Title: Applying Guidance in a Limited Interval Improves Sample and Distribution Quality in Diffusion Models
Venue: NeurIPS 2024
Accepted: yes
Keywords: generative models

diffusion models

classifier-free guidance
Abstract: Guidance is a crucial technique for extracting the best performance out of image-generating diffusion models. Traditionally, a constant guidance weight has been applied throughout the sampling chain of an image. We show that guidance is clearly harmful toward the beginning of the chain (high noise levels), largely unnecessary toward the end (low noise levels), and only beneficial in the middle. We thus restrict it to a specific range of noise levels, improving both the inference speed and result quality. This limited guidance interval improves the record FID in ImageNet-512 significantly, from 1.81 to 1.40. We show that it is quantitatively and qualitatively beneficial across different sampler parameters, network architectures, and datasets, including the large-scale setting of Stable Diffusion XL. We thus suggest exposing the guidance interval as a hyperparameter in all diffusion models that use guidance.

Applying Guidance in a Limited Interval Improves Sample and Distribution Quality in Diffusion Models

Abstract

Guidance is a crucial technique for extracting the best performance out of imagegenerating diffusion models. Traditionally, a constant guidance weight has been applied throughout the sampling chain of an image. We show that guidance is clearly harmful toward the beginning of the chain (high noise levels), largely unnecessary toward the end (low noise levels), and only beneficial in the middle. We thus restrict it to a specific range of noise levels, improving both the inference speed and result quality. This limited guidance interval improves the record FID in ImageNet-512 significantly, from 1.81 to 1.40. We show that it is quantitatively and qualitatively beneficial across different sampler parameters, network architectures, and datasets, including the large-scale setting of Stable Diffusion XL. We thus suggest exposing the guidance interval as a hyperparameter in all diffusion models that use guidance.

1 Introduction

Denoising diffusion models [17, 28, 38, 39, 40, 41, 43, 20] have enabled rapid advances in highquality image synthesis based on text prompts and other forms of input [13, 35, 44]. They scale effortlessly to large-scale datasets [4, 5, 34], and also to other modalities such as video [8, 7, 16, 19], 3D shapes [26, 31, 33, 37], and audio [24, 32].

Diffusion models convert an initial image of pure noise to a novel generated image through repeated application of image denoising. This sampling chain typically contains dozens of steps, and in each step a little bit of the denoised result is blended into the noisy image. The sampling process first gravitates towards the mean of the training data, followed by the determination of image features in an approximate coarse-to-fine manner based on the remaining noise. This iterative process, where the image is formed little by little, offers considerable flexibility in terms of encouraging or discouraging certain kinds of behavior at each step.

Negative prompts [3] are a widely used concept, where the sampling process is given an additional anti-goal that is to be avoided. For example, 'nudity' might be a common negative prompt in text-based image generators. At every sampling step, the denoiser is executed twice: once for the positive and once for the negative prompt, and the positive result is then extrapolated further away from the negative one based on a weight parameter. This works remarkably well in practice. Classifier-free guidance (CFG) [18] builds on this general concept. It uses an unconditional model (no class information or text prompts) as a negative prompt, causing the result image to align more strongly with the conditioning signal.

In practice, all large-scale image generators rely heavily on CFG. It allows a mathematically justified way of truncating the distribution of generated images [12, 18], basically trading variation for

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perceptually higher image quality. By convention, the same guidance weight is used in all sampling steps. We observe that this is sub-optimal because CFG behaves very differently on high, middle, and low noise levels. On high noise levels, it drastically reduces the variation in the results, basically leading them towards a handful of 'template images' per prompt. On middle levels, it causes the sampling to more decisively choose some set of features, leading to crisper and perceptually more pleasing results. On low levels, it is largely unnecessary. Similar observations have been made in the Stable Diffusion community [1, 2, 21], and Muse [10] and Masked DiT v2 [14] observe that making guidance weight noise level-dependent improves the results. In the context of prompt inversion, Mahajan et al. [27] notice that limiting the inversion to specific noise levels leads to improved result quality. However, these works do not quantify the effect on distribution metrics with the exception of Sadat et al. [36], whose 'dynamic CFG' limits a linearly varying guidance weight to an interval of noise levels. Interestingly, they conclude that dynamic CFG leads to rather poor results, while a more complicated condition annealing scheme is required for good quantitative results.

We suggest that guidance should be simply limited to an interval of sampling steps in the middle, where the net effect is positive, without otherwise changing the guidance weight. This avoids most of the detrimental effects of guidance, while also reducing computational cost. We show that an optimal guidance interval improves the state-of-the-art FID [23] in ImageNet-512 from 1.81 to 1.40 and also leads to an improved visual quality. The benefits are consistent across sampler parameters, network architectures, and datasets, including Stable Diffusion XL. Code is available at https://github.com/kynkaat/guidance-interval

2 Background

The concepts in this and the following section are illustrated in Figure 1 using a synthetic 1D example. In this example, generation is performed by ideal analytic denoisers, avoiding all approximations that a learned denoiser might cause. While this renders classifier-free guidance strictly harmful in the scenario, the example allows us to intuitively visualize the kinds of harm it causes.

The goal of a denoising diffusion model is to draw samples from a data distribution p data ( x ) . Let us define a series of smoothed distributions p ( x ; σ ) , so that each individual distribution is the convolution between p data and a Gaussian noise distribution with standard deviation σ . Following the EDM formulation [22], the evolution of a sample x ∼ p ( x ; σ ) w.r.t. a change in σ is described by the ordinary differential equation (ODE):

where D θ is a denoiser model with parameters θ , optimized to minimize the expected L 2 denoising error:

Here, p train ( σ ) is the training distribution of noise levels, which we consider to be an implementation detail of D θ . To generate a sample from the data distribution, we first draw an initial sample

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x 0 ∼ p ( x ; σ max ) , where σ max is chosen to be large enough so that p ( x ; σ max ) is approximately equal to pure Gaussian distribution and thus trivial to sample from. We then follow the ODE of Equation 1 to evolve x 0 towards σ = 0 , i.e., the data distribution. Figure 1a illustrates the target distribution (orange). Figure 1b depicts the diffused target distribution over the σ axis and a set of sample trajectories computed by solving Equation 1 from several different initial conditions.

We can think of classifier-free guidance [18] as constructing a modified ODE where d x / d σ is defined as a linear combination between a conditional ODE and an unconditional ODE:

where w is the guidance weight and c is the condition information given to the denoiser D θ (cf. Figure 1c). Setting w > 1 results in extrapolating the effect of the condition with respect to the unconditional result, i.e., the sample is effectively pushed away from the unconditional result. This extrapolation can be seen [12, 18] as raising the conditional likelihood p ( c | x ; σ ) to a power greater than one, which, intuitively, aims to concentrate the probability mass to the regions that most agree with the condition. However, as illustrated in Figure 1(d, e) and the next section, this 'oversteering' may direct the trajectories away from the data distribution and cause mode drops.

Most commonly, a single denoiser model D θ is trained to accept either conditional or unconditional input by dropping the conditioning information 10-20% of the time during training. Alternatively, we can train two separate models D θ cond. ( x | c ; σ ) and D θ uncond. ( x ; σ ) . This makes it possible to reduce the capacity of the unconditional model considerably to improve the overall sampling speed [23].

Sampling the ODE is done by taking a number of discrete steps that bring the noise level from σ max to zero, giving rise to a sequence of images x 0 , x 1 , · · · , x N , each with its corresponding noise level σ i . Various discretization schemes and solvers have been proposed [22]. Regardless of the specifics, the computational cost is directly proportional to the number of sampling steps N .

3 Our method

In Figure 2, we continue to probe the downsides of CFG using the previous toy example. We observe that applying guidance at all noise levels - as is typical - causes the sampling trajectories to drift quite far from the the smoothed data distribution (Figure 2b). This is caused by the unconditional trajectories effectively repelling the guided trajectories, as discussed above, yielding badly skewed intermediate distributions. As a result, the sampler drops one of the modes (almost) entirely.

As most of the drift seems to be caused at high noise levels, we disable CFG in those sampling steps (Figure 2c). This correctly recovers both modes of the conditional distribution. In addition, disabling guidance at low noise levels (Figure 2d) has only a small effect on the resulting distribution, providing a simple way to reduce the sampling cost with minimal effect on outputs.

Although this toy example is grossly simplified, we hypothesize that broadly similar effects occur in full-scale diffusion models as well. In Section 4 we can see, e.g., image compositions becoming

less varied due to guidance, somewhat akin to the mode dropping observed in the toy example. That behaviour is difficult to explain by local sharpening of probability distributions alone (Section 2).

3.1 Practice

Motivated by the above observations, we propose to only apply guidance in a continuous interval of noise levels in the middle of the sampling chain and disable it elsewhere. Concretely, we redefine the ODE of Equation 4 by replacing w with a piecewise constant function:

Here, σ hi denotes the point in the sampling chain where we enable guidance and σ lo is the point where we turn it off. In our formulation, traditional CFG is recovered by setting σ lo = 0 and σ hi = ∞ .

Virtually all existing deterministic samplers can be seen as numerical Runge-Kutta solutions to the ODE of Equation 4, obtained through a number of discrete steps. While the correspondence might not be obvious in all cases, we can nevertheless characterize the steps with respect to σ as detailed in Appendix A. For example, in the case of Stable Diffusion XL [30], we have 32 steps corresponding to the transitions σ 0 → σ 1 , σ 1 → σ 2 , . . . , σ 31 → σ 32 , where σ 0 = 14 . 61 , σ 1 = 13 . 41 , σ 2 = 12 . 28 , . . . , σ 31 = 0 . 03 , and σ 32 = 0 .

The underlying assumption common to all Runge-Kutta methods is that d x / d σ should be sufficiently smooth within each step. In Equation 6, however, we intentionally introduce discontinuities at σ lo and σ hi. In order to satisfy the smoothness requirement, we must thus ensure that both transitions happen exactly at step boundaries so that the value of w ( σ ) stays constant within each step. In practice, we choose to do this by rounding σ lo and σ hi appropriately, i.e., by setting σ hi = σ i and σ lo = σ j for some i < j . Note that this leads to a seemingly high numerical precision in the values of σ lo and σ hi , which should not be taken as an indication of extremely precise tuning.

4 Results

We will first evaluate and ablate our method quantitatively using ImageNet [11]. Limiting the guidance interval leads to clearly identifiable qualitative changes in the images, which we subsequently demonstrate also in the large-scale context using Stable Diffusion XL [30]. Please refer to Appendix B for additional results.

4.1 Main results

We mainly evaluate our method on ImageNet at 512 × 512 , using the current state-of-the-art approach EDM2 [23] as a baseline. 1 We use the small (EDM2-S) and the largest (EDM2-XXL) models as-is with the default sampling parameters: 32 deterministic steps with a 2 nd order Heun sampler [22].

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Table 1 shows that our method improves FID [15] and the more recently proposed FDDINOv2 [42] significantly. Using EDM2-S, FID improves from 2.23 to 1.68, already beating the state-of-the-art in this dataset. With EDM2-XXL, the record further improves to 1.40 and FDDINOv2 also improves from 33.09 to 29.16.

To find the optimal parameters for each case, we performed a full grid search over w , σ lo, and σ hi . In the case of EDM2-XXL, the best FID is achieved by applying guidance at 6 of the 32 steps, corresponding to noise levels σ ∈ (0 . 19 , 1 . 61] , with weight w = 2 . 0 . The best FDDINOv2 is obtained with slightly higher noise levels σ ∈ (0 . 60 , 5 . 00] and a slightly higher weight w = 2 . 9 .

For additional validation, we also tested our method on diffusion transformers [29] using the DiTXL/2 model 2 with default sampling parameters: 250 step iDDPM [28]. Limiting the guidance interval leads to significant improvements with this model as well. The best FID results were obtained by using guidance with w = 2 . 5 in 75 of the 250 sampling steps, corresponding to the interval σ ∈ (0 . 34 , 1 . 02] . The best FDDINOv2 is again obtained with slightly higher noise levels (0 . 45 , 1 . 23] and weight w = 4 . 0 .

4.2 Ablations

Figure 3 shows that standard classifier-free guidance is quite sensitive to the guidance weight. When the weight is too high, the output image distribution is excessively truncated, and the harm caused outside the useful interval starts to outweigh the benefits obtained within. In contrast, limiting the

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guidance interval allows the use of much higher guidance weight, and FID or FDDINOv2 are far less sensitive to the exact choice.

Figure 4 shows precision and recall [25] curves for CFG and our method, evaluated with varying guidance weights in DINOv2 feature space, as suggested by Stein at al. [42]. Compared to CFG, our method achieves better FDDINOv2 primarily by improving Recall without significantly affecting Precision. This is consistent with the qualitative observation that the results are more varied.

Figure 5 probes the sensitivity of our results to the chosen guidance interval. In this test, we sweep over σ lo and σ hi, while keeping the other interval endpoint, σ hi or σ lo, and the guidance weight w as the optimal choices as reported in Table 1. The left side shows a sweep over σ hi, i.e., the highest noise level with guidance. Including too high noise levels to the guidance interval leads to truncation of the image distribution, which can be seen as an increase in FID. Furthermore, too narrow an interval (low σ hi) yields sub-optimal results. For both EDM2 models the optimal choice for σ hi is located at the middle noise levels. The right side shows a sweep over σ lo, i.e., the lowest noise level with guidance. Applying guidance at low noise levels does not bring additional benefits, compared to the middle levels. Thus, guidance can be disabled in most of the low noise levels to decrease sampling cost, an observation also made in [9].

To estimate the optimal guidance interval in practice, the upper and lower guidance limits can be determined separately, without the need for a two-dimensional search. This happens by first establishing the optimal upper limit by keeping the lower limit at zero. This can be done because the lower limit affects the result only weakly, and in a predictable way (Figure 5, right). Once the optimal upper limit is known, the lower limit is determined. Optionally, a bisection method can be used for accelerating both search operations. Finally, one can reduce the sample size of FID evaluation from 50k to, say, 5k, at least for an initial run, which accelerates the process by 10 × .

We have found that the optimal choice of σ lo and σ hi is not overly sensitive to the other sampling parameters. For example, if we halve or double the number of steps with EDM2-S, the optimal guidance interval remains unchanged. With 16 steps, our method improves FID from 2.49 to 1.84, and with 64 steps, from 2.27 to 1.70.

In an additional test, we tried applying various smooth weighting functions to the guidance weight (less guidance towards the ends of the interval), but these tests did not improve the results over the simple binary inclusion. We also tried estimating the importance of guidance at individual noise levels by enabling or disabling it at each sampling step at a time. However, these tests consistently underestimated the downsides of guidance, suggesting that they build up cumulatively over multiple consecutive steps.

4.3 Qualitative analysis

With the rise of recent large-scale image generators, ImageNet can hardly be considered a meaningful benchmark for gauging perceptual image quality. Thus, we primarily focus on evaluating our method in the context of Stable Diffusion XL (SD-XL), but we also provide corresponding results for

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ImageNet using EDM2-XXL. For SD-XL, we use the official pre-trained checkpoint 3 with a standard 32-step deterministic Heun sampler, where the first step corresponds to σ = 14 . 61 .

With SD-XL, we apply guidance at 50% of the sampling steps, corresponding to noise levels σ ∈ (0 . 28 , 5 . 42] , with weight w = 16 . These parameters were chosen by visual inspection. The beneficial interval is wider than in ImageNet, likely due to the more varied dataset used in the

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Figure 8: Effect of changing the guidance interval ( σ lo , σ hi ] with w = 16 . Top: Decreasing σ hi , i.e., disabling guidance at high noise levels, while keeping σ lo = 0 . 28 . High values lead to simplified image composition and oversaturated colors (left); low values cause the image to become increasingly convoluted (right). Bottom: Increasing σ lo, i.e., disabling guidance at low noise levels, while keeping σ hi = 5 . 42 . The value can be made relatively high with no noticeable impact, reducing sampling cost.

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training of SD-XL. Consequently, our method leads to over 20% speed-up due to a lower number of unconditional model evaluations [1].

Figure 6 shows a comparison between standard classifier-free guidance with low and high weights (left and middle columns) and our method with high guidance weight (right column). When the guidance weight is increased in standard CFG (middle), the composition of the image tends to change drastically, towards some limited set of per-class 'templates'. Furthermore, the colors saturate unnaturally as the guidance weight increases. When we limit the guidance interval (right), image diversity is preserved to a significant degree and the color saturation is also reduced, although excessively large guidance weights can still lead to over-saturation.

Figure 7 shows the effect of increasing the guidance weight with our method. With low weight, the images appear blurry, inconsistent, and lacking in detail. Increasing the weight improves the rendition of details while retaining the original image composition.

As the task of selecting the best guidance interval ( σ lo , σ hi ] with SD-XL is necessarily subjective, we provide a visual ablation of this choice in Figure 8. Modifying the upper limit σ hi , i.e., disabling guidance at high noise levels, has two distinct effects. First, it affects the overall image composition - higher values lead to more simplified image layouts whereas low values lead to unnecessary complexity. Second, high values lead to oversaturated colors whereas lower σ hi leads to a blander color scheme. Similar to EDM2 results, changing the lower limit σ lo has only a modest effect - guidance can be disabled from most of the low noise levels with no noticeable impact while improving the inference speed.

Lastly, Figure 9 compares the effects of increasing the guidance weight in standard CFG vs. our method with EDM2-XXL.

5 Conclusions

Classifier-free guidance is an indispensable tool for improving the results of practically all imagegenerating diffusion models. As our simple modification improves the results both numerically and visually, and also reduces sampling cost, we recommend exposing the guidance interval as an additional sampler parameter.

Future work could investigate whether the optimal guidance interval can be automatically derived from the ODE, and the role played by the non-idealities in the trained denoiser. A recent work by Biroli et al. [6] predicts from a dataset the interval where the generated images specialize to a certain class. A follow-up study could examine whether their 'speciation' interval overlaps with the interval that is beneficial for guidance.

References

Alex Birch. Turning off classifier-free guidance at low noise levels. Idea mentioned on Twitter. https://twitter.com/Birchlabs/status/1640033271512702977 , 2023. Accessed 19 Mar 2024.

Alex Goodwin. Stable Diffusion dynamic thresholding. GitHub repository. https://github.com/ mcmonkeyprojects/sd-dynamic-thresholding , 2023. Accessed 19 Mar 2024.

AUTOMATIC1111. Stable Diffusion web UI. GitHub repository. https://github.com/AUTOMATIC 1111/stable-diffusion-webui , 2022. Accessed 19 Mar 2024.

Y. Balaji, S. Nah, X. Huang, A. Vahdat, J. Song, K. Kreis, M. Aittala, T. Aila, S. Laine, B. Catanzaro, T. Karras, and M.-Y. Liu. eDiff-I: Text-to-image diffusion models with ensemble of expert denoisers. CoRR , abs/2211.01324, 2022.

J. Betker, G. Goh, L. Jing, T. Brooks, J. Wang, L. Li, L. Ouyang, J. Zhuang, J. Lee, Y. Guo, W. Manassra, P. Dhariwal, C. Chu, Y. Jiao, and A. Ramesh. Improving image generation with better captions. Technical report, OpenAI, 2023.

G. Biroli, T. Bonnaire, V. de Bortoli, and M. Mézard. Dynamical regimes of diffusion models. CoRR , abs/2402.18491, 2024.

A. Blattmann, R. Rombach, H. Ling, T. Dockhorn, S. W. Kim, S. Fidler, and K. Kreis. Align your latents: High-resolution video synthesis with latent diffusion models. In Proc. CVPR , 2023.

T. Brooks, B. Peebles, C. Holmes, W. DePue, Y. Guo, L. Jing, D. Schnurr, J. Taylor, T. Luhman, E. Luhman, C. Ng, R. Wang, and A. Ramesh. Video generation models as world simulators. Blog post, OpenAI, 2024.

A. Castillo, J. Kohler, J. C. Pérez, J. P. Pérez, A. Pumarola, B. Ghanem, P. Arbeláez, and A. Thabet. Adaptive guidance: Training-free acceleration of conditional diffusion models. CoRR , abs/2312.12487, 2023.

H. Chang, H. Zhang, J. Barber, A. Maschinot, J. Lezama, L. Jiang, M.-H. Yang, K. Murphy, W. T. Freeman, M. Rubinstein, Y. Li, and D. Krishnan. Muse: Text-to-image generation via masked generative transformers. CoRR , abs/2301.00704, 2023.

J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. ImageNet: A large-scale hierarchical image database. In Proc. CVPR , 2009.

S. Dieleman. Guidance: A cheat code for diffusion models. Blog post. https://sander.ai/2022/05/ 26/guidance.html , 2022.

R. Gal, Y. Alaluf, Y. Atzmon, O. Patashnik, A. H. Bermano, G. Chechik, and D. Cohen-Or. An image is worth one word: Personalizing text-to-image generation using textual inversion. In Proc. ICLR , 2023.

S. Gao, P. Zhou, M.-M. Cheng, and S. Yan. Mdtv2: Masked diffusion transformer is a strong image synthesizer. In Proc. ICCV , 2023.

M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter. GANs trained by a two time-scale update rule converge to a local Nash equilibrium. In Proc. NIPS , 2017.

J. Ho, W. Chan, C. Saharia, J. Whang, R. Gao, A. Gritsenko, D. P. Kingma, B. Poole, M. Norouzi, D. J. Fleet, and T. Salimans. Imagen Video: High definition video generation with diffusion models. CoRR , abs/2210.02303, 2022.

J. Ho, A. Jain, and P. Abbeel. Denoising diffusion probabilistic models. In Proc. NeurIPS , 2020.

J. Ho and T. Salimans. Classifier-free diffusion guidance. In Proc. NeurIPS 2021 Workshop on Deep Generative Models and Downstream Applications , 2021.

J. Ho, T. Salimans, A. A. Gritsenko, W. Chan, M. Norouzi, and D. J. Fleet. Video diffusion models. In Proc. ICLR Workshop on Deep Generative Models for Highly Structured Data , 2022.

A. Hyvärinen. Estimation of non-normalized statistical models by score matching. Journal of Machine Learning Research , 6(24):695-709, 2005.

Jeremy Howard and Rekil Prashanth. Adjusting guidance weight as a function of time. Idea mentioned on Twitter. https://twitter.com/jeremyphoward/status/1584771100378288129 , 2022. Accessed 19 Mar 2024.

T. Karras, M. Aittala, T. Aila, and S. Laine. Elucidating the design space of diffusion-based generative models. In Proc. NeurIPS , 2022.

T. Karras, M. Aittala, J. Lehtinen, J. Hellsten, T. Aila, and S. Laine. Analyzing and improving the training dynamics of diffusion models. In Proc. CVPR , 2024.

Z. Kong, W. Ping, J. Huang, K. Zhao, and B. Catanzaro. DiffWave: A versatile diffusion model for audio synthesis. In Proc. ICLR , 2021.

T. Kynkäänniemi, T. Karras, S. Laine, J. Lehtinen, and T. Aila. Improved precision and recall metric for assessing generative models. In Proc. NeurIPS , 2019.

C.-H. Lin, J. Gao, L. Tang, T. Takikawa, X. Zeng, X. Huang, K. Kreis, S. Fidler, M.-Y. Liu, and T.-Y. Lin. Magic3D: High-resolution text-to-3D content creation. In Proc. CVPR , 2023.

S. Mahajan, T. Rahman, K. M. Yi, and L. Sigal. Prompting hard or hardly prompting: Prompt inversion for text-to-image diffusion models. In Proc. CVPR , 2024.

A. Nichol and P. Dhariwal. Improved denoising diffusion probabilistic models. In Proc. ICML , 2021.

W. Peebles and S. Xie. Scalable diffusion models with transformers. In Proc. ICCV , 2023.

D. Podell, Z. English, K. Lacey, A. Blattmann, T. Dockhorn, J. Müller, J. Penna, and R. Rombach. SDXL: Improving latent diffusion models for high-resolution image synthesis. In Proc. ICLR , 2024.

B. Poole, A. Jain, J. T. Barron, and B. Mildenhall. DreamFusion: Text-to-3D using 2D diffusion. In Proc. ICLR , 2023.

V. Popov, I. Vovk, V. Gogoryan, T. Sadekova, and M. Kudinov. Grad-TTS: A diffusion probabilistic model for text-to-speech. In Proc. ICML , 2021.

A. Raj, S. Kaza, B. Poole, M. Niemeyer, B. Mildenhall, N. Ruiz, S. Zada, K. Aberman, M. Rubenstein, J. Barron, Y. Li, and V. Jampani. DreamBooth3D: Subject-driven text-to-3D generation. In Proc. ICCV , 2023.

R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer. High-resolution image synthesis with latent diffusion models. In Proc. CVPR , 2022.

N. Ruiz, Y. Li, V. Jampani, Y. Pritch, M. Rubinstein, and K. Aberman. DreamBooth: Fine tuning text-to-image diffusion models for subject-driven generation. In Proc. CVPR , 2023.

S. Sadat, J. Buhmann, D. Bradley, O. Hilliges, and R. M. Weber. Cads: Unleashing the diversity of diffusion models through condition-annealed sampling. In Proc. ICLR , 2024.

J. R. Shue, E. R. Chan, R. Po, Z. Ankner, J. Wu, and G. Wetzstein. 3D neural field generation using triplane diffusion. In Proc. CVPR , 2023.

J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In Proc. ICML , 2015.

J. Song, C. Meng, and S. Ermon. Denoising diffusion implicit models. In Proc. ICLR , 2021.

Y. Song and S. Ermon. Generative modeling by estimating gradients of the data distribution. In Proc. NeurIPS , 2019.

Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole. Score-based generative modeling through stochastic differential equations. In Proc. ICLR , 2021.

G. Stein, J. C. Cresswell, R. Hosseinzadeh, Y. Sui, B. L. Ross, V. Villecroze, Z. Liu, A. L. Caterini, J. E. T. Taylor, and G. Loaiza-Ganem. Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models. In Proc. NeurIPS , 2023.

P. Vincent. A connection between score matching and denoising autoencoders. Neural Computation , 23(7):1661-1674, 2011.

L. Zhang, A. Rao, and M. Agrawala. Adding conditional control to text-to-image diffusion models. In Proc. ICCV , 2023.

A Characterizing sampling steps in noise levels

In the main paper, we reported the guidance interval measured in noise levels σ . Here, we show for each model how the indices of sampling steps are mapped to noise levels. For EDM2 models and SD-XL, we use the discretization from [22]. The i th sampling step corresponds to noise level that is given by:

where N is the total number of sampling steps, σ min = 0 . 002 , σ max = 80 . With SD-XL, we use ρ = 3 , which is the default value in the official code, with EDM2 models we use ρ = 7 . With DiT, we use the iDDPM discretization from [22] which maps the i th sampling step to the corresponding noise level in the following way:

where u M = 0 , u j -1 = √ u 2 j +1 max (¯ α j -1 / ¯ α j ,C 1 ) -1 and ¯ α j = sin 2 ( π 2 j M ( C 2 +1) ) . We use the default parameters C 1 = 0 . 001 , C 2 = 0 . 008 , M = 1000 and j 0 = 0 from [22].

B Additional qualitative results

Figures 10 and 11 show further comparisons between classifier-free guidance and our method. Figures 12 and 13 show additional examples from our method where we increase the guidance weight. Figures 14, 15 and 16 compare classifier-free guidance to our method when the guidance weight is increased.

C Broader impacts

Large-scale diffusion models, such as Stable Diffusion XL, might have various negative societal effects related to the spread of disinformation or amplifying harmful biases and stereotypes. Our method improves the result quality of these models which can potentially further magnify these issues. In the large-scale setting, our method decreases the cost of sampling, but diffusion models continue to require a lot of computing power, which may contribute to wider issues such as climate change.

D Licenses

The pre-trained EDM2 [23] models are licensed under the CC BY-NC-SA 4.0 International License by NVIDIA corporation. The pre-trained SD-XL [30] model is available under the CreativeML Open RAIL++-M License by Stability AI. ImageNet [11] dataset uses a custom non-commercial license.

A pointillist painting of a raccoon looking at the sea.

A wild west town with cowboys and saloons, set at sunset

A blue jay standing on a large basket of rain-

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper discovers that the effect of classifier-free guidance in image-generating diffusion models varies with noise levels. It proposes a very simple method of performing guidance only at intermediate noise levels, demonstrating that this method can improve sample quality, diversity, and sampling cost both quantitatively and qualitatively. The quantitative effects are shown using EDM2 and DiT models trained on ImageNet, while the qualitative effects are demonstrated using the pre-trained Stable Diffusion XL.\n\nSTRENGTHS:\n**Originality**\n\n- The toy examples in Figures 1 and 2 are very intuitive and help in understanding the behavior of guidance at high, middle, and low noise levels. This interpretation of classifier-free guidance is novel.\n\n**Significance**\n- The method is very simple and can be easily applied to other diffusion models. \n\n- The FID and FD_{DINOv2} in Table 1 are strong.\n\n**Quality**\n- The ablation studies in Figures 3 and 4 effectively demonstrate the efficacy of the proposed method. \n\n- The qualitative results in Figures 5 through 8 clearly show the behavior of guidance at high, middle, and low noise levels in Stable Diffusion XL.\n\nWEAKNESSES:\nWhile the paper shows good intuition and strong results, several questions remain. \n\n- Does the **optimal interval vary with the model's performance?** The results in Table 1 imply this, as the optimal intervals for EDM2 and DiT differ, and the optimal guidance weight for DiT is higher. If the optimal interval varies with model performance, the proposed method will require an **exhaustive hyperparameter search for each diffusion model**, which is inconvenient.\n\n- Will guidance at high noise levels be detrimental for **conditions denser than the class and text conditions** considered in this paper? Examples include InstructPix2Pix [A] using image conditions, ControlNet [B] using various spatial conditions, and Stable Video Diffusion [C] generating videos from images. Additionally, a text-to-image model based on the T5 encoder (DeepFloyd IF [D]), which provides richer text representation than the CLIP text encoder of Stable Diffusion XL, is also an example.\n\n[A] Brooks et al., InstructPix2Pix Learning to Follow Image Editing Instructions, CVPR 2023.\n\n[B] Zhang et al., Adding Conditional Control to Text-to-Image Diffusion Models, ICCV 2023.\n\n[C] Blattmann et al., Scaling Latent Video Diffusion Models to Large Datasets.\n\n[D] DeepFloyd, https://huggingface.co/DeepFloyd\n\nQUESTIONS:\nLooking at Figures 1 and 2, the guidance scale at each interval significantly impacts the ODE trajectory. Do the authors think this tendency would also be observed in **flow matching** generative models [E] (or rectified flows [F]) that learn **straight trajectories**, compared to previous VP and VE diffusion models?\n\n[E] Lipman et al., Flow Matching for Generative Modeling, ICLR 2023.\n\n[F] Liu et al., Learning to Generate and Transfer Data with Rectified Flow, ICLR 2023.\n\nLIMITATIONS:\nThe authors mention in the paper checklist that they discussed limitations in section 4, but the limitations are not clearly presented in that section. It is recommended to address the limitations in the conclusion section as well.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-06-24"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper investigates the behavior of classifier-free guidance and proposes an adjustment in its application during sampling. Instead of applying a constant weight for the guidance scale across all sampling steps, the authors suggest that the guidance should be deactivated at high and low noise scales and applied only at intermediate levels. Experimental results with EDM2 and SDXL models indicate that guidance at high noise scales is detrimental, while at lower noise levels, its contribution is negligible. Thus, the guidance interval should be incorporated as part of the sampling hyperparameters for conditional diffusion models.\n\nSTRENGTHS:\n- As classifier-free guidance is an essential part of all diffusion models, the paper takes an important step to reduce the harmful effects of high guidance scales.\n- The proposed method is straightforward to implement and can be integrated into any model, making it valuable to the broader NeurIPS community.\n- The authors provide thorough experiments analyzing the impact of applying CFG over an interval.\n- The effectiveness of the method is also shown on toy datasets.\n- The paper is well-written and easy to understand.\n\nWEAKNESSES:\nThe main weakness of the work is a lack of discussion of existing works that address the issues of high-guidance scales. For example, the concept of using a non-constant guidance weight for CFG is also introduced in [1] (referred to as dynamic-CFG), and the current method of the paper is dynamic-CFG with a specific weight schedule (namely $w$ for $\\sigma \\in [\\sigma_{lo}, \\sigma_{hi}]$ and 1 otherwise). In addition, similar ideas have been explored in [1, 2, 3] in the form using a linear/cosine schedule for the guidance weight or annealing the conditioning vector to improve the diversity of generations at higher guidance scales. Moreover, half of the conclusion of the paper is already stated in [4] (i.e. guidance does nothing at lower noise levels). Accordingly, the paper would benefit from a more comprehensive related work section. \n\n[1] Sadat, S., Buhmann, J., Bradley, D., Hilliges, O. and Weber, R.M., CADS: Unleashing the Diversity of Diffusion Models through Condition-Annealed Sampling. In The Twelfth International Conference on Learning Representations (2024).\n\n[2] Chang H, Zhang H, Barber J, Maschinot AJ, Lezama J, Jiang L, Yang MH, Murphy K, Freeman WT, Rubinstein M, Li Y. Muse: Text-to-image generation via masked generative transformers. arXiv preprint arXiv:2301.00704. 2023 Jan 2.\n\n[3] Gao, Shanghua, et al. \"Masked diffusion transformer is a strong image synthesizer.\" Proceedings of the IEEE/CVF International Conference on Computer Vision. 2023.\n\n[4] Castillo A, Kohler J, Pérez JC, Pérez JP, Pumarola A, Ghanem B, Arbeláez P, Thabet A. Adaptive guidance: Training-free acceleration of conditional diffusion models. arXiv preprint arXiv:2312.12487. 2023 Dec 19.\n\nQUESTIONS:\n1. Is Figure 3 compiled with a fixed EMA profile for the EDM2 model or is the EMA profile also optimized along with the guidance parameters?\n\n2. Can you explain lines 112-118 in more detail? Does that mean you always use the low and high guidance scales based on the sampling discretization? This part was not very clear to me.\n\n3. Can you also plot Precision/Recall curves for the method to show the impact of interval guidance on quality and diversity in isolation?\n\n4. One major advantage of high classifier-free guidance is image-text alignment. Does the proposed method have a noticeable impact in this area? For example, in Figure 7, it seems that some parts of the prompt are more strongly emphasized by the normal CFG (e.g., the sky is orange).\n\n5. Does the optimal guidance interval change when modifying the sampling algorithm, or are the thresholds mainly model dependent?\n\nLIMITATIONS:\nThe authors have addressed limitations and social impact of the work.", "TITLE": "Official Review", "RECOMMENDATION": 7, "REVIEWER_CONFIDENCE": 5, "SOUNDNESS_CORRECTNESS": 4, "DATE": "2024-07-03"}

comments: SUMMARY:
This paper explains applying guidance in a limited interval improves sample and distribution quality in diffusion models, as shown in this title.

STRENGTHS:
The author provided intuitive figures, Fig.1 and 2, helped to understand the core of this paper easily.
The author provided a lot of experiments and results to support his idea.

WEAKNESSES:
This paper provided an optimization approach in improving diffusion with guidance. The author proposed, doing guidance in the middle stage is good. 
However, this work does not provide a novel idea or fundamental issue in diffusion with guidance. There are more fundamental problems needed to be solved. Despite the author showed good experiments and good writing, unfortunately, the contribution of this paper is minor.

QUESTIONS:
No questions

LIMITATIONS:
N/A
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 3
DATE: 2024-07-08