mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2024-06-07 21:20:49 +00:00
196 lines
10 KiB
Python
196 lines
10 KiB
Python
import sys
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import argparse
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import json
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import os
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import gradio as gr
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import torch
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import tqdm
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import modules.artists
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from modules.paths import script_path, sd_path
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import modules.styles
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config_filename = "config.json"
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sd_model_file = os.path.join(script_path, 'model.ckpt')
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if not os.path.exists(sd_model_file):
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sd_model_file = "models/ldm/stable-diffusion-v1/model.ckpt"
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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=os.path.join(sd_path, sd_model_file), help="path to checkpoint of model",)
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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='GFPGANv1.3.pth')
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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-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)")
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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='embeddings', help="embeddings directory for textual inversion (default: embeddings)")
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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("--always-batch-cond-uncond", action='store_true', help="a workaround test; may help with speed if you use --lowvram")
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parser.add_argument("--unload-gfpgan", action='store_true', help="unload GFPGAN every time after processing images. Warning: seems to cause memory leaks")
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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("--esrgan-models-path", type=str, help="path to directory with ESRGAN models", default=os.path.join(script_path, 'ESRGAN'))
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parser.add_argument("--opt-split-attention", action='store_true', help="enable optimization that reduce vram usage by a lot for about 10%% decrease in performance")
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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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cmd_opts = parser.parse_args()
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if torch.has_cuda:
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device = torch.device("cuda")
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elif torch.has_mps:
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device = torch.device("mps")
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else:
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device = torch.device("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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class State:
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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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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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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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state = State()
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artist_db = modules.artists.ArtistsDatabase(os.path.join(script_path, 'artists.csv'))
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styles_filename = os.path.join(script_path, 'styles.csv')
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prompt_styles = modules.styles.load_styles(styles_filename)
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face_restorers = []
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class Options:
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class OptionInfo:
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def __init__(self, default=None, label="", component=None, component_args=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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data = None
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data_labels = {
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"outdir_samples": OptionInfo("", "Output directory for images; if empty, defaults to two directories below"),
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"outdir_txt2img_samples": OptionInfo("outputs/txt2img-images", 'Output directory for txt2img images'),
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"outdir_img2img_samples": OptionInfo("outputs/img2img-images", 'Output directory for img2img images'),
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"outdir_extras_samples": OptionInfo("outputs/extras-images", 'Output directory for images from extras tab'),
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"outdir_grids": OptionInfo("", "Output directory for grids; if empty, defaults to two directories below"),
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"outdir_txt2img_grids": OptionInfo("outputs/txt2img-grids", 'Output directory for txt2img grids'),
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"outdir_img2img_grids": OptionInfo("outputs/img2img-grids", 'Output directory for img2img grids'),
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"save_to_dirs": OptionInfo(False, "When writing images, create a directory with name derived from the prompt"),
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"grid_save_to_dirs": OptionInfo(False, "When writing grids, create a directory with name derived from the prompt"),
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"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}),
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"outdir_save": OptionInfo("log/images", "Directory for saving images using the Save button"),
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"samples_save": OptionInfo(True, "Save indiviual samples"),
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"samples_format": OptionInfo('png', 'File format for individual samples'),
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"grid_save": OptionInfo(True, "Save image grids"),
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"return_grid": OptionInfo(True, "Show grid in results for web"),
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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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"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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"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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"enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
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"font": OptionInfo("", "Font for image grids that have text"),
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"enable_emphasis": OptionInfo(True, "Use (text) to make model pay more attention to text text and [text] to make it pay less attention"),
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"save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters."),
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"ESRGAN_tile": OptionInfo(192, "Tile size for upscaling. 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 upscaling. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "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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"upscale_at_full_resolution_padding": OptionInfo(16, "Inpainting at full resolution: padding, in pixels, for the masked region.", gr.Slider, {"minimum": 0, "maximum": 128, "step": 4}),
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"show_progressbar": OptionInfo(True, "Show progressbar"),
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"show_progress_every_n_steps": OptionInfo(0, "Show show image creation progress every N sampling steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),
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"multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job. Broken in PyCharm console."),
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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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}
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def __init__(self):
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self.data = {k: v.default for k, v in self.data_labels.items()}
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def __setattr__(self, key, value):
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if self.data is not None:
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if key in self.data:
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self.data[key] = value
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return super(Options, self).__setattr__(key, value)
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def __getattr__(self, item):
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if self.data is not None:
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if item in self.data:
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return self.data[item]
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if item in self.data_labels:
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return self.data_labels[item].default
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return super(Options, self).__getattribute__(item)
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def save(self, filename):
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with open(filename, "w", encoding="utf8") as file:
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json.dump(self.data, file)
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def load(self, filename):
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with open(filename, "r", encoding="utf8") as file:
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self.data = json.load(file)
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opts = Options()
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if os.path.exists(config_filename):
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opts.load(config_filename)
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sd_upscalers = []
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sd_model = None
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progress_print_out = sys.stdout
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class TotalTQDM:
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def __init__(self):
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self._tqdm = None
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def reset(self):
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self._tqdm = tqdm.tqdm(
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desc="Total progress",
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total=state.job_count * state.sampling_steps,
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position=1,
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file=progress_print_out
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)
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def update(self):
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if not opts.multiple_tqdm:
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return
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if self._tqdm is None:
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self.reset()
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self._tqdm.update()
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def clear(self):
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if self._tqdm is not None:
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self._tqdm.close()
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self._tqdm = None
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total_tqdm = TotalTQDM()
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