Image variation The Stable Diffusion model can also generate variations from an input image. It uses a fine-tuned version of a Stable Diffusion model by Justin Pinkney from Lambda. The original codebase can be found at LambdaLabsML/lambda-diffusers and additional official checkpoints for image variation can be found at lambdalabs/sd-image-variations-diffusers. Make sure to check out the Stable Diffusion Tips section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!  StableDiffusionImageVariationPipeline  class diffusers.StableDiffusionImageVariationPipeline  < source > ( vae: AutoencoderKL image_encoder: CLIPVisionModelWithProjection unet: UNet2DConditionModel scheduler: KarrasDiffusionSchedulers safety_checker: StableDiffusionSafetyChecker feature_extractor: CLIPImageProcessor requires_safety_checker: bool = True  )   Parameters   vae (AutoencoderKL) —