stable-diffusion-finetune/scripts/txt2img.py

import argparse, os, sys, glob
import torch
import numpy as np
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange
from torchvision.utils import make_grid
import time
from pytorch_lightning import seed_everything

from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler


def chunk(it, size):
    it = iter(it)
    return iter(lambda: tuple(islice(it, size)), ())


def load_model_from_config(config, ckpt, verbose=False):
    print(f"Loading model from {ckpt}")
    pl_sd = torch.load(ckpt, map_location="cpu")
    if "global_step" in pl_sd:
        print(f"Global Step: {pl_sd['global_step']}")
    sd = pl_sd["state_dict"]
    model = instantiate_from_config(config.model)
    m, u = model.load_state_dict(sd, strict=False)
    if len(m) > 0 and verbose:
        print("missing keys:")
        print(m)
    if len(u) > 0 and verbose:
        print("unexpected keys:")
        print(u)

    model.cuda()
    model.eval()
    return model


if __name__ == "__main__":
    parser = argparse.ArgumentParser()

    parser.add_argument(
        "--prompt",
        type=str,
        nargs="?",
        default="a painting of a virus monster playing guitar",
        help="the prompt to render"
    )

    parser.add_argument(
        "--outdir",
        type=str,
        nargs="?",
        help="dir to write results to",
        default="outputs/txt2img-samples"
    )

    parser.add_argument(
        "--skip_grid",
        action='store_true',
        help="do not save a grid, only individual samples. Helpful when evaluating lots of samples",
    )

    parser.add_argument(
        "--skip_save",
        action='store_true',
        help="do not save indiviual samples. For speed measurements.",
    )

    parser.add_argument(
        "--ddim_steps",
        type=int,
        default=50,
        help="number of ddim sampling steps",
    )

    parser.add_argument(
        "--plms",
        action='store_true',
        help="use plms sampling",
    )

    parser.add_argument(
        "--ddim_eta",
        type=float,
        default=0.0,
        help="ddim eta (eta=0.0 corresponds to deterministic sampling",
    )
    parser.add_argument(
        "--n_iter",
        type=int,
        default=1,
        help="sample this often",
    )

    parser.add_argument(
        "--H",
        type=int,
        default=256,
        help="image height, in pixel space",
    )

    parser.add_argument(
        "--W",
        type=int,
        default=256,
        help="image width, in pixel space",
    )

    parser.add_argument(
        "--C",
        type=int,
        default=4,
        help="latent channels",
    )
    parser.add_argument(
        "--f",
        type=int,
        default=8,
        help="downsampling factor, most often 8 or 16",
    )

    parser.add_argument(
        "--n_samples",
        type=int,
        default=8,
        help="how many samples to produce for each given prompt. A.k.a batch size",
    )

    parser.add_argument(
        "--n_rows",
        type=int,
        default=0,
        help="rows in the grid (default: n_samples)",
    )

    parser.add_argument(
        "--scale",
        type=float,
        default=5.0,
        help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))",
    )

    parser.add_argument(
        "--dyn",
        type=float,
        help="dynamic thresholding from Imagen, in latent space (TODO: try in pixel space with intermediate decode)",
    )
    parser.add_argument(
        "--from-file",
        type=str,
        help="if specified, load prompts from this file",
    )

    parser.add_argument(
        "--config",
        type=str,
        default="logs/f8-kl-clip-encoder-256x256-run1/configs/2022-06-01T22-11-40-project.yaml",
        help="path to config which constructs model",
    )
    parser.add_argument(
        "--ckpt",
        type=str,
        default="logs/f8-kl-clip-encoder-256x256-run1/checkpoints/last.ckpt",
        help="path to checkpoint of model",
    )
    parser.add_argument(
        "--seed",
        type=int,
        default=42,
        help="the seed (for reproducible sampling)",
    )
    opt = parser.parse_args()
    seed_everything(opt.seed)

    config = OmegaConf.load(f"{opt.config}")
    model = load_model_from_config(config, f"{opt.ckpt}")

    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
    model = model.to(device)

    if opt.plms:
        sampler = PLMSSampler(model)
    else:
        sampler = DDIMSampler(model)

    os.makedirs(opt.outdir, exist_ok=True)
    outpath = opt.outdir

    batch_size = opt.n_samples
    n_rows = opt.n_rows if opt.n_rows > 0 else batch_size
    if not opt.from_file:
        prompt = opt.prompt
        assert prompt is not None
        data = [batch_size * [prompt]]

    else:
        print(f"reading prompts from {opt.from_file}")
        with open(opt.from_file, "r") as f:
            data = f.read().splitlines()
            data = list(chunk(data, batch_size))

    sample_path = os.path.join(outpath, "samples")
    os.makedirs(sample_path, exist_ok=True)
    base_count = len(os.listdir(sample_path))
    grid_count = len(os.listdir(outpath)) - 1

    with torch.no_grad():
        with model.ema_scope():
            tic = time.time()
            all_samples = list()
            for n in trange(opt.n_iter, desc="Sampling"):
                for prompts in tqdm(data, desc="data"):
                    uc = None
                    if opt.scale != 1.0:
                        uc = model.get_learned_conditioning(batch_size * [""])
                    if isinstance(prompts, tuple):
                        prompts = list(prompts)
                    c = model.get_learned_conditioning(prompts)
                    shape = [opt.C, opt.H//opt.f, opt.W//opt.f]
                    samples_ddim, _ = sampler.sample(S=opt.ddim_steps,
                                                     conditioning=c,
                                                     batch_size=opt.n_samples,
                                                     shape=shape,
                                                     verbose=False,
                                                     unconditional_guidance_scale=opt.scale,
                                                     unconditional_conditioning=uc,
                                                     eta=opt.ddim_eta,
                                                     dynamic_threshold=opt.dyn)

                    x_samples_ddim = model.decode_first_stage(samples_ddim)
                    x_samples_ddim = torch.clamp((x_samples_ddim+1.0)/2.0, min=0.0, max=1.0)

                    if not opt.skip_save:
                        for x_sample in x_samples_ddim:
                            x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
                            Image.fromarray(x_sample.astype(np.uint8)).save(os.path.join(sample_path, f"{base_count:05}.png"))
                            base_count += 1
                    all_samples.append(x_samples_ddim)

            if not opt.skip_grid:
                # additionally, save as grid
                grid = torch.stack(all_samples, 0)
                grid = rearrange(grid, 'n b c h w -> (n b) c h w')
                grid = make_grid(grid, nrow=n_rows)

                # to image
                grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()
                Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
                grid_count += 1

            toc = time.time()

    print(f"Your samples are ready and waiting for you here: \n{outpath} \n"
          f"Sampling took {toc-tic}s, i.e. produced {opt.n_iter * opt.n_samples / (toc - tic):.2f} samples/sec."
          f" \nEnjoy.")