Original: D IFFUSION M ODELS ALREADY HAVE A S EMANTIC L ATENT S PACE
Revised: D IFFUSION M ODELS ALREADY HAVE A S EMANTIC L ATENT S PACE

Original: A BSTRACT
Revised: A BSTRACT

Original: Diffusion models achieve outstanding generative performance in various domains.
Revised: Diffusion models achieve outstanding generative performance in various domains.

Original: Despite their great success, they lack semantic latent space which is essential for controlling the generative process.
Revised: Despite their great success, they lack semantic latent space which is essential for controlling the generative process.

Original: To address the problem, we propose asy mmetric r everse p rocess (Asyrp) which discovers the semantic latent space in frozen pretrained diffusion models.
Revised: To address the problem, we propose asy mmetric r everse p rocess (Asyrp) which discovers the semantic latent space in frozen pretrained diffusion models.

Original: Our semantic latent space, named h-space , has nice properties to accommodate semantic image manipulation: homogeneity, linearity, robustness, and consistency across timesteps.
Revised: Our semantic latent space, named h-space , has nice properties to accommodate semantic image manipulation: homogeneity, linearity, robustness, and consistency across timesteps.

Original: In addition, we introduce a principled design of the generative process for versatile editing and quality boosting by quantifiable measures: editing strength of an interval and quality deficiency at a timestep.
Revised: In addition, we introduce a principled design of the generative process for versatile editing and quality boosting by quantifiable measures: editing strength of an interval and quality deficiency at a timestep.

Original: Our method is applicable to various architectures (DDPM++, iDDPM, and ADM) and datasets (CelebA-HQ, AFHQ-dog, LSUN-church, LSUNbedroom, and M ET F ACES ).
Revised: Our method is applicable to various architectures (DDPM++, iDDPM, and ADM) and datasets (CelebA-HQ, AFHQ-dog, LSUN-church, LSUNbedroom, and M ET F ACES ).

Original: Project page: https://kwonminki.github.io/Asyrp/
Revised: Project page: https://kwonminki.github.io/Asyrp/

Original:  I NTRODUCTION
Revised:  I NTRODUCTION

Original: In image synthesis, diffusion models have advanced to achieve state-of-the-art performance regarding quality and mode coverage since the introduction of denoising diffusion probabilistic models (Ho et al., 2020).
Revised: In image synthesis, diffusion models have advanced to achieve state-of-the-art performance regarding quality and mode coverage since the introduction of denoising diffusion probabilistic models (Ho et al., 2020).

Original: They disrupt images by adding noise through multiple steps of forward process and generate samples by progressive denoising through multiple steps of reverse (i.e., generative) process.
Revised: They disrupt images by adding noise through multiple steps of forward process and generate samples by progressive denoising through multiple steps of reverse (i.e., generative) process.

Original: We refer to the intermediate noisy images as latent variables.

Original: Since their deterministic version enables nearly perfect reconstruction of input images (Song et al., 2020a), they are suitable for image editing, which renders target attributes on the real images.
Revised: Since their deterministic version provides nearly perfect reconstruction of original images (Song et al., 2020a), they are suitable for image editing, which renders target attributes on the real images.

Original: However, simply editing the latent variables  causes degraded results (Appendix D5 in Kim & Ye (2021)).
Revised: However, simply editing the latent variables (i.e., intermediate noisy images) causes degraded results (Kim & Ye, 2021).

Original: Instead, they require complicated procedures: providing guidance in the reverse process or finetuning models for an attribute.
Revised: Instead, they require complicated procedures: providing guidance in the reverse process or finetuning models for an attribute.

Original: Figure 1(a-c) briefly illustrates the existing approaches.
Revised: Figure 1(a-c) briefly illustrates the existing approaches.

Original: Image guidance mixes the latent variables of the input image with unconditional latent variables (Choi et al. (2021), Lugmayr et al.
Revised: Image guidance mixes the latent variables of the guiding image with unconditional latent variables (Choi et al., 2021; Lugmayr et al., 2022;

Original: , Meng et al.
Revised: Meng et al., 2021).

Original: Though it provides some control, it is ambiguous to specify which attribute to reflect among the ones in the input and  unconditional result and it lacks intuitive control for the magnitude of change.
Revised: Though it provides some control, it is ambiguous to specify which attribute to reflect among the ones in the guide and the unconditional result, and it lacks intuitive control for the magnitude of change.

Original: Classifier guidance manipulates images by imposing gradients of a classifier on the latent variables in the reverse process to match the target class (Dhariwal & Nichol (2021), Avrahami et al.
Revised: Classifier guidance manipulates images by imposing gradients of a classifier on the latent variables in the reverse process to match the target class (Dhariwal & Nichol, 2021;

Original:  Liu et al.
Revised: Avrahami et al., 2022; Liu et al., 2021).

Original: It requires training an extra classifier for the latent variables, i.e., noisy images.
Revised: It requires training an extra classifier for the latent variables, i.e., noisy images.

Original: Furthermore, computing gradients through the classifier during sampling is costly.
Revised: Furthermore, computing gradients through the classifier during sampling is costly.

Original: Finetuning the whole model can steer the resulting images to the target attribute without the above problems (Kim & Ye, 2021).
Revised: Finetuning the whole model can steer the resulting images to the target attribute without the above problems (Kim & Ye, 2021).

Original: Still, it requires multiple models to reflect multiple descriptions.
Revised: Still, it requires multiple models to reflect multiple descriptions.

Original: On the other hand, generative adversarial networks (Goodfellow et al., 2020) inherently provide straightforward image editing in their latent space.
Revised: On the other hand, generative adversarial networks (Goodfellow et al., 2020) inherently provide straightforward image editing in their latent space.

Original: Given a latent vector for an original image, we can find the direction in the latent space that maximizes the similarity of the resulting image with a target description in CLIP embedding (Patashnik et al., 2021).
Revised: Given a latent vector for an original image, we can find the direction in the latent space that maximizes the similarity of the resulting image with a target description in CLIP embedding (Patashnik et al., 2021).

Original: The latent direction found on one  image leads to the same manipulation of other images.
Revised: The latent direction found on one [Figure] image leads to the same manipulation of other images.

Original: However, given a real image, finding its exact latent vector is often challenging and produces unexpected appearance changes.[Figure]
Revised: However, given a real image, finding its exact latent vector is often challenging and produces unexpected appearance changes.

Original: It would allow admirable image editing if the diffusion models with the nearly perfect inversion property have such a semantic latent space.
Revised: It would allow admirable image editing if the diffusion models with the nearly perfect inversion property have such a semantic latent space.

Original: Preechakul et al.
Revised: Preechakul et al.

Original: (2022) introduces an additional input to the reverse diffusion process: a latent vector from an original image embedded by an extra encoder.
Revised: (2022) introduces an additional input to the reverse diffusion process: a latent vector from an original image embedded by an extra encoder.

Original: This latent vector contains the semantics to condition the process.
Revised: This latent vector contains the semantics to condition the process.

Original: However, it requires training from scratch and does not match with pretrained diffusion models.
Revised: However, it requires training from scratch and does not match with pretrained diffusion models.

Original: In this paper, we propose an asy mmetric r everse p rocess ( Asyrp ) which discovers the semantic latent space of a frozen diffusion model where modifications in the space synthesize various attributes on the input images.
Revised: In this paper, we propose an asy mmetric r everse p rocess ( Asyrp ) which discovers the semantic latent space of a frozen diffusion model such that modifications in the space edits attributes of the original images.

Original: Our semantic latent space, named h-space , has practical properties  for editing applications as follows.
Revised: Our semantic latent space, named h-space , has the properties necessary for editing applications as follows.

Original: The same shift in this space results in the same attribute change in all images.
Revised: The same shift in this space results in the same attribute change in all images.

Original: Linear changes in this space lead to linear changes in attributes.
Revised: Linear changes in this space lead to linear changes in attributes.

Original: The changes do not degrade the quality of the resulting images.
Revised: The changes do not degrade the quality of the resulting images.

Original: The changes throughout the timesteps are almost identical to each other for a desired attribute change.
Revised: The changes throughout the timesteps are almost identical to each other for a desired attribute change.

Original: Figure 1(d) illustrates some of these properties and § 5. provides detailed analyses.
Revised: Figure 1(d) illustrates some of these properties and § 5.3 provides detailed analyses.

Original: To the best of our knowledge, it is the first attempt to discover the semantic latent space in the frozen pretrained diffusion models.
Revised: To the best of our knowledge, it is the first attempt to discover the semantic latent space in the frozen pretrained diffusion models.

Original: Spoiler alert: our semantic latent space is different from the intermediate latent variables in the diffusion process.
Revised: Spoiler alert: our semantic latent space is different from the intermediate latent variables in the diffusion process.

Original: Moreover, we introduce a principled design of the generative process for versatile editing and quality boosting by quantifiable measures: editing strength of an interval and quality deficiency at a timestep.
Revised: Moreover, we introduce a principled design of the generative process for versatile editing and quality boosting by quantifiable measures: editing strength of an interval and quality deficiency at a timestep.

Original: Extensive experiments demonstrate that our method is generally applicable to various architectures (DDPM++, iDDPM, and ADM) and datasets (CelebA-HQ, AFHQ-dog, LSUN-church, LSUN-bedroom, and M ET F ACES ).
Revised: Extensive experiments demonstrate that our method is generally applicable to various architectures (DDPM++, iDDPM, and ADM) and datasets (CelebA-HQ, AFHQ-dog, LSUN-church, LSUN-bedroom, and M ET F ACES ).

Original:  B ACKGROUND
Revised:  B ACKGROUND

Original: We briefly describe essential backgrounds.
Revised: We briefly describe essential backgrounds.

Original: The rest of the related work is deferred to Appendix A.
Revised: The rest of the related work is deferred to Appendix A.

Original: 2. D ENOISING D IFFUSION P ROBABILITY M ODEL (DDPM)
Revised: 2. D ENOISING D IFFUSION P ROBABILITY M ODEL (DDPM)

Original: DDPM is a latent variable model that learns a data distribution by denoising noisy images (Ho et al., 2020).
Revised: DDPM is a latent variable model that learns a data distribution by denoising noisy images (Ho et al., 2020).

Original: The forward process diffuses the data samples through Gaussian transitions parameterized with a Markov process:
Revised: The forward process diffuses the data samples through Gaussian transitions parameterized with a Markov process:

Original: [Equation]
Revised: [Equation]

Original: where { β t } T t =1 is the variance schedule and α t = (cid:81) t s =1 (1 − β s ) .
Revised: where { β t } Tt =1 is the variance schedule and α t = (cid:81) ts =1 (1 − β s ) .

Original: Then the reverse process becomes p θ [Equation] x 0: T ) :
Revised: Then the reverse process becomes p θ ( x 0: T ) :

Original: = p ( x T ) (cid:81) Tt =1 p θ [Equation] x t − 1 | x t ) , starting from x T ∼ N (0 , I ) with noise predictor ϵ θt :
Revised: = p ( x T ) (cid:81) Tt =1 p θ ( x t − 1 | x t ) , starting from x T ∼ N (0 , I ) with noise predictor ϵ θt :

Original: [Equation]
Revised: [Equation]

Original: where z t ∼ N (0 , I ) and σ 2 t is a variance of the reverse process which is set to σ 2 t = β t by DDPM.
Revised: where z t ∼ N (0 , I ) and σ 2 t is a variance of the reverse process which is set to σ 2 t = β t by DDPM.

Revised: [Equation]

Original: 2. D ENOISING D IFFUSION I MPLICIT M ODEL (DDIM)
Revised: 2. D ENOISING D IFFUSION I MPLICIT M ODEL (DDIM)

Original: DDIM redefines Eq.
Revised: DDIM redefines Eq.

Original: (1) as
Revised: (1) as

Original: [Equation]
Revised: [Equation]

Original: which is a non-Markovian process (Song et al., 2020a).
Revised: which is a non-Markovian process (Song et al., 2020a).

Original: Accordingly, the reverse process becomes
Revised: Accordingly, the reverse process becomes

Original: [Equation]
Revised: [Equation]

Original: where σ t = η (cid:112) (1 − α t − 1 ) /
Revised: where σ t = η (cid:112) (1 − α t − 1 ) /

Original: (1 − α t ) (cid:112) 

Revised: (1 − α t ) (cid:112)

Original: − α t /α
Revised: − α t /α

Original: t − 1 .
Revised: t − 1 .

Original: When η = 1 for all t , it becomes DDPM.
Revised: When η = 1 for all t , it becomes DDPM.

Original: As η = 0 , the process becomes deterministic and guarantees nearly perfect inversion.
Revised: As η = 0 , the process becomes deterministic and guarantees nearly perfect inversion.

Original: 2. I MAGE MANIPULATION WITH CLIP
Revised: 2. I MAGE MANIPULATION WITH CLIP

Original: CLIP learns multimodal embeddings with an image encoder E I and a text encoder E T whose similarity indicates semantic similarity between images and texts (Radford et al., 2021).
Revised: CLIP learns multimodal embeddings with an image encoder E I and a text encoder E T whose similarity indicates semantic similarity between images and texts (Radford et al., 2021).

Original: Compared to directly minimizing the cosine distance between the edited image and the target description (Patashnik et al., 2021), directional loss with cosine distance achieves homogeneous editing without mode collapse (Gal et al., 2021):
Revised: Compared to directly minimizing the cosine distance between the edited image and the target description (Patashnik et al., 2021), directional loss with cosine distance achieves homogeneous editing without mode collapse (Gal et al., 2021):

Original: [Equation]
Revised: [Equation]

Original: where ∆ T = E T ( y target ) − E T ( y source ) and ∆ I = E I (cid:0) x edit (cid:1) − E
Revised: where ∆ T = E T ( y target ) − E T ( y source ) and ∆ I = E I (cid:0) x edit (cid:1) − E

Original: I
Revised: I

Original: ( x source ) for edited image x edit , target description y target , original image x source , and source description y source .
Revised: ( x source ) for edited image x edit , target description y target , original image x source , and source description y source .

Original: We use the prompts ‘smiling face’ and ‘face’ as the target and source descriptions for facial attribute smiling .
Revised: We use the prompts ‘smiling face’ and ‘face’ as the target and source descriptions for facial attribute smiling .

Original:  D ISCOVERING SEMANTIC LATENT SPACE IN DIFFUSION MODELS
Revised:  D ISCOVERING SEMANTIC LATENT SPACE IN DIFFUSION MODELS

Original: This section explains why naive approaches do not work and proposes a new controllable reverse process.
Revised: This section explains why naive approaches do not work and proposes a new controllable reverse process.

Original: Then we describe the techniques for controlling the generative process.
Revised: Then we describe the techniques for controlling the generative process.

Original: Throughout this paper, we use an abbreviated version of Eq.
Revised: Throughout this paper, we use an abbreviated version of Eq.

Original: [Equation]
Revised: [Equation]

Original: where P t ( ϵ θt ( x t )) denotes the predicted x 0 and D t ( ϵ θt ( x t )) denotes the direction pointing to x t .
Revised: where P t ( ϵ θt ( x t )) denotes the predicted x 0 and D t ( ϵ θt ( x t )) denotes the direction pointing to x t .

Original: We omit σ t z t for brevity, except when η ̸ = 0 .
Revised: We omit σ t z t for brevity, except when η ̸ = 0 .

Original: We further abbreviate P t ( ϵ θt ( x t )) as P t and D t ( ϵ θt ( x t )) as D t when the context clearly specifies the arguments.
Revised: We further abbreviate P t ( ϵ θt ( x t )) as P t and D t ( ϵ θt ( x t )) as D t when the context clearly specifies the arguments.

Original: 3. P ROBLEM
Revised: 3. P ROBLEM

Original: We aim to allow semantic latent manipulation of images x 0 generated from x T given a pretrained and frozen diffusion model.
Revised: We aim to allow semantic latent manipulation of images x 0 generated from x T given a pretrained and frozen diffusion model.

Original: The easiest idea to manipulate x 0 is simply updating x T to optimize the directional CLIP loss given text prompts with Eq.
Revised: The easiest idea to manipulate x 0 is simply updating x T to optimize the directional CLIP loss given text prompts with Eq.

Original: However, it leads to distorted images or incorrect manipulation (Kim & Ye, 2021).
Revised: However, it leads to distorted images or incorrect manipulation (Kim & Ye, 2021).

Original: An alternative approach is to shift the noise ϵ θt predicted by the network at each sampling step.
Revised: An alternative approach is to shift the noise ϵ θt predicted by the network at each sampling step.

Original: However, it does not achieve manipulating x 0 because the intermediate changes in P t and D t cancel out each other resulting in the same p θ ( x 0: T ) , similarly to destructive interference.
Revised: However, it does not achieve manipulating x 0 because the intermediate changes in P t and D t cancel out each other resulting in the same p θ ( x 0: T ) , similarly to destructive interference.

Original: Theorem 1.
Revised: Theorem 1.

Original: Let ϵ θt be a predicted noise during the original reverse process at t and ˜ ϵ θt be its shifted counterpart.
Revised: Let ϵ θt be a predicted noise during the original reverse process at t and ˜ ϵ θt be its shifted counterpart.

Original: Then, ∆ x t = ˜ x t − 1 − x t − 1 is negligible where ˜ x t − 1 = √
Revised: Then, ∆ x t = ˜ x t − 1 − x t − 1 is negligible where ˜ x t − 1 = √

Original: α t − 1 P t (˜ ϵ θt ( x t ))
Revised: α t − 1 P t (˜ ϵ θt ( x t ))

Original: +
Revised: +

Original: D t (˜ ϵ θt ( x t )) .
Revised: D t (˜ ϵ θt ( x t )) .

Original: I.e., the shifted terms of ˜ ϵ θt in P t and D t destruct each other in the reverse process.
Revised: I.e., the shifted terms of ˜ ϵ θt in P t and D t destruct each other in the reverse process.

Original: Appendix C proves above theorem.
Revised: Appendix C proves above theorem.

Original: Figure 13(a-b) shows that ˜ x 0 is almost identical to x 0 .
Revised: Figure 13(a-b) shows that ˜ x 0 is almost identical to x 0 .

Original: 3. A SYMMETRIC REVERSE PROCESS
Revised: 3. A SYMMETRIC REVERSE PROCESS

Original: In order to break the interference, we propose a new controllable reverse process with asymmetry:
Revised: In order to break the interference, we propose a new controllable reverse process with asymmetry:

Original: [Equation]
Revised: [Equation]

Original: [Figure]
Revised: [Figure]

Original: i.e., we modify only P t by shifting ϵ θt to ˜ ϵ θt while preserving D t .
Revised: i.e., we modify only P t by shifting ϵ θt to ˜ ϵ θt while preserving D t .

Original: Intuitively, it modifies the original reverse process according to ∆ ϵ t =
Revised: Intuitively, it modifies the original reverse process according to ∆ ϵ t =

Original: ˜ ϵ θt − ϵ θt while it does not alter the direction toward x t so that x t − 1 follows the original flow D t at each sampling step.
Revised: ˜ ϵ θt − ϵ θt while it does not alter the direction toward x t so that x t − 1 follows the original flow D t at each sampling step.

Original: Figure 2 illustrates the above intuition.
Revised: Figure 2 illustrates the above intuition.

Original: As in Avrahami et al.
Revised: As in Avrahami et al.

Original: (2022), we use the modified P edit t and the original P source t as visual inputs for the directional CLIP loss in Eq.
Revised: (2022), we use the modified P edit t and the original P source t as visual inputs for the directional CLIP loss in Eq.

Original: (4), and regularize the difference between the modified P edit t and the original P source t .
Revised: (4), and regularize the difference between the modified P edit t and the original P source t .

Original: We find ∆ ϵ = arg min ∆ ϵ E t L ( t ) where
Revised: We find ∆ ϵ = arg min ∆ ϵ E t L ( t ) where

Original: [Equation]
Revised: [Equation]

Original: Although ∆ ϵ indeed renders the attribute in the x edit0 , ϵ -space lacks the necessary properties of the semantic latent space in diffusion models that will be described in the following.
Revised: Although ∆ ϵ indeed renders the attribute in the x edit0 , ϵ -space lacks the necessary properties of the semantic latent space in diffusion models that will be described in the following.

Original: 3. h-space
Revised: 3. h-space

Original: Note that ϵ θt is implemented as U-Net in all state-of-the-art diffusion models.
Revised: Note that ϵ θt is implemented as U-Net in all state-of-the-art diffusion models.

Original: We choose its bottleneck, the deepest feature maps h t , to control ϵ θt .
Revised: We choose its bottleneck, the deepest feature maps h t , to control ϵ θt .

Original: By design, h t has smaller spatial resolutions and high-level semantics than ϵ θt .
Revised: By design, h t has smaller spatial resolutions and high-level semantics than ϵ θt .

Original: Accordingly, the sampling equation becomes
Revised: Accordingly, the sampling equation becomes

Original: [Equation]
Revised: [Equation]

Original: (8) where ϵ θt ( x t | ∆ h t ) adds ∆ h t to the original feature maps h t .
Revised: (8) where ϵ θt ( x t | ∆ h t ) adds ∆ h t to the original feature maps h t .

Original: The ∆ h t minimizing the same loss in
Revised: The ∆ h t minimizing the same loss in

Original: Eq.
Revised: Eq.

Original: (7) with P t ( ϵ θt ( x t | ∆ h t )) instead of P t (˜ ϵ θt ( x t )) successfully manipulates the attributes.
Revised: (7) with P t ( ϵ θt ( x t | ∆ h t )) instead of P t (˜ ϵ θt ( x t )) successfully manipulates the attributes.

Original: We observe that h-space in Asyrp has the following properties that others do not have
Revised: We observe that h-space in Asyrp has the following properties that others do not have

Original: • The same ∆ h leads to the same effect on different samples.
Revised: • The same ∆ h leads to the same effect on different samples.

Original: • Linearly scaling ∆ h controls the magnitude of attribute change, even with negative scales.
Revised: • Linearly scaling ∆ h controls the magnitude of attribute change, even with negative scales.

Original: • Adding multiple ∆ h manipulates the corresponding multiple attributes simultaneously.
Revised: • Adding multiple ∆ h manipulates the corresponding multiple attributes simultaneously.

Original: • ∆ h preserves the quality of the resulting images without degradation.
Revised: • ∆ h preserves the quality of the resulting images without degradation.

Original: • ∆ h t is roughly consistent across different timesteps t .
Revised: • ∆ h t is roughly consistent across different timesteps t .

Original: The above properties are demonstrated thoroughly in § 5.3.
Revised: The above properties are demonstrated thoroughly in § 5.3.

Original: Appendix D.3 provides details of h-space andresults with suboptimal  choices.
Revised: Appendix D.3 provides details of h-space and suboptimal results from alternative choices.

Original: 3. I MPLICIT NEURAL DIRECTIONS
Revised: 3. I MPLICIT NEURAL DIRECTIONS

Original: Although ∆ h succeeds in manipulating images, directly optimizing ∆ h t on multiple timesteps requires many iterations of training with a carefully chosen learning rate and its scheduling.
Revised: Although ∆ h succeeds in manipulating images, directly optimizing ∆ h t on multiple timesteps requires many iterations of training with a carefully chosen learning rate and its scheduling.

Original: Instead, we define an implicit function f t ( h t ) which produces ∆ h t for given h t and t .
Revised: Instead, we define an implicit function f t ( h t ) which produces ∆ h t for given h t and t .

Original: f t is implemented as a small neural network with two 1 × 1 convolutions concatenating timestep t .
Revised: f t is implemented as a small neural network with two 1 × 1 convolutions concatenating timestep t .

Original: See Appendix E for the details.
Revised: See Appendix E for the details.

Original: Accordingly, we optimize the same loss in Eq.
Revised: Accordingly, we optimize the same loss in Eq.

Original: (7) with P edit t
Revised: (7) with P edit t

Original: [Equation]
Revised: [Equation]

Original: Learning f t is more robust to learning rate settings and converges faster than learning every ∆ h t .
Revised: Learning f t is more robust to learning rate settings and converges faster than learning every ∆ h t .

Original: In addition, as f t learns an implicit function for given timesteps and bottleneck features, it generalizes
Revised: In addition, as f t learns an implicit function for given timesteps and bottleneck features, it generalizes

Original: [Figure]
Revised: [Figure]

Original: to unseen timesteps and bottleneck features.
Revised: to unseen timesteps and bottleneck features.

Original: The generalization allows us to borrow the accelerated training scheme of DDIM defined on a subsequence { x τ i } ∀ i ∈ [1 ,S ] where { τ i } is a subsequence of [1 , ..., T ] and S < T .
Revised: The generalization allows us to borrow the accelerated training scheme of DDIM defined on a subsequence { x τ i } ∀ i ∈ [1 ,S ] where { τ i } is a subsequence of [1 , ..., T ] and S < T .

Original: Then, we can use the generative process with a custom subsequence { ˜ τ i } with length ˜ S < T through normalization: ∆˜ h ˜ τ
Revised: Then, we can use the generative process with a custom subsequence { ˜ τ i } with length ˜ S < T through normalization: ∆˜ h ˜ τ

Original: = f ˜ τ
Revised: = f ˜ τ

Original: ( h ˜ τ ) S/ ˜ S .
Revised: ( h ˜ τ ) S/ ˜ S .

Original: It preserves the amount of (cid:80) ∆ h t , ∆˜ h ˜ τ ˜ S =
Revised: It preserves the amount of (cid:80) ∆ h t , ∆˜ h ˜ τ ˜ S =

Original: ∆ h t S .
Revised: ∆ h t S .

Original: Therefore, we can use f t trained on any subsequence for any length of the generative process.
Revised: Therefore, we can use f t trained on any subsequence for any length of the generative process.

Original: See Appendix F for details.
Revised: See Appendix F for details.

Original: We use f t to get ∆ h t for all experiments except Figure 6.
Revised: We use f t to get ∆ h t for all experiments except Figure 6.

Original:  G ENERATIVE PROCESS DESIGN
Revised:  G ENERATIVE PROCESS DESIGN

Original: This section describes the entire editing process, which consists of three phases: editing with Asyrp, traditional denoising, and quality boosting.
Revised: This section describes the entire editing process, which consists of three phases: editing with Asyrp, traditional denoising, and quality boosting.

Original: We design formulas to determine the length of each phase with quantifiable measures.
Revised: We design formulas to determine the length of each phase with quantifiable measures.

Original: 4. E DITING PROCESS WITH A SYRP
Revised: 4. E DITING PROCESS WITH A SYRP

Original: Diffusion models generate the high-level context in the early stage and imperceptible fine details in the later stage (Choi et al., 2022).
Revised: Diffusion models generate the high-level context in the early stage and imperceptible fine details in the later stage (Choi et al., 2022).

Original: Likewise, we modify the generative process in the early stage to achieve semantic changes.
Revised: Likewise, we modify the generative process in the early stage to achieve semantic changes.

Original: We refer to the early stage as the editing interval [ T, t edit ] .
Revised: We refer to the early stage as the editing interval [ T, t edit ] .

Original: LPIPS( x , P T ) and LPIPS( x , P t ) calculate the perceptual distance between the original image and the predicted image at time steps T and t , respectively.
Revised: LPIPS( x , P T ) and LPIPS( x , P t ) calculate the perceptual distance between the original image and the predicted image at time steps T and t , respectively.

Original: Intuitively, the high-level content is already determined by the predicted terms at the respective timesteps and LPIPS measures the remaining component to be edited through the remaining reverse process.
Revised: Intuitively, the high-level content is already determined by the predicted terms at the respective timesteps and LPIPS measures the remaining component to be edited through the remaining reverse process.

Original: Consequently, we define editing strength of an interval [ T, t ] :
Revised: Consequently, we define editing strength of an interval [ T, t ] :

Original: [Equation]
Revised: [Equation]

Original: indicating the perceptual change from timestep T to t in the original generative process.
Revised: indicating the perceptual change from timestep T to t in the original generative process.

Original: Figureillustrates LPIPS( x , · ) for P t and x t with examples and the inset depicts editing strength.
Revised: Figureillustrates LPIPS( x , · ) for P t and x t with examples and the inset depicts editing strength .

Original: The shorter editing interval has the lower ξ t , and the longer editing interval brings more changes to the resulting images.
Revised: The shorter editing interval has the lower ξ t , and the longer editing interval brings more changes to the resulting images.

Original: We seek the shortest editing interval which will bring enough distinguishable changes in the images in general.
Revised: We seek the shortest editing interval which will bring enough distinguishable changes in the images in general.

Original: We empirically find that t edit with LPIPS( x , P t edit )
Revised: We empirically find that t edit with LPIPS( x , P t edit )

Original: = 0 .
Revised: = 0 .

Original: 33 builds the shortest editing interval with enough editing strength as P t edit has nearly all visual attributes in x .
Revised: 33 builds the shortest editing interval with enough editing strength as P t edit has nearly all visual attributes in x .

Original: However, some attributes require more visual changes than others, e.g., pixar > smile .
Revised: However, some attributes require more visual changes than others, e.g., pixar > smile .

Original: For such attributes, we increase the editing strength ξ t by δ = 0 .
Revised: For such attributes, we increase the editing strength ξ t by δ = 0 .

Original: 33 d ( E T ( y source ) , E T ( y target ))
Revised: 33 d ( E T ( y source ) , E T ( y target ))

Original: where
Revised: where

Original: E T ( · ) produces CLIP text embedding, y ( · ) denotes the descriptions, and d ( · , · ) computes the cosine distance between the arguments.
Revised: E T ( · ) produces CLIP text embedding, y ( · ) denotes the descriptions, and d ( · , · ) computes the cosine distance between the arguments.

Original: Choosing t edit with LPIPS( x , P t edit )
Revised: Choosing t edit with LPIPS( x , P t edit )

Original: = 0 .
Revised: = 0 .

Original: 33 − δ expands the editing interval to a suitable length.
Revised: 33 − δ expands the editing interval to a suitable length.

Original: It consistently produces good results in various settings.
Revised: It consistently produces good results in various settings.

Original: The supporting experiments are shown in Appendix G.
Revised: The supporting experiments are shown in Appendix G.

Original: 4. Q UALITY BOOSTING WITH STOCHASTIC NOISE INJECTION
Revised: 4. Q UALITY BOOSTING WITH STOCHASTIC NOISE INJECTION

Original: Although DDIM achieves nearly perfect inversion by removing stochasticity ( η = 0 ), Karras et al.
Revised: Although DDIM achieves nearly perfect inversion by removing stochasticity ( η = 0 ), Karras et al.

Original: (2022) demonstrate that stochasticity improves image quality.
Revised: (2022) demonstrate that stochasticity improves image quality.

Original: Likewise, we inject stochastic noise in the boosting interval [ t boost , 0] .
Revised: Likewise, we inject stochastic noise in the boosting interval [ t boost , 0] .

Original: Though the longer boosting interval would achieve higher quality, boosting over excessively long intervals would modify the content.
Revised: Though the longer boosting interval would achieve higher quality, boosting over excessively long intervals would modify the content.

Original: Hence, we want to determine the shortest interval that shows enough quality boosting to guarantee minimal change in the content.
Revised: Hence, we want to determine the shortest interval that shows enough quality boosting to guarantee minimal change in the content.

Original: We consider the noise in the image as the capacity for the quality boosting and define quality deficiency at t : γ t = LPIPS( x , x t ) indicating the amount of noise in x t compared to the original image.
Revised: We consider the noise in the image as the capacity for the quality boosting and define quality deficiency at t : γ t = LPIPS( x , x t ) indicating the amount of noise in x t compared to the original image.

Original: We use x t instead of P t because we consider the actual image rather than the semantics.
Revised: We use x t instead of P t because we consider the actual image rather than the semantics.

Original: Figure 3 inset depicts editing flexibility and quality deficiency.
Revised: Figure 3 inset depicts editing flexibility and quality deficiency.

Original: We empirically find that t boost with γ t boost = 1 . 2 achieves quality boosting with minimal content change.
Revised: We empirically find that t boost with γ t boost = 1 . 2 achieves quality boosting with minimal content change.

Original: We confirmed that the editing strength of the intervals [ t boost , 0] is guaranteed to be less than 0.25.
Revised: We confirmed that the editing strength of the intervals [ t boost , 0] is guaranteed to be less than 0.25.

Original: In Figure 3, after t boost , LPIPS( x , x t ) sharply drops in the original generative process while LPIPS( x , P t ) changes little.
Revised: In Figure 3, after t boost , LPIPS( x , x t ) sharply drops in the original generative process while LPIPS( x , P t ) changes little.

Original: Note that most of the quality degradation of the resulting images is caused by DDIM reverse process, not by Asyrp.
Revised: Note that most of the quality degradation of the resulting images is caused by DDIM reverse process, not by Asyrp.

Original: We use this quality boosting for all experiments except ablation in Appendix H.
Revised: We use this quality boosting for all experiments except ablation in Appendix H.

Original: 4. O VERALL PROCESS OF IMAGE EDITING
Revised: 4. O VERALL PROCESS OF IMAGE EDITING

Original: Using t edit and t boost determined by the above formulas, we modify the generative process of DDIM with
Revised: Using t edit and t boost determined by the above formulas, we modify the generative process of DDIM with

Original: [Equation]
Revised: [Equation]

Original: The visual overview and comprehensive algorithms of the entire process are in Appendix I.
Revised: The visual overview and comprehensive algorithms of the entire process are in Appendix I.

Original:  E XPERIMENTS
Revised:  E XPERIMENTS

Original: In this section, we show the effectiveness of semantic latent editing in h-space with Asyrp on various attributes, datasets and architectures in § 5.1.
Revised: In this section, we show the effectiveness of semantic latent editing in h-space with Asyrp on various attributes, datasets and architectures in § 5.1.

Original: Moreover, we provide quantitative results including user study in § 5.2.
Revised: Moreover, we provide quantitative results including user study in § 5.2.

Original: Lastly, we provide detailed analyses for the properties of the semantic latent space on h-space and alternatives in § 5.3.
Revised: Lastly, we provide detailed analyses for the properties of the semantic latent space on h-space and alternatives in § 5.3.

Original: Implementation details.
Revised: Implementation details.

Original: We implement our method on various settings: CelebA-HQ (Karras et al., 2018) and LSUN-bedroom/-church (Yu et al., 2015) on DDPM++ (Song et al., 2020b)
Revised: We implement our method on various settings: CelebA-HQ (Karras et al., 2018) and LSUN-bedroom/-church (Yu et al., 2015) on DDPM++ (Song et al., 2020b)

Original: (Meng et al., 2021); AFHQ-dog (Choi et al., 2020) on iDDPM (Nichol & Dhariwal, 2021); and M ET F ACES (Karras et al., 2020) on ADM with P2-weighting (Dhariwal & Nichol, 2021) (Choi et al., 2022).
Revised: (Meng et al., 2021); AFHQ-dog (Choi et al., 2020) on iDDPM (Nichol & Dhariwal, 2021); and M ETF ACES (Karras et al., 2020) on ADM with P2-weighting (Dhariwal & Nichol, 2021) (Choi et al., 2022).

Original: Please note that all models are official pretrained checkpoints and are kept frozen.
Revised: Please note that all models are official pretrained checkpoints and are kept frozen.

Original: Detailed settings including the coefficients for λ CLIP and λ recon , and source/target descriptions can be found in Appendix J.1.
Revised: Detailed settings including the coefficients for λ CLIP and λ recon , and source/target descriptions can be found in Appendix J.1.

Original: We train f t with S = 40 for 1 epoch using 1000 samples.
Revised: We train f t with S = 40 for 1 epoch using 1000 samples.

Original: The real samples are randomly chosen from each dataset for in-domain-like attributes.
Revised: The real samples are randomly chosen from each dataset for in-domain-like attributes.

Original: For out-of-domain-like attributes, we randomly draw 1,000 latent variables x T ∼ N (0 , I ) .
Revised: For out-of-domainlike attributes, we randomly draw 1,000 latent variables x T ∼ N (0 , I ) .

Original: Details are described in Appendix J.2.
Revised: Details are described in Appendix J.2.

Original: Training takes about 20 minutes with three RTX 3090 GPUs.
Revised: Training takes about 20 minutes with three RTX 3090 GPUs.

Original: All the images in the figures are not used for training.
Revised: All the images in the figures are not used for training.

Original: We set ˜ S
Revised: We set ˜ S

Original: = 1 , 000 for inference.
Revised: = 1 , 000 for inference.

Original: The code isattached to the supplementary materials and will be available online with publication.
Revised: The code is available at https://github.com/kwonminki/Asyrp official

Original: 5. V ERSATILITY OF h-space WITH A SYRP
Revised: 5. V ERSATILITY OF h-space WITH A SYRP

Original: Figure 4 shows the effectiveness of our method on various datasets and any existing U-Net based architectures.
Revised: Figure 4 shows the effectiveness of our method on various datasets and any existing U-Net based architectures.

Original: Our method can synthesize the attributes that are not even included in the training
Revised: Our method can synthesize the attributes that are not even included in the training

Original: [Figure]
Revised: [Figure]

Original: Recon.
Revised: Recon.

Original: Smiling
Revised: Smiling

Original: Sad
Revised: Sad

Original: Angry
Revised: Angry

Original: Man ↔ Woman
Revised: Man ↔ Woman

Original: Young
Revised: Young

Original: Curly hair
Revised: Curly hair

Original: [Figure]
Revised: [Figure]

Original: Department
Revised: Department

Original: Factory
Revised: Factory

Original: Gothic
Revised: Gothic

Original: Red bricks
Revised: Red bricks

Original: Temple
Revised: Temple

Original: Wooden
Revised: Wooden

Original: [Figure]
Revised: [Figure]

Original: Sleepy
Revised: Sleepy

Original: [Figure]
Revised: [Figure]

Original: Hotel
Revised: Hotel

Original: [Figure] [Figure]
Revised: [Figure] [Figure]

Original: dataset, such as church −→ { department , factory , and temple } .
Revised: dataset, such as church −→ { department , factory , and temple } .

Original: Even for dogs, our method synthesizes smiling Poodle and Yorkshire, the species that barely smile in the dataset.
Revised: Even for dogs, our method synthesizes smiling Poodle and Yorkshire, the species that barely smile in the dataset.

Original: Figureprovides results for changing human faces to different identities, painting styles, and ancient primates.
Revised: Figureprovides results for changing human faces to different identities, painting styles, and ancient primates.

Original: More result can be found in Appendix N. Versatility of our method is surprising because we do not alter the models but only shift the bottleneck feature maps in h-space with Asyrp during inference.
Revised: More result can be found in Appendix N. Versatility of our method is surprising because we do not alter the models but only shift the bottleneck feature maps in h-space with Asyrp during inference.

Original: [Table]
Revised: [Table]

Original: [Figure] [Figure]
Revised: [Figure] [Figure]

Original: 5. Q UANTITATIVE COMPARISON
Revised: 5. Q UANTITATIVE COMPARISON

Original: Considering that our method can be combined with various diffusion models without finetuning, we do not find such a versatile competitor.
Revised: Considering that our method can be combined with various diffusion models without finetuning, we do not find such a versatile competitor.

Original: Nonetheless, we compare Asyrp against DiffusionCLIP using the official code that edits the real images by finetuning the whole model.
Revised: Nonetheless, we compare Asyrp against DiffusionCLIP using the official code that edits the real images by finetuning the whole model.

Original: We asked 80 participants to choose the images with better quality, natural attribute change and overall preference for given total of 40 sets of original images, ours, and DiffusionCLIP.
Revised: We asked 80 participants to choose the images with better quality, natural attribute change and overall preference for given total of 40 sets of original images, ours and DiffusionCLIP.

Original: Table 1 shows that Asyrp outperforms DiffusionCLIP in the all perspectives including the attributes unseen in the training dataset.
Revised: Table 1 shows that Asyrp outperforms DiffusionCLIP in the all perspectives including the attributes unseen in the training dataset.

Original: We list the settings for fair comparison including the questions and example images in Appendix
Revised: We list the settings for fair comparison including the questions and example images in Appendix

Original: K.1.
Revised: K.1.

Original: See § K.2 for more evaluation metrics: segmentation consistency (SC) and directional CLIP similarity( S dir ).
Revised: See § K.2 for more evaluation metrics: segmentation consistency (SC) and directional CLIP similarity( S dir ).

Original: 5. A NALYSIS ON h-space
Revised: 5. A NALYSIS ON h-space

Original: We provide detailed analyses to validate the properties of semantic latent space for diffusion models: homogeneity, linearity, robustness, and consistency across timesteps.
Revised: We provide detailed analyses to validate the properties of semantic latent space for diffusion models: homogeneity, linearity, robustness, and consistency across timesteps.

Original: Homogeneity.
Revised: Homogeneity.

Original: Figure 6 illustrates homogeneity of h-space compared to ϵ -space.
Revised: Figure 6 illustrates homogeneity of h-space compared to ϵ -space.

Original: One ∆ h t optimized for an image results in the same attribute change to other input images.
Revised: One ∆ h t optimized for an image results in the same attribute change to other input images.

Original: On the other hand, one ∆ ϵ t optimized for an image distorts other input images.
Revised: On the other hand, one ∆ ϵ t optimized for an image distorts other input images.

Original: In Figure 10, applying ∆ h mean t =
Revised: In Figure 10, applying ∆ h mean t =

Original: N (cid:80) ∆ h i t produces almost identical results where i indicates indices of N = 20 random samples.
Revised: N (cid:80) ∆ h i t produces almost identical results where i indicates indices of N = 20 random samples.

Original: Linearity.
Revised: Linearity.

Original: In Figure 7, we observe that linearly scaling a ∆ h reflects the amount of change in the visual attributes.
Revised: In Figure 7, we observe that linearly scaling a ∆ h reflects the amount of change in the visual attributes.

Original: Surprisingly, it generalizes to negative scales that are not seen during training.
Revised: Surprisingly, it generalizes to negative scales that are not seen during training.

Original: Moreover, Figure 8 shows that combinations of different ∆ h ’s yield their combined semantic changes in the resulting images.
Revised: Moreover, Figure 8 shows that combinations of different ∆ h ’s yield their combined semantic changes in the resulting images.

Original: Appendix N.2 provides mixed interpolation between multiple attributes.
Revised: Appendix N.2 provides mixed interpolation between multiple attributes.

Original: [Figure]
Revised: [Figure]

Original: N+S N eanderthal
Revised: N+S N eanderthal

Original: N+Y
Revised: N+Y

Original: Y+S
Revised: Y+S

Original: Y+S+N
Revised: Y+S+N

Original: [Figure]
Revised: [Figure]

Original: and

Original: [Figure]
Revised: [Figure]

Original: Robustness.
Revised: Robustness.

Original: Figure 9 compares the effect of adding random noise in h-space and ϵ -space.
Revised: Figure 9 compares the effect of adding random noise in h-space and ϵ -space.

Original: The random noises are chosen to be the vectors with random directions and magnitude of the example ∆ h t and ∆ ϵ t in Figure 6 on each space.
Revised: The random noises are chosen to be the vectors with random directions and magnitude of the example ∆ h t and ∆ ϵ t in Figure 6 on each space.

Original: Perturbation in h-space leads to realistic images with a minimal difference or some semantic changes.
Revised: Perturbation in h-space leads to realistic images with a minimal difference or some semantic changes.

Original: On the contrary, perturbation in ϵ -space severely distorts the resulting images.
Revised: On the contrary, perturbation in ϵ -space severely distorts the resulting images.

Original: See Appendix D.2 for more analyses.
Revised: See Appendix D.2 for more analyses.

Original: Consistency across timesteps.
Revised: Consistency across timesteps.

Original: Recall that ∆ h t for all samples are homogeneous and replacing them by their mean ∆ h mean t yields similar results.
Revised: Recall that ∆ h t for all samples are homogeneous and replacing them by their mean ∆ h mean t yields similar results.

Original: Interestingly, in Figure 10, we observe that adding a time-invariant ∆ h global = 1 T e (cid:80) t ∆ h mean t instead of ∆ h t also yields similar results where T e denotes the length of the editing interval [ T, t edit ] .
Revised: Interestingly, in Figure 10, we observe that adding a time-invariant ∆ h global = 1 T e (cid:80) t ∆ h mean t instead of ∆ h t also yields similar results where T e denotes the length of the editing interval [ T, t edit ] .

Original: Though we use ∆ h t to deliver the best quality and manipulation, using ∆ h mean t or even ∆ h global with some compromise would be worth trying for simplicity.
Revised: Though we use ∆ h t to deliver the best quality and manipulation, using ∆ h mean t or even ∆ h global with some compromise would be worth trying for simplicity.

Original: We report more detail about mean and global direction in Appendix L.1.
Revised: We report more detail about mean and global direction in Appendix L.1.

Original:  C ONCLUSION
Revised:  C ONCLUSION

Original: We proposed a new generative process, Asyrp, which facilitates image editing in a semantic latent space h-space for pretrained diffusion models.
Revised: We proposed a new generative process, Asyrp, which facilitates image editing in a semantic latent space h-space for pretrained diffusion models.

Original: h-space has nice properties as in the latent space of GANs: homogeneity, linearity, robustness, and consistency across timesteps.
Revised: h-space has nice properties as in the latent space of GANs: homogeneity, linearity, robustness, and consistency across timesteps.

Original: The full editing process is designed to achieve versatile editing and high quality by measuring editing strength and quality deficiency at timesteps.
Revised: The full editing process is designed to achieve versatile editing and high quality by measuring editing strength and quality deficiency at timesteps.

Original: We hope that our approach and detailed analyses help cultivate a new paradigm of image editing in the semantic latent space of diffusion models.
Revised: We hope that our approach and detailed analyses help cultivate a new paradigm of image editing in the semantic latent space of diffusion models.

Original: Combining previous finetuning or guidance techniques would be an interesting research direction.
Revised: Combining previous finetuning or guidance techniques would be an interesting research direction.

Revised: A CKNOWLEDGMENTS

Revised: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the

Revised: Korea government (Ministry of Science and ICT) (No. 2021-0-00155)

Original: Table of Contents
Revised: Table of Contents

Original: E Implicit neural directions
Revised: E Implicit neural directions

Original: F 
Revised: F Quality improvements by

Original:  ∆ h t
Revised: -accelerated sampling with scaled ∆ h t

Original: G Editing strength and editing flexibility
Revised: G Editing strength and editing flexibility

Original: G.1 Editing strength and t edit . . .
Revised: G.1 Editing strength and t edit . . .

Original: G.2 Editing flexibility and t boost . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Revised: G.2 Editing flexibility and t boost . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Original: H Quality boosting
Revised: H Quality boosting

Original: I Algorithm
Revised: I Algorithm

Original: J Training details
Revised: J Training details

Original: J.
Revised: J.

Original: Loss coefficients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Revised: Loss coefficients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Original: J.
Revised: J.

Original: Training with random sampling instead of the training datasets . . . . . . . . . .
Revised: Training with random sampling instead of the training datasets . . . . . . . . . .

Original: K Evaluation
Revised: K Evaluation

Original: K.1 User study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Revised: K.1 User study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Original: K.2 Segmentation consistency and directional CLIP similarity . .
Revised: K.2 Segmentation consistency and directional CLIP similarity . .

Original: L Directions
Revised: L Directions

Original: L.1 Global direction . .
Revised: L.1 Global direction . .

Original: L.2 Compare three methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Revised: L.2 Compare three methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Original: [Table] [Table]
Revised: [Table] [Table]

Original: N.
Revised: N.

Original: [Table]
Revised: [Table]

Original: ImageNet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Revised: ImageNet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Original: N.2 Multi-
Revised: N.2 Multi-

Original: interpolation . .
Revised: interpolation . .

Original: N. [Table] More results on all datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Revised: N.3 More results on all datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Original: [Figure]
Revised: [Figure]

Original: A R ELATED WORK
Revised: A R ELATED WORK

Original: After Sohl-Dickstein et al.
Revised: After Sohl-Dickstein et al.

Original: (2015), denoising diffusion probabilistic models (DDPMs) provide a universal approach for generative modeling (Ho et al., 2020).
Revised: (2015), denoising diffusion probabilistic models (DDPMs) provide a universal approach for generative modeling (Ho et al., 2020).

Original: On the other hand, Song et al.
Revised: On the other hand, Song et al.

Original: (2020b) suggests score-based model and unifies SDEs incorporating diffusion models with score-based models.
Revised: (2020b) suggests score-based model and unifies SDEs incorporating diffusion models with score-based models.

Original: Subsequent works renovate diffusion models by focusing on architectures, scheduling, weighting, and fast sampling (Nichol & Dhariwal (2021), Karras et al.
Revised: Subsequent works renovate diffusion models by focusing on architectures, scheduling, weighting, and fast sampling (Nichol & Dhariwal (2021), Karras et al.

Original: (2022), Choi et al.
Revised: (2022), Choi et al.

Original: , Song et al.
Revised: , Song et al.

Original: (2020a), Watson et al.
Revised: (2020a), Watson et al.

Original: They mainly consider random generation rather than controlled generation.
Revised: They mainly consider random generation rather than controlled generation.

Original: In the meantime, Dhariwal & Nichol (2021) introduces classifier guidance not only improving the quality of images but also retrieving specific class of images.
Revised: In the meantime, Dhariwal & Nichol (2021) introduces classifier guidance not only improving the quality of images but also retrieving specific class of images.

Original: Since it can apply any guidance, its variants have emerged (Sehwag et al.
Revised: Since it can apply any guidance, its variants have emerged (Sehwag et al.

Original: , Avrahami et al.
Revised: , Avrahami et al.

Original: Liu et al.
Revised: Liu et al.

Original: (2021), Nichol et al.
Revised: (2021), Nichol et al.

Original: However it requires a noise-dependent classifier (or any off-the-shelf models) and additional cost to compute gradients for the guidance during its sampling process.
Revised: However it requires a noise-dependent classifier (or any off-the-shelf models) and additional cost to compute gradients for the guidance during its sampling process.

Original: The other works try to control the generative process using image-space guidance (Choi et al.
Revised: The other works try to control the generative process using image-space guidance (Choi et al.

Original: (2021), Meng et al.
Revised: (2021), Meng et al.

Original: Lugmayr et al.
Revised: Lugmayr et al.

Original: , Avrahami et al.
Revised: , Avrahami et al.

Original: They manipulate resulting images by matching noisy images with target images during the reverse process.
Revised: They manipulate resulting images by matching noisy images with target images during the reverse process.

Original: Still, it is hard to expect delicate control of the reverse process from the image guidance.
Revised: Still, it is hard to expect delicate control of the reverse process from the image guidance.

Original: Furthermore, Preechakul et al.
Revised: Furthermore, Preechakul et al.

Original: (2022) introduces an extra encoder which encodes the semantic features of a real image in order to condition the generative process.
Revised: (2022) introduces an extra encoder which encodes the semantic features of a real image in order to condition the generative process.

Original: Although the semantics allow one to control diffusion models, it requires additional training from scratch with the encoder and inherently can not use the other pretrained diffusion models.
Revised: Although the semantics allow one to control diffusion models, it requires additional training from scratch with the encoder and inherently can not use the other pretrained diffusion models.

Original: For controllability, Rombach et al.
Revised: For controllability, Rombach et al.

Original: (2022) and Vahdat et al.
Revised: (2022) and Vahdat et al.

Original: (2021) apply another approach which adapts VAE (Kingma & Welling, 2013) and autoencoder (Rumelhart et al., 1985) to diffusion models.
Revised: (2021) apply another approach which adapts VAE (Kingma & Welling, 2013) and autoencoder (Rumelhart et al., 1985) to diffusion models.

Original: In spite of their great success in editing, their diffusion models learn the distribution of the learned embeddings in VAE or autoencoder, not the images.
Revised: In spite of their great success in editing, their diffusion models learn the distribution of the learned embeddings in VAE or autoencoder, not the images.

Original: Kim & Ye (2021) proposes another strategy: fine-tuning a whole diffusion model for image editing.
Revised: Kim & Ye (2021) proposes another strategy: fine-tuning a whole diffusion model for image editing.

Original: It shows valid performance but it requires each fine-tuned model corresponding each attribute.
Revised: It shows valid performance but it requires each fine-tuned model corresponding each attribute.

Original: In comparison, Asyrp enables outstanding manipulation without high computation, specifically designed architectures, or fine-tuning whole models.
Revised: In comparison, Asyrp enables outstanding manipulation without high computation, specifically designed architectures, or fine-tuning whole models.

Original: Meanwhile, generative adversarial network Goodfellow et al.
Revised: Meanwhile, generative adversarial network Goodfellow et al.

Original: (2020) address their latent space for image editing (Ling et al.
Revised: (2020) address their latent space for image editing (Ling et al.

Original: (2021), H¨ark¨onen et al.
Revised: (2021), H¨ark¨onen et al.

Original: (2020), Chefer et al.
Revised: (2020), Chefer et al.

Original: (2021), Shen et al.
Revised: (2021), Shen et al.

Original: Y¨uksel et al.
Revised: Y¨uksel et al.

Original: (2021), Patashnik et al.
Revised: (2021), Patashnik et al.

Original: (2021), Gal et al.
Revised: [Table] , Gal et [Table] [Table]

Original: (2021), Dai et al. 
Revised: , Dai [Table] al. [Table]

Original: (2019), Xu et al. 
Revised: , Xu [Table] al. [Table]

Original: However they have to conduct ‘inversion’ to their latent space for real image editing and ‘GAN inversion’ is often challenging and produces unexpected appearance changes.
Revised: However they have to conduct ‘inversion’ to their latent space for real image editing and ‘GAN inversion’ is often challenging and produces unexpected appearance changes.

Original: On the contrary, Asyrp enables to use latent space of real images by nearly perfect easy inversion of
Revised: On the contrary, Asyrp enables to use latent space of real images by nearly perfect easy inversion of

Original: DDIM.
Revised: DDIM.

Original: B M ORE DISCUSSION
Revised: B M ORE DISCUSSION

Original: In this section, we discuss the pros and cons of diffusion-model-based and GAN-based methods.
Revised: In this section, we discuss the pros and cons of diffusion-model-based and GAN-based methods.

Original: And we provide guidelines for further improvements.
Revised: And we provide guidelines for further improvements.

Original: GAN-based latent manipulation methods Patashnik et al.
Revised: GAN-based latent manipulation methods Patashnik et al.

Original: ; Gal et al.
Revised: ; Gal et al.

Original: (2021)require careful inversion from real images to latent codes for real image editing.
Revised: (2021)require careful inversion from real images to latent codes for real image editing.

Original: On the contrary, our proposed method based on diffusion models has a powerful advantage; the sophisticated inversion method is not necessary.
Revised: On the contrary, our proposed method based on diffusion models has a powerful advantage; the sophisticated inversion method is not necessary.

Original: This means that we can obtain the latent code of an arbitrary real image even if the image is not in the trained domain.
Revised: This means that we can obtain the latent code of an arbitrary real image even if the image is not in the trained domain.

Original: On the other hand, several inversion methods have been proposed for GANs to obtain the latent of the real image, and the corresponding latent manipulation method should be considered for each inversion method.
Revised: On the other hand, several inversion methods have been proposed for GANs to obtain the latent of the real image, and the corresponding latent manipulation method should be considered for each inversion method.

Original: For example, it is difficult to apply the method of editing in w space to the method of inversion using w + space.
Revised: For example, it is difficult to apply the method of editing in w space to the method of inversion using w + space.

Original: However, GANs have the advantage of fast sampling.
Revised: However, GANs have the advantage of fast sampling.

Original: In addition, diffusion models have a relatively slow sampling time.
Revised: In addition, diffusion models have a relatively slow sampling time.

Original: Additionally, we have to be aware of the time steps of diffusion models, which is still less well known.
Revised: Additionally, we have to be aware of the time steps of diffusion models, which is still less well known.

Original: The advantage of being free from Inversion provides the following milestones.
Revised: The advantage of being free from Inversion provides the following milestones.

Original: The manipulation in the latent of the diffusion models is the same as the editing in real images.
Revised: The manipulation in the latent of the diffusion models is the same as the editing in real images.

Original: It can be expanded to segmentation, clustering, classification, etc.
Revised: It can be expanded to segmentation, clustering, classification, etc.

Original: in h-space for real-world images.
Revised: in h-space for real-world images.

Original: It would be an interesting research direction to employ previous techniques.
Revised: It would be an interesting research direction to employ previous techniques.

Original: Our method can be used in conjunction with gradient guidance methods.
Revised: Our method can be used in conjunction with gradient guidance methods.

Original: Although we do not focus on random sampling, ours works effectively for sampling with stochastic.
Revised: Although we do not focus on random sampling, ours works effectively for sampling with stochastic.

Original: (See § M.)
Revised: (See § M.)

Original: It may bring more diverse methods to steer diffusion models.
Revised: It may bring more diverse methods to steer diffusion models.

Original: h-space in the latent diffusion models such as stable diffusion, is another interesting research direction.
Revised: h-space in the latent diffusion models such as stable diffusion, is another interesting research direction.

Original: The main contribution of our paper is only modifying P t while preserving D t , and can be adapted with latent diffusion models.
Revised: The main contribution of our paper is only modifying P t while preserving D t , and can be adapted with latent diffusion models.

Original: However, since the latent meaning may be different due to structural differences, research on this is needed.
Revised: However, since the latent meaning may be different due to structural differences, research on this is needed.

Original: Furthermore, all of the properties of h-space according to the time step has not been fully discussed so far.
Revised: Furthermore, all of the properties of h-space according to the time step has not been fully discussed so far.

Original: Research on them can be expected to expand further.
Revised: Research on them can be expected to expand further.

Original: Limitations.
Revised: Limitations.

Original: Editing with Asyrp seldom yields changes in overall style or peripheral objects but edits attributes of the main object.
Revised: Editing with Asyrp seldom yields changes in overall style or peripheral objects but edits attributes of the main object.

Original: Style transfer using frozen diffusion models is our future work.
Revised: Style transfer using frozen diffusion models is our future work.

Original: Societal impact.
Revised: Societal impact / Ethics statement.

Original: Techniques for high-quality image manipulation such as Asyrp should be accompanied by social and/or technical solutions to prevent abuse.
Revised: Techniques for high-quality image manipulation such as Asyrp should be accompanied by social and/or technical solutions to prevent abuse.

Revised: We acknowledge the potential ethical implications that may arise from the use of our image manipulation technique,

Revised: Asyrp.

Revised: We advocate for the development and implementation of social and technical solutions to prevent potential abuses such as spreading disinformation or propaganda.

Revised: We are committed to ensuring fairness and non-discrimination, legal compliance, and research integrity in our work.

Original: C P ROOF OF T HEOREM
Revised: C P ROOF OF T HEOREM

Original: Proof of Theorem 1 .
Revised: Proof of Theorem 1 .

Original: Let ϵ θt be a predicted noise during the original reverse process at t and ˜ ϵ θt be its shifted counterpart.
Revised: Let ϵ θt be a predicted noise during the original reverse process at t and ˜ ϵ θt be its shifted counterpart.

Original: Then, ∆ x t = ˜ x t − 1 − x t − 1 is negligible where ˜ x t − 1 = √
Revised: Then, ∆ x t = ˜ x t − 1 − x t − 1 is negligible where ˜ x t − 1 = √

Original: α t − 1 P t (˜ ϵ θt ( x t ))
Revised: α t − 1 P t (˜ ϵ θt ( x t ))

Original: +
Revised: +

Original: D t (˜ ϵ θt ( x t )) .
Revised: D t ( ˜ ϵ θt ( x t )) .

Original: Define ˜ ϵ θt ( x t ) = 
Revised: Define ˜ ϵ θt ( x t ) = ϵ

Original: ϵ θt ( x t ) + ∆ ϵ t , { β t } Tt =

Revised: [Equation] ( x t ) + ∆ ϵ t , { β t } Tt =

Original: = { β 1 = β min , ..., β T = β max } , and α t = (cid:81) ts =1 (1 − β s ) .
Revised: = { β 1 = β min , ..., β T = β max } , and α t = (cid:81) ts =1 (1 − β s ) .

Original: Note that β max is defined as a small value (e.g., β max = 0 . 001 ) and { β t } Tt =1 are defined by a decreasing schedule from β T = β max to β 1 = β min ≈ 

Revised: Note that β max is defined as a small value (e.g., β max = 0 . 001 ) and { β t } Tt =1 are defined by a decreasing schedule from β T = β max to β 1 = β min ≈

Original: (e.g., β min = 0 . 00001 ).
Revised: (e.g., β min = 0 . 00001 ).

Original: Then,
Revised: Then,

Original: [Equation]

Original: [Equation]

Original: is negligible

Original: [Equation]
Revised: [Equation]

Original: D A DDITIONAL SUPPORTS FOR h-space WITH A SYRP D. R ANDOM PERTURBATION ON ϵ -space WITHOUT A SYRP
Revised: D A DDITIONAL SUPPORTS FOR h-space WITH A SYRP D. R ANDOM PERTURBATION ON ϵ -space WITHOUT A SYRP

Original: In § 3.1, we argue that if both P t and D t are shifted, we can not manipulate x 0 .
Revised: In § 3.1, we argue that if both P t and D t are shifted, we can not manipulate x 0 .

Original: In Figure 13, we do not observe the noticeable difference between (a) and (b) which are the result of the original reverse process of DDIM and the one with shifting both terms, respectively.
Revised: In Figure 13, we do not observe the noticeable difference between (a) and (b) which are the result of the original reverse process of DDIM and the one with shifting both terms, respectively.

Original: D. R OBUSTNESS AND SEMANTICS IN h-space AND ϵ -space WITH A SYRP
Revised: D. R OBUSTNESS AND SEMANTICS IN h-space AND ϵ -space WITH A SYRP

Original: In § 3.2, we also argue that h-space is more robust than ϵ -space with Asyrp.
Revised: In § 3.2, we also argue that h-space is more robust than ϵ -space with Asyrp.

Original: In Figure 13, we observe that small random noise z ∼ N (0 , I ) in ϵ -space degrades the resulting image without semantic changes (c) and much larger random noise in h-space yields random semantic changes without severe artifacts (d).
Revised: In Figure 13, we observe that small random noise z ∼ N (0 , I ) in ϵ -space degrades the resulting image without semantic changes (c) and much larger random noise in h-space yields random semantic changes without severe artifacts (d).

Original: Note that diffusion models are designed as latent variable models with learned Gaussian transitions and the reverse process should also be close to Gaussian.
Revised: Note that diffusion models are designed as latent variable models with learned Gaussian transitions and the reverse process should also be close to Gaussian.

Original: Based on the assumption, we manage ϵ θt to follow the original Gaussian distribution as follows.
Revised: Based on the assumption, we manage ϵ θt to follow the original Gaussian distribution as follows.

Original: Adding z ∼ N (0 , σ I ) to ϵ θt expands the distribution of the predicted noise and may produce distorted images.
Revised: Adding z ∼ N (0 , σ I ) to ϵ θt expands the distribution of the predicted noise and may produce distorted images.

Original: To preserve the distribution, we scale ˜ ϵ θt
Revised: To preserve the distribution, we scale ˜ ϵ θt

Original: =
Revised: =

Original: ( ϵ θt + z ) / √ 1 2 + σ 2 .
Revised: ( ϵ θt + z ) / √ 1 2 + σ 2 .

Original: Still, the resulting images are almost identical compared to the original images where ˜ ϵ θt = ϵ θt + z as shown in Figure 13.
Revised: Still, the resulting images are almost identical compared to the original images where ˜ ϵ θt = ϵ θt + z as shown in Figure 13.

Original: It is no wonder that the scaling does not improve the distorted results since the additive random noise disturbs the denoising operation of the predicted random noise.[Figure] [Figure]
Revised: It is no wonder that the scaling does not improve the distorted results since the additive random noise disturbs the denoising operation of the predicted random noise.

Original: D. C HOICE OF h-space IN U-N ET
Revised: D. C HOICE OF h-space IN U-N ET

Original: As shown in Figure 14, there are many other candidates for h-space in the architecture.
Revised: As shown in Figure 14, there are many other candidates for h-space in the architecture.

Original: Among the layers, we choose the 8th layer, the bridge of the U-Net based architecture.
Revised: Among the layers, we choose the 8th layer, the bridge of the U-Net based architecture.

Original: The layer is not influenced by any skip connection, has the smallest spatial dimension with compressed information, and is located just before the upsampling blocks.
Revised: The layer is not influenced by any skip connection, has the smallest spatial dimension with compressed information, and is located just before the upsampling blocks.

Original: Thus, we assume that it could possibly be considered as the most suitable latent embedding.
Revised: Thus, we assume that it could possibly be considered as the most suitable latent embedding.

Original: To confirm the assumption, we train f t on the other layers.
Revised: To confirm the assumption, we train f t on the other layers.

Original: The results are shown in Figure 15.
Revised: The results are shown in Figure 15.

Original: We carefully tuned the training hyperparameters ( λ CLIP and λ recon ) for fair comparison.
Revised: We carefully tuned the training hyperparameters ( λ CLIP and λ recon ) for fair comparison.

Original: The 1st to the 6th layers hardly bring visible changes.
Revised: The 1st to the 6th layers hardly bring visible changes.

Original: The 7th and 9th layers bring not only the desired changes but also difficulty in finding optimal hyperparameters.
Revised: The 7th and 9th layers bring not only the desired changes but also difficulty in finding optimal hyperparameters.

Original: After the 9th layer, the results bear severe artifacts.
Revised: After the 9th layer, the results bear severe artifacts.

Original: E I MPLICIT NEURAL DIRECTIONS
Revised: E I MPLICIT NEURAL DIRECTIONS

Original: Figure 16 illustrates the neural implicit function f t .
Revised: Figure 16 illustrates the neural implicit function f t .

Original: It has only two 1x1 convolution layers withchannels.
Revised: It has only two 1x1 convolution layers withchannels.

Original: Note that we haven’t explored the network architecture much.
Revised: Note that we haven’t explored the network architecture much.

Original: Layer 1~

Original: Layer

Original: Layer 8 ( h-space )

Original: Layer

Original: Layer

Original: Layer

Original: Layer

Original: Layer

Original: Layer

Original: Layer

Original: Layer

Original: Original

Original: [Figure]
Revised: [Figure]

Original: F Q UALITY IMPROVEMENTS BY NON - ACCELERATED SAMPLING WITH SCALED ∆ h t
Revised: F Q UALITY IMPROVEMENTS BY NON - ACCELERATED SAMPLING WITH SCALED ∆ h t

Original: Figure 17 shows the quality improvements by non-accelerated sampling with scaled ∆ h t described in § 3.4.
Revised: Figure 17 shows the quality improvements by non-accelerated sampling with scaled ∆ h t described in § 3.4.

Original: Even with different number of inference steps, we observe similar changes of an attribute if we preserve the sum of ∆ h t .
Revised: Even with different number of inference steps, we observe similar changes of an attribute if we preserve the sum of ∆ h t .

Original: This scaling technique allows non-accelerated sampling withsteps for the models trained by accelerated training with 40 steps.
Revised: This scaling technique allows non-accelerated sampling withsteps for the models trained by accelerated training with 40 steps.

Original: Using non-accelerated sampling[Figure] with scaled ∆ h t leads to the same magnitude of manipulation and higher-quality images.
Revised: Using non-accelerated sampling with scaled ∆ h t leads to the same magnitude of manipulation and higher-quality images.

Original: In our experiments, it takes about 1.5 seconds to sample for 40 steps and 40 seconds for 1000 steps.
Revised: In our experiments, it takes about 1.5 seconds to sample for 40 steps and 40 seconds for 1000 steps.

Original: G E DITING STRENGTH AND EDITING FLEXIBILITY G. E DITING STRENGTH AND t edit
Revised: G E DITING STRENGTH AND EDITING FLEXIBILITY G. E DITING STRENGTH AND t edit

Original: Figure 18 shows the results according to t edit .
Revised: Figure 18 shows the results according to t edit .

Original: If t edit is too high, the length of the editing process becomes too short resulting in insufficient changes.
Revised: If t edit is too high, the length of the editing process becomes too short resulting in insufficient changes.

Original: On the contrary, too low t edit causes excessively unnecessary manipulation from the long editing process.
Revised: On the contrary, too low t edit causes excessively unnecessary manipulation from the long editing process.

Original: We observe that t edit is one of the important hyperparameters.
Revised: We observe that t edit is one of the important hyperparameters.

Original: We argue that the formula for choosing t edit using editing strength is reasonable because it applies to all five different datasets despite its sensitivity, even though the choice of LPIPS = 0 .
Revised: We argue that the formula for choosing t edit using editing strength is reasonable because it applies to all five different datasets despite its sensitivity, even though the choice of LPIPS = 0 .

Original: 33 is empirical.
Revised: 33 is empirical.

Original: We provide ablation of thresholds in Figure 22.
Revised: We provide ablation of thresholds in Figure 22.

Original: Additionally, these results imply why we need to use sufficiently low t edit in the unseen domains.
Revised: Additionally, these results imply why we need to use sufficiently low t edit in the unseen domains.

Original: Editing interval with insufficient editing strength struggles to escape from the training domain.
Revised: Editing interval with insufficient editing strength struggles to escape from the training domain.

Revised: G. E DITING FLEXIBILITY AND t boost

Revised: Table 2 shows the prompts, t edit , and t boost .

Revised: Intuitively, the attributes with larger visual changes have smaller cosine similarity and require longer editing interval.

Revised: Figure 19 shows the average

Original: Dataset src txt trg txt
Revised: LPIPS( x , P t ) and LPIPS( x , x t ) of 100 samples on all datasets. Note that t edit and t boost dif- Layer 1~ Layer

Revised: Layer 8 ( h-space )

Revised: Layer

Revised: Layer

Revised: Layer

Revised: Layer

Revised: Layer

Revised: Layer

Revised: Layer

Revised: Layer

Original: CLIP similarity t edit t boost 
Revised: Original [Figure] fer across datasets and attributes, and they are chosen by the formulas in § 4. We provide ablation of t boost in Figure 23.

Original: CelebA-HQ
Revised: We validate the effectiveness of our quality boosting ( § 4.2) in the original DDIM process and in Asyrp.

Original: Person
Revised: [Figure]

Original: Young person 0.
Revised: Dataset src [Table] trg txt

Original: 167 Face Smiling face 0. 167 Face
Revised: CLIP similarity t edit t boost

Original: Sad face 0.
Revised: [Table] [Table] [Table]

Original: 167 Face
Revised: [Table]

Original: Angry face 0. 167 Face Tanned face 0.
Revised: [Table] [Table]

Original: 167 Face
Revised: [Table]

Original: Disgustedface 0.
Revised: Disgusted

Original: 167 Person
Revised: [Table]

Original: Person with makeup 0.
Revised: [Table] makeup 0.

Original: 167 Human
Revised: [Table]

Original: Zombie 0.
Revised: Zombie 0.

Original: 167 Person
Revised: 167 Person

Original: Person with bald head 0.
Revised: Person with bald head 0.

Original: 167 Person
Revised: 167 Person

Original: Person with curly hair 0.
Revised: Person with curly hair 0.

Original: 167 Human
Revised: 167 Human

Original: Neanderthal 0.
Revised: Neanderthal 0.

Original: 167 Person
Revised: 167 Person

Original: Mark Zuckerberg 0.
Revised: Mark Zuckerberg 0.

Original: 167 Person
Revised: [Table]

Original: Nicolas Cage 0.
Revised: Nicolas Cage 0.

Original: 167 Human
Revised: 167 Human

Original: Painting in the style of Pixar 0.
Revised: Painting in the style of Pixar 0.

Original: 167 Photo
Revised: 167 Photo

Original: Painting in Modigliani style 0.
Revised: Painting in Modigliani style 0.

Original: 167 Photo
Revised: 167 Photo

Original: Self-portrait by Frida Kahlo 0.
Revised: Self-portrait by Frida Kahlo 0.

Original: LSUN-church
Revised: [Table]

Original: Church
Revised: Church

Original: Gothic Church 0.
Revised: [Table]

Original: 293 Church
Revised: 293 Church

Original: Temple 0.
Revised: Temple 0.

Original: 293 Church
Revised: 293 Church

Original: Department store 0.
Revised: Department store 0.

Original: 293 Church
Revised: 293 Church

Original: Wooden House 0.
Revised: Wooden House 0.

Original: 293 Church
Revised: 293 Church

Original: Ancient traditional Asian tower 0.
Revised: Ancient traditional Asian tower 0.

Original: 293 Church
Revised: 293 Church

Original: Red brick wall Church 0.
Revised: Red brick wall Church 0.

Original: 293 Church
Revised: 293 Church

Original: Factory 0.
Revised: Factory 0.

Original: LSUN-bedroom

Original: Bedroom
Revised: [Table]

Original: Princess Bedroom 0.
Revised: Princess Bedroom 0.

Original: 221 Bedroom
Revised: [Table]

Original: Hotel Bedroom 0.
Revised: Hotel Bedroom 0.

Original: AFHQ
Revised: AFHQ

Original: Dog
Revised: Dog

Original: Happy Dog 0.
Revised: Happy Dog 0.

Original: 167 Dog
Revised: 167 Dog

Original: Sleepy Dog 
Revised: Sleepy Dog 0.

Original: [Table]
Revised: 167 Dog

Original: Table 2 shows the prompts, t edit
Revised: Angry Dog 0.

Original: [Table]
Revised: 167 Dog

Original: and t boost .
Revised: Wolf 0.

Original: Intuitively, the attributes with larger visual changes have smaller cosine similarity and require longer editing interval. Figure 19 shows the average LPIPS( x , P t ) and LPIPS( x , x t ) of 100 samples on all datasets.
Revised: 167 Dog

Original: Note that t edit and t boost differ across datasets and attributes
Revised: Yorkshire Terrier 0.

Original: [Table]
Revised: [Table]

Original: and they are chosen by the formulas in § 4. We provide ablation oft boost in Figure
Revised: 180 Painting of

Original: [Table]
Revised: [Table]

Original: H Q UALITY BOOSTING

Original: We validate the effectiveness of our quality boosting ( § 4.2) in the original DDIM process and in

Original: Asyrp.

Original: Figure 20 shows the effect of quality boosting with the original DDIM reverse process.
Revised: Figure 20 shows the effect of quality boosting with the original DDIM reverse process.

Original: Although the reverse process of DDIM has a nearly-perfect inversion property, we observe some noise by zooming in.
Revised: Although the reverse process of DDIM has a nearly-perfect inversion property, we observe some noise by zooming in.

Original: Our quality boosting improves the quality of a sample and concurrently keeps nearly perfect inversion property.
Revised: Our quality boosting improves the quality of a sample and concurrently keeps nearly [Figure] [Figure] [Figure]

Revised: Pixar

Original: We observe that t boost is not sensitive, but the larger interval brings the less preservation.
Revised: Young Smiling 0. [Figure] Young

Revised: Smiling 0.

Revised: perfect inversion property.

Original: Figure 21 shows quality improvements by our quality boosting in Asyrp.
Revised: Figure 21 shows quality improvements by our quality boosting in Asyrp.

Original: I A LGORITHM
Revised: I A LGORITHM

Original: Figure 24 illustrates generative process.
Revised: Figure 24 illustrates generative process.

Original: Algorithm 1 and 2 describe training algorithm and inference algorithm of Asyrp, respectively.
Revised: Algorithm 1 and 2 describe training algorithm and inference algorithm of Asyrp, respectively

Original:  J T RAINING DETAILS J. L OSS COEFFICIENTS
Revised: . J T RAINING DETAILS J. L OSS COEFFICIENTS

Original: Table 3 reports loss coefficients for each attribute.
Revised: Table 3 reports loss coefficients for each attribute.

Original: λ CLIP s near 0.8 are suitable for most in-domain attributes.
Revised: λ CLIP s near 0.8 are suitable for most in-domain attributes.

Original: For unseen domains, higher coefficient leads to more noticeable changes.
Revised: For unseen domains, higher coefficient leads to more noticeable changes.

Original: Note that we use λ recon = CLIP similarity ∗ 3 which reduces L1 loss when an attribute needs a lot of change. 
Revised: Note that we use λ recon = CLIP similarity ∗ 3 which reduces L1 loss when an attribute needs a lot of change. [Figure]

Original: J. T RAINING WITH RANDOM SAMPLING INSTEAD OF THE TRAINING DATASETS
Revised: J. T RAINING WITH RANDOM SAMPLING INSTEAD OF THE TRAINING DATASETS

Original: Apparently, for training, inverting real-images can be replaced by random sampling.
Revised: Apparently, for training, inverting real-images can be replaced by random sampling.

Original: It refers to using x T ∼ N (0 , I ) instead of x T
Revised: It refers to using x T ∼ N (0 , I ) instead of x T

Original: = q ( x 0 ) · (cid:81)
Revised: = q ( x 0 ) · (cid:81)

Original: Tt =
Revised: Tt =

Original: 1 q ( x t | x t − 1 ) where x 0 ∼ p data ( x ) .
Revised: 1 q ( x t | x t − 1 ) where x 0 ∼ p data ( x ) .

Original: It allows us to train Asyrp only with the pretrained network and without extra dataset.
Revised: It allows us to train Asyrp only with the pretrained network and without extra dataset.

Original: Using random samples has tradeoff between preservation of contents and possible amount in editing.
Revised: Using random samples has tradeoff between preservation of contents and possible amount in editing.

Original: It take advantages when a target attribute requires large amount changes.
Revised: It take advantages when a target attribute requires large amount changes.

Original: We assume that the inversion of real-image is in the long tail of a Gaussian distribution because of realistic background or detailed clothes.
Revised: We assume that the inversion of real-image is in the long tail of a Gaussian distribution because of realistic background or detailed clothes.

Original: On the other hand, random noise is considered to be closer to the mean of the normal, so it is easier to find directions.
Revised: On the other hand, random noise is considered to be closer to the mean of the normal, so it is easier to find directions.

Original: It can easily bring larger changes but also easily alter the contents.
Revised: It can easily bring larger changes but also easily alter the contents.

Original: On the contrary, training with inversion shows the opposite property.
Revised: On the contrary, training with inversion shows the opposite property.

Original: [Figure]

Original: [Figure]

Original: [Figure] [Figure] [Figure] We train f t with random sampling for attributes whose identity preservation is not important to take advantage of these properties.
Revised: We train f t with random sampling for attributes whose identity preservation is not important to take advantage of these properties.

Original: The rightmost column in Table 3 shows the choices.
Revised: The rightmost column in Table 3 shows the choices.

Revised: K E VALUATION K. U SER STUDY

Original: [-Input: x 0 (Input image), { f it } Mi =
-]
Revised: [-Input: x 0 (Input image), { f it } Mi =
-]We conduct user study to compare the performance of Asyrp and DiffusionCLIP (Kim & Ye, 2021) on Celeba-HQ (Liu et al., 2015) and LSUN-church (Yu et al., 2015). We use official checkpoints provided by DiffusionCLIP except for some facial attributes whose checkpoint do not exist. We tried our best to tune their hyperparameters following the manual for fair comparison. Example images are shown in Figure 25-27.

Revised: We use smiling and sad for in-domain CelebA-HQ attributes, Pixar and Neanderthal for unseen-domain CelebA-HQ attributes, and department store , ancient , and wooden for

Revised: LSUN-church.

Revised: In unseen domain and Lsun-church, we use official checkpoints provided by DiffusionCLIP.

Revised: We also randomly select 8 images for each CelebA-HQ attribute and 12 images for each LSUN-church attribute.

Revised: We observe that DiffusionCLIP works better in changing the holistic style of images.

Revised: At the same time, it is short of the ability to bring semantic changes and suffers noisy results and a lack of diversity.

Revised: The problems would be caused by fine-tuning the whole diffusion model.

Revised: We use the following questions for the survey.

Revised: 1) Quality: Which image quality do you think is better?

Revised: (clear and less noisy) 2)

Original: {+Attribute: Which image do you think is “Attribute(e.g., Smiling) naturally”? Overall: Which image do you think is better considering the above evaluation criteria? Input: x 0 (Input image), { f it } Mi =
+} (M Neural implicit functions for M attributes), { c i } Mi =1 (user defined M scaling coefficients for M attributes), ϵ θ (frozen pretrained model), S for (# of inversion steps), S gen (# of inference steps), t edit (computed from § 4.1), t boost (computed from § 4.2) 1 Function Editor( x 0 , ϵ θ , { c i } Mi =1 , S for , S gen , ∗ ) : // step 1: Semantic encoding
Revised: {+Attribute: Which image do you think is “Attribute(e.g., Smiling) naturally”? Overall: Which image do you think is better considering the above evaluation criteria? Input: x 0 (Input image), { f it } Mi =
+} (M Neural implicit functions for M attributes), { c i } Mi =1 (user defined M scaling coefficients for M attributes), ϵ θ (frozen pretrained model), S for (# of inversion steps), S gen (# of inference steps), t edit (computed from § 4.1), t boost (computed from § 4.2) 1 Function Editor( x 0 , ϵ θ , { c i } Mi =1 , S for , S gen , ∗ ) : // step 1: Semantic encoding

Original: Define { τ s } S for s =

Revised: Define { τ s } S for s =

Original: s.t τ 1 = 0 , τ S for =
Revised: s.t τ 1 = 0 , τ S for =

Original: T for s = 1 , 2 , ..., S for − 1 do ϵ ←−
Revised: T for s = 1 , 2 , ..., S for − 1 do ϵ ←−

Original: ϵ θ ( x τ s , τ s ) x
Revised: ϵ θ ( x τ s , τ s ) x

Original: τ s +1 = √
Revised: τ s +1 = √

Original: α τ s x τ s + √ 1 −
Revised: α τ s x τ s + √ 1 −

Original: α τ s ϵ // step 2: Manipulation
Revised: α τ s ϵ // step 2: Manipulation

Original: Define { ˜ τ s } S gen s =1 s.t ˜ τ 1 = 0 , ˜ τ S edit =
Revised: Define { ˜ τ s } S gen s =1 s.t ˜ τ 1 = 0 , ˜ τ S edit =

Original: t edit , ˜ τ S noise = t boost and ˜ τ S gen = T ˜ x
Revised: t edit , ˜ τ S noise = t boost and ˜ τ S gen = T ˜ x

Original: ˜ τ Sgen = x ˜ τ
Revised: ˜ τ Sgen = x ˜ τ

Original: Sfor for s = S gen , S gen − 1 , ..., 2 do // phase 1: editing if s ≥ S edit then
Revised: Sfor for s = S gen , S gen − 1 , ..., 2 do // phase 1: editing if s ≥ S edit then

Original: Extract feature map h ˜ τ s from ϵ θ (˜ x ˜ τ s )
Revised: Extract feature map h ˜ τ s from ϵ θ (˜ x ˜ τ s )

Original: ∆ h ˜ τ s = S for S gen ( (cid:80) Mi
Revised: ∆ h ˜ τ s = S for S gen ( (cid:80) Mi

Original: =1 c
Revised: =1 c

Original: i f i ˜ τ s ( h ˜ τ s ))
Revised: i f i ˜ τ s ( h ˜ τ s ))

Original: ˜
Revised: ˜

Original: ϵ
Revised: ϵ

Original: =
Revised: =

Original: ϵ θ (˜ x ˜ τ s | ∆ h ˜ τ s )
Revised: ϵ θ (˜ x ˜ τ s | ∆ h ˜ τ s )

Original: ϵ
Revised: ϵ

Original: =
Revised: =

Original: ϵ θ (˜ x ˜ τ s ) σ ˜ τ s =// phase
Revised: ϵ θ (˜ x ˜ τ s ) σ ˜ τ s =// phase

Original: else if s ≥ S noise then ˜ ϵ
Revised: else if s ≥ S noise then ˜ ϵ

Original: = ϵ =
Revised: = ϵ =

Original: ϵ θ (˜ x ˜ τ s ) σ ˜ τ s =// phase
Revised: ϵ θ (˜ x ˜ τ s ) σ ˜ τ s =// phase

Original: quality boosting
Revised: quality boosting

Original: else ˜ ϵ
Revised: else ˜ ϵ

Original: = ϵ =
Revised: = ϵ =

Original: ϵ θ (˜ x˜ τ s ) σ ˜ τ s = (cid:112) (1 − α ˜ τ
Revised: ϵ θ (˜ x

Original: s − 1 ) / (1 − α ˜ τ s ) (cid:112) 1 − α ˜ τ s /α ˜
Revised: [Table]

Original: τ s − z ∼ N (0 , 1) ˜ x 
Revised: return ˜ x 0 (manipulated image)

Original: ˜ τ s − 1 = √
Revised: As for LSUN-church, we provide a set of four images at once and add a question: 3) Diversity:

Original: α ˜ τ s − 1 ( ˜ x ˜ τs − √ 1 − α ˜ τs
Revised: Which group do you think has a more diverse style?

Original: ˜ ϵ √ α ˜ τs ) + (cid:113) 1 − α ˜ τ s − 1 − σ 2˜ τ s ϵ + σ ˜ τ s z return ˜ x 0 (manipulated image)
Revised: Overall: Which image do you think is better considering the above evaluation criteria?

Original: K E VALUATION K. U SER STUDY
Revised: K. S EGMENTATION CONSISTENCY AND DIRECTIONAL CLIP SIMILARITY

Original: Weconduct user study to comparethe performance of Asyrp and DiffusionCLIP (Kim & Ye, 2021) on Celeba-HQ (Liu et al., 2015) and LSUN-church (Yu et al., 2015).
Revised: We compare Asyrp and DiffusionCLIP using directional CLIP similarity ( S dir ) and segmentationconsistency (SC) following the protocols in DiffusionCLIP in Table 4 and Table 5.

Original: We use official checkpoints provided by DiffusionCLIP except for some facial attributes whose checkpoint do not exist. We tried our best to tune their hyperparameters following the manual for fair comparison.
Revised: A pretrained

Original: Example images are shown in Figure 25-27.
Revised: CLIP (Radford et al., 2021) and segmentation models (Yu et al. ; Zhou et al. ; Lee et al. (2020)) are used to compute S dir and SC, respectively.

Original: We use smiling and sad  for in-domain CelebA-HQ attributes, Pixar and Neanderthal for unseen-domain CelebA-HQ attributes, and  department store , ancient , and wooden for LSUN-church.
Revised: We choose three attributes ( smiling , sad , tanned ) for CelebA-HQ-in-domain, two attributes ( Pixar , Neaderthal ) for CelebA-HQunseen-domain and three attributes ( department store , ancient , red brick ) for LSUNchurch.

Original: In unseen domain and Lsun-church, we use official checkpoints provided by DiffusionCLIP. We also randomly select 8 images for each CelebA-HQ attribute and 12 images for each LSUN-church attribute.
Revised: For a fair comparison, we use official checkpoints of DiffusionCLIP and provide scores of the attributes ( tanned , red brick ) following Kim & Ye (2021). Regarding attributes without the official checkpoints ( smiling , sad ), we train DiffusionCLIP by ourselves with the official code.

Original: Input: ϵ θ (pretrained model), { x ( i ) 0 } Ni =1 (images to precompute), f t (Neural implicit function of an attribute), y src (source text), y tar (target text), S for (# of inversion steps), K (# of training epochs), t edit (computed from § 4.1), t boost (computed from § 4.2) //
Revised: Input: ϵ θ (pretrained model), { x ( i ) 0 } Ni =1 (images to precompute), f t (Neural implicit function of an attribute), y src (source text), y tar (target text), S for (# of inversion steps), K (# of training epochs), t edit (computed from § 4.1), t boost (computed from § 4.2) //

Original: 1 Define { τ s } S for s =

Revised: 1 Define { τ s } S for s =

Original: s.t τ 1 = 0 , τ S for = T 2 for i = 1 , 2 , ..., N do for s = 1 , 2 , ..., S for − 1 do ϵ ←−
Revised: s.t τ 1 = 0 , τ S for = T 2 for i = 1 , 2 , ..., N do for s = 1 , 2 , ..., S for − 1 do ϵ ←−

Original: ϵ θ ( x ( i ) τ s , τ s )
Revised: ϵ θ ( x ( i ) τ s , τ s )

Original: [Table]
Revised: [Table]

Original: ( i
Revised: ( i

Original: [Table] [Table]
Revised: [Table] [Table]

Original: Save the latent x ( i ) τ
Revised: Save the latent x ( i ) τ

Original: Sfor 7 τ S edit =
Revised: Sfor 7 τ S edit =

Original: t edit // step 2: Update f t 8 for epoch
Revised: t edit // step 2: Update f t 8 for epoch

Original: = 1 , 2 , ...
Revised: = 1 , 2 , ...

Original: , K do for i = 1 , 2 , ..., N do
Revised: , K do for i = 1 , 2 , ..., N do

Original: Clone the latent ˜ x
Revised: Clone the latent ˜ x

Original: ( i )
Revised: ( i )

Original: τ
Revised: τ

Original: Sfor = x ( i ) τ
Revised: Sfor = x ( i ) τ

Original: Sfor for s = S for , S for − 1 , ..., S edit do
Revised: Sfor for s = S for , S for − 1 , ..., S edit do

Original: Extract feature map h τ s from ϵ θ (˜ x
Revised: Extract feature map h τ s from ϵ θ (˜ x

Original: ( i ) τ s )
Revised: ( i ) τ s )

Original: [Table]
Revised: [Table]

Original: ( h
Revised: ( h

Original: [Table]
Revised: [Table]

Original: s )
Revised: s )

Original: [Figure]
Revised: [Figure]

Original: [Table]
Revised: [Table]

Original: [Figure]
Revised: [Figure]

Original: [Table]
Revised: [Table]

Original: [Figure]
Revised: [Figure]

Original: [Table]
Revised: [Table]

Original: [Figure]
Revised: [Figure]

Original: [Table]
Revised: [Table]

Original: [Figure]
Revised: [Figure]

Original: [Table]
Revised: [Table]

Original: [Figure]
Revised: [Figure]

Original: [Table]
Revised: [Table]

Original: [Figure]
Revised: [Figure]

Original: [Table]
Revised: [Table]

Original: [Figure]
Revised: [Figure]

Original: [Table]
Revised: [Table]

Original: [Equation]
Revised: [Equation]

Original: [Figure]
Revised: [Figure]

Original: [Equation]
Revised: [Equation]

Original: [Figure]
Revised: [Figure]

Original: [Table]
Revised: [Table]

Original: [Figure]
Revised: [Figure]

Original: [Table]
Revised: [Table]

Original: [Figure]
Revised: [Figure]

Original: [Table]
Revised: [Table]

Original: [Figure]
Revised: [Figure]

Original: [Table]
Revised: [Table]

Original: [Figure]
Revised: [Figure]

Original: [Table]
Revised: [Table]

Original: [Figure]
Revised: [Figure]

Original: [Table]
Revised: [Table]

Original: [Figure]
Revised: [Figure]

Original: [Table]
Revised: [Table]

Original: [Figure]
Revised: [Figure]

Original: [Table]
Revised: [Table]

Original: [Figure]
Revised: [Figure]

Original: [Table]
Revised: [Table]

Original: [Figure]
Revised: [Figure]

Original: [Table]
Revised: [Table]

Original: [Figure]
Revised: [Figure]

Original: [Table]
Revised: [Table]

Original: [Figure]
Revised: [Figure]

Original: [Table]
Revised: [Table]

Original: [Figure]
Revised: [Figure]

Original: [Table]
Revised: [Table]

Original: We observe that DiffusionCLIP works better in changing the holistic style of images.
Revised: We use 100 samples per attribute. Asyrp outperforms DiffusionCLIP on S dir for all attributes.

Original: At the same time, it is short of the ability to bring semantic changes and suffers noisy results and a lack of diversity.
Revised: On SC, DiffusionCLIP achieves better or competitive scores.

Original: The problems would be caused by fine-tuning the whole diffusion model. We use the following questions for the survey. 1) Quality: Which image quality do you think is better? (clear and less noisy) 2)
Revised: Because DiffusionCLIP manipulates images mostly by focusing on texture or color while preserving structure and shape, it takes advantage of getting higher SC scores.

Original: Attribute: Which image do you think is “Attribute(e.g., Smiling) naturally”?
Revised: However, in Figure 28, results of smiling show that it is not proper to edit attributes which require structural manipulation.

Original: Overall: Which image do you think is better considering the above evaluation criteria?
Revised: Note that better SC scores do not guarantee better qualitative performance.

Original: As for LSUN-church, we provide a set of four images at once and add a question: 3) Diversity: Which group do you think has a more diverse style?
Revised: Results of Pixar also show the similar tendency of each method. We allow more structural changes than DiffusionCLIP while editing.

Original: Overall: Which image do you think is better considering the above evaluation criteria?
Revised: Lower SC of our method comes from desirable structural changes as shown in Figure 28.

Original: K. S EGMENTATION CONSISTENCY AND DIRECTIONAL CLIP SIMILARITY
Revised: [Equation] L D IRECTIONS L. G LOBAL DIRECTION

Original: We compare Asyrp and DiffusionCLIP using directional CLIP similarity ( S dir ) and segmentationconsistency (SC) following the protocols in DiffusionCLIP in Table 4 and Table 5.
Revised: Figure 29 and Figure 30 show that the effects of mean direction and global direction are quite similar with ∆ h t by f t in various attributes.

Original: A pretrained CLIP (Radford et al., 2021) and segmentation models (Yu et al.
Revised: We compute mean direction and global direction from 20 different images.

Original: ; Zhou et al.
Revised: We argue that h-space is roughly homogeneous across samples and timesteps. However, we observe that h-space is not completely independent to the conditions especially on unseen-domain [Table] [Table]

Original: ; Lee et al.
Revised: [Figure] [Figure] [Figure]

Original: (2020)) are used to compute S dirand SC, respectively.
Revised: Department store ancient red brick S dir SC S dir

Original: We choose three attributes ( smiling , sad , tanned ) for CelebA-HQ-in-domain, two attributes ( Pixar , Neaderthal ) for CelebA-HQunseen-domain and three attributes ( department store , ancient , red brick ) for LSUNchurch. For a fair comparison, we use official checkpoints of DiffusionCLIP and provide scores of the attributes ( tanned , red brick ) following Kim & Ye (2021).
Revised: SC

Original: Regarding attributes without the official checkpoints ( smiling , sad ), we train DiffusionCLIP by ourselves with the official
Revised: S dir SC Asyrp (ours) 0. DIffusionCLIP 0.

Original: [Table]
Revised: [Table]

Original: [Figure] [Figure]
Revised: (See Figure 29).

Original: [Equation]
Revised: Note that unseen-domains require a longer editing interval with small t edit . Therefore, we conjecture that the consistency of h-space decreases at the end of the generative process. It is supported by additional experiments that L2 distance between ∆ h t and global direction gradually increases along with timesteps. We leave a more detailed analysis on h-space at different timesteps as a future work.

Original: Real image
Revised: Real image

Original: Asyrp (ours)
Revised: Asyrp (ours)

Original: DiffusionCLIP
Revised: DiffusionCLIP

Original: Real image
Revised: Real image

Original: Asyrp (ours)
Revised: Asyrp (ours)

Original: DiffusionCLIP
Revised: DiffusionCLIP

Original: D e p a r t m e n t s t o r e
Revised: D e p a r t m e n t s t o r e

Original: W ood e n
Revised: W ood e n

Original: Real image
Revised: Real image

Original: Asyrp (ours)
Revised: Asyrp (ours)

Original: DiffusionCLIP
Revised: DiffusionCLIP

Original: Real image
Revised: Real image

Original: Asyrp (ours)
Revised: Asyrp (ours)

Original: DiffusionCLIP
Revised: DiffusionCLIP

Original: A n c i e n t
Revised: A n c i e n t

Original: R e d b r
Revised: R e d b r

Original: i c k
Revised: i c k

Original: [Figure]

Original: [Figure]

Original: [Figure]

Original: [Figure]

Original: [Figure]

Original: 79.03 DiffusionCLIP 0.

Original: [Figure]

Original: [Figure]

Original: L D IRECTIONS L. G LOBAL DIRECTION

Original: Figure 29 and Figure 30 show that the effects of mean direction and global direction are quite similar with ∆ h t by f t in various attributes.

Original: We compute mean direction and global direction from 20 different images.

Original: We argue that h-space is roughly homogeneous across samples and timesteps.

Original: However, we observe that h-space is not completely independent to the conditions especially on unseen-domain

Original: [Table]

Original: [Table]

Original: Smiling

Original: (See Figure 29).

Original: Note that unseen-domains require a longer editing interval with small t edit .

Original: Therefore, we conjecture that the consistency of h-space decreases at the end of the generative process.

Original: It is supported by additional experiments that L2 distance between ∆ h t and global direction gradually increases along with timesteps.

Original: We leave a more detailed analysis on h-space at different timesteps as a future work.

Original: [Figure]

Original: [Figure]

Original: direction

Original: L. C OMPARE THREE METHODS
Revised: L. C OMPARE THREE METHODS

Original: In this section, we compare three methods: implicit neural direction f t , optimized ∆ h t , and optimized ∆ h global .
Revised: In this section, we compare three methods: implicit neural direction f t , optimized ∆ h t , and optimized ∆ h global .

Original: Training time f t ≈ ∆ h global < ∆ h t
Revised: Training time f t ≈ ∆ h global < ∆ h t

Original: We have to optimize each ∆ h t for each time step t .
Revised: We have to optimize each ∆ h t for each time step t .

Original: Additionally, it needs specific hyperparameters for each ∆ h t , e.g., higher learning rates for larger t .
Revised: Additionally, it needs specific hyperparameters for each ∆ h t , e.g., higher learning rates for larger t .

Original: On the contrary, time-consuming for f t is similar to optimizing ∆ h global .
Revised: On the contrary, time-consuming for f t is similar to optimizing ∆ h global .

Original: Quality f t ≈ ∆ h t > ∆ h global f t and ∆ h t , where directions can be obtained for each timestep, have the best quality.
Revised: f t and ∆ h t , where directions can be obtained for each timestep, have the best quality.

Original: As can be shown in Figure 10, ∆ h global is sometimes accompanied by slight differences in hair, etc.
Revised: As can be shown in Figure 10, ∆ h global is sometimes accompanied by slight differences in hair, etc.

Original: Extensibility f t > ∆ h t > ∆ h global ∆ h t can be obtained from f t , and ∆ h global can be obtained by aggregating ∆ h t .
Revised: Original Asyrp (Ours) DiffusionCLIP

Revised: [Figure]

Original: We opt to use f t for above three advantages.
Revised: We opt to use f t for above three advantages.

Original: M R ANDOM SAMPLING
Revised: M R ANDOM SAMPLING

Original: [Figure]

Original: Although we do not focus on these results, Asyrp can be used for conditional sampling.

Original: We conduct extra experiments: generating images with target attributes using Asyrp not from inversion but from random Gaussian noises.
Revised: We conduct extra experiments: generating images with target attributes using Asyrp not from inversion but from random Gaussian noises.

Original: As a consequence, the generative process can be used for conditional random sampling.
Revised: As a consequence, the generative process can be used for conditional random sampling.

Original: We provide the results in Figure 31.
Revised: We provide the results in Figure 31.

Original: However, it is beyond the scope of this paper.
Revised: However, it is beyond the scope of this paper.

Revised: [Figure]

Revised: [Figure]

Revised: [Figure]

Original: N M ORE SAMPLES N. I MAGE N ET
Revised: N M ORE SAMPLES N. I MAGE N ET

Original: We conduct extra experiments: editing images with target class using Asyrp with ImageNet pretrained model.
Revised: We conduct extra experiments: editing images with target class using Asyrp with ImageNet pretrained model.

Original: We verified that models trained on large datasets, such as ImageNet, can be edited using Asyrp.
Revised: We verified that models trained on large datasets, such as ImageNet, can be edited using Asyrp.

Original: However, we also observed that in this case the latent space is not partitioned by classes.
Revised: However, we also observed that in this case the latent space is not partitioned by classes.

Original: For an orange, we have different latents for a single orange, for many oranges, for a cross-section of cut orange, and for a single piece of orange.
Revised: For an orange, we have different latents for a single orange, for many oranges, for a cross-section of cut orange, and for a single piece of orange.

Original: Therefore, we learned the implicit function by collecting similar images to find the direction.
Revised: Therefore, we learned the implicit function by collecting similar images to find the direction.

Original: [Figure]
Revised: [Figure]

Original: N. 
Revised: N. M ULTI - INTERPOLATION

Original: [Figure]

Original: Figure 33 provides mixed interpolation between multiple attributes.
Revised: Figure 33 provides mixed interpolation between multiple attributes.

Original: We observe that any interpolation with any attribute is possible.
Revised: We observe that any interpolation with any attribute is possible

Original:  N. M ORE RESULTS ON ALL DATASETS
Revised: . N. M ORE RESULTS ON ALL DATASETS

Original: We provide more results on CelebA-HQ (Figure 34), LSUN-church (Figure 35), AFHQ, LSUNbedroom, M ET F ACES (Figure 36).
Revised: We provide more results on CelebA-HQ (Figure 34), LSUN-church (Figure 35), AFHQ, LSUNbedroom, M ET F ACES (Figure 36).

Original: [Figure]
Revised: [Figure]

Original: un
Revised: un

Original: [Figure]
Revised: [Figure]

Original: ng (
Revised: ng (

Original: [Figure]
Revised: [Figure]

Original: ) +young (trained) -young (untrained) +young (trained) -young
Revised: ) +young (trained) -young (untrained) +young (trained) -young

Original: (untrained) - s m ili ng
Revised: (untrained) - s m ili ng

Original: [Figure]
Revised: [Figure]

Original: i n e d )
Revised: i n e d )

Original: [Figure]
Revised: [Figure]

Original: [Figure] [Figure]
Revised: [Figure] [Figure]