658 lines
33 KiB
Python
658 lines
33 KiB
Python
import argparse
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import datetime
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import json
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import os
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import sys
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import time
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from PIL import Image
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import gradio as gr
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import tqdm
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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
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from modules import localization, script_loading, 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
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demo = None
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parser = cmd_args.parser
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script_loading.preload_extensions(extensions_dir, parser)
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script_loading.preload_extensions(extensions_builtin_dir, parser)
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if os.environ.get('IGNORE_CMD_ARGS_ERRORS', None) is None:
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cmd_opts = parser.parse_args()
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else:
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cmd_opts, _ = parser.parse_known_args()
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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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}
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ui_reorder_categories = [
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"inpaint",
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"sampler",
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"checkboxes",
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"hires_fix",
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"dimensions",
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"cfg",
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"seed",
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"batch",
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"override_settings",
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"scripts",
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]
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cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or cmd_opts.server_name) and not cmd_opts.enable_insecure_extension_access
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devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_esrgan, devices.device_codeformer = \
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(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'])
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device = devices.device
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weight_load_location = None if cmd_opts.lowram else "cpu"
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batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)
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parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
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xformers_available = False
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config_filename = cmd_opts.ui_settings_file
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os.makedirs(cmd_opts.hypernetwork_dir, exist_ok=True)
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hypernetworks = {}
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loaded_hypernetworks = []
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def reload_hypernetworks():
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from modules.hypernetworks import hypernetwork
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global hypernetworks
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hypernetworks = hypernetwork.list_hypernetworks(cmd_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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job = ""
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job_no = 0
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job_count = 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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def skip(self):
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self.skipped = True
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def interrupt(self):
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self.interrupted = True
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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):
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self.sampling_step = 0
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self.job_count = -1
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self.processing_has_refined_job_count = False
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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.current_latent = None
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self.current_image = None
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self.current_image_sampling_step = 0
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self.id_live_preview = 0
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self.skipped = False
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self.interrupted = 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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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 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 != -1:
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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
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if opts.show_progress_grid:
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self.assign_current_image(modules.sd_samplers.samples_to_image_grid(self.current_latent))
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else:
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self.assign_current_image(modules.sd_samplers.sample_to_image(self.current_latent))
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self.current_image_sampling_step = self.sampling_step
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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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styles_filename = cmd_opts.styles_file
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prompt_styles = modules.styles.StyleDatabase(styles_filename)
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interrogator = modules.interrogate.InterrogateModels("interrogate")
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face_restorers = []
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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):
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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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def options_section(section_identifier, options_dict):
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for k, v in options_dict.items():
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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
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return modules.sd_models.checkpoint_tiles()
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def refresh_checkpoints():
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import modules.sd_models
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return modules.sd_models.list_models()
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def list_samplers():
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import modules.sd_samplers
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return modules.sd_samplers.all_samplers
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hide_dirs = {"visible": not cmd_opts.hide_ui_dir_config}
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tab_names = []
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options_templates = {}
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options_templates.update(options_section(('saving-images', "Saving images/grids"), {
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"samples_save": OptionInfo(True, "Always save all generated images"),
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"samples_format": OptionInfo('png', 'File format for images'),
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"samples_filename_pattern": OptionInfo("", "Images filename pattern", component_args=hide_dirs),
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"save_images_add_number": OptionInfo(True, "Add number to filename when saving", component_args=hide_dirs),
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"grid_save": OptionInfo(True, "Always save all generated image grids"),
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"grid_format": OptionInfo('png', 'File format for grids'),
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"grid_extended_filename": OptionInfo(False, "Add extended info (seed, prompt) to filename when saving grid"),
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"grid_only_if_multiple": OptionInfo(True, "Do not save grids consisting of one picture"),
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"grid_prevent_empty_spots": OptionInfo(False, "Prevent empty spots in grid (when set to autodetect)"),
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"n_rows": OptionInfo(-1, "Grid row count; use -1 for autodetect and 0 for it to be same as batch size", gr.Slider, {"minimum": -1, "maximum": 16, "step": 1}),
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"enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
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"save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters."),
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"save_images_before_face_restoration": OptionInfo(False, "Save a copy of image before doing face restoration."),
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"save_images_before_highres_fix": OptionInfo(False, "Save a copy of image before applying highres fix."),
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"save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
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"save_mask": OptionInfo(False, "For inpainting, save a copy of the greyscale mask"),
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"save_mask_composite": OptionInfo(False, "For inpainting, save a masked composite"),
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"jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
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"webp_lossless": OptionInfo(False, "Use lossless compression for webp images"),
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"export_for_4chan": OptionInfo(True, "If the saved image file size is above the limit, or its either width or height are above the limit, save a downscaled copy as JPG"),
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"img_downscale_threshold": OptionInfo(4.0, "File size limit for the above option, MB", gr.Number),
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"target_side_length": OptionInfo(4000, "Width/height limit for the above option, in pixels", gr.Number),
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"img_max_size_mp": OptionInfo(200, "Maximum image size, in megapixels", gr.Number),
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"use_original_name_batch": OptionInfo(True, "Use original name for output filename during batch process in extras tab"),
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"use_upscaler_name_as_suffix": OptionInfo(False, "Use upscaler name as filename suffix in the extras tab"),
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"save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),
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"do_not_add_watermark": OptionInfo(False, "Do not add watermark to images"),
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"temp_dir": OptionInfo("", "Directory for temporary images; leave empty for default"),
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"clean_temp_dir_at_start": OptionInfo(False, "Cleanup non-default temporary directory when starting webui"),
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}))
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options_templates.update(options_section(('saving-paths', "Paths for saving"), {
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"outdir_samples": OptionInfo("", "Output directory for images; if empty, defaults to three directories below", component_args=hide_dirs),
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"outdir_txt2img_samples": OptionInfo("outputs/txt2img-images", 'Output directory for txt2img images', component_args=hide_dirs),
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"outdir_img2img_samples": OptionInfo("outputs/img2img-images", 'Output directory for img2img images', component_args=hide_dirs),
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"outdir_extras_samples": OptionInfo("outputs/extras-images", 'Output directory for images from extras tab', component_args=hide_dirs),
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"outdir_grids": OptionInfo("", "Output directory for grids; if empty, defaults to two directories below", component_args=hide_dirs),
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"outdir_txt2img_grids": OptionInfo("outputs/txt2img-grids", 'Output directory for txt2img grids', component_args=hide_dirs),
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"outdir_img2img_grids": OptionInfo("outputs/img2img-grids", 'Output directory for img2img grids', component_args=hide_dirs),
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"outdir_save": OptionInfo("log/images", "Directory for saving images using the Save button", component_args=hide_dirs),
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}))
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options_templates.update(options_section(('saving-to-dirs', "Saving to a directory"), {
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"save_to_dirs": OptionInfo(True, "Save images to a subdirectory"),
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"grid_save_to_dirs": OptionInfo(True, "Save grids to a subdirectory"),
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"use_save_to_dirs_for_ui": OptionInfo(False, "When using \"Save\" button, save images to a subdirectory"),
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"directories_filename_pattern": OptionInfo("[date]", "Directory name pattern", component_args=hide_dirs),
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"directories_max_prompt_words": OptionInfo(8, "Max prompt words for [prompt_words] pattern", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1, **hide_dirs}),
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}))
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options_templates.update(options_section(('upscaling', "Upscaling"), {
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"ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
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"ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
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"realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": shared_items.realesrgan_models_names()}),
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"upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),
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}))
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options_templates.update(options_section(('face-restoration', "Face restoration"), {
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"face_restoration_model": OptionInfo("CodeFormer", "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in face_restorers]}),
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"code_former_weight": OptionInfo(0.5, "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
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"face_restoration_unload": OptionInfo(False, "Move face restoration model from VRAM into RAM after processing"),
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}))
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options_templates.update(options_section(('system', "System"), {
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"show_warnings": OptionInfo(False, "Show warnings in console."),
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"memmon_poll_rate": OptionInfo(8, "VRAM usage polls per second during generation. Set to 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}),
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"samples_log_stdout": OptionInfo(False, "Always print all generation info to standard output"),
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"multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job."),
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"print_hypernet_extra": OptionInfo(False, "Print extra hypernetwork information to console."),
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}))
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options_templates.update(options_section(('training', "Training"), {
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"unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
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"pin_memory": OptionInfo(False, "Turn on pin_memory for DataLoader. Makes training slightly faster but can increase memory usage."),
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"save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training of embedding or HN can be resumed with the matching optim file."),
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"save_training_settings_to_txt": OptionInfo(True, "Save textual inversion and hypernet settings to a text file whenever training starts."),
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"dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
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"dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
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"training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
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"training_write_csv_every": OptionInfo(500, "Save an csv containing the loss to log directory every N steps, 0 to disable"),
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"training_xattention_optimizations": OptionInfo(False, "Use cross attention optimizations while training"),
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"training_enable_tensorboard": OptionInfo(False, "Enable tensorboard logging."),
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"training_tensorboard_save_images": OptionInfo(False, "Save generated images within tensorboard."),
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"training_tensorboard_flush_every": OptionInfo(120, "How often, in seconds, to flush the pending tensorboard events and summaries to disk."),
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}))
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options_templates.update(options_section(('sd', "Stable Diffusion"), {
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"sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": list_checkpoint_tiles()}, refresh=refresh_checkpoints),
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"sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
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"sd_vae_checkpoint_cache": OptionInfo(0, "VAE Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
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"sd_vae": OptionInfo("Automatic", "SD VAE", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list),
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"sd_vae_as_default": OptionInfo(True, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"),
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"inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
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"initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for img2img", gr.Slider, {"minimum": 0.5, "maximum": 1.5, "step": 0.01}),
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"img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
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"img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
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"img2img_background_color": OptionInfo("#ffffff", "With img2img, fill image's transparent parts with this color.", ui_components.FormColorPicker, {}),
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"enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."),
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"enable_emphasis": OptionInfo(True, "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"),
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"enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
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"comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
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"CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
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"upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),
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}))
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options_templates.update(options_section(('compatibility', "Compatibility"), {
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"use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
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"use_old_karras_scheduler_sigmas": OptionInfo(False, "Use old karras scheduler sigmas (0.1 to 10)."),
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"no_dpmpp_sde_batch_determinism": OptionInfo(False, "Do not make DPM++ SDE deterministic across different batch sizes."),
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"use_old_hires_fix_width_height": OptionInfo(False, "For hires fix, use width/height sliders to set final resolution rather than first pass (disables Upscale by, Resize width/height to)."),
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}))
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options_templates.update(options_section(('interrogate', "Interrogate Options"), {
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"interrogate_keep_models_in_memory": OptionInfo(False, "Interrogate: keep models in VRAM"),
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"interrogate_return_ranks": OptionInfo(False, "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators)."),
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"interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
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"interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
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"interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
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"interrogate_clip_dict_limit": OptionInfo(1500, "CLIP: maximum number of lines in text file (0 = No limit)"),
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"interrogate_clip_skip_categories": OptionInfo([], "CLIP: skip inquire categories", gr.CheckboxGroup, lambda: {"choices": modules.interrogate.category_types()}, refresh=modules.interrogate.category_types),
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"interrogate_deepbooru_score_threshold": OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
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"deepbooru_sort_alpha": OptionInfo(True, "Interrogate: deepbooru sort alphabetically"),
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"deepbooru_use_spaces": OptionInfo(False, "use spaces for tags in deepbooru"),
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"deepbooru_escape": OptionInfo(True, "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)"),
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"deepbooru_filter_tags": OptionInfo("", "filter out those tags from deepbooru output (separated by comma)"),
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}))
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options_templates.update(options_section(('extra_networks', "Extra Networks"), {
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"extra_networks_default_view": OptionInfo("cards", "Default view for Extra Networks", gr.Dropdown, {"choices": ["cards", "thumbs"]}),
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"extra_networks_default_multiplier": OptionInfo(1.0, "Multiplier for extra networks", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
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"extra_networks_card_width": OptionInfo(0, "Card width for Extra Networks (px)"),
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"extra_networks_card_height": OptionInfo(0, "Card height for Extra Networks (px)"),
|
|
"extra_networks_add_text_separator": OptionInfo(" ", "Extra text to add before <...> when adding extra network to prompt"),
|
|
"sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": [""] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
|
|
}))
|
|
|
|
options_templates.update(options_section(('ui', "User interface"), {
|
|
"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"),
|
|
"do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
|
|
"add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
|
|
"add_model_name_to_info": OptionInfo(True, "Add model name to generation information"),
|
|
"disable_weights_auto_swap": OptionInfo(True, "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint."),
|
|
"send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"),
|
|
"send_size": OptionInfo(True, "Send size when sending prompt or image to another interface"),
|
|
"font": OptionInfo("", "Font for image grids that have text"),
|
|
"js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
|
|
"js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
|
|
"show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
|
|
"samplers_in_dropdown": OptionInfo(True, "Use dropdown for sampler selection instead of radio group"),
|
|
"dimensions_and_batch_together": OptionInfo(True, "Show Width/Height and Batch sliders in same row"),
|
|
"keyedit_precision_attention": OptionInfo(0.1, "Ctrl+up/down precision when editing (attention:1.1)", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
|
|
"keyedit_precision_extra": OptionInfo(0.05, "Ctrl+up/down precision when editing <extra networks:0.9>", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
|
|
"quicksettings": OptionInfo("sd_model_checkpoint", "Quicksettings list"),
|
|
"hidden_tabs": OptionInfo([], "Hidden UI tabs (requires restart)", ui_components.DropdownMulti, lambda: {"choices": [x for x in tab_names]}),
|
|
"ui_reorder": OptionInfo(", ".join(ui_reorder_categories), "txt2img/img2img UI item order"),
|
|
"ui_extra_networks_tab_reorder": OptionInfo("", "Extra networks tab order"),
|
|
"localization": OptionInfo("None", "Localization (requires restart)", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)),
|
|
}))
|
|
|
|
options_templates.update(options_section(('ui', "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"),
|
|
"show_progress_every_n_steps": OptionInfo(10, "Show new live preview image every N sampling steps. Set to -1 to show after completion of batch.", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}),
|
|
"show_progress_type": OptionInfo("Approx NN", "Image creation progress preview mode", gr.Radio, {"choices": ["Full", "Approx NN", "Approx cheap"]}),
|
|
"live_preview_content": OptionInfo("Prompt", "Live preview subject", gr.Radio, {"choices": ["Combined", "Prompt", "Negative prompt"]}),
|
|
"live_preview_refresh_period": OptionInfo(1000, "Progressbar/preview update period, in milliseconds")
|
|
}))
|
|
|
|
options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
|
|
"hide_samplers": OptionInfo([], "Hide samplers in user interface (requires restart)", gr.CheckboxGroup, lambda: {"choices": [x.name for x in list_samplers()]}),
|
|
"eta_ddim": OptionInfo(0.0, "eta (noise multiplier) for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
|
"eta_ancestral": OptionInfo(1.0, "eta (noise multiplier) for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
|
"ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),
|
|
's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
|
's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
|
's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
|
'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
|
|
'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma"),
|
|
'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"]}),
|
|
'uni_pc_order': OptionInfo(3, "UniPC order (must be < sampling steps)", gr.Slider, {"minimum": 1, "maximum": 50, "step": 1}),
|
|
'uni_pc_lower_order_final': OptionInfo(True, "UniPC lower order final"),
|
|
}))
|
|
|
|
options_templates.update(options_section(('postprocessing', "Postprocessing"), {
|
|
'postprocessing_enable_in_main_ui': OptionInfo([], "Enable postprocessing operations in txt2img and img2img tabs", 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()]}),
|
|
'upscaling_max_images_in_cache': OptionInfo(5, "Maximum number of images in upscaling cache", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
|
|
}))
|
|
|
|
options_templates.update(options_section((None, "Hidden options"), {
|
|
"disabled_extensions": OptionInfo([], "Disable those extensions"),
|
|
"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):
|
|
if self.data is not None:
|
|
if key in self.data or key in self.data_labels:
|
|
assert not cmd_opts.freeze_settings, "changing settings is disabled"
|
|
|
|
info = opts.data_labels.get(key, None)
|
|
comp_args = info.component_args if info else None
|
|
if isinstance(comp_args, dict) and comp_args.get('visible', True) is False:
|
|
raise RuntimeError(f"not possible to set {key} because it is restricted")
|
|
|
|
if cmd_opts.hide_ui_dir_config and key in restricted_opts:
|
|
raise RuntimeError(f"not possible to set {key} because it is restricted")
|
|
|
|
self.data[key] = value
|
|
return
|
|
|
|
return super(Options, self).__setattr__(key, value)
|
|
|
|
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)
|
|
|
|
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 == 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:
|
|
errors.display(e, f"changing setting {key} to {value}")
|
|
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)
|
|
if data_label is None:
|
|
return None
|
|
|
|
return data_label.default
|
|
|
|
def save(self, filename):
|
|
assert not cmd_opts.freeze_settings, "saving settings is disabled"
|
|
|
|
with open(filename, "w", encoding="utf8") as file:
|
|
json.dump(self.data, file, indent=4)
|
|
|
|
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):
|
|
with open(filename, "r", encoding="utf8") as file:
|
|
self.data = json.load(file)
|
|
|
|
bad_settings = 0
|
|
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):
|
|
print(f"Warning: bad setting value: {k}: {v} ({type(v).__name__}; expected {type(info.default).__name__})", file=sys.stderr)
|
|
bad_settings += 1
|
|
|
|
if bad_settings > 0:
|
|
print(f"The program is likely to not work with bad settings.\nSettings file: {filename}\nEither fix the file, or delete it and restart.", file=sys.stderr)
|
|
|
|
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()}
|
|
return json.dumps(d)
|
|
|
|
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 = {k: v for k, v in 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
|
|
else:
|
|
value = expected_type(value)
|
|
|
|
return value
|
|
|
|
|
|
|
|
opts = Options()
|
|
if os.path.exists(config_filename):
|
|
opts.load(config_filename)
|
|
|
|
settings_components = None
|
|
"""assinged from ui.py, a mapping on setting anmes to gradio components repsponsible for those settings"""
|
|
|
|
latent_upscale_default_mode = "Latent"
|
|
latent_upscale_modes = {
|
|
"Latent": {"mode": "bilinear", "antialias": False},
|
|
"Latent (antialiased)": {"mode": "bilinear", "antialias": True},
|
|
"Latent (bicubic)": {"mode": "bicubic", "antialias": False},
|
|
"Latent (bicubic antialiased)": {"mode": "bicubic", "antialias": True},
|
|
"Latent (nearest)": {"mode": "nearest", "antialias": False},
|
|
"Latent (nearest-exact)": {"mode": "nearest-exact", "antialias": False},
|
|
}
|
|
|
|
sd_upscalers = []
|
|
|
|
sd_model = None
|
|
|
|
clip_model = None
|
|
|
|
progress_print_out = sys.stdout
|
|
|
|
|
|
class TotalTQDM:
|
|
def __init__(self):
|
|
self._tqdm = None
|
|
|
|
def reset(self):
|
|
self._tqdm = tqdm.tqdm(
|
|
desc="Total progress",
|
|
total=state.job_count * state.sampling_steps,
|
|
position=1,
|
|
file=progress_print_out
|
|
)
|
|
|
|
def update(self):
|
|
if not opts.multiple_tqdm or cmd_opts.disable_console_progressbars:
|
|
return
|
|
if self._tqdm is None:
|
|
self.reset()
|
|
self._tqdm.update()
|
|
|
|
def updateTotal(self, new_total):
|
|
if not opts.multiple_tqdm or cmd_opts.disable_console_progressbars:
|
|
return
|
|
if self._tqdm is None:
|
|
self.reset()
|
|
self._tqdm.total = new_total
|
|
|
|
def clear(self):
|
|
if self._tqdm is not None:
|
|
self._tqdm.refresh()
|
|
self._tqdm.close()
|
|
self._tqdm = None
|
|
|
|
|
|
total_tqdm = TotalTQDM()
|
|
|
|
mem_mon = modules.memmon.MemUsageMonitor("MemMon", device, opts)
|
|
mem_mon.start()
|
|
|
|
|
|
def listfiles(dirname):
|
|
filenames = [os.path.join(dirname, x) for x in sorted(os.listdir(dirname)) if not x.startswith(".")]
|
|
return [file for file in filenames if os.path.isfile(file)]
|
|
|
|
|
|
def html_path(filename):
|
|
return os.path.join(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 ""
|