Under Development
Please note: This section is a work in progress. Information and features are subject to change and may be incomplete.
ReNU: Preserving Texture and Color Fidelity in Diffusion Models
A proprietary framework designed to address persistent challenges in diffusion-based generative models. ReNU (Regularized Neural Units) integrates enhanced penalty functions into the reverse diffusion process to significantly improve the preservation of fine texture details and ensure faithful color reproduction in generated images. [cite: 4, 5] The system is particularly effective for applications requiring high-fidelity image synthesis, such as virtual try-on.
abstract
ReNU introduces a novel framework that enhances diffusion models by integrating specialized penalty functions. [cite: 5] [cite_start]This approach leverages perceptual texture analysis using VGG19, LAB color histogram matching with CIEDE2000 metrics, and edge-aware structural similarity. [cite: 6] [cite_start]The result is a significant improvement in both texture and color fidelity, addressing common shortcomings like latent space compression artifacts and cumulative denoising errors in state-of-the-art generative models. [cite: 12]
methodology
The core of ReNU is an extended U-Net based diffusion model featuring a dual-stream architecture for texture and color processing. [cite: 29, 32] [cite_start]A composite loss function is applied at each denoising step, combining perceptual texture losses (SSIM, Gabor, VGG19), a LAB-space color histogram loss, and an edge preservation term (Dice coefficient). [cite: 50, 56, 71] [cite_start]Training follows a three-phase progressive schedule: 1) Texture-Only, 2) Joint Texture-Color, and 3) Full Objective fine-tuning, to balance detail preservation with accurate color reproduction. [cite: 79, 81, 83]
current Progress
The ReNU framework has been successfully implemented and evaluated on the VTAB+Texture dataset. [cite: 103] [cite_start]Quantitative results demonstrate state-of-the-art performance, achieving an SSIM score of 0.81, an FID of 24.7, and a Color Difference (CIEDE2000) of 3.9, outperforming both baseline Stable Diffusion and prior texture-preserving methods. [cite: 110] [cite_start]Qualitative results confirm that ReNU generates images with visibly sharper textures and more consistent color fidelity. [cite: 114]
expected Completion
Q2 2025