# A Two-Stage System for Layout-Controlled Image Generation using Large Language Models and Diffusion Models

arXiv: https://arxiv.org/abs/2511.06888 · HTML: https://mlsanity.com/paper/2511.06888/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models.
- Builds on 21 of its 24 references that are in the map.
- First posted on arXiv 10 Nov 2025.

## 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, 21 of 21)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [BEiT: BERT Pre-Training of Image Transformers](https://mlsanity.com/paper/2106.08254/) (2021)
- [GLIGEN: Open-Set Grounded Text-to-Image Generation](https://mlsanity.com/paper/2301.07093/) (2023)
- [Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2301.13826/) (2023)
- [LayoutGPT: Compositional Visual Planning and Generation with Large Language Models](https://mlsanity.com/paper/2305.15393/) (2023)
- [LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models](https://mlsanity.com/paper/2305.13655/) (2023)
- [LayoutGAN: Generating Graphic Layouts with Wireframe Discriminators](https://mlsanity.com/paper/1901.06767/) (2019)
- [LayoutTransformer: Layout Generation and Completion with Self-attention](https://mlsanity.com/paper/2006.14615/) (2020)
- [LayoutVAE: Stochastic Scene Layout Generation From a Label Set](https://mlsanity.com/paper/1907.10719/) (2019)
- [Variational Transformer Networks for Layout Generation](https://mlsanity.com/paper/2104.02416/) (2021)
- [Visual Programming for Text-to-Image Generation and Evaluation](https://mlsanity.com/paper/2305.15328/) (2023)
- [LayoutNUWA: Revealing the Hidden Layout Expertise of Large Language Models](https://mlsanity.com/paper/2309.09506/) (2023)

## Built on by (most cited first, 1 of 1)

- [IV-CoT: Implicit Visual Chain-of-Thought for Structure-Aware Text-to-Image Generation](https://mlsanity.com/paper/2606.24849/) (2026)

## Abstract (arXiv)

Text-to-image diffusion models exhibit remarkable generative capabilities, but lack precise control over object counts and spatial arrangements. This work introduces a two-stage system to address these compositional limitations. The first stage employs a Large Language Model (LLM) to generate a structured layout from a list of objects. The second stage uses a layout-conditioned diffusion model to synthesize a photorealistic image adhering to this layout. We find that task decomposition is critical for LLM-based spatial planning; by simplifying the initial generation to core objects and completing the layout with rule-based insertion, we improve object recall from 57.2% to 99.9% for complex scenes. For image synthesis, we compare two leading conditioning methods: ControlNet and GLIGEN. After domain-specific finetuning on table-setting datasets, we identify a key trade-off: ControlNet preserves text-based stylistic control but suffers from object hallucination, while GLIGEN provides superior layout fidelity at the cost of reduced prompt-based controllability. Our end-to-end system successfully generates images with specified object counts and plausible spatial arrangements, demonstrating the viability of a decoupled approach for compositionally controlled synthesis.