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, encodes it and creates text embeddings which are denoised by the UNet, and then decoded by a VQGAN into a video. ModelScopeT2V generates watermarked videos due to the datasets it was trained on. To use a watermark-free model, try the cerspense/zeroscope_v2_76w model with the TextToVideoSDPipeline first, and then upscale it’s output with the cerspense/zeroscope_v2_XL checkpoint using the VideoToVideoSDPipeline. Load a ModelScopeT2V checkpoint into the DiffusionPipeline along with a prompt to generate a video.   Copied import torch