Elastic Token Compression for Pixel-Space Diffusion Transformers
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
arXiv:2610.36671v1 Announce Type: cross Abstract: Diffusion vision-language models (DVLMs) process their entire input at every denoising step. Since visual tokens make up 89-97% of this input on the...
The paper proposes InterpTok, a method that fine‑tunes the MAETok image tokenizer to better handle codes that lie between training samples. By training the decoder on mixtures of image codes and using a discriminator plus a mixing‑ratio loss, InterpTok preserves reconstruction quality while dramatically improving interpolation and generation metrics. The approach yields sharper, more natural image compositions and significantly lowers FID scores for diffusion models trained on the new tokenizer.
BudgetPix introduces a compute‑adaptive tokenization framework for pixel‑space image diffusion, dynamically allocating computational resources based on visual complexity and spatial layout. It comprises an adaptive encoder using an entropy‑guided quadtree and multi‑scale patch embedder, a scale‑aware decoder, and a flexible training schedule that allows pixel‑space denoisers to operate across variable token counts. The method integrates with existing diffusion architectures, achieving comparable fidelity to larger models while using only a fraction of the compute budget, and demonstrates improved quality‑efficiency tradeoffs in both text‑to‑image and class‑conditional generation.
arXiv:2606. 20076v1 Announce Type: cross Abstract: Latent Diffusion Models (LDMs) have become dominant in visual synthesis, but their quality-compute trade-off is largely constrained by the tokenizer's fixed compression ratio.
PixelMoE introduces a pixel-space generative framework that applies LLM-style Mixture-of-Experts (MoE) to diffusion models, enabling sparse scaling of model capacity without diffusion-specific routing. The approach combines Token-Choice routing, fine-grained routed experts, a shared expert, and bias-based load balancing, while also employing Pixel-Space Hierarchical Flow Matching to adjust spatial resolution during denoising. PixelMoE outperforms dense counterparts at comparable active parameter counts, achieving strong FID scores on class-conditional ImageNet-1K.
Unified image restoration (UIR) aims to recover high-quality (HQ) content from low-quality (LQ) images with different degradations using a single model. Most recent methods adapt large pretrained text-to-image (T2I) latent diffusion models for their strong capacity and generative priors.