Diffusion Image Inpainting Using x0 Reparameterization
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Solution Overview
Problem
Diffusion models generate poor results with unwanted artifacts like blurry outputs during image inpainting and converge slowly due to conventional training techniques, limiting the quality and speed of image generation.
Innovation Solution
The diffusion model is trained using x0 reparameterization and a perceptual loss, incorporating a sample-based and adaptively-blurred perceptual loss to predict a clean output image, improving image quality and reducing artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional epsilon reparameterization is used for diffusion model training, then the model can generate images, but the output contains unwanted artifacts and blurry regions with poor quality
Solution Approach 1:
The patent changes the reparameterization approach from conventional epsilon reparameterization to x0 reparameterization. Instead of predicting noise epsilon directly, the model predicts the clean image x0 at each diffusion step. This parameter change fundamentally alters what the model learns to predict, leading to significantly reduced artifacts and improved image quality in the generated outputs.
2Productivity
If conventional training techniques are used for diffusion models, then the model can be trained, but the convergence speed is slow
Solution Approach 1:
The patent introduces x0 reparameterization that changes the training objective from predicting noise to predicting the clean image. This parameter transformation accelerates convergence because the model directly learns to reconstruct the original image structure rather than indirectly learning noise patterns. The perceptual loss function further enhances this by providing more meaningful gradient signals during training.
Solution Approach 2:
The patent employs perceptual loss as a feedback mechanism during training. Instead of simple pixel-wise loss, the perceptual loss compares feature representations from a pre-trained network, providing more informative feedback about image quality and structure. This feedback guides the model more effectively toward high-quality solutions, accelerating convergence.
3Device complexity
If the diffusion model predicts noise directly, then the training process is simpler, but the final output image has lower quality and more artifacts
Solution Approach 1:
The patent changes the prediction target from noise epsilon to clean image x0. While this may seem to increase complexity, the x0 reparameterization framework actually simplifies the training dynamics by providing more stable gradients and more direct learning signals. The quality improvement is substantial while the added complexity is manageable within the existing diffusion framework.
Data Source
AI summary
Systems and methods for image processing are described. Embodiments of the present disclosure receive an image comprising a first region that includes content and a second region to be inpainted. Noise is then added to the image to obtain a noisy image, and a plurality of intermediate output images are generated based on the noisy image using a diffusion model trained using a perceptual loss. The intermediate output images predict a final output image based on a corresponding intermediate noise level of the diffusion model. The diffusion model then generates the final output image based on the intermediate output image. The final output image includes inpainted content in the second region that is consistent with the content in the first region.