STEER AWAY FROM MODE COLLISIONS: IMPROVING COMPOSITION IN DIFFUSION MODELS
Authors: Debottam Dutta
Jianchong Chen
RAJALAXMI RAJAGOPALAN
Yu-Lin Wei
Romit Roy Choudhury
Venue: ICLR 2026 Poster (2026)
Keywords: Composition
classifier-free guidance
diffusion
text2image
corrector sampling
Primary area: generative models

Abstract: We propose to improve multi-concept prompt fidelity in text-to-image diffusion
models. We begin with common failure cases—prompts like “a cat and a dog”
that sometimes yields images where one concept is missing, faint, or colliding
awkwardly with another. We hypothesize that this happens when the diffusion
model drifts into mixed modes that over-emphasize a single concept it learned
strongly during training. Instead of re-training, we introduce a corrective sampling 
strategy that steers away from regions where the joint prompt behavior overlaps 
too strongly with any single concept in the prompt. The goal is to steer
towards “pure” joint modes where all concepts can coexist with balanced visual
presence. We further show that existing multi-concept guidance schemes can operate 
in unstable weight regimes that amplify imbalance; we characterize favorable 
regions and adapt sampling to remain within them. Our approach, CO3, is
plug-and-play, requires no model tuning, and complements standard classifier-free
guidance. Experiments on diverse multi-concept prompts indicate improvements
in concept coverage, balance and robustness, with fewer dropped or distorted concepts 
compared to standard baselines and prior compositional methods. Results
suggest that lightweight corrective guidance can substantially mitigate brittle 
semantic alignment behavior in modern diffusion systems. Code is available at
https://github.com/debottam-dutta7/co3

Published as a conference paper at ICLR 2026
STEER AWAY FROM MODE COLLISIONS: IMPROVING COMPOSITION IN DIFFUSION MODELS
Debottam Dutta Jianchong Chen Rajalaxmi Rajagopalan
Yu-Lin Wei Romit Roy Choudhury
University of Illinois at Urbana-Champaign
ABSTRACT
We propose to improve multi-concey prompt fidelity in text-to-image diffusion models. We begin with common failure cases prompts like "a cat and dog" that sometimes yields images where one concept is missing, faint, or colliding awkwardly with another. We hypothesize that this happens when the diffusion model drifts into mixed modes that over-emphasize a single concept it learned strongly during training. Instead of re-training, we introduce corrective sam- pling strategy that steers away from regions where the joint prompt behavior over- laps too strongly with any single concept in the prompt. The goal is to steer towards "pure" joint modes where all concepts can coexist with balanced visual presence. We further show that existing multi-concept guidance schemes can op- erate in unstable weight regimes that amplify imbalance; we characterize favor- able regions and adapt sampling remain within them. Our approach, CO3, is plug-and-play requires no model tuning, and complements standard classifier-free guidance. Experiments on diverse multi-concept prompts indicate improvements in concept coverage, balance and robustness, with fewer dropped or distorted con- cepts compared to standard baselines and prior compositional methods. Results suggest that lightweight corrective guidance can substantially mitigate brittle se- mantic alignment behavior in modern diffusion systems. Code available at bttpps://itte com debot tam duttaa/coa
INTRODUCTION
Recent diffusion models (Ho et al. 2020 Ramesh et al. 2022a Rombach et al. 2022) have ushered significant breakthroughs in text-to-Image (T21) synthesis, producing high-fidelity images from textual descriptions. However, ensuring the generated images faithfully adhere to the prompt, challenge known as semantic alignment (Chefer et al. 2023 [aihang Hu et al. 2024 Liu al. al. 2023))
remains problem. Concretely, for a given prompt C. T21 models like StableDiffussin (Rombach al. 2022) sample from the modes (or high probability regions) of the learned distribution, p(æ C). While such models can produce high resolution images in general, every often, the results are surprisingly misaligned even for very simple prompts containing few concepts, e.g., C="a cat and a dog". Diagnosing exactly why this behavior emerges periodically is difficult. It is conceivable that the complex training process in high dimensions, especially in conjunction with text embeddings, creates some problematic modes in p( C).
We hypothesize that problematic modes in p(x | C)) arise when they overlap with modes of indi- vidual concept distribution p( c:). Such overlap biases the generation toward single concept C; reducing the prominence of others. For instance, across images c₁ = "'cat in the training dataset, a few may have an inconspicuous may still fall under the mode of p(x C).. We attribute this to training instabilities and relatively less coverage of multi-concept prompts C, which cause the model assign high probability even to weakly conforming images. Said differently, an image of a big cat and an inconspicuous dog can get assigned high probabilities under p(x) C),, causing semantic misalignment.
partial C2 "dog" in the background. This image
Preventing such problematic modes warrants strict and specialized training paradigms; difficult task for such large models. However, "curing" them after their occurrence is more viable approach
"Corresponding author: Email: dd24 aiziiiliioos

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Published as a conference paper at ICLR 2026
Proposed corrector distribution
p(x(c)
p I1p((1e)
p(x(c))
p(x(c2)
Figure 1: The figure illustrates our hypothesis on mode overlap using simple 2D toy example. (a) Two modes of the distribution p(r "a cat and a dog") (in green contour) has significant overlap with the modes of the individual concept distributions p(r "a cat") (in red contour) and pe(a "a dog") (in orange contour). (b) The proposed (unnormalized) corrector distribution p(x "a cat and a dog")/(p(z) "a cat")p(x "a dog")) suppresses these overlaps, steering the generation away from problematic modes. The arrows indicate the denoising directions.
Assuming our hypothesis is true, we propose a cure for problematic modes. Our intuitive idea is go away from problematic modes and move towards modes under which none of the individual concepts are strong. To realize this, we propose to design a corrector that generates samples from the following distribution:
pf C)
p(r C
II,
p(E c.)
Figure illustrates the intuition behind our proposal. Our corrector distribution p(x C)) assigns low probability to regions where p(x C) overlaps with individual p(r c;); we deem them as degenerate modes dominated by single concept. By suppressing these overlaps, the corrector em- phasizes pure p(r C) modes where all concepts coexist without one overwhelming the others. From probabilistic perspective, this acts corrective factor: while p(z C") may assign high probability to weakly conforming images due to training noise or limited multi-concept data, divid- ing by the marginals removes this bias and sharpens the distribution toward genuine multi-concept samples. As a result, the modes we target are more semantically aligned and less prone to concept suppression or distortion.
Correction sampling from p(x C.) can be achieved by composing scores from constituent compo- nent distributions V, log p(x C)2 V log p(x c:)): (Liu al. 2022). While there many ways to compose, we analyse and show that composition through weighted sum of Tweedie-meanss the Tweedie denoised space-offers more general framework that subsumes existing approaches.
In particular, we show that two classes of correction sampling noise-resampling and latent correc- tion (Bao et al. 2024; Rassin al. 2023 taihang Hu et al. 2024 Kwon & Ye 2025 2025) become special cases of Tweedie-ment composition under different weighting schemes. This allows to design hybrid composition framework that serves the purpose of resampling at time T, and then toggles to latent correction at later steps. By latent, we mean that the correction steps are accom- plished in between each DDIM time step, allowing CO3 be a plug-and-play model-agnostic and gradient-free approach for T2I models. Comparison of CO3 against SOTA baselines shows stronger semantic alignment to prompts (measured using multiple metrics), giving empirical evidence of our hypothesis.
2 BACKGROUND
Conditional generation using Classifier-Free Guidance (CFG). In diffusion- based Text-to- Image (T2I) generation (Rombach et al.) 2022 Saharia et al.) 2022a Ramesh et al. (022b), given the noisy latent 2; at timestep a denoised estimate can be derived using Tweedie's formula:
It VI aj c0 (2,, c,t)
(2)
Va
2

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Published as a conference paper at ICLR 2026
where eg denotes the predicted noise conditioned the text prompt and de is the cumulative product of the noising schedule. This step corresponds to the denoising stage, recovering an estimate of the clean signal Zn- In the DDIM sampler (Song et al. 202I). under the noise-free condition, the subsequent step deterministically evolves To to 24-1 without introducing additional stochasticity:
It- - v 80 + VI V1-a:-1 C,
Here, the same predicted noise eœ is reused, eliminating the renoisification step present in stochastic samplers such as DD et al. 2020). This perspective highlights how the Tweedie decompo- sition denoising via Zo, followed by deterministic reconstruction of 2;-1, is naturally aligned with DDIM sampling.
In practice, most T2I models adopt classifier-free guidance (CFG) (Ho & Salimans 2022 where predictions from both the conditional and unconditional models are combined:
A.c X c, (1 A) en( 2,()
where À > 1 controls the guidance strength. c, is the composed noise prediction under CFG. Then the denoising and DDIM steps proceed as before, but using c in place of ea ca( c,
Tweedie View with CFG. Substituting A.E into the Tweedie denoising and DDIM update yields:
A.c Cr.
Et
1
Stmendie (ec 2.
(5)
Vot
v 04
xt-1
04-1
Ptweedie (et
2.+ + /I āc-1 Et
(6)
Vat
where Ptweedie (6 A.0 == Ft V
Org 6t is the Tweedie mean from the CFG noise at l. Thus, CFG in the Tweedie framework can be interpreted as modifying the denoised estimate 2n to aA. before the deterministic step to -1 (Chung et al. 2024 Kwon Ye 2023).
Correction-based approaches for conditional generation. A number of recent works have ad- dressed the challenge of compositional text-to-image generation using correction-bee baaed approaches (Chefer et al. 2023: Liu et al., [2023] Bao et al. 2024; Rassin et al. 2223) taihang Hu et al., (024)). During sampling with classifier- free guidance, these methods iteratively correct the latent variable
by applying gradient updates of the form
- = x - ((,,, k 1, M 1.
and then use the final refined latent zzM to predict the next DDIM step IT-1, using Eqs. 5 Here L(xe, c) is a task-specific loss function that enforces better alignment with the target condition and s is step size. This iterative update can be interpreted correcting the diffusion guidance process using corrector distribution of the form:
Pe (z c) x exp(-I(xx, c)),
(8)
which refines the generative process at each timestep. In the special case where the step size s = of 1 Or (the noise scale timestep 1), the update rule becomes equivalent to iterative Tweedie- mean correction (see Eq. above), where x; k+1 is reinterpreted the next estimated Tweedie mean given x for the distribution (e(
Composable-diffusion Generating samples that satisfy multiple conditions fci...., cx) can be formulated as sampling from the joint distribution
Po ci....,ck) x p(ro) II Polck xo).
To achieve this, Liu et al. (202)) proposed composable diffasion, which directly composes the output scores (predicted noises) from different conditional diffusion models using CFG during sampling.
Specifically, in the text-to-image setting, if the prompt C' can be decomposed into constituent con- cepts {c1, ,,, ). the outputs of the corresponding diffusion chains can be combined
A.C ct Du (c E
+ X2( + + >x (E
(10)
3

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where e, denotes the unconditional score, and A; controls the guidance strength for concept Cg- The next sample is then predicted via the Tweedie formulation:
1t-1
Eqweodic
A.c. Et
-V1 V1- A.C
(11)
Although this approach is model-agnostic and conceptually simple, its performance is often poor, since the above linear composition of scores incorrect doesn't I correspond to the score of the diffusion forward distribution [r ci....,ck) at any timestep & >> (Du et al. 2024).
In summary, both the correction- approach and the composable diffusion idea can be inter- preted different ways of approximating Tweedie- meanns &tweddie (c time t.
CO3: CONCEPT CONTRASTING CORRECTOR
We aim combine the strengths of correction-base approaches and composable diffusion. On one hand, correction-basse methods are powerful and explicitly improve compositional alignment, but they are subject the complex gradient of the base model. On the other hand, composable diffusion is fully model-agnostic, but suffers from poor performance because linear score composition is not consistent with the diffusion forward process Du et al. (024)..
To take advantage of both, assume that prompt C can be decomposed into constituent concepts (ci, C2,-.... CK). We propose an explicit Concept Contrasting Corrector (CO3) distribution based on our hypothesis on mode overlap discussed in Sec. | Specifically, we define the corrector distri- bution each timestep as:
(r C)u0
Pe C)
(12)
I PT cx)
where (Wo, wi...., wxk are composition weights. As discussed in Sec. [ this corrector steers the generation toward regions where the distribution pr(2%, C') is high, while simultaneously avoiding regions of overlap with the individual concept distributions {Pr(2, [+)]x. Observe that this en- courages the model generate samples that satisfy all concepts in C' without over-emphasizing any single concept. We present the CO3 corrector pseudo code in Algorithm !
To sample from the unnormalized probability distribution in Eq. 12 an well-known approach is to compose the concept distributions (2, cx) } in the space of score functions (Liu et al. 2022 Du al. 2024). We break-away from this approach and compose the distribution in the Tweedie mean space; we show how this offers more general framework for composition. In Tweedie-denoise space we define composition as:
Ttweedie = Wy &tweedie (c, Cc2.C + Wi &tteedie (2 + + WK @eweedie (et Aoox]
(13)
where Wi and "01,...,up. WK
concept weights. Note: Ptmendic A..cu is
Algorithm DDIM with CO3 Corrector
the Tweedie mean corresponding to the CFG composed noise prediction A.ce for concept C with guidance weight A.
Require: 2r, number of DDIM steps T, timestep
threshold T., prompt C, set of concepts {cu) for down to do if T. then x
How should composition weights be cho- sen? We analyze the effect of different weight assignments to Eq. [3] in particular, how the constraint on the concept weights ws influences both the theoretical interpretation and the empirical behavior of the composi- tional Tweedie mean.
(2) + CO3( C,c,,..., ck)
> Proposed correction
(2)
x
end if Se DDIMMstep(z.)
Reverse DDIM
7: end for
valid compositional Tweedie mean Ttweedie in Eq. 13 must be one that can be expressed in the definition given in equation 5 DDIM goes through senies of Tweedie means for image generation as described section 2 Proposition [ shows that this is guaranteed only when the weights satisfy
the normalization condition Σ, w;

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Proposition 1. Let wiweddie 1c ( 21 oi Ae be the tweedie mean from CFG composed noise E A(f -eg) for some X Let, 2 Tewediie be the composed tweedie-mean defined as &tweddie Ex wx&tweedsells 122 Then,
a) CO3-corrector: Prmeedic can be expressed in the form of a tweedie-mean at time t, i.e. dtweddie = F,CCC. S.C if and only if >x wk = Here x = > and CFG composed noise 2.c c
- + WKE -G.).
b) cO3-resapple: itweedic = -o Σx W is weighted noise if and only if Es Wkk = O.
Remarks: See Appendix section A.2 for proof. Proposition 1 a) shows that when Σx Wk = 1,
denoted as CO3-corrector, the composed Tweedie-mean corresponds to the CFG composed noise
-AC & + + " - -E,) with the same guidance scale X. This preserves the relative guidance strength between unconditional and conditional scores proposed by classifier free guidance (CFG)
E
(see Eq. 4.. This is in contrast to composable diffusion methods (Liu et al. 2022 Du et al. 2024)
which use arbitrary guidance weights {Λr} for composing different concepts (see Eq. I0); hence this does not preserve the CFG form. We believe this explains, at least partly, why composable diffusion often produces out of manifold samples. See Sec. A.3 in Appendix for more discussion.
shows that when the weights sum zero, denoted as resampler, composed Tweedie mean is independent of the current sample 2; and is only a function of the concept noises. Instead of correcting the current sample Tt, it replaces 24 with a weighted combination of the concept noises. In other words, when Σx Wg = 0, the composition is sampling from noise, and only valid at = T where 2T NO 1).
Proposition
To further characterize the behavior of the composition under the two conditions E④ Wt = 1 and Σg W/: = O, we evaluate the performance of different weighting strategies across timesteps of the diffusion chain. Figure[]revedd complementary dynamics between the resampler and the corrector. Specifically, the resampler (Ex w/ = 0) is effective at high & values near T, but its effectiveness diminishes the denoising process progresses toward lower l. Although resampling is not theoreti- cally valid for < T, we observe practical improvements until approximately t ze 0.97' In contrast,
the corrector ( k Wy 1) exhibits gradual improvement with increasing timesteps and saturates around t xe 0.75I' These findings invite a hybrid strategy into CO3: apply resampling during early timesteps (t Tn), followed by correction until threshold (Tc < t < TR), beyond which further gains are marginal.
The pseudocode for the weiihtt resampler (CO3-resampler) and the unit-sum- (CO3-corrector) corrector is provided in Algorithm 2 and Algorithm 3 respectively. Importantly, the same diffusion denoiser model can be reused compute the individual concept scores under different input conditions Ck, thereby eliminating the needfor any costly backward passes to obtain gradients. Furthermore, the framework operates without reliance on model-specific architectural details, making it fully model-agnostic and gradient-free.
Closeness-Aware Concept Weight Modulation: To make the CO3 corrector more adaptive, we an- chor the weight wig (wg=1.0 in Algo. 2 and wp
2.0 in Algo. 3. and assign weights to each con- cept based on how close the current noise predic- tion e is, to the noise corresponding that concept, est, Intuitively, if sample looks closer to concept Ck than all others, we want penalize Cs more strongly (i.e., give a larger negative weight), while reducing the strength of the other concepts. This en- courages the sampler to move away from the near- mode, preventing collapse toward one dominant concept. Formally, let dj = ||e_ c* I| denote the distance between the current sample and the mode of concept cj- We convert distances ) k= into affinity scores using an exponential kernel:
ImageReward
CO3-resampler Ww 0)
CO3-corrector (w. 11
timesteps
Characterization of Resampler and Corrector steps. Resampling is more powerful high while the Corrector improves slowly with more timesteps and saturates.
Figure 2:
a4 exp(-
B >
(14)
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Algorithm 2 CO3 CO3-resampler ( Wk 0)
Algorithm 3 CO3 cor-ecorrctor ( we
Require: 2g, denoiser v., a,, work, guidance X, Require: 2₁. denoiser 1b ãt, Wo:K, guidance X,
prompt C, concepts {c
prompt C, concepts {cE)
1: for p to doo
1: for p 1 to P do
2: c = (2 C)2 E = (b ck),
2:
c = (1 C), E = (2 Cx),
3:
k == I..., K
I....,KK K
4:
Peweedie ( c. = Ut VI dge
A.c
tweedic k= Wy Tr.etreedie ( A.ct] Tweedie mean composition
4:
5:
Stweedie = - -VVI at Ek Wh Projection onto noise space
T [ mean normalization
6:
2 tteeddie + vI āi of Uncond. manifold correction
[| @tweedie t Diweedie * 6:
(2) 2t
3 Etwerdie + vI 0t Eg
7:
Tt =
7:
8: end for
9:
return 24
8: Tt + Zt
end for
10: return It.
so that concepts closer to the current sample (smaller d4) receive higher affinity. These affinity scores are then normalized to define the weights:
Wk
(15)
2j=1 aj
which ensures each weight is negative and the total sum satisfies Σ wkk -1. As result, con- cepts that are closer to the current sample receive stronger negative weights, while farther concepts are down-weighted
proportion to their distance. This weight modulation scheme samples from the whole hyper-plane Σк= Wi = (for C03-correction) or Σ Wk = (for CO3-resampling) instead of fixed weights for the entire course of generation.
EXPERIMENTS
4.1 EXPERIMENTAL SETUP
We implement our method on SDXL (Podell et al. 2023) using 50-step DDIM sampler with guidance scale = 5.0. CO3 applied during the first 20% denoising steps (T, 0.8T), with the initial three steps reserved for resampling. For weight modulation, set 3 = 0.8. All experiments are performed on single NVIDIA A6000 GPU (48GB). Additional implementation details provided in the Appendix section A.4
Implementation details:
We first evaluate our method on simpler prompts (two con- cepts) using the Attend- benchmark from Chefer et al. (2023) which contains categories of prompts: Animal-Animal, Animal-Object, and Object-Object. We then take the best perform- ing baselines from this task and compare them with CO3 on more complex prompts (three or more concepts) from 12ICompbench Huang et al. (2023) and rare or novel concept prompts from RareBench (Park al. 2024). Specifically, we consider CCmplexx" category from T2ICompbench and "Concat", "Relations" and "Complex" from RareBench; the subset of categories which stress multi-concept interactions, attribute entanglement and rare-concept scenarios.
Evaluation benchmark and metrics:
For evaluation, we adopt BLIP-VQA (Huang et al. 2023), ImageReward (Xu et al. 2023) and DINOv2 (Oquab et al., 2023) scores for simpler prompts. While BLIP-VQA and ImageReward (widely used in recent works (taihang Hu et al. 2024 Hu et al.) 2024 measure the faithfulness of generated-imet -mmdges to prompss DINOv2 measures the quality of concepts generated by each model via a classification score. R2F Park et al. (2024) notes that BLIP-VQA can be unreliable in rare-concept scenarios; hence, following their recommendation, we report metrics that more closely reflect human judgement, namely, ImageReward and human evaluation (from 11 participants).

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Published as a conference paper at ICLR 2026
Table 1: Quantitative comparison different methods on two concept prompts from Chefer et al. (023). We evaluate the generated images using three metrics: BLIP-VQA, ImageReward and DI- NOv2 scores on categories
Animal- Objjnt--)jet((AAO) and Object-Ohject(OOO),
The top performing model is highlighted in and the 2nd best in
Properties
BLIP-VQA
ImageRoward
DINOv2
train
grad-
model
A-A
A-O
0-0
A-
A-O
0-0
AA
A-O
0-0
agnostic
Rombboc
0.3233
0.5958
0.2730
40,2733
0.4622
40,5521
02163
0.2611
0.3010
A d-cxeiteyChhfer ot.
X
0.0980
0.7865
0.5155
0.824
1,2300
0:741
0.2245
0.259
0.21
2025
al.
2025
0.3348
0.769
06395
-0.2455
0.8388
0.3315
0.2187
0.2406
0.970
Divide
x 0.7301
0.5337
0.5499
1,2516
0.2029
0.3411
IniNN
1io st al.
2024
x
0.7264
0.1998
0.5406
1,0082
1.3927
1,1383
0.2252
0.2654
0.953
SDX SDXD et
2023
0.6950
0.3654
0.4926
0.7820
15554
0.6789
0.2234
0.2618
0.2629
Magma Zhtingg 61
V
x
0,6782
0.8744
0.5398
0.6150
1 1.985
0.7856
0.2122
0.2636
0.2624
2024
0.6816
0.4652
1.622
0.2221
0.2100
2033
To01 Iaihang Hu
x
0.6257
0.3308
0.6440
0.3895
15736
1.0118
0.2226
0.2620
0.2545
2024
Teooddomm Kwon Kannn A Yo
0.7390
0.3029
0.4683
1,0023
1.3127
0.7959
0.2519
0.3105
0.3237
2025
Comp--iif
0.5656
0.459
-1.1399
40,2068
-0.0955
0.3497
0.7441 0.3878
0.5146
1.2342
1.6744
1.0158
0.2623
0.3259
0.3285
Comparison methods: We compare C03 against state-of-the-art approaches, including optimization- correction methods (Attend-Excin (Chefer et al. (023), SynGin (Rassin et al,, 2023), Divide-Bind (Liu 2023), Magnet Zhuang et al. (2024). ToME (taihang Hu al. 2224));
composable generation methods Comp-Diff (Liu et al. 2022). Tweediemix (K (Kwon & Ye 2025 ;
LLM augmented method R2F (Park al. 2024) and the coieeopptiiiztionn method InitNO GGuo et al. 2024). For fair comparison against Tweediemix's 's sampling strategy, we implemented Tweed- iemix without its L.RAA-inetuned customization stage. All optimization- and require gradient computations at inference, with InitNO being model- agnostic but still incurring costly backpropagation. In contrast, only Composable Diffusion, Tweed- iemix and CO3 are purely sampling- basdd, model-agnostic, and completely gradient-free.
approaches are
architecture-dependee
4.2 EXPERIMENTAL RESULTS
Table 2: Quantitative results on multi-concept prompts from T2ICompbench and rare-concepts from RareBench. We report ImageReward (IR) and Human Evaluation (HE) scores.
121Compbench
RareBench
Complex
Concat
Relations
Complex
Method
IR:1
HE: 1
IR 7
Hl: 1
IR: n
HE: 1
1K: 1
HE 1
SDXL Podell al. 2023
0,3083
0.59K3
0.1018
0.5416
0.360)
0.5016
1.1907
0.6283
SynGen SDXLT Kassin cl 2023
0,2889
0.5133
0.0576
0.3177
1.1452
ToMe Salhare Ti 2022
0.2891
0.5866
-0.204]
0,4500
-0.0632
0,4983
1.0200
0.48666
R2F of
0.3561
0.3620
CO3
0.4405
0.8033
0.1610
0.5167
0,2588
0.6200
12211
0.61166
Quantitative Comparison:
Simpler prompts (Attend-Exeite): Table T demonstrates the extent to which CO3, despite be- ing model-agnostic and optimization-free, competes or outperforms optimization-base and model- agnostic baselines on ImageReward. Gains are substantive in the Animal-Animal and Ani- mal-Object categories. On 3LIP-VQA. CO3 delivers the best performance in these two categories and improves over the base SDXL on the Object category, which contains prompts of the form: "a [colorl] [objectl] and [color2] [object2]]" " Unlike methods such as ToMe [tahang Hu et al. 2204), SynGen (Rassin et al. 2023 Attend-Excite (Chefer et al.] 2023). and BivideBBindLiig et al. (003), , which rely on model-specifee architectures or explicit attention based binding losses, CO3 architecture-inee cebittecturr-indeeedeenn independent, campling-basedd and free of explicit subject-attis attrrbute binding. Interest- ingly, by simply targeting "pure" modes under p( C), CO3 discovers layouts that better capture
"ddapted without the (L.ORA finetuned) concept-customizzatio stage.
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multi-object relations Aanimals-Animal Animal-Objects) and finds high-probability regions that preserve attribute bindings (Objects).
DivideBiidd
Nettend Zacitte
SDXL
Composttle Diftuion
Syniee
Tikl
Collours)
3.d
gROP CONA
Figure 3: Qualitative comparison of different methods simpler prompts.
Multiple and rare-concept prompts (T2ICompbench and RareBench): Table shows that CO3 consistently outperforms or matches the pest-performing baselines across all categories and metrics. We note that CO3 is not designed specifically for rare-concept scenarios it is general composi- tional sampling correction that does not rely on rarity-aware priors or LLM--based prompt augmenta- tion. Yet, CO3 matches or exceeds R2F on RareBench, despite R2F being tailored for rare-concept generation. We believe this indicates that the early-alignment phase of CO3, which mitigates con- cept dominance and encourages sampling from "pure" modes, is naturally beneficial for rare-concept generation as well.
These performance gains empirically validates our hypothesis on mode overlap and also highlights CO3's effectiveness in correcting the compositional limitations of the base model.
owe (rie for The catrr aallons stery otly sther sirpiti strpitt.. (lbo.
SDXL
Tofl
R2F
CD2
Brighted tortle and winnrr wibky all Lonnen ot sasttigg Srae bottl Serdhs
5DX
ner
Figure 4: Qualitative comparison of CO3 with competing methods complex and rare concept prompts.
Qualitative Comparison: Figure 3 compares C03 on Attend-Exeite (simpler) prompts with several baselines. We illustrate common issues in compositional aligmment-concee attribute mixing, and object binding- across all categories. Divide-Bind (Liu et al. 2023) and Attend-Excite (Cheter et al.) (023) enhance the attention layer of the base model, but often yield cropped or incomplete concepts (rows 1, 3). Comp-Diff (Liu et al. (022) suffers from missing or merged concepts (rows 2, 3). Syn- Gen (Rassin et al. 2023) and ToMe (taihang Hu ct al. 2024) perform well on Object prompts of the form "a [color]] [object]] and a [color2] [ob- ject2], but still exhibit overlapping mixed concepts (rows 2, 3; columns 5, 6). In contrast, CO3 achieves stronger object binding in the Animals-Animan and Animals -bjaectss categories while also improving results
Table 3: Ablations study conducted the C03 method. Starting with the base-model SDXL we
progressively add different components.
missing,
ImageRewand 7
BLIP-VQA 1
Avg
Method
0-0
SDXL
0,7820
1.5474
0.6789
0.8654 0.4926
0.4355
Resampling
10881
1,6755
0.8363
0.735)
036666
0.4588
0.9437
Conrector
1.0630
1.6429
0.8049
0.7177
018744
(14776
0.9463
weight-mod.
1,2342
1.6744
1.0158
0,744]
0.8878
0,5146
1.0118
0.9543
1,5140
0.8894
0.7149
0.8011
0.4969
0.9085
weightt mod.
1,1322
1,5865
1.0067
0.7211
0.7011
0.5206
0.9797
Figure 4 extends qualitative results to complex prompts from the Complex category of T21Compbench and rare-concept prompts from Rarebench Park et al. (2024), comparing C03
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50XL -Cporrecto
SOML
GOX "ressepling
SEXL FOCTOO
eCorroctor SIUEE
a bire and
a birdd and a 13on
3 Lion
monkey uith 8000
S montey with 8 bov
Figure 5: Qualitative results of C03 with different configurations on SDXL.
with the top-performing SDXL models (from Table D The left half shows prompts from T2ICompbench's Complex category, which involves complex subject-attribut & inter-concept re- lations. Only CO3 is able to faithfully generate all concepts and relations, while the other methods often miss concepts or fail to capture relations. The right half shows prompts from RareBench, which involve "banana shaped bottle" or "Knighted turtle", while the other methods often suffer from training data-bias generating regular banana or human knight instead.
novel concepts. C03 is able generate extremely concepts such as
Ablations Studies are preformed on the key components of CO3, starting from the base SIXL. model (Table & Figure 5 Adding resampling in the first three DDIM steps sub- stantially improves average performance, particularly for the Animals and Animal-Objects categories. Since image layout is largely determined in the early timesteps, CO3- resampler iteratively explores better initial noise configurations that align with the prompt. Incorporating CO3-corrector over the next steps further improves prompts requiring fine-grained details such as color or texture, boosting performance on the Ob- jects category in both ImageReward and BLIP-VQA. Additional gains come from the Closeness- Aware Con-
RWART
Composstil Oiflusion CoStours)
horoe
cept Weight Modulation strategy, which consistently benefits all categories. We hypothesize that as the dif- fusion trajectory evolves, the latent state often drifts closer to one concept mode e;; the modulation mech- anism assigns o; more negative weight, repelling the latent and mitigating concept dominance (Tunanyan al. 2023) commonly observed in stable-diffusion.
Did
TOX Wnd
Including the Corrector alone already improves perfor- mance in categories where attributes must attach to spe- cific objects. This is most clearly visible in Rows 4- of the ablation table: prompts of the form "an [at- tributel ][objec]] and [atribuuee]loojeet22"" show large gains, because early-timestep dominance (one object absorbing both attributes) is the failure mode that the Conrector resolves. Importantly, the average perfor- mance of SDXL Corrector + medght-modulltion (last row) is very close to SIXLL + Resampling + Corrector weight-modulatton (Row 4), which indicates that the Corrector alone is powerful even without Resampling. However, the full improvement is achieved when the Corrector is applied after the Resampling steps.
Figure Model Agnostic behavior: Qualitative comparison of generation from PixART-E(Chen et al. 2024) base diffusion model, PixART-E CO3, and PixART-E + Composable Diffusion (Comp-diff).
Generalization of C03 on PixArt and comparison with model- agnostic baselines. cDiff denotes Comp- diff (Liu et al. 2022
Table 4:
Comparison with Optimization-Free Approaches: To further demonstrate CO3's model-agnostic nature and generalization to other base models, we eval- uate on PixArt-E (Chen et al.] 2024) (Table 4 We compare against both the base model and the sampling- based, optimization sion (Comp-diff) (Liu et al. 2022). Unlike Comp-diff, which composes diffusion chains within the DDIM pre- diction step. CO3 applies composition as corrector. As shown in Fig. [ Comp-diff suffers from missing or mixing concepts, while CO3 consistently preserves all concepts with correct bindings in the output.
ImageReward
BLJP-VQA
Composable Diffu-
Mothed
A-A
A-
0.0
A-A
A-O
0-0
PicAnt
L066 1,887 1403
0.91
0361
0.339
PixAnt <Dif
0.047
0.399
0.472
PixArt

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Published as a conference paper at ICLR 2026
5 RELATED WORK
Optimization-base correction approaches: (1) Text-embedding optimization works like T2I- Zero variants (Tunanyan et al., 2023; Zhuang et al.] 2024), and style or prompt inversion (Li et al.) 2023a), improve semantic alignment by optimizing or restructuring the text representation during inference. These methods tweak or invert token embeddings reflects entity-attribute bindings or multi-concept prompts.
that the denoising trajectory better
(2) Attention-map optimization works (Zhang et al.) 2024; Meral et al. 2024: Tumanyan et al. 2023 Hertz et al. 2022) directly manipulate the cross- and self-attention maps during sampling. These methods typically inject constraints losses that (i) boost token- region correspondence for each entity, (ii) reduce overlap between different concepts, or (iii) preserve early layout information across timesteps. Attend-and-Exxiit (Chefer al., 2023) increases the activation of object tokens.
Dividc-and-Binn -Bnnd (Liu et al. (023) maximizes total variation to elicit distinct excitations for multiple objects and aligns autribute-entiyy attention maps, while A-STAR (Agarwal et al.) 2023) further reduces cross-token overlap and preserves early attention signals. SynGen (Rassin et al. 2023)
leverages syntactic parses to penalize mismatched attention overlaps, ensuring linguistic binding between entities and modifiers. InitNO (Guo et al. 2024) optimizes the initial noise so that sampling begins in more favorable regions that yield stronger, less-conflicted attention.
Composable generation works: These works view conditional diffusion models as energy/score functions, enabling algebraic composition. Composed-Diffuuse (Liu et al.] 2022) frames score- composition under CFG and demonstrates test-time generalization, but suffers from concept mix- ing and missing. Follow-ups propose training-fiee -ree and model-agnostic methods like energy- parameterized diffusion and Metropolis/MCMMC-ooreee condition generation (Du et al.] 2024). but their performance is often poor (Chefer et all]3202) Feng al. 2022). On different line, while Skrreta et al. (2025) proposes density estimation approach for composing diffusion chains for concept interpolation, Park et al. (2024) interpolates distribu- tions of frequent and rare-concepts for improved compositional generation. Kwon & Ye 2025) uses a composition similar to our resampler but not in the context of a corrector. Instead they use multiple DDIM forward- forward-backwarc backward steps to sample initial noise.
samplers that markedly improve multi-
Layout augmentation image generation: (1) Layout-to-imag' methods: (Xie et al. 2023 Phung
al. 2024; Kim al., 2023 Zhao et al., 2023) use strategy with explicit spatial priors boxes,
masks, or region texts to bridge text and image: training- -frenn controllers steer attention so objects
materialize in designated regions. Other works add instance-level handles, for fine-grained place-
ment and attributes across multiple entities (Wang et al. 2024). In parallel, fine-tuning approaches inject layout channels into the backbone (Li al. 2023b Mou et al. 2023 Zhang et al., (202)).
(2) LLM-augmented methods: Collectively, these works leverage LLM ressoning/reprrsenttiiins to better bridge linguistic structure and the denoising trajectory by (i) decomposing complex prompts
into regional sub-tasks (Yang et al., 2024 Hu et al., 2024) (ii) infer layouts from text (Qu et al.,
2023). and (iii) act as stronger text encoders or timestep-aware adapters (Saharia et al. 2022b).
CONCLUSION & FUTURE WORK
We propose a gradient- free, model-agnostic image composition method that switches between noise- resampling and correction. Our Conceps Contrasting Corrector based on the hypothesis that com- position is degraded by "problematic" mrodes-those that overlap with individual concept modes, resulting in strong alignment with particular concepts while suppressing others. Our corrector em- ploys a Closeness-Aww Aware Weight Modulation scheme that emphasizes "pure" modes where all con- cepts coexist without any dominating ones. Crucially, we attribute shortcomings of past composition methods to the choice of composition weights and show that only when the weights sum to 1, the interpretation of Tweedie- meedic--man correction and the CFG guidance strength are preserved. Results demonstrate that CO3 outperforms baseline approaches for several diffusion model choices and serves as a plug-and-play module offering improved compositional generation performance.
While our corrector suppresses concept overlap, the model's predicted score landscape remains biased by training data quality, occasionally leading to poor alignment in unrealistic prompts (Fig g) While our work offers significant boost multi-concept compositional generation, failure cases still persist and we believe advanced energy-based compositional samplers that explicitly model landscape probabilities can further mitigate these issues. We intend to investigate these edge cases and explore applications beyond compositional generation in future work.
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ACKNOWLEDGMENTS
Acknowledgments: We thank the anonymous reviewers for their valuable feedback. This work was partially supported by NSF #2008338, #1909568, #2148588 and AERI-2018966. This work used DELTA NCSA through allocation CIS230230 from the Advanced Cyberinfrastructure Coordi- nation Ecosystem: Services & Support (ACCESS) program, which is supported by U.S. National Science Foundation grants #2138259, #2138286, #2138307, #2137603, and #2138296
REPRODUCIBILITY STATEMENT
All method implementations, inference pipelines are included in the supplementary material. This includes instructions for environment setup, dependencies, and reproducible random seeds. The datasets used in our experiments are publicly available. We provide detailed descriptions of the dataset in Sec. 4. Hyperparameters, inference schedules, and evaluation pipelines are described in Sec. 4, with further details in the Appendix A.4
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A APPENDIX
A.1 BROADER IMPACTS
C03 improves compositional text-to-image generation by adjusting the sampling distribution of diffusion models. At the same time, it introduces certain risks, The method could be misused to create deceptive or misleading visuals, contributing to the spread of misinformation. When applied to depictions of public figures, it may compromise personal privacy. Moreover, the automatically produced content can raise concerns around copyright and intellectual property.
A.2 COMPOSITION IN TWEEDIE-DENOISEN SPACE FOR A VALID CFG
Classifier free guidance: In CFG, to sample from p(ælc), compose the conditonal and uncondi- tonal predicted noise at each time step t as:
xe = + A(E =
(16)
where of is the noise predicted for the distribution Pe(xelc) time t, i.e., Vz, Pexel( Then we use this composed noise to sample the next step as,
A.c
0.0
A.c It
It-1
Vā--iato + /1 ac
(17)
āt
L Let, Ttweedic
Ft / ōre A. be the tweedie mean from CFG composed noise time t. Then we can rewrite the above equation as,
02g Van
It-1
tweedie + 11--0-144
(18)
Composition of CFG scores: (Liu al. 2022) introduced the idea composableddiffusion where they compose scores from different diffusion models or conditional distributions generate im- ages from the composed distribution. They assume that, p(zo|C) IIp(xolc; and proposed the modified CFG composition for each as,
A.C = c0 + A. cf
Σ
-
(19)
The issue with above composition is that for arbitrary ); the composed CFG noise doesn't satisfy
the CFG equation in equation 16 i.e., 2².0 + E A.C for any X in equation equation 16 where e, is the noise predicted for the distribution Pe P=(x((C) at time
A.C
To conretize this, we state the following simple result.
Lemma 1. Let's define CFG function time as, fcro(a; X) c + x(a G) Then for any X and K > 1, En wxJcra((;;)) fcrG wxek; 2) only if Ex uk = 1.
Proof. Let, 27 = EE wwfcrolee; X). Then,
Σ ux(e x(EE - E())
Σ Whet + +A() 11 whek Σ whet)
Wk A Σ WkE
c
wk)
ol +
Whet - E
if Σ WR 1
= foro Σ
w 2)
if Σ Wp = 1
If Ex we + 1, then 2, # ScrolE, weet;:) A).
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Remark: One immediate implication of this lemma is that, whenever sample from composed distribution with the CFG composed score of the form Ě² = W1 + =22e +.. w.. A.. then et corresponds to the CFG noise for the distribution p(z|C) = P(, ci) only when Σ; w; 1.
(et dioj ;= It of Et A.C be the tweedie mean from CFG com- posed noise Et" of +Me ).) some x. Let, &tweedie be the composed tweedie-mean defined as Dtweedie = Ex ur&rwwedieee (t). Then,
Proposition 2. (Restated) Let Ltweedie
a) CO3-corrector: Ltmendic can be expressed in the form of a tweedie-meun at time l, i.e. Ptweedie = I ot
2.C if and only if Ex wik = 1. Here X = X and CFG composed noise
G
E = + *1 WhEt -
J.C.
b) CO3-resampler: itweedie = -ooo Ex wxet is weighted noise if and only if Es wk = O.
Proof. a) The proof can be obtained by simplifying the comp cation of Lemma We have,
expression and using direct appli-
Ltweedie
w Wweedee
Wk (xie
o+ ft
2,0K)
Wk Σ
N.ck
WhEt
k
Et
Σ
Wk 0z Σ fora(d+)
( (from Lemma
b
Ft at JCFG
Σ wkee
Σ wk = 1 k
So, we have Peweedie 21 - &l A.C where & A.C. = foFa Ex wke(*) )- = ct +xE wxee" c.).
If Σx We + 1, then Dtweedie cannot be expressed in the form 0t El x.C for any X and &.C.C
b) From the 4-th equality of part a)
a Etwediie
It Σ Wk oe
Σ
Wket
Acck
K
-,A wke (from Lemma
A.3 COMPARISON OF COMPOSITIONS IN SCORE SPACE
In this section we compare our composition framework with Composable-Diffusionn described in section we propose composition in Tweedie-denoised space as
As already
ttweedie = wo Ptweedte A.C + Ltweedie (c +
+
WK Stweedie le
A.CK)
(20)
(21)
which leads to
5.0 iff Σ w
Temeedic Ti E
(22)
where -.. 6 + X1 xx WxEt - -
(23)
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Thus, when mixing scores from different conditional distributions, the above condition need to be satisfied for Dtweedie to correspond to any coherent joint conditional model. Contrast this with the Composable Difolll-Dffffoonn 's
(Liu et al. 2022)) noise/score composition:
A,C commpiif
& + A1 (e - + >2 <() +...... Ax( -E,)
(24)
The above composition allows arbitrary weights, which in general violate the unit-sum constraint. In such cases, the result simply heuristic linear combination of scores, and the resultant Tweedie mean isn't valid and doesn't correspond to any well-defined distribution under the given forward process.
By contrast, CO3 composes Tweedie- means from different conditional distributions. Lemma shows that these weights have satisfy the same unit-sum condition above for the resultant composed Tweedie-mean to be a valid one. CO3 maintains this condition and this preserves the update on the Tweedie manifold of the underlying sampler.
A.4 MORE IMPLEMENTATION DETAILS
A.4.1 C03 IMPLEMENTATION DETAILS
For the results in Table we T, 10(nmber of time-steps correct), T, 3(number of resampling steps) and P 5(number of corrector iteration). For concept aware weight modulation, we use exponential kernel with 3 0.8 while anchoring wo at 1.0 for CO3-resampler and 2.0 for CO3-corrector.
We use Stanza (Qi et 2020) to parse the prompts. We parse the prompts extract different noun chunks and filter each of them to remove articles and adjectives. The remaining proper noun is used as concept in CO3. For example, if C is "a black cat and brown dog", we consider ej ="cat" and ="dog".
A.4.2 BASELINE METHODS
Attend-Fxcite Chefer et al. 2023). Divide-Bind (Liu et al.
2023), Init (Guo et al. 2024), Syn-
Gen (Rassin et 2023) and Composable Diffusion (Liu et al. (022) are methods based on SD1.5. We run experiments on these models using their publicly available code. Tweediemix (Kwon & Ye Zagnet
2025), Magnet Zhuang et al. (2024), R2F (Park al. 2024) and ToMe (taihang Hu al. 2024) are
SDXL based we use their publicly available code-base implementation. We also adapted Compos- able Diffusion and SynGen SDXL.
A.4.3 ADDITIONAL. HUMAN EVALUATION
studies (with 11 participants) across all benchmarks considered in this paper. For evaluation,
We conducted new
Table
Human Evaluation results on Attend-Excii prompts. Higher the score, the better.
follow the protocol from R2F Park et al.] (2024). For each prompt, im- ages from C03 and baseline methods displayed side by side to enable direct comparison. Method names are anonymized and their order is randomly shuffled to reduce bias. Participants provide rat- ings in {0, 1, 3, 4, 5). which are then normalized to [0, 1]. Results are shown below (higher is bet- ter). Results on T2ICompbench and RareBench are included in Table of the main text. Table shows results on Aitend-Excite prompts.
Animuls- Objects
Method
Animals
Objects
SIXL
015266
0.0050
0.7083
Syntien
0.3667
0.4366
0.7183
Magner
TaMe
0.4383
0.008
0.6166
CO3cours)
0.8817
0 0.7466
0.8250
A.5 GENERALIZATION TO HIGHER-ORDER SOLVERS AND LOW INFERENCE STEPS REGIMES
In addition to the main 50-step SDXL with DDIM experiments, we include results using both (i) low-step DDIM and (ii) low-step high-order DPM++ solvers (10-20 steps).
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Please note that, since DDIM sampling has direct connections with denoising and generation via the Tweedic mean (Section 2 in the main paper), CO3 applies naturally to DDIM. However, our experiments below show that CO3 continues provide consistent improvements under higher-order solvers and aggressive sampling budgets as shown in Table & Table
Table 6: Performance of CO3 with DPM+ solver and in the low inference steps regime.
Steps
InsageReward
BLIPAQQA f
Avg
Animals Objects
Animals Oitjocts
Objjcct
Objects
SD-DDIM
0,7820
1.5574
0,6789
0.6950
0.8658
0.6925
COSD
20
1.2311
1,0743
1.0188
0.7441
0.8878
0.5146
1.112
SD-DPM-+
20
0.7089
0.8537
CO--DPM
20
1.1424
1.5249
0.5377
0.7582
0.8942
0.4944
019563
SD-DPM++
10
0.8844
1.5074
0.0081
0.6708
0.8537
0.29774
0.8145
CO3 DPM++
10
1.0371
1.4200
0.9206
0.7331
0.8765
0,4443
0.9106
Table 7: Performance of CO3 with DDIM solver in the low inference steps regime.
InsageReward
BLI--VQA
Animals Ohjects
Animals Objects
Methed
Steps
Animals
Objects
Animtals
Objects
SD w/ DDIM
0,7820
1.5574
0.6799
0.6950
0,8658
0.8926
0,8452
DDIM
1.2341
1.1588
0.7441
0.5146
SD DDIM
6965
14567
0.5516
0.6832
0.4947
0,7907
DDIM
1.6440
0.6698
0,4315
0.8671
SD
CO3 w/ DDIM
0.5872
1,5900
0.7117
0.6294
0.8589
0,4453
0,8037
A.6 PROMPTS WITH AND WITHOUT BACKGROUNDS
CO3 strongly concept obedient. Many CO3 generated images from Attend-Excite prompts seem to lack background details. To investigate this behavior, added background concepts to the same prompts shown in Figure [ of the main text. Figure shows the resulated images from this experiment. We observe that, CO3 tends to strongly obey the concepts specified in the prompt; when the background is not specified it is conservative with hallucinating the background. However, if the background explicitly specified, CO3 brings the same degree of richness and quality both the foreground and the background.
A.7 TIME AND MEMORY COMPLEXITY
Table 8: Runtime and memory comparison between SDXL, ToMe, and CO3 at different sampling budgets.
ImageReward
BLIP-VQA f
Avg
Time(s) 1
Memory
Animuls Objects
Animals Objects
Method
Animals
Objects
Animals
Objects
SDXL (50 steps)
0.4906
1.1503
0.1131
0.6428
0.8378
0.4752
0.6183
7.20
8.5GB
(50 steps)
0.3894
0.8807
0.6440
16.58
CO3 (50 steps)
1,2341
1.6743
L0158
0.7440
0.8877
0.5145
1.0120
19.90
9,5GB
CO3 (20 stops)
0,8532
1,6440
0.7481
0.6698
0.8611
0.4315
0.8679
10.65
9.5GB
We compare CO3 with its most competitive baseline ToMe taihang Hu al. (2024) in terms of runtime peak memory and time requirement to generate one sample in Table conducted on NVIDIA 1100 gpu.
The experiments are
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a bird and a turtle by the beach
bird and turtle
horse and a monkey, mountain background
horse and monkey
lion and yellow clock living room
lion yellow
XII
VIII VIII
Figure 7: Concept obedient behavior of CO3: CO3 tends to strongly obey the concepts specified in the prompt; when the background is not specified it is conservative with hallucinating the back- ground.
Although CO3 takes more time compared to ToMe with 50 time-steps, it requires significantly less memory. C03 is lightweight, and its peak memory usage is only slightly above that of the base SDXL model. ToMe, being gradient-base based method, has substantial memory requirements.
We also emphasize that CO3 is model-agnostic and can be applied to any diffusion- model. In contrast, ToMe model-architecture dependent, as its attribute-binding mechanism operates by manipulating internal attention activations of the base model.
For fair comparison, we additionally tested CO3 with 20 inference steps. As shown in the table below, the average performance of CO3 with 20 steps remains comparable to, and in many cases better than, ToMe with 50 steps. CO3 with 20 steps takes 10.65 seconds, which is faster than ToMe.
A.8 ABLATIONS ON HYPERPARAMETERS
In this section, we analyze the contribution of the following five factors to the performance of our CO3 corrector.
Notation recap: / is the exponential decay factor in the affinity scores; scales the composed score for CFG; num_res amp ing is the number of resampling steps at the start of diffusion; num_ts is the number of carly timesteps where the corrector is used; num steps is the number of iterations per corrector application.
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A.8.1 NUMBER OF RESAMPLING STEPS
Table 9 shows results for different resampling steps. We observe that using more resampling steps can blur concept separation early in the diffusion process rather than helping to disentangle concepts.
Table 9: num resarrssapplnn sweep. other settings are identical.
Within each group, only num.resampling changes; all
Reward
BLIP-VQA
settings
resampling
Animals
Animals, Objecct
Objects
Animals
Animals, Objectt
Objects
1.2218
1,7020
0.7375
0.8811
0-4782
0.8, nom_s=7,, non-slepp=3
1,1705
1,7031
0.9512
0.7259
0.8828
0.469
1.1484
1.6915
0.3. num_rem?
5
1.1297
1.6948
0.8996
0.7145
0.8774
0.4697
1,2566
1,7062
0,7422
0.8813
0,4834
1.1, nsm_i=77, 7, non_sle
1,1927
1.7164
0.9934
0.7292
0.8835
0,811
A.8.2 CORRECTOR APPLICATION TIMESTEPS
In CO3, the corrector is applied during the first num_t s diffusion steps. Table 10 shows that larger num_ts generally yields better results, especially for animals and objects. This indicates the cor- rector remains beneficial beyond only the carliest timesteps.
Table 10: num_steps=5, > 0.8. (Best values bold)
num_ sweep with all other settings fixed: B = 1.1, rum_rmsamplnng-,
ImageReward (1)
BLIP-VQA (1)
num_ss
Animals
Animals Objects
Objects
Animals
Animals Objects
Objects
1.1862
1.7136
0.8885
0.7430
0.8783
0.4630
1.2510
1.7117
0.9750
0.7432
0.8801
0,4812
7
1.2566
1.7062
0.9916
0.7422
0,8813
0,4834
10
1.3149
1,6816
1.0095
0.7576
0.8832
0.5074
A.8.3 CORRECTOR ITERATIONS
Table reports the effect of increasing the number of corrector iterations. Despite setting num raising num sum_stes reduces performance substantially, suggesting that applying the corrector only the very beginning is not sufficient; employing until much later in the diffusion trajectory is more effective.
Table 11: num.steps. Within each group, only num_steps changes; all other settings are identi- cal.
Image Reward (*)
BLIP-VQA (1)
Fixed settings
num_steps
Animals
Animals Objects
Objects
Animals
Animals Objects
Objects
(a) 3 1.1. num_tss4 nom pam_rescmplinn pesponpingg = 0.9
1.2622
1.7129
0.9959
0.7542
0,8843
0.4707
1,2452
1.6994
0.9753
0.7448
0.8809
0.4706
1.2151
1.6701
0.9437
0.7318
0.8781
0.4556
(b) 1.1.
1.2755
1.7049
0,9424
0.7538
0.8762
0.4611
2..plng...
10
1.2503
1.6969
0.9191
0.7446
0,8798
0.4617
0.8
1.2480
1.6658
0.9063
0.7356
0.8750
0.4577
A.8.4 EXPONENTIAL DECAY FACTOR 33 OF affinity scores
We vary in equation 14 from 0.3 to 1.1. Tables 12 and demonstrate that the performance generally increases with larger 3, with the strongest gains in the animals and objects categories, while few settings exhibit minor regressions.
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Table 12: Exponential decay factor 3 with all other settings fixed: num resampling=,, num_steps=5, À 0.8. We report two blocks: num_ts=6 and num.ts=7. (Best values for each block are bold)
nUmLI
8
Imagekeward (1)
BLIP-YQA ct)
Animals
Animalk,[hjjets
Objects
Animils_Ohjecr
Objjets
0.6
1.1945
1.7009
0.5010
0.7517
0.8736
0.4645
0.7
1,2043
1.7109
0.9152
0.7310
015300
0.4706
1.2157
1.70124
0.916
0.7398
0.8776
1.0
1,2282
1.7183
09996
0.7350
0,8808
0.4770
1.1
1.2510
1,7117
0,9750
01.443
0.8301
0,4811
0.3
1,1484
1.6155
0.9005
0.7240
0,8809
0.469]
7
088
1.2218
1,7020
0.9405
0.7575
0.5811
01478
Exponential decay factor 3 with all other settings fixed: num_resampl ing=3,, num steps=5. 0.9. We report two blocks: num and num ts7 (Best values for each block are bold.)
Table 13:
Image Reward
BLIP-YQA
BUmLis
8
Animals
Animals Objects
Objects
Animals
Animals Objects
Objects
0.3
L,1437
1,7020
0.9518
0.7248
0,8816
0.4764
0.7
1,2051
1,7130
0.9860
0.7407
0,8851
0.1788
0.9
1,2003
1.7159
03883
0.7416
0,8842
0.189]
1.1
1,2098
1,7180
0.9956
0.7571
0.8841
0.455
0.3
1.1273
L6938
0.9490
0.7274
0.8348
0.4796
7
0.8
1.2142
1,7000
0.9937
0.7563
038845
0.4952
A.8.5 C03 FAILURE CASES
Despite correcting for the "problematic" modes, there still remain open challenges in image com- position as shown in Figure 8 The score and, hence, the correction landscape is heavily influenced by the training schemes employed in diffusion model training, i.e., the quantity and quality of the multi-concept bindings in the training set. In addition, the usage of unrealistic prompts perhaps not encountered training also results in poor text/concept alignment. We leave this investigation for the future.
A.9 MORE QUALITATIVE RESULTS ON MULTI-CONCEPT PROMPTS
We added more qualitative comparison results on the three categories from Attend-Exeite prompts in Figure 9 Complex category from T21compnench in Figure I0 Additional results on Rarebench prompts are included in Figure
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PixART
Composable Diffusion
Co3(ours)
brown balloon and a pink car
wrong wrong-attribbnn attrribtt
dog black apple
wrong- attributt
and red balloon
missing
a turtle a yellow car
missing
and a bear
missing
elephant with a bow
unrealistic
purple car and a pink apple
unrealistic
Figure 8: Failure scenarios of PixART-E base diffusion model, PixART-E + CO3, and PiXART-E Composable Diffusion.
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SEXL
Co3lcurs)
SDXL
Co3(ours)
SEXL
Co3lours)
dogg and black apple
pink crown.
purple
lion and vellow cloce
orange chair clocke
horse
autll and backnack
blue bell coom and
600
catt with plazies
a brown chair
dog
bear and bench
White gleases orange
cat
horse ard green apple
blue crown
and
mouse and biue clock
purple their
ar.
Figure 9: Qualitative results of SDXL base diffusion model, and SDXL + CO3 for animal- animal.animal-ohae object, and object-object categories from Eitend-Excite prompts.
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SOAL
Synten
Tolle
C23
SAA on the waEee cofeee Talle
The Me Ce the MAr
The oe wel the Te
THE CONT LOTT CErSEe Clas.
THE COOE MPS SIRLM.
MN WIEE The NET
Figure 10: Qualitative results on Complex category from T21compbench
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R2F
915
1203
Calty shat teaa SHEEE ponk
YOU aliietor
SUUPERT ta and SIE seatine BELLWAN Wooee LEmee of crull
Woory TENO FOC IUPRUTON REPHOWI
Figure 11: Qualitative results on RareBench prompts.
25


Reviews:
[{"review_id": "6oADp6QXj8", "reviewer": "Reviewer_Qf6F", "summary": "The paper addresses the problem of multi-concept prompt fidelity in text-to-image diffusion models. The authors propose CO3, a lightweight, re-training-free corrective sampling strategy that adjusts inference to maintain balanced representation among multiple concepts. By analyzing the instability of existing compositional guidance, the method identifies stable composition-weight regions and steers sampling toward them. CO3 integrates seamlessly with classifier-free guidance and demonstrates improved concept balance, coverage, and robustness across diverse prompts compared with prior compositional baselines.", "questions": "1. Compatibility with low‑step and high‑order solvers.\n- Main experiments use SDXL with a 50‑step DDIM sampler. Can CO3 be applied effectively with high‑order solvers such as DPM++ at around 10 steps, and with distilled text‑to‑image models that operate at 4 steps or fewer? Any guidance on tuning composition weights in these low‑step regimes would be helpful.\n\n2. Human evaluation.\n- ImageReward and BLIP‑VQA are useful proxy metrics, but has a human evaluation been conducted to assess concept presence and balance under multi‑concept prompts? If not, a small user study could substantiate the claimed improvements in perceived fidelity.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. Correction‑based compositional guidance with theoretical grounding and broad evidence.\n- The method unifies ideas from correction‑based approaches and composable diffusion into a single re‑training‑free sampling framework, and provides a reasonable theoretical justification for composition‑weight choices and stability regions. The paper offers extensive quantitative and qualitative comparisons that demonstrate improvements in multi‑concept fidelity while remaining compatible with standard classifier‑free guidance.", "weaknesses": "1. Comparison on multi‑concepts is incomplete.\n- The paper focuses on improving multi‑concept prompt fidelity, especially when one concept is rare or easily dominated by another. A direct theoretical and empirical comparison with R2F [1], ideally on RareBench, would clarify relative strengths on the rare‑concept regime where fidelity typically degrades most.\n\n2. Model coverage could be broadened beyond SDXL.\n- To strengthen the claim of generality, evaluations on additional recent open‑source text‑to‑image diffusion models, for example FLUX, would make the comparisons more persuasive and reduce the risk that gains are specific to one backbone.\n\n\nReference\n\n[1] Park et al., Rare‑to‑Frequent (R2F): Unlocking Compositional Generation Power of Diffusion Models on Rare Concepts with LLM Guidance.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "lEmc9CTdtl", "reviewer": "Reviewer_3fep", "summary": "This paper tackles the problem of multi-concept compositional generation in text-to-image diffusion models. The authors hypothesize such models struggle with compositional generation because the learned joint distribution for composite prompts overlaps significantly with individual concept distributions, leading to dominance by a single concept. To address this, they propose CO3, a plug-and-play corrective sampling method that suppresses individual concepts by re-weighting Tweedie means of each concept, rather than directly re-weighting in the noise (or score) space. Experiments on SD1.5, SDXL, and Pixart-$\\Sigma$ show improved prompt alignment and concept coexistence.", "questions": "Please refer to weaknesses section.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. CO3 requires no retraining or gradient computation and can be applied to any diffusion model. This makes it broadly applicable and practical.\n2. The paper provides an intuitive theoretical explanation showing that directly performing weighted averaging of scores does not guarantee a valid Tweedie mean or consistent classifier-free guidance formulation; by instead re-weighting in the Tweedie-denoised space, CO3 ensures mathematical validity.\n3. Novelty. While not the first to explore Tweedie-denoised space reweighting for concept composition [1], CO3 offers a clear theoretical formulation and a practical two-stage approach, early resampling followed by later correction, that together provide consistent empirical improvements and strong interpretability.\n\n[1] Kwon & Ye, TweedieMix: Improving Multi-Concept Fusion for Diffusion-based Image/Video Generation, ICLR 2025.", "weaknesses": "My main concern lies in the evaluation, particularly regarding benchmark coverage and baseline selection.\n\n1. The evaluated prompts are relatively limited, focusing mainly on compositions involving animals and objects. Incorporating commonly used benchmarks such as T2I-CompBench, which include more diverse attribute and object combinations, would strengthen the empirical validation.\n2. A relevant baseline is missing [1]. In addition, comparisons with recent **LLM-guided generation methods** [2, 3, 4] would be beneficial to quantitatively assess the relative performance and effectiveness of CO3 in enhancing compositional fidelity. If the proposed method is orthogonal to LLM-guided approaches, it would be particularly valuable to demonstrate how CO3 complements them when combined.\n3. Including comparisons with recent **state-of-the-art text-to-image models**, such as **Stable Diffusion 3.5** or **Flux**, would improve the paper’s relevance and help readers better understand the practical significance of the proposed method.\n\n[1] Yu et al., Improving Compositional Generation with Diffusion Models Using Lift Scores, ICML 2025.\n\n[2] Yang et al., Mastering text-to-image diffusion: Recaptioning, planning, and generating with multimodal llms, ICML 2024.\n\n[3] Lian et al., LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models, TMLR 2024.\n\n[4] Hu et al., ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment, arXiv preprint.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "2fhI06Lss1", "reviewer": "Reviewer_Md3g", "summary": "The authors proposed a method for better prompt-aligned multi-concept image generation. Specifically, existing models often neglect or mix multiple concepts in a single generation scenario. The proposed method tends to squeeze the distribution, hindering the generation process from walking through some problematic regions. The probability normalization approach is designed for this purpose.", "questions": "1. Isn’t \\epsilon have a similar formulation as \\tilda(\\epsilon) in Lemma 1 204-205 (CFG, with Lambda)? Is it a typo?\n2. What is the \"x\" in Lemma 1, a) 209? is it x_{t}?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The diagnosis of the multi-concept image generation problem is interesting—specifically, the overlap of individual concept distributions and the original multi-concept distribution's regions.\n2. The distribution correction idea is theoretically intuitive and good.", "weaknesses": "1. A known post-hoc guidance is called in a new name, “correction guidance”, for a better sound of the paper. It sounds unnatural.\n2. Only Subjective metrics were used. What about objective ones, such as CLIP-T, DSG (Davidsonian Scene Graph), or simple classification metrics?\n3. The proposed method is based on a modern and relatively stronger model, SDXL, while it is often compared with older methods with weaker diffusion models (2023 methods, such as Attend and Excite or Divide-Bind with SD 1.5 or 2.0). Also, many other modern methods are neglected in the paper ( Not a fair comparison.\n4. In the ablation study, the corrector’s contribution is somehow questioned: a) What is the performance of SDXL+Corrector (w/o Re-sampler)? b) In all 6 metrics, it has gains only in 3 metrics, out of 6. The other 3 are either the same or even worse.\n5. There is no limitation section.\n6. In Table 3, it has the worst speed performance, and even the metrics are not great compared with ToMe.\n7. Limited color diversity in qualitative results in Figure 3.\n8. No qualitative Ablation study.\n9. It drops the image quality in Figure 5.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "chKQkj46AW", "reviewer": "Reviewer_agn3", "summary": "This paper proposed the CO3, Contrasting concepts compose better, to improve muti-concept prompt fidelity in text-to-image diffusion models. Instead of re-training, the authors introduced a corrective sampling strategy, to steer away from regions where the joint prompt behavior overlaps strongly with any single concept in the prompt. Specifically, they analyze and show that composition through weighted sum of Tweedie-means in the Tweedie denoised space, offers a more general framework. Experiments on diverse multi-concept prompts demonstrate improvements in concept coverage, balance and robustness, with fewer dropped or distorted concepts compared to standard baselines and prior compositional works.", "questions": "1)\tSome very latest and more  closely relevant works are not compared, such as, Magnet (We Never Know How Text-to-Image Diffusion Models Work, Until We Learn How Vision-Language Models Function), TWEEDIEMIX ICLR 2025, ConceptWeaver, etc. \n2)\tIt is confusing that why using arbitrary weights in (10) (i.e., Liu et al. 200) does not lead to a valid Tweedie-mean. There seems a lack of theoretical analysis or empirical validation.\n3)\tHow to connect the equation (12) to (13). On other words, why (13) is representing the samping from the unnormalized probability distribution in Eq. (12).\n4)\tMore experiments are tested on two concept prompts. Can this model generalize to the scenario with the prompts of three or more concepts?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1)\tThe paper is well-written, and the structure is well-organized. \n2)\tThe proposed CO3 is plug-and-play, model-agnostic, and gradient-free. And the results shows stronger semantic alignment to prompts. \n3)\tCombing the strengths of correction-based approaches and composable diffusion seem reasonable and effective.", "weaknesses": "1)\tSome very latest and more relevant works are not compared, such as, Magnet (We Never Know How Text-to-Image Diffusion Models Work, Until We Learn How Vision-Language Models Function), TWEEDIEMIX ICLR 2025, ConceptWeaver, etc. \n2)\tIt is confusing that why using arbitrary weights in (10) (i.e., Liu et al. 200) does not lead to a valid Tweedie-mean. There seems a lack of theoretical analysis or empirical validation.\n3)\tHow to connect the equation (12) to (13). On other words, why (13) is representing the samping from the unnormalized probability distribution in Eq. (12).\n4)\tMore experiments are tested on two concept prompts. Can this model generalize to the scenario with the prompts of three or more concepts?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)