diff --git a/ldm/models/diffusion/ddim.py b/ldm/models/diffusion/ddim.py index 99a08c0..aa3fbec 100644 --- a/ldm/models/diffusion/ddim.py +++ b/ldm/models/diffusion/ddim.py @@ -6,7 +6,7 @@ from tqdm import tqdm from functools import partial from einops import rearrange -from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like +from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, extract_into_tensor from ldm.models.diffusion.sampling_util import renorm_thresholding, norm_thresholding, spatial_norm_thresholding @@ -226,3 +226,86 @@ class DDIMSampler(object): noise = torch.nn.functional.dropout(noise, p=noise_dropout) x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise return x_prev, pred_x0 + + @torch.no_grad() + def encode(self, x0, c, t_enc, use_original_steps=False, return_intermediates=None, + unconditional_guidance_scale=1.0, unconditional_conditioning=None): + num_reference_steps = self.ddpm_num_timesteps if use_original_steps else self.ddim_timesteps.shape[0] + + assert t_enc <= num_reference_steps + num_steps = t_enc + + if use_original_steps: + alphas_next = self.alphas_cumprod[:num_steps] + alphas = self.alphas_cumprod_prev[:num_steps] + else: + alphas_next = self.ddim_alphas[:num_steps] + alphas = torch.tensor(self.ddim_alphas_prev[:num_steps]) + + x_next = x0 + intermediates = [] + inter_steps = [] + for i in tqdm(range(num_steps), desc='Encoding Image'): + t = torch.full((x0.shape[0],), i, device=self.model.device, dtype=torch.long) + if unconditional_guidance_scale == 1.: + noise_pred = self.model.apply_model(x_next, t, c) + else: + assert unconditional_conditioning is not None + e_t_uncond, noise_pred = torch.chunk( + self.model.apply_model(torch.cat((x_next, x_next)), torch.cat((t, t)), + torch.cat((unconditional_conditioning, c))), 2) + noise_pred = e_t_uncond + unconditional_guidance_scale * (noise_pred - e_t_uncond) + + xt_weighted = (alphas_next[i] / alphas[i]).sqrt() * x_next + weighted_noise_pred = alphas_next[i].sqrt() * ( + (1 / alphas_next[i] - 1).sqrt() - (1 / alphas[i] - 1).sqrt()) * noise_pred + x_next = xt_weighted + weighted_noise_pred + if return_intermediates and i % ( + num_steps // return_intermediates) == 0 and i < num_steps - 1: + intermediates.append(x_next) + inter_steps.append(i) + elif return_intermediates and i >= num_steps - 2: + intermediates.append(x_next) + inter_steps.append(i) + + out = {'x_encoded': x_next, 'intermediate_steps': inter_steps} + if return_intermediates: + out.update({'intermediates': intermediates}) + return x_next, out + + @torch.no_grad() + def stochastic_encode(self, x0, t, use_original_steps=False, noise=None): + # fast, but does not allow for exact reconstruction + # t serves as an index to gather the correct alphas + if use_original_steps: + sqrt_alphas_cumprod = self.sqrt_alphas_cumprod + sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod + else: + sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas) + sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas + + if noise is None: + noise = torch.randn_like(x0) + return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 + + extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise) + + @torch.no_grad() + def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None, + use_original_steps=False): + + timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps + timesteps = timesteps[:t_start] + + time_range = np.flip(timesteps) + total_steps = timesteps.shape[0] + print(f"Running DDIM Sampling with {total_steps} timesteps") + + iterator = tqdm(time_range, desc='Decoding image', total=total_steps) + x_dec = x_latent + for i, step in enumerate(iterator): + index = total_steps - i - 1 + ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long) + x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning) + return x_dec \ No newline at end of file diff --git a/scripts/img2img.py b/scripts/img2img.py new file mode 100644 index 0000000..52292b4 --- /dev/null +++ b/scripts/img2img.py @@ -0,0 +1,296 @@ +"""make variations of input image""" + +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, repeat +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 + + +def load_img(path): + image = np.array(Image.open(path).convert("RGB")) + image = image.astype(np.float32) / 255.0 + image = image[None].transpose(0, 3, 1, 2) + image = torch.from_numpy(image) + return 2.*image - 1. + + +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( + "--init-img", + type=str, + nargs="?", + help="path to the input image" + ) + + parser.add_argument( + "--outdir", + type=str, + nargs="?", + help="dir to write results to", + default="outputs/img2img-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( + "--fixed_code", + action='store_true', + help="if enabled, uses the same starting code across all 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=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( + "--strength", + type=float, + default=0.3, + help="strength for noising/unnoising. 1.0 corresponds to full destruction of information in init image", + ) + + 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: + raise NotImplementedError("check for 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 + + assert os.path.isfile(opt.init_img) + init_image = load_img(opt.init_img).to(device) + init_image = repeat(init_image, '1 ... -> b ...', b=batch_size) + init_latent = model.get_first_stage_encoding(model.encode_first_stage(init_image)) # move to latent space + + sampler.make_schedule(ddim_num_steps=opt.ddim_steps, ddim_eta=opt.ddim_eta, verbose=False) + + assert 0. <= opt.strength <= 1., 'can only work with strength in [0.0, 1.0]' + t_enc = int(opt.strength * opt.ddim_steps) + print(f"target t_enc is {t_enc} steps") + + 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) + + # encode (scaled latent) + z_enc = sampler.stochastic_encode(init_latent, torch.tensor([t_enc]*batch_size).to(device)) + # decode it + samples = sampler.decode(z_enc, c, t_enc, unconditional_guidance_scale=opt.scale, + unconditional_conditioning=uc,) + + x_samples = model.decode_first_stage(samples) + x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0) + + if not opt.skip_save: + for x_sample in x_samples: + 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) + + 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.") + + +if __name__ == "__main__": + main()