152 lines
6.3 KiB
Python
152 lines
6.3 KiB
Python
import argparse
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import json
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import os
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import torch
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import torch.distributed as dist
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from diffusers import FlowMatchEulerDiscreteScheduler
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from diffusers.utils import export_to_video
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from fastvideo.distill.solver import PCMFMScheduler
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from fastvideo.models.mochi_hf.modeling_mochi import MochiTransformer3DModel
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from fastvideo.models.mochi_hf.pipeline_mochi import MochiPipeline
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from fastvideo.utils.parallel_states import initialize_sequence_parallel_state, nccl_info
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def initialize_distributed():
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local_rank = int(os.getenv("RANK", 0))
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world_size = int(os.getenv("WORLD_SIZE", 1))
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print("world_size", world_size)
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torch.cuda.set_device(local_rank)
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dist.init_process_group(backend="nccl", init_method="env://", world_size=world_size, rank=local_rank)
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initialize_sequence_parallel_state(world_size)
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def main(args):
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initialize_distributed()
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print(nccl_info.sp_size)
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device = torch.cuda.current_device()
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# Peiyuan: GPU seed will cause A100 and H100 to produce different results .....
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if args.scheduler_type == "euler":
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scheduler = FlowMatchEulerDiscreteScheduler()
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else:
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linear_quadratic = True if "linear_quadratic" in args.scheduler_type else False
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scheduler = PCMFMScheduler(
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1000,
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args.shift,
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args.num_euler_timesteps,
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linear_quadratic,
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args.linear_threshold,
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args.linear_range,
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)
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if args.transformer_path is not None:
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transformer = MochiTransformer3DModel.from_pretrained(args.transformer_path)
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else:
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transformer = MochiTransformer3DModel.from_pretrained(args.model_path, subfolder="transformer/")
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pipe = MochiPipeline.from_pretrained(args.model_path, transformer=transformer, scheduler=scheduler)
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pipe.enable_vae_tiling()
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if args.lora_checkpoint_dir is not None:
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print(f"Loading LoRA weights from {args.lora_checkpoint_dir}")
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config_path = os.path.join(args.lora_checkpoint_dir, "lora_config.json")
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with open(config_path, "r") as f:
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lora_config_dict = json.load(f)
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rank = lora_config_dict["lora_params"]["lora_rank"]
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lora_alpha = lora_config_dict["lora_params"]["lora_alpha"]
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lora_scaling = lora_alpha / rank
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pipe.load_lora_weights(args.lora_checkpoint_dir, adapter_name="default")
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pipe.set_adapters(["default"], [lora_scaling])
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print(f"Successfully Loaded LoRA weights from {args.lora_checkpoint_dir}")
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# pipe.to(device)
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pipe.enable_model_cpu_offload(device)
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# Generate videos from the input prompt
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if args.prompt_embed_path is not None:
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prompt_embeds = (torch.load(args.prompt_embed_path, map_location="cpu",
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weights_only=True).to(device).unsqueeze(0))
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encoder_attention_mask = (torch.load(args.encoder_attention_mask_path, map_location="cpu",
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weights_only=True).to(device).unsqueeze(0))
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prompts = None
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elif args.prompt_path is not None:
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prompts = [line.strip() for line in open(args.prompt_path, "r")]
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prompt_embeds = None
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encoder_attention_mask = None
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else:
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prompts = args.prompts
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prompt_embeds = None
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encoder_attention_mask = None
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if prompts is not None:
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with torch.autocast("cuda", dtype=torch.bfloat16):
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for prompt in prompts:
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generator = torch.Generator("cpu").manual_seed(args.seed)
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video = pipe(
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prompt=[prompt],
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height=args.height,
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width=args.width,
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num_frames=args.num_frames,
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num_inference_steps=args.num_inference_steps,
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guidance_scale=args.guidance_scale,
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generator=generator,
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).frames
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if nccl_info.global_rank <= 0:
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os.makedirs(args.output_path, exist_ok=True)
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suffix = prompt.split(".")[0]
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export_to_video(
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video[0],
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os.path.join(args.output_path, f"{suffix}.mp4"),
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fps=30,
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)
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else:
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with torch.autocast("cuda", dtype=torch.bfloat16):
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generator = torch.Generator("cpu").manual_seed(args.seed)
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videos = pipe(
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prompt_embeds=prompt_embeds,
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prompt_attention_mask=encoder_attention_mask,
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height=args.height,
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width=args.width,
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num_frames=args.num_frames,
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num_inference_steps=args.num_inference_steps,
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guidance_scale=args.guidance_scale,
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generator=generator,
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).frames
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if nccl_info.global_rank <= 0:
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export_to_video(videos[0], args.output_path + ".mp4", fps=30)
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if __name__ == "__main__":
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# arg parse
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parser = argparse.ArgumentParser()
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parser.add_argument("--prompts", nargs="+", default=[])
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parser.add_argument("--num_frames", type=int, default=163)
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parser.add_argument("--height", type=int, default=480)
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parser.add_argument("--width", type=int, default=848)
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parser.add_argument("--num_inference_steps", type=int, default=64)
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parser.add_argument("--guidance_scale", type=float, default=4.5)
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parser.add_argument("--model_path", type=str, default="data/mochi")
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parser.add_argument("--seed", type=int, default=42)
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parser.add_argument("--output_path", type=str, default="./outputs.mp4")
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parser.add_argument("--transformer_path", type=str, default=None)
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parser.add_argument("--prompt_embed_path", type=str, default=None)
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parser.add_argument("--prompt_path", type=str, default=None)
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parser.add_argument("--scheduler_type", type=str, default="euler")
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parser.add_argument("--encoder_attention_mask_path", type=str, default=None)
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parser.add_argument(
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"--lora_checkpoint_dir",
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type=str,
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default=None,
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help="Path to the directory containing LoRA checkpoints",
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)
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parser.add_argument("--shift", type=float, default=8.0)
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parser.add_argument("--num_euler_timesteps", type=int, default=100)
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parser.add_argument("--linear_threshold", type=float, default=0.025)
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parser.add_argument("--linear_range", type=float, default=0.5)
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args = parser.parse_args()
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main(args)
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