BudgetPix: Compute-Adaptive Tokenization for Pixel-Space Image Diffusion

arXiv:2610.12307 · cs.CV · Submitted 2026-10-08 · Read on arXiv

cs.CV

Submitted: 2026-10-08

Updated: 2026-10-08

Terminology

• Transformer — 37× in this paper · explained in 2 episodes
• Tokenization — 18× in this paper · explained in 2 episodes
• Ablation — 9× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 9× in this paper
• Embeddings — 9× in this paper · explained in 2 episodes
• Fine-Tuning — 8× in this paper · explained in 5 episodes
• Diffusion Model — 6× in this paper · explained in 8 episodes
• Diffusion Models — 6× in this paper · explained in 6 episodes
• Fidelity — 5× in this paper · explained in 2 episodes
• Generative Model — 4× in this paper · explained in 2 episodes
• Generative Models — 4× in this paper · explained in 2 episodes
• Decode — 3× in this paper · explained in 2 episodes
• International Conference on Computer Vision — 3× in this paper
• Ablation study — 2× in this paper · explained in 1 episode
• Adaptive Layout — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Computer Vision and Pattern Recognition Workshops — 2× in this paper
• FID and Inception Score — 2× in this paper
• Generative Modeling — 2× in this paper · explained in 3 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Adaptive Sampling — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Edge Devices — 1× in this paper · explained in 2 episodes
• Flow Matching — 1× in this paper · explained in 7 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Nash Equilibrium — 1× in this paper · explained in 2 episodes
• Prompt Adherence — 1× in this paper · explained in 2 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Resource Constraints — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Vision-Language Model — 1× in this paper
• Vision-Language Model (VLM) — 1× in this paper · explained in 3 episodes

Sources

• PixelFlow: Pixel-Space Generative Models with Flow
• ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment
• Superpixel Tokenization for Vision Transformers: Preserving Semantic Integrity in Visual Tokens

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