Title: Uni-Instruct: One-step Diffusion Model through Unified Diffusion Divergence Instruction
Venue: NeurIPS 2025
Accepted: yes
Keywords: Diffusion Distillation

One Step Generation
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.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n## 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)\n\nQUESTIONS:\n* 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\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-29"}

comments: SUMMARY:
This paper proposes a diffusion expansion of f-divergence theory, mathematically unifying KL-based and Score-based distillation methods, offering significant theoretical depth.

STRENGTHS AND WEAKNESSES:
strengths：
1. The paper provides a detailed derivation and proof of gradient equivalence, ensuring the theoretical rigor and reliability.
2. Achieved new state-of-the-art results on multiple benchmarks.
3.It is not only applicable to image generation but also extends to text-to-3D generation tasks.

weaknesses
1. 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.
2. The format is inconsistent; the representation of ImageNet 64x64 appears in multiple forms in the paper.
3. It is unclear how the performance in more complex face datasets, such as FFHQ-64.

QUESTIONS:
1. Can this method be used on stable diffusion like DMD, and what is the effect?
2. Have you tried other ways of initialization? Can you briefly describe why SiD is used as the initialization?

LIMITATIONS:
Yes.
TITLE: Official Review
CLARITY: 2
IMPACT: 3
ORIGINALITY: 2
DATE: 2025-06-30

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\nPlease see weaknesses.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-01"}

comments: 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.

STRENGTHS AND WEAKNESSES:
### Strengths
- The paper gives a comprehensive summary of existing one-step diffusion models.
- The proposed $f$-divergence expansion theorem provides a unification of existing one-step diffusion models.
- The proposed method achieves state-of-the-art performance across all one-step models

### Weaknesses
- The focused research area requires too much prior knowledge, which may not be accessible to all readers.
- There lacks an algorithm description in the main text.

QUESTIONS:
1. Should the title of Section 2.1 be "Diffusion Models" instead of "One-step Diffusion Models"?
2. Is it possible to provide the algorithm description in the main text instead of the appendix?

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-01