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hao-ai-lab-FastVideo/examples/inference/basic/basic_turbodiffusion_i2v.py
f4704847c2 [bugfix] Add configs for TurboDiffusion T2V/I2V models (#993)
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
Co-authored-by: Will Lin <wlsaidhi@gmail.com>
2026-01-06 16:36:45 -06:00

38 lines
1.3 KiB
Python

import os
# Set SLA attention backend BEFORE fastvideo imports
os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "SLA_ATTN"
from fastvideo import VideoGenerator
# Use local model path
MODEL_PATH = "loayrashid/TurboWan2.2-I2V-A14B-Diffusers"
OUTPUT_PATH = "video_samples_turbodiffusion_i2v"
def main() -> None:
# TurboDiffusion I2V: 1-4 step image-to-video generation
generator = VideoGenerator.from_pretrained(
MODEL_PATH,
num_gpus=2,
)
# Example prompt and image for I2V
prompt = ("Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside.")
# Use an example image path
image_path = "https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/wan_i2v_input.JPG"
video = generator.generate_video(
prompt,
image_path=image_path,
output_path=OUTPUT_PATH,
save_video=True,
seed=42,
)
if __name__ == "__main__":
main()