mirror of
https://github.com/THUDM/CogVideo.git
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102 lines
3.5 KiB
Python
102 lines
3.5 KiB
Python
"""
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This is a parallel inference script for CogVideo. The original script
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can be found from the xDiT project at
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https://github.com/xdit-project/xDiT/blob/main/examples/cogvideox_example.py
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By using this code, the inference process is parallelized on multiple GPUs,
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and thus speeded up.
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You can also use the run.sh file in the same folder to automate running this
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code for batch generation of videos.
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Usage:
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1. pip install xfuser
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2. run the following command to generate video
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torchrun --nproc_per_node=4 cogvideox_xdit.py --model <cogvideox-model-path> \
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--ring_degree 2 --use_cfg_parallel --height 480 --width 720 --num_frames 9 \
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--prompt 'A small dog.'
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"""
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import time
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import torch
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import torch.distributed
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from diffusers import AutoencoderKLTemporalDecoder
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from xfuser import xFuserCogVideoXPipeline, xFuserArgs
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from xfuser.config import FlexibleArgumentParser
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from xfuser.core.distributed import (
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get_world_group,
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get_data_parallel_rank,
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get_data_parallel_world_size,
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get_runtime_state,
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is_dp_last_group,
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)
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from diffusers.utils import export_to_video
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def main():
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parser = FlexibleArgumentParser(description="xFuser Arguments")
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args = xFuserArgs.add_cli_args(parser).parse_args()
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engine_args = xFuserArgs.from_cli_args(args)
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# Check if ulysses_degree is valid
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num_heads = 30
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if engine_args.ulysses_degree > 0 and num_heads % engine_args.ulysses_degree != 0:
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raise ValueError(
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f"ulysses_degree ({engine_args.ulysses_degree}) must be a divisor of the number of heads ({num_heads})"
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)
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engine_config, input_config = engine_args.create_config()
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local_rank = get_world_group().local_rank
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pipe = xFuserCogVideoXPipeline.from_pretrained(
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pretrained_model_name_or_path=engine_config.model_config.model,
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engine_config=engine_config,
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torch_dtype=torch.bfloat16,
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)
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if args.enable_sequential_cpu_offload:
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pipe.enable_model_cpu_offload(gpu_id=local_rank)
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pipe.vae.enable_tiling()
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else:
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device = torch.device(f"cuda:{local_rank}")
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pipe = pipe.to(device)
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torch.cuda.reset_peak_memory_stats()
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start_time = time.time()
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output = pipe(
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height=input_config.height,
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width=input_config.width,
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num_frames=input_config.num_frames,
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prompt=input_config.prompt,
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num_inference_steps=input_config.num_inference_steps,
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generator=torch.Generator(device="cuda").manual_seed(input_config.seed),
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guidance_scale=6,
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).frames[0]
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end_time = time.time()
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elapsed_time = end_time - start_time
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peak_memory = torch.cuda.max_memory_allocated(device=f"cuda:{local_rank}")
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parallel_info = (
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f"dp{engine_args.data_parallel_degree}_cfg{engine_config.parallel_config.cfg_degree}_"
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f"ulysses{engine_args.ulysses_degree}_ring{engine_args.ring_degree}_"
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f"tp{engine_args.tensor_parallel_degree}_"
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f"pp{engine_args.pipefusion_parallel_degree}_patch{engine_args.num_pipeline_patch}"
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)
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if is_dp_last_group():
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world_size = get_data_parallel_world_size()
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resolution = f"{input_config.width}x{input_config.height}"
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output_filename = f"results/cogvideox_{parallel_info}_{resolution}.mp4"
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export_to_video(output, output_filename, fps=8)
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print(f"output saved to {output_filename}")
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if get_world_group().rank == get_world_group().world_size - 1:
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print(f"epoch time: {elapsed_time:.2f} sec, memory: {peak_memory/1e9} GB")
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get_runtime_state().destory_distributed_env()
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if __name__ == "__main__":
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main()
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