noise_level increases the variance in the final un-noised images. The noise is applied in two ways: A noise schedule is applied directly to the embeddings. A vector of sinusoidal time embeddings are appended to the output. In both cases, the amount of noise is controlled by the same noise_level. The embeddings are normalized before the noise is applied and un-normalized after the noise is applied.  StableUnCLIPImg2ImgPipeline  class diffusers.StableUnCLIPImg2ImgPipeline  < source > ( feature_extractor: CLIPImageProcessor image_encoder: CLIPVisionModelWithProjection image_normalizer: StableUnCLIPImageNormalizer image_noising_scheduler: KarrasDiffusionSchedulers tokenizer: CLIPTokenizer text_encoder: CLIPTextModel unet: UNet2DConditionModel scheduler: KarrasDiffusionSchedulers vae: AutoencoderKL  )   Parameters   feature_extractor (CLIPImageProcessor) —