ality, worse quality, low resolution",
    num_frames=16,
    guidance_scale=7.5,
    num_inference_steps=50,
    generator=torch.Generator("cpu").manual_seed(49),
)
frames = output.frames[0]
export_to_gif(frames, "animation.gif")   ModelscopeT2V ModelscopeT2V adds spatial and temporal convolutions and attention to a UNet, and it is trained on image-text and video-text datasets to enhance what it learns during training. The model takes a prompt