PokeFusion Attention: Enhancing Reference-Free Style-Conditioned Generation

• URL: http://arxiv.org/abs/2602.03220v1
• Date: Tue, 03 Feb 2026 07:44:01 GMT
• Title: PokeFusion Attention: Enhancing Reference-Free Style-Conditioned Generation
• Authors: Jingbang Tang,
• Abstract summary: This paper studies reference-free style-conditioned character generation in text-to-image diffusion models.<n>Existing approaches rely on text-only prompting, or introduce reference-based adapters that depend on external images at inference time.<n>We propose PokeFusion Attention, a lightweight decoder-level cross-attention mechanism.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper studies reference-free style-conditioned character generation in text-to-image diffusion models, where high-quality synthesis requires both stable character structure and consistent, fine-grained style expression across diverse prompts. Existing approaches primarily rely on text-only prompting, which is often under-specified for visual style and tends to produce noticeable style drift and geometric inconsistency, or introduce reference-based adapters that depend on external images at inference time, increasing architectural complexity and limiting deployment flexibility.We propose PokeFusion Attention, a lightweight decoder-level cross-attention mechanism that fuses textual semantics with learned style embeddings directly inside the diffusion decoder. By decoupling text and style conditioning at the attention level, our method enables effective reference-free stylized generation while keeping the pretrained diffusion backbone fully frozen.PokeFusion Attention trains only decoder cross-attention layers together with a compact style projection module, resulting in a parameter-efficient and plug-and-play control component that can be easily integrated into existing diffusion pipelines and transferred across different backbones.Experiments on a stylized character generation benchmark (Pokemon-style) demonstrate that our method consistently improves style fidelity, semantic alignment, and character shape consistency compared with representative adapter-based baselines, while maintaining low parameter overhead and inference-time simplicity.

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