Every example calls VideoGenerator.generate with a nested request instead of generate_video keywords, and passes settings outside the from_pretrained convenience keywords through VideoGenerator.from_config at their typed paths. Each request and generator config equals what the keyword call built. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_012M91wnVFmPEvJ9h39r5BH7
47 lines
1.8 KiB
Python
47 lines
1.8 KiB
Python
from fastvideo import VideoGenerator, PipelineConfig
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def main():
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config = PipelineConfig.from_pretrained("Wan-AI/Wan2.1-T2V-1.3B-Diffusers")
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config.text_encoder_precisions = ["fp16"]
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generator = VideoGenerator.from_config({
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"model_path": "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
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"engine": {
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"use_fsdp_inference": False, # Disable FSDP for MPS
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"offload": {
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"dit": True,
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"text_encoder": True,
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"pin_cpu_memory": True,
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},
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"disable_autocast": False,
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"num_gpus": 1,
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},
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"pipeline": {"experimental": {"pipeline_config": config}},
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})
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# Create sampling parameters with reduced number of frames
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sampling = {
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"num_frames": 25, # Reduce from default 81 to 25 frames bc we have to use the SDPA attn backend for mps
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"height": 256,
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"width": 256,
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}
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prompt = ("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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video = generator.generate({"prompt": prompt, "sampling": sampling})
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prompt2 = ("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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video2 = generator.generate({"prompt": prompt2, "sampling": sampling})
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if __name__ == "__main__":
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main()
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