Guided Score Identity Distillation for Data-Free One-Step Text-to-Image Generation

## Abstract

Abstract Diffusion-based text-to-image generation models trained on extensive text-image pairs have shown the capacity to generate photorealistic images consistent with textual descriptions. However, a significant limitation of these models is their slow sample generation, which requires iterative refinement through the same network. In this paper, we enhance Score identity Distillation (SiD) by developing long and short classifier-free guidance (LSG) to efficiently distill pretrained Stable Diffusion models without using real training data. SiD aims to optimize a model-based explicit score matching loss, utilizing a score-identity-based approximation alongside the proposed LSG for practical computation. By training exclusively with fake images synthesized with its one-step generator, SiD equipped with LSG rapidly improves FID and CLIP scores, achieving state-of-the-art FID performance while maintaining a competitive CLIP score. Specifically, its data-free distillation of Stable Diffusion 1.5 achieves a record low FID of 8.15 on the COCO-2014 validation set, with a CLIP score of 0.304 at an LSG scale of 1.5, and an FID of 9.56 with a CLIP score of 0.313 at an LSG scale of 2. Our code and distilled one-step text-to-image generators are available at https://github.com/mingyuanzhou/SiD-LSG .

## 1 Introduction

The pursuit of generating high-resolution, photorealistic images that matches the textual descriptions has consistently driven the machine learning community in developing powerful text-to-image generative models.
Text-to-image diffusion models (Nichol et al., ; Ramesh et al., ; Saharia et al., ; Rombach et al., ; Podell et al., ) are currently leading the way in delivering unprecedentedly visual quality, diverse generation, and accurate text-image correspondences. They are renowned for their straightforward implementation and stability during optimization, and they receive substantial acclaim for the robust support from the open-source community (Rombach et al., ; Podell et al., ).

Despite these advantages, a significant limitation of diffusion models is their slow sampling process, which involves iterative refinement characterized by repeated forward passes through the generation network. Originally, this required a stochastic sampler with thousands of steps (Song and Ermon, ; Ho et al., ; Dhariwal and Nichol, ; Song et al., ). Recent advancements in deterministic samplers based on ordinary differential equations have significantly reduced the required number of sampling steps to just tens or hundreds (Song et al., ; Liu et al., ; Lu et al., ; Karras et al., ).

To further reduce the number of steps below ten or make a single step diffusion-based generator, the focus has shifted toward distilling the iterative-refinement based multi-step text-to-image generative progress into a single or a few steps, using a wide variety of acceleration techniques (Zheng et al., ; Meng et al., ; Liu et al., ; Luo et al., ; Nguyen and Tran, ; Sauer et al., ; Xu et al., ; Yin et al., ). However, they often result in clearly reduced ability to match the original data distribution, reflected as clearly worsening FIDs. All of them, with the exception of SwiftBrush (Nguyen and Tran, ), also require access to the original training data or the assistance of extra regression or adversarial losses.

It is commonly believed that student models used for distillation sacrifice performance for increased speed. However, recent findings from Score identity Distillation (SiD) (Zhou et al., ) present a notable discovery. The SiD-based single-step student model, although trained in a data-free manner, not only simplifies the multi-step generation process required by the teacher diffusion model, EDM by Karras et al. (), but also excels in performance. It surpasses the teacher model in terms of Fréchet inception distance (FID) (Heusel et al., ) on the CIFAR10-32x32, FFHQ-64x64, and AFHQ-v2 64x64 datasets. It only slightly underperforms in FID in comparison on ImageNet 64x64.

The success of SiD in distilling diffusion models in the pixel space has inspired us to apply it to open-source text-to-image latent diffusion models, specifically Stable Diffusion (SD) versions 1.5 and 2.1-base, aiming to significantly enhance their generation speed while maintaining performance. However, adapting SiD poses several notable challenges: first, SiD has primarily been applied to distill pre-trained EDM models, which utilize noise scheduling and preconditioning methods markedly different from the DDPM noise scheduling employed in SD; second, SiD-EDM does not incorporate classifier-free guidance (CFG) (Ho and Salimans, ), which is integral to SD; third, both the complexities and sizes of the data and model in EDM are much smaller than those in SD.

To address these challenges, we explore the integration of CFG into SiD-based diffusion distillation. We have developed strategies that either enhance CFG on the pretrained score network, reduce it on the fake score network, or apply a combination of both, known as long and short guidance (LSG). This method allows SiD to efficiently distill SD models into one-step generators without needing training data.
Remarkably, without relying on additional regression or adversarial losses—key components of recent distillation methods—or real data, our approach sets new benchmarks by achieving the lowest zero-shot FID score of 8.15 on the COCO-2014 (Lin et al., ) validation set. This significantly outperforms all previous distillation methods while maintaining competitive CLIP scores.

Figure: Figure 2: Rapid advancements in distilling Stable Diffusion 1.5 are showcased by the newly proposed SiD method, which employs long-short guidance (LSG). Key parameters include a batch size of 512, a learning rate of 1e-6, and an LSG scale of 2. This data-free approach achieves a zero-shot FID of 9.56 on the COCO-2014 validation set, along with a competitive CLIP score of 0.313. By reducing the LSG scale to 1.5, the FID can be further lowered to a record 8.15 among diffusion distillation models, with a corresponding CLIP score of 0.304. The series of images, generated from the same set of random noises post-training the SiD generator with varying counts of synthesized images, illustrates progressions at 0, 0.02, 0.1, 0.2, 0.5, 1, 2, 3, 4, and 5 million images. These are equivalent to 0, 40, 200, 400, 1k, 2k, 4K, 6K, 8k, and 10k training iterations respectively, organized from the top left to the bottom right. The progression of FIDs and CLIPs is detailed in the orange solid curves in the left plot of Fig. 4. The corresponding COCO-2014 validation text prompts are listed in Appendix E.
Refer to caption: extracted/5781662/sd15_2222/000000.png

The development of our SiD-LSG benefits tremendously from previous work on generative models, CFGs, and acceleration methods. We provide a detailed review of related work in Appendix B.

## 2 Empowering Score identity Distillation with long-short guidance

The effectiveness of SiD has been demonstrated in distilling both unconditional and label-conditional EDM-based diffusion models provided by Karras et al. (), which are pretrained on pixel spaces up to a resolution of 64x64. In this paper, we expand the application of SiD to distill SD, the leading open-source platform for text-to-image diffusion models that operate on the latent space of an image encoder-decoder (Rombach et al., ). Our focus is specifically on SD-v1-5 (SD1.5) and SD-v2-1-base (SD2.1-base), which are two versions extensively benchmarked for diffusion distillation.

The first challenge we tackle is adapting SiD, which was initially tested with EDM noise scheduling, to the classical DDPM scheduling employed by SD. A significant hurdle involves incorporating CFG (Ho and Salimans, ),
which is essential for diffusion models to enhance their photorealism and text alignment, into the SiD loss functions. Addressing these challenges primarily requires modifying the derivation and loss functions of SiD. A third challenge arises because SD models are significantly larger and trained on data that is not only larger, but also more complex and of higher resolution. Overcoming this challenge requires addressing numerous technical details, such as establishing the minimum hardware requirements and computational precision. Additionally, it involves configuring the appropriate software settings to align with the constraints of the available computing platforms.

### 2.1 Semi-implicit distributions, score identities, and model-based score matching losses

We denote ${\boldsymbol{c}}$ as the text representation of a pretrained text encoder, such as CLIP (Radford et al., ).
Our objective is to distill a student model $p_{\theta}(\boldsymbol{x}_{g}\,|\,{\boldsymbol{c}})$ from a pretrained text-to-image diffusion model, such as SD1.5, which can generate text-guided random samples in a single step as:
$\boldsymbol{x}_{g}=G_{\theta}(\boldsymbol{z},{\boldsymbol{c}}),~{}\boldsymbol{
z}\sim p(\boldsymbol{z}),$
where $G_{\theta}$ is a neural network parameterized by $\theta$ that deterministically transforms noise $\boldsymbol{z}\sim p(\boldsymbol{z})$ into generated data $\boldsymbol{x}_{g}$ under the guidance of text ${\boldsymbol{c}}$. The distribution of $\boldsymbol{x}_{g}$ is often implicit (Mohamed and Lakshminarayanan, ; Tran et al., ; Yin and Zhou, ), lacking an analytic probability density function (PDF) but is straightforward to sample from.
The marginals of the real and generated data under the forward diffusion process can be expressed as:

$$ $\textstyle p_{\text{data}}(\boldsymbol{x}_{t}\,|\,{\boldsymbol{c}})=\int q( \boldsymbol{x}_{t}\,|\,\boldsymbol{x}_{0})p_{\text{data}}(\boldsymbol{x}_{0}\, |\,{\boldsymbol{c}})\,\mathrm{d}\boldsymbol{x}_{0},\quad p_{\theta}( \boldsymbol{x}_{t}\,|\,{\boldsymbol{c}})=\int q(\boldsymbol{x}_{t}\,|\, \boldsymbol{x}_{g})p_{\theta}(\boldsymbol{x}_{g}\,|\,{\boldsymbol{c}})\, \mathrm{d}\boldsymbol{x}_{g}.$ $$

This structure, characterized by explicit conditional layers but implicit marginals, exemplifies a semi-implicit distribution (Yin and Zhou, ; Yu et al., ). This concept is employed by Zhou et al. () to develop SiD, a method whose single-step data-free distillation capability has so far been demonstrated only on both unconditional and label-conditional diffusion models based on EDM.

We define the forward diffusion transition as $q(\boldsymbol{x}_{t}\,|\,\boldsymbol{x}_{0})=\mathcal{N}(a_{t}\boldsymbol{x}_{
0},\sigma_{t}^{2}\mathbf{I}),$
and unlike in SiD, which adheres to EDM noise scheduling where $a_{t}=1$, we allow $a_{t}$ to vary within $[0,1]$ to align with the DDPM scheduling used by SD during its training. This necessitates generalizing the equations used in SiD by permitting $a_{t}\neq 1$. We note that other diffusion types, such as categorical (Austin et al., ; Hoogeboom et al., ; Gu et al., ; Hu et al., ), Poisson (Chen and Zhou, ), and beta diffusions (Zhou et al., ), also align with the semi-implicit framework and can potentially be adapted similarly.

Score identities. The scores $S(\boldsymbol{x}_{t}):=\nabla_{\boldsymbol{x}_{t}}\ln p_{\text{data}}(
\boldsymbol{x}_{t}\,|\,{\boldsymbol{c}})$ and $\nabla_{\boldsymbol{x}_{t}}\ln p_{\boldsymbol{\theta}}(\boldsymbol{x}_{t}\,|\,
{\boldsymbol{c}})$ are generally unknown. However, the score of the forward conditional $q(\boldsymbol{x}_{t}\,|\,\boldsymbol{x})\sim\mathcal{N}(\boldsymbol{x},\sigma_
{t}^{2}\mathbf{I})$
is analytic:

$$ $\displaystyle\text{ If }\boldsymbol{x}_{t}=a_{t}\boldsymbol{x}+\sigma_{t} \boldsymbol{\epsilon}_{t},~{}\boldsymbol{\epsilon}_{t}\sim\mathcal{N}(0, \mathbf{I}),~{}~{}\text{ then }\nabla_{\boldsymbol{x}_{t}}\ln q(\boldsymbol{x} _{t}\,|\,\boldsymbol{x})=\sigma_{t}^{-2}{(a_{t}\boldsymbol{x}-\boldsymbol{x}_{ t})}=-\sigma_{t}^{-1}{\boldsymbol{\epsilon}_{t}}.$ $$

Exploiting the semi-implicit constructions,
we follow SiD to present the following three identities:

$$ $\displaystyle\mathbb{E}[\boldsymbol{x}_{0}\,|\,\boldsymbol{x}_{t},{\boldsymbol {c}}]=\textstyle\int\boldsymbol{x}_{0}q(\boldsymbol{x}_{0}\,|\,\boldsymbol{x}_ {t},{\boldsymbol{c}})\,\mathrm{d}\boldsymbol{x}_{0}=(\boldsymbol{x}_{t}+\sigma _{t}^{2}\nabla_{\boldsymbol{x}_{t}}\ln p_{{\text{data}}}(\boldsymbol{x}_{t}\,| \,{\boldsymbol{c}}))/a_{t},$ $\displaystyle\mathbb{E}[\boldsymbol{x}_{g}\,|\,\boldsymbol{x}_{t},{\boldsymbol {c}}]=\textstyle\int\boldsymbol{x}_{g}q(\boldsymbol{x}_{g}\,|\,\boldsymbol{x}_ {t})\,\mathrm{d}\boldsymbol{x}_{g}=(\boldsymbol{x}_{t}+\sigma_{t}^{2}\nabla_{ \boldsymbol{x}_{t}}\ln p_{\theta}(\boldsymbol{x}_{t}\,|\,{\boldsymbol{c}}))/a_ {t},$ $\displaystyle\mathbb{E}_{p_{\theta}(\boldsymbol{x}_{t}\,|\,{\boldsymbol{c}})} \left[u^{T}(\boldsymbol{x}_{t})\nabla_{\boldsymbol{x}_{t}}\ln p_{\theta}( \boldsymbol{x}_{t}\,|\,{\boldsymbol{c}})\right]=\mathbb{E}_{q(\boldsymbol{x}_{ t}\,|\,\boldsymbol{x}_{g})p_{\theta}(\boldsymbol{x}_{g}\,|\,{\boldsymbol{c}})} \left[u^{T}(\boldsymbol{x}_{t})\nabla_{\boldsymbol{x}_{t}}\ln q(\boldsymbol{x} _{t}\,|\,\boldsymbol{x}_{g},{\boldsymbol{c}})\right].$ $$

MESM loss. A pretrained text-to-image diffusion model, such as SD, provides a score network $S_{\phi}$ parameterized by $\phi$ that estimates the true data score as

$$ $-\sigma_{t}\nabla_{\boldsymbol{x}_{t}}\ln p_{\text{data}}(\boldsymbol{x}_{t}\, |\,{\boldsymbol{c}})\approx-\sigma_{t}S_{\phi}(\boldsymbol{x}_{t},{\boldsymbol {c}}):={\sigma_{t}^{-1}}({\boldsymbol{x}_{t}-a_{t}f_{\phi}(\boldsymbol{x}_{t}, t,{\boldsymbol{c}})})=\boldsymbol{\epsilon}_{\phi}(\boldsymbol{x}_{t},{ \boldsymbol{c}}).$ $$

It adopts $f_{\phi}(\boldsymbol{x}_{t},t,{\boldsymbol{c}})$ as the functional approximation of the conditional expectation of the real image $\boldsymbol{x}_{0}$ given noisy image $\boldsymbol{x}_{t}$ and text ${\boldsymbol{c}}$, expressed as $\mathbb{E}[\boldsymbol{x}_{0}\,|\,\boldsymbol{x}_{t},{\boldsymbol{c}}]$, adopts $\boldsymbol{\epsilon}_{\phi}(\boldsymbol{x}_{t},t,{\boldsymbol{c}})$ to predict the noise inside $\boldsymbol{x}_{t}$, and adopts $-\sigma_{t}^{-1}\boldsymbol{\epsilon}_{\phi}(\boldsymbol{x}_{t},{\boldsymbol{c
}})$ as the functional approximation of the true score $\nabla_{\boldsymbol{x}_{t}}\ln p_{\text{data}}(\boldsymbol{x}_{t}\,|\,{
\boldsymbol{c}})$.
Given time step $t\sim p(t)$ and text ${\boldsymbol{c}}$, we define the model-based explicit score-matching (MESM) distillation loss, which is a form of Fisher divergence (Lyu, ; Holmes and Walker, ; Yang et al., ; Yu and Zhang, ), as

$$ $\displaystyle{\mathcal{L}}_{\theta}=\mathbb{E}_{\boldsymbol{x}_{t}\sim p_{ \theta}(\boldsymbol{x}_{t})}[\|S_{\phi}(\boldsymbol{x}_{t},{\boldsymbol{c}})- \nabla_{\boldsymbol{x}_{t}}\ln p_{\theta}(\boldsymbol{x}_{t}\,|\,{\boldsymbol{ c}})\|_{2}^{2}].$ (1) $$

Loss approximation for distillation. The MESM loss in (1) is in general intractable to compute as
$\nabla_{\boldsymbol{x}_{t}}\ln p_{\theta}(\boldsymbol{x}_{t}\,|\,{\boldsymbol{
c}})$ is unknown. To denoise the noisy fake data $\boldsymbol{x}_{t}$ generated as

$$ $\displaystyle\boldsymbol{x}_{t}=a_{t}\boldsymbol{x}_{g}+\sigma_{t}\boldsymbol{ \epsilon}_{t},~{}~{}\boldsymbol{\epsilon}_{t}\sim\mathcal{N}(0,\mathbf{I}),~{} ~{}\boldsymbol{x}_{g}=G_{\theta}(\boldsymbol{z},{\boldsymbol{c}}),~{}~{} \boldsymbol{z}\sim p(\boldsymbol{z}),$ (2) $$

there exists an optimal denoising network
defined as
$f_{\psi^{*}(\theta)}(\boldsymbol{x}_{t},t,{\boldsymbol{c}})=\mathbb{E}[
\boldsymbol{x}_{g}\,|\,\boldsymbol{x}_{t},{\boldsymbol{c}}]=(\boldsymbol{x}_{t
}+\sigma_{t}^{2}\nabla_{\boldsymbol{x}_{t}}\ln p_{\theta}(\boldsymbol{x}_{t}\,
|\,{\boldsymbol{c}}))/a_{t}.$ Given this optimal denoising network, the MESM loss in (1) would become

$$ $\displaystyle{\mathcal{L}}_{\theta}=\mathbb{E}_{\boldsymbol{x}_{t}\sim p_{ \theta}(\boldsymbol{x}_{t})}[\|a_{t}\sigma_{t}^{-2}(f_{\phi}(\boldsymbol{x}_{t },t,{\boldsymbol{c}})-f_{\psi^{*}(\theta)}(\boldsymbol{x}_{t},t,{\boldsymbol{c }})\|_{2}^{2}].$ (3) $$

As $\psi^{*}(\theta)$ is unknown in practice, approximation is needed. We follow SiD to introduce an approximate optimization algorithm that alternates between optimize $\psi$ given $\theta$ using

$$ $\displaystyle\min_{\psi}\hat{\mathcal{L}}_{\psi}(\boldsymbol{x}_{t},c,t)= \textstyle\frac{a^{2}_{t}}{\sigma^{2}_{t}}\|f_{\psi}(\boldsymbol{x}_{t},t,{ \boldsymbol{c}})-\boldsymbol{x}_{g}\|_{2}^{2}=\|\boldsymbol{\epsilon}_{\psi}( \boldsymbol{x}_{t},t,{\boldsymbol{c}})-\boldsymbol{\epsilon}_{t}\|_{2}^{2},$ (4) $$

and optimize $\theta$ given $\psi$ using

$$ $\displaystyle\min_{\theta}\textstyle\tilde{L}_{\theta}(\boldsymbol{x}_{t},t, \phi,\psi)$ $\displaystyle\textstyle=\omega(t)\frac{a_{t}^{2}}{{\sigma_{t}^{4}}}(f_{\phi}( \boldsymbol{x}_{t},t,{\boldsymbol{c}})-f_{\psi}(\boldsymbol{x}_{t},t,{ \boldsymbol{c}}))^{T}(f_{\psi}(\boldsymbol{x}_{t},t,{\boldsymbol{c}})- \boldsymbol{x}_{g}),$ (5) $\displaystyle=\textstyle\omega(t)\frac{1}{{\sigma_{t}^{2}}}(\boldsymbol{ \epsilon}_{\psi}(\boldsymbol{x}_{t},t,{\boldsymbol{c}})-\boldsymbol{\epsilon}_ {\phi}(\boldsymbol{x}_{t},t,{\boldsymbol{c}}))^{T}(\boldsymbol{\epsilon}_{t}- \boldsymbol{\epsilon}_{\psi}(\boldsymbol{x}_{t},t,{\boldsymbol{c}})),$ $$

where $\boldsymbol{x}_{t}$ is generated as in (2) and $\omega(t)$ are weighted coefficients that will be specified. Note that (5) simplifies the corresponding one in SiD by fixing $\alpha=1$, a parameter that can be tuned in SiD to ehance performance but fixed at one in this paper. We have strategically set $\alpha=1$ to focus more on analyzing the injection of CFGs into SiD, leaving the tuning of this parameter for future work.

### 2.2 SiD with classifier-free guidance

An essential practice for enhancing photorealism and alignment with text instructions in text-to-image diffusion models involves incorporating CFG into reverse diffusion. This principle also applies to distillation methods for these models, where CFG must be integrated into the appropriate terms of their distillation loss functions. Therefore, a key distinction in the distillation of SD, compared to previous unconditional and label-conditional diffusion models, lies in the need to introduce CFG.

SiD presents a unique opportunity to apply CFG to enhance its text-to-image generation performance. First, we note CFG enhances text guidance by modifying the distribution of $\boldsymbol{x}_{t}$ given text ${\boldsymbol{c}}$ as

$$ $\textstyle p(\boldsymbol{x}_{t}\,|\,{\boldsymbol{c}},\kappa)\propto p({ \boldsymbol{c}}\,|\,\boldsymbol{x}_{t})^{\kappa}p(\boldsymbol{x}_{t})\propto \left(\frac{p(\boldsymbol{x}_{t}\,|\,{\boldsymbol{c}})}{p(\boldsymbol{x}_{t})} \right)^{\kappa}p(\boldsymbol{x}_{t}),$ $$

which means $\nabla_{\boldsymbol{x}_{t}}\ln p(\boldsymbol{x}_{t}\,|\,{\boldsymbol{c}},
\kappa)=\nabla_{\boldsymbol{x}_{t}}\ln p(\boldsymbol{x}_{t})+\kappa[\nabla_{
\boldsymbol{x}_{t}}\ln p(\boldsymbol{x}_{t}\,|\,{\boldsymbol{c}})-\nabla_{
\boldsymbol{x}_{t}}\ln p(\boldsymbol{x}_{t})]$.
Second, the score network, when reaching its optimal, is related to the true score as $f(\boldsymbol{x}_{t},t,{\boldsymbol{c}})={a_{t}^{-1}}({\boldsymbol{x}_{t}+
\sigma_{t}^{2}\nabla_{\boldsymbol{x}_{t}}\ln p(\boldsymbol{x}_{t}\,|\,c)})={a_
{t}^{-1}}({\boldsymbol{x}_{t}-\sigma_{t}\boldsymbol{\epsilon}(\boldsymbol{x}_{
t},t,{\boldsymbol{c}})}).$
Therefore, with “$\boldsymbol{\cdot}$” representing $\phi$ or $\psi$, we can express the score network $f_{\boldsymbol{\cdot}}(\boldsymbol{x}_{t},t,{\boldsymbol{c}})$ under CFG with scale $\kappa$ as

$$ $\displaystyle f_{\boldsymbol{\cdot},\kappa}(\boldsymbol{x}_{t},t,{\boldsymbol{ c}})=f_{\boldsymbol{\cdot}}(\boldsymbol{x}_{t},t)+\kappa[f_{\boldsymbol{\cdot} }(\boldsymbol{x}_{t},t,c)-f_{\boldsymbol{\cdot}}(\boldsymbol{x}_{t},t)].$ (6) $$

Similarly, for noise prediction network, we have $\boldsymbol{\epsilon}_{\boldsymbol{\cdot},\kappa}(\boldsymbol{x}_{t},t,{
\boldsymbol{c}})=\boldsymbol{\epsilon}_{\boldsymbol{\cdot}}(\boldsymbol{x}_{t}
,t)+\kappa[\boldsymbol{\epsilon}_{\boldsymbol{\cdot}}(\boldsymbol{x}_{t},t,c)-
\boldsymbol{\epsilon}_{\boldsymbol{\cdot}}(\boldsymbol{x}_{t},t)].$
With CFG, the score and noise prediction networks are related in the same way, which means that $\boldsymbol{\epsilon}_{\boldsymbol{\cdot},\kappa}(\boldsymbol{x}_{t},t,{
\boldsymbol{c}})={\sigma_{t}^{-1}}({\boldsymbol{x}_{t}-a_{t}{f_{\boldsymbol{
\cdot},\kappa}(\boldsymbol{x}_{t},t,{\boldsymbol{c}})}})$ and $f_{\boldsymbol{\cdot},\kappa}(\boldsymbol{x}_{t},t,{\boldsymbol{c}})={a_{t}^{-
1}}({\boldsymbol{x}_{t}-\sigma_{t}\boldsymbol{\epsilon}_{\boldsymbol{\cdot},
\kappa}(\boldsymbol{x}_{t},t,{\boldsymbol{c}})}).$

Inspecting the two losses in (4) and (5) suggests four potential places to inject CFG. More specifically, with $\kappa_{1},\kappa_{2},\kappa_{3},\kappa_{4}\in\mathbb{R}_{+}$, where $\mathbb{R}_{+}=\{x:x\geq 0\}$, we modify the losses with CFGs as

$$ $\displaystyle\hat{\mathcal{L}}_{\psi}(\boldsymbol{x}_{t},c,t)$ $\displaystyle=\textstyle\frac{a^{2}_{t}}{\sigma^{2}_{t}}\|f_{\psi,\kappa_{1}}( \boldsymbol{x}_{t},t,{\boldsymbol{c}})-\boldsymbol{x}_{g}\|_{2}^{2},=\| \boldsymbol{\epsilon}_{\psi,\kappa_{1}}(\boldsymbol{x}_{t},t,{\boldsymbol{c}}) -\boldsymbol{\epsilon}_{t}\|_{2}^{2},$ (7) $\displaystyle\textstyle\tilde{L}_{\theta}(\boldsymbol{x}_{t},t,\phi,\psi)$ $\displaystyle\textstyle=\omega(t)\frac{a_{t}^{2}}{{\sigma_{t}^{4}}}(f_{\phi, \kappa_{4}}(\boldsymbol{x}_{t},t,{\boldsymbol{c}})-f_{\psi,\kappa_{2}}( \boldsymbol{x}_{t},t,{\boldsymbol{c}}))^{T}(f_{\psi,\kappa_{3}}(\boldsymbol{x} _{t},t,{\boldsymbol{c}})-\boldsymbol{x}_{g}).$ (8) $$

Figure: Algorithm 1 SiD-LSG: Score identity Distillation with Long-Short classifier-free Guidance

### 2.3 Long and Short Guidance

Previous works equipped with a fake score network $f_{\psi}$ typically only consider adding CFG when evaluating the pretrained score network $f_{\phi}$, such as in DMD (Yin et al., ).
In the context of SiD, this corresponds to setting $\kappa_{1}=\kappa_{2}=\kappa_{3}=1$ and $\kappa_{4}>1$.
In this paper, we discover that a broad spectrum of combinations of $\kappa_{1}$, $\kappa_{2}$, $\kappa_{3}$, and $\kappa_{4}$ can all significantly enhance performance compared to not using any CFG at all. These settings are found to lead to different balances of minimizing FID, which reflects how well the generated data match the training data in distribution, and maximizing the CLIP score (Radford et al., ), which reflects how well the generated images follow the textual guidance.

This flexibility to accommodate various CFG combinations expands the design space for SD distillation, balancing generation quality and text adherence. However, given the vastness of the search space, we are motivated to develop strategies that constrain the scope of exploration.

Long strategy: Enhancing CFG of the pretrained score network $f_{\phi}$.
Aligning with established practices, the most common approach involves enhancing the CFG of the pretrained score network $f_{\phi}$. An example setting under this strategy in SiD is: $\kappa_{1}=\kappa_{2}=\kappa_{3}=1$ and $\kappa_{4}=3$. The rationale is that by biasing the teacher $f_{\phi}$ to favor generations more aligned with ${\boldsymbol{c}}$ using a CFG scale greater than 1, such as $\kappa_{4}=3$, the student generator is compelled to follow suit. We will present experimental results to demonstrate the effectiveness of this strategy while also discussing its limitations.

Short strategy: Weakening CFG of the fake score network $f_{\psi}$.
With the availability of a fake score network $f_{\psi}$, we have developed a new strategy to enhance SiD’s text-to-image generation capabilities by reducing the CFG of $f_{\psi}$ in SiD. An exemplary configuration is setting $\kappa_{1}=\kappa_{4}=1$ while allowing $\kappa_{2}=\kappa_{3}$ to vary within $(0,1)$. The underlying idea is that by diminishing $f_{\psi}$’s capacity to detect generations aligned with ${\boldsymbol{c}}$ through a reduced CFG scale ($0<\kappa_{2}<1$), the student generator is incentivized to produce images that better align with the textual guidance to compensate for this reduction. We will present experiments to assess the efficacy and limitations of this strategy.

Long and short CFGs. We introduce an innovative strategy termed long-short guidance (LSG), where we enhance CFG during the training of $f_{\psi}$ by setting $\kappa_{1}>1$, and maintain CFGs on $f_{\psi}$ and $f_{\phi}$ during the training of $G_{\theta}$ at the same or a lower scale by setting $1\leq\kappa_{2}=\kappa_{3}=\kappa_{4}\leq\kappa_{1}$. The logic behind this approach is that enhancing the CFG of $f_{\psi}$ during training effectively reduces the CFG at evaluation time when used at the same or a reduced level. To our knowledge, we are the first to incorporate CFG into the training of the fake score network. LSG has been found to achieve a better balance between FID and CLIP scores, optimizing performance across these metrics.

## 3 Experiments

Figure: Figure 3: Left (Long CFG of the true score network): This plot illustrates the progression of FID and CLIP scores, each influenced by different CFGs applied to the true score network. The $\kappa$’s not specified in the legends are set as 1. FID scores are plotted on the primary y-axis, while CLIP scores are displayed on the secondary y-axis in corresponding line styles but with slight transparency. Together, these lines demonstrate how various CFGs impact model performance. Right (Short CFG of the fake score network): Analogous plot to the left where the CFGs of the fake score network are either weakened during evaluation or enhanced during training.
Refer to caption: x1.png

We summarize the parameter settings, such as batch size, learning rate, and optimizer configurations, in Table 3 in Appendix C. Unless specified in ablation studies, the settings are uniformly applied across all guidance strategies.
We present the details of our method in Algorithm 1, where
we generate an image in one step and generalize the setting in DMD and SiD to set $\omega(t)$ as

$$ $\displaystyle\boldsymbol{x}_{g}=G_{\theta}(\boldsymbol{z},{\boldsymbol{c}})=f_ {\theta}(\sigma_{t_{init}}\boldsymbol{z},t_{init},{\boldsymbol{c}}),~{}~{} \boldsymbol{z}\sim\mathcal{N}(0,\mathbf{I});~{}~{}~{}~{}\textstyle\omega(t)= \frac{\sigma_{t}^{4}}{a_{t}^{2}}\frac{C}{\|\boldsymbol{x}_{g}-f_{\phi,\kappa_{ 4}}(\boldsymbol{x}_{t},t,{\boldsymbol{c}})\|_{1,sg}}~{},$ (9) $$

where $C$ is the total number of pixels in $\boldsymbol{x}_{g}$.
We initialize both ${\psi}$ and ${\theta}$ using the pretrained ${\phi}$ from SD.
We have performed an ablation study that chooses $t_{init}\in\{354,550,625,675,800,900,999\}$ and found that the model’s performance under FP32 optimization is not sensitive to these choices.
To align with the setting in SiD, we set $t_{init}=625$, which would result in $\sigma_{t_{init}}/a_{t_{init}}=2.5$ under the DDPM schedule used to train SD1.5.
We conduct a comprehensive study to evaluate the performance of SiD using the proposed LSG for distilling SD1.5. Additionally, we apply the same LSG scales to distill SD2.1-base, further assessing the adaptability and effectiveness of our approach across different model versions.

We consider a standard setting that utilizes the Aesthetics6+ prompt (Cherti et al., ) for training and evaluates performance by computing zero-shot FID on the COCO-2014 validation set. We adhere to the standard protocol by generating 30k images to compare with the 40,504 images in the COCO-2014 validation set for calculating zero-shot FID. Additionally, we employ the ViT-g-14-laion2b_s12b_b42k encoder (Ilharco et al., ; Cherti et al., ) to compute the CLIP score (Radford et al., ).
The FID and CLIP scores presented in the figures are calculated using randomly sampled prompts from the COCO-2014 validation set. When reporting the FID and CLIP results of SiD in Table 1, we follow
DMD (Yin et al., ) to use the exact evaluation code(^1^11[https://github.com/mingukkang/GigaGAN/tree/main/evaluation](https://github.com/mingukkang/GigaGAN/tree/main/evaluation)) provided by GigaGAN (Kang et al., ), where a pre-defined list of 30k text prompts selected from the COCO-2014 validation set is used to generate the 30k images, which are used for computing FID and CLIP scores with images from the validation set as the reference.

**Table 1: Comparison of image generation methods across various metrics. Inference times are estimated using a Nvidia A100 GPU as reference. The numbers of the methods marked with ^† are produced by running the publicly available model checkpoints. Other data are sourced from corresponding scientific papers with comparisons as reported. The value followed by symbol $\sim$ indicates it is estimated based on the plots shown in the paper.**
| Method | Res. | Time ($\downarrow$) | # Steps | # Param. | FID ($\downarrow$) | CLIP ($\uparrow$) |
| --- | --- | --- | --- | --- | --- | --- |
| Autoregressive Models |  |  |  |  |  |  |
| DALL·E (Ramesh et al., ) | 256 | - | - | 12B | 27.5 | - |
| CogView2 (Ding et al., ) | 256 | - | - | 6B | 24.0 | - |
| Parti-750M (Yu et al., ) | 256 | - | - | 750M | 10.71 | - |
| Parti-3B (Yu et al., ) | 256 | 6.4s | - | 3B | 8.10 | - |
| Parti-20B (Yu et al., ) | 256 | - | - | 20B | 7.23 | - |
| Make-A-Scene (Gafni et al., ) | 256 | 25.0s | - | - | 11.84 | - |
| Masked Models |  |  |  |  |  |  |
| Muse (Chang et al., ) | 256 | 1.3 | 24 | 3B | 7.88 | 0.32 |
| Diffusion Models |  |  |  |  |  |  |
| GLIDE (Nichol et al., ) | 256 | 15.0s | 250 | 5B | 12.24 | - |
| DALL·E 2 (Ramesh et al., ) | 256 | - | 250+27 | 5.5B | 10.39 | - |
| LDM (Rombach et al., ) | 256 | 3.7s | 250 | 1.45B | 12.63 | - |
| Imagen (Ho et al., ) | 256 | 9.1s | - | 3B | 7.27 | - |
| eDiff-I (Balaji et al., ) | 256 | 32.0s | 25+10 | 9B | 6.95 | - |
| Generative Adversarial Networks (GANs) |  |  |  |  |  |  |
| LAFITE (Zhou et al., ) | 256 | 0.02s | 1 | 75M | 26.94 | - |
| StyleGAN-T (Sauer et al., ) | 512 | 0.10s | 1 | 1B | 13.90 | $\sim$0.293 |
| GigaGAN (Kang et al., ) | 512 | 0.13s | 1 | 1B | 9.09 | - |
| Distilled Stable Diffusion 2.1 |  |  |  |  |  |  |
| ^†ADD (SD-Turbo) (Sauer et al., ) | 512 | - | 1 | - | 16.25 | 0.335 |
| Distilled Stable Diffusion XL |  |  |  |  |  |  |
| ^†ADD (SDXL-Turbo) (Sauer et al., ) | 512 | - | 1 | - | 19.08 | 0.343 |
| Stable Diffusion 1.5 and its accelerated or distilled versions |  |  |  |  |  |  |
| SD1.5 (CFG=3) (Rombach et al., ) | 512 | 2.59s | 250 | 0.9B | 8.78 | - |
| SD1.5 (CFG=7.5) (Rombach et al., ) | 512 | 2.59s | 250 | 0.9B | 13.45 | 0.322 |
| \hdashlineDPM++ (4 step) (Lu et al., ) | 512 | 0.26s | 4 | 0.9B | 22.44 | 0.31 |
| UniPC (4 step) (Zhao et al., ) | 512 | 0.26s | 4 | 0.9B | 22.30 | 0.31 |
| LCM-LoRA (4 step) (Luo et al., ) | 512 | 0.19s | 4 | 0.9B | 23.62 | 0.30 |
| LCM-LoRA (1 step) (Luo et al., ) | 512 | 0.07s | 1 | 0.9B | 77.90 | 0.24 |
| InstaFlow-0.9B  (Liu et al., ) | 512 | 0.09s | 1 | 0.9B | 13.10 | 0.28 |
| InstaFlow-1.7B  (Liu et al., ) | 512 | 0.12s | 1 | 1.7B | 11.83 | - |
| UFOGen (Xu et al., ) | 512 | 0.09s | 1 | 0.9B | 12.78 | - |
| DMD (CFG=3) (Yin et al., ) | 512 | 0.09s | 1 | 0.9B | 11.49 | - |
| DMD (CFG=8) (Yin et al., ) | 512 | 0.09s | 1 | 0.9B | 14.93 | 0.32 |
| BOOT (Gu et al., ) | 512 | 0.09s | 1 | 0.9B | 48.20 | 0.26 |
| Guided Distillation (Meng et al., ) | 512 | - | 1 | 0.9B | 37.3 | 0.27 |
| SiD-LSG ($\kappa=1.5$) | 512 | 0.09s | 1 | 0.9B | 8.71 | 0.302 |
| SiD-LSG ($\kappa=1.5$, double the training time) | 512 | 0.09s | 1 | 0.9B | 8.15 | 0.304 |
| SiD-LSG ($\kappa=2$) | 512 | 0.09s | 1 | 0.9B | 9.56 | 0.313 |
| SiD-LSG ($\kappa=3$) | 512 | 0.09s | 1 | 0.9B | 13.21 | 0.314 |
| SiD-LSG ($\kappa=4.5$) | 512 | 0.09s | 1 | 0.9B | 16.59 | 0.317 |
| Stable Diffusion 2.1-base and its distilled versions |  |  |  |  |  |  |
| SD2.1-base (Rombach et al., ) | 512 | 0.09s | 1 | 0.9B | 202.14 | 0.08 |
| SD2.1 base (Rombach et al., ) | 512 | 0.77s | 25 | 0.9B | 13.45 | 0.30 |
| \hdashlineSwiftBrush (Nguyen and Tran, ) | 512 | 0.09 | 1 | 0.9B | 16.67 | 0.29 |
| SiD-LSG ($\kappa=1.5$) | 512 | 0.09s | 1 | 0.9B | 9.52 | 0.308 |
| SiD-LSG ($\kappa=2$) | 512 | 0.09s | 1 | 0.9B | 10.97 | 0.318 |
| SiD-LSG ($\kappa=3$) | 512 | 0.09s | 1 | 0.9B | 13.50 | 0.321 |
| SiD-LSG ($\kappa=4.5$) | 512 | 0.09s | 1 | 0.9B | 16.54 | 0.322 |

**Table 2: Comparison of HPSv2 score and Precision/Recall on the COCO-2014 validation set. The HPSv2 scores of ADD (SD-Turbo) are produced based on the publicly available model checkpoint. The HPSv2 scores of the other baselines are quoted from SwiftBrush (Nguyen and Tran, ). The Precision and Recall on COCO-2014 are obtained using the 30K images generated by the corresponding model checkpoints. The SwiftBrush checkpoint was provided upon request by the authors.**
| Teacher | Student | Human Preference Score v2 $\uparrow$ | COCO-2014 Precision and Recall $\uparrow$ |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Anime | Photo | Concept Art | Paintings | Precision | Recall |  |  |
| SD2.1 | ADD (SD-Turbo) (Sauer et al., ) | 27.48 | 26.89 | 26.86 | 27.46 | 0.65 | 0.35 |
| SD1.5 | LCM (Luo et al., ) | 22.61 | 22.71 | 22.74 | 22.91 | - | - |
| SD1.5 | InstaFlow (Liu et al., ) | 25.98 | 26.32 | 25.79 | 25.93 | 0.53 | 0.45 |
| SD1.5 | BOOT (Gu et al., ) | 25.29 | 25.16 | 24.40 | 24.61 | - | - |
| SD2.1-base | SwiftBrush (Nguyen and Tran, ) | 26.91 | 27.21 | 26.32 | 26.37 | 0.47 | 0.46 |
| SD1.5 | SiD-LSG ($\kappa=1.5$) | 26.58 | 26.80 | 26.02 | 26.02 | 0.59 | 0.52 |
| SiD-LSG ($\kappa=1.5$, double the training time) | 26.58 | 26.80 | 26.01 | 26.02 | 0.60 | 0.53 |  |
| SiD-LSG ($\kappa=2$) | 26.94 | 27.03 | 26.35 | 26.27 | 0.64 | 0.48 |  |
| SiD-LSG ($\kappa=3$) | 27.10 | 27.11 | 26.47 | 26.46 | 0.65 | 0.40 |  |
|  | SiD-LSG ($\kappa=4.5$) | 27.39 | 27.30 | 26.65 | 26.58 | 0.67 | 0.34 |
| SD2.1-base | SiD-LSG ($\kappa=1.5$) | 26.65 | 26.87 | 26.19 | 26.14 | 0.60 | 0.49 |
| SiD-LSG ($\kappa=2$) | 26.90 | 27.08 | 26.43 | 26.47 | 0.62 | 0.44 |  |
| SiD-LSG ($\kappa=3$) | 27.27 | 27.22 | 26.75 | 26.72 | 0.64 | 0.38 |  |
|  | SiD-LSG ($\kappa=4.5$) | 27.42 | 27.31 | 26.81 | 26.79 | 0.63 | 0.34 |

### 3.1 Long and/or short guidance strategies

Long the CFG of the pretrained score network.
For the long strategy,
we set $\kappa_{1}=\kappa_{2}=\kappa_{3}=1$ and $\kappa_{4}>1$, specifically exploring values of $\kappa_{4}$ from the set $\{2,2.5,3,3.5,7.5\}$. As shown in the left panel of Figure 4, our experiments results indicate that this setup yields highly competitive performance, even after processing as few as 2.5 million fake images (approximately 5,000 iterations with a mini-batch size of 512). The parameter $\kappa_{4}$ plays a crucial role in balancing FID and CLIP scores. For achieving lower FID scores, $\kappa_{4}=2$ can yield an FID close to 10, while for higher CLIP scores, $\kappa_{4}=7.5$ can result in a CLIP score around 0.31. However, our primary objective is to devise a strategy that lowers FID while minimizing CLIP degradation.

Short the CFG of the fake score network.
For the short strategy, we set $\kappa_{1}=\kappa_{4}=1$ and explore $\kappa_{2}=\kappa_{3}\in\{0.5,0.125\}$. As illustrated in the right panel of Figure 4, this approach delivers competitive performance, with $\kappa_{2}$ dictating the balance between FID and CLIP scores. However, compared to the long strategy, this configuration generally produces inferior results, as suggested by lower CLIP scores when FIDs are controlled to similar levels.
From Figure 4, it’s also evident that when No CFG is employed, where $\kappa_{1}=\kappa_{2}=\kappa_{3}=\kappa_{4}=1$, the results are underwhelming, underscoring the importance of injecting CFGs into SiD to distill text-to-image diffusion models.

LSG: Long and Short classifier-free Guidance.
Below, we explore how to effectively integrate the long and short strategies to enhance text guidance in diffusion distillation. Initially, we discovered an alternative approach to shorting the CFG of the fake score network $f_{\psi}$: by amplifying the CFG during the training of $f_{\psi}$. This approach represents the “simplest” form of LSG strategy. Specifically, we set $\kappa_{1}$ to 2 or 3, while keeping $\kappa_{2}=\kappa_{3}=\kappa_{4}$ at 1. As illustrated in the right panel of Figure 4, setting $\kappa_{1}=3$ (green lines) proves as effective as the short strategy with $\kappa_{2}=\kappa_{3}=0.125$ (red lines), and $\kappa_{1}=2$ (black lines) outperforms the short strategy with $\kappa_{2}=\kappa_{3}=0.5$ (blue lines) and is comparable to the long strategy with $\kappa_{4}=3$ (red lines in the left panel). These findings validate this “simplest” LSG as an effective guidance strategy in text-guided diffusion distillation.

Nevertheless, merely matching the performance of the best long or short strategy is insufficient to justify adopting this “simplest” LSG. The efficacy of LSG is notably enhanced when the CFG scale applied to the fake score network during its training exceeds 1 and is maintained throughout the generator’s training. This strategy strikes an effective balance between minimizing FID and maximizing CLIP. Specifically, for this recommended LSG configuration, we evaluated $\kappa_{1}=\kappa_{2}=\kappa_{3}=\kappa_{4}$ values within $\{1.5,2,3,4.5\}$, presenting the results in Figure 4 for distilling both SD 1.5 and 2.1-base models.
Comparisons between SD1.5 outcomes in the left panel of Figure 4 and those in Figure 4 demonstrate superior performance with this LSG setting, indicated by higher CLIP scores at controlled FID levels, and lower FID scores at controlled CLIP levels. Generally, within the range of 1.5 to 4.5, a lower guidance scale correlates with better FID but worse CLIP, and vice versa, as evidenced by the curves for both SD 1.5 and 2.1-base in Figure 4.

### 3.2 Ablation study

Figure: Figure 5: This figure illustrates the progression of FID and CLIP scores during an ablation study of distilling SD1.5 using SiD-LSG. The default settings of batch size 512, learning rate 1e-6, LSG scale 2, and Prompt Aesthetics6+ are maintained unless specified otherwise. Left: The number of training fake images is doubled from 10M to 20M under LSG scales of 1.5 and 2.0. Middle: Variations in batch size and learning rate settings under LSG 1.5. Right: Comparison of training prompts Aesthetics6+, Aesthetics6.25+, and Aesthetics6.5+.
Refer to caption: x5.png

We investigate the impact of extended training durations under two different guidance scales, variations in batch size, and the selection of training prompts. Initially, we doubled the number of fake images used to train the generator from 10M to 20M and monitored the evolution of the FID and CLIP scores. From the left panel of Figure 5, we observe continuous improvements in both FID and CLIP scores for an LSG of 1.5 when training is extended beyond 10M fake images, and sustained enhancements in CLIP scores for an LSG of 2.0. Notably, by doubling the training input from 10M to 20M fake images, the FID under LSG 1.5 decreased from 8.71 to a record low of 8.15 among diffusion distillation methods.

Additionally, we assessed the effects of batch size by considering two additional settings: a batch size of 8192 with a learning rate of 4e-6, a configuration used in SiD to distill the EDM model pretrained on ImageNet 64x64 (Zhou et al., ), and a batch size of 64 with a learning rate of 1e-6. The middle panel of Figure 5 demonstrates that while there are initial differences in the convergence speed in terms of the number of fake images processed, the performances eventually converge to similar levels. We note that while smaller batch sizes may seem to converge faster, they require more time to process the same number of images. This is due to more frequent model parameter updates, higher communication costs between GPUs, and additional overheads typically associated with smaller batch sizes.

Lastly, we explored the effect of changing training prompts, shifting from the Aesthetics6+ prompts to Aesthetics6.25+ and Aesthetics6.5+ prompts (Cherti et al., ). The performance, as shown in the right panel of Figure 5, appears comparable across these variations. Specifically, under an LSG of 2.0, switching from Aesthetics6+ to Aesthetics6.25+ enabled us to further reduce the FID from the 9.56 reported in Table 1 to 9.21, although the CLIP score decreased slightly from 0.313 to 0.311, indicating no significant performance disparity between them.

The results of the ablation study show that SiD-LSG has low sensitivity to variations in batch size and training prompts and its performance could potentially be further enhanced with extended training.

### 3.3 Quantitative and qualitative evaluations

Figure: Figure 6: Qualitative comparison of one-step distillation methods using identical text prompts and random seeds.
Refer to caption: extracted/5781662/sid_sd_plots/qualitative.jpg

Figure: Figure 7: Visual comparison of two SiD-LSG models: one preferred for FID and the other for CLIP. All images are generated from the same text prompt: “A distinguished older gentleman in a vintage study, surrounded by books and dim lighting, his face marked by wisdom and time. 8K, hyper-realistic, cinematic, post-production." The model with a lower guidance scale of $\kappa=1.5$, which achieves a record-low one-step-generation FID of 8.15 and a competitive CLIP score of 0.304, produces images that are more diverse but align less closely with specific text details, such as “dim lighting.” Conversely, the model with a higher guidance scale of $\kappa=4.5$, achieving a high CLIP score of 0.322 and noted for state-of-the-art human preference scores (HPSv2) as shown in Table 2, presents a relatively high FID of 16.54, indicating less diversity but superior text alignment and visual quality.
Refer to caption: extracted/5781662/sid_sd_images/figure9_sd15/000009.png

We present comprehensive results from prior studies across various experimental settings, including both one-step and multi-step generation methods. When evaluation results are available in existing literature (Yin et al., ; Liu et al., ; Kang et al., ; Nguyen and Tran, ), we directly cite them; otherwise, if model checkpoints are accessible, either publicly or provided upon request by the authors, we utilize the evaluation code from GigaGAN to produce the reported results. For our SiD-LSG, we select $\kappa_{1}=\kappa_{2}=\kappa_{3}=\kappa_{4}\in\{1.5,2.0,3.0,4.5\}$.

For comparisons of FID and CLIP scores, the results are detailed in Table 1. Among all one-step distillation methods, our approach notably excels in zero-shot text-conditioned image generation on the COCO-2014 dataset, as reflected by both FID-30K and CLIP scores. Specifically, with the guidance scale set as 2, our method attains FID scores as low as 9.56 and 10.97, and a CLIP score above 0.31 and around 0.32, using SD 1.5 and 2.1-base as the pretrained backbones, respectively. Notably, by setting the guidance scale to 1.5 and doubling the training time, our data-free method achieves a record-low FID of 8.15, along with a CLIP score of 0.304, when distilling SD1.5. These results remain highly competitive when compared to other generative approaches, such as autoregressive models and GANs, and are even comparable to previous multi-step diffusion-based sampling methods. Analyzing different $\kappa$ values, we observe a trade-off between FID and CLIP scores: smaller $\kappa$ values generally yield better FID metrics, while larger values enhance CLIP scores, aligning with past findings on the impact of guidance scale.

Beyond FID and CLIP scores, we also assess Precision and Recall (Kynkäänniemi et al., ) as well as Human Preference Score (HPSv2) (Wu et al., ), which are presented in Table 2. We reuse the same 30k images from previous evaluations for the Precision and Recall calculations. For HPSv2, we follow their established protocol, generating images from 800 text prompts per category. Our SiD-LSG models outperform other baselines in HPSv2 scores across all categories, as well as in Precision and Recall metrics. Notably, with $\kappa=4.5$, SiD-LSG reaches peak performance in distilling SD2.1-base. Regarding Precision and Recall, higher $\kappa$ values lead to improved Precision, while lower values result in better Recall.

For qualitative verification, we utilize our models with $\kappa=4.5$, selecting six prompts from across all HPSv2 categories to generate images. To ensure a fair comparison, we maintain the same random seed for image generation across all methods. The visual results are illustrated in Figure 6, where SiD consistently shows superior text-image alignment and visual fidelity.

To contextualize the numerical differences in metrics such as FID, CLIP, and HPSv2,
we present Figures 8 and 8 to illustrate what these differences mean for visual perception: the model with the best FID score excels in diversity, while the model with the best CLIP score stands out in text alignment and aesthetic quality.

For broader impact, please refer to the discussion in Appendix A.
For limitations and computational requirements, please refer to a detailed discussion in Appendix D.

## 4 Conclusion

In this paper, we introduce a novel approach that combines Classifier-Free Guidance (CFG) with Score identity Distillation (SiD) to efficiently distill Stable Diffusion models into effective one-step generators. Utilizing our innovative Long and Short CFG strategies (LSG), we have distilled these models using only synthetic images generated by the one-step generator. This method not only validates the practical potential of SiD but also establishes new benchmarks for one-step diffusion distillation, achieving remarkable zero-shot FID scores on the COCO-2014 validation set. Designed for enhanced efficiency without sacrificing performance, our approach allows for learning from the teacher model without requiring real images or incorporating additional regression or adversarial losses. We have made our code and distilled models publicly available to facilitate further research.

## Acknowledgments and Disclosure of Funding

We are grateful for the contributions of Michael (Qijia) Zhou in preparing the data and code. We also extend our thanks to Tianwei Yin for clarifying our queries regarding the evaluation protocol of DMD on FID and CLIP scores. Additionally, we appreciate Thuan Hoang Nguyen for responding to our questions about SwiftBrush and for kindly providing a model checkpoint upon request. M. Zhou, H. Zheng, and Z. Wang acknowledge the support of NSF-IIS 2212418 and NIH-R37 CA271186.

## Appendix A Broader impact

The broader impact of our work is multifaceted. On one hand, it significantly reduces the energy required to operate state-of-the-art diffusion models, contributing to more sustainable AI practices. On the other hand, the ease of distilling and deploying models that might be trained on data with questionable content or intentions presents ethical challenges. Therefore, it is crucial for the community to engage in discussions on how to minimize risks while enhancing the benefits of such advancements. This involves developing robust guidelines and frameworks to govern the use and deployment of distilled models, ensuring they are used responsibly and ethically.

## Appendix B Related work

Generative modeling of high-dimensional data has long been a focal point in machine learning research. This area primarily concentrates on replicating various data distributions: the original data distribution, conditional distributions influenced by labels, noisy or incomplete measurements, textual descriptions, or the joint distribution of data and other modalities. This has spurred the development of a diverse range of generative models and methodologies. Initially, these models were only capable of handling simpler, low-dimensional data such as $28\times 28$ grayscale or binarized MNIST digits [Hinton et al., , Salakhutdinov and Hinton, , Vincent et al., ], vector-quantized local descriptors [Fei-Fei and Perona, , Chong et al., ], or patches of natural and hyperspectral images [Zhou et al., , Xing et al., , Polatkan et al., ]. Early models often utilized neural networks with stochastic binary hidden layers or shallow hierarchical Bayesian models, which are simpler to train but have limited capacities.

Deep generative models.
To tackle the generation of high-dimensional data, such as images comprising millions of pixels, substantial advancements in generative models have been made over the past decade.
This period has marked the emergence of diverse deep generative models, including variational auto-encoders (VAEs) [Kingma and Welling, , Rezende et al., ], normalizing flows [Papamakarios et al., ], generative adversarial networks (GANs) [Goodfellow et al., , Reed et al., , Karras et al., , Wang et al., ], autoregressive models [Gregor et al., , Mansimov et al., ],
and diffusion models [Sohl-Dickstein et al., , Song and Ermon, , Ho et al., , Song and Ermon, , Song et al., , , Dhariwal and Nichol, , Karras et al., , Peebles and Xie, , Zheng et al., ]. Additionally, essential resources for creating effective text-to-image synthesis systems, such as large language models [Vaswani et al., , Devlin et al., , Radford et al., , , Raffel et al., , He et al., , Brown et al., , Achiam et al., , Touvron et al., , Jiang et al., ], large vision-language models [Radford et al., , Ilharco et al., , Cherti et al., ], advanced visual tokenization and compression [Rolfe, , van den Oord et al., , Esser et al., , Rombach et al., , Podell et al., ], and extensive training datasets
[Thomee et al., , Changpinyo et al., , Schuhmann et al., ], have transitioned from proprietary tools of major tech companies to publicly available assets.

Previously, text-to-image models primarily utilized GANs and focused on small-scale, object-centric domains like flowers, birds, and face images [Reed et al., , Zhang et al., , Xu et al., , Zhang et al., ].
Powered by text encoders that leverage pretrained large language models or vision-language models, which offer profound language comprehension and extract semantically rich latent representations, and supported by an extensive collection of text-image pairs, three main families of generative models—GANs [Zhou et al., , Sauer et al., , Kang et al., ], autoregressive models [Ramesh et al., , Zhang et al., , Ding et al., , Gafni et al., , Yu et al., , Chang et al., ], and diffusion models [Nichol et al., , Ramesh et al., , Saharia et al., , Rombach et al., , Xu et al., , Wang et al., , Qin et al., ]—have effectively capitalized on these technological advancements. These developments have facilitated the creation of text-to-image synthesis systems that demonstrate exceptional photorealism and sophisticated language understanding.

Acceleration of diffusion-based generation.
Early-stage diffusion models were notably slow in sampling, spurring extensive research aimed at speeding up the reverse diffusion process. Researchers have approached this by interpreting diffusion models through stochastic or ordinary differential equations and using advanced numerical solvers to enhance efficiency [Song et al., , , Liu et al., , Lu et al., , Zhang and Chen, , Karras et al., ]. Additional strategies include truncating the diffusion chain to initiate generation from more structured distributions [Pandey et al., , Zheng et al., , Lyu et al., ], integrating these models with GANs to boost generation speed [Xiao et al., , Wang et al., ], and exploring flow matching in diffusion modeling [Liu et al., , Lipman et al., , Albergo et al., ].
More recently, research has pivoted towards distilling reverse diffusion chains to refine and expedite the generation process, a direction that continues to evolve with new methodologies and insights
[Luhman and Luhman, , Salimans and Ho, , Zheng et al., , Meng et al., , Song et al., , Song and Dhariwal, , Kim et al., , Sauer et al., , Xu et al., , Yin et al., , Luo et al., , Zhou et al., ].

Text-to-image diffusion distillation. Recent efforts aim to accelerate the sampling process from pre-trained diffusion teachers like SD [Rombach et al., ]. Sauer et al. [] focused on distilling diffusion models into generators capable of one or two-step operations through adversarial training. Xu et al. [] introduced UFOGen, utilizing a time-step-dependent discriminator for generator initialization. Luo et al. [] applied consistency distillation [Song et al., ] to text-guided latent diffusion models [Ramesh et al., ] for efficient, high-fidelity text-to-image generation. Building on the idea of using a pre-trained 2D text-to-image diffusion model for text-to-3D synthesis [Poole et al., , Wang et al., ], SwiftBrush [Nguyen and Tran, ] showcases its effectiveness of distilling pre-trained stable diffusion models. Distribution Matching Distillation (DMD) by Yin et al. [] further enhances distillation quality by adding a regression loss term.

How does SiD-LSG differ from previous methods?
SiD-LSG introduces several unique features that distinguish it from previous text-to-image diffusion distillation methods. Firstly, similar to SwiftBrush [Nguyen and Tran, ], SiD-LSG is a data-free distillation method, which eliminates the need for original training datasets or synthetic data generated with SDE/ODE solvers during the distillation process. However, unlike SwiftBrush, SiD-LSG necessitates gradient backpropagation through the score networks, a critical step akin to gradient backpropagation through the discriminator in methods employing adversarial losses. Additionally, SiD-LSG applies CFG to both the training and evaluation of the fake score network. This is a departure from previous methods, which typically apply CFG only during the evaluation of the pretrained score network. Finally, SiD-LSG aims to minimize a model-based explicit score-matching loss, a type of Fisher divergence. In contrast, previous methods often focus on minimizing losses based on KL divergence, consistency, regression, GAN-based adversarial tactics, or a combination thereof.

## Appendix C Training and evaluation details

The hyperparameters tailored for our study are outlined in Table 3. It’s important to note that the time and memory costs reported in Table 3 do not account for those incurred during periodic evaluations of the FID and CLIP scores of the single-step generator, nor do they include the resources used to save model checkpoints during the distillation process. These costs can vary significantly depending on the computing and storage platforms used and the frequency of these operations.

**Table 3: Hyperparameter settings and comparison of distillation time and memory usage between different long and short guidance strategies of SiD. Note in order to run the long-short guidance (LSG) with $\kappa_{1}=\kappa_{2}=\kappa_{3}=\kappa_{4}>1$, we need to turn off the EMA network for some cases (indicated with “no EMA”), otherwise it will be out of memory.**
| Computing platform | Hyperparameters | Long Strategy | Short Strategy | Long-Short | Long-Short |
| --- | --- | --- | --- | --- | --- |
| General Settings | CFG scales | $\kappa_{4}>1$ | $0<\kappa_{2}=\kappa_{3}<1$ | $\kappa_{1}>1$ | $\kappa_{1,2,3,4}>1$ |
| $\kappa_{1}=\kappa_{2}=\kappa_{3}=1$ | $\kappa_{1}=\kappa_{4}=1$ | $\kappa_{2}=\kappa_{3}=\kappa_{4}=1$ | $\kappa_{1}=\kappa_{2}=\kappa_{3}=\kappa_{4}$ |  |  |
| Batch size | 512 |  |  |  |  |
| Learning rate | 1e-6 |  |  |  |  |
| Half-life of EMA | 50k images |  |  |  |  |
| Optimizer under FP32 | Adam ($\beta_{1}=0$, $\beta_{2}=0.999$, $\epsilon=1\text{e-8}$) |  |  |  |  |
| Optimizer under FP16 | Adam ($\beta_{1}=0$, $\beta_{2}=0.999$, $\epsilon=1\text{e-6}$) |  |  |  |  |
| $\alpha$ | 1.0 |  |  |  |  |
| Time parameters | $(t_{\min},t_{\text{init}},t_{\max})=(20,625,979)$ |  |  |  |  |
| RTX-A5000 (24G), FP16 | xFormers available and enabled | Yes |  |  |  |
| Batch size per GPU | 1 |  |  |  |  |
| # of GPUs | 8 |  |  |  |  |
| # of gradient accumulation round | 64 |  |  |  |  |
| Max memory in GB allocated | 22.9 | 22.9 | 22.6 | 22.0 (no EMA) |  |
| Max memory in GB reserved | 23.0 | 23.0 | 23.0 | 22.1 (no EMA) |  |
| Time in seconds per 1k images | 74 | 75 | 80 | 102 |  |
| Time in hours per 1M images | 21 | 21 | 22 | 29 |  |
| RTX-A6000 (48G), FP32 | xFormers available and enabled | Yes |  |  |  |
| Batch size per GPU | 1 |  |  |  |  |
| # of GPUs | 8 |  |  |  |  |
| # of gradient accumulation round | 64 |  |  |  |  |
| Max memory in GB allocated | 45.7 | 45.7 | 45.0 | 45.8 (no EMA) |  |
| Max memory in GB reserved | 45.7 | 45.7 | 45.9 | 46.0 (no EMA) |  |
| Time in seconds per 1k images | 365 | 365 | 366 | 502 |  |
| Time in hours per 1M images | 102 | 102 | 102 | 139 |  |
| H100 (80G), FP16 | xFormers available and enabled | No |  |  |  |
| Batch size per GPU | 4 |  |  |  |  |
| # of GPUs | 8 |  |  |  |  |
| # of gradient accumulation round | 16 |  |  |  |  |
| Max memory in GB allocated | 55.8 | 55.8 | 47.7 | 63.9 |  |
| Max memory in GB reserved | 57.3 | 58.3 | 49.2 | 65.4 |  |
| Time in seconds per 1k images | 17 | 17 | 18 | 23 |  |
| Time in hours per 1M images | 5 | 5 | 5 | 6 |  |
| H100 (80G), FP32 | xFormers available and enabled | No |  |  |  |
| Batch size per GPU | 1 |  |  |  |  |
| # of GPUs | 8 |  |  |  |  |
| # of gradient accumulation round | 64 |  |  |  |  |
| Max memory in GB allocated | 58.9 | 57.4 | 53.4 | 62.9 |  |
| Max memory in GB reserved | 60.0 | 59.0 | 54.0 | 64.0 |  |
|  | Time in seconds per 1k images | 74 | 74 | 76 | 90 |
|  | Time in hours per 1M images | 21 | 21 | 21 | 25 |

Figure: Figure 9: Comparison between FP16 and FP32 under three different guidance strategies. Top: Long strategy with $\kappa_{1}=\kappa_{2}=\kappa_{3}=1$ and $\kappa_{4}=3$. Middle: Short strategy with $\kappa_{1}=\kappa_{4}=1$ and $\kappa_{2}=\kappa_{3}=0.5$. Bottom: Long-short guidance (LSG) with $\kappa_{1}=\kappa_{2}=\kappa_{3}=\kappa_{4}=2$.
Refer to caption: x8.png

## Appendix D Limitations and computational requirements

Memory and speed. Table 3 in Appendix C offers a detailed examination of the computational resources required for the SiD distillation process employing various CFG strategies—Long, Short, basic Long-Short, and the recommended Long-Short—across different NVIDIA GPU platforms (RTX-A5000 with 24GB, RTX-A6000 with 48GB, and H100 with 80GB). Key observations include:

SiD-LSG can be effectively run on the RTX-A5000 with 24GB memory by enabling xFormers [Lefaudeux et al., ] and switching to FP16 for model and gradient precision. On the RTX-A6000 with 48GB memory, it operates under FP32 with xFormers enabled. However, on our available H100 with 80GB memory, where xFormers are not supported, there is a noticeable increase in GPU memory consumption.

The recommended LSG strategy, which provides an improved balance between FID and CLIP scores, demands approximately 20% more computation time per iteration and exhibits about 10% more peak memory usage on the H100 compared to the Long or Short strategies. This underlines a trade-off between achieving performance enhancements and managing resource utilization.

FP16 versus FP32.
SiD-LSG can be trained under FP16 mixed precision, which significantly conserves memory and enhances processing speed, as detailed in Table 3. Although this reduced precision in optimization leads to quicker improvements in both FID and CLIP scores, it also restricts the potential for achieving the lowest FID and highest CLIP scores compared to results under FP32. These effects are demonstrated in the ablation study shown in Figure 9.
Additionally, FP16 operation is less stable and requires the use of gradient clipping to maintain training stability. For instance, we employ torch.nn.utils.clip_grad_value_(G.parameters(), 1) to prevent sudden model divergence—a precaution that is not necessary with FP32.

Further investigation is required to optimize FP16 performance to equal that of FP32, potentially through refined loss scaling techniques or by implementing an effective warmup period using FP16 before transitioning to FP32. We plan to address these challenges in future studies.

Reaching a performance plateau. Zhou et al. [] demonstrate through extensive comparisons that the SiD distilled one-step generator can reduce the FID at an exponential decay rate, showing a log-log linear relationship between the number of iterations and FID. This approach can match or even surpass the performance of both unconditional and label-conditional teacher diffusion models trained under the EDM framework [Karras et al., ], provided sufficient training.

However, results shown in Figures 4 and 5 indicate that the SiD-LSG distilled one-step generator on SD1.5 quickly reaches a performance plateau in reducing FID and/or increasing CLIP scores, particularly when the LSG scale exceeds 2. Additionally, data from Table 1 demonstrate that after processing 10M images (approximately 20k iterations at a batch size of 512), SiD-LSG still does not match the text-image alignment performance of the teacher model, which achieves higher CLIP scores after 250 generation steps.
Notably, by reducing the LSG scale to 1.5 and doubling the training duration, SiD-LSG achieves a record-low FID of 8.15, establishing a new benchmark among all diffusion distillation methods. This achievement also surpasses the teacher model’s FID of 8.78, which was obtained with a CFG scale of 3 and 250 generation steps. However, this reduction in the LSG scale to 1.5 also results in a significant decline in its CLIP score.

This suggests significant potential for further performance enhancements, possibly by extending beyond single-step generation, increasing the model size, or incorporating real data and additional regression or adversarial losses into the distillation process. These avenues for improvement will be the focus of future studies.

## Appendix E Prompts and additional example images

We list the prompts used in Figure 1 as follows:

- 1.
A distinguished older gentleman in a vintage study, surrounded by books and dim lighting, his face marked by wisdom and time. 8K, hyper-realistic, cinematic, post-production.
- 2.
saharian landscape at sunset , 4k ultra realism, BY Anton Gorlin, trending on artstation, sharp focus, studio photo, intricate details, highly detailed, by greg rutkowski.
- 3.
chinese red blouse, in the style of dreamy and romantic compositions, floral explosions –ar 24:37 –stylize 750 –v 6
- 4.
Digital 2D, Miyazaki’s style, ultimate detailed, tiny finnest details, futuristic, sci-fi, magical dreamy landscape scenery, small cute girl living alone with plushified friendly big tanuki in the gigantism of wilderness, intricate round futuristic simple multilayered architecture, habitation cabin in the trees, dramatic soft lightning, rule of thirds, cinematic.
- 5.
poster art for the collection of the asian woman, in the style of gloomy, dark orange and white, dynamic anime, realistic watercolors, nintencore, weathercore, mysterious realism –ar 69:128 –s 750 –niji 5
- 6.
A fantasy-themed portrait of a female elf with golden hair and violet eyes, her attire shimmering with iridescent colors, set in an enchanted forest. 8K, best quality, fine details.
- 7.
’very beautiful girl in bright leggings, white short top, charismatic personality, professional photo, style of jessica drossin, super realistic photo, hyper detail, great attention to skin and eyes, professional photo.
- 8.
(steampunk atmosphere, a stunning girl with a mecha musume aesthetic, adorned in intricate cyber gogle,) digital art, fractal, 32k UHD high resolution, highres, professional photography, intricate details, masterpiece, perfect anatomy, cinematic angle , cinematic lighting, (dynamic warrior pose:1)
- 9.
(Pirate ship sailing into a bioluminescence sea with a galaxy in the sky), epic, 4k, ultra.
- 10.
tshirt design, colourful, no background, yoda with sun glasses, dancing at a festival, ink splash, 8k.

We list the prompts used in Figure 2, which are taken from the COCO-2014 validation set, as follows:

- 1.
many cars stuck in traffic on a high way
- 2.
an old blue car with a surfboard on top’, 3. ‘a sole person sits in the front pew of a large church.
- 3.
a shot of the hollywood sign at santa monica blvd.
- 4.
a bunch of flowers, in front of a forest.
- 5.
there is some sort of vegetables in a bowl
- 6.
a man in a pink shirt stands staring against a green wall.
- 7.
small girl in green shirt holding a slice of pizza to her face
- 8.
two dogs sitting in the back seat of a car looking out the windwo

We list the prompts used in Figure 6 as follows:

- 1.
Half-length head portrait of the goddess of autumn with wheat ears on her head, depicted as dreamy and beautiful, by wlop.
- 2.
Walter White dressed as a medieval-style king.
- 3.
A serene meadow with a tree, river, bridge, and mountains in the background under a slightly overcast sunrise sky.
- 4.
A closeup portrait of a gray owl with spreaded wings attacking in cinematic lighting, digital painting by Greg Rutkowski used as album cover art on Artstation.
- 5.
A hyena fursona sitting in the grass in a savannah at sunset.
- 6.
A puppy staring through a red sectioned window.

We list the prompts used in Figure 10 as follows:

- 1.
A fantasy-themed portrait of a female elf with golden hair and violet eyes, her attire shimmering with iridescent colors, set in an enchanted forest. 8K, best quality, fine details.
- 2.
pumpkins, autumn sunset in the old village, cobblestone houses, streets, plants, flowers, entrance, realistic, stunningly beautiful
- 3.
"Highly detailed mysterious egyptian (sphynx cat), skindentation:1.2, bright eyes, ancient egypt pyramid background, photorealistic, (hyper-realistic:1.2), cinematic, masterpiece:1.1, cinematic lighting"
- 4.
"vw bus, canvas art, abstract art printing, in the style of brian mashburn, light red and light brown, theo prins, charming character illustrations, pierre pellegrini, vintage cut-and-paste, rusty debris –ar 73:92 –stylize 750 –v 6"
- 5.
painterly style, seductive female League of legends Jinx character fighting at war, raging, crazy smile, crazy eyes, rocket lancher, guns, crazy face expression, character design, body is adorned with glowing golden runes, intense green aura around her, body dynamic epic action pose, intricate, highly detailed, epic and dynamic composition, dynamic angle, intricate details, multicolor explosion, blur effect, sharp focus, uhd, hdr, colorful shot, stormy weather, tons of flying debris around her, dark city background, modifier=CarnageStyle, color=blood_red, intensity=1.6
- 6.
A charismatic chef in a bustling kitchen, his apron dusted with flour, smiling as he presents a beautifully prepared dish. 8K, hyper-realistic, cinematic, post-production.
- 7.
A young adventurer with tousled hair and bright eyes, wearing a leather jacket and a backpack, ready to explore distant lands. 8K, hyper-realistic, cinematic, post-production.
- 8.
"A watercolor painting of a vibrant flower field in spring, with a rainbow of blossoms under a bright blue sky. 8K, best quality, fine details.",
- 9.
"digital art of a beautiful tiger pokemon under an apple tree, cartoon style,Matte Painting,Magic Realism,Bright colors,hyper quality,high detail,high resolution, –video –s 750 –v 6.0 –ar 1:2"
- 10.
"painterly style, Goku fighting at war, raging, blue hair, character design, body is adorned with glowing golden runes, yellow aura around him, body dynamic epic action pose, intricate, highly detailed, epic and dynamic composition, dynamic angle, intricate details, multicolor explosion, blur effect, sharp focus, uhd, hdr, colorful shot, stormy weather, tons of flying debris around him, dark city background, modifier=CarnageStyle, color=blood_red, intensity=1.6"
- 11.
A stunning steampunk city with towering skyscrapers and intricate clockwork mechanisms, gears and pistons move in a complex symphony, steam billows from chimneys, airships navigate the bustling skylanes, a vibrant metropolis
- 12.
"Samurai looks at the enemy, stands after the battle, fear and horror on his face, tired and beaten, sand on his face mixed with sweat, an atmosphere of darkness and horror, hyper realistic photo, In post - production, enhance the details, sharpness, and contrast to achieve the hyper - realistic effect"
- 13.
A portrait of an elemental entity with strong rim lighting and intricate details, painted digitally by Alvaro Castagnet, Peter Mohrbacher, and Dan Mumford
- 14.
"A regal female portrait with an ornate headdress decorated with colorful gemstones and feathers, her robes rich with intricate designs and bright hues. 8K, best quality, fine details.",
- 15.
"A detailed painting of Atlantis by multiple artists, featuring intricate detailing and vibrant colors.",
- 16.
"A landscape featuring mountains, a valley, sunset light, wildlife and a gorilla, reminiscent of Bob Ross’s artwork.

Figure: A fantasy-themed portrait of a female elf with golden hair and violet eyes, […]
Refer to caption: extracted/5781662/sid_sd_images/figure8/000000.png

## Peer Reviews

confidence: 3
contribution: 2
initial_rating: 6
presentation: 3
questions: Please refer to the Weaknesses.
soundness: 3
strengths: The paper introduces two distinct strategies for CFG in SiD: the long CFG and short CFG strategies, each with unique effects on model performance.
1. In the long CFG, it is applied to the pretrained score network (the teacher model) with a higher guidance scale (typically $\tau$>1), which encourages the pretrained model to generate images more aligned with the textual prompt, which in turn compels the student generator to follow this alignment. The primary effect is a strong improvement in text adherence and quality, with the capability to achieve a balanced FID and CLIP score. 
2. In the short CFG, it is applied to the fake score network (the student model) with a reduced guidance scale (typically $\tau$ values between 0 and 1). By reducing CFG, this approach lessens the fake score network’s alignment with the text prompt, which incentivizes the student generator to improve text alignment and quality. This strategy tends to result in a trade-off where lower CLIP scores (text alignment) can occur, but FID scores (distribution match) remain competitive.
summary: This paper focuses on a novel approach to accelerating text-to-image diffusion models through Score Identity Distillation with Long-Short Guidance (SiD-LSG), specifically targeting data-free one-step generation. By combining Score Identity Distillation with Classifier-Free Guidance (CFG), the authors efficiently distill Stable Diffusion models into one-step generators without needing real data. The proposed method incorporates both long and short CFG strategies, which balance FID and CLIP scores, enabling efficient and high-quality text-to-image generation. Experimental results demonstrate the effectiveness of the proposed method on the COCO dataset, achieving a record-low data-free FID of 8.15.
weaknesses: 1. The main contribution of this paper is introducing a variant of CFG into SiD, i.e., long and short CFGs, which aim to balance semantic alignment and the quality of generated images. However, there seems to be a contradiction, as no single $\tau$ value optimally balances both CLIP and FID scores (see results in Tables 1 and 2). Are there intrinsic reasons for this phenomenon?

2. A more intuitive ablation study would better showcase the effectiveness of the proposed long and short CFG strategies. Specifically, in Tables 1 and 2, rather than using SiD-LSG alone, could you provide the results (both CLIP and FID) for SiD with the same $\tau$ value across different configurations: SiD (short CFG only), SiD (long CFG only), SiD (CFG only), and SiD (original)?

---

confidence: 2
contribution: 3
initial_rating: 5
presentation: 2
questions: See the weaknesses.
soundness: 3
strengths: - The paper adapts Score identity Distillation (SiD) to work with text-to-image models.
- SiD-LSG shows state-of-the-art distillation results in a data-free setting. With a small CFG, it achieves impressive FID scores (8.15 for SD1.5 and 9.52 for SD2.1). With a large CFG, it achieves high CLIP scores, with sacrification in FID ones.
- The paper presents a comprehensive set of experiments.
summary: The paper proposes a new distillation technique (SiD-LSG) for data-free one-step text-to-image generation. First, it adapts Score identity Distillation (SiD) to work with text-to-image models. Second, it proposes a Long and Short Classifier-Free Guidance (LSG) scheme to improve the distillation effectiveness. SiD-LSG shows state-of-the-art distillation results in a data-free setting. With a small CFG, it achieves impressive FID scores (8.15 for SD1.5 and 9.52 for SD2.1). With a large CFG, it achieves high CLIP scores, with sacrification in FID ones.
weaknesses: - L268-269: The claim "we are the first to incorporate CFG into the training of the fake score network" is incorrect. SwiftBrush uses CFG 4.5 for both teachers (see Implementation Details in Section 4.1 in their paper).
- While the paper proposes an interesting short guidance setting ($\kappa 2 = \kappa 3 < 1$), it does not apply to the long and short guidance ($\kappa 2 = \kappa 3 > 1$). I do not see the connection between the short guidance setting and the long and short guidance setting.
- In the end, the long and short guidance scheme is very simple and uninteresting ($\kappa 1 = \kappa 2 = \kappa 3 = \kappa 4 = 1.5). It just applies the same CFG for both teachers, which SwiftBrush already did.
- Some recent works (SwiftBrush v2, DMD2) should be mentioned and compared with.
- The training protocol is expensive. The batch size is large (512 by default), requiring 64 gradient accumulation rounds on H100 (80G), FP32 setting. SiD-LSG necessitates gradient backpropagation through the score networks, which requires massive memory usage. Hence, it can only use the batch size per GPU as 1 on H100 (80G), FP32 setting.
- Fig 3, 4, 6, 9: The curves for FID scores and CLIP scores are hard to differentiate. They have the same color and with just a small transparency difference. The authors should improve these figures.
- Fig. 6, which is discussed in Section 3.2, is in the Appendix. It is not good. The authors should move the figure to the main paper and squeeze the text to fit in the 10-page limit.

---

confidence: 4
contribution: 3
initial_rating: 8
presentation: 4
questions: - Does this approach valid for v-prediction networks? how does this alter the theory?
soundness: 4
strengths: - The paper is well written and easy to follow
- The novelty and relation to SiD is clear.
- The theory and mathematical background is solid.
- The method is novel and important.
- The ablation is comprehensive.
- The results of the methods are SoTA.
summary: The authors propose an enhancement to Score Identity Distillation (SiD), a recently introduced method for distilling a pre-trained diffusion network into a single-step generator. This paper’s method specifically targets pre-trained text-to-image models based on DDPM, adapting SiD to distill two epsilon-prediction networks: SD1.5 and SD2.1.base.

Following the SiD framework, the one-step generator ($\theta$) is optimized by training an additional fake multi-step generator ($\psi$). Furthermore, classifier-free guidance (CFG) is integrated into the distillation process, allowing for varied CFG values that emphasize different target objectives. This added flexibility contributes to enhanced model performance.

The results achieve state-of-the-art FID scores and demonstrate strong CLIP scores on the COCO-2014 validation prompts.
weaknesses: - Given SiD the method novelty is limited to the adaptation of the SiD theory to DDPM and t2i models.
- The distillation is on relatively old pre-trained networks.

---

confidence: 2
contribution: 3
initial_rating: 5
presentation: 3
questions: If a model itself does not use CFG, then the proposed method will not work？ For example, FLUX does not use CFG for inference but uses word embedding instead. Just open discussion.
soundness: 4
strengths: 1.  The main contribution is The proposed method enhances the Score Identity Distillation technique by incorporating Classifier-Free Guidance , although these two techniques have been proposed in other studies.

2. By reducing the number of steps required for image generation to one, the method significantly improves the efficiency of the generation process, and the proposed method achieves state-of-the-art performance in terms of Fréchet Inception Distance (FID) on the COCO-2014 validation set, setting a new benchmark for data-free one-step distillation.
summary: This paper mainly studies how to accelerate the diffusion-based generative model to generate high-quality images. The paper presents a novel method called Guided Score Identity Distillation with Long-Short Guidance (SiD-LSG) for data-free one-step text-to-image generation, which enhances the Score Identity Distillation (SiD) technique by incorporating Long and Short Classifier-Free Guidance (LSG).
weaknesses: 1.  Regarding training efficiency, SiD needs to generate false data for training, and adding CFG will further increase the model overhead, resulting in more training time and resources.

2. The data-free nature of the SiD and SID-LSG means it does not leverage real-world data to further refine the generated images, which could potentially improve photorealism and text alignment.

Meta-review: This paper focuses on accelerating diffusion-based generative models. It introduces a novel method, Guided Score Identity Distillation with Long-Short Guidance (SiD-LSG), designed for data-free one-step text-to-image generation. The approach builds upon the existing Score Identity Distillation (SiD) framework by integrating Long and Short Classifier-Free Guidance (LSG) to enhance its effectiveness.

A key strength of this work lies in its data-free nature, making it much more generalizable especially when access to real training data is limited. The experimental results also demonstrate state-of-the-art performance. The method's scalability and adaptability to various diffusion models further enhance its significance.

Given these strengths and the consistent positive feedback from the reviewers, I recommend acceptance of the paper.

Decision: Accept (Poster)