diff options
Diffstat (limited to 'modules')
66 files changed, 1987 insertions, 972 deletions
diff --git a/modules/api/api.py b/modules/api/api.py index 6a456861..224bbfc6 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -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 @@ -22,20 +22,15 @@ from modules.textual_inversion.textual_inversion import create_embedding, train_ 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_models import checkpoints_list, unload_model_weights, reload_model_weights, checkpoint_alisases +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 from typing import Dict, List, Any import piexif import piexif.helper - - -def upscaler_to_index(name: str): - try: - return [x.name.lower() for x in shared.sd_upscalers].index(name.lower()) - except Exception as e: - raise HTTPException(status_code=400, detail=f"Invalid upscaler, needs to be one of these: {' , '.join([x.name for x in shared.sd_upscalers])}") from e +from contextlib import closing def script_name_to_index(name, scripts): @@ -83,6 +78,8 @@ def encode_pil_to_base64(image): image.save(output_bytes, format="PNG", pnginfo=(metadata if use_metadata else None), quality=opts.jpeg_quality) elif opts.samples_format.lower() in ("jpg", "jpeg", "webp"): + if image.mode == "RGBA": + image = image.convert("RGB") parameters = image.info.get('parameters', None) exif_bytes = piexif.dump({ "Exif": { piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(parameters or "", encoding="unicode") } @@ -108,7 +105,6 @@ def api_middleware(app: FastAPI): from rich.console import Console console = Console() except Exception: - import traceback rich_available = False @app.middleware("http") @@ -139,11 +135,12 @@ 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 +185,9 @@ 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 +205,11 @@ 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.api_server_stop: + 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 +282,7 @@ 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: @@ -321,19 +325,19 @@ class Api: args.pop('save_images', None) with self.queue_lock: - p = StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **args) - p.scripts = script_runner - p.outpath_grids = opts.outdir_txt2img_grids - p.outpath_samples = opts.outdir_txt2img_samples - - shared.state.begin() - if selectable_scripts is not None: - p.script_args = script_args - processed = scripts.scripts_txt2img.run(p, *p.script_args) # Need to pass args as list here - else: - p.script_args = tuple(script_args) # Need to pass args as tuple here - processed = process_images(p) - shared.state.end() + with closing(StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **args)) as p: + p.scripts = script_runner + p.outpath_grids = opts.outdir_txt2img_grids + p.outpath_samples = opts.outdir_txt2img_samples + + shared.state.begin(job="scripts_txt2img") + if selectable_scripts is not None: + p.script_args = script_args + processed = scripts.scripts_txt2img.run(p, *p.script_args) # Need to pass args as list here + else: + p.script_args = tuple(script_args) # Need to pass args as tuple here + processed = process_images(p) + shared.state.end() b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else [] @@ -377,20 +381,20 @@ class Api: args.pop('save_images', None) with self.queue_lock: - p = StableDiffusionProcessingImg2Img(sd_model=shared.sd_model, **args) - p.init_images = [decode_base64_to_image(x) for x in init_images] - p.scripts = script_runner - p.outpath_grids = opts.outdir_img2img_grids - p.outpath_samples = opts.outdir_img2img_samples - - shared.state.begin() - if selectable_scripts is not None: - p.script_args = script_args - processed = scripts.scripts_img2img.run(p, *p.script_args) # Need to pass args as list here - else: - p.script_args = tuple(script_args) # Need to pass args as tuple here - processed = process_images(p) - shared.state.end() + with closing(StableDiffusionProcessingImg2Img(sd_model=shared.sd_model, **args)) as p: + p.init_images = [decode_base64_to_image(x) for x in init_images] + p.scripts = script_runner + p.outpath_grids = opts.outdir_img2img_grids + p.outpath_samples = opts.outdir_img2img_samples + + shared.state.begin(job="scripts_img2img") + if selectable_scripts is not None: + p.script_args = script_args + processed = scripts.scripts_img2img.run(p, *p.script_args) # Need to pass args as list here + else: + p.script_args = tuple(script_args) # Need to pass args as tuple here + processed = process_images(p) + shared.state.end() b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else [] @@ -514,6 +518,10 @@ class Api: return options def set_config(self, req: Dict[str, Any]): + checkpoint_name = req.get("sd_model_checkpoint", None) + if checkpoint_name is not None and checkpoint_name not in checkpoint_alisases: + raise RuntimeError(f"model {checkpoint_name!r} not found") + for k, v in req.items(): shared.opts.set(k, v) @@ -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] @@ -583,44 +602,42 @@ class Api: def create_embedding(self, args: dict): try: - shared.state.begin() + shared.state.begin(job="create_embedding") filename = create_embedding(**args) # create empty embedding sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings() # reload embeddings so new one can be immediately used - shared.state.end() return models.CreateResponse(info=f"create embedding filename: {filename}") except AssertionError as e: - shared.state.end() return models.TrainResponse(info=f"create embedding error: {e}") + finally: + shared.state.end() + def create_hypernetwork(self, args: dict): try: - shared.state.begin() + shared.state.begin(job="create_hypernetwork") filename = create_hypernetwork(**args) # create empty embedding - shared.state.end() return models.CreateResponse(info=f"create hypernetwork filename: {filename}") except AssertionError as e: - shared.state.end() return models.TrainResponse(info=f"create hypernetwork error: {e}") + finally: + shared.state.end() def preprocess(self, args: dict): try: - shared.state.begin() + shared.state.begin(job="preprocess") preprocess(**args) # quick operation unless blip/booru interrogation is enabled shared.state.end() - return models.PreprocessResponse(info = 'preprocess complete') + return models.PreprocessResponse(info='preprocess complete') except KeyError as e: - shared.state.end() return models.PreprocessResponse(info=f"preprocess error: invalid token: {e}") - except AssertionError as e: - shared.state.end() + except Exception as e: return models.PreprocessResponse(info=f"preprocess error: {e}") - except FileNotFoundError as e: + finally: shared.state.end() - return models.PreprocessResponse(info=f'preprocess error: {e}') def train_embedding(self, args: dict): try: - shared.state.begin() + shared.state.begin(job="train_embedding") apply_optimizations = shared.opts.training_xattention_optimizations error = None filename = '' @@ -633,15 +650,15 @@ class Api: finally: if not apply_optimizations: sd_hijack.apply_optimizations() - shared.state.end() return models.TrainResponse(info=f"train embedding complete: filename: {filename} error: {error}") - except AssertionError as msg: - shared.state.end() + except Exception as msg: return models.TrainResponse(info=f"train embedding error: {msg}") + finally: + shared.state.end() def train_hypernetwork(self, args: dict): try: - shared.state.begin() + shared.state.begin(job="train_hypernetwork") shared.loaded_hypernetworks = [] apply_optimizations = shared.opts.training_xattention_optimizations error = None @@ -659,9 +676,10 @@ class Api: sd_hijack.apply_optimizations() shared.state.end() return models.TrainResponse(info=f"train embedding complete: filename: {filename} error: {error}") - except AssertionError: + except Exception as exc: + return models.TrainResponse(info=f"train embedding error: {exc}") + finally: shared.state.end() - return models.TrainResponse(info=f"train embedding error: {error}") def get_memory(self): try: @@ -701,3 +719,15 @@ class Api: def launch(self, server_name, port): self.app.include_router(self.router) 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.") diff --git a/modules/api/models.py b/modules/api/models.py index 1ff2fb33..b5683071 100644 --- a/modules/api/models.py +++ b/modules/api/models.py @@ -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") @@ -267,10 +274,6 @@ class PromptStyleItem(BaseModel): prompt: Optional[str] = Field(title="Prompt") negative_prompt: Optional[str] = Field(title="Negative Prompt") -class ArtistItem(BaseModel): - name: str = Field(title="Name") - score: float = Field(title="Score") - category: str = Field(title="Category") class EmbeddingItem(BaseModel): step: Optional[int] = Field(title="Step", description="The number of steps that were used to train this embedding, if available") diff --git a/modules/call_queue.py b/modules/call_queue.py index 447bb764..3b94f8a4 100644 --- a/modules/call_queue.py +++ b/modules/call_queue.py @@ -1,10 +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()
@@ -20,17 +19,18 @@ def wrap_queued_call(func): def wrap_gradio_gpu_call(func, extra_outputs=None):
+ @wraps(func)
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:
id_task = None
with queue_lock:
- shared.state.begin()
+ shared.state.begin(job=id_task)
progress.start_task(id_task)
try:
@@ -47,6 +47,7 @@ def wrap_gradio_gpu_call(func, extra_outputs=None): def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
+ @wraps(func)
def f(*args, extra_outputs_array=extra_outputs, **kwargs):
run_memmon = shared.opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled and add_stats
if run_memmon:
@@ -56,16 +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
@@ -108,4 +107,3 @@ 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..278a605e 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")
@@ -62,7 +62,7 @@ parser.add_argument("--opt-split-attention-invokeai", action='store_true', help= parser.add_argument("--opt-split-attention-v1", action='store_true', help="prefer older version of split attention optimization for automatic choice of optimization")
parser.add_argument("--opt-sdp-attention", action='store_true', help="prefer scaled dot product cross-attention layer optimization for automatic choice of optimization; requires PyTorch 2.*")
parser.add_argument("--opt-sdp-no-mem-attention", action='store_true', help="prefer scaled dot product cross-attention layer optimization without memory efficient attention for automatic choice of optimization, makes image generation deterministic; requires PyTorch 2.*")
-parser.add_argument("--disable-opt-split-attention", action='store_true', help="does not do anything")
+parser.add_argument("--disable-opt-split-attention", action='store_true', help="prefer no cross-attention layer optimization for automatic choice of optimization")
parser.add_argument("--disable-nan-check", action='store_true', help="do not check if produced images/latent spaces have nans; useful for running without a checkpoint in CI")
parser.add_argument("--use-cpu", nargs='+', help="use CPU as torch device for specified modules", default=[], type=str.lower)
parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
@@ -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('--api-server-stop', action='store_true', help='enable server stop/restart/kill via api')
diff --git a/modules/codeformer_model.py b/modules/codeformer_model.py index ececdbae..f293acf5 100644 --- a/modules/codeformer_model.py +++ b/modules/codeformer_model.py @@ -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
@@ -17,14 +15,11 @@ model_dir = "Codeformer" model_path = os.path.join(models_path, model_dir)
model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth'
-have_codeformer = False
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 +100,8 @@ 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')
@@ -127,15 +122,11 @@ def setup_model(dirname): return restored_img
- global have_codeformer
- have_codeformer = True
-
global codeformer
codeformer = FaceRestorerCodeFormer(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
diff --git a/modules/config_states.py b/modules/config_states.py index db65bcdb..6f1ab53f 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 @@ -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): diff --git a/modules/devices.py b/modules/devices.py index 1ed6ffdc..620ed1a6 100644 --- a/modules/devices.py +++ b/modules/devices.py @@ -15,13 +15,6 @@ def has_mps() -> bool: else: return mac_specific.has_mps -def extract_device_id(args, name): - for x in range(len(args)): - if name in args[x]: - return args[x + 1] - - return None - def get_cuda_device_string(): from modules import shared diff --git a/modules/errors.py b/modules/errors.py index f6b80dbb..5271a9fe 100644 --- a/modules/errors.py +++ b/modules/errors.py @@ -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
diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py index 2fced999..02a1727d 100644 --- a/modules/esrgan_model.py +++ b/modules/esrgan_model.py @@ -1,15 +1,13 @@ -import os
+import sys
import numpy as np
import torch
from PIL import Image
-from basicsr.utils.download_util import load_file_from_url
import modules.esrgan_model_arch as arch
from modules import modelloader, images, devices
-from modules.upscaler import Upscaler, UpscalerData
from modules.shared import opts
-
+from modules.upscaler import Upscaler, UpscalerData
def mod2normal(state_dict):
@@ -134,7 +132,7 @@ class UpscalerESRGAN(Upscaler): scaler_data = UpscalerData(self.model_name, self.model_url, self, 4)
scalers.append(scaler_data)
for file in model_paths:
- if "http" in file:
+ if file.startswith("http"):
name = self.model_name
else:
name = modelloader.friendly_name(file)
@@ -143,26 +141,25 @@ class UpscalerESRGAN(Upscaler): self.scalers.append(scaler_data)
def do_upscale(self, img, selected_model):
- model = self.load_model(selected_model)
- if model is None:
+ try:
+ model = self.load_model(selected_model)
+ except Exception as e:
+ print(f"Unable to load ESRGAN model {selected_model}: {e}", file=sys.stderr)
return img
model.to(devices.device_esrgan)
img = esrgan_upscale(model, img)
return img
def load_model(self, path: str):
- if "http" in path:
- filename = load_file_from_url(
+ if path.startswith("http"):
+ # TODO: this doesn't use `path` at all?
+ filename = modelloader.load_file_from_url(
url=self.model_url,
model_dir=self.model_download_path,
file_name=f"{self.model_name}.pth",
- progress=True,
)
else:
filename = path
- if not os.path.exists(filename) or filename is None:
- print(f"Unable to load {self.model_path} from {filename}")
- return None
state_dict = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None)
diff --git a/modules/extensions.py b/modules/extensions.py index 624832a0..abc6e2b1 100644 --- a/modules/extensions.py +++ b/modules/extensions.py @@ -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)
diff --git a/modules/extra_networks.py b/modules/extra_networks.py index 34a3ba63..41799b0a 100644 --- a/modules/extra_networks.py +++ b/modules/extra_networks.py @@ -26,12 +26,15 @@ class ExtraNetworkParams: self.named = {}
for item in self.items:
- parts = item.split('=', 2)
+ parts = item.split('=', 2) if isinstance(item, str) else [item]
if len(parts) == 2:
self.named[parts[0]] = parts[1]
else:
self.positional.append(item)
+ def __eq__(self, other):
+ return self.items == other.items
+
class ExtraNetwork:
def __init__(self, name):
@@ -100,6 +103,9 @@ def activate(p, extra_network_data): except Exception as e:
errors.display(e, f"activating extra network {extra_network_name}")
+ if p.scripts is not None:
+ p.scripts.after_extra_networks_activate(p, batch_number=p.iteration, prompts=p.prompts, seeds=p.seeds, subseeds=p.subseeds, extra_network_data=extra_network_data)
+
def deactivate(p, extra_network_data):
"""call deactivate for extra networks in extra_network_data in specified order, then call
diff --git a/modules/extra_networks_hypernet.py b/modules/extra_networks_hypernet.py index aa2a14ef..b6a6dc0e 100644 --- a/modules/extra_networks_hypernet.py +++ b/modules/extra_networks_hypernet.py @@ -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)
diff --git a/modules/extras.py b/modules/extras.py index 830b53aa..e9c0263e 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -73,8 +73,7 @@ def to_half(tensor, enable): def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format, config_source, bake_in_vae, discard_weights, save_metadata):
- shared.state.begin()
- shared.state.job = 'model-merge'
+ shared.state.begin(job="model-merge")
def fail(message):
shared.state.textinfo = message
diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py index d5f0a49b..a3448be9 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)
@@ -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):
@@ -174,31 +174,6 @@ def send_image_and_dimensions(x): return img, w, h
-
-def find_hypernetwork_key(hypernet_name, hypernet_hash=None):
- """Determines the config parameter name to use for the hypernet based on the parameters in the infotext.
-
- Example: an infotext provides "Hypernet: ke-ta" and "Hypernet hash: 1234abcd". For the "Hypernet" config
- parameter this means there should be an entry that looks like "ke-ta-10000(1234abcd)" to set it to.
-
- If the infotext has no hash, then a hypernet with the same name will be selected instead.
- """
- hypernet_name = hypernet_name.lower()
- if hypernet_hash is not None:
- # Try to match the hash in the name
- for hypernet_key in shared.hypernetworks.keys():
- result = re_hypernet_hash.search(hypernet_key)
- if result is not None and result[1] == hypernet_hash:
- return hypernet_key
- else:
- # Fall back to a hypernet with the same name
- for hypernet_key in shared.hypernetworks.keys():
- if hypernet_key.lower().startswith(hypernet_name):
- return hypernet_key
-
- return None
-
-
def restore_old_hires_fix_params(res):
"""for infotexts that specify old First pass size parameter, convert it into
width, height, and hr scale"""
@@ -265,19 +240,30 @@ 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)
-
- 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
+ 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
+ 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:
@@ -306,11 +292,19 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model if "RNG" not in res:
res["RNG"] = "GPU"
- return res
+ if "Schedule type" not in res:
+ res["Schedule type"] = "Automatic"
+ if "Schedule max sigma" not in res:
+ res["Schedule max sigma"] = 0
-settings_map = {}
+ if "Schedule min sigma" not in res:
+ res["Schedule min sigma"] = 0
+ if "Schedule rho" not in res:
+ res["Schedule rho"] = 0
+
+ return res
infotext_to_setting_name_mapping = [
@@ -318,6 +312,10 @@ 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 +328,7 @@ 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 +420,7 @@ 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 +437,3 @@ def connect_paste(button, paste_fields, input_comp, override_settings_component, outputs=[],
show_progress=False,
)
-
-
diff --git a/modules/gfpgan_model.py b/modules/gfpgan_model.py index 0131dea4..8e0f13bd 100644 --- a/modules/gfpgan_model.py +++ b/modules/gfpgan_model.py @@ -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
@@ -27,7 +25,7 @@ def gfpgann(): return None
models = modelloader.load_models(model_path, model_url, user_path, ext_filter="GFPGAN")
- if len(models) == 1 and "http" in models[0]:
+ if len(models) == 1 and models[0].startswith("http"):
model_file = models[0]
elif len(models) != 0:
latest_file = max(models, key=os.path.getctime)
@@ -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)
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..51941c11 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
@@ -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 = {}
@@ -358,17 +353,6 @@ def load_hypernetworks(names, multipliers=None): shared.loaded_hypernetworks.append(hypernetwork)
-def find_closest_hypernetwork_name(search: str):
- if not search:
- return None
- search = search.lower()
- applicable = [name for name in shared.hypernetworks if search in name.lower()]
- if not applicable:
- return None
- applicable = sorted(applicable, key=lambda name: len(name))
- return applicable[0]
-
-
def apply_single_hypernetwork(hypernetwork, context_k, context_v, layer=None):
hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context_k.shape[2], None)
@@ -451,18 +435,6 @@ def statistics(data): return total_information, recent_information
-def report_statistics(loss_info:dict):
- keys = sorted(loss_info.keys(), key=lambda x: sum(loss_info[x]) / len(loss_info[x]))
- for key in keys:
- try:
- print("Loss statistics for file " + key)
- info, recent = statistics(list(loss_info[key]))
- print(info)
- print(recent)
- except Exception as e:
- print(e)
-
-
def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, dropout_structure=None):
# Remove illegal characters from name.
name = "".join( x for x in name if (x.isalnum() or x in "._- "))
@@ -770,12 +742,11 @@ 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()
hypernetwork.eval()
- #report_statistics(loss_dict)
sd_hijack_checkpoint.remove()
diff --git a/modules/images.py b/modules/images.py index 4e8cd993..b5412548 100644 --- a/modules/images.py +++ b/modules/images.py @@ -1,6 +1,6 @@ +from __future__ import annotations
+
import datetime
-import sys
-import traceback
import pytz
import io
@@ -12,7 +12,7 @@ import re import numpy as np
import piexif
import piexif.helper
-from PIL import Image, ImageFont, ImageDraw, PngImagePlugin
+from PIL import Image, ImageFont, ImageDraw, ImageColor, PngImagePlugin
import string
import json
import hashlib
@@ -21,6 +21,8 @@ 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)
@@ -139,6 +141,11 @@ class GridAnnotation: def draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin=0):
+
+ color_active = ImageColor.getcolor(opts.grid_text_active_color, 'RGB')
+ color_inactive = ImageColor.getcolor(opts.grid_text_inactive_color, 'RGB')
+ color_background = ImageColor.getcolor(opts.grid_background_color, 'RGB')
+
def wrap(drawing, text, font, line_length):
lines = ['']
for word in text.split():
@@ -168,9 +175,6 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin=0): fnt = get_font(fontsize)
- color_active = (0, 0, 0)
- color_inactive = (153, 153, 153)
-
pad_left = 0 if sum([sum([len(line.text) for line in lines]) for lines in ver_texts]) == 0 else width * 3 // 4
cols = im.width // width
@@ -179,7 +183,7 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin=0): assert cols == len(hor_texts), f'bad number of horizontal texts: {len(hor_texts)}; must be {cols}'
assert rows == len(ver_texts), f'bad number of vertical texts: {len(ver_texts)}; must be {rows}'
- calc_img = Image.new("RGB", (1, 1), "white")
+ calc_img = Image.new("RGB", (1, 1), color_background)
calc_d = ImageDraw.Draw(calc_img)
for texts, allowed_width in zip(hor_texts + ver_texts, [width] * len(hor_texts) + [pad_left] * len(ver_texts)):
@@ -200,7 +204,7 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin=0): pad_top = 0 if sum(hor_text_heights) == 0 else max(hor_text_heights) + line_spacing * 2
- result = Image.new("RGB", (im.width + pad_left + margin * (cols-1), im.height + pad_top + margin * (rows-1)), "white")
+ result = Image.new("RGB", (im.width + pad_left + margin * (cols-1), im.height + pad_top + margin * (rows-1)), color_background)
for row in range(rows):
for col in range(cols):
@@ -336,8 +340,20 @@ 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 +370,23 @@ 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()
@@ -390,7 +410,7 @@ 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(',')
@@ -399,7 +419,7 @@ 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)
@@ -407,7 +427,7 @@ 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:
@@ -446,8 +466,7 @@ 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
@@ -482,20 +501,29 @@ def get_next_sequence_number(path, basename): return result + 1
-def save_image_with_geninfo(image, geninfo, filename, extension=None, existing_pnginfo=None):
+def save_image_with_geninfo(image, geninfo, filename, extension=None, existing_pnginfo=None, pnginfo_section_name='parameters'):
+ """
+ Saves image to filename, including geninfo as text information for generation info.
+ For PNG images, geninfo is added to existing pnginfo dictionary using the pnginfo_section_name argument as key.
+ For JPG images, there's no dictionary and geninfo just replaces the EXIF description.
+ """
+
if extension is None:
extension = os.path.splitext(filename)[1]
image_format = Image.registered_extensions()[extension]
- existing_pnginfo = existing_pnginfo or {}
- if opts.enable_pnginfo:
- existing_pnginfo['parameters'] = geninfo
-
if extension.lower() == '.png':
- pnginfo_data = PngImagePlugin.PngInfo()
- for k, v in (existing_pnginfo or {}).items():
- pnginfo_data.add_text(k, str(v))
+ existing_pnginfo = existing_pnginfo or {}
+ if opts.enable_pnginfo:
+ existing_pnginfo[pnginfo_section_name] = geninfo
+
+ 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)
@@ -608,7 +636,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i """
temp_file_path = f"{filename_without_extension}.tmp"
- save_image_with_geninfo(image_to_save, info, temp_file_path, extension, params.pnginfo)
+ save_image_with_geninfo(image_to_save, info, temp_file_path, extension, existing_pnginfo=params.pnginfo, pnginfo_section_name=pnginfo_section_name)
os.replace(temp_file_path, filename_without_extension + extension)
@@ -625,12 +653,18 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i oversize = image.width > opts.target_side_length or image.height > opts.target_side_length
if opts.export_for_4chan and (oversize or os.stat(fullfn).st_size > opts.img_downscale_threshold * 1024 * 1024):
ratio = image.width / image.height
-
+ resize_to = None
if oversize and ratio > 1:
- image = image.resize((round(opts.target_side_length), round(image.height * opts.target_side_length / image.width)), LANCZOS)
+ resize_to = round(opts.target_side_length), round(image.height * opts.target_side_length / image.width)
elif oversize:
- image = image.resize((round(image.width * opts.target_side_length / image.height), round(opts.target_side_length)), LANCZOS)
+ resize_to = round(image.width * opts.target_side_length / image.height), round(opts.target_side_length)
+ if resize_to is not None:
+ try:
+ # Resizing image with LANCZOS could throw an exception if e.g. image mode is I;16
+ image = image.resize(resize_to, LANCZOS)
+ except Exception:
+ image = image.resize(resize_to)
try:
_atomically_save_image(image, fullfn_without_extension, ".jpg")
except Exception as e:
@@ -648,8 +682,15 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i return fullfn, txt_fullfn
-def read_info_from_image(image):
- items = image.info or {}
+IGNORED_INFO_KEYS = {
+ 'jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif',
+ 'loop', 'background', 'timestamp', 'duration', 'progressive', 'progression',
+ 'icc_profile', 'chromaticity', 'photoshop',
+}
+
+
+def read_info_from_image(image: Image.Image) -> tuple[str | None, dict]:
+ items = (image.info or {}).copy()
geninfo = items.pop('parameters', None)
@@ -665,9 +706,8 @@ 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 IGNORED_INFO_KEYS:
+ items.pop(field, None)
if items.get("Software", None) == "NovelAI":
try:
@@ -678,8 +718,7 @@ 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
diff --git a/modules/img2img.py b/modules/img2img.py index 4c12c2c5..a5f1c148 100644 --- a/modules/img2img.py +++ b/modules/img2img.py @@ -1,10 +1,12 @@ import os
+from pathlib import Path
import numpy as np
from PIL import Image, ImageOps, ImageFilter, ImageEnhance, ImageChops, UnidentifiedImageError
+import gradio as gr
-from modules import sd_samplers
-from modules.generation_parameters_copypaste import create_override_settings_dict
+from modules import sd_samplers, images as imgutil
+from modules.generation_parameters_copypaste import create_override_settings_dict, parse_generation_parameters
from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
from modules.shared import opts, state
import modules.shared as shared
@@ -13,7 +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, use_png_info=False, png_info_props=None, png_info_dir=None):
processing.fix_seed(p)
images = shared.listfiles(input_dir)
@@ -21,9 +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.")
@@ -34,6 +37,14 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args): state.job_count = len(images) * p.n_iter
+ # extract "default" params to use in case getting png info fails
+ prompt = p.prompt
+ negative_prompt = p.negative_prompt
+ seed = p.seed
+ cfg_scale = p.cfg_scale
+ sampler_name = p.sampler_name
+ steps = p.steps
+
for i, image in enumerate(images):
state.job = f"{i+1} out of {len(images)}"
if state.skipped:
@@ -49,23 +60,59 @@ 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
+ if use_png_info:
+ try:
+ info_img = img
+ if png_info_dir:
+ info_img_path = os.path.join(png_info_dir, os.path.basename(image))
+ info_img = Image.open(info_img_path)
+ geninfo, _ = imgutil.read_info_from_image(info_img)
+ parsed_parameters = parse_generation_parameters(geninfo)
+ parsed_parameters = {k: v for k, v in parsed_parameters.items() if k in (png_info_props or {})}
+ except Exception:
+ parsed_parameters = {}
+
+ p.prompt = prompt + (" " + parsed_parameters["Prompt"] if "Prompt" in parsed_parameters else "")
+ p.negative_prompt = negative_prompt + (" " + parsed_parameters["Negative prompt"] if "Negative prompt" in parsed_parameters else "")
+ p.seed = int(parsed_parameters.get("Seed", seed))
+ p.cfg_scale = float(parsed_parameters.get("CFG scale", cfg_scale))
+ p.sampler_name = parsed_parameters.get("Sampler", sampler_name)
+ p.steps = int(parsed_parameters.get("Steps", steps))
+
proc = modules.scripts.scripts_img2img.run(p, *args)
if proc is None:
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)
@@ -78,7 +125,7 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args): processed_image.save(os.path.join(output_dir, filename))
-def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, selected_scale_tab: int, height: int, width: int, scale_by: float, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, *args):
+def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, selected_scale_tab: int, height: int, width: int, scale_by: float, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, img2img_batch_use_png_info: bool, img2img_batch_png_info_props: list, img2img_batch_png_info_dir: str, request: gr.Request, *args):
override_settings = create_override_settings_dict(override_settings_texts)
is_batch = mode == 5
@@ -115,7 +162,7 @@ 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)
@@ -161,6 +208,8 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s p.scripts = modules.scripts.scripts_img2img
p.script_args = args
+ p.user = request.username
+
if shared.cmd_opts.enable_console_prompts:
print(f"\nimg2img: {prompt}", file=shared.progress_print_out)
@@ -170,7 +219,7 @@ 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, use_png_info=img2img_batch_use_png_info, png_info_props=img2img_batch_png_info_props, png_info_dir=img2img_batch_png_info_dir)
processed = Processed(p, [], p.seed, "")
else:
diff --git a/modules/interrogate.py b/modules/interrogate.py index 111b1322..a3ae1dd5 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
@@ -185,8 +184,7 @@ class InterrogateModels: def interrogate(self, pil_image):
res = ""
- shared.state.begin()
- shared.state.job = 'interrogate'
+ shared.state.begin(job="interrogate")
try:
if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
lowvram.send_everything_to_cpu()
@@ -216,8 +214,7 @@ 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()
diff --git a/modules/launch_utils.py b/modules/launch_utils.py index 35a52310..0e0dbca4 100644 --- a/modules/launch_utils.py +++ b/modules/launch_utils.py @@ -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:
@@ -136,15 +142,15 @@ def git_clone(url, dir, name, commithash=None): if commithash is None:
return
- current_hash = run(f'"{git}" -C "{dir}" rev-parse HEAD', None, f"Couldn't determine {name}'s hash: {commithash}").strip()
+ current_hash = run(f'"{git}" -C "{dir}" rev-parse HEAD', None, f"Couldn't determine {name}'s hash: {commithash}", live=False).strip()
if current_hash == commithash:
return
run(f'"{git}" -C "{dir}" fetch', f"Fetching updates for {name}...", f"Couldn't fetch {name}")
- run(f'"{git}" -C "{dir}" checkout {commithash}', f"Checking out commit for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}")
+ 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)
diff --git a/modules/localization.py b/modules/localization.py index ee9c65e7..e8f585da 100644 --- a/modules/localization.py +++ b/modules/localization.py @@ -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)}"
diff --git a/modules/lowvram.py b/modules/lowvram.py index e254cc13..d95bcfbf 100644 --- a/modules/lowvram.py +++ b/modules/lowvram.py @@ -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)
diff --git a/modules/mac_specific.py b/modules/mac_specific.py index d74c6b95..735847f5 100644 --- a/modules/mac_specific.py +++ b/modules/mac_specific.py @@ -4,16 +4,21 @@ from modules.sd_hijack_utils import CondFunc from packaging import version -# has_mps is only available in nightly pytorch (for now) and macOS 12.3+. -# check `getattr` and try it for compatibility +# before torch version 1.13, has_mps is only available in nightly pytorch and macOS 12.3+, +# use check `getattr` and try it for compatibility. +# in torch version 1.13, backends.mps.is_available() and backends.mps.is_built() are introduced in to check mps availabilty, +# since torch 2.0.1+ nightly build, getattr(torch, 'has_mps', False) was deprecated, see https://github.com/pytorch/pytorch/pull/103279 def check_for_mps() -> bool: - if not getattr(torch, 'has_mps', False): - return False - try: - torch.zeros(1).to(torch.device("mps")) - return True - except Exception: - return False + if version.parse(torch.__version__) <= version.parse("2.0.1"): + if not getattr(torch, 'has_mps', False): + return False + try: + torch.zeros(1).to(torch.device("mps")) + return True + except Exception: + return False + else: + return torch.backends.mps.is_available() and torch.backends.mps.is_built() has_mps = check_for_mps() diff --git a/modules/modelloader.py b/modules/modelloader.py index be23071a..098bcb79 100644 --- a/modules/modelloader.py +++ b/modules/modelloader.py @@ -1,3 +1,5 @@ +from __future__ import annotations + import os import shutil import importlib @@ -8,6 +10,29 @@ from modules.upscaler import Upscaler, UpscalerLanczos, UpscalerNearest, Upscale from modules.paths import script_path, models_path +def load_file_from_url( + url: str, + *, + model_dir: str, + progress: bool = True, + file_name: str | None = None, +) -> str: + """Download a file from `url` into `model_dir`, using the file present if possible. + + Returns the path to the downloaded file. + """ + os.makedirs(model_dir, exist_ok=True) + if not file_name: + parts = urlparse(url) + file_name = os.path.basename(parts.path) + cached_file = os.path.abspath(os.path.join(model_dir, file_name)) + if not os.path.exists(cached_file): + print(f'Downloading: "{url}" to {cached_file}\n') + from torch.hub import download_url_to_file + download_url_to_file(url, cached_file, progress=progress) + return cached_file + + def load_models(model_path: str, model_url: str = None, command_path: str = None, ext_filter=None, download_name=None, ext_blacklist=None) -> list: """ A one-and done loader to try finding the desired models in specified directories. @@ -46,9 +71,7 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None if model_url is not None and len(output) == 0: if download_name is not None: - from basicsr.utils.download_util import load_file_from_url - dl = load_file_from_url(model_url, places[0], True, download_name) - output.append(dl) + output.append(load_file_from_url(model_url, model_dir=places[0], file_name=download_name)) else: output.append(model_url) @@ -59,7 +82,7 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None def friendly_name(file: str): - if "http" in file: + if file.startswith("http"): file = urlparse(file).path file = os.path.basename(file) @@ -95,8 +118,7 @@ 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) diff --git a/modules/models/diffusion/ddpm_edit.py b/modules/models/diffusion/ddpm_edit.py index 3fb76b65..b892d5fc 100644 --- a/modules/models/diffusion/ddpm_edit.py +++ b/modules/models/diffusion/ddpm_edit.py @@ -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): diff --git a/modules/paths.py b/modules/paths.py index 5f6474c0..bada804e 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"]),
@@ -39,17 +38,3 @@ for d, must_exist, what, options in path_dirs: else:
sys.path.append(d)
paths[what] = d
-
-
-class Prioritize:
- def __init__(self, name):
- self.name = name
- self.path = None
-
- def __enter__(self):
- self.path = sys.path.copy()
- sys.path = [paths[self.name]] + sys.path
-
- def __exit__(self, exc_type, exc_val, exc_tb):
- sys.path = self.path
- self.path = None
diff --git a/modules/postprocessing.py b/modules/postprocessing.py index 736315e2..136e9c88 100644 --- a/modules/postprocessing.py +++ b/modules/postprocessing.py @@ -9,8 +9,7 @@ from modules.shared import opts def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir, show_extras_results, *args, save_output: bool = True):
devices.torch_gc()
- shared.state.begin()
- shared.state.job = 'extras'
+ shared.state.begin(job="extras")
image_data = []
image_names = []
@@ -54,7 +53,9 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir, for image, name in zip(image_data, image_names):
shared.state.textinfo = name
- existing_pnginfo = image.info or {}
+ parameters, existing_pnginfo = images.read_info_from_image(image)
+ if parameters:
+ existing_pnginfo["parameters"] = parameters
pp = scripts_postprocessing.PostprocessedImage(image.convert("RGB"))
diff --git a/modules/processing.py b/modules/processing.py index 29a3743f..21d1492c 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -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)
@@ -171,15 +174,18 @@ 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
+ self.user = None
+
@property
def sd_model(self):
return shared.sd_model
@@ -288,8 +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:
@@ -311,7 +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.
@@ -320,28 +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.
"""
+ 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]
- if cache[0] is not None and (required_prompts, steps) == 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)
+ 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:
@@ -543,7 +551,7 @@ def program_version(): return res
-def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0):
+def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0, use_main_prompt=False):
index = position_in_batch + iteration * p.batch_size
clip_skip = getattr(p, 'clip_skip', opts.CLIP_stop_at_last_layers)
@@ -567,7 +575,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
"Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
"Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
- "Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
+ "Seed resize from": (None if p.seed_resize_from_w <= 0 or p.seed_resize_from_h <= 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
"Denoising strength": getattr(p, 'denoising_strength', None),
"Conditional mask weight": getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None,
"Clip skip": None if clip_skip <= 1 else clip_skip,
@@ -579,16 +587,21 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter "NGMS": None if p.s_min_uncond == 0 else p.s_min_uncond,
**p.extra_generation_params,
"Version": program_version() if opts.add_version_to_infotext else None,
+ "User": p.user if opts.add_user_name_to_info else None,
}
generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None])
+ prompt_text = p.prompt if use_main_prompt else all_prompts[index]
negative_prompt_text = f"\nNegative prompt: {p.all_negative_prompts[index]}" if p.all_negative_prompts[index] else ""
- return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip()
+ return f"{prompt_text}{negative_prompt_text}\n{generation_params_text}".strip()
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:
@@ -654,8 +667,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: else:
p.all_subseeds = [int(subseed) + x for x in range(len(p.all_prompts))]
- def infotext(iteration=0, position_in_batch=0):
- return create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, comments, iteration, position_in_batch)
+ def infotext(iteration=0, position_in_batch=0, use_main_prompt=False):
+ return create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, comments, iteration, position_in_batch, use_main_prompt)
if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
model_hijack.embedding_db.load_textual_inversion_embeddings()
@@ -674,10 +687,11 @@ 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
@@ -698,11 +712,11 @@ 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)
@@ -737,7 +751,7 @@ 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()
@@ -814,7 +828,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: grid = images.image_grid(output_images, p.batch_size)
if opts.return_grid:
- text = infotext()
+ text = infotext(use_main_prompt=True)
infotexts.insert(0, text)
if opts.enable_pnginfo:
grid.info["parameters"] = text
@@ -822,10 +836,10 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: index_of_first_image = 1
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)
+ images.save_image(grid, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0], opts.grid_format, info=infotext(use_main_prompt=True), 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()
@@ -860,6 +874,8 @@ 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)
@@ -892,6 +908,8 @@ 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
@@ -971,7 +989,8 @@ 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))
@@ -1054,8 +1073,14 @@ 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))
+ if self.scripts is not None:
+ self.scripts.before_hr(self)
+
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)
sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio())
@@ -1065,8 +1090,12 @@ 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()
@@ -1093,12 +1122,31 @@ 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()
@@ -1115,7 +1163,7 @@ 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
@@ -1126,7 +1174,11 @@ 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
@@ -1148,8 +1200,17 @@ 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
diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py index b4aff704..0069d8b0 100644 --- a/modules/prompt_parser.py +++ b/modules/prompt_parser.py @@ -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)
diff --git a/modules/realesrgan_model.py b/modules/realesrgan_model.py index 99983678..0700b853 100644 --- a/modules/realesrgan_model.py +++ b/modules/realesrgan_model.py @@ -1,15 +1,13 @@ 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.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 +34,7 @@ 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 = []
@@ -45,9 +42,10 @@ class UpscalerRealESRGAN(Upscaler): if not self.enable:
return img
- info = self.load_model(path)
- if not os.path.exists(info.local_data_path):
- print(f"Unable to load RealESRGAN model: {info.name}")
+ try:
+ info = self.load_model(path)
+ except Exception:
+ errors.report(f"Unable to load RealESRGAN model {path}", exc_info=True)
return img
upsampler = RealESRGANer(
@@ -65,21 +63,17 @@ class UpscalerRealESRGAN(Upscaler): return image
def load_model(self, path):
- try:
- info = next(iter([scaler for scaler in self.scalers if scaler.data_path == path]), None)
-
- if info is None:
- print(f"Unable to find model info: {path}")
- return None
-
- if info.local_data_path.startswith("http"):
- 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)
- return None
+ for scaler in self.scalers:
+ if scaler.data_path == path:
+ if scaler.local_data_path.startswith("http"):
+ scaler.local_data_path = modelloader.load_file_from_url(
+ scaler.data_path,
+ model_dir=self.model_download_path,
+ )
+ if not os.path.exists(scaler.local_data_path):
+ raise FileNotFoundError(f"RealESRGAN data missing: {scaler.local_data_path}")
+ return scaler
+ raise ValueError(f"Unable to find model info: {path}")
def load_models(self, _):
return get_realesrgan_models(self)
@@ -135,5 +129,4 @@ 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)
diff --git a/modules/restart.py b/modules/restart.py new file mode 100644 index 00000000..18eacaf3 --- /dev/null +++ b/modules/restart.py @@ -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) diff --git a/modules/safe.py b/modules/safe.py index e8f50774..b1d08a79 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
@@ -11,7 +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
-
diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py index 40f388a5..77ee55ee 100644 --- a/modules/script_callbacks.py +++ b/modules/script_callbacks.py @@ -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)
diff --git a/modules/script_loading.py b/modules/script_loading.py index 57b15862..306a1f35 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 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)
diff --git a/modules/scripts.py b/modules/scripts.py index c902804b..7d9dd59f 100644 --- a/modules/scripts.py +++ b/modules/scripts.py @@ -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.
@@ -105,6 +117,21 @@ class Script: pass
+ def after_extra_networks_activate(self, p, *args, **kwargs):
+ """
+ Calledafter extra networks activation, before conds calculation
+ allow modification of the network after extra networks activation been applied
+ won't be call if p.disable_extra_networks
+
+ **kwargs will have those items:
+ - batch_number - index of current batch, from 0 to number of batches-1
+ - prompts - list of prompts for current batch; you can change contents of this list but changing the number of entries will likely break things
+ - seeds - list of seeds for current batch
+ - subseeds - list of subseeds for current batch
+ - extra_network_data - list of ExtraNetworkParams for current stage
+ """
+ pass
+
def process_batch(self, p, *args, **kwargs):
"""
Same as process(), but called for every batch.
@@ -175,6 +202,11 @@ class Script: return f'script_{tabname}{title}_{item_id}'
+ def before_hr(self, p, *args):
+ """
+ This function is called before hires fix start.
+ """
+ pass
current_basedir = paths.script_path
@@ -238,7 +270,7 @@ 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 +296,12 @@ 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 +312,9 @@ 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 +327,7 @@ 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 +355,73 @@ 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
- self.titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.selectable_scripts]
+ script.args_from = len(self.inputs)
+ script.args_to = len(self.inputs)
- inputs = [None]
- inputs_alwayson = [True]
+ controls = wrap_call(script.ui, script.filename, "ui", script.is_img2img)
- def create_script_ui(script, inputs, inputs_alwayson):
- script.args_from = len(inputs)
- script.args_to = len(inputs)
+ if controls is None:
+ return
- controls = wrap_call(script.ui, script.filename, "ui", script.is_img2img)
+ script.name = wrap_call(script.title, script.filename, "title", default=script.filename).lower()
+ api_args = []
- if controls is None:
- return
+ for control in controls:
+ control.custom_script_source = os.path.basename(script.filename)
- script.name = wrap_call(script.title, script.filename, "title", default=script.filename).lower()
- api_args = []
+ arg_info = api_models.ScriptArg(label=control.label or "")
- for control in controls:
- control.custom_script_source = os.path.basename(script.filename)
+ for field in ("value", "minimum", "maximum", "step", "choices"):
+ v = getattr(control, field, None)
+ if v is not None:
+ setattr(arg_info, field, v)
- arg_info = api_models.ScriptArg(label=control.label or "")
+ api_args.append(arg_info)
- for field in ("value", "minimum", "maximum", "step", "choices"):
- v = getattr(control, field, None)
- if v is not None:
- setattr(arg_info, field, v)
+ script.api_info = api_models.ScriptInfo(
+ name=script.name,
+ is_img2img=script.is_img2img,
+ is_alwayson=script.alwayson,
+ args=api_args,
+ )
- api_args.append(arg_info)
+ if script.infotext_fields is not None:
+ self.infotext_fields += script.infotext_fields
- script.api_info = api_models.ScriptInfo(
- name=script.name,
- is_img2img=script.is_img2img,
- is_alwayson=script.alwayson,
- args=api_args,
- )
+ if script.paste_field_names is not None:
+ self.paste_field_names += script.paste_field_names
- if script.infotext_fields is not None:
- self.infotext_fields += script.infotext_fields
+ self.inputs += controls
+ script.args_to = len(self.inputs)
- if script.paste_field_names is not None:
- self.paste_field_names += script.paste_field_names
+ def setup_ui_for_section(self, section, scriptlist=None):
+ if scriptlist is None:
+ scriptlist = self.alwayson_scripts
- inputs += controls
- inputs_alwayson += [script.alwayson for _ in controls]
- script.args_to = len(inputs)
+ for script in scriptlist:
+ if script.alwayson and script.section != section:
+ continue
- for script in self.alwayson_scripts:
- with gr.Group() as group:
- create_script_ui(script, inputs, inputs_alwayson)
+ with gr.Group(visible=script.alwayson) as group:
+ self.create_script_ui(script)
script.group = group
- dropdown = gr.Dropdown(label="Script", elem_id="script_list", choices=["None"] + self.titles, value="None", type="index")
- inputs[0] = dropdown
+ def prepare_ui(self):
+ self.inputs = [None]
- for script in self.selectable_scripts:
- with gr.Group(visible=False) as group:
- create_script_ui(script, inputs, inputs_alwayson)
+ def setup_ui(self):
+ self.titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.selectable_scripts]
- 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")
+ self.inputs[0] = dropdown
+
+ 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 +446,7 @@ class ScriptRunner: )
self.script_load_ctr = 0
+
def onload_script_visibility(params):
title = params.get('Script', None)
if title:
@@ -421,10 +457,10 @@ 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 +480,21 @@ 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 +502,15 @@ 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 after_extra_networks_activate(self, p, **kwargs):
+ for script in self.alwayson_scripts:
+ try:
+ script_args = p.script_args[script.args_from:script.args_to]
+ script.after_extra_networks_activate(p, *script_args, **kwargs)
+ except Exception:
+ errors.report(f"Error running after_extra_networks_activate: {script.filename}", exc_info=True)
def process_batch(self, p, **kwargs):
for script in self.alwayson_scripts:
@@ -468,8 +518,7 @@ 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 +526,7 @@ 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 +534,7 @@ 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 +542,21 @@ 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)):
@@ -533,6 +577,15 @@ class ScriptRunner: self.scripts[si].args_to = args_to
+ def before_hr(self, p):
+ for script in self.alwayson_scripts:
+ try:
+ script_args = p.script_args[script.args_from:script.args_to]
+ script.before_hr(p, *script_args)
+ except Exception:
+ errors.report(f"Error running before_hr: {script.filename}", exc_info=True)
+
+
scripts_txt2img: ScriptRunner = None
scripts_img2img: ScriptRunner = None
scripts_postproc: scripts_postprocessing.ScriptPostprocessingRunner = None
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index f93df0a6..3b6f95ce 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
@@ -43,7 +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:
@@ -68,16 +68,19 @@ def apply_optimizations(): if selection == "None":
matching_optimizer = None
+ elif selection == "Automatic" and shared.cmd_opts.disable_opt_split_attention:
+ matching_optimizer = None
elif matching_optimizer is None:
matching_optimizer = optimizers[0]
if matching_optimizer is not None:
- print(f"Applying optimization: {matching_optimizer.name}... ", 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 ''
@@ -155,9 +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()
@@ -194,6 +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
@@ -215,6 +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
diff --git a/modules/sd_hijack_clip.py b/modules/sd_hijack_clip.py index cc6e8c21..3b5a7666 100644 --- a/modules/sd_hijack_clip.py +++ b/modules/sd_hijack_clip.py @@ -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
diff --git a/modules/sd_hijack_clip_old.py b/modules/sd_hijack_clip_old.py index a3476e95..c5c6270b 100644 --- a/modules/sd_hijack_clip_old.py +++ b/modules/sd_hijack_clip_old.py @@ -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}")
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py index 2ec0b049..53e27ade 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
@@ -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
@@ -59,7 +57,7 @@ class SdOptimizationSdpNoMem(SdOptimization): name = "sdp-no-mem"
label = "scaled dot product without memory efficient attention"
cmd_opt = "opt_sdp_no_mem_attention"
- priority = 90
+ priority = 80
def is_available(self):
return hasattr(torch.nn.functional, "scaled_dot_product_attention") and callable(torch.nn.functional.scaled_dot_product_attention)
@@ -73,7 +71,7 @@ class SdOptimizationSdp(SdOptimizationSdpNoMem): name = "sdp"
label = "scaled dot product"
cmd_opt = "opt_sdp_attention"
- priority = 80
+ priority = 70
def apply(self):
ldm.modules.attention.CrossAttention.forward = scaled_dot_product_attention_forward
@@ -116,7 +114,7 @@ class SdOptimizationInvokeAI(SdOptimization): class SdOptimizationDoggettx(SdOptimization):
name = "Doggettx"
cmd_opt = "opt_split_attention"
- priority = 20
+ priority = 90
def apply(self):
ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward
@@ -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()
diff --git a/modules/sd_models.py b/modules/sd_models.py index 91b3eb11..f65f4e36 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
@@ -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:
@@ -247,7 +247,12 @@ def read_state_dict(checkpoint_file, print_global_state=False, map_location=None _, extension = os.path.splitext(checkpoint_file)
if extension.lower() == ".safetensors":
device = map_location or shared.weight_load_location or devices.get_optimal_device_name()
- pl_sd = safetensors.torch.load_file(checkpoint_file, device=device)
+
+ if not shared.opts.disable_mmap_load_safetensors:
+ pl_sd = safetensors.torch.load_file(checkpoint_file, device=device)
+ else:
+ pl_sd = safetensors.torch.load(open(checkpoint_file, 'rb').read())
+ pl_sd = {k: v.to(device) for k, v in pl_sd.items()}
else:
pl_sd = torch.load(checkpoint_file, map_location=map_location or shared.weight_load_location)
@@ -313,8 +318,6 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer timer.record("apply half()")
- devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
- devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
devices.dtype_unet = model.model.diffusion_model.dtype
devices.unet_needs_upcast = shared.cmd_opts.upcast_sampling and devices.dtype == torch.float16 and devices.dtype_unet == torch.float16
@@ -423,7 +426,7 @@ 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 +535,8 @@ 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 638e0ac9..71581b76 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -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]:]
@@ -125,15 +134,17 @@ class CFGDenoiser(torch.nn.Module): x_in = x_in[:-batch_size]
sigma_in = sigma_in[:-batch_size]
- # TODO add infotext entry
+ 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:
@@ -265,6 +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
@@ -304,6 +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())
@@ -365,6 +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):
@@ -398,5 +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
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 66c6330a..b9c53875 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -4,8 +4,10 @@ import os import sys
import threading
import time
+import logging
import gradio as gr
+import torch
import tqdm
import modules.interrogate
@@ -17,6 +19,8 @@ from modules.paths_internal import models_path, script_path, data_path, sd_confi from ldm.models.diffusion.ddpm import LatentDiffusion
from typing import Optional
+log = logging.getLogger(__name__)
+
demo = None
parser = cmd_args.parser
@@ -43,19 +47,6 @@ restricted_opts = { "outdir_init_images"
}
-ui_reorder_categories = [
- "inpaint",
- "sampler",
- "checkboxes",
- "hires_fix",
- "dimensions",
- "cfg",
- "seed",
- "batch",
- "override_settings",
- "scripts",
-]
-
# https://huggingface.co/datasets/freddyaboulton/gradio-theme-subdomains/resolve/main/subdomains.json
gradio_hf_hub_themes = [
"gradio/glass",
@@ -76,6 +67,9 @@ cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or cmd_op devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_esrgan, devices.device_codeformer = \
(devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'esrgan', 'codeformer'])
+devices.dtype = torch.float32 if cmd_opts.no_half else torch.float16
+devices.dtype_vae = torch.float32 if cmd_opts.no_half or cmd_opts.no_half_vae else torch.float16
+
device = devices.device
weight_load_location = None if cmd_opts.lowram else "cpu"
@@ -153,12 +147,15 @@ class State: def request_restart(self) -> None:
self.interrupt()
self.server_command = "restart"
+ log.info("Received restart request")
def skip(self):
self.skipped = True
+ log.info("Received skip request")
def interrupt(self):
self.interrupted = True
+ log.info("Received interrupt request")
def nextjob(self):
if opts.live_previews_enable and opts.show_progress_every_n_steps == -1:
@@ -182,7 +179,7 @@ class State: return obj
- def begin(self):
+ def begin(self, job: str = "(unknown)"):
self.sampling_step = 0
self.job_count = -1
self.processing_has_refined_job_count = False
@@ -196,10 +193,13 @@ class State: self.interrupted = False
self.textinfo = None
self.time_start = time.time()
-
+ self.job = job
devices.torch_gc()
+ log.info("Starting job %s", job)
def end(self):
+ duration = time.time() - self.time_start
+ log.info("Ending job %s (%.2f seconds)", self.job, duration)
self.job = ""
self.job_count = 0
@@ -269,6 +269,10 @@ 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
@@ -314,7 +318,12 @@ 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}),
+ "font": OptionInfo("", "Font for image grids that have text"),
+ "grid_text_active_color": OptionInfo("#000000", "Text color for image grids", ui_components.FormColorPicker, {}),
+ "grid_text_inactive_color": OptionInfo("#999999", "Inactive text color for image grids", ui_components.FormColorPicker, {}),
+ "grid_background_color": OptionInfo("#ffffff", "Background color for image grids", ui_components.FormColorPicker, {}),
"enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
"save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters."),
@@ -380,6 +389,7 @@ options_templates.update(options_section(('system', "System"), { "multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job."),
"print_hypernet_extra": OptionInfo(False, "Print extra hypernetwork information to console."),
"list_hidden_files": OptionInfo(True, "Load models/files in hidden directories").info("directory is hidden if its name starts with \".\""),
+ "disable_mmap_load_safetensors": OptionInfo(False, "Disable memmapping for loading .safetensors files.").info("fixes very slow loading speed in some cases"),
}))
options_templates.update(options_section(('training', "Training"), {
@@ -403,6 +413,7 @@ 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."),
@@ -412,18 +423,19 @@ 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"), {
@@ -432,6 +444,7 @@ 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"), {
@@ -471,7 +484,6 @@ options_templates.update(options_section(('ui', "User interface"), { "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
"send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"),
"send_size": OptionInfo(True, "Send size when sending prompt or image to another interface"),
- "font": OptionInfo("", "Font for image grids that have text"),
"js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
"js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
"js_modal_lightbox_gamepad": OptionInfo(False, "Navigate image viewer with gamepad"),
@@ -486,7 +498,7 @@ 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(),
@@ -495,8 +507,16 @@ options_templates.update(options_section(('ui', "User interface"), { options_templates.update(options_section(('infotext', "Infotext"), {
"add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
"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"), {
@@ -518,6 +538,10 @@ 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"]}),
@@ -633,6 +657,10 @@ 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)
diff --git a/modules/shared_items.py b/modules/shared_items.py index 2a8713c8..89792e88 100644 --- a/modules/shared_items.py +++ b/modules/shared_items.py @@ -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"
diff --git a/modules/styles.py b/modules/styles.py index 34e1b5e1..ec0e1bc5 100644 --- a/modules/styles.py +++ b/modules/styles.py @@ -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
diff --git a/modules/sysinfo.py b/modules/sysinfo.py new file mode 100644 index 00000000..5f15ac4f --- /dev/null +++ b/modules/sysinfo.py @@ -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)
diff --git a/modules/textual_inversion/autocrop.py b/modules/textual_inversion/autocrop.py index 8e667a4d..1675e39a 100644 --- a/modules/textual_inversion/autocrop.py +++ b/modules/textual_inversion/autocrop.py @@ -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):
diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py index b9621fc9..7ee05061 100644 --- a/modules/textual_inversion/dataset.py +++ b/modules/textual_inversion/dataset.py @@ -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
diff --git a/modules/textual_inversion/image_embedding.py b/modules/textual_inversion/image_embedding.py index 5858a55f..81cff7bf 100644 --- a/modules/textual_inversion/image_embedding.py +++ b/modules/textual_inversion/image_embedding.py @@ -1,8 +1,10 @@ import base64
import json
+import warnings
+
import numpy as np
import zlib
-from PIL import Image, ImageDraw, ImageFont
+from PIL import Image, ImageDraw
import torch
@@ -129,14 +131,17 @@ def extract_image_data_embed(image): def caption_image_overlay(srcimage, title, footerLeft, footerMid, footerRight, textfont=None):
+ from modules.images import get_font
+ if textfont:
+ warnings.warn(
+ 'passing in a textfont to caption_image_overlay is deprecated and does nothing',
+ DeprecationWarning,
+ stacklevel=2,
+ )
from math import cos
image = srcimage.copy()
fontsize = 32
- if textfont is None:
- from modules.images import get_font
- textfont = get_font(fontsize)
-
factor = 1.5
gradient = Image.new('RGBA', (1, image.size[1]), color=(0, 0, 0, 0))
for y in range(image.size[1]):
@@ -147,12 +152,12 @@ def caption_image_overlay(srcimage, title, footerLeft, footerMid, footerRight, t draw = ImageDraw.Draw(image)
- font = ImageFont.truetype(textfont, fontsize)
+ font = get_font(fontsize)
padding = 10
_, _, w, h = draw.textbbox((0, 0), title, font=font)
fontsize = min(int(fontsize * (((image.size[0]*0.75)-(padding*4))/w)), 72)
- font = ImageFont.truetype(textfont, fontsize)
+ font = get_font(fontsize)
_, _, w, h = draw.textbbox((0, 0), title, font=font)
draw.text((padding, padding), title, anchor='lt', font=font, fill=(255, 255, 255, 230))
@@ -163,7 +168,7 @@ def caption_image_overlay(srcimage, title, footerLeft, footerMid, footerRight, t _, _, w, h = draw.textbbox((0, 0), footerRight, font=font)
fontsize_right = min(int(fontsize * (((image.size[0]/3)-(padding))/w)), 72)
- font = ImageFont.truetype(textfont, min(fontsize_left, fontsize_mid, fontsize_right))
+ font = get_font(min(fontsize_left, fontsize_mid, fontsize_right))
draw.text((padding, image.size[1]-padding), footerLeft, anchor='ls', font=font, fill=(255, 255, 255, 230))
draw.text((image.size[0]/2, image.size[1]-padding), footerMid, anchor='ms', font=font, fill=(255, 255, 255, 230))
diff --git a/modules/textual_inversion/logging.py b/modules/textual_inversion/logging.py index 734a4b6f..45823eb1 100644 --- a/modules/textual_inversion/logging.py +++ b/modules/textual_inversion/logging.py @@ -2,11 +2,51 @@ import datetime import json
import os
-saved_params_shared = {"model_name", "model_hash", "initial_step", "num_of_dataset_images", "learn_rate", "batch_size", "clip_grad_mode", "clip_grad_value", "gradient_step", "data_root", "log_directory", "training_width", "training_height", "steps", "create_image_every", "template_file", "gradient_step", "latent_sampling_method"}
-saved_params_ti = {"embedding_name", "num_vectors_per_token", "save_embedding_every", "save_image_with_stored_embedding"}
-saved_params_hypernet = {"hypernetwork_name", "layer_structure", "activation_func", "weight_init", "add_layer_norm", "use_dropout", "save_hypernetwork_every"}
+saved_params_shared = {
+ "batch_size",
+ "clip_grad_mode",
+ "clip_grad_value",
+ "create_image_every",
+ "data_root",
+ "gradient_step",
+ "initial_step",
+ "latent_sampling_method",
+ "learn_rate",
+ "log_directory",
+ "model_hash",
+ "model_name",
+ "num_of_dataset_images",
+ "steps",
+ "template_file",
+ "training_height",
+ "training_width",
+}
+saved_params_ti = {
+ "embedding_name",
+ "num_vectors_per_token",
+ "save_embedding_every",
+ "save_image_with_stored_embedding",
+}
+saved_params_hypernet = {
+ "activation_func",
+ "add_layer_norm",
+ "hypernetwork_name",
+ "layer_structure",
+ "save_hypernetwork_every",
+ "use_dropout",
+ "weight_init",
+}
saved_params_all = saved_params_shared | saved_params_ti | saved_params_hypernet
-saved_params_previews = {"preview_prompt", "preview_negative_prompt", "preview_steps", "preview_sampler_index", "preview_cfg_scale", "preview_seed", "preview_width", "preview_height"}
+saved_params_previews = {
+ "preview_cfg_scale",
+ "preview_height",
+ "preview_negative_prompt",
+ "preview_prompt",
+ "preview_sampler_index",
+ "preview_seed",
+ "preview_steps",
+ "preview_width",
+}
def save_settings_to_file(log_directory, all_params):
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py index a009d8e8..dbd856bd 100644 --- a/modules/textual_inversion/preprocess.py +++ b/modules/textual_inversion/preprocess.py @@ -7,7 +7,7 @@ from modules import paths, shared, images, deepbooru from modules.textual_inversion import autocrop
-def preprocess(id_task, process_src, process_dst, process_width, process_height, preprocess_txt_action, process_keep_original_size, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False, process_multicrop=None, process_multicrop_mindim=None, process_multicrop_maxdim=None, process_multicrop_minarea=None, process_multicrop_maxarea=None, process_multicrop_objective=None, process_multicrop_threshold=None):
+def preprocess(id_task, process_src, process_dst, process_width, process_height, preprocess_txt_action, process_keep_original_size, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.15, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False, process_multicrop=None, process_multicrop_mindim=None, process_multicrop_maxdim=None, process_multicrop_minarea=None, process_multicrop_maxarea=None, process_multicrop_objective=None, process_multicrop_threshold=None):
try:
if process_caption:
shared.interrogator.load()
@@ -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)
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index d489ed1e..bb6f211c 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
@@ -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()
diff --git a/modules/timer.py b/modules/timer.py index ba92be33..da99e49f 100644 --- a/modules/timer.py +++ b/modules/timer.py @@ -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
diff --git a/modules/txt2img.py b/modules/txt2img.py index 2e7d202d..6aa79f23 100644 --- a/modules/txt2img.py +++ b/modules/txt2img.py @@ -4,10 +4,10 @@ from modules.generation_parameters_copypaste import create_override_settings_dic from modules.shared import opts, cmd_opts
import modules.shared as shared
from modules.ui import plaintext_to_html
+import gradio as gr
-
-def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, hr_sampler_index: int, hr_prompt: str, hr_negative_prompt, override_settings_texts, *args):
+def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, hr_sampler_index: int, hr_prompt: str, hr_negative_prompt, override_settings_texts, request: gr.Request, *args):
override_settings = create_override_settings_dict(override_settings_texts)
p = processing.StableDiffusionProcessingTxt2Img(
@@ -48,6 +48,8 @@ def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, step p.scripts = modules.scripts.scripts_txt2img
p.script_args = args
+ p.user = request.username
+
if cmd_opts.enable_console_prompts:
print(f"\ntxt2img: {prompt}", file=shared.progress_print_out)
diff --git a/modules/ui.py b/modules/ui.py index 001b9792..39d226ad 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -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):
@@ -150,7 +155,7 @@ def process_interrogate(interrogation_function, mode, ii_input_dir, ii_output_di img = Image.open(image)
filename = os.path.basename(image)
left, _ = os.path.splitext(filename)
- print(interrogation_function(img), file=open(os.path.join(ii_output_dir, f"{left}.txt"), 'a'))
+ print(interrogation_function(img), file=open(os.path.join(ii_output_dir, f"{left}.txt"), 'a', encoding='utf-8'))
return [gr.update(), None]
@@ -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")
@@ -505,10 +482,10 @@ 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:
@@ -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:
@@ -642,6 +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)),
@@ -752,6 +733,10 @@ def create_ui(): img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, elem_id="img2img_batch_input_dir")
img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, elem_id="img2img_batch_output_dir")
img2img_batch_inpaint_mask_dir = gr.Textbox(label="Inpaint batch mask directory (required for inpaint batch processing only)", **shared.hide_dirs, elem_id="img2img_batch_inpaint_mask_dir")
+ with gr.Accordion("PNG info", open=False):
+ img2img_batch_use_png_info = gr.Checkbox(label="Append png info to prompts", **shared.hide_dirs, elem_id="img2img_batch_use_png_info")
+ img2img_batch_png_info_dir = gr.Textbox(label="PNG info directory", **shared.hide_dirs, placeholder="Leave empty to use input directory", elem_id="img2img_batch_png_info_dir")
+ img2img_batch_png_info_props = gr.CheckboxGroup(["Prompt", "Negative prompt", "Seed", "CFG scale", "Sampler", "Steps"], label="Parameters to take from png info", info="Prompts from png info will be appended to prompts set in ui.")
img2img_tabs = [tab_img2img, tab_sketch, tab_inpaint, tab_inpaint_color, tab_inpaint_upload, tab_batch]
@@ -780,6 +765,8 @@ 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")
@@ -790,7 +777,7 @@ 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")
@@ -799,7 +786,7 @@ 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():
@@ -889,6 +876,8 @@ 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)
@@ -949,6 +938,9 @@ def create_ui(): img2img_batch_output_dir,
img2img_batch_inpaint_mask_dir,
override_settings,
+ img2img_batch_use_png_info,
+ img2img_batch_png_info_props,
+ img2img_batch_png_info_dir,
] + custom_inputs,
outputs=[
img2img_gallery,
@@ -1053,6 +1045,7 @@ 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
@@ -1462,195 +1455,10 @@ 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"),
@@ -1662,7 +1470,7 @@ 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")]
@@ -1672,10 +1480,7 @@ 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()
@@ -1703,58 +1508,20 @@ 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
@@ -1782,7 +1549,7 @@ 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,
]
)
@@ -1796,70 +1563,6 @@ 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
@@ -1903,3 +1606,17 @@ 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"])
+
diff --git a/modules/ui_common.py b/modules/ui_common.py index 27ab3ebb..57c2d0ad 100644 --- a/modules/ui_common.py +++ b/modules/ui_common.py @@ -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
+
diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py index 515ec262..dff522ef 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
@@ -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)
@@ -415,9 +424,19 @@ sort_ordering = [ (False, lambda x: x.get('name', 'z')),
(True, lambda x: x.get('name', 'z')),
(False, lambda x: 'z'),
+ (True, lambda x: x.get('commit_time', '')),
+ (True, lambda x: x.get('created_at', '')),
+ (True, lambda x: x.get('stars', 0)),
]
+def get_date(info: dict, key):
+ try:
+ return datetime.strptime(info.get(key), "%Y-%m-%dT%H:%M:%SZ").strftime("%Y-%m-%d")
+ except (ValueError, TypeError):
+ return ''
+
+
def refresh_available_extensions_from_data(hide_tags, sort_column, filter_text=""):
extlist = available_extensions["extensions"]
installed_extension_urls = {normalize_git_url(extension.remote): extension.name for extension in extensions.extensions}
@@ -442,7 +461,10 @@ def refresh_available_extensions_from_data(hide_tags, sort_column, filter_text=" for ext in sorted(extlist, key=sort_function, reverse=sort_reverse):
name = ext.get("name", "noname")
+ stars = int(ext.get("stars", 0))
added = ext.get('added', 'unknown')
+ update_time = get_date(ext, 'commit_time')
+ create_time = get_date(ext, 'created_at')
url = ext.get("url", None)
description = ext.get("description", "")
extension_tags = ext.get("tags", [])
@@ -453,7 +475,7 @@ 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
@@ -469,7 +491,8 @@ def refresh_available_extensions_from_data(hide_tags, sort_column, filter_text=" code += f"""
<tr>
<td><a href="{html.escape(url)}" target="_blank">{html.escape(name)}</a><br />{tags_text}</td>
- <td>{html.escape(description)}<p class="info"><span class="date_added">Added: {html.escape(added)}</span></p></td>
+ <td>{html.escape(description)}<p class="info">
+ <span class="date_added">Update: {html.escape(update_time)} Added: {html.escape(added)} Created: {html.escape(create_time)}</span><span class="star_count">stars: <b>{stars}</b></a></p></td>
<td>{install_code}</td>
</tr>
@@ -490,8 +513,14 @@ 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 +535,8 @@ 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)
@@ -546,7 +576,7 @@ def create_ui(): with gr.Row():
hide_tags = gr.CheckboxGroup(value=["ads", "localization", "installed"], label="Hide extensions with tags", choices=["script", "ads", "localization", "installed"])
- sort_column = gr.Radio(value="newest first", label="Order", choices=["newest first", "oldest first", "a-z", "z-a", "internal order", ], type="index")
+ sort_column = gr.Radio(value="newest first", label="Order", choices=["newest first", "oldest first", "a-z", "z-a", "internal order",'update time', 'create time', "stars"], type="index")
with gr.Row():
search_extensions_text = gr.Text(label="Search").style(container=False)
@@ -555,9 +585,9 @@ 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(
diff --git a/modules/ui_extra_networks.py b/modules/ui_extra_networks.py index 19fbaae5..1efd00b0 100644 --- a/modules/ui_extra_networks.py +++ b/modules/ui_extra_networks.py @@ -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
@@ -29,8 +30,8 @@ def fetch_file(filename: str = ""): raise ValueError(f"File cannot be fetched: {filename}. Must be in one of directories registered by extra pages.")
ext = os.path.splitext(filename)[1].lower()
- if ext not in (".png", ".jpg", ".jpeg", ".webp"):
- raise ValueError(f"File cannot be fetched: {filename}. Only png and jpg and webp.")
+ if ext not in (".png", ".jpg", ".jpeg", ".webp", ".gif"):
+ raise ValueError(f"File cannot be fetched: {filename}. Only png, jpg, webp, and gif.")
# would profit from returning 304
return FileResponse(filename, headers={"Accept-Ranges": "bytes"})
@@ -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)
diff --git a/modules/ui_extra_networks_checkpoints.py b/modules/ui_extra_networks_checkpoints.py index a17aa9c9..8b9ab71b 100644 --- a/modules/ui_extra_networks_checkpoints.py +++ b/modules/ui_extra_networks_checkpoints.py @@ -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):
diff --git a/modules/ui_extra_networks_hypernets.py b/modules/ui_extra_networks_hypernets.py index 6187e000..7c19b532 100644 --- a/modules/ui_extra_networks_hypernets.py +++ b/modules/ui_extra_networks_hypernets.py @@ -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):
diff --git a/modules/ui_extra_networks_textual_inversion.py b/modules/ui_extra_networks_textual_inversion.py index 6944d559..58a61c55 100644 --- a/modules/ui_extra_networks_textual_inversion.py +++ b/modules/ui_extra_networks_textual_inversion.py @@ -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):
diff --git a/modules/ui_gradio_extensions.py b/modules/ui_gradio_extensions.py new file mode 100644 index 00000000..b824b113 --- /dev/null +++ b/modules/ui_gradio_extensions.py @@ -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
diff --git a/modules/ui_settings.py b/modules/ui_settings.py new file mode 100644 index 00000000..0c560b30 --- /dev/null +++ b/modules/ui_settings.py @@ -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,
+ )
diff --git a/modules/ui_tempdir.py b/modules/ui_tempdir.py index f05049e1..fb75137e 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, format="png"):
already_saved_as = getattr(pil_image, 'already_saved_as', None)
if already_saved_as and os.path.isfile(already_saved_as):
register_tmp_file(shared.demo, already_saved_as)
+ filename = already_saved_as
- 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..e682bbaa 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 = 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) |