545 lines
20 KiB
Python
545 lines
20 KiB
Python
import argparse, os, glob
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import datetime, time
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import pandas as pd
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import torch
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import torchvision
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import torchvision.transforms as transforms
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import random
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from pytorch_lightning import seed_everything
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from PIL import Image
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from omegaconf import OmegaConf
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from tqdm import tqdm
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from einops import rearrange, repeat
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from collections import OrderedDict
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from unifolm_wma.models.samplers.ddim import DDIMSampler
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from unifolm_wma.utils.utils import instantiate_from_config
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torch.backends.cuda.matmul.allow_tf32 = True
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torch.backends.cudnn.allow_tf32 = True
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def get_filelist(data_dir: str, postfixes: list[str]) -> list[str]:
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"""
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Get list of files in `data_dir` with extensions in `postfixes`.
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Args:
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data_dir (str): Directory path.
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postfixes (list[str]): List of file extensions (e.g., ['csv', 'jpg']).
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Returns:
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list[str]: Sorted list of matched file paths.
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"""
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patterns = [
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os.path.join(data_dir, f"*.{postfix}") for postfix in postfixes
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]
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file_list = []
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for pattern in patterns:
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file_list.extend(glob.glob(pattern))
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file_list.sort()
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return file_list
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def load_model_checkpoint(model: torch.nn.Module,
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ckpt: str) -> torch.nn.Module:
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"""
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Load model weights from checkpoint file.
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Args:
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model (torch.nn.Module): The model to load weights into.
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ckpt (str): Path to the checkpoint file.
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Returns:
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torch.nn.Module: Model with weights loaded.
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"""
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state_dict = torch.load(ckpt, map_location="cpu")
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if "state_dict" in list(state_dict.keys()):
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state_dict = state_dict["state_dict"]
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try:
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loaded = model.load_state_dict(state_dict, strict=False)
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print("Missing keys:")
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for k in loaded.missing_keys:
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print(f" {k}")
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print("Unexpected keys:")
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for k in loaded.unexpected_keys:
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print(f" {k}")
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except:
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# Rename the keys for 256x256 model
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new_pl_sd = OrderedDict()
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for k, v in state_dict.items():
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new_pl_sd[k] = v
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for k in list(new_pl_sd.keys()):
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if "framestride_embed" in k:
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new_key = k.replace("framestride_embed", "fps_embedding")
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new_pl_sd[new_key] = new_pl_sd[k]
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del new_pl_sd[k]
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model.load_state_dict(new_pl_sd, strict=False)
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else:
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new_pl_sd = OrderedDict()
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for key in state_dict['module'].keys():
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new_pl_sd[key[16:]] = state_dict['module'][key]
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model.load_state_dict(new_pl_sd)
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print('>>> model checkpoint loaded.')
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return model
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def load_prompts(prompt_file: str) -> list[str]:
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"""
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Load prompts from a text file, one per line.
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Args:
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prompt_file (str): Path to the prompt file.
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Returns:
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list[str]: List of prompt strings.
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"""
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f = open(prompt_file, 'r')
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prompt_list = []
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for idx, line in enumerate(f.readlines()):
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l = line.strip()
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if len(l) != 0:
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prompt_list.append(l)
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f.close()
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return prompt_list
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def load_data_prompts(
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data_dir: str,
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savedir: str,
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video_size: tuple[int, int] = (256, 256),
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video_frames: int = 16
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) -> tuple[list[str], list[torch.Tensor], list[str], list[float], list[float],
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list[int]]:
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"""
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Load image prompts, process them into video format, and retrieve metadata.
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Args:
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data_dir (str): Directory containing images and CSV file.
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savedir (str): Output directory to check if inference was already done.
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video_size (tuple[int, int], optional): Target size of video frames.
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video_frames (int, optional): Number of frames in each video.
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Returns:
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tuple: (filenames, video tensors, prompts, fps values, fs values, num_generations)
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"""
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transform = transforms.Compose([
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transforms.Resize(min(video_size)),
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transforms.CenterCrop(video_size),
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transforms.ToTensor(),
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transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
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])
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# Load prompt csv
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prompt_file = get_filelist(data_dir, ['csv'])
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assert len(prompt_file) > 0, "Error: found NO image prompt file!"
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# Load image prompts
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file_list = get_filelist(data_dir, ['jpg', 'png', 'jpeg', 'JPEG', 'PNG'])
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data_list = []
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filename_list = []
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prompt_list = []
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fps_list = []
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fs_list = []
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num_gen_list = []
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prompt_csv = pd.read_csv(prompt_file[0])
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n_samples = len(file_list)
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for idx in range(n_samples):
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image = Image.open(file_list[idx]).convert('RGB')
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image_tensor = transform(image).unsqueeze(1)
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frame_tensor = repeat(image_tensor,
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'c t h w -> c (repeat t) h w',
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repeat=video_frames)
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_, filename = os.path.split(file_list[idx])
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if not is_inferenced(savedir, filename):
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video_id = filename[:-4]
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prompt_csv['videoid'] = prompt_csv['videoid'].map(str)
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if not (prompt_csv['videoid'] == video_id).any():
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continue
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data_list.append(frame_tensor)
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filename_list.append(filename)
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ins = prompt_csv[prompt_csv['videoid'] ==
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video_id]['instruction'].values[0]
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prompt_list.append(ins)
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fps = prompt_csv[prompt_csv['videoid'] ==
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video_id]['fps'].values[0]
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fps_list.append(fps)
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fs = prompt_csv[prompt_csv['videoid'] == video_id]['fs'].values[0]
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fs_list.append(fs)
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num_gen = prompt_csv[prompt_csv['videoid'] ==
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video_id]['num_gen'].values[0]
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num_gen_list.append(int(num_gen))
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return filename_list, data_list, prompt_list, fps_list, fs_list, num_gen_list
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def is_inferenced(save_dir: str, filename: str) -> bool:
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"""
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Check if a result video already exists.
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Args:
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save_dir (str): Directory where results are saved.
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filename (str): Base filename to check.
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Returns:
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bool: True if file exists, else False.
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"""
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video_file = os.path.join(save_dir, f"{filename[:-4]}.mp4")
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return os.path.exists(video_file)
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def save_results_seperate(prompt: str | list[str],
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samples: torch.Tensor,
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filename: str,
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fakedir: str,
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fps: int = 8) -> None:
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"""
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Save generated video samples as .mp4 files.
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Args:
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prompt (str | list[str]): The prompt text.
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samples (torch.Tensor): Generated video tensor of shape [B, C, T, H, W].
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filename (str): Output filename.
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fakedir (str): Directory to save output videos.
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fps (int, optional): Frames per second.
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Returns:
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None
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"""
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prompt = prompt[0] if isinstance(prompt, list) else prompt
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# Save video
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videos = [samples]
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savedirs = [fakedir]
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for idx, video in enumerate(videos):
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if video is None:
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continue
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video = video.detach().cpu()
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video = torch.clamp(video.float(), -1., 1.)
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n = video.shape[0]
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for i in range(n):
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grid = video[i, ...]
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grid = (grid + 1.0) / 2.0
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grid = (grid * 255).to(torch.uint8).permute(1, 2, 3, 0)
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path = os.path.join(savedirs[idx], f'{filename.split(".")[0]}.mp4')
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torchvision.io.write_video(path,
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grid,
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fps=fps,
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video_codec='h264',
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options={'crf': '0'})
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def get_latent_z(model: torch.nn.Module, videos: torch.Tensor) -> torch.Tensor:
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"""
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Encode videos to latent space.
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Args:
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model (torch.nn.Module): Model with encode_first_stage function.
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videos (torch.Tensor): Video tensor of shape [B, C, T, H, W].
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Returns:
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torch.Tensor: Latent representation of shape [B, C, T, H, W].
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"""
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b, c, t, h, w = videos.shape
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x = rearrange(videos, 'b c t h w -> (b t) c h w')
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z = model.encode_first_stage(x)
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z = rearrange(z, '(b t) c h w -> b c t h w', b=b, t=t)
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return z
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def image_guided_synthesis(model: torch.nn.Module,
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prompts: list[str],
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videos: torch.Tensor,
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noise_shape: list[int],
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ddim_steps: int = 50,
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ddim_eta: float = 1.0,
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unconditional_guidance_scale: float = 1.0,
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fs: int | None = None,
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text_input: bool = False,
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timestep_spacing: str = 'uniform',
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guidance_rescale: float = 0.0,
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**kwargs) -> torch.Tensor:
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"""
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Run DDIM-based image-to-video synthesis with hybrid/text+image guidance.
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Args:
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model (torch.nn.Module): Diffusion model.
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prompts (list[str]): Text prompts.
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videos (torch.Tensor): Input images/videos of shape [B, C, T, H, W].
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noise_shape (list[int]): Latent noise shape [B, C, T, H, W].
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ddim_steps (int, optional): Number of DDIM steps.
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ddim_eta (float, optional): Eta value for DDIM.
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unconditional_guidance_scale (float, optional): Guidance scale.
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fs (int | None, optional): FPS input for sampler.
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text_input (bool, optional): If True, use text guidance.
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timestep_spacing (str, optional): Timestep schedule spacing.
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guidance_rescale (float, optional): Rescale guidance effect.
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**kwargs: Additional sampler args.
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Returns:
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torch.Tensor: Synthesized videos of shape [B, 1, C, T, H, W].
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"""
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ddim_sampler = DDIMSampler(model)
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batch_size = noise_shape[0]
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fs = torch.tensor([fs] * batch_size, dtype=torch.long, device=model.device)
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if not text_input:
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prompts = [""] * batch_size
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b, c, t, h, w = videos.shape
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img = videos[:, :, 0]
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img_emb = model.embedder(img)
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img_emb = model.image_proj_model(img_emb)
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img_emb = rearrange(img_emb, 'b (t l) c -> (b t) l c', t=t)
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cond_emb = model.get_learned_conditioning(prompts)
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cond_emb = cond_emb.repeat_interleave(repeats=t, dim=0)
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cond = {"c_crossattn": [torch.cat([cond_emb, img_emb], dim=1)]}
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if model.model.conditioning_key == 'hybrid':
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z = get_latent_z(model, videos)
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img_cat_cond = z[:, :, :1, :, :]
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img_cat_cond = repeat(img_cat_cond,
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'b c t h w -> b c (repeat t) h w',
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repeat=z.shape[2])
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cond["c_concat"] = [img_cat_cond]
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uc = None
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cond_mask = None
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kwargs.update({"unconditional_conditioning_img_nonetext": None})
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batch_variants = []
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if ddim_sampler is not None:
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samples, _, _, _ = ddim_sampler.sample(
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S=ddim_steps,
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batch_size=batch_size,
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shape=noise_shape[1:],
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conditioning=cond,
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eta=ddim_eta,
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mask=cond_mask,
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x0=None,
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verbose=False,
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unconditional_guidance_scale=unconditional_guidance_scale,
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unconditional_conditioning=uc,
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fs=fs,
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timestep_spacing=timestep_spacing,
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guidance_rescale=guidance_rescale,
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**kwargs)
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# Reconstruct from latent to pixel space
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batch_images = model.decode_first_stage(samples)
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batch_variants.append(batch_images)
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batch_variants = torch.stack(batch_variants)
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return batch_variants.permute(1, 0, 2, 3, 4, 5)
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def run_inference(args: argparse.Namespace, gpu_num: int, gpu_no: int) -> None:
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"""
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Run inference pipeline on prompts and image inputs.
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Args:
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args (argparse.Namespace): Parsed command-line arguments.
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gpu_num (int): Number of GPUs.
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gpu_no (int): Index of the current GPU.
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Returns:
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None
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"""
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# Load config
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config = OmegaConf.load(args.config)
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# Set use_checkpoint as False as when using deepspeed, it encounters an error "deepspeed backend not set"
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config['model']['params']['wma_config']['params'][
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'use_checkpoint'] = False
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model = instantiate_from_config(config.model)
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model = model.cuda(gpu_no)
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model.perframe_ae = args.perframe_ae
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assert os.path.exists(args.ckpt_path), "Error: checkpoint Not Found!"
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model = load_model_checkpoint(model, args.ckpt_path)
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model.eval()
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# Run over data
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assert (args.height % 16 == 0) and (
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args.width % 16
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== 0), "Error: image size [h,w] should be multiples of 16!"
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assert args.bs == 1, "Current implementation only support [batch size = 1]!"
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# Get latent noise shape
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h, w = args.height // 8, args.width // 8
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channels = model.model.diffusion_model.out_channels
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n_frames = args.video_length
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print(f'>>> Generate {n_frames} frames under each generation ...')
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noise_shape = [args.bs, channels, n_frames, h, w]
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fakedir = os.path.join(args.savedir, "samples")
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os.makedirs(fakedir, exist_ok=True)
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# Prompt file setting
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assert os.path.exists(args.prompt_dir), "Error: prompt file Not Found!"
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filename_list, data_list, prompt_list, fps_list, fs_list, num_gen_list = load_data_prompts(
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args.prompt_dir,
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args.savedir,
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video_size=(args.height, args.width),
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video_frames=n_frames)
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num_samples = len(prompt_list)
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samples_split = num_samples // gpu_num
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print('>>> Prompts testing [rank:%d] %d/%d samples loaded.' %
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(gpu_no, samples_split, num_samples))
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indices = list(range(samples_split * gpu_no, samples_split * (gpu_no + 1)))
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fps_list_rank = [fps_list[i] for i in indices]
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fs_list_rank = [fs_list[i] for i in indices]
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prompt_list_rank = [prompt_list[i] for i in indices]
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data_list_rank = [data_list[i] for i in indices]
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filename_list_rank = [filename_list[i] for i in indices]
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with torch.no_grad(), torch.cuda.amp.autocast():
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# Create a new result csv
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for idx, indice in enumerate(
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tqdm(range(0, len(prompt_list_rank), args.bs),
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desc=f'Sample batch')):
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fps = fps_list_rank[indice:indice + args.bs]
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fs = fs_list_rank[indice:indice + args.bs]
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prompts = prompt_list_rank[indice:indice + args.bs]
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num_gen = num_gen_list[indice:indice + args.bs]
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videos = data_list_rank[indice:indice + args.bs]
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filenames = filename_list_rank[indice:indice + args.bs]
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if isinstance(videos, list):
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videos = torch.stack(videos, dim=0).to("cuda")
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else:
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videos = videos.unsqueeze(0).to("cuda")
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results = []
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print(
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f">>> {prompts[0]}, frame_stride:{fs[0]}, and {num_gen[0]} generation ..."
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)
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for _ in range(num_gen[0]):
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batch_samples = image_guided_synthesis(
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model, prompts, videos, noise_shape, args.ddim_steps,
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args.ddim_eta, args.unconditional_guidance_scale,
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fps[0] // fs[0], args.text_input, args.timestep_spacing,
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args.guidance_rescale)
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results.extend(batch_samples)
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videos = repeat(batch_samples[0][:, :, -1, :, :].unsqueeze(2),
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'b c t h w -> b c (repeat t) h w',
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repeat=batch_samples[0].shape[2])
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batch_samples = [torch.concat(results, axis=2)]
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# Save each example individually
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for nn, samples in enumerate(batch_samples):
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prompt = prompts[nn]
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filename = filenames[nn]
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save_results_seperate(prompt,
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samples,
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filename,
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fakedir,
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fps=8)
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def get_parser() -> argparse.ArgumentParser:
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"""
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Create and return the argument parser.
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Returns:
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argparse.ArgumentParser: Parser for command-line arguments.
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"""
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parser = argparse.ArgumentParser()
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parser.add_argument("--savedir",
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type=str,
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default=None,
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help="Path to save the results.")
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parser.add_argument("--ckpt_path",
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type=str,
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default=None,
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help="Path to the model checkpoint.")
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parser.add_argument("--config",
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type=str,
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help="Path to the YAML configuration file.")
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parser.add_argument(
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"--prompt_dir",
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type=str,
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default=None,
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help="Directory containing videos and corresponding prompts.")
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parser.add_argument(
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"--ddim_steps",
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type=int,
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default=50,
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help="Number of DDIM steps. If non-positive, DDPM is used instead.")
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parser.add_argument(
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"--ddim_eta",
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type=float,
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default=1.0,
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help="Eta for DDIM sampling. Set to 0.0 for deterministic results.")
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parser.add_argument("--bs",
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type=int,
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default=1,
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help="Batch size for inference. Must be 1.")
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parser.add_argument("--height",
|
|
type=int,
|
|
default=320,
|
|
help="Height of the generated images in pixels.")
|
|
parser.add_argument("--width",
|
|
type=int,
|
|
default=512,
|
|
help="Width of the generated images in pixels.")
|
|
parser.add_argument(
|
|
"--unconditional_guidance_scale",
|
|
type=float,
|
|
default=1.0,
|
|
help="Scale for classifier-free guidance during sampling.")
|
|
parser.add_argument("--seed",
|
|
type=int,
|
|
default=123,
|
|
help="Random seed for reproducibility.")
|
|
parser.add_argument("--video_length",
|
|
type=int,
|
|
default=16,
|
|
help="Number of frames in the generated video.")
|
|
parser.add_argument(
|
|
"--text_input",
|
|
action='store_true',
|
|
default=False,
|
|
help=
|
|
"Whether to provide a text prompt as input to the image-to-video model."
|
|
)
|
|
parser.add_argument(
|
|
"--timestep_spacing",
|
|
type=str,
|
|
default="uniform",
|
|
help=
|
|
"Strategy for timestep scaling. See Table 2 in the paper: 'Common Diffusion Noise Schedules and Sample Steps are Flawed' (https://huggingface.co/papers/2305.08891)."
|
|
)
|
|
parser.add_argument(
|
|
"--guidance_rescale",
|
|
type=float,
|
|
default=0.0,
|
|
help=
|
|
"Rescale factor for guidance as discussed in 'Common Diffusion Noise Schedules and Sample Steps are Flawed' (https://huggingface.co/papers/2305.08891)."
|
|
)
|
|
parser.add_argument(
|
|
"--perframe_ae",
|
|
action='store_true',
|
|
default=False,
|
|
help=
|
|
"Use per-frame autoencoder decoding to reduce GPU memory usage. Recommended for models with resolutions like 576x1024."
|
|
)
|
|
return parser
|
|
|
|
|
|
if __name__ == '__main__':
|
|
parser = get_parser()
|
|
args = parser.parse_args()
|
|
seed = args.seed
|
|
if seed < 0:
|
|
seed = random.randint(0, 2**31)
|
|
seed_everything(seed)
|
|
rank, gpu_num = 0, 1
|
|
run_inference(args, gpu_num, rank)
|