mirror of
https://github.com/vladmandic/sdnext.git
synced 2026-01-29 05:02:09 +03:00
1321 lines
79 KiB
Python
1321 lines
79 KiB
Python
from functools import lru_cache
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import io
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import os
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import sys
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import time
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import json
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import threading
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import contextlib
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from types import SimpleNamespace
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from urllib.parse import urlparse
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from enum import Enum
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import psutil
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import requests
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import gradio as gr
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import fasteners
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import orjson
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import diffusers
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from rich.console import Console
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from modules import errors, devices, shared_items, shared_state, cmd_args, theme, history
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from modules.paths import models_path, script_path, data_path, sd_configs_path, sd_default_config, sd_model_file, default_sd_model_file, extensions_dir, extensions_builtin_dir # pylint: disable=W0611
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from modules.dml import memory_providers, default_memory_provider, directml_do_hijack
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from modules.onnx_impl import initialize_onnx, execution_providers
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from modules.memstats import memory_stats
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from modules.ui_components import DropdownEditable
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import modules.interrogate
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import modules.memmon
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import modules.styles
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import modules.paths as paths
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from installer import print_dict
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from installer import log as central_logger # pylint: disable=E0611
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errors.install([gr])
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demo: gr.Blocks = None
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api = None
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log = central_logger
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progress_print_out = sys.stdout
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parser = cmd_args.parser
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url = 'https://github.com/vladmandic/automatic'
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cmd_opts, _ = parser.parse_known_args()
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hide_dirs = {"visible": not cmd_opts.hide_ui_dir_config}
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xformers_available = False
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locking_available = True
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clip_model = None
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interrogator = modules.interrogate.InterrogateModels(os.path.join("models", "interrogate"))
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sd_upscalers = []
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detailers = []
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face_restorers = []
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yolo = None
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tab_names = []
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extra_networks = []
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options_templates = {}
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hypernetworks = {}
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loaded_hypernetworks = []
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settings_components = None
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latent_upscale_default_mode = "None"
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latent_upscale_modes = {
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"Latent Nearest": {"mode": "nearest", "antialias": False},
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"Latent Nearest-exact": {"mode": "nearest-exact", "antialias": False},
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"Latent Area": {"mode": "area", "antialias": False},
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"Latent Bilinear": {"mode": "bilinear", "antialias": False},
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"Latent Bicubic": {"mode": "bicubic", "antialias": False},
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"Latent Bilinear antialias": {"mode": "bilinear", "antialias": True},
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"Latent Bicubic antialias": {"mode": "bicubic", "antialias": True},
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# "Latent Linear": {"mode": "linear", "antialias": False}, # not supported for latents with channels=4
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# "Latent Trilinear": {"mode": "trilinear", "antialias": False}, # not supported for latents with channels=4
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}
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restricted_opts = {
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"samples_filename_pattern",
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"directories_filename_pattern",
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"outdir_samples",
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"outdir_txt2img_samples",
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"outdir_img2img_samples",
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"outdir_extras_samples",
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"outdir_control_samples",
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"outdir_grids",
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"outdir_txt2img_grids",
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"outdir_save",
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"outdir_init_images"
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}
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resize_modes = ["None", "Fixed", "Crop", "Fill", "Outpaint", "Context aware"]
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compatibility_opts = ['clip_skip', 'uni_pc_lower_order_final', 'uni_pc_order']
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console = Console(log_time=True, log_time_format='%H:%M:%S-%f')
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dir_timestamps = {}
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dir_cache = {}
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max_workers = 8
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if os.environ.get("SD_HFCACHEDIR", None) is not None:
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hfcache_dir = os.environ.get("SD_HFCACHEDIR")
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if os.environ.get("HF_HUB_CACHE", None) is not None:
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hfcache_dir = os.environ.get("HF_HUB_CACHE")
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elif os.environ.get("HF_HUB", None) is not None:
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hfcache_dir = os.path.join(os.environ.get("HF_HUB"), '.cache')
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else:
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hfcache_dir = os.path.join(os.path.expanduser('~'), '.cache', 'huggingface', 'hub')
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os.environ["HF_HUB_CACHE"] = hfcache_dir
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log.debug(f'Huggingface cache: folder="{hfcache_dir}"')
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class Backend(Enum):
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ORIGINAL = 1
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DIFFUSERS = 2
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state = shared_state.State()
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if not hasattr(cmd_opts, "use_openvino"):
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cmd_opts.use_openvino = False
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def readfile(filename, silent=False, lock=False):
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global locking_available # pylint: disable=global-statement
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data = {}
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lock_file = None
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locked = False
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if lock and locking_available:
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try:
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lock_file = fasteners.InterProcessReaderWriterLock(f"{filename}.lock")
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lock_file.logger.disabled = True
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locked = lock_file.acquire_read_lock(blocking=True, timeout=3)
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except Exception as err:
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lock_file = None
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locking_available = False
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log.error(f'File read lock: file="{filename}" {err}')
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locked = False
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try:
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# if not os.path.exists(filename):
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# return {}
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t0 = time.time()
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with open(filename, "rb") as file:
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b = file.read()
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data = orjson.loads(b) # pylint: disable=no-member
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# if type(data) is str:
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# data = json.loads(data)
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t1 = time.time()
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if not silent:
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log.debug(f'Read: file="{filename}" json={len(data)} bytes={os.path.getsize(filename)} time={t1-t0:.3f}')
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except FileNotFoundError as err:
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log.debug(f'Reading failed: {filename} {err}')
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except Exception as err:
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if not silent:
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log.error(f'Reading failed: {filename} {err}')
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try:
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if locking_available and lock_file is not None:
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lock_file.release_read_lock()
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if locked and os.path.exists(f"{filename}.lock"):
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os.remove(f"{filename}.lock")
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except Exception:
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locking_available = False
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return data
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def writefile(data, filename, mode='w', silent=False, atomic=False):
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import tempfile
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global locking_available # pylint: disable=global-statement
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lock_file = None
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locked = False
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def default(obj):
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log.error(f'Save: file="{filename}" not a valid object: {obj}')
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return str(obj)
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try:
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t0 = time.time()
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# skipkeys=True, ensure_ascii=True, check_circular=True, allow_nan=True
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if type(data) == dict:
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output = json.dumps(data, indent=2, default=default)
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elif type(data) == list:
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output = json.dumps(data, indent=2, default=default)
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elif isinstance(data, object):
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simple = {}
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for k in data.__dict__:
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if data.__dict__[k] is not None:
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simple[k] = data.__dict__[k]
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output = json.dumps(simple, indent=2, default=default)
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else:
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raise ValueError('not a valid object')
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except Exception as err:
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log.error(f'Save failed: file="{filename}" {err}')
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return
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try:
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if locking_available:
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lock_file = fasteners.InterProcessReaderWriterLock(f"{filename}.lock") if locking_available else None
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lock_file.logger.disabled = True
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locked = lock_file.acquire_write_lock(blocking=True, timeout=3) if lock_file is not None else False
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except Exception as err:
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locking_available = False
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lock_file = None
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log.error(f'File write lock: file="{filename}" {err}')
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locked = False
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try:
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if atomic:
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with tempfile.NamedTemporaryFile(mode=mode, encoding="utf8", delete=False, dir=os.path.dirname(filename)) as f:
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f.write(output)
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f.flush()
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os.fsync(f.fileno())
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os.replace(f.name, filename)
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else:
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with open(filename, mode=mode, encoding="utf8") as file:
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file.write(output)
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t1 = time.time()
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if not silent:
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log.debug(f'Save: file="{filename}" json={len(data)} bytes={len(output)} time={t1-t0:.3f}')
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except Exception as err:
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log.error(f'Save failed: file="{filename}" {err}')
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try:
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if locking_available and lock_file is not None:
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lock_file.release_write_lock()
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if locked and os.path.exists(f"{filename}.lock"):
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os.remove(f"{filename}.lock")
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except Exception:
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locking_available = False
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# early select backend
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default_backend = 'diffusers'
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early_opts = readfile(cmd_opts.config, silent=True)
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early_backend = early_opts.get('sd_backend', default_backend)
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backend = Backend.DIFFUSERS if early_backend.lower() == 'diffusers' else Backend.ORIGINAL
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if cmd_opts.backend is not None: # override with args
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backend = Backend.DIFFUSERS if cmd_opts.backend.lower() == 'diffusers' else Backend.ORIGINAL
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if cmd_opts.use_openvino: # override for openvino
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backend = Backend.DIFFUSERS
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from modules.intel.openvino import get_device_list as get_openvino_device_list # pylint: disable=ungrouped-imports
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elif cmd_opts.use_ipex or devices.has_xpu():
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from modules.intel.ipex import ipex_init
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ok, e = ipex_init()
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if not ok:
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log.error(f'IPEX initialization failed: {e}')
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elif cmd_opts.use_directml:
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name = 'directml'
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from modules.dml import directml_init
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ok, e = directml_init()
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if not ok:
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log.error(f'DirectML initialization failed: {e}')
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devices.backend = devices.get_backend(cmd_opts)
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devices.device = devices.get_optimal_device()
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cpu_memory = round(psutil.virtual_memory().total / 1024 / 1024 / 1024, 2)
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mem_stat = memory_stats()
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gpu_memory = mem_stat['gpu']['total'] if "gpu" in mem_stat else 0
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native = backend == Backend.DIFFUSERS
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class OptionInfo:
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def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, section=None, refresh=None, folder=None, submit=None, comment_before='', comment_after=''):
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self.default = default
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self.label = label
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self.component = component
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self.component_args = component_args
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self.onchange = onchange
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self.section = section
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self.refresh = refresh
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self.folder = folder
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self.comment_before = comment_before # HTML text that will be added after label in UI
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self.comment_after = comment_after # HTML text that will be added before label in UI
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self.submit = submit
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self.exclude = ['sd_model_checkpoint', 'sd_model_refiner', 'sd_vae', 'sd_unet', 'sd_text_encoder', 'sd_model_dict']
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def needs_reload_ui(self):
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return self
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def link(self, label, uri):
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self.comment_before += f"[<a href='{uri}' target='_blank'>{label}</a>]"
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return self
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def js(self, label, js_func):
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self.comment_before += f"[<a onclick='{js_func}(); return false'>{label}</a>]"
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return self
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def info(self, info):
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self.comment_after += f"<span class='info'>({info})</span>"
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return self
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def html(self, info):
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self.comment_after += f"<span class='info'>{info}</span>"
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return self
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def needs_restart(self):
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self.comment_after += " <span class='info'>(requires restart)</span>"
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return self
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def validate(self, opt, value):
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if opt in self.exclude:
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return True
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args = self.component_args if self.component_args is not None else {}
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if callable(args):
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try:
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args = args()
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except Exception:
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args = {}
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choices = args.get("choices", [])
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if callable(choices):
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try:
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choices = choices()
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except Exception:
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choices = []
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if len(choices) > 0:
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if not isinstance(value, list):
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value = [value]
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for v in value:
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if v not in choices:
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log.warning(f'Setting validation: "{opt}"="{v}" default="{self.default}" choices={choices}')
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return False
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minimum = args.get("minimum", None)
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maximum = args.get("maximum", None)
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if (minimum is not None and value < minimum) or (maximum is not None and value > maximum):
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log.error(f'Setting validation: "{opt}"={value} default={self.default} minimum={minimum} maximum={maximum}')
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return False
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return True
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def __str__(self) -> str:
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args = self.component_args if self.component_args is not None else {}
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if callable(args):
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args = args()
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choices = args.get("choices", [])
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return f'OptionInfo: label="{self.label}" section="{self.section}" component="{self.component}" default="{self.default}" refresh="{self.refresh is not None}" change="{self.onchange is not None}" args={args} choices={choices}'
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def options_section(section_identifier, options_dict):
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for v in options_dict.values():
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v.section = section_identifier
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return options_dict
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def list_checkpoint_titles():
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import modules.sd_models # pylint: disable=W0621
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return modules.sd_models.checkpoint_titles()
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list_checkpoint_tiles = list_checkpoint_titles # alias for legacy typo
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default_checkpoint = list_checkpoint_titles()[0] if len(list_checkpoint_titles()) > 0 else "model.safetensors"
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def is_url(string):
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parsed_url = urlparse(string)
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return all([parsed_url.scheme, parsed_url.netloc])
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def reload_hypernetworks():
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from modules.hypernetworks import hypernetwork
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global hypernetworks # pylint: disable=W0603
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hypernetworks = hypernetwork.list_hypernetworks(opts.hypernetwork_dir)
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def refresh_checkpoints():
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import modules.sd_models # pylint: disable=W0621
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return modules.sd_models.list_models()
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def refresh_vaes():
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import modules.sd_vae # pylint: disable=W0621
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modules.sd_vae.refresh_vae_list()
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def refresh_upscalers():
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import modules.modelloader # pylint: disable=W0621
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modules.modelloader.load_upscalers()
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def list_samplers():
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import modules.sd_samplers # pylint: disable=W0621
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modules.sd_samplers.set_samplers()
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return modules.sd_samplers.all_samplers
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def temp_disable_extensions():
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disable_safe = ['sd-webui-controlnet', 'multidiffusion-upscaler-for-automatic1111', 'a1111-sd-webui-lycoris', 'sd-webui-agent-scheduler', 'clip-interrogator-ext', 'stable-diffusion-webui-rembg', 'sd-extension-chainner', 'stable-diffusion-webui-images-browser']
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disable_diffusers = ['sd-webui-controlnet', 'multidiffusion-upscaler-for-automatic1111', 'a1111-sd-webui-lycoris', 'sd-webui-animatediff']
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disable_themes = ['sd-webui-lobe-theme', 'cozy-nest', 'sdnext-modernui']
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disable_original = []
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disabled = []
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if modules.shared.cmd_opts.theme is not None:
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theme_name = modules.shared.cmd_opts.theme
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else:
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theme_name = f'{modules.shared.opts.theme_type.lower()}/{modules.shared.opts.gradio_theme}'
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if theme_name == 'lobe':
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disable_themes.remove('sd-webui-lobe-theme')
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elif theme_name == 'cozy-nest' or theme_name == 'cozy':
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disable_themes.remove('cozy-nest')
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elif '/' not in theme_name: # set default themes per type
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if theme_name == 'standard' or theme_name == 'default':
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theme_name = 'standard/black-teal'
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if theme_name == 'modern':
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theme_name = 'modern/Default'
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if theme_name == 'gradio':
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theme_name = 'gradio/default'
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if theme_name == 'huggingface':
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theme_name = 'huggingface/blaaa'
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if theme_name.lower().startswith('standard') or theme_name.lower().startswith('default'):
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modules.shared.opts.data['theme_type'] = 'Standard'
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modules.shared.opts.data['gradio_theme'] = theme_name[9:]
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elif theme_name.lower().startswith('modern'):
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modules.shared.opts.data['theme_type'] = 'Modern'
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modules.shared.opts.data['gradio_theme'] = theme_name[7:]
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disable_themes.remove('sdnext-modernui')
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elif theme_name.lower().startswith('gradio'):
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modules.shared.opts.data['theme_type'] = 'None'
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modules.shared.opts.data['gradio_theme'] = theme_name
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elif theme_name.lower().startswith('huggingface'):
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modules.shared.opts.data['theme_type'] = 'None'
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modules.shared.opts.data['gradio_theme'] = theme_name
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else:
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modules.shared.log.error(f'UI theme invalid: theme="{theme_name}" available={["standard/*", "modern/*", "none/*"]} fallback="standard/black-teal"')
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modules.shared.opts.data['theme_type'] = 'Standard'
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modules.shared.opts.data['gradio_theme'] = 'black-teal'
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for ext in disable_themes:
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if ext.lower() not in opts.disabled_extensions:
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disabled.append(ext)
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if cmd_opts.safe:
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for ext in disable_safe:
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if ext.lower() not in opts.disabled_extensions:
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disabled.append(ext)
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if native:
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for ext in disable_diffusers:
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if ext.lower() not in opts.disabled_extensions:
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disabled.append(ext)
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if not native:
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for ext in disable_original:
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if ext.lower() not in opts.disabled_extensions:
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disabled.append(ext)
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cmd_opts.controlnet_loglevel = 'WARNING'
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return disabled
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def get_default_modes():
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default_offload_mode = "none"
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if not (cmd_opts.lowvram or cmd_opts.medvram):
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if "gpu" in mem_stat:
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if gpu_memory <= 4:
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cmd_opts.lowvram = True
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default_offload_mode = "sequential"
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log.info(f"Device detect: memory={gpu_memory:.1f} optimization=lowvram")
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elif gpu_memory <= 8:
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cmd_opts.medvram = True
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default_offload_mode = "model"
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log.info(f"Device detect: memory={gpu_memory:.1f} optimization=medvram")
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else:
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default_offload_mode = "none"
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log.info(f"Device detect: memory={gpu_memory:.1f} optimization=none")
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elif cmd_opts.medvram:
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default_offload_mode = "model"
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elif cmd_opts.lowvram:
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default_offload_mode = "sequential"
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if devices.backend == "directml": # Force BMM for DirectML instead of SDP
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default_cross_attention = "Dynamic Attention BMM" if native else "Sub-quadratic"
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elif devices.backend == "cpu":
|
|
default_cross_attention = "Scaled-Dot-Product" if native else "Doggettx's"
|
|
elif devices.backend == "mps":
|
|
default_cross_attention = "Scaled-Dot-Product" if native else "Doggettx's"
|
|
else: # cuda, rocm, ipex, openvino
|
|
default_cross_attention ="Scaled-Dot-Product"
|
|
|
|
if devices.backend == "rocm":
|
|
default_sdp_options = ['Memory attention', 'Math attention']
|
|
elif devices.backend == "zluda":
|
|
default_sdp_options = ['Math attention']
|
|
else:
|
|
default_sdp_options = ['Flash attention', 'Memory attention', 'Math attention']
|
|
if (cmd_opts.lowvram or cmd_opts.medvram) and ('Flash attention' not in default_sdp_options):
|
|
default_sdp_options.append('Dynamic attention')
|
|
|
|
return default_offload_mode, default_cross_attention, default_sdp_options
|
|
|
|
|
|
startup_offload_mode, startup_cross_attention, startup_sdp_options = get_default_modes()
|
|
|
|
options_templates.update(options_section(('sd', "Execution & Models"), {
|
|
"sd_backend": OptionInfo(default_backend, "Execution backend", gr.Radio, {"choices": ["diffusers", "original"] }),
|
|
"sd_model_checkpoint": OptionInfo(default_checkpoint, "Base model", DropdownEditable, lambda: {"choices": list_checkpoint_titles()}, refresh=refresh_checkpoints),
|
|
"sd_model_refiner": OptionInfo('None', "Refiner model", gr.Dropdown, lambda: {"choices": ['None'] + list_checkpoint_titles()}, refresh=refresh_checkpoints),
|
|
"sd_vae": OptionInfo("Automatic", "VAE model", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list),
|
|
"sd_unet": OptionInfo("None", "UNET model", gr.Dropdown, lambda: {"choices": shared_items.sd_unet_items()}, refresh=shared_items.refresh_unet_list),
|
|
"sd_text_encoder": OptionInfo('None', "Text encoder model", gr.Dropdown, lambda: {"choices": shared_items.sd_te_items()}, refresh=shared_items.refresh_te_list),
|
|
"sd_model_dict": OptionInfo('None', "Use separate base dict", gr.Dropdown, lambda: {"choices": ['None'] + list_checkpoint_titles()}, refresh=refresh_checkpoints),
|
|
"sd_checkpoint_autoload": OptionInfo(True, "Model autoload on start"),
|
|
"sd_checkpoint_autodownload": OptionInfo(True, "Model auto-download on demand"),
|
|
"sd_textencoder_cache": OptionInfo(True, "Cache text encoder results", gr.Checkbox, {"visible": False}),
|
|
"sd_textencoder_cache_size": OptionInfo(4, "Text encoder results LRU cache size", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
|
|
"stream_load": OptionInfo(False, "Load models using stream loading method", gr.Checkbox, {"visible": not native }),
|
|
"prompt_mean_norm": OptionInfo(False, "Prompt attention normalization", gr.Checkbox),
|
|
"comma_padding_backtrack": OptionInfo(20, "Prompt padding", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1, "visible": not native }),
|
|
"prompt_attention": OptionInfo("native", "Prompt attention parser", gr.Radio, {"choices": ["native", "compel", "xhinker", "a1111", "fixed"] }),
|
|
"latent_history": OptionInfo(16, "Latent history size", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
|
|
"sd_checkpoint_cache": OptionInfo(0, "Cached models", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1, "visible": not native }),
|
|
}))
|
|
|
|
options_templates.update(options_section(('cuda', "Compute Settings"), {
|
|
"math_sep": OptionInfo("<h2>Execution precision</h2>", "", gr.HTML),
|
|
"precision": OptionInfo("Autocast", "Precision type", gr.Radio, {"choices": ["Autocast", "Full"]}),
|
|
"cuda_dtype": OptionInfo("Auto", "Device precision type", gr.Radio, {"choices": ["Auto", "FP32", "FP16", "BF16"]}),
|
|
|
|
"model_sep": OptionInfo("<h2>Model options</h2>", "", gr.HTML),
|
|
"no_half": OptionInfo(False if not cmd_opts.use_openvino else True, "Full precision for model (--no-half)", None, None, None),
|
|
"no_half_vae": OptionInfo(False if not cmd_opts.use_openvino else True, "Full precision for VAE (--no-half-vae)"),
|
|
"upcast_sampling": OptionInfo(False if sys.platform != "darwin" else True, "Upcast sampling"),
|
|
"upcast_attn": OptionInfo(False, "Upcast attention layer"),
|
|
"cuda_cast_unet": OptionInfo(False, "Fixed UNet precision"),
|
|
"nan_skip": OptionInfo(False, "Skip Generation if NaN found in latents", gr.Checkbox),
|
|
"rollback_vae": OptionInfo(False, "Attempt VAE roll back for NaN values"),
|
|
|
|
"cross_attention_sep": OptionInfo("<h2>Cross Attention</h2>", "", gr.HTML),
|
|
"cross_attention_optimization": OptionInfo(startup_cross_attention, "Attention optimization method", gr.Radio, lambda: {"choices": shared_items.list_crossattention(native) }),
|
|
"sdp_options": OptionInfo(startup_sdp_options, "SDP options", gr.CheckboxGroup, {"choices": ['Flash attention', 'Memory attention', 'Math attention', 'Dynamic attention', 'Sage attention'] }),
|
|
"xformers_options": OptionInfo(['Flash attention'], "xFormers options", gr.CheckboxGroup, {"choices": ['Flash attention'] }),
|
|
"dynamic_attention_slice_rate": OptionInfo(4, "Dynamic Attention slicing rate in GB", gr.Slider, {"minimum": 0.1, "maximum": gpu_memory, "step": 0.1, "visible": native}),
|
|
"sub_quad_sep": OptionInfo("<h3>Sub-quadratic options</h3>", "", gr.HTML, {"visible": not native}),
|
|
"sub_quad_q_chunk_size": OptionInfo(512, "Attention query chunk size", gr.Slider, {"minimum": 16, "maximum": 8192, "step": 8, "visible": not native}),
|
|
"sub_quad_kv_chunk_size": OptionInfo(512, "Attention kv chunk size", gr.Slider, {"minimum": 0, "maximum": 8192, "step": 8, "visible": not native}),
|
|
"sub_quad_chunk_threshold": OptionInfo(80, "Attention chunking threshold", gr.Slider, {"minimum": 0, "maximum": 100, "step": 1, "visible": not native}),
|
|
|
|
"other_sep": OptionInfo("<h2>Execution options</h2>", "", gr.HTML),
|
|
"opt_channelslast": OptionInfo(False, "Use channels last "),
|
|
"cudnn_deterministic": OptionInfo(False, "Use deterministic mode"),
|
|
"cudnn_benchmark": OptionInfo(False, "Full-depth cuDNN benchmark feature"),
|
|
"diffusers_fuse_projections": OptionInfo(False, "Fused projections"),
|
|
"torch_expandable_segments": OptionInfo(False, "Torch expandable segments"),
|
|
"cuda_mem_fraction": OptionInfo(0.0, "Torch memory limit", gr.Slider, {"minimum": 0, "maximum": 2.0, "step": 0.05}),
|
|
"torch_gc_threshold": OptionInfo(80, "Torch memory threshold for GC", gr.Slider, {"minimum": 0, "maximum": 100, "step": 1}),
|
|
"torch_malloc": OptionInfo("native", "Torch memory allocator", gr.Radio, {"choices": ['native', 'cudaMallocAsync'] }),
|
|
|
|
"cuda_compile_sep": OptionInfo("<h2>Model Compile</h2>", "", gr.HTML),
|
|
"cuda_compile": OptionInfo([] if not cmd_opts.use_openvino else ["Model", "VAE"], "Compile Model", gr.CheckboxGroup, {"choices": ["Model", "VAE", "Text Encoder", "Upscaler"]}),
|
|
"cuda_compile_backend": OptionInfo("none" if not cmd_opts.use_openvino else "openvino_fx", "Model compile backend", gr.Radio, {"choices": ['none', 'inductor', 'cudagraphs', 'aot_ts_nvfuser', 'hidet', 'migraphx', 'ipex', 'onediff', 'stable-fast', 'deep-cache', 'olive-ai', 'openvino_fx']}),
|
|
"cuda_compile_mode": OptionInfo("default", "Model compile mode", gr.Radio, {"choices": ['default', 'reduce-overhead', 'max-autotune', 'max-autotune-no-cudagraphs']}),
|
|
"cuda_compile_fullgraph": OptionInfo(True if not cmd_opts.use_openvino else False, "Model compile fullgraph"),
|
|
"cuda_compile_precompile": OptionInfo(False, "Model compile precompile"),
|
|
"cuda_compile_verbose": OptionInfo(False, "Model compile verbose mode"),
|
|
"cuda_compile_errors": OptionInfo(True, "Model compile suppress errors"),
|
|
"deep_cache_interval": OptionInfo(3, "DeepCache cache interval", gr.Slider, {"minimum": 1, "maximum": 10, "step": 1}),
|
|
|
|
"ipex_sep": OptionInfo("<h2>IPEX</h2>", "", gr.HTML, {"visible": devices.backend == "ipex"}),
|
|
"ipex_optimize": OptionInfo([], "IPEX Optimize for Intel GPUs", gr.CheckboxGroup, {"choices": ["Model", "VAE", "Text Encoder", "Upscaler"], "visible": devices.backend == "ipex"}),
|
|
|
|
"openvino_sep": OptionInfo("<h2>OpenVINO</h2>", "", gr.HTML, {"visible": cmd_opts.use_openvino}),
|
|
"openvino_devices": OptionInfo([], "OpenVINO devices to use", gr.CheckboxGroup, {"choices": get_openvino_device_list() if cmd_opts.use_openvino else [], "visible": cmd_opts.use_openvino}), # pylint: disable=E0606
|
|
"openvino_accuracy": OptionInfo("performance", "OpenVINO accuracy mode", gr.Radio, {"choices": ['performance', 'accuracy'], "visible": cmd_opts.use_openvino}),
|
|
"openvino_disable_model_caching": OptionInfo(False, "OpenVINO disable model caching", gr.Checkbox, {"visible": cmd_opts.use_openvino}),
|
|
"openvino_disable_memory_cleanup": OptionInfo(True, "OpenVINO disable memory cleanup after compile", gr.Checkbox, {"visible": cmd_opts.use_openvino}),
|
|
|
|
"directml_sep": OptionInfo("<h2>DirectML</h2>", "", gr.HTML, {"visible": devices.backend == "directml"}),
|
|
"directml_memory_provider": OptionInfo(default_memory_provider, 'DirectML memory stats provider', gr.Radio, {"choices": memory_providers, "visible": devices.backend == "directml"}),
|
|
"directml_catch_nan": OptionInfo(False, "DirectML retry ops for NaN", gr.Checkbox, {"visible": devices.backend == "directml"}),
|
|
|
|
"olive_sep": OptionInfo("<h2>Olive</h2>", "", gr.HTML),
|
|
"olive_float16": OptionInfo(True, 'Olive use FP16 on optimization'),
|
|
"olive_vae_encoder_float32": OptionInfo(False, 'Olive force FP32 for VAE Encoder'),
|
|
"olive_static_dims": OptionInfo(True, 'Olive use static dimensions'),
|
|
"olive_cache_optimized": OptionInfo(True, 'Olive cache optimized models'),
|
|
}))
|
|
|
|
options_templates.update(options_section(('diffusers', "Diffusers Settings"), {
|
|
"diffusers_pipeline": OptionInfo('Autodetect', 'Diffusers pipeline', gr.Dropdown, lambda: {"choices": list(shared_items.get_pipelines()) }),
|
|
"diffuser_cache_config": OptionInfo(True, "Use cached model config when available"),
|
|
"diffusers_move_base": OptionInfo(False, "Move base model to CPU when using refiner"),
|
|
"diffusers_move_unet": OptionInfo(False, "Move base model to CPU when using VAE"),
|
|
"diffusers_move_refiner": OptionInfo(False, "Move refiner model to CPU when not in use"),
|
|
"diffusers_extract_ema": OptionInfo(False, "Use model EMA weights when possible"),
|
|
"diffusers_generator_device": OptionInfo("GPU", "Generator device", gr.Radio, {"choices": ["GPU", "CPU", "Unset"]}),
|
|
"diffusers_offload_mode": OptionInfo(startup_offload_mode, "Model offload mode", gr.Radio, {"choices": ['none', 'balanced', 'model', 'sequential']}),
|
|
"diffusers_offload_max_gpu_memory": OptionInfo(round(gpu_memory * 0.75, 1), "Max GPU memory for balanced offload mode in GB", gr.Slider, {"minimum": 0, "maximum": gpu_memory, "step": 0.01,}),
|
|
"diffusers_offload_max_cpu_memory": OptionInfo(round(cpu_memory * 0.75, 1), "Max CPU memory for balanced offload mode in GB", gr.Slider, {"minimum": 0, "maximum": cpu_memory, "step": 0.01,}),
|
|
"diffusers_vae_upcast": OptionInfo("default", "VAE upcasting", gr.Radio, {"choices": ['default', 'true', 'false']}),
|
|
"diffusers_vae_slicing": OptionInfo(True, "VAE slicing"),
|
|
"diffusers_vae_tiling": OptionInfo(cmd_opts.lowvram or cmd_opts.medvram, "VAE tiling"),
|
|
"diffusers_model_load_variant": OptionInfo("default", "Preferred Model variant", gr.Radio, {"choices": ['default', 'fp32', 'fp16']}),
|
|
"diffusers_vae_load_variant": OptionInfo("default", "Preferred VAE variant", gr.Radio, {"choices": ['default', 'fp32', 'fp16']}),
|
|
"custom_diffusers_pipeline": OptionInfo('', 'Load custom Diffusers pipeline'),
|
|
"diffusers_eval": OptionInfo(True, "Force model eval"),
|
|
"diffusers_to_gpu": OptionInfo(False, "Load model directly to GPU"),
|
|
"disable_accelerate": OptionInfo(False, "Disable accelerate"),
|
|
"diffusers_pooled": OptionInfo("default", "Diffusers SDXL pooled embeds", gr.Radio, {"choices": ['default', 'weighted']}),
|
|
"diffusers_zeros_prompt_pad": OptionInfo(False, "Use zeros for prompt padding", gr.Checkbox),
|
|
"huggingface_token": OptionInfo('', 'HuggingFace token'),
|
|
"enable_linfusion": OptionInfo(False, "Apply LinFusion distillation on load"),
|
|
|
|
"onnx_sep": OptionInfo("<h2>ONNX Runtime</h2>", "", gr.HTML),
|
|
"onnx_execution_provider": OptionInfo(execution_providers.get_default_execution_provider().value, 'Execution Provider', gr.Dropdown, lambda: {"choices": execution_providers.available_execution_providers }),
|
|
"onnx_cpu_fallback": OptionInfo(True, 'ONNX allow fallback to CPU'),
|
|
"onnx_cache_converted": OptionInfo(True, 'ONNX cache converted models'),
|
|
"onnx_unload_base": OptionInfo(False, 'ONNX unload base model when processing refiner'),
|
|
}))
|
|
|
|
options_templates.update(options_section(('quantization', "Quantization Settings"), {
|
|
"bnb_quantization": OptionInfo([], "BnB quantization enabled", gr.CheckboxGroup, {"choices": ["Model", "VAE", "Text Encoder"], "visible": native}),
|
|
"bnb_quantization_type": OptionInfo("nf4", "BnB quantization type", gr.Radio, {"choices": ['nf4', 'fp8', 'fp4'], "visible": native}),
|
|
"bnb_quantization_storage": OptionInfo("uint8", "BnB quantization storage", gr.Radio, {"choices": ["float16", "float32", "int8", "uint8", "float64", "bfloat16"], "visible": native}),
|
|
"optimum_quanto_weights": OptionInfo([], "Optimum.quanto quantization enabled", gr.CheckboxGroup, {"choices": ["Model", "VAE", "Text Encoder", "ControlNet"], "visible": native}),
|
|
"optimum_quanto_weights_type": OptionInfo("qint8", "Optimum.quanto quantization type", gr.Radio, {"choices": ['qint8', 'qfloat8_e4m3fn', 'qfloat8_e5m2', 'qint4', 'qint2'], "visible": native}),
|
|
"optimum_quanto_activations_type": OptionInfo("none", "Optimum.quanto quantization activations ", gr.Radio, {"choices": ['none', 'qint8', 'qfloat8_e4m3fn', 'qfloat8_e5m2'], "visible": native}),
|
|
"torchao_quantization": OptionInfo([], "TorchAO quantization enabled", gr.CheckboxGroup, {"choices": ["Model", "VAE", "Text Encoder"], "visible": native}),
|
|
"torchao_quantization_type": OptionInfo("int8", "TorchAO quantization type", gr.Radio, {"choices": ["int8+act", "int8", "int4", "fp8+act", "fp8", "fpx"], "visible": native}),
|
|
"nncf_compress_weights": OptionInfo([], "NNCF compression enabled", gr.CheckboxGroup, {"choices": ["Model", "VAE", "Text Encoder", "ControlNet"], "visible": native}),
|
|
"nncf_compress_weights_mode": OptionInfo("INT8", "NNCF compress mode", gr.Radio, {"choices": ['INT8', 'INT8_SYM', 'INT4_ASYM', 'INT4_SYM', 'NF4'] if cmd_opts.use_openvino else ['INT8']}),
|
|
"nncf_compress_weights_raito": OptionInfo(1.0, "NNCF compress ratio", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01, "visible": cmd_opts.use_openvino}),
|
|
"nncf_quantize": OptionInfo([], "NNCF OpenVINO quantization enabled", gr.CheckboxGroup, {"choices": ["Model", "VAE", "Text Encoder"], "visible": cmd_opts.use_openvino}),
|
|
"nncf_quant_mode": OptionInfo("INT8", "NNCF OpenVINO quantization mode", gr.Radio, {"choices": ['INT8', 'FP8_E4M3', 'FP8_E5M2'], "visible": cmd_opts.use_openvino}),
|
|
|
|
"quant_shuffle_weights": OptionInfo(False, "Shuffle the weights between GPU and CPU when quantizing", gr.Checkbox, {"visible": native}),
|
|
}))
|
|
|
|
options_templates.update(options_section(('advanced', "Inference Settings"), {
|
|
"token_merging_sep": OptionInfo("<h2>Token merging</h2>", "", gr.HTML),
|
|
"token_merging_method": OptionInfo("None", "Token merging method", gr.Radio, {"choices": ['None', 'ToMe', 'ToDo']}),
|
|
"tome_ratio": OptionInfo(0.0, "ToMe token merging ratio", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.05}),
|
|
"todo_ratio": OptionInfo(0.0, "ToDo token merging ratio", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.05}),
|
|
|
|
"freeu_sep": OptionInfo("<h2>FreeU</h2>", "", gr.HTML),
|
|
"freeu_enabled": OptionInfo(False, "FreeU"),
|
|
"freeu_b1": OptionInfo(1.2, "1st stage backbone factor", gr.Slider, {"minimum": 1.0, "maximum": 2.0, "step": 0.01}),
|
|
"freeu_b2": OptionInfo(1.4, "2nd stage backbone factor", gr.Slider, {"minimum": 1.0, "maximum": 2.0, "step": 0.01}),
|
|
"freeu_s1": OptionInfo(0.9, "1st stage skip factor", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
|
"freeu_s2": OptionInfo(0.2, "2nd stage skip factor", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
|
|
|
"pag_sep": OptionInfo("<h2>Perturbed-Attention Guidance</h2>", "", gr.HTML),
|
|
"pag_apply_layers": OptionInfo("m0", "PAG layer names"),
|
|
|
|
"hypertile_sep": OptionInfo("<h2>HyperTile</h2>", "", gr.HTML),
|
|
"hypertile_hires_only": OptionInfo(False, "HyperTile hires pass only"),
|
|
"hypertile_unet_enabled": OptionInfo(False, "HyperTile UNet"),
|
|
"hypertile_unet_tile": OptionInfo(0, "HyperTile UNet tile size", gr.Slider, {"minimum": 0, "maximum": 1024, "step": 8}),
|
|
"hypertile_unet_swap_size": OptionInfo(1, "HyperTile UNet swap size", gr.Slider, {"minimum": 1, "maximum": 10, "step": 1}),
|
|
"hypertile_unet_depth": OptionInfo(0, "HyperTile UNet depth", gr.Slider, {"minimum": 0, "maximum": 4, "step": 1}),
|
|
"hypertile_vae_enabled": OptionInfo(False, "HyperTile VAE", gr.Checkbox),
|
|
"hypertile_vae_tile": OptionInfo(128, "HyperTile VAE tile size", gr.Slider, {"minimum": 0, "maximum": 1024, "step": 8}),
|
|
"hypertile_vae_swap_size": OptionInfo(1, "HyperTile VAE swap size", gr.Slider, {"minimum": 1, "maximum": 10, "step": 1}),
|
|
|
|
"hidiffusion_sep": OptionInfo("<h2>HiDiffusion</h2>", "", gr.HTML),
|
|
"hidiffusion_raunet": OptionInfo(True, "Apply RAU-Net"),
|
|
"hidiffusion_attn": OptionInfo(True, "Apply MSW-MSA"),
|
|
"hidiffusion_steps": OptionInfo(8, "Aggressive at step", gr.Slider, {"minimum": 1, "maximum": 10, "step": 1}),
|
|
"hidiffusion_t1": OptionInfo(-1, "Override T1 ratio", gr.Slider, {"minimum": -1, "maximum": 1.0, "step": 0.05}),
|
|
"hidiffusion_t2": OptionInfo(-1, "Override T2 ratio", gr.Slider, {"minimum": -1, "maximum": 1.0, "step": 0.05}),
|
|
|
|
"inference_batch_sep": OptionInfo("<h2>Batch</h2>", "", gr.HTML),
|
|
"sequential_seed": OptionInfo(True, "Batch mode uses sequential seeds"),
|
|
"batch_frame_mode": OptionInfo(False, "Parallel process images in batch"),
|
|
"inference_other_sep": OptionInfo("<h2>Other</h2>", "", gr.HTML),
|
|
"inference_mode": OptionInfo("no-grad", "Torch inference mode", gr.Radio, {"choices": ["no-grad", "inference-mode", "none"]}),
|
|
"sd_vae_sliced_encode": OptionInfo(False, "VAE sliced encode", gr.Checkbox, {"visible": not native}),
|
|
}))
|
|
|
|
options_templates.update(options_section(('system-paths', "System Paths"), {
|
|
"models_paths_sep_options": OptionInfo("<h2>Models paths</h2>", "", gr.HTML),
|
|
"models_dir": OptionInfo('models', "Base path where all models are stored", folder=True),
|
|
"ckpt_dir": OptionInfo(os.path.join(paths.models_path, 'Stable-diffusion'), "Folder with stable diffusion models", folder=True),
|
|
"diffusers_dir": OptionInfo(os.path.join(paths.models_path, 'Diffusers'), "Folder with Huggingface models", folder=True),
|
|
"hfcache_dir": OptionInfo(hfcache_dir, "Folder for Huggingface cache", folder=True),
|
|
"vae_dir": OptionInfo(os.path.join(paths.models_path, 'VAE'), "Folder with VAE files", folder=True),
|
|
"unet_dir": OptionInfo(os.path.join(paths.models_path, 'UNET'), "Folder with UNET files", folder=True),
|
|
"te_dir": OptionInfo(os.path.join(paths.models_path, 'Text-encoder'), "Folder with Text encoder files", folder=True),
|
|
"lora_dir": OptionInfo(os.path.join(paths.models_path, 'Lora'), "Folder with LoRA network(s)", folder=True),
|
|
"styles_dir": OptionInfo(os.path.join(paths.data_path, 'styles.csv'), "File or Folder with user-defined styles", folder=True),
|
|
"wildcards_dir": OptionInfo(os.path.join(paths.models_path, 'wildcards'), "Folder with user-defined wildcards", folder=True),
|
|
"embeddings_dir": OptionInfo(os.path.join(paths.models_path, 'embeddings'), "Folder with textual inversion embeddings", folder=True),
|
|
"hypernetwork_dir": OptionInfo(os.path.join(paths.models_path, 'hypernetworks'), "Folder with Hypernetwork models", folder=True),
|
|
"control_dir": OptionInfo(os.path.join(paths.models_path, 'control'), "Folder with Control models", folder=True),
|
|
"yolo_dir": OptionInfo(os.path.join(paths.models_path, 'yolo'), "Folder with Yolo models", folder=True),
|
|
"codeformer_models_path": OptionInfo(os.path.join(paths.models_path, 'Codeformer'), "Folder with codeformer models", folder=True),
|
|
"gfpgan_models_path": OptionInfo(os.path.join(paths.models_path, 'GFPGAN'), "Folder with GFPGAN models", folder=True),
|
|
"esrgan_models_path": OptionInfo(os.path.join(paths.models_path, 'ESRGAN'), "Folder with ESRGAN models", folder=True),
|
|
"bsrgan_models_path": OptionInfo(os.path.join(paths.models_path, 'BSRGAN'), "Folder with BSRGAN models", folder=True),
|
|
"realesrgan_models_path": OptionInfo(os.path.join(paths.models_path, 'RealESRGAN'), "Folder with RealESRGAN models", folder=True),
|
|
"scunet_models_path": OptionInfo(os.path.join(paths.models_path, 'SCUNet'), "Folder with SCUNet models", folder=True),
|
|
"swinir_models_path": OptionInfo(os.path.join(paths.models_path, 'SwinIR'), "Folder with SwinIR models", folder=True),
|
|
"ldsr_models_path": OptionInfo(os.path.join(paths.models_path, 'LDSR'), "Folder with LDSR models", folder=True),
|
|
"clip_models_path": OptionInfo(os.path.join(paths.models_path, 'CLIP'), "Folder with CLIP models", folder=True),
|
|
"other_paths_sep_options": OptionInfo("<h2>Other paths</h2>", "", gr.HTML),
|
|
"openvino_cache_path": OptionInfo('cache', "Directory for OpenVINO cache", folder=True),
|
|
"accelerate_offload_path": OptionInfo('cache/accelerate', "Directory for disk offload with Accelerate", folder=True),
|
|
"onnx_cached_models_path": OptionInfo(os.path.join(paths.models_path, 'ONNX', 'cache'), "Folder with ONNX cached models", folder=True),
|
|
"onnx_temp_dir": OptionInfo(os.path.join(paths.models_path, 'ONNX', 'temp'), "Directory for ONNX conversion and Olive optimization process", folder=True),
|
|
"temp_dir": OptionInfo("", "Directory for temporary images; leave empty for default", folder=True),
|
|
"clean_temp_dir_at_start": OptionInfo(True, "Cleanup non-default temporary directory when starting webui"),
|
|
}))
|
|
|
|
options_templates.update(options_section(('saving-images', "Image Options"), {
|
|
"keep_incomplete": OptionInfo(True, "Keep incomplete images"),
|
|
"samples_save": OptionInfo(True, "Save all generated images"),
|
|
"samples_format": OptionInfo('jpg', 'File format', gr.Dropdown, {"choices": ["jpg", "png", "webp", "tiff", "jp2"]}),
|
|
"jpeg_quality": OptionInfo(90, "Image quality", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
|
|
"img_max_size_mp": OptionInfo(1000, "Maximum image size (MP)", gr.Slider, {"minimum": 100, "maximum": 2000, "step": 1}),
|
|
"webp_lossless": OptionInfo(False, "WebP lossless compression"),
|
|
"save_selected_only": OptionInfo(True, "Save only saves selected image"),
|
|
"include_mask": OptionInfo(False, "Include mask in outputs"),
|
|
"samples_save_zip": OptionInfo(True, "Create ZIP archive"),
|
|
"image_background": OptionInfo("#000000", "Resize background color", gr.ColorPicker, {}),
|
|
|
|
"image_sep_metadata": OptionInfo("<h2>Metadata/Logging</h2>", "", gr.HTML),
|
|
"image_metadata": OptionInfo(True, "Include metadata"),
|
|
"save_txt": OptionInfo(False, "Create image info text file"),
|
|
"save_log_fn": OptionInfo("", "Append image info JSON file", component_args=hide_dirs),
|
|
"image_sep_grid": OptionInfo("<h2>Grid Options</h2>", "", gr.HTML),
|
|
"grid_save": OptionInfo(True, "Save all generated image grids"),
|
|
"grid_format": OptionInfo('jpg', 'File format', gr.Dropdown, {"choices": ["jpg", "png", "webp", "tiff", "jp2"]}),
|
|
"n_rows": OptionInfo(-1, "Row count", gr.Slider, {"minimum": -1, "maximum": 16, "step": 1}),
|
|
"grid_background": OptionInfo("#000000", "Grid background color", gr.ColorPicker, {}),
|
|
"font": OptionInfo("", "Font file"),
|
|
"font_color": OptionInfo("#FFFFFF", "Font color", gr.ColorPicker, {}),
|
|
|
|
"image_sep_browser": OptionInfo("<h2>Image Gallery</h2>", "", gr.HTML),
|
|
"browser_cache": OptionInfo(True, "Use image gallery cache"),
|
|
"browser_folders": OptionInfo("", "Additional image browser folders"),
|
|
"browser_fixed_width": OptionInfo(False, "Use fixed width thumbnails"),
|
|
"viewer_show_metadata": OptionInfo(True, "Show metadata in full screen image browser"),
|
|
|
|
"save_sep_options": OptionInfo("<h2>Intermediate Image Saving</h2>", "", gr.HTML),
|
|
"save_init_img": OptionInfo(False, "Save init images"),
|
|
"save_images_before_highres_fix": OptionInfo(False, "Save image before hires"),
|
|
"save_images_before_refiner": OptionInfo(False, "Save image before refiner"),
|
|
"save_images_before_detailer": OptionInfo(False, "Save image before detailer"),
|
|
"save_images_before_color_correction": OptionInfo(False, "Save image before color correction"),
|
|
"save_mask": OptionInfo(False, "Save inpainting mask"),
|
|
"save_mask_composite": OptionInfo(False, "Save inpainting masked composite"),
|
|
"gradio_skip_video": OptionInfo(False, "Do not display video output in UI"),
|
|
|
|
"image_sep_watermark": OptionInfo("<h2>Watermarking</h2>", "", gr.HTML),
|
|
"image_watermark_enabled": OptionInfo(False, "Include invisible watermark"),
|
|
"image_watermark": OptionInfo('', "Invisible watermark string"),
|
|
"image_watermark_position": OptionInfo('none', 'Image watermark position', gr.Dropdown, {"choices": ["none", "top/left", "top/right", "bottom/left", "bottom/right", "center", "random"]}),
|
|
"image_watermark_image": OptionInfo('', "Image watermark file"),
|
|
}))
|
|
|
|
options_templates.update(options_section(('saving-paths', "Image Naming & Paths"), {
|
|
"saving_sep_images": OptionInfo("<h2>Save options</h2>", "", gr.HTML),
|
|
"save_images_add_number": OptionInfo(True, "Numbered filenames", component_args=hide_dirs),
|
|
"use_original_name_batch": OptionInfo(True, "Batch uses original name"),
|
|
"save_to_dirs": OptionInfo(False, "Save images to a subdirectory"),
|
|
"directories_filename_pattern": OptionInfo("[date]", "Directory name pattern", component_args=hide_dirs),
|
|
"samples_filename_pattern": OptionInfo("[seq]-[model_name]-[prompt_words]", "Images filename pattern", component_args=hide_dirs),
|
|
"directories_max_prompt_words": OptionInfo(8, "Max words per pattern", gr.Slider, {"minimum": 1, "maximum": 99, "step": 1, **hide_dirs}),
|
|
|
|
"outdir_sep_dirs": OptionInfo("<h2>Folders</h2>", "", gr.HTML),
|
|
"outdir_samples": OptionInfo("", "Images folder", component_args=hide_dirs, folder=True),
|
|
"outdir_txt2img_samples": OptionInfo("outputs/text", 'Folder for text generate', component_args=hide_dirs, folder=True),
|
|
"outdir_img2img_samples": OptionInfo("outputs/image", 'Folder for image generate', component_args=hide_dirs, folder=True),
|
|
"outdir_control_samples": OptionInfo("outputs/control", 'Folder for control generate', component_args=hide_dirs, folder=True),
|
|
"outdir_extras_samples": OptionInfo("outputs/extras", 'Folder for processed images', component_args=hide_dirs, folder=True),
|
|
"outdir_save": OptionInfo("outputs/save", "Folder for manually saved images", component_args=hide_dirs, folder=True),
|
|
"outdir_video": OptionInfo("outputs/video", "Folder for videos", component_args=hide_dirs, folder=True),
|
|
"outdir_init_images": OptionInfo("outputs/init-images", "Folder for init images", component_args=hide_dirs, folder=True),
|
|
|
|
"outdir_sep_grids": OptionInfo("<h2>Grids</h2>", "", gr.HTML),
|
|
"outdir_grids": OptionInfo("", "Grids folder", component_args=hide_dirs, folder=True),
|
|
"outdir_txt2img_grids": OptionInfo("outputs/grids", 'Folder for txt2img grids', component_args=hide_dirs, folder=True),
|
|
"outdir_img2img_grids": OptionInfo("outputs/grids", 'Folder for img2img grids', component_args=hide_dirs, folder=True),
|
|
"outdir_control_grids": OptionInfo("outputs/grids", 'Folder for control grids', component_args=hide_dirs, folder=True),
|
|
}))
|
|
|
|
options_templates.update(options_section(('ui', "User Interface Options"), {
|
|
"theme_type": OptionInfo("Standard", "Theme type", gr.Radio, {"choices": ["Modern", "Standard", "None"]}),
|
|
"theme_style": OptionInfo("Auto", "Theme mode", gr.Radio, {"choices": ["Auto", "Dark", "Light"]}),
|
|
"gradio_theme": OptionInfo("black-teal", "UI theme", gr.Dropdown, lambda: {"choices": theme.list_themes()}, refresh=theme.refresh_themes),
|
|
"autolaunch": OptionInfo(False, "Autolaunch browser upon startup"),
|
|
"font_size": OptionInfo(14, "Font size", gr.Slider, {"minimum": 8, "maximum": 32, "step": 1, "visible": True}),
|
|
"aspect_ratios": OptionInfo("1:1, 4:3, 3:2, 16:9, 16:10, 21:9, 2:3, 3:4, 9:16, 10:16, 9:21", "Allowed aspect ratios"),
|
|
"motd": OptionInfo(True, "Show MOTD"),
|
|
"compact_view": OptionInfo(False, "Compact view"),
|
|
"return_grid": OptionInfo(True, "Show grid in results"),
|
|
"return_mask": OptionInfo(False, "Inpainting include greyscale mask in results"),
|
|
"return_mask_composite": OptionInfo(False, "Inpainting include masked composite in results"),
|
|
"disable_weights_auto_swap": OptionInfo(True, "Do not change selected model when reading generation parameters"),
|
|
"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"),
|
|
"quicksettings_list": OptionInfo(["sd_model_checkpoint"], "Quicksettings list", gr.Dropdown, lambda: {"multiselect":True, "choices": list(opts.data_labels.keys())}),
|
|
}))
|
|
|
|
options_templates.update(options_section(('live-preview', "Live Previews"), {
|
|
"notification_audio_enable": OptionInfo(False, "Play a notification upon completion"),
|
|
"notification_audio_path": OptionInfo("html/notification.mp3","Path to notification sound", component_args=hide_dirs, folder=True),
|
|
"show_progress_every_n_steps": OptionInfo(1, "Live preview display period", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),
|
|
"show_progress_type": OptionInfo("Approximate", "Live preview method", gr.Radio, {"choices": ["Simple", "Approximate", "TAESD", "Full VAE"]}),
|
|
"live_preview_refresh_period": OptionInfo(500, "Progress update period", gr.Slider, {"minimum": 0, "maximum": 5000, "step": 25}),
|
|
"live_preview_taesd_layers": OptionInfo(3, "TAESD decode layers", gr.Slider, {"minimum": 1, "maximum": 3, "step": 1}),
|
|
"logmonitor_show": OptionInfo(True, "Show log view"),
|
|
"logmonitor_refresh_period": OptionInfo(5000, "Log view update period", gr.Slider, {"minimum": 0, "maximum": 30000, "step": 25}),
|
|
}))
|
|
|
|
options_templates.update(options_section(('sampler-params', "Sampler Settings"), {
|
|
"show_samplers": OptionInfo([], "Show samplers in user interface", gr.CheckboxGroup, lambda: {"choices": [x.name for x in list_samplers()]}),
|
|
'eta_noise_seed_delta': OptionInfo(0, "Noise seed delta (eta)", gr.Number, {"precision": 0}),
|
|
"scheduler_eta": OptionInfo(1.0, "Noise multiplier (eta)", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
|
"schedulers_solver_order": OptionInfo(0, "Solver order (where", gr.Slider, {"minimum": 0, "maximum": 5, "step": 1, "visible": False}),
|
|
|
|
# managed from ui.py for backend original
|
|
"schedulers_brownian_noise": OptionInfo(True, "Use Brownian noise", gr.Checkbox, {"visible": False}),
|
|
"schedulers_discard_penultimate": OptionInfo(True, "Discard penultimate sigma", gr.Checkbox, {"visible": False}),
|
|
"schedulers_use_karras": OptionInfo(True, "Use Karras sigmas", gr.Checkbox, {"visible": False}),
|
|
|
|
"schedulers_use_loworder": OptionInfo(True, "Use simplified solvers in final steps", gr.Checkbox, {"visible": False}),
|
|
"schedulers_prediction_type": OptionInfo("default", "Override model prediction type", gr.Radio, {"choices": ['default', 'epsilon', 'sample', 'v_prediction'], "visible": False}),
|
|
"schedulers_sigma": OptionInfo("default", "Sigma algorithm", gr.Radio, {"choices": ['default', 'karras', 'exponential', 'polyexponential'], "visible": False}),
|
|
|
|
# managed from ui.py for backend diffusers
|
|
"schedulers_beta_schedule": OptionInfo("default", "Beta schedule", gr.Dropdown, {"choices": ['default', 'linear', 'scaled_linear', 'squaredcos_cap_v2'], "visible": False}),
|
|
"schedulers_use_thresholding": OptionInfo(False, "Use dynamic thresholding", gr.Checkbox, {"visible": False}),
|
|
"schedulers_timestep_spacing": OptionInfo("default", "Timestep spacing", gr.Dropdown, {"choices": ['default', 'linspace', 'leading', 'trailing'], "visible": False}),
|
|
'schedulers_timesteps': OptionInfo('', "Timesteps", gr.Textbox, {"visible": False}),
|
|
"schedulers_rescale_betas": OptionInfo(False, "Rescale betas with zero terminal SNR", gr.Checkbox, {"visible": False}),
|
|
'schedulers_beta_start': OptionInfo(0, "Beta start", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.00001, "visible": native}),
|
|
'schedulers_beta_end': OptionInfo(0, "Beta end", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.00001, "visible": native}),
|
|
'schedulers_timesteps_range': OptionInfo(1000, "Timesteps range", gr.Slider, {"minimum": 250, "maximum": 4000, "step": 1, "visible": native}),
|
|
'schedulers_shift': OptionInfo(1, "Sampler shift", gr.Slider, {"minimum": 0.1, "maximum": 10, "step": 0.1, "visible": native}),
|
|
'schedulers_dynamic_shift': OptionInfo(True, "Sampler dynamic shift", gr.Checkbox, {"visible": native}),
|
|
|
|
# managed from ui.py for backend original k-diffusion
|
|
"always_batch_cond_uncond": OptionInfo(False, "Disable conditional batching", gr.Checkbox, {"visible": not native}),
|
|
"enable_quantization": OptionInfo(True, "Use quantization", gr.Checkbox, {"visible": not native}),
|
|
's_churn': OptionInfo(0.0, "Sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01, "visible": not native}),
|
|
's_min_uncond': OptionInfo(0.0, "Sigma negative guidance minimum ", gr.Slider, {"minimum": 0.0, "maximum": 4.0, "step": 0.01, "visible": not native}),
|
|
's_tmin': OptionInfo(0.0, "Sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01, "visible": not native}),
|
|
's_noise': OptionInfo(1.0, "Sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01, "visible": not native}),
|
|
's_min': OptionInfo(0.0, "Sigma min", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01, "visible": not native}),
|
|
's_max': OptionInfo(0.0, "Sigma max", gr.Slider, {"minimum": 0.0, "maximum": 100.0, "step": 1.0, "visible": not native}),
|
|
"schedulers_sep_compvis": OptionInfo("<h2>CompVis specific config</h2>", "", gr.HTML, {"visible": not native}),
|
|
'uni_pc_variant': OptionInfo("bh2", "UniPC variant", gr.Radio, {"choices": ["bh1", "bh2", "vary_coeff"], "visible": not native}),
|
|
'uni_pc_skip_type': OptionInfo("time_uniform", "UniPC skip type", gr.Radio, {"choices": ["time_uniform", "time_quadratic", "logSNR"], "visible": not native}),
|
|
"ddim_discretize": OptionInfo('uniform', "DDIM discretize img2img", gr.Radio, {"choices": ['uniform', 'quad'], "visible": not native}),
|
|
}))
|
|
|
|
options_templates.update(options_section(('postprocessing', "Postprocessing"), {
|
|
'postprocessing_enable_in_main_ui': OptionInfo([], "Additional postprocessing operations", gr.Dropdown, lambda: {"multiselect":True, "choices": [x.name for x in shared_items.postprocessing_scripts()]}),
|
|
'postprocessing_operation_order': OptionInfo([], "Postprocessing operation order", gr.Dropdown, lambda: {"multiselect":True, "choices": [x.name for x in shared_items.postprocessing_scripts()]}),
|
|
|
|
"postprocessing_sep_img2img": OptionInfo("<h2>Img2Img & Inpainting</h2>", "", gr.HTML),
|
|
"img2img_color_correction": OptionInfo(False, "Apply color correction"),
|
|
"mask_apply_overlay": OptionInfo(True, "Apply mask as overlay"),
|
|
"img2img_background_color": OptionInfo("#ffffff", "Image transparent color fill", gr.ColorPicker, {}),
|
|
"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 image processing", gr.Slider, {"minimum": 0.1, "maximum": 1.5, "step": 0.01, "visible": not native}),
|
|
"img2img_extra_noise": OptionInfo(0.0, "Extra noise multiplier for img2img", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01, "visible": not native}),
|
|
|
|
# "postprocessing_sep_detailer": OptionInfo("<h2>Detailer</h2>", "", gr.HTML),
|
|
"detailer_model": OptionInfo("Detailer", "Detailer model", gr.Radio, lambda: {"choices": [x.name() for x in detailers], "visible": False}),
|
|
"detailer_classes": OptionInfo("", "Detailer classes", gr.Textbox, { "visible": False}),
|
|
"detailer_conf": OptionInfo(0.6, "Min confidence", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.05, "visible": False}),
|
|
"detailer_max": OptionInfo(2, "Max detected", gr.Slider, {"minimum": 1, "maximum": 10, "step": 1, "visible": False}),
|
|
"detailer_iou": OptionInfo(0.5, "Max overlap", gr.Slider, {"minimum": 0, "maximum": 1.0, "step": 0.05, "visible": False}),
|
|
"detailer_min_size": OptionInfo(0.0, "Min object size", gr.Slider, {"minimum": 0.1, "maximum": 1, "step": 0.05, "visible": False}),
|
|
"detailer_max_size": OptionInfo(1.0, "Max object size", gr.Slider, {"minimum": 0.1, "maximum": 1, "step": 0.05, "visible": False}),
|
|
"detailer_padding": OptionInfo(20, "Item padding", gr.Slider, {"minimum": 0, "maximum": 100, "step": 1, "visible": False}),
|
|
"detailer_blur": OptionInfo(10, "Item edge blur", gr.Slider, {"minimum": 0, "maximum": 100, "step": 1, "visible": False}),
|
|
"detailer_strength": OptionInfo(0.5, "Detailer strength", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01, "visible": False}),
|
|
"detailer_models": OptionInfo(['face-yolo8n'], "Detailer models", gr.Dropdown, lambda: {"multiselect":True, "choices": list(yolo.list), "visible": False}),
|
|
"code_former_weight": OptionInfo(0.2, "CodeFormer weight parameter", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01, "visible": False}),
|
|
"detailer_unload": OptionInfo(False, "Move detailer model to CPU when complete"),
|
|
|
|
"postprocessing_sep_face_restore": OptionInfo("<h2>Face restore</h2>", "", gr.HTML),
|
|
"face_restoration_model": OptionInfo("Face restorer", "Face restoration", gr.Radio, lambda: {"choices": ['None'] + [x.name() for x in face_restorers]}),
|
|
|
|
"postprocessing_sep_upscalers": OptionInfo("<h2>Upscaling</h2>", "", gr.HTML),
|
|
"upscaler_unload": OptionInfo(False, "Unload upscaler after processing"),
|
|
"upscaler_tile_size": OptionInfo(192, "Upscaler tile size", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
|
|
"upscaler_tile_overlap": OptionInfo(8, "Upscaler tile overlap", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}),
|
|
}))
|
|
|
|
options_templates.update(options_section(('control', "Control Options"), {
|
|
"control_max_units": OptionInfo(4, "Maximum number of units", gr.Slider, {"minimum": 1, "maximum": 10, "step": 1}),
|
|
"control_move_processor": OptionInfo(False, "Processor move to CPU after use"),
|
|
"control_unload_processor": OptionInfo(False, "Processor unload after use"),
|
|
}))
|
|
|
|
options_templates.update(options_section(('interrogate', "Interrogate"), {
|
|
"interrogate_keep_models_in_memory": OptionInfo(False, "Interrogate: keep models in VRAM"),
|
|
"interrogate_return_ranks": OptionInfo(True, "Interrogate: include ranks of model tags matches in results"),
|
|
"interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
|
|
"interrogate_clip_min_length": OptionInfo(32, "Interrogate: minimum description length", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
|
|
"interrogate_clip_max_length": OptionInfo(192, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
|
|
"interrogate_clip_skip_categories": OptionInfo(["artists", "movements", "flavors"], "Interrogate: skip categories", gr.CheckboxGroup, lambda: {"choices": modules.interrogate.category_types()}, refresh=modules.interrogate.category_types),
|
|
"interrogate_deepbooru_score_threshold": OptionInfo(0.65, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
|
|
"deepbooru_sort_alpha": OptionInfo(False, "Interrogate: deepbooru sort alphabetically"),
|
|
"deepbooru_use_spaces": OptionInfo(False, "Use spaces for tags in deepbooru"),
|
|
"deepbooru_escape": OptionInfo(True, "Escape brackets in deepbooru"),
|
|
"deepbooru_filter_tags": OptionInfo("", "Filter out tags from deepbooru output"),
|
|
}))
|
|
|
|
options_templates.update(options_section(('extra_networks', "Networks"), {
|
|
"extra_networks_sep1": OptionInfo("<h2>Networks UI</h2>", "", gr.HTML),
|
|
"extra_networks_show": OptionInfo(True, "UI show on startup"),
|
|
"extra_networks": OptionInfo(["All"], "Available networks", gr.Dropdown, lambda: {"multiselect":True, "choices": ['All'] + [en.title for en in extra_networks]}),
|
|
"extra_networks_sort": OptionInfo("Default", "Sort order", gr.Dropdown, {"choices": ['Default', 'Name [A-Z]', 'Name [Z-A]', 'Date [Newest]', 'Date [Oldest]', 'Size [Largest]', 'Size [Smallest]']}),
|
|
"extra_networks_view": OptionInfo("gallery", "UI view", gr.Radio, {"choices": ["gallery", "list"]}),
|
|
"extra_networks_card_cover": OptionInfo("sidebar", "UI position", gr.Radio, {"choices": ["cover", "inline", "sidebar"]}),
|
|
"extra_networks_height": OptionInfo(0, "UI height (%)", gr.Slider, {"minimum": 0, "maximum": 100, "step": 1}), # set in ui_javascript
|
|
"extra_networks_sidebar_width": OptionInfo(35, "UI sidebar width (%)", gr.Slider, {"minimum": 10, "maximum": 80, "step": 1}),
|
|
"extra_networks_card_size": OptionInfo(160, "UI card size (px)", gr.Slider, {"minimum": 20, "maximum": 2000, "step": 1}),
|
|
"extra_networks_card_square": OptionInfo(True, "UI disable variable aspect ratio"),
|
|
"extra_networks_fetch": OptionInfo(True, "UI fetch network info on mouse-over"),
|
|
"extra_network_skip_indexing": OptionInfo(False, "Build info on first access", gr.Checkbox),
|
|
|
|
"extra_networks_model_sep": OptionInfo("<h2>Models</h2>", "", gr.HTML),
|
|
"extra_network_reference": OptionInfo(False, "Use reference values when available", gr.Checkbox),
|
|
"extra_networks_embed_sep": OptionInfo("<h2>Embeddings</h2>", "", gr.HTML),
|
|
"diffusers_convert_embed": OptionInfo(False, "Auto-convert SD 1.5 embeddings to SDXL ", gr.Checkbox, {"visible": native}),
|
|
"extra_networks_styles_sep": OptionInfo("<h2>Styles</h2>", "", gr.HTML),
|
|
"extra_networks_styles": OptionInfo(True, "Show built-in styles"),
|
|
"extra_networks_wildcard_sep": OptionInfo("<h2>Wildcards</h2>", "", gr.HTML),
|
|
"wildcards_enabled": OptionInfo(True, "Enable file wildcards support"),
|
|
"extra_networks_lora_sep": OptionInfo("<h2>LoRA</h2>", "", gr.HTML),
|
|
"extra_networks_default_multiplier": OptionInfo(1.0, "Default strength", gr.Slider, {"minimum": 0.0, "maximum": 2.0, "step": 0.01}),
|
|
"lora_preferred_name": OptionInfo("filename", "LoRA preferred name", gr.Radio, {"choices": ["filename", "alias"]}),
|
|
"lora_add_hashes_to_infotext": OptionInfo(False, "LoRA add hash info"),
|
|
"lora_force_diffusers": OptionInfo(False if not cmd_opts.use_openvino else True, "LoRA force loading of all models using Diffusers"),
|
|
"lora_maybe_diffusers": OptionInfo(False, "LoRA force loading of specific models using Diffusers"),
|
|
"lora_fuse_diffusers": OptionInfo(False if not cmd_opts.use_openvino else True, "LoRA use fuse when possible"),
|
|
"lora_apply_tags": OptionInfo(0, "LoRA auto-apply tags", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}),
|
|
"lora_in_memory_limit": OptionInfo(0, "LoRA memory cache", gr.Slider, {"minimum": 0, "maximum": 24, "step": 1}),
|
|
"lora_quant": OptionInfo("NF4","LoRA precision in quantized models", gr.Radio, {"choices": ["NF4", "FP4"]}),
|
|
"lora_load_gpu": OptionInfo(True if not cmd_opts.lowvram else False, "Load LoRA directly to GPU"),
|
|
}))
|
|
|
|
options_templates.update(options_section((None, "Internal options"), {
|
|
"diffusers_version": OptionInfo("", "Diffusers version", gr.Textbox, {"visible": False}),
|
|
"disabled_extensions": OptionInfo([], "Disable these extensions"),
|
|
"sd_checkpoint_hash": OptionInfo("", "SHA256 hash of the current checkpoint"),
|
|
"tooltips": OptionInfo("UI Tooltips", "UI tooltips", gr.Radio, {"choices": ["None", "Browser default", "UI tooltips"], "visible": False}),
|
|
}))
|
|
|
|
options_templates.update(options_section((None, "Hidden options"), {
|
|
"batch_cond_uncond": OptionInfo(True, "Do conditional and unconditional denoising in one batch", gr.Checkbox, {"visible": False}),
|
|
"CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 8, "step": 1, "visible": False}),
|
|
"dataset_filename_join_string": OptionInfo(" ", "Filename join string", gr.Textbox, { "visible": False }),
|
|
"dataset_filename_word_regex": OptionInfo("", "Filename word regex", gr.Textbox, { "visible": False }),
|
|
"diffusers_force_zeros": OptionInfo(False, "Force zeros for prompts when empty", gr.Checkbox, {"visible": False}),
|
|
"disable_all_extensions": OptionInfo("none", "Disable all extensions (preserves the list of disabled extensions)", gr.Radio, {"choices": ["none", "user", "all"]}),
|
|
"disable_nan_check": OptionInfo(True, "Disable NaN check", gr.Checkbox, {"visible": False}),
|
|
"embeddings_templates_dir": OptionInfo("", "Embeddings train templates directory", gr.Textbox, { "visible": False }),
|
|
"extra_networks_card_fit": OptionInfo("cover", "UI image contain method", gr.Radio, {"choices": ["contain", "cover", "fill"], "visible": False}),
|
|
"grid_extended_filename": OptionInfo(True, "Add extended info to filename when saving grid", gr.Checkbox, {"visible": False}),
|
|
"grid_save_to_dirs": OptionInfo(False, "Save grids to a subdirectory", gr.Checkbox, {"visible": False}),
|
|
"hypernetwork_enabled": OptionInfo(False, "Enable Hypernetwork support", gr.Checkbox, {"visible": False}),
|
|
"img2img_fix_steps": OptionInfo(False, "For image processing do exact number of steps as specified", gr.Checkbox, { "visible": False }),
|
|
"interrogate_clip_dict_limit": OptionInfo(2048, "CLIP: maximum number of lines in text file", gr.Slider, { "visible": False }),
|
|
"keyedit_delimiters": OptionInfo(r".,\/!?%^*;:{}=`~()", "Ctrl+up/down word delimiters", gr.Textbox, { "visible": False }),
|
|
"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, "visible": False}),
|
|
"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, "visible": False}),
|
|
"live_preview_content": OptionInfo("Combined", "Live preview subject", gr.Radio, {"choices": ["Combined", "Prompt", "Negative prompt"], "visible": False}),
|
|
"live_previews_enable": OptionInfo(True, "Show live previews", gr.Checkbox, {"visible": False}),
|
|
"lora_functional": OptionInfo(False, "Use Kohya method for handling multiple LoRA", gr.Checkbox, { "visible": False }),
|
|
"lyco_dir": OptionInfo(os.path.join(paths.models_path, 'LyCORIS'), "Folder with LyCORIS network(s)", gr.Text, {"visible": False}),
|
|
"model_reuse_dict": OptionInfo(False, "Reuse loaded model dictionary", gr.Checkbox, {"visible": False}),
|
|
"pad_cond_uncond": OptionInfo(True, "Pad prompt and negative prompt to be same length", gr.Checkbox, {"visible": False}),
|
|
"pin_memory": OptionInfo(True, "Pin training dataset to memory", gr.Checkbox, { "visible": False }),
|
|
"save_optimizer_state": OptionInfo(False, "Save resumable optimizer state when training", gr.Checkbox, { "visible": False }),
|
|
"save_training_settings_to_txt": OptionInfo(True, "Save training settings to a text file", gr.Checkbox, { "visible": False }),
|
|
"sd_disable_ckpt": OptionInfo(False, "Disallow models in ckpt format", gr.Checkbox, {"visible": False}),
|
|
"sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, { "choices": ["None"], "visible": False }),
|
|
"sd_lora": OptionInfo("", "Add LoRA to prompt", gr.Textbox, {"visible": False}),
|
|
"sd_vae_checkpoint_cache": OptionInfo(0, "Cached VAEs", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1, "visible": False}),
|
|
"show_progress_grid": OptionInfo(True, "Show previews as a grid", gr.Checkbox, {"visible": False}),
|
|
"show_progressbar": OptionInfo(True, "Show progressbar", gr.Checkbox, {"visible": False}),
|
|
"training_enable_tensorboard": OptionInfo(False, "Enable tensorboard logging", gr.Checkbox, { "visible": False }),
|
|
"training_image_repeats_per_epoch": OptionInfo(1, "Image repeats per epoch", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1, "visible": False }),
|
|
"training_tensorboard_flush_every": OptionInfo(120, "Tensorboard flush period", gr.Number, { "visible": False }),
|
|
"training_tensorboard_save_images": OptionInfo(False, "Save generated images within tensorboard", gr.Checkbox, { "visible": False }),
|
|
"training_write_csv_every": OptionInfo(0, "Save loss CSV file every n steps", gr.Number, { "visible": False }),
|
|
"ui_scripts_reorder": OptionInfo("", "UI scripts order", gr.Textbox, { "visible": False }),
|
|
"unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training", gr.Checkbox, { "visible": False }),
|
|
"upscaler_for_img2img": OptionInfo("None", "Default upscaler for image resize operations", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers], "visible": False}, refresh=refresh_upscalers),
|
|
"use_save_to_dirs_for_ui": OptionInfo(False, "Save images to a subdirectory when using Save button", gr.Checkbox, {"visible": False}),
|
|
"use_upscaler_name_as_suffix": OptionInfo(True, "Use upscaler as suffix", gr.Checkbox, {"visible": False}),
|
|
}))
|
|
|
|
options_templates.update()
|
|
|
|
|
|
class Options:
|
|
data = None
|
|
data_labels = options_templates
|
|
filename = None
|
|
typemap = {int: float}
|
|
|
|
def __init__(self):
|
|
self.data = {k: v.default for k, v in self.data_labels.items()}
|
|
|
|
def __setattr__(self, key, value): # pylint: disable=inconsistent-return-statements
|
|
if self.data is not None:
|
|
if key in self.data or key in self.data_labels:
|
|
if cmd_opts.freeze:
|
|
log.warning(f'Settings are frozen: {key}')
|
|
return
|
|
if cmd_opts.hide_ui_dir_config and key in restricted_opts:
|
|
log.warning(f'Settings key is restricted: {key}')
|
|
return
|
|
self.data[key] = value
|
|
return
|
|
return super(Options, self).__setattr__(key, value) # pylint: disable=super-with-arguments
|
|
|
|
def get(self, item):
|
|
if self.data is not None:
|
|
if item in self.data:
|
|
return self.data[item]
|
|
if item in self.data_labels:
|
|
return self.data_labels[item].default
|
|
return super(Options, self).__getattribute__(item) # pylint: disable=super-with-arguments
|
|
|
|
def __getattr__(self, item):
|
|
if self.data is not None:
|
|
if item in self.data:
|
|
return self.data[item]
|
|
if item in self.data_labels:
|
|
return self.data_labels[item].default
|
|
return super(Options, self).__getattribute__(item) # pylint: disable=super-with-arguments
|
|
|
|
def set(self, key, value):
|
|
"""sets an option and calls its onchange callback, returning True if the option changed and False otherwise"""
|
|
oldval = self.data.get(key, None)
|
|
if oldval is None:
|
|
oldval = self.data_labels[key].default
|
|
if oldval == value:
|
|
return False
|
|
try:
|
|
setattr(self, key, value)
|
|
except RuntimeError:
|
|
return False
|
|
if self.data_labels[key].onchange is not None:
|
|
try:
|
|
self.data_labels[key].onchange()
|
|
except Exception as err:
|
|
log.error(f'Error in onchange callback: {key} {value} {err}')
|
|
errors.display(err, 'Error in onchange callback')
|
|
setattr(self, key, oldval)
|
|
return False
|
|
return True
|
|
|
|
def get_default(self, key):
|
|
"""returns the default value for the key"""
|
|
data_label = self.data_labels.get(key)
|
|
return data_label.default if data_label is not None else None
|
|
|
|
def save_atomic(self, filename=None, silent=False):
|
|
if filename is None:
|
|
filename = self.filename
|
|
if cmd_opts.freeze:
|
|
log.warning(f'Setting: fn="{filename}" save disabled')
|
|
return
|
|
try:
|
|
# output = json.dumps(self.data, indent=2)
|
|
diff = {}
|
|
unused_settings = []
|
|
|
|
if os.environ.get('SD_CONFIG_DEBUG', None) is not None:
|
|
log.debug('Settings: user')
|
|
for k, v in self.data.items():
|
|
log.trace(f' Config: item={k} value={v} default={self.data_labels[k].default if k in self.data_labels else None}')
|
|
log.debug('Settings: defaults')
|
|
for k in self.data_labels.keys():
|
|
log.trace(f' Setting: item={k} default={self.data_labels[k].default}')
|
|
|
|
for k, v in self.data.items():
|
|
if k in self.data_labels:
|
|
if type(v) is list:
|
|
diff[k] = v
|
|
if self.data_labels[k].default != v:
|
|
diff[k] = v
|
|
else:
|
|
if k not in compatibility_opts:
|
|
diff[k] = v
|
|
if not k.startswith('uiux_'):
|
|
unused_settings.append(k)
|
|
writefile(diff, filename, silent=silent)
|
|
if len(unused_settings) > 0:
|
|
log.debug(f"Settings: unused={unused_settings}")
|
|
except Exception as err:
|
|
log.error(f'Settings: fn="{filename}" {err}')
|
|
|
|
def save(self, filename=None, silent=False):
|
|
threading.Thread(target=self.save_atomic, args=(filename, silent)).start()
|
|
|
|
def same_type(self, x, y):
|
|
if x is None or y is None:
|
|
return True
|
|
type_x = self.typemap.get(type(x), type(x))
|
|
type_y = self.typemap.get(type(y), type(y))
|
|
return type_x == type_y
|
|
|
|
def load(self, filename=None):
|
|
if filename is None:
|
|
filename = self.filename
|
|
if not os.path.isfile(filename):
|
|
log.debug(f'Settings: fn="{filename}" created')
|
|
self.save(filename)
|
|
return
|
|
self.data = readfile(filename, lock=True)
|
|
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(',')]
|
|
unknown_settings = []
|
|
for k, v in self.data.items():
|
|
info: OptionInfo = self.data_labels.get(k, None)
|
|
if info is not None:
|
|
if not info.validate(k, v):
|
|
self.data[k] = info.default
|
|
if info is not None and not self.same_type(info.default, v):
|
|
log.warning(f"Setting validation: {k}={v} ({type(v).__name__} expected={type(info.default).__name__})")
|
|
self.data[k] = info.default
|
|
if info is None and k not in compatibility_opts and not k.startswith('uiux_'):
|
|
unknown_settings.append(k)
|
|
if len(unknown_settings) > 0:
|
|
log.warning(f"Setting validation: unknown={unknown_settings}")
|
|
|
|
def onchange(self, key, func, call=True):
|
|
item = self.data_labels.get(key)
|
|
item.onchange = func
|
|
if call:
|
|
func()
|
|
|
|
def dumpjson(self):
|
|
d = {k: self.data.get(k, self.data_labels.get(k).default) for k in self.data_labels.keys()}
|
|
metadata = {
|
|
k: {
|
|
"is_stored": k in self.data and self.data[k] != self.data_labels[k].default, # pylint: disable=unnecessary-dict-index-lookup
|
|
"tab_name": v.section[0]
|
|
} for k, v in self.data_labels.items()
|
|
}
|
|
return json.dumps({"values": d, "metadata": metadata})
|
|
|
|
def add_option(self, key, info):
|
|
self.data_labels[key] = info
|
|
|
|
def reorder(self):
|
|
"""reorder settings so that all items related to section always go together"""
|
|
section_ids = {}
|
|
settings_items = self.data_labels.items()
|
|
for _k, item in settings_items:
|
|
if item.section not in section_ids:
|
|
section_ids[item.section] = len(section_ids)
|
|
self.data_labels = dict(sorted(settings_items, key=lambda x: section_ids[x[1].section]))
|
|
|
|
def cast_value(self, key, value):
|
|
"""casts an arbitrary to the same type as this setting's value with key
|
|
Example: cast_value("eta_noise_seed_delta", "12") -> returns 12 (an int rather than str)
|
|
"""
|
|
if value is None:
|
|
return None
|
|
default_value = self.data_labels[key].default
|
|
if default_value is None:
|
|
default_value = getattr(self, key, None)
|
|
if default_value is None:
|
|
return None
|
|
expected_type = type(default_value)
|
|
if expected_type == bool and value == "False":
|
|
value = False
|
|
elif expected_type == type(value):
|
|
pass
|
|
else:
|
|
value = expected_type(value)
|
|
return value
|
|
|
|
profiler = None
|
|
opts = Options()
|
|
config_filename = cmd_opts.config
|
|
opts.load(config_filename)
|
|
cmd_opts = cmd_args.settings_args(opts, cmd_opts)
|
|
if cmd_opts.use_xformers:
|
|
opts.data['cross_attention_optimization'] = 'xFormers'
|
|
opts.data['uni_pc_lower_order_final'] = opts.schedulers_use_loworder # compatibility
|
|
opts.data['uni_pc_order'] = max(2, opts.schedulers_solver_order) # compatibility
|
|
log.info(f'Engine: backend={backend} compute={devices.backend} device={devices.get_optimal_device_name()} attention="{opts.cross_attention_optimization}" mode={devices.inference_context.__name__}')
|
|
if not native:
|
|
log.warning('Backend=original is in maintainance-only mode')
|
|
opts.data['diffusers_offload_mode'] = 'none'
|
|
|
|
prompt_styles = modules.styles.StyleDatabase(opts)
|
|
reference_models = readfile(os.path.join('html', 'reference.json'))
|
|
cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or (cmd_opts.server_name or False)) and not cmd_opts.insecure
|
|
devices.args = cmd_opts
|
|
devices.opts = opts
|
|
devices.onnx = [opts.onnx_execution_provider]
|
|
devices.set_cuda_params()
|
|
if opts.onnx_cpu_fallback and 'CPUExecutionProvider' not in devices.onnx:
|
|
devices.onnx.append('CPUExecutionProvider')
|
|
device = devices.device
|
|
|
|
batch_cond_uncond = opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)
|
|
parallel_processing_allowed = not cmd_opts.lowvram
|
|
mem_mon = modules.memmon.MemUsageMonitor("MemMon", devices.device)
|
|
history = history.History()
|
|
if devices.backend == "directml":
|
|
directml_do_hijack()
|
|
elif devices.backend == "zluda":
|
|
from modules.zluda import initialize_zluda
|
|
initialize_zluda()
|
|
initialize_onnx()
|
|
try:
|
|
log.info(f'Device: {print_dict(devices.get_gpu_info())}')
|
|
except Exception as ex:
|
|
log.error(f'Device: {ex}')
|
|
|
|
|
|
class TotalTQDM: # compatibility with previous global-tqdm
|
|
# import tqdm
|
|
def __init__(self):
|
|
pass
|
|
def reset(self):
|
|
pass
|
|
def update(self):
|
|
pass
|
|
def updateTotal(self, new_total):
|
|
pass
|
|
def clear(self):
|
|
pass
|
|
total_tqdm = TotalTQDM()
|
|
|
|
|
|
def restart_server(restart=True):
|
|
if demo is None:
|
|
return
|
|
log.warning('Server shutdown requested')
|
|
try:
|
|
sys.tracebacklimit = 0
|
|
stdout = io.StringIO()
|
|
stderr = io.StringIO()
|
|
with contextlib.redirect_stdout(stdout), contextlib.redirect_stdout(stderr):
|
|
demo.server.wants_restart = restart
|
|
demo.server.should_exit = True
|
|
demo.server.force_exit = True
|
|
demo.close(verbose=False)
|
|
demo.server.close()
|
|
demo.fns = []
|
|
time.sleep(1)
|
|
sys.tracebacklimit = 100
|
|
# os._exit(0)
|
|
except (Exception, BaseException) as err:
|
|
log.error(f'Server shutdown error: {err}')
|
|
if restart:
|
|
log.info('Server will restart')
|
|
|
|
|
|
def restore_defaults(restart=True):
|
|
if os.path.exists(cmd_opts.config):
|
|
log.info('Restoring server defaults')
|
|
os.remove(cmd_opts.config)
|
|
restart_server(restart)
|
|
|
|
|
|
def listdir(path):
|
|
if not os.path.exists(path):
|
|
return []
|
|
mtime = os.path.getmtime(path)
|
|
if path in dir_timestamps and mtime == dir_timestamps[path]:
|
|
return dir_cache[path]
|
|
else:
|
|
dir_cache[path] = [os.path.join(path, f) for f in os.listdir(path)]
|
|
dir_timestamps[path] = mtime
|
|
return dir_cache[path]
|
|
|
|
|
|
def walk_files(path, allowed_extensions=None):
|
|
if not os.path.exists(path):
|
|
return
|
|
if allowed_extensions is not None:
|
|
allowed_extensions = set(allowed_extensions)
|
|
for root, _dirs, files in os.walk(path, followlinks=True):
|
|
for filename in files:
|
|
if allowed_extensions is not None:
|
|
_, ext = os.path.splitext(filename)
|
|
if ext not in allowed_extensions:
|
|
continue
|
|
yield os.path.join(root, filename)
|
|
|
|
|
|
def html_path(filename):
|
|
return os.path.join(paths.script_path, "html", filename)
|
|
|
|
|
|
def html(filename):
|
|
path = html_path(filename)
|
|
if os.path.exists(path):
|
|
with open(path, encoding="utf8") as file:
|
|
return file.read()
|
|
return ""
|
|
|
|
|
|
@lru_cache(maxsize=1)
|
|
def get_version():
|
|
version = None
|
|
if version is None:
|
|
try:
|
|
import subprocess
|
|
res = subprocess.run('git log --pretty=format:"%h %ad" -1 --date=short', stdout = subprocess.PIPE, stderr = subprocess.PIPE, shell=True, check=True)
|
|
ver = res.stdout.decode(encoding = 'utf8', errors='ignore') if len(res.stdout) > 0 else ' '
|
|
githash, updated = ver.split(' ')
|
|
res = subprocess.run('git remote get-url origin', stdout = subprocess.PIPE, stderr = subprocess.PIPE, shell=True, check=True)
|
|
origin = res.stdout.decode(encoding = 'utf8', errors='ignore') if len(res.stdout) > 0 else ''
|
|
res = subprocess.run('git rev-parse --abbrev-ref HEAD', stdout = subprocess.PIPE, stderr = subprocess.PIPE, shell=True, check=True)
|
|
branch = res.stdout.decode(encoding = 'utf8', errors='ignore') if len(res.stdout) > 0 else ''
|
|
version = {
|
|
'app': 'sd.next',
|
|
'updated': updated,
|
|
'hash': githash,
|
|
'url': origin.replace('\n', '') + '/tree/' + branch.replace('\n', '')
|
|
}
|
|
except Exception:
|
|
version = { 'app': 'sd.next' }
|
|
return version
|
|
|
|
|
|
def req(url_addr, headers = None, **kwargs):
|
|
if headers is None:
|
|
headers = { 'Content-type': 'application/json' }
|
|
try:
|
|
res = requests.get(url_addr, timeout=30, headers=headers, verify=False, allow_redirects=True, **kwargs)
|
|
except Exception as err:
|
|
log.error(f'HTTP request error: url={url_addr} {err}')
|
|
res = { 'status_code': 500, 'text': f'HTTP request error: url={url_addr} {err}' }
|
|
res = SimpleNamespace(**res)
|
|
return res
|
|
|
|
|
|
sd_model: diffusers.DiffusionPipeline = None # dummy and overwritten by class
|
|
sd_refiner: diffusers.DiffusionPipeline = None # dummy and overwritten by class
|
|
sd_model_type: str = '' # dummy and overwritten by class
|
|
sd_refiner_type: str = '' # dummy and overwritten by class
|
|
sd_loaded: bool = False # dummy and overwritten by class
|
|
compiled_model_state = None
|
|
listfiles = listdir
|
|
|
|
from modules.modeldata import Shared # pylint: disable=ungrouped-imports
|
|
sys.modules[__name__].__class__ = Shared
|