Training-Free Occluded Text Rendering via Glyph Priors and Attention-Guided Semantic Blending

TL;DR

This paper introduces a training-free method for rendering occluded text in images, utilizing a dual-stream inference framework and glyph priors to improve text readability and occlusion alignment without fine-tuning.

Contribution

The authors propose a novel training-free occluded text rendering framework that decouples text layout from occluder insertion using dual-stream inference and glyph priors.

Findings

Significantly improves text readability in occluded scenarios.

Achieves more stable object-on-text compositions.

Does not require model fine-tuning.

Abstract

We present a training-free framework for occluded text rendering with a pretrained FLUX.1-dev backbone. The task requires a model to render recognizable typography and place an occluding object over the intended text region. This setting remains difficult for existing text-to-image generators: the occluder often drifts away from the text, while the text may be distorted or appear to float on top of the occluding object. To address this problem, we propose a restarted dual-stream inference framework that decouples text-layout preservation from occluder insertion. A Base Stream provides a clean typographic reference and same-step key/value (K/V) features, while the Edit Stream is conditioned on the occlusion prompt. We further adopt the spectral glyph-prior idea from FreeText and adapt it to stabilize the target text structure during early-to-mid denoising. In the reasoning pass, our method localizes the target text, estimates a text-band region from token-conditioned attention and glyph support, and derives an anchor-aware hard fusion mask for the occluder. In the final edit pass, generation restarts from the same initial noise and applies hard mask-guided image-token K/V replacement at selected attention sites, preserving the Base layout outside the mask while injecting the occluder appearance from the Edit Stream inside the mask. Experiments on representative occluded text scenarios demonstrate substantially improved text readability and competitive occlusion alignment, yielding more stable object-on-text compositions without any model fine-tuning.