Fast Direct: Query-Efficient  Online Black-box Guidance  for Diffusion-model Target Generation
Authors: Kim Yong Tan
Yueming Lyu
Ivor Tsang
Yew-Soon Ong
Venue: ICLR 2025 Poster (2025)
Keywords: Diffusion model
Black-box target generation
Online guided diffusion model
Query-efficient
Primary area: generative models

Abstract: Guided diffusion-model generation is a promising direction for customizing the generation process of a pre-trained diffusion model to address specific downstream tasks. Existing guided diffusion models either rely on training the guidance model with pre-collected datasets or require the objective functions to be differentiable. However, for most real-world tasks, offline datasets are often unavailable, and their objective functions are often not differentiable, such as image generation with human preferences, molecular generation for drug discovery, and material design. Thus, we need an **online** algorithm capable of collecting data during runtime and supporting a **black-box** objective function. Moreover, the **query efficiency** of the algorithm is also critical because the objective evaluation of the query is often expensive in real-world scenarios. In this work, we propose a novel and simple algorithm, **Fast Direct**, for query-efficient online black-box target generation. Our Fast Direct builds a pseudo-target on the data manifold to update the noise sequence of the diffusion model with a universal direction, which is promising to perform query-efficient guided generation. Extensive experiments on twelve high-resolution ($\small {1024 \times 1024}$) image target generation tasks and six 3D-molecule target generation tasks show $\textbf{6}\times$ up to $\textbf{10}\times$ query efficiency improvement and $\textbf{11}\times$ up to $\textbf{44}\times$ query efficiency improvement, respectively.

Published as a conference paper at ICLR 2025
FAST DIRECT: QUERY-EFHICIENT ONLINE BLACK- BOX GUIDANCE FOR DIFFUSION-MODEL TARGET GENERATION
Kim Yong Tan
Yueming Lyu 233x Ivor Tsang 123.3 Yew-Soon Ong!.23
'College of Computing and Data Science, Nanyang Technological University, Singapore
"Cettre for Frontier Al Research, Agency for Science, Technology and Research, Singapore
'Institute High Performance Computing. Agency for Science, Technology and Research, Singapore
(KIMYONGOO 01, ASYSOngJantu edu. sg [Tyu_Yueming ng, Tvor_ @cfar. a-sstarredu...
ABSTRACT
Guided dlffission-odde generation is a promising direction for customizing the generation process of pre-trained diffusion model to address specific down- stream tasks. Existing guided diffusion models either rely on training the guid- ance model with pre-collected datasets or require the objective functions to be differentiable. However, for most real-world tasks, offline datasets are often un- available, and their objective functions are often not differentiable, such as image generation with human preferences, molecular generation for drug discovery, and material design. Thus, we need an online algorithm capable of collecting data during runtime and supporting black-box objective function. Moreover, the query efficiency of the algorithm is also critical because the objective evaluation of the query is often expensive in real-world scenarios. In this work, we propose a novel and simple algorithm, Fast Direct, for query-efficient online black-bor target generation. Our Fast Direct builds pseudo-target on the data manifold update the noise sequence of the diffusion model with a universal direction, which is promising to perform query-effi efficieett guided generation. Extensive experiments on twelve high-resolution (1024 1024) image target generation tasks and six 3D-molecule target generation tasks show 6x up to 10x query efficiency improve- ment and 11x x up to 44x query efficiency improvement, respectively. Our im- plementation is publicly available at: https: /ttttpp:://ttubbb..p. com/ /sto////mmgggg///// guide- st htw.sstaabbeeaafasooooooooooeeee
direct
INTRODUCTION
Diffusion models have become the state-of-the-are generative model for image synthesis (Ho et al.
2020 Nichol & Dhariwal 2021: Dhariwal & Nichol 2021) and video synthesis (Ho et al. [022]
etc. Its remarkable success is due to its powerful capability in modeling complex multi-mode high- dimensional data distributions.
One promising direction for utilizing the generative power of diffusion models through target generation, which allows users customize the generation process to specific downstream objectives, effectively extending the models' capabilities beyond synthesis problems to engineering optimization and science discovery problems, such as image generation with human preferences,
drug discovery (Corso et al. 2022 Guan et al. 2023) and material design (Vlassis & Sun 2023
Giannone et al. 2023).
The pre-trained diffusion models often struggle to generate desired samples for these applications, especially when the target data lies outside the training data distribution. Therefore, the target gen- eration often involves model fine-tuning or guidance techniques. KKrishnamoorhhy et al. (2023) pro- poses to train the diffusion model with re-weighted training loss, while Clark et al. (2023); Black Clark et al. (2023); Fan et al. (2024); Yang et al. (2024) advocates for fine-tuning the parameters of the pre-
"Corresponding author

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Published as a conference paper at ICLR 2025
trained model. As opposed to training-time approaches, et al. (2023) introduces an inference- time approach, which replaces the classifier in classifier guidance (Dhariwal & Nichol 2021 with a differentiable objective function to achieve the downstream target. However, the requirement of the differentiable objective limits its practical usage for real applications with black-box feedback.
Most real-world applications of target generation involve evaluating black-box objectives in an online manner. For example, image generation with human preferences requires human users to evaluate the generated images; drug design requires real-world experiments evaluate the generated molecules. These applications typically require several iterative query-feedbac cycles with a black- box objective achieve satisfactory target generation. As objective evaluations are often expensive, it becomes crucial to develop query-efficient algorithms to minimize the cost of these evaluations.
Although the online black-box target generation tasks are important, the existing works are not suit- able to address this task. Bansal et al. (2033); y; Krishnamoorthy et al. (2023): Lu et al. (2023) require training an offline guidance model with pre-col pre-collected data, while [ansal et al. (2023): Clark et al.) Prabhudaaa et al. (2023); He et al. (2023) require the objective function to be differentiable. Most recently, Black et al. (2023); Fan et al. (2024): Yang et al. (2024) can be employed for on- line black-box target generation, but they require online updates of the huge number of parameters, which is both time-consuming and not query-efficient.
In Section 3.1.
first propose novel guided noise sequence optimization (GNSO) technique to guide the diffusion model sampling process towards a given target. GNSO updates the noise sequence in a universal direction the data manifold. Our GNSO is efficient: it enables fast adaptation (with around only 50 steps) from any initial generation towards the given input target. Moreover, GNSO is robust: empirically, even if the input target image noisy, it can still generate a clear image semantically similar to the target image. The GNSO itself cannot directly handle black- box target generation tasks, but provides backbone for further designing black-box algorithms.
In Section 3.2, based on our GNSO technique, we further propose a novel algorithm, Fast Direct, to tackle the online black-box target generation tasks in query- query-efficient manner. Fast Direct builds a pseudo-target on the data manifold as the input for our GNSO, to guide the diffusion model at inference time. Notably, our Fast Direct is easy to implement and supports any stochastic diffusion scheduler. Moreover, our Fast Direct provides a very flexible framework for extension. Any designs of update methods for the pseudo-target can serve as a plug-in for our Fast Direct.
In Section we evaluate our Fast Direct algorithm on the real-world applications: high-resolution (1024 x 1024) image target generation tasks and 3D-molecule target generation tasks. For image tasks, we employ the black-box API of the modern Large Language Model (LLM), Gemini 1.5, as the black-box objective, which is much more practical compared with the synthetic toy score function used in the literature. Extensive experiments twelve image target generation tasks and six 3D-molecule target generation tasks show significant query efficiency improvement: 6-times up to 10-times query efficiency improvement on image tasks and 11-times up to 44-times query efficiency improvement on 3D-molecule tasks, compared with baselines. Additionally, we evalu- ate Fast Direct on compressibility, incompressibility. and atheistic quality tasks, demonstrating its generalization ability on the unseen prompts. Our contributions are summarized as follows:
We propose novel guided noise sequence optimization (GNSO) technique to guide the diffu- sion model sampling process toward a given target in an efficient and robust manner.
Based on GNSO, we further propose novel algorithm, Fast Direct, to address online black-box target generation tasks in a query-efficient manner.
Fast Direct achieves significant query efficiency improvement in real-world applications: high- resolution image target generation tasks and 3D-molecule target generation tasks, Additionally, we demonstrate its generalization capability for unseen prompts.
2 RELATED WORKS
2.1 GUIDED GENERATION
Diffusion guided generation (also called inference-time guidance) refers to the technique to guide the sampling trajectory of the pre-trained diffusion to generate target data (Croitoru al., 2023
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Chen et al. 2024).
The key advantage of this approach is that it does not require updating the model parameters, which is computationally expensive, especially for large models.
Dhariwal & Nichol (2021) proposed classifier guidance. However, it requires guidance model trained on noisy images with different noise scales, which is generally not readily available and often requires training from scratch for each domain. Chung et al. (2022); Bansal et al. (2023): He
ett al.] (2023) extend the classifier guidance by using a differentiable objective function defined only for clean data. Instead of using noisy images, the predicted clean images at each sampling step are used as the input for the guidance objective function. In this way, the guidance process can operate on the clean image space. While the predicted clean image naturally imperfect. empirically it still provides informative feedback to guide image generation (Bansal et al. 2023).
However, this approximation can harm image quality, Bansal al. (2023) proposed universal guid- ance that comprises backward guidance followed by self-recurrence step to preserve image quality. On the other hand, [H al. (2023) leverages the differentiability of a well-trained auto-encoder to project the image to the data manifold and thus preserve image quality during the guidance process. Instead of guiding the sampling trajectory, Karunratanakul et al. (2024): [Eyring et al. (2024) treat the diffusion process as black-box and only optimize the initial (prior) noise.
While the aforementioned approaches assume a differentiable objective function, Lu al. (2023)
tackles black-box objective by learning differentiable proxy neural network h to match their gradients, i.c., Vf Vh. Li et al. (2024) climinates the for differentiable proxy model by employing importance sampling weighted by the objective values during the sampling process. Most recently, DNO (Tang et al. (024) proposes optimizing the diffusion noise sequence by using ZO-SGD (Nesterov & Spokoiny, 2017) to tackle the black-box objective function. However, it runs at the instance level; namely, each run only produces one image.
2.2 DIFFUSION-MODEL FINE-TUNING
ceffusiin-mode fine-tuning refers to the technique that updating the pre-trained diffusion-model parameters to improve its performance on a specific use case. In this section, we review the fine- tuning methods that support online learning of the black-box objective function.
Black et al. (2023) ): Fan et al. (2024) formulate the diffusion fine-tuning problem as a reinforce-
ment learning (RL) problem within Markov Decision Processes (MDPs), and propose an iterative
algorithm to fine-tune the diffusion model by using Proximal Policy Optimization (PPO) (Schulman) jet al., 2017 Uehara et al. 2024) loss function. Fan et al.] (2024) integrates the PPO with a KI. regularization prevent the fine-tuned model from deviating too much from the pre-trained model.
On the other hand, Yang et al. (2024) does not require an absolute objective value; instead, it uses the relative reward on pair of samples by integrating DPO (Direct Preference Optimization) (Rafailov) ct al. 2024). a technique for fine-tuning large language models, into the diffusion model.
Fine-tuming the diffusion model requires large amount of GPU memory. Existing work mitigates memory consumption by using the LoRA technique (Low-Rank Adaptation) (Hu et al. [021 and only fine-tunes parameters of the attention blocks in UNet. We categorize the related works based on whether they support the online and black-box box objective tasks in Appendix Table
METHODS
In this section, we first present a novel inference- time guidance generation method by guided noise sequence optimization. Based on this method, further present our query-efficien online black- box guidance algorithm, Fast Direct, to address online black-box guidance tasks.
3.1 NOISE SEQUENCE OPTIMIZATION WITH TARGET GUIDANCE
Take i...d. Gaussian samples (Eo....,,EE) NN I), for k E (1,--. , K}. the inference process of the diffusion model can be formulated as follows:
DC So Ck);
(1)
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Algorithm 1: Guided Noise Sequence Optimization
Input: Stepsize a, target data x", number of diffusion sampling steps K, pre-trained diffusion
sampler Sg, number of iterations T.
Output: Generated target æKK
Take i..dd noise {ED,... {K} (O, I)
2 Compute the norm of noise *
nurm = | for {0,-.. :. J.
for t < to T do
2o < Eg
for k < 1 to K do
3g f- So (o 6k)
for k -- 0 to
éG - 6x a(x" xKK)
C₁ - l %
mum
Line 4 Line 6: Call diffusion-model sampler to generate data æx-
Line 7 Line 9: Update the noise eg by moving along direction x:"
Updea Drnchond
Offacion Genarned Images bilttrr (o... 100)
-
d=x--f
d=x'-xx
Figure 1: Demonstration guided generation for given target by Algorithm D Column 2 (Update Direction) indicates the update term of Algorithm I Line 8. Rows to 3 analyze how different update directions can affect the generated images. Rows 4 and show that by using the update direction of x² æk. the diffusion model can generate visually satisfying images even when the target image noisy. The noisy target image (x*) of the row is obtained by clean image added with noise (0, I), and row 5 is added with noise NO, 9 I).
where .. denotes the diffusion model sampler that depends on the concrete SDE solver used, 20 6, is Gaussian sample, and ææ denotes the generated data.
Question 1: Given an input target x", can we guide a pre-trained diffuusion model at inference time generate the target data efficiently and robustly?
To answer this question, we need to develop an algorithm to address two challenges simultaneously: (1) Guidance Efficiency: the algorithm can generate the target data x' by using only few guidance update steps. (2) Robust to noisy target: given a noisy target (e.g., noise-perturbed image), the algorithm can generate meaningful target data (e.g.. clear image).
We propose novel guided noise sequence optimization method to address the above two challenges, which is presented Algorithm[] [ Our method optimizes the noise sequence by updating the noise along the direction on the data manifold. Specifically, Algorithm [ consists of two procedures: data generation procedure and a noise update procedure. In the data generation procedure, Line Line 6 in Algorithm | employ current noise sequence {Eo.... Ex to generate data æk. In the noise update procedure, Lines 7 in Algorithm I we update each noise E₁ by moving along a universal direction x" xx. Then, Algorithm continues employ the updated noise generate a new data Øx By looping the data generation procedure and noise update procedure, Algorithm generate clear and meaningful target.
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The direction x" K point from the current generation data zx to the target x". Intuitively, moving in this direction will shift the diffusion sampling trajectory toward the target. We highlight the advantage of updating noise with the universal direction x" - æ compared with direction x² - 2y as the following remark.
Remark: In our Algorithm D we guided the noise update by moving along universal direction
xx - KKK This direction lies in the data manifold, which alleviates the quality degeneration of the generated data. We employ the direction x" # instead of the direction x 2% because the internal Øy during inference sampling process may be far away from the data manifold (e.g., the ai can be very noisy and distorted image), which may degenerate the quality.
We empirically evaluate Algorithm I with different update directions: d==x" 2K and d' xx 2x. Additionally, we consider the approximation ææ R 2: Lastly, we set the noisy target to test the robustness. We use the stable-diffusion model as the pre-trained model. The stepsize a is We present the generated images every 10 iterations for each case in Figure
set to a = 2 x 10-3
From Figure | we can observe that Algorithm | (with update direction x" x Øk) ) can successfully guide the diffusion process towards the target image, while x 2y leads to degenerated image. The approximation x" 0x can achieve comparable target image but not as accurate as x" 2 K-
Interestingly, even the input target x" is perturbed by noise and lies out of the data manifold, Algo- rithm will generate a pseudo-data on data manifold that is similar to the noisy target x" but with much higher quality compared with the input noisy target. This property is important for designing black-box guidance methods because the update direction is an approximation or estimation of the true gradient of the black-box objective.
Moreover, we present the updating direction x= KK' for K' E {K, K/2, K/4, K/8) in Ap- pendix C
We observe that the generated image quality decreases as the æ becomes noisier.
3.2 QUERY-EFFICIENT ONLINE BLACK- -LACK-BOX GUIDANCE
In section 3.1 we address the diffusion guidance sampling with given optimal target input. How- ever, in reality, we often only have access to the black-box objective function f(z), where the optimal target data x= arg min f(x) is unknown. In this section, we further investigate the dif- fusion guidance sampling with only black-box objective feedback. For this task, it is natural to ask the following question.
Can we guide a pre-trained diffusion model at inference time to generate target data with only black-box objective feedback in query-effccient online manner?
Question 2:
To answer this question, we need to address two challenges: (1) the Black-bor box challenge and (2) the Online guidance challenge. The black-box challenge means that we cannot access the gradient of the objective. The online guidance challenge means we don't 't have prior dataset to train a surrogate (classifier) for guidance; we can only access the black-box objective through query feedback online. Usually, the query evaluation is expensive. Thus, query efficiency is critical.
We start addressing the above two challenges based on our target guidance generation Algorithm I Although Algorithm I itself cannot handle black-box guidance tasks because it needs input target x², it provides basis for us design to generate target even updating with a noisy direction x - æx-
online black-box guidance methods. To be
specific. Algorithm| enablee This property is important for black-box guidance tasks because we can use a noisy estimation of the gradient of the black-box objective to guide the generation.
The key idea is to set pseudo target å" to guide the generation process based on our Algorithm T
We present our Fast Direct method as in Algorithm 2 In Algorithm 2 we call Algorithm [ inside the for-loop w.r.t. the number of batch queries t. The only difference compared with Alg. iss that instead of given fixed target x=" The choice of models for updating the pseudo target a= in Algorithm 2 is flexible, which supports various black-box target generation method designs based on our Fast Direct algorithm framework. In this work, we set the pscudo target o through nonparametric methods without additional training.
set pseudo target x* in Line 10 of Algorithm 2
'The &u is the "predicted clean image" at step ls, see the Eq. 12 of DDIM (Song et al. 2020). Note that it is undefined for the initial step, so simply set Da := dt.
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Algorithm 2: Fast Direct
Input: Max number of batch queries N, batch size B, step-size a, number of diffusion sampling steps K, pre-trained diffusion sampler Sg. Output: Set of optimized samples (#k.""' ,*k), pseudo target model that learns from
dataset 'D
Initialize D 1- -0
for i 4- 1 to N do
do parallel for B instances
Take i.i.d noise fEu. [x] ~ (0.
Compute the norm of noise chorm = ||eu|] for E (0,..., K).
&
for 1. < to do
2o f- 60
for k
to K do Ig f Sa Sz
10
Set a pseudo target s' by the pscudo target model with input æk-
for h: 0 to K do
12
E f- Ck + a(x" xx)
6/ f- Ey Tex %
14
Query black-box objective score y + f(xx). Increment dataset DD <- DD U {( y)).
16
Update pseudo target model with dataset D.
Specifically, employ two methods for setting the pseudo target s", namely, Gaussian process (GP) update and historical optimal update.
Set pseudo target 4* by GP update: For the GP update case, we set the pseudo target 2' as the one gradient step update using the gradient of the posterior mean prediction of the GP surrogate model as follows:
aK Vf(=k:
where æx denotes the generated data in Algorithm 2 and f faxx; x") denotes the posterior predic- tion of the GP surrogate model (Seeger 2004) evaluated at æK, which has closed-form as below:
f x k(æK X")
(K(("" X") +X) γ
(3)
where X = [xx I and = ly yd denotes the collected data and its corresponding score value in the historical set in Algorithm 2 respectively. When the historical set is empty, we simply set the gradient fx 2) 0.
In this work, we employ shift-invariant kernels that can be rewritten in the form as k(z), #2) g(1221 22|l2). e.g., Gaussian kernel and Matérn kernel. We show that the gradient of the GP surrogate when employing these shift-invariant kernels lies on the low-dimensional data manifold. We present this property in Proposition Detailed proof of Proposition [ can be found in Ap- pendix D This property enables degenerated data, which is important for high-dimension diffusion- lems, e.g., high-resolution (1024 1024) target image generation tasks.
generate meaningful data on the data manifold instead of
target generation prob-
Proposition 1. Given prior data X" [x', "....... x and its corresponding score y = - .v",, ler f(x; x") denotes the posterior mean of the GP model. For shift- invariant kernels k( x2) = g[ll #21l2), V fx X²) lies subspace spanned by xx,xx xx") where x² denotes the ith data sample in x
When setting the pseudo target t" as Eq. we know the pseudo target ø" lies on the low-dimensional subspace spanned by data according to Proposition [ Usually, the gradient descent update in ()) will lead to a new pseudo target x* with a lower score. In addition, based on the empirical observation from Algorithm [ we know that even the pseudo target d" is noisy, Algorithm can still generate similar data that is meaningful and lies on the data manifold. These two properties enable Algorithm 2 to generate data with lower and lower scores along the data manifold.
Remark:

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Set pseudo target 2" by historical optimal update: For the historical optimal update case, set pseudo target á" as the empirical optimal point from the historical dataset D in Algorithm as:
= arg min f(x). mED
Set pseudo target x" as (q. is an empirical approximation of the optimal target x In Algorithm 2 each iteration w.r.t the batch query i, we employ the gener- ated data that achieves the current best score as the pseudo target Ø' to guide the generation process. Intuitively, given this pseudo target &², Algorithm 2 will generate batch of data that is similar to 2" on the low-dimensional data manifold, which is promising to further improve the score. By iter- atively repeating this procedure, the generated data progressively achieves lower objective scores.
arg men fax).
EXPERIMENTS
In this section, we evaluate our algorithm in two domains, images and molccules, and compare against four baseline methods: DDPO (Black et al. 2023), DPOK (Fan al. 2024), D3PO (Yang et al. 2024). and DNO (Tang et al., 2024). Moreover, we further evaluate our algorithm in com- pressibility, incompressibility. and aesthetic quality tasks in Appendix
4.1 IMAGE BLACK-BOX TARGET GENERATION TASK
Prompt Alignment. We consider the image-prompt alignment problem. While the current state-of-the-art image generative models excel at generating highly realistic images, they sometimes struggle faithfully generate images that are accurately aligned with the input prompts, especially those complex prompts involving rare object combinations, object counting, or specific object positioning.
Problem:
Pre-trained Model: Sight-igghtning. We use the SDXL. (Podell et al. 2023) diffusion model as the backbone text-to-image model. It can generate 1024x 1024 high-resolution realistic image. In our experiment, we use the distilled version, SDXL-Lightning (Lin et al. (024). for its high sampling efficiency, which can generate images with comparable quality with just K steps. We use the official implementation
Objective Function: Gemini 1.5. We leverage Gemini 1.5 (Reid al. 2024), an advanced multi- modal LLM service, as our black-box objective function to evaluate the alignment between input prompts and generated images. To avoid confusion, the term query refers the input Gemini, while prompt refers to the text used for image generation.
The query to Gemini 1.5 consists of the generated images with the question like: "Does the prompt $promot accurately describe the image? Rate from I to 5". We state the complete query in Appendix G
Because it is closed-source paid service, we limit the number of batch queries in our ex- periments, referred to as the batch query budget. We use the Gemini 1.5 Flash model (code: gemini-1.5-$11 w-w15----h0000 for its cost-efficiency, and set the temperature to 0 for experiment consistency and reproducibility. For conciseness, we call it simply Gemini in the following section.
Experiment Procedure. We identify 12 prompts that the pre-trained model SDXL-Lighhing strug- gles to generate, and refer to these as the 12 tasks in our experiment, where the goal is to generate images that accurately aligned with the input prompts. We compare Fast Direct algorithm against each baseline method, and each experiment is constrained with a 50 batch query budget.
We perform inference-tine guidance using Fast Direct (Algorithm to maximize the Gemini rating. We use the GP model in Eq. 2 for the pseudo-target, and set the kernel as a Gaussian kernel, and we
follow (Hvarfner et al. 2024) to set the lengthscale as À = Vd, where d = x 128 x 128 is the latent dimensionality of SDXL. We use N 50 iterations to utilize the 50 batch query budget, and set the batch size as B- = 32 and step size as a = -80. We use the Fu arDesc wwteescreeecchheee ler (Karras) al. 2022) sampler as suggested by the SDXL-Lightning implementation (Lin al., 2024), and the DDIMSchedule (Song et al. 2020) (DDIM) sampler for a fair comparison with the basclines.

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For all baseline methods, we perform 50 iterations, each iteration utilizes one batch query for updat- ing. We set the batch size to 32, and leave the rest of the hyperparameters as default. We state more details for baselines in the Appendix D Note that for DNO, in which each experiment trial can only produce one image, we run 16 independent experiment trials and report the average result F
Dass Experiment Results. We present the results for the "deer-elephant" task in this section and defer the results for the other 11 tasks to Appendix | due to space constraints. We compare the images generated by our method with those generated by the baseline methods at each 10 batch query intervals in Fig. 2 We can observe that our algorithm successfully satisfies the visual objectives for both samplers within the 50 batch query budget, while the baseline methods fall short within the same budget. Similar phenomena are observed for the other 11 tasks in Appendix J We grant baseline methods with extra batch query budget, and we observed that DDPO and DPOK almost achieve the visual objectives, whereas D3PO and DNO remain unsuccessful in this particular task.
We collect the 32 randomly generated images using our algorithm (by utilizing 50 batch query bud- get) and present them in Fig. 3 We can observe that most images well satisfy the visual objective. Similar phenomena are observed on the other 11 tasks as shown in Appendix |
DDPO
DPOK
D3PO
DNO
Fast Direct
DOeN
Fast Direct
Number of Batch Queries
Figure 2:
The generated images over each number of batch queries on the prompt "deer-eleplant" extra batch query budget (until 500) is given the baseline methods for demonstration.
For the guantitative results, we report the average objective values over each batch query across the 12 tasks in Fig. 4 For DNO, which only produces one image each experiment, we average the results from 16 independent experiments. We observe that the Fast Direct achieves nearly a full score within the 50 batch query budget in all tasks, which is significantly higher than the baseline methods. Moreover, we observe that Fast Direct achieve a similar score for both samplers, suggesting it is invariant to the sampler. We further perform an ablation study in Appendix B and show that Fast Direct is insensitive to the peper-parameters: step size a and batch size B.
We further employ tasks 1 to 3 and assign an extra batch query budget of 200 to the baseline methods. We report the accumulated objective values, which represent the best objective values achieved up to each number of batch queries, in Appendix Fig. [1 We observe that our algorithm, utilizing only 50 batch query budget, consistently outperforms all baseline methods even when they are assigned with expanded batch query budget of 200.
To quantify this advantage, in Table D we identify the minimum number of batch query budget (denoted as N*) our algorithm requires surpass each baseline method with their 200 batch query
NNte that DNO spent 16 x 50 800 batch queries for each task.
*The complete prompt is: A yellow reindeer and a blue elephant.
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Figure 3: The 32 randomly generated images for the prompt "deer-eleplant" 3 guided by Fast Direct (w/ EDM) by utilizing 50 batch query budget.
Bol: Sieer mari
bal- mote
Teboo Daude
Deloo byybeddag
Namberrd SURCT QUATIIS
Figure 4: The average Gemini rating (from 5, higher is better) of the generated images over each number of batch queries on the 12 different tasks (the prompts abbreviation shown in bracket, see the complete prompts in Appendix 33.
200 and budget. We further calculate ours butch-query-fficiency gain compared to baselines as N report (in the bracket) in Table [ We can observe that our algorithm at least 667% and up to 1000% more batch-query-efffiiine than the bascline methods.
4.2 MOLECULE BLACK-BOX TARGET GENERATION TASK
Drug Discovery. We consider the drug discovery problem. One of the key problems is to find drug molecules that have a strong binding affinity with the target protein receptor. The binding affinity quantifies the strength of the interaction between two biomolecules, so a stronger binding affinity indicates higher drug efficacy.
Problem:
traditional approach to this problem requires human-designed molecules, which are highly time- consuming. Recent advancements in AI pose great potential to speed up this process by generating molecules using advanced generative models such as diffusion models. Although the current pre- trained molecule models, such TargetDiff, are capable of generating relatively realistic molecules, they cannot generate molecules with the user's desired high binding affinity.
Pre-trained Model: TargetDiff. We TargetDiff (Guan et al. 2023 the backbone molecules generative model. It is pre-trained on the CrossDocked2020 dataset (Francoeer et al.) 2020). and generate realistic 3D molecular structures conditioned on the given protein receptor. For sam-

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Table 1: The minimum number of batch query budget (denoted as N) for Fast Direct (ours) to outperform each baseline (when each baseline is granted with 200 batch query budget) on images and molecules tasks. The values in brackets show the batch-query-efiicee efficienef gain ( (2 of Fast Direct (ours) over the baselines.
Minimum number of batch queries (NV) for Fast Direct to outperform each basclines: DDPO
DPOK
D3PO
Image Tasks
Task
15 (13.34x)
15 (13.34x)
10 (20.00x)
Task 2
8 (25.00x)
(25.00x)
4 (50.00x)
Task
37 (5.41x)
46 (4.34x)
16 (12.50x)
Average
(10.00xx
23 (8.70 x)
(6.67x)
Molecules Tasks
Task
6 (3).33x)
(40.00x)
25 (8.00x)
Task 2
9 (22.22xx
(50.00x)
11 (18.18x)
Average
x26.677)
(44.44x
18 (II.ITx)
pling efficiency, we use the DDIMSched;ler sampler with 200 sampling steps as we find that it can generate comparable results. During the generation process, we fix the atom type ac- cording to the dataset reference and only allow the atom position to be varied. We use the official implementation in our experiment.
Objective Function:
Molecules Binding Affinity. Measurement of the binding affinity requires real-world experiment, which is highly expensive (David et al.. 2020). In our experiment, leverage the Vina score (kcal/mol) calculated by the AutoDock Vina simulation software (Eberhardi)
et al. (021) to estimate the binding affinity. A lower (more negative) Vina score indicates stronger estimated binding affinity. Therefore, our objective is to minimize the Vina score.
Experiment Procedure. We conduct experiments on 6 tasks, where the goal of each task is to optimize the Vina score on the protein receptors of ID from to 6, respectively. Similar to image tasks, each algorithm constrained 50 batch query budget.
We perform inference-ting -ieen guidance using our Algorithm to minimize the Vina score. We use the historical optimal in Eq. 4 for the pseudo-target. We use N 50 iterations to utilize the 50 batch query budget, and set the batch size as B = 32, and the step size as = 10-2, We then conduct experiments on the baseline methods for 50 fine-tuning iterations. For DDPO and DPOK, the batch size is set as the same 32; while D3PO requires binary rewards, so we double the batch size as 64 6 We keep the hyperparameters as default for all baseline methods.
Experiment Results. We report the average Vina score (recall, lower is better) for each batch of queries across tasks in Fig. I0 We observe that our algorithm consistently achieves much lower Vina score compared to the bascline methods across all tasks. Similar to the images task, we grant extra batch query budget of 200 to the baselines for tasks and 2, and report the accumulated objective values in Appendix Fig and list the natch-query-efficiee that our algorithm is at least 1111% and up to 4444% more batch-query--fie baseline methods.
gain in Table We observe
compared to the
CONCLUSION
In this work, we proposed Fast Direct for diffusion model target generation, which effectively addresses the challenges of limited batch query budgets and black-box objective functions, demon- strating its potential for various applications, including image generation and drug discovery. Fast Direct is highly practical, it is easy to implement, supports any type of SDE solver, and has only one hyper-parameter to tune (step size a). Our algorithm is based on the surprising empirical ob- servation that the universal update direction (i.e., x² ØK in Algorithm [] can efficiently guide the diffusion trajectory toward the target, even when the target x² is extremely noisy. This phe- nomenon suggests an intriguing robustness the diffusion inference process. Future research could investigate its underlying theoretical principles.
https:: / /i thub.
However, unlike image tasks where Gemini can evaluate rewards for two images in a single query, the Vina simulation lacks this capability, requiring 64 individual queries for hatch of size 64.
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ACKNOWLEDGMENTS
This research is supported by the National Research Foundation, Singapore and Infocomm Media Development Authority under its Trust Tech Funding Initiative, the Center for Frontier Artificial Intelligence Research Center, Institute of High Performance Computing, *tar,, and the College of Computing and Data Science at Nanyang Technological University. Any opinions, findings and con- clusions or recommendations expressed in this material are those the author(s) and do not reflect the views of National Research Foundation, Singapore and Infocomm Media Development Author- ity. Yueming Lyu is supported by Career Development Fund (CDF) of the Agency for Science, Technology and Research (A (A*STAR) (No: (243512014 and No: C233312007).
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A MORE EXPERIMENTS: COMPRESSIBILITY, INCOMPRESSIBILITTE AND AESTHETIC QUALITY
Problem. We follow DDPO (Black al. 2023) evaluate algorithm the three black-box optimization tasks for images: compressibility. incompressibility. and aesthetic quality.
Objective Function. For compressibility, we aimed minimize the compressed JPEG size (MB) of the generated images. For incompressibility, it's simply the inverse. For aesthetic quality, the aesthetic score evaluated by the pre-trained LAION aesthetics predictor (?), which is trained on human ratings of the images' aesthetic quality, and we aimed to maximize the aesthetic score.
Experiment Procedure. For each task, we uniformly sample from the 45 common animals (as proposed by DDPO (Black et al. 2023)) as input prompts and optimize the objective using Fast Direct in Algorithm 2 The prompt for each instance within batch is sampled randomly and independently. We set N = 50 batch query budget for compressibility and incompressibility. and = 100 for aesthetic quality. We use the same pre-trained model, nyper-parametert and GP settings as in Section 4.1 The 45 common animal prompts used in the experiment are as follows:
cat deer lizard
dog horse monkey goat beetle ant
rabbit tiger
zebra spider bird raccoon fox whale dolphin chicken duck goose bee hedgehog
sheep wolf squirrel
lion
bear
butterfly
fish
shark
mouse pig
snake turtle frog
turkey fly llama
camel
bat gorilla
kangaroo
For DNO, each experimental trial generates only a single image, and the objective score is highly accurate evaluation, run 45 independent
dependent the specific prompt. To ensure experiment trials using each prompt and report the average results. Consequently, DNO requires 45x more batch queries per task compared to other methods.
Experiment Result, We present the objective scores for each task in the left column of Fig. 5 and provide the generated images for the three tasks in the supplementary materials. For compressibility and incompressibility. we observe that Fast Direct achieves significantly better scores than the base- lines. For aesthetic quality, Fast Direct with the EDM sampler achieves significantly better than with DDIM, likely due to EDM being a more advanced sampler. capable of generating higher- quality images. DNO achieves comparable scores; however, note that each experiment optimizes and generates only a single image.
Generalization Ability. We follow DDPO to evaluate generalization ability. For Fast Direct, we freeze the learned GP model to generate 16 unseen images using distinct unseen animal prompts. Specifically, in this phase, Lines 17 and are removed from Algorithm 2 and our algorithm does not access the objective function. For DDPO, DPOK, and D3PO, the fine-tuned models are frozen to generate images with the unseen animal prompts. DNO not applicable to the generalization experiment, as it performs per-image optimization and lacks generalization ability. For the unseen prompts, since DDPO didn't publish the unseen prompts, we created our own as follows:
elephant owl
cagle
pigeon flamingo
hippo
hamster otter giraffe
panda
reindeer
penguin
seal
koala
parrot
cheetah
In F we present the images generated using the unseen prompt "hippo" by each algorithm across the three tasks. The remaining 15 prompts for the three tasks provided in the supplementary material. A similar phenomenon is observed for all 15 other unseen prompts across the three tasks. For quantitative evaluation, we report the objective values for the unseen prompts in the right column of 5 We can observe that Fast Direct with the GP model has generalization ability to unseen prompts.
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Compressibility
Compressibility (Unseen Prompts) (4)
0.40
0.35
0.35
JPEO Size (MB)
0.25
0.25
0.20
0.20
0.15
0.10
0.10
20
40
60
80
100
20
40
60
80
100
Incompressibility (1)
Incompressibility (Unseen Prompts) (1)
0.8
0.7
JPEG Size (MD)
0.6
0.5
0.3
100
Aesthetic Quality
Aesthetic Quality (Unseen Prompts)
6.2
Aesthetic Score
6.0
6.2
5.9
Aechatic
5.8
5.6
5.8
20
40
100
60
80
100
Number of Batch Queries
Fast Direct (ours) EDM
Fast Direct (ours) Aw DDIM
DDPO
DPOK
D3PO
DNO
Figure 5: Left column: The average objective score of the generated images over each number of batch queries the 3 black-box optimization tasks, the images generated using the 45 common animals that were used in DDPO (Tang et al. 2024) as the input prompts. Right column: The objective score of the images generated by unseen prompts, which demonstrates the generalization capability. Note that DNO is not applicable to this task.
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DDPO
DPOK
D3PO
Fast Direct
Fast Direct
Number of Batch Queries
(a) Compressiblitt
DDPO
DPOK
D3PO
Fast Direct (wl O3MM
Fast Direct (wl EDM1
Number Batch Queries
(b) Incompresshility
DDPO
DPOK
D3PO
Fast Direct (w 00mg
Fast Direct
Number of Batch Quories
(c) Acsthetic Quality
Figure 6: The generated images using the unseen prompt "hippo" in the aesthetic quality task, extra batch query budgets (until 500) are given to the baseline methods for demonstration.
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HEPO
(a) Pre-trained
(b) Compressiility
Eol
(c) Incompresibiliity
(d) Aesthetic Quality
Figure 7: The generated images using the 16 unseen prompts. Top left is the image generated by the pre-trained model, the rest are the Fast Direct generated images in the corresponding tasks.
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B ABLATION STUDY
We perform an ablation study on the target image generation Task-1. For step size analysis, we fix the batch size 32, then perform experiment with different step size {20, 40, 80, 160, 320); for batch size analysis, we fix the step size as 80, then perform experiment with different batch size B {4, 8, 16, 32, 64). Additionally, we report the run time for different batch sizes. We report the result in Fig. 8
We observe that the performance steadily increases for any step size and any batch size, which suggests that our algorithm is not sensitive to the hyperparameter settings. The run time scales linearly with the batch size. our target image generation experiments in Section 4.1 the batch size is set as 32, each experiment takes approximately 6.4 hours to process 32 images in parallel.
Step Size
Batch Size
Runtime vs Batch Size
5.0
(HOUPS)
Gemmi Catra
3.5-
3.0-
2.5
30
Number Batern Quer
Number of Batch Queries
Figure 8: Left: Gemini rating for different step size settings. Middle: Gemini rating for different batch size settings. Right: Run time (hours) for different batch size settings.
C ANALYSIS OF UNIVERSAL DIRECTION
In Fig. demonstrate Algorithm with update direction d x² KK for K' {K, K/ /2, K/4, K/8). shows that the generated image quality decreases as the K' decreases. This is because as K' decreases, the g becomes noisier and may move further away from the data manifold.
Xz/4
X5yt
Figure 9: The update direction d' = x æg for K" E (K, K/2, K/4, K/8), the generated images quality decrease the g" being more more noisy.
D BASELINES DETAILS
For DDPO, DPOK, and D3PO, we fine-tune the model to maximize the Gemini rating. We fine-tune the model with 50 epochs; each epoch utilizes batch query for model updating. For DDPO and DPOK, we set the batch size to 32; for D3PO, it requires a relative reward, so we doubled the batch size 64 F
As these RL-based methods require closed-form expression of the logarithm probabilities, we follow their official implementation to use the DDIMScheduler (Song et al. 2020) sampler.
For D3PO, evaluate 64 images only requires 32 queries calls, Appendix G
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For DNO, we optimize the noise sequence w.r.t the Gemini rating. Recall that the Gemini is a black- box, so we use the non-differemtiable mode, with the number of samples for gradient approximation set as 32, and optimize for 50 iterations; each iteration utilizes one batch query for updating. How- ever, each experiment of DNO only produces one image, where the score is highly dependent on the initial prior. Thus, for more accurate evaluation, 16 images and report the average results. Note that this requires 16 x 50 = 800 batch queries for each task, which 16 times compared with our Fast Direct and other baselines.
16 independent experiments generate
E PROOF OF PROPOSITION
for GP with a shift-invariant kernel that can be rewritten as k(21, x2) = g(|2] - =2(12), the gradient of the GP prediction
Proof. Let a = (K(("", ) + AT)
AT) -1 = [a]. [a.."..,. .o,]
V (æ; x²) = Vk(æ, )
(5)
0 Vk(x, x²))
(6)
|| x 9' (|<=->\|l)((= x¹)
03
Σ (x) x²)
(8)
1=1
where æ² denotes the 7th sample in X n [w ... x²). and c;(x)) = T g'(12 x 22112), and
g'(+) denotes the derivative of g(.).
From E8. (1. n). Thus, know Vf(;; ") lies subspace spanned by [x, x , xx"
that the gradient [z(=; is weighted of x² for i
F OVERVIEW OF RELATED WORKS
We present the summary of related works according to their category and supported problem sce- narios in Table
Table Overview of existing approaches.
Algorithm Category
Algorithm
Online Black-box box
Truining/ / Fine-tune
DRaFT (Clark et al.
2026
DDOM (Knshnam
2023
RCGDM Yuan
DPO Waffave el a
DDPO Wlack et m
2023
DPOK (Fan al.
D3PO Yung
2025
LGD (Song ct al.
2025
MPG (ri al.
20445
Inforence-ting
DNO (2024] KKaumralaaaa et al
2024
Guidance
ReNO [Ering el al 2024
CEP Lu ct. 2025
DNOT Lang clai. 2024
CASBO (Ly al 2024
Fast (ours)
G EXPERIMENT DETAILS
We use the following query question for the Gemini:
Does the prompt $promp: accurately describe the image?
Rate from 1 (inaccurate) to 5 (accurate).
Answer in the format: Score- (score), Reason- (reason).
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where the Sprompt is substituted by the imput prompt used for image generation. We extract the integer score as the objective value.
The D3PO takes relative reward, specially crafted for its selective query question:
Given this two images, which image is better aligned with the prompt Sprompt?
Answer in the format: Choice-(1/2) Reason= (reason)..
where we extract the choice (1/2) as the selections.
The Gemini 1.5 Flash can faithfully respond with the correct format to ensure experiment is consistent.
H MOLECULES GENERATION EXPERIMENT RESULTS
We report the average Vina score in the molecules generation task in Fig. 10
Task 1 (BSD ASPTE)
Task (GLMU, (GLMU_STRPM) STRPN)
Task 3 (GRK4 HUMAN)
-6-
-6-
-0-
Vina Score (kcal/ml))
-6-
-10
-8-
-12
10
20
50
10
20
30
40
50
Task-4 (GSTPL_ HUMANI
Task-5 (GUX]_ (GUJE_HYPJE)
Task-6 (HDAC8, HUMAN)
-8
-8-
Number of Batch Feedbacks
Fast
DPOK
D3PO
Figure 10: The Vina score (lower is better) of the generated molecules for each number of batch queries on the six protein receptors.
I MORE EXPERIMENT RESULTS
We report the accumulated objective values over the number of batch queries for the images task in Fig. I1 and for the molecules task in Fig. 12
J MORE GENERATED IMAGES BY FAST DIRECT FOR IMAGE-PROMPT ALIGNMENT TASK
Table 3 shows the list the complete prompts used for each task in our image generation experiment.
From Fig. 13 to Fig. 23 we present the generated images of each algorithm over each number of
batch queries for prompts 2 to 12. From Fig. to Fig. 34 showcase the 32 randomly generated
by the pre-trained model, followed by the resultant images guided by the Fast Direct for prompts 12.
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Accumalated Gemini Rating
Idner-alephant)
[trattic-llght)
[apple]
3.
3
125
100
175
25
150 200
Number of Batch Queries
Fast Direct (ours)
DIPO
CIOCK
D3PO
DNO
Figure 11: The accumulated Gemini rating (from 1 to 5, higher is better) over the number of batch queries. The accumulated plot displays the maximum Gemini rating achieved for each number of batch queries.
Task-1 (BSD (BSD_ASPTE) ASPTE)
Task-2 (GLMU_STRPN)
Accumalated Vina Score
0
-2
-2
-4
-4
-6-
-8-
-10
-10-
-12
0
50
100
150
200
0
50
100
150
200
Number of Batch Feedbacks
Fast Direct (ours)
DDPO
DPOK
D3PO
Figure 12: The accumulated Vina score (lower is better) over the number of batch queries. The accumulated plot displays the minimum Vina score achieved up to each number batch queries.
Table 3: Target Image Generation Tasks
Task ID
Abbreviated Prompt
Complete Prompt
deer-elephant
yellow reindeer and blue elephant.
2
traffic-light
A traffic light with yellow top, green at middle, and red at bot-
tom.
3
apple
Seven red apples arranged in a circle.
4
cyber-dog
A natural fluffy dog talking to cybertic dog.
5
puppy-nose
Side view of a puppy lying on floor, one butterfly stopping on its nose.
6
robot-plant
A cute cybernetic robot plants tree in the forest.
7
ocean
helicopter floating under the ocean.
8
sand-glass
transparent glass filled with a mixture of water and sand, with one feather floating inside.
9
penguin
photo realistic photo showing a penguin standing on very small floating ice, with tree is fire in the background.
10
basket
Exactly one orange in a basket of apples.
11
ice-cube
glass of water with exactly one ice cube.
12
cat-butterfly
A cat with butterfly wings.
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DDPO
DPOK
D3PO
DNO
Fast Direct
fer Domn
Fast Direct
10
60
Number
Figure 13: The generated images over each number of batch queries on the prompt "traffic-light", extra batch query budgets (until 500) are given the baseline methods for demonstration.
DDPO
DPOK
D3PO
DNO
Fast Direct
for comn
Fast Direct
10
Number of Batch Queries
Figure 14: The generated images over each number of batch queries on the prompt "apple", extra batch query budgets (until 500) are given the baseline methods for demonstration.
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DDPO
DPOK
D3PO
DNO
Fast Direct
Fast Direct
Quaries
Figure 15: The generated images over each number of batch queries on the prompt "cyber-dog" for each algorithm.
DDPO
DPOK
D3PO
DNO
Fast Direct
Fast Direct
Queries
Figure 16: The generated images over each number of batch queries on the prompt "puppy-nose" for each algorithm.
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DDPO
D3PO
DNO
Fast Direct
Fast Direct
Quaries
Figure 17: The generated images over each number of batch queries on the prompt "robot-plant" for each algorithm.
DDPO
DPOK
D3PO
DNO
Fast Direct
Fast Direct
Queries
Figure 18: The generated images over each number batch queries on the prompt "ocean" for each algorithm.
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DDPO
DPOK
DNO
Fast Direct
Fast Direct
Quaries
Figure 19: The generated images over each number of batch queries on the prompt "sand-glass" for each algorithm.
DDPO
DPOK
D3PO
DNO
Fast Direct
Fast Direct
Queries
Figure 20: The generated images over each number of batch queries on the prompt "penguin" for each algorithm.
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Published as a conference paper at ICLR 2025
DDPO
D3PO
DNO
Fast Direct
Fast Direct
Quaries
Figure 21: The generated images over each number of batch queries on the prompt "basket" for each algorithm.
DDPO
DPOK
D3PO
DNO
Fast Direct
Fast Direct
Queries
Figure 22: The generated images over each number of batch queries on the prompt "ice-cube" for each algorithm.
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Published as a conference paper at ICLR 2025
DDPO
DPOK
D3PO
DNO
Fast Direct
Fast Direct
Querles
Figure 23: The generated images over each number of batch queries on the prompt "cat-butterfly" for each algorithm.
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Published as a conference paper at ICLR 2025
B
Pre-trained
(b) Fast Diroct
Figure 24: The 32 randomly generated images for the prompt "traffic-light" guided by Fast Direct by utilizing 50 batch query budget.
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Published as a conference paper at ICLR 2025
(a) Pre-trained
(b) Fast Direct
Figure 25: The 32 randomly generated images for the prompt "apple" guided by Fast Direct by utilizing 50 batch query budget.
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Published as a conference paper at ICLR 2025
(a) Pre-trained
(b) Fast Direct
Figure 26: The 32 randomly generated images for the prompt "cyber-dog" guided by Fast Direct by utilizing 50 batch query budget.
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Published as a conference paper at ICLR 2025
(a) Pre-trained
2d
(b) Fast Direct
Figure 27: The 32 randomly generated images for the prompt "puppy-nose" guided by Fast Direct by utilizing 50 batch query budget.
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Published as a conference paper at ICLR 2025
(a) Pre-trained
(b) Past Disect
Figure 28: The 32 randomly generated images for the prompt "robot-plant" guided by Fast Direct by utilizing 50 batch query budget.
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Published as a conference paper at ICLR 2025
(a) Pre-trained
(b) Fast Direct
Figure 29: The 32 randomly generated images for the prompt "ocean" guided by Fast Direct by utilizing 50 batch query budget.
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Published as a conference paper at ICLR 2025
(a) Pre-triined
(b) Fast Direct
Figure 30: The 32 randomly generated images for the prompt "sand-glass" guided by Fast Direct by utilizing 50 batch query budget.
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Published as a conference paper at ICLR 2025
(a) Pre-trained
(b) Past Direct
Figure 31: The 32 randomly generated images for the prompt "penguin" guided by Fast Direct by utilizing 50 batch query budget.
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Published as a conference paper at ICLR 2025
(a) Pre-trained
(b) Fast Direct
Figure 32: The 32 randomly generated images for the prompt "basker" guided by Fast Direct by utilizing 50 batch query budget.
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Published as a conference paper at ICLR 2025
Pre-trinned
(b) Past Direct
Figure 33: The 32 randomly generated images for the prompt "ice-cube" guided by Fast Direct by utilizing 50 batch query budget.
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Published as a conference paper at ICLR 2025
M
b
(a) Pre-trained
(b) Fast Direct
Figure 34: The 32 randomly generated images for the prompt "cattbutterfly" guided by Fast Direct by utilizing 50 batch query budget.
38


Reviews:
[{"review_id": "8WMFoMpU9j", "reviewer": "Reviewer_TjGV", "summary": "This paper proposed an LLM-scored/non-differentiable-scored pipeline for generating aligned outputs via diffusion models during the inference procedure. The paper utilizes Gaussian Process (GP) estimation or historical optimal to insert the guidance of the score. With two different generative tasks, the method outperforms several inference-time diffusion guidance methods with fewer iteration steps.", "questions": "In Figure 1 (also in the figures in the Appendix), the initial image of the proposed method is different to the other baselines, which seems to be a factor that influences the final generation performance. Can the authors clarify here?\n(comment: several references could be listed here).", "limitations": "", "rating": 5, "confidence": 1, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "The method utilizes GP/historical optimal as the estimation of the guidance direction, illustrating clear improvements based on the score evaluation. The Bayesian-based optimization in the field of inference-time diffusion guidance is shown to be a useful tool for improving generation quality.", "weaknesses": "Lack of ablation studies of the hyper-parameters used in the proposed pipeline (step-size alpha, lambda). Additional experiments on the chosen hyper-parameters (especially for the step-size alpha since this is the guidance scale) are encouraged.\n\nFor both image and molecule generation tasks, the evaluation is based on the same metric used in the optimization, which may enrol incomplete or unfair comparisons to the other baselines. The paper could use the same LLM score during the inferences of the baselines (if it is available for them) or post additional evaluation metrics (such as the score from different LLMs and human evaluation scores) to strengthen the argument.", "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": "KzkxFf7Bnt", "reviewer": "Reviewer_dSoP", "summary": "This work proposed a new online black-box algorithm, Fast Direct, to provide guidance for diffusion model without the need of pretrained guidance model or differentiable objective function. The important findings of the noise update direction in Guided Noise Sequence Optimization laid the foundation of the Fast Direct algorithm. The Fast Direct showed consistent query efficiency compared to 3 baselines.", "questions": "1. For comparison with three baselines, were there reward models finetuned with Gemini scores? Or were the Gimini scores directly used for finetuning the diffusion model? It was not really clear to me from my reading.\n\n2. In the claim of selecting K instead of k for the update direction in Section 3.1 and the observation from Figure 1, I'd really love to also see the effect of choosing K/2 and K/4 to get a sense how the degree of \"far away from the data manifold\" correlates quality degradation. \n\n3. While aiming to deal with the situation of missing a guidance model, could you also add a comparison of the results using this method versus using a trained guidance model? That would make the story more convincing.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 4, "strengths": "1. This work used a data driven method for image generations, different from the three baselines in previous works focusing more on finetuning the diffusion model. This should be a good indicator of originality.\n\n2. The proposed solution was a training free solution, which also led to a more query efficient method.", "weaknesses": "In case of the query being OOD, and the Gemini continues to dislike the generated image, the proposed solution might be less efficient compared to the methods involved finetuning the diffusion model.", "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": "jwaEzLv3mT", "reviewer": "Reviewer_hGsH", "summary": "The paper, titled \"Fast Direct: Query-Efficient Online Black-Box Guidance for Diffusion-Model Target Generation,\" introduces an innovative approach to guiding diffusion models in black-box target generation tasks. It addresses scenarios where the objective functions are non-differentiable and where training datasets are unavailable, making it applicable to real-world applications such as drug discovery and human-preference-driven image generation. The authors propose a new method, Fast Direct, leveraging a Guided Noise Sequence Optimization (GNSO) technique. Through extensive experimentation on image and molecular generation tasks, the paper demonstrates that Fast Direct outperforms several baseline methods in query efficiency and generation quality.", "questions": "Why were DDPO, DPOK, and D3PO chosen as baselines? Are they the most relevant and competitive in black-box target generation? If there are other methods that could be appropriate, a brief comparison or justification for their exclusion could enhance the robustness of the evaluation.\nHave you considered alternative pseudo-target selection strategies? While the paper presents GP and historical-optimal updates, discussing the potential effects of alternative methods (e.g., reinforcement learning-based or clustering-based approaches) might highlight the generalizability of Fast Direct.\nCould you provide more insights into the parameter tuning process for Fast Direct? As query efficiency is a primary focus, understanding the sensitivity of Fast Direct to parameters like batch size and step size would clarify the required fine-tuning efforts.\nCould you expand on the potential for scalability? Especially in high-dimensional tasks like high-resolution image generation, it would be helpful to know if there are constraints or limitations for Fast Direct in terms of computational efficiency.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Novelty and Relevance: The Fast Direct algorithm introduces a unique approach to guiding diffusion models without requiring differentiable objectives or offline data, making it relevant for applications with black-box or non-differentiable feedback.\nEfficiency: The method shows impressive improvements in query efficiency across tasks, which is valuable in settings with expensive evaluations, such as in drug design.\nExtensive Experiments: The paper includes rigorous experiments across various image and molecular generation tasks, showing consistent improvements over baseline methods in both query efficiency and task performance.\nGeneralizability: The proposed approach is designed to be flexible, supporting various black-box objectives and stochastic diffusion schedulers, which suggests it could be adapted to other domains and applications beyond those tested.", "weaknesses": "Limited Theoretical Justification: While empirical results are promising, the paper lacks a thorough theoretical analysis of the robustness and convergence properties of Fast Direct’s update strategy. Providing an in-depth theoretical foundation could strengthen the paper’s contributions and clarify the algorithm's potential limitations.\nBaseline Comparisons: Although Fast Direct shows efficiency gains over DDPO, DPOK, and D3PO, a discussion is missing on why these methods were specifically chosen as baselines and how representative they are of the current state of the art in black-box guidance.\nScalability and Computational Costs: The paper could provide more detail on the computational overhead introduced by Fast Direct, especially for high-dimensional tasks like image generation. Scalability analysis in terms of computational cost and time efficiency would be beneficial, particularly as query budgets increase.\nLack of Ablation Studies: The paper does not explore the sensitivity of Fast Direct’s performance to its key hyperparameters, such as step size and batch size. Given the flexibility mentioned for the pseudo-target update, ablation studies could reveal the robustness of these parameters and how they influence the results across tasks.", "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": "W5SG0s9sks", "reviewer": "Reviewer_KTQQ", "summary": "This paper introduces an inference-time black-box online guidance method for diffusion models. The authors first propose Guided Noise Sequence Optimization (GNSO), an efficient and robust technique for guiding a diffusion model sampling process toward a desired target sample. Building upon GNSO, they present Fast Direct, a novel approach that leverages pseudo-target samples derived from an online, black-box reward model as guidance targets. This approach enables guidance for any reward model, making it highly versatile. Compared to existing black-box online guidance methods, this approach offers the advantage of inference-time guidance, significantly reducing the number of reward model queries required to steer the sampling process toward the desired target.", "questions": "1. How was the point in time considered complete for guidance iterations determined? Explicitly stating this criterion within the text would provide greater clarity.\n2. Are there any comparison results with the inference time guidance method? Although online black box inference time guidance methods are limited, incorporating a comparison with existing methods, even if the performance of the presented methodology is lower, would offer valuable insights. Additionally, considering the paper's focus on tasks that do not allow backpropagation through the reward model, performing neural network-based reward model tasks (compressibility, incompressibility, and aesthetic scores) commonly used in diffusion model reinforcement learning would make it possible to compare with those methods.\n3. What is the number of diffusion sampling steps used in each experiment? \n4. What are the specific advantages of guiding $\\epsilon_k$ compared to directly guiding $x_k$?\n5. The contribution summary in the introduction is not in a clear summary form. It is recommended to reorganize the detailed contributions into a separate paragraph and condense the summary into two or three concise lines.\n6. Is the value of $||\\epsilon_k||$ used in line 13 fixed as a value pre-calculated in line 3? Or is it a value that continuously changes as $\\epsilon_k$ is updated by the loop in line 4?\n7. Why was the experiment conducted on $x^{\\star} − x_k$ instead of $x^{\\star} − \\hat{x}_k$ (predicted clean image) in Figure 1 (ablation)? Given that $x^\\star$ represents a clean image, comparing it to the predicted clean image $\\hat{x}_k$ at the same noise level appears to be a more intuitive approach for assessing the difference.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. This paper advances the efficiency of reward model queries in steering diffusion models with online black-box reward models. By employing an inference-time guidance method, it avoids the computationally intensive process of model fine-tuning, making it a more practical and efficient solution.\n2. From a user perspective, inference-time guidance offers a more immediate and flexible approach to personalization compared to model fine-tuning. This makes the proposed methodology highly promising for real-world applications.\n3. The paper proposes a novel approach to online black-box guidance, leveraging reward models to generate pseudo-targets for the GNSO, a technique originally used for same-dimensional target guidance.\n4. The proposed methodology has been validated across various domains (image and molecule) and tasks (text-image alignment and drug discovery), demonstrating superior performance and efficiency compared to existing black-box online reward model-based guidance methods.\n5. The paper presents its methodology clearly and concisely, making it easy to understand and follow.", "weaknesses": "1. The number of experimental tasks is insufficient, and the number of prompts used in the experiment is limited. A direct comparison with reinforcement learning methodologies for diffusion models, such as those in [1], would benefit from evaluating common tasks like compressibility, incompressibility, and aesthetic scores. Additionally, the manual selection of a small number of prompts by the author raises concerns about potential bias toward the proposed methodology.\n2. The comparison with fine-tuning baselines may be unfair due to their intended purpose, which differs from the proposed inference-time guidance approach. However, given the absence of other black-box and online inference-time guidance methods, this comparison may be justifiable.\n3. The methodology's optimization of noise during sampling is incompatible with the ODE sampler, a popular choice due to its deterministic nature and high performance [2]. This limitation could hinder the method's practical applicability.\n4. In the text-image alignment experiments, the proposed method utilized the EDM sampler, while the baseline employed the DDIM sampler. This experimental setup may favor the proposed method, potentially compromising the fairness of the comparison.\n\n[1] Black, Kevin, et al. \"Training diffusion models with reinforcement learning.\" *ICLR* (2024).\n\n[2] Karras, Tero, et al. \"Elucidating the design space of diffusion-based generative models.\" *NeurIPS* (2022).", "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)