mirror of
https://github.com/sd-webui/stable-diffusion-webui.git
synced 2024-12-14 23:02:00 +03:00
375 lines
10 KiB
Python
375 lines
10 KiB
Python
import argparse, os, re
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import torch
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import numpy as np
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from random import randint
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from omegaconf import OmegaConf
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from PIL import Image
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from tqdm import tqdm, trange
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from itertools import islice
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from einops import rearrange
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import time
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from pytorch_lightning import seed_everything
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from torch import autocast
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from contextlib import nullcontext
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from ldm.util import instantiate_from_config
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from optimUtils import split_weighted_subprompts, logger
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from transformers import logging
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# from samplers import CompVisDenoiser
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logging.set_verbosity_error()
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def chunk(it, size):
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it = iter(it)
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return iter(lambda: tuple(islice(it, size)), ())
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def load_model_from_config(ckpt, verbose=False):
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print(f"Loading model from {ckpt}")
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pl_sd = torch.load(ckpt, map_location="cpu")
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if "global_step" in pl_sd:
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print(f"Global Step: {pl_sd['global_step']}")
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sd = pl_sd["state_dict"]
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return sd
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config = "optimizedSD/v1-inference.yaml"
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DEFAULT_CKPT = "models/ldm/stable-diffusion-v1/model.ckpt"
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--prompt",
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type=str,
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nargs="?",
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default="a painting of a virus monster playing guitar",
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help="the prompt to render",
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)
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parser.add_argument(
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"--outdir",
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type=str,
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nargs="?",
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help="dir to write results to",
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default="outputs/txt2img-samples",
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)
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parser.add_argument(
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"--skip_grid",
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action="store_true",
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help="do not save a grid, only individual samples. Helpful when evaluating lots of samples",
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)
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parser.add_argument(
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"--skip_save",
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action="store_true",
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help="do not save individual samples. For speed measurements.",
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)
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parser.add_argument(
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"--ddim_steps",
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type=int,
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default=50,
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help="number of ddim sampling steps",
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)
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parser.add_argument(
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"--fixed_code",
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action="store_true",
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help="if enabled, uses the same starting code across samples ",
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)
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parser.add_argument(
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"--ddim_eta",
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type=float,
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default=0.0,
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help="ddim eta (eta=0.0 corresponds to deterministic sampling",
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)
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parser.add_argument(
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"--n_iter",
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type=int,
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default=1,
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help="sample this often",
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)
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parser.add_argument(
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"--H",
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type=int,
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default=512,
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help="image height, in pixel space",
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)
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parser.add_argument(
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"--W",
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type=int,
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default=512,
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help="image width, in pixel space",
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)
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parser.add_argument(
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"--C",
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type=int,
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default=4,
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help="latent channels",
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)
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parser.add_argument(
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"--f",
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type=int,
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default=8,
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help="downsampling factor",
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)
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parser.add_argument(
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"--n_samples",
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type=int,
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default=5,
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help="how many samples to produce for each given prompt. A.k.a. batch size",
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)
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parser.add_argument(
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"--n_rows",
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type=int,
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default=0,
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help="rows in the grid (default: n_samples)",
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)
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parser.add_argument(
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"--scale",
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type=float,
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default=7.5,
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help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))",
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)
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parser.add_argument(
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"--device",
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type=str,
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default="cuda",
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help="specify GPU (cuda/cuda:0/cuda:1/...)",
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)
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parser.add_argument(
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"--from-file",
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type=str,
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help="if specified, load prompts from this file",
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)
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parser.add_argument(
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"--seed",
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type=int,
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default=None,
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help="the seed (for reproducible sampling)",
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)
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parser.add_argument(
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"--unet_bs",
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type=int,
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default=1,
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help="Slightly reduces inference time at the expense of high VRAM (value > 1 not recommended )",
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)
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parser.add_argument(
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"--turbo",
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action="store_true",
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help="Reduces inference time on the expense of 1GB VRAM",
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)
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parser.add_argument(
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"--precision",
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type=str,
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help="evaluate at this precision",
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choices=["full", "autocast"],
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default="autocast",
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)
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parser.add_argument(
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"--format",
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type=str,
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help="output image format",
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choices=["jpg", "png"],
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default="png",
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)
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parser.add_argument(
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"--sampler",
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type=str,
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help="sampler",
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choices=["ddim", "plms", "heun", "euler", "euler_a", "dpm2", "dpm2_a", "lms"],
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default="plms",
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)
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parser.add_argument(
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"--ckpt",
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type=str,
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help="path to checkpoint of model",
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default=DEFAULT_CKPT,
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)
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opt = parser.parse_args()
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tic = time.time()
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os.makedirs(opt.outdir, exist_ok=True)
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outpath = opt.outdir
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grid_count = len(os.listdir(outpath)) - 1
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if opt.seed is None:
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opt.seed = randint(0, 1000000)
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seed_everything(opt.seed)
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# Logging
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logger(vars(opt), log_csv="logs/txt2img_logs.csv")
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sd = load_model_from_config(f"{opt.ckpt}")
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li, lo = [], []
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for key, value in sd.items():
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sp = key.split(".")
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if (sp[0]) == "model":
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if "input_blocks" in sp:
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li.append(key)
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elif "middle_block" in sp:
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li.append(key)
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elif "time_embed" in sp:
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li.append(key)
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else:
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lo.append(key)
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for key in li:
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sd["model1." + key[6:]] = sd.pop(key)
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for key in lo:
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sd["model2." + key[6:]] = sd.pop(key)
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config = OmegaConf.load(f"{config}")
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model = instantiate_from_config(config.modelUNet)
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_, _ = model.load_state_dict(sd, strict=False)
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model.eval()
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model.unet_bs = opt.unet_bs
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model.cdevice = opt.device
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model.turbo = opt.turbo
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modelCS = instantiate_from_config(config.modelCondStage)
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_, _ = modelCS.load_state_dict(sd, strict=False)
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modelCS.eval()
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modelCS.cond_stage_model.device = opt.device
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modelFS = instantiate_from_config(config.modelFirstStage)
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_, _ = modelFS.load_state_dict(sd, strict=False)
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modelFS.eval()
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del sd
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if opt.device != "cpu" and opt.precision == "autocast":
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model.half()
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modelCS.half()
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start_code = None
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if opt.fixed_code:
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start_code = torch.randn(
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[opt.n_samples, opt.C, opt.H // opt.f, opt.W // opt.f], device=opt.device
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)
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batch_size = opt.n_samples
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n_rows = opt.n_rows if opt.n_rows > 0 else batch_size
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if not opt.from_file:
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assert opt.prompt is not None
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prompt = opt.prompt
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print(f"Using prompt: {prompt}")
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data = [batch_size * [prompt]]
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else:
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print(f"reading prompts from {opt.from_file}")
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with open(opt.from_file, "r") as f:
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text = f.read()
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print(f"Using prompt: {text.strip()}")
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data = text.splitlines()
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data = batch_size * list(data)
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data = list(chunk(sorted(data), batch_size))
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if opt.precision == "autocast" and opt.device != "cpu":
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precision_scope = autocast
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else:
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precision_scope = nullcontext
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seeds = ""
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with torch.no_grad():
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all_samples = list()
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for n in trange(opt.n_iter, desc="Sampling"):
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for prompts in tqdm(data, desc="data"):
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sample_path = os.path.join(outpath, "_".join(re.split(":| ", prompts[0])))[
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:150
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]
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os.makedirs(sample_path, exist_ok=True)
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base_count = len(os.listdir(sample_path))
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with precision_scope("cuda"):
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modelCS.to(opt.device)
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uc = None
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if opt.scale != 1.0:
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uc = modelCS.get_learned_conditioning(batch_size * [""])
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if isinstance(prompts, tuple):
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prompts = list(prompts)
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subprompts, weights = split_weighted_subprompts(prompts[0])
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if len(subprompts) > 1:
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c = torch.zeros_like(uc)
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totalWeight = sum(weights)
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# normalize each "sub prompt" and add it
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for i in range(len(subprompts)):
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weight = weights[i]
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# if not skip_normalize:
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weight = weight / totalWeight
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c = torch.add(
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c,
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modelCS.get_learned_conditioning(subprompts[i]),
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alpha=weight,
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)
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else:
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c = modelCS.get_learned_conditioning(prompts)
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shape = [opt.n_samples, opt.C, opt.H // opt.f, opt.W // opt.f]
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if opt.device != "cpu":
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mem = torch.cuda.memory_allocated() / 1e6
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modelCS.to("cpu")
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while torch.cuda.memory_allocated() / 1e6 >= mem:
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time.sleep(1)
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samples_ddim = model.sample(
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S=opt.ddim_steps,
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conditioning=c,
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seed=opt.seed,
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shape=shape,
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verbose=False,
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unconditional_guidance_scale=opt.scale,
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unconditional_conditioning=uc,
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eta=opt.ddim_eta,
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x_T=start_code,
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sampler=opt.sampler,
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)
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modelFS.to(opt.device)
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print(samples_ddim.shape)
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print("saving images")
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for i in range(batch_size):
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x_samples_ddim = modelFS.decode_first_stage(
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samples_ddim[i].unsqueeze(0)
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)
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x_sample = torch.clamp(
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(x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0
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)
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x_sample = 255.0 * rearrange(
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x_sample[0].cpu().numpy(), "c h w -> h w c"
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)
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Image.fromarray(x_sample.astype(np.uint8)).save(
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os.path.join(
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sample_path,
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"seed_"
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+ str(opt.seed)
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+ "_"
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+ f"{base_count:05}.{opt.format}",
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)
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)
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seeds += str(opt.seed) + ","
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opt.seed += 1
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base_count += 1
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if opt.device != "cpu":
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mem = torch.cuda.memory_allocated() / 1e6
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modelFS.to("cpu")
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while torch.cuda.memory_allocated() / 1e6 >= mem:
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time.sleep(1)
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del samples_ddim
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print("memory_final = ", torch.cuda.memory_allocated() / 1e6)
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toc = time.time()
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time_taken = (toc - tic) / 60.0
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print(
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(
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"Samples finished in {0:.2f} minutes and exported to "
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+ sample_path
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+ "\n Seeds used = "
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+ seeds[:-1]
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).format(time_taken)
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)
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