Title: LayerCraft: Enhancing Text-to-Image Generation with CoT Reasoning and Layered Object Integration
Venue: NeurIPS 2025
Accepted: yes
Keywords: Multi-control image generation

Diffusion Transformer

LLM agent framework
Abstract: Text-to-image (T2I) generation has made remarkable progress, yet existing systems still lack intuitive control over spatial composition, object consistency, and multi-step editing. We present **LayerCraft**, a modular framework that uses large language models (LLMs) as autonomous agents to orchestrate structured, layered image generation and editing. LayerCraft supports two key capabilities: (1) *structured generation* from simple prompts via chain-of-thought (CoT) reasoning, enabling it to decompose scenes, reason about object placement, and guide composition in a controllable, interpretable manner; and (2) *layered object integration*, allowing users to insert and customize objects---such as characters or props---across diverse images or scenes while preserving identity, context, and style. The system comprises a coordinator agent, the **ChainArchitect** for CoT-driven layout planning, and the **Object Integration Network (OIN)** for seamless image editing using off-the-shelf T2I models without retraining. Through applications like batch collage editing and narrative scene generation, LayerCraft empowers non-experts to iteratively design, customize, and refine visual content with minimal manual effort. Code will be released upon acceptance.

LayerCraft: Enhancing Text-to-Image Generation with CoT Reasoning and Layered Object Integration

Abstract

Text-to-image (T2I) generation has made remarkable progress, yet existing systems still lack intuitive control over spatial composition, object consistency, and multistep editing. We present LayerCraft , a modular framework that uses large language models (LLMs) as autonomous agents to orchestrate structured, layered image generation and editing. LayerCraft supports two key capabilities: (1) structured generation from simple prompts via chain-of-thought (CoT) reasoning, enabling it to decompose scenes, reason about object placement, and guide composition in a controllable, interpretable manner; and (2) layered object integration , allowing users to insert and customize objects-such as characters or props-across diverse images or scenes while preserving identity, context, and style. The system comprises a coordinator agent, the ChainArchitect for CoT-driven layout planning, and the Object Integration Network (OIN) for seamless image editing using off-the-shelf T2I models without retraining. Through applications like batch collage editing and narrative scene generation, LayerCraft empowers non-experts to iteratively design, customize, and refine visual content with minimal manual effort. Code will be released at https://github.com/PeterYYZhang/LayerCraft .

1 Introduction

Text-to-image (T2I) generation has rapidly evolved with advances in diffusion models [14, 35, 32], transformer-based architectures [42], and scalable encoder-decoder frameworks [36]. Recent systems [9, 4, 6] produce visually impressive results from simple prompts. However, they still fall short in offering precise, intuitive control over spatial composition, multi-object interactions, and iterative customization.

Existing approaches to fine-grained T2I control often require architectural modifications or finetuning [55, 53, 37], which limits generality and usability. Others support instance-level manipulation [44, 48, 17], but often falter in complex scenes or suffer from spatial inconsistency. More structured methods like LayoutGPT [10] and GenArtist [45] attempt procedural generation, but neglect 3D spatial reasoning or rely on inefficient pipelines with excessive external tools. Even advanced multi-modal agents like GPT-4o 2 fail to maintain background consistency or facial identity over multiple editing iterations.

LayerCraft is our answer to these limitations: a fully automatic, modular framework for structured T2I generation and editing, designed to balance expressive control, compositional accuracy, and system efficiency. LayerCraft treats image synthesis as a step-by-step reasoning process, orchestrated by a team of specialized agents that handle prompt interpretation, spatial planning, and object integration. As shown in Figure 1, our framework supports applications such as batch collage editing with consistent object insertion, and narrative-driven image generation using structured reasoning and layout planning.

LayerCraft Coordinator serves as the central interface, managing interactions between users and agents. It processes instructions, coordinates agent outputs, and integrates user feedback throughout the generation process.

ChainArchitect performs chain-of-thought (CoT) reasoning to decompose prompts into structured layout plans. It first generates the background, then infers a spatial layout, represented as a dependency-aware 3D scene graph, to determine bounding boxes and relationships among objects. This planning phase supports complex multi-object scenes and facilitates layer-wise, editable image construction.

Object Integration Network (OIN) uses the original FLUX [18] T2I model to seamlessly inpaint objects into specific regions. By applying dual LoRA adapters, OIN integrates both background and reference conditions while preserving generative quality. Its attention-mixing mechanism ensures that inserted objects align contextually and stylistically with the base image.

LayerCraft introduces several advantages over prior work: (1) it eliminates the need for model fine-tuning or external tools, making it accessible and lightweight; (2) it offers interpretable, spatially aware image construction via CoT-guided layout planning; and (3) it supports consistent object editing across single or multiple images without sacrificing visual quality. Compared to LayoutGPT [10] and GenArtist [45], which struggle with spatial coherence and integration complexity, LayerCraft provides a unified, agent-based framework capable of general-purpose generation and editing.

Our experiments demonstrate that LayerCraft excels in various creative workflows, from narrative scene composition to iterative and batch image editing, empowering both experts and non-experts to produce controllable, high-quality images with minimal effort.

2 Related Work

Controllable Image Generation. Text-to-image (T2I) generation has seen rapid progress, led by advances in diffusion models-from pixel-space methods like GLIDE [30] and Imagen [38] to more efficient latent-space frameworks such as Stable Diffusion [35] and Raphael [50]. Enhancements in multimodal alignment (e.g., DALLE-2 [33], Playground [20]) and architectural designs (e.g., Diffusion Transformers [31], PixArt [6], FLUX [18]) have substantially improved the quality and diversity of generated content. However, fine-grained and interpretable control remains challenging, especially in scenes with multiple objects or complex layouts. Personalization methods like DreamBooth [37] and Textual Inversion [11] support user-specific concepts but require task-specific

fine-tuning. Structured control approaches such as ControlNet [55] and GLIGEN [21] offer spatial conditioning via edge maps or boxes but rely on detailed inputs and lack high-level scene reasoning.

Recent techniques like Raphael [50] improve specialization through expert models at the cost of high computation. Lightweight alternatives like Attend [5] reduce overhead but struggle with compositional complexity. Autoregressive frameworks (e.g., LlamaGen [39], Show-O [49], Janus-Pro [8]) explore prompt-based synthesis via language models but often lack spatial structure. Meanwhile, emerging MLLMs such as GPT-4o and Gemini 2.0 Flash 3 show generative promise, though they require substantial resources and offer limited layout control.

In contrast, LayerCraft introduces a modular, agent-based framework for structured multi-object generation and editing with minimal user input. The ChainArchitect employs chain-of-thought (CoT) reasoning to produce interpretable, 3D-aware layouts, enabling precise spatial planning without manual annotations. The Object Integration Network (OIN) leverages dual-LoRA fine-tuning on a pre-trained diffusion model (FLUX) to enable parameter-efficient object integration with strong visual fidelity and contextual coherence. Unlike methods such as OminiControl [40], which emphasize attention-based spatial aligned and subject driven generation, LayerCraft supports broader workflows, including image-guided inpainting, iterative editing, and batch collage generation, within a unified, lightweight pipeline that generalizes effectively across diverse scenarios.

Agent-Based Generation. The rise of large language models (LLMs) has greatly advanced zero- and few-shot learning across diverse domains [1, 41]. With multimodal training [2, 23, 60], LLMs have evolved into powerful agents for reasoning and creative generation [51, 47, 25, 26, 27, 16]. Among these, LayoutGPT [10] uses LLMs to generate spatial layouts from text prompts. While effective for simple scenes, its reliance on static layout models limits its ability to handle complex prompts and spatial relationships, particularly due to the absence of multi-step reasoning. Other frameworks like GenArtist [45] and LLM Blueprints [12] follow a 'generate-then-edit' paradigm, refining initial layouts or images through external editing modules. This often leads to stylistic drift and unstable outputs, due to fragmented control and lack of shared context across steps.

In contrast, LayerCraft offers an integrated multi-agent framework that unifies layout planning and refinement, and iterative object integration. The LayerCraft Coordinator orchestrates agent interactions and incorporates user feedback throughout the process. The ChainArchitect improves upon LayoutGPT by applying chain-of-thought (CoT) reasoning to generate structured, 3D-aware layouts, enabling compositional planning without external layout tools. The Object Integration Network (OIN) complements this with image-guided inpainting via dual-LoRA fine-tuning on a pretrained model, supporting adaptive, context-aware generation while maintaining high visual fidelity. Unlike modular pipelines that rely on third-party components or model modifications, LayerCraft remains self-contained and parameter-efficient, offering a robust and consistent user experience.

Chain of Thought Reasoning. Chain-of-thought (CoT) prompting has proven effective in improving language model reasoning by decomposing complex tasks into intermediate steps [46, 57]. However, in multimodal settings, existing CoT approaches often rely on model finetuning over specialized datasets [29, 58], limiting their applicability in zero-shot or flexible generation scenarios.

LayerCraft takes a different approach by incorporating CoT reasoning without requiring additional fine-tuning. The LayerCraft Coordinator uses CoT to iteratively revise and enrich user prompts, while the ChainArchitect applies CoT-style decomposition to translate high-level instructions into structured, 3D-aware layouts. This allows LayerCraft to reason over complex spatial relationships and multi-object configurations in a fully zero-shot, training-free setting. By leveraging CoT within a modular agent framework, LayerCraft achieves interpretable, step-wise control in multimodal image generation, offering a robust alternative to methods that depend on task-specific finetuning or static layout templates.

3 Methodology

This section elaborates the detailed design of LayerCraft, overviewed in Figure 2. Leveraging GPT-4o as the central coordinator, LayerCraft enables self-monitoring, user-agent interaction, and

User-Agent Interaction

ChainArchitect

LayerCraft Coordinator

Object Integration Network

CoT Reasoning

1.Background Generation

2.

Agent Collaboration

3. Intermediate Self-Check

oVo

Object List:

Background

Noisy Image

Conc

1.Alice,a cute git

23

Cat

TaskDecomposition

CoT Reasoning

Prompt

Caterpillar

Invoke

Invoke

CoT Reasoning

1.

Thinking for order

OIN

2.

Thinking for prompts

3.Thinking for coarse position

4.

Thinking...

Generating

Layout

1.Background Prompt

2.

Object list

with order

3.

AskQuestions

CoT Reasoning

aesthetically refined outputs and multi-turn editing. The framework consists of three main agents: (1) LayerCraft Coordinator, which processes user instructions and orchestrates agent collaboration; (2) ChainArchitect, a layout planning agent that generates backgrounds, assigns objects and their spatial relationships; and (3) Object Integration Network (OIN), which integrates objects seamlessly into the background based on given mask.

3.1 LayerCraft Coordinator

The LayerCraft Coordinator acts as the central orchestrator for the entire framework, overseeing the system's operation, ensuring smooth user-agent interactions, and directing agent collaboration. This component also serves as the primary interface for user input, streamlining communication between the user and the system.

Agent-Agent Interaction The framework integrates multiple specialized agents, each responsible for a specific task such as content recognition, reference image generation, layout planning (ChainArchitect), and final image generation and inpainting (OIN). The Coordinator plays a crucial role in orchestrating these agents, breaking down tasks, assigning responsibilities, and ensuring effective communication between them. Since generative models can produce intermediate outputs with inherent randomness, the Coordinator rigorously checks the consistency of both textual and visual outputs. If discrepancies are detected, it formulates corrective measures and delegates the task to the appropriate agent for regeneration. This enables LayerCraft to ensure that the final output meets the user's specifications.

User-Agent Interaction Although the system operates autonomously, users can modify or refine the output by interacting with the agents. For example, a user may request more details for a specific object or a customized layout for a particular region. The Coordinator facilitates multiple rounds of interaction, refining the image iteratively until the user's requirements are fully met.

Chain-of-Thought (CoT) Enrichment. The Coordinator enhances generation by iteratively enriching the text prompt using a Chain-of-Thought (CoT) approach. Starting from the user's input, it 'asks itself' which objects should appear and how they should be arranged to meet user intent (see Figure 1, right). This reasoning produces detailed descriptions of background and foreground elements, filling gaps in the original prompt.

If the Coordinator determines the user's prompt is already sufficiently detailed, it skips CoT reasoning and proceeds directly to task delegation. This adaptive strategy improves efficiency by avoiding unnecessary steps when the input is complete.

3.2 ChainArchitect

ChainArchitect advances traditional layout generation models (e.g., LayoutGPT [10]) by integrating Chain-of-Thought (CoT) reasoning to better handle complex prompts involving multiple objects and intricate spatial relationships.

Given a user input prompt P i , which may range from detailed to brief, the LLM identifies relevant objects and generates a structured list O = { O i | i ∈ N } alongside a background description P b i . For instance, if the prompt mentions a 'car,' ChainArchitect infers a suitable context such as a 'road.' The background description P b i is passed to the FLUX model by the Coordinator to generate the background image I bg , which serves as a spatial reference for placing foreground objects.

To ensure the generated layout follows a consistent, interpretable format (e.g., JSON), ChainArchitect uses in-context exemplars 12 that define object classes, spatial positions, and scene style, thereby aligning the output with the user's intent. Additionally, ChainArchitect leverages GPT-4o's vision capabilities to analyze the background image viewpoint, improving object placement accuracy.

For foreground objects, ChainArchitect performs explicit spatial reasoning: it determines an optimal generation order (placing distant objects before closer ones to manage occlusion) and models interobject relationships, such as relative positioning ('A is on top of B') and orientation ('Person A is facing left'). This structured reasoning enables coherent and realistic multi-object layouts even in complex scenes.

3.3 Object-Integration Network (OIN)

The Object Integration Network (OIN) facilitates the seamless incorporation of objects into pre-existing backgrounds, as illustrated in Figure 3. OIN processes a masked background (delineated by a bounding box), a reference object image, and a text prompt to synthesize a contextually coherent and visually consistent integration of the specified object into the background environment.

A Parameter Reuse Method for Multiple Conditional Generation via Dual LoRA Leveraging the robust pretrained capabilities of the FLUX text-image model on text-to-image task, we implement a parameter-efficient adaptation methodology for conditional generation. This approach enables the framework to process masked backgrounds and reference object images for highly precise subject-driven inpainting. Our implementation follows a two-phase training protocol:

In the initial phase, we develop two independent LoRA adaptorsW bg and W obj -that enhance the model's capacity to interpret conditional images for inpainting and subject-driven generation tasks. Following the technique proposed in OminiControl [40], we incorporate positional embeddings for background image tokens using encodings identical to the initial noise, while reference image tokens utilize biased embeddings to accommodate spatially aligned and unaligned processing requirements.

Nx

MLoRA Finetuning

OriginalWeights

-IQKV

.-MMA

M:Latent Mask

Obtained by mask

on image

Projection

+PE2

+PE

indino

Projection

+PE

X

Prompt

The second phase initializes the model with the trained LoRA modules, enabling comprehensive understanding of both background and object conditions ( C bg and C obj ). To circumvent the quadratic memory complexity associated with processing extensive token sequences and to maintain clarity in condition relationships, we bifurcate the latent sequence into two components: [ C T , X, C bg ] for background processing and [ C T , X, C obj ] for object integration. These components undergo parallel processing with query, key, and value projections utilizing distinct weight sets: M W both qkv for joint conditions, M qkv for FLUX's foundational weights, M W inp qkv for inpainting-specialized LoRA weights,

LayerCraft(Ours)

SD3.5

FLUX.1-Schnell

FLUX.1-Dev

DALL-E 3

Show-O

Janus-pro 7B

LLM Blueprint

GenArtist

GPT4o-Image

6 apples placed from

far to close on the

table, 2 are far

away, while 4 are

closer

7 cookies arranged in 3 horizontal lines,

chocolate cokie nthe left,astrwberry

top view. At the top, there are α

Layer

on the right. In the middle, there is α

cookie in the middlle, and a banana cookie

large long rectangular cookie with *layer

Layer craft

layer:

layer:

layercraft

50669

Lswery

cookie

layer craft

Craft’ written on it, Below this, there are

three more cookies placed side by side in

the reverse order as the first row.

Two hot dogs sit on α

green plate near a soda

cup which are sitting on

α white picnic table,

while α red bike on the

right of a blue car are

parked nearby.

A woman stands in

front of 5 portraits

of women.

and M W obj qkv for object-specific LoRA weights. This architecture generates dual query, key, and value outputs: [ Q 1 , K 1 , V 1 ] for background elements and [ Q 2 , K 2 , V 2 ] for object features.

The attention mechanism computes outputs through the following formulations:

where M ( X 1 , X 2 ) denotes the replacement of the masked region's latent sequence X 1 with X 2 according to the bounding box mask. This methodology preserves both the generative capabilities of the model and its interpretation of the respective conditions, as the resultant image is generated without LoRA layer activation while conditions are processed using their corresponding LoRA weights. Consequently, the training objective focuses on establishing the relationship between textual input and the conditional elements.

Discussion: OIN supports LayerCraft's design of the usage of intermediate reference images. It also enables LayerCraft to be an extremely efficient pipeline because during reference image creation, we employ Flux as the primary generator; when reference images are unnecessary, the LayerCraft coordinator can selectively load inpainting LoRA weights. This approach stands in contrast to frameworks such as GenArtist, which necessitates more than ten external models, resulting in computational inefficiency due to model loading/unloading cycles and introducing stylistic inconsistencies stemming from distributional variances across different models. We will provide more details and analysis related to OIN in the supplementary materials.

4 Experiments

Implementation Details We use OpenAI's GPT-4o [1] as the base LLM for both the LayerCraft Coordinator and ChainArchitect agent, with the temperature set to 0.1 to balance control and creativity. Our text-to-image backbone is FLUX.1-dev [18], implemented via the Hugging Face Diffusers library [43].

The Object Integration Network (OIN) is built using Diffusers and PEFT, and trained with a batch size of 1 and gradient accumulation over 4 steps on 4 NVIDIA A6000 Ada GPUs (48GB each). We use a LoRA rank of 4 and enable gradient checkpointing for memory efficiency. OIN is trained for 20,000 iterations on a 50K subset of IPA300K, while OminiControl is fine-tuned for 50,000 iterations. Additional samples are drawn from the remaining dataset for qualitative evaluation.

Dataset Preparation (IPA300K) To ensure diversity, we use ChatGPT (via O1) to generate a list of 500 unique objects across various categories. For each object, we create 20 descriptive prompts with

CollageImagesInput

LayerCraft(Ours) LayerCraft (w/o OIN)

GPT-40

GenArtist(Hacked)

varying attributes. Following the procedure in [40], we generate 10 scene-level and 1 studio-level description per prompt to facilitate paired image generation using FLUX.1-dev with 4 random seeds. This results in paired images-one with the object in isolation and one within a complex scene. To obtain accurate object localization, we apply Grounding DINO [24] and SAM 2 [34] to extract bounding boxes from the scene images. Additional image pairs are generated with smaller object sizes to reflect realistic subject-driven inpainting cases in our framework. Bounding boxes are expanded by 15% at the bottom and 10% on each side to reduce the impact of shadows or reflections. After filtering mismatched pairs using LLM-based validation, we obtain a final dataset of 300,000 high-quality pairs, which we name I mage-guided in P ainting A ssets (IPA300K). The dataset will be released on HuggingFace.

4.1 Visual Comparison with State-of-the-Art Methods

Figure 4 provides a qualitative comparison of our LayerCraft framework against a diverse set of stateof-the-art baselines, including generic diffusion models [19, 3, 9], agent-based approaches [45, 12], autoregressive models [49, 8], and GPT-4o. We crafted prompts that vary in object attributes, quantities, and spatial configurations to rigorously evaluate each method's ability to interpret and realize complex scene compositions.

Our method consistently outperforms competitors by accurately capturing both the object counts and their spatial arrangements. For example, when prompted to generate two apples positioned farther away and four apples closer to the viewpoint, LayerCraft faithfully reproduces the specified quantity and spatial layout. In contrast, while Stable Diffusion 3.5 and FLUX.1-Dev produce the correct number of apples, they fail to preserve the intended spatial relationships. GPT-4o also struggles with correct object counting, and models like PixArtα and DALL·E 3 frequently generate incorrect object counts. Furthermore, FLUX.1-Schnell and Show-o exhibit notable errors across multiple dimensions, including color, positioning, and object consistency.

Additional visual comparisons are included in the supplementary materials. Figure 5 demonstrates LayerCraft's effectiveness in editing collage photos via a single prompt. Compared to GPT-4o, our framework delivers superior consistency in maintaining coherent backgrounds and faithful human face details, as also illustrated in Figure 6. We further evaluate an ablation without the Object Integration Network (OIN), which forgoes intermediate reference images and results in inconsistent clothing details. Even when using a manually "hacked" version of GenArtist with ground truth bounding boxes and intermediate prompts, the output suffers from blurry faces and inconsistent attire, highlighting the critical role of our intermediate representations and integrated refinement process.

Overall, these results highlight LayerCraft's strengths in robust multi-object control, spatial coherence, and consistent detail preservation, which collectively set it apart from prior approaches.

4.2 Comparision on T2I-Compbench

We evaluate our LayerCraft framework against two categories of state-of-the-art approaches: multi-agent systems (upper part) and generic models (lower part) on T2I-Compbench [15] since the GenEval doesn't have the statistics for the agent based models, but we'll include our results in the supplementary materials. As shown in Table 1, LayerCraft excels in all metrics across attribute binding, object relationship, and numeracy, outperforming others due to its instance-level control capabilities.

In contrast to agent-based generation approaches, which typically employ a 'generate-then-edit' pipeline, LayerCraft generates each object sequentially under explicit positional and relational constraints. The generate-thenedit paradigm can propagate early errors into later stages, producing visible artifacts in the final output. Generic diffusion and transformer models fare even worse: lacking the ability to reason over complex textual instructions, they systematically underperform our framework across all evaluated dimensions.

4.3 More Quantitative Comparisons

To comprehensively assess the effectiveness of our approach, we conduct extensive quantitative evaluations across multiple benchmarks targeting different aspects of image generation and editing capabilities. First, we evaluate compositional generalization on the GenEval benchmark [13], which systematically tests models' ability to handle complex compositional reasoning through six dis-

LayerCraft (Ours)

GPT-40

tinct subcategories (Table 2). LayerCraft achieves an overall score of 0.84, demonstrating strong performance across all evaluated dimensions. Specifically, our model achieves perfect scores (1.0) on Single Object generation, indicating robust object rendering capabilities, and excels in Two Objects composition (0.94), suggesting effective spatial reasoning. The model also shows superior color understanding with a score of 0.89 on the Colors subcategory, while maintaining competitive performance on Counting (0.82) and Position (0.75) tasks. These results notably outperform or match GPT-4o across most subcategories and significantly surpass non-agent baselines including Show-O,

SDXL, DALL-E 3, and SD3.5, highlighting the effectiveness of our layered integration framework for handling complex compositional requirements.

To validate real-world applicability and user preference, we conducted a comprehensive user study with 30 participants evaluating interactive complex prompt generation across diverse scenarios (Table 3). Participants assessed outputs from five state-of-the-art systems-LayerCraft, GPT-4o, LLM-Blueprint, FLUX.1 Dev, and GenArtist-using 15 carefully curated challenging prompts that span various artistic styles, compositional complexities, and semantic requirements. Each generated image was rigorously evaluated on a five-point Likert scale (1 = poor, 5 = excellent) across four critical criteria: (1) prompt consistency, measuring how accurately the generated image reflects all specified elements; (2) naturalness, assessing the realism and coherence of the generated content; (3) visual appeal, evaluating aesthetic qualities including color harmony, compositional balance, and stylistic consistency; and (4) overall quality, providing a holistic assessment. LayerCraft achieved the highest average scores in three out of four criteria, with particularly strong performance in prompt consistency (4.5/5.0) and naturalness (4.5/5.0), surpassing GPT-4o (4.4 and 4.3 respectively). LLM-Blueprint, FLUX.1 Dev, and GenArtist showed consistently lower scores across all categories, with particularly notable deficiencies in prompt consistency and visual appeal, suggesting limitations in their ability to handle complex, multi-faceted generation requirements. Additionally, to demonstrate multi-turn editing capability, we perform multi-round editing tests on the MagicBrush [54] benchmark (Table 4), where our model achieves stronger semantic alignment as reflected by higher CLIP-I/T and DINO scores. These comprehensive evaluations underscore the effectiveness of our structured multi-agent pipeline for complex generation and editing scenarios.

5 Ablation Study

Ablation on CoT Variants To rigorously assess the contribution of Chain-of-Thought (CoT) reasoning in our layout generation process, we conducted a comprehensive ablation study by comparing

the full LayerCraft pipeline with systematically simplified variants. The purpose is to quantify the individual and collective impact of key CoT components on generation quality and spatial coherence.

Specifically, we evaluated the following variants:

Without Generation Order : Removes the CoT-driven ordering mechanism used to determine the sequence of object placement.

Without Object Relationship : Omits relational reasoning such as spatial prepositions or interobject dependencies.

Without Both Order and Relationship : Disables both sequential placement and object relationship modeling.

Without All CoT for Layout Generation : Fully removes CoT reasoning from the ChainArchitect, falling back to a single-pass layout prediction without relationships with background.

Due to computational constraints, we employed a stratified sampling strategy and evaluated the models on 20% of the test data, ensuring balanced representation across object types and scene configurations.

As shown in Table 5, the complete LayerCraft pipeline consistently outperforms all ablated versions across key metrics, including object count accuracy, spatial arrangement fidelity, and overall realism. Notably, the absence of generation order and relationship reasoning leads to degraded spatial coherence and increased placement conflicts. The full removal of CoT results in the most significant performance drop, underscoring the critical role of iterative reasoning in managing compositional complexity.

Limitations While LayerCraft delivers strong spatial control and compositional accuracy, its use of Chain-of-Thought reasoning and multi-agent coordination introduces additional computational overhead. This can impact efficiency, particularly for complex scenes with many interacting objects. Although spatial accuracy is enhanced through background-guided bounding boxes, the primary cost lies in maintaining agent interactions. Future work will focus on streamlining these processes to improve runtime performance while preserving generation quality.

6 Conclusion

We have presented LayerCraft, a novel agent-based framework for text-to-image (T2I) generation that addresses key challenges in compositional control, spatial reasoning, and multi-object fidelity. By integrating three specialized agents, LayerCraft Coordinator , ChainArchitect , and Object Integration Network (OIN) , our system supports structured planning, iterative reasoning, and object-aware image refinement in a fully automated pipeline.

LayerCraft excels in generating complex scenes with accurate spatial layouts and consistent object attributes, all without requiring model finetuning. It also enables consistent multi-image editing, making it particularly effective for tasks such as photo collage editing from a single prompt. Extensive experiments demonstrate superior performance over existing methods in both accuracy and visual coherence.

With instance-level control, real-time interactivity, and a modular design, LayerCraft offers a scalable and user-friendly solution for high-quality image synthesis across a wide range of creative and practical applications.

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Appendix

7 Broader Impact Statement

LayerCraft significantly advances text-to-image (T2I) generation by providing precise control over composition and object integration, driven by Chain-of-Thought (CoT) reasoning. This research promises substantial positive societal impact, primarily by democratizing visual content creation for non-experts, making sophisticated design tools accessible to a broader audience. It will revolutionize creative and professional workflows in industries like advertising and gaming, drastically accelerating content creation and fostering innovation. This capability also catalyzes new forms of digital storytelling and education , enabling richer visual narratives. While acknowledging risks like misuse for misinformation or bias propagation, which we condemn and will address through ethical guidelines and further research, our core focus remains on LayerCraft's transformative power to empower human creativity and broadly benefit society.

8 More Examples on Batch Collage Editing

In this section, we present additional examples of batch collage image editing. Figure 7 illustrates LayerCraft's ability to seamlessly integrate a Van Cleef necklace across multiple photos of a girl. Our model first intelligently identifies optimal placement bounding boxes for the necklace, then generates a consistent reference image to ensure uniformity throughout the process before engaging the Object Integration Network (OIN) for the final result. In contrast, GPT-4o struggles with this task, failing

I want her to wear a Van Cleef Neckless

Proposing layouts...

LayerCraft

Generating the Van Cleef Neckless... Calling OIN...

LayerCraft

Ours

GPT-40

to preserve facial identity and generating inconsistent necklaces, as highlighted by the red boxes. Figure 8 provides further demonstrations of our model's robust capabilities. The upper panel shows the zoomed-in version of the teaser image. The lower panel showcases a striking outfit modification, seamlessly changing a black man's attire to a white blazer. Figure 9 effectively illustrates the generation of a cohesive Audi advertisement, featuring a single car consistently integrated across five distinct scenes.

9 Additional comparisons on T2I generation with other SOTA methods

In this section, Figure 15 presents a detailed qualitative comparison with state-of-the-art methods, including expanded versions of examples from the main paper for clearer visualization. As demonstrated, our model consistently yields better results, particularly in terms of object numeracy and accurate spatial relationships. Furthermore, LayerCraft exhibits significantly fewer artifacts compared to other agent-based methods. For instance, LLM Blueprint generates an anomalous red object beneath the table in the hot dog example. GenArtist, even in its teaser image, struggles with perspective accuracy: while the hotdogs are in focus, the distant car and bike remain sharply defined despite the blurry far end of the table, diminishing overall realism. Our method, conversely, avoids such inconsistencies, producing more coherent and realistic compositions.

I want to hold a graduation bear for my photo collage.

Generating the graduation bear..

Analyzing Images... Proposing Layouts and prompts... Calling OIN... Here is the result!

LayerCraft

Proposing layouts...

LayerCraft

Generating the white blazer... Calling OIN...

LayerCraft

Iwanttodesign5Audiadvertisementsusingthese5scenes.

Proposing layouts..

LayerCraft

Generating the Audi sport car...

CallingOIN...

LayerCraft

Condition Img

Ominicontrol

OIN (Ours)

10 Additional Analysis on Objection Integration Network

10.1 Ablation on Attention Mixing

In our work, attention outputs are derived through the strategic blending of dual attention maps utilizing mask indices in the latent space. Specifically, our approach computes attention outputs independently for each branch and subsequently integrates the hidden states according to a latent mask derived from the original masked region. This strategic integration ensures that the model effectively learns optimal object placement within the background while preserving background integrity. Furthermore, since the Multi-Modal attention mechanism within the FLUX architecture processes textual and image tokens concurrently, we address the cross-modal correlations by implementing a weighted average of textual tokens from both branches. This dual-branch integration synergistically enhances the model's comprehension of the conditional inputs. To validate our approach, we conducted comparative analyses against two alternative methodologies. The first alternative, inspired by OmniControl [40], involves extending the input sequence and computing the attention matrix for the entire augmented sequence. However, this methodology encountered significant convergence

challenges during optimization. The second approach implements a weighted summation of attention outputs, which resulted in artifacts characterized by pronounced boundaries in the masked regions and unintended modifications to background elements. Figure 10 presents visual evidence of these failure cases comparing with the successful results obtained using our method. Our proposed methodology demonstrates superior performance compared to these alternatives, as illustrated in more results of OIN in Figures 13 and 14.

10.2 Comparisons with Concurrent Subject-driven Inpainting Methods

&

Condition Img

(sunO)NIO

ACE++

EasyControl

In addition, we provide qualitative comparisons with concurrent approaches that support subjectdriven inpainting to contextualize our contributions as being on par with, or even surpassing, the current state of the art [28, 56]. Figure 11 displays these results. A detailed examination reveals that our Object Integration Network (OIN) excels in capturing finer details, as exemplified by the orange bottle where text and logos are well-preserved, unlike the blurred text in ACE++'s output or the reverted logo in EasyControl's result. Another instance is the violin, whose features (e.g., the chin rest) are maintained only by OIN, while they disappear in the results from ACE++ and EasyControl. Similarly, the lighted earring on the lady vanishes in EasyControl's output. While these concurrent methods could theoretically replace OIN within our pipeline, OIN offers superior efficiency by requiring only a single loading and unloading of models, as it only necessitates loading the correct LoRA weights. In terms of speed, OIN and ACE++ generate results within one minute, depending on GPU type, whereas EasyControl is two to three times slower, further underscoring OIN's significant contribution to the overall pipeline.

11 Handling Difficult Bounding Box Proposal.

To propose a correct bounding box using LLM only will be difficult sometimes, so we adopt a iterative refinement process to solve this problem. Figure 12 demonstrates the process. Initially, the ChainArchitect proposes an initial bounding box. This proposal is then visualized directly on the image, allowing the LLM to iteratively refine its size and position. This refinement process proves highly effective in achieving appropriate bounding box localization.

Bounding Box Refinement...

Figure 12: Our approach to handling bounding box proposals in editing tasks demonstrates remarkable effectiveness. Initially, the Large Language Model (ChainArchitect) proposes an initial bounding box. This proposal is then visualized directly on the image, allowing the LLM to iteratively refine its size and position. This refinement process proves highly effective in achieving appropriate bounding box localization.

12 Overall Process Decomposed Demo

In this section we will demonstrate how the process happen within the framework. We use the example of living room decoration to demonstrate the CoT thinking process of the framework, it is demonstrated at the end of the supplementary materials.

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horizontal lines, top view.

At the top, there are a

chocolate cookie on the

7 cookies arranged in 3

from neor to far.

5 shrimps are arranged

first pink stickynote says

from top to bottom. The

3rectangular sticky notes

"hello',the second blue

green plate neara soda

Two hot dogs sit ona

right of a blue car are

cup which are sitting on

white picnic table,

while a red bike on the

front of 5 portraits of

A woman stands in

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parked nearby.

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layer craft

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LLM Blueprint

GenArtist

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LayerCraft (ours)

Layer craft

USER: I want to decorate this room.

LAYERCRAFT COT THINKING PROCESS: This image shows a minimalist, modern living room with a clean, warm aesthetic. Let me identify the objects and describe the background in JSON format.

The shelving unit and built-in seating are considered separate objects but could be viewed as part of the architectural design. I've included the structural elements like flooring, ceiling, and wall paneling in the background description since they define the space rather than being foreground objects.

Looking at the image, I can suggest several items that could be added to enhance the minimalist space while maintaining its clean aesthetic. Let me think about what would complement this room:

These additions would enhance the functionality and warmth of the space while respecting its minimalist design language and natural materials.

Analyzing the image based on the provided grid regions, I'll determine suitable placement areas for the suggested objects while considering perspective and available space:

This analysis considers both the physical space available in each region and the design principles of balance, flow, and functionality while maintaining the minimalist aesthetic of the room.

Based on our analysis, here are the bounding boxes and prompts for each suggested object, ordered from far to close in terms of generation order:

This arrangement considers depth perception (generating background objects first), spatial relationships, and the existing elements in the room. The bounding boxes are designed to place objects in appropriate locations while maintaining a balanced, functional layout that enhances the minimalist aesthetic of the space.

USER: I've done the previous decorating and want to put this cute teddy bear in.

LAYERCRAFT: Looking at the new item you want to add to the room, I can help place the teddy bear in the previously discussed space.

}

These placements would work well with your previous decorating plans.

Peer reviews:
comments: SUMMARY:
This paper proposed a general LLM centered agent framework general realistic image with object layout from user prompt. The two key components are structured layout planning with CoT reasoning, and layered object inpainting/integration. The system has light training requirement, good generalization, and good visual quality.

STRENGTHS AND WEAKNESSES:
Strength
- general agent framework for image generation with good layout planning and reasoning
- enhanced Flex inpainting and object integration model with flexible conditioning and efficient attention mechanism
- show case various image generation and editing applications, such as batch object insertion and multi-object layout editing
Weakness
- Key modules are from GPT and Flux models
- the generation results are in pixel format, without object layer separation, limiting further editing use cases
- CoT may incur longer computing time

QUESTIONS:
- explain the impact of CoT on inference time
- why not using original Flux infill model for object inpainting? needs explanation and comparison
- how soon can native GPT catch up to paper's result quality? if in a short time, what's the impact of this work?

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 2
DATE: 2025-06-21

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes LayerCraft, multi-agent framework for controllable text-to-image (T2I) generation and editing. The method uses Chain-of-Thought (CoT) reasoning via GPT-4o to decompose prompts into structured scene layouts. A layout planner (ChainArchitect) then produces 3D-aware object graphs and layouts. A lightweight Object Integration Network (OIN) leverages dual LoRA adapters on a FLUX-based model for reference-driven, inpainted object placement. The system targets problems in compositional scene generation and batch editing across multiple images. It is evaluated both qualitatively and quantitatively on T2I-CompBench and custom tasks.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n- The paper is easy to follow\n- The collage editing is useful and has practical applications.\n\nWeaknesses:\n\n- The paper touts Chain-of-Thought as central but provides no direct comparisons against simpler prompt decomposition strategies or language-model-free layout generators (e.g., rule-based or template-based). The authors should compare  CoT-enabled ChainArchitect to vanilla GPT-4o prompting without reasoning steps.\n- While the paper claims consistency and ability to handle occluded objects, it does not explore cases with hard relative positioning (e.g., occlusion, 3D viewpoint shifts) where CoT should help most.\n- I would argue that the proposed method has interesting similarities with LLM-Blueprint [1]. The method used in LLM-Blueprint is also based on generating Layouts from the prompts while maintaining spatial reasoning and consistency. The objects are also superimposed on the background through an iterative mechanism. How different is the overall idea of the proposed paper from the LLM-Blueprint?\n- The human evaluation should be carried out and metrics should be reported. Human evaluation is important for compositional scenes.\n- The failure cases should have been discussed.\n\n\nReferences: \n\n\n[1] H. Gani et al. \"LLM Blueprint: Enabling Text-to-Image Generation with Complex and Detailed Prompts\". ICLR 2024.\n\nQUESTIONS:\n- It seems that the spatial accuracy of the layouts stems from the use of COT-based GPT-4o. Did the authors try using any other LLM for generating layouts?\n\nLIMITATIONS:\nCheck the weaknesses.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-24"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces LayerCraft, a modular, agent-based framework for structured text-to-image generation and editing. The system comprises three main components: a LayerCraft Coordinator to manage the overall process and user interaction, a ChainArchitect that uses Chain-of-Thought (CoT) reasoning to decompose prompts into structured scene layouts with objects and relationships, and an Object Integration Network (OIN) that leverages a dual-LoRA adapted diffusion model (FLUX) to integrate or edit objects in the scene. The paper demonstrates LayerCraft's capabilities by evaluating its performance on the T2I-CompBench benchmark.\n\nSTRENGTHS AND WEAKNESSES:\nPaper strengths:\n- The paper is easy to follow and technically sound.\n- The paper shows good performance on T2I-CompBench which demonstrates the effectiveness of the proposed method.\n\nPaper weaknesses:\n\n- Incremental novelty of core components: The core concepts it is built upon LLM agents, Chain-of-Thought for planning, and LoRA-based model tuning, which are established techniques in the field. The contribution is more of a system integration and refinement of these ideas, rather than a methodology contribution.\n- Lack of interactivity evaluation: The paper emphasizes LayerCraft's ability to support multi-turn editing and user-agent interaction (e.g., Figure 1). However, this interactive capability is not formally evaluated. A user study or quantitative analysis of multi-turn editing scenarios (e.g., measuring efficiency, success rate, or user satisfaction) would be important to demonstrate the interactivity of the framework.\n\n- Complexity and potential for error propagation: The multi-agent, multi-step pipeline is inherently complex. It is also susceptible to cascading errors; a mistake made by the ChainArchitect in the layout planning stage could lead to a flawed final image that the OIN cannot recover from. Therefore, a deeper analysis of failure modes and the system's robustness would be beneficial.\n\nQUESTIONS:\n- Did you experiment with simpler pipelines (e.g., a single LLM agent, or directly using a VLM like GPT-4o for both planning and editing)? If so, what were their specific failure modes that motivated your three-agent design?\n- How often does the pipeline fail on complex prompts, and which component is typically the bottleneck?\n- Could you clarify how LayerCraft manages its context over extended interactions, particularly in a long, iterative editing session?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents LayerCraft, a modular framework that uses large language models (LLMs) as autonomous agents to orchestrate structured, layered image generation and editing.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\nThe proposed framework is well-designed and conceptually sound. I find the overall idea to be novel and thoughtfully constructed. The experimental results also show a noticeable improvement, which supports the validity of the approach. Overall, this is a solid piece of work with meaningful contributions.\nWeaknesses:\nThe main weakness of the paper lies in the lack of certain key experiments, which limits the strength of the empirical validation. Additional experimental results are necessary to fully support the claims made in the paper. Please refer to the Questions section below for more detailed suggestions.\n\nQUESTIONS:\n1. Lack of Evaluation on Broader Benchmarks (e.g., GenEval):\nTo better validate the effectiveness and generalizability of the proposed method, the authors are encouraged to conduct experiments on additional, more diverse benchmarks—particularly GenEval. This dataset offers a broad range of prompt types and evaluation scenarios, and results on it would help demonstrate the robustness and scalability of the proposed approach beyond the current settings.\n2. Missing Results on Complex Cases in T2I-CompBench:\nWhile some experiments are provided on T2I-CompBench, the results appear to focus only on simpler or partial aspects of the benchmark. To present a more complete evaluation, the authors should include performance on the “complex” subset of T2I-CompBench, which is specifically designed to challenge models with compositional and multi-object prompts. This would offer a more rigorous test of the agent's capabilities.\n3. Lack of Comparison with GPT-4o and Other Advanced LLMs:\nWith the emergence of powerful multimodal models such as GPT-4o, which can perform text-to-image generation and complex reasoning with strong alignment, it is important for the authors to compare their agent-based framework against such models. This comparison is crucial to justify the added complexity of using agents and to clearly demonstrate the advantages of this approach in terms of controllability, interpretability, or performance.\n4. Missing Comparison in Layout Generation with Prior Work:\nSince the paper involves layout generation as part of the image synthesis pipeline, it is important to benchmark this component against prior layout-guided methods, such as LLM-Grounded Diffusion. A direct comparison would help clarify whether the proposed layout generation method improves spatial accuracy, semantic alignment, or structural consistency. Without this, it remains unclear how competitive or effective the layout strategy is compared to existing solutions.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}