gative_image_embeds=zero_image_emb,
...     height=768,
...     width=768,
...     num_inference_steps=50,
... )

>>> image = out.images[0]
>>> image.save("cat_with_hat.png")  KandinskyInpaintCombinedPipeline  class diffusers.KandinskyInpaintCombinedPipeline  < source > ( text_encoder: MultilingualCLIP tokenizer: XLMRobertaTokenizer unet: UNet2DConditionModel scheduler: Union movq: VQModel prior_prior: PriorTransformer prior_image_encoder: CLIP