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 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", ) opt = parser.parse_args() 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() 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 = [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.")