der", torch_dtype=torch.float16
... )
>>> pipe.to("cuda")

>>> image = pipe(
...     image_embeds=image_emb,
...     negative_image_embeds=zero_image_emb,
...     height=768,
...     width=768,
...     num_inference_steps=150,
... ).images[0]

>>> image.save("starry_cat.png")  KandinskyV22Img2ImgPipeline  class diffusers.KandinskyV22Img2ImgPipeline  < source > ( unet: UNet2DConditionModel scheduler: DDPMScheduler movq: VQModel  )   Parameters  