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 transformers import T5EncoderModel, T5Tokenizer from diffusion.model.utils import prepare_prompt_ar from diffusion import IDDPM, DPMS, SASolverSampler from tools.download import find_model from diffusion.model.nets import PixArtMS_XL_2, PixArt_XL_2 from diffusion.data.datasets import get_chunks from diffusion.data.datasets.utils import * def get_args(): parser = argparse.ArgumentParser() parser.add_argument('--image_size', default=1024, type=int) parser.add_argument('--version', default='sigma', type=str) parser.add_argument( "--pipeline_load_from", default='output/pretrained_models/pixart_sigma_sdxlvae_T5_diffusers', type=str, help="Download for loading text_encoder, " "tokenizer and vae from https://huggingface.co/PixArt-alpha/pixart_sigma_sdxlvae_T5_diffusers" ) 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('--sdvae', action='store_true', help='sd vae') parser.add_argument('--bs', default=1, type=int) parser.add_argument('--cfg_scale', default=4.5, type=float) parser.add_argument('--sampling_algo', default='dpm-solver', type=str, choices=['iddpm', 'dpm-solver', 'sa-solver']) 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): for chunk in tqdm(list(get_chunks(items, bs)), unit='batch'): prompts = [] if bs == 1: save_path = os.path.join(save_root, f"{prompts[0][:100]}.jpg") if os.path.exists(save_path): continue 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) for prompt in chunk: prompts.append(prepare_prompt_ar(prompt, base_ratios, device=device, show=False)[0].strip()) latent_size_h, latent_size_w = latent_size, latent_size caption_token = tokenizer(prompts, max_length=max_sequence_length, padding="max_length", truncation=True, return_tensors="pt").to(device) caption_embs = text_encoder(caption_token.input_ids, attention_mask=caption_token.attention_mask)[0] emb_masks = caption_token.attention_mask caption_embs = caption_embs[:, None] null_y = null_caption_embs.repeat(len(prompts), 1, 1)[:, None] print(f'finish embedding') with torch.no_grad(): if args.sampling_algo == 'iddpm': # Create sampling noise: n = len(prompts) z = torch.randn(n, 4, latent_size_h, latent_size_w, device=device).repeat(2, 1, 1, 1) model_kwargs = dict(y=torch.cat([caption_embs, null_y]), cfg_scale=cfg_scale, data_info={'img_hw': hw, 'aspect_ratio': ar}, mask=emb_masks) diffusion = IDDPM(str(sample_steps)) # Sample images: samples = diffusion.p_sample_loop( model.forward_with_cfg, z.shape, z, clip_denoised=False, model_kwargs=model_kwargs, progress=True, device=device ) samples, _ = samples.chunk(2, dim=0) # Remove null class samples elif args.sampling_algo == 'dpm-solver': # Create sampling noise: n = len(prompts) z = 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) dpm_solver = DPMS(model.forward_with_dpmsolver, condition=caption_embs, uncondition=null_y, cfg_scale=cfg_scale, model_kwargs=model_kwargs) samples = dpm_solver.sample( z, steps=sample_steps, order=2, skip_type="time_uniform", method="multistep", ) elif args.sampling_algo == 'sa-solver': # Create sampling noise: n = len(prompts) model_kwargs = dict(data_info={'img_hw': hw, 'aspect_ratio': ar}, mask=emb_masks) sa_solver = SASolverSampler(model.forward_with_dpmsolver, device=device) samples = sa_solver.sample( S=25, batch_size=n, shape=(4, latent_size_h, latent_size_w), eta=1, conditioning=caption_embs, unconditional_conditioning=null_y, unconditional_guidance_scale=cfg_scale, model_kwargs=model_kwargs, )[0] samples = samples.to(weight_dtype) samples = vae.decode(samples / vae.config.scaling_factor).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" assert args.sampling_algo in ['iddpm', 'dpm-solver', 'sa-solver'] # only support fixed latent size currently latent_size = args.image_size // 8 max_sequence_length = {"alpha": 120, "sigma": 300}[args.version] pe_interpolation = {256: 0.5, 512: 1, 1024: 2} # trick for positional embedding interpolation micro_condition = True if args.version == 'alpha' and args.image_size == 1024 else False sample_steps_dict = {'iddpm': 100, 'dpm-solver': 20, 'sa-solver': 25} sample_steps = args.step if args.step != -1 else sample_steps_dict[args.sampling_algo] weight_dtype = torch.float16 print(f"Inference with {weight_dtype}") # model setting micro_condition = True if args.version == 'alpha' and args.image_size == 1024 else False if args.image_size in [512, 1024, 2048, 2880]: model = PixArtMS_XL_2( input_size=latent_size, pe_interpolation=pe_interpolation[args.image_size], micro_condition=micro_condition, model_max_length=max_sequence_length, ).to(device) else: model = PixArt_XL_2( input_size=latent_size, pe_interpolation=pe_interpolation[args.image_size], model_max_length=max_sequence_length, ).to(device) print("Generating sample from ckpt: %s" % args.model_path) state_dict = find_model(args.model_path) if 'pos_embed' in state_dict['state_dict']: 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() model.to(weight_dtype) base_ratios = eval(f'ASPECT_RATIO_{args.image_size}_TEST') if args.sdvae: # pixart-alpha vae link: https://huggingface.co/PixArt-alpha/PixArt-alpha/tree/main/sd-vae-ft-ema vae = AutoencoderKL.from_pretrained("output/pretrained_models/sd-vae-ft-ema").to(device).to(weight_dtype) else: # pixart-Sigma vae link: https://huggingface.co/PixArt-alpha/pixart_sigma_sdxlvae_T5_diffusers/tree/main/vae vae = AutoencoderKL.from_pretrained(f"{args.pipeline_load_from}/vae").to(device).to(weight_dtype) tokenizer = T5Tokenizer.from_pretrained(args.pipeline_load_from, subfolder="tokenizer") text_encoder = T5EncoderModel.from_pretrained(args.pipeline_load_from, subfolder="text_encoder").to(device) null_caption_token = tokenizer("", max_length=max_sequence_length, padding="max_length", truncation=True, return_tensors="pt").to(device) null_caption_embs = text_encoder(null_caption_token.input_ids, attention_mask=null_caption_token.attention_mask)[0] work_dir = os.path.join(*args.model_path.split('/')[:-2]) work_dir = '/'+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+).*', args.model_path).group(1) step_name = re.search(r'.*step_(\d+).*', args.model_path).group(1) except: 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}_samp{args.sampling_algo}_seed{seed}") os.makedirs(save_root, exist_ok=True) visualize(items, args.bs, sample_steps, args.cfg_scale)