diff --git a/ldm/stable_txt2img.py b/ldm/stable_txt2img.py deleted file mode 100644 index 1f9cc4af52..0000000000 --- a/ldm/stable_txt2img.py +++ /dev/null @@ -1,287 +0,0 @@ -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 torch import autocast -from contextlib import contextmanager, nullcontext - -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(): - 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 individual 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( - "--laion400m", - action='store_true', - help="uses the LAION400M model", - ) - parser.add_argument( - "--fixed_code", - action='store_true', - help="if enabled, uses the same starting code across samples ", - ) - 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=2, - help="sample this often", - ) - parser.add_argument( - "--H", - type=int, - default=512, - help="image height, in pixel space", - ) - parser.add_argument( - "--W", - type=int, - default=512, - 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", - ) - parser.add_argument( - "--n_samples", - type=int, - default=3, - 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=7.5, - help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))", - ) - parser.add_argument( - "--from-file", - type=str, - help="if specified, load prompts from this file", - ) - parser.add_argument( - "--config", - type=str, - default="configs/stable-diffusion/v1-inference.yaml", - help="path to config which constructs model", - ) - parser.add_argument( - "--ckpt", - type=str, - default="models/ldm/stable-diffusion-v1/model.ckpt", - help="path to checkpoint of model", - ) - parser.add_argument( - "--seed", - type=int, - default=42, - help="the seed (for reproducible sampling)", - ) - parser.add_argument( - "--precision", - type=str, - help="evaluate at this precision", - choices=["full", "autocast"], - default="autocast" - ) - - - parser.add_argument( - "--embedding_path", - type=str, - help="Path to a pre-trained embedding manager checkpoint") - - opt = parser.parse_args() - - if opt.laion400m: - print("Falling back to LAION 400M model...") - opt.config = "configs/latent-diffusion/txt2img-1p4B-eval.yaml" - opt.ckpt = "models/ldm/text2img-large/model.ckpt" - opt.outdir = "outputs/txt2img-samples-laion400m" - - seed_everything(opt.seed) - - config = OmegaConf.load(f"{opt.config}") - model = load_model_from_config(config, f"{opt.ckpt}") - model.embedding_manager.load(opt.embedding_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) - - 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 - - start_code = None - if opt.fixed_code: - start_code = torch.randn([opt.n_samples, opt.C, opt.H // opt.f, opt.W // opt.f], device=device) - - precision_scope = autocast if opt.precision=="autocast" else nullcontext - with torch.no_grad(): - with precision_scope("cuda"): - 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, - x_T=start_code) - - 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}.jpg")) - base_count += 1 - - if not opt.skip_grid: - 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'{prompt.replace(" ", "-")}-{grid_count:04}.jpg')) - grid_count += 1 - - toc = time.time() - - print(f"Your samples are ready and waiting for you here: \n{outpath} \n" - f" \nEnjoy.") - - -if __name__ == "__main__": - main()