xora/examples/image_to_video.py

import torch
from xora.models.autoencoders.causal_video_autoencoder import CausalVideoAutoencoder
from xora.models.transformers.transformer3d import Transformer3DModel
from xora.models.transformers.symmetric_patchifier import SymmetricPatchifier
from xora.schedulers.rf import RectifiedFlowScheduler
from xora.pipelines.pipeline_video_pixart_alpha import VideoPixArtAlphaPipeline
from pathlib import Path
from transformers import T5EncoderModel, T5Tokenizer
import safetensors.torch
import json
import argparse

def load_vae(vae_dir):
    vae_ckpt_path = vae_dir / "diffusion_pytorch_model.safetensors"
    vae_config_path = vae_dir / "config.json"
    with open(vae_config_path, 'r') as f:
        vae_config = json.load(f)
    vae = CausalVideoAutoencoder.from_config(vae_config)
    vae_state_dict = safetensors.torch.load_file(vae_ckpt_path)
    vae.load_state_dict(vae_state_dict)
    return vae.cuda().to(torch.bfloat16)

def load_unet(unet_dir):
    unet_ckpt_path = unet_dir / "diffusion_pytorch_model.safetensors"
    unet_config_path = unet_dir / "config.json"
    transformer_config = Transformer3DModel.load_config(unet_config_path)
    transformer = Transformer3DModel.from_config(transformer_config)
    unet_state_dict = safetensors.torch.load_file(unet_ckpt_path)
    transformer.load_state_dict(unet_state_dict, strict=True)
    return transformer.cuda()

def load_scheduler(scheduler_dir):
    scheduler_config_path = scheduler_dir / "scheduler_config.json"
    scheduler_config = RectifiedFlowScheduler.load_config(scheduler_config_path)
    return RectifiedFlowScheduler.from_config(scheduler_config)

def main():
    # Parse command line arguments
    parser = argparse.ArgumentParser(description='Load models from separate directories')
    parser.add_argument('--separate_dir', type=str, required=True, help='Path to the directory containing unet, vae, and scheduler subdirectories')
    args = parser.parse_args()

    # Paths for the separate mode directories
    separate_dir = Path(args.separate_dir)
    unet_dir = separate_dir / 'unet'
    vae_dir = separate_dir / 'vae'
    scheduler_dir = separate_dir / 'scheduler'

    # Load models
    vae = load_vae(vae_dir)
    unet = load_unet(unet_dir)
    scheduler = load_scheduler(scheduler_dir)

    # Patchifier (remains the same)
    patchifier = SymmetricPatchifier(patch_size=1)

    # text_encoder = T5EncoderModel.from_pretrained("PixArt-alpha/PixArt-XL-2-1024-MS", subfolder="text_encoder").to("cuda")
    # tokenizer = T5Tokenizer.from_pretrained("PixArt-alpha/PixArt-XL-2-1024-MS", subfolder="tokenizer")

    # Use submodels for the pipeline
    submodel_dict = {
        "transformer": unet,  # using unet for transformer
        "patchifier": patchifier,
        "text_encoder": None,
        "tokenizer": None,
        "scheduler": scheduler,
        "vae": vae,
    }

    model_name_or_path = "PixArt-alpha/PixArt-XL-2-1024-MS"
    pipeline = VideoPixArtAlphaPipeline(
                                                        **submodel_dict
                                                        ).to("cuda")

    num_inference_steps = 20
    num_images_per_prompt = 1
    guidance_scale = 3
    height = 512
    width = 768
    num_frames = 57
    frame_rate = 25

    # Sample input stays the same
    sample = torch.load("/opt/sample_media.pt")
    for key, item in sample.items():
        if item is not None:
            sample[key] = item.cuda()

    # media_items = torch.load("/opt/sample_media.pt")

    # Generate images (video frames)
    images = pipeline(
        num_inference_steps=num_inference_steps,
        num_images_per_prompt=num_images_per_prompt,
        guidance_scale=guidance_scale,
        generator=None,
        output_type="pt",
        callback_on_step_end=None,
        height=height,
        width=width,
        num_frames=num_frames,
        frame_rate=frame_rate,
        **sample,
        is_video=True,
        vae_per_channel_normalize=True,
    ).images

    print("Generated video frames.")

if __name__ == "__main__":
    main()