446 lines
28 KiB
Python
446 lines
28 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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from collections import OrderedDict
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import gradio as gr
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import tqdm
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import modules.artists
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import modules.interrogate
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import modules.memmon
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import modules.sd_models
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import modules.styles
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import modules.devices as devices
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from modules import sd_samplers, sd_models, localization
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from modules.hypernetworks import hypernetwork
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from modules.paths import models_path, script_path, sd_path
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sd_model_file = os.path.join(script_path, 'model.ckpt')
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default_sd_model_file = sd_model_file
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parser = argparse.ArgumentParser()
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parser.add_argument("--config", type=str, default=os.path.join(sd_path, "configs/stable-diffusion/v1-inference.yaml"), help="path to config which constructs model",)
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parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",)
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parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to directory with stable diffusion checkpoints")
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parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
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parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default=None)
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parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats")
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parser.add_argument("--no-half-vae", action='store_true', help="do not switch the VAE model to 16-bit floats")
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parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)")
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parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
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parser.add_argument("--embeddings-dir", type=str, default=os.path.join(script_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
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parser.add_argument("--aesthetic_embeddings-dir", type=str, default=os.path.join(script_path, 'aesthetic_embeddings'),
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help="aesthetic_embeddings directory(default: aesthetic_embeddings)")
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parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory")
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parser.add_argument("--localizations-dir", type=str, default=os.path.join(script_path, 'localizations'), help="localizations directory")
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parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
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parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage")
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parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a lot of speed for very low VRM usage")
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parser.add_argument("--lowram", action='store_true', help="load stable diffusion checkpoint weights to VRAM instead of RAM")
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parser.add_argument("--always-batch-cond-uncond", action='store_true', help="disables cond/uncond batching that is enabled to save memory with --medvram or --lowvram")
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parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.")
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parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
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parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site (doesn't work for me but you might have better luck)")
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parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None)
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parser.add_argument("--ngrok-region", type=str, help="The region in which ngrok should start.", default="us")
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parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer'))
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parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN'))
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parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(models_path, 'ESRGAN'))
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parser.add_argument("--bsrgan-models-path", type=str, help="Path to directory with BSRGAN model file(s).", default=os.path.join(models_path, 'BSRGAN'))
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parser.add_argument("--realesrgan-models-path", type=str, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(models_path, 'RealESRGAN'))
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parser.add_argument("--scunet-models-path", type=str, help="Path to directory with ScuNET model file(s).", default=os.path.join(models_path, 'ScuNET'))
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parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(models_path, 'SwinIR'))
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parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(models_path, 'LDSR'))
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parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers")
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parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
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parser.add_argument("--deepdanbooru", action='store_true', help="enable deepdanbooru interrogator")
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parser.add_argument("--opt-split-attention", action='store_true', help="force-enables Doggettx's cross-attention layer optimization. By default, it's on for torch cuda.")
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parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.")
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parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
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parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
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parser.add_argument("--use-cpu", nargs='+',choices=['all', 'sd', 'interrogate', 'gfpgan', 'bsrgan', 'esrgan', 'scunet', 'codeformer'], help="use CPU as torch device for specified modules", default=[], type=str.lower)
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parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
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parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)
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parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False)
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parser.add_argument("--ui-config-file", type=str, help="filename to use for ui configuration", default=os.path.join(script_path, 'ui-config.json'))
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parser.add_argument("--hide-ui-dir-config", action='store_true', help="hide directory configuration from webui", default=False)
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parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default=os.path.join(script_path, 'config.json'))
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parser.add_argument("--gradio-debug", action='store_true', help="launch gradio with --debug option")
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parser.add_argument("--gradio-auth", type=str, help='set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
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parser.add_argument("--gradio-img2img-tool", type=str, help='gradio image uploader tool: can be either editor for ctopping, or color-sketch for drawing', choices=["color-sketch", "editor"], default="editor")
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parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
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parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(script_path, 'styles.csv'))
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parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
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parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
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parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False)
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parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
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parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencoders model', default=None)
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parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
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cmd_opts = parser.parse_args()
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restricted_opts = [
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"samples_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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devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_bsrgan, devices.device_esrgan, devices.device_scunet, 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', 'bsrgan', 'esrgan', 'scunet', '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 = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
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loaded_hypernetwork = None
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aesthetic_embeddings = {}
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def update_aesthetic_embeddings():
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global aesthetic_embeddings
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aesthetic_embeddings = {f.replace(".pt",""): os.path.join(cmd_opts.aesthetic_embeddings_dir, f) for f in
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os.listdir(cmd_opts.aesthetic_embeddings_dir) if f.endswith(".pt")}
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aesthetic_embeddings = OrderedDict(**{"None": None}, **aesthetic_embeddings)
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update_aesthetic_embeddings()
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def reload_hypernetworks():
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global hypernetworks
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hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
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hypernetwork.load_hypernetwork(opts.sd_hypernetwork)
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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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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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textinfo = 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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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 get_job_timestamp(self):
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return datetime.datetime.now().strftime("%Y%m%d%H%M%S") # shouldn't this return job_timestamp?
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state = State()
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artist_db = modules.artists.ArtistsDatabase(os.path.join(script_path, 'artists.csv'))
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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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localization.list_localizations(cmd_opts.localizations_dir)
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def realesrgan_models_names():
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import modules.realesrgan_model
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return [x.name for x in modules.realesrgan_model.get_realesrgan_models(None)]
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class OptionInfo:
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def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, show_on_main_page=False, 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 = None
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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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hide_dirs = {"visible": not cmd_opts.hide_ui_dir_config}
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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"),
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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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"jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
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"export_for_4chan": OptionInfo(True, "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG"),
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"use_original_name_batch": OptionInfo(False, "Use original name for output filename during batch process in 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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}))
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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(False, "Save images to a subdirectory"),
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"grid_save_to_dirs": OptionInfo(False, "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("", "Directory name pattern"),
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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}),
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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 x4+", "R-ESRGAN x4+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": realesrgan_models_names()}),
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"SWIN_tile": OptionInfo(192, "Tile size for all SwinIR.", gr.Slider, {"minimum": 16, "maximum": 512, "step": 16}),
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"SWIN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
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"ldsr_steps": OptionInfo(100, "LDSR processing steps. Lower = faster", gr.Slider, {"minimum": 1, "maximum": 200, "step": 1}),
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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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"use_scale_latent_for_hires_fix": OptionInfo(False, "Upscale latent space image when doing hires. fix"),
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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(None, "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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"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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}))
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options_templates.update(options_section(('training', "Training"), {
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"unload_models_when_training": OptionInfo(False, "Unload VAE and CLIP from VRAM when training"),
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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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}))
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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": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models),
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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_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
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"sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
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"img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
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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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"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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"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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"use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
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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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"filter_nsfw": OptionInfo(False, "Filter NSFW content"),
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'CLIP_stop_at_last_layers': OptionInfo(1, "Stop At last layers of CLIP model", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
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"random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
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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_use_builtin_artists": OptionInfo(True, "Interrogate: use artists from artists.csv"),
|
|
"interrogate_return_ranks": OptionInfo(False, "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators)."),
|
|
"interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
|
|
"interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
|
|
"interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
|
|
"interrogate_clip_dict_limit": OptionInfo(1500, "CLIP: maximum number of lines in text file (0 = No limit)"),
|
|
"interrogate_deepbooru_score_threshold": OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
|
|
"deepbooru_sort_alpha": OptionInfo(True, "Interrogate: deepbooru sort alphabetically"),
|
|
"deepbooru_use_spaces": OptionInfo(False, "use spaces for tags in deepbooru"),
|
|
"deepbooru_escape": OptionInfo(True, "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)"),
|
|
}))
|
|
|
|
options_templates.update(options_section(('ui', "User interface"), {
|
|
"show_progressbar": OptionInfo(True, "Show progressbar"),
|
|
"show_progress_every_n_steps": OptionInfo(0, "Show image creation progress every N sampling steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),
|
|
"return_grid": OptionInfo(True, "Show grid 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(False, "Add model name to generation information"),
|
|
"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."),
|
|
'quicksettings': OptionInfo("sd_model_checkpoint", "Quicksettings list"),
|
|
'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(('sampler-params', "Sampler parameters"), {
|
|
"hide_samplers": OptionInfo([], "Hide samplers in user interface (requires restart)", gr.CheckboxGroup, lambda: {"choices": [x.name for x in sd_samplers.all_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}),
|
|
}))
|
|
|
|
|
|
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:
|
|
self.data[key] = value
|
|
|
|
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 save(self, filename):
|
|
with open(filename, "w", encoding="utf8") as file:
|
|
json.dump(self.data, file)
|
|
|
|
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):
|
|
item = self.data_labels.get(key)
|
|
item.onchange = func
|
|
|
|
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)
|
|
|
|
|
|
opts = Options()
|
|
if os.path.exists(config_filename):
|
|
opts.load(config_filename)
|
|
|
|
sd_upscalers = []
|
|
|
|
sd_model = None
|
|
|
|
clip_model = None
|
|
|
|
from modules.aesthetic_clip import AestheticCLIP
|
|
aesthetic_clip = AestheticCLIP()
|
|
|
|
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.close()
|
|
self._tqdm = None
|
|
|
|
|
|
total_tqdm = TotalTQDM()
|
|
|
|
mem_mon = modules.memmon.MemUsageMonitor("MemMon", device, opts)
|
|
mem_mon.start()
|