export_to_gif(frames, "animation.gif")  no negative prompt  negative prompt applied  Model-specific parameters There are some pipeline parameters that are unique to each model such as adjusting the motion in a video or adding noise to the initial image. Stable Video Diffusion Text2Video-Zero  Stable Video Diffusion provides additional micro-conditioning for the frame rate with the fps parameter and for motion with the motion_bucket_id parameter. Together, these parameters allow for adjusting the amount of motion in the generated video. There is also a noise_aug_strength parameter that increases the amount of noise added to the initial image. Varying this parameter affects how similar the generated video and initial image are. A higher noise_aug_strength also increases the amount of motion. To learn more, read the Micro-conditioning guide.   Control video generation Video generation can be controlled similar to how text-to-image, image-to-image, and inpainting can be controlled with a ControlNetModel. The only difference is you need to use the CrossFrameAttnProcessor so each frame attends to the first frame.  Text2Video-Zero Text2Video-Zero video generation can be conditioned on pose and edge images for even greater control over a subject’s motion in the generated video or to preserve the identity of a subject/object in the video. You can also use Text2Video-Zero with InstructPix2Pix for editing videos with text. pose control edge control InstructPix2Pix  Start by downloading a video and extracting the pose images from it.   Copied from huggingface_hub import hf_hub_download