mirror of
https://github.com/THUDM/CogVideo.git
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112 lines
5.1 KiB
Python
112 lines
5.1 KiB
Python
"""
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This script demonstrates how to generate a video from a text prompt using CogVideoX with 🤗Huggingface Diffusers Pipeline.
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Note:
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This script requires the `diffusers>=0.30.0` library to be installed, after `diffusers 0.31.0` release,
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need to update.
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Run the script:
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$ python cli_demo.py --prompt "A girl ridding a bike." --model_path THUDM/CogVideoX-2b
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"""
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import argparse
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import torch
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from diffusers import CogVideoXPipeline, CogVideoXDDIMScheduler, CogVideoXDPMScheduler
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from diffusers.utils import export_to_video
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def generate_video(
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prompt: str,
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model_path: str,
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output_path: str = "./output.mp4",
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num_inference_steps: int = 50,
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guidance_scale: float = 6.0,
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num_videos_per_prompt: int = 1,
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dtype: torch.dtype = torch.bfloat16,
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):
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"""
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Generates a video based on the given prompt and saves it to the specified path.
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Parameters:
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- prompt (str): The description of the video to be generated.
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- model_path (str): The path of the pre-trained model to be used.
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- output_path (str): The path where the generated video will be saved.
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- num_inference_steps (int): Number of steps for the inference process. More steps can result in better quality.
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- guidance_scale (float): The scale for classifier-free guidance. Higher values can lead to better alignment with the prompt.
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- num_videos_per_prompt (int): Number of videos to generate per prompt.
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- dtype (torch.dtype): The data type for computation (default is torch.bfloat16).
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"""
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# 1. Load the pre-trained CogVideoX pipeline with the specified precision (bfloat16).
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# add device_map="balanced" in the from_pretrained function and remove the enable_model_cpu_offload()
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# function to use Multi GPUs.
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pipe = CogVideoXPipeline.from_pretrained(model_path, torch_dtype=dtype)
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# 2. Set Scheduler.
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# Can be changed to `CogVideoXDPMScheduler` or `CogVideoXDDIMScheduler`.
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# We recommend using `CogVideoXDDIMScheduler` for CogVideoX-2B and `CogVideoXDPMScheduler` for CogVideoX-5B.
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# pipe.scheduler = CogVideoXDDIMScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
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pipe.scheduler = CogVideoXDPMScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
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# 3. Enable CPU offload for the model, enable tiling.
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# turn off if you have multiple GPUs or enough GPU memory(such as H100) and it will cost less time in inference
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pipe.enable_model_cpu_offload()
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pipe.vae.enable_tiling()
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# 4. Generate the video frames based on the prompt.
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# `num_frames` is the Number of frames to generate.
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# This is the default value for 6 seconds video and 8 fps,so 48 frames and will plus 1 frame for the first frame.
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# for diffusers `0.30.1` and after version, this should be 49.
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video = pipe(
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prompt=prompt,
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num_videos_per_prompt=num_videos_per_prompt, # Number of videos to generate per prompt
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num_inference_steps=num_inference_steps, # Number of inference steps
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num_frames=49, # Number of frames to generate,changed to 49 for diffusers version `0.31.0` and after.
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use_dynamic_cfg=True, ## This id used for DPM Sechduler, for DDIM scheduler, it should be False
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guidance_scale=guidance_scale, # Guidance scale for classifier-free guidance, can set to 7 for DPM scheduler
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generator=torch.Generator().manual_seed(42), # Set the seed for reproducibility
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).frames[0]
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# 5. Export the generated frames to a video file. fps must be 8 for original video.
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export_to_video(video, output_path, fps=8)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Generate a video from a text prompt using CogVideoX")
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parser.add_argument("--prompt", type=str, required=True, help="The description of the video to be generated")
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parser.add_argument(
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"--model_path", type=str, default="THUDM/CogVideoX-5b", help="The path of the pre-trained model to be used"
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)
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parser.add_argument(
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"--output_path", type=str, default="./output.mp4", help="The path where the generated video will be saved"
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)
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parser.add_argument(
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"--num_inference_steps", type=int, default=50, help="Number of steps for the inference process"
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)
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parser.add_argument("--guidance_scale", type=float, default=6.0, help="The scale for classifier-free guidance")
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parser.add_argument("--num_videos_per_prompt", type=int, default=1, help="Number of videos to generate per prompt")
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parser.add_argument(
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"--dtype", type=str, default="bfloat16", help="The data type for computation (e.g., 'float16' or 'bfloat16')"
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)
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args = parser.parse_args()
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# Convert dtype argument to torch.dtype.
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# For CogVideoX-2B model, use torch.float16.
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# For CogVideoX-5B model, use torch.bfloat16.
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dtype = torch.float16 if args.dtype == "float16" else torch.bfloat16
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# main function to generate video.
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generate_video(
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prompt=args.prompt,
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model_path=args.model_path,
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output_path=args.output_path,
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num_inference_steps=args.num_inference_steps,
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guidance_scale=args.guidance_scale,
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num_videos_per_prompt=args.num_videos_per_prompt,
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dtype=dtype,
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)
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