FreeControl: Efficient, Training-Free Structural Control via One-Step Attention Extraction
Authors: Jiang Lin
Xinyu Chen
Song Wu
Zhiqiu Zhang
Jizhi Zhang
Ye Wang
Qiang Tang
qian Wang
Jian Yang
Zili Yi
Venue: NeurIPS 2025 poster ()
Keywords: Diffusion models
Controlled Image Generation
Primary area: applications
TL;DR: We present FreeControl, a training-free method for structural control in diffusion models that achieves strong semantic alignment with less than 2\% additional inference cost.

Abstract: Controlling the spatial and semantic structure of diffusion-generated images remains a challenge. Existing methods like ControlNet rely on handcrafted condition maps and retraining, limiting flexibility and generalization. Inversion-based approaches offer stronger alignment but incur high inference cost due to dual-path denoising.
We present \textbf{FreeControl}, a training-free framework for semantic structural control in diffusion models. Unlike prior methods that extract attention across multiple timesteps, FreeControl performs \textit{one-step attention extraction} from a single, optimally chosen timestep and reuses it throughout denoising.  This enables efficient structural guidance without inversion or retraining.
To further improve quality and stability, we introduce \textit{Latent-Condition Decoupling (LCD)}: a principled separation of the timestep condition and the noised latent used in attention extraction. LCD provides finer control over attention quality and eliminates structural artifacts. FreeControl also supports compositional control via reference images assembled from multiple sources, enabling intuitive scene layout design and stronger prompt alignment. FreeControl introduces a new paradigm for test-time control—enabling structurally and semantically aligned, visually coherent generation directly from raw images, with the flexibility for intuitive compositional design and compatibility with modern diffusion models at ~5\% additional cost.

FreeControl: Efficient, Training-Free Structural Control via One-Step Attention Extraction
Jiang Lin
Xinyu Chen', Song Wu', Zhiqiu Zhang',
Jizhi Zhang',
Ye Wang',
Qiang Tang', Qian Wang", Jian Yang',
Zili Yi2
I Nanjing University, Suzhou, China JIUTIAN Research, Beijing, China
University of British Columbia, Vancouver, Canada Jilin University, Changchun, China
lin0smail, nju. edu. cn, yi@nju.edu.
Structure Conditioned Generation
Control Strength (rigid-fllexible)
Compose Content
2025
Figure 1: FreeControl enables efficient, structure-awarc generation from image references. Top- left: structure-condinee conditioned generation using reference image on the left. Top-right: Tunable Control strength via adjustable attention injection. Bottom: compositional generation from user-assembled reference images enables intuitive spatial and semantic layout control.
Abstract
Controlling the spatial and semantic structure of diffusion -eeeerrteee images re- mains a challenge. Existing methods like ControlNet rely on handcrafted condition maps and retraining, limiting flexibility and generalization. Inversion- based ap- proaches offer stronger alignment but incur high inference cost due to dual-pate denoising. We present FreeControl, training-free framework for semantic struc-
"Corresponding authors: yi@nnj.edu.cn/ project lead
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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tural control in diffusion models. Unlike prior methods that extract attention across multiple timesteps, FreeControl performs one-step attention extraction from a sin- gle, optimally chosen key timestep and reuses it throughout denoising. This enables efficient structural guidance without inversion or retraining. To further improve Decoupling (LCD):: princi-
quality and stability, we introduce Latent-Conditg pled separation of the key timestep and the noised latent used in attention extraction. LCD provides finer control over attention quality and eliminates structural artifacts. FreeControl also supports compositional control via reference images assembled from multiple sources, enabling intuitive scene layout design and stronger prompt alignment. FreeControl introduces a new paradigm for test-time control-enabee structurally and semantically aligned. visually coherent generation directly from raw images, with the flexibility for intuitive compositional design and compatibility with modern diffusion models at -5% additional cost.
1 Introduction
While diffusion models 8
2 have revolutionized generative image synthesis, they remain difficult to control. There often lacks intuitive ways to specify what appears where, or how objects should relate spatially and semantically manamically-mmkkin them less suitable for tasks like scene layout, object rearrangement, or design prototyping.
33
36
32
5
28
A prevalent approach to this challenge involves conditioning generation on external control maps, as exemplified by ControlNet 447 and T2I-Adapter (24). These methods inject spatial guidance via edge maps, depth cues, or segmentation masks. While effective, they depend on handcrafted pre-pree pre-processing and require separate training for each control type and base model. In particular, ControlNet demands large-scale paired datasets and substantial training resources per condition, making it expensive to scale across modalities or architectures. Moreover, the control signals themselves are limited: Canny edges are often overly rigid and may conflict with prompt semantics, while segmentation- based guidance is restricted by limited category labels, preventing nuanced or open-ended structure control. In contrast, test-time augmented methods [38 [3 19 44) such as DDIM inversion (38) and TRAC [99 extract structure from reference images by reconstructing their latent trajectory and injecting features throughout denoising. These techniques incur high inference cost, requiring full or dual-path denoising and considerable memory.
We propose a training-free free, test-time augmented framework for semantic structural control using raw reference images. Our method performs single additional denoising step at a model-specific key timestep, chosen to extract maximally informative self-attention. This attention matrix captures both spatial structure and semantic intent, and is consistently injected into the main generation process to guide the arrangement and content of the generated image. The strength and scope of guidance are tunable, enabling both flexible layout guidance and strong structural adherence, depending on user intent. Unlike prior test-time augmentation methods 7 4] approach climinates the need for inversion or reconstruction entirely- removing both the computational burden and architectural complexity of dual-path denoising. With only 5% additional cost over baseline inference, it delivers high-quality structural control without retraining, making it directly compatible with fine-tuned [35] or coRA-augmentte models (10).
By collapsing multi-step extraction into a single attention signal, our one-step approach creates a tractable point of analysis-eeabling us to systematically study and refine the quality of structural guidance through Latent-Condition Decoupling (LCD). LCD separates the roles of the noised latent and the key timestep, revealing how each factor shapes the extracted structure. This lets improve alignment quality, reduce artifacts, and offer tunable control over structural granularity- from coarse layout to fine semantic detail.
To support intuitive, layout-aware control beyond segmentation maps or prompt tuning, we introduce a composition- based conditioning strategy. As shown in fig. [ users can directly assemble reference images by cropping and combining objects from different sources, enabling them to express both spatial layout and semantic intent in a natural visual form, and generate images with content that aligns with their expectations. This flexibility transforms structural and semantic conditioning into a designable interface for high-level scene control.

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In experiments, FreeControl outperforms existing structural control methods in both spatial alignment and visual fidelity, while maintaining high efficiency. Qualitative results further demonstrate its advantage in semantic-level control, producing generations that more faithfully adhere to the intended prompts.
Our contributions are as follows:
We present training-free, test-time augmented method for semantic structural control from raw reference images, eliminating the need for handcrafted inputs, inversion, or retraining.
We propose one-step attention extraction framework that uses single denoising step at a key timestep to guide generation, with attention maps injected across layers during inference.
We introduce Latent-Conditio Decoupling (LCD), principled method that separates the key timestep from the noised latent attention extraction, enabling stronger control and improved stability.
We introduce composition- based conditioning approach that allows users to define both spatial layout and semantic intent through assembled reference images, enabling intuitive control beyond segmentation maps prompt tuning.
Related Work
Diffusion Models. Diffusion models 8 38 28 have emerged as a leading framework for high-
quality image synthesis, with success across tasks such text-to-image generation [3 36 32],
image-to-image translation 12 22 15), and image editing [7] 3 45 13 37). Foundational models
like DDPM [8] and DDIM 38 introduced the core denoising process, while later developments
such as LDM [33]. DiT [27,. and SD3 [6] have scaled diffusion to high-resolution, semantically
rich generation. The field has also moved from U-Net-based backbones (34 to more expressive transformer-basee architectures [41].
Structural Guidance via Training-Base Based Conditioning. Training- based methods such as Control- Net [47 and 121-Adapter (24] guide spatial structure using condition maps like edges or segmentation. While they achieve strong low-level alignment, they require retraining for each control type and base inteoluuuuu high computational cost and model proliferation. Their reliance on handcrafted inputs also leads to brittle performance when structure maps are noisy or conflict with prompts. 121-Adapter lighter but similarly struggles with complex scenes and still demands per-condition training. High-level alternatives like GLIGEN (18) and IP-Adapter (46) support layout-aware and visual conditioning using bounding boxes or global image features. However, IP- AP-Adateer still needs moss-model-speciff training and shows varied quality depending on the dataset. Though these methods improve compositional flexibility, they lack fine-grained structural control-e.g, for object contours or pose limiting their utility in dense structure transfer tasks.
Test-Time Control via Inversion and Attention Reuse. Test-time approaches offer another path to structure control. DDIM inversion B8 40 21 and Null-Text Inversion 23 reconstruct noise latents from reference images, enabling attention reuse for editing. Though effective, they are computationally heavy and depend prompt alignment. Prompt-to-Prompt 7 modifies cross- attention for semantic edits while preserving layout but cannot incorporate visual references.
Plug-and-Play [4 injects image features during inference but offers coarse structure control. It requires dual attention modulation and ResNet backbones, limiting efficiency and compatibility with transformer-basee models like DiTs (27) TRAC [19] improves efficiency by avoiding inversion, but still extracts attention at many timesteps, incurring high cost.
Crucially, both inversion- based and inversion-fis free methods operate under the same assumption: that structure must extracted progressively across a denoising trajectory. Yet, across timesteps, the role of attention remains consistent-cappuriig capturing spatial layout and semantic structure. This raises fundamental question: if attention serves the same purpose at every step, repeated extraction truly necessary?
3

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Noise
Generited Image
Consistent Injection
One Step Extraction
DiT Block
Craft noise latent key timestop 2*
extructed layens
Denoise t*
20
Figure 2: The illustration of one-step attention extraction framework. The query attention matrices the later layers (blue layers) are extracted from a forward forward-simulated latent at single key timestep and are injected consistently in the generation process to enable structural guidance.
3 Methods
3.1 Semantic Structural Control via One-Step Extraction
One-Step Attention Extraction Motivated by the insight that structural information remains conceptually consistent across timesteps, introduce one-step attention extraction strategy to replace multi-step guidance. As demonstrated in fig. 2 rather than accumulating structure through repeated attention capture, we extract attention matrices from a single, designated timestep and reuse them throughout the denoising process. This approach preserves structural alignment while significantly reducing computational overhead.
key design consideration in this framework is the selection of the optimal timestep t for attention extraction. We conduct empirical evaluation across range of candidate steps and identify the one (661) that yields the strongest structural alignment in the final output. In contrast to inversion- based methods, which require traversing full reverse denoising trajectory to reach t", adopt lightweight forward simulation strategy: we apply the forward noise process directly to the reference latent Xa simulate the noised latent timestep 1*, bypassing reverse diffusion entirely. Specifically, we compute the noised latent as:
X Je- (1 )
where e (O, 1) standard Gaussian noise, and σ+- E [0, 1] is the timestep-dependent noise scale factor at optimal timestep t* A single denoising step is then applied to to produce intermediate attention maps. From this, we extract the self-attention query matrices Q,") at each transformer layer 1. During generation, these matrices are injected at every timestep f by replacing the model's s dynamically computed queries:
(2)
The key (K) and value (V) matrices remain dynamically computed from the evolving latent X preserving responsiveness to the generative context while maintaining consistent structural queries. This procedure introduces no per-image tuning and requires only a single additional denoising step, making our method highly efficient, training-frees and broadly applicable across diffusion architectures.
Layer-Aware Injection for Preserving Appearance Quality. While one-step attention extraction provides effective structural control, indiscriminate injection across all layers can degrade visual quality. In particular, injecting structural Q matrices into early layers of the diffusion transformer interferes with low-level synthesis tasks-such as color, lighting, and texture modeling- resulting in desaturation, flat textures, or unnatural shading. This occurs because early layers are primarily responsible for fine appearance features, and rigid structural guidance can disrupt their generative flexibility.

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In contrast, deeper transformer layers capture higher-level semantic and spatial information, making them more suitable targets for structural injection. To balance structure and uppearance, we adopt layer-aware injection strategy that applies structural Q matrices only to mid-to-late layers (the blue layers in fig. 2.. This preserves structure alignment while allowing early layers focus on generating detailed and visually rich content.
Low Noise 0.25)
High Noise (op =0.85)
Coarse Structure (t" (0)
Fine Strueture (t -640)
Figure 3: Left: Noisy artifacts induced by the noise term. Right: Different granularity of structural
control under different key timesteps.
3.2 Latent-Condition Decoupling (LCD) for Enhanced Attention Quality
With attention now extracted from single forward-simulated timestep, we gain stable and isolated point of intervention for improving control. We introduce Latent-Condition Decoupling (LCD) to exploit this opportunity. Rather than varying the key timestep 1*, L.C disentangles the two influences on attention quality: (1) the noised latent X,- provided as input, and (2) the key timestep passed the model. By isolating and manipulating these factors independently, we gain deeper insight into how structural guidance arises- and unlock both improved fidelity and fine-grained control.
To isolate the contribution of the noised latent, we fix the key timestep (at previously optimal 661) and vary the construction of x,-. As shown in fig. B we find that latents generated with high noise levels (i.e., large σ.-) tend to introduce visible noise artifacts such as scattered dots in the final image. These high-noise latents also degrade the attention maps extracted for structural guidance, likely due to the model's inability to reason clearly over heavily corrupted input.
Based on this insight, propose simplified latent construction that removes the stochastic noise term entirely. Rather than performing forward diffusion with sampled noise, we directly construct a scaled ž, which serves substitute for Xt-
=1-00) Xo.
where a here becomes tunable scale factor, independent of the key timestep. The removal of the noise term improves the stability of the proposed method. This noise-free latent simulates the amplitude characteristies of an intermediate timestep while preserving the spatial coherence of the original image latent. Empirical evaluation shows that moderate values (e.g.. o E [0.25, 0.5)) yield the best results.
We then fix the latent input x and vary the key timestep passed to the diffusion transformer. As shown in fig. 3, the choice of key timestep affects the granularity of structural control. Conditioning with timestep near zero yields prominent but coarse structure struture-capturiing large shapes and global layout while omitting fine detail. In contrast, using a timestep closer to the original key timestep (e.g.. 661) achieves finer structure transfer, preserving contours, texture boundaries, and detailed object shapes.
This observation opens the door to user-driven structural tradeoffs: by adjusting the conditioning timestep, one can control the rigidity of structural guidance. Lower key timesteps provide more compositional fexiiility-suitable for creative reinterpretations or stylistic variation-whil higher key timesteps enforce stricter alignment with the reference structure. This tunable granularity makes LCD not only a tool for quality improvement, but also mechanism for interactive control.
3.3 Unlocking the Full Potential of FreeControl with Compositional Generation
With the core attention control mechanism place, FreeControl serves as a flexible framework for structural guidance, enabling users to intuitively define spatial and semantic layout without modifying
5

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the model. Rather than relying on directly control both the content and position of visual elements through image composition.
segmentation maps or prompt engineering, users can
Compositional Reference Images for Semantic Layouts. While structural control methods like ControlNet or T21-Adapter are effective at enforeing edges spatial layouts, they often struggle to preserve semantic intent- intent--articularly in complex scenes. FreeControl addresses this by enabling compositional reference images, where users can define both *what* should appear and #where* should appear using direct visual assembly.
For instance, like SAM [14] and paste it onto a new background (e.g., sky). The resulting image encodes both spatial structure and semantic intent, and serves direct condition for generation. This "design by composition" approach allows users to guide the layout in a natural, intuitive way-without requiring segmentation maps or prompt engineering. Examples are shown in fig. which comprise cases such as transferring digital text to real writing and scene composition.
extract an object (e.g., kite) from source image using segmentation tool
To improve robustness when assembling such references, Gaussian blur could be optionally applied to the compositional image before passing it to the model. This lightweight preprocessing step reduces sharp boundaries and high-frreueet noise, helping the model focus on the intended structure while avoiding artifacts from poy-t seams.
4 Experiments
4.1 Implementation details
We conduct all experiments using the FLUX.11des [16] model with the FlowMachhFulerDiscreee scheduler, timestep range of 1000 to 400, and guidance scale of 6.5. Quantitative results use 25 denoising steps; 50 steps are used elsewhere for improved visual quality. The key timestep t* is fixed at 661. Attention is extracted once and injected into the last 25 transformer layers of the model's single transformer block in the quantitative evaluations, and may reduced elsewhere to demonstrate results of lower structural control. Compositional image generation is disabled unless specifically ablated. Inference is performed on a single NVIDIA RTX A6000 GPU with 48 GB of memory, and the inference time is measured over 100 runs
4.2 Quatitative Comparison
We evaluate on 5,000 images sampled from the COCO 2017 20 validation set, resized to 5 512x512. Each image is paired with its corresponding caption, which is used as the input text prompt for controlled generation.
Dataset.
Metrics. We report FID for visual fidelity, SSIM and PSNR for low-level similarity, and CLIP-Text Similarity [3I] for semantic alignment between images and prompts. For Canny-conditioned models, we quantify structural fidelity with the F1 score computed between the input Canny edge map and the Canny edge map extracted from each generated image. For depth-conditiones models, we report pixel level accuracy as the mean squared error (MSE) between the input depth map and the depth map predicted from the generated image.
Comparison Methods. We compare FreeControl against five strong baselines: ControlNet [47] UniControlNet [49] UniControl first four baselines are implemented on Stable Diffusion v1.5, while Flux ControlNet is built on FLUX.I-dev [6] All SD based models are run with 20 denoising steps, and Flux-based meth- ods- ods-including FreeControl-usse use 25 steps, following the respective official configurations.
30 Control [17), and Flux-ControlNet 43
44 The
Note that ControlNet-style methods require pre-processed condition maps (c.g., Canny edge or depth), while FreeControl directly uses the raw image as structural input, with no preprocessing(we also did not count the preprocessing time for the comparison methods in table 2p.. The Canny edge is computed with the high threshold set to 200, and the low threshold set to 100.
Results. Table 1 reports quantitative comparisons across several metrics. FreeControl outperforms all baselines in terms of structural similarity (SSIM and PSNR), while maintaining competitive CLIP-Text alignment with prompt semantics. Compared to ControlNet and UniControl-style meth-

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Table 1: Comparison with controlled-generatio methods. The best scores are in bold, and the second scores are under lined.
Configuration
F1 1 MSE
FID +
SSIM f
PSNR f
CLIP-T
ControlNet SD1.5 (Canny)
47
0.23 /
18.18
0.2585
10.55
0.3083
ControlNet++ (Canny) 17
0.30
22.06
0.2784
10.59
0.2986
UniControl (Canny)
60
0.35
21.22
0.3714
11.66
0.3103
UniControINet (Canny)
49
0.26
17.97
0.2783
10.59
0.3137
FLUX. ControlNet (Canny)
43
0.16
27.11
0.2515
10.65
0.3009
ControlNet SD1.5 (Depth) 47
/00.64
18.09
0.2383
10.22
0,3107
FLUX.1-dev ControlNet (Depth)
44
* /47.04
19.27
0.1968
10.74
0.3087
ControlNet++ (Depth) [17
/27.779
23.23
0.2093
9.71
0,3020
UniControl (Depth) [30
/33.51
28.24
0.2255
10.09
0.3105
UniControlNet (Depth) [49
* * 34.72
22.25
0.2038
10.12
0.3156
Ours (Iterative Extraction)
0.30/ 20.76
16.43
0.8078
19.11
0.3043
Ours (One Step Extraction)
0.28 21.18
15.64
0.7564
17.49
0.3087
Canny
Canny
Cyherpunk Prompt
Dopth
Dopth
Animate Prompt
Original Image
Condition
UniControl
UniContrulNet FLUX ControlNet
Ours
Qualitative comparisons on wwwuctuee-codiitionnn image generation. Rows and 3 show results where all methods conditioned using the original caption of the reference image. Rows 2 and 4 present generations under stylized prompts to evaluate each method's s ability to generalize beyond the original content.
Figure 4:
ods-which rely on handcrafted edge or depth inputs our method achieves higher visual fidelity without requiring retraining specialized condition maps.
In edge-conditioned tasks, FreeControl achieves an F1 score 0.30 with lower mean squared error (MSE), produces Canny edge results comparable to UniControl and ControlNet++ while using only the raw reference image. Additionally, compared the iterative extraction, our method performs competitively while exerting significantly less computational resources.
These results demonstrate that FreeControl not only preserves spatial structure and semantic content effectively but also serves as lightweight, training-fres alternative to existing structure-conditionn generation pipelines. For further reference regarding the flexibility and fidelity of our method, also provide quantitative comparisons that benchmark under different settings in the Appendix.
4.3 Qualitative Results
present qualitative comparisons fig. 4 where FreeControl conditioned on raw reference images, while bascline methods rely on preprocessed control signals. Under the original prompt,

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FreeControl delivers superior structural alignment and visual fidelity. In contrast, comparison methods either fail to align accurately with the intended structure or generate artifacts such as blur or noise, undermining image quality. We further demonstrate results on representative stylized prompts to evaluate generalization beyond the original setting. FreeControl successfully preserves structural integrity while adapting to new prompts, demonstrating robust guidance under prompt variation. Canny-based methods rigidly adhere to edge maps. often at odds with prompt semantics-resullee in unnatural appearances and ghost artifacts. Depth-based methods suffer from insufficient detail in the control signal, leading to misalignment, semantic drift, and diminished image fidelity, Overall, the results underscore FreeControl's 's ability maintain consistent structural control and prompt adherence, even when guided by raw image inputs rather than handcrafted control maps.
4.4 Inference Time
We benchmark inference speed for FLUX ControlNet (Canny), FLUX ControlNet (Depth), the vanilla FLUX pipeline, and our method. All models are run with 25 denoising steps and produce 1024 1024 px outputs on an NVIDIA RTX A6000, For each pipeline, we fix a single prompt and single source inage-togethee with its corresponding condition map (canny edges or depth)-and execute the generation 100 times, recording the elapsed time every run. Note that we exclude the pre-process time of the comparison methods for fair comparison. The aggregate statistics from these 100-run trials are reported in table [2, and our method, being a test-time augmented method, performs equally efficiently as the training- based methods. Beyond that, the additional memory usage brought by our method is around GB, which is also negligible.
Table 2: Inference time of different methods.
Configuration
Average Inference Time
Max
Min
Variance
FLUX Original Pipeline FLUX. FLUX..--dv [16
24.89
24.98
24.18
0.0101
FLUX.1 1-ve ControlNet (Canny) 43
26.01
26.52
25.61
0.0054
FLUX.I-dev ControlNet (Depth)
26.01
26.09
25.80
0.0034
Ours
26.11
26.16
25.32
0.0117
Ours(lterative Extraction)
45.16
48.09
45.01
0.1012
4.5
Compatibility with Fine-Tuned or LoRA-Augmented Models
ControlNet [47 often exhibit limited compatibility with finetuned or augmented via LoRA 9 This instability arises because ControlNet relies heavily on the original backbone's parameters-it its control branches are trained jointly with the base model and assume specific internal feature distributions. However, our method is not dependent on any specific model architecture or weights, demonstrating strong adaptability across different model variants. To validate this, we conduct experiments on both fine-tuned M and (Canny) and FLUX ControlNet (Depth). As shown in fig. [ our method demonstrates superior compatibility by providing stable results with consistent structure and fine adaptation to the model changes the community models, whereas ControlNet fails to be compatible with them and produce artifacts and distorted results. More results can be found in the Appendix.
(26) models, comparing our method with ControlNet FLUX
5
Ablation Study
5.1
One-Step vs. Iterative Attention Extraction
To validate the effectiveness of extracting attention from single timestep, we compare our method against a baseline that mimics iterative attention extraction across multiple denoising steps similar to inversion-based or reconstruction-baste and injected step-by-step. rather than reused. As shown table [ and table 2 one-step injection achieves comparable structural fidelity while significantly reducing computational overhead. This result supports our hypothesis that structural information can be captured once and reused without loss of guidance, due to the shared purpose of structural encoding across timesteps.
strategies. In this baseline, attention matrices are extracted

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Fine- Tuned
LoRA Auggeeted
Original Images
Conditions
FLUX ControlNet
Ours
Figure 5: Examples of compatibility with fine-tuned or LoRA-augmented models.
Prompt
fisherishi oyberporrk helmeit adorved intricuthe neon-le fry parttorns and chreinn actents, resiting on sleek pades hal 'llominatted by polsating Iolographic lightss, set agglinst a sark w bre cacksrop with Rlckering digital billonedds and elus in wish
0.0
sigma
1.0
key timestep
1000.0
number of modified layers
37
Figure 6: Visual analysis of structural control effects by varying injection depth, sigma, and key timestep in FreeControl.
5.2 Injection Depth vs. Sigma vs. Key timestep
The injection depth (number of transformer layers) influences the strength of structural control, while the injection quality (i.e., the content the attention matrix) determines its focus. To isolate the effects of each factor, we vary it individually while holding others fixed at empirically optimal values. As shown in fig. 6 varying the injection depth reveals different trade-off: injecting into fewer transformer layers relaxes structure in less critical regions. improving texture and color fidelity, whereas deeper injection increases rigidity at the cost of visual richness especially in color saturation. The choice of key timestep. on the other hand, influences the focus of attention that is, what kind of structural information being injected. Earlier timesteps (e.g.. / 0) yield more abstract, layout-level attention that allows greater freedom in fine details; later timesteps (e.g., / 661) still capture high-level structure with greater specificity and finer granularity, resulting in more detailed structural alignment. The sigma value, in contrast, remains relatively stable at moderate settings, with

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Prompt
howeriig liglnt/tossee blases atop jagged crimson dlfff, surronndedd by fiory rnd skyy and sitrr clouds. winding Stone path elows like molton cracks, while blood-red oesan crashes below, "aak towers flank the scene, silnoweriie against the bwnning sky, Thi world is infowse, swrreal inforro possion, Violewce and awe, with velcanic energy and dramatic d4pramian style.
Original Image
Layer-aware Injection
Full-layer Injection
Figure 7: Qualitative Comparison between Layer-Aware Injection and Full-Layer Injection
structural control gradually diminishing it approaches 1 first affecting fine details, then larger structures. Based on these observations, we recommend adjusting layer depth and injection range to tune structural strength, while selecting the appropriate key timestep and sigma to steer the level of structural detail captured in the generation.
5.3 Impact of Layer-Aware Injection
As diseussioned in section 3.1 mid-to-late layer queries encode structural layout rather than raw appearance, aligning better with our structural control objective. However, early-layer queries primarily encode low-level appearance statistics, especially color. When injected, they conflict with the prompp-conditioned key/value features, which often imply a different color palette. This feature mismatch causes the model to lose chromatic fidelity, leading muted grayscale-like outputs. To verify this, we test prompts with deliberately shifted color themes. As shown in fig. 7 full-layer injection causes localized color fading in regions where prompt colors diverge from the reference, while our layer-aware injection maintains both structure and visual quality.
6 Limitations
While FreeControl is efficient, training-free, and offers strong structural control, it does not support condition maps like edges segmentation. This limits scenarios where users prefer editing sketches or symbolic inputs. Although compositional image assembly provides flexibility, some use cases may still benefit from explicit support for sparse conditions.
7 Conclusion
This paper revisits central assumption in attention-basee structural control for diffusion models: that effective guidance requires multi-step extraction. We show that a single-step extraction- wwhen properly conditioned-ccet offer strong, reusable structural signals without inversion or retraining, Decoupling (LCD) reveals that attention quality depends not just the timestep, but on how the noised latent and conditioning signal are configured. This enables more stable and controllable generation. Beyond efficiency, FreeControl supports intuitive control by allowing users to compose reference images that express both layout and intent-bridginn structure and semantics without relying on edge maps segmentation masks. Overall, our findings suggest attention can serve not just as an internal mechanism, but as a practical, tunable approach for control diffusion models.
Our
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8 Acknowledgments
This work was supported by the National Natural Science Foundation China (Grant No. 62406134), 134), Jiangsu Provincial Science & Technology Major Project (Grant No. BG2024042), the Suzhou Key Technologies Project (Grant No. SYG2024136) and the Nanjing University-Chine Mobile Communications Group Co. Ltd. Joint Institute.
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A Quantitative Results on Stylized Prompts (COCO Dataset)
FreeControl conditions generation directly on the raw image, rather than on derived conditions like edges depth. This design provides rich and detailed structural prior, which has proven effective in preserving the original layout and composition during generation. While prior experiments focus on tasks where the target image shares semantic similarity with the reference, real-world cases often involve prompts that diverge stylistically or conceptually from the original image. To broaden the evaluation scope and assess FreeControl's robustness more diverse generative settings, we conduct a benchmark using stylized prompts.
We construct this benchmark the COCO [20] validation set. For each image, retain the original as the structural reference and generate stylized prompts by combining the original caption with one of five target styles (c.g., Cyberpunk, Vaporwave). These stylized prompts created using GP [25] conditioned on both the caption and the style keyword.
This setup introduces challenging mismatch between the style and the visual structure, making it difficult for models to retain both prompt adherence and structural fidelity. We compare FreeControl against Flux-ControlNe [47, which serves as strong baseline for structure-conditioned generation on same backbone. As shown in t FreeControl consistently preserves structure better (as measured by Fl and MSE) while generating content more semantically aligned with the stylized prompts (via CLIP-T). This demonstrates the strength of our method in transferring structure faithfully when prompt semantics diverge from the original image.
B More Results on Compatibility with Fine-Tuned or LoRA-Augmente Models
To further support the findings discussed in the main paper, we provide additional qualitative results on fine-tuned and LoRA-augmenter diffusion models. Specifically, evaluate FreeControl and FLUX ControlNet variants on community models that are fine-tuned [ or LoRA-augmented 26 29
As shown in figs. to 11 our method consistently preserves structure and semantic fidelity across diverse model variants, producing stable and visually coherent outputs. In contrast, ControlNet- approaches exhibit visible artifacts, color shifts, or loss of structural alignment under the same settings.
These results further confirm that FreeControl maintains strong compatibility across both fine-tuned and LoRA- LoRA-augman -uugmenntd backbones, benefiting from its training free nature and independence from specific model weights or feature distributions.
C Additional Visual Results
We provide more visual results for the readers to reference. fig. 12 showcases additional generation results based on compositional reference images. Users can crop and paste objects into layout to specify spatial intent, allowing for precise control over the scene's 's composition. With touch of creativity, FreeControl empowers users to bring their imaginative visions to life, generating stunning, dynamic visuals that reflect their unique concepts (like giant floating whale in the sky). fig. T3 and fig. 14 provide further comparisons between FreeControl and FLUX ControlNet.
Table 3: Quantitative evaluation the COCO validation using stylized prompts. The best scores are in bold.
Method
F1 t
MSE 4
CLIP-T SSIM 1
PSNR
FLUX ControlNet (Canny)
0.19
N/A
0.2629
9.72
0.2646
FLUX ControlNet (Depth)
N/A
41.41
0.2029
9.98
0.2448
Ours
0.25
26.24
0.4825
15.07
0.2981
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D Expanded Baseline Comparison Results
We have added quantitative comparison experiments with two image editing models, In-Context Edit [48] and Taming Rectified Flow (42). improve the fairness and completeness the evaluation. The test images are consistent with the previous comparison experiments. We use image captions and stylized prompts as text inputs respectively, and the corresponding results are shown in table and 5
According the results, Our method performs comparably to, and often surpasses, Taming Rectified Flow across several metrics. ICEdit, built on the FLUX-Fill model [16] for image inpainting, achieves relatively high similarity metrics (e.g., PSNR) primarily because it keeps all content outside the edited region untouched. However, this strategy limits its ability to satisfy the desired balance between structural control and free content generation. As result, its CLIP-T score is lower, and it often struggles with stability and controllability when following editing instructions.
Table 4: Quantitative Results with image-editing methods on the COCO validation subset.
Method
F1 t
MSE 4
SSIM f
PSNR t
CLIP-T t
FID J.
In Cn-CCntext Edit
0.47
17.30
0.7781
21.34
0.3024
8.64
Taming Rectified Flow
0.19
28.31
0.4390
16.90
0.3164
16.35
Ours
0.28
21.18
0.7564
17.49
0.3087
15.64
Table 5: Quantitative Results with image-editing methods on the COCO validation subset using
stylized prompts.
Method
F1
MSE 4
SSIM 1
PSNR
CLIP-T f
In-Context Edit
0.30
35.96
0.5436
14.70
0.2543
Taming Rectified Flow
0.17
38.49
0.4034
16.37
0,2585
Ours
0.25
26.24
0.4825
15.07
0.2981
E
Additional Quantitative Ablation Analysis
We have supplemented more comprehensive ablation studies justify the choice of key parameters in our method, such as the key timestep t", numbers of modified transformer layers and a. The results presented in table 6 Thanks to LCD, by flexibly tuning the hyperparameters, structural control of varying strength and granularity, producing stable and controllable results that cater different user requirements.
achieve
Table 6: Ablation results on the COCO validation subset. Each entry in the Parameters column indicates the number modified layers, the key timestep t", and the a, in that order.
Parameters
f
MSE 1
SSIM 1
PSNR f
CLIP-T t
FID 1
20-661-0.25
0.27
23.01
0.5251
16.61
0.3083
17.84
25-561-0.25
0.27
22.44
0.5232
16.61
0.3077
17.42
25-661-0.0
0.30
21.74
0.5630
17.20
0.3061
17.78
25-661-0.25
0.28
21.86
0.5438
16.87
0.3048
18.00
25-661-0.5
0.24
24.64
0.5097
16.54
0.3072
22.51
25-761-0.25
0.28
23.47
0.5492
17.07
0.3048
21.40
30-661-0.22
0.30
22.20
0.5461
16.88
0.3020
20.20
22

---

F Evaluation under Challenging Structural Scenarios
F.1 Semantic Entanglement and Object Occlusion
We conduct stress tests on both real and synthetic images featuring semantic entanglement and severe object overlap. Selected results are shown in fig. [5] For each example, the left image is the original input, and the right image is the generated result. As observed, FreeControl consistently avoids distorted or unrealistic artifacts such as extra limbs or warped body structures. The outputs remain natural-looking and visually coherent, demonstrating strong robustness even under highly challenging compositional scenarios.
F.2 Preservation of Facial Identity
Our method provides flexible control over facial identity preservation, allowing users to adjust the strength of identity retention via hyperparameters. Under non-conflicting text-image guidance, tuning parameters such as the number of modified transformer layers enables strong structural control while preserving facial details, making FreeControl suitable for dieenttt--enniiteee tasks. As shown in fig. [66 with higher control strength, our approach demonstrates strong ability to retain facial identity. Conversely, for artistic creation or diversity-oriented generation, relaxing the control allows for slight, intentional changes in facial features, leading to more expressive results.
Applicability to UNet-based Models (i.e., Stable Diffusion) G
Our method is designed to operate purely at the attention level, making it architecture- We have implemented on UNct-based models (e.g.. SD1.5 [33]. SDXL (28 and observed strong structural control behavior after only minimal hyperparameters adjustments to fit the model. Several qualitative examples of structural control are presented in fig. 17 and fig. 18 We further note that, due to the inherent capacity limitations of UNet- based models, the degree of controllability can diminish in highly complex scenarios. In practice, we find that leveraging FLUX models [16] yields more stable and visually coherent generations, and we recommend their use when high-fidelity control is desired.
H Further Discussion on the Design Space
H.1 Independence from ROPE
The query matrices FreeControl extracts are captured before RoPE [39] is applied, so the injected queries contain positional encoding they are entirely image-driven. While RoPE still affects key and value during generation, it does not alter what FreeControl injects. Furthermore, FreeControl works identically U-Net architectures (which do not use RoPE), showing that structural consistency stems from the extracted queries themselves, not from positional priors.
To directly confirm this point, we ran controlled test by removing RoPE entirely from the FLUX model. As expected, the base model collapsed into near-random noise, since was never trained to operate without positional encoding. Crucially, when we applied FreeControl under the same no-RoPE setup, the one-step injection still imposed clear, image-driven structure on the output. The result looked like "structured noise" faithfully echoing the condition image's 's layout strong evidence that FreeControl's guidance originates from the injected queries themselves, not from RoPE.
H.2
Key timestep Choice
The key timestep fundamentally governs the granularity of structural information that FreeControl can extract. In diffusion models, each denoising step is influenced not only by progressively refined latents but also by a changing timestep input that biases the network toward different levels of detail. Conceptually, the key timestep acts like a focus knob: adjusting it continuously shifts the model's representational emphasis from global layout patterns fine-grained textures.
By holding the latent fixed and sweeping only the key timestep, our experiments reveal natural progression in structural granularity encoded within the query matrices. Both quantitative metrics and
23

---

visual evidence show smooth evolution from coarse shape-level representation toward detailed texture-level encoding. Among all tested values, key timestep (661) emerges as a sweet spot, offering the best trade-off between global consistency and structural precision, making it the most suitable extraction point for query-based control.
H.3 Layer-wise Query Matrices Similarity
We extract query matrices at different depths under two configurations: with LCD and without LCD, where the only difference lies whether noise is added to zg during the forward diffusion process (as defined in Sec. 3.1 of main paper). We then compare these two sets of query matrices with the query matrices from every timestep of the multi-step extraction variant and compute the cosine similarity, as reported in table 7 The layer-wise similarity shows a clear low-to-high trend: shallow layers tend to have lower similarity, while deeper layers converge more. This aligns with our layer-aware injection choice in Sec. 3.1 early layers focus more on appearance elements rather than structure, diverging more across timesteps and contributing less to shared structural signals.
Table 7:
Cosine Similarity Between One-Step and Multi-Step Extracted Query Matrices. The "Global" row reports the average similarity of query matrices across all layers.
Cosine Similarity
Layer Depth
w/ LCD
w/o LCD
Early
0,5418
0,6680
Mid
0,5861
0.6853
Last
0,6011
0.7638
Global
0.5769
0.7063
24

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Canny or Depth
FLUX ControlNet (Canny or Depth)
Original Image
Ours
Figure 8: Visual results comparing our method and FLUX ControlNet on Lora-Augmented models (Ghibli-Style LoRA).
25

---

Canny or Depth
FLUX ControlNet
Original Image
Ours
(Canny or Depth)
Figure 9; Visual results comparing our method and FLUX ControlNet on Lora-Augmented models (Canopus-Pixar-3D-e Pixar-DD-Stlle LoRA).
26

---

WURRU
Canny or Depth
FLUX ControlNet (Canny or Depth)
Original Image
Ours
Figure 10:
Visual results comparing our method and FLUX ControlNet on finetuned models (AWPortrait Fine-Tune).
27

---

Canny or Depth
FLUX ControlNet
Original Image
Ours
(Canny or Depth)
Figure 11:
Visual results comparing our method and FLUX ControlNet on finetuned models (UltraRcal Fine-Tune).
28

---

Compose Content
NEON
NEON
Figure 12: More visual results compositional generation.
29

---

9
Original Image
Canny
Depth
FLUUX ControlNet (Canny)
FLUX ControlNet
Ours
(Depth)
Figure 13: Qualitative comparisons structure-conditioned image generation.
30

---

Original Image
Canny
Depth
FLUX ControlNet FLUX ControlNet (Canny)
Ours
(Depth)
Figure 14: Qualitative comparisons structure-conditionee image generation.
31

---

(a)
(b)
(c)
Figure 13: Examples generated images under Semantic Entanglement and Object Occlusion. For each pair, the image the left is the original image, and the image on the right is the generated result.
(a)
(b)
(c)
Figure 16: Examples of Facial Identity Control. Adjusting control strength, together with a suitable prompt, enables strong structural preservation of facial features. For each pair, the image on the left is the original image, and the image on the right is the generated result.
(b)
(c)
Figure 17: Generation Examples on SDXL model Using Our Method. For each pair, the image on the left is the original image, and the image on the right is the generated result.
(a)
(b)
(C)
Figure 18: Generation Examples SD- 1.5 model Using Our Method. For each pair, the image the left is the original image, and the image on the right is the generated result.
32


Reviews:
[{"review_id": "CWeiGDT8qB", "reviewer": "Reviewer_pcsA", "summary": "This paper introduces FreeControl, a training-free test-time framework for semantic structural control in diffusion models. Unlike existing methods requiring handcrafted condition maps and retraining, FreeControl extracts a structural self-attention matrix in a single step from a user-supplied reference image and reuses it during diffusion denoising, greatly improving efficiency. The framework also proposes \"Latent-Condition Decoupling\" (LCD), which disentangles the roles of the noised latent and timestep condition to yield finer, tunable structure control and mitigate artifacts. FreeControl extends to compositional generation, letting users assemble layouts from multiple references, and experimentally demonstrates competitive or better performance versus existing structural control methods with significant gains in efficiency.", "questions": "1. Can the authors provide more discussion or experiments on how FreeControl handles highly complex or ambiguous compositional reference images? Are there failure cases for semantic or spatially entangled references?\n2. Scalability Beyond FLUX and COCO: Have the authors attempted to apply FreeControl on other diffusion backbones or datasets (e.g., SDXL, Imagen, domain-specific datasets)? Are there nontrivial engineering challenges in porting the proposed method?", "limitations": "see the weakness part", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strength：\n1. FreeControl achieves good results with limited extra inference time, which is appealing and practical.\n2. Experiments cover a wide range of relevant baselines (including both ControlNet and UniControl variants) and tasks\n\nWeakness:\n1. Ablations are mostly limited to the number of injected layers, sigma, and timestep. It would be beneficial to analyze the limits or failures of the approach (e.g., what compositional references are too complex, or when does injection of attention matrices degrade rather than help).\n 2. It would be helpful to see broader validation with different init models or datasets.", "was_revised": true, "final_justification": "The author addressed all my concerns in the rebuttal, I am inclined to accept the paper.", "rebuttal": "### Rebuttal to Reviewer 1\n\nWe sincerely thank the reviewer for highlighting areas where additional clarification and analysis would strengthen the paper. We address each concern directly below.\n\n---\n\n**(1) Failure cases for complex or ambiguous compositions**  \nWe agree that it is important to analyze the limitations of FreeControl. To this end, we explicitly tested cases involving **highly complex or semantically entangled references**, and conducted an extensive **pressure test** under extreme compositional setups to evaluate its robustness.\n\n\nWe began with **object occlusion cases**, ranging from mild overlaps — where only small regions are covered — to extreme situations where objects are almost entirely obscured with only small portions remaining. In both settings, FreeControl handled the occlusion well, producing results with **clear semantic retention and structure preservation**.  \n\nWe then escalated testing to scenes with **multiple objects (including humans) crowding into one image**, each with different conceptual depths in the scene. Even under these conditions, semantic meaning and structure remained well‑preserved across several trials.  \n\nIn the **most extreme test**, where two humans were heavily overlapped and the person behind had only partial limbs visible, we observed that under **medium control strength**, the model naturally merged them into a single person—producing outputs that remained visually plausible and coherent, with no distorted or unnatural artifacts. However, by **slightly increasing the control strength**, **FreeControl** was able to correctly disentangle and restore the separate individuals.\n\nIt is worth noting that even in these **highly challenging and intentionally destructive cases**, FreeControl **never produced distorted or unrealistic outputs** — for example, no unnatural extra limbs or warped bodies. Across all tests, the results remained **natural‑looking and visually coherent**. We honestly believe the stability of the method is in a **very good state**; it remains fairly robust even under stress. (Unfortunately, we can only describe these outcomes in words rather than include images in the rebuttal.)\n\n---\n\n**(2) Scalability beyond FLUX, COCO, and dataset scope**  \nWe attempted and implemented FreeControl on other diffusion backbones, including **SDXL** and **SD1.5**. As expected, the **generation quality is bounded by the underlying model**, but the **structural control effect remains prominent**, demonstrating that the method generalizes effectively.  \n\nFreeControl is **architecturally agnostic** because it operates entirely at the attention level. Porting to new backbones only requires **trivial code changes and light adaptation to the new model structure**. It would, admittedly, take more time to tune the hyperparameters and carefully check the details to bring the method to a stable and robust state—particularly with models like SD 1.5, which are inherently less stable. We also note that the appendix shows adaptation to **LoRA and fine‑tuned models**, further underscoring generality.  \n\nAs for the dataset, we initially consider the COCO dataset to be a sufficiently large dataset that could fairly evaluates the overall performance. To provide additional angles on flexibility, the appendix also includes experiments on a **stylized prompt** version of COCO. During our experiments on various other data sources, our method delivered stable outcomes (even on roughly assembled compositional images with artifacts). \n\nWe recognize that explaining these points explicitly is key to showing the method’s applicability, and we will **add a new section in the appendix** detailing these cross‑model experiments, dataset considerations, and engineering notes, making the portability and robustness of FreeControl clearer to readers.\n\nOn the other hand, if your concern is less about generalization and more about benchmarking on other datasets to improve representativeness, we are already working on such an evaluation and **will be happy to provide it once it is complete**. \n\nAdditionally, we have included new minibatch experiments with expanded baseline comparisons and quantitative ablations in response to another reviewer’s request. A complete version with formal discussion will be included in the full paper. Please feel free to check the current results below and the discussions could be found in the responses to other reviewers — we’d be happy to hear your thoughts!\n\n---\n#### Table A: Mini‑Batch Expanded Baseline Comparison Results\n\n| Method                 |  F1 ↑ | MSE  ↓  | SSIM ↑  | PSNR ↑ | CLIP-T ↑ | FID ↓ |\n|-----------------------|------|-------|----------|--------|-----------|--------|\n| In‑Context Edit       |  0.43 | 24.63 | 0.8050   | 19.68  | 0.2787    | 67.63  |\n| Taming Rectified Flow |  0.19 | 28.22 | 0.6131   | 17.41  | 0.3160    | 72.49  |\n| FreeControl           |  0.28 | 21.21 | 0.7580   | 17.43  |0.3076       | 66.72  |\n\n#### Table B: Mini‑Batch Expanded Baseline Comparison Results on Stylized COCO\n\n| Method                 |  F1 ↑   | MSE ↓  | SSIM ↑ | PSNR ↑ | CLIP-T ↑ |\n|------------------------|-------| ------- |-------|-------|---------|\n| In‑Context Edit        |  0.28 | 43.27 | 0.5680   | 14.55  | 0.2248    |\n| Taming Rectified Flow  |  0.17 | 39.22 | 0.5610   | 16.88  | 0.2606    |\n| FreeControl            |  0.23    |     27.09      | 0.7087      | 15.98   | 0.2419       |\n\n#### Table C: Mini‑Batch Quantitative Ablation Results\n\n| Layers | Timestep | Sigma | F1 ↑ | MSE ↓ | SSIM ↑ | PSNR ↑ | CLIP-T ↑ | FID ↓ |\n|--------|----------|-------|------|--------|--------|--------|-----------|--------|\n| 25     | 561      | 0.25  | 0.27 | 22.60  | 0.7504 | 17.42  | 0.3072    | 71.40  |\n| 25     | 761      | 0.25  | 0.28 | 23.89  | 0.7727 | 17.95  | 0.3042    | 78.03  |\n| 25     | 661      | 0.00  | 0.29 | 22.15  | 0.7874 | 18.10  | 0.3056    | 69.94  |\n| 25     | 661      | 0.50  | 0.24 | 21.94  | 0.7338 | 17.46  | 0.3041    | 79.13  |\n| 30     | 661      | 0.25  | 0.30 | 22.79  | 0.7835 | 17.82  | 0.3014    | 75.58  |\n| 25     | 661      | 0.25  | 0.28 | 21.89  | 0.7681 | 17.70  | 0.3062    | 70.70  |\n\n#### Table D: Mini‑Batch Quantitative Ablation Results on Stylized COCO\n\n| Layers | Timestep | Sigma | F1 ↑ | MSE ↓ | SSIM ↑ | PSNR ↑ | CLIP-T ↑ |\n|--------|----------|-------|------|--------|--------|--------|-----------|\n| 25     | 561      | 0.25  | 0.22 | 27.80  | 0.6910 | 15.66  | 0.2425    |\n| 25     | 761      | 0.25  | 0.25 | 26.92  | 0.7485 | 17.03  | 0.2411    |\n| 25     | 661      | 0.00  | 0.26 | 25.20  | 0.7589 | 17.20  | 0.2606    |\n| 25     | 661      | 0.50  | 0.19 | 25.87  | 0.6801 | 15.62  | 0.2396    |\n| 25     | 661      | 0.25  | 0.25 | 30.80  | 0.7258 | 16.30  | 0.2427    |\n| 30     | 661      | 0.25  | 0.28 | 24.08  | 0.7728 | 17.28  | 0.2404    |\n\n\n---"}, {"review_id": "lBM95Mq4zc", "reviewer": "Reviewer_t9ys", "summary": "This paper proposes a method for structure-preserving image generation by reusing self-attention query matrices extracted from a single timestep in a diffusion transformer (DiT) model. By selecting an optimal timestep and injecting the extracted queries into specific transformer layers, the method enables spatial control without retraining. The authors analyze how different injection layers and conditioning timesteps affect the granularity of structural control, introducing a decoupled formulation where the latent input and timestep condition are adjusted separately.\n\nThe approach also extends to compositional generation, allowing users to assemble reference images from multiple sources to define both spatial layout and semantic content. Rather than relying on segmentation maps or handcrafted conditions, this method offers an alternative interface for test-time control using raw visual inputs. While efficient and training-free, it is primarily applicable when structural cues can be visually inferred from the reference image.", "questions": "1. Impact of RoPE on Structural Consistency\nSince DiT models typically employ Rotary Positional Embedding (RoPE), which directly influences the query representations, it is possible that the preserved spatial structure in your one-step attention injection is partially attributed to RoPE rather than the content of the reference image alone. Have you investigated the impact of RoPE in this context? Specifically, have you tried performing attention injection without RoPE, or with alternative positional encoding schemes? Including such ablations would help clarify whether the structural consistency truly stems from the extracted queries or is largely retained due to positional priors.", "limitations": "yes", "rating": 3, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n1. Efficient and Training-Free Structural Control\nThe proposed method introduces a one-step attention extraction mechanism that eliminates the need for multi-step inversion or model retraining. This allows structural guidance to be applied efficiently at test time with minimal overhead (~5% additional cost), making it compatible with various diffusion architectures without fine-tuning. The design is simple yet practical, lowering the barrier to applying spatial control in generative workflows.\n2. Analysis of Layer-Wise Injection and Latent-Condition Decoupling\nThe paper provides a detailed empirical analysis of how injecting attention queries into different transformer layers affects visual quality and structural adherence. Moreover, the introduction of Latent-Condition Decoupling (LCD) offers a principled way to disentangle the effect of timestep conditioning and latent input, allowing finer control over the level of structure transferred. This contributes to a more interpretable and tunable design.\n\nWeaknesses\n1. Limited Novelty\nWhile the idea of injecting attention queries is not new and has been widely explored in prior works, the paper’s main contribution lies in demonstrating that queries extracted from a single timestep can suffice for structure-aware editing. This is a practical and well-executed insight, but it may be viewed as a relatively minor extension rather than a fundamentally novel concept.\n2. Lack of Generalization Across Architectures\nAll experiments are conducted using a single DiT-based model, FLUX.1-dev. While the results are strong, the paper does not explore whether the proposed method generalizes across other diffusion architectures, such as U-Net-based models. This raises questions about the broader applicability and robustness of the technique beyond the tested setting.\n3. Insufficient Analysis of One-Step Extraction\nAlthough the method achieves strong empirical results, the paper lacks a deeper analysis of how the one-step attention compares to multi-step (iterative) attention extraction. For instance, it would be valuable to understand how similar the extracted attention matrices are, or what trade-offs exist between them. Similarly, while Latent-Condition Decoupling is introduced as a key mechanism, there is little discussion or visualization of how the timestep condition affects the internal representations or latent structure of the model. A more detailed analysis would strengthen the paper’s claims and interpretability.", "was_revised": false, "final_justification": "", "rebuttal": "### Rebuttal to Reviewer 2\n\nWe sincerely thank the reviewer for their thoughtful and constructive feedback. Below, we respond point by point and summarize both new and existing evidence.\n\n---\n\n**(1) Novelty and Conceptual Contribution**  \nPrior works on attention injection largely **use attention matrices “as it is”** — the matrices they extract, and the control they deliver, tend to be vague, unstable, and time‑consuming to apply because they rely on multi‑step extraction. FreeControl changes this by reducing the process to **one carefully chosen timestep**, which isolates a single, stable query signal and allows us to identify and manipulate the **key factors** embedded in that signal.  \n\nThis one‑step simplification is not just a convenience — it enables something fundamentally new by reducing the problem to one query matrix: we could now easily **manipulate the content encapsulated in the query matrix** itself, giving FreeControl the ability to deliver **precise, flexible, and interpretable structural control**. Because the process collapses to a single controllable factor, we can:  \n- adjust the latent input directly (noise vs. no‑noise) to improve quality,  \n- provide a clean lever for structural granularity via LCD, and  \n- outperform training‑based methods on structural control despite being a test‑time augmentation method with only ~5% inference overhead.  \n\n---\n\n**(2) Generalization Beyond DiT and FLUX**  \nFreeControl is designed to operate purely at the attention level, making it architecture‑agnostic. We have implemented it on U‑Net–based models (SD1.5, SDXL) and observed strong structural control behavior after only **minimal adjustments to fit the model**. It would, honestly, take some time to tune the hyperparameters and carefully check the details to achieve stable and robust performance.\n\nOur method:  \n- is **not DiT‑specific**, and  \n- works even on models without RoPE, further confirming that FreeControl’s structural adherence does not rely on positional encoding quirks.\n\n\n---\n\n**(3) Understanding One‑Step Extraction: Similarity, Timestep Intuition, and Trade‑offs**  \nThe reviewer requested a deeper look at one‑step extraction, and we provide several perspectives:\n\n### Layer‑wise Similarity, Continuity, and LCD Alignment\nWe extracted both the query matrices from LCD and from the optimal key timestep without LCD (as defined in Sec. 3.1) and compared each against the query matrices from every timestep of the multi‑step extraction variant. A few key points emerged from this analysis.  \n\n #### Table A: One‑Step vs. Iterative Q‑Matrix Similarity\n\n| Layer Depth |Key Timestep Cosine Similarity |  LCD Cosine Similarity | \n|-------------|-------------------|-------------------------|\n| Early       | 0.667969               |        0.541797                     |\n| Mid         | 0.685312               |        0.586172                     |\n| Late        | 0.763750               |        0.601094                     |\n| Global        | 0.706328              |      0.576875                       |\n\n**First**, the layer‑wise similarity shows a clear low‑to‑high trend: shallow layers tend to have lower similarity, while deeper layers converge more. This aligns with our **layer‑aware injection** choice in Sec. 3.1 — early layers focus more on appearance elements rather than structure, diverging more across timesteps and contributing less to shared structural signals.  \n\n**Second**, we observed strong **cross‑timestep continuity**. Each query matrix is most similar to those from adjacent timesteps and steadily diverges from those further away. This continuity reinforces that the structural signal evolves gradually along the denoising trajectory, validating our full sweep across timesteps that identified ≈ 661 as the optimal point, and showing how control quality predictably falls off as we move away from that region.  \n\n**Third**, LCD adds an important refinement. When we compared LCD‑generated query matrices to the multi‑step variant, similarities ranged **0.4–0.7** and peaked around the same optimal timestep region. As timesteps drift further from that point, similarity drops. This indicates that LCD sharpens the structural guidance precisely where it is most effective, aligning closely with the paper’s observation that LCD improves on the “raw” optimal point while maintaining separation from less informative regions.  \n\n---\n\n### Timestep‑by‑Timestep Injection\nWe extracted Q from every timestep in the multi‑step extraction process and used each as the one‑step query‑matrix injection candidate, injecting it across the denoising process. The reviewer can refer to the middle of Fig. 5 for a comparable trend (the actual changes across timesteps are even more pronounced). As seen there, the queries from very early timesteps are too coarse, those from very late timesteps overfit to fine details, and only those from the middle range provide balanced, usable structure.  \n\n---\n\n### Trade‑offs and Additional Analysis\nTo our knowledge, there is not a major trade‑off between our one‑step method and multi‑step extraction in most applications. Breaking this down across speed and quality: in terms of speed, as shown in Table 2, one‑step extraction provides a dominant advantage — even more so when compared to inversion‑based approaches. On quality, one‑step extraction combined with LCD offers the ability to isolate and adjust the injected attention matrix as needed, tuning granularity, semantic focus, and strength. This flexibility produces cleaner, less noisy outputs and often better visuals than the multi‑step variant. While multi‑step extraction has one unique edge — by collecting attention at different stages of denoising it can sometimes capture richer information from the reference image for highly specialized cases like blending or computational photography — the raw restoration achievable under LCD can already be tuned to a very satisfying state, further narrowing that gap while preserving the one‑step method’s flexibility and efficiency.\n\nThe trade‑offs between timestep condition and latent changes are originally **visualized in Fig. 5**, offering intuitive understanding of the design choice. We have also run a **mini‑batch quantitative ablations** of these settings in Table B and Table C, and will expand this into a more comprehensive study in the paper and appendix. We attempted to visualize the model’s **internal representations**, but found DiT features difficult to reduce into meaningful visuals. Instead, we rely on qualitative examples (as in Fig. 5) combined with new quantitative results and matrix‑similarity analysis to provide a more rigorous discussion.\n\n#### Table B: Mini‑Batch Quantitative Ablation Analysis\n\n| Layers | Timestep | Sigma | F1 ↑ | MSE ↓ | SSIM ↑ | PSNR ↑ | CLIP-T ↑ | FID ↓ |\n|--------|----------|-------|------|--------|--------|--------|-----------|--------|\n| 25     | 561      | 0.25  | 0.27 | 22.60  | 0.7504 | 17.42  | 0.3072    | 71.40  |\n| 25     | 761      | 0.25  | 0.28 | 23.89  | 0.7727 | 17.95  | 0.3042    | 78.03  |\n| 25     | 661      | 0.00  | 0.29 | 22.15  | 0.7874 | 18.10  | 0.3056    | 69.94  |\n| 25     | 661      | 0.50  | 0.24 | 21.94  | 0.7338 | 17.46  | 0.3041    | 79.13  |\n| 30     | 661      | 0.25  | 0.30 | 22.79  | 0.7835 | 17.82  | 0.3014    | 75.58  |\n| 25     | 661      | 0.25  | 0.28 | 21.89  | 0.7681 | 17.70  | 0.3062    | 70.70  |\n\n#### Table C: Mini‑Batch Quantitative Ablation Results on Stylized COCO\n\n| Layers | Timestep | Sigma | F1 ↑ | MSE ↓ | SSIM ↑ | PSNR ↑ | CLIP-T ↑ |\n|--------|----------|-------|------|--------|--------|--------|-----------|\n| 25     | 561      | 0.25  | 0.22 | 27.80  | 0.6910 | 15.66  | 0.2425    |\n| 25     | 761      | 0.25  | 0.25 | 26.92  | 0.7485 | 17.03  | 0.2411    |\n| 25     | 661      | 0.00  | 0.26 | 25.20  | 0.7589 | 17.20  | 0.2606    |\n| 25     | 661      | 0.50  | 0.19 | 25.87  | 0.6801 | 15.62  | 0.2396    |\n| 25     | 661      | 0.25  | 0.25 | 30.80  | 0.7258 | 16.30  | 0.2427    |\n| 30     | 661      | 0.25  | 0.28 | 24.08  | 0.7728 | 17.28  | 0.2404    |\n\n---\n\n**(4) RoPE Clarification**  \nThe Q matrices FreeControl extracts are captured **before RoPE is applied**, so the injected queries contain **no positional encoding** — they are entirely image‑driven. While RoPE still affects K and V during generation, it does not alter what FreeControl injects. Furthermore, FreeControl works identically on U‑Net architectures (which do **not** use RoPE), showing that structural consistency stems from the extracted queries themselves, not from positional priors.  \n\nTo directly confirm this point, we ran a controlled test by **removing RoPE entirely from the FLUX model**. As expected, the base model collapsed into near‑random noise, since it was never trained to operate without positional encoding. Crucially, when we applied FreeControl under the same no‑RoPE setup, the one‑step injection still imposed **clear, image‑driven structure** on the output. The result looked like **“structured noise”** faithfully echoing the condition image’s layout — **strong evidence** that FreeControl’s guidance originates from the injected queries themselves, not from RoPE."}, {"review_id": "MHmEoL7qAB", "reviewer": "Reviewer_c3mp", "summary": "This paper proposes a training-free method for image editing without training an adapter or running inversion. Specifically, the authors designed a framework including one step attention extraction and latent conditioned decoupling. Both quantative and qualitative results show the superior performance compared with controlnet.", "questions": "1. Fig 1 can be improved, from this figure solely, it's hard to tell what it is trying to say, e.g., what is the input output? prompt?\n2. There are some missing related works, for example, reference-based modulation[1] and RL based editing[2] are both highly relavant.\n3. Eq 1 seems to be linear interpolation instead of forward process?\n4. Ablations on L133 section is missing. \"mid-to-late layers\" (L142) needs to be explained.\n5. As mentioned in L166, how does the equation in L160 related on t?\n\n\n[1] RB-Modulation: Training-Free Stylization using Reference-Based Modulation\n[2] Training Diffusion Models with Reinforcement Learning", "limitations": "no", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 1, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strength\n\n1. The work is motivated correctly and the paper is structured well.\n2. The general idea is explained well.\n3. Results demonstrate good performance.\n\nWeakness\n\n1. Experiments needs to be improved. The authors only compared with controlnets, while I think it's more approapriate to compare with other inversion based methods, which do not take additional inputs and is more aligned with the proposed method.\n2. More details need to be clarified. There are several details that make it very confusing in understanding how the method works. For example the magic number 661 (L154) appears without context. See the question section\n3. More ablation studies are needed to clarify the key design choice, besides the visual comparisons in sec 5.2.", "was_revised": true, "final_justification": "My concern has been addressed. The authors need to put some effort in adding more ablations and rationales in the rebuttal to the final draft for this paper to meet the bar of acceptance.", "rebuttal": "### Rebuttal to Reviewer 3\nWe sincerely thank the reviewer for the careful read and valuable feedback. We address your points in depth below and clarify areas that may have caused confusion.\n\n---\n\n### (1) Baseline Scope (ControlNet vs. Inversion Methods)\n\n**Initial baseline choice**  \nInversion-based methods like **DDIM Inversion** or **Null‑Text Inversion** perform very different tasks: they reconstruct the full latent trajectory to edit an image, which makes them perform **less optimally on structure‑controlled regeneration**. Because they must run the entire reverse denoising process, they are typically 2–3× slower than FreeControl and are designed to alter what’s already present, rather than enforcing where new content should appear.  \n\nBy contrast, **ControlNet‑style approaches** are explicitly designed for **structural guidance**, using external maps (edges, depth, etc.) to align generation. This makes them the most relevant comparison point for FreeControl’s goal of test‑time structural control.  \n\n**Added methods and new runs**  \nThat said, we agree with the reviewer that **additional baselines improve fairness and completeness**. We have now added **In‑Context Edit** and **Taming Rectified Flow** to our experiments.  \n\nWe have already run a **mini‑batch comparison** with these methods, and their preliminary results are reflected in the rebuttal tables. We will **expand this evaluation** with full runs for the final paper and appendix, ensuring FreeControl is compared against a broader range of recent methods as requested.  \n\nAs shown in Table A, FreeControl performs comparably to, and often surpasses, Taming Rectified Flow across several metrics. ICEdit, built on the FLUX.Fill model for image completion, benefits in similarity metrics due to stronger content retention. However, its CLIP-T score is lower, and it often struggles with stability and controllability when following editing instructions. Visual results from the experiments in Table B further highlight its limitations—despite favorable metrics, ICEdit frequently produces unnatural, low-quality outcomes that appear unrealistic or visually jarring, such as disjointed objects, missing body parts, and incoherent scenes. In contrast, our method is significantly more stable and consistently produces higher-quality, more natural results.\n\n#### Table A: Mini‑Batch Expanded Baseline Comparison Results\n\n| Method                 |  F1 ↑ | MSE  ↓  | SSIM ↑  | PSNR ↑ | CLIP-T ↑ | FID ↓ |\n|-----------------------|------|-------|----------|--------|-----------|--------|\n| In‑Context Edit       |  0.43 | 24.63 | 0.8050   | 19.68  | 0.2787    | 67.63  |\n| Taming Rectified Flow |  0.19 | 28.22 | 0.6131   | 17.41  | 0.3160    | 72.49  |\n| FreeControl           |  0.28 | 21.21 | 0.7580   | 17.43  |0.3076       | 66.72  |\n\n#### Table B: Mini‑Batch Expanded Baseline Comparison Results on Stylized COCO\n\n| Method                 |  F1 ↑   | MSE ↓  | SSIM ↑ | PSNR ↑ | CLIP-T ↑ |\n|------------------------|-------| ------- |-------|-------|---------|\n| In‑Context Edit        |  0.28 | 43.27 | 0.5680   | 14.55  | 0.2248    |\n| Taming Rectified Flow  |  0.17 | 39.22 | 0.5610   | 16.88  | 0.2606    |\n| FreeControl            | 0.23    |     27.09 | 0.7087      | 15.98   | 0.2419       |\n\n---\n\n### (2) Timestep Condition (“Magic Number 661”) and LCD\n\nWe appreciate the chance to clarify this, as some confusion comes from mixing two different timestep concepts:  \n\n– **t in Eq. 1** refers to the timestep in the forward simulation used to create the latent for query extraction.  \n– **Timestep condition** is the timestep value fed into the diffusion transformer when queries are extracted.  \n\nIn **Section 3.1**, they are treated in sync — *t* and the timestep condition have the same value, as diffusion models normally do. In **Section 3.2**, however, LCD decouples the latent and the timestep condition, so that in **L166** it points to the timestep condition, not the *t* in the forward process, and the equation around **L160** produces the latent in a way that no longer takes *t* as a parameter (*sorry for the missing formula numbering — since the equation was unnumbered, we can only refer to it by this line number*).  \n\nSection 3.2 then walks through how these two inputs interact: the latent determines what information can be extracted, while the timestep condition shifts how the model interprets it.  \n\nThe reviewer notes that **661 appears with no context in L154** — but in fact it is first introduced and explained earlier, where the forward process for query extraction is described.  \n\nThe number **661** is not arbitrary — it was empirically selected by sweeping across candidate timesteps to find the best balance of structural fidelity and flexibility. We have also run a **mini‑batch quantitative ablation** specifically showing how different timestep condition choices (including 661 and nearby values) affect structural alignment and generation quality, and those results are included in Table C. We also include a quantitative ablation study on the Stylized COCO dataset; please refer to the corresponding table provided in our responses to other reviewers.\n\n#### Table C:  Mini‑Batch Quantitative Ablation Analysis\n\n| Layers | Timestep | Sigma | F1 ↑ | MSE ↓ | SSIM ↑ | PSNR ↑ | CLIP-T ↑ | FID ↓ |\n|--------|----------|-------|------|--------|--------|--------|-----------|--------|\n| 25     | 561      | 0.25  | 0.27 | 22.60  | 0.7504 | 17.42  | 0.3072    | 71.40  |\n| 25     | 761      | 0.25  | 0.28 | 23.89  | 0.7727 | 17.95  | 0.3042    | 78.03  |\n| 25     | 661      | 0.00  | 0.29 | 22.15  | 0.7874 | 18.10  | 0.3056    | 69.94  |\n| 25     | 661      | 0.50  | 0.24 | 21.94  | 0.7338 | 17.46  | 0.3041    | 79.13  |\n| 30     | 661      | 0.25  | 0.30 | 22.79  | 0.7835 | 17.82  | 0.3014    | 75.58  |\n| 25     | 661      | 0.25  | 0.28 | 21.89  | 0.7681 | 17.70  | 0.3062    | 70.70  |\n\n\n\n\nSadly, we cannot include the full sweep plot here because rebuttals do not allow image attachments, but the **middle of Fig. 5** offers a similar reference. It is not identical to the sweep — the latent there is fixed (derived from the forward process around L160), whereas the initial findings in Eq. 1 used matching latents (latents created at the same timestep as the timestep condition). This still provides a reasonable approximation of how the timestep condition influences structural control.\n\n---\n\n### (3) Ablation Depth and Design Choices\n\nWe acknowledge the reviewer’s point that the ablation section would benefit from stronger quantitative backing. Section 5.2 already examines the key factors — injection depth, σ (noise factor), and timestep condition — and Fig. 5 shows how each parameter affects generation. We initially leaned on qualitative comparisons because the changes are **immediately and clearly visible** — for example, deeper injection tightens structure but dulls colors, and adjusting the timestep condition shifts how rigid the layout becomes — and these visualizations were considered the most direct way to convey how each design choice influences the results.  \n\nThat said, we agree that numbers are needed to complement the visuals. **We have already run a mini‑batch of quantitative ablations** as in Table C. **We are extending this work and will add fuller quantitative results and a more detailed analysis from multiple dimensions in the paper and appendix to justify these design choices more completely.**\n\n---\n\n### Responses to the reviewer’s specific questions\n\n**1. Fig. 1 clarity**  \nWe agree Fig. 1 needs clearer labeling. In the revised paper, we will provide an updated version that explicitly marks *Reference Image*, *Prompt*, and *Generated Output* so the input–output flow of FreeControl is obvious at a glance.  \n\n**2. Missing related work**  \nWe agree that additional related work should be acknowledged. Citations to **RB‑Modulation**and **RL‑based editing** will be added. We will also further review the literature and include additional relevant references to strengthen the related work section.\n\n**3. Eq. 1 “linear interpolation”**  \nEq. 1 uses the **forward process from a Euler-discretized FlowMatch scheduler**. It applies the forward process at timestep *t* to simulate a noised latent for query extraction. We omit the calculation of the sigma value here for simplicity, as the t-dependent sigma is later replaced by a fixed value.  In LCD mode, we remove the stochastic term to avoid speckle artifacts and improve stability.  \n\n**4. Ablations around L133**  \nThe ablations the reviewer flagged as “missing” are in fact already included in Sec. 5.2 (L274) and illustrated in Fig. 5. That section varies layer injection depth, σ, and timestep condition, showing how each choice affects control strength and image fidelity. **We have also added quantitative ablations, as discussed above, to provide numerical backing for these choices.**  \n\n**5. “Mid‑to‑late layers” explanation**  \nThis phrase refers to layers closer to the model’s output side — beyond the very early layers that model color and texture. Injecting queries here provides strong semantic and structural guidance **without disturbing** early‑stage appearance modeling, as shown in Fig. 5.  \n\n**6. Question about Eq. 160 and timestep (L166)**  \nThere is a misunderstanding here: **the *t* in Eq. 1 is not the same as the timestep condition.** In Eq. 1, *t* is used in the forward process to generate the latent. In **Section 3.2**, we decouple these by introducing LCD — the equation around **L160** generates the latent in a way that no longer depends on *t*. The timestep mentioned in **L166** is the **timestep condition** passed to the diffusion transformer during denoising, **not** the *t* used in the forward process."}, {"review_id": "xtzVpMPR7H", "reviewer": "Reviewer_RWpJ", "summary": "This paper proposes a new training-free strategy for structural control by using only one-step attention extraction. Specifically, the method extracts the attention query matrix from a specific time step and certain layers during the inversion process and injects it into each time step during denoising. This approach greatly simplifies the process and offers a novel direction for future research. Experimental results demonstrate the effectiveness of the proposed method.", "questions": "Please try to solve the questions proposed in weaknesses section.", "limitations": "yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "## Strengths\n1. The proposed method is simple yet effective. By extracting attention features from only a single step, it significantly reduces the operational complexity compared to previous approaches.\n2. The presented results are impressive. Moreover, the method demonstrates natural and coherent composition when combining multiple contents.\n3. The overall writing is clear and easy to follow, making the paper accessible and understandable.\n\n## Weaknesses\n- The paper lacks sufficient quantitative and comprehensive experimental analysis to justify the selection of specific time steps and layers (e.g., time step 661 used in the paper). Relying solely on empirical evaluation seems somewhat arbitrary and raises questions about whether better choices of time steps and layers might exist. Moreover, if the underlying model (e.g., FLUX) were replaced with SD3 or another DiT-based model, would the same choices still apply? Are the observed patterns model-specific or generalizable? A more rigorous analysis—similar to what was provided in **Stable Flow: Vital Layers for Training-Free Image Editing [CVPR 2025]**—would help to support the design decisions and strengthen the method's justification.\n- The comparison baselines used in the paper are mostly outdated models, which weakens the credibility of the experimental results. Since the proposed method is built upon a strong prior model (Flux .1-dev based on DiT), the improved performance may partially result from the strength of the underlying model itself rather than the proposed strategy. It would be more convincing to include comparisons with recent SOTA models that are also based on Flux or DiT architectures. The authors are encouraged to consider adding comparisons with the following works:\n    - **Taming Rectified Flow for Inversion and Editing**\n    - **OminiControl: Minimal and Universal Control for Diffusion Transformer**\n    - **EasyControl: Adding Efficient and Flexible Control for Diffusion Transformer**\n    - **In-Context Edit: Enabling Instructional Image Editing with In-Context Generation in Large Scale Diffusion Transformer**\n    \n    Alternatively, adapting the proposed method to smaller-scale DiT models such as SD3 could help demonstrate its robustness and generalizability.\n    \n- In the examples involving human generation and editing, it appears that facial identity is not well preserved. This suggests that the method's controllability may still be limited, especially in more fine-grained or identity-sensitive tasks.", "was_revised": true, "final_justification": "The authors have provided detailed and thoughtful responses during the rebuttal, which have largely addressed my initial concerns. I appreciate the additional experiments and clarifications, and I believe the planned inclusion of the qualitative and quantitative comparisons discussed in the rebuttal will further improve the clarity and persuasiveness of the paper. Overall, I support acceptance.", "rebuttal": "### Rebuttal to Weakness 1: Quantitative Analysis of Ablations\n\nWe appreciate Reviewer 4’s call for deeper quantitative analysis of the ablations — this is a fair point and will strengthen the paper. Our initial focus on **qualitative illustrations** in Sec. 5.2 and Fig. 5 was driven by two considerations:\n\n1. The shifts caused by hyperparameters (layer depth, σ, timestep condition) produce **very visible changes** — such as structure tightening or relaxing and granularity change — rather than subtle metric tweaks. We believed showing these striking differences directly would help readers intuitively understand how each setting affects the result.  \n\n2. Because these parameters can combine in many ways, exhaustive metrics for every combination would create a prohibitively large experiment grid.\n\nThat said, we fully agree that numerical backing is important. **We have already run a minibatch of quantitative ablations** and include those results below, with more comprehensive runs planned for the camera‑ready.\n\n#### Table A: Mini‑Batch Quantitative Ablation Results\n\n| Layers | Timestep | Sigma | F1 ↑ | MSE ↓ | SSIM ↑ | PSNR ↑ | CLIP-T ↑ | FID ↓ |\n|--------|----------|-------|------|--------|--------|--------|-----------|--------|\n| 25     | 561      | 0.25  | 0.27 | 22.60  | 0.7504 | 17.42  | 0.3072    | 71.40  |\n| 25     | 761      | 0.25  | 0.28 | 23.89  | 0.7727 | 17.95  | 0.3042    | 78.03  |\n| 25     | 661      | 0.00  | 0.29 | 22.15  | 0.7874 | 18.10  | 0.3056    | 69.94  |\n| 25     | 661      | 0.50  | 0.24 | 21.94  | 0.7338 | 17.46  | 0.3041    | 79.13  |\n| 30     | 661      | 0.25  | 0.30 | 22.79  | 0.7835 | 17.82  | 0.3014    | 75.58  |\n| 25     | 661      | 0.25  | 0.28 | 21.89  | 0.7681 | 17.70  | 0.3062    | 70.70  |\n\n#### Table B: Mini‑Batch Quantitative Ablation Results on Stylized COCO\n\n| Layers | Timestep | Sigma | F1 ↑ | MSE ↓ | SSIM ↑ | PSNR ↑ | CLIP-T ↑ |\n|--------|----------|-------|------|--------|--------|--------|-----------|\n| 25     | 561      | 0.25  | 0.22 | 27.80  | 0.6910 | 15.66  | 0.2425    |\n| 25     | 761      | 0.25  | 0.25 | 26.92  | 0.7485 | 17.03  | 0.2411    |\n| 25     | 661      | 0.00  | 0.26 | 25.20  | 0.7589 | 17.20  | 0.2606    |\n| 25     | 661      | 0.50  | 0.19 | 25.87  | 0.6801 | 15.62  | 0.2396    |\n| 25     | 661      | 0.25  | 0.25 | 30.80  | 0.7258 | 16.30  | 0.2427    |\n| 30     | 661      | 0.25  | 0.28 | 24.08  | 0.7728 | 17.28  | 0.2404    |\n\n\n\nOn **timestep 661**: the discussion in **L166** and the sweep in **Sec. 5.2/Fig. 5** already show that we evaluated the entire range, and 661 provided the best balance for Flux. There is also a clear **trend from both directions** — from timestep 0 moving up toward 661 *and* from timestep 1000 moving down toward 661 — where **structural alignment and generation quality steadily improve, peaking around the optimal region near 661**. The partial quantitative results are reflected in Table A, and a more complete version will be provided later.\n\nWhen we tested FreeControl on other backbones, the exact numbers shifted — as expected — but the **principles for selecting these hyperparameters and how they affect control remained consistent**. These principles are carefully embedded into the **method section** so they can guide future applications of FreeControl to new models.\n\nWe thank the reviewer again for highlighting this — it prompted us to expand our quantitative experiments and commit to adding **more rigorous analysis from multiple dimensions** in the paper and appendix to fully justify these design choices. An additional analysis regarding matrix similarity is also provided in our response to Reviewer T9YS, which may be referenced for further insights.\n\n\n---\n\n### Rebuttal to Weakness 2: Baselines\nWe sincerely thank Reviewer 4 for emphasizing the importance of comparing FreeControl against recent Flux/DiT‑based control methods and for providing helpful recommendations. **This was a valuable point, and we acted on it directly.**\n\nUsing released code, we ran **In‑Context Edit** and **Taming Rectified Flow**, adding their results in **Table A** below. The other suggested methods currently have no public implementations, but we will include them in the camera‑ready version once code becomes available.\n\nAs shown in **Table A**, FreeControl performs comparably to — and in several cases surpasses — Taming Rectified Flow across key metrics. **In‑Context Edit (ICEdit)**, built on the FLUX.Fill model for **image completion (filling areas in existing images)**, scores strongly on similarity metrics because its base model is highly conservative in retaining existing content, but is performing less optimally on CLIP-T on both Table A and B. Visual results from the experiments in Table B further highlight its limitations: despite favorable metrics, ICEdit frequently produces unnatural and low-quality outputs that appear unrealistic or visually jarring—such as disjointed objects, missing body parts, or incoherent scenes. In contrast, our method demonstrates significantly greater stability and consistently generates higher-quality, more natural, and human-acceptable results\n\nWe chose **Flux** intentionally to demonstrate FreeControl’s effect on a strong, modern backbone — but stronger generative quality alone does not guarantee easier structural control. That is why we prioritized adding **new baselines built on Flux itself**. These comparisons show that our method performs comparably or better than other Flux‑based methods on metrics, and that those methods often produce generations with severe visual flaws. In contrast, FreeControl consistently produces stable, natural‑looking images, which is essential for applicability.  \n\nWe also note that **Table 1** of the paper already includes **ControlNet‑Flux**, the latest Flux/DiT ControlNet adaptation, ensuring FreeControl is  also evaluated against modern Flux‑based pipelines.\n\n\n\n#### Table A: Mini‑Batch Expanded Baseline Comparison Results\n\n| Method                 |  F1 ↑ | MSE  ↓  | SSIM ↑  | PSNR ↑ | CLIP-T ↑ | FID ↓ |\n|-----------------------|------|-------|----------|--------|-----------|--------|\n| In‑Context Edit       |  0.43 | 24.63 | 0.8050   | 19.68  | 0.2787    | 67.63  |\n| Taming Rectified Flow |  0.19 | 28.22 | 0.6131   | 17.41  | 0.3160    | 72.49  |\n| FreeControl           |  0.28 | 21.21 | 0.7580   | 17.43  |0.3076       | 66.72  |\n\n#### Table B: Mini‑Batch Expanded Baseline Comparison Results on Stylized COCO\n\n| Method                 |  F1 ↑   | MSE ↓  | SSIM ↑ | PSNR ↑ | CLIP-T ↑ |\n|------------------------|-------| ------- |-------|-------|---------|\n| In‑Context Edit        |  0.28 | 43.27 | 0.5680   | 14.55  | 0.2248    |\n| Taming Rectified Flow  |  0.17 | 39.22 | 0.5610   | 16.88  | 0.2606    |\n| FreeControl            | 0.23    |     27.09     | 0.7087      | 15.98   | 0.2419       |\n\n\n\n---\n\n### Rebuttal to Weakness 3: Facial Identity\n\nWe thank Reviewer 4 for pointing out the identity issue in some of our human examples. The reviewer is correct that some samples appear looser — this was intentional, as in Fig. 1 we used relaxed control settings (σ/timestep) to illustrate FreeControl’s ability to support different levels of structural strength and granularity, such as a comic‑style look. When identity retention is required, FreeControl supports much tighter guidance through the same hyperparameter adjustments described in the method section. \n\nWe have prepared additional facial examples to demonstrate this, but unfortunately, since rebuttals cannot include new images or figures, we are unable to show them directly here. You could refer to the wolf in the **middle‑left** of Fig. 1 for a rough approximation of this fine‑grained control — though we note that FreeControl actually supports even **higher** control of details when the hyper-parameters are tuned for identity‑sensitive tasks. We will provide a comprehensive discussion on this topic, accompanied by a complete quantitative ablation study in the full paper.\n\nOverall, as long as the prompt itself does not explicitly request facial changes, FreeControl could maintain identity features under higher control. For example, in one prepared (but not attachable) sample, adding a “cyberpunk face implant” preserved the subject’s facial details to a high extent while incorporating the requested implant — but if the prompt describes a more direct change to **global facial identity**, then in this case the facial identity is not guaranteed to be preserved unless control is pushed to an extent where it begins to limit the creative freedom of other aspects of the image (and since rebuttals cannot include new images, we explain this nuance here in words rather than figures)."}]

Author rebuttal:

Meta-review: This paper proposes Free-control, a training-free test-time framework for semantic structural control in diffusion models based on attention maps. Reviewers are largely positive about the contribution, commenting on the significance of the improvement over methods like controlnet, the practicality of the approach, and its effectiveness. There are also some concerns: the analysis was limited to a select few layers and time-steps and to see more variety in the datasets and architectures evaluated on, both of which were addressed during rebuttal. Another concern was the sensitivity to the choice of layers (and other hyperparameters) and the "how" and "why" things work, both of which were addressed by the authors.

A lingering concern by one reviewer is that of novelty: the proposed method is "simple and elegant", but indeed of limited novelty compared to prior work in this space. In subsequent AC-reviewer discussion, one reviewer decided to increase their score indicating that despite the limited novelty, this paper's contribution is significant. Another reviewer already previously increased their score and is support of acceptance as well.
Decision: Accept (poster)