42 lines
1.5 KiB
Python
42 lines
1.5 KiB
Python
import os
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# Set SLA attention backend BEFORE fastvideo imports
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os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "SLA_ATTN"
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from fastvideo import VideoGenerator
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# Use local model path
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MODEL_PATH = "loayrashid/TurboWan2.2-I2V-A14B-Diffusers"
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OUTPUT_PATH = "video_samples_turbodiffusion_i2v"
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def main() -> None:
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# TurboDiffusion I2V: 1-4 step image-to-video generation
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# Uses RCM scheduler with sigma_max=200 for I2V
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generator = VideoGenerator.from_pretrained(
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MODEL_PATH,
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num_gpus=2,
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override_pipeline_cls_name="TurboDiffusionI2VPipeline",
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)
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# Example prompt and image for I2V
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prompt = ("Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside.")
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# Use an example image path
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image_path = "https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/wan_i2v_input.JPG"
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video = generator.generate_video(
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prompt,
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image_path=image_path,
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output_path=OUTPUT_PATH,
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save_video=True,
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num_inference_steps=4,
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seed=42,
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guidance_scale=1.0,
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)
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if __name__ == "__main__":
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main()
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