cogvideox_example.py

import logging
import time
import torch
import torch.distributed
from diffusers import AutoencoderKLTemporalDecoder
from xfuser import xFuserCogVideoXPipeline, xFuserArgs
from xfuser.config import FlexibleArgumentParser
from xfuser.core.distributed import (
    get_world_group,
    get_data_parallel_rank,
    get_data_parallel_world_size,
    get_runtime_state,
    is_dp_last_group,
)
from diffusers.utils import export_to_video


def main():
    parser = FlexibleArgumentParser(description="xFuser Arguments")
    args = xFuserArgs.add_cli_args(parser).parse_args()
    engine_args = xFuserArgs.from_cli_args(args)

    engine_config, input_config = engine_args.create_config()
    local_rank = get_world_group().local_rank
    
    assert engine_args.use_parallel_vae is False, "parallel VAE not implemented for CogVideo"

    pipe = xFuserCogVideoXPipeline.from_pretrained(
        pretrained_model_name_or_path=engine_config.model_config.model,
        engine_config=engine_config,
        torch_dtype=torch.bfloat16,
    )
    if args.enable_sequential_cpu_offload:
        pipe.enable_sequential_cpu_offload(gpu_id=local_rank)
        logging.info(f"rank {local_rank} sequential CPU offload enabled")
    elif args.enable_model_cpu_offload:
        pipe.enable_model_cpu_offload(gpu_id=local_rank)
        logging.info(f"rank {local_rank} model CPU offload enabled")
    else:
        device = torch.device(f"cuda:{local_rank}")
        pipe = pipe.to(device)

    if args.enable_tiling:
        pipe.vae.enable_tiling()

    if args.enable_slicing:
        pipe.vae.enable_slicing()

    # warmup
    output = pipe(
        height=input_config.height,
        width=input_config.width,
        num_frames=input_config.num_frames,
        prompt=input_config.prompt,
        num_inference_steps=1,
        generator=torch.Generator(device="cuda").manual_seed(input_config.seed),
    ).frames[0]

    torch.cuda.reset_peak_memory_stats()
    start_time = time.time()

    output = pipe(
        height=input_config.height,
        width=input_config.width,
        num_frames=input_config.num_frames,
        prompt=input_config.prompt,
        num_inference_steps=input_config.num_inference_steps,
        generator=torch.Generator(device="cuda").manual_seed(input_config.seed),
    ).frames[0]

    end_time = time.time()
    elapsed_time = end_time - start_time
    peak_memory = torch.cuda.max_memory_allocated(device=f"cuda:{local_rank}")

    parallel_info = (
        f"dp{engine_args.data_parallel_degree}_cfg{engine_config.parallel_config.cfg_degree}_"
        f"ulysses{engine_args.ulysses_degree}_ring{engine_args.ring_degree}_"
        f"tp{engine_args.tensor_parallel_degree}_"
        f"pp{engine_args.pipefusion_parallel_degree}_patch{engine_args.num_pipeline_patch}"
    )
    if is_dp_last_group():
        resolution = f"{input_config.width}x{input_config.height}"
        output_filename = f"results/cogvideox_{parallel_info}_{resolution}.mp4"
        export_to_video(output, output_filename, fps=8)
        print(f"output saved to {output_filename}")

    if get_world_group().rank == get_world_group().world_size - 1:
        print(f"epoch time: {elapsed_time:.2f} sec, memory: {peak_memory/1e9} GB")
    get_runtime_state().destory_distributed_env()


if __name__ == "__main__":
    main()