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
67 lines
2.7 KiB
Python
67 lines
2.7 KiB
Python
import os
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import time
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from fastvideo import VideoGenerator
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OUTPUT_PATH = "video_samples_dmd2"
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def main():
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os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "VIDEO_SPARSE_ATTN"
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load_start_time = time.perf_counter()
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model_name = "FastVideo/FastWan2.1-T2V-1.3B-Diffusers"
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generator = VideoGenerator.from_config({
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"model_path": model_name,
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"engine": {
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# FastVideo will automatically handle distributed setup
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"num_gpus": 1,
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"use_fsdp_inference": False, # set to True if GPU is out of memory
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# Adjust these offload parameters if you have < 32GB of VRAM
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"offload": {
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"text_encoder": 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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"dit": False,
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"vae": False,
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},
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"attention": {"vsa_sparsity": 0.8},
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},
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})
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load_end_time = time.perf_counter()
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load_time = load_end_time - load_start_time
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prompt = (
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"A neon-lit alley in futuristic Tokyo during a heavy rainstorm at night. The puddles reflect glowing signs in kanji, advertising ramen, karaoke, and VR arcades. A woman in a translucent raincoat walks briskly with an LED umbrella. Steam rises from a street food cart, and a cat darts across the screen. Raindrops are visible on the camera lens, creating a cinematic bokeh effect."
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)
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start_time = time.perf_counter()
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video = generator.generate({
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"prompt": prompt,
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"sampling": {"num_frames": 81},
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"output": {"output_path": OUTPUT_PATH, "save_video": True},
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})
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end_time = time.perf_counter()
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gen_time = end_time - start_time
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# Generate another video with a different prompt, without reloading the
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# 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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start_time = time.perf_counter()
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video2 = generator.generate({
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"prompt": prompt2,
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"sampling": {"num_frames": 81},
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"output": {"output_path": OUTPUT_PATH, "save_video": True},
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})
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end_time = time.perf_counter()
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gen_time2 = end_time - start_time
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print(f"Time taken to load model: {load_time} seconds")
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print(f"Time taken to generate video: {gen_time} seconds")
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print(f"Time taken to generate video2: {gen_time2} seconds")
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if __name__ == "__main__":
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main()
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