42 lines
1.8 KiB
Python
42 lines
1.8 KiB
Python
from fastvideo import VideoGenerator
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# from fastvideo.configs.sample import SamplingParam
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OUTPUT_PATH = "video_samples"
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def main():
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# FastVideo will automatically use the optimal default arguments for the
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# model.
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# If a local path is provided, FastVideo will make a best effort
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# attempt to identify the optimal arguments.
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generator = VideoGenerator.from_pretrained(
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"Wan-AI/Wan2.1-T2V-14B-Diffusers",
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# FastVideo will automatically handle distributed setup
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num_gpus=4,
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use_fsdp_inference=True,
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dit_cpu_offload=False,
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vae_cpu_offload=False,
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text_encoder_cpu_offload=True,
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pin_cpu_memory=True, # set to false if low CPU RAM or hit obscure "CUDA error: Invalid argument"
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# image_encoder_cpu_offload=False,
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)
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# sampling_param = SamplingParam.from_pretrained("Wan-AI/Wan2.1-T2V-1.3B-Diffusers")
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# sampling_param.num_frames = 45
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# sampling_param.image_path = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg"
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# Generate videos with the same simple API, regardless of GPU count
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prompt = (
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"A watermelon wearing a helmet is crushed by a hydraulic press, causing it to flatten and burst open."
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)
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video = generator.generate_video(prompt, output_path=OUTPUT_PATH, save_video=True)
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# video = generator.generate_video(prompt, sampling_param=sampling_param, output_path="wan_t2v_videos/")
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# Generate another video with a different prompt, without reloading the
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# model!
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prompt2 = (
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"The video shows a green and orange object being flattened as if it were under a hydraulic press, with the press moving down and compressing the object")
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video2 = generator.generate_video(prompt2, output_path=OUTPUT_PATH, save_video=True)
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if __name__ == "__main__":
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main()
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