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import yaml
import argparse
import torch
import torchvision
from PIL import Image
import logging
import sys
# --- Diffusers and Transformers Imports ---
from diffusers import AutoencoderKLWan, UniPCMultistepScheduler, HunyuanVideoTransformer3DModel, FlowMatchEulerDiscreteScheduler
from diffusers.utils import load_image
from transformers import CLIPVisionModel
# --- Low-pass Pipelines ---
from pipeline_wan_image2video_lowpass import WanImageToVideoPipeline
from pipeline_cogvideox_image2video_lowpass import CogVideoXImageToVideoPipeline
from pipeline_hunyuan_video_image2video_lowpass import HunyuanVideoImageToVideoPipeline
from lp_utils import get_hunyuan_video_size
from diffusers.utils import export_to_video
# --- Basic Logging Setup ---
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s', stream=sys.stdout)
logger = logging.getLogger(__name__)
def main(args):
# 1. Configuration
IMAGE_PATH = args.image_path
PROMPT = args.prompt
OUTPUT_PATH = args.output_path
MODEL_CACHE_DIR = args.model_cache_dir
with open(args.config, 'r') as f:
config = yaml.safe_load(f)
model_path = config['model']['path']
model_dtype_str = config['model']['dtype']
model_dtype = getattr(torch, model_dtype_str)
device = "cuda" if torch.cuda.is_available() else "cpu"
logger.info(f"Using device: {device}")
# 2. Pipeline preparation
if "Wan" in model_path:
image_encoder = CLIPVisionModel.from_pretrained(model_path,
subfolder="image_encoder",
torch_dtype=torch.float32,
cache_dir=MODEL_CACHE_DIR
)
vae = AutoencoderKLWan.from_pretrained(model_path,
subfolder="vae",
torch_dtype=torch.float32,
cache_dir=MODEL_CACHE_DIR
)
pipe = WanImageToVideoPipeline.from_pretrained(model_path,
vae=vae,
image_encoder=image_encoder,
torch_dtype=model_dtype,
cache_dir=MODEL_CACHE_DIR
)
# Recommended setup (See https://github.com/huggingface/diffusers/blob/3c8b67b3711b668a6e7867e08b54280e51454eb5/src/diffusers/pipelines/wan/pipeline_wan.py#L58C13-L58C23)
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config, flow_shift=3.0 if config['generation']['height'] == '480' else 5.0)
elif "CogVideoX" in model_path:
pipe = CogVideoXImageToVideoPipeline.from_pretrained(
model_path,
torch_dtype=model_dtype,
cache_dir=MODEL_CACHE_DIR
)
elif "HunyuanVideo" in model_path:
transformer = HunyuanVideoTransformer3DModel.from_pretrained(
model_path,
subfolder="transformer",
torch_dtype=torch.bfloat16,
cache_dir=MODEL_CACHE_DIR
)
pipe = HunyuanVideoImageToVideoPipeline.from_pretrained(
model_path, transformer=transformer,
torch_dtype=torch.float16,
cache_dir=MODEL_CACHE_DIR
)
pipe.scheduler = FlowMatchEulerDiscreteScheduler.from_config(
pipe.scheduler.config,
flow_shift= config['model']['flow_shift'],
invert_sigmas = config['model']['flow_reverse']
)
pipe.to(device)
logger.info("Pipeline loaded successfully.")
# 3. Prepare inputs
input_image = load_image(Image.open(IMAGE_PATH))
generator = torch.Generator(device=device).manual_seed(42)
pipe_kwargs = {
"image": input_image,
"prompt": PROMPT,
"generator": generator,
}
params_from_config = {**config.get('generation', {}), **config.get('alg', {})}
for key, value in params_from_config.items():
if value is not None:
pipe_kwargs[key] = value
logger.info("Starting video generation...")
log_subset = {k: v for k, v in pipe_kwargs.items() if k not in ['image', 'generator']}
logger.info(f"Pipeline arguments: {log_subset}")
if "HunyuanVideo" in model_path:
pipe_kwargs["height"], pipe_kwargs["width"] = get_hunyuan_video_size(config['video']['resolution'], input_image)
# 4. Generate video
video_output = pipe(**pipe_kwargs)
video_frames = video_output.frames[0] # Output is a list containing a list of PIL Images
logger.info(f"Video generation complete. Received {len(video_frames)} frames.")
# # 5. Save video
# video_tensors = [torchvision.transforms.functional.to_tensor(frame) for frame in video_frames]
# video_tensor = torch.stack(video_tensors) # Shape: (T, C, H, W)
# video_tensor = video_tensor.permute(0, 2, 3, 1) # Shape: (T, H, W, C) for write_video
# video_tensor = (video_tensor * 255).clamp(0, 255).to(torch.uint8).cpu()
# logger.info(f"Saving video to: {OUTPUT_PATH}")
# torchvision.io.write_video(
# OUTPUT_PATH,
# video_tensor,
# fps=config['video']['fps'],
# video_codec='h264',
# options={'crf': '18', 'preset': 'slow'}
# )
export_to_video(video_frames, OUTPUT_PATH, fps=config['video']['fps'])
logger.info("Video saved successfully. Run complete.")
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="Arguments")
parser.add_argument("--config", type=str, default="./configs/hunyuan_video_alg.yaml")
parser.add_argument("--image_path", type=str, default="./assets/a red double decker bus driving down a street.jpg")
parser.add_argument("--prompt", type=str, default="a red double decker bus driving down a street")
parser.add_argument("--output_path", type=str, default="output.mp4")
parser.add_argument("--model_cache_dir", type=str, default=None)
args = parser.parse_args()
main(args) |