+ frames = pipe(image, decode_chunk_size=2, generator=generator, num_frames=25).frames[0] Using all these tricks togethere should lower the memory requirement to less than 8GB VRAM.  Micro-conditioning Stable Diffusion Video also accepts micro-conditioning, in addition to the conditioning image, which allows more control over the generated video: fps: the frames per second of the generated video. motion_bucket_id: the motion bucket id to use for the generated video. This can be used to control the motion of the generated video. Increasing the motion bucket id increases the motion of the generated video. noise_aug_strength: the amount of noise added to the conditioning image. The higher the values the less the video resembles the conditioning image. Increasing this value also increases the motion of the generated video. For example, to generate a video with more motion, use the motion_bucket_id and noise_aug_strength micro-conditioning parameters:   Copied import torch