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 einops import rearrange
from torchvision.utils import make_grid
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
import pdb 
import random 


def load_model_from_config(config, ckpt, verbose=False):
    print(f"Loading model from {ckpt}")
    pl_sd = torch.load(ckpt, map_location="cpu")
    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(
        "--ddim_steps",
        type=int,
        default=200,
        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=4,
        help="how many samples to produce for the given prompt",
    )

    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(
        "--seed",
        type=int,
        default=42,
        help="the seed (for reproducible sampling)",
    )

    parser.add_argument(
        "--ema",
        action='store_true',
    )

    parser.add_argument(
        "--ckpt_path", 
        type=str, 
        default="/data/pretrained_models/ldm/text2img-large/model.ckpt", 
        help="Path to pretrained ldm text2img model")

    parser.add_argument(
        "--embedding_path", 
        type=str, 
        help="Path to a pre-trained embedding manager checkpoint")

    parser.add_argument(
        "--use_generic", 
        type=bool, 
        default=False
    )
    
    opt = parser.parse_args()
    seed = random.randint(1,500)
    seed_everything(seed)

    if opt.use_generic:
        config = OmegaConf.load("configs/finetune_generic.yaml")
    else:
        config = OmegaConf.load("configs/finetune.yaml")
    

    model = load_model_from_config(config, opt.ckpt_path)  # TODO: check path

    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, device=device)

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


    sample_path = os.path.join(outpath, "samples")
    os.makedirs(sample_path, exist_ok=True)
    base_count = len(os.listdir(sample_path))
    start_code = None

    all_samples=list()
    with torch.no_grad():
        with model.ema_scope():
            uc = None
            if opt.scale != 1.0:
                uc = model.get_learned_conditioning(opt.n_samples * [""])
            for n in trange(opt.n_iter, desc="Sampling"):
                c = model.get_learned_conditioning(opt.n_samples * [prompt])
                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)


                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)

                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:04}.png"))
                    base_count += 1
                all_samples.append(x_samples_ddim)
                prompt = prompt

    # 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=opt.n_samples)

    # 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'{prompt.replace(" ", "-")}.jpg'))

    print(f"Your samples are ready and waiting for you here: \n{outpath} \nEnjoy.")