STEPS: Scene Text Editing with Preserved Style Using Diffusion and Contrastive Style Encoding

arXiv:2609.38636 · cs.CV, cs.LG · Submitted 2026-09-29 · Read on arXiv

cs.CV, cs.LG

Submitted: 2026-09-29

Updated: 2026-09-29

Terminology

• Scene Text Editing — 14× in this paper
• Diffusion Model — 9× in this paper · explained in 8 episodes
• Embeddings — 8× in this paper · explained in 2 episodes
• Benchmark — 5× in this paper · explained in 2 episodes
• Consistency — 5× in this paper · explained in 3 episodes
• Transformer — 5× in this paper · explained in 2 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Diffusion Models — 4× in this paper · explained in 6 episodes
• Scene Text Editing with Preserved Style — 4× in this paper
• Ablation — 3× in this paper · explained in 1 episode
• Vision-Language Model — 3× in this paper
• Ablation Studies — 2× in this paper · explained in 1 episode
• Latent Space — 2× in this paper · explained in 7 episodes
• Loss Function — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Cross-Attention Mechanism — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Flow Matching — 1× in this paper · explained in 8 episodes
• Generative Adversarial Networks (GANs) — 1× in this paper · explained in 3 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• U-net architecture — 1× in this paper · explained in 3 episodes
• Variational Auto-Encoder — 1× in this paper
• Vision-Language Model (VLM) — 1× in this paper · explained in 3 episodes

Sources

• Blended Latent Diffusion
• DiffUTE: Universal Text Editing Diffusion Model
• TextDiffuser-2: Unleashing the Power of Language Models for Text Rendering
• Recognition-Synergistic Scene Text Editing
• Improving Diffusion Models for Scene Text Editing with Dual Encoders
• FLUX-Text: A Simple and Advanced Diffusion Transformer Baseline for Scene Text Editing
• TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models
• Character-Aware Models Improve Visual Text Rendering
• DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps
• Exploring Stroke-Level Modifications for Scene Text Editing
• AnyText2: Visual Text Generation and Editing With Customizable Attributes
• AnyText: Multilingual Visual Text Generation And Editing
• IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models

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