Solving the Output Size Issue in Inpainting Pipelines

When using Stable Diffusion for inpainting, you might encounter unexpected dimension changes or distorted aspect ratios in your output. That’s because models like Stable Diffusion typically perform best when images have dimensions that are multiples of 8, 16, or even 32. 

Stable Diffusion’s internal architecture (the UNet) works in downsampling and upsampling stages. Convolution layers and attention blocks often assume image dimensions divisible by 2. 

After multiple halves, the model can handle images more efficiently if they start at a size that’s a power-of-two multiple (8, 16, 32, etc.):

• Memory efficiency: Avoid having to handle partial “rows” or “columns” that don’t fit neatly into the model’s computational blocks.
• Speed: GPU kernels optimized for standard sizes (like 512, 768, etc.) often run faster than custom, arbitrary dimensions.
• Quality: Proper alignment reduces boundary artifacts. You won’t see harsh “edges” where the model struggled with partial tiles.

If your original image is, for instance, 777×653, the model may try to auto-resize it to something like 768×640 (some models can work only with 512×512), discarding those extra pixels. This resizing can lead to:

• Distortion: Subtle shape warping, especially noticeable when dealing with delicate features like a dog’s face or fur.
• Alignment issues: If you plan to overlay the inpainted region back onto the original 777×653 image, you’ll need extra manual steps to match dimensions.
• Loss of detail: Automatic downsampling can blur fine textures (like the hair of a Spitz).

Clearly, we need a reliable strategy to keep the final result exactly 777×653, preserving every pixel of the original framing.

Padding & cropping approach

Rather than resizing the image to a “safe” dimension or cropping it to lose the extra pixels, we rely on a pad-and-crop technique. Here’s the high-level process:

1. Calculate multiples of 8

For 777×653, the nearest multiples of 8 that are equal or larger are 784×656.

2. Pad the image

Create a blank 784×656 canvas, place the original 777×653 image in the top-left corner, and fill any empty area with black or a neutral color.

3. Model inference

Feed the padded 784×656 image and its corresponding mask into the inpainting pipeline. The model sees a dimension it prefers – no forced auto-resize.

4. Crop to original size

After inpainting, you’ll receive a 784×656 output. Simply crop it back to the first 777 pixels in width and 653 pixels in height. No stretching or squashing required.

This approach ensures the original dimension remains intact once the padding is removed.

Potential pitfalls without proper padding

Offset masks: If you forget to apply the same padding logic to your inpainting mask, the region you want to inpaint might end up shifted or partially excluded.

Unwanted borders: If you pad incorrectly or forget to crop back, you could end up with black bars (or extra blank space) in the final image.

Strange artifacts: The model might fill the newly padded areas incorrectly if your mask extends into those regions, causing bizarre patterns at the edges.

A small summary comparison might look like this:

Case	Input size	Model’s output size	Final result	Observed artifacts
Naive Resize	777×653	512×512	512×512	Slight distortion, lost edges
Pad & Crop (Proposed)	777×653	512×512	777×653 (cropped)	Minimal distortion, preserved details

Why our approach beats simple resizing or cropping

1. Aspect ratio integrity

Unlike resizing, padding does not force you to alter the height-to-width ratio. If a Spitz’ face is naturally oval, it stays that way.

2. Preserved content

Cropping out extra pixels could cut off important parts of the image (like fur edges or ears). Padding ensures no original detail is lost.

3. Consistent dimensions for post-processing

Once the inpainted image is cropped back down, it aligns perfectly with any original references – no extra transforms required.

Conclusion & next steps

Perfect inpainting often hinges on small details – like ensuring your dimensions remain accurate throughout the pipeline. This pad-and-crop strategy lays the groundwork for higher-level enhancements.

By padding images to the nearest multiples of 8 (and then cropping afterward), you avoid the pitfalls of forced auto-resizing. The result is a flawless alignment between your original and inpainted images – 777×653 in, 777×653 out.

Next, we’ll explore ControlNet and why it might not be the magic bullet for fur inpainting that we hoped. Advanced structural guidance can be helpful, but in the real world, it sometimes struggles with the ultra-fine details of Japanese Spitz fur, leading us to adopt more specialized solutions like LoRA. 

Stay tuned!