Files
hao-ai-lab-FastVideo/examples/inference/basic/basic_dmd.py
T

60 lines
2.4 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_cpu_offload=True,
pin_cpu_memory=True, # set to false if low CPU RAM or hit obscure "CUDA error: Invalid argument"
dit_cpu_offload=False,
vae_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 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."
)
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
# Generate another video with a different prompt, without reloading the
# model!
prompt2 = (
"A majestic lion strides across the golden savanna, its powerful frame "
"glistening under the warm afternoon sun. The tall grass ripples gently in "
"the breeze, enhancing the lion's commanding presence. The tone is vibrant, "
"embodying the raw energy of the wild. Low angle, steady tracking shot, "
"cinematic.")
start_time = time.perf_counter()
video2 = generator.generate_video(prompt2, output_path=OUTPUT_PATH, save_video=True)
end_time = time.perf_counter()
gen_time2 = 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 to generate video2: {gen_time2} seconds")
if __name__ == "__main__":
main()