In-Token Learning for High-Fidelity Image Restoration via Diffusion Transformers

arXiv:2609.33523 · cs.CV · Submitted 2026-09-27 · Read on arXiv

cs.CV

Submitted: 2026-09-27

Updated: 2026-09-27

Code: https://github.com/Xingfu-Yi/Fill2SR

Terminology

• Transformer — 19× in this paper · explained in 2 episodes
• Fidelity — 16× in this paper · explained in 2 episodes
• Alignment — 15× in this paper · explained in 1 episode
• Rectified Flow — 13× in this paper · explained in 3 episodes
• Ablation — 9× in this paper · explained in 1 episode
• Flow Matching — 8× in this paper · explained in 8 episodes
• Embeddings — 7× in this paper · explained in 2 episodes
• Generalization — 6× in this paper · explained in 13 episodes
• Hallucination — 6× in this paper · explained in 3 episodes
• Diffusion Model — 5× in this paper · explained in 8 episodes
• Diffusion Models — 5× in this paper · explained in 6 episodes
• Consistency — 4× in this paper · explained in 3 episodes
• Direct Low-Quality Guidance — 4× in this paper
• Benchmark — 3× in this paper · explained in 2 episodes
• Latent Space — 3× in this paper · explained in 7 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Latent Diffusion Model — 2× in this paper · explained in 2 episodes
• Latent Diffusion Models — 2× in this paper · explained in 2 episodes
• Zero-Shot Transfer — 2× in this paper · explained in 4 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Decode — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• Over-smoothing — 1× in this paper · explained in 4 episodes
• Prompt Engineering — 1× in this paper · explained in 4 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Reference-Conditioned Degradation Transfer — 1× in this paper
• Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes

Sources

• In-Context LoRA for Diffusion Transformers
• FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space
• Flow Matching for Generative Modeling
• Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
• SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

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