Title: Diffusion Self-Guidance for Controllable Image Generation
Venue: NeurIPS 2023
Accepted: yes
Keywords: generative models

image editing

diffusion

guidance
Abstract: Large-scale generative models are capable of producing high-quality images from detailed prompts. However, many aspects of an image are difficult or impossible to convey through text. We introduce self-guidance, a method that provides precise control over properties of the generated image by guiding the internal representations of diffusion models. We demonstrate that the size, location, and appearance of objects can be extracted from these representations, and show how to use them to steer the sampling process. Self-guidance operates similarly to standard classifier guidance, but uses signals present in the pretrained model itself, requiring no additional models or training. We demonstrate the flexibility and effectiveness of self-guided generation through a wide range of challenging image manipulations, such as modifying the position or size of a single object (keeping the rest of the image unchanged), merging the appearance of objects in one image with the layout of another, composing objects from multiple images into one, and more. We also propose a new method for reconstruction using self-guidance, which allows extending our approach to editing real images.

Diffusion Self-Guidance for Controllable Image Generation

Abstract

Large-scale generative models are capable of producing high-quality images from detailed text descriptions. However, many aspects of an image are difficult or impossible to convey through text. We introduce self-guidance, a method that provides greater control over generated images by guiding the internal representations of diffusion models. We demonstrate that properties such as the shape, location, and appearance of objects can be extracted from these representations and used to steer the sampling process. Self-guidance operates similarly to standard classifier guidance, but uses signals present in the pretrained model itself, requiring no additional models or training. We show how a simple set of properties can be composed to perform challenging image manipulations, such as modifying the position or size of specific objects, merging the appearance of objects in one image with the layout of another, composing objects from multiple images into one, and more. We also show that self-guidance can be used for editing real images. See our project page for results and an interactive demo: https://dave.ml/selfguidance

1 Introduction

Generative image models have improved rapidly in recent years with the adoption of large text-image datasets and scalable architectures [7, 10, 12, 27, 28, 32, 34, 38]. These models are able to create realistic images given a text prompt describing just about anything. However, despite the incredible abilities of these systems, discovering the right prompt to generate the exact image a user has in mind can be surprisingly challenging. A key issue is that all desired aspects of an image must be communicated through text, even those that are difficult or even impossible to convey precisely.

To address this limitation, previous work has introduced methods [9, 14, 18, 30] that tune pretrained models to better control details that a user has in mind. These details are often supplied in the form of reference images along with a new textual prompt [2, 4] or other forms of conditioning [1, 31, 39]. However, these approaches all either rely on fine-tuning with expensive paired data (thus limiting the scope of possible edits) or must undergo a costly optimization process to perform the few manipulations they are designed for. While some methods [11, 21, 22, 36] can perform zero-shot editing of an input image using a target caption describing the output, these methods only allow for limited control, often restricted to structure-preserving appearance manipulation or uncontrolled image-to-image translation.

By consequence, many simple edits still remain out of reach. For example, how can we move or resize one object in a scene without changing anything else? How can we take the appearance of an object in one image and copy it over to another, or combine the layout of one scene with the appearance of a second one? How can we generate images with certain items having precise shapes at specific positions on the canvas? This degree of control has been explored in the past in smaller scale settings [5, 8, 19, 24, 37, 40], but has not been convincingly demonstrated with modern large-scale diffusion models [26, 32, 38].

'a photo of

a giant macaron and a croissant splashing in the Seine with the Eiffel Tower in the background'

(a) Original

(b) Swap objects

(c) Enlarge

macaron

(d) Replace

macaron

(e) Copy scene appearance

(f) Copy scene layout

'a DSLR

photo

of a

meatball

and a

donut

falling from

the clouds onto

a

neighborhood'

(a) Original

(b) Move

donut

(c) Shrink

donut

(d) Replace

donut

(e) Copy scene appearance

(f) Copy scene layout

We propose self-guidance , a zero-shot approach which allows for direct control of the shape, position, and appearance of objects in generated images. Self-guidance leverages the rich representations learned by pretrained text-to-image diffusion models - namely, intermediate activations and attention to steer attributes of entities and interactions between them. These constraints can be user-specified or transferred from other images, and rely only on knowledge internal to the diffusion model. Through a variety of challenging image manipulations, we demonstrate that self-guidance using only a few simple properties allows for granular, disentangled manipulation of the contents of generated images (Figure 1). Further, we show that self-guidance can also be used to reconstruct and edit real images.

Our key contributions are as follows:

We introduce self-guidance , which takes advantage of the internal representations of pretrained text-to-image diffusion models to provide disentangled, zero-shot control over the generative process without requiring auxiliary models or supervision.

We find that properties such as the size, location, shape, and appearance of objects can be extracted from these representations and used to meaningfully guide sampling in a zero-shot manner.

We demonstrate that this small set of properties, when composed, allows for a wide variety of surprisingly complex image manipulations, including control of relationships between objects and the way modifiers bind to them.

Finally, by reconstructing captioned images using their layout and appearance as computed by self-guidance, we show that we can extend our method to editing real images.

2 Background

2.1 Diffusion generative models

Diffusion models learn to transform random noise into high-resolution images through a sequential sampling process [12, 33, 35]. This sampling process aims to reverse a fixed time-dependent destructive process that corrupts data by adding noise. The learned component of a diffusion model is a neural network /epsilon1 θ that tries to estimate the denoised image, or equivalently the noise /epsilon1 t that was added to create the noisy image z t = α t x + σ t /epsilon1 t . This network is trained with loss:

where y is an additional conditioning signal like text, and w ( t ) is a function weighing the contributions of denoising tasks to the training objective, commonly set to 1 [12, 15]. A common choice for /epsilon1 θ is a U-Net architecture with self- and cross-attention at multiple resolutions to attend to conditioning text in y [28, 29, 32]. Diffusion models are score-based models, where /epsilon1 θ can be seen as an estimate of the score function for the noisy marginal distributions: /epsilon1 θ ( z t ) ≈ -σ t ∇ z t log p ( z t ) [35].

Given a trained model, we can generate samples given conditioning y by starting from noise z T ∼ N (0 , I ) , and then alternating between estimating the noise component and updating the noisy image:

where the update could be based on DDPM [12], DDIM [34], or another sampling method (see Appendix for details). Unfortunately, naïvely sampling from conditional diffusion models does not produce high-quality images that correspond well to the conditioning y . Instead, additional techniques are utilized to modify the sampling process by altering the update direction ˆ /epsilon1 t .

2.2 Guidance

A key capability of diffusion models is the ability to adapt outputs after training by guiding the sampling process. From the score-based perspective, we can think of guidance as composing score functions to sample from richer distributions or to introduce conditioning on auxiliary information [7, 17, 35]. In practice, using guidance involves altering the update direction ˆ /epsilon1 t at each iteration.

Classifier guidance can generate conditional samples from an unconditional model by combining the unconditional score function for p ( z t ) with a classifier p ( y | z t ) to generate samples from p ( z t | y ) ∝ p ( y | z t ) p ( z t ) [7, 35]. To use classifier guidance, one needs access to a labeled dataset and has to learn a noise-dependent classifier p ( y | z t ) that can be differentiated with respect to the noisy image z t . While sampling, we can incorporate classifier guidance by modifying ˆ /epsilon1 t :

where s is an additional parameter controlling the guidance strength. Classifier guidance moves the sampling process towards images that are more likely according to the classifier [7], achieving a similar effect to truncation in GANs [3], and can also be applied with pretrained classifiers by first denoising the intermediate noisy image (though this requires additional approximations [1]).

In general, we can use any energy function g ( z t ; t, y ) to guide the diffusion sampling process, not just the probabilities from a classifier. g could be the approximate energy from another model [17], a similarity score from a CLIP model [23], an arbitrary time-independent energy as in universal guidance [1], bounding box penalties on attention [6], or any attributes of the noisy images. We can incorporate this additional guidance alongside classifier-free guidance [13] to obtain high-quality text-to-image samples that also have low energy according to g :

where s is the classifier-free guidance strength and v is an additional guidance weight for g . As with classifier guidance, we scale by σ t to convert the score function to a prediction of /epsilon1 t . The main contribution of our work is to identify energy functions g useful for controlling properties of objects and interactions between them.

2.3 Where can we find signal for controlling diffusion?

While guidance is a flexible way of controlling the sampling process, energy functions typically used [1, 39] require auxiliary models (adapted to be noise-dependent) as well as data annotated with properties we would like to control. Can we circumvent these costs? Recent work [11, 36] has shown that the intermediate outputs of the diffusion U-Net encode valuable information [16, 25] about the structure and content of the generated images. In particular, the self and cross-attention maps { A i,t ∈ R H i × W i × K } often encode structural information [11] about object position and shape, while the network activations { Ψ i,t ∈ R H i × W i × D i } allow for maintaining coarse appearance [36] when extracted from appropriate layers. While these editing approaches typically share attention and activations naively between subsequent sampling passes, drastically limiting the scope of possible manipulations, we ask: what if we tried to harness model internals in a more nuanced way?

̂

Pre-trained Di /uniFB00 usion Model

'a photo of

a burger

and

an ice cream cone

f

loating

in the ocean

'

conv

attend

z

t

attention

feats

Ψ

t

At

/uni03F5

t

Self-Guidance

∇

z

t

none

centroid

shape

appearance

∇

z

t

∇

z

t

Samples

3 Self-guidance

Inspired by the rich representations learned by diffusion models, we propose self-guidance, which places constraints on intermediate activations and attention maps to steer the sampling process and control entities named in text prompts (see Fig. 2). These constraints can be user-specified or copied from existing images, and rely only on knowledge internal to the diffusion model.

We identify a number of properties useful for meaningfully controlling generated images, derived from the set of softmax-normalized attention matrices { A i,t ∈ R H i × W i × K } and activations { Ψ i,t ∈ R H i × W i × D i } extracted from the standard denoising forward pass /epsilon1 θ ( z t ; t, y ) . To control an object mentioned in the text conditioning y at token indices k , we can manipulate the corresponding attention channel(s) A i,t, · , · ,k ∈ R H i × W i ×| k | and activations Ψ i,t (extracted at timestep t from a noisy image z t given text conditioning y ) by adding guidance terms to Eqn. 4.

Object position. To represent the position of an object (omitting attention layer index and timestep for conciseness), we find the center of mass of each relevant attention channel:

We can use this property to guide an object to an absolute target position on the image. For example, to move ' burger ' to position (0 . 3 , 0 . 5) , we can minimize ‖ (0 . 3 , 0 . 5) -centroid ( k ) ‖ 1 . We can also perform a relative transformation, e.g., move ' burger ' to the right by (0 . 1 , 0 . 0) by minimizing ‖ centroid orig ( k ) + (0 . 1 , 0 . 0) -centroid ( k ) ‖ 1 .

Object size. To compute an object's size, we spatially sum its corresponding attention channel:

In practice, we find it beneficial to differentiably threshold the attention map A thresh before computing its size, to eliminate the effect of background noise. We do this by taking a soft threshold at the midpoint of the per-channel minimum and maximum values (see Appendix for details). As with position, one can guide to an absolute size ( e.g. half the canvas) or a relative one ( e.g. 10% larger).

Object shape. For even more granular control than position and size, we can represent the object's exact shape directly through the thresholded attention map itself:

This shape can then be guided to match a specified binary mask (either provided by a user or extracted from the attention from another image) with ‖ target_shape -shape ( k ) ‖ 1 . Note that we can

'distant shot of the tokyo tower with a massive sun

in the sky'

'a photo

of

a

fluffy cat sitting on a

museum bench

looking at an

oil painting of cheese'

'a photo

of a

raccoon in a

barrel

going down a waterfall'

apply any arbitrary transformation (scale, rotation, translation) to this shape before using it as a guidance target, which allows us to manipulate objects while maintaining their silhouette.

Object appearance. Considering thresholded attention a rough proxy for object extent, and spatial activation maps as representing local appearance (since they ultimately must be decoded into an unnoised RGB image), we can reach a notion of object-level appearance by combining the two:

4 Composing self-guidance properties

The small set of properties introduced in Section 3 can be composed to perform a wide range of image manipulations, including many that are intractable through text. We showcase this collection of manipulations and, when possible, compare to prior work that accomplishes similar effects. All experiments were performed on Imagen [32], producing 1024 × 1024 samples. For more samples and details on the implementation of self-guidance, please see the Appendix.

Adjusting specific properties. By guiding one property to change and all others to keep their original values, we can modify single objects in isolation (Fig. 3b-3e). For a caption C = y with words at indices { c i } , in which O = { o j } ⊆ C are objects, we can move an object o k at time t with:

/negationslash

Where shape orig and shape are extracted from the generation of the initial and edited image, respectively. Critically, T lets us define whatever transformation of the H i × W i spatial attention map we

'a photo of a

parrot riding a horse

down

a

city street'

(a) Original

(b) New appearances

(c) ControlNet [39]

(d) PtP [11]

want. To move an object, T translates the attention mask the desired amount. We can also resize objects (Fig. 3f-3g) with Eqn. 9 by changing T to up- or down-sample shape matrices.

Constraining per-object layout but not appearance finds new 'styles' for the same scene (Fig. 4):

/negationslash

We can alternatively choose to guide all words, not just nouns or objects, changing summands to ∑ c = o k ∈ C instead of ∑ c = o k ∈ O . See Appendix for further discussion.

/negationslash

Composition between images. We can compose properties across multiple images into a cohesive sample, e.g. the layout of an image A with the appearance of objects in another image B (Fig. 5):

We can also borrow only appearances, dropping the first term to sample new arrangements for the same objects, as in the last two columns of Figure 5.

Highlighting the compositionality of self-guidance terms, we can further inherit the appearance and/or shape of objects from several images and combine them into one (Fig. 6). Say we have J images, where we are interested in keeping a single object o k j from each one. We can collage these objects 'in-place' i.e. maintaining their shape, size, position, and appearance - straightforwardly:

We can also take only the appearances of the objects from these images and copy the layout from another image, useful if object positions in the J images are not mutually compatible (Fig. 6f).

→

Appearance

'

a

photo of a

suitcase, a bowling ball, and a phone washed up on

a

beach after

a

shipwreck

'

Layout

→

No layout conditioning

'

a

photo of

a

picnic blanket,

a

fruit tree,

and a car by the

lake

'

(a) Take

blanket

(b) Take

tree

(c) Take

car

(d)

Result

(e) + Target layout

(f)

Final result

'

a

top-down

photo

of a

tea kettle, a

bowl

of fruit, and a

cup of

matcha

'

(a) Take

matcha

(b) Take

kettle

(c) Take

fruit

(d)

Result

(e) + Target layout

(f)

Final result

'

a

photo

of a

dog wearing a

knit

sweater

and a

baseball

cap drinking

a

cocktail

'

(a) Take

sweater

(b) Take

cocktail

(c) Take

cap

(d)

Result*

(e) + Target layout

(f)

Final result

O

purple wizard outfit

Ours

DreamBooth

chef outfit

superman

outfit

floating

in milk

pouring tea

floating

in the

sea

'a photo of

a

hot dog, fries, and a

soda on a solid background'

(a) Real image

(b) Reconstruct

(c) Swap w.

fries

↓

↓

, height

↑

'a photo

of an

eclair

and a

shot

of

(g) Real image

(h) Reconstruct

(i) Move

↓

(k) Width, height

↑

CANDP

Editing with real images. Our approach is not limited to only images generated by a model, whose internals we have access to by definition. By running T noised versions of a (captioned) existing image through a denoiser - one for each forward process timestep - we extract a set of intermediates that can be treated as if it came from a reverse sampling process (see Appendix for more details). In Fig. 8, we show that, by guiding shape and appearance for all tokens, we generate faithful reconstructions of real images. More importantly, we can manipulate these real images just as we can generated ones, successfully controlling properties such as appearance, position, or size. We can also transfer the appearance of an object of interest into new contexts (Fig. 7), from only one source image, and without any fine-tuning:

Attributes and interactions. So far we have focused only on the manipulation of objects, but we can apply our method to any concept in the image, as long as it appears in the caption. We demonstrate manipulation of verbs and adjectives in Fig. 9, and show an example where certain self-guidance constraints can help in enforcing attribute binding in the generation process.

5 Discussion

We introduce a method for guiding the diffusion sampling process to satisfy properties derived from the attention maps and activations within the denoising model itself. While we propose a number of

Move

laughing

to the right

'

a

cat and a

monkey laughing

on a

road

'

(a) Original

(b) Modified

Change

messy

location

'

a

photo

of a

messy

room

'

(c) At

〈

0

.

3

,

0

.

6

〉

(d) At

〈

0

.

8

,

0

.

8

〉

Move

red

to jacket,

yellow

to shoes

' green hat, blue book, yellow shoes, red jacket '

(e) Original

(f) Fixed

Appearance features leak layout (a) Unguided (b) ' lime ' guided

'

a

photo

of a

squirrel

trying

to catch

a

lime

mid-air

'

Multi-token layout leaks appearance (c) Real image (d) Layout guided

'

a

picture of

a

cake

'

Interacting objects entangled

' a potato sitting on a couch with a bowl of popcorn watching football on TV '

(e) Original

(f) Move potato

→

Alltokens

"cake"

such properties, many more certainly exist, as do alternative formulations of those presented in this paper. Among the proposed collection of properties, a few limitations stand out.

The reliance on cross-attention maps imposes restrictions by construction, precluding control over any object that is not described in the conditioning text prompt and hindering fully disentangled control between interacting objects due to correlations in attention maps (Fig. 10e-10f). Selectively applying attention guidance at certain layers or timesteps may result in more effective disentanglement.

Our experiments also show that appearance features often contain undesirable information about spatial layout (Fig. 10a-10b), perhaps since the model has access to positional information in its architecture. The reverse is also sometimes true: guiding the shape of multiple tokens occasionally betrays the appearance of an object (Fig. 10c-10d), implying that hidden high-frequency patterns arising from interaction between attention channels may be used to encode appearance. These findings suggest that our method could serve as a window into the inner workings of diffusion models and provide valuable experimental evidence to inform future research.

Broader impact

The use-cases showcased in this paper, while transformative for creative uses, carry the risk of producing harmful content that can negatively impact society. In particular, self-guidance allows for a level of control over the generation process that might enable potentially harmful image manipulations, such as pulling in the appearance or layout from real images into arbitrary generated content (e.g., as in Fig. 7). One such dangerous manipulation might be the injection of a public figure into an image containing illicit activity. In our experiments, we mitigate this risk by deliberately refraining from generating images containing humans. Additional safeguards against these risks include methods for embedded watermarking [20] and automated systems for safe filtering of generated imagery.

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Appendix

A. Implementation details

We apply our self-guidance term following best practices for classifier-free guidance on Imagen [32]. Specifically, where N is the number of DDPM steps, we take the first 3 N 16 steps with self-guidance and the last N 32 without. The remaining 25 N 32 steps are alternated between using self-guidance and not using it. We use N = 1024 steps. Our method works with 256 and 512 steps as well, though self-guidance weights occasionally require adjustment. We set v = 7500 in Eqn. 4 as an overall scale for gradients of the functions g defined below - we find that the magnitude of per-pixel gradients is quite small (often in the range of 10 -7 to 10 -6 , so such a high weight is needed to induce changes.

We apply centroid , size , and shape terms on all cross-attention interactions in the model we use. In total, there are 36 of these, across the encoder, bottleneck, and decoder, at 8 × 8 , 16 × 16 , and 32 × 32 resolutions. We apply the appearance term using the activations of the penultimate layer in the decoder (two layers before the prediction readout) and the final cross-attention operation.We experimented with features from other parts of the U-Net denoiser, namely early in the encoder before positional information can propagate through the image (to prevent appearance-layout entanglement), but found these to work significantly worse. To avoid degenerate solutions, we apply a stop-gradient to the attention in the appearance term so only information about activations is back-propagated. We take the mean spatially of all shape terms and across activation dimensions for all appearance terms, which we omit in all equations for conciseness.

Attention mask binarization. In practice, it is beneficial to differentiably binarize the attention map (with sharpness controlled by s ) before computing its size or utilizing its shape, to eliminate the effect of background noise (this is empirically less important when guiding centroids, so we do not binarize in that case). We do this by taking a soft threshold at the midpoint of the per-channel minimum and maximum values. More specifically, we apply a shifted sigmoid on the attention normalized to have minimum 0 and maximum 1, followed by another such normalization to ensure the high value is 1 and the low 0 after applying the sigmoid. We use s = 10 and redefine Eqn. 6.

'

a

photo of

a

carrot and an onion in a

hot

'

a

photo

of

an oak tree

and a

pineapple

outside an arctic

igloo

'

'

a

photo

of

an owl

and a

pig

running at the

racetrack

'

(b) Edited

tub outdoors

'

'

a

photo of a

kangaroo and a

punching bag

at the gym

'

'

a

photo

of a

chicken

walking across the

street with

an Italian sports

car waiting for it

'

'

a

photo

of a

boombox

on a camel near a pond

'

B. Using self-guidance

Maximizing consistency. In general, we find that sharing the same sequence of noise in the DDPM process between an image and its edited version is not necessary to maintain high levels of consistency, but can help if extreme precision is desired. We find that maintaining object silhouettes under transformations such as resizing and repositioning is more effective if applying a transformation T to the original shape , rather than expressing the same change through centroid and size .

Guiding 'background' words. To keep all objects of the scene fixed but one (Fig. 3), one can either guide all other tokens in the prompt (including 'a photo of' and other abstract terms) to keep their shape, or only select the other salient objects and hold those fixed. In general, since abstract words are often used for message passing and have attention patterns that are correlated with the layout of the scene, we prefer not to guide their layouts to maximize compositionality.

Mitigating appearance-layout entanglement. When words or concepts span multiple tokens, we can mean-pooling attention maps across these tokens before processing them, though do not find this to improve results. We also find that corrupting target shapes with Gaussian noise helps mitigate this effect, providing some evidence for this hypothesis.

Moving objects. We use w 0 ∈ [0 . 5 , 2] , w 1 ∈ [0 . 03 , 0 . 3] , w 2 ∈ [0 . 5 , 5] in Eqn. 9. Alternatively, we can express o k 's new location through its centroid , adding a term to keep size fixed:

/negationslash

'

a

photo of

a

koala picking flowers next

'

a

photo

of a

capybara

wearing a

robe

sitting

by the fireplace

'

'

a

photo

of a

bird

drinking

coffee at a

1950s

style

diner

'

(b) Edited

Where target_centroid can be computed as a shfited version of the timestep-and-attentionspecific centroid orig if desired, or selected to be an absolute value on the canvas (repeated across all timesteps). We generally use weights w 0 ∈ [0 . 5 , 2] , w 1 ∈ [0 . 03 , 0 . 3] , w 2 ∈ [0 . 5 , 2] , w 3 ∈ [1 , 3] .

Resizing objects. We can follow Eqn. 9 to resize objects as well, by setting T to upsample or downsample the original mask. We can similarly use Eqn. 17, omitting the final term and setting the target size to a desired value, either computed as a function of size orig ( o k ) or provided as an absolute proportion of pixels on the canvas that the object should cover. We use the same weight range for all weights except we set w 2 ∈ [1 , 3] , w 3 = 0 for Eqn. 17.

Sampling new appearances. We set w 0 ∈ [0 . 1 , 1] in Eqn. 10. Generally, higher values lead to extremely precise layout preservation at the expense of diversity in appearance.

Sampling new layouts. Just as we can find new appearances for a scene of a given layout, we can perform the opposite operation, finding new layouts for scenes where objects have a given appearance:

We almost always use w 0 ∈ [0 . 05 , 0 . 25] .

Collaging objects in-place. Eqn. 12 can be easily generalized to more than one object per image (adding another sum across all objects) or to the case where prompts vary between images (mapping from k j to the corresponding indices in the new image). We set w 0 ∈ [0 . 5 , 1] , w 1 ∈ [0 . 05 , 0 . 3] .

Collaging objects with a new layout. As shown in Fig. 6f, we can also collage objects into a new layout specified by a target image J +1 , in addition to the J images specifying object appearance:

As in Eqn. 12, we set w 0 ∈ [0 . 5 , 1] , w 1 ∈ [0 . 05 , 0 . 3] .

to a mansion

'

'

a

photo of

a

rabbit with

a

birthday balloon and a

(b) Edited

'

a photo of cleats, a bright soccer ball, and a cone

'

'

a

calculator, a

toy car,

and a

pillow on

a

rug

'

(a) Original

'

a

DSLR

photo of

a

backpack at the grand canyon

'

a

DSLR

photo

of a

backpack

wet in the water

'

'

a

photo

of

a

pair

of sunglasses

being

worn

by

a

bear

'

'

a

photo

of

a

pair

of sunglasses

on a

pile

of snow

'

(a) Original

(b) Object in new contexts

Transferring object appearances to new layouts. Nothing requires the indices (or in fact, the objects those indices refer to) to be the same in the image being generated and the original image being used as a source, as long as there is a mapping specified between the indices in the old and new images which should correspond. Call this mapping m . We can then take the appearance of an object o k in a source image and transfer it to an image with any new prompt as follows, as specified in Eqn. 13 (with typical weights w 0 ∈ [0 . 01 , 0 . 1] ):

Merging layout and appearance. We use w 0 ∈ [1 , 2] and w 1 ∈ [0 . 1 , 0 . 3] in Eqn. 11.

Editing with real images. Importantly, our method is not limited to editing generated images to whose internals it has access by definition. We find that we can also meaningfully guide generation

'

party hat

'

'

a

photo of a

chow chow

wearing a superman

outfit

'

'

a

dslr

photo

of

a

teapot

floating

in the

sea

'

(a) Original

(b) Ours

(b) Random samples without self-guidance

using the attention and activations extracted from a set of forward-process denoisings of a real image (given a caption) to 'approximate' the reverse process, despite any mismatch in distributions one might imagine. Concretely, we generate T corrupted versions of a real image x , { α t x + σ t /epsilon1 t } T 1 , where /epsilon1 t ∼ N (0 , 1) . We then extract the attention A t and activations Ψ t from the denoising network at each of these timesteps in parallel and concatenate them into a lengthT sequence. We treat this sequence identically to a sequence of T internals given by subsequent sampling steps, and can thus transfer the appearance of objects from real images, output images that look like real images with moved or resized objects, and so on.

In Fig. 7, the prompts we use to transfer appearance are 'A photo of a Chow Chow...' and 'A DSLR photo of a teapot...'. While our method works on less specific descriptions as well, it is not as reliable when object appearance is more out-of-distribution. For context, we show unguided samples under the prompts from Fig. 7 in Fig. 16, which still deviate significantly from the desired appearance, showing the efficacy of our approach. A weakness of our simple approach is that it has no constraints on the shape of the generated objects, which we leave to future work.

Weight selection heuristics. We find weights that work well to remain more or less consistent across different images given an edit, but ideal weights do vary somewhat (within predictable ranges) between different combinations of terms. Our heuristics for weight selection per term are: the more weights there are, the higher per-term weights can be without causing artifacts (and indeed, need to be, to provide ample contribution to the final result); appearance terms should have weights 1 or 2 orders of magnitude lower than layout terms; layout summary statistics ( centroid and size ) should have slightly lower weights than terms on the per-pixel shape ; total weight of terms should not add up to more than ∼ 5 to avoid artifacts.

C. Additional results

We show further non-cherry-picked results for the edits we show in the main paper. Our general protocol consists of selecting an interesting prompt manually, verifying that our model creates compelling samples aligning with this prompt without self-guidance, beginning with the typical weights we use for an edit, and trying around 3-5 other weight configurations to find the one that works best for the prompt - in most cases, this is the starting set of weights. Then, we use the first 8 images we generate, without further filtering. We generate all results with different seeds to showcase the strength of guidance even without shared DDPM noise. We show more results for moving (Fig. 11) and resizing (Fig. 12) objects, sampling new appearances for given layouts (Fig. 13) as well as new layouts for a given set of objects (Fig. 14), and transferring the appearance of real objects into new contexts (Fig. 15). We also include an ablation on hyperparameter values (Fig. 17) as well as preliminary results of an implementation of self-guidance on an open-source diffusion model in Fig. 18.

Original:

'a photo of a giant macaron and a croissant in the seine with the eiffel tower visible'

Edit:

w

0

=0.4

0.8

1.6

3.2

6.4

w

1

=0.04

0.08

0.16

0.32

0.64

w

=0.5

1.0

2.0

4.0

8.0

w

0

:

fix other

token shapes

w

1

:

fix appearance

w

2

:

guide macaron

shape to be larger

Original:

'a photo of a meatball and a donut falling from the clouds onto a neighborhood'

Edit:

move meatball

w

0

=0.4

0.8

1.6

3.2

6.4

w

1

=0.04

0.08

0.16

0.32

0.64

w

=0.5

1.0

2.0

4.0

8.0

w

0

:

fix other

token shapes

w

1

:

fix appearance

w

2

:

translate

meatball shape

2

2

'a watermelon and a pitcher of beer on a picnic table'

watermelon centroid (0.3, 0.7)

watermelon centroid (0.7, 0.7)

volleyball centroid (0.85, 0.15)

volleyball centroid (0.2, 0.15)

'a sea otter playing

volleyball

at the beach'

frog centroid (0.3, 0.5)

frog centroid (0.65, 0.5)

'a fancy fountain pen, a

globe

, and whiskey on a desk'

globe centroid (0.35, 0.35)

globe centroid (0.9, 0.9)

balloon size 0.05

balloon size 0.2

cat size 0.03

cat size 0.25

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces a new method for detailed and versatile image editing and controlled image synthesis with large-scale text-to-image diffusion models. In particular, the authors propose self-guidance: Self-guidance uses the internal representations of the denoiser neural network of diffusion models for guidance. The work finds that the attention and feature maps of the denoiser network encode properties like objects' size, shape, position and appearance in an interpretable way. This is, some heuristics on the internal attention/feature maps can be defined that represent these properties well and then we can do guidance, leveraging these internal representations (note that this works only for objects and words that were mentioned in the text prompts driving generation). Hence, given an image (either generated by the diffusion model itself or a real one, reconstructed by the diffusion model), the novel self-guidance technique enables editing operations such as freely modifying objects' size, shape, position and appearance, without introducing noticeable artifacts. Importantly, this does not require any additional training or use of auxiliary models, and no additional paired data for training, which makes this a very powerful technique. The authors convincingly validate their proposed editing method mostly qualitatively by showing lots of interesting editing examples.\n\nSTRENGTHS:\nGenerally, the main strength of the paper is the introduction of the novel self-guidance technique, which it then uses for detailed and advanced image editing and controllable image synthesis. To the best of my knowledge no previous image editing methods with text-to-image diffusion models reach this level of fine control with respect to objects' shape, size, position, appearance, layout, and composition, while introducing almost no artifacts.\n\n**Clarity:** The paper is very well written and easy to follow and understand. It is very clear.\n\n**Originality:** The main idea of self-guidance leveraging the model's internal representations is novel and original. That said, it is closely related to, and probably inspired by, existing technique such as Prompt-to-prompt editing (Hertz et al. \"Prompt-to-Prompt Image Editing with Cross Attention Control\") and paint-with-words (Balaji et al. \"eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers\").\n\n**Significance:** Controllable image generation and image editing with large-scale text-to-image diffusion is an important direction with many relevant applications. The paper proposes a very powerful and versatile method with little requirements, which makes this a significant contribution.\n\n**Quality:** The overall quality of the paper is high. It is clear, well written, all claims are supported, limitations and societal impacts are appropriately discussed, background is provided, and related work is thoroughly discussed.\n\nWEAKNESSES:\nWhile the paper has few major issues, there are some concerns:\n- Editing and control is limited with respect to objects (and words more generally) that appear in the text prompt. This also means that given real images require an appropriate caption to be editable. Note that this limitation is acknowledged by the authors.\n- The method comes with a ton of hyperparameters: The guidance weights for the different edits need to be chosen manually and it seems like some trial and error can be necessary. Also, more detailed ablations on this would be helpful, like showing different guidance weights for same image/seed/prompt/edit, etc.\n- When performing a simple edit like moving a single object, we actually require a lot more guidance terms for keeping all the other objects in place and preserving their appearance. This means that even edits that may appear simple are actually quite complicated and require guidance weights for all different terms.\n- The way the different edits and controls are defined is largely heuristic (how size, shape, appearance, etc. is calculated from the attention/feature maps). It seems to work well, but in theory it would be great if there was a more principled approach to this.  \n- The way the cross-attention maps are leveraged in self-guidance is related to the \"paint-with-words\" technique introduced in eDiff-I (Balaji et al. \"eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers\"). I believe this should be acknowledged and cited.\n- The approach is validated only in pixel-space text-to-image diffusion models. Can the same method also work in latent diffusion models? The most popular, and publicly available, large-scale text-to-image model is Stable Diffusion. It would be great to see the method also applied there, if possible.\n\nIn conclusion, there are concerns, weaknesses and routes for further improving the paper. However, the strengths outweigh these weaknesses. Consequently, I am leaning towards suggesting acceptance of the paper. I am looking forward to the rebuttal by the authors.\n\nQUESTIONS:\nI also have a variety of questions that did not significantly impact the rating of the paper:\n- I believe the paper intrinsically relies on a typical U-Net architecture for the denoiser network that gives rise to the useful attention and feature maps. There are various recent works that use pure vision transformer denoiser networks or advanced architectures like recurrent interface networks. Can the approach be extended to these architectures, too? I would be interested in the authors' thoughts on this.\n- In a similar spirit, do the authors believe that similar techniques can also be used in diffusion models that model video, 3D, audio, graph/molecule data, etc.?\n- While centroid, size and shape leverage all cross attention maps, the appearance term uses only the activations of the penultimate layer in the decoder as well as the final cross-attention operation. Why is that? Can the authors provide more intuitions for the qualitative differences in how the attention/activations are used in the different operations?\n- The paper entirely leverages DDPM-style sampling. Does the method also work with deterministic probability flow/DDIM sampling? Moreover, when using given real images, could there be an alternative way of editing when probability flow ODE/DDIM is used to deterministically encode a given image and then re-generate again while introducing the desired edits? I would be curious about the authors' perspective on this.\n\nVery Minor nitpick: \n- Please properly introduce the data $x$ as well as the noise schedule $\\alpha_t$, $\\sigma_t$ around equation 1.\n- I would suggest to properly introduce and define the indices in Line 155 and below, this is, what exactly is $i$, etc. to avoid any potential confusion.\n\nLIMITATIONS:\nLimitations and potential negative societal impact have been appropriately discussed and are well addressed. There are no concerns in that regard.", "TITLE": "Official Review", "RECOMMENDATION": 7, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2023-07-05"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nLarge-scale text-to-image generative methods have demonstrated impressive performance given a detailed text prompt. However, many aspects of an image are hard or impossible to specify with texts. The authors proposed self-guidance to guide the generation of diffusion models. Experimental results demonstrated that object size, location, and appearance of objects can be controlled from normalized attention matrices and feature activations.\n\nSTRENGTHS:\n1. The motivation and background is explained clearly. Arguments and claims are supported by references or experimental results.\n2. The proposed approach is simple and effective. The presented empirical results on multiple tasks demonstrated the effectiveness of the approach. The authors claimed that all results are non-cherry-picked, which is quite impressive.\n3. Adding geometrical/compositional control to the image generative models is an interesting and important research topic. The authors proposed a simple and smart method to control existing generative models with diffusion guidance.\n4. Multiple tasks were considered, including adjusting individual properties, compositional generation, and real image editing. This proposed approach showed potentials to a range of real-world applications.\n\nWEAKNESSES:\n1. From the abstract, the authors aimed to add more control over the image generation beyond text prompts. However, the proposed approach is not a good solution in this regard – it is strongly based on the tokens in the text prompt. There are two limitations:\n   * Can we control fine-grained objects/parts not naturally present in the text prompt? For example, control the dog mouth when generating “a photo of a dog wearing a baseball cap”? All current results are on the object-level with no finer control.\n   * The proposed approach is limited to objects/parts easily describable by texts. This is a disadvantage compared to the keypoint inputs from DragGAN. For instance, annotating a keypoint would be more straightforward than inputting “left-front leg of the dog”, for both the user and the model.\n2. Based on the framework, designing more control seems difficult, e.g., 3D viewpoint of objects. Compared to parallel works, ControlNet [2] finetuned the LDM model with 3D representations and DragGAN [1] used finer keypoint inputs to accomplish this.\n3. The proposed method is built on existing large-scale image diffusion models. It heavily relies on a good representation learned by these trained models. The aforementioned limitations are mostly due to the latent representations having limited feature disentanglement or 3D awareness. The proposed approach doesn’t seem to have any easy fixes to address these limitations.\n\nReferences:\n1. X. Pan et al. Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold.\n2. L. Zhang et al. Adding conditional control to text-to-image diffusion models.\n\nQUESTIONS:\n1. To see how entangled the representations are, can the authors generate a video or a dense sampling of intermediate images to interpolate the “control space”? For instance, changing the layout of the objects in Figure 5 over 16 frames, or changing the styles in Figure 4 over 16 frames? In this way we could see: (i) if a continuous control corresponds to smooth and continuous changes in the image space, and (ii) how the other objects are influenced by the controlled changes made to the target object.\n\nLIMITATIONS:\n1. The input space of the proposed control is still limited by texts: see weakness 1.\n2. The proposed approach is haunted by entangled representation: see weakness 2 and 3.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2023-07-06"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces diffusion self-guidance, an inference-time technique for controllable image generation using pre-trained text-to-image diffusion models. The key finding is that internal representations of the denoiser network carry meaningful information about the scene, and one can build custom energy functions around these representations to align image generation with user-defined scene properties, including object location, size, shape and appearance. Further, an appealing property of self-guidance is that the energy functions can be flexibly composed to support the simultaneous manipulation of multiple image attributes. Extensive experiments are conducted to demonstrate the effectiveness of self-guidance across a broad spectrum of image manipulation tasks.\n\nSTRENGTHS:\n- The proposed method enables controllable image generation / manipulation using existing text-to-image diffusion model checkpoints without costly fine-tuning. As a training-free method, it achieves controllability by steering the generation towards matching the desired internal representations at inference time. This guidance-based design is conceptually simple, easy to implement, computationally efficient and highly flexible.\n\n- The method represents object properties using aggregated statistics of denoiser representations. It thus reveals the internal working of the generative process. To this end, the impact of the paper goes beyond controllable image generation / manipulation.\n\n- Self-guidance enables several edits that are not possible with concurrent methods. These include change in object location, size and modification of scene layout. In the meantime, self-guidance allows the composition of multiple edits in a single generation pass. This is another unique property that is rarely seen in previous and concurrent works.\n\n- Finally, self-guidance offers compelling editing capabilities and supports a wide range of image manipulation needs. Among them are the mixing of objects and layout from multiple source images, transfer of attributes defined by non-object words, and the editing of real images.\n\nWEAKNESSES:\nI do not have major concerns about the proposed method. Some minor concerns are as follows.\n\n- The main text presents self-guidance in its generic form and provides the ingredients for defining a specific energy function. However, all concrete examples are left to the supplement. I understand that space is limited and the authors want to make room for the results (which are indeed compelling). However, it is awkward to alternate between the main text and the supplement in order to decipher how those edits are actually achieved. One possibility is to organize all instantiations of self-guidance in a big table and put them in the main text for the ease of understanding.\n\n- I did not find ablation experiments on hyper-parameters such as the number of inference steps, guidance strength and the range of steps to apply guidance. In practice, the editing quality is often quite sensitive to these hyper-parameters. While their choices are briefly discussed in the supplement, some visual examples will help visualize their effects.\n\n- The experiments are conducted using Imagen, whose design is substantially different from the publicly available Stable Diffusion (cascaded vs. latent diffusion). It is a well-known fact that an editing method may not work equally well on different diffusion models. Given that most players in the space of controllable image generation rely on Stable Diffusion, I would encourage the authors to showcase a few editing examples using Stable Diffusion in order to calibrate the performance of self-guidance.\n\nQUESTIONS:\nPlease find my comments in the section above.\n\nLIMITATIONS:\nThis paper includes a discussion on the limitations and potential societal impact of the proposed method.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 5, "SOUNDNESS_CORRECTNESS": 4, "DATE": "2023-07-07"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a method for image editing using pretrained text-to-image diffusion models. The method guides the sampling process with energy functions that are added similarly to classifier guidance. These energy functions are computed as the difference between some object's property and the target state we want to change it to. A property is either an object's location, size, shape, or appearance. These properties are represented by the cross-attention maps between the object's word token and the image, which has previously been shown to produce meaningful masks. Multiple energy functions can be composed to achieve composite edits. Experiments qualitatively demonstrate the appeal of the method.\n\nSTRENGTHS:\nThe method is practical since it does not require retraining while still achieving impressive results (as demonstrated qualitatively with some samples).\n\nThe paper proposes a simple method with a clear and concise presentation.\n\nThe addressed problem (image editing using pretrained diffusion models) is of interest to both academics and practitioners.\n\nWEAKNESSES:\nQuantitative evaluations of the method (for example with user studies that rank/compare different editing results) would make this work more insightful. Currently, it is unclear whether the shown examples are representative of what to expect when using this method and where the limitations lie. A potential limitation that could be exposed through quantitative studies is the number of energy functions that can be composed at once. Editing real images is possible as shown in Figure 7, but it isn't clear how well it works when compared to generated images. Furthermore, from some examples (e.g., Figure 8 a-b) it looks like the non-edited parts of the image also change and do not remain consistent with the original image, as is usually desirable in image editing settings.\n\nThere are missing details that could help in reproducing and better understanding the results. See the questions part.\n\nQUESTIONS:\nWhat pretrained diffusion model is used in the experiments? Does this method perform differently for different diffusion models (Stable Diffusion vs. eDiff-I vs. Imagen vs. etc.)? I'm asking this because different diffusion models incorporate text conditioning differently (both architecturally and during training).\n\nHow sensitive is the method w.r.t. hyperparameters like the attention layer, time step, and guidance strength? Is it necessary to tune the hyperparameters for every image individually? Maybe even for every energy function individually?\n\nLIMITATIONS:\nMany limitations regarding the method and potential negative societal impacts are discussed. Some that might be reasonable to also mention are listed above.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2023-07-12"}