Generative Visual Prompt: Unifying Distributional Control of Pre-Trained Generative Models
Authors: Chen Henry Wu
Saman Motamed
Shaunak Srivastava
Fernando De la Torre
Venue: NeurIPS 2022 Accept (2022)
Keywords: generative models
energy-based models
normalizing flows
generative adversarial networks
diffusion models
amortized inference

Abstract: Generative models (e.g., GANs, diffusion models) learn the underlying data distribution in an unsupervised manner. However, many applications of interest require sampling from a particular region of the output space or sampling evenly over a range of characteristics. For efficient sampling in these scenarios, we propose Generative Visual Prompt (PromptGen), a framework for distributional control over pre-trained generative models by incorporating knowledge of other off-the-shelf models. PromptGen defines control as energy-based models (EBMs) and samples images in a feed-forward manner by approximating the EBM with invertible neural networks, avoiding optimization at inference. Our experiments demonstrate how PromptGen can efficiently sample from several unconditional generative models (e.g., StyleGAN2, StyleNeRF, diffusion autoencoder, NVAE) in a controlled or/and de-biased manner using various off-the-shelf models: (1) with the CLIP model as control, PromptGen can sample images guided by text, (2) with image classifiers as control, PromptGen can de-bias generative models across a set of attributes or attribute combinations, and (3) with inverse graphics models as control, PromptGen can sample images of the same identity in different poses. (4) Finally, PromptGen reveals that the CLIP model shows a "reporting bias" when used as control, and PromptGen can further de-bias this controlled distribution in an iterative manner. The code is available at https://github.com/ChenWu98/Generative-Visual-Prompt.

Generative Visual Prompt: Unifying Distributional Control of Pre-Trained Generative Models
Chen Henry Wu, Saman Motamed, Shaunak Srivastava, Fernando De la Torre Robotics Institute, Camegie Mellon University, Pittsburgh, PA ftorre wwtpeessccueeee edu, {saman.moatenn moatamed, shaunak 999 j@gmail. com
Abstract
Generative models (e.g., GANs, diffusion models) learn the underlying data distri- bution in an unsupervised manner. However, many applications interest require sampling from a particular region of the output space or sampling evenly over a range of characteristics. For efficient sampling in these scenarios, we propose Gen- erative Visual Prompt (PromptGen), framework for distributional control over pre-trained generative models by incorporating knowledge of other off-the-shet models. PromptGen defines control as energy-based models (EBMs) and samples images in feed-forward manner by approximating the EBM with invertible neural networks, avoiding optimization at inference. Our experiments demonstrate how PromptGen can efficiently sample from several unconditional generative models (e.g., StyleGAN2, StyleNeRF, diffusion autoencoder, NVAE) in a controlled or/and de-biased manner using various off-the-shelf models: (1) with the CLIP model as control, PromptGen can sample images guided by text, (2) with image classifiers control, PromptGen can de-bias generative models across a set of attributes attribute combinations, and (3) with inverse graphics models as control, PromptGin can sample images of the same identity in different poses. (4) Finally, PromptGen reveals that the CLIP model shows a "reporting bias" when used as control, and PromptGen can further de-bias this controlled distribution in an iterative manner.' !
Introduction
Generative models learn the underlying high-ddmensional data distribution and have achieved promis- ing performance on image synthesis [4, 75, 36, 21]. Though being well praised, they still face two main limitations. First, since generative models typically trained in unsupervised way, they lack controllability, meaning that it is unclear how to sample from a specific region of the space. Second, generative models are prone to inherit the imbalance and bias of training data [60, 31]. For instance, StyleGAN2 is more likely to produce images of white individuals, see Figure ((e). Previous works have studied these challenges separately, and typical methods include editing of "style" codes [23, 68, 31] and explicit conditions [43, 69]. However, these methods are either model-dependent (i.e., requiring well-structured style space) or label-intensive (i.c., requiring all training samples to be labeled for explicit conditions), limiting their generality and practical use.
To address the above challenges, this paper advocates a unified formulation, distributional control of generative models, which enables controllability by incorporating the knowledge of off-the-shelf models (e.g., CLIP [58], classifiers, inverse graphics models [16]). Based on this unified view, propose to learn distributions in the latent space of pre-trained generative model while keeping the pre-trained weights fixed. Given its conceptual similarity prompting [42, 85, 86, 30, 22], we term our framework as Generative Visual Prompt (PromptGen). PromptGen requires no training data, and the only supervision comes from off-the-shelf models that help define the control. Specifically,
TThe code is available at https: wttp.//cccceeeeeeeeeeeeeeieeeeee
Prompt.
36th Conference on Neural Information Processing Systems (NcurIPS 2022).

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(a) "baby""-ontren"" controlled
(b) race deblased
(c) gender debiased makeup"
A photo of baby
CLIP
Race Classifter
Gender Classifter
pose-controlled
(o) Pre-trained StyleGAN2
"w/o makeup controlled
Inverse Graphics Model
Pose
photo of a person without makeup
Figure I: PromptGen uses different off-the-shelf models (e.g., CLIP [58], inverse graphics models, and classifiers) to control the output distribution of pre-trained and fixed generative model (e.g.. StyleGAN2). Colored boxes (c.g., the blue CLIP) indicate the control. See the text for details.
PromptGen allows the user define controls using an energy-based model (EBM) approximated by an invertible neural network (INN). Unlike methods that require optimization at inference in EBM sampling [49, 14, 45, 52], PromptGen samples images in feed-forward manner, which is highly efficient. Moreover, PromptGen stands alone at inference, meaning that we can discard the off-the-shelf models, which define the control, after training.
We illustrate the benefits of PromptGen with several experiments. Figure 1 demonstrates our main findings with StyleGAN2 trained on FFHQ [35] without any labels. while we also show results for StyleNeRF [21], diffusion autoencoder [57]. and NVAE [75] in Section 4. Figure ((a) illustrates how we can sample from StyleGAN2 based on text descriptions such as photo of baby". Figure 1(b) shows that PromptGen can leverage race classifier (potentially trained on a different dataset) to sample uniformly across all races, di-bin the pre-trained StyleGAN2. Figure ((d) illustrates that PromptGen can generate images of the same identity in different poses, guided by pose regressor.
Finally, it is worth pointing that PromptGen and modularity for control composition, but also enables iterative controls when some controls are contingent others. For instance, one may train text-controlled distribution and then de-bias this distribution. To achieve this, we view the composition of the INN and the generative model as a new "generative model" be controlled. Figure 1(f) illustrates that PromptGen reveals "reporting bias" of the CLIP model [58], where "without makeup" is perhaps surprisingly positively correlated with female, and Figure I(c) shows that PromptGen can further mitigate this bias with iterative control.
only offers generality for algorithmic design
Table
Comparison between methods.
PPGM [49]
Guided
StyleFlow [1]
PromptGen
LACE [52]
DDPM [10]
Arbitrary control (c.g., CLIP)
x
Low-dimensional latent space Stands alone at inference Feed-forward (i.e., no inference-time optim.)
X
X
Iterative distributional control
X
X
V
2 Related Work
Over the past few years, generative models have gained the ability to generate images with high visual quality. A few the most widely used methods include generative adversarial networks (GANs) [18], VAE [40], invertible neural networks [1] 12), and diffusion models [25. 71]. In particular, generative models trained on large amounts of unlabeled data, e.g., BigGAN [4], StyleGANs [35, 36, 34, 64), Glow [39], and diffusion models [51. 10), achieve promising image synthesis results.

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Despite their success, controllability and de- he-biasing are still two fundamental challenges generative models face. For controllability, existing methods include explicit conditioning at training [43. 69] or local editing of the learned representation, e.g. "style" codes [1, 56, 46, 68, 80]. For de-biasing, existing methods include local editing of "style" codes [60, 31J and importance sampling for either training [20] or inference [29]. Existing works study these problems separately, each requiring specific design for the task studied. On the contrary, PromptGen a unified framework for arbitrary controls defined by off-the-shelf models. The benefits of unifying tasks have been shown by recent trend of works in multiple research areas across vision and language [59, 48, 63, 81, 61J. Moreover, fine-tune generative models [17] for domain adaptation, which is orthogonal to PromptGen: since PromptGen maps a generative model to another generative model (Algorithm 1). fine-tuning can be applied before after PromptGen training. We leave this exploration to future studies.
Previous methods usually sample from EBM [41] with Markov Chain Monte Carlo (MCMC) [73, 76, 14, 19, 13, 45]. Among them, plug-and-play generative models (PPGMs) [49] and LACE [52] define latent-space EBMs. However, MCMC requires inference-time optimization, which is inefficient and requires the off-the-shelf models to be available at inference; this is also the criticism for diffusion models, regardless of being gradient-guides [51, 67] or not [25, 26, 50J. In contrast, PrompiGen achieves efficient, feed-forward sampling. Table 1 shows comparison with previous methods. Our INN training is similar to that proposed by [54] to sample from physical systems, which is later used by [78] to solve inverse problems with two composed INNs. Notably, [54] and [78] do not leverage low-dimensional latent space, and [78] requires training separate model for each image sample. In this paper, use INN to model arbitrary EBMs for various generative models.
StyleGAN2 StyleNeRF Diffusion N(O,I) - p(z) p(z)
Control
G fo
Control
G
OO
INN fo p(=|C) G p(x (C)
NVAE
Genorative model
Iteration
Overview of Generative Visual Prompt (PromptGen). Given pre-trained generative model and a control C, we learn distribution p(=[C) in G's latent space, while keeping fixed. Each control C can have multiple components, e.g., C = {text "a photo of baby", gender gender=male). PromptGen views the composition G o fe as a new "generative model" for iterative control.
Figure 2:
3 Method
Figure illustrates our PromptGen framework. To begin, the user selects a pre-trained generative model G. PromptGen then lets the user specify a control C as an energy-based model (EBM) p(z(C). We train an INN fo to approximate p(z[C). PromptGen views the functional composition Go fo as new generative model and can perform iterative control. Algorithm 1 describes the overall procedure.
Algorithm 1: Generative Visual Prompt (PromptGen)
Input: Generative model G idd repeat
Input: control C of the current iteration
2. Define an EBM p(z|C) for C (Section 3.1)
3. Train an INN ʃa Rd + Rd approximate p(=(C) (Section 3.2)
4. G e Go Goo
until user stops the iteration
return G: dRd + X, which is a feed-forward network
3.1 Latent-Space EBM for Distributional Control
The plug-and-play generative model [49] was first proposed to use latent-space EBM for controllable image synthesis. If a fixed generative model is used, then theoretically, any image-space EBM can be viewed as latent-space EBM, as shown by [19] and [52]. We define the latent-space EBM following similar formulation as [49, 19, 52], with some new energy functions. We then extend the formulation
3

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to incorporate the moment constraint [7], which was adopted for language modeling [37], but unlike [37], we define the moment constraint in the latent space accommodate generative vision models.
We define a control C as M independent properties (yi..... ym J. e.g., can be a text description and y/2 can be attribute. The controllability be defined the EBM (detailed in Appendix B.1)
p(z(C) Pw (a)e BB(
M
Ec(x)
Σ X.,
2.),
Pe( -Fclo')
(1)
Zx
which reweights the image prior (a()) with energy Fe(x)), where images with smaller energy are preferred. Using pre-trained generative model G : Fd + X' that maps latent code an image æ, the image prior Pae (=( ) is defined by sampling a latent code z from (0,, I) and mapping it
x = G(z). Appendix B.2 shows that this EBM equivalent the latent-space EBM
p(=|C) P=(z)e Be(G(=))
2
M Ec(G(z)) Σ XE(GG()),))
Z -= pa
Latent-space EBM allows us to use any off-the-sher model to specify the control. The following are some examples that are discussed this paper (explained in Appendix B.1):
Classifier energy: Given a classifier P(-læ) and the target class a that we want to sample images from, define the classifier energy Ectassiner (2c, a) log P(a (a:).
CLIP energy: Using the CLIP model [58], we define the CLIP energy as the cosine distance between the embeddings of the image and the text t. averaged over L differentiable augmentations [84, 46]:
1 Σ cos (CLIPimg (x) (LLP) Exup( 1) -
Inverse graphics energy: Given an inverse graphics model, fx-P,, which infers image parameters (e.g., pose and expression), and the target parameters p. we define the inverse graphics energy as
Ennv-grppics (x, p) (z)
(4)
where .( is the geodesic distance between the inferred parameters and the target parameters.
Moment constraint: Some controls cannot be directly defined by off-the-shelf models, and the moment constraint [7, 37] is one of them. Given mapping X R (e.g.. Y can be classifier that outputs the probability simplex), the moment constraint defines the target distribution p(x (C) as
p(x|C)= arg min DD (p(x)()llpeect)). plan C)
S.t.
E Basp(ate) (x()) = μ
(5)
construint
Deviation from the pre trained distribution
where μ is the user-specified vector. For example, if want generate images that are uniformly distributed across races, we may use a race classifier as y and define (IAI-),.... |A (1)) where A is the set of races. It means that would like to find distribution that stays close the original, pre-trat pre-trained generative model G' and 2) mitigates G's bias. In this paper, we generalize the moment constraint to the latent space, and approximate the above objective as (detailed in Appendix B.6);
P=(z) exp 3 (T(=))
p(z(C)= =
Z =
(z(x') exp (BT (TT(G((²)) dz', N レー=ı exp (B ( (z))
(6)
3 = arg min (N) P. (x))
(7)
Σy- exp (3 T 7 (xxld'))
Notably, to optimize in Eq. (7), we leverage existing gradient-based optimization tools such as stochastic gradient descent (SGD) and Adam [38].

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050 log detí
fyu
Io
Bel
8E
INN fo
(( P (optional)
Figure 3: Illustration for Algorithm In the forward pass (solid curves), we sample e ~ NV I). map to latent code fc(c) with INN fo. and map image G(z) with fixed generative model G. Dashed curves show the gradients. Details are provided in Section 3.2.
Algorithm 2: Approximating Latent-Space EBM with INN
while not converged do
1. Sample ~ (O,,
Map to latent code z = fo(C)
3. Map to an image a G(z)
4. Optimize with gradient Vo (- det( 0Sa log (z) + Fe((x))
dc
3.2
Approximating EBM with Invertible Neural Network
Given the functional form of the EBM (C= our next step to approximate it with an efficient sampling network. To achieve this, train distribution (=)2) that minimizes the KL divergence DKL (po( (2)](2)]122 (=))pppp(()))))))))))))))))))))) Estimating DKL (po (z) [[]p(((() requires easily sampling z ~ Po and tractably computing po(z), Inspired by [54], model pe with an INN So that defines a bijection
fo(e).
which has two merits besides the invertibility: (1) one can easily sample z by sampling e ~ (O and mapping it fo( and (2) (=(=) 2)) has closed-form solution:
050 Ofo)1. de
log po =) log (c I) log det(
fo(c).
(8)
Based on these properties of INN, we can rewrite our KI. divergence objective DAL (po))))((((C()) as (full derivations Appendix B.3) the following form:
(=(2)
(pL)))))) = Ex Px~po(x) log
(z)e Fzc /Z
(9)
-Be. [- log det(
-log
0
log P= (2) + Ec(x)) H. + log Z.
de
Since H.(o,) and log Z are independent of 0, our training objective becomes
arg min
-
log |det(Ofo/Oe)
log logp=(z) + Ec(x)
(10)
Eye
Cusengttrr
Cenery
Figure gives an illustration our process, and Algorithm describes the algorithmic details.
PromptGen in a class-embedding space (Figure 4(g) Previous works [4, 64] have shown that class conditioning boosts generative models' performances on ImageNet [62]. Specifically, class- conditioned generative models map a latent code z and a class embedding to x G(z, y). To extend PromptGen to these models, we train an INN ho to map & ~ WV,,, I²I) to y = h where and o are the mean and standard deviation of G's class embeddings. The motivation for defining the distribution of ((,, a 2 but not (0, J) that we want the learned INN ho to be volume- preserving, which is casier to train. The training objective is to minimize DKL (pe(= (z, y) f)lpp(z y(C)).
5

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which is equivalent to (full derivations in Appendix B.4):
log det( a50- de ) 1og
arg min
(11)
log det( Tho
) log P + Ec (z)
ag
where Py (v) = NNull,, I2 is the estimated class-embedding distribution.
PromptGen with conditional INN To generalize the control continuous values, e.g., the scene parameters p in Eq. (4), we condition the INN on p. The condition is modeled by replacing = = fe(e) with = (c. p). Space limited, we provide details and derivations in Appendix B.5. This extension results in similar architecture to StyleFlow [1]. but StyleFlow [1] uses MLE training on image-label pairs and is only applicable to explicit condition, which is not capable of modeling EBMs. When the condition is modeled by the equivariant operations proposed by [79], PromptGen also satisfies the homomorphism property [79] in the latent space.
(a) w/ StyleNeRF on FFH (baby)
(b) w/ NVAE FFHQ (baby)
(c) w/ DiffAE on FFHQ (baby)
(d) w/ StyleGAN2 on AFHQ-Cat (British shorthair)
(c) w/ StyleGAN2 on Landscape (autumn)
(f) w/ StyleGAN2 on Landscape (winter)
photo of glow and light dog
An ink wash a church near forest under moonlight
(g) w/ BigGAN trained on ImageNet 512"
Figure 4: PromptGen is applicable to different generative models in various domains. Figure 4(a),
Figure 4(b), and Figure 4(c) are PromptGen applied to StyleNeRF [21], NVAE [75], and diffusion autoencoder [57], with text description a photo of a baby. Figure 4(d) shows PromptGien applied to StyleGAN2 [36] trained
AFHQ-Cats [6], with text description photo of British shorthair cat Figure 4(e) and Figure 4(f) are PromptGien applied to StyleGAN2 trained on Landscape-HO [70], with text descriptions photo of /autumn, winter) scene, Figure 4(g) is the extension to the embedding space of BigGAN [4] on ImageNet [62]. All images are resized for visualization. See Appendix C for results on more datasets and text descriptions.

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(a) StyleCLIP [56]
(h) StyleGANZ-NADA [17]
(c) PromptGen (ours) w/ StyleGAN2
Image synthesis based on text description, guided by the CLIP model. As the pre-trained generative model, we use StyleGAN2 trained on FFHQ 1024 [35] with truncation y = 0.7. The text
Figure 5:
description used in this experiment is photo of baby. StyleCLIP requires optimization at inference, while StyleGAN2-NADA and PromptGen do not. All images are resized for visualization.
4
Experiments
This section describes the experimental validation of PromptGen. See Appendix A for experiments on synthetic data and Appendix C, D, E, and F for additional experiments on images and 3D meshes. Additional experimental details are provided in Appendix B.8.
4.1 Image Synthesis based on Text Description
This experiment illustrates the capability of PromptGen sample images from generative model driven by a text description using the pre-trained CLIP model [58] (the ViT-B/32 version). We used the CLIP energy from Eq. (3), with text descriptions such a photo of baby.
Figure shows a comparison between PromptGen and two previous -LIP-guided image generation methods, StyleCLIP [56] and StyleGAN2-NADA [17]. We observe that PromptGen generates diverse and high-quality images of babies, while StyleCLIP struggles in controllability and image quality, and StyleGAN2-NADA generates baby-like adults.? These results show that (1) locally editing the latent code (i.e., StyleCLIP) is not always an effective method for controlling generative models (e.g.. not all images' latent code can be locally edited into a baby); (2) domain adaptation (i.e., StYLGGAN-NADA) is not effective in seeking modes in a generative model. Figure 4(a), Figure 4(b), and Figure 4(c) show how PromptGen applies to StyleNeRF [21], NVAE [75], and diffusion autoencoder (DiffAE; [57]). Although these generative models are all trained on FFHQ, baby images sampled from them have distinct characteristics. Figures 4(d)-4(f) show PromptGen applied to and landscape generation. Figure 4(g) shows the extension (Section 3.2) of our PromptGen to the class-embeddingg space of BigGAN [4], a class-conditional GAN trained on ImageNet [62]. We observe that PromptGen helps BigGAN generate images with complex text descriptions, which are out of BigGAN's training image distribution; however, the diversity seems to limited in this extension.
Zace
Aa
Pace
Pegpetion 0
R-22
Maille Neclew
Progractoo
JKL
Progurtion
SYAGANG
Prompkan
ReaGAN2
Modid
Moddd
Model
(a) Race dist. (FFHQ)
(b) Age dist. (FFHQ)
(c) Race dist. (MetFaces)
Figure 6: With the moment constraint, PromptGen de-biases StyleGAN2 (on FFHQ and MetFaces 10243, truncation va 0.7). See Appendix D for image samples and Table for quantitative results.
4.2 De-Biasing Fre-Trained Generative Models
An important problem in generative models is to generate fair distributions w.r.t a set of attributes of interest. For instance, Figure 6(a) shows that StyleGAN2 generates images with bias across races and
Since about distributions, none the images in this paper cherry- lemon-picked.
7

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ages. PromptGen de-biases StyleGAN2 models trained on FFHQ 1024 and MetFaces 1024 [33] in terms of categorical attributes, using the moment constraint defined Eq. (5) and Eq. (6). As control, we used a classifier trained on FairFace 224 [32] as 7 We defined μ (IAI-1 ..... |AI-)), where is the set of races. Similar to the energy weights X; defined in Eq. (2), we propose rescale the trained B3 as X3. Figure shows that PromptGen de-biases the race and age effectively.
Existing de-biasing baselines consider binary attributes [31]. For a fair comparison with them, we adopted their setting to use binary classifiers trained on CelebA [47] for de-biasing and evaluation. Since classifiers trained on CelebA also suffer from the spurious correlation between attributes, we did not use the moment constraint for this experiment. Instead, since PromptGen allows conditional image generation with the classifier energy, we generated the same number of samples conditioned on each attribute or attribute combination. Table 2 shows that PromptGen has competitive performance on de-biasing for attributes and attribute combinations.
Table 2: Comparison with baselines for de-biasing binary attributes and their correlations. Following [31], we use classifiers on CelebA [47]. Baseline performances are copied from [31]. PromptGen has competitive performance, even in the cases where FairStyle achieves nearly perfect performance.
FFHQ (binary attributes)
geender
Deveglasses
D Mend hair
D gender
age
D K1.
age aygglayyelssss
- eyeglasses
D
FFHQ (real data)
0.015
0.186
0.246
0.355
0.242
StyleGAN2 [36]
0.018
0.180
0.279
0.384
0,250
StyleHlow [1]
0.023
0.061
01.214
0.162
0.121
FairGen [72]
4.21 x10
7.07 x10
0.0373
0.0330
0.00185
FairStyle [31]
3,20 x10-7
0
0,0257
0.0157
0.000241
PromptGen (ours)
1.71 x x10
1.72 x x10
0.0008
0.000558
0.000415
0.000628
Figure 7:
Using inverse graphics model DECA [16] and scene parameters Prese E S3(3), PromptGen controls the pose of StyleGAN2 while preserving the identity, All images are resized for visualization.
Table 3: Pose-controlled face generation. In this experiment, PromptGin uses StyleGAN2 as the pre-trained generative model. Results for GRAF [66], pi-GAN [5], GIRAFFE [53]. and StyleNeRE [21] are from [21]; results for DiscoFaceGAN (DFG) [9] and
[69] are from [69].
StyleGAN2
GRAF
Pi-GAN
GIRAFFE
StyleNeRF
DFG
GAN-Control
PromptGen
Resolution
1024
256
2562
2562
1024
256
5122
1024
Pose
X
V
V
V
FIDI
3
71
85
35
13
Dist. w/ same IDJ
0.83
0.68
0.45
Dist. w/ diff. ID
1.73
1.90
1.37
4.3 Pose-Guided Face Synthesis
With an inverse graphics model, PromptGen can control the pose of faces generated by StyleGAN2.
We used the DECA model [16], which infers the parameters of FLAME [44], parametric facial graphics model. We set p E SO(3) as FLAME's three neck poses and used the conditional INN
extension introduced Section 3.2. To enable generating different poses of the same identity (ID), we propose II) energy using the IR-SE50 model [8]. Specifically, given canonical pose Po. we define 20 fe Po), and the ID energy defined as (detailed in Appendix B.7)
EiD x) 1 cos (R(zo), R(x)), To G(zo), G(z),
(12)

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where R is the IR-SE50 model [8] that computes face embeddings. Figure shows that PrompiGen generates faces of the same ID in different poses, even without being explicitly trained with poses as conditions. We computed the FID score [24] of each model using Clean-FID [55]; following [36], we did not use the truncation trick when computing the FII score. Table 3 shows that PromptGen outperforms existing models in terms of the FID score. Following [69], we then reported the average IR-SE50 [8] embedding distances for images with the same ID and with different IDs. Results show that PromptGen achieves the best ID preservation, with a slight sacrifice of ID diversity.
4.4 Iterative Distributional Control via Functional Composition
This section discusses an interesting bias that PromptGen reveals about the CLIP model. Figure 8(a) shows images generated by PromptGen (with StyleGAN2) with the CLIP model and the text de- scription a photo a person without makeup, where more females are generated than males. This bias should not be attributed to image pre-training data since images contain bias in the opposite direction, i.e., men are less likely to have makeup. We argue that this bias should be explained by CLIP having learned "reporting bias" in vision-language pre-training data: people are more likely to say "a person without makeup" when the person is female (detailed analysis in Appendix F).
Besides revealing the above "reporting bias", PromptGen control, enabled by the functional composition in Algorithm Specifically, in the second iteration, we de-biased the gender distribution of G' fo instead of G, where fo is the INN learned for the text control. For de-biasing, we used the moment constraint with & trained for G fo. Figure 8(b) shows that females and males are uniformly distributed after the moment constraint in the second iteration.
also mitigate this bias via iterative
PromptGen (iteration with text photo of person without makeup. Gender distribution: female: 81.6%; male: 18.4%.
(b) PromptGen (iteration 2). Gender de li-biising (A 2) for the distribution learned iteration Gender distribution: female: 49.3%; male: 50.7%.
Figure 8: A curious case of the CLIP model: with description photo of person without makeup, PromptGen generates more female images than male, showing that CLIP learns a "reporting bias". Besides revealing this "reporting bias", PromptGen can also mitigate this bias via iterative control, allowing us to de-bias the text-controlled distribution. All images are resized for visualization.
4.5 Inference Latency
We compared our PromptGin and the plug-and-play generative model (PPGM) [49] in terms the inference latency. PPGM uses Langevin dynamics [76] to optimize over the latent space at inference, while PromptGin samples images feed-forward manner. In this experiment, we used PPGM and PromptGen to approximate the same EBM with CLIP energy. Inference times were estimated on

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an NVIDIA RTX A4000 GPU. Table 4 shows that our PromptGen has the highest performance and efficieney. Compared our PrompiGen, PPGM requires 100x inference time achieve comparable results. Moreover, learning So in PromptGen requires training-time optimization, and this amortized optimization useful when one wants to reuse a controlled distribution many times.
Table 4: Comparison between PromptGen and PPGM, when upproximating the same EBM with CLIP energy. n the number of inference-time optimization steps used by PPGM. All models used Style- GAN2 as the pre-trained generative model. The text descriptions
photo of a (baby, boy, girl).
PPGM (n = 10)
PPGM (n 50)
PromptCien (ours)
CLIP energy (baby)( CLIP energy (girl)
0.7327
0.7134
0.7038
0.7257
0.7184
0.7199
CLIP energy (boy))
0.7263
0.7114
0.7081
Inference time per sample (sec.) Back-propagation through CLIP inference
4.4
21.5
0.2
10
50
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Conclusions and Future Work
This paper proposes PromptGen, unified framework to learn latent distributions for distributional control of pre-trained generative models, by leveraging the knowledge of other off-the-sheif models. Unlike previous approaches, which require optimization for generating each sample at inference, PromptGen trains an invertible neural network to approximate the latent distribution. Thus, Prompt- neural
Gen can be viewed as amortization of the inference-time optimization into network, allowing for efficient, Teed-forward sampling at inference. Further, this amortization also makes PromptGen not require the availability of those off-the-shelf models at inference. In practice, PromptGen offers generality for algorithmic design and modularity for control composition, and it also enables iterative controls via functional composition. Experiments validate that PromptGen applies to various generative models (StyleGAN2. StyleNeRF, diffusion autoencoder, and NVAE), control types (continuous, discrete, and moment constraint), off-the-shelf models (CLIP, classifiers, and inverse graphics models), and data domains (faces, churches, animals, ImageNet, and landscapes).
Limitation and future work: We provide an error analysis in Appendix F and a discussion on societal impact in Section 6. PromptGen is restricted by the pre-trained generative model's coverage [3, 28]. Notably, since PromptGen focuses on mode-seeking and reweighting instead of domain adaptation, the support set of the output distribution will not change after PromptGen learning. We find it interesting to further explore combining PromptGen and domain adaptation [17] achieve better generalization and adaptability. PromptGen depends on the off-the-shelf models that provide knowledge about the control (Appendix F); it can be beneficial to explore how to learn energy functions [15, 27] (besides our moment constraint) for fine-grained control with less bias.
Societal Impact
Like any technology, PromptGen has both benefits and drawbacks for society. On the positive side, we have demonstrated that PromptGien can uncover biases learned by text-image models (like CLIP) and de-bias text-image models and pre-trained generative models, suggesting that PromptGen may be helpful tool for fair AI if used properly. The efficient inference of PromptGen also helps reduce the computational expense, giving a positive impact on the environment. However, improved controllability makes it easier to synthesize targeted images; for instance, the creation of deceptive media such as DeepFakes [77, 74] and privacy leakage. To battle these cases, we expect researchers and practitioners to use technologies that can detect fake media and mitigate privacy leakage. We also encourage practitioners to consider these risks when using PromptGen to develop systems.
Acknowledgments and Disclosure of Funding
The authors would like to thank the anonymous reviewers, Zoltán Ádám Milacski, Jianchun Chen, and Shubhra Aich for their valuable feedback drafts of this paper.
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Checklist
1. For all authors...
(a) Do the main claims made in the abstract and introduction accurately reflect the paper's
contributions and scope? [Yes]
(b) Did you descrihe the limitations of your work? [Yes]
In Section 5 and Appendix F.
(c) Did you discuss any potential negative societal impacts of your work? [Yes]
In Section 5.
(d) Have you read the ethics review guidelines and ensured that your paper conforms to them? [Yes]
2. If you are including theoretical results...
(a) Did you state the full of assumptions all theoretical results? [Yes]
In Section 3 and Appendix B.
(b) Did you include complete proofs of all theoretical results? [Yes]
In Section and Appendix B.
3. If you ran experiments...
(a) Did you include the code, data, and instructions needed to reproduce the main experi- mental results (either in the supplemental material or as URL)? [N/A] We will make our code publicly available on GitHub, with instruction for each experi- ment. We made great efforts to ensure that most experiments will be run in one line of command.
(b) Did you specify all the training details (e.g.. data splits, hyperparameters, how they were chosen)? [Yes]
In Appendix B.8; other training details will be included in the code release.
(c) Did you report error bars (c.g., with respect to the random seed after running experi- ments multiple times)? [No]
(d) Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] In Appendix B.8.
4. If you using existing assets (e.g., code, data, models) curating/releasing assets...
(a) If your work uses existing assets, did you cite the creators? [Yes] We have cited the original papers.
(b) Did you mention the license of the assets? [Yes]
License will be included in the code.
(c) Did you include any new assets either in the supplemental material or as a URL? [N/A] We do not have new assets.
(d) Did you discuss whether and how consent was obtained from people whose data you're using/curating? [Yes]
All data
published datasets.
(c) Did you discuss whether the data you are using/curating contains personally identifiable information or offensive content? [N/A]
All data use are published datasets.
5. If you used crowdsourcing conducted research with human subjects...
(a) Did you include the full text of instructions given to participants and screenshots, if applicable? [N/A]
We do not have user study.
(b) Did you describe any potential participant risks, with links to Institutional Review Board (IRB) approvals, applicable? [N/A]
We do not have user study.
(c) Did you include the estimated hourly wage paid participants and the total amount spent on participant compensation? [N/A]
We do have user study.
16


Reviews:
[{"review_id": "q0cBDN592SC", "reviewer": "Reviewer_byc9", "summary": "This paper introduces an approach to use supervision from off-the-shelf pre-trained models, including classifiers, scoring functions, and multi-modal encoders, as controls for the generation of images with custom properties. In particular, the proposed approach transforms random noise, using an invertible neural network, that is fed to a pre-trained generative model.", "questions": "* The authors attempt at differentiating this paper from prior work suggests that [prior methods] “are either model-dependent (i.e., requiring a well-structured style space) or label-intensive (i.e., requiring all training samples to be labeled for explicit conditions), limiting their generality and practical use.” However, there are several prior works that enable sampling from a specific region of the latent space in a frozen generative model without doing any optimization at inference time (e.g. [1]) (not needing labels at training time, using a frozen generative model, using interpolation of previously learnt vectors at inference time, applicable to both GAN and VAE models). Additionally, it is unclear what being “model-dependent” means here. Does it mean learning to navigate the latent space that is specific to a single pre-trained generative model and may not generalize to other pretrained generative models? If so, the parameters $\\theta$ in PromptGen also rely on the pretrained generative model at hand (In Algorithm 2, the gradients pass through the frozen $G_{\\phi}$.) Conceptually, both PromptGen and the aforementioned approaches apply to different pre-trained generative models, though the parameters are going to be tuned for each frozen model. The authors might have missed such prior research in their literature review or the writeup does not properly communicate that. Please clarify the distinctions and positioning of this work.\n\n*  Method clarification: It is unclear where the different formulations of energy for the classifier/CLIP type models/and scoring functions (i.e. the inverse graphics case) come from. Is this well-defined in an EBM? Is this inspired by prior work? Is this a design choice? \n\n*  Prompt tuning uses a (small) dataset for tuning additional parameters that are (usually) prepended to a meaningful (textual) input to steer the model’s outputs in a direction of interest. Some variants do it in the discrete natural language format and some in continuous embedding space. Some form of concatenation to an existing meaningful input is the fingerprint of what is usually dubbed as “prompt tuning”. So in my opinion, drawing a parallel between “learning transformations over random noise” and “prompt tuning” seems a bit tangential and potentially distracting at first. Perhaps authors meant other connections? If yes, would appreciate clarifications.\n\n[1] Shen, Yujun, et al. \"Interpreting the latent space of gans for semantic face editing.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020.\n", "limitations": "The authors have briefly described the societal impact of their work. Additionally, one can argue that more granular controls for generative models might make it easier for generating targeted Deepfakes and its resulting potential negative impacts.", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "+This paper includes a through set of experiments that showcase the effectiveness of the proposed approach across several pre-trained generative models (e.g. StyleNeRF, NVAE, BigGAN, StyleGAN2) and different types of controls (binary properties, continuous values, text descriptions). \n\n+A unified approach to custom controls for successfully steering pre-trained generative models can facilitate several down-stream applications and has high significance. In particular, success in compositional controls makes this work even more interesting.\n\n-Both the positioning of the work and the explanation of the method can be further clarified.  See below for more details.\n", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}, {"review_id": "_bhYvOU8pN8", "reviewer": "Reviewer_5eQN", "summary": "This work proposes a method to control pre-trained generative models (e.g. to condition samples on a text prompt, or to control the value of an attribute, or to debias samples). The control can be specified using a different model (e.g. CLIP or inverse graphics or a classifier) via an energy-based formulation that outputs a controlled-version of the pre-trained generative model. ", "questions": "- Functional composition is an important property that needs further evidence. Consider providing results that show how well CLIP captions can be composed (e.g. C_1=“photo of an Asian man” and C_2=“photo of a person with eyeglasses” and C_3=“photo of a bald person”). If you could expound on the performance difference between using a multi-component control C (as suggested in Figure 2’s caption) or using two separate controls C_1 and C_2, that would be super helpful.\n- Table 2: could you please add at least one more column here so we can ensure the attributes aren’t cherry-picked? How about hairColor (say “black” if you need a binary attribute)?\n- Fig 9: the latents learnt by the GAN looks fairly well separated between the two clusters. How well is PromptGen able to cope with less structured latent spaces?\n- Could you clarify whether equation 8 holds for all invertible NNs, or only the architecture you've chosen?", "limitations": "Section F in the appendix covers some interesting failure modes. But it places the burden of failure mostly on the pre-trained generative model or control model. It would be nice to see where the overall idea/method might fail.", "rating": 7, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "**Strengths**\n- Compatibility with a wide range of control models (classifiers, inverse graphics, embedding)\n- Compatibility with a wide range of generative models (results include StyleNerf, NVAE, StyleGAN2, and BigGan)\n- Compositionality of controls\n- No optimization at inference\n- The writing is easy to follow. (But here's a specific point. The following motif is repeated at several points in the paper: PromptGen leverages “the knowledge of various off-the-shelf models” for distributional control of pre-trained generative models. It is quite unclear especially in the abstract what “off-the-shelf models” you are referring to, and whether you are still talking about generative models. Perhaps it would be clearer to state that you are trying to control pre-trained generative models using knowledge from “other models” rather than “off-the-shelf models”?)\n\n**Weaknesses/concerns**:\n- Most results are on faces. The authors could have evaluated on different data or chosen a wider variety of prompts. So far it is only clear that the model can control/yield pictures of babies, cats, and persons (especially controlling for age, make-up, or race).\n- When evaluated on non-facial data, the results lack diversity (PromptGen on ImageNet 512 in Figure 5(d)). “A photo of a glow and light dog” seems to yield a single specimen. The melancholy robot always has the same helmet and the background is a consistent color. There’s no further results from ImageNet in the supplementary. Also the size of the generated samples is too small given they were 512x512.\n- CLIP control doesn’t always seem to work well e.g. in Fig 12(a), at least a third of the images have cats with open eyes. It is unclear whether it is CLIP to blame, or a bias in the image dataset.", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}, {"review_id": "_GN8gxqmk-", "reviewer": "Reviewer_QXTM", "summary": "This work proposes a unified method called PromptGen to control the image synthesis of pre-trained generative models, such as StyleGAN, NVAE and StyleNeRF. The basic idea is to 1) first formulate the latent variable z distribution conditioned on the control \\mathcal{C}, i.e., $p(x|\\mathcal{C})$ as a latent-space EBM, where the latent-space EBM can use pre-trained image classifiers, CLIP model and inverse graphics model to specify the control; and 2) train an invertible neural network (INN) to approximate the latent-space EBM using an KL divergence as the training objective. In experiments across different image datasets, this work shows the efficacy and efficiency of PromptGen in the tasks of image synthesis based on text description, de-biasing generative models, pose-guided face synthesis, and iterative control via functional composition.\n", "questions": "(1) Can more quantitative results on the controllability be added to show the performance of PromptGen? \n\n(2) In Figure 5(d), I think we see a severe mode collapse issue, as the background colors and object patterns are very similar across different generated images. How do you explain this phenomenon?\n\n(3) For the text-guided generation, can PromptGen perform well with more complex sentences, where we have a composition of multiple attributes, such as “a photo of a smiling baby with glasses”? These kinds of results will better demonstrate the compositionality of the proposed method. \n\n(4) For the generative models with a class-embedding space, I wonder why we need to train another latent-space generator $y=h_\\theta(\\xi)$? I feel like we can also directly train a single latent-space generation $z=f_\\theta(\\epsilon, y)$ to approximate the EBM. Any justification?\n", "limitations": "This work has well addressed their limitations. But I didn’t see many discussions about their negative societal impact. I think this work shares with other image synthesis tools similar potential benefits and risks, which have been discussed extensively in [1]. I suggest adding more discussions about the risks of controllable image synthesis. \n\n[1] Vaccari, C. and Chadwick, A. (2020). Deepfakes and disinformation: Exploring the impact of synthetic political video on deception, uncertainty, and trust in news. Social Media+ Society. \n", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n\n(1) Controlling generative models using EBM with MCMC Langevin dynamics is effective but slow (it needs multiple inference/optimization iterations). Thus, this work proposes to train another latent generator $z=f_\\theta(\\epsilon)$ that approximates the latent-space EBM, so the sampling can be performed with one forward pass, which I think is the main originality and significance of this work.\n\n(2) For the second contribution, with the latent generator $z=f_\\theta(\\epsilon)$, this work also demonstrates 1) the generality for different controllable generation tasks, and 2) the iterative controls by composing the latent generators and the pre-trained generative model. \n\n(3) The paper is well-written and easy to read. Extensive experiments were performed to show the wide applicability, effectiveness and efficiency of the proposed method.\n\nWeaknesses:\n\nMy main concern is that in experiments, this work didn’t provide sufficient quantitative results of evaluating the controllability. I like the quantitative results of evaluating the de-biasing performance, but regarding other controllable generation tasks, such as text-guided generation and pose-guided generation, I didn’t see the quantitative results of how the proposed method controls the generation to satisfy the specified attribute or text. \n\nFor other concerns, please see the questions below.\n", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}, {"review_id": "X_hfVOZcQe", "reviewer": "Reviewer_gbfq", "summary": "This paper proposes PromptGen, a method for sampling pre-trained generator networks (e.g. StyleGAN) conditioned on a prompt (label, text, etc) using off-the-shelf discriminative models (classifiers, CLIP etc). The proposed method uses an invertible neural network (INN) to express a distribution p(z ; C) over generator latents z given constraint C. A prompted sample is obtained by sampling p(z ; C) and then passing through the generator to obtain a data-space sample (all experiments are conducted with images but in principle the method could be applied to other modalities). INNs are trained either for a specific instant of the prompt C, or in a prompt conditional setting.\n\nThe method is demonstrated on a range of tasks, including de-biasing StyleGAN, and generating conditioned on a text prompt (with CLIP).\n\n\n\n", "questions": "* The prospect of using such models to reveal the limitations of pre-trained models (e.g. CLIP and the makeup example) is potentially very useful. But how do we know if the limitation belongs to the prompting model, or if it is a limitation of the INR-based p(z ; C)? For some prompts, p(z ; C) might be very complex, and therefore challenging to learn. So how do we know that we haven't just failed to (fully) learn the mapping when training the INR? If there isn't a good way of knowing, then this is a limitation, as in general it won't be possible to confidently use the proposed method to diagnose other models.\n* For the text conditional experiments, is the INN trained just for a single prompt? Why not train a text-conditional model? It is impractical to train separate models for each text input, and is a limitation if it is not possible with the proposed approach.\n* Can PromptGen be applied to diffusion models?   \n* Could you expand on this comment in the limitations:  \"Except for the case of generative models with a class-embedding space, PromptGen focuses on mode-seeking and mode-reweighting instead of domain adaptation\" - I didn't really understand what was meant by domain adaptation vs mode seeking in this context. ", "limitations": "* The authors adequately addressed the fact that the method is dependent on the data coverage of the generator network used. E.g. if you're using StyleGAN you're going to get faces and not spaceships. \n* The need to train a potentially very powerful conditional INN for complex tasks such as text-conditional generation wasn't really addressed (as mentioned in the weaknesses section). For this use case the burden of training such a network could easily be so high that a practicioner may prefer an iterative inference time method (like PPGN), even with its higher inference costs.\n* Potential negative societal impacts were not really discussed, however the experimental work on de-biasing pre-trained models (StyleGAN, CLIP) communicates to the reader that bias is an issue with such models that should be addressed.", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths\n* Unlike iterative inference time methods like PPGM, PromptGen can be applied (after training) to sample latents in a single forward pass, resulting in significantly improved inference speeds. This is particularly useful in cases where the INN can effectively represent a constraint that can be applied to all samples, e.g. StyleGAN race de-biasing. \n* The compositionality of the proposed method, where prompting mechanisms can be applied in sequence, is a desirable property. \n* The experiments: race / gender de-biasing, text-conditioning are well chosen for their real-world importance.\n\nWeaknesses\n* The broad usefulness of the method depends on the quality and coverage of the pre-trained generator network. GAN generators in general are prone to mode-dropping, especially those trained on complex natural image distributions (BigGAN). This is addressed in the limitations section.\n* For complex conditional tasks, like text-conditioned generation, the INN will have to learn a very complex mapping (e.g. from text -> latents). This will require lots of data / compute / optimization etc and as such might be prohibitive in practice. \n* In general there is a balance between the benefits of amortization: fast inference speeds, and the costs: training time and compute. And which is preferable depends on a user's constraints. I think the paper would benefit from a discussion of these issues. 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Author rebuttal:

Decision: Accept