import argparse import json import os import gradio as gr import torch from modules.paths import script_path, sd_path config_filename = "config.json" sd_model_file = os.path.join(script_path, 'model.ckpt') if not os.path.exists(sd_model_file): sd_model_file = "models/ldm/stable-diffusion-v1/model.ckpt" parser = argparse.ArgumentParser() 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",) parser.add_argument("--ckpt", type=str, default=os.path.join(sd_path, sd_model_file), help="path to checkpoint of model",) parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN')) parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default='GFPGANv1.3.pth') parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats") 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 accleration in browser)") parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI") parser.add_argument("--embeddings-dir", type=str, default='embeddings', help="embeddings dirtectory for textual inversion (default: embeddings)") parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui") parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrficing a little speed for low VRM usage") parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrficing a lot of speed for very low VRM usage") parser.add_argument("--always-batch-cond-uncond", action='store_true', help="a workaround test; may help with speed in you use --lowvram") parser.add_argument("--unload-gfpgan", action='store_true', help="unload GFPGAN every time after processing images. Warning: seems to cause memory leaks") parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast") 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)") cmd_opts = parser.parse_args() cpu = torch.device("cpu") gpu = torch.device("cuda") device = gpu if torch.cuda.is_available() else cpu batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram) class State: interrupted = False job = "" def interrupt(self): self.interrupted = True state = State() class Options: class OptionInfo: def __init__(self, default=None, label="", component=None, component_args=None): self.default = default self.label = label self.component = component self.component_args = component_args data = None data_labels = { "outdir_samples": OptionInfo("", "Output dictectory for images; if empty, defaults to two directories below"), "outdir_txt2img_samples": OptionInfo("outputs/txt2img-images", 'Output dictectory for txt2img images'), "outdir_img2img_samples": OptionInfo("outputs/img2img-images", 'Output dictectory for img2img images'), "outdir_extras_samples": OptionInfo("outputs/extras-images", 'Output dictectory for images from extras tab'), "outdir_grids": OptionInfo("", "Output dictectory for grids; if empty, defaults to two directories below"), "outdir_txt2img_grids": OptionInfo("outputs/txt2img-grids", 'Output dictectory for txt2img grids'), "outdir_img2img_grids": OptionInfo("outputs/img2img-grids", 'Output dictectory for img2img grids'), "save_to_dirs": OptionInfo(False, "When writing images/grids, create a directory with name derived from the prompt"), "save_to_dirs_prompt_len": OptionInfo(10, "When using above, how many words from prompt to put into directory name", gr.Slider, {"minimum": 1, "maximum": 32, "step": 1}), "outdir_save": OptionInfo("log/images", "Directory for saving images using the Save button"), "samples_save": OptionInfo(True, "Save indiviual samples"), "samples_format": OptionInfo('png', 'File format for indiviual samples'), "grid_save": OptionInfo(True, "Save image grids"), "return_grid": OptionInfo(True, "Show grid in results for web"), "grid_format": OptionInfo('png', 'File format for grids'), "grid_extended_filename": OptionInfo(False, "Add extended info (seed, prompt) to filename when saving grid"), "grid_only_if_multiple": OptionInfo(True, "Do not save grids consisting of one picture"), "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}), "jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}), "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"), "enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"), "font": OptionInfo("arial.ttf", "Font for image grids that have text"), "enable_emphasis": OptionInfo(True, "Use (text) to make model pay more attention to text text and [text] to make it pay less attention"), "save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters."), } 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 load(self, filename): with open(filename, "r", encoding="utf8") as file: self.data = json.load(file) opts = Options() if os.path.exists(config_filename): opts.load(config_filename) sd_upscalers = {} sd_model = None