simulacra_imagen_sample.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

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


def main(opt):
    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(
        "--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(
        "--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",
    )
    #opt = parser.parse_args()
    opt.n_rows = 1
    opt.config = "stable-diffusion/configs/stable-diffusion/txt2img-1p4B-multinode-clip-encoder-high-res-512-inference.yaml"
    opt.ckpt = "256f8ft512-2022-06-15-pruned.ckpt"
    opt.dyn = None

    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)

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

    with torch.no_grad():
        with model.ema_scope():
            for n in trange(opt.n_iter, desc="Sampling"):
                all_samples = list()
                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 = [4, opt.H//8, opt.W//8]
                    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)
                    all_samples.append(x_samples_ddim)

                # print(all_samples)
                # outs = torch.stack(all_samples)
                # print(outs.shape)
                outs = all_samples[0]
                for index, out in enumerate(outs):
                    print(out.shape)
                    outpath = str(opt.seed) + "_" + opt.prompt.replace(" ", "_").replace("/","_") + "_" + str(index + 1) + ".png"
                    out = 255. * rearrange(out.cpu().numpy(), 'c h w -> h w c')
                    Image.fromarray(out.astype(np.uint8)).save(outpath)

                gridpath = str(opt.seed) + "_" + opt.prompt.replace(" ", "_").replace("/","_") + "_grid" + ".png"
                grid = torch.stack(all_samples, 0)
                grid = rearrange(grid, 'n b c h w -> (n b) c h w')
                grid = make_grid(grid, nrow=opt.n_samples)

                # to image
                grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()
                Image.fromarray(grid.astype(np.uint8)).save(gridpath)

if __name__ == "__main__":
    pass