from fastvideo import VideoGenerator from fastvideo.api import ( EngineConfig, GenerationRequest, GeneratorConfig, OffloadConfig, OutputConfig, ) OUTPUT_PATH = "video_samples" def main(): # FastVideo will automatically use the optimal default arguments for the # model. # If a local path is provided, FastVideo will make a best effort # attempt to identify the optimal arguments. generator = VideoGenerator.from_config( GeneratorConfig( model_path="Wan-AI/Wan2.1-T2V-1.3B-Diffusers", # FastVideo will automatically handle distributed setup engine=EngineConfig( num_gpus=2, use_fsdp_inference=True, execution_backend="ray", offload=OffloadConfig( dit=False, vae=False, text_encoder=True, pin_cpu_memory=True, # set to false if low CPU RAM or hit obscure "CUDA error: Invalid argument" # image_encoder=False, ), ), ) ) # Generate videos with the same simple API, regardless of GPU count 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." ) video = generator.generate( GenerationRequest(prompt=prompt, output=OutputConfig(output_path=OUTPUT_PATH, save_video=True))) # 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.") video2 = generator.generate( GenerationRequest(prompt=prompt2, output=OutputConfig(output_path=OUTPUT_PATH, save_video=True))) if __name__ == "__main__": main()