Applying Guidance in a Limited Interval Improves Sample and Distribution Quality in Diffusion Models
Authors: Tuomas Kynkäänniemi
Miika Aittala
Tero Karras
Samuli Laine
Timo Aila
Jaakko Lehtinen
Venue: NeurIPS 2024 poster (2024)
Keywords: generative models
diffusion models
classifier-free guidance
Primary area: diffusion_based_models
TL;DR: We improve quantitative and qualitative results of image-generating diffusion models by applying classifier-free guidance in a limited interval.

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
Tuomas Kynkäänniemi Aalto University
Miika Aittala NVIDIA
Tero Karras NVIDIA
Samuli Laine NVIDIA
Timo Aila NVIDIA
Jaakko Lehtinen Aalto University, NVIDIA
Abstract
Guidance a crucial technique for extracting the best performance out of image- generating diffusion models. Traditionally, constant guidance weight has been applied throughout the sampling chain of an image. We show that guidance clearly harmful toward the beginning of the chain (high noise levels), largely unnecessary toward the end (low noise levels), and only beneficial the middle. We thus restrict it to specific range of noise levels, improving both the inference speed and result quality. This limited guidance interval improves the record FID ImageNet-511 512 significantly, from 1.81 to 1 1.40. We show that it is quantitatively and qualitatively beneficial across different sampler parameters, network architectures, and datasets, including the large-scat 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 high- quality image synthesis based on text prompts and other forms of input [13, 35, 44J. 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 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-fiee 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 widely used concept, where the sampling process is given an additional anti-goal that to be avoided. For example, "nudity" might be common negative prompt in text-based image generators. At every sampling step. the denoiser is executed twice: once for positive and once the negative prompt, and the positive result then extrapolated further away from the negative one based on weight parameter. This works remarkably well in practice. Classifier-free guidance (CFG) [18] builds on this general concept. uses an unconditional model (no class information or text prompts) as 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 mathematically justified way of truncating the distribution of generated images [12, 18), basically trading variation for
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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(a) PDFs
(b) Unconditional
(c) Conditional
(d) Guided, w 6 (e) Tangent vectors
Figure I: Visualizing the effect of guidance. (a) The unconditional (orange) and conditional (green)
PDFs. In (b) through (e), the orange unconditional density is visualized in the background. (b) Sample trajectories for the unconditional distribution. (c) Trajectories for the conditional distribution. (d) Trajectories for the guided distribution with 6. (e) The tangent vectors dx /d the intersection point of the three marked trajectories. The difference of the unconditional (red) and conditional (green) vectors is magnified per Equation causing the unexpected detour in low-probability areas and mode drop. See Figure 2 for details and comparison to our approach.
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 the results, basically leading them towards handful of "template images" per prompt. On middle levels, it causes the sampling more decisively choose some 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-dependen improves the results. In the context of prompt inversion, Mahajan 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 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 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-aar FID [23] in ImageNer-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://gitthu: http:///gtthuu..oo/k.ooaae com wttp://ow/.kkaaaggcceeeeee -uudtanc--nntrvall
2 Background
The concepts in this and the following section are illustrated in Figure using a synthetic ID 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 to intuitively visualize the kinds of harm it causes.
The goal of denoising diffusion model to draw samples from a data distribution Paax (x). Let us define series of smoothed distributions p(x; so that each individual distribution is the convolution between Pdatn and a Gaussian noise distribution with standard deviation a. Following the EDM formulation [22], the evolution of sample p(x; σ) w.r.t. change in o is described by the ordinary differential equation (ODE): dx/do - (Do σ) - x)/0.
(1)
where Dg is a denoiser model with parameters 0, optimized to minimize the expected Lg denoising error:
argming pyp.
n;o) y
Here, Purain (a) the training distribution noise levels, which we consider to be an implementation detail of Do. To generate sample from the data distribution, we first draw initial sample

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22
-1.5
0.6
0.9 0.1
0.6
(a) PDFs
(b) Guidance everywhere
(c) Guidance at E (0, (d) Guidance at a E 00.1,
Figure 2: Illustration the detrimental effects of guidance at high a in synthetic ID scenario. (a) PDFs of the unconditional (orange) and conditional (green) data distributions used in this example. (b) Activating guidance (weight w = 6) everywhere leads to a catastrophic mode drop. The zoomed-out inset shows how guidance pushes the sampling trajectories outside the distribution during early sampling. (c) Disabling guidance at high o resolves the issue and restores both modes. (d) Disabling guidance at low has little effect and can be done to reduce computational cost.
XD ~ p(x; muax), where max is chosen be large enough so that p(x; mmxx is approximately equal to pure Gaussian distribution and thus trivial to sample from. We then follow the ODE of Equation evolve Xo towards (orange). Figure 1b depicts the diffused target distribution over the axis and set of sample trajectories computed by solving Equation from several different initial conditions.
i.e., the data distribution. Figure la illustrates the target distribution
We can think of classifier-fre free guidance [18] as constructing a modified ODE where dx/da is defined as linear combination between conditional ODE and unconditional ODE:
dx/do w[ (Do(x) - x)/o] + (1 - w)[ (Poo (x) xa]
- (wDg((x)) (1- ((-)))))))(_-)))))>>>>=)))>>>>)) -x)
(4)
where w is the guidance weight and e is the condition information given to the denoiser Da (ef. Fig- ure Ic). Setting w: 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; x; o) 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, single denoiser model D3 is trained to accept either conditional unconditional input by dropping the conditioning information 10-20% of the time during training. A Alternatively, we can train two separate models Docnn [x|c; o7) and Douccond (x; rr. 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 number of discrete steps that bring the noise level from mmax to zero, giving rise to sequence of images Xo, XI, xN. each with its corresponding noise level o, Various discretization schemes and solvers have been proposed [22]. Regardless of the specifies, the computational cost is directly proportional to the number of sampling steps N.
3 Our method
In Figure 2, we continue probe the downsides of CFG using the previous toy example. We observe that applying guidance at all noise levels - as 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 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 simple way reduce the sampling cost with minimal effect on outputs.
Although this toy example is grossly simplified, we hypothesize that broadly similar effects oceur in full-scale diffusion models well. In Section 4 we see, e.g., image compositions becoming
3

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ImageNet-511
Quality metric
Model size
Guidance interval
Guidance weight
FID 4
FDrnov
Mgarams
Gflops
FID
FDpeso
FDansos
EDM2-S
[23] w/ CFG [18]
2.23
52.32
280
Full
EDM2-S
[23] w/ guidance interval
1.68
46.25
280
102
(0.28, 2.90] (0.60, 5,00
2.1
3.2
EDM2-XXL
[23] w/ CFG [18]
1.81
33.09
1523
552
Full
Full
1.2
1.7
EDM2-XXL
[23] w/ guidance interval
29.16
1523
552
[0.19.161]
(0.60, 5.00
DIT-XL/2
(29] w/ CFG [18)
3,04
51.97
675
525
Full
Full
1.5
2.0
DIT-XL/2
[29] w/ guidance interval
2.40
43.94
675
525
(0.34, (0.45, 1.23)
2.5
4.0
Table I: Quantitative results on ImageNet-512. Limiting the classifier-free guidance (CFG) to an interval improves both FID and DINOV2 significantly, without altering the model complexity. The sampling cost is a bit lower due to fewer guidance evaluations. This holds for small (S) and large (XXL) variants of the state-of-te offte- EDM2 model [23], as well as diffusion transformers [29]. The model complexity numbers are copied from the EDM2 paper.
less varied due 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 by replacing w with piecewise constant function:
dx/do (w(o))o(x(c;o)) + (1 - ((a)) -x)/,,
(5)
where w()) w E e (1)
otherwise.
Here, оbi denotes the point in the sampling chain where we enable guidance and 0% is the point where we turn it off. In our formulation, traditional CFG is recovered by setting σh = 0 and σbi = co.
Virtually all existing deterministic samplers can be ODE of Equation 4, obtained through a number of discrete steps. While the correspondence might not be obvious in all cases, Appendix A. For example, in the case of Stable Diffusion XL [30], we have 32 steps corresponding where do 14.61, as 13.41, 02
numerical Runge Kutta solutions the
nevertheless characterize the steps with respect to as detailed in
to the transitions on - 01, σ 02,--.. 031 032,
12.28, 031 = 0.03, and 032 = O.
The underlying assumption common all Runge- Kutta methods is that dx/do should be sufficiently smooth within each step. In Equation 6, however, we intentionally introduce discontinuities at a) and Thi In order to satisfy the smoothness requirement, exactly at step boundaries so that the value of w(r) stays constant within each step. In practice, we choose to do this by rounding d and dm appropriately, i.e., by setting Fna σ, and dio o, for some < j. Note that this leads to a seemingly high numerical precision in the values of d% and ou, which should not be taken an indication of extremely precise tuning.
must thus ensure that both transitions happen
Results
We will first evaluate and ablate 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 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-arr approach EDM2 [23] baseline. We use the small (EDM2-S) and the largest (EDM2-XXL) models as-is with the default sampling parameters: 32 deterministic steps with a 2nd order Heun sampler [22].
hhttps://githan htt.ttthbb.co////aa///eee/

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12
Ours, a (0.28, 2.90]
S.CT
120
5. Ours, é (0.60, 5.00]
CPG
XXL. Ours, a (0.19, 1.61]
XXL, Oues (0.60, 5.00 XXL.
10
100
8
FDDINO02
FID
80
60
46.25
1.81
33.00
29.16
1.0
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3.0
3.5
4.0
1.0
1.5
2.0
2.5
3.0
3.5
1.0
Guidance weight w
Guidance weight w
Figure 3: FID and FDDINOv2 function of guidance weight for classifier- guidance (orange, red) and our method where the guidance has been limited to the stated interval (blue, green). The shaded regions indicate the min/max three evaluations.
1.0
Ours, (0.60, 5.00 S,CFG XXL. Ouns a (0.60, 5.00] XXL, CPG
0.9
0.8
4.0
Precision
= 4.0
29.16
52.32
0.7
46.25
33.09
w = 4.0
0.6
0.5
W
1.0
0.4
0.3
0.4
0.6
0.7
Recall
Figure 4: Precision and recall curves for classifier-free guidance (orange, red) and our method (blue, green), when the guidance weight w varied from 1.0 to 4.0 in 0.1 increments. Black points indicate the minimum and maximum guidance weights in the sweep, while colored triangles show the precision/recall tradeoffs that achieve the best FDDINO.2. We used the DINOv2 feature space in this plot, following the recommendation by Stein et al. [42]. The curves represent median over three evaluations.
Table 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-ae in this dataset. With EDM2-XXI,, the record further improves 1.40 and FDDINOw2 also improves from 33.09 to 29.16.
To find the optimal parameters for each case, performed full grid search over w, 0%o and Ohi- In the case EDM2-XXL, the best FID is achieved by applying guidance at the 32 steps, corresponding to noise levels (0.19, 1.61], with weight = 2.0. The best FD obtained with slightly higher noise levels a E (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 DiT- XL/2 model 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 in 75 of the 250 sampling steps, corresponding to the interval (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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2.1,m,, 0.28
Ours, 2.1.
XXL, Ours, w = 2.0, oh. 0.19
XXL, Oun, = 2.0. = 1.61
5
5
FID
4
1.68
1:40 1:40--68
1
1:40
2.90
31.22
0.002
0.028
2.90
Highest noise level with guidance, Fhi
Lowest noise level with guidance, do
Figure 5: Sensitivity of FID to the chosen guidance interval. Left: Sweep over dhi with optimal and w. Right: Sweep over de with optimal ohi and w. The shaded regions indicate the min/max over three evaluations.
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 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 probes the sensitivity of our results to the chosen guidance interval. In this test, sweep over de and This while keeping the other interval endpoint, Tni or djo and the guidance weight w as the optimal choices as reported in Table 1. The left side shows sweep over This i... the highest noise level with guidance. Including too high noise levels to the guidance interval leads to truncation the image distribution, which can be seen Tni yields sub-optimal results. For both EDM2 models the optimal choice for Tni is located at the middle noise levels. The right side shows sweep Applying guidance at low noise levels does not bring additional benefits, compared to the middle levels. Thus, guidance observation also made in [9].
increase in FID. Furthermore,
narrow interval (low
oi. i.e., the lowest noise level with guidance.
be disabled in most of the low noise levels to decrease sampling cost, an
To estimate the optimal guidance interval in practice, the upper and lower guidance limits can be determined separately, without the need for 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 predictable way (Figure 5, right). Once the optimal upper limit is known, the lower limit is determined. Optionally, 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 initial run, which accelerates the process by 10x.
We have found that the optimal choice oia and mhi 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 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 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 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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CFG with low guidance 2, a E oo) fuzzy details, high diversity
CFG with high guidance w = 16, a E (0, oc) crisp details, low diversity
Ours with high guidance w = 16, a E (0.28, 5.42] crisp details, high diversity
Rembrandt painting of a raccoon.
adorable painting of a Dachshurd.
(no guidance) fuzzy details, high diversity
5, E (0, oo) crisp details, low diversity
5, (0.19, 1.61] crisp details, high diversity
ImageNet class 64: green mamba
5pco6
ImageNer class 959: carbonara
Figure 6: Traditional CFG vs. our method. Left: Low w yields diverse but fuzzy images that lack detail. Middle: Increasing adds crispness, but reduces diversity and oversaturates the colors. Right: Our method reduces these effects while retaining the crisp look.
7

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W
W 12
0 = 16
4k dslr photo a raccoon wearing an astronaut helmet, photorealistic.
A highly detailed paper origami of Dachshund table next to porcelain teapot, 4k dslr.
201
3
w
w
3
w
ImageNet class castle
ImageNet class 933: cheeseburger
Figure 7: Effect of guidance weight w with our method. We limit the guidance to E (0.28, 5.42) with SD-XL (top) and (0.19, 1.61] with EDM2-XXL (bottom). Higher leads clearer and more well-defined image details while keeping the color palette and overall composition unchanged.
ImageNet using EDM2-XXL. For SD-XL, 32-step deterministic Heun sampler, where the first step corresponds to = 14.61.
the official pre-trained checkpoint with standard
With SD-XL, we apply guidance at 50% of the sampling steps, corresponding to noise levels E (0.28, 5.42], with weight 16. These parameters were chosen by visual inspection. The beneficial interval is wider than in ImageNet, likely due the more varied dataset used in the

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hh
8.39
Th
5.42
Thi 4.24
Thi 1.75
ØP 0
0..88
1.00
O 2.42
3.72
A fantasy landscape alien planet in which there are many buildings. There is beautiful bridge with pond in the center. There large
the sky. The sky is orange. Digital art, artstation
Figure 8: Effect of changing the guidance interval (ma: anl with w 16. Top:
Decreasing This i.e., 0.28. High values lead to simplified
disabling guidance at high noise levels, while keeping image composition and oversaturated colors (left); low values cause the image become increasingly convoluted (right). Bottom: Increasing %oo i.e., disabling guidance at low noise levels, while keeping Thi 5.42. The value can be made relatively high with no noticeable impact, reducing sampling cost.
CFG
Ours, a E (0.19, 1.61] W
W 3
W
3
5
ImageNet class 483: castle
Figure 9: Effect of increasing guidance weight w with CFG vs. our method. Left: Increasing the guidance weight with CFG leads to changes in image composition and contrast. Right: With our method, increasing improves image details but retains the overall composition and realistic colors.

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training of SD-XL. Consequently, our method leads to over 20% speed-up due to lower number of unconditional model evaluations [1].
Figure shows a comparison between standard classifier-free guidance with low and high weights (left and middle columns) and 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 a significant degree and the color saturation is also reduced, although excessively large guidance weights can still lead to over- setrrsaturation.
Figure 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 (o): Tbl with SD-XL necessarily subjective, provide visual ablation of this choice in Figure 8. Modifying the upper limit Thi i.e. dis- abling guidance at high noise levels, has two distinct effects. First, 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 Thi leads to blander color scheme. Similar to EDM2 results, changing the lower limit doo has only modest effeet guidance can be disabled from most of the low noise levels with no noticeable impact while improving the inference speed.
Lastly, Figure compares the effects of increasing the guidance weight in standard CFG vs. our method with EDM2-XXI.
Conclusions
Classifier-free guidance an indispensable tool for improving the results of practically all image- generating diffusion models. As 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. follow-up study could examine whether their "speciation" interval overlaps with the interval that is beneficial for guidance.
Acknowledgements
This work partially supported by the European Research Council (ERC Consolidator Grant 866435), and made use of computational resources provided by the Aalto Science-ITT project and the Finnish IT Center for Science (CSC).
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A Characterizing sampling steps in noise levels
In the main paper, we reported the guidance interval measured in noise levels a. Here, 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 ith sampling step corresponds to noise level that given by:
( mmax
N - m
Tima
where N is the total number of sampling steps, Omin = 0.002, max = 80. With SD-XL, we use p which the default value in the official code, with EDM2 models p With DIT, we use the iDDPM discretization from [22] which maps the ith sampling step to the corresponding noise level in the following way:
3 U L M-1-J0 +1¹
+1 where UM 0, 1j-1 m CC) 1 and āj sin² 3ICCH1T) We use the default
parameters C1 0.001, C2 0.008, M 1000 and jo from [22].
B Additional qualitative results
Figures 10 and 11 show further comparisons between classifier-free guidance and our method. Figures 12 and 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 lot of computing power, which may contribute to wider issues such 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 Al. ImageNet [11] dataset uses custom non commemmraaaal license.
13

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CFG with low guidance w 2, a (0, oo) fuzzy details, high diversity
CFG with high guidance 16, a (0, oo) crisp details, low diversity
Ours with high guidance w 16, a E (0.28, 5.42] crisp details, high diversity
A pointillist painting of raccoon looking at the sea.
A wild west town with cowboys and saloons, set at sunset
A blue joy standing on a large basket of rain- bow macarons.
Figure 10: More SD-XL results that demonstrate how CFG with low w yields fuzzy images that lack detail (left) and CFG with high w leads to reduced diversity and oversaturated colors. Our method (right) produces images with crisp details while maintaining natural colors. The degree the negative effects with CFG varies between prompts.
14

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CFG with low guidance (no guidance) fuzzy details, high diversity
CFG with high guidance 5, E (0, oo) crisp details, low diversity
Ours with high guidance 5. (0.19, 1.61] crisp details, high diversity
ImageNet class 17: jay
ImageNet class loggerhead
ImageNet class 497: church
Figure 11: Additional EDM2-XXL results that demonstrate how CFG with low yields fuzzy images that lack detail (left) and CFG with high w leads to reduced diversity and oversaturated colors. Our method (right) produces images with crisp details while maintaining natural colors.
15

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12
16
highly detailed zoomed-it digital painting of cat dressed as a witch wearing a wizard hat haunted house, artstation.
fantasy landscope the Shire during sunrise. The Sun near the horizon and there fog farm fields. Highly detailed fantasy art, artstation.
A 4K dstr photo of hedgehog sitting in small boat in the middle of pond. It is wearing Hawañan shirt and straw hat. is reading a book. There are few leaves the background.
Figure 12: More SD-XL results showing the effect of changing w with our method. We limit the guidance to (0.28, 5.42]. Increasing produces images with more well-defined details while maintaining the color palette and the original image composition.
16

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3
5
3
ImageNet class 324: cabbage butterffy
ImageNet class 360: otter
ImageNet class 105: koala
ImageNet class S55: fire truck
ImageNet class 397; pufferfish
ImageNet class 698: palace
ImageNet class 849: teapot
ImageNet class 970: alp
Figure 13: More EDM2-XXL results showing the effect of changing w with our method. We limit the guidance E (0.19, 1.61]. Increasing produces images with more well-defined details while maintaining the color palette and the original image composition.
17

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4
12
16
CFG
Ours, a (0.28, 5.42)
A highly detailed paper origumi of a Dachshund on table next to porcelain teapot, 4k dsír.
Figure 14: Effect of increasing guidance weight with CFG method. Top: Increasing the guidance weight with CFG leads to large changes in the image composition. Note how the dog's 's head moves changes. Bottom: Our method leads well-defined image details and retains the overall composition to a significant degree.
18

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u 2=2
W #=4 4
W ==8 8
W
12
W
16
CFG
Ours, a E (0.28, 5.42)
A fantasy landscape on an alien planet in which there are many buildings. There is beautiful bridge with a pond in the center. There is one large moon in the sky. The sky is orange. Digital art, artstation
Figure 15: Effect of increasing guidance weight w with CFG vs. our method. Top:
Increasing the
guidance weight with CFG leads to large changes in the image composition. Bottom: Our method leads to well-defined image details and retains the overall composition to significant degree.
19

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CFG
Ours, a E (0.19, 1.61]
3
==5 5
= 3 ==
ImageNet class 33: loggerbead
ImageNet class green mamba
ImageNet class I: goldfish
ImageNet class 504: coffee mug
Figure 16: Effect of increasing guidance weight w with CFG vs. our method. Left: Increasing the guidance weight with CFG leads to large changes in the image composition. Right: Our method leads to well-defined image details and retains the overall composition to a significant degree.
20

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Reviews:
[{"review_id": "xRdCjyAys9", "reviewer": "Reviewer_2mKR", "summary": "This paper explains applying guidance in a limited interval improves sample and distribution quality in diffusion models, as shown in this title.", "questions": "No questions", "limitations": "N/A", "rating": 3, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 1, "strengths": "The author provided intuitive figures, Fig.1 and 2, helped to understand the core of this paper easily.\nThe 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. \nHowever, 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.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Respectfully, this terse review appears to present a personal preference, but makes no factually supported arguments to rebut. CFG is a crucial but poorly understood component of diffusion image generators, and despite its apparent simplicity, our technique extracts its key benefits while significantly suppressing its downsides.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "49PjQOnFmV", "reviewer": "Reviewer_JNG5", "summary": "This 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.", "questions": "1. 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?", "limitations": "The authors have addressed limitations and social impact of the work.", "rating": 7, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 2, "strengths": "- 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.", "weaknesses": "The 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.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for the review. We will next address concerns and the explicit questions:\n\n>“The main weakness of the work is a lack of discussion of existing works that address the issues of high-guidance scales.”\n\nThank you for providing pointers to additional relevant previous work. We are glad to cite them. All of these papers propose guidance schedules that are continuous at high noise levels to enhance the results of their (unrelated) main methods, without evaluating the effects of these in isolation (apart from [1]). While our guidance interval can be seen as a step function schedule, this schedule was not advocated in any of these works. The closest to our method is Dynamic CFG [1] that modulates guidance weight linearly in an interval that mostly covers high noise levels. In contrast, we found guidance to be strictly detrimental in these regions, and disabling it there leads to our state-of-the-art FIDs. \n\nIn our initial experiments, we tried a linear schedule before finding it to be clearly worse than the simpler guidance interval. With EDM2-S, we obtained FID 2.02 at best with a linear schedule, whereas guidance interval achieves FID 1.68. Therefore, we focused the paper specifically on studying the guidance interval and its effects on the resulting image distribution. We also provide novel data confirming that guidance is harmful at high noise levels through our toy example and experiments with EDM2 and SD-XL.\n\n_[1] Sadat et al.: CADS: Unleashing the Diversity of Diffusion Models through Condition-Annealed Sampling. In Proc. ICLR 2024._\n\n>“1. 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\nWe use a fixed EMA profile in Figure 3. For EDM2-S, we use EMA lengths 2.5% and 8.5% respectively for FID and FD_DINOv2, and for EDM2-XXL, we use EMA lengths 1.5% for both FID and FD_DINOv2. To determine these, we ran a sweep over the EMA lengths and found that the EMA length that works best with our guidance interval also works best with CFG.\n\n>“2. 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\nThis is correct. This part means that we either enable or disable CFG at the noise levels given by the noise level discretization of the sampler.\n\n>“3. Can you also plot Precision/Recall curves for the method to show the impact of interval guidance on quality and diversity in isolation?”\n\nWe are happy to add a plot with precision and recall curves. They clearly show that our approach allows increasing precision with a smaller penalty in recall in comparison to CFG.\n\n>“4. 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\nWe have not performed a quantitative experiment using text-image alignment metrics, but generally, good text-image alignment seems to require roughly similar (i.e., quite high) guidance weights for both traditional CFG and our method. Prompts that benefit from added saturation and bright colors may work better with traditional CFG, as the artifacts that our method aims to remove may end up strengthening the alignment.\n\n>“5. Does the optimal guidance interval change when modifying the sampling algorithm, or are the thresholds mainly model dependent?”\n\nThe guidance interval is not overly sensitive to the sampler parameters, at least as long as the sampling algorithm remains the same (see 3rd paragraph of 4.2). We did not experiment with different sampling algorithms (DDIM, Euler, etc.) because the current state-of-the-art results are obtained with a 2nd order Heun sampler with 32 step deterministic steps. We expect that the optimal guidance interval depends mainly on the model.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "5wIk20N7kX", "reviewer": "Reviewer_HBPT", "summary": "This 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.", "questions": "Looking 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.", "limitations": "The 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.", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "**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.", "weaknesses": "While 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", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for the review. We will next address the explicit questions:\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\nYes, based on our experiments with EDM2 and DiT the optimal guidance interval appears to be model specific, and it probably depends on the model’s performance as well. Finding the optimal sampling hyperparameters requires only model evaluations, not re-training, but it can indeed become somewhat expensive if done in a naive fashion. However, there are several ways to reduce the cost.\n\nFirst, 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 as zero, which is very close to optimal except for the computational cost (see Figure 4, right). Once the optimal upper limit is known, the lower limit can be determined afterwards. Second, the lower limit seems to affect the output in a monotonic fashion, making binary search applicable there. 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 10x.\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\nThus far, we have not experimented with conditioning signals other than class and text conditioning using CLIP. It is likely that major changes to the conditioning scheme will impact the optimal guidance interval.\n\n>“Looking 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\nAs discussed in [1], the trajectories generated by flow matching (FM) for a white noise latent follow the score function of progressively less noisy distributions (Eq. 7 in [1]), similar to diffusion. Thus, guidance has a similar statistical interpretation and behavior in both diffusion and flow matching.\n\nThe trajectories in FM are straight in the sense that they don't take entirely unnecessary detours, but they are still necessarily curved for any non-trivial data. This can be seen, e.g., in the first rectified flows of Figs. 3 and 4 of [2]. The EDM sampler used in our experiments is similarly designed to avoid unnecessary trajectory curvature to allow efficient sampling. We therefore expect that our findings translate to flow matching.\n\nThat said, FM allows for curious variants where the latent distribution is, e.g., another set of images instead of the typical Gaussian noise. To our knowledge, all state of the art applications of FM use noise latents, but the behavior of CFG in these more exotic settings seems to be a largely unexplored topic, and it is unclear if it (or guidance interval) would be applicable at all. Similarly, iterated reflows [2] would potentially behave differently.\n\n_[1] Zheng et al.: Guided Flows for Generative Modeling and Decision Making. Arxiv preprint, 2023._\\\n_[2] Liu et al.: Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow. In Proc. ICLR, 2023._", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: This paper received split recommendations after the post-rebuttal discussion. The rebuttal addressed most reviewer concerns about missing references, experimental details, and technical clarity. After an engaged author-reviewer discussion, most remaining doubts were clarified, and only Reviewer 2mKR recommended rejection on the grounds of insufficient technical contribution. Although the paper does not offer a theoretical breakthrough in diffusion with guidance, it presents a simple, generic, and effective solution that can practically impact the widely studied topic of diffusion model inference. Reviewer comments and corrections should be carefully included in the final version.
Decision: Accept (poster)