# Trajectory-Guided Diffusion for Foreground-Preserving Background Generation in Multi-Layer Documents

arXiv: https://arxiv.org/abs/2601.21857 · HTML: https://mlsanity.com/paper/2601.21857/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models.
- Builds on 11 of its 50 references that are in the map.
- First posted on arXiv 29 Jan 2026.

## Placed in

- Diffusion models / [Image generation, text-to-image and editing](https://mlsanity.com/diffusion/image-generation-text-to-image-and-editing/)

## Builds on (most cited first, 11 of 11)

- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2308.06721/) (2023)
- [Emerging Properties in Unified Multimodal Pretraining](https://mlsanity.com/paper/2505.14683/) (2025)
- [DiffEdit: Diffusion-based semantic image editing with mask guidance](https://mlsanity.com/paper/2210.11427/) (2022)
- [ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment](https://mlsanity.com/paper/2403.05135/) (2024)
- [Transparent Image Layer Diffusion using Latent Transparency](https://mlsanity.com/paper/2402.17113/) (2024)
- [CreatiDesign: A Unified Multi-Conditional Diffusion Transformer for Creative Graphic Design](https://mlsanity.com/paper/2505.19114/) (2025)
- [LayerFusion: Harmonized Multi-Layer Text-to-Image Generation with Generative Priors](https://mlsanity.com/paper/2412.04460/) (2024)
- [CreatiPoster: Towards Editable and Controllable Multi-Layer Graphic Design Generation](https://mlsanity.com/paper/2506.10890/) (2025)
- [Planning and Rendering: Towards Product Poster Generation with Diffusion Models](https://mlsanity.com/paper/2312.08822/) (2023)
- [Spatial-Aware Latent Initialization for Controllable Image Generation](https://mlsanity.com/paper/2401.16157/) (2024)

## Abstract (arXiv)

We revisit diffusion-based generation for structured visual content and identify a fundamental limitation of existing approaches: foreground preservation and background stylization are typically enforced through external interventions, such as hard masking or corrective post-processing, rather than arising from the generative process itself. Here, we define background as the generative content outside designated foreground regions (e.g., text and layout elements), while preserving the structural integrity of the foreground. We propose a dynamical systems perspective on diffusion, in which controllable generation is formulated as trajectory shaping in latent space. Under this view, we introduce Auxiliary Context Diffusion (ACD), a state-space control framework that integrates heterogeneous signals (layout-derived foreground indicators, document summaries, and style representations) directly into the diffusion dynamics. This formulation induces time-scale separation in the generative process, where foreground regions become dynamically stabilized while background regions remain expressive. To address stylistic drift across multi-page documents, we further introduce style directions as persistent latent constraints that guide diffusion trajectories within a shared stylistic subspace. Unlike prior approaches that entangle style with prompt conditioning, our formulation enables reusable and consistent style control across pages. We validate the proposed perspective through controlled experiments on synthetic document benchmarks, demonstrating that trajectory-level control provides a unified and extensible mechanism for structured generation without retraining, hard masking, or corrective post-processing. These results suggest a new direction for controllable diffusion in document-centric and multimodal applications.