from fastvideo import VideoGenerator
import argparse

OUTPUT_PATH = "video_samples_wan2_2_5B_ti2v"


def main(text_encoder_path: str):
    # 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.
    model_name = "Wan-AI/Wan2.2-TI2V-5B-Diffusers"
    generator = VideoGenerator.from_config({
        "model_path": model_name,
        "engine": {
            # FastVideo will automatically handle distributed setup
            "num_gpus": 1,
            "use_fsdp_inference": True,
            "offload": {
                "dit": True,
                "vae": False,
                "text_encoder": False,
                "pin_cpu_memory": True,  # set to false if low CPU RAM or hit obscure "CUDA error: Invalid argument"
            },
            # AbsMaxFP8 is the quantization method used by ComfyUI;
            # check fastvideo/layers/quantization/* for more quantization methods
            "quantization": {"text_encoder_quant": "AbsMaxFP8"},
        },
        "pipeline": {
            "components": {
                # for Wan 2.2, this is the path to "umt5_xxl_fp8_e4m3fn_scaled.safetensors"
                "text_encoder_weights": text_encoder_path,
            },
        },
    })

    # I2V is triggered just by passing in an image_path argument
    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."
    image_path = "https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/wan_i2v_input.JPG"
    video = generator.generate({
        "prompt": prompt,
        "inputs": {"image_path": image_path},
        "output": {"output_path": OUTPUT_PATH, "save_video": True},
    })


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--text_encoder_path",
        type=str,
        required=True,
        help="Path to the quantized text encoder safetensors file.",
    )
    args = parser.parse_args()
    main(args.text_encoder_path)