LayerCraft: Enhancing Text-to-Image Generation with CoT Reasoning and Layered Object Integration
Authors: Yuyao Zhang
Jinghao Li
Yu-Wing Tai
Venue: NeurIPS 2025 poster ()
Keywords: Multi-control image generation
Diffusion Transformer
LLM agent framework
Primary area: applications

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.

Craft: Enhancing Text-to-lmage Generation with CoT Reasoning and Layered Object Integration
Yuyao ZHANG Dartmouth College
Jinghao LI' CUHK
Yu-Wing TAI Dartmouth College
went drew "Alice in wonderland"
hold graduation for collage.
Gerereting backgreund. Hore the flying catorsilar, L the girl Alice and a Cheskire Cot.
LayerCraft
Reosoning relecionetip, posttion. Refiring generation arder.. Genercting the loyout.
gradustion
LaywCroft
LayerCraft
Here resut!
Andlying Imoges...
Proposity Layeuts and prompts..
LayerCraft
Calling CIN.
wontt nenove the secand from left out and put this las
LayerCraft
Madiyingg Layout
Genereting.
LayerCraft
Figure 1: Application demonstrations for LayerCraft. Left: Demonstrates batch collage editing capabilities. A user uploads graduation photos and LayerCraft seamlessly integrates a graduation bear across all images. The system first generates a reference bear for consistency, then analyzes optimal placement while preserving facial identity and background integrity. Right: Illustrates the structured text-to-image generation process. From simple "Alice in Wonderland" prompt, LayerCraft employs chain-of-thought reasoning to sequentially generate background elements, determine object layout, and compose the final image. The framework supports post-generation customization, shown with the lion integration.
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 (LLMMs) autonomous agents 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-suun such as characters or props across diverse images scenes while preserving identity, context, and style. The system com- prises 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 col- lage editing and narrative scene generation, LayerCraft empowers non-experts iteratively design, customize, and refine visual content with minimal manual effort. Code will be released at fihhtts::. : // //gggtut.b..../ com http://eeeeee/theg///eaa Laarnn/g/yyyeaaatta
'This work was done while Jinghao Li was exchange student Dartmouth College.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Introduction
Text-to-image (T2I) generation has rapidly evolved with advances in diffusion models 14| 35
32]. transformer-basel architectures [42]. and scalable encoder-decoder frameworks [36.. Recent systems 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 fine- 371. which limits generality and usability. Others support instance-level manipu- 17 but often falter in complex scenes or suffer from spatial inconsistency. More structured methods like LayoutGPT [TO] 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-4g fail maintain background consisteney or facial identity over multiple editing iterations.
tuning [55
53
lation
48
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 by a team of specialized agents that handle prompt interpretation, spatial planning, and object integration. As shown in Figure our framework supports applications such as batch collage editing with consistent object insertion, and narrative-driven image generation using structured reasoning and layout planning.
step-by-step reasoning process, orchestrated
LayerCraft Coordinator serves as the central interface, managing interactions between users and agents. processes instructions, coordinates agent outputs, and integrates user feedback throughout the generation process.
ChainArchitect performs thain-of-thought (CoT) reasoning to decompose prompts into structured layout plans. It first generates the background, then infers spatial layout, represented dependency-aware 3D scene graph, to determine bounding boxes and relationships among objects. This planning phase supports complex multi-object scenes and facilitates layer- editable image construction.
Object Integration Network (OIN) uses the original FLUX [88 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 accessible and lightweight; (2) 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 LayoutGPT [TO] 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 foodels-from pixel-space methods like GLIDE [30 and Imagen [8] 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 [T8) have substantially improved the quality and diversity of generated content. However, fine -neineedded and interpretable control remains challeng- ing, especially in scenes with multiple objects or complex layouts. Personalization methods like DreamBooth (37 and Textual Inversion [|] support user-specific concepts but require task-specific
card/

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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 [4]. Janus-Pro [8] explore prompt-based synthesis via language models but often lack spatial structure. Meanwhile, emerging show generative promise, though they require
MLLMs such as GPT-4o and Gemini 2.0 Flast gl 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 thain-of-thoughe (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 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 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 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 L.MSs to generate spatial layouts from text prompts. While effective
for simple scenes, its reliance 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 [55 and LLM Blueprints [12] follow
geenerate-then-edit paradigm, refining initial layouts or images through external editing modules. This often leads to stylistic drift and unstable outputs, due 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 u LayoutGPT by applying Shain-of-though (CoT) reasoning to generate structured, 3D-aware layouts, enabling compositional planning without external layout tools. The Object Integration Network (OIN) complements this with mmage-gudde inpainting via dual-LoRA fine-tuning on pre- trained 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-thouggh (CoT)) prompting has proven effective in improving language model reasoning by decomposing complex tasks into intermediate steps [46] (77 However, in multimodal settings, existing CoT approaches often rely on model finetuning over specialized datasets 22 58), limiting their applicability in zero-shot or flexible generation scenarios.
LayerCraft takes 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 nujti-objectt 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 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
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User-Agent Interaction
ChainArchitect
LayerCraft Coordinator
Object Integration Network
Generation Agent Coltkkennn on
CoT Reasoning
Baokgraand
Allan sutts
Task Decompasition
CoT Reasoning
Poons
Coterpäiiar
Invoke
Invoke
Reasoning
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Figure 2: LayerCraft is framework with three key components: the LayerCraft Coordinator, which processes user instructions and manages collaboration; ChainArchites which enhances prompts to plan layouts, identify objects and relationships, and assign bounding boxes using Chain-of-Thougg reasoning; and the Object Integration Network (OIN), which enables image-guided inpainting for seamless object integration using the LoRA fine-tuned FLUX model.
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) Architec,,, 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 the central orchestrator for the entire framework, overseeing the system's 's operation, ensuring smooth user-agent interactions, and directing agent collaboration. This component also serves 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 (ChainAr- chitect), and final image generation and inpainting (OIN). The Coordinator plays 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, formulates corrective measures and delegates the task the appropriate agent for regeneration. This enables LayerCraft to ensure that the final output meets the user's 's specifications.
User-Agent Interaction Although the system operates autonomously, users can modify or refine the output by interacting with the agents. For example, user may request more details for specific object or a customized layout for particular region. The Coordinator facilitates multiple rounds of interaction, refining the image iteratively until the user's requirements are fully met.
Chain-of- Chain-of-Thought (CoT) Enrichment. The Coordinator enhances generation by iteratively enrich- ing the text prompt using Chain-of- (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] T 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.

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3.2 ChainArchitect
ChainArchitec advances traditional layout generation models (e.g.. LayoutGPT 100 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. which may range from detailed to brief, the LLM identifies relevant objects and generates a structured list O {O, i E N} alongside background description Pb.. For instance, if the prompt mentions a "car," ChainArchitect infers a suitable context such as "road." The background description Po, is passed the FLUX model by the Coordinator to generate the background image Thg. which serves as spatial reference for placing foreground objects.
To ensure the generated layout follows consistent, interpretable format (e.g., JSON), ChainArchitect uses in-context exemplars [2 that define object classes, spatial positions, and scene style, thereby aligning the output with the user's intent. Additionally, ChainArchitect leverages GPT-4o's 's vision capabilities to analyze the background image viewpoint, improving object placement accuracy.
For foreground objects, ChainArchitect performs explicit spatial reasoning: it determines optimal generation order (placing distant objects before closer ones to manage occlusion) and models inter- object relationships, such relative positioning ("A is 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)
N
LLRAA Finstuning Original Weights
The Object Integration Network (OIN) facilitates the seam- less incorporation of objects into pre-existing backgrounds, as illustrated in Figure OIN processes a masked back- ground (delineated by a bounding box), a reference object image, and text prompt to synthesize a contextually co- herent and visually consistent integration of the specified object into the background environment.
Parameter Reuse Method for Multiple Conditional Generation via Dual LoRA Leveraging the robust pre- trained capabilities of the FLUX text-image model on text-to-image task, we implement parameter-efficient adaptation methodology for conditional generation. This approach enables the framework process masked back- grounds and reference object images for highly precise subject-driven inpainting. Our implementation follows a two-phase training protocol:
Num
Prjjedinn
Pronset
Figure 3: Architecture of the Object In- tegration Network (OIN). The system processes text prompt, a background image with designated bounding box, and a reference object to produce a seam- lessly integrated result. Red, yellow, and blue indicators represent the utilization of combined LoRA weights, background inpainting weights, and subject-driven generation weights respectively. "FF" and "MM Attn" denote feedforward lay- ers and multi-modal attention layer in the FLUX model.
In the initial phase, we develop independent LoRA adaptors-Way and Was that enhance the model's ca- pacity 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 encod- ings identical to the initial noise, while reference image tokens utilize biased embeddings to accommodate spa- tially aligned and unaligned processing requirements.
The second phase initializes the model with the trained LoRA modules, enabling comprehensive understanding of both background and object conditions (Cny and Cobz). 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: [Cr, X, Cig. for background processing and [CT, X, Cars] for object integration. These components undergo parallel processing with query, key, and value projections utilizing distinct weight sets: MW for joint
conditions, Myku for FLUX's foundational weights, M Wene for speeialinn--eecializeedd LoRA weights,
Make
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Figure 4: Visual comparisons with state-of-the-an thr- generic texl-to-image generation models are presented. On the left, the prompts are annotated with distinct colors to highlight critical attributes and relationships.
and M Woi for object-specific LoRA weights. This architecture generates dual query, key, and value outputs: QQ,, V11 for background elements and [Q2, K2, V2] for object features.
The attention mechanism computes outputs through the following formulations:
Q1K? Vd
[C,, [byl =
Softmax
V1,
O2Kg
X2? Cotyl = Softmax
V2,
Vd
C
C7 M(X x²), Chg: Cobs
Output
where M(X1, X²) denotes the replacement of the masked region's 's latent sequence X with X² according to the bounding box mask. This methodology preserves both the generative capabilities of the model and its interpretation of the respective conditions, 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 'ss 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 the primary generator; when reference images are unnecessary. the LayerCraft coordi- nator can selectively load inpainting LoRA weights. This approach stands in contrast to frameworks such GenArtist, which necessitates more than external models, resulting in computational inefficiency due to model loading/unloadee 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.
Experiments
the base LLM for both the LayerCraft Coordinator and Architet agent, with the temperature set to 0. to balance control and creativity. Our text-to-image backbone is ILUX.I-dev [18], implemented via the Hugging Face Diffusers
Implementation Details We OpenAl's GPT-4o 1
library [43]
The Object Integration Network (OIN) is built using Diffusers and PEFT, and trained with batch size of and gradient accumulation over 4 steps on 4 NVIDIA A6000 Ada GPUs (48GB each). We 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 list of 500 unique objects across various categories. For each object, create 20 descriptive prompts with

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Collage Images Input (oyerCraft(Ours)
LayerCraft (w/o OIN)
GPT-4o
GenArtist (Hacked)
Prompt: Let her wear Chinese traditional dress.
Figure 5: More example usage of LayerCraft. We can see that our model can generate results with consistent background, and object identity comparing GPT-4o. also illustrates the importance of pipeline's 's design with OIN and intermediate reference images. For GenArtist, even if provide the grouth truth bounding boxes and extra instructions, they still failed.
varying attributes. Following the procedure [40], we generate 10 scene-level and studio-level description per prompt 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 34 extract bounding boxes from the scene images. Additional image pairs are generated with smaller object sizes 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 final dataset of 300,000 high-quality pairs, which we name Image-guided in inPainting Assets (IPA300K). The dataset will be released on HuggingFace.
Table 1: Comparison with other methods on 121-Compbench 15
The symbol denotes that higher values correspond better performance. Our LayerCraft system achieves the state-of-the-an performance on the benchmark.
Attribute Binding
Object Relationship
Method
Numeracy
Colort
Shapet
Texture
Spatialt
Non-Spatialf
LayoutGPT 10
0.2921
0.3716
0.3310
0.1153
0.2989
0,4193
Attn-Exct [5
0.6400
0.4517
0.5963
0.1455
0.3109
GORS 15
0.6603
0.4785
0.6287
0.1815
0.3193
RPG-Diffusion 52
0.6024
0.4597
0.5326
0.2115
0.3104
0.4968
CompAgent 59
0.7400
0.6305
0.7102
0.3698
0,3104
GenArtist 45
0.8482
0.6948
0.7709
0.5437
0.3346
SDXL
0.6369
0.5408
0.5637
0.2032
0.3110
0.5145
PixArt- 7
0.6886
0.5582
0.7044
0.2082
0.3179
0,5001
Playground v2.5 20
0.6381
0.4790
0.6297
0.2062
0.3108
0.5329
Hunyuan-DiT 22
0.6342
0.4641
0.5328
0.2337
0.3063
0.5153
DALI-E33
0.7785
0.6205
0.7036
0.2865
0.3003
SD v3 [9]
0.8085
0.5793
0.7317
0.3144
0.3131
0.6088
FL.UX.I-De
0.7407
0.5718
0.6922
0.2863
0.3127
0.5872
LayerCraft (Ours)
0.8643
0.7046
0.8147
0.6432
0.3508
0.6331
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 state- of-the-art baselines, including generic diffusion models [19 9 agent-based approaches [45] [2], 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 interpret and realize complex scene compositions.
7

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Our method consistently outperforms competitors by accurately capturing both the object counts and their spatial arrangements. For example, when prompted 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 1-.e 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 PixArt-a and DALL-E 3 frequently generate incorrect object counts. Furthermore, FLUX.I-Schnell I-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 S demonstrates LayerCraft's effectiveness in editing collage photos via single prompt. Compared GPT-40, our framework delivers superior consistency in maintaining coherent backgrounds and faithful human face details, as also illustrated Figure [ 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 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 apart from prior approaches.
4.2 Comparision on 121-Compbench
We evaluate LayerCraft framework against two cat- egories of state-of-the-ar approaches: multi-agent sys- tems (upper part) and generic models (lower part) T21-Compbench [15] since the GenEval doesn't have the statisties for the agent based models, but we'll include our results in the supplementary materials. As shown in Table [ LayerCraft excels in all metrics across attribute binding, object relationship, and numeracy, outperforming others due to its instance-level control capabilities.
Prompt: I want to decorate this room.
In contrast to agent-based generation approaches, which typically employ "generate-then-ediff pipeline, Layer- Craft generates each object sequentially under explicit positional and relational constraints. The generate-then- edit paradigm can propagate early errors into later stages, producing visible artifacts in the final output. Generic diffusion and transformer models fare even worse: lack- ing the ability to reason over complex textual instructions, they systematically underperform our framework across all evaluated dimensions.
LayerCraft (Ours)
4.3 More Quantitative Comparisons
GPT-4o
To comprehensively assess the effectiveness of our ap- proach, we conduct extensive quantitative evaluations multiple benchmarks targeting different aspects of image generation and editing capabilities. First, we eval- uate compositional generalization on the GenEval bench- mark [13), which systematically tests models' ability to handle complex compositional reasoning through six dis- 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 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 match GPT-4o across most subcategories and significantly surpass non-agent baselines including Show-O,
Figure 6: Another example in- door decoration, which demonstrates our model's strong consistency.

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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 Wystems-LayerCrrade 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 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). L.LM L..M-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 where model achieves stronger semantic alignment 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.
Table 2: Comparison of GenEval results across models. LayerCraft matches or outperforms GPT-4o on most subcategories while significantly surpassing non-agent baselines.
Model
Overall
Single Obj.
Two Ohj.
Counting
Colors
Position
Attr. Bind.
LayerCraft 0.84
1.00
0.94
0.82
0.89
0.75
0.62
GPT-4o
0.84
0.99
0.92
0.85
0.92
0.75
0.61
Show-O
0.53
0.95
0.52
0.49
0.82
0.11
0.28
SDXL
0.55
0.98
0.74
0.39
0.85
0.15
0.23
FLUX.I-Dee
0.66
0.98
0.81
0.74
0.79
0.22
0.45
DALL-E
0.67
0.96
0.87
0.47
0.83
0.43
0.45
SD3.5
0.71
0,98
0.89
0.73
0.83
0.34
0.47
Janus-pro Prous-pro7oo
0.80
0.99
0.89
0.59
0.90
0.79
0.66
Table 3: Mean (1) and standard deviation (σ) of user ratings (1-5). Higher values indicate better performance.
System
Consistency
Naturalness
Visual Appeal
Overall
LayerCraft
4.5 + 0.82
4.5 + 0.73
4,4 0.78
4.2 0.75
GPT-4o
+: 0.79
4.3 4 0.86
4,5 0.67
4.2 0.84
LLM-BBlueprit
3.0 + 1.28
2.9 1.09
2.6 1.15
2,9 + 0.94
FLUX.1 Dev
3.2 + 1.38
3.6 0.97
3.5 1.12
3.3 + 1.07
GenArtist
3.1 + 1.08
3.5 1.18
3.6 1.26
3.5 = 1.02
Table 4: Multi-turn editing results MagicBrush. Lower is better for L1/L2; higher is better for CLIP-I, DINO, and CLIP-T.
Method
L1 1
L2 1
CLIP-It
CLIP-T DINO
HIVE
0.1521
0.0557
0.8004
0.6463
0.2673
InstructPix2Pix
0.1584
0.0598
0.7924
0.6177
0.2726
MagicBrush
0.0964
0.0353
0.8924
0.8273
0,2754
GenArtist
0.0858
0.0298
0.9071
0.8492
0.3067
LayerCraft (ours)
0.0863
0.0299
0.9121
0.8541
0.3157
5
Ablation Study
Ablation on CoT Variants To rigorously assess the contribution of Chain-of-Thougee (CoT) reason- ing in our layout generation process, we conducted comprehensive ablation study by comparing

---

Table 5: Ablation Study for CoT on 121-Compbench.
Attribute Binding
Object Relationship
Method
Numeracy
Color?
Shapet
Texture
Spatialf
Non- Non-Spatialf
LayerCraft
0.8643
0.7046
0.8147
0.6432
0.3508
0.6331
w/o generation order
0.8524
0.6792
0.7853
0.4210
0.3147
0.6305
w/o object relationship
0,8512
0.6867
0.7842
0,4062
0.2854
0,6301
w/o order relationship
0.8413
0.6463
0.7531
0.3847
0.2752
0.6023
w/o CoT for Layout Generation
0.6394
0.5639
0.7216
0.2831
0.3013
0.5663
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 determine the sequence of object placement.
Without Object Relationship:
Omits relational reasoning such as spatial prepositions or inter-
object dependencies.
Without Both Order and Relationship: Disables both sequential placement and object relation- ship 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 computational constraints, employed 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 metries, including object count accuracy, spatial arrangement fidelity, and overall real- ism. 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-Thougeee 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-gui lies in maintaining agent interactions. Future work will focus on streamlining these processes to improve runtime performance while preserving generation quality.
bounding boxes, the primary cost
Conclusion
We have presented LayerCraft, 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, LayerCrafi 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 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 modular design, LayerCraft offers scalable and user-friendly solution for high-quality image synthesis across wide range of creative and practical applications.
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Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [Yes]
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Justification: Broader societal impacts, including benefits such as accessibility and creative empowerment, as well risks such as misuse for disinformation, will be discussed in the supplementary materials, along with recommendations for responsible use and model deployment.
Guidelines:
The answer NA means that there no socictal impact of the work performed.
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Does the paper describe safeguards that have been put in place for responsible release of data models that have high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
Question:
Answer: [Yes]
Justification: Safeguards will be implemented and detailed upon the release the trained model. These include measures such as gated access, usage restrictions, and output water- marking to mitigate risks of misuse. The paper acknowledges potential concerns around image generation and editing, and commits responsible deployment practices aligned with community standards.
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We recognize that providing effective safeguards is challenging, and many papers do require this, but we encourage authors to take this into account and make best faith effort.
12. Licenses for existing assets
Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?
Answer: [Yes]
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Justification:
The paper uses Flux and GPT-4o API, both under open-source licenses. These are cited in the references and licensed appropriately.
Guidelines:
The answer NA means that the paper does use existing assets.
The authors should cite the original paper that produced the code package or dataset.
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For existing datasets that are re- e-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.
If this information is not available online, the authors are encouraged to reach out to the asset's 's creators.
13. New assets
Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [Yes]
Justification: The paper introduces new agent pipeline (Layercraft), Object Integration Network (OIN) and dataset (IPA300K). We will provide code, model weights, and integration instructions in the supplemental.
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Institutional review board (IRB) approvals or equivalent for research with human subjects
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Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or equivalent approval/review based on the requirements of your country or institution) were obtained?
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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 broader audience. It will revolu- tionize 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 bias propagation, which we condemn and will address through ethical guidelines and further research, our core focus remains on LayerCraft's '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 SntureeJillstieenn 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
21

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want her wear Van Cleef Neckless
Proposing layouts...
LayerCraft
Generating the Van Cleef Neckless... Calling OIN...
LayerCraft
Ours
GPT-4o
Figure 7: An example of batch collage image editing. LayerCraft effectively proposes bounding boxes for necklace placement and generates consistent reference image, leading to seamless integration across multiple images with single prompt. In contrast, GPT-4o fails to preserve facial identity and generates inconsistent necklaces as highlighted by the red boxes.
to preserve facial identity and generating inconsistent necklaces, as highlighted by the red boxes. [esure@]rrovide. further demonstrations of our model's 's robust capabilities. The upper panel shows the zoomed-in version of the teaser image. The lower panel showeases striking outfit modification. seamlessly changing a black man's attire to white blazer. Figure 9 effectively illustrates the generation of cohesive Audi advertisement, featuring single car consistently integrated across five distinct scenes.
9 Additional comparisons on T2I generation with other SOTA methods
In this section, Figure 15 presents detailed qualitative comparison with tate--of-the-art methods, including expanded versions of examples from the main paper for clearer visualization. As demon- strated, 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-bar based methods. For instance, LLM B Blueprint generates an anomalous red object be- neath 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.
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I want to hold graduation bear for my photo collage.
Generating the graduation bear...
LayerCraft
Analyzing Images...
Proposing Layouts and prompts...
Calling OIN... Here is the result!
LayerCraft
I want him to wear white blazer
Proposing layouts...
LayerCraft
Generating the white blazer... Calling OIN...
LayerCraft
Figure 8: This figure provides more user scenario, the upper part is a larger and clearer demo for the teaser image. The lower panel demonstrates an outfit modification, showing a black man's 's attire seamlessly changed to white blazer. These examples highlight the robust capabilities of our model.
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I wantt to design 5 Audi advertisements using these 5 scenes.
Proposing layouts...
LayerCraft
Generating the Audi sport car... Calling OIN...
LayerCraft
Figure 9: Further examples of batch collage image editing and generation. It illustrates the generation of consistent Audi advertisement featuring a single car across five distinct scenes.
Condition Img
Masked Background
Ominicontrol
OIN (Ours)
Figure 10: Failure case for direct attention mix in Section 10.1 | Analllis of Objection Integration Network". One can see the background is changed and the boundary easy see.
Additional Analysis on Objection Integration Network
10.1 Ablation on Attention Mixing
In work, attention outputs 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 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-moot modal correlations by implementing weighted average of textual tokens from both branches. This dual- branch integration synergistically enhances the model's comprehension 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
the conditional inputs. To validate our approach, we
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challenges during optimization. The second approach implements a weighted summation of attention outputs, which resulted artifacts characterized by pronounced boundaries in the masked regions and unintended modifications to background elements. Figure [0 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, illustrated in more results of OIN in Figures 13 and 14
10.2 Comparisons with Concurrent Subject-driven Inpainting Methods
Masked Bg
& Condition Img
OIN(Ours)
ACE..
EasyControl
Figure 11: Visual comparisons with concurrent works on subject-driven inpainting task. The first row is our Object- Interttinterttionn Network, the second row is ACE+ [28] and the last row is EasyControl (56)
In addition, we provide qualitative comparisons with concurrent approaches that support subject- driven inpainting to contextualize our contributions being on par with, even surpassing, the current state of the art [28, (6). Figure IT displays these results. detailed examination reveals that our Object Integration Network (OIN) excels in capturing finer details, exemplified by the orange bottle where text and logos are well peel-eeee,r,ed, unlike the blurred text ACE++'s output the reverted logo in EasyContool'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 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 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 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 adopt iterative refinement process to solve this problem. Figure [2 demonstrates the process. Initially, the Chain proposes an initial bounding box. This proposal is then visualized directly on the image, allowing the L.M to iteratively refine its size and position. This refinement process proves highly effective in achieving appropriate bounding box localization.
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Bounding Box Refinement...
Bounding Box for Necklace Placement
Further Enjrged Bounding Box Necklare Placeeet
Figure 12: Our approach to handling bounding box proposals in editing tasks demonstrates remarkable effectiveness. Initially, the Large Language Model (ChainArchitect) proposes 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 will demonstrate how the process happen within the framework. We use the example living room decoration to demonstrate the CoT thinking process of the framework, demonstrated at the end of the supplementary materials.
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blank background
Shverse foctiondoress. with osse gownt or apps or gracetully over suriif park
scoop wrth deconctonn in lingttyy tihhen. it nett on martle rolling
wee given bankgyrund with Deganizes calequardd phetogiaply whidwerly
shribs. rediees on vibranit lown of county....
Ribs Blank area in the given background with large, colortul boach chace. performers and curious enicollens, nell ec...
the tleri area in the given Leckground with a and modenn formel wulls, pastt nollcp lerase pertruyed
goldien sthouatt againal
blank bec Uerue ziichen applienre
with multie cooking bn fificions, with desoriation a modern mit inna
Figure 13: More resluts of OIN
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the blank weea the gheen backgrouun wes Arrts.Arree Arcrent engire for Artue A320
fommly deconstion mechanicy
motal cralle sur corbod lighing.
twnn tedigrourd spanent
ceremnoia with
shelt
needed smong wosthorod bookd and artioue brood itams...
slank
arcrult tey, WE cesenssinn Parched proconousty on rocky outcrog at
All the Hlnk ama in the gven beckgmund with A durah advenhurme sounted
dieee to the cameeaa capturing closer....
ven eggat
stir, resorption oute lirany. 2000 eu shows off refieed under gondle famps,
nill beciógraund stanty wasden busi youth, with description ed a cory can bedrooon, nem capcured
Figure 14: More results of OIN
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encrrppli dridy wirus
Tho an
fromef
chout tole,
Lect chic eit
ur arting
butt dridy
right cls
hello
963.5
kello
FLUX
Layer craft
.come
kello
welcome
DALL-E
ShowOO
NAC
Jeruk-pro
LLM Bueepint
Gandertie
HeBba
hello
GPT4e
layer
LayerCroft (sund)
Layer
Figure 15: More comparisons with state-of-the-ae art models. We also include the examples used in the main paper and make them larger for details.
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features built-in USER: I want to decorate this room.
LAYERCRAFT COT THINKING PROCESS: This image shows minimalist, modern living room with a clean, warm aesthetic. Let me identify the objects and describe the background in JSON format.
{
"objects":
"name": "reading nook", " "description": "A built-in wooden seating area dark cushions and white pillous nestled within the wooden shelving unit"
"name": "pendant light", "description". "A circular, disc-shaped hanging light fixture with diffuse glow suspended from the ceiling"
"name": "area rug", "description"; "A large, neutral-tone rectangular rug covering part of the wooden floor"
"name": "shelving unit", "description"; "Bull-iin light wood shelving spanning the back wall with various decorative items displayed"
" "name": "decorative objects", "description". "Small sculptures, books, framed artwork arranged sparsely on the shelves"
"name": "sliding glass doors", "description": "Large foor-to-ceilinn glase doors/ windows on the left side offering vievs to the outdoors"
"background":
"description": "A spacious, minimalist room with light wooden flooring, white ceiling, and light wood wall paneling. The shelving along the back wall and large sliding glass doors that allow natural light to flood the space. The vieupoint is from the center of the room looking toward the back wall with the built- shelving and seating area, with the glass doors visible on the left side.", Annclude__eeement
["wooden flooring", "vhite ceiling", "light wood wall paneling", "natural lighting"]
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 the background description since they define the space rather than being foreground objects.
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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:
addigisn [
"name": "coffee table", "description"; "A low, rectangular wooden coffee table with clean lines, placed in front of the reading nook on the area rug"
"name": "side table",
"description": "A small, round side table in light wood brass next to the reading nook"
"name": "floor lamp", "description". "A slim, architectural floor lamp with warm lighting placed in the corner opposite the glass doors"
"name": "accent chair", "description"; "A minimalist armchair in complementary neutral tone (tan leather or light gray fabric) positioned at an angle to the reading nook"
"name": "throw blanket",
"description": "A textured throw blanket in subtle pattern muted color draped corner of the reading nook"
"name": "indoor plant",
"description": "A tall potted plant like fiddle leaf fig snake plant placed the glass doors to bring natural elements"
" "name": "decorative boul", "description": "A sculptural boul vessel of the open shelves to add visual interest"
}
}
These additions would enhance the functionality and warmth of the space while respecting its minimalist design language and natural materials.
Analyzing the image based the provided grid regions, I'll determine suitable placement areas for the suggested objects while considering perspective and avail- able space:
{
"object__lacementee" [
" "object": "coffee table",
"suttable_rogions": ["center", "betveen center and center center-right", "betveen lover center and center "between lower center right and center"),
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"reasoning": "The central floor area the rug provides ample space for coffee table, positioned in front the reading nook"
" "object": "side table", " "suitable_region""" [ ("center-right", "between center and center-right"),
"reasoning": "A small side table would fit well beside the reading nook on the right side"
"object": "floor lamp", "suitable_regia
["lower left", "between lower left and center- "]
" "reasoning": "The
opposite the glass doors provides good placement for tall floor lamp without blocking views or pathways" " obbject" "accent chair",
"euitableeregions". ["lower center -let", "between
lover center-left and center", "center-left"" betueen center and center"],
" "reasoning"; "An accent chair would fit well in this area, creating a conversation zone with the reading nook while maintaining open space"
},
"object": "throw blanket",
"suitable_regionss" ("center-right"]]
"reasoning": "The throw blanket would be placed on the reading nook which is already in this region"
"object": "indoor plant",
couitable_regionae ["upper left", "betueen upper left and center-left"), "reasoning"; "Near the glass doors to receive natural light while adding greenery to that corner of the room"
"object": "wall art",
esuitable_rogions" ["upper center -iter-letee left", "upper center", "between upper "between upper center and center "reasoning": "The visible wall space opposite the shelving unit would accommodate wall art while maintaining visual balance"
and center"
"object": "decorative bowl", "suitable_regione ""center-right "betveen center and center-right),, "reasoning": "Could be placed existing shelves in built-in unit"
of the
" cnnetcaal_cosiidraattioosss Snkewinggperspecttin "The image is taken from position looking toward the back wall with the built-in shelving, with the glass doors on the left side".
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"floor_space" "The central and lower areas have the most available floor space for furniture placement",
"traffic_flov" "Pathways should be maintained between the entrance (presumed to be behind the viewing position) and the glass doors", "focal_ "focal_pointe: pointtoi "The reading nook and shelving unit already serve as focal points, so additional elements should complement rather than compete with them"
This analysis considers both the physical space available in each region and the de- sign 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:
{
"objects":
" "type": "indoor plant", "position"; "upper left",
gemerattionorrde" 1,
"prompt": "A tall, elegant fiddle leaf fig plant with large, glossy green leaves in minimalist ceramic pot, placed receive natural light, rendered contemporary style that complements modern interior design." "bounding.box". [50, 150, 150, 350]
large windows clean,
"type": "decorative boul", "position": "center-right", "generation_order" orderr;; 2,
"prompt": "A sculptural, handcrafted ceramic bowl in a soft matte finish with subtle organic texture and asymmetrical form, placed on an open shelf, rendered in minimalist Scandinavian style.". "bounding.box": [420, 220, 470, 260]
"type": "throw blanket", "position"; "center mintttr-rggh",. "genorattio__orde""". 3,
"prompt": "A soft, textured throw blanket in muted beige with subtle geometric pattern, casually draped the corner of the built-in seating area, rendered adds comfort to the minimalist space.", "bounding.box": [420, 250, 500, 300]
a warm, inviting style that
"type": "side table", "position": "betveen center and center-righ "generation_ooder"!! 4,
"prompt": "A small, round side table with slender brass legs and light oak top, positioned beside the reading nook at perfect arm's reach height, rendered in a clean, architectural style.",
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"bounding_box"" [350, 250, 420, 320]
"type": "floor lamp",
"position": "between lower left and center -let"", "goneratioonorder": 5,
"prompt": "A tall, architectural floor lamp with slender brushed brass stem shade casting a warm glow, positioned in the corner of the room, rendered in contenporary style that emphasizes clean lines.". "bounding_box" [70, 320, 150, 450]
minimal white
" "type": "accent chair"
"position": "center-left between center -lft and center",
"generation_order". 6,
" "prompt": "A minimalist lounge chair with gentle curves, upholstered in natural tan leather with light wooden frame, positioned at an inviting angle to create conversation space, rendered in Scandinavian modern style.", boonding.bbx".. [100, 250, 250, 380]
"type": "coffee table", "position": "center to between lower center and center",
deeneration_odde" 7,
"prompt": "A low, rectangular coffee table with clean lines and rounded corners, crafted from light oak with subtle grain pattern and minimalist design, positioned centrally the area rug, rendered in contemporary style balances form and function." "bounding. box": [200, 300, 350, 400]
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, can help place the teddy bear the previously discussed space.
"additional_objec""
" "type" "teddy bear".
" "position": "lover center-righ
"generation_order" 8,
"prompt": "A small, golden- brown teddy bear with a smiling face and soft plush texture, sitting upright one of the built-in shelves, adding touch of warmth and playfulness to the minimalist space.",
"bounding_box". box": [290, 300, 480, 490]
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These placements would work well with your previous decorating plans.
35


Reviews:
[{"review_id": "06TF7g6SSq", "reviewer": "Reviewer_ytVC", "summary": "This paper presents LayerCraft, a modular framework that uses large language models (LLMs) as autonomous agents to orchestrate structured, layered image generation and editing.", "questions": "1. 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.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "The author rebuttal has addressed my concerns, so I remain my original rating.", "rebuttal": "General Responds: We sincerely thank all reviewers for their thoughtful and constructive feedback. We are encouraged that multiple reviewers found the paper well-structured, technically sound, and easy to follow (9VLN, f8N8, ytVC). Reviewer ytVC noted that the proposed framework is \"well-designed and conceptually sound\" and appreciated the \"novel and thoughtfully constructed\" nature of our approach, recognizing that it demonstrates \"meaningful contributions.\" Reviewer zjmb similarly highlighted the generality of our framework, praising the \"good layout planning and reasoning\" and \"flexible conditioning and efficient attention mechanism\" in our integration model. Reviewers also acknowledged the good performance on T2I-CompBench (9VLN) and the practical applications of our batch editing and collage editing features (f8N8). We are pleased that these aspects of our work were well-received, and we address the remaining concerns with detailed clarifications in the following responses.\n\n\n**Q1: Evaluation on Broader Benchmarks (GenEval).**\n\n**A:** We now include GenEval results in Table 1. LayerCraft achieves 0.84 overall score, outperforming or matching GPT-4o across most subcategories (e.g., 1.0 on Single Object, 0.94 on Two Objects, 0.89 on Color), while significantly surpassing non-agent methods like Show-O, SDXL, DALL-E 3, and SD3.5. These new results will be included in the camera-ready version.\n\n| Model | Overall | Single Object | Two Objects | Counting | Colors | Position | Attr. Binding |\n|-------|---------|---------------|-------------|----------|--------|----------|---------------|\n| **LayerCraft** | **0.84** | **1.0** | **0.94** | 0.82 | 0.89 | 0.75 | 0.62 |\n| GPT-4o | **0.84** | 0.99 | 0.92 | **0.85** | **0.92** | 0.75 | 0.61 |\n| Show-O | 0.53 | 0.95 | 0.52 | 0.49 | 0.82 | 0.11 | 0.28 |\n| SDXL | 0.55 | 0.98 | 0.74 | 0.39 | 0.85 | 0.15 | 0.23 |\n| FLUX.1-Dev | 0.66 | 0.98 | 0.81 | 0.74 | 0.79 | 0.22 | 0.45 |\n| DALL-E 3 | 0.67 | 0.96 | 0.87 | 0.47 | 0.83 | 0.43 | 0.45 |\n| SD3.5 | 0.71 | 0.98 | 0.89 | 0.73 | 0.83 | 0.34 | 0.47 |\n| Janus-pro 7B | 0.80 | 0.99 | 0.89 | 0.59 | 0.90 | **0.79** | **0.66** |\n\n*Comparison on GenEval Results*\n\n**Q2: Missing Results on Complex T2I-CompBench.**\n\n**A:** We now include Complex T2I-CompBench results in Table 2. LayerCraft scores 0.4631 on the complex subset, the highest among all methods. The result confirms LayerCraft's robustness on multi-object, compositional, and instruction-heavy prompts.\n\n| **LayerCraft** | SD-XL | Pixart-α | Attn-Exct | GORS | DALL-E 3 | CompAgent | GenArtist |\n|----------------|-------|----------|-----------|------|-----------|-----------|-----------|\n| **0.4631** | 0.3237 | 0.3433 | 0.3401 | 0.3328 | 0.3773 | 0.3972 | 0.4499 |\n\n*Comparison of complex on T2I-Compbench*\n\n**Q3: Lack of Comparison with GPT-4o and Other Advanced LLMs.**\n\n**A:** Visual comparisons (Figures 4–6) demonstrate that LayerCraft performs better than GPT-4o in spatial reasoning, controllability, identity preservation, and batch collage consistency. While GPT-4o struggles with maintaining identity, ensuring consistency across images, and handling fine-grained edits, LayerCraft overcomes these challenges through layer-wise control, background-guided spatial reasoning, and intermediate representations. This advantage stems from LayerCraft's unified conditional mechanism, which contrasts with GPT-4o's less integrated structure that can cause mismatches and brittle editing behavior.\n\n**Q4: Missing Comparison in Layout Generation with Prior Work.**\n\n**A:** We appreciate the request for layout generation comparisons. Direct quantitative comparison is challenging because existing methods like LLM-Grounded Diffusion do not use background-guided reasoning or support layer-wise generation. Instead, we provide an ablation study: removing our CoT-based layout strategy reduces spatial accuracy from 0.6432 to 0.2831, showing the clear benefit of our approach. These results highlight how our method offers more precise and coherent layouts than conventional layout predictors."}, {"review_id": "eEq8qhfSKT", "reviewer": "Reviewer_9VLN", "summary": "This 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.", "questions": "- 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?", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Paper 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.", "was_revised": true, "final_justification": "The work is technically sound and demonstrates solid empirical results. However, I still find the overall novelty impact to be limited, as much of the contribution lies in careful system integration rather than fundamentally new methodologies. Considering both strengths and limitations, I maintain my borderline accept score.", "rebuttal": "General Responds:\nWe sincerely thank all reviewers for their thoughtful and constructive feedback. We are encouraged that multiple reviewers found the paper well-structured, technically sound, and easy to follow (9VLN, f8N8, ytVC). Reviewer ytVC noted that the proposed framework is \"well-designed and conceptually sound\" and appreciated the \"novel and thoughtfully constructed\" nature of our approach, recognizing that it demonstrates \"meaningful contributions.\" Reviewer zjmb similarly highlighted the generality of our framework, praising the \"good layout planning and reasoning\" and \"flexible conditioning and efficient attention mechanism\" in our integration model. Reviewers also acknowledged the good performance on T2I-CompBench (9VLN) and the practical applications of our batch editing and collage editing features (f8N8). We are pleased that these aspects of our work were well-received, and we address the remaining concerns with detailed clarifications in the following responses.\n\n**W1: Incremental novelty of core components.**\n\n**A:** While LayerCraft uses established tools, its core innovation lies in how these components are combined to enable structured, controllable generation. And we respectfully disagree with the characterization of our contributions as merely incremental.\nSpecifically:\n\n- Our Object Integration Network (OIN) uses dual-LoRA modules not for weight adaptation like conventional LoRA finetuning, but for conditional understanding, allowing multiple control while preserving the original generative ability. Additionally, our parallel attention streams with attention score mixing mitigate the quadratic memory cost typically caused by long control sequences. This design significantly outperforms concurrent methods, as shown in Supplementary Figures 4 and 5, which compare our object injection results against a naive extension from OmniControl, ACE++, and EasyControl.\n- Our Layer-wise Progressive Generation avoids error cascades common in generate-then-edit pipelines.\n- We introduce background-guided layout planning and 3D-aware scene graphs to enhance spatial reasoning and editing precision. In contrast, previous work arranges layouts without considering the content or perspective of the background image.\n- Our intermediate reference images enable consistent batch collage editing. This is an important capability for collage editing where GPT-4o and other existing methods fall short (see Figure 5 and Supplementary Figure 6).\n\n**W2: Lack of interactivity evaluation**\n\n**A:** Given the limited time for rebuttal, we conducted a small-scale user study (20 participants) to evaluate complex prompt generation. A larger-scale study including detailed user interaction study will be included in the camera-ready version. In this preliminary study, we assessed the generation quality of five systems: LayerCraft, GPT-4o, LLM-Blueprint, FLUX.1 Dev, and GenArtist, using 15 challenging prompts. A total of 20 participants rated each generated image on a five-point Likert scale (1 = poor, 5 = excellent) across four criteria: (1) prompt consistency, (2) naturalness, (3) visual appeal (color, composition, style), and (4) overall quality.\n\nAs shown in the table below, LayerCraft achieved the highest average scores in three out of four criteria. In contrast, LLM-Blueprint, FLUX.1 Dev, and GenArtist showed noticeably lower scores across all categories, especially in prompt consistency and visual appeal, indicating limitations in structured layout generation or visual coherence. These results demonstrate that LayerCraft not only matches GPT-4o in generation quality but also surpasses it in prompt fidelity and naturalness, reinforcing the value of our structured, multi-agent pipeline for complex, multi-turn editing scenarios.\n\n| System | Consistency | Naturalness | Visual appeal | Overall |\n|--------|-------------|-------------|---------------|---------|\n| | μ±σ | μ±σ | μ±σ | μ±σ |\n| LayerCraft | **4.6** ± 0.762 | **4.5** ± 0.687 | 4.4 ± 0.762 | **4.5** ± 0.71 |\n| GPT-4o | 4.5 ± 0.764 | 4.3 ± 0.811 | **4.5** ± 0.623 | 4.4 ± 0.82 |\n| LLM-Blueprint | 3.0 ± 1.35 | 2.9 ± 1.13 | 2.5 ± 1.21 | 2.9 ± 0.97 |\n| FLUX.1 Dev | 3.2 ± 1.45 | 3.7 ± 0.94 | 3.5 ± 1.17 | 3.3 ± 1.10 |\n| GenArtist | 3.1 ± 1.05 | 3.5 ± 1.213 | 3.6 ± 1.30 | 3.6 ± 1.01 |\n\n*Mean (μ) and standard deviation (σ) of user ratings (1–5) for each criterion. Higher values indicate better performance.*\n\n**W3: Complexity and potential for error propagation**\n\n**A:** We incorporate three levels of error mitigation in our pipeline:\n\n- Background Generation Validation: During intial background synthesis, our LayerCraft performs viewpoint suitability assessment to ensure the generated perspective accommodates subsequent object placements. When inadequacies are detected, the system automatically reformulates prompts and regenerates backgrounds.\n- Visual-Grounded Layout Generation: Unlike text-only methods that struggle with spatial accuracy, our approach uses background images as visual guides to generate layouts. This grounding makes object placement more precise and predictable, and it also supports flexible adjustments through user and agent interactions.\n- Context Handling: LayerCraft stores intermediate images and spatially analyzed layouts in a structured JSON format, which can be retrieved and reused when needed.\n\n**Q1: Did you test simpler pipelines like a single LLM agent or GPT-4o? What were the failure modes?**\n\n**A:** Yes, we conducted an ablation study (Table 2) to evaluate simplified versions of our pipeline, including single-agent setups that remove key components of our Chain-of-Thought (CoT) reasoning. Specifically, we tested variants without (i) generation order, (ii) object relationship modeling, (iii) both, and (iv) all CoT reasoning. These simplified pipelines are conceptually aligned with approaches based on a single LLM agent or direct GPT-4o usage.\n\nOur results show that removing CoT-driven reasoning, especially layout ordering and inter-object relationships, leads to significant degradation in spatial coherence, object count accuracy, and visual realism. The full removal of CoT results in the steepest drop in performance, with frequent placement conflicts and incoherent scene compositions.\n\nThese failure modes underscore the limitations of simpler pipelines. Without structured, iterative reasoning, such models struggle to handle compositional complexity and fail to support consistent multi-object placement.\n\n**Q2: How often does the pipeline fail on complex prompt?**\n\n**A:** Across our experiments, we did not observe notable failure cases in layout accuracy, spatial reasoning, or generation quality. One limitation we did notice is that the scale of inserted objects can vary depending on the background image. This variation arises because ChainArchitect is designed to inject objects in a visually natural way, yet prompts or input images often lack explicit size constraints for target objects. Overall, the system performs robustly across diverse prompts, and most inconsistencies stem from VLM limitations rather than LayerCraft's design.\n\n**Q3: How does LayerCraft handle context during long interactions?**\n\n**A:** LayerCraft maintains and updates structured scene layouts and intermediate images. These are serialized and passed between turns, enabling both agents and users to make informed editing decisions over time. During Batch Editing, the ChainArchitect will be reinitialized every time for new images to ensure the max performance."}, {"review_id": "PDaFydZ3DW", "reviewer": "Reviewer_f8N8", "summary": "The 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.", "questions": "- 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?", "limitations": "Check the weaknesses.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "The authors responded confidently to most of my queries. However, I still think that there could have been more challenging qualitative scenarios. Given the strengths and limitations, I will raise my rating by one mark.", "rebuttal": "General Responds: \nWe sincerely thank all reviewers for their thoughtful and constructive feedback. We are encouraged that multiple reviewers found the paper well-structured, technically sound, and easy to follow (9VLN, f8N8, ytVC). Reviewer ytVC noted that the proposed framework is \"well-designed and conceptually sound\" and appreciated the \"novel and thoughtfully constructed\" nature of our approach, recognizing that it demonstrates \"meaningful contributions.\" Reviewer zjmb similarly highlighted the generality of our framework, praising the \"good layout planning and reasoning\" and \"flexible conditioning and efficient attention mechanism\" in our integration model. Reviewers also acknowledged the good performance on T2I-CompBench (9VLN) and the practical applications of our batch editing and collage editing features (f8N8). We are pleased that these aspects of our work were well-received, and we address the remaining concerns with detailed clarifications in the following responses.\n\n**W1:Compare CoT-enabled ChainArchitect to vanilla GPT-4o prompting without reasoning steps**\n\n**A:** Our ablation study (Table 2) provides direct comparison with simpler strategies. The configuration without CoT (last row) effectively approximates vanilla GPT-4o prompting, showing spatial accuracy degradation from 0.6432 to 0.2831. This substantial gap validates our CoT approach.\n\n**W2: No Hard Spatial Relations**\n\n**A:** We acknowledge that including more explicit examples of occlusion and 3D viewpoint shifts would strengthen the paper. While we cannot add new results during the rebuttal phase, we kindly refer reviewers to the following existing results: the photo collage in Figure 1, Figure 5 in the main paper, and Figure 2 in the supplementary materials, which showcase instances of cloth deformation and 3D viewpoint variation. Regarding occlusion, our framework handles it naturally through the use of overlapping bounding boxes and sequential object injection during composition. We will include more comprehensive and focused examples in the camera-ready version.\n\n**W3: Similarities with LLM-Blueprint**\n\n**A:** We appreciate the pointer to LLM Blueprint [1]. While both works use LLMs for layout generation, LayerCraft differs in three key ways: (1) We employ modular agents with role-specific prompts and memory, enabling more interpretable and debuggable scene planning. (2) We integrate layer-wise reasoning and spatial anchoring based on background cues, which is not present in LLM Blueprint and eliminates the cascading errors due to \"generate-then-edit\" pipeline. (3) Our pipeline supports batch editing with global consistency thanks to our Intermediate Representations and Object-Integration Network (OIN), which is beyond the scope of LLM Blueprint. We will include a detailed discussion in related work.\n\n**W4: Lack of Human Evaluation and Multi-turn Editing Results**\n\n**A:** \n\n#### User Study\n\nGiven the limited time for rebuttal, we conducted a small-scale user study (20 participants) to evaluate interactive complex prompt generation. A larger-scale study will be included in the camera-ready version. In this preliminary study, we assessed the generation quality of five systems: LayerCraft, GPT-4o, LLM-Blueprint, FLUX.1 Dev, and GenArtist, using 15 challenging prompts. A total of 20 participants rated each generated image on a five-point Likert scale (1 = poor, 5 = excellent) across four criteria: (1) prompt consistency, (2) naturalness, (3) visual appeal (color, composition, style), and (4) overall quality.\n\nAs shown in the table below, LayerCraft achieved the highest average scores in three out of four criteria. In contrast, LLM-Blueprint, FLUX.1 Dev, and GenArtist showed noticeably lower scores across all categories, especially in prompt consistency and visual appeal, indicating limitations in structured layout generation or visual coherence. These results demonstrate that LayerCraft not only matches GPT-4o in generation quality but also surpasses it in prompt fidelity and naturalness, reinforcing the value of our structured, multi-agent pipeline for complex, multi-turn editing scenarios.\n\n| System | Consistency | Naturalness | Visual appeal | Overall |\n|--------|-------------|-------------|---------------|---------|\n| | μ±σ | μ±σ | μ±σ | μ±σ |\n| LayerCraft | **4.6** ± 0.762 | **4.5** ± 0.687 | 4.4 ± 0.762 | **4.5** ± 0.71 |\n| GPT-4o | 4.5 ± 0.764 | 4.3 ± 0.811 | **4.5** ± 0.623 | 4.4 ± 0.82 |\n| LLM-Blueprint | 3.0 ± 1.35 | 2.9 ± 1.13 | 2.5 ± 1.21 | 2.9 ± 0.97 |\n| FLUX.1 Dev | 3.2 ± 1.45 | 3.7 ± 0.94 | 3.5 ± 1.17 | 3.3 ± 1.10 |\n| GenArtist | 3.1 ± 1.05 | 3.5 ± 1.213 | 3.6 ± 1.30 | 3.6 ± 1.01 |\n\n*Mean (μ) and standard deviation (σ) of user ratings (1–5) for each criterion. Higher values indicate better performance.*\n\nAdditionally, we conducted a quantitative evaluation on multi-turn editing using 1/5 of the MagicBrush benchmark, where the ground-truth(GT) target exists. For the comparisons, we copied the reported results from GenArtist. L1 and L2 are for measuring the average pixel-level difference between the edited image and GT targets. CLIP-I and Dino measures the cosine similarity between the image embeddings of GT and generated results. CLIP-T demonstrates the cosine similarity results between text and results. The results are shown below:\n\n| Method | L1↓ | L2↓ | CLIP-I↑ | DINO↑ | CLIP-T↑ |\n|--------|-----|-----|---------|-------|---------|\n| HIVE | 0.1521 | 0.0557 | 0.8004 | 0.6463 | 0.2673 |\n| InstructPix2Pix | 0.1584 | 0.0598 | 0.7924 | 0.6177 | 0.2726 |\n| MagicBrush | 0.0964 | 0.0353 | 0.8924 | 0.8273 | 0.2754 |\n| GenArtist | **0.0858** | **0.0298** | 0.9071 | 0.8492 | 0.3067 |\n| LayerCraft (ours) | 0.0863 | 0.0299 | **0.9121** | **0.8541** | **0.3157** |\n\n*Multi-turn quantitative comparison on MagicBrush. Lower is better for L1/L2; higher is better for CLIP-I, DINO, and CLIP-T.*\n\nLayerCraft achieves the best performance across most metrics, including CLIP-I, DINO, and CLIP-T, indicating stronger visual-textual alignment and perceptual similarity. While GenArtist shows slightly better L1 and L2 scores, our method excels in semantic alignment, which is more critical for realistic, context-aware edits.\n\n**W5: Failure Cases**\n\n**A:** Across our experiments, we did not observe notable failure cases in terms of layout accuracy, spatial reasoning, and generation quality. However, one limitation we did observe is that the scale of inserted objects can vary depending on the background image. This variation arises because the ChainArchitect is designed to inject objects in a contextually natural way, but the prompts or input images often lack explicit size specifications for the target objects.\n\n**Q1: Using any other LLMs**\n\n**A:** Due to the limited time available during the rebuttal phase, we were unable to conduct extensive experiments with additional LLMs. However, we tested Claude Sonnet 3.7 and Gemini 2.5 and observed minimal performance differences in most cases. We also experimented with Gemma-3 and LLaVA, whose results were comparable. Notably, we found that once the prompt is decomposed into structured representations, the performance bottleneck is no longer tied to model size. This aligns with recent findings, e.g. \"Scaling LLM Test-Time Compute Optimally Can Be More Effective than Scaling Model Parameters, ICLR 2025\", which highlights that test-time compute and inference-time reasoning are more critical to leveraging pre-trained LLMs effectively. We will include the additional experiments and findings in the camera-ready version."}, {"review_id": "ZVVlWEfMhD", "reviewer": "Reviewer_zjmb", "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.", "questions": "- explain the impact of CoT on inference time\n- why not using original Flux infill model for object inpainting? needs explanation and comparison\n- 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", "rating": 3, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strength\n- general agent framework for image generation with good layout planning and reasoning\n- enhanced Flex inpainting and object integration model with flexible conditioning and efficient attention mechanism\n- show case various image generation and editing applications, such as batch object insertion and multi-object layout editing\nWeakness\n- Key modules are from GPT and Flux models\n- the generation results are in pixel format, without object layer separation, limiting further editing use cases\n- CoT may incur longer computing time", "was_revised": true, "final_justification": "After reading the rebuttal from authors, I appreciate the detailed explanations, but still have doubt on the output layer representation. If the final result is not in layer only the intermediate steps have layers, this is not justifying the paper's main claim and should not be qualified for publication.", "rebuttal": "General Responds:\nWe sincerely thank all reviewers for their thoughtful and constructive feedback. We are encouraged that multiple reviewers found the paper well-structured, technically sound, and easy to follow (9VLN, f8N8, ytVC). Reviewer ytVC noted that the proposed framework is \"well-designed and conceptually sound\" and appreciated the \"novel and thoughtfully constructed\" nature of our approach, recognizing that it demonstrates \"meaningful contributions.\" Reviewer zjmb similarly highlighted the generality of our framework, praising the \"good layout planning and reasoning\" and \"flexible conditioning and efficient attention mechanism\" in our integration model. Reviewers also acknowledged the good performance on T2I-CompBench (9VLN) and the practical applications of our batch editing and collage editing features (f8N8). We are pleased that these aspects of our work were well-received, and we address the remaining concerns with detailed clarifications in the following responses.\n\n**W1: Regarding Dependency on GPT and FLUX.1 dev**\n\n**A:** While we leverage GPT-4o and FLUX.1 dev as foundation models, our contributions lie in the novel architectural design and integration methodology. The whole design of LayerCraft system and the parameter reuse dual-LoRA architecture of OIN should be highlighted. Notably, LayerCraft outperforms all compared methods, including GPT-4o, both quantitatively and qualitatively.\n\n**W2: Pixel Format without Layer Separation**\n\n**A:** We respectfully clarify that LayerCraft actually maintains full layer separation throughout the generation process. Our intermediate representations include Layout, reference images, and the intermediate results at each layer, this is precisely what enables flexible post-generation editing.\n\n**Q1: Impact of CoT on Inference Time**\n\n**A:** The CoT reasoning adds approximately 80% to inference time compared to direct generation. However, this overhead is justified by substantial quality improvements that the spatial accuracy improves from 0.2831 to 0.6432 (127% improvement). Moreover, our overall runtime remains faster than that of the GPT-4o API, which typically requires several minutes for comparable generation and editing tasks.\n\n**Q2: Why Not Use Original FLUX-Fill Model**\n\n**A:** Our OIN addresses fundamental limitations that FLUX fill cannot overcome. FLUX infill does not support reference image processing, which is crucial for subject-driven inpainting. Our dual-LoRA architecture introduces a new approach: LoRA weights handle conditions while maintaining FLUX's generation quality, enabling consistent style in both generation and editing. OIN also outperforms other concurrent methods (see Figure 6 in the supplementary materials).\n\n**Q3: How soon can native GPT catch up to paper's result quality? If in a short time, what's the impact of this work?**\n\n**A:** We agree that generative LLMs like GPT-4o are rapidly improving, and it is possible that future versions may natively support more controllable layout-to-image generation. However, LayerCraft is designed as a modular framework that leverages LLM capabilities rather than replacing them. Our work demonstrates how LLMs can be orchestrated with visual modules to achieve structured, editable, and interpretable generation. These capabilities remain challenging for end-to-end diffusion models or monolithic GPT pipelines."}]

Author rebuttal:

Meta-review: This paper received ratings of 4443. The paper presents a modular framework utilizing large language models for structured image generation and editing. The proposed method achieves state-of-the-art performance on various benchmarks with strong controllability and spatial reasoning ability. The method is technically sound and practical, particularly in batch editing. Some concerns were raised about the novelty of its components and the lack of challenging qualitative examples, but he authors have addressed these points mostly during the rebuttal, providing additional results and clarifications. The AC agrees that the final layer representation (like RGBA) is not the key consideration. Overall, the AC recommends the acceptance to NeurIPS 2025.
Decision: Accept (poster)