Trajectory-Guided Diffusion for Foreground-Preserving Background Generation in Multi-Layer Documents
On the map: Diffusion models → Image generation, text-to-image and editing
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.
Key facts. No citing papers in the map yet (data through 7 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.
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11 of 50 references.
Closest ancestors · 7
Same direction, newest first
- 2025CreatiDesign: A Unified Multi-Conditional Diffusion Transformer for Creative Graphic Design25 May
- 2024LayerFusion: Harmonized Multi-Layer Text-to-Image Generation with Generative Priors5 Dec
- 2024ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment8 Mar
- 2024Spatial-Aware Latent Initialization for Controllable Image Generation29 Jan
- 2023Planning and Rendering: Towards Product Poster Generation with Diffusion Models14 Dec
- 2023IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models13 Aug
- 2022DiffEdit: Diffusion-based semantic image editing with mask guidance20 Oct
Other references · 4
- 2022Prompt-to-Prompt Image Editing with Cross Attention Controlcited by 2,409
- 2025Emerging Properties in Unified Multimodal Pretrainingcited by 855
- 2024Transparent Image Layer Diffusion using Latent Transparencycited by 111
- 2025CreatiPoster: Towards Editable and Controllable Multi-Layer Graphic Design Generationcited by 17
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- 2026Scheduled Style Injection: Expanding the Style-Content Pareto Frontier in Training-Free Diffusion-based Style Transfer
- 2024Training-Free Sketch-Guided Diffusion with Latent Optimization