mirror of
https://github.com/vladmandic/sdnext.git
synced 2026-01-29 05:02:09 +03:00
1052 lines
58 KiB
Python
1052 lines
58 KiB
Python
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 datetime
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import contextlib
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import urllib.request
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from urllib.parse import urlparse
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from enum import Enum
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import gradio as gr
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import tqdm
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import fasteners
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from rich.console import Console
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from modules import errors, ui_components, shared_items, cmd_args
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from modules.paths_internal 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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import modules.interrogate
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import modules.memmon
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import modules.styles
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import modules.devices as devices # pylint: disable=R0402
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import modules.paths_internal 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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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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clip_model = None
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interrogator = modules.interrogate.InterrogateModels("interrogate")
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sd_upscalers = []
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face_restorers = []
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tab_names = []
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options_templates = {}
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hypernetworks = {}
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loaded_hypernetworks = []
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gradio_theme = gr.themes.Base()
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settings_components = None
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pipelines = [
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'Autodetect',
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'Stable Diffusion', 'Stable Diffusion XL', 'Kandinsky V1', 'Kandinsky V2', 'DeepFloyd IF', 'Shap-E',
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'Stable Diffusion Img2Img', 'Stable Diffusion XL Img2Img', 'Kandinsky V1 Img2Img', 'Kandinsky V2 Img2Img', 'DeepFloyd IF Img2Img', 'Shap-E Img2Img'
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]
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latent_upscale_default_mode = "None"
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latent_upscale_modes = {
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"Latent": {"mode": "bilinear", "antialias": False},
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"Latent (antialiased)": {"mode": "bilinear", "antialias": True},
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"Latent (bicubic)": {"mode": "bicubic", "antialias": False},
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"Latent (bicubic antialiased)": {"mode": "bicubic", "antialias": True},
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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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}
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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_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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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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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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class Backend(Enum):
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ORIGINAL = 1
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DIFFUSERS = 2
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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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class State:
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skipped = False
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interrupted = False
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paused = False
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job = ""
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job_no = 0
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job_count = 0
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total_jobs = 0
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processing_has_refined_job_count = False
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job_timestamp = '0'
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sampling_step = 0
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sampling_steps = 0
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current_latent = None
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current_image = None
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current_image_sampling_step = 0
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id_live_preview = 0
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textinfo = None
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time_start = None
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need_restart = False
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server_start = None
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oom = False
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def skip(self):
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log.debug('Requested skip')
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self.skipped = True
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def interrupt(self):
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log.debug('Requested interrupt')
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self.interrupted = True
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def pause(self):
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self.paused = not self.paused
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log.debug(f'Requested {"pause" if self.paused else "continue"}')
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def nextjob(self):
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if opts.live_previews_enable and opts.show_progress_every_n_steps == -1:
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self.do_set_current_image()
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self.job_no += 1
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self.sampling_step = 0
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self.current_image_sampling_step = 0
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def dict(self):
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obj = {
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"skipped": self.skipped,
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"interrupted": self.interrupted,
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"job": self.job,
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"job_count": self.job_count,
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"job_timestamp": self.job_timestamp,
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"job_no": self.job_no,
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"sampling_step": self.sampling_step,
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"sampling_steps": self.sampling_steps,
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}
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return obj
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def begin(self, title=""):
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self.total_jobs += 1
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self.current_image = None
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self.current_image_sampling_step = 0
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self.current_latent = None
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self.id_live_preview = 0
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self.interrupted = False
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self.job = title
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self.job_count = -1
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self.job_no = 0
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self.job_timestamp = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
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self.paused = False
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self.processing_has_refined_job_count = False
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self.sampling_step = 0
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self.skipped = False
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self.textinfo = None
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self.time_start = time.time()
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devices.torch_gc()
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def end(self):
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self.job = ""
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self.job_count = 0
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self.job_no = 0
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self.paused = False
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self.interrupted = False
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self.skipped = False
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devices.torch_gc()
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def set_current_image(self):
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"""sets self.current_image from self.current_latent if enough sampling steps have been made after the last call to this"""
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if not parallel_processing_allowed:
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return
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if abs(self.sampling_step - self.current_image_sampling_step) >= opts.show_progress_every_n_steps and opts.live_previews_enable and opts.show_progress_every_n_steps > 0:
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self.do_set_current_image()
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def do_set_current_image(self):
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if self.current_latent is None:
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return
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import modules.sd_samplers # pylint: disable=W0621
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try:
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image = modules.sd_samplers.samples_to_image_grid(self.current_latent) if opts.show_progress_grid else modules.sd_samplers.sample_to_image(self.current_latent)
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self.assign_current_image(image)
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self.current_image_sampling_step = self.sampling_step
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except Exception:
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# log.error(f'Error setting current image: step={self.sampling_step} {e}')
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pass
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def assign_current_image(self, image):
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self.current_image = image
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self.id_live_preview += 1
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state = State()
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state.server_start = time.time()
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if not hasattr(cmd_opts, "use_openvino"):
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cmd_opts.use_openvino = False
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if cmd_opts.use_openvino:
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backend = Backend.DIFFUSERS
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cmd_opts.backend = 'diffusers'
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else:
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backend = Backend.DIFFUSERS if (cmd_opts.backend is not None) and (cmd_opts.backend.lower() == 'diffusers') else Backend.ORIGINAL # initial since we don't have opts loaded yet
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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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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 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_tiles():
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import modules.sd_models # pylint: disable=W0621
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return modules.sd_models.checkpoint_tiles()
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default_checkpoint = list_checkpoint_tiles()[0] if len(list_checkpoint_tiles()) > 0 else "model.ckpt"
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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 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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disabled = []
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if backend == Backend.DIFFUSERS:
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for ext in ['sd-webui-controlnet', 'multidiffusion-upscaler-for-automatic1111', 'a1111-sd-webui-lycoris']:
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if ext not in opts.disabled_extensions:
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disabled.append(ext)
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log.info(f'Diffusers disabling uncompatible extensions: {disabled}')
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if opts.lyco_patch_lora and backend != Backend.DIFFUSERS:
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cmd_opts.lyco_dir = opts.lora_dir
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if 'Lora' not in opts.disabled_extensions:
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disabled.append('Lora')
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cmd_opts.controlnet_loglevel = 'WARNING'
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return disabled
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def list_builtin_themes():
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files = [os.path.splitext(f)[0] for f in os.listdir('javascript') if f.endswith('.css')]
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return files
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def list_themes():
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fn = os.path.join('html', 'themes.json')
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if not os.path.exists(fn):
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refresh_themes()
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if os.path.exists(fn):
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with open(fn, mode='r', encoding='utf=8') as f:
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res = json.loads(f.read())
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else:
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res = []
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builtin = list_builtin_themes()
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default = ["gradio/default", "gradio/base", "gradio/glass", "gradio/monochrome", "gradio/soft"]
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external = {x['id'] for x in res if x['status'] == 'RUNNING' and 'test' not in x['id'].lower()}
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log.info(f'Themes: builtin={len(builtin)} default={len(default)} external={len(external)}')
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themes = sorted(builtin) + sorted(default) + sorted(external, key=str.casefold)
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return themes
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def refresh_themes():
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import requests
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try:
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req = requests.get('https://huggingface.co/datasets/freddyaboulton/gradio-theme-subdomains/resolve/main/subdomains.json', timeout=5)
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if req.status_code == 200:
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res = req.json()
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fn = os.path.join('html', 'themes.json')
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writefile(res, fn)
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list_themes()
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else:
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log.error('Error refreshing UI themes')
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except Exception:
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log.error('Exception refreshing UI themes')
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def readfile(filename, silent=False):
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data = {}
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try:
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if not os.path.exists(filename):
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return {}
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with fasteners.InterProcessLock(f"{filename}.lock"):
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with open(filename, "r", encoding="utf8") as file:
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data = json.load(file)
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if type(data) is str:
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data = json.loads(data)
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if not silent:
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log.debug(f'Reading: {filename} len={len(data)}')
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except Exception as e:
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log.error(f'Reading failed: {filename} {e}')
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return data
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def writefile(data, filename, mode='w'):
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def default(obj):
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log.error(f"Saving: {filename} not a valid object: {obj}")
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return str(obj)
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try:
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with fasteners.InterProcessLock(f"{filename}.lock"):
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# skipkeys=True, ensure_ascii=True, check_circular=True, allow_nan=True
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output = json.dumps(data, indent=2, default=default)
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log.debug(f'Saving: {filename} len={len(output)}')
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with open(filename, mode, encoding="utf8") as file:
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file.write(output)
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except Exception as e:
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log.error(f'Saving failed: {filename} {e}')
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if devices.backend == "cpu":
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cross_attention_optimization_default = "Doggettx's"
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elif devices.backend == "mps":
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cross_attention_optimization_default = "Doggettx's"
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elif devices.backend == "ipex":
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cross_attention_optimization_default = "Scaled-Dot-Product"
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elif devices.backend == "directml":
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cross_attention_optimization_default = "Sub-quadratic"
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elif devices.backend == "rocm":
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cross_attention_optimization_default = "Sub-quadratic"
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else: # cuda
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cross_attention_optimization_default ="Scaled-Dot-Product"
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options_templates.update(options_section(('sd', "Execution & Models"), {
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"sd_backend": OptionInfo("diffusers" if cmd_opts.use_openvino else "original", "Execution backend", gr.Radio, lambda: {"choices": ["original", "diffusers"] }),
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"sd_checkpoint_autoload": OptionInfo(True, "Model autoload on server start"),
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"sd_model_checkpoint": OptionInfo(default_checkpoint, "Base model", gr.Dropdown, lambda: {"choices": list_checkpoint_tiles()}, refresh=refresh_checkpoints),
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"sd_model_refiner": OptionInfo('None', "Refiner model", gr.Dropdown, lambda: {"choices": ['None'] + list_checkpoint_tiles()}, refresh=refresh_checkpoints),
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"sd_checkpoint_cache": OptionInfo(0, "Number of cached models", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
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"sd_vae_checkpoint_cache": OptionInfo(0, "Number of cached VAEs", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
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"sd_vae": OptionInfo("Automatic", "VAE model", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list),
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"sd_model_dict": OptionInfo('None', "Use baseline data from a different model", gr.Dropdown, lambda: {"choices": ['None'] + list_checkpoint_tiles()}, refresh=refresh_checkpoints),
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"stream_load": OptionInfo(False, "Load models using stream loading method"),
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"model_reuse_dict": OptionInfo(False, "When loading models attempt to reuse previous model dictionary"),
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"prompt_attention": OptionInfo("Full parser", "Prompt attention parser", gr.Radio, lambda: {"choices": ["Full parser", "Compel parser", "A1111 parser", "Fixed attention"] }),
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"prompt_mean_norm": OptionInfo(True, "Prompt attention mean normalization"),
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"comma_padding_backtrack": OptionInfo(20, "Prompt padding for long prompts", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
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"sd_disable_ckpt": OptionInfo(False, "Disallow usage of models in ckpt format"),
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}))
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options_templates.update(options_section(('optimizations', "Optimizations"), {
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"cross_attention_optimization": OptionInfo(cross_attention_optimization_default, "Cross-attention optimization method", gr.Radio, lambda: {"choices": shared_items.list_crossattention() }),
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"cross_attention_options": OptionInfo([], "Cross-attention advanced options", gr.CheckboxGroup, lambda: {"choices": ['xFormers enable flash Attention', 'SDP disable memory attention']}),
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"sub_quad_q_chunk_size": OptionInfo(512, "Sub-quadratic cross-attention query chunk size", gr.Slider, {"minimum": 16, "maximum": 8192, "step": 8}),
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"sub_quad_kv_chunk_size": OptionInfo(512, "Sub-quadratic cross-attention kv chunk size", gr.Slider, {"minimum": 0, "maximum": 8192, "step": 8}),
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"sub_quad_chunk_threshold": OptionInfo(80, "Sub-quadratic cross-attention chunking threshold", gr.Slider, {"minimum": 0, "maximum": 100, "step": 1}),
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"token_merging_ratio": OptionInfo(0.0, "Token merging ratio", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}),
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"token_merging_ratio_img2img": OptionInfo(0.0, "Token merging ratio for img2img", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}),
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"token_merging_ratio_hr": OptionInfo(0.0, "Token merging ratio for hires pass", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}),
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"inference_mode": OptionInfo("no-grad", "Torch inference mode", gr.Radio, lambda: {"choices": ["no-grad", "inference-mode", "none"]}),
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"sd_vae_sliced_encode": OptionInfo(False, "VAE Slicing (original)"),
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}))
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options_templates.update(options_section(('cuda', "Compute Settings"), {
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# "memmon_poll_rate": OptionInfo(2, "VRAM usage polls per second during generation", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}),
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"precision": OptionInfo("Autocast", "Precision type", gr.Radio, lambda: {"choices": ["Autocast", "Full"]}),
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"cuda_dtype": OptionInfo("FP32" if sys.platform == "darwin" or cmd_opts.use_openvino else "BF16" if devices.backend == "ipex" else "FP16", "Device precision type", gr.Radio, lambda: {"choices": ["FP32", "FP16", "BF16"]}),
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"no_half": OptionInfo(False, "Use full precision for model (--no-half)", None, None, None),
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"no_half_vae": OptionInfo(False, "Use full precision for VAE (--no-half-vae)"),
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"upcast_sampling": OptionInfo(True if sys.platform == "darwin" else False, "Enable upcast sampling"),
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"upcast_attn": OptionInfo(False, "Enable upcast cross attention layer"),
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"cuda_cast_unet": OptionInfo(False, "Use fixed UNet precision"),
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"disable_nan_check": OptionInfo(True, "Disable NaN check in produced images/latent spaces", gr.Checkbox, {"visible": False}),
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"rollback_vae": OptionInfo(False, "Attempt VAE roll back when produced NaN values (experimental)"),
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"opt_channelslast": OptionInfo(False, "Use channels last as torch memory format "),
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"cudnn_benchmark": OptionInfo(False, "Enable full-depth cuDNN benchmark feature"),
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"ipex_optimize": OptionInfo(True if devices.backend == "ipex" else False, "Enable IPEX Optimize for Intel GPUs"),
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"directml_memory_provider": OptionInfo(default_memory_provider, 'DirectML memory stats provider', gr.Radio, lambda: {"choices": memory_providers}),
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|
"cuda_compile_sep": OptionInfo("<h2>Model Compile</h2>", "", gr.HTML),
|
|
"cuda_compile": OptionInfo(True if cmd_opts.use_openvino else False, "Enable model compile"),
|
|
"cuda_compile_backend": OptionInfo("openvino_fx" if cmd_opts.use_openvino else "none", "Model compile backend", gr.Radio, lambda: {"choices": ['none', 'inductor', 'cudagraphs', 'aot_ts_nvfuser', 'hidet', 'ipex', 'openvino_fx']}),
|
|
"cuda_compile_mode": OptionInfo("default", "Model compile mode", gr.Radio, lambda: {"choices": ['default', 'reduce-overhead', 'max-autotune']}),
|
|
"cuda_compile_fullgraph": OptionInfo(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"),
|
|
}))
|
|
|
|
options_templates.update(options_section(('diffusers', "Diffusers Settings"), {
|
|
"diffusers_pipeline": OptionInfo(pipelines[0], 'Diffusers pipeline', gr.Dropdown, lambda: {"choices": pipelines}),
|
|
"diffusers_move_base": OptionInfo(True, "Move base model to CPU when using refiner"),
|
|
"diffusers_move_unet": OptionInfo(True, "Move base model to CPU when using VAE"),
|
|
"diffusers_move_refiner": OptionInfo(True, "Move refiner model to CPU when not in use"),
|
|
"diffusers_extract_ema": OptionInfo(True, "Use model EMA weights when possible"),
|
|
"diffusers_generator_device": OptionInfo("default", "Generator device", gr.Radio, lambda: {"choices": ["default", "cpu"]}),
|
|
"diffusers_model_cpu_offload": OptionInfo(False, "Enable model CPU offload (--medvram)"),
|
|
"diffusers_seq_cpu_offload": OptionInfo(False, "Enable sequential CPU offload (--lowvram)"),
|
|
"diffusers_vae_upcast": OptionInfo("default", "VAE upcasting", gr.Radio, lambda: {"choices": ['default', 'true', 'false']}),
|
|
"diffusers_vae_slicing": OptionInfo(True, "Enable VAE slicing"),
|
|
"diffusers_vae_tiling": OptionInfo(False if cmd_opts.use_openvino else True, "Enable VAE tiling"),
|
|
"diffusers_attention_slicing": OptionInfo(False, "Enable attention slicing"),
|
|
"diffusers_model_load_variant": OptionInfo("default", "Diffusers model loading variant", gr.Radio, lambda: {"choices": ['default', 'fp32', 'fp16']}),
|
|
"diffusers_vae_load_variant": OptionInfo("default", "Diffusers VAE loading variant", gr.Radio, lambda: {"choices": ['default', 'fp32', 'fp16']}),
|
|
"diffusers_lora_loader": OptionInfo("diffusers", "Diffusers LoRA loading variant", gr.Radio, lambda: {"choices": ['diffusers', 'sequential apply', 'merge and apply']}),
|
|
"diffusers_force_zeros": OptionInfo(True, "Force zeros for prompts when empty"),
|
|
"diffusers_aesthetics_score": OptionInfo(False, "Require aesthetics score"),
|
|
}))
|
|
|
|
options_templates.update(options_section(('system-paths', "System Paths"), {
|
|
"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"),
|
|
"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 Hugggingface models", folder=True),
|
|
"vae_dir": OptionInfo(os.path.join(paths.models_path, 'VAE'), "Folder with VAE files", folder=True),
|
|
"sd_lora": OptionInfo("", "Add LoRA to prompt", gr.Textbox, {"visible": False}),
|
|
"lora_dir": OptionInfo(os.path.join(paths.models_path, 'Lora'), "Folder with LoRA network(s)", folder=True),
|
|
"lyco_dir": OptionInfo(os.path.join(paths.models_path, 'LyCORIS'), "Folder with LyCORIS network(s)", folder=True),
|
|
"styles_dir": OptionInfo(os.path.join(paths.data_path, 'styles.csv'), "File or Folder with user-defined styles", 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),
|
|
"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),
|
|
}))
|
|
|
|
options_templates.update(options_section(('saving-images', "Image Options"), {
|
|
"samples_save": OptionInfo(True, "Always save all generated images"),
|
|
"samples_format": OptionInfo('jpg', 'File format for generated images', gr.Dropdown, lambda: {"choices": ["jpg", "png", "webp", "tiff", "jp2"]}),
|
|
"jpeg_quality": OptionInfo(90, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
|
|
"img_max_size_mp": OptionInfo(250, "Maximum allowed image size in megapixels", gr.Number),
|
|
"webp_lossless": OptionInfo(False, "Use lossless compression for webp images"),
|
|
"save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),
|
|
"samples_save_zip": OptionInfo(True, "Create zip archive when downloading multiple images"),
|
|
|
|
"image_sep_metadata": OptionInfo("<h2>Metadata/Logging</h2>", "", gr.HTML),
|
|
"image_metadata": OptionInfo(True, "Include metadata in saved images"),
|
|
"save_txt": OptionInfo(False, "Create text file next to every image with generation parameters"),
|
|
"save_log_fn": OptionInfo("", "Create JSON log file for each saved image", component_args=hide_dirs),
|
|
"image_watermark_enabled": OptionInfo(False, "Include watermark in saved images"),
|
|
"image_watermark": OptionInfo('', "Image watermark string"),
|
|
"image_sep_grid": OptionInfo("<h2>Grid Options</h2>", "", gr.HTML),
|
|
"grid_save": OptionInfo(True, "Always save all generated image grids"),
|
|
"grid_format": OptionInfo('jpg', 'File format for grids', gr.Dropdown, lambda: {"choices": ["jpg", "png", "webp", "tiff", "jp2"]}),
|
|
"n_rows": OptionInfo(-1, "Grid row count", gr.Slider, {"minimum": -1, "maximum": 16, "step": 1}),
|
|
|
|
"save_sep_options": OptionInfo("<h2>Intermediate Image Saving</h2>", "", gr.HTML),
|
|
"save_init_img": OptionInfo(True, "Save copy of img2img init images"),
|
|
"save_images_before_highres_fix": OptionInfo(False, "Save copy of image before applying highres fix"),
|
|
"save_images_before_refiner": OptionInfo(False, "Save copy of image before running refiner"),
|
|
"save_images_before_face_restoration": OptionInfo(False, "Save copy of image before doing face restoration"),
|
|
"save_images_before_color_correction": OptionInfo(False, "Save copy of image before applying color correction"),
|
|
"save_mask": OptionInfo(False, "Save copy of the inpainting greyscale mask"),
|
|
"save_mask_composite": OptionInfo(False, "Save copy of inpainting masked composite"),
|
|
|
|
}))
|
|
|
|
options_templates.update(options_section(('saving-paths', "Image Naming & Paths"), {
|
|
"saving_sep_images": OptionInfo("<h2>Images</h2>", "", gr.HTML),
|
|
"save_images_add_number": OptionInfo(True, "Add number to filename when saving", component_args=hide_dirs),
|
|
"use_original_name_batch": OptionInfo(True, "Use original name for output filename during batch process"),
|
|
"use_upscaler_name_as_suffix": OptionInfo(True, "Use upscaler name as filename suffix in the extras tab"),
|
|
"samples_filename_pattern": OptionInfo("[seq]-[prompt_words]", "Images filename pattern", component_args=hide_dirs),
|
|
|
|
"outdir_sep_dirs": OptionInfo("<h2>Directories</h2>", "", gr.HTML),
|
|
"save_to_dirs": OptionInfo(False, "Save images to a subdirectory"),
|
|
"use_save_to_dirs_for_ui": OptionInfo(False, "Save images to a subdirectory when using Save button"),
|
|
"directories_filename_pattern": OptionInfo("[date]", "Directory name pattern", component_args=hide_dirs),
|
|
"directories_max_prompt_words": OptionInfo(8, "Max prompt words for [prompt_words] pattern", gr.Slider, {"minimum": 1, "maximum": 99, "step": 1, **hide_dirs}),
|
|
"outdir_samples": OptionInfo("", "Output directory for images", component_args=hide_dirs, folder=True),
|
|
"outdir_txt2img_samples": OptionInfo("outputs/text", 'Output directory for txt2img images', component_args=hide_dirs, folder=True),
|
|
"outdir_img2img_samples": OptionInfo("outputs/image", 'Output directory for img2img images', component_args=hide_dirs, folder=True),
|
|
"outdir_extras_samples": OptionInfo("outputs/extras", 'Output directory for images from extras tab', component_args=hide_dirs, folder=True),
|
|
"outdir_save": OptionInfo("outputs/save", "Directory for saving images using the Save button", component_args=hide_dirs, folder=True),
|
|
"outdir_init_images": OptionInfo("outputs/init-images", "Directory for saving init images when using img2img", component_args=hide_dirs, folder=True),
|
|
|
|
"outdir_sep_grids": OptionInfo("<h2>Grids</h2>", "", gr.HTML),
|
|
"grid_extended_filename": OptionInfo(True, "Add extended info (seed, prompt) to filename when saving grid"),
|
|
"grid_save_to_dirs": OptionInfo(False, "Save grids to a subdirectory"),
|
|
"outdir_grids": OptionInfo("", "Output directory for grids", component_args=hide_dirs, folder=True),
|
|
"outdir_txt2img_grids": OptionInfo("outputs/grids", 'Output directory for txt2img grids', component_args=hide_dirs, folder=True),
|
|
"outdir_img2img_grids": OptionInfo("outputs/grids", 'Output directory for img2img grids', component_args=hide_dirs, folder=True),
|
|
|
|
}))
|
|
|
|
options_templates.update(options_section(('ui', "User Interface"), {
|
|
"gradio_theme": OptionInfo("black-teal", "UI theme", gr.Dropdown, lambda: {"choices": list_themes()}, refresh=refresh_themes),
|
|
"theme_style": OptionInfo("Auto", "Theme mode", gr.Radio, {"choices": ["Auto", "Dark", "Light"]}),
|
|
"tooltips": OptionInfo("UI Tooltips", "UI tooltips", gr.Radio, {"choices": ["None", "Browser default", "UI tooltips"]}),
|
|
"return_grid": OptionInfo(True, "Show grid in results for web"),
|
|
"return_mask": OptionInfo(False, "For inpainting, include the greyscale mask in results for web"),
|
|
"return_mask_composite": OptionInfo(False, "For inpainting, include masked composite in results for web"),
|
|
"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"),
|
|
"font": OptionInfo("", "Font for image grids that have text"),
|
|
"keyedit_precision_attention": OptionInfo(0.1, "Ctrl+up/down precision when editing (attention:1.1)", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
|
|
"keyedit_precision_extra": OptionInfo(0.05, "Ctrl+up/down precision when editing <extra networks:0.9>", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
|
|
"keyedit_delimiters": OptionInfo(".,\/!?%^*;:{}=`~()", "Ctrl+up/down word delimiters"), # pylint: disable=anomalous-backslash-in-string
|
|
"quicksettings_list": OptionInfo(["sd_model_checkpoint"] if backend == Backend.ORIGINAL else ["sd_model_checkpoint", "sd_model_refiner"], "Quicksettings list", ui_components.DropdownMulti, lambda: {"choices": list(opts.data_labels.keys())}),
|
|
"ui_scripts_reorder": OptionInfo("", "UI scripts order"),
|
|
}))
|
|
|
|
options_templates.update(options_section(('live-preview', "Live Previews"), {
|
|
"show_progressbar": OptionInfo(True, "Show progressbar"),
|
|
"live_previews_enable": OptionInfo(True, "Show live previews of the created image"),
|
|
"show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"),
|
|
"notification_audio_enable": OptionInfo(False, "Play a sound when images are finished generating"),
|
|
"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": -1, "maximum": 32, "step": 1}),
|
|
"show_progress_type": OptionInfo("Approximate NN", "Live preview method", gr.Radio, {"choices": ["Full VAE", "Approximate NN", "Approximate simple", "TAESD"]}),
|
|
"live_preview_content": OptionInfo("Combined", "Live preview subject", gr.Radio, {"choices": ["Combined", "Prompt", "Negative prompt"]}),
|
|
"live_preview_refresh_period": OptionInfo(500, "Progress update period", gr.Slider, {"minimum": 0, "maximum": 5000, "step": 25}),
|
|
"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(["Default", "Euler a", "UniPC", "DEIS", "DDIM", "DPM 1S", "DPM 2M", "DPM++ 2M SDE", "DPM++ 2M SDE Karras", "DPM2 Karras", "DPM++ 2M Karras"], "Show samplers in user interface", gr.CheckboxGroup, lambda: {"choices": [x.name for x in list_samplers() if x.name != "PLMS"]}),
|
|
'uni_pc_variant': OptionInfo("bh1", "UniPC variant", gr.Radio, {"choices": ["bh1", "bh2", "vary_coeff"]}),
|
|
'uni_pc_skip_type': OptionInfo("time_uniform", "UniPC skip type", gr.Radio, {"choices": ["time_uniform", "time_quadratic", "logSNR"]}),
|
|
'eta_noise_seed_delta': OptionInfo(0, "Noise seed delta (eta)", gr.Number, {"precision": 0}),
|
|
"eta_ddim": OptionInfo(0.0, "Noise multiplier for DDIM (eta)", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
|
"schedulers_solver_order": OptionInfo(2, "Samplers solver order where applicable", gr.Slider, {"minimum": 1, "maximum": 5, "step": 1}),
|
|
|
|
"schedulers_sep_diffusers": OptionInfo("<h2>Diffusers specific config</h2>", "", gr.HTML),
|
|
"schedulers_prediction_type": OptionInfo("default", "Samplers override model prediction type", gr.Radio, lambda: {"choices": ['default', 'epsilon', 'sample', 'v-prediction']}),
|
|
"schedulers_use_karras": OptionInfo(True, "Samplers use Karras sigmas where applicable"),
|
|
"schedulers_use_loworder": OptionInfo(True, "Samplers use simplified solvers in final steps where applicable"),
|
|
"schedulers_use_thresholding": OptionInfo(False, "Samplers use dynamic thresholding where applicable"),
|
|
"schedulers_dpm_solver": OptionInfo("sde-dpmsolver++", "Samplers DPM solver algorithm", gr.Radio, lambda: {"choices": ['dpmsolver', 'dpmsolver++', 'sde-dpmsolver++']}),
|
|
"schedulers_beta_schedule": OptionInfo("default", "Samplers override beta schedule", gr.Radio, lambda: {"choices": ['default', 'linear', 'scaled_linear', 'squaredcos_cap_v2']}),
|
|
'schedulers_beta_start': OptionInfo(0, "Samplers override beta start", gr.Number, {}),
|
|
'schedulers_beta_end': OptionInfo(0, "Samplers override beta end", gr.Number, {}),
|
|
|
|
"schedulers_sep_kdiffusers": OptionInfo("<h2>K-Diffusion specific config</h2>", "", gr.HTML),
|
|
"always_batch_cond_uncond": OptionInfo(False, "Disable conditional batching enabled on low memory systems"),
|
|
"enable_quantization": OptionInfo(True, "Enable samplers quantization for sharper and cleaner results"),
|
|
"eta_ancestral": OptionInfo(1.0, "Noise multiplier for ancestral samplers (eta)", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
|
's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
|
's_min_uncond': OptionInfo(0.0, "sigma negative guidance minimum ", gr.Slider, {"minimum": 0.0, "maximum": 4.0, "step": 0.01}),
|
|
's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
|
's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
|
'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma"),
|
|
'never_discard_next_to_last_sigma': OptionInfo(False, "Never discard next-to-last sigma"),
|
|
|
|
"schedulers_sep_compvis": OptionInfo("<h2>CompVis specific config</h2>", "", gr.HTML),
|
|
"ddim_discretize": OptionInfo('uniform', "DDIM discretize img2img", gr.Radio, {"choices": ['uniform', 'quad']}),
|
|
}))
|
|
|
|
options_templates.update(options_section(('postprocessing', "Postprocessing"), {
|
|
'postprocessing_enable_in_main_ui': OptionInfo([], "Enable additional postprocessing operations", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
|
|
'postprocessing_operation_order': OptionInfo([], "Postprocessing operation order", ui_components.DropdownMulti, lambda: {"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 to match original colors"),
|
|
"img2img_fix_steps": OptionInfo(False, "For image processing do exact number of steps as specified"),
|
|
"img2img_background_color": OptionInfo("#ffffff", "Image transparent color fill", ui_components.FormColorPicker, {}),
|
|
"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}),
|
|
"CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 8, "step": 1, "visible": False}),
|
|
|
|
"postprocessing_sep_face_restoration": OptionInfo("<h2>Face Restoration</h2>", "", gr.HTML),
|
|
"face_restoration_model": OptionInfo("CodeFormer", "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in face_restorers]}),
|
|
"code_former_weight": OptionInfo(0.2, "CodeFormer weight parameter", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
|
|
"face_restoration_unload": OptionInfo(False, "Move face restoration model from VRAM into RAM after processing"),
|
|
|
|
"postprocessing_sep_upscalers": OptionInfo("<h2>Upscaling</h2>", "", gr.HTML),
|
|
'upscaling_max_images_in_cache': OptionInfo(5, "Maximum number of images in upscaling cache", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
|
|
"upscaler_for_img2img": OptionInfo("None", "Default upscaler for image resize operations", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),
|
|
"realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Real-ESRGAN available models", gr.CheckboxGroup, lambda: {"choices": shared_items.realesrgan_models_names()}),
|
|
"ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
|
|
"ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap in pixels for ESRGAN upscalers", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
|
|
"SCUNET_tile": OptionInfo(256, "Tile size for SCUNET upscalers", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
|
|
"SCUNET_tile_overlap": OptionInfo(8, "Tile overlap for SCUNET upscalers", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}),
|
|
}))
|
|
|
|
options_templates.update(options_section(('training', "Training"), {
|
|
"unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible"),
|
|
"pin_memory": OptionInfo(True, "Pin training dataset to memory"),
|
|
"save_optimizer_state": OptionInfo(False, "Save resumable optimizer state when training"),
|
|
"save_training_settings_to_txt": OptionInfo(True, "Save training settings to a text file on training start"),
|
|
"dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
|
|
"dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
|
|
"embeddings_templates_dir": OptionInfo(os.path.join(paths.script_path, 'train', 'templates'), "Embeddings train templates directory", folder=True),
|
|
"training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch", gr.Number, {"precision": 0}),
|
|
"training_write_csv_every": OptionInfo(0, "Save CSV file containing the loss to log directory"),
|
|
"training_enable_tensorboard": OptionInfo(False, "Enable tensorboard logging"),
|
|
"training_tensorboard_save_images": OptionInfo(False, "Save generated images within tensorboard"),
|
|
"training_tensorboard_flush_every": OptionInfo(120, "Tensorboard flush period"),
|
|
}))
|
|
|
|
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_dict_limit": OptionInfo(2048, "CLIP: maximum number of lines in text file"),
|
|
"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', "Extra Networks"), {
|
|
"extra_networks_card_cover": OptionInfo("sidebar", "UI position", gr.Radio, lambda: {"choices": ["cover", "inline", "sidebar"]}),
|
|
"extra_networks_height": OptionInfo(53, "UI height (%)", gr.Slider, {"minimum": 10, "maximum": 100, "step": 1}),
|
|
"extra_networks_sidebar_width": OptionInfo(35, "UI sidebar width (%)", gr.Slider, {"minimum": 10, "maximum": 80, "step": 1}),
|
|
"extra_networks_card_lazy": OptionInfo(True, "UI card preview lazy loading"),
|
|
"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_card_fit": OptionInfo("cover", "UI image contain method", gr.Radio, lambda: {"choices": ["contain", "cover", "fill"]}),
|
|
"extra_network_skip_indexing": OptionInfo(False, "Do not automatically build extra network pages", gr.Checkbox),
|
|
"lyco_patch_lora": OptionInfo(False, "Use LyCoris handler for all LoRA types", gr.Checkbox),
|
|
"lora_functional": OptionInfo(False, "Use Kohya method for handling multiple LoRA", gr.Checkbox),
|
|
"extra_networks_default_multiplier": OptionInfo(1.0, "Multiplier for extra networks", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
|
"sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: { "choices": ["None"] + list(hypernetworks.keys()), "visible": False }, refresh=reload_hypernetworks),
|
|
}))
|
|
|
|
options_templates.update(options_section((None, "Hidden options"), {
|
|
"disabled_extensions": OptionInfo([], "Disable these extensions"),
|
|
"disable_all_extensions": OptionInfo("none", "Disable all extensions (preserves the list of disabled extensions)", gr.Radio, {"choices": ["none", "user", "all"]}),
|
|
"sd_checkpoint_hash": OptionInfo("", "SHA256 hash of the current checkpoint"),
|
|
}))
|
|
|
|
options_templates.update()
|
|
|
|
|
|
class Options:
|
|
data = None
|
|
data_labels = options_templates
|
|
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 __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 e:
|
|
log.error(f'Error in onchange callback: {key} {value} {e}')
|
|
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(self, filename):
|
|
if cmd_opts.freeze:
|
|
log.warning(f'Settings saving is disabled: {filename}')
|
|
return
|
|
try:
|
|
# output = json.dumps(self.data, indent=2)
|
|
diff = {}
|
|
unused_settings = []
|
|
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:
|
|
unused_settings.append(k)
|
|
diff[k] = v
|
|
writefile(diff, filename)
|
|
if len(unused_settings) > 0:
|
|
log.debug(f"Unused settings: {unused_settings}")
|
|
except Exception as e:
|
|
log.error(f'Saving settings failed: {filename} {e}')
|
|
|
|
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):
|
|
if not os.path.isfile(filename):
|
|
log.debug(f'Created default config: {filename}')
|
|
self.save(filename)
|
|
return
|
|
self.data = readfile(filename)
|
|
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 = self.data_labels.get(k, None)
|
|
if info is not None and not self.same_type(info.default, v):
|
|
log.error(f"Error: bad setting value: {k}: {v} ({type(v).__name__}; expected {type(info.default).__name__})")
|
|
if info is None and k not in compatibility_opts:
|
|
unknown_settings.append(k)
|
|
if len(unknown_settings) > 0:
|
|
log.debug(f"Unknown settings: {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,
|
|
"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
|
|
|
|
opts = Options()
|
|
config_filename = cmd_opts.config
|
|
opts.load(config_filename)
|
|
cmd_opts = cmd_args.compatibility_args(opts, cmd_opts)
|
|
if cmd_opts.backend is None:
|
|
backend = Backend.DIFFUSERS if cmd_opts.use_openvino or opts.data.get('sd_backend', 'original') == 'diffusers' else Backend.ORIGINAL
|
|
else:
|
|
backend = Backend.DIFFUSERS if cmd_opts.use_openvino or cmd_opts.backend.lower() == 'diffusers' else Backend.ORIGINAL
|
|
opts.data['sd_backend'] = 'diffusers' if backend == Backend.DIFFUSERS else 'original'
|
|
opts.data['uni_pc_lower_order_final'] = opts.schedulers_use_loworder
|
|
opts.data['uni_pc_order'] = opts.schedulers_solver_order
|
|
opts.data['diffusers_lora_loader'] = 'diffusers' # TODO broken in diffusers=0.21
|
|
log.info(f'Engine: backend={backend} compute={devices.backend} mode={devices.inference_context.__name__} device={devices.get_optimal_device_name()}')
|
|
log.info(f'Device: {print_dict(devices.get_gpu_info())}')
|
|
|
|
prompt_styles = modules.styles.StyleDatabase(opts)
|
|
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.device, devices.device_interrogate, devices.device_gfpgan, devices.device_esrgan, devices.device_codeformer = (devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'esrgan', 'codeformer'])
|
|
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)
|
|
if devices.backend == "directml":
|
|
directml_do_hijack()
|
|
|
|
|
|
def reload_gradio_theme(theme_name=None):
|
|
global gradio_theme # pylint: disable=global-statement
|
|
if not theme_name:
|
|
theme_name = opts.gradio_theme
|
|
default_font_params = {}
|
|
res = 0
|
|
try:
|
|
req = urllib.request.Request("https://fonts.googleapis.com/css2?family=IBM+Plex+Mono", method="HEAD")
|
|
res = urllib.request.urlopen(req, timeout=3.0).status # pylint: disable=consider-using-with
|
|
except Exception:
|
|
res = 0
|
|
if res != 200:
|
|
log.info('No internet access detected, using default fonts')
|
|
default_font_params = {
|
|
'font':['Helvetica', 'ui-sans-serif', 'system-ui', 'sans-serif'],
|
|
'font_mono':['IBM Plex Mono', 'ui-monospace', 'Consolas', 'monospace']
|
|
}
|
|
if theme_name in list_builtin_themes():
|
|
gradio_theme = gr.themes.Default(**default_font_params)
|
|
elif theme_name.startswith("gradio/"):
|
|
if theme_name == "gradio/default":
|
|
gradio_theme = gr.themes.Default(**default_font_params)
|
|
if theme_name == "gradio/base":
|
|
gradio_theme = gr.themes.Base(**default_font_params)
|
|
if theme_name == "gradio/glass":
|
|
gradio_theme = gr.themes.Glass(**default_font_params)
|
|
if theme_name == "gradio/monochrome":
|
|
gradio_theme = gr.themes.Monochrome(**default_font_params)
|
|
if theme_name == "gradio/soft":
|
|
gradio_theme = gr.themes.Soft(**default_font_params)
|
|
else:
|
|
try:
|
|
gradio_theme = gr.themes.ThemeClass.from_hub(theme_name)
|
|
except Exception:
|
|
log.error("Theme download error accessing HuggingFace")
|
|
gradio_theme = gr.themes.Default(**default_font_params)
|
|
log.info(f'Loading UI theme: name={theme_name} style={opts.theme_style}')
|
|
|
|
|
|
class TotalTQDM: # compatibility with previous global-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 e:
|
|
log.error(f'Server shutdown error: {e}')
|
|
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 listfiles(dirname):
|
|
filenames = [os.path.join(dirname, x) for x in sorted(os.listdir(dirname), key=str.lower) if not x.startswith(".")]
|
|
return [file for file in filenames if os.path.isfile(file)]
|
|
|
|
|
|
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 ""
|
|
|
|
|
|
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
|
|
|
|
|
|
class Shared(sys.modules[__name__].__class__): # this class is here to provide sd_model field as a property, so that it can be created and loaded on demand rather than at program startup.
|
|
@property
|
|
def sd_model(self):
|
|
import modules.sd_models # pylint: disable=W0621
|
|
return modules.sd_models.model_data.get_sd_model()
|
|
|
|
@sd_model.setter
|
|
def sd_model(self, value):
|
|
import modules.sd_models # pylint: disable=W0621
|
|
modules.sd_models.model_data.set_sd_model(value)
|
|
|
|
@property
|
|
def sd_refiner(self):
|
|
import modules.sd_models # pylint: disable=W0621
|
|
return modules.sd_models.model_data.get_sd_refiner()
|
|
|
|
@sd_refiner.setter
|
|
def sd_refiner(self, value):
|
|
import modules.sd_models # pylint: disable=W0621
|
|
modules.sd_models.model_data.set_sd_refiner(value)
|
|
|
|
@property
|
|
def backend(self):
|
|
return Backend.ORIGINAL if not cmd_opts.use_openvino and opts.data['sd_backend'] == 'original' else Backend.DIFFUSERS
|
|
|
|
@property
|
|
def sd_model_type(self):
|
|
try:
|
|
if backend == Backend.ORIGINAL:
|
|
model_type = 'ldm'
|
|
elif "StableDiffusionXL" in self.sd_model.__class__.__name__:
|
|
model_type = 'sdxl'
|
|
elif "StableDiffusion" in self.sd_model.__class__.__name__:
|
|
model_type = 'sd'
|
|
elif "Kandinsky" in self.sd_model.__class__.__name__:
|
|
model_type = 'kandinsky'
|
|
else:
|
|
model_type = self.sd_model.__class__.__name__
|
|
except Exception:
|
|
model_type = 'unknown'
|
|
return model_type
|
|
|
|
@property
|
|
def sd_refiner_type(self):
|
|
try:
|
|
if backend == Backend.ORIGINAL:
|
|
model_type = 'ldm'
|
|
elif "StableDiffusionXL" in self.sd_refiner.__class__.__name__:
|
|
model_type = 'sdxl'
|
|
elif "StableDiffusion" in self.sd_refiner.__class__.__name__:
|
|
model_type = 'sd'
|
|
elif "Kandinsky" in self.sd_refiner.__class__.__name__:
|
|
model_type = 'kandinsky'
|
|
else:
|
|
model_type = self.sd_refiner.__class__.__name__
|
|
except Exception:
|
|
model_type = 'unknown'
|
|
return model_type
|
|
|
|
sd_model = None
|
|
sd_refiner = None
|
|
sd_model_type = ''
|
|
sd_refiner_type = ''
|
|
sys.modules[__name__].__class__ = Shared
|