Textflux
TextFlux is a high-fidelity multilingual scene text synthesis model based on an OCR-free diffusion transformer. It uses FLUX.1-Fill-dev as the base model and focuses on the scene text synthesis task.
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Release Time : 4/21/2025
Model Overview
TextFlux is an OCR-free framework that uses a diffusion transformer to achieve high-fidelity multilingual scene text synthesis. This framework simplifies the learning task by directly providing visual glyph guidance through the concatenation of rendered glyphs with the scene image space, enabling the model to focus on context reasoning and visual fusion.
Model Features
OCR-free architecture
A simplified structure without an OCR encoder
High-fidelity and consistent with scene style
Accurate rendering to maintain scene style consistency
Multilingual and low-resource adaptation
Excellent cross-lingual performance, adaptable to new languages with a small amount of data (e.g., <1,000 samples)
Zero-shot generalization ability
Can render characters not seen during training
Controllable multi-line text
Supports flexible multi-line synthesis and line-level control
Data-efficient
Only requires about 1% of the data compared to other methods
Model Capabilities
Scene text synthesis
Multilingual text generation
Image and text fusion
Zero-shot character rendering
Multi-line text control
Use Cases
Scene text generation
Billboard text synthesis
Generate realistic billboard text in natural scenes
High-fidelity text consistent with scene style
Multilingual sign generation
Generate scene signs containing multiple languages
Supports rendering of characters not seen during training
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