mirror of
https://github.com/sd-webui/stable-diffusion-webui.git
synced 2024-12-14 14:52:31 +03:00
010b27ce9a
* repo-merge * cutdown size * Create setup.py * webui.cmd * ldm * Update environment.yaml * Update environment.yaml
297 lines
8.2 KiB
Python
297 lines
8.2 KiB
Python
import argparse, os, sys, glob, random
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import torch
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import numpy as np
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import copy
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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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from torchvision.utils import make_grid
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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 contextmanager, nullcontext
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from ldm.util import instantiate_from_config
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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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ckpt = "models/ldm/stable-diffusion-v1/model.ckpt"
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device = "cuda"
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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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"--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=42,
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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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"--small_batch",
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action='store_true',
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help="Reduce inference time when generate a smaller batch of images",
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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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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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sample_path = os.path.join(outpath, "samples", "_".join(opt.prompt.split())[:255])
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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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grid_count = len(os.listdir(outpath)) - 1
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seed_everything(opt.seed)
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sd = load_model_from_config(f"{ckpt}")
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li = []
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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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config.modelUNet.params.ddim_steps = opt.ddim_steps
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if opt.small_batch:
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config.modelUNet.params.small_batch = True
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else:
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config.modelUNet.params.small_batch = False
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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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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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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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if 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([opt.n_samples, opt.C, opt.H // opt.f, opt.W // opt.f], device=device)
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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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prompt = opt.prompt
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assert prompt is not None
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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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data = f.read().splitlines()
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data = list(chunk(data, batch_size))
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precision_scope = autocast if opt.precision=="autocast" else nullcontext
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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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with precision_scope("cuda"):
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modelCS.to(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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c = modelCS.get_learned_conditioning(prompts)
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shape = [opt.C, opt.H // opt.f, opt.W // opt.f]
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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(S=opt.ddim_steps,
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conditioning=c,
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batch_size=opt.n_samples,
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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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modelFS.to(device)
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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(samples_ddim[i].unsqueeze(0))
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x_sample = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
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# for x_sample in x_samples_ddim:
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x_sample = 255. * rearrange(x_sample[0].cpu().numpy(), 'c h w -> h w c')
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Image.fromarray(x_sample.astype(np.uint8)).save(
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os.path.join(sample_path, f"{base_count:05}.png"))
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base_count += 1
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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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# if not opt.skip_grid:
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# all_samples.append(x_samples_ddim)
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del samples_ddim
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print("memory_final = ", torch.cuda.memory_allocated()/1e6)
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# if not skip_grid:
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# # additionally, save as grid
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# grid = torch.stack(all_samples, 0)
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# grid = rearrange(grid, 'n b c h w -> (n b) c h w')
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# grid = make_grid(grid, nrow=n_rows)
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# # to image
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# grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()
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# Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
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# grid_count += 1
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toc = time.time()
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time_taken = (toc-tic)/60.0
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print(("Your samples are ready in {0:.2f} minutes and waiting for you here \n" + sample_path).format(time_taken)) |