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43
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, restart
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")
@ -188,7 +189,9 @@ class Api:
self.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=models.FlagsModel)
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/latent-upscale-modes", self.get_latent_upscale_modes, methods=["GET"], response_model=List[models.LatentUpscalerModeItem])
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])
@ -206,6 +209,11 @@ class Api:
self.add_api_route("/sdapi/v1/scripts", self.get_scripts_list, methods=["GET"], response_model=models.ScriptsList)
self.add_api_route("/sdapi/v1/script-info", self.get_script_info, methods=["GET"], response_model=List[models.ScriptInfo])
if shared.cmd_opts.add_stop_route:
self.add_api_route("/sdapi/v1/server-kill", self.kill_webui, methods=["POST"])
self.add_api_route("/sdapi/v1/server-restart", self.restart_webui, methods=["POST"])
self.add_api_route("/sdapi/v1/server-stop", self.stop_webui, methods=["POST"])
self.default_script_arg_txt2img = []
self.default_script_arg_img2img = []
@ -278,7 +286,7 @@ class Api:
script_args[0] = selectable_idx + 1
# Now check for always on scripts
if request.alwayson_scripts and (len(request.alwayson_scripts) > 0):
if request.alwayson_scripts:
for alwayson_script_name in request.alwayson_scripts.keys():
alwayson_script = self.get_script(alwayson_script_name, script_runner)
if alwayson_script is None:
@ -538,9 +546,20 @@ class Api:
for upscaler in shared.sd_upscalers
]
def get_latent_upscale_modes(self):
return [
{
"name": upscale_mode,
}
for upscale_mode in [*(shared.latent_upscale_modes or {})]
]
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 +719,16 @@ 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)
def kill_webui(self):
restart.stop_program()
def restart_webui(self):
if restart.is_restartable():
restart.restart_program()
return Response(status_code=501)
def stop_webui(request):
shared.state.server_command = "stop"
return Response("Stopping.")

7
modules/api/models.py
View file

@ -241,6 +241,9 @@ class UpscalerItem(BaseModel):
model_url: Optional[str] = Field(title="URL")
scale: Optional[float] = Field(title="Scale")
class LatentUpscalerModeItem(BaseModel):
name: str = Field(title="Name")
class SDModelItem(BaseModel):
title: str = Field(title="Title")
model_name: str = Field(title="Model Name")
@ -249,6 +252,10 @@ 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")

25
modules/call_queue.py
View file

@ -1,11 +1,9 @@
from functools import wraps
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()
@ -25,7 +23,7 @@ def wrap_gradio_gpu_call(func, extra_outputs=None):
def f(*args, **kwargs):
# if the first argument is a string that says "task(...)", it is treated as a job id
if len(args) > 0 and type(args[0]) == str and args[0][0:5] == "task(" and args[0][-1] == ")":
if args and type(args[0]) == str and args[0].startswith("task(") and args[0].endswith(")"):
id_task = args[0]
progress.add_task_to_queue(id_task)
else:
@ -59,16 +57,14 @@ 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
@ -111,4 +107,3 @@ 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")
@ -106,4 +106,4 @@ parser.add_argument("--skip-version-check", action='store_true', help="Do not ch
parser.add_argument("--no-hashing", action='store_true', help="disable sha256 hashing of checkpoints to help loading performance", default=False)
parser.add_argument("--no-download-sd-model", action='store_true', help="don't download SD1.5 model even if no model is found in --ckpt-dir", default=False)
parser.add_argument('--subpath', type=str, help='customize the subpath for gradio, use with reverse proxy')
parser.add_argument('--add-stop-route', action='store_true', help='add /_stop route to stop server')
parser.add_argument('--add-stop-route', action='store_true', help='enable server stop/restart/kill via api')

15
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
@ -22,9 +20,7 @@ codeformer = None
def setup_model(dirname):
global model_path
if not os.path.exists(model_path):
os.makedirs(model_path)
os.makedirs(model_path, exist_ok=True)
path = modules.paths.paths.get("CodeFormer", None)
if path is None:
@ -105,8 +101,8 @@ 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 +131,6 @@ 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)

46
modules/errors.py
View file

@ -1,8 +1,42 @@
import sys
import textwrap
import traceback
exception_records = []
def record_exception():
_, e, tb = sys.exc_info()
if e is None:
return
if exception_records and exception_records[-1] == e:
return
exception_records.append((e, tb))
if len(exception_records) > 5:
exception_records.pop(0)
def report(message: str, *, exc_info: bool = False) -> None:
"""
Print an error message to stderr, with optional traceback.
"""
record_exception()
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):
record_exception()
lines = message.strip().split("\n")
max_len = max([len(x) for x in lines])
@ -12,9 +46,15 @@ def print_error_explanation(message):
print('=' * max_len, file=sys.stderr)
def display(e: Exception, task):
def display(e: Exception, task, *, full_traceback=False):
record_exception()
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:
@ -28,6 +68,8 @@ already_displayed = {}
def display_once(e: Exception, task):
record_exception()
if task in already_displayed:
return

23
modules/extensions.py
View file

@ -1,17 +1,13 @@
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 = []
if not os.path.exists(extensions_dir):
os.makedirs(extensions_dir)
os.makedirs(extensions_dir, exist_ok=True)
def active():
@ -54,10 +50,9 @@ 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
@ -72,8 +67,8 @@ class Extension:
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
@ -94,7 +89,7 @@ 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
@ -116,7 +111,7 @@ 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)

3
modules/extra_networks.py
View file

@ -32,6 +32,9 @@ class ExtraNetworkParams:
else:
self.positional.append(item)
def __eq__(self, other):
return self.items == other.items
class ExtraNetwork:
def __init__(self, name):

4
modules/extra_networks_hypernet.py
View file

@ -9,7 +9,7 @@ class ExtraNetworkHypernet(extra_networks.ExtraNetwork):
def activate(self, p, params_list):
additional = shared.opts.sd_hypernetwork
if additional != "None" and additional in shared.hypernetworks and len([x for x in params_list if x.items[0] == additional]) == 0:
if additional != "None" and additional in shared.hypernetworks and not any(x for x in params_list if x.items[0] == additional):
hypernet_prompt_text = f"<hypernet:{additional}:{shared.opts.extra_networks_default_multiplier}>"
p.all_prompts = [f"{prompt}{hypernet_prompt_text}" for prompt in p.all_prompts]
params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
@ -17,7 +17,7 @@ class ExtraNetworkHypernet(extra_networks.ExtraNetwork):
names = []
multipliers = []
for params in params_list:
assert len(params.items) > 0
assert params.items
names.append(params.items[0])
multipliers.append(float(params.items[1]) if len(params.items) > 1 else 1.0)

50
modules/generation_parameters_copypaste.py
View file

@ -55,7 +55,7 @@ def image_from_url_text(filedata):
if filedata is None:
return None
if type(filedata) == list and len(filedata) > 0 and type(filedata[0]) == dict and filedata[0].get("is_file", False):
if type(filedata) == list and filedata and type(filedata[0]) == dict and filedata[0].get("is_file", False):
filedata = filedata[0]
if type(filedata) == dict and filedata.get("is_file", False):
@ -265,19 +265,30 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
else:
prompt += ("" if prompt == "" else "\n") + line
if shared.opts.infotext_styles != "Ignore":
found_styles, prompt, negative_prompt = shared.prompt_styles.extract_styles_from_prompt(prompt, negative_prompt)
if shared.opts.infotext_styles == "Apply":
res["Styles array"] = found_styles
elif shared.opts.infotext_styles == "Apply if any" and found_styles:
res["Styles array"] = found_styles
res["Prompt"] = prompt
res["Negative prompt"] = negative_prompt
for k, v in re_param.findall(lastline):
if v[0] == '"' and v[-1] == '"':
v = unquote(v)
try:
if v[0] == '"' and v[-1] == '"':
v = unquote(v)
m = re_imagesize.match(v)
if m is not None:
res[f"{k}-1"] = m.group(1)
res[f"{k}-2"] = m.group(2)
else:
res[k] = v
m = re_imagesize.match(v)
if m is not None:
res[f"{k}-1"] = m.group(1)
res[f"{k}-2"] = m.group(2)
else:
res[k] = v
except Exception:
print(f"Error parsing \"{k}: {v}\"")
# Missing CLIP skip means it was set to 1 (the default)
if "Clip skip" not in res:
@ -306,6 +317,18 @@ 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 +341,10 @@ 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'),
@ -330,6 +357,7 @@ infotext_to_setting_name_mapping = [
('Token merging ratio hr', 'token_merging_ratio_hr'),
('RNG', 'randn_source'),
('NGMS', 's_min_uncond'),
('Pad conds', 'pad_cond_uncond'),
]
@ -421,7 +449,7 @@ def connect_paste(button, paste_fields, input_comp, override_settings_component,
vals_pairs = [f"{k}: {v}" for k, v in vals.items()]
return gr.Dropdown.update(value=vals_pairs, choices=vals_pairs, visible=len(vals_pairs) > 0)
return gr.Dropdown.update(value=vals_pairs, choices=vals_pairs, visible=bool(vals_pairs))
paste_fields = paste_fields + [(override_settings_component, paste_settings)]
@ -438,5 +466,3 @@ def connect_paste(button, paste_fields, input_comp, override_settings_component,
outputs=[],
show_progress=False,
)

12
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
@ -72,11 +70,8 @@ gfpgan_constructor = None
def setup_model(dirname):
global model_path
if not os.path.exists(model_path):
os.makedirs(model_path)
try:
os.makedirs(model_path, exist_ok=True)
from gfpgan import GFPGANer
from facexlib import detection, parsing # noqa: F401
global user_path
@ -112,5 +107,4 @@ 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()

44
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,
@ -354,20 +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,
'user': lambda self: self.p.user,
'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()
@ -391,7 +406,7 @@ class FilenameGenerator:
prompt_no_style = self.prompt
for style in shared.prompt_styles.get_style_prompts(self.p.styles):
if len(style) > 0:
if style:
for part in style.split("{prompt}"):
prompt_no_style = prompt_no_style.replace(part, "").replace(", ,", ",").strip().strip(',')
@ -400,7 +415,7 @@ class FilenameGenerator:
return sanitize_filename_part(prompt_no_style, replace_spaces=False)
def prompt_words(self):
words = [x for x in re_nonletters.split(self.prompt or "") if len(x) > 0]
words = [x for x in re_nonletters.split(self.prompt or "") if x]
if len(words) == 0:
words = ["empty"]
return sanitize_filename_part(" ".join(words[0:opts.directories_max_prompt_words]), replace_spaces=False)
@ -408,7 +423,7 @@ class FilenameGenerator:
def datetime(self, *args):
time_datetime = datetime.datetime.now()
time_format = args[0] if len(args) > 0 and args[0] != "" else self.default_time_format
time_format = args[0] if (args and args[0] != "") else self.default_time_format
try:
time_zone = pytz.timezone(args[1]) if len(args) > 1 else None
except pytz.exceptions.UnknownTimeZoneError:
@ -447,8 +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
@ -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

42
modules/img2img.py
View file

@ -1,4 +1,5 @@
import os
from pathlib import Path
import numpy as np
from PIL import Image, ImageOps, ImageFilter, ImageEnhance, ImageChops, UnidentifiedImageError
@ -14,7 +15,7 @@ from modules.ui import plaintext_to_html
import modules.scripts
def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args):
def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=False, scale_by=1.0):
processing.fix_seed(p)
images = shared.listfiles(input_dir)
@ -22,9 +23,10 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args):
is_inpaint_batch = False
if inpaint_mask_dir:
inpaint_masks = shared.listfiles(inpaint_mask_dir)
is_inpaint_batch = len(inpaint_masks) > 0
if is_inpaint_batch:
print(f"\nInpaint batch is enabled. {len(inpaint_masks)} masks found.")
is_inpaint_batch = bool(inpaint_masks)
if is_inpaint_batch:
print(f"\nInpaint batch is enabled. {len(inpaint_masks)} masks found.")
print(f"Will process {len(images)} images, creating {p.n_iter * p.batch_size} new images for each.")
@ -50,14 +52,31 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args):
continue
# Use the EXIF orientation of photos taken by smartphones.
img = ImageOps.exif_transpose(img)
if to_scale:
p.width = int(img.width * scale_by)
p.height = int(img.height * scale_by)
p.init_images = [img] * p.batch_size
image_path = Path(image)
if is_inpaint_batch:
# try to find corresponding mask for an image using simple filename matching
mask_image_path = os.path.join(inpaint_mask_dir, os.path.basename(image))
# if not found use first one ("same mask for all images" use-case)
if mask_image_path not in inpaint_masks:
if len(inpaint_masks) == 1:
mask_image_path = inpaint_masks[0]
else:
# try to find corresponding mask for an image using simple filename matching
mask_image_dir = Path(inpaint_mask_dir)
masks_found = list(mask_image_dir.glob(f"{image_path.stem}.*"))
if len(masks_found) == 0:
print(f"Warning: mask is not found for {image_path} in {mask_image_dir}. Skipping it.")
continue
# it should contain only 1 matching mask
# otherwise user has many masks with the same name but different extensions
mask_image_path = masks_found[0]
mask_image = Image.open(mask_image_path)
p.image_mask = mask_image
@ -66,7 +85,7 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args):
proc = process_images(p)
for n, processed_image in enumerate(proc.images):
filename = os.path.basename(image)
filename = image_path.name
if n > 0:
left, right = os.path.splitext(filename)
@ -93,7 +112,8 @@ 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
@ -115,7 +135,7 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
if image is not None:
image = ImageOps.exif_transpose(image)
if selected_scale_tab == 1:
if selected_scale_tab == 1 and not is_batch:
assert image, "Can't scale by because no image is selected"
width = int(image.width * scale_by)
@ -172,7 +192,7 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
if is_batch:
assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
process_batch(p, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, args)
process_batch(p, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, args, to_scale=selected_scale_tab == 1, scale_by=scale_by)
processed = Processed(p, [], p.seed, "")
else:

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()

32
modules/launch_utils.py
View file

@ -7,7 +7,7 @@ import platform
import json
from functools import lru_cache
from modules import cmd_args
from modules import cmd_args, errors
from modules.paths_internal import script_path, extensions_dir
args, _ = cmd_args.parser.parse_known_args()
@ -68,7 +68,13 @@ def git_tag():
try:
return subprocess.check_output([git, "describe", "--tags"], shell=False, encoding='utf8').strip()
except Exception:
return "<none>"
try:
from pathlib import Path
changelog_md = Path(__file__).parent.parent / "CHANGELOG.md"
with changelog_md.open(encoding="utf-8") as file:
return next((line.strip() for line in file if line.strip()), "<none>")
except Exception:
return "<none>"
def run(command, desc=None, errdesc=None, custom_env=None, live: bool = default_command_live) -> str:
@ -141,10 +147,10 @@ def git_clone(url, dir, name, commithash=None):
return
run(f'"{git}" -C "{dir}" fetch', f"Fetching updates for {name}...", f"Couldn't fetch {name}")
run(f'"{git}" -C "{dir}" checkout {commithash}', f"Checking out commit for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}")
run(f'"{git}" -C "{dir}" checkout {commithash}', f"Checking out commit for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}", live=True)
return
run(f'"{git}" clone "{url}" "{dir}"', f"Cloning {name} into {dir}...", f"Couldn't clone {name}")
run(f'"{git}" clone "{url}" "{dir}"', f"Cloning {name} into {dir}...", f"Couldn't clone {name}", live=True)
if commithash is not None:
run(f'"{git}" -C "{dir}" checkout {commithash}', None, "Couldn't checkout {name}'s hash: {commithash}")
@ -188,7 +194,7 @@ def run_extension_installer(extension_dir):
print(run(f'"{python}" "{path_installer}"', errdesc=f"Error running install.py for extension {extension_dir}", custom_env=env))
except Exception as e:
print(e, file=sys.stderr)
errors.report(str(e))
def list_extensions(settings_file):
@ -198,8 +204,8 @@ def list_extensions(settings_file):
if os.path.isfile(settings_file):
with open(settings_file, "r", encoding="utf8") as file:
settings = json.load(file)
except Exception as e:
print(e, file=sys.stderr)
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')
@ -223,23 +229,28 @@ def prepare_environment():
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==0.0.17')
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")
taming_transformers_repo = os.environ.get('TAMING_TRANSFORMERS_REPO', "https://github.com/CompVis/taming-transformers.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")
taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
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")
try:
# the existance of this file is a signal to webui.sh/bat that webui needs to be restarted when it stops execution
os.remove(os.path.join(script_path, "tmp", "restart"))
os.environ.setdefault('SD_WEBUI_RESTARTING ', '1')
except OSError:
pass
if not args.skip_python_version_check:
check_python_version()
@ -286,7 +297,6 @@ def prepare_environment():
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(taming_transformers_repo, repo_dir('taming-transformers'), "Taming Transformers", taming_transformers_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)

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)}"

6
modules/lowvram.py
View file

@ -15,6 +15,8 @@ def send_everything_to_cpu():
def setup_for_low_vram(sd_model, use_medvram):
sd_model.lowvram = True
parents = {}
def send_me_to_gpu(module, _):
@ -96,3 +98,7 @@ def setup_for_low_vram(sd_model, use_medvram):
diff_model.middle_block.register_forward_pre_hook(send_me_to_gpu)
for block in diff_model.output_blocks:
block.register_forward_pre_hook(send_me_to_gpu)
def is_enabled(sd_model):
return getattr(sd_model, 'lowvram', False)

3
modules/modelloader.py
View file

@ -95,8 +95,7 @@ def cleanup_models():
def move_files(src_path: str, dest_path: str, ext_filter: str = None):
try:
if not os.path.exists(dest_path):
os.makedirs(dest_path)
os.makedirs(dest_path, exist_ok=True)
if os.path.exists(src_path):
for file in os.listdir(src_path):
fullpath = os.path.join(src_path, file)

4
modules/models/diffusion/ddpm_edit.py
View file

@ -230,9 +230,9 @@ class DDPM(pl.LightningModule):
missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
sd, strict=False)
print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
if len(missing) > 0:
if missing:
print(f"Missing Keys: {missing}")
if len(unexpected) > 0:
if unexpected:
print(f"Unexpected Keys: {unexpected}")
def q_mean_variance(self, x_start, t):

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"]),

115
modules/processing.py
View file

@ -1,4 +1,5 @@
import json
import logging
import math
import os
import sys
@ -6,14 +7,14 @@ import hashlib
import torch
import numpy as np
from PIL import Image, ImageFilter, ImageOps
from PIL import Image, ImageOps
import random
import cv2
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
@ -106,6 +106,9 @@ class StableDiffusionProcessing:
"""
The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
"""
cached_uc = [None, None]
cached_c = [None, None]
def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_min_uncond: float = 0.0, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None):
if sampler_index is not None:
print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr)
@ -171,12 +174,13 @@ class StableDiffusionProcessing:
self.prompts = None
self.negative_prompts = None
self.extra_network_data = None
self.seeds = None
self.subseeds = None
self.step_multiplier = 1
self.cached_uc = [None, None]
self.cached_c = [None, None]
self.cached_uc = StableDiffusionProcessing.cached_uc
self.cached_c = StableDiffusionProcessing.cached_c
self.uc = None
self.c = None
@ -290,8 +294,9 @@ class StableDiffusionProcessing:
self.sampler = None
self.c = None
self.uc = None
self.cached_c = [None, None]
self.cached_uc = [None, None]
if not opts.experimental_persistent_cond_cache:
StableDiffusionProcessing.cached_c = [None, None]
StableDiffusionProcessing.cached_uc = [None, None]
def get_token_merging_ratio(self, for_hr=False):
if for_hr:
@ -313,7 +318,7 @@ class StableDiffusionProcessing:
self.all_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, self.styles) for x in self.all_prompts]
self.all_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, self.styles) for x in self.all_negative_prompts]
def get_conds_with_caching(self, function, required_prompts, steps, cache):
def get_conds_with_caching(self, function, required_prompts, steps, caches, extra_network_data):
"""
Returns the result of calling function(shared.sd_model, required_prompts, steps)
using a cache to store the result if the same arguments have been used before.
@ -322,27 +327,29 @@ class StableDiffusionProcessing:
representing the previously used arguments, or None if no arguments
have been used before. The second element is where the previously
computed result is stored.
caches is a list with items described above.
"""
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]
for cache in caches:
if cache[0] is not None and (required_prompts, steps, opts.CLIP_stop_at_last_layers, shared.sd_model.sd_checkpoint_info, extra_network_data) == cache[0]:
return cache[1]
cache = caches[0]
with devices.autocast():
cache[1] = function(shared.sd_model, required_prompts, steps)
cache[0] = (required_prompts, steps, opts.CLIP_stop_at_last_layers, shared.sd_model.sd_checkpoint_info)
cache[0] = (required_prompts, steps, opts.CLIP_stop_at_last_layers, shared.sd_model.sd_checkpoint_info, extra_network_data)
return cache[1]
def setup_conds(self):
sampler_config = sd_samplers.find_sampler_config(self.sampler_name)
self.step_multiplier = 2 if sampler_config and sampler_config.options.get("second_order", False) else 1
self.uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, self.negative_prompts, self.steps * self.step_multiplier, self.cached_uc)
self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, self.prompts, self.steps * self.step_multiplier, self.cached_c)
self.uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, self.negative_prompts, self.steps * self.step_multiplier, [self.cached_uc], self.extra_network_data)
self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, self.prompts, self.steps * self.step_multiplier, [self.cached_c], self.extra_network_data)
def parse_extra_network_prompts(self):
self.prompts, extra_network_data = extra_networks.parse_prompts(self.prompts)
return extra_network_data
self.prompts, self.extra_network_data = extra_networks.parse_prompts(self.prompts)
class Processed:
@ -591,6 +598,9 @@ 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:
@ -676,10 +686,11 @@ 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
extra_network_data = None
for n in range(p.n_iter):
p.iteration = n
@ -700,11 +711,11 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
if len(p.prompts) == 0:
break
extra_network_data = p.parse_extra_network_prompts()
p.parse_extra_network_prompts()
if not p.disable_extra_networks:
with devices.autocast():
extra_networks.activate(p, extra_network_data)
extra_networks.activate(p, p.extra_network_data)
if p.scripts is not None:
p.scripts.process_batch(p, batch_number=n, prompts=p.prompts, seeds=p.seeds, subseeds=p.subseeds)
@ -739,7 +750,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
del samples_ddim
if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
if lowvram.is_enabled(shared.sd_model):
lowvram.send_everything_to_cpu()
devices.torch_gc()
@ -826,8 +837,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
if opts.grid_save:
images.save_image(grid, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)
if not p.disable_extra_networks and extra_network_data:
extra_networks.deactivate(p, extra_network_data)
if not p.disable_extra_networks and p.extra_network_data:
extra_networks.deactivate(p, p.extra_network_data)
devices.torch_gc()
@ -862,6 +873,8 @@ def old_hires_fix_first_pass_dimensions(width, height):
class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
sampler = None
cached_hr_uc = [None, None]
cached_hr_c = [None, None]
def __init__(self, enable_hr: bool = False, denoising_strength: float = 0.75, firstphase_width: int = 0, firstphase_height: int = 0, hr_scale: float = 2.0, hr_upscaler: str = None, hr_second_pass_steps: int = 0, hr_resize_x: int = 0, hr_resize_y: int = 0, hr_sampler_name: str = None, hr_prompt: str = '', hr_negative_prompt: str = '', **kwargs):
super().__init__(**kwargs)
@ -894,6 +907,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
self.hr_negative_prompts = None
self.hr_extra_network_data = None
self.cached_hr_uc = StableDiffusionProcessingTxt2Img.cached_hr_uc
self.cached_hr_c = StableDiffusionProcessingTxt2Img.cached_hr_c
self.hr_c = None
self.hr_uc = None
@ -973,7 +988,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
latent_scale_mode = shared.latent_upscale_modes.get(self.hr_upscaler, None) if self.hr_upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "nearest")
if self.enable_hr and latent_scale_mode is None:
assert len([x for x in shared.sd_upscalers if x.name == self.hr_upscaler]) > 0, f"could not find upscaler named {self.hr_upscaler}"
if not any(x.name == self.hr_upscaler for x in shared.sd_upscalers):
raise Exception(f"could not find upscaler named {self.hr_upscaler}")
x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
@ -1056,6 +1072,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
with devices.autocast():
extra_networks.activate(self, self.hr_extra_network_data)
with devices.autocast():
self.calculate_hr_conds()
sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio(for_hr=True))
samples = self.sampler.sample_img2img(self, samples, noise, self.hr_c, self.hr_uc, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)
@ -1067,8 +1086,12 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
return samples
def close(self):
super().close()
self.hr_c = None
self.hr_uc = None
if not opts.experimental_persistent_cond_cache:
StableDiffusionProcessingTxt2Img.cached_hr_uc = [None, None]
StableDiffusionProcessingTxt2Img.cached_hr_c = [None, None]
def setup_prompts(self):
super().setup_prompts()
@ -1095,12 +1118,31 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
self.all_hr_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, self.styles) for x in self.all_hr_prompts]
self.all_hr_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, self.styles) for x in self.all_hr_negative_prompts]
def calculate_hr_conds(self):
if self.hr_c is not None:
return
self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, self.hr_negative_prompts, self.steps * self.step_multiplier, [self.cached_hr_uc, self.cached_uc], self.hr_extra_network_data)
self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, self.hr_prompts, self.steps * self.step_multiplier, [self.cached_hr_c, self.cached_c], self.hr_extra_network_data)
def setup_conds(self):
super().setup_conds()
self.hr_uc = None
self.hr_c = None
if self.enable_hr:
self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, self.hr_negative_prompts, self.steps * self.step_multiplier, self.cached_uc)
self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, self.hr_prompts, self.steps * self.step_multiplier, self.cached_c)
if shared.opts.hires_fix_use_firstpass_conds:
self.calculate_hr_conds()
elif lowvram.is_enabled(shared.sd_model): # if in lowvram mode, we need to calculate conds right away, before the cond NN is unloaded
with devices.autocast():
extra_networks.activate(self, self.hr_extra_network_data)
self.calculate_hr_conds()
with devices.autocast():
extra_networks.activate(self, self.extra_network_data)
def parse_extra_network_prompts(self):
res = super().parse_extra_network_prompts()
@ -1117,7 +1159,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
sampler = None
def __init__(self, init_images: list = None, resize_mode: int = 0, denoising_strength: float = 0.75, image_cfg_scale: float = None, mask: Any = None, mask_blur: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: float = None, **kwargs):
def __init__(self, init_images: list = None, resize_mode: int = 0, denoising_strength: float = 0.75, image_cfg_scale: float = None, mask: Any = None, mask_blur: int = None, mask_blur_x: int = 4, mask_blur_y: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: float = None, **kwargs):
super().__init__(**kwargs)
self.init_images = init_images
@ -1128,7 +1170,11 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
self.image_mask = mask
self.latent_mask = None
self.mask_for_overlay = None
self.mask_blur = mask_blur
if mask_blur is not None:
mask_blur_x = mask_blur
mask_blur_y = mask_blur
self.mask_blur_x = mask_blur_x
self.mask_blur_y = mask_blur_y
self.inpainting_fill = inpainting_fill
self.inpaint_full_res = inpaint_full_res
self.inpaint_full_res_padding = inpaint_full_res_padding
@ -1150,8 +1196,17 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
if self.inpainting_mask_invert:
image_mask = ImageOps.invert(image_mask)
if self.mask_blur > 0:
image_mask = image_mask.filter(ImageFilter.GaussianBlur(self.mask_blur))
if self.mask_blur_x > 0:
np_mask = np.array(image_mask)
kernel_size = 2 * int(4 * self.mask_blur_x + 0.5) + 1
np_mask = cv2.GaussianBlur(np_mask, (kernel_size, 1), self.mask_blur_x)
image_mask = Image.fromarray(np_mask)
if self.mask_blur_y > 0:
np_mask = np.array(image_mask)
kernel_size = 2 * int(4 * self.mask_blur_y + 0.5) + 1
np_mask = cv2.GaussianBlur(np_mask, (1, kernel_size), self.mask_blur_y)
image_mask = Image.fromarray(np_mask)
if self.inpaint_full_res:
self.mask_for_overlay = image_mask

6
modules/prompt_parser.py
View file

@ -336,11 +336,11 @@ def parse_prompt_attention(text):
round_brackets.append(len(res))
elif text == '[':
square_brackets.append(len(res))
elif weight is not None and len(round_brackets) > 0:
elif weight is not None and round_brackets:
multiply_range(round_brackets.pop(), float(weight))
elif text == ')' and len(round_brackets) > 0:
elif text == ')' and round_brackets:
multiply_range(round_brackets.pop(), round_bracket_multiplier)
elif text == ']' and len(square_brackets) > 0:
elif text == ']' and square_brackets:
multiply_range(square_brackets.pop(), square_bracket_multiplier)
else:
parts = re.split(re_break, text)

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)

23
modules/restart.py Normal file
View file

@ -0,0 +1,23 @@
import os
from pathlib import Path
from modules.paths_internal import script_path
def is_restartable() -> bool:
"""
Return True if the webui is restartable (i.e. there is something watching to restart it with)
"""
return bool(os.environ.get('SD_WEBUI_RESTART'))
def restart_program() -> None:
"""creates file tmp/restart and immediately stops the process, which webui.bat/webui.sh interpret as a command to start webui again"""
(Path(script_path) / "tmp" / "restart").touch()
stop_program()
def stop_program() -> None:
os._exit(0)

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

35
modules/script_callbacks.py
View file

@ -1,16 +1,16 @@
import sys
import traceback
from collections import namedtuple
import inspect
import os
from collections import namedtuple
from typing import Optional, Dict, Any
from fastapi import FastAPI
from gradio import Blocks
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:
@ -111,6 +111,7 @@ callback_map = dict(
callbacks_before_ui=[],
callbacks_on_reload=[],
callbacks_list_optimizers=[],
callbacks_list_unets=[],
)
@ -123,6 +124,7 @@ def app_started_callback(demo: Optional[Blocks], app: FastAPI):
for c in callback_map['callbacks_app_started']:
try:
c.callback(demo, app)
timer.startup_timer.record(os.path.basename(c.script))
except Exception:
report_exception(c, 'app_started_callback')
@ -271,16 +273,28 @@ 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'
filename = stack[0].filename if stack else 'unknown file'
callbacks.append(ScriptCallback(filename, fun))
def remove_current_script_callbacks():
stack = [x for x in inspect.stack() if x.filename != __file__]
filename = stack[0].filename if len(stack) > 0 else 'unknown file'
filename = stack[0].filename if stack else 'unknown file'
if filename == 'unknown file':
return
for callback_list in callback_map.values():
@ -430,3 +444,10 @@ 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)

184
modules/scripts.py
View file

@ -1,12 +1,12 @@
import os
import re
import sys
import traceback
import inspect
from collections import namedtuple
import gradio as gr
from modules import shared, paths, script_callbacks, extensions, script_loading, scripts_postprocessing
from modules import shared, paths, script_callbacks, extensions, script_loading, scripts_postprocessing, errors, timer
AlwaysVisible = object()
@ -20,6 +20,9 @@ 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 +85,15 @@ 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.
@ -238,7 +250,7 @@ def load_scripts():
def register_scripts_from_module(module):
for script_class in module.__dict__.values():
if type(script_class) != type:
if not inspect.isclass(script_class):
continue
if issubclass(script_class, Script):
@ -264,12 +276,12 @@ 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
current_basedir = paths.script_path
timer.startup_timer.record(scriptfile.filename)
global scripts_txt2img, scripts_img2img, scripts_postproc
@ -280,11 +292,9 @@ 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
@ -297,6 +307,7 @@ 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
@ -324,69 +335,73 @@ 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
@ -411,6 +426,7 @@ class ScriptRunner:
)
self.script_load_ctr = 0
def onload_script_visibility(params):
title = params.get('Script', None)
if title:
@ -421,10 +437,10 @@ 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]
@ -444,14 +460,21 @@ 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:
@ -459,8 +482,7 @@ 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:
@ -468,8 +490,7 @@ 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:
@ -477,8 +498,7 @@ 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:
@ -486,8 +506,7 @@ 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:
@ -495,24 +514,21 @@ 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)):

20
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
@ -43,7 +43,7 @@ def list_optimizers():
optimizers.extend(new_optimizers)
def apply_optimizations():
def apply_optimizations(option=None):
global current_optimizer
undo_optimizations()
@ -60,7 +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:
@ -74,12 +74,13 @@ def apply_optimizations():
matching_optimizer = optimizers[0]
if matching_optimizer is not None:
print(f"Applying optimization: {matching_optimizer.name}... ", end='')
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 ''
@ -157,9 +158,9 @@ class StableDiffusionModelHijack:
def __init__(self):
self.embedding_db.add_embedding_dir(cmd_opts.embeddings_dir)
def apply_optimizations(self):
def apply_optimizations(self, option=None):
try:
self.optimization_method = apply_optimizations()
self.optimization_method = apply_optimizations(option)
except Exception as e:
errors.display(e, "applying cross attention optimization")
undo_optimizations()
@ -196,6 +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
@ -217,6 +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

2
modules/sd_hijack_clip.py
View file

@ -167,7 +167,7 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
chunk.multipliers += [weight] * emb_len
position += embedding_length_in_tokens
if len(chunk.tokens) > 0 or len(chunks) == 0:
if chunk.tokens or not chunks:
next_chunk(is_last=True)
return chunks, token_count

2
modules/sd_hijack_clip_old.py
View file

@ -74,7 +74,7 @@ def forward_old(self: sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase, text
self.hijack.comments += hijack_comments
if len(used_custom_terms) > 0:
if used_custom_terms:
embedding_names = ", ".join(f"{word} [{checksum}]" for word, checksum in used_custom_terms)
self.hijack.comments.append(f"Used embeddings: {embedding_names}")

9
modules/sd_hijack_optimizations.py
View file

@ -1,7 +1,5 @@
from __future__ import annotations
import math
import sys
import traceback
import psutil
import torch
@ -48,7 +46,7 @@ class SdOptimizationXformers(SdOptimization):
priority = 100
def is_available(self):
return shared.cmd_opts.force_enable_xformers or (shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0))
return shared.cmd_opts.force_enable_xformers or (shared.xformers_available and torch.cuda.is_available() and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0))
def apply(self):
ldm.modules.attention.CrossAttention.forward = xformers_attention_forward
@ -140,8 +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():
@ -605,7 +602,7 @@ def sdp_attnblock_forward(self, x):
q, k, v = (rearrange(t, 'b c h w -> b (h w) c') for t in (q, k, v))
dtype = q.dtype
if shared.opts.upcast_attn:
q, k = q.float(), k.float()
q, k, v = q.float(), k.float(), v.float()
q = q.contiguous()
k = k.contiguous()
v = v.contiguous()

27
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
@ -95,8 +95,7 @@ except Exception:
def setup_model():
if not os.path.exists(model_path):
os.makedirs(model_path)
os.makedirs(model_path, exist_ok=True)
enable_midas_autodownload()
@ -164,6 +163,7 @@ 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 +171,14 @@ 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:
@ -421,7 +421,7 @@ 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
@ -506,6 +506,11 @@ 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
@ -525,6 +530,8 @@ 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:

63
modules/sd_samplers_kdiffusion.py
View file

@ -20,7 +20,7 @@ samplers_k_diffusion = [
('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, "brownian_noise": True}),
('DPM++ 2M SDE', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {"brownian_noise": True, 'discard_next_to_last_sigma': 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'}),
@ -29,7 +29,7 @@ samplers_k_diffusion = [
('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, "brownian_noise": True}),
('DPM++ 2M SDE Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {'scheduler': 'karras', "brownian_noise": True, 'discard_next_to_last_sigma': True}),
('DPM++ 2M SDE Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {'scheduler': 'karras', "brownian_noise": True}),
]
samplers_data_k_diffusion = [
@ -44,6 +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):
"""
@ -61,6 +69,7 @@ class CFGDenoiser(torch.nn.Module):
self.init_latent = None
self.step = 0
self.image_cfg_scale = None
self.padded_cond_uncond = False
def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
denoised_uncond = x_out[-uncond.shape[0]:]
@ -125,6 +134,18 @@ class CFGDenoiser(torch.nn.Module):
x_in = x_in[:-batch_size]
sigma_in = sigma_in[:-batch_size]
self.padded_cond_uncond = False
if shared.opts.pad_cond_uncond and tensor.shape[1] != uncond.shape[1]:
empty = shared.sd_model.cond_stage_model_empty_prompt
num_repeats = (tensor.shape[1] - uncond.shape[1]) // empty.shape[1]
if num_repeats < 0:
tensor = torch.cat([tensor, empty.repeat((tensor.shape[0], -num_repeats, 1))], axis=1)
self.padded_cond_uncond = True
elif num_repeats > 0:
uncond = torch.cat([uncond, empty.repeat((uncond.shape[0], num_repeats, 1))], axis=1)
self.padded_cond_uncond = True
if tensor.shape[1] == uncond.shape[1] or skip_uncond:
if is_edit_model:
cond_in = torch.cat([tensor, uncond, uncond])
@ -255,6 +276,13 @@ 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
@ -294,6 +322,31 @@ 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())
@ -355,6 +408,9 @@ class KDiffusionSampler:
samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
if self.model_wrap_cfg.padded_cond_uncond:
p.extra_generation_params["Pad conds"] = True
return samples
def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
@ -388,5 +444,8 @@ class KDiffusionSampler:
's_min_uncond': self.s_min_uncond
}, disable=False, callback=self.callback_state, **extra_params_kwargs))
if self.model_wrap_cfg.padded_cond_uncond:
p.extra_generation_params["Pad conds"] = True
return samples

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)

48
modules/shared.py
View file

@ -44,19 +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",
@ -273,6 +260,10 @@ class OptionInfo:
self.comment_after += f"<span class='info'>({info})</span>"
return self
def html(self, html):
self.comment_after += html
return self
def needs_restart(self):
self.comment_after += " <span class='info'>(requires restart)</span>"
return self
@ -318,6 +309,7 @@ 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"),
@ -407,6 +399,7 @@ 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."),
@ -416,17 +409,19 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
"enable_emphasis": OptionInfo(True, "Enable emphasis").info("use (text) to make model pay more attention to text and [text] to make it pay less attention"),
"enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
"comma_padding_backtrack": OptionInfo(20, "Prompt word wrap length limit", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1}).info("in tokens - for texts shorter than specified, if they don't fit into 75 token limit, move them to the next 75 token chunk"),
"CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}).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"),
"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 network; 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"),
"experimental_persistent_cond_cache": OptionInfo(False, "persistent cond cache").info("Experimental, keep cond caches across jobs, reduce overhead."),
}))
options_templates.update(options_section(('compatibility', "Compatibility"), {
@ -435,6 +430,7 @@ options_templates.update(options_section(('compatibility', "Compatibility"), {
"no_dpmpp_sde_batch_determinism": OptionInfo(False, "Do not make DPM++ SDE deterministic across different batch sizes."),
"use_old_hires_fix_width_height": OptionInfo(False, "For hires fix, use width/height sliders to set final resolution rather than first pass (disables Upscale by, Resize width/height to)."),
"dont_fix_second_order_samplers_schedule": OptionInfo(False, "Do not fix prompt schedule for second order samplers."),
"hires_fix_use_firstpass_conds": OptionInfo(False, "For hires fix, calculate conds of second pass using extra networks of first pass."),
}))
options_templates.update(options_section(('interrogate', "Interrogate Options"), {
@ -488,9 +484,10 @@ 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"), {
@ -498,7 +495,14 @@ options_templates.update(options_section(('infotext', "Infotext"), {
"add_model_name_to_info": OptionInfo(True, "Add model name to generation information"),
"add_user_name_to_info": OptionInfo(False, "Add user name to generation information when authenticated"),
"add_version_to_infotext": OptionInfo(True, "Add program version to generation information"),
"disable_weights_auto_swap": OptionInfo(True, "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint."),
"disable_weights_auto_swap": OptionInfo(True, "Disregard checkpoint information from pasted infotext").info("when reading generation parameters from text into UI"),
"infotext_styles": OptionInfo("Apply if any", "Infer styles from prompts of pasted infotext", gr.Radio, {"choices": ["Ignore", "Apply", "Discard", "Apply if any"]}).info("when reading generation parameters from text into UI)").html("""<ul style='margin-left: 1.5em'>
<li>Ignore: keep prompt and styles dropdown as it is.</li>
<li>Apply: remove style text from prompt, always replace styles dropdown value with found styles (even if none are found).</li>
<li>Discard: remove style text from prompt, keep styles dropdown as it is.</li>
<li>Apply if any: remove style text from prompt; if any styles are found in prompt, put them into styles dropdown, otherwise keep it as it is.</li>
</ul>"""),
}))
options_templates.update(options_section(('ui', "Live previews"), {
@ -520,6 +524,10 @@ 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"]}),
@ -635,6 +643,10 @@ 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"

67
modules/styles.py
View file

@ -1,6 +1,7 @@
import csv
import os
import os.path
import re
import typing
import shutil
@ -28,6 +29,44 @@ def apply_styles_to_prompt(prompt, styles):
return prompt
re_spaces = re.compile(" +")
def extract_style_text_from_prompt(style_text, prompt):
stripped_prompt = re.sub(re_spaces, " ", prompt.strip())
stripped_style_text = re.sub(re_spaces, " ", style_text.strip())
if "{prompt}" in stripped_style_text:
left, right = stripped_style_text.split("{prompt}", 2)
if stripped_prompt.startswith(left) and stripped_prompt.endswith(right):
prompt = stripped_prompt[len(left):len(stripped_prompt)-len(right)]
return True, prompt
else:
if stripped_prompt.endswith(stripped_style_text):
prompt = stripped_prompt[:len(stripped_prompt)-len(stripped_style_text)]
if prompt.endswith(', '):
prompt = prompt[:-2]
return True, prompt
return False, prompt
def extract_style_from_prompts(style: PromptStyle, prompt, negative_prompt):
if not style.prompt and not style.negative_prompt:
return False, prompt, negative_prompt
match_positive, extracted_positive = extract_style_text_from_prompt(style.prompt, prompt)
if not match_positive:
return False, prompt, negative_prompt
match_negative, extracted_negative = extract_style_text_from_prompt(style.negative_prompt, negative_prompt)
if not match_negative:
return False, prompt, negative_prompt
return True, extracted_positive, extracted_negative
class StyleDatabase:
def __init__(self, path: str):
self.no_style = PromptStyle("None", "", "")
@ -67,10 +106,34 @@ class StyleDatabase:
if os.path.exists(path):
shutil.copy(path, f"{path}.bak")
fd = os.open(path, os.O_RDWR|os.O_CREAT)
fd = os.open(path, os.O_RDWR | os.O_CREAT)
with os.fdopen(fd, "w", encoding="utf-8-sig", newline='') as file:
# _fields is actually part of the public API: typing.NamedTuple is a replacement for collections.NamedTuple,
# and collections.NamedTuple has explicit documentation for accessing _fields. Same goes for _asdict()
writer = csv.DictWriter(file, fieldnames=PromptStyle._fields)
writer.writeheader()
writer.writerows(style._asdict() for k, style in self.styles.items())
writer.writerows(style._asdict() for k, style in self.styles.items())
def extract_styles_from_prompt(self, prompt, negative_prompt):
extracted = []
applicable_styles = list(self.styles.values())
while True:
found_style = None
for style in applicable_styles:
is_match, new_prompt, new_neg_prompt = extract_style_from_prompts(style, prompt, negative_prompt)
if is_match:
found_style = style
prompt = new_prompt
negative_prompt = new_neg_prompt
break
if not found_style:
break
applicable_styles.remove(found_style)
extracted.append(found_style.name)
return list(reversed(extracted)), prompt, negative_prompt

162
modules/sysinfo.py Normal file
View file

@ -0,0 +1,162 @@
import json
import os
import sys
import traceback
import platform
import hashlib
import pkg_resources
import psutil
import re
import launch
from modules import paths_internal, timer
checksum_token = "DontStealMyGamePlz__WINNERS_DONT_USE_DRUGS__DONT_COPY_THAT_FLOPPY"
environment_whitelist = {
"GIT",
"INDEX_URL",
"WEBUI_LAUNCH_LIVE_OUTPUT",
"GRADIO_ANALYTICS_ENABLED",
"PYTHONPATH",
"TORCH_INDEX_URL",
"TORCH_COMMAND",
"REQS_FILE",
"XFORMERS_PACKAGE",
"GFPGAN_PACKAGE",
"CLIP_PACKAGE",
"OPENCLIP_PACKAGE",
"STABLE_DIFFUSION_REPO",
"K_DIFFUSION_REPO",
"CODEFORMER_REPO",
"BLIP_REPO",
"STABLE_DIFFUSION_COMMIT_HASH",
"K_DIFFUSION_COMMIT_HASH",
"CODEFORMER_COMMIT_HASH",
"BLIP_COMMIT_HASH",
"COMMANDLINE_ARGS",
"IGNORE_CMD_ARGS_ERRORS",
}
def pretty_bytes(num, suffix="B"):
for unit in ["", "K", "M", "G", "T", "P", "E", "Z", "Y"]:
if abs(num) < 1024 or unit == 'Y':
return f"{num:.0f}{unit}{suffix}"
num /= 1024
def get():
res = get_dict()
text = json.dumps(res, ensure_ascii=False, indent=4)
h = hashlib.sha256(text.encode("utf8"))
text = text.replace(checksum_token, h.hexdigest())
return text
re_checksum = re.compile(r'"Checksum": "([0-9a-fA-F]{64})"')
def check(x):
m = re.search(re_checksum, x)
if not m:
return False
replaced = re.sub(re_checksum, f'"Checksum": "{checksum_token}"', x)
h = hashlib.sha256(replaced.encode("utf8"))
return h.hexdigest() == m.group(1)
def get_dict():
ram = psutil.virtual_memory()
res = {
"Platform": platform.platform(),
"Python": platform.python_version(),
"Version": launch.git_tag(),
"Commit": launch.commit_hash(),
"Script path": paths_internal.script_path,
"Data path": paths_internal.data_path,
"Extensions dir": paths_internal.extensions_dir,
"Checksum": checksum_token,
"Commandline": sys.argv,
"Torch env info": get_torch_sysinfo(),
"Exceptions": get_exceptions(),
"CPU": {
"model": platform.processor(),
"count logical": psutil.cpu_count(logical=True),
"count physical": psutil.cpu_count(logical=False),
},
"RAM": {
x: pretty_bytes(getattr(ram, x, 0)) for x in ["total", "used", "free", "active", "inactive", "buffers", "cached", "shared"] if getattr(ram, x, 0) != 0
},
"Extensions": get_extensions(enabled=True),
"Inactive extensions": get_extensions(enabled=False),
"Environment": get_environment(),
"Config": get_config(),
"Startup": timer.startup_record,
"Packages": sorted([f"{pkg.key}=={pkg.version}" for pkg in pkg_resources.working_set]),
}
return res
def format_traceback(tb):
return [[f"{x.filename}, line {x.lineno}, {x.name}", x.line] for x in traceback.extract_tb(tb)]
def get_exceptions():
try:
from modules import errors
return [{"exception": str(e), "traceback": format_traceback(tb)} for e, tb in reversed(errors.exception_records)]
except Exception as e:
return str(e)
def get_environment():
return {k: os.environ[k] for k in sorted(os.environ) if k in environment_whitelist}
re_newline = re.compile(r"\r*\n")
def get_torch_sysinfo():
try:
import torch.utils.collect_env
info = torch.utils.collect_env.get_env_info()._asdict()
return {k: re.split(re_newline, str(v)) if "\n" in str(v) else v for k, v in info.items()}
except Exception as e:
return str(e)
def get_extensions(*, enabled):
try:
from modules import extensions
def to_json(x: extensions.Extension):
return {
"name": x.name,
"path": x.path,
"version": x.version,
"branch": x.branch,
"remote": x.remote,
}
return [to_json(x) for x in extensions.extensions if not x.is_builtin and x.enabled == enabled]
except Exception as e:
return str(e)
def get_config():
try:
from modules import shared
return shared.opts.data
except Exception as e:
return str(e)

17
modules/textual_inversion/autocrop.py
View file

@ -77,27 +77,27 @@ def focal_point(im, settings):
pois = []
weight_pref_total = 0
if len(corner_points) > 0:
if corner_points:
weight_pref_total += settings.corner_points_weight
if len(entropy_points) > 0:
if entropy_points:
weight_pref_total += settings.entropy_points_weight
if len(face_points) > 0:
if face_points:
weight_pref_total += settings.face_points_weight
corner_centroid = None
if len(corner_points) > 0:
if corner_points:
corner_centroid = centroid(corner_points)
corner_centroid.weight = settings.corner_points_weight / weight_pref_total
pois.append(corner_centroid)
entropy_centroid = None
if len(entropy_points) > 0:
if entropy_points:
entropy_centroid = centroid(entropy_points)
entropy_centroid.weight = settings.entropy_points_weight / weight_pref_total
pois.append(entropy_centroid)
face_centroid = None
if len(face_points) > 0:
if face_points:
face_centroid = centroid(face_points)
face_centroid.weight = settings.face_points_weight / weight_pref_total
pois.append(face_centroid)
@ -187,7 +187,7 @@ def image_face_points(im, settings):
except Exception:
continue
if len(faces) > 0:
if faces:
rects = [[f[0], f[1], f[0] + f[2], f[1] + f[3]] for f in faces]
return [PointOfInterest((r[0] +r[2]) // 2, (r[1] + r[3]) // 2, size=abs(r[0]-r[2]), weight=1/len(rects)) for r in rects]
return []
@ -298,8 +298,7 @@ def download_and_cache_models(dirname):
download_url = 'https://github.com/opencv/opencv_zoo/blob/91fb0290f50896f38a0ab1e558b74b16bc009428/models/face_detection_yunet/face_detection_yunet_2022mar.onnx?raw=true'
model_file_name = 'face_detection_yunet.onnx'
if not os.path.exists(dirname):
os.makedirs(dirname)
os.makedirs(dirname, exist_ok=True)
cache_file = os.path.join(dirname, model_file_name)
if not os.path.exists(cache_file):

2
modules/textual_inversion/dataset.py
View file

@ -32,7 +32,7 @@ class DatasetEntry:
class PersonalizedBase(Dataset):
def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, cond_model=None, device=None, template_file=None, include_cond=False, batch_size=1, gradient_step=1, shuffle_tags=False, tag_drop_out=0, latent_sampling_method='once', varsize=False, use_weight=False):
re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex) > 0 else None
re_word = re.compile(shared.opts.dataset_filename_word_regex) if shared.opts.dataset_filename_word_regex else None
self.placeholder_token = placeholder_token

4
modules/textual_inversion/preprocess.py
View file

@ -47,7 +47,7 @@ def save_pic_with_caption(image, index, params: PreprocessParams, existing_capti
caption += shared.interrogator.generate_caption(image)
if params.process_caption_deepbooru:
if len(caption) > 0:
if caption:
caption += ", "
caption += deepbooru.model.tag_multi(image)
@ -67,7 +67,7 @@ def save_pic_with_caption(image, index, params: PreprocessParams, existing_capti
caption = caption.strip()
if len(caption) > 0:
if caption:
with open(os.path.join(params.dstdir, f"{basename}.txt"), "w", encoding="utf8") as file:
file.write(caption)

37
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):
@ -241,7 +251,7 @@ class EmbeddingDatabase:
if self.previously_displayed_embeddings != displayed_embeddings:
self.previously_displayed_embeddings = displayed_embeddings
print(f"Textual inversion embeddings loaded({len(self.word_embeddings)}): {', '.join(self.word_embeddings.keys())}")
if len(self.skipped_embeddings) > 0:
if self.skipped_embeddings:
print(f"Textual inversion embeddings skipped({len(self.skipped_embeddings)}): {', '.join(self.skipped_embeddings.keys())}")
def find_embedding_at_position(self, tokens, offset):
@ -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()

46
modules/timer.py
View file

@ -1,11 +1,30 @@
import time
class TimerSubcategory:
def __init__(self, timer, category):
self.timer = timer
self.category = category
self.start = None
self.original_base_category = timer.base_category
def __enter__(self):
self.start = time.time()
self.timer.base_category = self.original_base_category + self.category + "/"
def __exit__(self, exc_type, exc_val, exc_tb):
elapsed_for_subcategroy = time.time() - self.start
self.timer.base_category = self.original_base_category
self.timer.add_time_to_record(self.original_base_category + self.category, elapsed_for_subcategroy)
self.timer.record(self.category)
class Timer:
def __init__(self):
self.start = time.time()
self.records = {}
self.total = 0
self.base_category = ''
def elapsed(self):
end = time.time()
@ -13,18 +32,29 @@ class Timer:
self.start = end
return res
def record(self, category, extra_time=0):
e = self.elapsed()
def add_time_to_record(self, category, amount):
if category not in self.records:
self.records[category] = 0
self.records[category] += e + extra_time
self.records[category] += amount
def record(self, category, extra_time=0):
e = self.elapsed()
self.add_time_to_record(self.base_category + category, e + extra_time)
self.total += e + extra_time
def subcategory(self, name):
self.elapsed()
subcat = TimerSubcategory(self, name)
return subcat
def summary(self):
res = f"{self.total:.1f}s"
additions = [x for x in self.records.items() if x[1] >= 0.1]
additions = [(category, time_taken) for category, time_taken in self.records.items() if time_taken >= 0.1 and '/' not in category]
if not additions:
return res
@ -34,5 +64,13 @@ class Timer:
return res
def dump(self):
return {'total': self.total, 'records': self.records}
def reset(self):
self.__init__()
startup_timer = Timer()
startup_record = None

396
modules/ui.py
View file

@ -1,21 +1,23 @@
import datetime
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
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, sysinfo
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 +37,8 @@ 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
@ -76,6 +80,7 @@ extra_networks_symbol = '\U0001F3B4' # 🎴
switch_values_symbol = '\U000021C5' # ⇅
restore_progress_symbol = '\U0001F300' # 🌀
detect_image_size_symbol = '\U0001F4D0' # 📐
up_down_symbol = '\u2195\ufe0f' # ↕️
def plaintext_to_html(text):
@ -231,9 +236,8 @@ 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)]
@ -368,25 +372,6 @@ 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)
@ -405,27 +390,17 @@ 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)
dropdown.change(
fn=lambda x: gr.Dropdown.update(visible=len(x) > 0),
fn=lambda x: gr.Dropdown.update(visible=bool(x)),
inputs=[dropdown],
outputs=[dropdown],
)
@ -456,6 +431,8 @@ 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")
@ -524,6 +501,9 @@ 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 +596,8 @@ def create_ui():
outputs=[
txt2img_prompt,
txt_prompt_img
]
],
show_progress=False,
)
enable_hr.change(
@ -641,6 +622,7 @@ def create_ui():
(subseed_strength, "Variation seed strength"),
(seed_resize_from_w, "Seed resize from-1"),
(seed_resize_from_h, "Seed resize from-2"),
(txt2img_prompt_styles, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()),
(denoising_strength, "Denoising strength"),
(enable_hr, lambda d: "Denoising strength" in d),
(hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
@ -779,6 +761,8 @@ 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")
@ -789,7 +773,7 @@ def create_ui():
selected_scale_tab = gr.State(value=0)
with gr.Tabs():
with gr.Tab(label="Resize to") as tab_scale_to:
with gr.Tab(label="Resize to", elem_id="img2img_tab_resize_to") as tab_scale_to:
with FormRow():
with gr.Column(elem_id="img2img_column_size", scale=4):
width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
@ -798,7 +782,7 @@ def create_ui():
res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="img2img_res_switch_btn")
detect_image_size_btn = ToolButton(value=detect_image_size_symbol, elem_id="img2img_detect_image_size_btn")
with gr.Tab(label="Resize by") as tab_scale_by:
with gr.Tab(label="Resize by", elem_id="img2img_tab_resize_by") as tab_scale_by:
scale_by = gr.Slider(minimum=0.05, maximum=4.0, step=0.05, label="Scale", value=1.0, elem_id="img2img_scale")
with FormRow():
@ -888,6 +872,8 @@ 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 +888,8 @@ def create_ui():
outputs=[
img2img_prompt,
img2img_prompt_img
]
],
show_progress=False,
)
img2img_args = dict(
@ -1051,6 +1038,7 @@ def create_ui():
(subseed_strength, "Variation seed strength"),
(seed_resize_from_w, "Seed resize from-1"),
(seed_resize_from_h, "Seed resize from-2"),
(img2img_prompt_styles, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()),
(denoising_strength, "Denoising strength"),
(mask_blur, "Mask blur"),
*modules.scripts.scripts_img2img.infotext_fields
@ -1460,195 +1448,10 @@ 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 +1463,7 @@ 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 +1473,7 @@ 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()
@ -1701,58 +1501,20 @@ def create_ui():
gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
footer = shared.html("footer.html")
footer = footer.format(versions=versions_html())
footer = footer.format(versions=versions_html(), api_docs="/docs" if shared.cmd_opts.api else "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/API")
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 +1542,7 @@ 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 +1556,6 @@ 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
@ -1901,3 +1599,17 @@ def setup_ui_api(app):
app.add_api_route("/internal/quicksettings-hint", quicksettings_hint, methods=["GET"], response_model=List[QuicksettingsHint])
app.add_api_route("/internal/ping", lambda: {}, methods=["GET"])
app.add_api_route("/internal/profile-startup", lambda: timer.startup_record, methods=["GET"])
def download_sysinfo(attachment=False):
from fastapi.responses import PlainTextResponse
text = sysinfo.get()
filename = f"sysinfo-{datetime.datetime.utcnow().strftime('%Y-%m-%d-%H-%M')}.txt"
return PlainTextResponse(text, headers={'Content-Disposition': f'{"attachment" if attachment else "inline"}; filename="{filename}"'})
app.add_api_route("/internal/sysinfo", download_sysinfo, methods=["GET"])
app.add_api_route("/internal/sysinfo-download", lambda: download_sysinfo(attachment=True), methods=["GET"])

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

46
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, restart
from modules.paths_internal import config_states_dir
from modules.call_queue import wrap_gradio_gpu_call
@ -46,13 +44,16 @@ 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
shared.opts.save(shared.config_filename)
shared.state.request_restart()
if restart.is_restartable():
restart.restart_program()
else:
restart.stop_program()
def save_config_state(name):
@ -113,8 +114,7 @@ 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()
@ -138,7 +138,10 @@ def extension_table():
<table id="extensions">
<thead>
<tr>
<th><abbr title="Use checkbox to enable the extension; it will be enabled or disabled when you click apply button">Extension</abbr></th>
<th>
<input class="gr-check-radio gr-checkbox all_extensions_toggle" type="checkbox" {'checked="checked"' if all(ext.enabled for ext in extensions.extensions) else ''} onchange="toggle_all_extensions(event)" />
<abbr title="Use checkbox to enable the extension; it will be enabled or disabled when you click apply button">Extension</abbr>
</th>
<th>URL</th>
<th>Branch</th>
<th>Version</th>
@ -170,7 +173,7 @@ def extension_table():
code += f"""
<tr>
<td><label{style}><input class="gr-check-radio gr-checkbox" name="enable_{html.escape(ext.name)}" type="checkbox" {'checked="checked"' if ext.enabled else ''}>{html.escape(ext.name)}</label></td>
<td><label{style}><input class="gr-check-radio gr-checkbox extension_toggle" name="enable_{html.escape(ext.name)}" type="checkbox" {'checked="checked"' if ext.enabled else ''} onchange="toggle_extension(event)" />{html.escape(ext.name)}</label></td>
<td>{remote}</td>
<td>{ext.branch}</td>
<td>{version_link}</td>
@ -325,6 +328,11 @@ def normalize_git_url(url):
def install_extension_from_url(dirname, url, branch_name=None):
check_access()
if isinstance(dirname, str):
dirname = dirname.strip()
if isinstance(url, str):
url = url.strip()
assert url, 'No URL specified'
if dirname is None or dirname == "":
@ -337,7 +345,8 @@ def install_extension_from_url(dirname, url, branch_name=None):
assert not os.path.exists(target_dir), f'Extension directory already exists: {target_dir}'
normalized_url = normalize_git_url(url)
assert len([x for x in extensions.extensions if normalize_git_url(x.remote) == normalized_url]) == 0, 'Extension with this URL is already installed'
if any(x for x in extensions.extensions if normalize_git_url(x.remote) == normalized_url):
raise Exception(f'Extension with this URL is already installed: {url}')
tmpdir = os.path.join(paths.data_path, "tmp", dirname)
@ -453,7 +462,7 @@ def refresh_available_extensions_from_data(hide_tags, sort_column, filter_text="
existing = installed_extension_urls.get(normalize_git_url(url), None)
extension_tags = extension_tags + ["installed"] if existing else extension_tags
if len([x for x in extension_tags if x in tags_to_hide]) > 0:
if any(x for x in extension_tags if x in tags_to_hide):
hidden += 1
continue
@ -490,8 +499,14 @@ 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():
@ -506,7 +521,8 @@ def create_ui():
with gr.TabItem("Installed", id="installed"):
with gr.Row(elem_id="extensions_installed_top"):
apply = gr.Button(value="Apply and restart UI", variant="primary")
apply_label = ("Apply and restart UI" if restart.is_restartable() else "Apply and quit")
apply = gr.Button(value=apply_label, variant="primary")
check = gr.Button(value="Check for updates")
extensions_disable_all = gr.Radio(label="Disable all extensions", choices=["none", "extra", "all"], value=shared.opts.disable_all_extensions, elem_id="extensions_disable_all")
extensions_disabled_list = gr.Text(elem_id="extensions_disabled_list", visible=False).style(container=False)
@ -555,9 +571,9 @@ def create_ui():
available_extensions_table = gr.HTML()
refresh_available_extensions_button.click(
fn=modules.ui.wrap_gradio_call(refresh_available_extensions, extra_outputs=[gr.update(), gr.update(), gr.update()]),
fn=modules.ui.wrap_gradio_call(refresh_available_extensions, extra_outputs=[gr.update(), gr.update(), gr.update(), gr.update()]),
inputs=[available_extensions_index, hide_tags, sort_column],
outputs=[available_extensions_index, available_extensions_table, hide_tags, install_result, search_extensions_text],
outputs=[available_extensions_index, available_extensions_table, hide_tags, search_extensions_text, install_result],
)
install_extension_button.click(

18
modules/ui_extra_networks.py
View file

@ -4,6 +4,7 @@ from pathlib import Path
from modules import shared
from modules.images import read_info_from_image, save_image_with_geninfo
from modules.ui import up_down_symbol
import gradio as gr
import json
import html
@ -185,6 +186,8 @@ class ExtraNetworksPage:
if search_only and shared.opts.extra_networks_hidden_models == "Never":
return ""
sort_keys = " ".join([html.escape(f'data-sort-{k}={v}') for k, v in item.get("sort_keys", {}).items()]).strip()
args = {
"background_image": background_image,
"style": f"'display: none; {height}{width}'",
@ -198,10 +201,23 @@ class ExtraNetworksPage:
"search_term": item.get("search_term", ""),
"metadata_button": metadata_button,
"search_only": " search_only" if search_only else "",
"sort_keys": sort_keys,
}
return self.card_page.format(**args)
def get_sort_keys(self, path):
"""
List of default keys used for sorting in the UI.
"""
pth = Path(path)
stat = pth.stat()
return {
"date_created": int(stat.st_ctime or 0),
"date_modified": int(stat.st_mtime or 0),
"name": pth.name.lower(),
}
def find_preview(self, path):
"""
Find a preview PNG for a given path (without extension) and call link_preview on it.
@ -296,6 +312,8 @@ def create_ui(container, button, tabname):
page_elem.change(fn=lambda: None, _js='function(){applyExtraNetworkFilter(' + json.dumps(tabname) + '); return []}', inputs=[], outputs=[])
gr.Textbox('', show_label=False, elem_id=tabname+"_extra_search", placeholder="Search...", visible=False)
gr.Dropdown(choices=['Default Sort', 'Date Created', 'Date Modified', 'Name'], value='Default Sort', elem_id=tabname+"_extra_sort", multiselect=False, visible=False, show_label=False, interactive=True)
gr.Button(up_down_symbol, elem_id=tabname+"_extra_sortorder")
button_refresh = gr.Button('Refresh', elem_id=tabname+"_extra_refresh")
ui.button_save_preview = gr.Button('Save preview', elem_id=tabname+"_save_preview", visible=False)

4
modules/ui_extra_networks_checkpoints.py
View file

@ -14,7 +14,7 @@ class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage):
def list_items(self):
checkpoint: sd_models.CheckpointInfo
for name, checkpoint in sd_models.checkpoints_list.items():
for index, (name, checkpoint) in enumerate(sd_models.checkpoints_list.items()):
path, ext = os.path.splitext(checkpoint.filename)
yield {
"name": checkpoint.name_for_extra,
@ -24,6 +24,8 @@ class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage):
"search_term": self.search_terms_from_path(checkpoint.filename) + " " + (checkpoint.sha256 or ""),
"onclick": '"' + html.escape(f"""return selectCheckpoint({json.dumps(name)})""") + '"',
"local_preview": f"{path}.{shared.opts.samples_format}",
"sort_keys": {'default': index, **self.get_sort_keys(checkpoint.filename)},
}
def allowed_directories_for_previews(self):

4
modules/ui_extra_networks_hypernets.py
View file

@ -12,7 +12,7 @@ class ExtraNetworksPageHypernetworks(ui_extra_networks.ExtraNetworksPage):
shared.reload_hypernetworks()
def list_items(self):
for name, path in shared.hypernetworks.items():
for index, (name, path) in enumerate(shared.hypernetworks.items()):
path, ext = os.path.splitext(path)
yield {
@ -23,6 +23,8 @@ class ExtraNetworksPageHypernetworks(ui_extra_networks.ExtraNetworksPage):
"search_term": self.search_terms_from_path(path),
"prompt": json.dumps(f"<hypernet:{name}:") + " + opts.extra_networks_default_multiplier + " + json.dumps(">"),
"local_preview": f"{path}.preview.{shared.opts.samples_format}",
"sort_keys": {'default': index, **self.get_sort_keys(path + ext)},
}
def allowed_directories_for_previews(self):

4
modules/ui_extra_networks_textual_inversion.py
View file

@ -13,7 +13,7 @@ class ExtraNetworksPageTextualInversion(ui_extra_networks.ExtraNetworksPage):
sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload=True)
def list_items(self):
for embedding in sd_hijack.model_hijack.embedding_db.word_embeddings.values():
for index, embedding in enumerate(sd_hijack.model_hijack.embedding_db.word_embeddings.values()):
path, ext = os.path.splitext(embedding.filename)
yield {
"name": embedding.name,
@ -23,6 +23,8 @@ class ExtraNetworksPageTextualInversion(ui_extra_networks.ExtraNetworksPage):
"search_term": self.search_terms_from_path(embedding.filename),
"prompt": json.dumps(embedding.name),
"local_preview": f"{path}.preview.{shared.opts.samples_format}",
"sort_keys": {'default': index, **self.get_sort_keys(embedding.filename)},
}
def allowed_directories_for_previews(self):

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

289
modules/ui_settings.py Normal file
View file

@ -0,0 +1,289 @@
import gradio as gr
from modules import ui_common, shared, script_callbacks, scripts, sd_models, sysinfo
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 changed 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("Sysinfo", id="sysinfo", elem_id="settings_tab_sysinfo"):
gr.HTML('<a href="./internal/sysinfo-download" class="sysinfo_big_link" download>Download system info</a><br /><a href="./internal/sysinfo">(or open as text in a new page)</a>', elem_id="sysinfo_download")
with gr.Row():
with gr.Column(scale=1):
sysinfo_check_file = gr.File(label="Check system info for validity", type='binary')
with gr.Column(scale=1):
sysinfo_check_output = gr.HTML("", elem_id="sysinfo_validity")
with gr.Column(scale=100):
pass
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=[],
)
def check_file(x):
if x is None:
return ''
if sysinfo.check(x.decode('utf8', errors='ignore')):
return 'Valid'
return 'Invalid'
sysinfo_check_file.change(
fn=check_file,
inputs=[sysinfo_check_file],
outputs=[sysinfo_check_output],
)
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,
)

2
modules/ui_tempdir.py
View file

@ -31,7 +31,7 @@ def check_tmp_file(gradio, filename):
return False
def save_pil_to_file(self, pil_image, dir=None):
def save_pil_to_file(self, pil_image, dir=None, format="png"):
already_saved_as = getattr(pil_image, 'already_saved_as', None)
if already_saved_as and os.path.isfile(already_saved_as):
register_tmp_file(shared.demo, already_saved_as)

6
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 = int((img.width * scale) // 8 * 8)
dest_h = int((img.height * scale) // 8 * 8)
for _ in range(3):
shape = (img.width, img.height)
@ -77,7 +77,7 @@ class Upscaler:
pass
def find_models(self, ext_filter=None) -> list:
return modelloader.load_models(model_path=self.model_path, model_url=self.model_url, command_path=self.user_path)
return modelloader.load_models(model_path=self.model_path, model_url=self.model_url, command_path=self.user_path, ext_filter=ext_filter)
def update_status(self, prompt):
print(f"\nextras: {prompt}", file=shared.progress_print_out)