Fill2SR: Repurposing Inpainting Diffusion Transformers for Real-World Super-Resolution

arXiv:2609.33582 · 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 — 13× in this paper · explained in 2 episodes
• Benchmark — 12× in this paper · explained in 2 episodes
• Rectified Flow — 12× in this paper · explained in 3 episodes
• Diffusion Model — 8× in this paper · explained in 8 episodes
• Diffusion Models — 6× in this paper · explained in 6 episodes
• Fidelity — 6× in this paper · explained in 2 episodes
• alpha — 6× in this paper · explained in 1 episode
• Consistency — 5× in this paper · explained in 3 episodes
• Flow Matching — 5× in this paper · explained in 8 episodes
• Adaptation — 4× in this paper · explained in 1 episode
• Rectified Flow Matching — 4× in this paper
• Ablation — 3× in this paper · explained in 1 episode
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Inpainting-Interface Evidence Adapter — 3× in this paper
• Robustness — 3× in this paper · explained in 5 episodes
• Hallucination — 2× in this paper · explained in 3 episodes
• Iterative Refinement — 2× in this paper · explained in 3 episodes
• Latent Diffusion Model — 2× in this paper · explained in 2 episodes
• Latent Diffusion Models — 2× in this paper · explained in 2 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• LoRA Fine-Tuning — 2× in this paper · explained in 3 episodes
• Over-smoothing — 2× in this paper · explained in 4 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Core — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Domain Shift — 1× in this paper · explained in 4 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Frozen model — 1× in this paper · explained in 3 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Parameter-Efficient Fine-Tuning — 1× in this paper · explained in 2 episodes
• Semantic Drift — 1× in this paper · explained in 3 episodes
• Variational Bayes — 1× in this paper · explained in 2 episodes

Sources

• Qwen3-VL Technical Report
• Auto-Encoding Variational Bayes
• Flow Matching for Generative Modeling
• Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
• SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations
• SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis
• Palette: Image-to-Image Diffusion Models
• Qwen-Image Technical Report

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