import os

# Set SLA attention backend BEFORE fastvideo imports
os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "SLA_ATTN"

from fastvideo import VideoGenerator

OUTPUT_PATH = "video_samples_turbodiffusion"


def main() -> None:
    # TurboDiffusion: 1-4 step video generation using RCM scheduler + SLA attention
    # FastVideo will automatically use TurboDiffusionPipeline when specified
    generator = VideoGenerator.from_pretrained(
        "loayrashid/TurboWan2.1-T2V-1.3B-Diffusers",
        # FastVideo will automatically handle distributed setup
        {
            "engine": {
                "num_gpus": 1,
                "use_fsdp_inference": False,  # set to True if GPU is out of memory
                "offload": {
                    # set to false if using RTX 4090
                    # "pin_cpu_memory": False,
                },
            },
        },
    )

    # Generate videos with the same simple API, regardless of GPU count
    # TurboDiffusion defaults: guidance_scale=1.0 and num_inference_steps=4 (from config)
    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({
        "prompt": prompt,
        "sampling": {
            "seed": 42,
        },
        "output": {
            "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({
        "prompt": prompt2,
        "sampling": {
            "seed": 42,
        },
        "output": {
            "output_path": OUTPUT_PATH,
            "save_video": True,
        },
    })


if __name__ == "__main__":
    main()