58 lines
2.0 KiB
Python
58 lines
2.0 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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OUTPUT_PATH = "video_samples_turbodiffusion"
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def main() -> None:
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# TurboDiffusion: 1-4 step video generation using RCM scheduler + SLA attention
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# FastVideo will automatically use TurboDiffusionPipeline when specified
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generator = VideoGenerator.from_pretrained(
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"loayrashid/TurboWan2.1-T2V-1.3B-Diffusers",
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# FastVideo will automatically handle distributed setup
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num_gpus=1,
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# TurboDiffusion uses a custom pipeline with RCM scheduler
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override_pipeline_cls_name="TurboDiffusionPipeline",
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)
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# Generate videos with the same simple API, regardless of GPU count
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# TurboDiffusion uses guidance_scale=1.0 (no CFG) and only 4 steps
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prompt = (
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"A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes "
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"wide with interest. The playful yet serene atmosphere is complemented by soft "
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"natural light filtering through the petals. Mid-shot, warm and cheerful tones."
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)
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video = generator.generate_video(
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prompt,
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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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# Generate another video with a different prompt, without reloading the model!
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prompt2 = (
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"A majestic lion strides across the golden savanna, its powerful frame "
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"glistening under the warm afternoon sun. The tall grass ripples gently in "
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"the breeze, enhancing the lion's commanding presence. The tone is vibrant, "
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"embodying the raw energy of the wild. Low angle, steady tracking shot, "
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"cinematic."
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)
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video2 = generator.generate_video(
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prompt2,
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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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