Conditional Text Generation Framework

• Conditional text generation frameworks are systems that synthesize or edit text conditioned on user-defined constraints such as typography, style, and layout.
• They integrate diffusion-transformer architectures with dual encoders and 3D rotary positional embeddings to achieve accurate word-level attribute control and stylistic consistency.
• These frameworks power applications like visual text rendering and graphic design editing, demonstrating state-of-the-art performance in integrating content and style.

A conditional text generation framework encompasses algorithmic and architectural methodologies that enable the synthesis or editing of text—typically image-based text—conditioned on user-specified constraints such as typography, style, background, layout, or semantic attributes. Recent advances in the domain leverage large-scale diffusion-transformer (DiT) architectures to precisely control both content and stylistic aspects, facilitating applications in visual text rendering, graphic design, and complex multimodal scenarios. These frameworks integrate specialized conditioning mechanisms, synthetic data pipelines, and novel fine-tuning paradigms to achieve state-of-the-art performance in tasks requiring word-level attribute control and high stylistic fidelity (Shi et al., 2024, Zhao et al., 23 Dec 2025).

1. Core Architectural Components

Conditional text generation frameworks for image-based text editing and synthesis are predominantly based on a diffusion transformer (DiT) backbone. Notably, modern solutions utilize multi-stage or unified DiT models, architected to allow explicit control over typography, font, and style. A typical architecture, as devised in FonTS and UTDesign, comprises the following elements:

• DiT backbone: A stack of fusion blocks that jointly attend to noisy text latents and auxiliary condition tokens (typography, style, content), followed by refinement blocks with self-attention and feedforward pathways.
• Dual encoders: Parallel content and style encoders (e.g., ViT-DINO for glyph shape and CLIP-ViT for style vector extraction), projecting features to a shared latent space.
• Positional embeddings: 3D rotary positional encoding to encode (character-index, x, y) and support variable-length/variable-script sequences.
• Condition token integration: At each denoising step, noisy text latent tokens are concatenated with content and style tokens according to a fixed schema before passing through the DiT (Zhao et al., 23 Dec 2025).

This design supports processing arbitrary scripts, maintaining both local (word/glyph-level) and global style consistency, and handling transparent RGBA outputs for compositing applications.

2. Mechanisms for Conditionality

Conditional control is attained through both architectural augmentation and data annotation:

• Enclosing Typography Control Tokens (ETC-tokens): Specialized tags (e.g., <b*> ... </b*>, <i*> ... </i*>, <font:k>) are injected into the prompt to mark spans that require designated attributes (font, bold, italic, underline). During model encoding, only target word spans are affected, enabling fine-grained, word-level typographic control (Shi et al., 2024).
• Style Control Adapter (SCA): Lightweight cross-attention adapters are inserted at each DiT block, fusing CLIP-encoded reference style images while preserving primary content control. The adapters are parameter-efficient and trained with cross-attention keys/values only, while all other DiT weights remain frozen.
• Tanh-gated attention for style: In UTDesign, style injection is modulated via a tanh-gating mechanism in fusion blocks, controlling the strength of style influence (Zhao et al., 23 Dec 2025).
• Multi-modal conditional encoders: For comprehensive design tasks, multi-modal encoders incorporate background images, prompts, and layout metadata, as seen in UTDesign's extension to a full text-to-design pipeline.

Conditionality is further reinforced through data synthesis strategies that encode typographic and stylistic annotation alongside each supervision sample, often via HTML-generated tags and detailed metadata.

3. Training Paradigms and Objectives

Training is staged to decouple different forms of control:

1. Typography Control Fine-Tuning (TC-FT): Only joint text-attention QKV projections are updated (approximately 5% of DiT parameters), with all other weights frozen. This approach enforces parameter efficiency while allowing the backbone to specialize for word-level typographic cues. Prompts are augmented with ETC-tokens, and a dummy prefix ("sks") is prepended to regularize attention and slow language drift (Shi et al., 2024).
2. Style Adapter Training: With DiT weights fixed, adapter weights are optimized using a style image embedding (from a CLIP vision encoder) that is agnostic to text, mitigating content leakage into styles. Training occurs in two phases: (a) large-scale style-general pretraining, and (b) focused domain adaptation to stylistic artistic text.
3. Diffusion Objective: Conditional flow-matching loss from Rectified Flow is standard. For a latent $z_t = a_t x_0 + b_t \epsilon$, with $\epsilon \sim \mathcal{N}(0, I)$, the loss is $$\mathcal{L}_{CFM} = \mathbb{E}_{t, x_0, \epsilon} \left\| v_\theta(z_t, t) - u_t(z_t | \epsilon)\right\|_2^2.$$
4. Feature Alignment & Post-training: In more generalized frameworks (UTDesign), an alignment loss matches MLLM-perceived style features to CLIP style features, and downstream fine-tuning combines SFT, LoRA, and DPO criteria.

Losses for VAE-based RGBA output reconstruction (MSE, LPIPS, and composite loss) are integrated to ensure sharp consistent text edges compatible with arbitrary backgrounds.

4. Data Synthesis and Supervision Strategies

Conditional text generation frameworks rely on synthetic datasets with meticulously annotated typography and style information:

• HTML-rendered Text Corpus: Multi-word snippets are rendered with and without specific typographic tags (bold, italic, underline), cycling through multiple fonts, backgrounds, and colors. For each snippet, variants are produced by systematically applying style tags to different word positions, enabling rich supervision for word-level control (Shi et al., 2024).
• Large-scale Stylized Glyph Dataset: UTDesign's SynthGlyph corpus encompasses 4,194 fonts, 6,857 unique characters (Chinese/LATIN), and approximately 28.8 million stylized RGBA glyphs, augmented with color, texture, and noise variations (Zhao et al., 23 Dec 2025).
• Paired Data Triplets: Each sample includes a content reference, a style reference, and the ground-truth stylized rendering, with tokenizer-prompt strings encoding attribute scope and font indices.

Supervision is provided exclusively by paired image and prompt strings, without requiring detailed pixel-level alignment or explicit bounding box annotations.

5. Quantitative Evaluation and Reported Results

Evaluation utilizes a combination of recognition and perceptual metrics as well as qualitative inspection:

Metric	Description	Sources
OCR-Acc	Fraction of characters recognized by PaddleOCR	(Shi et al., 2024)
Word-Acc	Word-level bold/italic/underline accuracy (GPT-4o/manual)	(Shi et al., 2024)
Font-Con/Style-Con	User-study, font/style consistency score	(Shi et al., 2024)
FID, LPIPS	Perceptual fidelity and similarity for stylized renders	(Zhao et al., 23 Dec 2025)
CLIP-Sim/CLIP score	Cross-modal alignment/similarity to reference style images	(Shi et al., 2024, Zhao et al., 23 Dec 2025)
Normalized ED, NED	Edit distance on predicted vs reference text	(Zhao et al., 23 Dec 2025)

Sample reported results for style-preserving text generation and editing include:

• FonTS: OCR-Acc 82.85%, Font-Con 63.64%, Word-Acc 55.00%, Style-Con 74.43% (Shi et al., 2024).
• UTDesign: Editing FID 10.81, Editing OCR-F 0.9518, Generation FID 72.07, Generation OCR-F 0.8716; outperforming open-source and proprietary baselines on multiple axes (Zhao et al., 23 Dec 2025).

Qualitative analysis emphasizes preservation of small-script features, compositional transparency, and seamless integration over complex backgrounds.

6. Applications and System Integration

Conditional text generation frameworks have been deployed in several application domains:

• Visual text rendering: Automated generation of artistic, typographically-controlled text for use in posters, advertisements, and digital media.
• Graphic design editing: High-precision insertion, modification, or removal of stylized text within graphical layouts, supporting multilingual and script-agnostic workflows.
• Text-to-design pipelines: End-to-end systems combine T2I models, multimodal condition encoders, and layout planners (e.g., MLLM-based) to automate the entire design generation process, from semantic intent to rendered artifact (Zhao et al., 23 Dec 2025).

A plausible implication is that the modular separation of typography and style pathways allows for extensible integration into broader generative multimodal systems.

7. Current Limitations and Research Directions

Contemporary conditional text generation frameworks address major limitations of earlier T2I methods—such as font inconsistency and lack of fine-grained control—but still face challenges in generalizing to complex layouts, supporting new scripts, and preserving semantic correctness under extreme geometric or stylistic transformations.

Data synthesis remains a bottleneck for rare scripts and low-resource languages. Another open research area is enhancing parameter efficiency further via progressive quantization and transfer learning, and the development of more granular metrics for perceptual and functional fidelity under real-world conditions (Shi et al., 2024, Zhao et al., 23 Dec 2025).