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hao-ai-lab-FastVideo/examples/inference/basic/basic_dmd.py
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2025-07-24 21:31:42 -07:00

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1.6 KiB
Python

import os
import time
from fastvideo import VideoGenerator
from fastvideo.configs.sample import SamplingParam
OUTPUT_PATH = "video_samples_dmd2"
def main():
os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "VIDEO_SPARSE_ATTN"
load_start_time = time.perf_counter()
model_name = "FastVideo/FastWan2.1-T2V-1.3B-Diffusers"
generator = VideoGenerator.from_pretrained(
model_name,
# FastVideo will automatically handle distributed setup
num_gpus=1,
use_fsdp_inference=True,
# Adjust these offload parameters if you have < 32GB of VRAM
text_encoder_offload=False,
use_cpu_offload=False,
VSA_sparsity=0.8,
)
load_end_time = time.perf_counter()
load_time = load_end_time - load_start_time
sampling_param = SamplingParam.from_pretrained(model_name)
prompt = (
"A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes "
"wide with interest. The playful yet serene atmosphere is complemented by soft "
"natural light filtering through the petals. Mid-shot, warm and cheerful tones."
)
start_time = time.perf_counter()
video = generator.generate_video(prompt, output_path=OUTPUT_PATH, save_video=True, sampling_param=sampling_param)
end_time = time.perf_counter()
gen_time = end_time - start_time
e2e_gen_time = end_time - start_time
print(f"Time taken to load model: {load_time} seconds")
print(f"Time taken to generate video: {gen_time} seconds")
print(f"Time taken for e2e generation: {e2e_gen_time} seconds")
if __name__ == "__main__":
main()