ts, weights)

pipeline = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16, use_safetensors=True).to("cuda")

image = pipeline(prompt, **prior_out, height=768, width=768).images[0]
image    ControlNet ⚠️ ControlNet is only supported for Kandinsky 2.2! ControlNet enables conditioning large pretrained diffusion models with additional inputs such as a depth map or edge detection. For example, you can 