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
import safetensors.torch
import json

# Paths for the separate mode directories
separate_dir = Path("/opt/models/xora-img2video")
unet_dir = separate_dir / 'unet'
vae_dir = separate_dir / 'vae'
scheduler_dir = separate_dir / 'scheduler'

# Load VAE from separate mode
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_state_dict = safetensors.torch.load_file(vae_ckpt_path)
vae = CausalVideoAutoencoder.from_pretrained_conf(
    config=vae_config,
    state_dict=vae_state_dict,
    torch_dtype=torch.bfloat16
).cuda()

# Load UNet (Transformer) from separate mode
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)
transformer = transformer.cuda()
unet = transformer

# Load Scheduler from separate mode
scheduler_config_path = scheduler_dir / "scheduler_config.json"
scheduler_config = RectifiedFlowScheduler.load_config(scheduler_config_path)
scheduler = RectifiedFlowScheduler.from_config(scheduler_config)

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

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

model_name_or_path = "PixArt-alpha/PixArt-XL-2-1024-MS"
pipeline = VideoPixArtAlphaPipeline.from_pretrained(model_name_or_path,
                                                    safety_checker=None,
                                                    revision=None,
                                                    torch_dtype=torch.float32,  # dtype adjusted
                                                    **submodel_dict,
                                                    ).to("cuda")

num_inference_steps = 20
num_images_per_prompt = 2
guidance_scale = 3
height = 512
width = 768
num_frames = 57
frame_rate = 25

# Assuming sample is a dict loaded from a .pt file
sample = torch.load("/opt/sample.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.")