Title: CREA: A Collaborative Multi-Agent Framework for Creative Image Editing and Generation
Venue: NeurIPS 2025
Accepted: yes
Keywords: creativity

t2i

diffusion

multi-agentic systems

llm
Abstract: Creativity in AI imagery remains a fundamental challenge, requiring not only the generation of visually compelling content but also the capacity to add novel, expressive, and artistically rich transformations to images. Unlike conventional editing tasks that rely on direct prompt-based modifications, creative image editing demands an autonomous, iterative approach that balances originality, coherence, and artistic intent. To address this, we introduce CREA, a novel multi-agent collaborative framework that mimics the human creative process. Our framework leverages a team of specialized AI agents who dynamically collaborate to conceptualize, generate, critique, and enhance images.  Through extensive qualitative and quantitative evaluations, we demonstrate that CREA significantly outperforms state-of-the-art methods in diversity, semantic alignment, and creative transformation. To the best of our knowledge, this is the first work to introduce the task of creative editing.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a multi-agent collaborative framework for creative image generation and editing. The pipeline consists of several agents. First, the Creative Director interprets the original image to create a conceptual blueprint. Second, the Prompt Agent generates a set of contrastive prompts that reflect the creativity principles based on the blueprint and merges them into a single prompt. Third, the Generative Executor sets the model parameters and uses a text-to-image (T2I) model to generate or edit the image creatively. Finally, the Critic evaluates the generated image using a multimodal large language model (MLLM), and the Creative Director reviews the judgment to decide whether further edits are needed. If so, the Prompt Architect updates the prompt for the next iteration. Compared to state-of-the-art generation and editing methods such as SDedit, InstructPix2Pix, SDXL, and FLUX, the proposed approach demonstrates improved creativity and diversity in the generated images. The authors also present strong results in personalization and video generation.\n\nSTRENGTHS AND WEAKNESSES:\nWeaknesses\n\n1. In the evaluation, the authors mainly use the prompt “a creative object,” which is not a very explicit instruction for the model. Since there are many ways an object can be creative, such a prompt is vague, hard to follow, and cannot clearly fulfill the user’s intent—especially compared to more concrete descriptions like specific materials or styles. The paper does not explain how the agent introduces more explicit constraints in the generation process. In addition to being “creative,” the authors should incorporate more diverse prompts that clearly specify how they want the object to be creative—e.g., through specific styles, materials, or design principles.\n\n2. The paper could be strengthened if the authors provided a detailed example of how the agent interacts across a full round of the editing pipeline, including the contrastive prompts and the generated images. The broader impact and limitations section could be moved to the appendix to make space.\n\n3. The evaluation metric is a bit questionable. The method favors both a high LPIPS score (which indicates more dissimilar images) and a high DINO score (which indicates more similar images). Wouldn’t these two metrics contradict each other? Also, why are the CLIP and DINO metrics missing in Table 2?\n\n4. The method has limited applicability. The creative editing results do not allow for explicit structural changes, and the model is focused on generating a single object rather than handling more complex scenes.\n\nMinor issue:\nThe references are broken after [45].\n\nStrength\n\n1. Multi-agent frameworks are a popular research topic, but there are few attempts to apply them to image or video generation. This work is among the first to propose a novel multi-agent framework for improving creativity in image generation.\n\n2. The proposed method is clean and straightforward, supported by theoretical principles of creativity.\n\n3. The experimental results demonstrate strong performance compared to baseline models. The authors also conduct comprehensive experiments, covering multiple applications and including a detailed ablation study in Section 4.3.\n\n4. In addition to image generation and editing, the authors also present a proof-of-concept experiment in video generation, demonstrating the generalizability of the proposed system.\n\nQUESTIONS:\nThere are missing details that make the creative process hard to follow. For example, what exactly is a contrastive prompt? Although the paper references creativity theory, it is unclear how the six principles are actually used by the agent to refine outputs. How does the Prompt Architect synthesize contrastive prompts based on distinct creativity principles?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes CREA, a novel collaborative multi-agent framework for creative image editing and generation. CREA imitates the human creative process by organizing a set of specialized agents—namely, a concept agent, generation agent, critique agent, and refinement agent—that work together in an iterative pipeline. The authors formalize the creative editing task, distinguishing it from standard prompt-based or semantic editing. Extensive experiments, including both quantitative metrics (e.g., diversity, semantic alignment, user studies) and qualitative visualizations, show that CREA significantly outperforms several strong baselines such as InstructPix2Pix and Plug-and-Play methods. The paper is positioned as the first to propose and formalize this agent-based creative editing task.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\nNovel Task Definition: The paper introduces and formalizes creative image editing as a new task distinct from conventional image editing, with a focus on artistic novelty and agentic control.\n\nModular Multi-Agent Framework: CREA’s agent decomposition—conceptualization, generation, critique, refinement—is both intuitive and grounded in cognitive creativity theories.\n\nStrong Empirical Results: The experiments cover multiple datasets, use both automatic metrics (LPIPS, FID, CLIP alignment) and human evaluation, and show consistent gains over strong baselines.\n\nReproducibility: The method is well-documented, with a clear architecture diagram, ablation studies, and statements regarding code release.\n\nOriginality: To the best of my knowledge, this is the first multi-agent approach designed specifically for the creative editing domain.\n\nWeaknesses\nAgent Implementation Details: The internal logic of some agents (e.g., the critique agent) is described at a high level. More details on how each agent’s output is used, especially how conflicting feedback is resolved in the refinement step, would improve clarity.\n\nScalability and Efficiency: CREA involves multiple inference rounds and agent coordination, which may limit real-time usability. A discussion of system latency and computational cost is missing.\n\nEvaluation Limitations: While the authors use CLIP-based and perceptual metrics, creativity is inherently subjective. The human evaluation setup is relatively small (50 samples); more robust or crowd-sourced studies would strengthen claims.\n\nGeneralization: CREA is evaluated primarily on artistic or fantasy domains. It is unclear how well it generalizes to domains like product design or real-world edits where creativity may be more constrained.\n\nQUESTIONS:\nCritique Agent Mechanism: Can the authors elaborate on how the critique agent produces structured feedback and how that feedback is utilized during refinement? Are the critiques learned or rule-based?\n\nFailure Cases: It would be helpful to include examples of failure modes—when CREA produces incoherent or uncreative edits—and how the system might be improved to mitigate these.\n\nSystem Latency and Efficiency: Please provide measurements (e.g., average number of iterations, inference time per step) to better understand CREA's feasibility for interactive use.\n\nComparison with Non-Agentic Iterative Methods: How does CREA compare to a monolithic iterative approach (e.g., looping Plug-and-Play generations with feedback) in terms of both performance and interpretability?\n\nEthics and Misuse: Given the potential use of such systems in generating deepfakes or manipulated visuals, have the authors considered misuse risks? Are any mitigation strategies discussed?\n\nLIMITATIONS:\nThe authors mention the subjective nature of creativity and the challenges of evaluation, which is appropriate. However, further discussion on scalability, domain transfer, and potential ethical misuse (e.g., for disinformation or deceptive content) is recommended.\n\nSuggested improvement: Include a section on computational cost and a mitigation discussion on possible societal misuse.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper presents an agentic framework CREA for text-to-image generation and editing, with a special focus on enhancing creativity of the generative process. The framework adopts several models from LLMs/VLMs to Flux-based ControlNets. The overall steps are pretty standard, beginning from a planning stage to generate creative text prompts, followed by conditional image generation, post-generation evaluation and refinement stages to rewrite the prompts and keep updating the generated images. Several baseline T2I and image editing models are compared to emphasize the strong creativity of CREA and its wide applications.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- The agent framework addresses creative image editing, which is a less-addressed dimension in previous work. The framework design, including the prompt planning, evaluation and refinement, make sense and seem to demonstrate good qualitative results. \n- The presented qualitative results are nice and the quantitative evaluation are also helpful in understanding the capabilities of the framework. \n- The overall writing is clear and easy to follow. \n\nWeaknesses:\nThe major weaknesses, as far as I could see, lie the following two dimensions:\n- Limited editing capabilities. While the qualitative demonstrations look nice, the editing prompts used in evaluation fail to address more general editing scenarios such as adding, moving, removing objects and etc -- operations beyond just changing appearance of the original object. Besides, the exact \"editing\" setup is not well-presented. Is the editing based on language instructions or changing phrases / expressions in the original prompts used for generation? I couldn't tell from the figures or tables as the editing prompts are not shown and the baselines are mix of instruction-based (instructpix2pix) and prompt-based editing models (turboedit, sdedit). \n- Insufficient/unfair evaluation. \n  - It is hard to tell if the improvement comes from the base model difference. For example, TurboEdit adopts SDXL-Turbo while CREA adopts Flux1.-dev, a better image generator that the former --  not to mention InstructPix2Px and SDEdit. \n  - Since  CREA is an agent framework, it may take several iterations of the whole pipeline to get a sample that passes the critic's judgement threshold. What's a more fair baseline here is to select the top-1 out of k generation results using the base model and compare to CREA. While this is not necessary in the rebuttal, it seems reasonable to show the overall evaluation results from each iteration of CREA to show that the generation results keep improving thanks to the critic and refinement. Otherwise, it could be that the whole framework is doing rejection sampling and eventually stop at a good enough sample.\n\nQUESTIONS:\nSee weaknesses. Please clarify accordingly if there are misunderstandings.\n\nLIMITATIONS:\nSee weaknesses.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces CREA, a new collaborative multi-agent AI framework designed for creative image editing and generation. CREA mimics the human creative process by using specialized AI agents—including a Creative Director, Prompt Architect, Generative Executor, Art Critic, and Refinement Strategist—to conceptualize, generate, critique, and refine visual content. Guided by creativity principles, CREA supports autonomous, disentangled, and user-guided edits for both images and videos. Experiments show CREA outperforms current state-of-the-art methods in diversity, semantic alignment, and human-perceived creativity.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1: This paper clearly aims to propose a new task of \"creative image editing,\" going beyond conventional, prompt-driven editing to focus on multi-faceted, disentangled, and artistically rich compositions. The motivation for an agentic, collaborative approach is well contextualized with respect to the limitations of current prompt-to-image systems.\n\n2: Experimental results showcase the qualitative advantages of CREA for both creative editing and generation, with side-by-side comparisons to leading methods (e.g., SDEdit, InstructPix2Pix, ConceptLab). The paper provides rigorous quantitative comparisons across a spectrum of metrics (CLIP, LPIPS, VENDI, DINO, and user studies); CREA achieves higher scores on most creative and diversity-based measures for both editing and generation tasks.\n\n3: The choice of six creativity principles for prompt generation and evaluation is grounded in established theories, lending interpretability and traceability to CREA's process.\n\n4: This paper is easy to understand and follow.\n\nWeaknesses:\n\n1: **Limited Novelty and Overlap with Existing Agent-Based Frameworks**. The core technical contribution of this paper shows overlap with prior work, particularly with agent-based systems for image generation like GenArtist [1]. While the authors argue that CREA's novelty lies in its multi-agent collaborative approach for creative editing, as opposed to GenArtist's single-agent structure, the fundamental concept of using agents for generation, critique, and refinement is not new. The framework's scope is narrowed to \"creativity,\" which may be perceived as applying an existing agent-based methodology to a more specific problem domain rather than introducing a paradigm-shifting approach.\n\n2: **Over-Engineering of the Prompt Generation Process**. A central contribution of the CREA framework is its complex, multi-stage pipeline designed to produce a \"high-creativity\" prompt (P_c). However, it is questionable whether this elaborate, multi-agent architecture is necessary. A powerful Large Language Model (e.g., GPT-4o, which is used by the agents themselves ) could likely achieve a comparable result with a much simpler, single-step instruction to expand a basic concept into a rich, creative prompt. Although the ablation study shows the full framework outperforms a \"base\" LLM prompt, it does not sufficiently justify whether the marginal gains in performance warrant the significant increase in system complexity, latency, and operational overhead.\n\n3: **Unfair Experimental Comparison Due to Simple Prompting**. While the paper claims superiority over existing editing and generation methods, the comparison setup raises fairness concerns. Competing baselines are primarily evaluated using simple prompts such as \"a creative <object>,” whereas the CREA framework benefits from an elaborate, agent-generated prompt construction pipeline that synthesizes multi-dimensional creativity cues. The authors do not investigate whether giving similarly enriched prompts to the baseline models would close the performance gap. This undermines the strength of the comparison and leaves open the question of whether the performance gain is due to the agentic framework or merely due to better prompting.\n\n4: **Limited and Unconvincing Visualization of Prompts and Outputs**. The qualitative examples throughout the paper showcase visual results conditioned on extremely simple or generic prompts (e.g., \"a couch\", \"a monster\", \"a cup\"), yet these do not clearly reflect the internal creative prompt transformations that are claimed to drive the system's creativity. Without visualizing or explicitly disclosing the final effective prompts used by the model (i.e., the ones passed to the text-to-image or editing model after multi-agent planning), it is difficult to assess whether the outputs are meaningfully creative or just well-curated samples. The limited and non-transparent prompt-to-image mappings weaken the interpretability and persuasiveness of the visual comparisons.\n\n[1] Wang, Zhenyu, et al. \"Genartist: Multimodal llm as an agent for unified image generation and editing.\" Advances in Neural Information Processing Systems 37 (2024): 128374-128395.\n\nQUESTIONS:\n1: Is the most important thing to generate a \"fancy\" image a better-oriented prompt?\n\n2: Does the whole system aim to generate a suitable prompt for generation model to generate \"creative\" images?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 1, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces CREA, a multi-agent framework for creative image editing and generation. It includes five main agents into its pipeline: creative director, prompt architect, generative executor, art critic and refinement strategist. The whole workflow is divided into 3 phases: pre-generation planning, post-generation evaluation and self-enhancement. During the generation and refinement, 6 creativity principles are provided as common guidelines. If the aggregated creativity index fails to meet the standard, the generation loop will repeat and update the prompt&image iteratively.\n\nSTRENGTHS AND WEAKNESSES:\n- The paper defines a new task of creative image editing and introduces the first work on such task.\n- The pipeline is flexible, can be easily extended for video generation.\n- Fig. 1 is not showed in a very intuitive way; the video can be displayed in frames; the generated prompts are also not shown. “Baseline” is not specified either.\n- The generation and refinement are mainly based on prompt design and manipulation, but what if the prompt contains ambiguity? (e.g., when the user wants to edit on a specific location which is tiny and has an irregular shape) The proposed method lacks fine-grained control. The editing ability can be greatly enhanced if various more visual inputs can be supported (e.g., masks, arrows, etc.).\n- In Fig. 3, the user’s initial intention or purpose is not clearly illustrated, and it’s hard to evaluate the visual quality of the generated images.\n- In the middle group of Fig. 3, the background is often modified when it is not supposed to be touched.\n- During the evaluation phase, only using creativity criteria is limited to obtain a comprehensive evaluation on the generated image. Metrics that measure aspects such as background preservation, identity preservation are missing.\n- In the user study results, the proposed method does not show a very obvious advantage compared with the other baselines.\n- In the comparisons with the baselines, the authors simply use “creative <object>” as the prompt, which is too ambiguous to guide the generation, lacking fairness.\n\nQUESTIONS:\n- While the proposed method involves GPT-4o in some phases, I am curious about the performance of a workflow that uses GPT-4o as both the agents and image generator.\n- Does CREA often tend to generate imaginative objects that does not exist in the real world? That might limit the use case of its application.\n\nLIMITATIONS:\nYes, it is discussed in the Appendix.\nBeyond that, my main concerns are about the fairness & comprehensiveness on the evaluation and comparison, and also the limited use scope of this workflow.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-06"}