mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2025-12-27 04:11:39 -08:00
Merge branch 'dev' into gradio-theme-support
This commit is contained in:
AUTOMATIC1111
GitHub
commit
e6cbfcfe5b
53 changed files with 936 additions and 341 deletions
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@ -3,9 +3,9 @@ import io
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import time
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import datetime
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import uvicorn
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import gradio as gr
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from threading import Lock
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from io import BytesIO
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from gradio.processing_utils import decode_base64_to_file
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from fastapi import APIRouter, Depends, FastAPI, Request, Response
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from fastapi.security import HTTPBasic, HTTPBasicCredentials
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from fastapi.exceptions import HTTPException
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@ -197,6 +197,9 @@ class Api:
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self.add_api_route("/sdapi/v1/reload-checkpoint", self.reloadapi, methods=["POST"])
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self.add_api_route("/sdapi/v1/scripts", self.get_scripts_list, methods=["GET"], response_model=ScriptsList)
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self.default_script_arg_txt2img = []
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self.default_script_arg_img2img = []
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def add_api_route(self, path: str, endpoint, **kwargs):
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if shared.cmd_opts.api_auth:
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return self.app.add_api_route(path, endpoint, dependencies=[Depends(self.auth)], **kwargs)
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@ -230,7 +233,7 @@ class Api:
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script_idx = script_name_to_index(script_name, script_runner.scripts)
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return script_runner.scripts[script_idx]
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def init_script_args(self, request, selectable_scripts, selectable_idx, script_runner):
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def init_default_script_args(self, script_runner):
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#find max idx from the scripts in runner and generate a none array to init script_args
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last_arg_index = 1
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for script in script_runner.scripts:
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@ -238,13 +241,24 @@ class Api:
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last_arg_index = script.args_to
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# None everywhere except position 0 to initialize script args
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script_args = [None]*last_arg_index
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script_args[0] = 0
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# get default values
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with gr.Blocks(): # will throw errors calling ui function without this
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for script in script_runner.scripts:
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if script.ui(script.is_img2img):
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ui_default_values = []
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for elem in script.ui(script.is_img2img):
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ui_default_values.append(elem.value)
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script_args[script.args_from:script.args_to] = ui_default_values
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return script_args
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def init_script_args(self, request, default_script_args, selectable_scripts, selectable_idx, script_runner):
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script_args = default_script_args.copy()
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# position 0 in script_arg is the idx+1 of the selectable script that is going to be run when using scripts.scripts_*2img.run()
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if selectable_scripts:
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script_args[selectable_scripts.args_from:selectable_scripts.args_to] = request.script_args
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script_args[0] = selectable_idx + 1
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else:
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# when [0] = 0 no selectable script to run
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script_args[0] = 0
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# Now check for always on scripts
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if request.alwayson_scripts and (len(request.alwayson_scripts) > 0):
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@ -265,6 +279,8 @@ class Api:
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if not script_runner.scripts:
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script_runner.initialize_scripts(False)
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ui.create_ui()
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if not self.default_script_arg_txt2img:
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self.default_script_arg_txt2img = self.init_default_script_args(script_runner)
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selectable_scripts, selectable_script_idx = self.get_selectable_script(txt2imgreq.script_name, script_runner)
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populate = txt2imgreq.copy(update={ # Override __init__ params
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@ -280,7 +296,7 @@ class Api:
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args.pop('script_args', None) # will refeed them to the pipeline directly after initializing them
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args.pop('alwayson_scripts', None)
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script_args = self.init_script_args(txt2imgreq, selectable_scripts, selectable_script_idx, script_runner)
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script_args = self.init_script_args(txt2imgreq, self.default_script_arg_txt2img, selectable_scripts, selectable_script_idx, script_runner)
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send_images = args.pop('send_images', True)
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args.pop('save_images', None)
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@ -317,6 +333,8 @@ class Api:
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if not script_runner.scripts:
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script_runner.initialize_scripts(True)
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ui.create_ui()
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if not self.default_script_arg_img2img:
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self.default_script_arg_img2img = self.init_default_script_args(script_runner)
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selectable_scripts, selectable_script_idx = self.get_selectable_script(img2imgreq.script_name, script_runner)
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populate = img2imgreq.copy(update={ # Override __init__ params
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@ -334,7 +352,7 @@ class Api:
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args.pop('script_args', None) # will refeed them to the pipeline directly after initializing them
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args.pop('alwayson_scripts', None)
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script_args = self.init_script_args(img2imgreq, selectable_scripts, selectable_script_idx, script_runner)
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script_args = self.init_script_args(img2imgreq, self.default_script_arg_img2img, selectable_scripts, selectable_script_idx, script_runner)
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send_images = args.pop('send_images', True)
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args.pop('save_images', None)
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@ -376,16 +394,11 @@ class Api:
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def extras_batch_images_api(self, req: ExtrasBatchImagesRequest):
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reqDict = setUpscalers(req)
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def prepareFiles(file):
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file = decode_base64_to_file(file.data, file_path=file.name)
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file.orig_name = file.name
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return file
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reqDict['image_folder'] = list(map(prepareFiles, reqDict['imageList']))
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reqDict.pop('imageList')
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image_list = reqDict.pop('imageList', [])
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image_folder = [decode_base64_to_image(x.data) for x in image_list]
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with self.queue_lock:
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result = postprocessing.run_extras(extras_mode=1, image="", input_dir="", output_dir="", save_output=False, **reqDict)
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result = postprocessing.run_extras(extras_mode=1, image_folder=image_folder, image="", input_dir="", output_dir="", save_output=False, **reqDict)
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return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])
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@ -4,6 +4,7 @@ from modules.paths_internal import models_path, script_path, data_path, extensio
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parser = argparse.ArgumentParser()
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parser.add_argument("-f", action='store_true', help=argparse.SUPPRESS) # allows running as root; implemented outside of webui
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parser.add_argument("--update-all-extensions", action='store_true', help="launch.py argument: download updates for all extensions when starting the program")
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parser.add_argument("--skip-python-version-check", action='store_true', help="launch.py argument: do not check python version")
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parser.add_argument("--skip-torch-cuda-test", action='store_true', help="launch.py argument: do not check if CUDA is able to work properly")
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@ -92,14 +92,18 @@ def cond_cast_float(input):
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def randn(seed, shape):
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from modules.shared import opts
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torch.manual_seed(seed)
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if device.type == 'mps':
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if opts.randn_source == "CPU" or device.type == 'mps':
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return torch.randn(shape, device=cpu).to(device)
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return torch.randn(shape, device=device)
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def randn_without_seed(shape):
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if device.type == 'mps':
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from modules.shared import opts
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if opts.randn_source == "CPU" or device.type == 'mps':
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return torch.randn(shape, device=cpu).to(device)
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return torch.randn(shape, device=device)
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@ -5,16 +5,22 @@ import traceback
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import time
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import git
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from modules import paths, shared
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from modules import shared
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from modules.paths_internal import extensions_dir, extensions_builtin_dir
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extensions = []
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if not os.path.exists(paths.extensions_dir):
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os.makedirs(paths.extensions_dir)
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if not os.path.exists(extensions_dir):
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os.makedirs(extensions_dir)
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def active():
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return [x for x in extensions if x.enabled]
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if shared.opts.disable_all_extensions == "all":
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return []
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elif shared.opts.disable_all_extensions == "extra":
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return [x for x in extensions if x.enabled and x.is_builtin]
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else:
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return [x for x in extensions if x.enabled]
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class Extension:
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@ -26,21 +32,29 @@ class Extension:
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self.can_update = False
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self.is_builtin = is_builtin
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self.version = ''
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self.remote = None
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self.have_info_from_repo = False
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def read_info_from_repo(self):
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if self.have_info_from_repo:
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return
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self.have_info_from_repo = True
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repo = None
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try:
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if os.path.exists(os.path.join(path, ".git")):
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repo = git.Repo(path)
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if os.path.exists(os.path.join(self.path, ".git")):
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repo = git.Repo(self.path)
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except Exception:
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print(f"Error reading github repository info from {path}:", file=sys.stderr)
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print(f"Error reading github repository info from {self.path}:", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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if repo is None or repo.bare:
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self.remote = None
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else:
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try:
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self.remote = next(repo.remote().urls, None)
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self.status = 'unknown'
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self.remote = next(repo.remote().urls, None)
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head = repo.head.commit
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ts = time.asctime(time.gmtime(repo.head.commit.committed_date))
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self.version = f'{head.hexsha[:8]} ({ts})'
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@ -85,11 +99,16 @@ class Extension:
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def list_extensions():
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extensions.clear()
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if not os.path.isdir(paths.extensions_dir):
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if not os.path.isdir(extensions_dir):
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return
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if shared.opts.disable_all_extensions == "all":
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print("*** \"Disable all extensions\" option was set, will not load any extensions ***")
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elif shared.opts.disable_all_extensions == "extra":
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print("*** \"Disable all extensions\" option was set, will only load built-in extensions ***")
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extension_paths = []
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for dirname in [paths.extensions_dir, paths.extensions_builtin_dir]:
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for dirname in [extensions_dir, extensions_builtin_dir]:
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if not os.path.isdir(dirname):
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return
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@ -98,9 +117,8 @@ def list_extensions():
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if not os.path.isdir(path):
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continue
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extension_paths.append((extension_dirname, path, dirname == paths.extensions_builtin_dir))
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extension_paths.append((extension_dirname, path, dirname == extensions_builtin_dir))
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for dirname, path, is_builtin in extension_paths:
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extension = Extension(name=dirname, path=path, enabled=dirname not in shared.opts.disabled_extensions, is_builtin=is_builtin)
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extensions.append(extension)
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@ -9,7 +9,7 @@ class ExtraNetworkHypernet(extra_networks.ExtraNetwork):
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def activate(self, p, params_list):
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additional = shared.opts.sd_hypernetwork
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if additional != "" and additional in shared.hypernetworks and len([x for x in params_list if x.items[0] == additional]) == 0:
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if additional != "None" and additional in shared.hypernetworks and len([x for x in params_list if x.items[0] == additional]) == 0:
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p.all_prompts = [x + f"<hypernet:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]
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params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
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@ -284,6 +284,10 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
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restore_old_hires_fix_params(res)
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# Missing RNG means the default was set, which is GPU RNG
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if "RNG" not in res:
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res["RNG"] = "GPU"
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return res
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@ -304,6 +308,7 @@ infotext_to_setting_name_mapping = [
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('UniPC skip type', 'uni_pc_skip_type'),
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('UniPC order', 'uni_pc_order'),
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('UniPC lower order final', 'uni_pc_lower_order_final'),
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('RNG', 'randn_source'),
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]
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@ -312,7 +312,7 @@ class Hypernetwork:
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def list_hypernetworks(path):
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res = {}
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for filename in sorted(glob.iglob(os.path.join(path, '**/*.pt'), recursive=True)):
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for filename in sorted(glob.iglob(os.path.join(path, '**/*.pt'), recursive=True), key=str.lower):
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name = os.path.splitext(os.path.basename(filename))[0]
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# Prevent a hypothetical "None.pt" from being listed.
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if name != "None":
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@ -261,9 +261,12 @@ def resize_image(resize_mode, im, width, height, upscaler_name=None):
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if scale > 1.0:
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upscalers = [x for x in shared.sd_upscalers if x.name == upscaler_name]
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assert len(upscalers) > 0, f"could not find upscaler named {upscaler_name}"
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if len(upscalers) == 0:
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upscaler = shared.sd_upscalers[0]
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print(f"could not find upscaler named {upscaler_name or '<empty string>'}, using {upscaler.name} as a fallback")
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else:
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upscaler = upscalers[0]
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upscaler = upscalers[0]
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im = upscaler.scaler.upscale(im, scale, upscaler.data_path)
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if im.width != w or im.height != h:
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@ -349,6 +352,7 @@ class FilenameGenerator:
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'prompt_no_styles': lambda self: self.prompt_no_style(),
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'prompt_spaces': lambda self: sanitize_filename_part(self.prompt, replace_spaces=False),
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'prompt_words': lambda self: self.prompt_words(),
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'clip_skip': lambda self: opts.data["CLIP_stop_at_last_layers"],
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}
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default_time_format = '%Y%m%d%H%M%S'
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|
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@ -151,13 +151,14 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
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override_settings=override_settings,
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)
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p.scripts = modules.scripts.scripts_txt2img
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p.scripts = modules.scripts.scripts_img2img
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p.script_args = args
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if shared.cmd_opts.enable_console_prompts:
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print(f"\nimg2img: {prompt}", file=shared.progress_print_out)
|
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p.extra_generation_params["Mask blur"] = mask_blur
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if mask:
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p.extra_generation_params["Mask blur"] = mask_blur
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|
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if is_batch:
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assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
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|
|
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|
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@ -32,7 +32,7 @@ def download_default_clip_interrogate_categories(content_dir):
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category_types = ["artists", "flavors", "mediums", "movements"]
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try:
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os.makedirs(tmpdir)
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os.makedirs(tmpdir, exist_ok=True)
|
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for category_type in category_types:
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torch.hub.download_url_to_file(f"https://raw.githubusercontent.com/pharmapsychotic/clip-interrogator/main/clip_interrogator/data/{category_type}.txt", os.path.join(tmpdir, f"{category_type}.txt"))
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os.rename(tmpdir, content_dir)
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|
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@ -41,7 +41,7 @@ def download_default_clip_interrogate_categories(content_dir):
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errors.display(e, "downloading default CLIP interrogate categories")
|
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finally:
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if os.path.exists(tmpdir):
|
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os.remove(tmpdir)
|
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os.removedirs(tmpdir)
|
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|
||||
|
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class InterrogateModels:
|
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|
|
|
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|
|
@ -55,12 +55,12 @@ def setup_for_low_vram(sd_model, use_medvram):
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if hasattr(sd_model.cond_stage_model, 'model'):
|
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sd_model.cond_stage_model.transformer = sd_model.cond_stage_model.model
|
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|
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# remove four big modules, cond, first_stage, depth (if applicable), and unet from the model and then
|
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# remove several big modules: cond, first_stage, depth/embedder (if applicable), and unet from the model and then
|
||||
# send the model to GPU. Then put modules back. the modules will be in CPU.
|
||||
stored = sd_model.cond_stage_model.transformer, sd_model.first_stage_model, getattr(sd_model, 'depth_model', None), sd_model.model
|
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sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.model = None, None, None, None
|
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stored = sd_model.cond_stage_model.transformer, sd_model.first_stage_model, getattr(sd_model, 'depth_model', None), getattr(sd_model, 'embedder', None), sd_model.model
|
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sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.embedder, sd_model.model = None, None, None, None, None
|
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sd_model.to(devices.device)
|
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sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.model = stored
|
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sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.embedder, sd_model.model = stored
|
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|
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# register hooks for those the first three models
|
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sd_model.cond_stage_model.transformer.register_forward_pre_hook(send_me_to_gpu)
|
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|
|
@ -69,6 +69,8 @@ def setup_for_low_vram(sd_model, use_medvram):
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sd_model.first_stage_model.decode = first_stage_model_decode_wrap
|
||||
if sd_model.depth_model:
|
||||
sd_model.depth_model.register_forward_pre_hook(send_me_to_gpu)
|
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if sd_model.embedder:
|
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sd_model.embedder.register_forward_pre_hook(send_me_to_gpu)
|
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parents[sd_model.cond_stage_model.transformer] = sd_model.cond_stage_model
|
||||
|
||||
if hasattr(sd_model.cond_stage_model, 'model'):
|
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|
|
|
|||
|
|
@ -18,9 +18,15 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir,
|
|||
|
||||
if extras_mode == 1:
|
||||
for img in image_folder:
|
||||
image = Image.open(img)
|
||||
if isinstance(img, Image.Image):
|
||||
image = img
|
||||
fn = ''
|
||||
else:
|
||||
image = Image.open(os.path.abspath(img.name))
|
||||
fn = os.path.splitext(img.orig_name)[0]
|
||||
|
||||
image_data.append(image)
|
||||
image_names.append(os.path.splitext(img.orig_name)[0])
|
||||
image_names.append(fn)
|
||||
elif extras_mode == 2:
|
||||
assert not shared.cmd_opts.hide_ui_dir_config, '--hide-ui-dir-config option must be disabled'
|
||||
assert input_dir, 'input directory not selected'
|
||||
|
|
|
|||
|
|
@ -3,6 +3,7 @@ import math
|
|||
import os
|
||||
import sys
|
||||
import warnings
|
||||
import hashlib
|
||||
|
||||
import torch
|
||||
import numpy as np
|
||||
|
|
@ -78,22 +79,28 @@ def apply_overlay(image, paste_loc, index, overlays):
|
|||
|
||||
|
||||
def txt2img_image_conditioning(sd_model, x, width, height):
|
||||
if sd_model.model.conditioning_key not in {'hybrid', 'concat'}:
|
||||
# Dummy zero conditioning if we're not using inpainting model.
|
||||
if sd_model.model.conditioning_key in {'hybrid', 'concat'}: # Inpainting models
|
||||
|
||||
# The "masked-image" in this case will just be all zeros since the entire image is masked.
|
||||
image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
|
||||
image_conditioning = sd_model.get_first_stage_encoding(sd_model.encode_first_stage(image_conditioning))
|
||||
|
||||
# Add the fake full 1s mask to the first dimension.
|
||||
image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
|
||||
image_conditioning = image_conditioning.to(x.dtype)
|
||||
|
||||
return image_conditioning
|
||||
|
||||
elif sd_model.model.conditioning_key == "crossattn-adm": # UnCLIP models
|
||||
|
||||
return x.new_zeros(x.shape[0], 2*sd_model.noise_augmentor.time_embed.dim, dtype=x.dtype, device=x.device)
|
||||
|
||||
else:
|
||||
# Dummy zero conditioning if we're not using inpainting or unclip models.
|
||||
# Still takes up a bit of memory, but no encoder call.
|
||||
# Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
|
||||
return x.new_zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)
|
||||
|
||||
# The "masked-image" in this case will just be all zeros since the entire image is masked.
|
||||
image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
|
||||
image_conditioning = sd_model.get_first_stage_encoding(sd_model.encode_first_stage(image_conditioning))
|
||||
|
||||
# Add the fake full 1s mask to the first dimension.
|
||||
image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
|
||||
image_conditioning = image_conditioning.to(x.dtype)
|
||||
|
||||
return image_conditioning
|
||||
|
||||
|
||||
class StableDiffusionProcessing:
|
||||
"""
|
||||
|
|
@ -190,6 +197,14 @@ class StableDiffusionProcessing:
|
|||
|
||||
return conditioning_image
|
||||
|
||||
def unclip_image_conditioning(self, source_image):
|
||||
c_adm = self.sd_model.embedder(source_image)
|
||||
if self.sd_model.noise_augmentor is not None:
|
||||
noise_level = 0 # TODO: Allow other noise levels?
|
||||
c_adm, noise_level_emb = self.sd_model.noise_augmentor(c_adm, noise_level=repeat(torch.tensor([noise_level]).to(c_adm.device), '1 -> b', b=c_adm.shape[0]))
|
||||
c_adm = torch.cat((c_adm, noise_level_emb), 1)
|
||||
return c_adm
|
||||
|
||||
def inpainting_image_conditioning(self, source_image, latent_image, image_mask=None):
|
||||
self.is_using_inpainting_conditioning = True
|
||||
|
||||
|
|
@ -241,6 +256,9 @@ class StableDiffusionProcessing:
|
|||
if self.sampler.conditioning_key in {'hybrid', 'concat'}:
|
||||
return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask)
|
||||
|
||||
if self.sampler.conditioning_key == "crossattn-adm":
|
||||
return self.unclip_image_conditioning(source_image)
|
||||
|
||||
# Dummy zero conditioning if we're not using inpainting or depth model.
|
||||
return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)
|
||||
|
||||
|
|
@ -459,6 +477,8 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
|
|||
"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,
|
||||
"ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta,
|
||||
"Init image hash": getattr(p, 'init_img_hash', None),
|
||||
"RNG": (opts.randn_source if opts.randn_source != "GPU" else None)
|
||||
}
|
||||
|
||||
generation_params.update(p.extra_generation_params)
|
||||
|
|
@ -990,6 +1010,12 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
|
|||
self.color_corrections = []
|
||||
imgs = []
|
||||
for img in self.init_images:
|
||||
|
||||
# Save init image
|
||||
if opts.save_init_img:
|
||||
self.init_img_hash = hashlib.md5(img.tobytes()).hexdigest()
|
||||
images.save_image(img, path=opts.outdir_init_images, basename=None, forced_filename=self.init_img_hash, save_to_dirs=False)
|
||||
|
||||
image = images.flatten(img, opts.img2img_background_color)
|
||||
|
||||
if crop_region is None and self.resize_mode != 3:
|
||||
|
|
|
|||
|
|
@ -1,6 +1,5 @@
|
|||
# this code is adapted from the script contributed by anon from /h/
|
||||
|
||||
import io
|
||||
import pickle
|
||||
import collections
|
||||
import sys
|
||||
|
|
@ -12,11 +11,9 @@ import _codecs
|
|||
import zipfile
|
||||
import re
|
||||
|
||||
|
||||
# PyTorch 1.13 and later have _TypedStorage renamed to TypedStorage
|
||||
TypedStorage = torch.storage.TypedStorage if hasattr(torch.storage, 'TypedStorage') else torch.storage._TypedStorage
|
||||
|
||||
|
||||
def encode(*args):
|
||||
out = _codecs.encode(*args)
|
||||
return out
|
||||
|
|
@ -27,7 +24,7 @@ class RestrictedUnpickler(pickle.Unpickler):
|
|||
|
||||
def persistent_load(self, saved_id):
|
||||
assert saved_id[0] == 'storage'
|
||||
return TypedStorage()
|
||||
return TypedStorage(_internal=True)
|
||||
|
||||
def find_class(self, module, name):
|
||||
if self.extra_handler is not None:
|
||||
|
|
|
|||
|
|
@ -553,3 +553,15 @@ def IOComponent_init(self, *args, **kwargs):
|
|||
|
||||
original_IOComponent_init = gr.components.IOComponent.__init__
|
||||
gr.components.IOComponent.__init__ = IOComponent_init
|
||||
|
||||
|
||||
def BlockContext_init(self, *args, **kwargs):
|
||||
res = original_BlockContext_init(self, *args, **kwargs)
|
||||
|
||||
add_classes_to_gradio_component(self)
|
||||
|
||||
return res
|
||||
|
||||
|
||||
original_BlockContext_init = gr.blocks.BlockContext.__init__
|
||||
gr.blocks.BlockContext.__init__ = BlockContext_init
|
||||
|
|
|
|||
|
|
@ -122,7 +122,7 @@ def list_models():
|
|||
elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
|
||||
print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)
|
||||
|
||||
for filename in model_list:
|
||||
for filename in sorted(model_list, key=str.lower):
|
||||
checkpoint_info = CheckpointInfo(filename)
|
||||
checkpoint_info.register()
|
||||
|
||||
|
|
@ -383,6 +383,14 @@ def repair_config(sd_config):
|
|||
elif shared.cmd_opts.upcast_sampling:
|
||||
sd_config.model.params.unet_config.params.use_fp16 = True
|
||||
|
||||
if getattr(sd_config.model.params.first_stage_config.params.ddconfig, "attn_type", None) == "vanilla-xformers" and not shared.xformers_available:
|
||||
sd_config.model.params.first_stage_config.params.ddconfig.attn_type = "vanilla"
|
||||
|
||||
# For UnCLIP-L, override the hardcoded karlo directory
|
||||
if hasattr(sd_config.model.params, "noise_aug_config") and hasattr(sd_config.model.params.noise_aug_config.params, "clip_stats_path"):
|
||||
karlo_path = os.path.join(paths.models_path, 'karlo')
|
||||
sd_config.model.params.noise_aug_config.params.clip_stats_path = sd_config.model.params.noise_aug_config.params.clip_stats_path.replace("checkpoints/karlo_models", karlo_path)
|
||||
|
||||
|
||||
sd1_clip_weight = 'cond_stage_model.transformer.text_model.embeddings.token_embedding.weight'
|
||||
sd2_clip_weight = 'cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight'
|
||||
|
|
|
|||
|
|
@ -14,6 +14,8 @@ config_sd2 = os.path.join(sd_repo_configs_path, "v2-inference.yaml")
|
|||
config_sd2v = os.path.join(sd_repo_configs_path, "v2-inference-v.yaml")
|
||||
config_sd2_inpainting = os.path.join(sd_repo_configs_path, "v2-inpainting-inference.yaml")
|
||||
config_depth_model = os.path.join(sd_repo_configs_path, "v2-midas-inference.yaml")
|
||||
config_unclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-l-inference.yaml")
|
||||
config_unopenclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-h-inference.yaml")
|
||||
config_inpainting = os.path.join(sd_configs_path, "v1-inpainting-inference.yaml")
|
||||
config_instruct_pix2pix = os.path.join(sd_configs_path, "instruct-pix2pix.yaml")
|
||||
config_alt_diffusion = os.path.join(sd_configs_path, "alt-diffusion-inference.yaml")
|
||||
|
|
@ -65,9 +67,14 @@ def is_using_v_parameterization_for_sd2(state_dict):
|
|||
def guess_model_config_from_state_dict(sd, filename):
|
||||
sd2_cond_proj_weight = sd.get('cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight', None)
|
||||
diffusion_model_input = sd.get('model.diffusion_model.input_blocks.0.0.weight', None)
|
||||
sd2_variations_weight = sd.get('embedder.model.ln_final.weight', None)
|
||||
|
||||
if sd.get('depth_model.model.pretrained.act_postprocess3.0.project.0.bias', None) is not None:
|
||||
return config_depth_model
|
||||
elif sd2_variations_weight is not None and sd2_variations_weight.shape[0] == 768:
|
||||
return config_unclip
|
||||
elif sd2_variations_weight is not None and sd2_variations_weight.shape[0] == 1024:
|
||||
return config_unopenclip
|
||||
|
||||
if sd2_cond_proj_weight is not None and sd2_cond_proj_weight.shape[1] == 1024:
|
||||
if diffusion_model_input.shape[1] == 9:
|
||||
|
|
|
|||
|
|
@ -60,3 +60,13 @@ def store_latent(decoded):
|
|||
|
||||
class InterruptedException(BaseException):
|
||||
pass
|
||||
|
||||
|
||||
if opts.randn_source == "CPU":
|
||||
import torchsde._brownian.brownian_interval
|
||||
|
||||
def torchsde_randn(size, dtype, device, seed):
|
||||
generator = torch.Generator(devices.cpu).manual_seed(int(seed))
|
||||
return torch.randn(size, dtype=dtype, device=devices.cpu, generator=generator).to(device)
|
||||
|
||||
torchsde._brownian.brownian_interval._randn = torchsde_randn
|
||||
|
|
|
|||
|
|
@ -70,8 +70,13 @@ class VanillaStableDiffusionSampler:
|
|||
|
||||
# Have to unwrap the inpainting conditioning here to perform pre-processing
|
||||
image_conditioning = None
|
||||
uc_image_conditioning = None
|
||||
if isinstance(cond, dict):
|
||||
image_conditioning = cond["c_concat"][0]
|
||||
if self.conditioning_key == "crossattn-adm":
|
||||
image_conditioning = cond["c_adm"]
|
||||
uc_image_conditioning = unconditional_conditioning["c_adm"]
|
||||
else:
|
||||
image_conditioning = cond["c_concat"][0]
|
||||
cond = cond["c_crossattn"][0]
|
||||
unconditional_conditioning = unconditional_conditioning["c_crossattn"][0]
|
||||
|
||||
|
|
@ -98,8 +103,12 @@ class VanillaStableDiffusionSampler:
|
|||
# Wrap the image conditioning back up since the DDIM code can accept the dict directly.
|
||||
# Note that they need to be lists because it just concatenates them later.
|
||||
if image_conditioning is not None:
|
||||
cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]}
|
||||
unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
|
||||
if self.conditioning_key == "crossattn-adm":
|
||||
cond = {"c_adm": image_conditioning, "c_crossattn": [cond]}
|
||||
unconditional_conditioning = {"c_adm": uc_image_conditioning, "c_crossattn": [unconditional_conditioning]}
|
||||
else:
|
||||
cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]}
|
||||
unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
|
||||
|
||||
return x, ts, cond, unconditional_conditioning
|
||||
|
||||
|
|
@ -176,8 +185,12 @@ class VanillaStableDiffusionSampler:
|
|||
|
||||
# Wrap the conditioning models with additional image conditioning for inpainting model
|
||||
if image_conditioning is not None:
|
||||
conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
|
||||
unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
|
||||
if self.conditioning_key == "crossattn-adm":
|
||||
conditioning = {"c_adm": image_conditioning, "c_crossattn": [conditioning]}
|
||||
unconditional_conditioning = {"c_adm": torch.zeros_like(image_conditioning), "c_crossattn": [unconditional_conditioning]}
|
||||
else:
|
||||
conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
|
||||
unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
|
||||
|
||||
samples = self.launch_sampling(t_enc + 1, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))
|
||||
|
||||
|
|
@ -195,8 +208,12 @@ class VanillaStableDiffusionSampler:
|
|||
# Wrap the conditioning models with additional image conditioning for inpainting model
|
||||
# dummy_for_plms is needed because PLMS code checks the first item in the dict to have the right shape
|
||||
if image_conditioning is not None:
|
||||
conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_concat": [image_conditioning]}
|
||||
unconditional_conditioning = {"c_crossattn": [unconditional_conditioning], "c_concat": [image_conditioning]}
|
||||
if self.conditioning_key == "crossattn-adm":
|
||||
conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_adm": image_conditioning}
|
||||
unconditional_conditioning = {"c_crossattn": [unconditional_conditioning], "c_adm": torch.zeros_like(image_conditioning)}
|
||||
else:
|
||||
conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_concat": [image_conditioning]}
|
||||
unconditional_conditioning = {"c_crossattn": [unconditional_conditioning], "c_concat": [image_conditioning]}
|
||||
|
||||
samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
|
||||
|
||||
|
|
|
|||
|
|
@ -92,14 +92,21 @@ class CFGDenoiser(torch.nn.Module):
|
|||
batch_size = len(conds_list)
|
||||
repeats = [len(conds_list[i]) for i in range(batch_size)]
|
||||
|
||||
if shared.sd_model.model.conditioning_key == "crossattn-adm":
|
||||
image_uncond = torch.zeros_like(image_cond)
|
||||
make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": c_crossattn, "c_adm": c_adm}
|
||||
else:
|
||||
image_uncond = image_cond
|
||||
make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": c_crossattn, "c_concat": [c_concat]}
|
||||
|
||||
if not is_edit_model:
|
||||
x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
|
||||
sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
|
||||
image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond])
|
||||
image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond])
|
||||
else:
|
||||
x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x] + [x])
|
||||
sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma])
|
||||
image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond] + [torch.zeros_like(self.init_latent)])
|
||||
image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond] + [torch.zeros_like(self.init_latent)])
|
||||
|
||||
denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps, tensor, uncond)
|
||||
cfg_denoiser_callback(denoiser_params)
|
||||
|
|
@ -116,13 +123,13 @@ class CFGDenoiser(torch.nn.Module):
|
|||
cond_in = torch.cat([tensor, uncond, uncond])
|
||||
|
||||
if shared.batch_cond_uncond:
|
||||
x_out = self.inner_model(x_in, sigma_in, cond={"c_crossattn": [cond_in], "c_concat": [image_cond_in]})
|
||||
x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict([cond_in], image_cond_in))
|
||||
else:
|
||||
x_out = torch.zeros_like(x_in)
|
||||
for batch_offset in range(0, x_out.shape[0], batch_size):
|
||||
a = batch_offset
|
||||
b = a + batch_size
|
||||
x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": [cond_in[a:b]], "c_concat": [image_cond_in[a:b]]})
|
||||
x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict([cond_in[a:b]], image_cond_in[a:b]))
|
||||
else:
|
||||
x_out = torch.zeros_like(x_in)
|
||||
batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size
|
||||
|
|
@ -135,9 +142,9 @@ class CFGDenoiser(torch.nn.Module):
|
|||
else:
|
||||
c_crossattn = torch.cat([tensor[a:b]], uncond)
|
||||
|
||||
x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": c_crossattn, "c_concat": [image_cond_in[a:b]]})
|
||||
x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b]))
|
||||
|
||||
x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]})
|
||||
x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict([uncond], image_cond_in[-uncond.shape[0]:]))
|
||||
|
||||
denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps)
|
||||
cfg_denoised_callback(denoised_params)
|
||||
|
|
@ -183,7 +190,7 @@ class TorchHijack:
|
|||
if noise.shape == x.shape:
|
||||
return noise
|
||||
|
||||
if x.device.type == 'mps':
|
||||
if opts.randn_source == "CPU" or x.device.type == 'mps':
|
||||
return torch.randn_like(x, device=devices.cpu).to(x.device)
|
||||
else:
|
||||
return torch.randn_like(x)
|
||||
|
|
|
|||
|
|
@ -40,6 +40,7 @@ restricted_opts = {
|
|||
"outdir_grids",
|
||||
"outdir_txt2img_grids",
|
||||
"outdir_save",
|
||||
"outdir_init_images"
|
||||
}
|
||||
|
||||
ui_reorder_categories = [
|
||||
|
|
@ -269,6 +270,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
|
|||
"use_upscaler_name_as_suffix": OptionInfo(False, "Use upscaler name as filename suffix in the extras tab"),
|
||||
"save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),
|
||||
"do_not_add_watermark": OptionInfo(False, "Do not add watermark to images"),
|
||||
"save_init_img": OptionInfo(False, "Save init images when using img2img"),
|
||||
|
||||
"temp_dir": OptionInfo("", "Directory for temporary images; leave empty for default"),
|
||||
"clean_temp_dir_at_start": OptionInfo(False, "Cleanup non-default temporary directory when starting webui"),
|
||||
|
|
@ -284,6 +286,7 @@ options_templates.update(options_section(('saving-paths', "Paths for saving"), {
|
|||
"outdir_txt2img_grids": OptionInfo("outputs/txt2img-grids", 'Output directory for txt2img grids', component_args=hide_dirs),
|
||||
"outdir_img2img_grids": OptionInfo("outputs/img2img-grids", 'Output directory for img2img grids', component_args=hide_dirs),
|
||||
"outdir_save": OptionInfo("log/images", "Directory for saving images using the Save button", component_args=hide_dirs),
|
||||
"outdir_init_images": OptionInfo("outputs/init-images", "Directory for saving init images when using img2img", component_args=hide_dirs),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('saving-to-dirs', "Saving to a directory"), {
|
||||
|
|
@ -299,6 +302,8 @@ options_templates.update(options_section(('upscaling', "Upscaling"), {
|
|||
"ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
|
||||
"realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": shared_items.realesrgan_models_names()}),
|
||||
"upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),
|
||||
"SCUNET_tile": OptionInfo(256, "Tile size for SCUNET upscalers. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
|
||||
"SCUNET_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SCUNET upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('face-restoration', "Face restoration"), {
|
||||
|
|
@ -347,6 +352,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
|
|||
"comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
|
||||
"CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
|
||||
"upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),
|
||||
"randn_source": OptionInfo("GPU", "Random number generator source. Changes seeds drastically. Use CPU to produce the same picture across different vidocard vendors.", gr.Radio, {"choices": ["GPU", "CPU"]}),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('compatibility', "Compatibility"), {
|
||||
|
|
@ -377,7 +383,7 @@ options_templates.update(options_section(('extra_networks', "Extra Networks"), {
|
|||
"extra_networks_card_width": OptionInfo(0, "Card width for Extra Networks (px)"),
|
||||
"extra_networks_card_height": OptionInfo(0, "Card height for Extra Networks (px)"),
|
||||
"extra_networks_add_text_separator": OptionInfo(" ", "Extra text to add before <...> when adding extra network to prompt"),
|
||||
"sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": [""] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
|
||||
"sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('ui', "User interface"), {
|
||||
|
|
@ -398,6 +404,7 @@ options_templates.update(options_section(('ui', "User interface"), {
|
|||
"dimensions_and_batch_together": OptionInfo(True, "Show Width/Height and Batch sliders in same row"),
|
||||
"keyedit_precision_attention": OptionInfo(0.1, "Ctrl+up/down precision when editing (attention:1.1)", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
|
||||
"keyedit_precision_extra": OptionInfo(0.05, "Ctrl+up/down precision when editing <extra networks:0.9>", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
|
||||
"keyedit_delimiters": OptionInfo(".,\/!?%^*;:{}=`~()", "Ctrl+up/down word delimiters"),
|
||||
"quicksettings": OptionInfo("sd_model_checkpoint", "Quicksettings list"),
|
||||
"hidden_tabs": OptionInfo([], "Hidden UI tabs (requires restart)", ui_components.DropdownMulti, lambda: {"choices": [x for x in tab_names]}),
|
||||
"ui_reorder": OptionInfo(", ".join(ui_reorder_categories), "txt2img/img2img UI item order"),
|
||||
|
|
@ -439,7 +446,8 @@ options_templates.update(options_section(('postprocessing', "Postprocessing"), {
|
|||
}))
|
||||
|
||||
options_templates.update(options_section((None, "Hidden options"), {
|
||||
"disabled_extensions": OptionInfo([], "Disable those extensions"),
|
||||
"disabled_extensions": OptionInfo([], "Disable these extensions"),
|
||||
"disable_all_extensions": OptionInfo("none", "Disable all extensions (preserves the list of disabled extensions)", gr.Radio, {"choices": ["none", "extra", "all"]}),
|
||||
"sd_checkpoint_hash": OptionInfo("", "SHA256 hash of the current checkpoint"),
|
||||
}))
|
||||
|
||||
|
|
@ -675,7 +683,7 @@ mem_mon.start()
|
|||
|
||||
|
||||
def listfiles(dirname):
|
||||
filenames = [os.path.join(dirname, x) for x in sorted(os.listdir(dirname)) if not x.startswith(".")]
|
||||
filenames = [os.path.join(dirname, x) for x in sorted(os.listdir(dirname), key=str.lower) if not x.startswith(".")]
|
||||
return [file for file in filenames if os.path.isfile(file)]
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -70,17 +70,6 @@ def gr_show(visible=True):
|
|||
sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
|
||||
sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
|
||||
|
||||
css_hide_progressbar = """
|
||||
.wrap .m-12 svg { display:none!important; }
|
||||
.wrap .m-12::before { content:"Loading..." }
|
||||
.wrap .z-20 svg { display:none!important; }
|
||||
.wrap .z-20::before { content:"Loading..." }
|
||||
.wrap.cover-bg .z-20::before { content:"" }
|
||||
.progress-bar { display:none!important; }
|
||||
.meta-text { display:none!important; }
|
||||
.meta-text-center { display:none!important; }
|
||||
"""
|
||||
|
||||
# Using constants for these since the variation selector isn't visible.
|
||||
# Important that they exactly match script.js for tooltip to work.
|
||||
random_symbol = '\U0001f3b2\ufe0f' # 🎲️
|
||||
|
|
@ -182,8 +171,8 @@ def create_seed_inputs(target_interface):
|
|||
with FormRow(elem_id=target_interface + '_seed_row', variant="compact"):
|
||||
seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1, elem_id=target_interface + '_seed')
|
||||
seed.style(container=False)
|
||||
random_seed = ToolButton(random_symbol, elem_id=target_interface + '_random_seed')
|
||||
reuse_seed = ToolButton(reuse_symbol, elem_id=target_interface + '_reuse_seed')
|
||||
random_seed = ToolButton(random_symbol, elem_id=target_interface + '_random_seed', label='Random seed')
|
||||
reuse_seed = ToolButton(reuse_symbol, elem_id=target_interface + '_reuse_seed', label='Reuse seed')
|
||||
|
||||
seed_checkbox = gr.Checkbox(label='Extra', elem_id=target_interface + '_subseed_show', value=False)
|
||||
|
||||
|
|
@ -479,7 +468,7 @@ def create_ui():
|
|||
height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height")
|
||||
|
||||
with gr.Column(elem_id="txt2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
|
||||
res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn")
|
||||
res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn", label="Switch dims")
|
||||
|
||||
if opts.dimensions_and_batch_together:
|
||||
with gr.Column(elem_id="txt2img_column_batch"):
|
||||
|
|
@ -1215,7 +1204,7 @@ def create_ui():
|
|||
|
||||
with gr.Column(elem_id='ti_gallery_container'):
|
||||
ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
|
||||
ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(grid=4)
|
||||
ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(columns=4)
|
||||
ti_progress = gr.HTML(elem_id="ti_progress", value="")
|
||||
ti_outcome = gr.HTML(elem_id="ti_error", value="")
|
||||
|
||||
|
|
@ -1566,22 +1555,6 @@ def create_ui():
|
|||
(train_interface, "Train", "ti"),
|
||||
]
|
||||
|
||||
css = ""
|
||||
|
||||
for cssfile in modules.scripts.list_files_with_name("style.css"):
|
||||
if not os.path.isfile(cssfile):
|
||||
continue
|
||||
|
||||
with open(cssfile, "r", encoding="utf8") as file:
|
||||
css += file.read() + "\n"
|
||||
|
||||
if os.path.exists(os.path.join(data_path, "user.css")):
|
||||
with open(os.path.join(data_path, "user.css"), "r", encoding="utf8") as file:
|
||||
css += file.read() + "\n"
|
||||
|
||||
if not cmd_opts.no_progressbar_hiding:
|
||||
css += css_hide_progressbar
|
||||
|
||||
interfaces += script_callbacks.ui_tabs_callback()
|
||||
interfaces += [(settings_interface, "Settings", "settings")]
|
||||
|
||||
|
|
@ -1592,7 +1565,7 @@ def create_ui():
|
|||
for _interface, label, _ifid in interfaces:
|
||||
shared.tab_names.append(label)
|
||||
|
||||
with gr.Blocks(css=css, theme=shared.gradio_theme, analytics_enabled=False, title="Stable Diffusion") as demo:
|
||||
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)
|
||||
|
|
@ -1655,6 +1628,7 @@ def create_ui():
|
|||
fn=get_settings_values,
|
||||
inputs=[],
|
||||
outputs=[component_dict[k] for k in component_keys],
|
||||
queue=False,
|
||||
)
|
||||
|
||||
def modelmerger(*args):
|
||||
|
|
@ -1731,7 +1705,7 @@ def create_ui():
|
|||
if init_field is not None:
|
||||
init_field(saved_value)
|
||||
|
||||
if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number, gr.Dropdown] and x.visible:
|
||||
if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number, gr.Dropdown, ToolButton] and x.visible:
|
||||
apply_field(x, 'visible')
|
||||
|
||||
if type(x) == gr.Slider:
|
||||
|
|
@ -1777,25 +1751,60 @@ def create_ui():
|
|||
return demo
|
||||
|
||||
|
||||
def reload_javascript():
|
||||
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():
|
||||
script_js = os.path.join(script_path, "script.js")
|
||||
head = f'<script type="text/javascript" src="file={os.path.abspath(script_js)}?{os.path.getmtime(script_js)}"></script>\n'
|
||||
head = f'<script type="text/javascript" src="{webpath(script_js)}"></script>\n'
|
||||
|
||||
inline = f"{localization.localization_js(shared.opts.localization)};"
|
||||
if cmd_opts.theme is not None:
|
||||
inline += f"set_theme('{cmd_opts.theme}');"
|
||||
|
||||
for script in modules.scripts.list_scripts("javascript", ".js"):
|
||||
head += f'<script type="text/javascript" src="file={script.path}?{os.path.getmtime(script.path)}"></script>\n'
|
||||
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="file={script.path}?{os.path.getmtime(script.path)}"></script>\n'
|
||||
head += f'<script type="module" src="{webpath(script.path)}"></script>\n'
|
||||
|
||||
head += f'<script type="text/javascript">{inline}</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'{head}</head>'.encode("utf8"))
|
||||
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
|
||||
|
||||
|
|
|
|||
|
|
@ -125,7 +125,7 @@ Requested path was: {f}
|
|||
|
||||
with gr.Column(variant='panel', elem_id=f"{tabname}_results"):
|
||||
with gr.Group(elem_id=f"{tabname}_gallery_container"):
|
||||
result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(grid=4)
|
||||
result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(columns=4)
|
||||
|
||||
generation_info = None
|
||||
with gr.Column():
|
||||
|
|
@ -145,8 +145,7 @@ Requested path was: {f}
|
|||
)
|
||||
|
||||
if tabname != "extras":
|
||||
with gr.Row():
|
||||
download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False, elem_id=f'download_files_{tabname}')
|
||||
download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False, elem_id=f'download_files_{tabname}')
|
||||
|
||||
with gr.Group():
|
||||
html_info = gr.HTML(elem_id=f'html_info_{tabname}', elem_classes="infotext")
|
||||
|
|
|
|||
|
|
@ -21,7 +21,7 @@ def check_access():
|
|||
assert not shared.cmd_opts.disable_extension_access, "extension access disabled because of command line flags"
|
||||
|
||||
|
||||
def apply_and_restart(disable_list, update_list):
|
||||
def apply_and_restart(disable_list, update_list, disable_all):
|
||||
check_access()
|
||||
|
||||
disabled = json.loads(disable_list)
|
||||
|
|
@ -43,6 +43,7 @@ def apply_and_restart(disable_list, update_list):
|
|||
print(traceback.format_exc(), file=sys.stderr)
|
||||
|
||||
shared.opts.disabled_extensions = disabled
|
||||
shared.opts.disable_all_extensions = disable_all
|
||||
shared.opts.save(shared.config_filename)
|
||||
|
||||
shared.state.interrupt()
|
||||
|
|
@ -63,6 +64,9 @@ def check_updates(id_task, disable_list):
|
|||
|
||||
try:
|
||||
ext.check_updates()
|
||||
except FileNotFoundError as e:
|
||||
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)
|
||||
|
|
@ -87,6 +91,8 @@ def extension_table():
|
|||
"""
|
||||
|
||||
for ext in extensions.extensions:
|
||||
ext.read_info_from_repo()
|
||||
|
||||
remote = f"""<a href="{html.escape(ext.remote or '')}" target="_blank">{html.escape("built-in" if ext.is_builtin else ext.remote or '')}</a>"""
|
||||
|
||||
if ext.can_update:
|
||||
|
|
@ -94,9 +100,13 @@ def extension_table():
|
|||
else:
|
||||
ext_status = ext.status
|
||||
|
||||
style = ""
|
||||
if shared.opts.disable_all_extensions == "extra" and not ext.is_builtin or shared.opts.disable_all_extensions == "all":
|
||||
style = ' style="color: var(--primary-400)"'
|
||||
|
||||
code += f"""
|
||||
<tr>
|
||||
<td><label><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" name="enable_{html.escape(ext.name)}" type="checkbox" {'checked="checked"' if ext.enabled else ''}>{html.escape(ext.name)}</label></td>
|
||||
<td>{remote}</td>
|
||||
<td>{ext.version}</td>
|
||||
<td{' class="extension_status"' if ext.remote is not None else ''}>{ext_status}</td>
|
||||
|
|
@ -119,7 +129,7 @@ def normalize_git_url(url):
|
|||
return url
|
||||
|
||||
|
||||
def install_extension_from_url(dirname, url):
|
||||
def install_extension_from_url(dirname, branch_name, url):
|
||||
check_access()
|
||||
|
||||
assert url, 'No URL specified'
|
||||
|
|
@ -140,10 +150,17 @@ def install_extension_from_url(dirname, url):
|
|||
|
||||
try:
|
||||
shutil.rmtree(tmpdir, True)
|
||||
with git.Repo.clone_from(url, tmpdir) as repo:
|
||||
repo.remote().fetch()
|
||||
for submodule in repo.submodules:
|
||||
submodule.update()
|
||||
if branch_name == '':
|
||||
# if no branch is specified, use the default branch
|
||||
with git.Repo.clone_from(url, tmpdir) as repo:
|
||||
repo.remote().fetch()
|
||||
for submodule in repo.submodules:
|
||||
submodule.update()
|
||||
else:
|
||||
with git.Repo.clone_from(url, tmpdir, branch=branch_name) as repo:
|
||||
repo.remote().fetch()
|
||||
for submodule in repo.submodules:
|
||||
submodule.update()
|
||||
try:
|
||||
os.rename(tmpdir, target_dir)
|
||||
except OSError as err:
|
||||
|
|
@ -289,16 +306,24 @@ def create_ui():
|
|||
with gr.Row(elem_id="extensions_installed_top"):
|
||||
apply = gr.Button(value="Apply and restart UI", 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)
|
||||
extensions_update_list = gr.Text(elem_id="extensions_update_list", visible=False).style(container=False)
|
||||
|
||||
info = gr.HTML()
|
||||
html = ""
|
||||
if shared.opts.disable_all_extensions != "none":
|
||||
html = """
|
||||
<span style="color: var(--primary-400);">
|
||||
"Disable all extensions" was set, change it to "none" to load all extensions again
|
||||
</span>
|
||||
"""
|
||||
info = gr.HTML(html)
|
||||
extensions_table = gr.HTML(lambda: extension_table())
|
||||
|
||||
apply.click(
|
||||
fn=apply_and_restart,
|
||||
_js="extensions_apply",
|
||||
inputs=[extensions_disabled_list, extensions_update_list],
|
||||
inputs=[extensions_disabled_list, extensions_update_list, extensions_disable_all],
|
||||
outputs=[],
|
||||
)
|
||||
|
||||
|
|
@ -358,13 +383,14 @@ def create_ui():
|
|||
|
||||
with gr.TabItem("Install from URL"):
|
||||
install_url = gr.Text(label="URL for extension's git repository")
|
||||
install_branch = gr.Text(label="Specific branch name", placeholder="Leave empty for default main branch")
|
||||
install_dirname = gr.Text(label="Local directory name", placeholder="Leave empty for auto")
|
||||
install_button = gr.Button(value="Install", variant="primary")
|
||||
install_result = gr.HTML(elem_id="extension_install_result")
|
||||
|
||||
install_button.click(
|
||||
fn=modules.ui.wrap_gradio_call(install_extension_from_url, extra_outputs=[gr.update()]),
|
||||
inputs=[install_dirname, install_url],
|
||||
inputs=[install_dirname, install_branch, install_url],
|
||||
outputs=[extensions_table, install_result],
|
||||
)
|
||||
|
||||
|
|
|
|||
|
|
@ -2,8 +2,10 @@ import glob
|
|||
import os.path
|
||||
import urllib.parse
|
||||
from pathlib import Path
|
||||
from PIL import PngImagePlugin
|
||||
|
||||
from modules import shared
|
||||
from modules.images import read_info_from_image
|
||||
import gradio as gr
|
||||
import json
|
||||
import html
|
||||
|
|
@ -252,10 +254,10 @@ def create_ui(container, button, tabname):
|
|||
|
||||
def toggle_visibility(is_visible):
|
||||
is_visible = not is_visible
|
||||
return is_visible, gr.update(visible=is_visible)
|
||||
return is_visible, gr.update(visible=is_visible), gr.update(variant=("secondary-down" if is_visible else "secondary"))
|
||||
|
||||
state_visible = gr.State(value=False)
|
||||
button.click(fn=toggle_visibility, inputs=[state_visible], outputs=[state_visible, container])
|
||||
button.click(fn=toggle_visibility, inputs=[state_visible], outputs=[state_visible, container, button])
|
||||
|
||||
def refresh():
|
||||
res = []
|
||||
|
|
@ -290,6 +292,7 @@ def setup_ui(ui, gallery):
|
|||
|
||||
img_info = images[index if index >= 0 else 0]
|
||||
image = image_from_url_text(img_info)
|
||||
geninfo, items = read_info_from_image(image)
|
||||
|
||||
is_allowed = False
|
||||
for extra_page in ui.stored_extra_pages:
|
||||
|
|
@ -299,7 +302,12 @@ def setup_ui(ui, gallery):
|
|||
|
||||
assert is_allowed, f'writing to {filename} is not allowed'
|
||||
|
||||
image.save(filename)
|
||||
if geninfo:
|
||||
pnginfo_data = PngImagePlugin.PngInfo()
|
||||
pnginfo_data.add_text('parameters', geninfo)
|
||||
image.save(filename, pnginfo=pnginfo_data)
|
||||
else:
|
||||
image.save(filename)
|
||||
|
||||
return [page.create_html(ui.tabname) for page in ui.stored_extra_pages]
|
||||
|
||||
|
|
|
|||
|
|
@ -13,7 +13,7 @@ def create_ui():
|
|||
extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil", elem_id="extras_image")
|
||||
|
||||
with gr.TabItem('Batch Process', elem_id="extras_batch_process_tab") as tab_batch:
|
||||
image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file", elem_id="extras_image_batch")
|
||||
image_batch = gr.Files(label="Batch Process", interactive=True, elem_id="extras_image_batch")
|
||||
|
||||
with gr.TabItem('Batch from Directory', elem_id="extras_batch_directory_tab") as tab_batch_dir:
|
||||
extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running.", elem_id="extras_batch_input_dir")
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue