![]() The model architectures are illustrated in the figure below - the chart on the left describes the process to train the image prior model, the figure in the center is the text-to-image generation process, and the figure on the right is image interpolation. ![]() Kandinsky 2.1 is a text-conditional diffusion model based on unCLIP and latent diffusion, composed of a transformer-based image prior model, a unet diffusion model, and a decoder. You now need to change the mask to: # For PIL input import PIL.ImageOps If you are using Kandinsky Inpaint in production. Please upgrade your inpainting code to follow the above. We have changed the mask format in Knaindsky and now using white pixels instead. This is inconsistent with all other pipelines in diffusers. Previously we accepted a mask format where black pixels represent the masked-out area. We introduced a breaking change for Kandinsky inpainting pipeline in the following pull request. □□□ Breaking change for Kandinsky Mask Inpainting □□□ ![]() Image = pipe(prompt=prompt, image=original_image, mask_image=mask).images # Let's mask out an area above the cat's head Negative_prompt = "low quality, bad quality" ![]() Pipe = om_pretrained( "kandinsky-community/kandinsky-2-1-inpaint", torch_dtype=torch.float16) Kandinsky 2.1 is available in diffusers! pip install diffusers transformers accelerateįrom diffusers import AutoPipelineForInpainting The Kandinsky model is created by Arseniy Shakhmatov, Anton Razzhigaev, Aleksandr Nikolich, Igor Pavlov, Andrey Kuznetsov and Denis Dimitrov This approach increases the visual performance of the model and unveils new horizons in blending images and text-guided image manipulation. It uses the CLIP model as a text and image encoder, and diffusion image prior (mapping) between latent spaces of CLIP modalities. Kandinsky 2.1 inherits best practices from Dall-E 2 and Latent diffusion while introducing some new ideas. ![]()
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