} for x in checkpoints_list.values()]
def get_sd_vaes(self):
return [{"model_name": x, "filename": vae_dict[x]} for x in vae_dict.keys()]
def get_hypernetworks(self):
return [{"name": name, "path": shared.hypernetworks[name]} for name in shared.hypernetworks]
@@ -700,4 +705,4 @@ class Api:
def launch(self, server_name, port):
self.app.include_router(self.router)
uvicorn.run(self.app, host=server_name, port=port)
uvicorn.run(self.app, host=server_name, port=port, timeout_keep_alive=0)

4
modules/api/models.py
View File

@@ -249,6 +249,10 @@ class SDModelItem(BaseModel):
filename: str = Field(title="Filename")
config: Optional[str] = Field(title="Config file")
class SDVaeItem(BaseModel):
model_name: str = Field(title="Model Name")
filename: str = Field(title="Filename")
class HypernetworkItem(BaseModel):
name: str = Field(title="Name")
path: Optional[str] = Field(title="Path")

23
modules/call_queue.py
View File

@@ -1,10 +1,8 @@
import html
import sys
import threading
import traceback
import time
from modules import shared, progress
from modules import shared, progress, errors
queue_lock = threading.Lock()
@@ -56,16 +54,14 @@ def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
try:
res = list(func(*args, **kwargs))
except Exception as e:
# When printing out our debug argument list, do not print out more than a MB of text
max_debug_str_len = 131072 # (1024*1024)/8
print("Error completing request", file=sys.stderr)
argStr = f"Arguments: {args} {kwargs}"
print(argStr[:max_debug_str_len], file=sys.stderr)
if len(argStr) > max_debug_str_len:
print(f"(Argument list truncated at {max_debug_str_len}/{len(argStr)} characters)", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
# When printing out our debug argument list,
# do not print out more than a 100 KB of text
max_debug_str_len = 131072
message = "Error completing request"
arg_str = f"Arguments: {args} {kwargs}"[:max_debug_str_len]
if len(arg_str) > max_debug_str_len:
arg_str += f" (Argument list truncated at {max_debug_str_len}/{len(arg_str)} characters)"
errors.report(f"{message}\n{arg_str}", exc_info=True)
shared.state.job = ""
shared.state.job_count = 0
@@ -108,4 +104,3 @@ def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
return tuple(res)
return f

4
modules/cmd_args.py
View File

@@ -11,7 +11,7 @@ parser.add_argument("--skip-python-version-check", action='store_true', help="la
parser.add_argument("--skip-torch-cuda-test", action='store_true', help="launch.py argument: do not check if CUDA is able to work properly")
parser.add_argument("--reinstall-xformers", action='store_true', help="launch.py argument: install the appropriate version of xformers even if you have some version already installed")
parser.add_argument("--reinstall-torch", action='store_true', help="launch.py argument: install the appropriate version of torch even if you have some version already installed")
parser.add_argument("--update-check", action='store_true', help="launch.py argument: chck for updates at startup")
parser.add_argument("--update-check", action='store_true', help="launch.py argument: check for updates at startup")
parser.add_argument("--test-server", action='store_true', help="launch.py argument: configure server for testing")
parser.add_argument("--skip-prepare-environment", action='store_true', help="launch.py argument: skip all environment preparation")
parser.add_argument("--skip-install", action='store_true', help="launch.py argument: skip installation of packages")
@@ -62,7 +62,7 @@ parser.add_argument("--opt-split-attention-invokeai", action='store_true', help=
parser.add_argument("--opt-split-attention-v1", action='store_true', help="prefer older version of split attention optimization for automatic choice of optimization")
parser.add_argument("--opt-sdp-attention", action='store_true', help="prefer scaled dot product cross-attention layer optimization for automatic choice of optimization; requires PyTorch 2.*")
parser.add_argument("--opt-sdp-no-mem-attention", action='store_true', help="prefer scaled dot product cross-attention layer optimization without memory efficient attention for automatic choice of optimization, makes image generation deterministic; requires PyTorch 2.*")
parser.add_argument("--disable-opt-split-attention", action='store_true', help="does not do anything")
parser.add_argument("--disable-opt-split-attention", action='store_true', help="prefer no cross-attention layer optimization for automatic choice of optimization")
parser.add_argument("--disable-nan-check", action='store_true', help="do not check if produced images/latent spaces have nans; useful for running without a checkpoint in CI")
parser.add_argument("--use-cpu", nargs='+', help="use CPU as torch device for specified modules", default=[], type=str.lower)
parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")

11
modules/codeformer_model.py
View File

@@ -1,13 +1,11 @@
import os
import sys
import traceback
import cv2
import torch
import modules.face_restoration
import modules.shared
from modules import shared, devices, modelloader
from modules import shared, devices, modelloader, errors
from modules.paths import models_path
# codeformer people made a choice to include modified basicsr library to their project which makes
@@ -105,8 +103,8 @@ def setup_model(dirname):
restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
del output
torch.cuda.empty_cache()
except Exception as error:
print(f'\tFailed inference for CodeFormer: {error}', file=sys.stderr)
except Exception:
errors.report('Failed inference for CodeFormer', exc_info=True)
restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1))
restored_face = restored_face.astype('uint8')
@@ -135,7 +133,6 @@ def setup_model(dirname):
shared.face_restorers.append(codeformer)
except Exception:
print("Error setting up CodeFormer:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
errors.report("Error setting up CodeFormer", exc_info=True)
# sys.path = stored_sys_path

13
modules/config_states.py
View File

@@ -3,8 +3,6 @@ Supports saving and restoring webui and extensions from a known working set of c
"""
import os
import sys
import traceback
import json
import time
import tqdm
@@ -13,7 +11,7 @@ from datetime import datetime
from collections import OrderedDict
import git
from modules import shared, extensions
from modules import shared, extensions, errors
from modules.paths_internal import script_path, config_states_dir
@@ -53,8 +51,7 @@ def get_webui_config():
if os.path.exists(os.path.join(script_path, ".git")):
webui_repo = git.Repo(script_path)
except Exception:
print(f"Error reading webui git info from {script_path}:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
errors.report(f"Error reading webui git info from {script_path}", exc_info=True)
webui_remote = None
webui_commit_hash = None
@@ -134,8 +131,7 @@ def restore_webui_config(config):
if os.path.exists(os.path.join(script_path, ".git")):
webui_repo = git.Repo(script_path)
except Exception:
print(f"Error reading webui git info from {script_path}:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
errors.report(f"Error reading webui git info from {script_path}", exc_info=True)
return
try:
@@ -143,8 +139,7 @@ def restore_webui_config(config):
webui_repo.git.reset(webui_commit_hash, hard=True)
print(f"* Restored webui to commit {webui_commit_hash}.")
except Exception:
print(f"Error restoring webui to commit {webui_commit_hash}:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
errors.report(f"Error restoring webui to commit{webui_commit_hash}")
def restore_extension_config(config):

18
modules/devices.py
View File

@@ -1,5 +1,7 @@
import sys
import contextlib
from functools import lru_cache
import torch
from modules import errors
@@ -154,3 +156,19 @@ def test_for_nans(x, where):
message += " Use --disable-nan-check commandline argument to disable this check."
raise NansException(message)
@lru_cache
def first_time_calculation():
"""
just do any calculation with pytorch layers - the first time this is done it allocaltes about 700MB of memory and
spends about 2.7 seconds doing that, at least wih NVidia.
"""
x = torch.zeros((1, 1)).to(device, dtype)
linear = torch.nn.Linear(1, 1).to(device, dtype)
linear(x)
x = torch.zeros((1, 1, 3, 3)).to(device, dtype)
conv2d = torch.nn.Conv2d(1, 1, (3, 3)).to(device, dtype)
conv2d(x)

20
modules/errors.py
View File

@@ -1,7 +1,19 @@
import sys
import textwrap
import traceback
def report(message: str, *, exc_info: bool = False) -> None:
"""
Print an error message to stderr, with optional traceback.
"""
for line in message.splitlines():
print("***", line, file=sys.stderr)
if exc_info:
print(textwrap.indent(traceback.format_exc(), " "), file=sys.stderr)
print("---", file=sys.stderr)
def print_error_explanation(message):
lines = message.strip().split("\n")
max_len = max([len(x) for x in lines])
@@ -12,9 +24,13 @@ def print_error_explanation(message):
print('=' * max_len, file=sys.stderr)
def display(e: Exception, task):
def display(e: Exception, task, *, full_traceback=False):
print(f"{task or 'error'}: {type(e).__name__}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
te = traceback.TracebackException.from_exception(e)
if full_traceback:
# include frames leading up to the try-catch block
te.stack = traceback.StackSummary(traceback.extract_stack()[:-2] + te.stack)
print(*te.format(), sep="", file=sys.stderr)
message = str(e)
if "copying a param with shape torch.Size([640, 1024]) from checkpoint, the shape in current model is torch.Size([640, 768])" in message:

27
modules/extensions.py
View File

@@ -1,11 +1,8 @@
import os
import sys
import threading
import traceback
import git
from modules import shared
from modules import shared, errors
from modules.gitpython_hack import Repo
from modules.paths_internal import extensions_dir, extensions_builtin_dir, script_path # noqa: F401
extensions = []
@@ -54,10 +51,9 @@ class Extension:
repo = None
try:
if os.path.exists(os.path.join(self.path, ".git")):
repo = git.Repo(self.path)
repo = Repo(self.path)
except Exception:
print(f"Error reading github repository info from {self.path}:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
errors.report(f"Error reading github repository info from {self.path}", exc_info=True)
if repo is None or repo.bare:
self.remote = None
@@ -65,14 +61,15 @@ class Extension:
try:
self.status = 'unknown'
self.remote = next(repo.remote().urls, None)
self.commit_date = repo.head.commit.committed_date
commit = repo.head.commit
self.commit_date = commit.committed_date
if repo.active_branch:
self.branch = repo.active_branch.name
self.commit_hash = repo.head.commit.hexsha
self.version = repo.git.describe("--always", "--tags") # compared to `self.commit_hash[:8]` this takes about 30% more time total but since we run it in parallel we don't care
self.commit_hash = commit.hexsha
self.version = self.commit_hash[:8]
except Exception as ex:
print(f"Failed reading extension data from Git repository ({self.name}): {ex}", file=sys.stderr)
except Exception:
errors.report(f"Failed reading extension data from Git repository ({self.name})", exc_info=True)
self.remote = None
self.have_info_from_repo = True
@@ -93,7 +90,7 @@ class Extension:
return res
def check_updates(self):
repo = git.Repo(self.path)
repo = Repo(self.path)
for fetch in repo.remote().fetch(dry_run=True):
if fetch.flags != fetch.HEAD_UPTODATE:
self.can_update = True
@@ -115,7 +112,7 @@ class Extension:
self.status = "latest"
def fetch_and_reset_hard(self, commit='origin'):
repo = git.Repo(self.path)
repo = Repo(self.path)
# Fix: `error: Your local changes to the following files would be overwritten by merge`,
# because WSL2 Docker set 755 file permissions instead of 644, this results to the error.
repo.git.fetch(all=True)

2
modules/extra_networks.py
View File

@@ -26,7 +26,7 @@ class ExtraNetworkParams:
self.named = {}
for item in self.items:
parts = item.split('=', 2)
parts = item.split('=', 2) if isinstance(item, str) else [item]
if len(parts) == 2:
self.named[parts[0]] = parts[1]
else:

18
modules/generation_parameters_copypaste.py
View File

@@ -35,7 +35,7 @@ def reset():
def quote(text):
if ',' not in str(text) and '\n' not in str(text):
if ',' not in str(text) and '\n' not in str(text) and ':' not in str(text):
return text
return json.dumps(text, ensure_ascii=False)
@@ -306,6 +306,18 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
if "RNG" not in res:
res["RNG"] = "GPU"
if "Schedule type" not in res:
res["Schedule type"] = "Automatic"
if "Schedule max sigma" not in res:
res["Schedule max sigma"] = 0
if "Schedule min sigma" not in res:
res["Schedule min sigma"] = 0
if "Schedule rho" not in res:
res["Schedule rho"] = 0
return res
@@ -318,6 +330,10 @@ infotext_to_setting_name_mapping = [
('Conditional mask weight', 'inpainting_mask_weight'),
('Model hash', 'sd_model_checkpoint'),
('ENSD', 'eta_noise_seed_delta'),
('Schedule type', 'k_sched_type'),
('Schedule max sigma', 'sigma_max'),
('Schedule min sigma', 'sigma_min'),
('Schedule rho', 'rho'),
('Noise multiplier', 'initial_noise_multiplier'),
('Eta', 'eta_ancestral'),
('Eta DDIM', 'eta_ddim'),

7
modules/gfpgan_model.py
View File

@@ -1,12 +1,10 @@
import os
import sys
import traceback
import facexlib
import gfpgan
import modules.face_restoration
from modules import paths, shared, devices, modelloader
from modules import paths, shared, devices, modelloader, errors
model_dir = "GFPGAN"
user_path = None
@@ -112,5 +110,4 @@ def setup_model(dirname):
shared.face_restorers.append(FaceRestorerGFPGAN())
except Exception:
print("Error setting up GFPGAN:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
errors.report("Error setting up GFPGAN", exc_info=True)

42
modules/gitpython_hack.py Normal file
View File

@@ -0,0 +1,42 @@
from __future__ import annotations
import io
import subprocess
import git
class Git(git.Git):
"""
Git subclassed to never use persistent processes.
"""
def _get_persistent_cmd(self, attr_name, cmd_name, *args, **kwargs):
raise NotImplementedError(f"Refusing to use persistent process: {attr_name} ({cmd_name} {args} {kwargs})")
def get_object_header(self, ref: str | bytes) -> tuple[str, str, int]:
ret = subprocess.check_output(
[self.GIT_PYTHON_GIT_EXECUTABLE, "cat-file", "--batch-check"],
input=self._prepare_ref(ref),
cwd=self._working_dir,
timeout=2,
)
return self._parse_object_header(ret)
def stream_object_data(self, ref: str) -> tuple[str, str, int, "Git.CatFileContentStream"]:
# Not really streaming, per se; this buffers the entire object in memory.
# Shouldn't be a problem for our use case, since we're only using this for
# object headers (commit objects).
ret = subprocess.check_output(
[self.GIT_PYTHON_GIT_EXECUTABLE, "cat-file", "--batch"],
input=self._prepare_ref(ref),
cwd=self._working_dir,
timeout=30,
)
bio = io.BytesIO(ret)
hexsha, typename, size = self._parse_object_header(bio.readline())
return (hexsha, typename, size, self.CatFileContentStream(size, bio))
class Repo(git.Repo):
GitCommandWrapperType = Git

15
modules/hypernetworks/hypernetwork.py
View File

@@ -2,8 +2,6 @@ import datetime
import glob
import html
import os
import sys
import traceback
import inspect
import modules.textual_inversion.dataset
@@ -11,7 +9,7 @@ import torch
import tqdm
from einops import rearrange, repeat
from ldm.util import default
from modules import devices, processing, sd_models, shared, sd_samplers, hashes, sd_hijack_checkpoint
from modules import devices, processing, sd_models, shared, sd_samplers, hashes, sd_hijack_checkpoint, errors
from modules.textual_inversion import textual_inversion, logging
from modules.textual_inversion.learn_schedule import LearnRateScheduler
from torch import einsum
@@ -325,17 +323,14 @@ def load_hypernetwork(name):
if path is None:
return None
hypernetwork = Hypernetwork()
try:
hypernetwork = Hypernetwork()
hypernetwork.load(path)
return hypernetwork
except Exception:
print(f"Error loading hypernetwork {path}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
errors.report(f"Error loading hypernetwork {path}", exc_info=True)
return None
return hypernetwork
def load_hypernetworks(names, multipliers=None):
already_loaded = {}
@@ -770,7 +765,7 @@ Last saved image: {html.escape(last_saved_image)}<br/>
</p>
"""
except Exception:
print(traceback.format_exc(), file=sys.stderr)
errors.report("Exception in training hypernetwork", exc_info=True)
finally:
pbar.leave = False
pbar.close()

52
modules/images.py
View File

@@ -1,6 +1,4 @@
import datetime
import sys
import traceback
import pytz
import io
@@ -21,6 +19,8 @@ from modules import sd_samplers, shared, script_callbacks, errors
from modules.paths_internal import roboto_ttf_file
from modules.shared import opts
import modules.sd_vae as sd_vae
LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
@@ -336,8 +336,20 @@ def sanitize_filename_part(text, replace_spaces=True):
class FilenameGenerator:
def get_vae_filename(self): #get the name of the VAE file.
if sd_vae.loaded_vae_file is None:
return "NoneType"
file_name = os.path.basename(sd_vae.loaded_vae_file)
split_file_name = file_name.split('.')
if len(split_file_name) > 1 and split_file_name[0] == '':
return split_file_name[1] # if the first character of the filename is "." then [1] is obtained.
else:
return split_file_name[0]
replacements = {
'seed': lambda self: self.seed if self.seed is not None else '',
'seed_first': lambda self: self.seed if self.p.batch_size == 1 else self.p.all_seeds[0],
'seed_last': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if self.p.batch_size == 1 else self.p.all_seeds[-1],
'steps': lambda self: self.p and self.p.steps,
'cfg': lambda self: self.p and self.p.cfg_scale,
'width': lambda self: self.image.width,
@@ -354,19 +366,23 @@ class FilenameGenerator:
'prompt_no_styles': lambda self: self.prompt_no_style(),
'prompt_spaces': lambda self: sanitize_filename_part(self.prompt, replace_spaces=False),
'prompt_words': lambda self: self.prompt_words(),
'batch_number': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if self.p.batch_size == 1 else self.p.batch_index + 1,
'generation_number': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if self.p.n_iter == 1 and self.p.batch_size == 1 else self.p.iteration * self.p.batch_size + self.p.batch_index + 1,
'batch_number': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if self.p.batch_size == 1 or self.zip else self.p.batch_index + 1,
'batch_size': lambda self: self.p.batch_size,
'generation_number': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if (self.p.n_iter == 1 and self.p.batch_size == 1) or self.zip else self.p.iteration * self.p.batch_size + self.p.batch_index + 1,
'hasprompt': lambda self, *args: self.hasprompt(*args), # accepts formats:[hasprompt<prompt1|default><prompt2>..]
'clip_skip': lambda self: opts.data["CLIP_stop_at_last_layers"],
'denoising': lambda self: self.p.denoising_strength if self.p and self.p.denoising_strength else NOTHING_AND_SKIP_PREVIOUS_TEXT,
'vae_filename': lambda self: self.get_vae_filename(),
}
default_time_format = '%Y%m%d%H%M%S'
def __init__(self, p, seed, prompt, image):
def __init__(self, p, seed, prompt, image, zip=False):
self.p = p
self.seed = seed
self.prompt = prompt
self.image = image
self.zip = zip
def hasprompt(self, *args):
lower = self.prompt.lower()
@@ -446,8 +462,7 @@ class FilenameGenerator:
replacement = fun(self, *pattern_args)
except Exception:
replacement = None
print(f"Error adding [{pattern}] to filename", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
errors.report(f"Error adding [{pattern}] to filename", exc_info=True)
if replacement == NOTHING_AND_SKIP_PREVIOUS_TEXT:
continue
@@ -488,14 +503,13 @@ def save_image_with_geninfo(image, geninfo, filename, extension=None, existing_p
image_format = Image.registered_extensions()[extension]
existing_pnginfo = existing_pnginfo or {}
if opts.enable_pnginfo:
existing_pnginfo['parameters'] = geninfo
if extension.lower() == '.png':
pnginfo_data = PngImagePlugin.PngInfo()
for k, v in (existing_pnginfo or {}).items():
pnginfo_data.add_text(k, str(v))
if opts.enable_pnginfo:
pnginfo_data = PngImagePlugin.PngInfo()
for k, v in (existing_pnginfo or {}).items():
pnginfo_data.add_text(k, str(v))
else:
pnginfo_data = None
image.save(filename, format=image_format, quality=opts.jpeg_quality, pnginfo=pnginfo_data)
@@ -665,9 +679,10 @@ def read_info_from_image(image):
items['exif comment'] = exif_comment
geninfo = exif_comment
for field in ['jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif',
'loop', 'background', 'timestamp', 'duration']:
items.pop(field, None)
for field in ['jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif',
'loop', 'background', 'timestamp', 'duration', 'progressive', 'progression',
'icc_profile', 'chromaticity']:
items.pop(field, None)
if items.get("Software", None) == "NovelAI":
try:
@@ -678,8 +693,7 @@ def read_info_from_image(image):
Negative prompt: {json_info["uc"]}
Steps: {json_info["steps"]}, Sampler: {sampler}, CFG scale: {json_info["scale"]}, Seed: {json_info["seed"]}, Size: {image.width}x{image.height}, Clip skip: 2, ENSD: 31337"""
except Exception:
print("Error parsing NovelAI image generation parameters:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
errors.report("Error parsing NovelAI image generation parameters", exc_info=True)
return geninfo, items

3
modules/img2img.py
View File

@@ -92,7 +92,8 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
elif mode == 2: # inpaint
image, mask = init_img_with_mask["image"], init_img_with_mask["mask"]
alpha_mask = ImageOps.invert(image.split()[-1]).convert('L').point(lambda x: 255 if x > 0 else 0, mode='1')
mask = ImageChops.lighter(alpha_mask, mask.convert('L')).convert('L')
mask = mask.convert('L').point(lambda x: 255 if x > 128 else 0, mode='1')
mask = ImageChops.lighter(alpha_mask, mask).convert('L')
image = image.convert("RGB")
elif mode == 3: # inpaint sketch
image = inpaint_color_sketch

4
modules/interrogate.py
View File

@@ -1,6 +1,5 @@
import os
import sys
import traceback
from collections import namedtuple
from pathlib import Path
import re
@@ -216,8 +215,7 @@ class InterrogateModels:
res += f", {match}"
except Exception:
print("Error interrogating", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
errors.report("Error interrogating", exc_info=True)
res += "<error>"
self.unload()

331
modules/launch_utils.py Normal file
View File

@@ -0,0 +1,331 @@
# this scripts installs necessary requirements and launches main program in webui.py
import subprocess
import os
import sys
import importlib.util
import platform
import json
from functools import lru_cache
from modules import cmd_args, errors
from modules.paths_internal import script_path, extensions_dir
args, _ = cmd_args.parser.parse_known_args()
python = sys.executable
git = os.environ.get('GIT', "git")
index_url = os.environ.get('INDEX_URL', "")
dir_repos = "repositories"
# Whether to default to printing command output
default_command_live = (os.environ.get('WEBUI_LAUNCH_LIVE_OUTPUT') == "1")
if 'GRADIO_ANALYTICS_ENABLED' not in os.environ:
os.environ['GRADIO_ANALYTICS_ENABLED'] = 'False'
def check_python_version():
is_windows = platform.system() == "Windows"
major = sys.version_info.major
minor = sys.version_info.minor
micro = sys.version_info.micro
if is_windows:
supported_minors = [10]
else:
supported_minors = [7, 8, 9, 10, 11]
if not (major == 3 and minor in supported_minors):
import modules.errors
modules.errors.print_error_explanation(f"""
INCOMPATIBLE PYTHON VERSION
This program is tested with 3.10.6 Python, but you have {major}.{minor}.{micro}.
If you encounter an error with "RuntimeError: Couldn't install torch." message,
or any other error regarding unsuccessful package (library) installation,
please downgrade (or upgrade) to the latest version of 3.10 Python
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 ""}
Use --skip-python-version-check to suppress this warning.
""")
@lru_cache()
def commit_hash():
try:
return subprocess.check_output([git, "rev-parse", "HEAD"], shell=False, encoding='utf8').strip()
except Exception:
return "<none>"
@lru_cache()
def git_tag():
try:
return subprocess.check_output([git, "describe", "--tags"], shell=False, encoding='utf8').strip()
except Exception:
return "<none>"
def run(command, desc=None, errdesc=None, custom_env=None, live: bool = default_command_live) -> str:
if desc is not None:
print(desc)
run_kwargs = {
"args": command,
"shell": True,
"env": os.environ if custom_env is None else custom_env,
"encoding": 'utf8',
"errors": 'ignore',
}
if not live:
run_kwargs["stdout"] = run_kwargs["stderr"] = subprocess.PIPE
result = subprocess.run(**run_kwargs)
if result.returncode != 0:
error_bits = [
f"{errdesc or 'Error running command'}.",
f"Command: {command}",
f"Error code: {result.returncode}",
]
if result.stdout:
error_bits.append(f"stdout: {result.stdout}")
if result.stderr:
error_bits.append(f"stderr: {result.stderr}")
raise RuntimeError("\n".join(error_bits))
return (result.stdout or "")
def is_installed(package):
try:
spec = importlib.util.find_spec(package)
except ModuleNotFoundError:
return False
return spec is not None
def repo_dir(name):
return os.path.join(script_path, dir_repos, name)
def run_pip(command, desc=None, live=default_command_live):
if args.skip_install:
return
index_url_line = f' --index-url {index_url}' if index_url != '' else ''
return run(f'"{python}" -m pip {command} --prefer-binary{index_url_line}', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}", live=live)
def check_run_python(code: str) -> bool:
result = subprocess.run([python, "-c", code], capture_output=True, shell=False)
return result.returncode == 0
def git_clone(url, dir, name, commithash=None):
# TODO clone into temporary dir and move if successful
if os.path.exists(dir):
if commithash is None:
return
current_hash = run(f'"{git}" -C "{dir}" rev-parse HEAD', None, f"Couldn't determine {name}'s hash: {commithash}").strip()
if current_hash == commithash:
return
run(f'"{git}" -C "{dir}" fetch', f"Fetching updates for {name}...", f"Couldn't fetch {name}")
run(f'"{git}" -C "{dir}" checkout {commithash}', f"Checking out commit for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}")
return
run(f'"{git}" clone "{url}" "{dir}"', f"Cloning {name} into {dir}...", f"Couldn't clone {name}")
if commithash is not None:
run(f'"{git}" -C "{dir}" checkout {commithash}', None, "Couldn't checkout {name}'s hash: {commithash}")
def git_pull_recursive(dir):
for subdir, _, _ in os.walk(dir):
if os.path.exists(os.path.join(subdir, '.git')):
try:
output = subprocess.check_output([git, '-C', subdir, 'pull', '--autostash'])
print(f"Pulled changes for repository in '{subdir}':\n{output.decode('utf-8').strip()}\n")
except subprocess.CalledProcessError as e:
print(f"Couldn't perform 'git pull' on repository in '{subdir}':\n{e.output.decode('utf-8').strip()}\n")
def version_check(commit):
try:
import requests
commits = requests.get('https://api.github.com/repos/AUTOMATIC1111/stable-diffusion-webui/branches/master').json()
if commit != "<none>" and commits['commit']['sha'] != commit:
print("--------------------------------------------------------")
print("| You are not up to date with the most recent release. |")
print("| Consider running `git pull` to update. |")
print("--------------------------------------------------------")
elif commits['commit']['sha'] == commit:
print("You are up to date with the most recent release.")
else:
print("Not a git clone, can't perform version check.")
except Exception as e:
print("version check failed", e)
def run_extension_installer(extension_dir):
path_installer = os.path.join(extension_dir, "install.py")
if not os.path.isfile(path_installer):
return
try:
env = os.environ.copy()
env['PYTHONPATH'] = os.path.abspath(".")
print(run(f'"{python}" "{path_installer}"', errdesc=f"Error running install.py for extension {extension_dir}", custom_env=env))
except Exception as e:
errors.report(str(e))
def list_extensions(settings_file):
settings = {}
try:
if os.path.isfile(settings_file):
with open(settings_file, "r", encoding="utf8") as file:
settings = json.load(file)
except Exception:
errors.report("Could not load settings", exc_info=True)
disabled_extensions = set(settings.get('disabled_extensions', []))
disable_all_extensions = settings.get('disable_all_extensions', 'none')
if disable_all_extensions != 'none':
return []
return [x for x in os.listdir(extensions_dir) if x not in disabled_extensions]
def run_extensions_installers(settings_file):
if not os.path.isdir(extensions_dir):
return
for dirname_extension in list_extensions(settings_file):
run_extension_installer(os.path.join(extensions_dir, dirname_extension))
def prepare_environment():
torch_index_url = os.environ.get('TORCH_INDEX_URL', "https://download.pytorch.org/whl/cu118")
torch_command = os.environ.get('TORCH_COMMAND', f"pip install torch==2.0.1 torchvision==0.15.2 --extra-index-url {torch_index_url}")
requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.20')
gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "https://github.com/TencentARC/GFPGAN/archive/8d2447a2d918f8eba5a4a01463fd48e45126a379.zip")
clip_package = os.environ.get('CLIP_PACKAGE', "https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip")
openclip_package = os.environ.get('OPENCLIP_PACKAGE', "https://github.com/mlfoundations/open_clip/archive/bb6e834e9c70d9c27d0dc3ecedeebeaeb1ffad6b.zip")
stable_diffusion_repo = os.environ.get('STABLE_DIFFUSION_REPO', "https://github.com/Stability-AI/stablediffusion.git")
k_diffusion_repo = os.environ.get('K_DIFFUSION_REPO', 'https://github.com/crowsonkb/k-diffusion.git')
codeformer_repo = os.environ.get('CODEFORMER_REPO', 'https://github.com/sczhou/CodeFormer.git')
blip_repo = os.environ.get('BLIP_REPO', 'https://github.com/salesforce/BLIP.git')
stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "cf1d67a6fd5ea1aa600c4df58e5b47da45f6bdbf")
k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "c9fe758757e022f05ca5a53fa8fac28889e4f1cf")
codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
if not args.skip_python_version_check:
check_python_version()
commit = commit_hash()
tag = git_tag()
print(f"Python {sys.version}")
print(f"Version: {tag}")
print(f"Commit hash: {commit}")
if args.reinstall_torch or not is_installed("torch") or not is_installed("torchvision"):
run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch", live=True)
if not args.skip_torch_cuda_test and not check_run_python("import torch; assert torch.cuda.is_available()"):
raise RuntimeError(
'Torch is not able to use GPU; '
'add --skip-torch-cuda-test to COMMANDLINE_ARGS variable to disable this check'
)
if not is_installed("gfpgan"):
run_pip(f"install {gfpgan_package}", "gfpgan")
if not is_installed("clip"):
run_pip(f"install {clip_package}", "clip")
if not is_installed("open_clip"):
run_pip(f"install {openclip_package}", "open_clip")
if (not is_installed("xformers") or args.reinstall_xformers) and args.xformers:
if platform.system() == "Windows":
if platform.python_version().startswith("3.10"):
run_pip(f"install -U -I --no-deps {xformers_package}", "xformers", live=True)
else:
print("Installation of xformers is not supported in this version of Python.")
print("You can also check this and build manually: https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers#building-xformers-on-windows-by-duckness")
if not is_installed("xformers"):
exit(0)
elif platform.system() == "Linux":
run_pip(f"install -U -I --no-deps {xformers_package}", "xformers")
if not is_installed("ngrok") and args.ngrok:
run_pip("install ngrok", "ngrok")
os.makedirs(os.path.join(script_path, dir_repos), exist_ok=True)
git_clone(stable_diffusion_repo, repo_dir('stable-diffusion-stability-ai'), "Stable Diffusion", stable_diffusion_commit_hash)
git_clone(k_diffusion_repo, repo_dir('k-diffusion'), "K-diffusion", k_diffusion_commit_hash)
git_clone(codeformer_repo, repo_dir('CodeFormer'), "CodeFormer", codeformer_commit_hash)
git_clone(blip_repo, repo_dir('BLIP'), "BLIP", blip_commit_hash)
if not is_installed("lpips"):
run_pip(f"install -r \"{os.path.join(repo_dir('CodeFormer'), 'requirements.txt')}\"", "requirements for CodeFormer")
if not os.path.isfile(requirements_file):
requirements_file = os.path.join(script_path, requirements_file)
run_pip(f"install -r \"{requirements_file}\"", "requirements")
run_extensions_installers(settings_file=args.ui_settings_file)
if args.update_check:
version_check(commit)
if args.update_all_extensions:
git_pull_recursive(extensions_dir)
if "--exit" in sys.argv:
print("Exiting because of --exit argument")
exit(0)
def configure_for_tests():
if "--api" not in sys.argv:
sys.argv.append("--api")
if "--ckpt" not in sys.argv:
sys.argv.append("--ckpt")
sys.argv.append(os.path.join(script_path, "test/test_files/empty.pt"))
if "--skip-torch-cuda-test" not in sys.argv:
sys.argv.append("--skip-torch-cuda-test")
if "--disable-nan-check" not in sys.argv:
sys.argv.append("--disable-nan-check")
os.environ['COMMANDLINE_ARGS'] = ""
def start():
print(f"Launching {'API server' if '--nowebui' in sys.argv else 'Web UI'} with arguments: {' '.join(sys.argv[1:])}")
import webui
if '--nowebui' in sys.argv:
webui.api_only()
else:
webui.webui()

6
modules/localization.py
View File

@@ -1,8 +1,7 @@
import json
import os
import sys
import traceback
from modules import errors
localizations = {}
@@ -31,7 +30,6 @@ def localization_js(current_localization_name: str) -> str:
with open(fn, "r", encoding="utf8") as file:
data = json.load(file)
except Exception:
print(f"Error loading localization from {fn}:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
errors.report(f"Error loading localization from {fn}", exc_info=True)
return f"window.localization = {json.dumps(data)}"

1
modules/paths.py
View File

@@ -20,7 +20,6 @@ assert sd_path is not None, f"Couldn't find Stable Diffusion in any of: {possibl
path_dirs = [
(sd_path, 'ldm', 'Stable Diffusion', []),
(os.path.join(sd_path, '../taming-transformers'), 'taming', 'Taming Transformers', []),
(os.path.join(sd_path, '../CodeFormer'), 'inference_codeformer.py', 'CodeFormer', []),
(os.path.join(sd_path, '../BLIP'), 'models/blip.py', 'BLIP', []),
(os.path.join(sd_path, '../k-diffusion'), 'k_diffusion/sampling.py', 'k_diffusion', ["atstart"]),

17
modules/processing.py
View File

@@ -1,4 +1,5 @@
import json
import logging
import math
import os
import sys
@@ -13,7 +14,7 @@ from skimage import exposure
from typing import Any, Dict, List
import modules.sd_hijack
from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common
from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet
from modules.sd_hijack import model_hijack
from modules.shared import opts, cmd_opts, state
import modules.shared as shared
@@ -23,7 +24,6 @@ import modules.images as images
import modules.styles
import modules.sd_models as sd_models
import modules.sd_vae as sd_vae
import logging
from ldm.data.util import AddMiDaS
from ldm.models.diffusion.ddpm import LatentDepth2ImageDiffusion
@@ -321,14 +321,13 @@ class StableDiffusionProcessing:
have been used before. The second element is where the previously
computed result is stored.
"""
if cache[0] is not None and (required_prompts, steps) == cache[0]:
if cache[0] is not None and (required_prompts, steps, opts.CLIP_stop_at_last_layers, shared.sd_model.sd_checkpoint_info) == cache[0]:
return cache[1]
with devices.autocast():
cache[1] = function(shared.sd_model, required_prompts, steps)
cache[0] = (required_prompts, steps)
cache[0] = (required_prompts, steps, opts.CLIP_stop_at_last_layers, shared.sd_model.sd_checkpoint_info)
return cache[1]
def setup_conds(self):
@@ -589,11 +588,15 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
def process_images(p: StableDiffusionProcessing) -> Processed:
if p.scripts is not None:
p.scripts.before_process(p)
stored_opts = {k: opts.data[k] for k in p.override_settings.keys()}
try:
# if no checkpoint override or the override checkpoint can't be found, remove override entry and load opts checkpoint
if sd_models.checkpoint_alisases.get(p.override_settings.get('sd_model_checkpoint')) is None:
override_checkpoint = p.override_settings.get('sd_model_checkpoint')
if override_checkpoint is not None and sd_models.checkpoint_alisases.get(override_checkpoint) is None:
p.override_settings.pop('sd_model_checkpoint', None)
sd_models.reload_model_weights()
@@ -674,6 +677,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
if shared.opts.live_previews_enable and opts.show_progress_type == "Approx NN":
sd_vae_approx.model()
sd_unet.apply_unet()
if state.job_count == -1:
state.job_count = p.n_iter

16
modules/realesrgan_model.py
View File

@@ -1,6 +1,4 @@
import os
import sys
import traceback
import numpy as np
from PIL import Image
@@ -9,7 +7,8 @@ from realesrgan import RealESRGANer
from modules.upscaler import Upscaler, UpscalerData
from modules.shared import cmd_opts, opts
from modules import modelloader
from modules import modelloader, errors
class UpscalerRealESRGAN(Upscaler):
def __init__(self, path):
@@ -36,8 +35,7 @@ class UpscalerRealESRGAN(Upscaler):
self.scalers.append(scaler)
except Exception:
print("Error importing Real-ESRGAN:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
errors.report("Error importing Real-ESRGAN", exc_info=True)
self.enable = False
self.scalers = []
@@ -76,9 +74,8 @@ class UpscalerRealESRGAN(Upscaler):
info.local_data_path = load_file_from_url(url=info.data_path, model_dir=self.model_download_path, progress=True)
return info
except Exception as e:
print(f"Error making Real-ESRGAN models list: {e}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
except Exception:
errors.report("Error making Real-ESRGAN models list", exc_info=True)
return None
def load_models(self, _):
@@ -135,5 +132,4 @@ def get_realesrgan_models(scaler):
]
return models
except Exception:
print("Error making Real-ESRGAN models list:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
errors.report("Error making Real-ESRGAN models list", exc_info=True)

27
modules/safe.py
View File

@@ -2,8 +2,6 @@
import pickle
import collections
import sys
import traceback
import torch
import numpy
@@ -11,7 +9,10 @@ import _codecs
import zipfile
import re
# PyTorch 1.13 and later have _TypedStorage renamed to TypedStorage
from modules import errors
TypedStorage = torch.storage.TypedStorage if hasattr(torch.storage, 'TypedStorage') else torch.storage._TypedStorage
def encode(*args):
@@ -136,17 +137,20 @@ def load_with_extra(filename, extra_handler=None, *args, **kwargs):
check_pt(filename, extra_handler)
except pickle.UnpicklingError:
print(f"Error verifying pickled file from {filename}:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
print("-----> !!!! The file is most likely corrupted !!!! <-----", file=sys.stderr)
print("You can skip this check with --disable-safe-unpickle commandline argument, but that is not going to help you.\n\n", file=sys.stderr)
errors.report(
f"Error verifying pickled file from {filename}\n"
"-----> !!!! The file is most likely corrupted !!!! <-----\n"
"You can skip this check with --disable-safe-unpickle commandline argument, but that is not going to help you.\n\n",
exc_info=True,
)
return None
except Exception:
print(f"Error verifying pickled file from {filename}:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
print("\nThe file may be malicious, so the program is not going to read it.", file=sys.stderr)
print("You can skip this check with --disable-safe-unpickle commandline argument.\n\n", file=sys.stderr)
errors.report(
f"Error verifying pickled file from {filename}\n"
f"The file may be malicious, so the program is not going to read it.\n"
f"You can skip this check with --disable-safe-unpickle commandline argument.\n\n",
exc_info=True,
)
return None
return unsafe_torch_load(filename, *args, **kwargs)
@@ -190,4 +194,3 @@ with safe.Extra(handler):
unsafe_torch_load = torch.load
torch.load = load
global_extra_handler = None

29
modules/script_callbacks.py
View File

@@ -1,18 +1,15 @@
import sys
import traceback
from collections import namedtuple
import inspect
from collections import namedtuple
from typing import Optional, Dict, Any
from fastapi import FastAPI
from gradio import Blocks
from modules import timer
from modules import errors, timer
def report_exception(c, job):
print(f"Error executing callback {job} for {c.script}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
errors.report(f"Error executing callback {job} for {c.script}", exc_info=True)
class ImageSaveParams:
@@ -113,6 +110,7 @@ callback_map = dict(
callbacks_before_ui=[],
callbacks_on_reload=[],
callbacks_list_optimizers=[],
callbacks_list_unets=[],
)
@@ -274,6 +272,18 @@ def list_optimizers_callback():
return res
def list_unets_callback():
res = []
for c in callback_map['callbacks_list_unets']:
try:
c.callback(res)
except Exception:
report_exception(c, 'list_unets')
return res
def add_callback(callbacks, fun):
stack = [x for x in inspect.stack() if x.filename != __file__]
filename = stack[0].filename if len(stack) > 0 else 'unknown file'
@@ -433,3 +443,10 @@ def on_list_optimizers(callback):
to it."""
add_callback(callback_map['callbacks_list_optimizers'], callback)
def on_list_unets(callback):
"""register a function to be called when UI is making a list of alternative options for unet.
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)

7
modules/script_loading.py
View File

@@ -1,8 +1,8 @@
import os
import sys
import traceback
import importlib.util
from modules import errors
def load_module(path):
module_spec = importlib.util.spec_from_file_location(os.path.basename(path), path)
@@ -27,5 +27,4 @@ def preload_extensions(extensions_dir, parser):
module.preload(parser)
except Exception:
print(f"Error running preload() for {preload_script}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
errors.report(f"Error running preload() for {preload_script}", exc_info=True)

180
modules/scripts.py
View File

@@ -1,12 +1,11 @@
import os
import re
import sys
import traceback
from collections import namedtuple
import gradio as gr
from modules import shared, paths, script_callbacks, extensions, script_loading, scripts_postprocessing, timer
from modules import shared, paths, script_callbacks, extensions, script_loading, scripts_postprocessing, errors, timer
AlwaysVisible = object()
@@ -20,6 +19,9 @@ class Script:
name = None
"""script's internal name derived from title"""
section = None
"""name of UI section that the script's controls will be placed into"""
filename = None
args_from = None
args_to = None
@@ -82,6 +84,15 @@ class Script:
pass
def before_process(self, p, *args):
"""
This function is called very early before processing begins for AlwaysVisible scripts.
You can modify the processing object (p) here, inject hooks, etc.
args contains all values returned by components from ui()
"""
pass
def process(self, p, *args):
"""
This function is called before processing begins for AlwaysVisible scripts.
@@ -264,8 +275,7 @@ def load_scripts():
register_scripts_from_module(script_module)
except Exception:
print(f"Error loading script: {scriptfile.filename}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
errors.report(f"Error loading script: {scriptfile.filename}", exc_info=True)
finally:
sys.path = syspath
@@ -281,11 +291,9 @@ def load_scripts():
def wrap_call(func, filename, funcname, *args, default=None, **kwargs):
try:
res = func(*args, **kwargs)
return res
return func(*args, **kwargs)
except Exception:
print(f"Error calling: {filename}/{funcname}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
errors.report(f"Error calling: {filename}/{funcname}", exc_info=True)
return default
@@ -298,6 +306,7 @@ class ScriptRunner:
self.titles = []
self.infotext_fields = []
self.paste_field_names = []
self.inputs = [None]
def initialize_scripts(self, is_img2img):
from modules import scripts_auto_postprocessing
@@ -325,69 +334,73 @@ class ScriptRunner:
self.scripts.append(script)
self.selectable_scripts.append(script)
def setup_ui(self):
def create_script_ui(self, script):
import modules.api.models as api_models
script.args_from = len(self.inputs)
script.args_to = len(self.inputs)
controls = wrap_call(script.ui, script.filename, "ui", script.is_img2img)
if controls is None:
return
script.name = wrap_call(script.title, script.filename, "title", default=script.filename).lower()
api_args = []
for control in controls:
control.custom_script_source = os.path.basename(script.filename)
arg_info = api_models.ScriptArg(label=control.label or "")
for field in ("value", "minimum", "maximum", "step", "choices"):
v = getattr(control, field, None)
if v is not None:
setattr(arg_info, field, v)
api_args.append(arg_info)
script.api_info = api_models.ScriptInfo(
name=script.name,
is_img2img=script.is_img2img,
is_alwayson=script.alwayson,
args=api_args,
)
if script.infotext_fields is not None:
self.infotext_fields += script.infotext_fields
if script.paste_field_names is not None:
self.paste_field_names += script.paste_field_names
self.inputs += controls
script.args_to = len(self.inputs)
def setup_ui_for_section(self, section, scriptlist=None):
if scriptlist is None:
scriptlist = self.alwayson_scripts
for script in scriptlist:
if script.alwayson and script.section != section:
continue
with gr.Group(visible=script.alwayson) as group:
self.create_script_ui(script)
script.group = group
def prepare_ui(self):
self.inputs = [None]
def setup_ui(self):
self.titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.selectable_scripts]
inputs = [None]
inputs_alwayson = [True]
def create_script_ui(script, inputs, inputs_alwayson):
script.args_from = len(inputs)
script.args_to = len(inputs)
controls = wrap_call(script.ui, script.filename, "ui", script.is_img2img)
if controls is None:
return
script.name = wrap_call(script.title, script.filename, "title", default=script.filename).lower()
api_args = []
for control in controls:
control.custom_script_source = os.path.basename(script.filename)
arg_info = api_models.ScriptArg(label=control.label or "")
for field in ("value", "minimum", "maximum", "step", "choices"):
v = getattr(control, field, None)
if v is not None:
setattr(arg_info, field, v)
api_args.append(arg_info)
script.api_info = api_models.ScriptInfo(
name=script.name,
is_img2img=script.is_img2img,
is_alwayson=script.alwayson,
args=api_args,
)
if script.infotext_fields is not None:
self.infotext_fields += script.infotext_fields
if script.paste_field_names is not None:
self.paste_field_names += script.paste_field_names
inputs += controls
inputs_alwayson += [script.alwayson for _ in controls]
script.args_to = len(inputs)
for script in self.alwayson_scripts:
with gr.Group() as group:
create_script_ui(script, inputs, inputs_alwayson)
script.group = group
self.setup_ui_for_section(None)
dropdown = gr.Dropdown(label="Script", elem_id="script_list", choices=["None"] + self.titles, value="None", type="index")
inputs[0] = dropdown
self.inputs[0] = dropdown
for script in self.selectable_scripts:
with gr.Group(visible=False) as group:
create_script_ui(script, inputs, inputs_alwayson)
script.group = group
self.setup_ui_for_section(None, self.selectable_scripts)
def select_script(script_index):
selected_script = self.selectable_scripts[script_index - 1] if script_index>0 else None
@@ -412,6 +425,7 @@ class ScriptRunner:
)
self.script_load_ctr = 0
def onload_script_visibility(params):
title = params.get('Script', None)
if title:
@@ -422,10 +436,10 @@ class ScriptRunner:
else:
return gr.update(visible=False)
self.infotext_fields.append( (dropdown, lambda x: gr.update(value=x.get('Script', 'None'))) )
self.infotext_fields.extend( [(script.group, onload_script_visibility) for script in self.selectable_scripts] )
self.infotext_fields.append((dropdown, lambda x: gr.update(value=x.get('Script', 'None'))))
self.infotext_fields.extend([(script.group, onload_script_visibility) for script in self.selectable_scripts])
return inputs
return self.inputs
def run(self, p, *args):
script_index = args[0]
@@ -445,14 +459,21 @@ class ScriptRunner:
return processed
def before_process(self, p):
for script in self.alwayson_scripts:
try:
script_args = p.script_args[script.args_from:script.args_to]
script.before_process(p, *script_args)
except Exception:
errors.report(f"Error running before_process: {script.filename}", exc_info=True)
def process(self, p):
for script in self.alwayson_scripts:
try:
script_args = p.script_args[script.args_from:script.args_to]
script.process(p, *script_args)
except Exception:
print(f"Error running process: {script.filename}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
errors.report(f"Error running process: {script.filename}", exc_info=True)
def before_process_batch(self, p, **kwargs):
for script in self.alwayson_scripts:
@@ -460,8 +481,7 @@ class ScriptRunner:
script_args = p.script_args[script.args_from:script.args_to]
script.before_process_batch(p, *script_args, **kwargs)
except Exception:
print(f"Error running before_process_batch: {script.filename}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
errors.report(f"Error running before_process_batch: {script.filename}", exc_info=True)
def process_batch(self, p, **kwargs):
for script in self.alwayson_scripts:
@@ -469,8 +489,7 @@ class ScriptRunner:
script_args = p.script_args[script.args_from:script.args_to]
script.process_batch(p, *script_args, **kwargs)
except Exception:
print(f"Error running process_batch: {script.filename}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
errors.report(f"Error running process_batch: {script.filename}", exc_info=True)
def postprocess(self, p, processed):
for script in self.alwayson_scripts:
@@ -478,8 +497,7 @@ class ScriptRunner:
script_args = p.script_args[script.args_from:script.args_to]
script.postprocess(p, processed, *script_args)
except Exception:
print(f"Error running postprocess: {script.filename}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
errors.report(f"Error running postprocess: {script.filename}", exc_info=True)
def postprocess_batch(self, p, images, **kwargs):
for script in self.alwayson_scripts:
@@ -487,8 +505,7 @@ class ScriptRunner:
script_args = p.script_args[script.args_from:script.args_to]
script.postprocess_batch(p, *script_args, images=images, **kwargs)
except Exception:
print(f"Error running postprocess_batch: {script.filename}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
errors.report(f"Error running postprocess_batch: {script.filename}", exc_info=True)
def postprocess_image(self, p, pp: PostprocessImageArgs):
for script in self.alwayson_scripts:
@@ -496,24 +513,21 @@ class ScriptRunner:
script_args = p.script_args[script.args_from:script.args_to]
script.postprocess_image(p, pp, *script_args)
except Exception:
print(f"Error running postprocess_batch: {script.filename}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
errors.report(f"Error running postprocess_image: {script.filename}", exc_info=True)
def before_component(self, component, **kwargs):
for script in self.scripts:
try:
script.before_component(component, **kwargs)
except Exception:
print(f"Error running before_component: {script.filename}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
errors.report(f"Error running before_component: {script.filename}", exc_info=True)
def after_component(self, component, **kwargs):
for script in self.scripts:
try:
script.after_component(component, **kwargs)
except Exception:
print(f"Error running after_component: {script.filename}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
errors.report(f"Error running after_component: {script.filename}", exc_info=True)
def reload_sources(self, cache):
for si, script in list(enumerate(self.scripts)):

37
modules/sd_hijack.py
View File

@@ -3,7 +3,7 @@ from torch.nn.functional import silu
from types import MethodType
import modules.textual_inversion.textual_inversion
from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors
from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors, sd_unet
from modules.hypernetworks import hypernetwork
from modules.shared import cmd_opts
from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr
@@ -43,11 +43,16 @@ def list_optimizers():
optimizers.extend(new_optimizers)
def apply_optimizations():
def apply_optimizations(option=None):
global current_optimizer
undo_optimizations()
if len(optimizers) == 0:
# a script can access the model very early, and optimizations would not be filled by then
current_optimizer = None
return ''
ldm.modules.diffusionmodules.model.nonlinearity = silu
ldm.modules.diffusionmodules.openaimodel.th = sd_hijack_unet.th
@@ -55,7 +60,7 @@ def apply_optimizations():
current_optimizer.undo()
current_optimizer = None
selection = shared.opts.cross_attention_optimization
selection = option or shared.opts.cross_attention_optimization
if selection == "Automatic" and len(optimizers) > 0:
matching_optimizer = next(iter([x for x in optimizers if x.cmd_opt and getattr(shared.cmd_opts, x.cmd_opt, False)]), optimizers[0])
else:
@@ -63,15 +68,19 @@ def apply_optimizations():
if selection == "None":
matching_optimizer = None
elif selection == "Automatic" and shared.cmd_opts.disable_opt_split_attention:
matching_optimizer = None
elif matching_optimizer is None:
matching_optimizer = optimizers[0]
if matching_optimizer is not None:
print(f"Applying optimization: {matching_optimizer.name}")
print(f"Applying attention optimization: {matching_optimizer.name}... ", end='')
matching_optimizer.apply()
print("done.")
current_optimizer = matching_optimizer
return current_optimizer.name
else:
print("Disabling attention optimization")
return ''
@@ -149,6 +158,13 @@ class StableDiffusionModelHijack:
def __init__(self):
self.embedding_db.add_embedding_dir(cmd_opts.embeddings_dir)
def apply_optimizations(self, option=None):
try:
self.optimization_method = apply_optimizations(option)
except Exception as e:
errors.display(e, "applying cross attention optimization")
undo_optimizations()
def hijack(self, m):
if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
model_embeddings = m.cond_stage_model.roberta.embeddings
@@ -168,11 +184,7 @@ class StableDiffusionModelHijack:
if m.cond_stage_key == "edit":
sd_hijack_unet.hijack_ddpm_edit()
try:
self.optimization_method = apply_optimizations()
except Exception as e:
errors.display(e, "applying cross attention optimization")
undo_optimizations()
self.apply_optimizations()
self.clip = m.cond_stage_model
@@ -185,6 +197,11 @@ class StableDiffusionModelHijack:
self.layers = flatten(m)
if not hasattr(ldm.modules.diffusionmodules.openaimodel, 'copy_of_UNetModel_forward_for_webui'):
ldm.modules.diffusionmodules.openaimodel.copy_of_UNetModel_forward_for_webui = ldm.modules.diffusionmodules.openaimodel.UNetModel.forward
ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = sd_unet.UNetModel_forward
def undo_hijack(self, m):
if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
m.cond_stage_model = m.cond_stage_model.wrapped
@@ -206,6 +223,8 @@ class StableDiffusionModelHijack:
self.layers = None
self.clip = None
ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = ldm.modules.diffusionmodules.openaimodel.copy_of_UNetModel_forward_for_webui
def apply_circular(self, enable):
if self.circular_enabled == enable:
return

12
modules/sd_hijack_optimizations.py
View File

@@ -1,6 +1,5 @@
from __future__ import annotations
import math
import sys
import traceback
import psutil
import torch
@@ -58,7 +57,7 @@ class SdOptimizationSdpNoMem(SdOptimization):
name = "sdp-no-mem"
label = "scaled dot product without memory efficient attention"
cmd_opt = "opt_sdp_no_mem_attention"
priority = 90
priority = 80
def is_available(self):
return hasattr(torch.nn.functional, "scaled_dot_product_attention") and callable(torch.nn.functional.scaled_dot_product_attention)
@@ -72,7 +71,7 @@ class SdOptimizationSdp(SdOptimizationSdpNoMem):
name = "sdp"
label = "scaled dot product"
cmd_opt = "opt_sdp_attention"
priority = 80
priority = 70
def apply(self):
ldm.modules.attention.CrossAttention.forward = scaled_dot_product_attention_forward
@@ -115,7 +114,7 @@ class SdOptimizationInvokeAI(SdOptimization):
class SdOptimizationDoggettx(SdOptimization):
name = "Doggettx"
cmd_opt = "opt_split_attention"
priority = 20
priority = 90
def apply(self):
ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward
@@ -139,8 +138,7 @@ if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers:
import xformers.ops
shared.xformers_available = True
except Exception:
print("Cannot import xformers", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
errors.report("Cannot import xformers", exc_info=True)
def get_available_vram():

26
modules/sd_models.py
View File

@@ -14,7 +14,7 @@ import ldm.modules.midas as midas
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
from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config, sd_unet
from modules.sd_hijack_inpainting import do_inpainting_hijack
from modules.timer import Timer
import tomesd
@@ -164,6 +164,7 @@ def model_hash(filename):
def select_checkpoint():
"""Raises `FileNotFoundError` if no checkpoints are found."""
model_checkpoint = shared.opts.sd_model_checkpoint
checkpoint_info = checkpoint_alisases.get(model_checkpoint, None)
@@ -171,14 +172,14 @@ def select_checkpoint():
return checkpoint_info
if len(checkpoints_list) == 0:
print("No checkpoints found. When searching for checkpoints, looked at:", file=sys.stderr)
error_message = "No checkpoints found. When searching for checkpoints, looked at:"
if shared.cmd_opts.ckpt is not None:
print(f" - file {os.path.abspath(shared.cmd_opts.ckpt)}", file=sys.stderr)
print(f" - directory {model_path}", file=sys.stderr)
error_message += f"\n - file {os.path.abspath(shared.cmd_opts.ckpt)}"
error_message += f"\n - directory {model_path}"
if shared.cmd_opts.ckpt_dir is not None:
print(f" - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}", file=sys.stderr)
print("Can't run without a checkpoint. Find and place a .ckpt or .safetensors file into any of those locations. The program will exit.", file=sys.stderr)
exit(1)
error_message += f"\n - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}"
error_message += "Can't run without a checkpoint. Find and place a .ckpt or .safetensors file into any of those locations."
raise FileNotFoundError(error_message)
checkpoint_info = next(iter(checkpoints_list.values()))
if model_checkpoint is not None:
@@ -313,8 +314,6 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
timer.record("apply half()")
devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
devices.dtype_unet = model.model.diffusion_model.dtype
devices.unet_needs_upcast = shared.cmd_opts.upcast_sampling and devices.dtype == torch.float16 and devices.dtype_unet == torch.float16
@@ -423,7 +422,7 @@ class SdModelData:
try:
load_model()
except Exception as e:
errors.display(e, "loading stable diffusion model")
errors.display(e, "loading stable diffusion model", full_traceback=True)
print("", file=sys.stderr)
print("Stable diffusion model failed to load", file=sys.stderr)
self.sd_model = None
@@ -508,6 +507,11 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None):
timer.record("scripts callbacks")
with devices.autocast(), torch.no_grad():
sd_model.cond_stage_model_empty_prompt = sd_model.cond_stage_model([""])
timer.record("calculate empty prompt")
print(f"Model loaded in {timer.summary()}.")
return sd_model
@@ -527,6 +531,8 @@ def reload_model_weights(sd_model=None, info=None):
if sd_model.sd_model_checkpoint == checkpoint_info.filename:
return
sd_unet.apply_unet("None")
if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
lowvram.send_everything_to_cpu()
else:

64
modules/sd_samplers_kdiffusion.py
View File

@@ -19,7 +19,8 @@ samplers_k_diffusion = [
('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True, "uses_ensd": True}),
('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {"uses_ensd": True, "second_order": True}),
('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {"second_order": True}),
('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {"second_order": True, "brownian_noise": True}),
('DPM++ 2M SDE', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {"brownian_noise": True}),
('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {"uses_ensd": True}),
('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {"uses_ensd": True}),
('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
@@ -27,7 +28,8 @@ samplers_k_diffusion = [
('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras', "uses_ensd": True, "second_order": True}),
('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras', "second_order": True}),
('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras', "second_order": True, "brownian_noise": True}),
('DPM++ 2M SDE Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {'scheduler': 'karras', "brownian_noise": True}),
]
samplers_data_k_diffusion = [
@@ -42,6 +44,14 @@ sampler_extra_params = {
'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
}
k_diffusion_samplers_map = {x.name: x for x in samplers_data_k_diffusion}
k_diffusion_scheduler = {
'Automatic': None,
'karras': k_diffusion.sampling.get_sigmas_karras,
'exponential': k_diffusion.sampling.get_sigmas_exponential,
'polyexponential': k_diffusion.sampling.get_sigmas_polyexponential
}
class CFGDenoiser(torch.nn.Module):
"""
@@ -123,6 +133,16 @@ class CFGDenoiser(torch.nn.Module):
x_in = x_in[:-batch_size]
sigma_in = sigma_in[:-batch_size]
# TODO add infotext entry
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 = torch.cat([tensor, empty.repeat((tensor.shape[0], -num_repeats, 1))], axis=1)
elif num_repeats > 0:
uncond = torch.cat([uncond, empty.repeat((uncond.shape[0], num_repeats, 1))], axis=1)
if tensor.shape[1] == uncond.shape[1] or skip_uncond:
if is_edit_model:
cond_in = torch.cat([tensor, uncond, uncond])
@@ -228,7 +248,7 @@ class KDiffusionSampler:
self.sampler_noises = None
self.stop_at = None
self.eta = None
self.config = None
self.config = None # set by the function calling the constructor
self.last_latent = None
self.s_min_uncond = None
@@ -253,6 +273,13 @@ class KDiffusionSampler:
try:
return func()
except RecursionError:
print(
'Encountered RecursionError during sampling, returning last latent. '
'rho >5 with a polyexponential scheduler may cause this error. '
'You should try to use a smaller rho value instead.'
)
return self.last_latent
except sd_samplers_common.InterruptedException:
return self.last_latent
@@ -292,6 +319,31 @@ class KDiffusionSampler:
if p.sampler_noise_scheduler_override:
sigmas = p.sampler_noise_scheduler_override(steps)
elif opts.k_sched_type != "Automatic":
m_sigma_min, m_sigma_max = (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (m_sigma_min, m_sigma_max)
sigmas_kwargs = {
'sigma_min': sigma_min,
'sigma_max': sigma_max,
}
sigmas_func = k_diffusion_scheduler[opts.k_sched_type]
p.extra_generation_params["Schedule type"] = opts.k_sched_type
if opts.sigma_min != m_sigma_min and opts.sigma_min != 0:
sigmas_kwargs['sigma_min'] = opts.sigma_min
p.extra_generation_params["Schedule min sigma"] = opts.sigma_min
if opts.sigma_max != m_sigma_max and opts.sigma_max != 0:
sigmas_kwargs['sigma_max'] = opts.sigma_max
p.extra_generation_params["Schedule max sigma"] = opts.sigma_max
default_rho = 1. if opts.k_sched_type == "polyexponential" else 7.
if opts.k_sched_type != 'exponential' and opts.rho != 0 and opts.rho != default_rho:
sigmas_kwargs['rho'] = opts.rho
p.extra_generation_params["Schedule rho"] = opts.rho
sigmas = sigmas_func(n=steps, **sigmas_kwargs, device=shared.device)
elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
@@ -337,13 +389,13 @@ class KDiffusionSampler:
if 'sigmas' in parameters:
extra_params_kwargs['sigmas'] = sigma_sched
if self.funcname == 'sample_dpmpp_sde':
if self.config.options.get('brownian_noise', False):
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={
extra_args = {
'cond': conditioning,
'image_cond': image_conditioning,
'uncond': unconditional_conditioning,
@@ -373,7 +425,7 @@ class KDiffusionSampler:
else:
extra_params_kwargs['sigmas'] = sigmas
if self.funcname == 'sample_dpmpp_sde':
if self.config.options.get('brownian_noise', False):
noise_sampler = self.create_noise_sampler(x, sigmas, p)
extra_params_kwargs['noise_sampler'] = noise_sampler

92
modules/sd_unet.py Normal file
View File

@@ -0,0 +1,92 @@
import torch.nn
import ldm.modules.diffusionmodules.openaimodel
from modules import script_callbacks, shared, devices
unet_options = []
current_unet_option = None
current_unet = None
def list_unets():
new_unets = script_callbacks.list_unets_callback()
unet_options.clear()
unet_options.extend(new_unets)
def get_unet_option(option=None):
option = option or shared.opts.sd_unet
if option == "None":
return None
if option == "Automatic":
name = shared.sd_model.sd_checkpoint_info.model_name
options = [x for x in unet_options if x.model_name == name]
option = options[0].label if options else "None"
return next(iter([x for x in unet_options if x.label == option]), None)
def apply_unet(option=None):
global current_unet_option
global current_unet
new_option = get_unet_option(option)
if new_option == current_unet_option:
return
if current_unet is not None:
print(f"Dectivating unet: {current_unet.option.label}")
current_unet.deactivate()
current_unet_option = new_option
if current_unet_option is None:
current_unet = None
if not (shared.cmd_opts.lowvram or shared.cmd_opts.medvram):
shared.sd_model.model.diffusion_model.to(devices.device)
return
shared.sd_model.model.diffusion_model.to(devices.cpu)
devices.torch_gc()
current_unet = current_unet_option.create_unet()
current_unet.option = current_unet_option
print(f"Activating unet: {current_unet.option.label}")
current_unet.activate()
class SdUnetOption:
model_name = None
"""name of related checkpoint - this option will be selected automatically for unet if the name of checkpoint matches this"""
label = None
"""name of the unet in UI"""
def create_unet(self):
"""returns SdUnet object to be used as a Unet instead of built-in unet when making pictures"""
raise NotImplementedError()
class SdUnet(torch.nn.Module):
def forward(self, x, timesteps, context, *args, **kwargs):
raise NotImplementedError()
def activate(self):
pass
def deactivate(self):
pass
def UNetModel_forward(self, x, timesteps=None, context=None, *args, **kwargs):
if current_unet is not None:
return current_unet.forward(x, timesteps, context, *args, **kwargs)
return ldm.modules.diffusionmodules.openaimodel.copy_of_UNetModel_forward_for_webui(self, x, timesteps, context, *args, **kwargs)

35
modules/shared.py
View File

@@ -6,6 +6,7 @@ import threading
import time
import gradio as gr
import torch
import tqdm
import modules.interrogate
@@ -43,19 +44,6 @@ restricted_opts = {
"outdir_init_images"
}
ui_reorder_categories = [
"inpaint",
"sampler",
"checkboxes",
"hires_fix",
"dimensions",
"cfg",
"seed",
"batch",
"override_settings",
"scripts",
]
# https://huggingface.co/datasets/freddyaboulton/gradio-theme-subdomains/resolve/main/subdomains.json
gradio_hf_hub_themes = [
"gradio/glass",
@@ -76,6 +64,9 @@ cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or cmd_op
devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_esrgan, devices.device_codeformer = \
(devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'esrgan', 'codeformer'])
devices.dtype = torch.float32 if cmd_opts.no_half else torch.float16
devices.dtype_vae = torch.float32 if cmd_opts.no_half or cmd_opts.no_half_vae else torch.float16
device = devices.device
weight_load_location = None if cmd_opts.lowram else "cpu"
@@ -314,6 +305,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
"grid_extended_filename": OptionInfo(False, "Add extended info (seed, prompt) to filename when saving grid"),
"grid_only_if_multiple": OptionInfo(True, "Do not save grids consisting of one picture"),
"grid_prevent_empty_spots": OptionInfo(False, "Prevent empty spots in grid (when set to autodetect)"),
"grid_zip_filename_pattern": OptionInfo("", "Archive filename pattern", component_args=hide_dirs).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"),
"n_rows": OptionInfo(-1, "Grid row count; use -1 for autodetect and 0 for it to be same as batch size", gr.Slider, {"minimum": -1, "maximum": 16, "step": 1}),
"enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
@@ -403,6 +395,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
"sd_vae_checkpoint_cache": OptionInfo(0, "VAE Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
"sd_vae": OptionInfo("Automatic", "SD VAE", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list).info("choose VAE model: Automatic = use one with same filename as checkpoint; None = use VAE from checkpoint"),
"sd_vae_as_default": OptionInfo(True, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"),
"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"),
"inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
"initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for img2img", gr.Slider, {"minimum": 0.5, "maximum": 1.5, "step": 0.01}),
"img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
@@ -414,15 +407,16 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
"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}).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#clip-skip").info("ignore last layers of CLIP nrtwork; 1 ignores none, 2 ignores one layer"),
"upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),
"randn_source": OptionInfo("GPU", "Random number generator source.", gr.Radio, {"choices": ["GPU", "CPU"]}).info("changes seeds drastically; use CPU to produce the same picture across different vidocard vendors"),
"randn_source": OptionInfo("GPU", "Random number generator source.", gr.Radio, {"choices": ["GPU", "CPU"]}).info("changes seeds drastically; use CPU to produce the same picture across different videocard vendors"),
}))
options_templates.update(options_section(('optimizations', "Optimizations"), {
"cross_attention_optimization": OptionInfo("Automatic", "Cross attention optimization", gr.Dropdown, lambda: {"choices": shared_items.cross_attention_optimizations()}),
"s_min_uncond": OptionInfo(0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 4.0, "step": 0.01}).link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9177").info("skip negative prompt for some steps when the image is almost ready; 0=disable, higher=faster"),
"s_min_uncond": OptionInfo(0.0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 4.0, "step": 0.01}).link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9177").info("skip negative prompt for some steps when the image is almost ready; 0=disable, higher=faster"),
"token_merging_ratio": OptionInfo(0.0, "Token merging ratio", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).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}).info("only applies if non-zero and overrides above"),
"pad_cond_uncond": OptionInfo(False, "Pad prompt/negative prompt to be same length").info("improves performance when prompt and negative prompt have different lengths; changes seeds"),
}))
options_templates.update(options_section(('compatibility', "Compatibility"), {
@@ -484,9 +478,10 @@ options_templates.update(options_section(('ui', "User interface"), {
"quicksettings_list": OptionInfo(["sd_model_checkpoint"], "Quicksettings list", ui_components.DropdownMulti, lambda: {"choices": list(opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that appear at the top of page rather than in settings tab").needs_restart(),
"ui_tab_order": OptionInfo([], "UI tab order", ui_components.DropdownMulti, lambda: {"choices": list(tab_names)}).needs_restart(),
"hidden_tabs": OptionInfo([], "Hidden UI tabs", ui_components.DropdownMulti, lambda: {"choices": list(tab_names)}).needs_restart(),
"ui_reorder": OptionInfo(", ".join(ui_reorder_categories), "txt2img/img2img UI item order").needs_restart(),
"ui_reorder_list": OptionInfo([], "txt2img/img2img UI item order", ui_components.DropdownMulti, lambda: {"choices": list(shared_items.ui_reorder_categories())}).info("selected items appear first").needs_restart(),
"hires_fix_show_sampler": OptionInfo(False, "Hires fix: show hires sampler selection").needs_restart(),
"hires_fix_show_prompts": OptionInfo(False, "Hires fix: show hires prompt and negative prompt").needs_restart(),
"disable_token_counters": OptionInfo(False, "Disable prompt token counters").needs_restart(),
}))
options_templates.update(options_section(('infotext', "Infotext"), {
@@ -515,6 +510,10 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
'k_sched_type': OptionInfo("Automatic", "scheduler type", gr.Dropdown, {"choices": ["Automatic", "karras", "exponential", "polyexponential"]}).info("lets you override the noise schedule for k-diffusion samplers; choosing Automatic disables the three parameters below"),
'sigma_min': OptionInfo(0.0, "sigma min", gr.Number).info("0 = default (~0.03); minimum noise strength for k-diffusion noise scheduler"),
'sigma_max': OptionInfo(0.0, "sigma max", gr.Number).info("0 = default (~14.6); maximum noise strength for k-diffusion noise schedule"),
'rho': OptionInfo(0.0, "rho", gr.Number).info("0 = default (7 for karras, 1 for polyexponential); higher values result in a more steep noise schedule (decreases faster)"),
'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}).info("ENSD; does not improve anything, just produces different results for ancestral samplers - only useful for reproducing images"),
'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma").link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/6044"),
'uni_pc_variant': OptionInfo("bh1", "UniPC variant", gr.Radio, {"choices": ["bh1", "bh2", "vary_coeff"]}),
@@ -630,6 +629,10 @@ class Options:
if self.data.get('quicksettings') is not None and self.data.get('quicksettings_list') is None:
self.data['quicksettings_list'] = [i.strip() for i in self.data.get('quicksettings').split(',')]
# 1.4.0 ui_reorder
if isinstance(self.data.get('ui_reorder'), str) and self.data.get('ui_reorder') and "ui_reorder_list" not in self.data:
self.data['ui_reorder_list'] = [i.strip() for i in self.data.get('ui_reorder').split(',')]
bad_settings = 0
for k, v in self.data.items():
info = self.data_labels.get(k, None)

38
modules/shared_items.py
View File

@@ -29,3 +29,41 @@ def cross_attention_optimizations():
return ["Automatic"] + [x.title() for x in modules.sd_hijack.optimizers] + ["None"]
def sd_unet_items():
import modules.sd_unet
return ["Automatic"] + [x.label for x in modules.sd_unet.unet_options] + ["None"]
def refresh_unet_list():
import modules.sd_unet
modules.sd_unet.list_unets()
ui_reorder_categories_builtin_items = [
"inpaint",
"sampler",
"checkboxes",
"hires_fix",
"dimensions",
"cfg",
"seed",
"batch",
"override_settings",
]
def ui_reorder_categories():
from modules import scripts
yield from ui_reorder_categories_builtin_items
sections = {}
for script in scripts.scripts_txt2img.scripts + scripts.scripts_img2img.scripts:
if isinstance(script.section, str):
sections[script.section] = 1
yield from sections
yield "scripts"

21
modules/textual_inversion/image_embedding.py
View File

@@ -1,8 +1,10 @@
import base64
import json
import warnings
import numpy as np
import zlib
from PIL import Image, ImageDraw, ImageFont
from PIL import Image, ImageDraw
import torch
@@ -129,14 +131,17 @@ def extract_image_data_embed(image):
def caption_image_overlay(srcimage, title, footerLeft, footerMid, footerRight, textfont=None):
from modules.images import get_font
if textfont:
warnings.warn(
'passing in a textfont to caption_image_overlay is deprecated and does nothing',
DeprecationWarning,
stacklevel=2,
)
from math import cos
image = srcimage.copy()
fontsize = 32
if textfont is None:
from modules.images import get_font
textfont = get_font(fontsize)
factor = 1.5
gradient = Image.new('RGBA', (1, image.size[1]), color=(0, 0, 0, 0))
for y in range(image.size[1]):
@@ -147,12 +152,12 @@ def caption_image_overlay(srcimage, title, footerLeft, footerMid, footerRight, t
draw = ImageDraw.Draw(image)
font = ImageFont.truetype(textfont, fontsize)
font = get_font(fontsize)
padding = 10
_, _, w, h = draw.textbbox((0, 0), title, font=font)
fontsize = min(int(fontsize * (((image.size[0]*0.75)-(padding*4))/w)), 72)
font = ImageFont.truetype(textfont, fontsize)
font = get_font(fontsize)
_, _, w, h = draw.textbbox((0, 0), title, font=font)
draw.text((padding, padding), title, anchor='lt', font=font, fill=(255, 255, 255, 230))
@@ -163,7 +168,7 @@ def caption_image_overlay(srcimage, title, footerLeft, footerMid, footerRight, t
_, _, w, h = draw.textbbox((0, 0), footerRight, font=font)
fontsize_right = min(int(fontsize * (((image.size[0]/3)-(padding))/w)), 72)
font = ImageFont.truetype(textfont, min(fontsize_left, fontsize_mid, fontsize_right))
font = get_font(min(fontsize_left, fontsize_mid, fontsize_right))
draw.text((padding, image.size[1]-padding), footerLeft, anchor='ls', font=font, fill=(255, 255, 255, 230))
draw.text((image.size[0]/2, image.size[1]-padding), footerMid, anchor='ms', font=font, fill=(255, 255, 255, 230))

35
modules/textual_inversion/textual_inversion.py
View File

@@ -1,6 +1,4 @@
import os
import sys
import traceback
from collections import namedtuple
import torch
@@ -14,7 +12,7 @@ import numpy as np
from PIL import Image, PngImagePlugin
from torch.utils.tensorboard import SummaryWriter
from modules import shared, devices, sd_hijack, processing, sd_models, images, sd_samplers, sd_hijack_checkpoint
from modules import shared, devices, sd_hijack, processing, sd_models, images, sd_samplers, sd_hijack_checkpoint, errors
import modules.textual_inversion.dataset
from modules.textual_inversion.learn_schedule import LearnRateScheduler
@@ -120,16 +118,29 @@ class EmbeddingDatabase:
self.embedding_dirs.clear()
def register_embedding(self, embedding, model):
self.word_embeddings[embedding.name] = embedding
ids = model.cond_stage_model.tokenize([embedding.name])[0]
return self.register_embedding_by_name(embedding, model, embedding.name)
def register_embedding_by_name(self, embedding, model, name):
ids = model.cond_stage_model.tokenize([name])[0]
first_id = ids[0]
if first_id not in self.ids_lookup:
self.ids_lookup[first_id] = []
self.ids_lookup[first_id] = sorted(self.ids_lookup[first_id] + [(ids, embedding)], key=lambda x: len(x[0]), reverse=True)
if name in self.word_embeddings:
# remove old one from the lookup list
lookup = [x for x in self.ids_lookup[first_id] if x[1].name!=name]
else:
lookup = self.ids_lookup[first_id]
if embedding is not None:
lookup += [(ids, embedding)]
self.ids_lookup[first_id] = sorted(lookup, key=lambda x: len(x[0]), reverse=True)
if embedding is None:
# unregister embedding with specified name
if name in self.word_embeddings:
del self.word_embeddings[name]
if len(self.ids_lookup[first_id])==0:
del self.ids_lookup[first_id]
return None
self.word_embeddings[name] = embedding
return embedding
def get_expected_shape(self):
@@ -207,8 +218,7 @@ class EmbeddingDatabase:
self.load_from_file(fullfn, fn)
except Exception:
print(f"Error loading embedding {fn}:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
errors.report(f"Error loading embedding {fn}", exc_info=True)
continue
def load_textual_inversion_embeddings(self, force_reload=False):
@@ -632,8 +642,7 @@ Last saved image: {html.escape(last_saved_image)}<br/>
filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
save_embedding(embedding, optimizer, checkpoint, embedding_name, filename, remove_cached_checksum=True)
except Exception:
print(traceback.format_exc(), file=sys.stderr)
pass
errors.report("Error training embedding", exc_info=True)
finally:
pbar.leave = False
pbar.close()

394
modules/ui.py
View File

@@ -2,20 +2,21 @@ import json
import mimetypes
import os
import sys
import traceback
from functools import reduce
import warnings
import gradio as gr
import gradio.routes
import gradio.utils
import numpy as np
from PIL import Image, PngImagePlugin # noqa: F401
from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru, sd_vae, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, timer
from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, sd_vae, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, errors, shared_items, ui_settings, timer
from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML
from modules.paths import script_path, data_path
from modules.paths import script_path
from modules.ui_common import create_refresh_button
from modules.ui_gradio_extensions import reload_javascript
from modules.shared import opts, cmd_opts
@@ -35,6 +36,8 @@ import modules.hypernetworks.ui
from modules.generation_parameters_copypaste import image_from_url_text
import modules.extras
create_setting_component = ui_settings.create_setting_component
warnings.filterwarnings("default" if opts.show_warnings else "ignore", category=UserWarning)
# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
@@ -231,9 +234,8 @@ def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info:
res = all_seeds[index if 0 <= index < len(all_seeds) else 0]
except json.decoder.JSONDecodeError:
if gen_info_string != '':
print("Error parsing JSON generation info:", file=sys.stderr)
print(gen_info_string, file=sys.stderr)
if gen_info_string:
errors.report(f"Error parsing JSON generation info: {gen_info_string}")
return [res, gr_show(False)]
@@ -272,12 +274,12 @@ def create_toprow(is_img2img):
with gr.Row():
with gr.Column(scale=80):
with gr.Row():
prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=3, placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)")
prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=3, placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
with gr.Row():
with gr.Column(scale=80):
with gr.Row():
negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)")
negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
button_interrogate = None
button_deepbooru = None
@@ -368,25 +370,6 @@ def apply_setting(key, value):
return getattr(opts, key)
def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
def refresh():
refresh_method()
args = refreshed_args() if callable(refreshed_args) else refreshed_args
for k, v in args.items():
setattr(refresh_component, k, v)
return gr.update(**(args or {}))
refresh_button = ToolButton(value=refresh_symbol, elem_id=elem_id)
refresh_button.click(
fn=refresh,
inputs=[],
outputs=[refresh_component]
)
return refresh_button
def create_output_panel(tabname, outdir):
return ui_common.create_output_panel(tabname, outdir)
@@ -405,22 +388,12 @@ def create_sampler_and_steps_selection(choices, tabname):
def ordered_ui_categories():
user_order = {x.strip(): i * 2 + 1 for i, x in enumerate(shared.opts.ui_reorder.split(","))}
user_order = {x.strip(): i * 2 + 1 for i, x in enumerate(shared.opts.ui_reorder_list)}
for _, category in sorted(enumerate(shared.ui_reorder_categories), key=lambda x: user_order.get(x[1], x[0] * 2 + 0)):
for _, category in sorted(enumerate(shared_items.ui_reorder_categories()), key=lambda x: user_order.get(x[1], x[0] * 2 + 0)):
yield category
def get_value_for_setting(key):
value = getattr(opts, key)
info = opts.data_labels[key]
args = info.component_args() if callable(info.component_args) else info.component_args or {}
args = {k: v for k, v in args.items() if k not in {'precision'}}
return gr.update(value=value, **args)
def create_override_settings_dropdown(tabname, row):
dropdown = gr.Dropdown([], label="Override settings", visible=False, elem_id=f"{tabname}_override_settings", multiselect=True)
@@ -456,6 +429,8 @@ def create_ui():
with gr.Row().style(equal_height=False):
with gr.Column(variant='compact', elem_id="txt2img_settings"):
modules.scripts.scripts_txt2img.prepare_ui()
for category in ordered_ui_categories():
if category == "sampler":
steps, sampler_index = create_sampler_and_steps_selection(samplers, "txt2img")
@@ -463,8 +438,8 @@ def create_ui():
elif category == "dimensions":
with FormRow():
with gr.Column(elem_id="txt2img_column_size", scale=4):
width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512, elem_id="txt2img_width")
height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512, elem_id="txt2img_height")
width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="txt2img_width")
height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height")
with gr.Column(elem_id="txt2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn", label="Switch dims")
@@ -505,10 +480,10 @@ def create_ui():
with FormRow(elem_id="txt2img_hires_fix_row4", variant="compact", visible=opts.hires_fix_show_prompts) as hr_prompts_container:
with gr.Column(scale=80):
with gr.Row():
hr_prompt = gr.Textbox(label="Prompt", elem_id="hires_prompt", show_label=False, lines=3, placeholder="Prompt for hires fix pass.\nLeave empty to use the same prompt as in first pass.")
hr_prompt = gr.Textbox(label="Hires prompt", elem_id="hires_prompt", show_label=False, lines=3, placeholder="Prompt for hires fix pass.\nLeave empty to use the same prompt as in first pass.", elem_classes=["prompt"])
with gr.Column(scale=80):
with gr.Row():
hr_negative_prompt = gr.Textbox(label="Negative prompt", elem_id="hires_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt for hires fix pass.\nLeave empty to use the same negative prompt as in first pass.")
hr_negative_prompt = gr.Textbox(label="Hires negative prompt", elem_id="hires_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt for hires fix pass.\nLeave empty to use the same negative prompt as in first pass.", elem_classes=["prompt"])
elif category == "batch":
if not opts.dimensions_and_batch_together:
@@ -524,6 +499,9 @@ def create_ui():
with FormGroup(elem_id="txt2img_script_container"):
custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
else:
modules.scripts.scripts_txt2img.setup_ui_for_section(category)
hr_resolution_preview_inputs = [enable_hr, width, height, hr_scale, hr_resize_x, hr_resize_y]
for component in hr_resolution_preview_inputs:
@@ -616,7 +594,8 @@ def create_ui():
outputs=[
txt2img_prompt,
txt_prompt_img
]
],
show_progress=False,
)
enable_hr.change(
@@ -779,6 +758,8 @@ def create_ui():
with FormRow():
resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize")
modules.scripts.scripts_img2img.prepare_ui()
for category in ordered_ui_categories():
if category == "sampler":
steps, sampler_index = create_sampler_and_steps_selection(samplers_for_img2img, "img2img")
@@ -792,8 +773,8 @@ def create_ui():
with gr.Tab(label="Resize to") as tab_scale_to:
with FormRow():
with gr.Column(elem_id="img2img_column_size", scale=4):
width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512, elem_id="img2img_width")
height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512, elem_id="img2img_height")
width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height")
with gr.Column(elem_id="img2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="img2img_res_switch_btn")
detect_image_size_btn = ToolButton(value=detect_image_size_symbol, elem_id="img2img_detect_image_size_btn")
@@ -888,6 +869,8 @@ def create_ui():
inputs=[],
outputs=[inpaint_controls, mask_alpha],
)
else:
modules.scripts.scripts_img2img.setup_ui_for_section(category)
img2img_gallery, generation_info, html_info, html_log = create_output_panel("img2img", opts.outdir_img2img_samples)
@@ -902,7 +885,8 @@ def create_ui():
outputs=[
img2img_prompt,
img2img_prompt_img
]
],
show_progress=False,
)
img2img_args = dict(
@@ -1183,8 +1167,8 @@ def create_ui():
with gr.Tab(label="Preprocess images", id="preprocess_images"):
process_src = gr.Textbox(label='Source directory', elem_id="train_process_src")
process_dst = gr.Textbox(label='Destination directory', elem_id="train_process_dst")
process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512, elem_id="train_process_width")
process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512, elem_id="train_process_height")
process_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_process_width")
process_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_process_height")
preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"], elem_id="train_preprocess_txt_action")
with gr.Row():
@@ -1276,8 +1260,8 @@ def create_ui():
template_file = gr.Dropdown(label='Prompt template', value="style_filewords.txt", elem_id="train_template_file", choices=get_textual_inversion_template_names())
create_refresh_button(template_file, textual_inversion.list_textual_inversion_templates, lambda: {"choices": get_textual_inversion_template_names()}, "refrsh_train_template_file")
training_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512, elem_id="train_training_width")
training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512, elem_id="train_training_height")
training_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_training_width")
training_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_training_height")
varsize = gr.Checkbox(label="Do not resize images", value=False, elem_id="train_varsize")
steps = gr.Number(label='Max steps', value=100000, precision=0, elem_id="train_steps")
@@ -1460,195 +1444,10 @@ def create_ui():
outputs=[],
)
def create_setting_component(key, is_quicksettings=False):
def fun():
return opts.data[key] if key in opts.data else opts.data_labels[key].default
info = opts.data_labels[key]
t = type(info.default)
args = info.component_args() if callable(info.component_args) else info.component_args
if info.component is not None:
comp = info.component
elif t == str:
comp = gr.Textbox
elif t == int:
comp = gr.Number
elif t == bool:
comp = gr.Checkbox
else:
raise Exception(f'bad options item type: {t} for key {key}')
elem_id = f"setting_{key}"
if info.refresh is not None:
if is_quicksettings:
res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
create_refresh_button(res, info.refresh, info.component_args, f"refresh_{key}")
else:
with FormRow():
res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
create_refresh_button(res, info.refresh, info.component_args, f"refresh_{key}")
else:
res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
return res
loadsave = ui_loadsave.UiLoadsave(cmd_opts.ui_config_file)
components = []
component_dict = {}
shared.settings_components = component_dict
script_callbacks.ui_settings_callback()
opts.reorder()
def run_settings(*args):
changed = []
for key, value, comp in zip(opts.data_labels.keys(), args, components):
assert comp == dummy_component or opts.same_type(value, opts.data_labels[key].default), f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}"
for key, value, comp in zip(opts.data_labels.keys(), args, components):
if comp == dummy_component:
continue
if opts.set(key, value):
changed.append(key)
try:
opts.save(shared.config_filename)
except RuntimeError:
return opts.dumpjson(), f'{len(changed)} settings changed without save: {", ".join(changed)}.'
return opts.dumpjson(), f'{len(changed)} settings changed{": " if len(changed) > 0 else ""}{", ".join(changed)}.'
def run_settings_single(value, key):
if not opts.same_type(value, opts.data_labels[key].default):
return gr.update(visible=True), opts.dumpjson()
if not opts.set(key, value):
return gr.update(value=getattr(opts, key)), opts.dumpjson()
opts.save(shared.config_filename)
return get_value_for_setting(key), opts.dumpjson()
with gr.Blocks(analytics_enabled=False) as settings_interface:
with gr.Row():
with gr.Column(scale=6):
settings_submit = gr.Button(value="Apply settings", variant='primary', elem_id="settings_submit")
with gr.Column():
restart_gradio = gr.Button(value='Reload UI', variant='primary', elem_id="settings_restart_gradio")
result = gr.HTML(elem_id="settings_result")
quicksettings_names = opts.quicksettings_list
quicksettings_names = {x: i for i, x in enumerate(quicksettings_names) if x != 'quicksettings'}
quicksettings_list = []
previous_section = None
current_tab = None
current_row = None
with gr.Tabs(elem_id="settings"):
for i, (k, item) in enumerate(opts.data_labels.items()):
section_must_be_skipped = item.section[0] is None
if previous_section != item.section and not section_must_be_skipped:
elem_id, text = item.section
if current_tab is not None:
current_row.__exit__()
current_tab.__exit__()
gr.Group()
current_tab = gr.TabItem(elem_id=f"settings_{elem_id}", label=text)
current_tab.__enter__()
current_row = gr.Column(variant='compact')
current_row.__enter__()
previous_section = item.section
if k in quicksettings_names and not shared.cmd_opts.freeze_settings:
quicksettings_list.append((i, k, item))
components.append(dummy_component)
elif section_must_be_skipped:
components.append(dummy_component)
else:
component = create_setting_component(k)
component_dict[k] = component
components.append(component)
if current_tab is not None:
current_row.__exit__()
current_tab.__exit__()
with gr.TabItem("Defaults", id="defaults", elem_id="settings_tab_defaults"):
loadsave.create_ui()
with gr.TabItem("Actions", id="actions", elem_id="settings_tab_actions"):
request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary', elem_id="settings_reload_script_bodies")
with gr.Row():
unload_sd_model = gr.Button(value='Unload SD checkpoint to free VRAM', elem_id="sett_unload_sd_model")
reload_sd_model = gr.Button(value='Reload the last SD checkpoint back into VRAM', elem_id="sett_reload_sd_model")
with gr.TabItem("Licenses", id="licenses", elem_id="settings_tab_licenses"):
gr.HTML(shared.html("licenses.html"), elem_id="licenses")
gr.Button(value="Show all pages", elem_id="settings_show_all_pages")
def unload_sd_weights():
modules.sd_models.unload_model_weights()
def reload_sd_weights():
modules.sd_models.reload_model_weights()
unload_sd_model.click(
fn=unload_sd_weights,
inputs=[],
outputs=[]
)
reload_sd_model.click(
fn=reload_sd_weights,
inputs=[],
outputs=[]
)
request_notifications.click(
fn=lambda: None,
inputs=[],
outputs=[],
_js='function(){}'
)
download_localization.click(
fn=lambda: None,
inputs=[],
outputs=[],
_js='download_localization'
)
def reload_scripts():
modules.scripts.reload_script_body_only()
reload_javascript() # need to refresh the html page
reload_script_bodies.click(
fn=reload_scripts,
inputs=[],
outputs=[]
)
restart_gradio.click(
fn=shared.state.request_restart,
_js='restart_reload',
inputs=[],
outputs=[],
)
settings = ui_settings.UiSettings()
settings.create_ui(loadsave, dummy_component)
interfaces = [
(txt2img_interface, "txt2img", "txt2img"),
@@ -1660,7 +1459,7 @@ def create_ui():
]
interfaces += script_callbacks.ui_tabs_callback()
interfaces += [(settings_interface, "Settings", "settings")]
interfaces += [(settings.interface, "Settings", "settings")]
extensions_interface = ui_extensions.create_ui()
interfaces += [(extensions_interface, "Extensions", "extensions")]
@@ -1670,10 +1469,7 @@ def create_ui():
shared.tab_names.append(label)
with gr.Blocks(theme=shared.gradio_theme, analytics_enabled=False, title="Stable Diffusion") as demo:
with gr.Row(elem_id="quicksettings", variant="compact"):
for _i, k, _item in sorted(quicksettings_list, key=lambda x: quicksettings_names.get(x[1], x[0])):
component = create_setting_component(k, is_quicksettings=True)
component_dict[k] = component
settings.add_quicksettings()
parameters_copypaste.connect_paste_params_buttons()
@@ -1704,55 +1500,17 @@ def create_ui():
footer = footer.format(versions=versions_html())
gr.HTML(footer, elem_id="footer")
text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False)
settings_submit.click(
fn=wrap_gradio_call(run_settings, extra_outputs=[gr.update()]),
inputs=components,
outputs=[text_settings, result],
)
for _i, k, _item in quicksettings_list:
component = component_dict[k]
info = opts.data_labels[k]
change_handler = component.release if hasattr(component, 'release') else component.change
change_handler(
fn=lambda value, k=k: run_settings_single(value, key=k),
inputs=[component],
outputs=[component, text_settings],
show_progress=info.refresh is not None,
)
settings.add_functionality(demo)
update_image_cfg_scale_visibility = lambda: gr.update(visible=shared.sd_model and shared.sd_model.cond_stage_key == "edit")
text_settings.change(fn=update_image_cfg_scale_visibility, inputs=[], outputs=[image_cfg_scale])
settings.text_settings.change(fn=update_image_cfg_scale_visibility, inputs=[], outputs=[image_cfg_scale])
demo.load(fn=update_image_cfg_scale_visibility, inputs=[], outputs=[image_cfg_scale])
button_set_checkpoint = gr.Button('Change checkpoint', elem_id='change_checkpoint', visible=False)
button_set_checkpoint.click(
fn=lambda value, _: run_settings_single(value, key='sd_model_checkpoint'),
_js="function(v){ var res = desiredCheckpointName; desiredCheckpointName = ''; return [res || v, null]; }",
inputs=[component_dict['sd_model_checkpoint'], dummy_component],
outputs=[component_dict['sd_model_checkpoint'], text_settings],
)
component_keys = [k for k in opts.data_labels.keys() if k in component_dict]
def get_settings_values():
return [get_value_for_setting(key) for key in component_keys]
demo.load(
fn=get_settings_values,
inputs=[],
outputs=[component_dict[k] for k in component_keys],
queue=False,
)
def modelmerger(*args):
try:
results = modules.extras.run_modelmerger(*args)
except Exception as e:
print("Error loading/saving model file:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
errors.report("Error loading/saving model file", exc_info=True)
modules.sd_models.list_models() # to remove the potentially missing models from the list
return [*[gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(4)], f"Error merging checkpoints: {e}"]
return results
@@ -1780,7 +1538,7 @@ def create_ui():
primary_model_name,
secondary_model_name,
tertiary_model_name,
component_dict['sd_model_checkpoint'],
settings.component_dict['sd_model_checkpoint'],
modelmerger_result,
]
)
@@ -1794,70 +1552,6 @@ def create_ui():
return demo
def webpath(fn):
if fn.startswith(script_path):
web_path = os.path.relpath(fn, script_path).replace('\\', '/')
else:
web_path = os.path.abspath(fn)
return f'file={web_path}?{os.path.getmtime(fn)}'
def javascript_html():
# Ensure localization is in `window` before scripts
head = f'<script type="text/javascript">{localization.localization_js(shared.opts.localization)}</script>\n'
script_js = os.path.join(script_path, "script.js")
head += f'<script type="text/javascript" src="{webpath(script_js)}"></script>\n'
for script in modules.scripts.list_scripts("javascript", ".js"):
head += f'<script type="text/javascript" src="{webpath(script.path)}"></script>\n'
for script in modules.scripts.list_scripts("javascript", ".mjs"):
head += f'<script type="module" src="{webpath(script.path)}"></script>\n'
if cmd_opts.theme:
head += f'<script type="text/javascript">set_theme(\"{cmd_opts.theme}\");</script>\n'
return head
def css_html():
head = ""
def stylesheet(fn):
return f'<link rel="stylesheet" property="stylesheet" href="{webpath(fn)}">'
for cssfile in modules.scripts.list_files_with_name("style.css"):
if not os.path.isfile(cssfile):
continue
head += stylesheet(cssfile)
if os.path.exists(os.path.join(data_path, "user.css")):
head += stylesheet(os.path.join(data_path, "user.css"))
return head
def reload_javascript():
js = javascript_html()
css = css_html()
def template_response(*args, **kwargs):
res = shared.GradioTemplateResponseOriginal(*args, **kwargs)
res.body = res.body.replace(b'</head>', f'{js}</head>'.encode("utf8"))
res.body = res.body.replace(b'</body>', f'{css}</body>'.encode("utf8"))
res.init_headers()
return res
gradio.routes.templates.TemplateResponse = template_response
if not hasattr(shared, 'GradioTemplateResponseOriginal'):
shared.GradioTemplateResponseOriginal = gradio.routes.templates.TemplateResponse
def versions_html():
import torch
import launch

32
modules/ui_common.py
View File

@@ -10,8 +10,11 @@ import subprocess as sp
from modules import call_queue, shared
from modules.generation_parameters_copypaste import image_from_url_text
import modules.images
from modules.ui_components import ToolButton
folder_symbol = '\U0001f4c2' # 📂
refresh_symbol = '\U0001f504' # 🔄
def update_generation_info(generation_info, html_info, img_index):
@@ -50,9 +53,10 @@ def save_files(js_data, images, do_make_zip, index):
save_to_dirs = shared.opts.use_save_to_dirs_for_ui
extension: str = shared.opts.samples_format
start_index = 0
only_one = False
if index > -1 and shared.opts.save_selected_only and (index >= data["index_of_first_image"]): # ensures we are looking at a specific non-grid picture, and we have save_selected_only
only_one = True
images = [images[index]]
start_index = index
@@ -70,6 +74,7 @@ def save_files(js_data, images, do_make_zip, index):
is_grid = image_index < p.index_of_first_image
i = 0 if is_grid else (image_index - p.index_of_first_image)
p.batch_index = image_index-1
fullfn, txt_fullfn = modules.images.save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
filename = os.path.relpath(fullfn, path)
@@ -83,7 +88,10 @@ def save_files(js_data, images, do_make_zip, index):
# Make Zip
if do_make_zip:
zip_filepath = os.path.join(path, "images.zip")
zip_fileseed = p.all_seeds[index-1] if only_one else p.all_seeds[0]
namegen = modules.images.FilenameGenerator(p, zip_fileseed, p.all_prompts[0], image, True)
zip_filename = namegen.apply(shared.opts.grid_zip_filename_pattern or "[datetime]_[[model_name]]_[seed]-[seed_last]")
zip_filepath = os.path.join(path, f"{zip_filename}.zip")
from zipfile import ZipFile
with ZipFile(zip_filepath, "w") as zip_file:
@@ -211,3 +219,23 @@ Requested path was: {f}
))
return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info, html_log
def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
def refresh():
refresh_method()
args = refreshed_args() if callable(refreshed_args) else refreshed_args
for k, v in args.items():
setattr(refresh_component, k, v)
return gr.update(**(args or {}))
refresh_button = ToolButton(value=refresh_symbol, elem_id=elem_id)
refresh_button.click(
fn=refresh,
inputs=[],
outputs=[refresh_component]
)
return refresh_button

20
modules/ui_extensions.py
View File

@@ -1,10 +1,8 @@
import json
import os.path
import sys
import threading
import time
from datetime import datetime
import traceback
import git
@@ -13,7 +11,7 @@ import html
import shutil
import errno
from modules import extensions, shared, paths, config_states
from modules import extensions, shared, paths, config_states, errors
from modules.paths_internal import config_states_dir
from modules.call_queue import wrap_gradio_gpu_call
@@ -46,8 +44,7 @@ def apply_and_restart(disable_list, update_list, disable_all):
try:
ext.fetch_and_reset_hard()
except Exception:
print(f"Error getting updates for {ext.name}:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
errors.report(f"Error getting updates for {ext.name}", exc_info=True)
shared.opts.disabled_extensions = disabled
shared.opts.disable_all_extensions = disable_all
@@ -113,8 +110,7 @@ def check_updates(id_task, disable_list):
if 'FETCH_HEAD' not in str(e):
raise
except Exception:
print(f"Error checking updates for {ext.name}:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
errors.report(f"Error checking updates for {ext.name}", exc_info=True)
shared.state.nextjob()
@@ -345,12 +341,12 @@ def install_extension_from_url(dirname, url, branch_name=None):
shutil.rmtree(tmpdir, True)
if not branch_name:
# if no branch is specified, use the default branch
with git.Repo.clone_from(url, tmpdir) as repo:
with git.Repo.clone_from(url, tmpdir, filter=['blob:none']) as repo:
repo.remote().fetch()
for submodule in repo.submodules:
submodule.update()
else:
with git.Repo.clone_from(url, tmpdir, branch=branch_name) as repo:
with git.Repo.clone_from(url, tmpdir, filter=['blob:none'], branch=branch_name) as repo:
repo.remote().fetch()
for submodule in repo.submodules:
submodule.update()
@@ -490,8 +486,14 @@ def refresh_available_extensions_from_data(hide_tags, sort_column, filter_text="
def preload_extensions_git_metadata():
t0 = time.time()
for extension in extensions.extensions:
extension.read_info_from_repo()
print(
f"preload_extensions_git_metadata for "
f"{len(extensions.extensions)} extensions took "
f"{time.time() - t0:.2f}s"
)
def create_ui():

69
modules/ui_gradio_extensions.py Normal file
View File

@@ -0,0 +1,69 @@
import os
import gradio as gr
from modules import localization, shared, scripts
from modules.paths import script_path, data_path
def webpath(fn):
if fn.startswith(script_path):
web_path = os.path.relpath(fn, script_path).replace('\\', '/')
else:
web_path = os.path.abspath(fn)
return f'file={web_path}?{os.path.getmtime(fn)}'
def javascript_html():
# Ensure localization is in `window` before scripts
head = f'<script type="text/javascript">{localization.localization_js(shared.opts.localization)}</script>\n'
script_js = os.path.join(script_path, "script.js")
head += f'<script type="text/javascript" src="{webpath(script_js)}"></script>\n'
for script in scripts.list_scripts("javascript", ".js"):
head += f'<script type="text/javascript" src="{webpath(script.path)}"></script>\n'
for script in scripts.list_scripts("javascript", ".mjs"):
head += f'<script type="module" src="{webpath(script.path)}"></script>\n'
if shared.cmd_opts.theme:
head += f'<script type="text/javascript">set_theme(\"{shared.cmd_opts.theme}\");</script>\n'
return head
def css_html():
head = ""
def stylesheet(fn):
return f'<link rel="stylesheet" property="stylesheet" href="{webpath(fn)}">'
for cssfile in scripts.list_files_with_name("style.css"):
if not os.path.isfile(cssfile):
continue
head += stylesheet(cssfile)
if os.path.exists(os.path.join(data_path, "user.css")):
head += stylesheet(os.path.join(data_path, "user.css"))
return head
def reload_javascript():
js = javascript_html()
css = css_html()
def template_response(*args, **kwargs):
res = shared.GradioTemplateResponseOriginal(*args, **kwargs)
res.body = res.body.replace(b'</head>', f'{js}</head>'.encode("utf8"))
res.body = res.body.replace(b'</body>', f'{css}</body>'.encode("utf8"))
res.init_headers()
return res
gr.routes.templates.TemplateResponse = template_response
if not hasattr(shared, 'GradioTemplateResponseOriginal'):
shared.GradioTemplateResponseOriginal = gr.routes.templates.TemplateResponse

263
modules/ui_settings.py Normal file
View File

@@ -0,0 +1,263 @@
import gradio as gr
from modules import ui_common, shared, script_callbacks, scripts, sd_models
from modules.call_queue import wrap_gradio_call
from modules.shared import opts
from modules.ui_components import FormRow
from modules.ui_gradio_extensions import reload_javascript
def get_value_for_setting(key):
value = getattr(opts, key)
info = opts.data_labels[key]
args = info.component_args() if callable(info.component_args) else info.component_args or {}
args = {k: v for k, v in args.items() if k not in {'precision'}}
return gr.update(value=value, **args)
def create_setting_component(key, is_quicksettings=False):
def fun():
return opts.data[key] if key in opts.data else opts.data_labels[key].default
info = opts.data_labels[key]
t = type(info.default)
args = info.component_args() if callable(info.component_args) else info.component_args
if info.component is not None:
comp = info.component
elif t == str:
comp = gr.Textbox
elif t == int:
comp = gr.Number
elif t == bool:
comp = gr.Checkbox
else:
raise Exception(f'bad options item type: {t} for key {key}')
elem_id = f"setting_{key}"
if info.refresh is not None:
if is_quicksettings:
res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
ui_common.create_refresh_button(res, info.refresh, info.component_args, f"refresh_{key}")
else:
with FormRow():
res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
ui_common.create_refresh_button(res, info.refresh, info.component_args, f"refresh_{key}")
else:
res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
return res
class UiSettings:
submit = None
result = None
interface = None
components = None
component_dict = None
dummy_component = None
quicksettings_list = None
quicksettings_names = None
text_settings = None
def run_settings(self, *args):
changed = []
for key, value, comp in zip(opts.data_labels.keys(), args, self.components):
assert comp == self.dummy_component or opts.same_type(value, opts.data_labels[key].default), f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}"
for key, value, comp in zip(opts.data_labels.keys(), args, self.components):
if comp == self.dummy_component:
continue
if opts.set(key, value):
changed.append(key)
try:
opts.save(shared.config_filename)
except RuntimeError:
return opts.dumpjson(), f'{len(changed)} settings changed without save: {", ".join(changed)}.'
return opts.dumpjson(), f'{len(changed)} settings changed{": " if len(changed) > 0 else ""}{", ".join(changed)}.'
def run_settings_single(self, value, key):
if not opts.same_type(value, opts.data_labels[key].default):
return gr.update(visible=True), opts.dumpjson()
if not opts.set(key, value):
return gr.update(value=getattr(opts, key)), opts.dumpjson()
opts.save(shared.config_filename)
return get_value_for_setting(key), opts.dumpjson()
def create_ui(self, loadsave, dummy_component):
self.components = []
self.component_dict = {}
self.dummy_component = dummy_component
shared.settings_components = self.component_dict
script_callbacks.ui_settings_callback()
opts.reorder()
with gr.Blocks(analytics_enabled=False) as settings_interface:
with gr.Row():
with gr.Column(scale=6):
self.submit = gr.Button(value="Apply settings", variant='primary', elem_id="settings_submit")
with gr.Column():
restart_gradio = gr.Button(value='Reload UI', variant='primary', elem_id="settings_restart_gradio")
self.result = gr.HTML(elem_id="settings_result")
self.quicksettings_names = opts.quicksettings_list
self.quicksettings_names = {x: i for i, x in enumerate(self.quicksettings_names) if x != 'quicksettings'}
self.quicksettings_list = []
previous_section = None
current_tab = None
current_row = None
with gr.Tabs(elem_id="settings"):
for i, (k, item) in enumerate(opts.data_labels.items()):
section_must_be_skipped = item.section[0] is None
if previous_section != item.section and not section_must_be_skipped:
elem_id, text = item.section
if current_tab is not None:
current_row.__exit__()
current_tab.__exit__()
gr.Group()
current_tab = gr.TabItem(elem_id=f"settings_{elem_id}", label=text)
current_tab.__enter__()
current_row = gr.Column(variant='compact')
current_row.__enter__()
previous_section = item.section
if k in self.quicksettings_names and not shared.cmd_opts.freeze_settings:
self.quicksettings_list.append((i, k, item))
self.components.append(dummy_component)
elif section_must_be_skipped:
self.components.append(dummy_component)
else:
component = create_setting_component(k)
self.component_dict[k] = component
self.components.append(component)
if current_tab is not None:
current_row.__exit__()
current_tab.__exit__()
with gr.TabItem("Defaults", id="defaults", elem_id="settings_tab_defaults"):
loadsave.create_ui()
with gr.TabItem("Actions", id="actions", elem_id="settings_tab_actions"):
request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary', elem_id="settings_reload_script_bodies")
with gr.Row():
unload_sd_model = gr.Button(value='Unload SD checkpoint to free VRAM', elem_id="sett_unload_sd_model")
reload_sd_model = gr.Button(value='Reload the last SD checkpoint back into VRAM', elem_id="sett_reload_sd_model")
with gr.TabItem("Licenses", id="licenses", elem_id="settings_tab_licenses"):
gr.HTML(shared.html("licenses.html"), elem_id="licenses")
gr.Button(value="Show all pages", elem_id="settings_show_all_pages")
self.text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False)
unload_sd_model.click(
fn=sd_models.unload_model_weights,
inputs=[],
outputs=[]
)
reload_sd_model.click(
fn=sd_models.reload_model_weights,
inputs=[],
outputs=[]
)
request_notifications.click(
fn=lambda: None,
inputs=[],
outputs=[],
_js='function(){}'
)
download_localization.click(
fn=lambda: None,
inputs=[],
outputs=[],
_js='download_localization'
)
def reload_scripts():
scripts.reload_script_body_only()
reload_javascript() # need to refresh the html page
reload_script_bodies.click(
fn=reload_scripts,
inputs=[],
outputs=[]
)
restart_gradio.click(
fn=shared.state.request_restart,
_js='restart_reload',
inputs=[],
outputs=[],
)
self.interface = settings_interface
def add_quicksettings(self):
with gr.Row(elem_id="quicksettings", variant="compact"):
for _i, k, _item in sorted(self.quicksettings_list, key=lambda x: self.quicksettings_names.get(x[1], x[0])):
component = create_setting_component(k, is_quicksettings=True)
self.component_dict[k] = component
def add_functionality(self, demo):
self.submit.click(
fn=wrap_gradio_call(lambda *args: self.run_settings(*args), extra_outputs=[gr.update()]),
inputs=self.components,
outputs=[self.text_settings, self.result],
)
for _i, k, _item in self.quicksettings_list:
component = self.component_dict[k]
info = opts.data_labels[k]
change_handler = component.release if hasattr(component, 'release') else component.change
change_handler(
fn=lambda value, k=k: self.run_settings_single(value, key=k),
inputs=[component],
outputs=[component, self.text_settings],
show_progress=info.refresh is not None,
)
button_set_checkpoint = gr.Button('Change checkpoint', elem_id='change_checkpoint', visible=False)
button_set_checkpoint.click(
fn=lambda value, _: self.run_settings_single(value, key='sd_model_checkpoint'),
_js="function(v){ var res = desiredCheckpointName; desiredCheckpointName = ''; return [res || v, null]; }",
inputs=[self.component_dict['sd_model_checkpoint'], self.dummy_component],
outputs=[self.component_dict['sd_model_checkpoint'], self.text_settings],
)
component_keys = [k for k in opts.data_labels.keys() if k in self.component_dict]
def get_settings_values():
return [get_value_for_setting(key) for key in component_keys]
demo.load(
fn=get_settings_values,
inputs=[],
outputs=[self.component_dict[k] for k in component_keys],
queue=False,
)

15
modules/ui_tempdir.py
View File

@@ -3,7 +3,7 @@ import tempfile
from collections import namedtuple
from pathlib import Path
import gradio as gr
import gradio.components
from PIL import PngImagePlugin
@@ -31,13 +31,16 @@ def check_tmp_file(gradio, filename):
return False
def save_pil_to_file(pil_image, dir=None):
def save_pil_to_file(self, pil_image, dir=None):
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
file_obj = Savedfile(f'{already_saved_as}?{os.path.getmtime(already_saved_as)}')
return file_obj
if not shared.opts.save_images_add_number:
filename += f'?{os.path.getmtime(already_saved_as)}'
return filename
if shared.opts.temp_dir != "":
dir = shared.opts.temp_dir
@@ -51,11 +54,11 @@ def save_pil_to_file(pil_image, dir=None):
file_obj = tempfile.NamedTemporaryFile(delete=False, suffix=".png", dir=dir)
pil_image.save(file_obj, pnginfo=(metadata if use_metadata else None))
return file_obj
return file_obj.name
# override save to file function so that it also writes PNG info
gr.processing_utils.save_pil_to_file = save_pil_to_file
gradio.components.IOComponent.pil_to_temp_file = save_pil_to_file
def on_tmpdir_changed():

4
modules/upscaler.py
View File

@@ -53,8 +53,8 @@ class Upscaler:
def upscale(self, img: PIL.Image, scale, selected_model: str = None):
self.scale = scale
dest_w = int(img.width * scale)
dest_h = int(img.height * scale)
dest_w = round((img.width * scale - 4) / 8) * 8
dest_h = round((img.height * scale - 4) / 8) * 8
for _ in range(3):
shape = (img.width, img.height)