arXiv Computer Vision
AffineTok: Semantic Affine Consistency for Diffusion-Friendly Visual Tokenizer
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arXiv AI
Sep 30
Representation by Design in Generation: Cross-View Class-Token Alignment in Diffusion Transformers
arXiv:2609.36348v1 Announce Type: cross Abstract: Generative and representation learning remain asymmetrically connected: semantic representations are used to improve diffusion generation, whereas th...
arXiv Computer Vision
Oct 2
Just Align $\bm{x}$: Aligning Predictions, Not Representations
arXiv:2610.00600v1 Announce Type: new Abstract: Representation alignment has become an effective way to accelerate diffusion training, but its benefits do not transfer reliably to pixel-space clean-i...
arXiv Computer Vision
3d ago
Which Tokens to Merge? Diffusion Dynamics for Efficient Image Generation
The paper introduces Dynamics-aware Token Merging (DaTMe), a training‑free method that accelerates diffusion models by selectively merging tokens based on diffusion dynamics. DaTMe builds an importance map from the Tweedie denoised estimate, combining temporal changes and structural high‑frequency content to decide which tokens to preserve and which to merge. Experiments on PixArt-a and FLUX show that DaTMe maintains visual fidelity across the entire image while reducing token computation.
arXiv AI
Sep 2
V-Co: A Closer Look at Visual Representation Alignment via Co-Denoising
V-Co investigates visual co-denoising for pixel-space diffusion models, using a unified JiT-based framework to isolate key design choices. The study identifies two essential components: a dual-stream architecture with flexible cross-stream interaction and a perceptual-drifting hybrid loss combined with RMS-based feature rescaling for stronger semantic supervision. Experiments on ImageNet-256 demonstrate that V-Co surpasses baseline pixel-space diffusion and strong prior pixel-diffusion methods at comparable model sizes while requiring fewer training epochs.
arXiv AI
Aug 11
UniSpace: Unified Visual Representation and Scalable Multimodal Modeling
arXiv:2608. 08676v1 Announce Type: cross Abstract: Semantic vision encoders have become a central visual interface for multimodal understanding and semantic conditioning in image generation.
arXiv Computer Vision
Oct 2
SemanTok: Predictable Semantic Tokens for Efficient Autoregressive Video Generation
arXiv:2610.00686v1 Announce Type: new Abstract: Recent video-based world models pair the scalability of autoregressive (AR) prediction with the visual quality of diffusion models. The choice of scene...