diff options
Diffstat (limited to 'modules')
37 files changed, 482 insertions, 207 deletions
diff --git a/modules/api/api.py b/modules/api/api.py index eee99bbb..fbd616a3 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -16,6 +16,7 @@ from secrets import compare_digest import modules.shared as shared from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing from modules.api import models +from modules.errors import print_error from modules.shared import opts from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images from modules.textual_inversion.textual_inversion import create_embedding, train_embedding @@ -23,6 +24,7 @@ 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 +110,6 @@ def api_middleware(app: FastAPI): from rich.console import Console console = Console() except Exception: - import traceback rich_available = False @app.middleware("http") @@ -139,11 +140,12 @@ 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() + print_error(message, exc_info=True) return JSONResponse(status_code=vars(e).get('status_code', 500), content=jsonable_encoder(err)) @app.middleware("http") @@ -189,6 +191,7 @@ class Api: self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[models.SamplerItem]) self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[models.UpscalerItem]) self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[models.SDModelItem]) + self.add_api_route("/sdapi/v1/sd-vae", self.get_sd_vaes, methods=["GET"], response_model=List[models.SDVaeItem]) self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[models.HypernetworkItem]) self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[models.FaceRestorerItem]) self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[models.RealesrganItem]) @@ -541,6 +544,9 @@ class Api: def get_sd_models(self): return [{"title": x.title, "model_name": x.model_name, "hash": x.shorthash, "sha256": x.sha256, "filename": x.filename, "config": find_checkpoint_config_near_filename(x)} for x in checkpoints_list.values()] + def get_sd_vaes(self): + return [{"model_name": x, "filename": vae_dict[x]} for x in vae_dict.keys()] + def get_hypernetworks(self): return [{"name": name, "path": shared.hypernetworks[name]} for name in shared.hypernetworks] @@ -700,4 +706,4 @@ class Api: def launch(self, server_name, port): self.app.include_router(self.router) - uvicorn.run(self.app, host=server_name, port=port) + uvicorn.run(self.app, host=server_name, port=port, timeout_keep_alive=0) diff --git a/modules/api/models.py b/modules/api/models.py index 1ff2fb33..47fdede2 100644 --- a/modules/api/models.py +++ b/modules/api/models.py @@ -249,6 +249,10 @@ class SDModelItem(BaseModel): filename: str = Field(title="Filename") config: Optional[str] = Field(title="Config file") +class SDVaeItem(BaseModel): + model_name: str = Field(title="Model Name") + filename: str = Field(title="Filename") + class HypernetworkItem(BaseModel): name: str = Field(title="Name") path: Optional[str] = Field(title="Path") diff --git a/modules/call_queue.py b/modules/call_queue.py index 447bb764..dba2a9b4 100644 --- a/modules/call_queue.py +++ b/modules/call_queue.py @@ -1,10 +1,9 @@ import html
-import sys
import threading
-import traceback
import time
from modules import shared, progress
+from modules.errors import print_error
queue_lock = threading.Lock()
@@ -56,16 +55,14 @@ 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)"
+ print_error(f"{message}\n{arg_str}", exc_info=True)
shared.state.job = ""
shared.state.job_count = 0
@@ -108,4 +105,3 @@ def wrap_gradio_call(func, extra_outputs=None, add_stats=False): return tuple(res)
return f
-
diff --git a/modules/cmd_args.py b/modules/cmd_args.py index 3eeb84d5..0974056d 100644 --- a/modules/cmd_args.py +++ b/modules/cmd_args.py @@ -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")
diff --git a/modules/codeformer_model.py b/modules/codeformer_model.py index ececdbae..76143e9f 100644 --- a/modules/codeformer_model.py +++ b/modules/codeformer_model.py @@ -1,6 +1,4 @@ import os
-import sys
-import traceback
import cv2
import torch
@@ -8,6 +6,7 @@ import torch import modules.face_restoration
import modules.shared
from modules import shared, devices, modelloader
+from modules.errors import print_error
from modules.paths import models_path
# codeformer people made a choice to include modified basicsr library to their project which makes
@@ -105,8 +104,8 @@ 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:
+ print_error('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 +134,6 @@ 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)
+ print_error("Error setting up CodeFormer", exc_info=True)
# sys.path = stored_sys_path
diff --git a/modules/config_states.py b/modules/config_states.py index db65bcdb..faeaf28b 100644 --- a/modules/config_states.py +++ b/modules/config_states.py @@ -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 @@ -14,6 +12,7 @@ from collections import OrderedDict import git from modules import shared, extensions +from modules.errors import print_error from modules.paths_internal import script_path, config_states_dir @@ -53,8 +52,7 @@ 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) + print_error(f"Error reading webui git info from {script_path}", exc_info=True) webui_remote = None webui_commit_hash = None @@ -134,8 +132,7 @@ 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) + print_error(f"Error reading webui git info from {script_path}", exc_info=True) return try: @@ -143,8 +140,7 @@ 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) + print_error(f"Error restoring webui to commit{webui_commit_hash}") def restore_extension_config(config): diff --git a/modules/errors.py b/modules/errors.py index f6b80dbb..41d8dc93 100644 --- a/modules/errors.py +++ b/modules/errors.py @@ -1,7 +1,23 @@ import sys
+import textwrap
import traceback
+def print_error(
+ message: str,
+ *,
+ exc_info: bool = False,
+) -> None:
+ """
+ Print an error message to stderr, with optional traceback.
+ """
+ for line in message.splitlines():
+ print("***", line, file=sys.stderr)
+ if exc_info:
+ print(textwrap.indent(traceback.format_exc(), " "), file=sys.stderr)
+ print("---")
+
+
def print_error_explanation(message):
lines = message.strip().split("\n")
max_len = max([len(x) for x in lines])
@@ -12,9 +28,13 @@ def print_error_explanation(message): print('=' * max_len, file=sys.stderr)
-def display(e: Exception, task):
+def display(e: Exception, task, *, full_traceback=False):
print(f"{task or 'error'}: {type(e).__name__}", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
+ te = traceback.TracebackException.from_exception(e)
+ if full_traceback:
+ # include frames leading up to the try-catch block
+ te.stack = traceback.StackSummary(traceback.extract_stack()[:-2] + te.stack)
+ print(*te.format(), sep="", file=sys.stderr)
message = str(e)
if "copying a param with shape torch.Size([640, 1024]) from checkpoint, the shape in current model is torch.Size([640, 768])" in message:
diff --git a/modules/extensions.py b/modules/extensions.py index 624832a0..92f93ad9 100644 --- a/modules/extensions.py +++ b/modules/extensions.py @@ -1,11 +1,9 @@ import os
-import sys
import threading
-import traceback
-
-import git
from modules import shared
+from modules.errors import print_error
+from modules.gitpython_hack import Repo
from modules.paths_internal import extensions_dir, extensions_builtin_dir, script_path # noqa: F401
extensions = []
@@ -54,10 +52,9 @@ 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)
+ print_error(f"Error reading github repository info from {self.path}", exc_info=True)
if repo is None or repo.bare:
self.remote = None
@@ -72,8 +69,8 @@ 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:
+ print_error(f"Failed reading extension data from Git repository ({self.name})", exc_info=True)
self.remote = None
self.have_info_from_repo = True
@@ -94,7 +91,7 @@ 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 +113,7 @@ 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)
diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py index d5f0a49b..071bd9ea 100644 --- a/modules/generation_parameters_copypaste.py +++ b/modules/generation_parameters_copypaste.py @@ -35,7 +35,7 @@ def reset(): def quote(text):
- if ',' not in str(text) and '\n' not in str(text):
+ if ',' not in str(text) and '\n' not in str(text) and ':' not in str(text):
return text
return json.dumps(text, ensure_ascii=False)
@@ -306,6 +306,18 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model if "RNG" not in res:
res["RNG"] = "GPU"
+ if "Schedule type" not in res:
+ res["Schedule type"] = "Automatic"
+
+ if "Schedule max sigma" not in res:
+ res["Schedule max sigma"] = 0
+
+ if "Schedule min sigma" not in res:
+ res["Schedule min sigma"] = 0
+
+ if "Schedule rho" not in res:
+ res["Schedule rho"] = 0
+
return res
@@ -318,6 +330,10 @@ infotext_to_setting_name_mapping = [ ('Conditional mask weight', 'inpainting_mask_weight'),
('Model hash', 'sd_model_checkpoint'),
('ENSD', 'eta_noise_seed_delta'),
+ ('Schedule type', 'k_sched_type'),
+ ('Schedule max sigma', 'sigma_max'),
+ ('Schedule min sigma', 'sigma_min'),
+ ('Schedule rho', 'rho'),
('Noise multiplier', 'initial_noise_multiplier'),
('Eta', 'eta_ancestral'),
('Eta DDIM', 'eta_ddim'),
diff --git a/modules/gfpgan_model.py b/modules/gfpgan_model.py index 0131dea4..d2f647fe 100644 --- a/modules/gfpgan_model.py +++ b/modules/gfpgan_model.py @@ -1,12 +1,11 @@ import os
-import sys
-import traceback
import facexlib
import gfpgan
import modules.face_restoration
from modules import paths, shared, devices, modelloader
+from modules.errors import print_error
model_dir = "GFPGAN"
user_path = None
@@ -112,5 +111,4 @@ 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)
+ print_error("Error setting up GFPGAN", exc_info=True)
diff --git a/modules/gitpython_hack.py b/modules/gitpython_hack.py new file mode 100644 index 00000000..e537c1df --- /dev/null +++ b/modules/gitpython_hack.py @@ -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 diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 570b5603..fcc1ef20 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -2,8 +2,6 @@ import datetime import glob
import html
import os
-import sys
-import traceback
import inspect
import modules.textual_inversion.dataset
@@ -12,6 +10,7 @@ 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.errors import print_error
from modules.textual_inversion import textual_inversion, logging
from modules.textual_inversion.learn_schedule import LearnRateScheduler
from torch import einsum
@@ -325,17 +324,14 @@ 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)
+ print_error(f"Error loading hypernetwork {path}", exc_info=True)
return None
- return hypernetwork
-
def load_hypernetworks(names, multipliers=None):
already_loaded = {}
@@ -770,7 +766,7 @@ Last saved image: {html.escape(last_saved_image)}<br/> </p>
"""
except Exception:
- print(traceback.format_exc(), file=sys.stderr)
+ print_error("Exception in training hypernetwork", exc_info=True)
finally:
pbar.leave = False
pbar.close()
diff --git a/modules/images.py b/modules/images.py index 4e8cd993..09f728df 100644 --- a/modules/images.py +++ b/modules/images.py @@ -1,6 +1,4 @@ import datetime
-import sys
-import traceback
import pytz
import io
@@ -18,9 +16,12 @@ import json import hashlib
from modules import sd_samplers, shared, script_callbacks, errors
+from modules.errors import print_error
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 +337,20 @@ def sanitize_filename_part(text, replace_spaces=True): class FilenameGenerator:
+ def get_vae_filename(self): #get the name of the VAE file.
+ if sd_vae.loaded_vae_file is None:
+ return "NoneType"
+ file_name = os.path.basename(sd_vae.loaded_vae_file)
+ split_file_name = file_name.split('.')
+ if len(split_file_name) > 1 and split_file_name[0] == '':
+ return split_file_name[1] # if the first character of the filename is "." then [1] is obtained.
+ else:
+ return split_file_name[0]
+
replacements = {
'seed': lambda self: self.seed if self.seed is not None else '',
+ 'seed_first': lambda self: self.seed if self.p.batch_size == 1 else self.p.all_seeds[0],
+ 'seed_last': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if self.p.batch_size == 1 else self.p.all_seeds[-1],
'steps': lambda self: self.p and self.p.steps,
'cfg': lambda self: self.p and self.p.cfg_scale,
'width': lambda self: self.image.width,
@@ -354,19 +367,23 @@ class FilenameGenerator: 'prompt_no_styles': lambda self: self.prompt_no_style(),
'prompt_spaces': lambda self: sanitize_filename_part(self.prompt, replace_spaces=False),
'prompt_words': lambda self: self.prompt_words(),
- 'batch_number': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if self.p.batch_size == 1 else self.p.batch_index + 1,
- 'generation_number': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if self.p.n_iter == 1 and self.p.batch_size == 1 else self.p.iteration * self.p.batch_size + self.p.batch_index + 1,
+ 'batch_number': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if self.p.batch_size == 1 or self.zip else self.p.batch_index + 1,
+ 'batch_size': lambda self: self.p.batch_size,
+ 'generation_number': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if (self.p.n_iter == 1 and self.p.batch_size == 1) or self.zip else self.p.iteration * self.p.batch_size + self.p.batch_index + 1,
'hasprompt': lambda self, *args: self.hasprompt(*args), # accepts formats:[hasprompt<prompt1|default><prompt2>..]
'clip_skip': lambda self: opts.data["CLIP_stop_at_last_layers"],
'denoising': lambda self: self.p.denoising_strength if self.p and self.p.denoising_strength else NOTHING_AND_SKIP_PREVIOUS_TEXT,
+ 'vae_filename': lambda self: self.get_vae_filename(),
+
}
default_time_format = '%Y%m%d%H%M%S'
- def __init__(self, p, seed, prompt, image):
+ def __init__(self, p, seed, prompt, image, zip=False):
self.p = p
self.seed = seed
self.prompt = prompt
self.image = image
+ self.zip = zip
def hasprompt(self, *args):
lower = self.prompt.lower()
@@ -446,8 +463,7 @@ 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)
+ print_error(f"Error adding [{pattern}] to filename", exc_info=True)
if replacement == NOTHING_AND_SKIP_PREVIOUS_TEXT:
continue
@@ -493,9 +509,12 @@ def save_image_with_geninfo(image, geninfo, filename, extension=None, existing_p existing_pnginfo['parameters'] = geninfo
if extension.lower() == '.png':
- pnginfo_data = PngImagePlugin.PngInfo()
- for k, v in (existing_pnginfo or {}).items():
- pnginfo_data.add_text(k, str(v))
+ if opts.enable_pnginfo:
+ pnginfo_data = PngImagePlugin.PngInfo()
+ for k, v in (existing_pnginfo or {}).items():
+ pnginfo_data.add_text(k, str(v))
+ else:
+ pnginfo_data = None
image.save(filename, format=image_format, quality=opts.jpeg_quality, pnginfo=pnginfo_data)
@@ -665,9 +684,10 @@ 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 +698,7 @@ 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)
+ print_error("Error parsing NovelAI image generation parameters", exc_info=True)
return geninfo, items
diff --git a/modules/img2img.py b/modules/img2img.py index d704bf90..4c12c2c5 100644 --- a/modules/img2img.py +++ b/modules/img2img.py @@ -92,7 +92,8 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s elif mode == 2: # inpaint
image, mask = init_img_with_mask["image"], init_img_with_mask["mask"]
alpha_mask = ImageOps.invert(image.split()[-1]).convert('L').point(lambda x: 255 if x > 0 else 0, mode='1')
- mask = ImageChops.lighter(alpha_mask, mask.convert('L')).convert('L')
+ mask = mask.convert('L').point(lambda x: 255 if x > 128 else 0, mode='1')
+ mask = ImageChops.lighter(alpha_mask, mask).convert('L')
image = image.convert("RGB")
elif mode == 3: # inpaint sketch
image = inpaint_color_sketch
diff --git a/modules/interrogate.py b/modules/interrogate.py index 111b1322..d36e1a5a 100644 --- a/modules/interrogate.py +++ b/modules/interrogate.py @@ -1,6 +1,5 @@ import os
import sys
-import traceback
from collections import namedtuple
from pathlib import Path
import re
@@ -12,6 +11,7 @@ from torchvision import transforms from torchvision.transforms.functional import InterpolationMode
from modules import devices, paths, shared, lowvram, modelloader, errors
+from modules.errors import print_error
blip_image_eval_size = 384
clip_model_name = 'ViT-L/14'
@@ -216,8 +216,7 @@ class InterrogateModels: res += f", {match}"
except Exception:
- print("Error interrogating", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
+ print_error("Error interrogating", exc_info=True)
res += "<error>"
self.unload()
diff --git a/modules/launch_utils.py b/modules/launch_utils.py index 35a52310..0bf4cb7e 100644 --- a/modules/launch_utils.py +++ b/modules/launch_utils.py @@ -8,6 +8,7 @@ import json from functools import lru_cache
from modules import cmd_args
+from modules.errors import print_error
from modules.paths_internal import script_path, extensions_dir
args, _ = cmd_args.parser.parse_known_args()
@@ -188,7 +189,7 @@ 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)
+ print_error(str(e))
def list_extensions(settings_file):
@@ -198,8 +199,8 @@ 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:
+ print_error("Could not load settings", exc_info=True)
disabled_extensions = set(settings.get('disabled_extensions', []))
disable_all_extensions = settings.get('disable_all_extensions', 'none')
@@ -223,19 +224,17 @@ 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")
@@ -286,7 +285,6 @@ 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)
diff --git a/modules/localization.py b/modules/localization.py index ee9c65e7..9a1df343 100644 --- a/modules/localization.py +++ b/modules/localization.py @@ -1,8 +1,7 @@ import json
import os
-import sys
-import traceback
+from modules.errors import print_error
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)
+ print_error(f"Error loading localization from {fn}", exc_info=True)
return f"window.localization = {json.dumps(data)}"
diff --git a/modules/paths.py b/modules/paths.py index 5f6474c0..5171df4f 100644 --- a/modules/paths.py +++ b/modules/paths.py @@ -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"]),
diff --git a/modules/processing.py b/modules/processing.py index 29a3743f..f628d88b 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -1,4 +1,5 @@ import json
+import logging
import math
import os
import sys
@@ -13,7 +14,7 @@ from skimage import exposure from typing import Any, Dict, List
import modules.sd_hijack
-from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common
+from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet
from modules.sd_hijack import model_hijack
from modules.shared import opts, cmd_opts, state
import modules.shared as shared
@@ -23,7 +24,6 @@ import modules.images as images import modules.styles
import modules.sd_models as sd_models
import modules.sd_vae as sd_vae
-import logging
from ldm.data.util import AddMiDaS
from ldm.models.diffusion.ddpm import LatentDepth2ImageDiffusion
@@ -321,14 +321,13 @@ class StableDiffusionProcessing: have been used before. The second element is where the previously
computed result is stored.
"""
-
- if cache[0] is not None and (required_prompts, steps) == cache[0]:
+ if cache[0] is not None and (required_prompts, steps, opts.CLIP_stop_at_last_layers, shared.sd_model.sd_checkpoint_info) == cache[0]:
return cache[1]
with devices.autocast():
cache[1] = function(shared.sd_model, required_prompts, steps)
- cache[0] = (required_prompts, steps)
+ cache[0] = (required_prompts, steps, opts.CLIP_stop_at_last_layers, shared.sd_model.sd_checkpoint_info)
return cache[1]
def setup_conds(self):
@@ -674,6 +673,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: if shared.opts.live_previews_enable and opts.show_progress_type == "Approx NN":
sd_vae_approx.model()
+ sd_unet.apply_unet()
+
if state.job_count == -1:
state.job_count = p.n_iter
diff --git a/modules/realesrgan_model.py b/modules/realesrgan_model.py index 99983678..c8d0c64f 100644 --- a/modules/realesrgan_model.py +++ b/modules/realesrgan_model.py @@ -1,12 +1,11 @@ import os
-import sys
-import traceback
import numpy as np
from PIL import Image
from basicsr.utils.download_util import load_file_from_url
from realesrgan import RealESRGANer
+from modules.errors import print_error
from modules.upscaler import Upscaler, UpscalerData
from modules.shared import cmd_opts, opts
from modules import modelloader
@@ -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)
+ print_error("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:
+ print_error("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)
+ print_error("Error making Real-ESRGAN models list", exc_info=True)
diff --git a/modules/safe.py b/modules/safe.py index e8f50774..b596f565 100644 --- a/modules/safe.py +++ b/modules/safe.py @@ -2,8 +2,6 @@ import pickle
import collections
-import sys
-import traceback
import torch
import numpy
@@ -11,6 +9,8 @@ import _codecs import zipfile
import re
+from modules.errors import print_error
+
# PyTorch 1.13 and later have _TypedStorage renamed to TypedStorage
TypedStorage = torch.storage.TypedStorage if hasattr(torch.storage, 'TypedStorage') else torch.storage._TypedStorage
@@ -136,17 +136,20 @@ 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)
+ print_error(
+ 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)
+ print_error(
+ 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 +193,3 @@ with safe.Extra(handler): unsafe_torch_load = torch.load
torch.load = load
global_extra_handler = None
-
diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py index 40f388a5..6aa9c3b6 100644 --- a/modules/script_callbacks.py +++ b/modules/script_callbacks.py @@ -1,16 +1,15 @@ -import sys
-import traceback
-from collections import namedtuple
import inspect
+from collections import namedtuple
from typing import Optional, Dict, Any
from fastapi import FastAPI
from gradio import Blocks
+from modules.errors import print_error
+
def report_exception(c, job):
- print(f"Error executing callback {job} for {c.script}", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
+ print_error(f"Error executing callback {job} for {c.script}", exc_info=True)
class ImageSaveParams:
@@ -111,6 +110,7 @@ callback_map = dict( callbacks_before_ui=[],
callbacks_on_reload=[],
callbacks_list_optimizers=[],
+ callbacks_list_unets=[],
)
@@ -271,6 +271,18 @@ def list_optimizers_callback(): return res
+def list_unets_callback():
+ res = []
+
+ for c in callback_map['callbacks_list_unets']:
+ try:
+ c.callback(res)
+ except Exception:
+ report_exception(c, 'list_unets')
+
+ return res
+
+
def add_callback(callbacks, fun):
stack = [x for x in inspect.stack() if x.filename != __file__]
filename = stack[0].filename if len(stack) > 0 else 'unknown file'
@@ -430,3 +442,10 @@ 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)
diff --git a/modules/script_loading.py b/modules/script_loading.py index 57b15862..26efffcb 100644 --- a/modules/script_loading.py +++ b/modules/script_loading.py @@ -1,8 +1,8 @@ import os
-import sys
-import traceback
import importlib.util
+from modules.errors import print_error
+
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)
+ print_error(f"Error running preload() for {preload_script}", exc_info=True)
diff --git a/modules/scripts.py b/modules/scripts.py index c902804b..a7168fd1 100644 --- a/modules/scripts.py +++ b/modules/scripts.py @@ -1,12 +1,12 @@ import os
import re
import sys
-import traceback
from collections import namedtuple
import gradio as gr
from modules import shared, paths, script_callbacks, extensions, script_loading, scripts_postprocessing
+from modules.errors import print_error
AlwaysVisible = object()
@@ -264,8 +264,7 @@ def load_scripts(): register_scripts_from_module(script_module)
except Exception:
- print(f"Error loading script: {scriptfile.filename}", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
+ print_error(f"Error loading script: {scriptfile.filename}", exc_info=True)
finally:
sys.path = syspath
@@ -280,11 +279,9 @@ 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)
+ print_error(f"Error calling: {filename}/{funcname}", exc_info=True)
return default
@@ -450,8 +447,7 @@ class ScriptRunner: 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)
+ print_error(f"Error running process: {script.filename}", exc_info=True)
def before_process_batch(self, p, **kwargs):
for script in self.alwayson_scripts:
@@ -459,8 +455,7 @@ 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)
+ print_error(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 +463,7 @@ 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)
+ print_error(f"Error running process_batch: {script.filename}", exc_info=True)
def postprocess(self, p, processed):
for script in self.alwayson_scripts:
@@ -477,8 +471,7 @@ 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)
+ print_error(f"Error running postprocess: {script.filename}", exc_info=True)
def postprocess_batch(self, p, images, **kwargs):
for script in self.alwayson_scripts:
@@ -486,8 +479,7 @@ 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)
+ print_error(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 +487,21 @@ 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)
+ print_error(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)
+ print_error(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)
+ print_error(f"Error running after_component: {script.filename}", exc_info=True)
def reload_sources(self, cache):
for si, script in list(enumerate(self.scripts)):
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 08d31080..487dfd60 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -3,7 +3,7 @@ from torch.nn.functional import silu from types import MethodType
import modules.textual_inversion.textual_inversion
-from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors
+from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors, sd_unet
from modules.hypernetworks import hypernetwork
from modules.shared import cmd_opts
from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr
@@ -43,11 +43,16 @@ def list_optimizers(): optimizers.extend(new_optimizers)
-def apply_optimizations():
+def apply_optimizations(option=None):
global current_optimizer
undo_optimizations()
+ if len(optimizers) == 0:
+ # a script can access the model very early, and optimizations would not be filled by then
+ current_optimizer = None
+ return ''
+
ldm.modules.diffusionmodules.model.nonlinearity = silu
ldm.modules.diffusionmodules.openaimodel.th = sd_hijack_unet.th
@@ -55,7 +60,7 @@ def apply_optimizations(): current_optimizer.undo()
current_optimizer = None
- selection = shared.opts.cross_attention_optimization
+ selection = option or shared.opts.cross_attention_optimization
if selection == "Automatic" and len(optimizers) > 0:
matching_optimizer = next(iter([x for x in optimizers if x.cmd_opt and getattr(shared.cmd_opts, x.cmd_opt, False)]), optimizers[0])
else:
@@ -67,11 +72,13 @@ def apply_optimizations(): matching_optimizer = optimizers[0]
if matching_optimizer is not None:
- print(f"Applying optimization: {matching_optimizer.name}")
+ print(f"Applying attention optimization: {matching_optimizer.name}... ", end='')
matching_optimizer.apply()
+ print("done.")
current_optimizer = matching_optimizer
return current_optimizer.name
else:
+ print("Disabling attention optimization")
return ''
@@ -149,6 +156,13 @@ class StableDiffusionModelHijack: def __init__(self):
self.embedding_db.add_embedding_dir(cmd_opts.embeddings_dir)
+ def apply_optimizations(self, option=None):
+ try:
+ self.optimization_method = apply_optimizations(option)
+ except Exception as e:
+ errors.display(e, "applying cross attention optimization")
+ undo_optimizations()
+
def hijack(self, m):
if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
model_embeddings = m.cond_stage_model.roberta.embeddings
@@ -168,11 +182,7 @@ class StableDiffusionModelHijack: if m.cond_stage_key == "edit":
sd_hijack_unet.hijack_ddpm_edit()
- try:
- self.optimization_method = apply_optimizations()
- except Exception as e:
- errors.display(e, "applying cross attention optimization")
- undo_optimizations()
+ self.apply_optimizations()
self.clip = m.cond_stage_model
@@ -185,6 +195,11 @@ class StableDiffusionModelHijack: self.layers = flatten(m)
+ if not hasattr(ldm.modules.diffusionmodules.openaimodel, 'copy_of_UNetModel_forward_for_webui'):
+ ldm.modules.diffusionmodules.openaimodel.copy_of_UNetModel_forward_for_webui = ldm.modules.diffusionmodules.openaimodel.UNetModel.forward
+
+ ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = sd_unet.UNetModel_forward
+
def undo_hijack(self, m):
if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
m.cond_stage_model = m.cond_stage_model.wrapped
@@ -206,6 +221,8 @@ 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
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py index 2ec0b049..fd186fa2 100644 --- a/modules/sd_hijack_optimizations.py +++ b/modules/sd_hijack_optimizations.py @@ -1,7 +1,5 @@ from __future__ import annotations
import math
-import sys
-import traceback
import psutil
import torch
@@ -11,6 +9,7 @@ from ldm.util import default from einops import rearrange
from modules import shared, errors, devices, sub_quadratic_attention
+from modules.errors import print_error
from modules.hypernetworks import hypernetwork
import ldm.modules.attention
@@ -140,8 +139,7 @@ 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)
+ print_error("Cannot import xformers", exc_info=True)
def get_available_vram():
diff --git a/modules/sd_models.py b/modules/sd_models.py index 91b3eb11..232eb9c4 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -14,7 +14,7 @@ import ldm.modules.midas as midas from ldm.util import instantiate_from_config
-from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config
+from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config, sd_unet
from modules.sd_hijack_inpainting import do_inpainting_hijack
from modules.timer import Timer
import tomesd
@@ -164,6 +164,7 @@ def model_hash(filename): def select_checkpoint():
+ """Raises `FileNotFoundError` if no checkpoints are found."""
model_checkpoint = shared.opts.sd_model_checkpoint
checkpoint_info = checkpoint_alisases.get(model_checkpoint, None)
@@ -171,14 +172,14 @@ def select_checkpoint(): return checkpoint_info
if len(checkpoints_list) == 0:
- print("No checkpoints found. When searching for checkpoints, looked at:", file=sys.stderr)
+ error_message = "No checkpoints found. When searching for checkpoints, looked at:"
if shared.cmd_opts.ckpt is not None:
- print(f" - file {os.path.abspath(shared.cmd_opts.ckpt)}", file=sys.stderr)
- print(f" - directory {model_path}", file=sys.stderr)
+ error_message += f"\n - file {os.path.abspath(shared.cmd_opts.ckpt)}"
+ error_message += f"\n - directory {model_path}"
if shared.cmd_opts.ckpt_dir is not None:
- print(f" - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}", file=sys.stderr)
- print("Can't run without a checkpoint. Find and place a .ckpt or .safetensors file into any of those locations. The program will exit.", file=sys.stderr)
- exit(1)
+ error_message += f"\n - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}"
+ error_message += "Can't run without a checkpoint. Find and place a .ckpt or .safetensors file into any of those locations."
+ raise FileNotFoundError(error_message)
checkpoint_info = next(iter(checkpoints_list.values()))
if model_checkpoint is not None:
@@ -423,7 +424,7 @@ 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
@@ -532,6 +533,8 @@ 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:
diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index dcec9e0e..f8a0c7ba 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -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):
"""
@@ -265,6 +273,13 @@ class KDiffusionSampler: try:
return func()
+ except RecursionError:
+ print(
+ 'Encountered RecursionError during sampling, returning last latent. '
+ 'rho >5 with a polyexponential scheduler may cause this error. '
+ 'You should try to use a smaller rho value instead.'
+ )
+ return self.last_latent
except sd_samplers_common.InterruptedException:
return self.last_latent
@@ -304,6 +319,31 @@ class KDiffusionSampler: if p.sampler_noise_scheduler_override:
sigmas = p.sampler_noise_scheduler_override(steps)
+ elif opts.k_sched_type != "Automatic":
+ m_sigma_min, m_sigma_max = (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
+ sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (m_sigma_min, m_sigma_max)
+ sigmas_kwargs = {
+ 'sigma_min': sigma_min,
+ 'sigma_max': sigma_max,
+ }
+
+ sigmas_func = k_diffusion_scheduler[opts.k_sched_type]
+ p.extra_generation_params["Schedule type"] = opts.k_sched_type
+
+ if opts.sigma_min != m_sigma_min and opts.sigma_min != 0:
+ sigmas_kwargs['sigma_min'] = opts.sigma_min
+ p.extra_generation_params["Schedule min sigma"] = opts.sigma_min
+ if opts.sigma_max != m_sigma_max and opts.sigma_max != 0:
+ sigmas_kwargs['sigma_max'] = opts.sigma_max
+ p.extra_generation_params["Schedule max sigma"] = opts.sigma_max
+
+ default_rho = 1. if opts.k_sched_type == "polyexponential" else 7.
+
+ if opts.k_sched_type != 'exponential' and opts.rho != 0 and opts.rho != default_rho:
+ sigmas_kwargs['rho'] = opts.rho
+ p.extra_generation_params["Schedule rho"] = opts.rho
+
+ sigmas = sigmas_func(n=steps, **sigmas_kwargs, device=shared.device)
elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
diff --git a/modules/sd_unet.py b/modules/sd_unet.py new file mode 100644 index 00000000..6d708ad2 --- /dev/null +++ b/modules/sd_unet.py @@ -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)
+
diff --git a/modules/shared.py b/modules/shared.py index 0897f937..acec7f18 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -314,6 +314,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids" "grid_extended_filename": OptionInfo(False, "Add extended info (seed, prompt) to filename when saving grid"),
"grid_only_if_multiple": OptionInfo(True, "Do not save grids consisting of one picture"),
"grid_prevent_empty_spots": OptionInfo(False, "Prevent empty spots in grid (when set to autodetect)"),
+ "grid_zip_filename_pattern": OptionInfo("", "Archive filename pattern", component_args=hide_dirs).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"),
"n_rows": OptionInfo(-1, "Grid row count; use -1 for autodetect and 0 for it to be same as batch size", gr.Slider, {"minimum": -1, "maximum": 16, "step": 1}),
"enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
@@ -403,6 +404,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), { "sd_vae_checkpoint_cache": OptionInfo(0, "VAE Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
"sd_vae": OptionInfo("Automatic", "SD VAE", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list).info("choose VAE model: Automatic = use one with same filename as checkpoint; None = use VAE from checkpoint"),
"sd_vae_as_default": OptionInfo(True, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"),
+ "sd_unet": OptionInfo("Automatic", "SD Unet", gr.Dropdown, lambda: {"choices": shared_items.sd_unet_items()}, refresh=shared_items.refresh_unet_list).info("choose Unet model: Automatic = use one with same filename as checkpoint; None = use Unet from checkpoint"),
"inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
"initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for img2img", gr.Slider, {"minimum": 0.5, "maximum": 1.5, "step": 0.01}),
"img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
@@ -414,12 +416,12 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), { "comma_padding_backtrack": OptionInfo(20, "Prompt word wrap length limit", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1}).info("in tokens - for texts shorter than specified, if they don't fit into 75 token limit, move them to the next 75 token chunk"),
"CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#clip-skip").info("ignore last layers of CLIP nrtwork; 1 ignores none, 2 ignores one layer"),
"upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),
- "randn_source": OptionInfo("GPU", "Random number generator source.", gr.Radio, {"choices": ["GPU", "CPU"]}).info("changes seeds drastically; use CPU to produce the same picture across different vidocard vendors"),
+ "randn_source": OptionInfo("GPU", "Random number generator source.", gr.Radio, {"choices": ["GPU", "CPU"]}).info("changes seeds drastically; use CPU to produce the same picture across different videocard vendors"),
}))
options_templates.update(options_section(('optimizations', "Optimizations"), {
"cross_attention_optimization": OptionInfo("Automatic", "Cross attention optimization", gr.Dropdown, lambda: {"choices": shared_items.cross_attention_optimizations()}),
- "s_min_uncond": OptionInfo(0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 4.0, "step": 0.01}).link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9177").info("skip negative prompt for some steps when the image is almost ready; 0=disable, higher=faster"),
+ "s_min_uncond": OptionInfo(0.0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 4.0, "step": 0.01}).link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9177").info("skip negative prompt for some steps when the image is almost ready; 0=disable, higher=faster"),
"token_merging_ratio": OptionInfo(0.0, "Token merging ratio", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9256").info("0=disable, higher=faster"),
"token_merging_ratio_img2img": OptionInfo(0.0, "Token merging ratio for img2img", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).info("only applies if non-zero and overrides above"),
"token_merging_ratio_hr": OptionInfo(0.0, "Token merging ratio for high-res pass", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).info("only applies if non-zero and overrides above"),
@@ -517,6 +519,10 @@ 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"]}),
diff --git a/modules/shared_items.py b/modules/shared_items.py index 2a8713c8..7f306a06 100644 --- a/modules/shared_items.py +++ b/modules/shared_items.py @@ -29,3 +29,14 @@ 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()
+
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index d489ed1e..b3dcb140 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -1,6 +1,4 @@ import os
-import sys
-import traceback
from collections import namedtuple
import torch
@@ -16,6 +14,7 @@ from torch.utils.tensorboard import SummaryWriter from modules import shared, devices, sd_hijack, processing, sd_models, images, sd_samplers, sd_hijack_checkpoint
import modules.textual_inversion.dataset
+from modules.errors import print_error
from modules.textual_inversion.learn_schedule import LearnRateScheduler
from modules.textual_inversion.image_embedding import embedding_to_b64, embedding_from_b64, insert_image_data_embed, extract_image_data_embed, caption_image_overlay
@@ -120,16 +119,29 @@ 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 +219,7 @@ 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)
+ print_error(f"Error loading embedding {fn}", exc_info=True)
continue
def load_textual_inversion_embeddings(self, force_reload=False):
@@ -632,8 +643,7 @@ 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
+ print_error("Error training embedding", exc_info=True)
finally:
pbar.leave = False
pbar.close()
diff --git a/modules/ui.py b/modules/ui.py index 001b9792..fb6b2498 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -2,7 +2,6 @@ import json import mimetypes
import os
import sys
-import traceback
from functools import reduce
import warnings
@@ -14,6 +13,7 @@ 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.errors import print_error
from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML
from modules.paths import script_path, data_path
@@ -231,9 +231,8 @@ 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:
+ print_error(f"Error parsing JSON generation info: {gen_info_string}")
return [res, gr_show(False)]
@@ -505,10 +504,10 @@ def create_ui(): with FormRow(elem_id="txt2img_hires_fix_row4", variant="compact", visible=opts.hires_fix_show_prompts) as hr_prompts_container:
with gr.Column(scale=80):
with gr.Row():
- hr_prompt = gr.Textbox(label="Prompt", elem_id="hires_prompt", show_label=False, lines=3, placeholder="Prompt for hires fix pass.\nLeave empty to use the same prompt as in first pass.", elem_classes=["prompt"])
+ hr_prompt = gr.Textbox(label="Hires prompt", elem_id="hires_prompt", show_label=False, lines=3, placeholder="Prompt for hires fix pass.\nLeave empty to use the same prompt as in first pass.", elem_classes=["prompt"])
with gr.Column(scale=80):
with gr.Row():
- hr_negative_prompt = gr.Textbox(label="Negative prompt", elem_id="hires_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt for hires fix pass.\nLeave empty to use the same negative prompt as in first pass.", elem_classes=["prompt"])
+ hr_negative_prompt = gr.Textbox(label="Hires negative prompt", elem_id="hires_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt for hires fix pass.\nLeave empty to use the same negative prompt as in first pass.", elem_classes=["prompt"])
elif category == "batch":
if not opts.dimensions_and_batch_together:
@@ -1753,8 +1752,7 @@ def create_ui(): 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)
+ print_error("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
diff --git a/modules/ui_common.py b/modules/ui_common.py index 27ab3ebb..5a9204a4 100644 --- a/modules/ui_common.py +++ b/modules/ui_common.py @@ -50,9 +50,10 @@ 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 +71,7 @@ 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 +85,10 @@ 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:
diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py index 515ec262..e2ee9d72 100644 --- a/modules/ui_extensions.py +++ b/modules/ui_extensions.py @@ -1,10 +1,8 @@ import json
import os.path
-import sys
import threading
import time
from datetime import datetime
-import traceback
import git
@@ -14,6 +12,7 @@ import shutil import errno
from modules import extensions, shared, paths, config_states
+from modules.errors import print_error
from modules.paths_internal import config_states_dir
from modules.call_queue import wrap_gradio_gpu_call
@@ -46,8 +45,7 @@ def apply_and_restart(disable_list, update_list, disable_all): try:
ext.fetch_and_reset_hard()
except Exception:
- print(f"Error getting updates for {ext.name}:", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
+ print_error(f"Error getting updates for {ext.name}", exc_info=True)
shared.opts.disabled_extensions = disabled
shared.opts.disable_all_extensions = disable_all
@@ -113,8 +111,7 @@ 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)
+ print_error(f"Error checking updates for {ext.name}", exc_info=True)
shared.state.nextjob()
@@ -490,8 +487,14 @@ 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():
diff --git a/modules/ui_tempdir.py b/modules/ui_tempdir.py index f05049e1..9fc7d764 100644 --- a/modules/ui_tempdir.py +++ b/modules/ui_tempdir.py @@ -3,7 +3,7 @@ import tempfile from collections import namedtuple
from pathlib import Path
-import gradio as gr
+import gradio.components
from PIL import PngImagePlugin
@@ -31,13 +31,16 @@ def check_tmp_file(gradio, filename): return False
-def save_pil_to_file(pil_image, dir=None):
+def save_pil_to_file(self, pil_image, dir=None):
already_saved_as = getattr(pil_image, 'already_saved_as', None)
if already_saved_as and os.path.isfile(already_saved_as):
register_tmp_file(shared.demo, already_saved_as)
+ filename = already_saved_as
- file_obj = Savedfile(f'{already_saved_as}?{os.path.getmtime(already_saved_as)}')
- return file_obj
+ if not shared.opts.save_images_add_number:
+ filename += f'?{os.path.getmtime(already_saved_as)}'
+
+ return filename
if shared.opts.temp_dir != "":
dir = shared.opts.temp_dir
@@ -51,11 +54,11 @@ def save_pil_to_file(pil_image, dir=None): file_obj = tempfile.NamedTemporaryFile(delete=False, suffix=".png", dir=dir)
pil_image.save(file_obj, pnginfo=(metadata if use_metadata else None))
- return file_obj
+ return file_obj.name
# override save to file function so that it also writes PNG info
-gr.processing_utils.save_pil_to_file = save_pil_to_file
+gradio.components.IOComponent.pil_to_temp_file = save_pil_to_file
def on_tmpdir_changed():
diff --git a/modules/upscaler.py b/modules/upscaler.py index 7b1046d6..3c82861d 100644 --- a/modules/upscaler.py +++ b/modules/upscaler.py @@ -53,8 +53,8 @@ class Upscaler: def upscale(self, img: PIL.Image, scale, selected_model: str = None): self.scale = scale - dest_w = int(img.width * scale) - dest_h = int(img.height * scale) + dest_w = round((img.width * scale - 4) / 8) * 8 + dest_h = round((img.height * scale - 4) / 8) * 8 for _ in range(3): shape = (img.width, img.height) |