import os import sys from pathlib import Path current_file_path = Path(__file__).resolve() sys.path.insert(0, str(current_file_path.parent.parent)) import warnings warnings.filterwarnings("ignore") # ignore warning import re import argparse from datetime import datetime from tqdm import tqdm import torch from torchvision.utils import save_image from diffusers.models import AutoencoderKL from diffusion.model.utils import prepare_prompt_ar from tools.download import find_model from diffusion.model.nets import PixArtMS_XL_2, PixArt_XL_2 from diffusion.model.t5 import T5Embedder from diffusion.data.datasets import get_chunks from diffusion.lcm_scheduler import LCMScheduler from diffusion.data.datasets import ASPECT_RATIO_512_TEST, ASPECT_RATIO_1024_TEST def get_args(): parser = argparse.ArgumentParser() parser.add_argument('--image_size', default=1024, type=int) parser.add_argument('--t5_path', default='output/pretrained_models/t5_ckpts', type=str) parser.add_argument('--tokenizer_path', default='output/pretrained_models/sd-vae-ft-ema', type=str) parser.add_argument('--txt_file', default='asset/samples.txt', type=str) parser.add_argument('--model_path', default='output/pretrained_models/PixArt-XL-2-1024x1024.pth', type=str) parser.add_argument('--bs', default=1, type=int) parser.add_argument('--cfg_scale', default=4.5, type=float) parser.add_argument('--sample_steps', default=4, type=int) parser.add_argument('--seed', default=0, type=int) parser.add_argument('--dataset', default='custom', type=str) parser.add_argument('--step', default=-1, type=int) parser.add_argument('--save_name', default='test_sample', type=str) return parser.parse_args() def set_env(seed=0): torch.manual_seed(seed) torch.set_grad_enabled(False) for _ in range(30): torch.randn(1, 4, args.image_size, args.image_size) @torch.inference_mode() def visualize(items, bs, sample_steps, cfg_scale): # 4. Prepare timesteps scheduler.set_timesteps(sample_steps, 50) timesteps = scheduler.timesteps for chunk in tqdm(list(get_chunks(items, bs)), unit='batch'): prompts = [] if bs == 1: prompt_clean, _, hw, ar, custom_hw = prepare_prompt_ar(chunk[0], base_ratios, device=device, show=False) # ar for aspect ratio if args.image_size == 1024: latent_size_h, latent_size_w = int(hw[0, 0] // 8), int(hw[0, 1] // 8) else: hw = torch.tensor([[args.image_size, args.image_size]], dtype=torch.float, device=device).repeat(bs, 1) ar = torch.tensor([[1.]], device=device).repeat(bs, 1) latent_size_h, latent_size_w = latent_size, latent_size prompts.append(prompt_clean.strip()) else: hw = torch.tensor([[args.image_size, args.image_size]], dtype=torch.float, device=device).repeat(bs, 1) ar = torch.tensor([[1.]], device=device).repeat(bs, 1) prompts.append(prepare_prompt_ar(prompt, base_ratios, device=device, show=False)[0].strip()) latent_size_h, latent_size_w = latent_size, latent_size with torch.no_grad(): caption_embs, emb_masks = t5.get_text_embeddings(prompts) caption_embs = caption_embs.float()[:, None] print('finish embedding') # Create sampling noise: n = len(prompts) latents = torch.randn(n, 4, latent_size_h, latent_size_w, device=device) model_kwargs = dict(data_info={'img_hw': hw, 'aspect_ratio': ar}, mask=emb_masks) # 7. LCM MultiStep Sampling Loop: for i, t in tqdm(list(enumerate(timesteps))): ts = torch.full((bs,), t, device=device, dtype=torch.long) # model prediction (v-prediction, eps, x) model_pred = model(latents, ts, caption_embs, **model_kwargs)[:, :4] # compute the previous noisy sample x_t -> x_t-1 latents, denoised = scheduler.step(model_pred, i, t, latents, return_dict=False) samples = vae.decode(denoised / 0.18215).sample torch.cuda.empty_cache() # Save images: os.umask(0o000) # file permission: 666; dir permission: 777 for i, sample in enumerate(samples): save_path = os.path.join(save_root, f"{prompts[i][:100]}.jpg") print("Saving path: ", save_path) save_image(sample, save_path, nrow=1, normalize=True, value_range=(-1, 1)) if __name__ == '__main__': args = get_args() # Setup PyTorch: seed = args.seed set_env(seed) device = "cuda" if torch.cuda.is_available() else "cpu" # only support fixed latent size currently latent_size = args.image_size // 8 lewei_scale = {512: 1, 1024: 2} # trick for positional embedding interpolation sample_steps = args.sample_steps # Initalize Scheduler: scheduler = LCMScheduler(beta_start=0.0001, beta_end=0.02, beta_schedule="linear", prediction_type="epsilon") # model setting if args.image_size == 512: model = PixArt_XL_2(input_size=latent_size, lewei_scale=lewei_scale[args.image_size]).to(device) else: model = PixArtMS_XL_2(input_size=latent_size, lewei_scale=lewei_scale[args.image_size]).to(device) print(f"Generating sample from ckpt: {args.model_path}") state_dict = find_model(args.model_path) del state_dict['state_dict']['pos_embed'] missing, unexpected = model.load_state_dict(state_dict['state_dict'], strict=False) print('Missing keys: ', missing) print('Unexpected keys', unexpected) model.eval() base_ratios = eval(f'ASPECT_RATIO_{args.image_size}_TEST') vae = AutoencoderKL.from_pretrained(args.tokenizer_path).to(device) t5 = T5Embedder(device="cuda", local_cache=True, cache_dir=args.t5_path, torch_dtype=torch.float) work_dir = os.path.join(*args.model_path.split('/')[:-2]) work_dir = f'/{work_dir}' if args.model_path[0] == '/' else work_dir # data setting with open(args.txt_file, 'r') as f: items = [item.strip() for item in f.readlines()] # img save setting try: epoch_name = re.search(r'.*epoch_(\d+).*.pth', args.model_path).group(1) step_name = re.search(r'.*step_(\d+).*.pth', args.model_path).group(1) except Exception: epoch_name = 'unknown' step_name = 'unknown' img_save_dir = os.path.join(work_dir, 'vis') os.umask(0o000) # file permission: 666; dir permission: 777 os.makedirs(img_save_dir, exist_ok=True) save_root = os.path.join(img_save_dir, f"{datetime.now().date()}_{args.dataset}_epoch{epoch_name}_step{step_name}_scale{args.cfg_scale}_step{sample_steps}_size{args.image_size}_bs{args.bs}_sampLCM_seed{seed}") os.makedirs(save_root, exist_ok=True) visualize(items, args.bs, sample_steps, args.cfg_scale)