Differential diffusion represents a significant improvement in inpainting techniques for AI image generation. This method, now available in native ComfyUI, addresses common issues with traditional inpainting such as harsh edges and inconsistent results.

The key difference lies in its approach to masking. Instead of binary black and white masks, soft inpainting employs a gradient. This allows for changes to be applied on a per-pixel basis, resulting in smoother transitions between original and edited areas.

Implementation in ComfyUI requires three main nodes: Gaussian Blur Mask, Differential Diffusion, and Inpaint Model Conditioning (update your ComfyUI if they aren't available). Notably, this technique works with standard generational checkpoints, eliminating the need for specialized inpainting models.

In practice, differential diffusion offers more control over the editing process. Users can fine-tune the softness of the mask and adjust denoise values to achieve the desired balance between change and preservation. This leads to more natural-looking edits, as demonstrated in tests where elements like glasses were seamlessly added to portraits.

While it requires some experimentation to master, differential diffusion provides a powerful new tool for AI artists and image editors seeking more precise and realistic results in their inpainting work.

You can see this workflow demonstrated over on the YouTube channel.