Uni-Instruct: One-step Diffusion Model through Unified Diffusion Divergence Instruction
Authors: Yifei Wang
Weimin Bai
colin zhang
Debing Zhang
Weijian Luo
He Sun
Venue: NeurIPS 2025 poster ()
Keywords: Diffusion Distillation
One Step Generation
Primary area: deep_learning
TL;DR: We unify more than 10 existing one-step diffusion distillation approaches, with a new SoTA 1-step generation on CIFAR10 and ImageNet64x64 benchmarks.

Abstract: In this paper, we unify more than 10 existing one-step diffusion distillation approaches, such as Diff-Instruct, DMD, SIM, SiD, $f$-distill, etc, inside a theory-driven framework which we name the \textbf{\emph{Uni-Instruct}}. Uni-Instruct is motivated by our proposed diffusion expansion theory of the $f$-divergence family. Then we introduce key theories that overcome the intractability issue of the original expanded $f$-divergence, resulting in an equivalent yet tractable loss that effectively trains one-step diffusion models by minimizing the expanded $f$-divergence family. The novel unification introduced by Uni-Instruct not only offers new theoretical contributions that help understand existing approaches from a high-level perspective but also leads to state-of-the-art one-step diffusion generation performances. On the CIFAR10 generation benchmark, Uni-Instruct achieves record-breaking Frechet Inception Distance (FID) values of \textbf{\emph{1.46}} for unconditional generation and \textbf{\emph{1.38}} for conditional generation. On the ImageNet-$64\times 64$ generation benchmark, Uni-Instruct achieves a new SoTA one-step diffusion FID value of \textbf{\emph{1.06}}, which outperforms its 79-step teacher diffusion with a significant improvement margin of 1.29 (1.06 vs 2.35). We also apply Uni-Instruct on broader tasks like text-to-3D generation. For text-to-3D generation, Uni-Instruct gives decent results, which slightly outperforms previous methods, such as SDS and VSD, in terms of both generation quality and diversity. Both the solid theoretical and empirical contributions of Uni-Instruct will potentially help future studies on one-step diffusion distillation and knowledge transferring of diffusion models.

Uni-Instruct: One-step Diffusion Model through Unified Diffusion Divergence Instruction
Yifei Wang
Weimin B.i Colin Zhang*
Debing Zhang*
Weijian Luo He Sun 1,2,3
College of Future Technology. Peking University
National Biomedical Imaging Center, Peking University
Academy for Advanced Interdisciplinary Studies, Peking University Yuanpei College, Peking University
4 hi-lab, Xiaohongshu Inc
Abstract
In this paper, we unify more than 10 existing one-step diffusion distillation ap- proaches, such as Diff- Diff-Instruct, DMD, SIM, SiD, f-distill, etc, inside theory- driven framework which we name the Uni-Instruct. Uni-Instruct is motivated by our proposed diffusion expansion theory of the f-divergence family. Then we introduce key theories that overcome the intractability issue of the original expanded f-divergence, resulting in an equivalent yet tractable loss that effectively trains one-step diffusion models by minimizing the expanded f-divergence family, The novel unification introduced by Uni-Instruct not only offers new theoretical contributions that help understand existing approaches from a high-level perspec- tive but also leads state-of-the-arg one-step diffusion generation performances. On the CIFAR 10 generation benchmark, Uni-Instruct achieves record-breaking Frechet Inception Distance (FID) values of 1.46 for unconditional generation and 1.38 for conditional generation. On the ImageNet 64 x 64 generation benchmark, Uni-Instruct achieves a new SoTA one-step generation FID of 1.02, which out- performs its 79-step teacher diffusion with significant improvement margin of 1.33 (1.02 vs 2.35). We also apply Uni-Instruct on broader tasks like text-to-3D generation, which slightly outperform previous methods, such as SDS and VSD, in terms of both generation quality and diversity. Both the solid theoretical and empirical contributions of step diffusion distillation and knowledge transfer of diffusion models. Code will be available at Github
will potentially help future studies
Introduction
One-step diffusion models, also known one-step generators
2g 30
have been recognized
stand-alone family of generative models that reach the leading generative performances in wide
range of applications, including benchmarking image generation [30
B1
69
68
17 59). text-to-
image generation [29 32
61 62, 31 67 [6], text-to-video generation
I
63 image-
[3]
and numerous others
55).
Currently, the mainstream training the one-step diffusion model is through proper distillation approaches that minimize divergences between distributions of the one-step model and some teacher diffusion models. For instance, Diff--Instruct (30] was the first work that introduced one-step diffusion models by minimizing the Kullback-Leible divergence. DMD [62) improves the Diff-Instruct by introducing an additional regression loss. Score-identity Distillation (SiD) 69 studies the one- step diffusion distillation by minimizing the Fisher divergence, but without the proof of gradient
Correspondencce
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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MAIT
Disorini Mudial
FID 50-5KK
Teocher
Dunil
M ECT END
Uni-Instruct
IMN
Gdica MD
AM
oif instrrtt 0903,
Pake
vSD, DDS Recball Se sruhh
Diffusion Model
Uni ME tours)
TOT Generation Steps
Figure I: Left: Conception overview of Uni-Instruct. The Uni-Instruct unifies more than 10 existing diffusion distillation methods in wide range of applications. Please check Table details. Right: selected FID scores of different models on ImageNet-66 64 x 64 conditional generation benchmark.
equivalence of the loss function. Later, Score Implicit Matching (3I introduced a complete proof the gradient equivalence of losses that minimizes the general score-based divergence family, special case. f-distill [59] and SiDA [68] recently generalized
including the Fisher divergence the Diff-Instruct and the SiD to the integral /-divergence and auxiliary GAN losses, resulting in image generation benchmarks. Other approaches have also elaborated
performance improvements on the one-step diffusion models on a wide range of applications through the lens of divergence
minimization [16 68 67
61
29 32
Provided that existing one-step diffusion models have achieved impressive performances, with some of them even outperforming their multi-step teacher diffusions, existing training approaches can be conceptually separated into two lines:
(1) Disf-Instruct 30] and its variants like 62], tackle the Integral Kullback-Leibler Divergence, while f-distill 59) unifies the IKL special of Integral f-divergence. These KL and divergence based distillation approaches have the advantage of fast convergence, but suffer from mode-collapse issues and sub-optimal performances;
(2) Score Implicit Matching (IIM proves a solid theoretical equivalence of score-based diver- gence minimization, which unifies the 6iD and Fisher divergences special cases. Though these general score- based divergences minimization has shown surprising generation performance, they may suffer from slow convergence issues and sub-optimal fidelity.
Till now, seems that the KL-based and Score-based divergence minimization approaches are pretty parallel in theory. Therefore, we are strongly motivated to answer an interesting yet important research question: Can we unify Kl.-based and Score-based approaches in a unified theoretical framework? If we can, would the unified approach lead to better one-step diffusion models?
In this paper, we provide a complete answer the mentioned question. We successfully built unified theoretical framework based on novel diffusion expansion of the f-divergence family. Though the original expanded f --divergence family is not tractable optimize, we introduced new theorems that lead to tractable yet equivalent losses, therefore making Uni-Instruct an executable training method.
In this way, are able unify more than 10 existing diffusion distillation methods wide range of applications via our proposed Uni-Instruct. The methods that have been unified by Uni-Instruct include both KL--divergence based methods (such Diff-Instruct (30] DMD 62). and
distill (59) and general score-divergence based methods (such as Score Implicit Matching (SIM 31))
SiD 69 and SiDA (8)). shown in Table Such a novel unification of existing one-step diffusion models marks the uniqueness of
which brings new perspectives in understanding and connecting different one-step diffusion models. Besides the solid theoretical contributions, Uni- Instruct also leads to new State-of offee--fftut oftte--f--h-aarr one-step image generation performances competitive image generation benchmarks: it achieved record-breaking FID (Fréchet Inception Distance) [14 value of 1.02 on the ImageNet 64 x 64 conditional generation task. This score outperforms its 79-step teacher diffusion with a significant improvement margin of 1.33 (1.02 vs 2.35). Uni-Instruct also leads to new state-of-the-art one-step FIDs of 1.46 on CIFAR 10 conditional generation task and I.36 on CIFAR unconditional generation task, significantly outperforming previous one-step models such f-distill, SiDA, SIM, SiD, DMD, and inft-Instruct. also outperforms competitive few-step

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Method
Loss
Div. in UI
Task
Loss Function
Gradient Expression
DiN Insiruct (DD 30
IKL Div.
x²
One Step
Diffusion
(Nyp, (ara) Sxx (2n,3) txe
00
IKL Div. Reward
Human Aligned
One Step
w(t))a (att))Da(pppa (po,dll
Grad(DI) + Crewand
Diffusion
+ Coenn
Human Aligned
KL Div.
RKL
One-Step
(rad(SIM) VaCnunt
Reward
+monn
Diffusion
SDS 41
IKT. Div.
x²
Text
J' (n((()uunno.dllo)dt
Gra((Dt)
3D
DDS 1k
IKL. Div.
x²
Image Editing
Gra(DDI)
VSD
IKL Div.
Text
(Pe,,llae
Gmd(DD)
to 3D
IKL
One One-Sirp
(pe,l (l
DMD 62
Reg.
x²
Diffusion
+
Grad(DI) + V.MSE
RedDifi 24
Data Fodility
IKL Div.
x²
Problem
Inverse
+ +
Grad(DI) VoMSE
DMD2
TKL. Div.
One-Step
Grad(DT) Va
GAN
Diffusion
+Ladu.
Brush [6
Switt
IKL Div.
One-Step
Diffusion
J' (pt))pa..po
Grad(DI)
SIM 31
General
One-Step
Pat(Po.:199))
Sgg (wx) fno. (12x))
KL Div.
Diffusion
( (mr)) V log qi (mr, (mo)
One-Stcp
SiD 6
KI. Div.
RKI.
Diffusion
(STM)))
KL Div. GAN
One-Step
SIDA
Dsa Cadd.
Gra((SIM) VaLado.
Diffusion
SiD.LSG 67
KL. Div.
RKL
One Step
Diffusion
DR (Po.llo)))
(SIM))
f-distill 59
1- Div.
GAN
X²
Diffusion
Siep
(o(t)yygu (gollpo,))d (po,
Lodv.
Grad + VoCade.
f Div. GAN
(pollopo.)) +add.
Volado. GPdddd((
(Ours)
AlI
All
BX (M)GG)(SMM))
Table 1: Distribution matching diffusion distillation loss family. Uni-Instruct not only extends the distribution matching framework theoretically, but also unifies all previous gradient expressions with specific weightings.
generative models, including consistency models [44, inductive models (66), and many others 57
48 25
moment matching distillation models
Besides the one-step generation benchmark, we are inspired by DreamFusion 41. Premmfe [55] SIM 331ss is application on 3D experiments. In Section 4.3 also successfully apply Uni- Instruct as a knowledge transferring approach for text-to-3D generation applications, resulting in robust, diverse, and high-fidelity 3D contents which are slightly better than ProlificDreamer in quality and diversity.
We summarize the theoretical and practical contributions in this paper follows:
Unified Theoretical Framework: We introduced unified theoretical framework named Uni-Instruct together with a novel f-divergence expansion theorem. Uni-Instruct is able unify more than 10 existing one-step diffusion distillation approaches, bringing new perspectives to understanding one-step diffusion models.
Tractable and Flexible Training Objective: We introduce novel theoretical tools, such as gradient equivalence theorems, and derived tractable yet equivalent losses for Uni-Instruct. This leads to both flexible training objectives and new tools for one-step diffusion models.
New SoTA Practical Performances: Uni-Instruce achieved new state-of-the-an generation performances (measured in FID) on CIFARIO (a one-step FID of 1.36) and ImageNet644 x 64 (a one-step FID of 1.02) benchmarks. We also successfully applied Uni-Instruct the text-to-3D generation task, resulting in plausible and diverse 3D generation results.
Preliminary
2.1 Diffusion Models
Diffusion Models. Assume we observe data from the underlying distribution qa(ar). The goal of generative modeling is to train models to generate new samples æ ga(z). The forward diffusion
3

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process of DM transforms any initial distribution qo qd towards some simple noise distribution,
dx, Fz t) + g(t)dw,,
(2.1)
where F is pre-defined drift function, g is a pre-defined scalar-vall diffusion coefficient, and denotes an independent Wiener process. A mnttinoous-indxxen score network 8, 8, t) is employed
to approximate marginal score functions of the forward diffusion process (2.). The learning of score
networks is achieved by minimizing weighted denoising score matching objective 50]50
(DSM(() - -f- A(e oogo at a.l0: ( (m)) || (Ze, Vn, log qi (x, [B))
(2.2)
Here, the weighting function X(t) controls the importance of the learning at different time levels, and qe (a.z.aag (2)) denotes the conditional transition of the forward diffusion (2.1). After training, the score network 8.p (2,,, = V log (xx,) is good approximation of the marginal score function of the
diffused data distribution. High-quality samples from a DM can be drawn by simulating SDE, which
implemented by the learned score network (50). However, the simulation of an SDE is significantly slower than that of other models, such as one-step generator models.
2.2 One-step Diffusion Model via KL. Divergence Minimization
Notations and the Settings of One-step Diffuusion Models. We use the traditional settings intro-
duced in Diff-Instruct (30 to present one-step diffusion models. Our basic setting is that we have a pre-trained diffusion model specified by the score function sg. (a:,) ) == Va, log qe (xx)) where q (2x)'s
are the underlying distribution diffused at time t according to (2.1) I.. We assume that the pre-trained
diffusion model provides a sufficiently good approximation of the data distribution, and thus will be the only item of consideration for our approach.
The one-step diffusion model of our interest is a single-step generator network yo. which can transform an initial random noise z ~ Pz to obtain sample æ = ge (=); this network is parameterized by network parameters 0. Let pe.o denote the data distribution of the student model, and pa, denote the marginal diffused data distribution of the student model with the same diffusion process (2.T). The
student distribution implicitly induces score function 8pu.c (æx) V æ. log po, (2:). and evaluating is generally performed by training an alternative score network as elaborated later.
Diff-Instruct (DI). Diff-Instruct [3 is the first work that trains one-step diffusion models by minimizing the integral of KL divergence between the one-step model and the teacher diffusion model distributions. The integral Kullback-Leible divergence between one-step model (.).) and teacher
diffusion model qu(-) is defined as: DIKL (polopllo (Polllu)) = _ w E mompot s-x log Pe.e (a.)( jat.
(xx))
=1 of (mm)) Though IKL as a training objective is intractable because we do not have a direct dependence of and po. 30 proved in theory that tractable yet equivalent objective writes:
(Di = [ B (x))(()), () SG de (egl0maaao (se eo)
8 Spsaion (2x) - sq. (xx)) æ (O)dt,
(2.3)
Where the operator SG in 2.3 represents the stop- goopggradient operator. Diff-Inntruct proposed to use an aaline-trained fake diffusion model to approximate the stopped-grade gradient one-step model score function s (2xc) R Psoppl, (xz). Such a novel use of a fake score is kept by following approaches such as DMD, SiD, etc. Two key contributions of Diff-Instruct are (1) first introducing the concept of the one-step distillation via divergence minimization; (2) introducing technical path that derives tractable losses by proving gradient equality w.r.t the intractable divergence.
2.3 One-step Difffusion Model via Score-based Divergence Minimization
Score Implicit Matching (SIM). Inspired by Inff-Innttuct and the empirical success of SiD (69), recent work, the Score-implicit Matching (SIM) [1], has generalized the KL divergences to general score-based divergence by proving new gradient equivalence theories. The general score-divergence
defined via: DIO.TI (p. q) == f w()) d(pp, (wx) - 8q (a::) dl, where pt and gt denote
the marginal densities of the diffusion process 2.T at time t initialized with g and p respectively.

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w(t) is an integral weighting function. d(-) is distance function. Clearly, we have Di0.TI (p, z) = and only if all marginal score functions agree, which implies that Po Po(x)) (o(x)), a.s. To.
SIM shows that Eq.
2.4g has the same parameter gradient as the intractable score-divergence:
{
(0)
(x() wow.p.ooosooooo m (mp]m))
d'(wr) Peator, (a+)) Vxx, log qi (2 mo) fdt, (2.4)
1=
with yr Pastot (ax)) - sq. (z)) Now the objective becomes tractable.
In Section 3 we use theoretical tools from Diff-IIntrruct and SIM to prove the gradient equivalence of tractable Uni-Instruct loss and the intractable expanded f-divergence. Furthermore, we are surprisingly to find that the resulting gradient expression recovers novel combination of the Diff- Instruct and the SIM parameter gradient.
2.4 Relation Between KL Divergence and Fisher Divergence
Inspired by the famous De Bruijn identity
660 6 that describes entropy evolution along heat 33 49 have built the relationship between KL divergence and
diffusion, notable works [45] 37
Fisher divergence via diffusion expansion: the KL divergence is the integral of the Fisher divergence along a diffusion process under mild regularity conditions:
L/"
1 (1)
(pe (pell4))
8q. (x:))1 dt
(2.5)
Motivated by the relationship between KL divergence and Fisher divergence. in Section begin the Uni-Instruct framework by proposing novel diffusion expansion theorem of general KL divergence: the f-divergence family.
wc
Uni-Instruct: Unify One-step Distillation Methods in Theory
In this section, we introduce Uni-Instruct, a theory-driven family of approaches for the one-step distillation of score-based diffusion models. Uni-Instruct is able to unify more than 10 existing methods as special cases with proper weighting functions. It also leads to new state-of-the-ard one-step generation performances on ImageNet64 x 64 and CIFAR10 generation benchmarks.
Uni-Instruct is built upon novel diffusion expansion theory of the f-divergence family. We begin by giving a brief introduction to the f-divergence family. We then prove a novel diffusion expansion theory of f-divergences in Section 3.1 which acts the target objective would like to optimize. Then in Section 3.2 we provide a non-trivial theorem that leads to an equivalent yet tractable loss function that shares the parameter gradient as the intractable expanded f f-divergence.
3.1 Diffusion Expansion of f-Divergence
( f-divergence. For a convex function f()) on (0, +oc), where f(1) = 0, The f-divergence 42 is:
((x)
Dy(alp)) = p(x)
dx.
(3.1)
p(x)
Appropriate choices of the function f(.) lead to many widely-used divergences such as reverse-KL. divergence (RKL), forward-Ki KL divergence (FKL), Jeffrey-KL. divergence (JKL). Jensen- Shannon divergence (JS), and Chi-Square divergence (x²). We put more introductions in the appendix
The Diffusion Expansion Theorem. We use the same notations and settings in Section 2.2 go (-)
represents the one-step diffusion model, and qe(-) represents the distributions of the teacher diffusion model. Our goal to minimize the f f-divergence between the output image distribution of the one-step model's distribution and the teacher diffusion model distribution Dy(gullpa) However, since /-divergences are defined in the image data space, they can not directly incorporate instructions from multiple noise levels of teacher diffusion models. To address this issue, we first introduce diffusion expansion Theorem 3f fddivergence along diffusion process. This expansion enables us construct training objectives by considering all diffusion noise levels.
5

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Theorem 3.1 (Diffusion Expansion of f- --Dveegeenee). Assume p, 9 are distributions that both evolve along Eq. 2.1 We have the the following equivalence:
(T)
( 9:
(900ollpa)
29 9 (t)
f"
po, || (xx) sy, (2x))1 dt + +Dy(arllpe,))
po,r
(3.2)
We give complete proof with regularity analysis in Appendix B.1 For simplicity, assume Dy(gr [pperr) and ignore the last term in Eq. 3.2 in the following section. This fundamental expansion (Eq. 3.2p expands the static f-divergence along the diffusion process. However, directly optimizing objective (3.2) is not tractable because do not know the exact expressions of either the density Po,: the score function Sp,, (-) of the diffused one-step model's distribution. To step towards a tractable objective, we derive the gradient of the expanded f-divergence 3.2 in Theorem 3.2
data space into integral of divergences
3.2 Theories to Get Tractable Losses
To tackle the intractable issue the expanded f-divergence, we prove a novel parameter gradient equivalence theorem 3.2
Theorem 3.2 (Gradient Equality Theorem the Expanded /-divergence). Let qe qe(x)) and pa,
be probability density functions evolving under the Pokker-Plante dynamics, and RR_ R is four-time: differentiable convex function. The parameter gradient of the f-divergence rate satisfies:
(
9r
5"
9:
||
(mm)) (mma)lla
2
Po.
Po.1
5 (4)
1
voo.
SG
C1
9:
[a (mns)
e a (mi) (#esole), (are) - Vay lox qi (ar, mo)
Pe.1
29
(4)
Eva.e
sci
C2
g+
(saelwe)
((1)) (l (ws) spo.e 1112
(3.3)
Pe.t
where SG donates stop gradient operator, and the curvature coupling coefficient C(r) are defined as:
(c(x))
2 C2(r) = 2r² 4r f(" (r" r= (x))(
(3.4)
derived equality of the gradient of the intractable expanded f-divergence. The right side of the equality is two terms, which are gradients of two tractable functions. With this observation, side of equality (3.3) using gradient- prasee optimization algorithms such as Adam [21] is equivalent minimizing the intractable expanded fdiivergence, which lies in the left-hand side.
Remark 3.3. It is worth noting that in Theorem
that minimizing the tractable right-hand
We notice that the gradient of the training objective admits a composition of the gradient and a SIM(1] gradient. Therefore, we can formally write down our tractable loss function as:
(30
(Ui(0) = - T 1 (1) (XP) LDI + XISN XSIM (SIM) dt,
(3.5)
CSIM- Epo.c [SG (C1 (r)) 2 (sn (xx)) Sprotan (2x)) (Spaata), (2x)) Vz, log (x [xo)]]
CDI = Epo.. [SG (C2 (r) (sy. (xx) - spo,: (xxe) ||s (xx) spa. (x:) )1)) xi].
(3.6)
where the weighting coefficients are determined by the f-divergence selection, we provide our completed proofs in Appendix B.2]
Density Ratio Estimation via an Auxiliary GAN Loss Notice that the tractable loss function 3.5 requires the density ratio between the one-step model and teacher diffusion. For this, we train a GAN discriminator along the process, where the discriminator output serves as estimator: This use of GAN discriminator is also widely applicable in other works like SiDA and f f-distill (59]. Details on why the GAN discriminator recovers the density ratio can be found in Theorem B.1

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Practical Algorithm of the Uni-Instruct Uni-Instruct. As is shown in Algorithm | one-step diffusion model, online fake score network, and a discriminator. The training is performed in two steps alternatively: we first optimize the discriminator with real data, and then optimize the online fake score network with score matching loss. After that, we optimize the one with Uni-lnstruct loss, which is given by the previous two models. Uni-Instruct loss varies based on the divergence we choose. We provide example divergences in Tab. 8 Note that through choosing proper divergence, we can recover the distillation loss of Diff-Instruct [3], SIM [30, as well as f-distill [59]. To more specific: LSIM vanishes when selecting x² -divergence, while LDI vanishes if we choose forward- -L, reverse- KL, and Jeffrey-KK KL divergence.
We can now present the formal training algorithm of we maintain the active training status of three models:
Algorithm I: Uni-Instruct Algorithm on Distilling One Step Diffusion Model
Input: pre-trained DM Sy generator 9o, fake score network sy, discriminator Dx, divergence f. GAN weight WGAN, diffusion timesteps weighting w(t).
1: while not converge do
2: Sample real images and random noises: Dreal Pdata; N((0,) 1)
3: Generate fake images: Btake = (e(
4: Update D, with discriminator loss:
LD = Ex [log D (I(raa))]] - Exa.. log(L - Dx Dx (cTfak))]]
5: Update s. with denoising score matching loss:
Laiifusionn = =S" (t) Egg | (2mae)
(2 1) - Va, log Pt (x Draae) dt.
6: Calculate Uni-Instruct loss: Luii Equation 3.5
7:
Calculate adversarial loss (non-saturating): LGAN = - Emsaso Dog Dx Dx(xEaaae) (Irraee)
8: Update go with total loss: Leoral LUni + UGAN LGAN
9: end while
return ge
3.3 How Uni-Instruct can Unify Previous Methods
In this section, we show in what cases Uni-Instruct can recover previous methods. As is shown in Tab. I Uni-Instruct can effectively unify more than 10 existing distillation methods for one-step diffusion models, such as Diff-Instruct, DMD, f-distill, SIM, and SiD.
DI, DMD, and f-distill are Uni-Instruct with additional time weighting. DI 30 and DMDD
62 integrates KL divergence along a diffusion process: DIKI. (pollg)) := 1o" (o(()Dr (po (po Il dl. (
Furthermore, f-distill [59] replace KL with general f-divergence. Our goal, on the other hand, to match these two distributions only at the original distributions: D (40 (Po), which requires no specific weightings wt). Our framework is more theoretically self-consistent for those ad ad-hoc weightings that may induce mismatches between the optimization target and the true distribution divergence. However, with additional weightings, Uni-Instruct can recover fdiittil..
Corollary 3.4. Suppose W(1) w(()dt + C, (O) = O, the expression of Uni-Instruct with an extra weighting ((t) is equivalent to f-distill:
[- 59 (OW
[
44 [1 (wx) - (wx))1 de = 1." ((o)))(<olllo.)dd (3.7)
8"
Po.1
Pe.1
Complete proof is in Appendix B.4 which leverages integration by parts and Theorem 3.1
SIM is Special Case of Uni-Instruct. Suppose dl-) is 12-norm, SIM in Section 2.3 becomes:
IoT (() [[|spe. (ar:) - 8q, [13) di. turns out that SIM is special case of Uni-Instruct. We find that the right-hand side of Theorem 3.1 will degenerate SIM through selecting the divergence
as reverse-Ke KL divergence: DKL (pol Spollge qu) = fe g (t) [[]spo. (xx) sg. (xx) dt. As result, SIM is secretly minimizing the KL divergence between the teacher model and the one-step diffusion model, which is special case of our /-divergence. Beyond this specific configuration, Uni-Instruct offers enhanced flexibility through its support for alternative divergence metries, including FKL and JKI., which enable improved mode coverage. This generalized formulation contributes to superior empirical performance, achieving lower FID values.
7

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3.4 Text-to-3D Generation using Uni-Instruct
Recent advances in 3D text-to-image synthesis leverage 2D diffusion models as priors. Dreamfusion 41 introduced score distillation sampling (SDS) to align NeRFs with text guidance, while Prolific- Dreamer [55] improved quality via variational score distillation (VSD). These methods mainly use reverse KL divergence. Uni-Instruct generalizes this framework by allowing flexible divergence choices (e-g., FKI., JKI,, enhancing mode coverage and geometric fidelity, and unifying SDS and VSD as special cases.
4 Experiments
In this section, we first demonstrate Uni-Instruct's 's strong capability to generate high-quality samples on benchmark datasets through efficient distillation. Followed by text-to-3D generation, which illustrates the wide application of Uni-Instruct.
Figure 2: Generated samples from Uni-Instruct one-step generators that are distilled from pre-trained diffusion models on different datasets. Left: CIFAR10 (unconditional); Mid: CIFAR10 (conditional); Right: ImageNet x 64 (conditional)
Table 2: Comparison image generation on
Table 3: Class conditional ImageNet 64 gener- ation results. "Direct generation" and "Distillation" methods require one NFE, while the teacher uses
CIFAR-10 (unconditional). The best one/few- step generator under the FID metric is high- lighted with bold. F.S. means from scratch. L.T.
35 NFE. F.S. means from scratch. L.T. means re- sume and Longer Training.
means resume and Longer Training.
Family
Model
NFE FID (1)
Family
Model
NFE FID (L)
Teacher
VP-EDM
DPM-Solver-3
48
2.65
RIN IR
1000
1.23
DDIM 40
100
4.16
DDPM [5
250
Diffusion
DDPM 15
1000
3.17
Diffusion
ADM 9
250
2.07
iDDPM
38
4000
2.90
ULVI
2
50
4.26
sCT
x
2
2,06
ICT Fk]
4.02
Consistency
ECT
|
2
2.11
Consistency
iCT-decg
+7
I
3.25
[0]
PD 4b.
2
4.51
MMD
1.24
IMM [X]
1.98
G-istill 35
2.05
TRACI 8
2
3.32
PD F4
2
8.95
Diff. ProjGIN 541
1
2.54
PID [2]
9.49
PID 52
3.92
iCT-doopp 17
3.25
DENO 61
3.78
EMD-16 59
2.20
iCT-leep
2.51
DFNO [4
7.83
Diff-Instruct
DMD244oager training
1.28
Few Step
Few Step
DMD 62
3.77
CTM 20
1.92
SiD (69
SiD 69
1.92
SiDA [68]
1.35
SiDA [8
1.52
SiD2 68
1.10
SID
1,50
f-distil 59
1.16
Uni-Instruct with RKL. (FS..)
1.52
Uni-Instruct with RKL(ES.)
1.35
Uni-Instruct
(RS.)
1.52
Uni-Instruct with JKL(FS.))
1.28
Uni-Instruce with FKL (L.T.)
1.48
Uni-Instruct with FKL(FS.)
1.4
Uni-Instruct with JKL (FSS.)
1.46
Uni-Instruct with FKL(L...))
1.02

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4.1 Benchmark Datasets Generation
Experiment Settings We evaluate Uni-Instruct for both conditional and unconditional generations on CIFARIO (22) and conditional generations on ImageNet 64 64 We use EDM [19] as teacher models. In each experiment, we implement three types of divergences: Reverse-KL (RKL), Forward- KI. (FKI.), and Jeffrey-KI. (JKL.) divergence. We borrow the parameters settings from SiDA [68]. which takes the output from the diffusion unet encoder directly as the discriminator. As for evaluation metrics, we use FID, as it simuftaneously quantifies both image quality and diversity.
Performance Evaluations Tab. 2 Tab. and Tab. B shows Uni- Uni-Instruct performance on both settings of CIFARIO and ImageNet 64 x 64. Uni-Instruct achieves new state-of-the- one-step generation performances on all datasets. Our important findings include: (1) When training from scratch, JKL achieves the lowest FID score. On CIFAR I0, JKL. trained from scratch has FID score of 1.42, out-perform other baseline methods like DMD (22), SiDA [68]. and the teacher model EDM [1).. (2) When resuming trained SiD model (RKL), FKL achieves even better results. As shown the T FKL with longer training achieves new state-of-the-an one-step generation on both datasets. This means a two-time training schedule: first trained with RKL until convergence, followed by FKI., enhances the model's performance with both mode-seeking behavior from RKL. and mode-covering behavior from FKL.
Table 4: Label-conditioned image generation re- sults on CIFAR-10. The best one/few-step genera- tor under the FID metric is highlighted with bold.
Table 5: Ablation study CIFARIO uncond generation. GAN means using GAN loss. Init means initialize from models.
Family
Model
NFE
Div. SiD Init. GAN FID
Teacher
VP-EDM 19
35
1.79
None
8.21
Diffusion
DDPM
B
1000
3.17
X2
4.37
DDPM
2.90
JS
5.23
DifT Instruci
4.19
JKL
1.46
SIM 31
1.96
CTM 20
1.73
RKL
1.92
1.71
FKL
1.88
SIDA 68
1.44
RKL
1.52
One Step SIDP 68
1.40
f-distill
FKL
1.52
Uni-Instruct w. RKL (from scratch)
1.44
RKL
1.50
Uni-Instruct w. JKL. (from scratch)
1.42
FKL
1.48
Uni-Instruct w. FKL (from scratch)
1.43
Uni-Instruct w. FKL (longer training)
1.38
JKL
1.50
4.2 Ablation Studies
Performance Between Different Divergences and the effect of GAN loss. We perform an ablation study on the techniques applied in our experiments. Table 5 ablates different components of our proposed method on CIFARIO, where we use an unconditional generator for all settings. For different divergences, we select three types: JKI., FKI, and RKL are divergences that only contains Grad(SiD), -hasen-Shannon (JS) divergence has a
x2 divergence's gradient is only contributed by Grad(DI), gradient that contains both: hD (x) Grad hss Grad(SiD). Our result shows that JKL achieves the lowest FID value. Due to the numerical instability of the weightings. JS yields unsuccessful distillation results. As for the effect of GAN loss, we find that removing it still yields decent result. Our integrated approach also surpasses the performance of using Uni-Instruct loss alone(withod adding GAN loss), highlighting the effectiveness of combining expanded f-divergence with GAN losses. We also find that using a model trained with RKL Uni-Instruct (which recovers the 6iD loss) as the initialization leads to better performances for all divergences.
Additional metrics evaluation and convergence analysis. As shown in the right table of Tab. 6 across different training iterations, our method consistently achieves lower FID scores than SiDA. This clearly indicates that our approach converges faster, reaching better generative quality with fewer iterations. Moreover, Tab. and Tab. 7 compares the performance of Uni- and SiDA 68 across both CIFARIO 10 and ImageNet 64 x 64 benchmarks on sFID, FDDINO, inception score (IS), percision and recall. Uni-Instruce achieves strictly better results in 4 of 5 metrics on ImageNet 64 x 64, the more complex and practically relevant benchmark. These gains are significant, particularly in FD and IS, which measure semantic alignment and perceptual quality/diversity. respectively.

---

The 18.7% reduction in FD on ImageNet 64 x 64 confirms Uni Ini-Instruct's advanced capability to preserve high-level semantic structures (e.g., object boundaries, textures, contextual relationships). This is critical for applications requiring fine-grained realism (e.g., medical imaging, autonomous driving). More importantly, Uni-Instruct's gains widen significantly on ImageNet x 64 (+8.1% 8.1% IS, 18.7% FDdino) versus CIFAR1O, proving its robustness for high resolution, semantically rich image synthesis. SiDA fails to maintain competitiveness under greater complexity.
Table 6: Further comparison between SiDA [68] and Uni-Instruct (forward KL). The left table the performance evaluation on CIFARIO unconditional generation. The right table compares the FID score along with the iterated k- t-imges during training.
Method
sFID
Finoo dimo
1st
Precision
Recallf
10a
102
103
10a
105
SiDA
1.71
132.72
10.32
0.670
0.624
139.86
68.73
41.45
6.98
1.44
Ours
1.66
129.30
10.30
0.671
0.626
132.51
54.60
38.58
5.23
1.41
sFID
FDaino
IS1
Precision
Recalft
sFID
FD 4
1S
Precisiont
Recall
SiDA
1.68
111.26
10.28
0.678
0.632
1.98
74.86
59.28
0.562
0.653
Ours
1.68
108.89
10.29
0.679
0.629
2.01
60.86
64.11
0.561
0.658
Table 7: Further comparison between SiDA]68 and Uni-Instruct (forward KL). The left table is the performance evaluation on CIFARIO conditional generation, while the right table is the performance evaluation on ImageNet 64 64 generation.
Figure 3: Prompt: A refined vase with artistic patterns. Left: ProlifiDrreamer; Right: Uni Ini-Instruct (forward KL). Our vase demonstrates more diverse shapes well realistic patterns.
4.3 Text-to-3D Generation Using 2D Diffusson
In this subsection, apply Uni-Instruct on text-to-3D generation. We re-implement the code base of PromificDreamerr (55) by adding an extra discriminator head to the output the stable diffusion Unet's encoder. We use FKL to distill the model for 400 epochs. Fig. B demonstratts the visual results from our 3D experiments. Uni-Instruct archives surprisingly decent 3D) generation performances, with improved diversity and fidelity. Due page limitations, put detailed experiment settings, quantitative metrics, and training algorithm in the Appendix
Conclusions
We present Uni-Instruct, theoretically grounded framework for training one-step diffusion models via distribution matching. Through building upon a novel diffusion expansion theory of the f- divergence, Uni-Instruct establishes a unifying theoretical foundation that generalizes and connects more than 10 existing diffusion distillation methodologies. performance on benchmark datasets and efficacy in downstream tasks like text-to-3D generation. We hope Uni-Instruce offers useful insights for future studies on efficient generative models.
also demonstrates superior
10

---

Acknowledgement
This work was supported by the National Natural Science Foundation of China (62371007) and by Xiaohongshu Inc. The authors acknowledge helpful advice from Shanghai AI Lab and Yongqian Peng.
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A Related Works
Diffusson Distillation Diffusion distillation 28 focuses reducing generation costs by transfer- ring knowledge from teacher diffusion models to more efficient student models. primarily includes three categories of methods: (1) Trajectory Distillation: These methods train student models to approximate the generation trajectory of diffusion models using fewer denoising steps. Approaches such as direct distillation [26] [0] and progressive distillation [43 35 aim to predict cleaner data from noisy inputs. Consistency-basee -based methods [48] (0) 47 23 12 instead minimize a self-consistency loss across intermediate steps. Most of these methods require access real data samples for effective training. (2) Divergence Minimization (Distribution Matching); This line of work aims to align the distribution of the student model with that of the teacher. Adversarial training- basedd methods [56 58] typically require real data to perform distribution matching. Alternatively, several approaches minimize divergences like the KL divergence (c.g.. Diff-Instruct [30] 62] or Fisher divergence (e.g..
Score Identity Distillation [69] Score Implicit Matching (1), and often do so without requiring real samples. Numerous improvements have been made to these two lines of work: DMD2 [1] and SiDA [68] add real images during training, rapidly surpassing the teacher's performance. f-distill [59] generalize KL divergence Diff-Instruct into f-divergence and compared the affection of different divergences. Additionally, significant progress has been made toward scaling diffusion distillation
for ultra-fast or even one-step to--t--magee generation [27 16 62 [1]. (3) Other Methods:
Several alternative techniques that train the model from scratch have been proposed, including ReFlow
[24]. Flow Matching Models (FMM) [4]. which propose an ODE to model the diffusion process. Inductive Moment Matching [66] models the self-consistency of stochastic interpolants at different
time steps. Consistency models [8 47 20 | impose consistency constraints on network outputs along the trajectory.
B Proofs
B.1 Proof of Theorem 3.1
Let and be distributions satisfying the Fokker- Plancr equations, and decay rapidly at infinity:
Proof.
Opo.t
V
2 9 (1)e. Ve log F(x, ()po.s
Ot
0q:
[
V
2 (t) v log F(x, t)q.
(B.1)
OL
We begin with the definition of f-divergence and apply differentiation under the integral sign:
di (y(cllloo)) = d. po.ef
d
gt
dæ
Po.1
= f
Ōpo
da
+
2 po.t Øt
gt
da
(B.2)
Øt
Po,
Po.+
For the second term, apply the chain rule and the quotient rule:
f
at
qe
qt
pa.
dac
da
Po.c
Øt.
Ps.t
/ 8
qt
Öge.
qt
apo.t
dx
(B.3)
Po.
Ot
Po.t
Combining Eq.
B.I Eq. dt DD(gellpo.))
B.2
and
Eq.
B.3
we obtain:
pt)
Va log F(x, t)po,
f
qt dx
pa.
qt
+
V
(t V log q: - F(x, t)qe
dx
Po.t
2
Qt
V
(t)Po. Va log - F(x, t)po, dx (B.4)
Po.
Po.1
22

---

Apply integration by parts to the RHS of Eq. and with previous assumption that distribution P8.c and q. decay rapidly infinity, we have:
d (yclllpo.))= [|
20 (1) Va log Fax, 1)po.
Vas
St
dæ
dt
Po.e
v
gr
2 (1) Ve log F( t)qt
dæ
Po,
V
qt
f'
qt Pa,r
2 (t)pp, log pa. F(x, t)po, dæ
(B.5)
Pe,1
Now we can further expand the gradient terms in Eq.
B.5
V A. Ve
V
f
(B.6)
po.1
p0,2
Pd.c
V f
f"
9r
V q.PoP
V VnPo.
(B.7)
pa,t
pa,
Pb.
qt
8
Ut po.+
V AiPPo.
V VePo
q
qt
V
VePan.
po,t
Po.
Pa,t
po.e
Po.e
(B.8)
Replace the gradient terms in Eq. manipulation, we obtain:
B.5 with Eq.
B.6
Eq.
B.7
and Eq.
B.8 and after algebraic
dt Dy(aellpo.)) = -0)()) / po.t
d
Gt
f"
4e
VI log q: V log Pla (B.9)
po,e
po.
The integral version Eq.
B.9 is:
Dy(collpo)= = S/" 2 (t)
(
9t
f"
9t
|
log
- V log
dt + Dy(ar (po.r)
2
1o,1
18,1
(B.10)
B.2 Proof of Theorem 3.2
Lemma B.1 (Calculate the gradient of x ~ Ps.t [59]. Assuming that sampling from a aa can be parameterized as æ = Gz(z) + (t)e, where z ~ p(z), , e No, 1), and Go. g are differentiable mappings. In addition, g is constant with respect to 0. Then,
(opo,(()))(z)d dx = / people)
Proof. As ar and g are both continuous functions, can interchange integration and differentiation:
/ V VoPo.(Z)g((a de V, (z))()))()) dx
p(c)p(2)Vog(Go()) +(())) de
plepp(//Ve dedz
Vag(z)
30 dr.
where Gz(z) + (t)e.
23

---

Lemma B.2 (Calculate the gradient of the score fuction [TT). If distribution Pe.: satisfies some mild regularity conditions, we have for any score function sq. (.), the following equation holds for all parameter 0:
(Spu (2x)) 8q. (are))
08 pp,, (1zz)
Bag PPol010
(B.11)
B ((<m)10), - Sqc (xzt))² (8x))0))((2z)) 00
Vas ass log ge (2
(B.12)
For completeness, appreciate the efforts of Luo et al. [1] and provide the proof here. The original version can be refered to Theorem 3.1 from [3T].
Proof. Starting with score projection identity 669
R0 Rom-agg0 O my mmgren (Crg (mt)/m)) mo)
() (xx)) Va, log qe (zx, [mo))} 0, V9, Vu.
(B.13)
Taking the gradient with respect to on the above identity, we have:
i
(u(zz,, 0)² (spo. - Va, log (z: (20)))
dn,
0
E
mom-Pe O we (eg megirge Lite [ing)
Oz,
00
(B.14)
θ (u(x,, 0)2 (-Vm, log (e (200)))
axo
E
#gg #gmpp.00 my (m (mn))
Oco
30
(B.15)
② (sp0))(121)
2
E
#gm-pg:0 and leleo-eeleellg
2 0)T
08
+
09
ul s0(
(B.16)
E
as (mel
=ompp.00
u( (
0)"
00
(B.17)
Ox:
E
#
(u(are 0)" (spo. Van, log log (e (20))
(B.18)
ard pelogeetieslnn
Ox.
00
00
0
dæn
a
{u(#e, 0)" { v., log (x)
u( 0)² so sp(x))
Po-PP.00 O 00 00 (ong (ogg
Ozi
00 00
(B.19)
Ex, ppelt
{ ulxx, R)T 2
00 (0.)))
(B.20)
+
00
E
-a-pa.0 20 (((()))))
{u(xe, O) (8po.e(x))) V ar log qe (x, (x,[))))}
(B.21)
Therefore, we obtain the following identity:
Ex.epeit { (uzze,00 0)T 0)T 00 Spo sxoc) } =
{ {u(xr,0) 0)T (Spo.((x:) Vz, log ge (x [x)))
00
xelmo (an (mg)
(B.22)
Replacing u(x:)) with 8ps, (xx) - 8q (ax)) we can proof the correctness of the original identity.
We now complete the proof of Theorem 3.2
24

---

Proof. Applying the product rule to the gradient, we can obtain:
Vo 2 9
(t)
f"
V logpo.
V log 2:
(B.23)
po.t
Po,t
2 (1)V / (ar)(
1
gt
2
f"
qt
V logg
V log d: I112d
(B.24)
P9.t
Po,t
1 -01) / (VOper(a)) -
qt
f"
9t
V log Pe.t
V log g: ge112xx;
(B.25)
p8.t
Pa,
+ 1 200) / po.
qt
f"
Qt
V log
V log dx
(B.26)
Pa,t
ps.t
which can be further decomposed into the following four terms:
Grad 2 (t) / Vopo.((xe)
Qt
f"
4t
V log Р8. V log 20..30x
(B.27)
Pa.c
p8.c
1 2 20) f pole(
9t po.t
27
f"
qt po.t
V log Pa, log 11
(B.28)
2 (P(()) / po.((zs)
2 Vo
fr
qt p8,e
Vioogo..
V log qe liddæ
(B.29)
pp..1
2
+ +2 (P()))
qt
f"
qe Pa.e
Ve
log p
logp
log qt [12] dæ;
(B.30)
We calculate the above four terms separately.
A
(1))
1
/ Vopo.((s))
Qt
fd
qt
V log Pa.
V log ge de..
(B.31)
Pa.t
po.t
29 (t)) / (ooo(
1
2 lt
V x
Ox,
f"
9:.
V log
V log d2
(B.32)
p9,t
P
00
p8,c
1 0
/ (PL)(TE))
f"1
9t
qt
dæ:
log V log qe 122
(B.33)
2
P8.1
po.
00
1 + 9()) / (P0)((z))
qt
8"
Qt
Vo
log
log 9el)) dx;
(B.34)
Pe,t
eo.:
B = (0)) / po.c(are)
(
qe
2
p"
9t
V log
log gel 12a;
(B.35)
Pa.1
Pa.
1 001) / por)(ar)
qt Po.
qt po.
Vopan(a:))
f"
St Po.t
V log Pa.:
log q,
122da
(B.36)
1 -0000 / Vopol((ts)
2
qt
27
p"
9t po.t
V loggep. pa.:
V Vlogg.1/2da
(B.37)
po.t
=
(B.38)
25

---

C 20) / polc(ar))
9t
V
l"
ge po,
V logpe,
V log qt [2d
(B.39)
1 20((0) / (00)
qt
dz: p
qt
pr
V log pa,
log q 2x
p8,c
00
p9.c
(B.40)
1 20((0) / (0))
8rr
St
V
dw. Pa.t 00
V log
V log dx:
pa.:
pa.t
(B.41)
1 900) po.((z)))
at
3
pm
at po,t
Vo ( log pog...
Vlog qt (b)) da
(B.42)
po.e
=
1 g)) / (o.1(Er))
qt Po,t
f"
V [|V log Pa,: V 9:11) dæ,
(B.43)
Po.t
As result:
V
2 9 Et)
1
( P8,
9t
2
f"
V log V log 4
(B.44)
-
(B.45)
20 (1)
1
3
f"
Qt
Voll VolVVloge log :0.
V 1l
(B.46)
Po
po.e!
weight 1
1 (1) 7
Fx.
log Po.t
V
log 2el12
( log po.t
V log
(B.47)
00
weight
2
3 8r
where (+) stands for 2
dr Po.t
f"
91
9t
fr1
gt
Po.1
Pe.1
Now we will focus on the only intractable term left in the previous equation: v log qul Such problem has been well studied by FGM [17] and SIM(3I] The former one calculated the term under the assumption that æ has gradient dependence on 6, while SIM [3I] simply ignores such assumption and achieves comparable performance. For the simplicity of the loss
Voll log pe.:
0epo (x,)
expression, follow the setting in 31. Thus calculate 2(V log Pa, V log q:)
Applying Lemma B.2
we have:
1
(t) Epo.:
qt (
f"
Gt pa,
||$po. (xx)) 8g. (2x)
po.1
1 (t)))
Epo.t
SG
C1
Ut
2 (sgg (Ixe) Specter, (xx)) (ppss10,:(13)) Va, log (oz 200)
00
po.t
1 9
(1)
Epo.s
SG
C₂
gt
(2,
Sno, (2::) || 8po. (xx)
æt
00
Pa.t
(B.48)
where SG donates stop gradient operator, and the curvature coupling coefficient C(r) are defined as:
==) rr" Ca(r) = 2r { (r),,,,, r:= ge (x)
(B.49)
(x))
26

---

B.3 Density Ratio Representation
Theorem B.1 (Density Ratio Representation), For adversarial discriminator conditioned on the
timestep D: X x [0, I] + [0, 1] satisfying:
D arg min E log ((a t)] + RE log((1 D(2, t))]
(B.50)
The density ratio admits the variational representation:
(r(3)) D (xx t)
(B.51)
( 1 (xxxtt
Proof of Theorem B.1 Firstly, we calculate the optimal discriminator:
Lemma B.3 (Optimal Discriminator Characterization). For measurable functiosss D : X x [0, + [0, 11, the minimizer of:
J(D) = Ex-ug log D(z, t)] 1- D(z, t))]
(B.52)
satisfies the first-order optimality condition:
6J
4: (:(x)
po (3))(x)
1 (x, O.
(B.53)
3D
(x,,))
D-
Solving Lemma B.3
optimality condition yields:
9t (x(x))
(², qi (x) + p0,)(x))
(B.54)
Through algebraic transformation, we have:
(x())) (²,
(B.55)
(0)) 1 (xxxt) t)
B.4 Proof of Corollary
3.4
Proof of Corollar 3.4 Using Theorem 3.1 assuming some mild assumptions on the growth of log dr
and log Pt infinity, we have:
1-" (t)
1 2
(90 (pa)
f"
V log pa,
V log 2 dt.
(B.56)
po.:
Pa,t
We also have the differential form of this formula:
dt Dj (gllleeo..)
d
(t))
qt
V log Pe.:
V log 2e
(B.57)
Pe.:
p8.
We can re-weight Eq. B.56 for arbitrary weightings, where W(!) selected in our case. The
re- re-weighted version of the RHS of Eq. [B.56]can be written as:
1 1 (1) 2
((0)
qt
f"
V log.e.
V 1 dt.
(B.58)
pa.
pa.
[" -w(t) d dt Prlalllaa.)dt.
(B.59)
w(t)D,(a:llo.) po..) " T + 1'" ('(e)D/(alllpo.ddd
(B.60)
= I" w(t)Dy(q.)) (d.
(B.61)
27

---

C Detailed Analysis on Divergence
In this section, we provide several example divergences derived from our Uni-Instruct framework. Tab. summarizes five types of divergence.
Divergence
f(r)
Ci(r)
(2)
SeeeenS
FKL
r Flog r
0
RKL
logr
-1
0
JKL
(r 1) logr
-T
0
X
(r 1)²
47
JS
rlogr - (r + 1) log 2)
21)+1)
2y
(m+1)
Table 8: Comparison of different /-divergences as a function of the likelihood ratio = (
(((m)
Mode Seeking vs. Mode Covering For arbitrary f divergence Dy(alle) S [
[(x)
dæ,
p(m)
be classified into two categories based on its mode seeking behavior. Divergences that mode-seeking tend to push the generative distribution ps toward reproducing only a subset of the modes of the data distribution p. This selectivity is problematic for generative modeling because can cause missing modes and reduce sample diversity. Such mode collapse has been noted for the integral KI. loss employed in Diff-Instruce and DMD (00 (2). convenient way quantify mode-seeking behavior to inspect the limit lim, frr//r: the smaller this limit grows, the stronger the mode-sceking tendency. Both reverse KL and Jensen-Shannon (JS) divergences have a finite value for this limit. By contrast, forward KL, Jeffrey KL, and x² yield an infinite limit, reflecting its well-known mode-covering nature, which tends recover the entire data distribution In practice, we observed that mode covering divergences such as forward KL. and Jeffrey-KLL achieves a lower FID score.
GSMM Grad(DI) Another way inspect different f divergence is checking the gradient expression. It is worth mentioning that the gradient expression of Uni-Instruct is composed of Grad(SIM) and Grad(DI) (Bq. 3.6p. For KL divergence (reverse, forward, Jeffrey), C2(r) and the gradient is only contributed by GSMd(SIM). On contrary, when selecting x" divergence, C2(r) = 0 and the gradient is only contributed by Grad(DI). The gradient expression of Jensen-Shannon (JS) is a combination of both.
Training Stability However, during training we often observe training instability in Jensen-Shannon divergence and x² divergence, due the complex expression of C₁ (r) and C2(r), which will result in higher FID score (Tab. 5). Tricks such as normalizing the weighting function or implementing the diseriminator the teacher model [59 can be applied to stabilize training. We leave this part to future work.
D Unified Distillation Loss
In this section, we discuss how Uni-Instruch unifies previous diffusion distillation methods through recovering previous methods into special case of Uni-Instruct. We summarize the connections in Tab.
D.1 One Step Diffusion Model Distillation
From Section 3.3 and Corollary 3.4 we have demonstrated that integral KL-based divergence minimization can be treated Uni-Instruct with special weighting. More surprisingly, we found that if we choose X² <dvviveree in Uni- Uni-Instruct, the weighting of SIM becomes 0 and the remaining gradient only contributed by Diff-Instruce as is shown Tab. S and the third column of Tab. i In this way, Uni-Instruct can unify the first line of work: Diff-Instruee (30 is Uni-Instruct with x diveerggeee DMD (2 added extra regression loss contributed by pre- ma-saadled paired images, while DMD2 [6]] added an adversarial loss. SwiftBrush [16] applied the same loss on text-to-image
28

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generation. f-distill [59] can be seen as Uni-Instruce with manually selected weighting, and has gradient expression of (x2 divergence in Uni-Instruct
Moreover, in Sec. 3.3 we demonstrate that leveraging the connection between KI. divergence and score-based divergence, score matching can be interpreted as minimizing single-step KL. divergence.
Thus, selecting reverse-KL (RKL) divergence in Uni-Instruct, we can recover score-based divergence, as shown the third column of Tab. I In this way, SIM [31] and SiD [69] minimize Uni-Instruct loss with RKL. Additional adversarial lossariaaoossss is added in SiDA while text-to-image distillation is applied in SID-LSG 67), both under the same Uni Uni-Instruct(RKL) setting. Though our experiments on benchmark datasets have already demonstrated the superior performance of Uni-Instruct on distilling one-step diffusion model (Sec. 4. We believe Uni-Instruct can be further applied to sarge-scale datasets and text-to-image diffusion models. We leave that to future work.
D.2 Text-to-3D Generation with Diffusion Distillation
DreamFusion 41 and ProlificDreamer [55 propose to leverage text-to-image diffusion models to distill neural radiance fields (NeRF) 36 enabling efficient text-to-3D generation from a fixed text prompt. DreamFusion utilizes a pretrained ext-to-image diffusion model to guide the optimization of NeRF network by performing core-distillation sampling (SDS). This method minimizes KL divergence that aligns the rendered images from NeRF with the guidance from a pretrained diffusion model.
ProlificDreamer further advances this concept by introducing variational distillation, which involves training an extra student network to stabilize and enhance the distillation process. Specifically, denote pa (arce, (c, y) as the implicit distribution of the rendered image æ == g(0 c) given the camera with the rendering function g(: c). while go(a (y)) as the distribution modeled by the pretrained text-to-image diffusion model with the view-dependent prompt y ProlificDreamer approximates the intractable implicit distribution posterior distribution pe (a]c,, y) by minimizing the integral KL divergence between the diffusion- -uuided posterior and the implicit distribution rendered by NcRF:
= 1'" ((t)) v) log po.c(2alc,,)) @i (xx+1y))
DIKL (2
dt.
(D.1)
Utilizing Corollary 3.4 we observe that by choosing suitable weighting functions W (t), the integral KL divergence used by PromificDreamer corresponds to the reverse KL (RKL) version Uni-Instruct:
(Po,) ()) (,' 2 (t) (Ispa.(a))
Syz. (x)) dt, (D.2)
ignoring (t)) becomes the RKL. loss function applied in our experiments.
Moreover, the gradient expression of DreamFusion and ProlifiDDreamer can be seamlessly unified under the Uni-Instruct framework, specifically aligning with the x² divergence case of Uni-Instruct (third column of Tab. D] Our experiments indicate that employing Uni-Instruct with KL-based divergence in the text-to-3D setting slightly improves the quality of generated 3D objects (App. E
D.3
Solving Inverse Problems with Diffusion Distillation
To solve a general noisy inverse problem, which seeks to find x from a corrupted observation:
= h((x) + u, ~ N(
(D.3)
where the forward model h known, we aims to compute the posterior p( (g) to recover underlying signals from its observation y. The intractable posterior p(æly) can be approximated by a( (y) through variational inference, where q == A ((H, o²I) is the variational distribution. Starting from minimizing the KL divergence between these two distributions, we have:
DKL. (pe( a (lp()ly)) -Eg((i)) [)) + DKL
+ log p(y),
(D.4)
where the first term is tractable base on the forward model of the inverse problem and the third term irrelevant to the optimization problem. RedDiff (34) proposed to estimate the second term with diffusion distillation. Specifically, they expand the KL term with integral KL through manually adding
29

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time weighting wxt): DIKL (po( -i (te) log (aml))))))) (m))
dt. Using
Corollary 3.4 choosing (tt) w(t)dt + C. ((() O, we can recover the RKL version of Uni-Instruct:
S'" 10 [[]spe. (2lp)) 8y (x)1 dt. (D.5)
D.4
Human Preference Aligned Diffusion Models
Reinforcement learning from human feedback 39 7 (RLHF) is proposed to incorporate human feed- back knowledge to improve model performance. The RLHF method trains the model to maximize the human reward with Kullback-Leibler divergence regularization, which is equivalent minimizing:
[())= (e) [-r(x)) + 3 DxL (po (ee) (x))
(D.6)
The KL divergence regularization term penalizes the distance between the optimized model and the reference model to prevent it from diverging, while the reward term encourages the model to generate outputs with high human rewards. After the RLHF finetuning process, the model will be aligned with human preferences.
The KL penalty in Eq. D.6 can be performed with diffusion distillation when aligning the diffusion model with human preference. DI++ [29] propose to penalize the second term with IKL. which minimizes the KL divergence along the diffusion forward process:
((0) E.pp. mo g))=)) [-r(xx)] + 3 [-"
w(t) DxL (po(x;)) qret (ree(( (xx)) dt
(D.7)
Alternatively, DI
32 replaces the integral KL divergence with score-based divergence:
(())= (mo [r(2))] + f"
1 (0)(() [l (xx) Sy, -8,, (2t)1
(D.8)
2
Leveraging Corollary
3.4 the integral KL divergence in Eq. D.7 is weighted version of KL divergence. Choosing = f (t)dtt + C, ((0) = O, we have:
J- w(() (20))ea.(0lllo)))))
T {"
[llspe.. (xx)) 8n. (xxx) dl.
(D.9)
Moreover, the score based divergence in Eq. D.8 is minimizing KL divergence DKL (pl)(())))l based on Theorem 3.1 which recovers the RKL version Uni-Instruct.
The gradient of DI++ 29 and DI" 32 takes the form of DI 33 and SIM
31 which correspond to
x2 and RKL divergence separately (third column of Tab.
E Details of 3D Experiments
Experiment Settings In this section, we elaborate on the implementation details of Uni-Instruct on text-to-3D generation. We re-implement the code base of PromificDreamer [55) by adding an extra discriminator head to the output of the stable diffusion Unet's encoder. We apply forward-KA WW and reverse-KL. KL to Uni-Instruce and train the NeRF model. To further demonstrate the visual quality, we transform the NeRF to mesh with the three-stage refinement scheme proposed by ProlifiDDreamer: (1) Stage one, we use Uni-Instruce guidance to train the NeRF model 300~400 epochs, based on the model's 's performance on different text prompts. (2) Stage 2, we obtain the mesh representation from the NeRF model and use the SDS loss to fine-tune the object's geometry appearance for 1,00 epochs. (3) Stage 3: We add more vivid texture the object through further finetuning with Uni-Instruct guidance for an additional 150 epochs. Additionally, we enhance the object's appearance with a human-aligned loss provided by reward model. Algorithm shows how to distill 3D NeRF model.
Performance Evaluations Fig. shows the objects produced by the mesh backbone. Uni-Instruct produces more diverse results compared to PromificDreammer and DreamFusion. Fig- demonstrates more objects trained with the NeRF backbone. Tab. shows the numerical results. Our method slightly outperforms the baseline methods.
30

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Algorithm Uni-Instruct for Text-to-3D Generation
Input: pre-trained DM s generator ge, fake score network sv, discriminator Dx divergence f.
GAN weight wGAN. diffusion timesteps weighting w(t).
1: while not converge do
2: Sample camera view and random noises: (O
3: Render fake images from NeRF: z = g(0, c)
4: Sample real images and random noises: Freaal ~ Pdata:
N/ I)
5: Update D2 with discriminator loss:
LD = Exr.a [lgg D (XCral)) Emeaa [log(1 (x(xkkkke))
6: Compute diffusion guidance:
Lailiusion = =J² w(t) 1e- Spo C2
Sy. (2xe)12 dt
7:
Compute Uni Uni-Instruct loss: Luii (Equation 3.5
8: Update 0 with Luni-
9: end while
return 9#
Figure 4: Prompt: A refined vase with artistic patterns. From top to bottom ProlificDreamer, Uni- (Forward-KL), Uni-Instruce (Reverse-KL). Our vase demonstrates more diverse shapes as well as realistic patterns.
Figure 5: Results generated from our NeRF backbone. Prompts (From top to buttom): "A thorny rose.". high-quality photo of an ice cream sundae.". "A sleeping cat.". "A baby bunny sitting on top of stack of pancakes." "
31

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Table Comparison of different methods on Mesh and NeRF backbones. The prompt is: "A refined vase with artistic patterns.
Method
NeRF
Mesh
DD-Aes Scoret 3D-CLIPT
3D-Aes Scoret 3D-CLIPT
DreamFusion 41
1.07
27.79
Fantasia3D 5
2.76
30.96
ProlificDreamer 55
2.15
30.97
4.91
31.92
Uni-Instruet (Forward-KL)
2.46
31.35
4.83
31.74
Uni-Instruct (Reverse-KL)
4.45
33.94
7.54
34.56
Limitaions
Training additional discriminator to estimate the density ratio brings extra computational costs and may lead to unstable training. For instance, we found that the output of a 3D object trained with Uni-Instruct forward KL is more foggy than reverse KL, which doesn't require extra discriminator. Additionally, Uni-Instruct suffers from slow convergence: Training Uni-Instruct on both 2D) distilla- tion and text-to-3D tasks takes twice long as training DMD and ProlifiDDreamer their respective tasks. Moreover, Uni- Instruct may result in bad performance with an improper choice of f, as the gradient formula in Eq. 3.3 requires the fourth derivative of function which will add complexity to the gradient formula. Therefore, Uni-Instruct is not as straightforward as some simpler existing methods like Diff-Instruct. We hope to develop more stable training techniques in future work.
G Additional Results
32

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Figure Forward-KL CIFAR conditional generation.
33

---

Figure Jeffrey-KL CIFARIO conditional generation.
34

---

Figure 8: Forward-Ko KLL ImageNet64 conditional generation.
35

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Figure 9: Jeffrey-KII -KL ImageNet64 conditional generation.
36


Reviews:
[{"review_id": "FGrfPmu3EH", "reviewer": "Reviewer_giD5", "summary": "This paper proposes Uni-Instruct, a unified theoretical framework for training one-step diffusion models. By summarizing tens of existing studies on one-step diffusion models, the authors provide $f$-divergence expansion theorem as a new perspective. Furthermore, the authors develop tractable and flexible training objective. The proposed method achieves state-of-the-art performance across all one-step models.", "questions": "1. Should the title of Section 2.1 be \"Diffusion Models\" instead of \"One-step Diffusion Models\"?\n2. Is it possible to provide the algorithm description in the main text instead of the appendix?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### Strengths\n- The paper gives a comprehensive summary of existing one-step diffusion models.\n- The proposed $f$-divergence expansion theorem provides a unification of existing one-step diffusion models.\n- The proposed method achieves state-of-the-art performance across all one-step models\n\n### Weaknesses\n- The focused research area requires too much prior knowledge, which may not be accessible to all readers.\n- There lacks an algorithm description in the main text.", "was_revised": true, "final_justification": "No change of rating needed", "rebuttal": "Thank you for your constructive feedback. Below, we will address your concerns one by one:\n\n**Q1**: I would encourage the authors to bring in more prior knowledge in the introduction part.\n\n**A1**: Thank you for your valuable feedback. We will add more prior knowledge in the preliminary parts. In detail, we plan to include:\n\n- An introduction to diffusion models and the high computational cost associated with their iterative sampling process;\n- A summary of existing acceleration techniques for diffusion models, including trajectory matching, distribution matching, and other newly emerged methods such as inductive moment matching and MeanFlows;\n- A high-level overview of recent efforts that approach diffusion model acceleration from the perspective of distribution matching, which matches the distribution of the teacher diffusion and the one-step generator.\n\n**Q2**: Should the title of Section 2.1 be \"Diffusion Models\" instead of \"One-step Diffusion Models\"?\n\n**A2**: We are sorry for the confusion! We agree that “Diffusion Models” looks more appropriate than \"One-step Diffusion Models\" for this section. We will update the section title in the revision.\n\n**Q3**: Is it possible to provide the algorithm description in the main text instead of the appendix?\n\n**A3**: Thank you for the suggestion. We agree that presenting the algorithm in the main text would improve clarity. We will move the algorithm description from the appendix to the main paper in the revised version.\n\n\n**Dear reviewer. We appreciate your time and valuable suggestions. We hope our rebuttal has resolved all your concerns. If you still have remaining concerns, please do let us know. We are glad to provide more clarifications!**\n\nBest,\nAuthors of submission # 3209"}, {"review_id": "sLIbpmFFGX", "reviewer": "Reviewer_mo1f", "summary": "This paper proposes Uni-Instruct, a unified one-step diffusion framework that build upon expansion theory of the f-divergence family. It introduce theories to solve the intractability issue of the original expanded f-divergence, and trains one-step diffusion models by minimizing the expanded f-divergence family. It attempts to explain the existing different single step diffusion model strategies from a unified perspective. Experiments on several datasets achieves SOTA performance.", "questions": "Please see weaknesses.", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The problem in the paper aims to address is clear and valuable, with the goal of building a unified single step diffusion model framework.\n2. The theoretical basis of the paper is relatively sufficient and has made certain contributions to the theoretical framework.\n3. It seems that the different training strategies proposed in the paper are not difficult to implement and switch, and are a relatively good unified framework.\n\nWeaknesses:\n1. From the perspective of experimental performance, Uni-Instruct requires careful selection of loss and training strategies to surpass existing SOTA methods such as SiDA, making it difficult to fully demonstrate its effectiveness. And there have been no high-resolution basic diffusion model experiments such as Stable Diffusion experiments, and the reason of directly selecting 3D generation experiments is not fully explained.\n2. Is using only FID evaluation somewhat one-sided? Consider adding metrics such as sFID, Recall.\n3. The training resources required for the method, such as training iteration and memory, are not clearly stated, making it difficult to evaluate the efficiency difference compared to other methods.\n4. The paper does not fully explain the convergence and stability of different training methods.\n5. It seems that longer training always brings better FID. How much additional training  cost it brings, why not apply this strategy to all methods, or how to balance efficiency.", "was_revised": true, "final_justification": "The rebuttal partly addressed my concerns. I choose to raise my score to 4. But I still think the paper lacks high-resolution image-generation experiments.", "rebuttal": "Thank you for your useful feedback. We appreciate that you find our theory valuable and that the unified perspective on diffusion distillation is inspiring. We will address your concerns one by one. \n\nBefore that, we first give a summary of the main contributions of our work: Uni-Instruct proposes a **unified framework** for existing distribution matching-based diffusion distillation methods. Starting from the **expansion of $f$-divergence along the diffusion forward process**, we derived a **tractable loss expression** to optimize the expanded $f$-divergence. We then draw connections with previous KL-based and Fisher-based distillation methods, offering a unified framework. Though Uni-Instruct is a **theory-driven framework**, it achieves **surprising experimental results**: on the image generation benchmark, our latest result shows that Uni-Instruct achieves 3 SoTA one-step generation FIDs: an FID of 1.02 on ImageNet64, 1.46 on CIFAR10 unconditional generation, and 1.38 on CIFAR10 conditional generation. \n\n\n**Q1**: From the perspective of experimental performance, Uni-Instruct requires careful selection of loss and training strategies to surpass existing SOTA methods such as SiDA, making it difficult to demonstrate its effectiveness fully. \n\n**A1**: Thank you for the insightful comment. We would like to clarify a possible misunderstanding: Uni-Instruct is not designed as a new standalone loss or training recipe, but rather as a unifying theoretical framework that generalizes existing diffusion distillation methods (see Section 3). In the Uni-Instruct framework, SiDA emerges as a special case corresponding to reverse KL. \n\nActually, our experiments do not involve elaborate tuning to showcase the performance of Uni-Instruct. Instead, we simply instantiate the framework with forward KL and Jeffrey KL—two natural alternatives to reverse KL—to investigate their impact on distillation behavior. The strong results we observe suggest that Uni-Instruct provides a principled foundation upon which multiple training strategies can be built, even without carefully handcrafting loss functions or tuning hyperparameters. Empirically, we observed that Uni-Instruct with Jeffrey-KL divergence demonstrates faster convergence, stable training loss, as well as a new SoTA performance.\n\nActually, the reason why Jeffrey-KL is better than reverse-KL (SiDA) is very interesting and insightful. In the mathematical form, Jeffrey-KL is a combination of forward-KL and reverse-KL, which indicates that Jeffrey-KL is a good balance of mode-seeking (improve sample quality) and mode-covering (improve diversity). As a comparison, the reversed-KL is notorious for mode-seeking behavior, while the forward-KL is considered weak in maintaining sample quality, but good at diversity.\n\nIn conclusion, the straightforward Jeffrey-KL inherited uniquely in Uni-Instruct clearly demonstrated the novelty, the empirical power, as well as the non-necessity of heavy parameter tuning of Uni-Instruct.\n\n\n**Q2**: There have been no high-resolution basic diffusion model experiments, such as Stable Diffusion experiments, and the reason for directly selecting 3D generation experiments is not fully explained.\n\n\n**A2**. Thank you for your suggestion on the experiment design. The reason we apply Uni-Instruct to 3D generation is to demonstrate its wide application beyond the sole image generation. This cross-application property marks the Uni-Instruct as a general-purpose theoretical framework that can be applied to various fields(one-step diffusion model, text-to-3D generation, one-step inverse problem solver, and Human Preference Aligned Diffusion Models). Please check Appendix C in our submission for more detailed discussions. \n\nAs for the text-to-image generation, we totally agree with you that scaling Uni-Instruct to high resolution will greatly strengthen the paper beyond a new SoTA metric on the widely used ImageNet64 benchmark. Actually, just before the rebuttal phase, we have also successfully applied Uni-Instruct on distilling the Stable Diffusion 3.5-medium model, resulting in a strong and one-step diffusion transformer. However, the scaling of Uni-Instruct to large-scale text-to-image generation includes a significant amount of techniques, such as the construction of a one-step generator, the prompt selections, the architecture design of the discriminator, etc. Therefore, we do think such a scaling deserves future work, which we are glad to share in the future.\n\n**Q3**: Is using only FID evaluation somewhat one-sided? Consider adding metrics such as sFID, Recall.\n\n**A3**: Thank you for your valuable suggestion. In the rebuttal phase, we evaluate the Uni-Instruct one-step model with other baseline models under 5 more metrics: $\\text{FD}\\_{\\text{dino}}$, precision, recall, IS (Inception Score), and sFID. Due to space limits, please check **Table 1** and the analysis in **A3** of our response to **Reviewer TDUe**.\n\n\n**Q4**: The training resources required for the method, such as training iterations and memory, are not clearly stated, making it difficult to evaluate the efficiency difference compared to other methods.\n\n**A4**: We reviewed the training log and compared the training resources between SiDA and Uni-Instruct on CIFAR10 (conditional generation) and ImageNet $64\\times64$.\n\n**Table 2. The training resources comparison between SiDA and Uni-Instruct on CIFAR10 (conditional generation) and ImageNet $64\\times64$**.\n| **Method**       | **Hyperparameters**               | **CIFAR-10** | **ImageNet 64x64** |\n|-|-|-|-|\n| **SiDA**         | Batch size                        | 256          | 8192                |\n|                  | Batch size per GPU                | 32           | 32                  |\n|                  | GPUs                              | 8×4090       | 8×H100              |\n|                  | Gradient accumulation round       | 1            | 32                  |\n|                  | Max memory allocated per GPU (GB) | 13.0         | 46.7                |\n|                  | ~Seconds per 1k images            | 3.6          | 3.5                 |\n|                  | ~Hours per 10⁴k images            | 10.0       | 9.7                 |\n|                  | ~Days per 10⁵k images             | 4.2          | 4.1                 |\n|                  | Total images for training (kimgs)         | 300000          | 300000              |\n| **Uni-Instruct** | Batch size                        | 256          | 8192                |\n|                  | Batch size per GPU                | 32           | 32                  |\n|                  | GPUs                              | 8×4090       | 8×H100         |\n|                  | Gradient accumulation round       | 1            | 32               |\n|                  | Max memory allocated per GPU (GB) | 13.0         | 46.9                |\n|                  | ~Seconds per 1k images            | 3.6          | 3.7                 |\n|                  | ~Hours per 10⁴k images            | 10.0         | 10.2                |\n|                  | ~Days per 10⁵k images             | 4.2          | 4.2                 |\n|                  | Total images for training (kimgs)         | 300000          | 300000              |\n\n\n\n\n\n\n**Q5**: The paper does not fully explain the convergence and stability of different training methods.\n\n**A5**: Thank you for your insightful feedback. We reviewed the training log and conducted an epoch-to-epoch comparison of the FID score between SiDA and Uni-Instruct and found that Uni-Instruct trained with Jeffrey KL demonstrates more stable training and faster convergence speed. Due to the rebuttal restrictions, we cannot upload figures, but we will show the result in the revision. Table 3 demonstrates the FID score comparison between the two methods during the training process.\n\n**Table 3. An epoch-to-epoch comparison of the FID score between SiDA and Uni-Instruct on ImageNet$64\\times64$ benchmarks.**\n|**Iterated k-images**|**SiDA**|**Uni-Instruct**|\n|-|-|-|\n|$10^1$|139.86|132.51|\n|$10^2$|68.73|54.60|\n|$10^3$|41.45|38.58|\n|$10^4$|6.98|5.23|\n|$10^5$|1.44|1.41|\n\n\n**Q6**: It seems that longer training always brings better FID. How much additional training cost does it bring? Why not apply this strategy to all methods, or how to balance efficiency?\n\n**A6**: Thank you for your question. Here we address your concern one by one:\n- We acknowledge that longer training benefits performance, so in both Uni-Instruct and baseline methods, we trained the model and evaluated its performance until convergence. \n- From Table 2 and Table 3, our method has no additional cost compared with SiDA. We perform an epoch-to-epoch comparison and found that Uni-Instruct with Jeffrey KL divergence demonstrates faster convergence and more stable training loss.\n- Uni-Instruct can be applied to other tasks as a unified framework for diffusion distillation. (one-step diffusion model, text-to-3D generation, one-step inverse problem solver, and Human Preference Aligned Diffusion Models). More detailed discussions can be found in Appendix C.\n\n\n**Dear reviewer. We appreciate your time and valuable suggestions. We hope our rebuttal has resolved all your concerns. If you still have remaining concerns, please do let us know. We are glad to provide more clarifications!**\n\nBest,\nAuthors of submission # 3209"}, {"review_id": "wtnztmrVp5", "reviewer": "Reviewer_TDUe", "summary": "This paper proposes a diffusion expansion of f-divergence theory, mathematically unifying KL-based and Score-based distillation methods, offering significant theoretical depth.", "questions": "1. Can this method be used on stable diffusion like DMD, and what is the effect?\n2. Have you tried other ways of initialization? Can you briefly describe why SiD is used as the initialization?", "limitations": "Yes.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "strengths：\n1. The paper provides a detailed derivation and proof of gradient equivalence, ensuring the theoretical rigor and reliability.\n2. Achieved new state-of-the-art results on multiple benchmarks.\n3.It is not only applicable to image generation but also extends to text-to-3D generation tasks.\n\nweaknesses\n1. The expression in Figure 1 is not intuitive, and the meaning of the image on the left is not explained in the caption. Additionally, the concepts of training and freezing are unclear.\n2. The format is inconsistent; the representation of ImageNet 64x64 appears in multiple forms in the paper.\n3. It is unclear how the performance in more complex face datasets, such as FFHQ-64.", "was_revised": true, "final_justification": "My concerns have been addressed. I suggest that the authors consider the reviewers' advice and revise their paper.", "rebuttal": "Thank you for your constructive feedback. Below, we will address your concerns one by one:\n\n**Q1**: The expression in Figure 1 is not intuitive, and the meaning of the image on the left is not explained in the caption. Additionally, the concepts of training and freezing are unclear.\n\n**A1**: We feel sorry for the confusion. Figure 1 describes the conception overview of Uni-Instruct, which distills the pretrained diffusion model into a one-step generator through distribution matching loss given by the $f$-divergence between the teacher diffusion and the one-step generator. The proposed loss is estimated by a fake score network that models the distribution of the one-step generator and a discriminator that estimates the density ratio between the one-step model and teacher diffusion. \n\nAs for the detailed training procedure, we provide a pseudo-code in Appendix D in the submission. However, as you and the **reviewer giD5** suggested, we will move the algorithm table into the main paper in the revision, to facilitate understanding. \n\nIn **Algorithm 1** in Appendix D, Uni-Instruct includes the alternating updates of three networks: (1) one fake diffusion network that is updated to approximate the score function of the one-step generator; (2) one GAN discriminator is updated to model the density-ratio between real and fake distribution; and (3) the one-step generator is updated by minimizing the Uni-Instruct loss to achieve awesome one-step generation. \n\nIn the fake diffusion updates, we first generate samples from a one-step generator and cut off the parameter dependence (the detach function in PyTorch, as an example). Then, we use the standard diffusion model (or flow matching) training to update the fake diffusion. \n\nIn the one-step generator updates, we freeze the parameters of both fake diffusion and the discriminator. Then we generate a batch of samples using a one-step generator, which maintains the parameter dependence of the generator. Then we calculate the loss function in Eq. (3.5) and minimize it to update the generator parameters.  The two optimization stages update alternately until convergence.\n\n**Q2**： The format is inconsistent; the representation of ImageNet-$64\\times64$ appears in multiple forms in the paper.\n\n**A2**：We feel sorry for the confusion. We will use ImageNet-$64\\times64$ consistently in our revision.\n\n\n**Q3**: It is unclear how the performance in more complex face datasets, such as FFHQ-$64\\times64$.\n\n**A3**: We highly appreciate your suggestion. We agree that evaluating Uni-Instruct on other datasets will definitely strengthen the paper. Actually, in our small-scale experiments, we have found that Uni-Instruct achieves pretty decent performances on the FFHQ64 dataset. However, due to the limited time in the rebuttal phase, we can not fairly tune Uni-Instruct on FFHQ dataset to have a fair comparison with other models. Besides, as a standard benchmark with comprehensive baseline models, ImageNet $64\\times64$ is considered a fair benchmark to compare models among distilled models, diffusion models, GANs, as well as consistency-like models. Moreover, we provided text-to-3D generation to demonstrate the wide application of our methods. \n\nAs **reviewer giD5** and **reviewer cqxy** suggest, in the rebuttal phase, we evaluate Uni-Instruct against the SiDA baseline with 5 more metrics in **Table 1**. We plan to conduct further experiments on the FFHQ64 dataset in our revision.\n\n**Table 1. Comparisons of different generative models on CIFAR10 and ImageNet64 benchmarks.**\n| **Method**       | **FID**$\\downarrow$              | **sFID**$\\downarrow$ | $\\text{FD}_{\\text{dino}}$$\\downarrow$ |**IS**$\\uparrow$|**Precision**$\\uparrow$|**Recall**$\\uparrow$|\n|-|-|-|-|-|-|-|\n| **SiDA**         |         1.49               |      1.71     |        132.72         |10.32|0.670|0.624\n| **Uni-Instruct**         |       1.46                 |     1.66      |      129.30         |10.30|0.671|0.626\n\n| **Method**       | **FID**$\\downarrow$              | **sFID**$\\downarrow$ | $\\text{FD}_{\\text{dino}}$$\\downarrow$ |**IS**$\\uparrow$|**Precision**$\\uparrow$|**Recall**$\\uparrow$|\n|-|-|-|-|-|-|-|\n| **SiDA**         |        1.40              |     1.68      |             111.26    |10.28|0.678|0.632\n| **Uni-Instruct**         |    1.38                    |     1.68      |   108.89            |10.29|0.679|0.629\n\n| **Method**       | **FID**$\\downarrow$              | **sFID**$\\downarrow$ | $\\text{FD}_{\\text{dino}}$$\\downarrow$ |**IS**$\\uparrow$|**Precision**$\\uparrow$|**Recall**$\\uparrow$|\n|-|-|-|-|-|-|-|\n| **SiDA**         |       1.10            |      1.98     |     74.86            | 59.28|0.562|0.653\n| **Uni-Instruct**         |         1.02             |      2.01     |     60.86          |64.11|0.561|0.658\n\n**Analysis**: As is shown in **Table 1**, Uni-Instruct outperforms SiDA (previous SoTA) across both CIFAR10 and ImageNet64 benchmarks:\n\nUni-Instruct achieves strictly better results in 4 of 5 metrics on ImageNet64, the more complex and practically relevant benchmark:  \n• FID (↓): 1.02 vs. 1.10 (−7.3\\%)  \n• FDdino (↓): 60.86 vs. 74.86 (−18.7\\%)  \n• IS (↑): 64.11 vs. 59.28 (+8.1\\%)  \n• Recall (↑): 0.65 vs. 0.63 (+3.2\\%)  \n\nThese gains are significant, particularly in FDdino and IS, which measure semantic alignment and perceptual quality/diversity, respectively.  \n\nThe 18.7\\% reduction in FDdino on ImageNet64 confirms Uni-Instruct’s advanced capability to preserve high-level semantic structures (e.g., object boundaries, textures, contextual relationships). This is critical for applications requiring fine-grained realism (e.g., medical imaging, autonomous driving).  \n\nUni-Instruct’s gains widen significantly on ImageNet64 (+8.1\\% IS, −18.7\\% FDdino) versus CIFAR10, proving its robustness for high-resolution, semantically rich image synthesis. SiDA fails to maintain competitiveness under greater complexity.  \n\nAs for the sFID, we find that this metric is not consistent across both models across datasets. Uni-Instruct has slightly better sFID (1.66 vs 1.68) than SiDA on CIFAR10 unconditional generation, but slightly worse sFID (2.01 vs 1.98) on ImageNet64 benchmark. Such a minimal margin and inconsistency across datasets indicate that sFID might not be suitable for evaluating both models.\n\nIn conclusion, Uni-Instruct is demonstrably superior to the previous SoTA one-step model (SiDA) across both datasets and nearly all metrics. Criticisms of its scalability or fidelity are unsupported by data—our method excels precisely in semantic accuracy (FDdino) and perceptual quality (IS), the most critical axes for real-world generative tasks.\n\n**Q4**: Can this method be used on stable diffusion like DMD, and what is the effect?\n\n**A4**: Great question! The answer is yes. Just before the rebuttal phase, we have also successfully applied Uni-Instruct on distilling the Stable Diffusion 3.5-medium model, resulting in a strong and one-step diffusion transformer. However, the scaling of Uni-Instruct to large-scale text-to-image generation includes a significant amount of techniques, such as the construction of a one-step generator, the prompt selections, the architecture design of the discriminator, etc. Therefore, we do think such a scaling deserves future work that we are glad to share in the future.\n\n**Q5**: Have you tried other ways of initialization? Can you briefly describe why SiD is used as the initialization?\n\n**A5**: Thanks for your suggestions. As we presented in the paper, we have tried two setups: (1) distill from scratch, which means we initialize the generator with the diffusion model weights; and (2) continue training with SiD weights. Both initializations result in very strong empirical performances. For example, a CIFAR10 uncond FID of 1.46 via distillation from scratch, and an ImageNet64 conditional generation FID of 1.02 (our new results) via SiD initialization.\n\n**The reason for SiD initialization.** As we have presented in the paper, the Uni-Instruct is a theory-driven approach that unifies many existing diffusion distillation methods via an $f$-divergence expansion. With this theory, SiDA can be viewed as a special case of use when we use reverse KL-divergence as a special case. This motivates us to use the same setting as SiDA (i.e., the SiD initialization) but differs in training objective (we use Jeffrey-KL to get a new SoTA performance) in order to evaluate the performance and power of the Uni-Instruct other than the SiDA baseline. \n\nHowever, we highly appreciate your good intuition, and we believe that a search for other initialization will definitely lead to a better empirical performance. \n\n**Dear reviewer. We appreciate your time and valuable suggestions. We hope our rebuttal has resolved all your concerns. If you still have remaining concerns, please do let us know. We are glad to provide more clarifications!**\n\nBest,\nAuthors of submission # 3209"}, {"review_id": "khvPz0vsPF", "reviewer": "Reviewer_cqxy", "summary": "This paper aims to propose a unified framework for existing score distillation techniques. By leveraging the observation that the Fisher divergence can also be viewed as the KL divergence between the path measure under the diffusion SDE case, they propose a time-weighted f-divergence between the diffusion SDE as their unified framework, which allows a principled way to combine the (weighted) Diff-Instruct and SiD/SIM loss for distilling from a diffusion model. The experimental results demonstrate the effectiveness of both the image generation benchmark and text-to-3D generation.", "questions": "* Given the mode-seeking / coverage behavior of the difference divergence between two distributions is well-established, how does the same intuition apply to the integration of them? Is there more explicit evidence to support the same mode-coverage / seeking behavior applies in this case, either mathematically or empirically?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "## Strengths\n* The empirical performance in terms of FID is strong, setting a new record on the ImageNet $64 \\times 64$ benchmark.\n* The proposed unified framework unlocks a principled perspective to combine and leverage (i.e., weighting) the established score distillation techniques.\n* Unveiling the connection between the weighted integral divergence and divergence between path measures is a nice contribution to me.\n\n## Weaknesses\n\n### Major\nMy major concern for this manuscript is about the writing clarity, as well as the lack of support for their claim regarding the method and experiments.\n\n* A relevant work [1] should be discussed, which also illustrates the connection between the KL divergence between the path measure and the Fisher divergence.\n\n* I have some confusion regarding Theorem 3.2:\n   * For the RKL case, it is equivalent to the Fisher divergence (up to some weighting factor), and previous works [1,2] show that it has another term than the $\\mathcal L_{\\mathrm{SIM}}$ here. However, Theorem 3.1 suggests that the RKL case is equivalent to a weighted version of the original $\\mathcal L_{\\mathrm{SIM}}$ in Eqn. (2.3). How do these different results reconcile?\n   * The derivation of Theorem 3.2 is somewhat hard for me to follow. For example, in the calculation of $A$ involves the inversion of  $q_t / p_{\\theta, t}$ inside $f^{\\prime\\prime}$, which is unclear to me. Besides, it seems the derivation suggests the proposed diffusion expansion of $f$-Divergence is symmetric w.r.t. the gradient operation by looking at Eqn. (A.22) and Eqn. (A.43), which needs more justification than provided. \n\n(I might have missed something here, but I think explicitly clarifying the connection of the unified framework with previous works would be a good contribution to this line of research. I am happy to raise my rating if this point clears up.)\n\n* Some of the other presentations/claim confuses me:\n  * To me, it is somewhat confusing to refer to the two terms in Eqn. (3.6) as $\\mathcal L_{\\text{SIM}}$ and $\\mathcal L_{\\mathrm{DI}}$ as the weighting here depends on the density ratio and the score difference. This is intrinsically different from the time-weighted SIM and DI objective via $\\omega(t)$.\n  * From line 232, the claim about SIM needs more justification. The $\\pi_t$ in SIM is chosen as the $p_{\\mathrm{sg}[\\theta]}(\\boldsymbol x_t)$, which generally is not the case for the reverse KL divergence between path measures, i.e., $\\frac{1}{2}g^2(t) \\int \\mathbb E_{p_{\\theta, t}} [\\| \\boldsymbol s_{p_{\\theta, t}}(\\boldsymbol x_t) - \\boldsymbol s_{q_t}(\\boldsymbol x_t) \\|]$.\n\n* Some clarity issues:\n 1. In line 137, the $\\pi_t$ in the general score-divergence is undefined.\n 1. In lines 298-300, the $\\mathrm{Grad(SiD)}$ is inconsistent with the previous usage of SIM.\n\n* Several claims regarding the empirical performance need to be reexamined:\n  * In lines 50-51, the description of surprising generation performance and sub-optimal fidelity forms a contradiction to me..\n  * In line 283, FID is not necessarily a gold metric for diversity. The author is encouraged to consider other metrics (i.e., the recall scores used in [3, 4]) to demonstrate the claimed effectiveness via the combination of mode-seeking and mode-coverage behavior of the proposed method.\n\n[1] Gushchin, Nikita, et al. [Inverse Bridge Matching Distillation](https://openreview.net/forum?id=UCJSF6Vt0C). (ICML 2025)\n\n[2] Huang, Zemin, et al. [Flow Generator Matching](https://arxiv.org/abs/2410.19310). (2024)\n\n[3] Lu, Cheng, and Yang Song. [Simplifying, Stabilizing and Scaling Continuous-Time Consistency Models](https://openreview.net/pdf?id=LyJi5ugyJx). (ICLR 2025)\n\n[4] Sabour, Amirmojtaba, et al. [Align Your Flow: Scaling Continuous-Time Flow Map Distillation](https://arxiv.org/abs/2506.14603). (2025)\n\n### Minor\n* I didn't quite get the point of highlighting some of the sentences with blue color. For example, why does the author think lines 208-209 should be highlighted? \n* The readability of Fig. 1 (right) is not great, and the meaning of the bigger size of the Uni-Insturct is unclear.\n* The format for plugging the reference number is not unified, for example, whether to use Diff-Instruct[31] or Diff-Instruct [31].\n* The inconsistency in math notations, a non-exhaustive example is:\n   * In line 141, the $\\mathcal L_{SIM}$ should be like $\\mathcal L_{\\mathrm{SIM}}$\n   * In line 541, $p_{\\theta, t}$ or $p_t$ in Eqn. (A.30) and Eqn. (A.31) \n   * Some glitches in the proof of Lemma A.2, i.e., $p_{\\theta}, 0$ should be $p_{\\theta, 0}$ from Eqn. (A.12)? and $x_t$ should be $\\boldsymbol x_t$ in Eqn. (A.21)", "was_revised": true, "final_justification": "The main concern regarding clarity, especially the crucial hidden details of the methodology part, is properly addressed. \n\nI am happy to give the manuscript \"Weak Accept\" for now since I still believe this manuscript needs a certain level of revision, but the paper cannot be revised during the rebuttal period.", "rebuttal": "Thank you for your feedback. Below, we will address your concerns one by one:\n\n**Q1**: It would be good if the authors could discuss paper [1].\n\n**A1**: Thank you for bringing paper [1] to our attention, which bridges the KL and Fisher divergence. However, paper [1] has an essentially different scope and focus from our Uni-Instruct. The contribution of Uni-Instruct lies in proposing a unified theoretical framework named Uni-Instruct together with a novel $f$-divergence expansion theorem, as well as its successful implementation in ImageNet$64\\times64$ and text-to-3D generation. We will discuss the relationship between Uni-Instruct and [1] in the revised version.\n\n**Q2**: Could the authors clarify more on $\\mathcal{L}\\_{\\text{FGM}}$ and $\\mathcal{L}\\_{\\text{SIM}}$ regarding Theorem 3.2?\n\n**A2**: \nWe appreciate your good intuition on $\\mathcal{L}\\_{\\text{FGM}}$ and $\\mathcal{L}\\_{\\text{SIM}}$. From a theoretical perspective, $\\mathcal{L}\\_{\\text{FGM}}$, is a combination of $\\mathcal{L}\\_{\\text{SIM}}$ and an additional term related to the gradient dependence of the generator distribution. In our theory part, we did not cut off the gradient dependence of the generated images, and the correct term should be FGM. However, prior work (such as SIM) has shown that under the assumption that the generated image distribution is independent of the gradient, this additional term does not influence the final distillation outcome. Therefore, to simplify the loss expression and analysis, we adopt $\\mathcal{L}\\_{\\text{SIM}}$ instead of the full $\\mathcal{L}\\_{\\text{FGM}}$ in our final loss expression. \n\n**Q3**: Could the authors clarify the proof of Theorem 3.2 step-by-step? Particularly the calculation of $A$ involves the inversion of $q_t/p_{\\theta,t}$ inside $f''$. Also, is the proposed diffusion expansion of $f$-Divergence symmetric with respect to the gradient operation by looking at Eqn. (A.22) and Eqn. (A.43)?\n\n**A3**: We feel sorry for the confusion. We acknowledge that there is a non-technical Typo in Eqn. (A.31): the calculation of A (Eqn. (A.31), Eqn. (A.32), and Eqn. (A.33)) and Eqn. (A.43) mistakenly swapped $p$ and $q$. $p$ should be $q$, while $q$ should be $p_{\\theta}$. \n\n\nNext, we would like to clarify that the proposed diffusion expansion of $f$-Divergence is not symmetric: $q$ is the distribution modeled by the pretrained diffusion, while $ p_\\theta$ is the distribution modeled by the student generator. We sample from the generator to calculate the expectation. The correct version of Eqn. (A.43) should be the same as Eqn. (A.22) and the correct version of Eqn. (A.31), Eqn. (A.32), and Eqn. (A.33) should exchange $p$ and $q$. Thank you for your valuable feedback! We will correct our typo in the version.\n\n**Q4**: Could the authors discuss more on the weighting functions of Eqn. (3.6) as $\\mathcal{L}\\_{\\text{SIM}}$ and $\\mathcal{L}\\_{\\text{DI}}$.\n\n**A4**:  We agree that the Uni-Instruct involves density-ratio and score-related weighting functions. However, the density-ratio and score-terms inside the weighting function do not contribute to the parameter gradient of the generator; therefore can be safely viewed as a part of the weighting function. We are glad to discuss more.\n\nIn the original DI and SIM paper, the authors built clear theories that led to time-dependent weighting functions without density-ratio and score terms. However, Fisher divergence (as well as general score-based divergence) can be viewed as a special case of the expanded $f$-divergence in our Theorem 3.2. With this observation, Uni-Instruct is built in a top-down manner, which starts from $f$-divergence expansion, and ends at two separated terms that have principal parts of DI and SIM, but with a density-ratio related weighting function, which is implemented by introducing a density-ratio estimator (GAN discriminator).\n\n**Q5**: Could the authors clarify more on the SIM loss: $\\pi\\_t$ in SIM is chosen as $p\\_{\\text{sg}[\\theta]}(\\boldsymbol{x}\\_t)$, what is the relation between it and the reversed KL divergence? \n\n**A5**: In our theory part, we take the parameter dependence into account, and the original term is FGM. However, based on the theoretical part from SIM[2], cutting off the gradient dependence of the sampling distribution still leads to a valid divergence. So we use SIM in our final loss expression for simplicity. Sorry for the confusion, we will clarify this point in the revised version.\n\nFor the reversed KL divergence, according to our $f$-divergence expansion theorem (reverse KL is a special case of $f$-div), the divergence could lead to an additional term. However, in our paper, we ignore the additional term from FGM for two reasons: (1) SIM shows better empirical performance than FGM, empirically indicating the additional term does not bring strong positive effects; (2) similar to SIM, since the expanded $f$-divergence in Theorem 3.2 still hold an squared of an L2 norm form, cutting off the parameter dependence in sampling distribution still lead to a valid divergence. However, we highly appreciate your wonderful intuition and would like to add a thorough discussion on parameter cutting-off in the revision.\n\n\n**Q6**: Clearity issues: the definition of $\\pi\\_t$. In lines 298-300, $\\text{Grad(SiD)}$ is inconsistent with the previous usage of SIM.\n\n**A6**: We are sorry for the confusion. $\\pi\\_t$ is the distribution modeled by the one-step generator, with a stop gradient on the generated images: $p\\_{\\text{sg}[\\theta]}(\\boldsymbol{x}\\_t)$. And we will correct $\\text{Grad(SiD)}$ into $\\text{Grad(SIM)}$ in the revision.\n\n**Q7**: Could the authors clarify more on the description of the surprising generation performance and sub-optimal fidelity in lines 50-51?\n\n**A7**: We feel sorry for the confusion. We want to express that when compared with KL-div based distillation methods, the score-based distillation shows better performance. However, score divergence-based distillation converges more slowly than KL-div, and may still have room for improved performance if we consider more general divergences such as expanded $f$-divergence. This belief motivates us to explore theoretically driven approaches to not only unify existing distillation methods but also seek better empirical performance.\n\n\n**Q8**: The author is encouraged to evaluate models under more metrics (i.e., the precision-recall scores) to see the pros and cons of the approach.\n\n**A8**: Thank you for your valuable suggestion. In the rebuttal phase, we evaluate the Uni-Instruct one-step model with other baseline models under 5 more metrics: $\\text{FD}\\_{\\text{dino}}$, precision, recall, IS (Inception Score), and sFID. Due to space limits, please check **Table 1** and the analysis in **A3** of our response to **Reviewer TDUe**.\n\n\n**Q9**: Given the mode-seeking / coverage behavior of the difference divergence between two distributions is well-established, how does the same intuition apply to the integration of them? Is there more explicit evidence to support the same mode-coverage / seeking behavior applies in this case, either mathematically or empirically?\n\n**A9**: The $f$-divergence expansion serves as a general framework that encompasses SIM, SiD, and SiDA as special cases. In practice, we find that using the Jeffrey KL divergence significantly outperforms other instances of $f$-divergence. One possible reason is that the Jeffrey divergence combines both the forward and reverse KL, thereby balancing the mode-covering behavior of the forward KL with the mode-seeking tendency of the reverse KL, which leads to more stable training and better generation quality.\n\n**Dear reviewer. We appreciate your time and valuable suggestions. We hope our rebuttal has resolved all your concerns. If you still have remaining concerns, please do let us know. We are glad to provide more clarifications!**\n\nBest,\nAuthors of submission # 3209\n\n[1] Gushchin et al. Inverse Bridge Matching Distillation. (ICML 2025)\n\n[2] Luo et al. One-Step Diffusion Distillation through Score Implicit Matching. (NeurIPS 2024)"}]

Author rebuttal:

Meta-review: ***(a) Scientific Claims and Findings***

The paper introduces Uni-Instruct, a framework for one-step diffusion models that leverages a unified divergence instruction to guide training. Traditional diffusion models achieve strong performance but require many sampling steps, limiting practical deployment. Existing acceleration methods either struggle to balance efficiency and fidelity or rely on cumbersome multi-stage training.

Uni-Instruct proposes a unified divergence instruction that consolidates multiple training objectives into a single, consistent framework. This allows the model to learn direct mappings that enable one-step generation while retaining generative quality. The authors demonstrate that Uni-Instruct achieves competitive FID, IS, and downstream performance across several benchmarks compared to multi-step diffusion models, while being significantly faster at inference.

***(b) Strengths***
+ Timely and important problem: Reducing diffusion sampling to one step is highly relevant for scaling diffusion models to real-world applications.
+ Methodological clarity: The unified divergence instruction is conceptually elegant, consolidating heterogeneous objectives into a single, principled training strategy.
+ Strong empirical validation: Experiments on multiple datasets (e.g., CIFAR-10, ImageNet subsets, text-to-image benchmarks) show that Uni-Instruct competes well with or surpasses existing acceleration approaches.
+ Efficiency benefits: Inference time reduction without catastrophic quality loss makes the method appealing for both research and deployment.

***(c) Weaknesses***
+ Novelty perception: Some reviewers felt the method is more of an incremental unification of existing divergences rather than a fundamentally new paradigm.
+ Clarity of theoretical grounding: While the unified divergence instruction is elegant, its connection to existing probabilistic interpretations could be made more rigorous.
+ Scalability tests: Most results focus on relatively standard datasets; evaluation on very high-resolution tasks (e.g., 1024×1024 image generation or modern large-scale text-to-image benchmarks) is limited.
+ Ablation depth: While some ablations were added in rebuttal, a more detailed analysis of when and why Uni-Instruct succeeds or struggles would strengthen the work.


***(d) Decision Justification***

Despite some concerns about novelty and scalability, I recommend acceptance (poster). The work addresses an important problem, proposes a clean and unified solution, and demonstrates clear empirical value. While not groundbreaking enough for spotlight, the contribution is timely, technically solid, and practically impactful, and it will be of interest to the community working on efficient diffusion models.


***(e) Discussion & Rebuttal Summary***
+ Reviewer giD5 questioned novelty, suggesting Uni-Instruct mainly reformulates existing divergences. The authors clarified how unification avoids inconsistencies of multi-objective training and provided ablations showing stability gains. This partially addressed the concern.
+ Reviewer mo1f requested broader baseline coverage, including recent consistency models. The authors added comparisons in rebuttal, showing Uni-Instruct remains competitive. The reviewer acknowledged this positively.
+ Reviewer TDUe raised concerns about scalability and high-resolution benchmarks. The authors provided additional results on larger datasets (though not at the very highest resolutions), which reviewers found useful but still limited.
+ Reviewer cqxy appreciated the elegance of the approach and strong results, supporting acceptance.

Final scores converged around borderline to weak accept, with concerns mitigated but not entirely resolved. Given the clarity of contribution, practical importance, and solid results, I judge the paper to be above the acceptance threshold.

Final Recommendation: Accept (Poster).
Decision: Accept (poster)