Davids048andClaude Opus 5.5
f571621ae7
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
54 lines
1.8 KiB
Python
54 lines
1.8 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_14B"
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def main() -> None:
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# TurboDiffusion 14B: 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-14B-Diffusers",
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# 14B model needs more GPUs
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num_gpus=2,
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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({
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"prompt": prompt,
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"sampling": {
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"seed": 42,
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},
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"output": {
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"output_path": OUTPUT_PATH,
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"save_video": True,
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},
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})
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# Generate another video with a different prompt, without reloading the model!
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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({
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"prompt": prompt2,
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"sampling": {
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"seed": 42,
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},
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"output": {
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"output_path": OUTPUT_PATH,
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"save_video": True,
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},
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})
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if __name__ == "__main__":
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main()
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