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test_matrixgame_similarity

Classes

Functions

fastvideo.tests.ssim.test_matrixgame_similarity.test_matrixgame_similarity 

test_matrixgame_similarity(prompt, ATTENTION_BACKEND, model_id)

Test that runs inference with different parameters and compares the output to reference videos using SSIM.

Source code in fastvideo/tests/ssim/test_matrixgame_similarity.py 
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@pytest.mark.parametrize("prompt", TEST_PROMPTS)
@pytest.mark.parametrize("ATTENTION_BACKEND", ["FLASH_ATTN"])
@pytest.mark.parametrize("model_id", list(MODEL_TO_PARAMS.keys()))
def test_matrixgame_similarity(prompt, ATTENTION_BACKEND, model_id):
    """
    Test that runs inference with different parameters and compares the output
    to reference videos using SSIM.
    """
    os.environ["FASTVIDEO_ATTENTION_BACKEND"] = ATTENTION_BACKEND

    script_dir = os.path.dirname(os.path.abspath(__file__))

    base_output_dir = os.path.join(script_dir, "generated_videos", model_id)
    output_dir = os.path.join(base_output_dir, ATTENTION_BACKEND)
    output_video_name = "video.mp4"

    os.makedirs(output_dir, exist_ok=True)

    BASE_PARAMS = MODEL_TO_PARAMS[model_id]
    num_inference_steps = BASE_PARAMS["num_inference_steps"]

    # Create action conditions for MatrixGame
    actions = create_action_presets(
        BASE_PARAMS["num_frames"], keyboard_dim=BASE_PARAMS["keyboard_dim"],
        seed=BASE_PARAMS["seed"]
    )
    latent_frames = (BASE_PARAMS["num_frames"] - 1) // 4 + 1
    grid_sizes = torch.tensor([latent_frames, 44, 80])

    init_kwargs = {
        "num_gpus": BASE_PARAMS["num_gpus"],
        "use_fsdp_inference": True,
        "dit_cpu_offload": False,
        "vae_cpu_offload": False,
        "text_encoder_cpu_offload": True,
        "pin_cpu_memory": True,
    }

    generation_kwargs = {
        "num_inference_steps": num_inference_steps,
        "output_path": output_dir,
        "image_path": TEST_IMAGE_PATHS[0],
        "height": BASE_PARAMS["height"],
        "width": BASE_PARAMS["width"],
        "num_frames": BASE_PARAMS["num_frames"],
        "seed": BASE_PARAMS["seed"],
        "mouse_cond": actions["mouse"].unsqueeze(0),
        "keyboard_cond": actions["keyboard"].unsqueeze(0),
        "grid_sizes": grid_sizes,
        "save_video": True,
    }

    generator = VideoGenerator.from_pretrained(model_path=BASE_PARAMS["model_path"], **init_kwargs)
    generator.generate_video(prompt, **generation_kwargs)

    if isinstance(generator.executor, MultiprocExecutor):
        generator.executor.shutdown()

    assert os.path.exists(output_dir), (
        f"Output video was not generated at {output_dir}"
    )

    reference_folder = os.path.join(
        script_dir, device_reference_folder, model_id, ATTENTION_BACKEND
    )

    if not os.path.exists(reference_folder):
        logger.error("Reference folder missing")
        raise FileNotFoundError(
            f"Reference video folder does not exist: {reference_folder}"
        )

    # Find the matching reference video based on the prompt
    reference_video_name = None

    for filename in os.listdir(reference_folder):
        if filename.endswith(".mp4"):
            reference_video_name = filename
            break

    if not reference_video_name:
        logger.error(
            f"Reference video not found for model: {model_id} with backend: {ATTENTION_BACKEND}"
        )
        raise FileNotFoundError("Reference video missing")

    reference_video_path = os.path.join(reference_folder, reference_video_name)
    generated_video_path = os.path.join(output_dir, output_video_name)

    logger.info(
        f"Computing SSIM between {reference_video_path} and {generated_video_path}"
    )
    ssim_values = compute_video_ssim_torchvision(
        reference_video_path, generated_video_path, use_ms_ssim=True
    )

    mean_ssim = ssim_values[0]
    logger.info(f"SSIM mean value: {mean_ssim}")
    logger.info(f"Writing SSIM results to directory: {output_dir}")

    success = write_ssim_results(
        output_dir,
        ssim_values,
        reference_video_path,
        generated_video_path,
        num_inference_steps,
        prompt,
    )

    if not success:
        logger.error("Failed to write SSIM results to file")

    min_acceptable_ssim = 0.98
    assert mean_ssim >= min_acceptable_ssim, (
        f"SSIM value {mean_ssim} is below threshold {min_acceptable_ssim} for {model_id} with backend {ATTENTION_BACKEND}"
    )