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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)")
parser.add_argument("--esrgan-models-path", type=str, help="path to directory with ESRGAN models", default=os.path.join(script_path, 'ESRGAN'))
parser.add_argument("--opt-split-attention", action='store_true', help="enable optimization that reduced vram usage by a lot for about 10% decrease in performance")
parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
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 = ""
job_no = 0
job_count = 0
sampling_step = 0
sampling_steps = 0
def interrupt(self):
self.interrupted = True
def nextjob(self):
self.job_no += 1
self.sampling_step = 0
state = State()
artist_db = modules.artists.ArtistsDatabase(os.path.join(script_path, 'artists.csv'))
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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."),
"ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscaling. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
"ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for ESRGAN upscaling. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
"random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
"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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}
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_model = None