Title: Linguistic Binding in Diffusion Models: Enhancing Attribute Correspondence through Attention Map Alignment
Venue: NeurIPS 2023
Accepted: yes
Keywords: syntax

diffusion

stable diffusion

attribute

attention
Abstract: Text-conditioned image generation models often generate incorrect associations between entities and their visual attributes. This reflects an impaired mapping between linguistic binding of entities and modifiers in the prompt and visual binding of the corresponding elements in the generated image. As one example, a query like ``a pink sunflower and a yellow flamingo'' may incorrectly produce an image of a yellow sunflower and a pink flamingo. To remedy this issue, we propose SynGen, an approach which first syntactically analyses the prompt to identify entities and their modifiers, and then uses a novel loss function that encourages the cross-attention maps to agree with the linguistic binding reflected by the syntax. Specifically, we encourage large overlap between attention maps of entities and their modifiers, and small overlap with other entities and modifier words. The loss is optimized during inference, without retraining or fine-tuning the model. Human evaluation on three datasets, including one new and challenging set, demonstrate significant improvements of SynGen compared with current state of the art methods. This work highlights how making use of sentence structure during inference can efficiently and substantially improve the faithfulness of text-to-image generation.

Linguistic Binding in Diffusion Models: Enhancing Attribute Correspondence through Attention Map Alignment

Abstract

Text-conditioned image generation models often generate incorrect associations between entities and their visual attributes. This reflects an impaired mapping between linguistic binding of entities and modifiers in the prompt and visual binding of the corresponding elements in the generated image. As one example, a query like 'a pink sunflower and a yellow flamingo ' may incorrectly produce an image of a yellow sunflower and a pink flamingo . To remedy this issue, we propose SynGen , an approach which first syntactically analyses the prompt to identify entities and their modifiers, and then uses a novel loss function that encourages the cross-attention maps to agree with the linguistic binding reflected by the syntax. Specifically, we encourage large overlap between attention maps of entities and their modifiers, and small overlap with other entities and modifier words. The loss is optimized during inference, without retraining or fine-tuning the model. Human evaluation on three datasets, including one new and challenging set, demonstrate significant improvements of SynGen compared with current state of the art methods. This work highlights how making use of sentence structure during inference can efficiently and substantially improve the faithfulness of text-to-image generation. 1

1 Introduction

Diffusion models for text-conditioned image generation produce impressive realistic images [1, 2, 3, 4]. Users control the generated content through natural-language text prompts that can be rich and complex. Unfortunately, in many cases the generated images are not faithful to the text prompt [5, 6]. Specifically, one very common failure mode results from improper binding , where modifier words fail to influence the visual attributes of the entity-nouns to which they are grammatically related.

Daniel Glickman

Yoav Goldberg

'a pink sunflower and

a yellow flamingo'

Stable

Diffusion

SynGen

(ours)

'a checkered bowl in a

cluttered room'

'a horned lion and

a spotted monkey'

(a) Semantic Leak in Prompt

(b) Semantic Leak out of Prompt

(c) Attribute Neglect

As an illustration, consider the prompt 'a pink sunflower and a yellow flamingo '. Given this prompt, current models often confuse the modifiers of the two entity-nouns, and generate an image of a yellow sunflower and a pink flamingo (Fig. 1, bottom left, semantic leak in prompt). In other cases, the attribute may semantically leak to areas in the image that are not even mentioned in the prompt (Fig. 1, bottom center, semantic leak outside prompt) or the attribute may be completely neglected and missed from the generated image (Fig. 1, bottom right, attribute neglect). Such mismatch can be addressed by providing non-textual control like visual examples [7, 8], but the problem of correctly controlling generated images using text remains open.

A possible reason for these failures is that diffusion models use text encoders like CLIP [9], which are known to fail to encode linguistic structures [10]. This makes the diffusion process 'blind" to the linguistic bindings, and as a result, generate objects that do not match their attributes. Building on this intuition, we propose to make the generation process aware of the linguistic structure of the prompt. Specifically, we suggest to intervene with the generation process by steering the cross-attention maps of the diffusion model. These cross-attention map serve as a link between prompt terms and the set of image pixels that correspond to these terms. Our linguistics-based approach therefore aims to generate an image where the visual binding between objects and their visual attributes adheres to the syntactic binding between entity-nouns and their modifiers in the prompt.

Several previous work devised solutions to improve the relations between prompt terms and visual components, with some success [11, 12, 13]. They did not focus on the problem of modifier-entity binding. Our approach specifically addresses this issue, by constructing a novel loss function that quantifies the distance between the attention patterns of grammatically-related (modifier, entity-noun) pairs, and the distance between pairs of unrelated words in the prompt. We then optimize the latent denoised image in the direction that separates the attention map of a given modifier from unrelated tokens and bring it closer to its grammatically-related noun. We show that by intervening in the latent code, we markedly improve the pairing between attributes and objects in the generated image while at the same time not compromising the quality of the generated image.

We evaluate our method on three datasets. (1) For a natural-language setting, we use the natural compositional prompts in the ABC-6K benchmark [13]; (2) To provide direct comparison with previous state-of-the-art in [11], we replicate prompts from their setting; (3) Finally, to evaluate binding in a challenging setting, we design a set of prompts that includes a variety of modifiers and entity-nouns. On all datasets, we find that SynGen shows significant improvement in performance based on human evaluation, sometimes doubling the accuracy. Overall, our work highlights the effectiveness of incorporating linguistic information into text-conditioned image generation models and demonstrates a promising direction for future research in this area.

(a)      Entity-Modifier Identification

Language-driven cross attention losses

Positive

Loss

U-Net

U-Net

…

a

red

crown

…

red

crown

amod

golden

amod

…

a

golden

Extract cross attention maps

A

and

a

red  crown

and

a

golden

amod

amod

conj

cc

Negative

Loss

red

crown

(b)      Diffusion Process

Zt-1

20

The main contributions of this paper are as follows: (1) A novel method to enrich the diffusion process with syntactic information, using inference-time optimization with a loss over cross-attention maps; (2) A new challenge set of prompts containing a rich number and types of modifiers and entities.

2 Syntax-Guided Generation

Our approach, which we call SynGen, builds on two key ideas. First, it is easy to analyze the syntactic structure of natural language prompts to identify bindings of entity-nouns and their modifiers. Second, one can steer the generation of images to adhere to these bindings by designing an appropriate loss over the cross-attention maps of the diffusion model. We describe the two steps of our approach: extracting syntactic bindings and then using them to control generation.

2.1 Identifying entity-nouns and their modifiers

To identify entity-nouns and their corresponding modifiers, we traverse the syntactic dependency graph, which defines the syntactic relation between words in the sentence. Concretely, we parse the prompt using spaCy's transformer-based dependency parser [14] and identify all entity-nouns (either proper-nouns or common-nouns) that are not serving as direct modifiers of other nouns.

These are the nouns that correspond to objects in the generated image. We then recursively collect all modifiers 2 of the noun into its modifier set. The set of modifier-labels includes a range of syntactic relations between nouns and their modifiers, such adjectivial modification (amod; 'the regal dog'), compounds (compound; 'the treasure map'), nominal modification through an intervening marker, adverbial modifiers (npadvmod; 'A watermelon -styled chair'), adjectivial complement (acomp; 'The apple is blue '), and coordination between modifiers (conj; 'A black and white dog').

2.2 Controlling generation with language-driven cross-attention losses

Consider a pair of a noun and its modifier. We expect the cross-attention map of the modifier to largely overlap with the cross-attention map of the noun, while remaining largely disjoint with the maps corresponding to other nouns and modifiers. To encourage the denoising process to obey these spatial relations between the attention maps, we design a loss that operates on all cross-attention maps. We then use this loss with a pretrained diffusion model during inference. Specifically, we

red

crown

golden

Cross attention maps

Generation

=

0=1

1          7       25        32        50

optimize the noised latents by taking a gradient step to reduce that loss. See illustration in Fig. 2. Fig. 3 illustrates the effect of the loss over the cross-attention maps.

Loss functions: Consider a text prompt with N tokens, for which our analysis extracted k nounmodifier sets { S 1 , S 2 , . . . , S k } . Let P ( S i ) represent all pairs ( m,n ) of tokens between the noun root n and its modifier descendants m in the i -th set S i . For illustration, the set of 'A black striped dog' contains two pairs ('black', 'dog') and ('striped', 'dog'). Next, denote by { A 1 , A 2 , . . . , A N } the attention maps of all N tokens in the prompt, and denote by dist ( A m , A n ) a measure of distance (lack of overlap) between attention maps A m and A n .

Our first loss aims to minimize that distance (maximize the overlap) over all pairs of modifiers and their corresponding entity-nouns ( m,n ) ,

We also construct a loss that compares pairs of modifiers and entity-nouns with the remaining words in the prompt, which are grammatically unrelated to these pairs. In other words, this loss is defined between words within the (modifiers, entity-nouns) set and words outside of it. Formally, let U ( S i ) represent the set of unmatched words obtained by excluding the words in S i from the full set of words and A u is the corresponding attention map for a given unrelated word u . The following loss encourages moving apart grammatically-unrealted pairs of words:

Our final loss combines the two loss terms:

For a measure of distance between attention maps we use a symmetric Kullback-Leibler divergence dist ( A i , A j ) = 1 2 D KL ( A i || A j ) + 1 2 D KL ( A j || A i ) , where A i , A j are attention maps normalized to a sum of 1, i and j are generic indices, and D KL ( A i || A j ) = ∑ pixels A i log( A i /A j ) .

Our test-time optimization approach resembles the one of [11], which defined a loss over the crossattention maps to update the latents at generation time. However, their loss aims to maximize the presence of the smallest attention map at a given timestep to guarantee a set of selected tokens is included in the generated image, and our loss depends on pairwise relations of linguistically-related words and aims to align the diffusion process to the linguistic-structure of the prompt.

2.3 The workflow

We use the loss of Eqs 1-3 to intervene in the first 25 out of 50 denoising steps. Empirically, using a smaller number of steps did not correct well improper binding, and using a larger number generated blurred images, as detailed in Appendix B. In each of the first 25 steps, a pretrained denoiser (U-Net) was first used to denoise the latent variable z t . Then, we obtained the cross-attention maps as in [15]. Next, we used the loss L to update the latent representation z t with a gradient step z ′ t = z t -α · ∇ z t L . Finally, the U-Net architecture denoises the updated latent variable z ′ t for the next timestep.

3 Experiments

3.1 Compared baseline methods

We compare SynGen with three baseline methods. (1) Stable Diffusion 1.4 (SD) [1]; (2) Structured Diffusion [13], extracts noun-phrases from the prompt and embeds them separately, to improve the mapping of the semantics in the cross-attention maps; and (3) Attend-and-Excite (A&E) [11], a method that given a predetermined set of tokens, updates the latent a certain number of timesteps, to eventually incorporate these tokens in the generated image. To automate token selection in A&E, we follow the recommendation by the authors to select the nouns using a part-of-speech tagger.

3.2 Datasets

We evaluate our approach using two existing benchmark datasets, and one new dataset that we designed to challenge methods in this area.

(1) ABC-6K [13]. This benchmark consists of 3.2K natural compositional prompts from MSCOCO [16], which were manually written by humans, using natural language and contain at least two color words modifying different noun-entities. In addition, the dataset contains 3.2K counterparts, where the position of modifiers in the original prompts are swapped. (e.g., 'a white bench in front of a green bush' and 'a green bench in front of a white bush'). We randomly sample 600 prompts.

(2) Data from Attend-and-Excite [11]. Originally introduced to evaluate the A&E method which focuses on entity-neglect, this dataset also showed that A&E improved over previous work in terms of improper binding.

Prompts in this dataset belong to three categories: (1) 'a {color} {in-animate object} and a {color} {in-animate object}'; (2) 'a {color} {in-animate object} and an {animal}'; (3) 'an {animal} and an {animal}'. Following the split in A&E, we sample 33 prompts from type (1) and 144 prompts from type (2), but exclude type (3), as it does not contain modifiers. This is a very simple dataset, which we use to facilitate direct comparison with previous work.

(3) Diverse Visual Modifier Prompts (DVMP). The above two datasets are limited in terms of number and types of modifiers, and the number of entity-nouns per prompt. To challenge our model, we design a dataset consisting of coordination sentences, in similar fashion to the dataset from A&E, but with strong emphasis on the number and types of modifiers per prompt. Specifically, we aim to compare the models with prompts that contain numerous and uncommon modifiers, creating sentences that would not usually be found in natural language or training data, such as 'a pink spotted panda'. DVMP was designed with two key aspects in mind:

Expanding the set of modifiers: We have extended the number of modifiers referring to an entitynoun from one to up to three. For instance, 'a blue furry spotted bird'. We also added types of modifiers besides colors, including material patterns ('a metal chair'), design patterns ('a checkered shoe'), and even nouns modifying other noun-entities ('a baby zebra').

Visually verifiable and semantically coherent: The modifiers selected for DVMP are visually verifiable, with a deliberate avoidance of nuanced modifiers. For instance, 'big' is a relative modifier dependent on its spatial context, and emotional states, such as in the prompt 'an excited dog', are largely excluded due to their subjective visual interpretation. Simultaneously, DVMP maintains semantic coherence by appropriately matching modifiers to noun-entities, thereby preventing the creation of nonsensical prompts like 'a sliced bowl' or 'a curved zebra'.

In total, we have generated 600 prompts through random sampling. For a comprehensive description of the dataset's creation, see Appendix F.

3.3 Human Evaluation

We evaluate image quality using Amazon Mechanical Turk (AMT). Raters were provided with a multiple-choice task, consisting of a single text prompt and four images, each generated by the baselines and SynGen. Raters could also indicate that all images are 'equally good' or 'equally bad'. We provided each prompt and its corresponding generations to three raters, and report the majority decision. In cases where there is no majority model winner, we count it toward 'no majority winner'.

We evaluate generated images in two main aspects: (1) concept separation (sometimes known as editability [17]) and (2) visual appeal . Concept separation refers to the ability of the model to distinctly depict different concepts or objects in the generated image. The effectiveness of concept separation is assessed by asking raters, 'Which image best matches the given description?'. To asses visual quality, raters were asked 'Which image is more visually appealing?'. To maintain fairness and reduce biases, the order of images was randomized in each task. Full rater instructions and further details are provided in Appendix G.1 of the supplemental materials.

We also experimented automatic evaluation, but find its quality subpar. For standardized evaluation purposes, it is detailed in Appendix G.2.

Fine-grained evaluation. In addition to a multiple-choice task, we evaluate concept separation using the following key metrics: (1) Proper Binding, quantifying how well the model associates attributes with their corresponding objects; (2) Improper Binding, measuring the instances where attributes are incorrectly linked to unrelated objects; and (3) Entity Neglect, capturing the frequency with which the model omits entities specified in the prompt.

To this end, we randomly select 200 prompts each from the DVMP and ABC-6K datasets, while using all 177 prompts available in the A&E dataset. Human evaluators were asked to mark if instances have correct or incorrect attribute-object mapping. Importantly, incorrect mappings are counted on a per-attribute basis-multiple incorrect mappings of a single attribute are considered one violation. For example, in the prompt 'the white dog chased the cat up the tree', if the modifier 'white' is incorrectly mapped to both 'cat' and 'tree', it is counted as one instance of violation. Evaluators also identify the number of entities mentioned in the prompt that are subsequently depicted in the generated image.

Based on these counts, we define the metric of Proper Binding as the ratio of correctly mapped attributes to the total number of attributes. Similarly, Improper Binding is defined as the ratio of incorrectly mapped attributes to the total number of attributes, while Entity Neglect is the complement of the ratio of mentioned entities that are depicted in the generated image to the total number of entities in the prompt. Rater instructions are provided in Appendix G.1.

4 Results

4.1 Quantitative Results

Table 1 provides results of the comparative experiment. SynGen is consistently ranked first in all three datasets, and by a large margin, sometimes double the approval rate of the second ranked method, A&E. These results are observed for concept separation, which measures directly the semantic leak, and for visual appeal.

The high number of 'no winner' cases reflects the large difficulty of some of the prompts, for which no method provides good enough generated images. Population results before majority aggregation are given in Appendix G.1 of the supplemental material. Comparisons with StableDiffusion are given in Fig. 19 of the supplemental.

Table 2 provides results of the individual experiment. We find that SynGen outperforms all models by a landslide in both proper and improper binding and is on par with state-of-the-art on entity neglect [11], despite not directly tackling this problem.

Table 1: Human evaluation of all methods on the three datasets. The table reports scores for concept separation (how well the image matches the prompt) and visual appeal. Values are the fraction of majority vote of three raters, normalized to sum to 100.

4.2 Qualitative Analysis

Figures 4-6 provide qualitative examples from the three datasets, comparing SynGen with the two strongest baselines.

The qualitative examples illustrate several failure modes of our baselines. First, semantic leak in prompt , occurs when a modifier of an entity-noun 'leaks' onto a different entity-noun in the prompt, as shown in Fig. 4, for the prompt 'a pink clock and a brown chair', in columns 3 and 4. In this case, all baselines incorrectly apply pink hues to the chair, despite the prompt explicitly defining it as brown. A more nuanced variant of this issue is semantic leak out of prompt , when a modifier is assigned to an entity-noun that is not mentioned in the prompt. For instance, the 'spiky' attribute in 'a spiky bowl and a green cat' leaks to a plant, which is not in the prompt, or the green coloration in the background of the images generated by the baselines, as seen in columns 5 and 6 in Fig. 5.

Attribute neglect occurs when a modifier from the prompt is absent from the generated image. As exhibited in Fig. 4, for 'a frog and a brown apple', both baselines do not include a brown color at all.

Entity casting is another failure type where a modifier is treated as a standalone entity, a phenomenon commonly observed with noun modifiers. For example, the prompt 'a wooden crown and a furry baby

'a monkey and a black bow'

'a pink clock and a brown chair'

'a frog and a brown apple'

Structured

Diffusion

Attend-and

Excite

SynGen

(ours)

'a wooden crown and a furry

baby rabbit'

Structured

Diffusion

Attend-and

Excite

SynGen

(ours)

'a red chair and a purple

camera and a baby lion'

'a spiky bowl and a green cat'

rabbit' (column 1 in Fig. 5) has all methods, apart from ours, generate human infants. Presumably, this occurs because 'baby' is interpreted as a noun rather than as a modifier, leading other methods to treat it as a separate object due to the lack of syntactic context. Conversely, SynGen correctly interprets 'baby' as a modifier and accurately binds it to the rabbit. Similarly, in the prompt 'a white fire hydrant sitting in a field next to a red building' (column 6 in Fig. 6), 'fire' is wrongly interpreted as an entity-noun, which leads to the unwarranted inclusion of a fire in the scene.

All methods, barring SynGen, grapple with entity entanglement [18, 19, 20, 21, 22], where some objects tend to strongly associate with their most common attribute (e.g., tomatoes are typically red). This is evident in columns 3 and 4 in Fig. 6, where other methods fail to visually associate the blue attribute with the dog in 'a blue and white dog sleeps in front of a black door'. Instead, they resort to typical attributes of the objects, generating a black and white dog.

Further qualitative analysis is provided in Appendix D.1.

'a brown brush glides through

beautiful blue hair'

'a blue and white dog sleeps in

front of a black door'

'a white fire hydrant sitting in a

field next to a red building'

Structured

Diffusion

Attend-and

Excite

SynGen

(ours)

4.3 Ablation study

The importance of using both positive and negative losses. We evaluated the relative importance of the two terms in our loss Eq. (3). The positive term L pos , which encourages alignment of the attention map of an object and its modifiers, and the negative loss term, L neg , which discourages alignment with other modifiers and objects. We sampled 100 prompts from the DVMP dataset and generated images with and without each of the two loss terms. See example in Fig. 7. Then, raters were asked to select the best of four variants. Table 3 shows that raters preferred the variant that combined both the positive and the negative terms. More examples are given in the supplemental Appendix B.

5 Related Work

Semantic leakage . [2] pointed out cases of semantic leakage in diffusion models, where properties of one entity-noun influence the depiction of another. [23] attributed this issue to a lack of understanding of syntax, specifically noting failures when processing texts requiring subtle syntactic binding comprehension. [6] identified semantic leakage issues in DALL-E , where properties of one entitynoun influence how other entity nouns are depicted. In this work, we pinpoint semantic leakage as a consequence of improper mapping between syntactic and visual binding.

Both

Losses

Only Positive

Loss

Only Negative

Loss

No

Intervention

"A bird and a white crown"

Attention-based interventions. [15] demonstrated that the cross-attention mechanism determines the spatial layout of entities in generated images. This result suggested that cross-attention is causally involved in the aforementioned issues. A&E [11] addresses the problem of entity omission, where certain entities mentioned in the prompt do not appear in the generated image. They propose a loss function that encourages each noun token in the image to significantly attend to a corresponding image patch, thereby preventing its omission. Our approach is similar to [11] in that it updates the latent representation through a loss function over attention maps, during image generation.

Syntax-based generation was also explored in [13], proposing the Structured Diffusion method. It aims to address the problem of missing entities and semantic leakage of attributes. This is achieved by parsing the prompt, extracting phrases corresponding to nouns and modifiers, and encoding them separately. They also intervene in the attention patterns, ensuring that each individual phrase influences the attention patterns. Our experiments show that it is better to implicitly influence the attention patterns through our loss which we dynamically optimize. In contrast, their intervention remains fixed.

Concurrent to this work, [24] proposed an alternative approach to combine syntactic control and attention-based optimization. They extract nouns from prompts and train a layout predictor to identify the corresponding pixels for each noun. Then, they optimize the latents by encouraging the pixels corresponding to the objects to attend to CLIP representations of phrases containing those objects. While similar in spirit, the current paper demonstrates intervention in the generation process solely based on syntax, without explicitly learning the correspondence between image entities and tokens.

6 Limitations

Like previous methods, the performance of SynGen degrades with the number of attributes to be depicted (see supplemental Fig. 12). However, its decline is remarkably less pronounced compared to other methods. This decay in performance can be attributed to two primary factors: (1) an image begins to lose its visual appeal when the negative loss term becomes excessively large; (2) an overly cluttered image poses challenges in crafting a cohesive 'narrative' for all the concepts. We expect that some of these issues can be addressed with more hyper-parameter tuning.

Naturally, the effectiveness of our method is intrinsically tied to the quality of the parser. When the parser fails to extract the stipulated syntactic relations, our method essentially operates akin to SD.

Finally, SynGen takes longer to generate images with modifiers in the prompt than SD and slightly slower than than A&E (see Appendix A).

7 Conclusions

In this work, we target the improper binding problem, a common failure mode of text-conditioned diffusion models, where objects and their attributes incorrectly correspond to the entity-nouns and their modifiers in the prompt. To address it, we propose SynGen, an inference-time intervention method, with a loss function that encourages syntax-related modifiers and entity-nouns to have overlapping cross-attention maps, and discourages an overlap from cross-attention maps of other words in the prompt. We challenge our method with three datasets, including DVMP - a new dataset that is specially-designed to draw out hard cases of improper-binding problem. Our method demonstrates improvement of over 100% across all three datasets over the previous state-of-the-art. Finally, our work highlights the importance of linguistic structure during denoising for attaining faithful text-to-image generation, suggesting promising avenues for future research.

References

Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 10684-10695, 2022.

Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125 , 2022.

Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S. Sara Mahdavi, Rapha Gontijo Lopes, Tim Salimans, Jonathan Ho, David J Fleet, and Mohammad Norouzi. Photorealistic text-to-image diffusion models with deep language understanding, 2022.

Yogesh Balaji, Seungjun Nah, Xun Huang, Arash Vahdat, Jiaming Song, Karsten Kreis, Miika Aittala, Timo Aila, Samuli Laine, Bryan Catanzaro, et al. ediffi: Text-to-image diffusion models with an ensemble of expert denoisers. arXiv preprint arXiv:2211.01324 , 2022.

Colin Conwell and Tomer Ullman. Testing relational understanding in text-guided image generation. arXiv preprint arXiv:2208.00005 , 2022.

Royi Rassin, Shauli Ravfogel, and Yoav Goldberg. DALLE-2 is seeing double: Flaws in word-to-concept mapping in Text2Image models. In Proceedings of the Fifth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP , pages 335-345, Abu Dhabi, United Arab Emirates (Hybrid), December 2022. Association for Computational Linguistics.

Lvmin Zhang and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models, 2023.

Yoad Tewel, Rinon Gal, Gal Chechik, and Yuval Atzmon. Key-locked rank one editing for text-to-image personalization. In ACM SIGGRAPH 2023 Conference Proceedings , SIGGRAPH '23, 2023.

Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. Learning transferable visual models from natural language supervision, 2021.

Mert Yuksekgonul, Federico Bianchi, Pratyusha Kalluri, Dan Jurafsky, and James Zou. When and why vision-language models behave like bags-of-words, and what to do about it? In The Eleventh International Conference on Learning Representations , 2023.

Hila Chefer, Yuval Alaluf, Yael Vinker, Lior Wolf, and Daniel Cohen-Or. Attend-and-excite: Attentionbased semantic guidance for text-to-image diffusion models. arXiv preprint arXiv:2301.13826 , 2023.

Nan Liu, Shuang Li, Yilun Du, Antonio Torralba, and Joshua B Tenenbaum. Compositional visual generation with composable diffusion models. In Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XVII , pages 423-439. Springer, 2022.

Weixi Feng, Xuehai He, Tsu-Jui Fu, Varun Jampani, Arjun Akula, Pradyumna Narayana, Sugato Basu, Xin Eric Wang, and William Yang Wang. Training-free structured diffusion guidance for compositional text-to-image synthesis. arXiv preprint arXiv:2212.05032 , 2022.

Matthew Honnibal and Ines Montani. spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing. To appear, 2017.

Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or. Prompt-toprompt image editing with cross attention control. arXiv preprint arXiv:2208.01626, 2022.

Tsung-Yi Lin, Michael Maire, Serge Belongie, Lubomir Bourdev, Ross Girshick, James Hays, Pietro Perona, Deva Ramanan, C. Lawrence Zitnick, and Piotr Dollár. Microsoft coco: Common objects in context, 2015.

Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patashnik, Amit H. Bermano, Gal Chechik, and Daniel Cohen-Or. An image is worth one word: Personalizing text-to-image generation using textual inversion, 2022.

Yuval Atzmon, Felix Kreuk, Uri Shalit, and Gal Chechik. A causal view of compositional zero-shot recognition. Advances in Neural Information Processing Systems , 33:1462-1473, 2020.

Massimiliano Mancini, Muhammad Ferjad Naeem, Yongqin Xian, and Zeynep Akata. Open world compositional zero-shot learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 5222-5230, 2021.

Yuval Atzmon, Jonathan Berant, Vahid Kezami, Amir Globerson, and Gal Chechik. Learning to generalize to new compositions in image understanding. arXiv preprint arXiv:1608.07639 , 2016.

Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, C. Lawrence Zitnick, and Ross B. Girshick. CLEVR: A diagnostic dataset for compositional language and elementary visual reasoning. CoRR , abs/1612.06890, 2016.

Ishan Misra, Abhinav Gupta, and Martial Hebert. From red wine to red tomato: Composition with context. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , pages 1160-1169, 2017.

Evelina Leivada, Elliot Murphy, and Gary Marcus. Dall-e 2 fails to reliably capture common syntactic processes. arXiv preprint arXiv:2210.12889 , 2022.

Qiucheng Wu, Yujian Liu, Handong Zhao, Trung Bui, Zhe Lin, Yang Zhang, and Shiyu Chang. Harnessing the spatial-temporal attention of diffusion models for high-fidelity text-to-image synthesis. arXiv preprint arXiv:2304.03869 , 2023.

Supplementary Material

A Implementation Details

Computing resources. Experiments were run on an NVIDIA DGX Station with four v100-SXM232GB GPUs. The overall duration of all experiments in the paper was about two weeks.

Efficiency. SynGen takes ~144% longer than SD and ~10.9% longer than A&E to generate images with modifiers in the prompt. To arrive to these numbers, we randomly sampled a set of 50 images from the A&E dataset, the DVMP set, and ABC-6K and timed the generations for each method. On average, SD needs 4 seconds to generate an image, A&E 8.8 seconds, and SynGen 9.76 seconds.

Hyperparameters. The hyperparameters we used consist of 50 diffusion steps, a guidance scale of 7.5, a scale-factor of 20, and 25 latent update steps. The choices of scale factor and latent update steps are described in Appendix B.2 and Appendix B.3 respectively.

Parser. Throughout this project, we use the spacy parser with the out-of-the-box en_core_web_trf model.

Attending word pieces. When a relatively uncommon word is encountered by the tokenizer of the text encoder, it is split to sub-words (i.e., word pieces). In the context of our loss function, when an entity (or modifier) is split into word pieces, we compute our distance function (the Symmetric-KL) for each word piece. Then, only the word piece that maximizes the distance is added to the loss.

Cross-attention maps. We describe more formally the cross-attention maps on which we intervene. Let N be the number of tokens in the prompt, and let D 2 be the dimensionality of the latent feature map in an intermediate denoising step. The denoising network defines a cross-attention map A patches → tokens ∈ R D 2 × N between each of D 2 patches in the latent feature map and each token. Intuitively, the attention maps designates which tokens are relevant for generating each patch.

Our goal it to derive an attention distribution A tokens → patches ∈ R N × D 2 such that its i -th row A tokens → patches i contains the attention distribution of token i over patches. For this goal, we define a score matrix S to be the transpose of A patches → tokens , i.e,. a matrix whose i th row contains the attention scores from each patch to token i . Since S is not normalized, we divide each row by its sum to get a distribution over patches. Unless stated otherwise, across the paper, we refer to A tokens → patches ∈ R N × D 2 when mentioning the 'cross-attention maps' A and its i th row A i corresponding to the attention map from the i th token.

B Additional Ablation Experiments

B.1 Further Investigation of the Positive and Negative Loss Terms

In Section 4.3, we investigate the importance of the positive and negative loss function terms using a human rater. Here, we accompany the rating with a qualitative analysis, to examine the effect of each term. To this end, we generate images for 15 randomly selected prompts, five from each dataset. Fig. 8 depicts a sample of the generated prompts.

We find that proper binding necessitates both the positive and negative terms: excluding the negative term from the loss function results in two noteworthy observations. First, the number of missing objects increase, evident by the missing crown, cat, metal chair, and tomato, in columns 1, 2, 4, and 5 in Fig. 8. One consequence of missing objects is the apparent improper binding, indicated by the red backpack and black shirt in columns 1 and 3.

On the other hand, excluding the positive term results in fuzzier separation between objects. For instance, the cat is not completely formed, and is 'merged' with the pillow; and while it appears that there is some green residue on the dog, it is not colored green. Moreover, the grass is green, which indicates a semantic leakage.

'a beige spotted

lion and a metal

chair'

Both Losses

Only Positive

Loss

Only Negative

Loss

'a black and white

cat is laying on a

green pillow on top

of a desk'

'an orange crown

and a red

backpack'

'a sliced tomato

and a spotted

elephant'

'a man reaches down

to a black and green

dog as they stand on

white grass'

No

Intervention

Putting these insights together, we observe that to some extent, the effect the loss terms is complementary. In addition to the increase of objects and proper binding, the images are more coherent (less cases of objects mixed into each other, such as the cat in the only-negative loss or the elephant in the only-positive loss).

B.2 Number of Timesteps for Intervention

Recall that our method intervenes in latent denoising generation. In this appendix, we study the effect of the hyperparameters determining the number of steps in which we intervene.

To identify an ideal number of timesteps to intervene, we experiment with 100 randomly selected prompts from the DVMP dataset, a fixed random seed, and a number of update steps from 5 to 50, in increments of 5. Examples of this experiment are shown in Fig. 9.

We observe that when intervening in a small number of timesteps, our method failed to adequately mitigate semantic leakage or that images are not completely formed. For instance, the apple in column 1 in the 15-steps example is cartoon-ish, while the dog is not. Conversely, intervening for the full 50 timesteps resulted in an increase rate of blurred images (potentially due to the significant modification of the latent, which shifts it away from the learned distribution). We conclude that the optimal number of timesteps for intervention is 25, as this allows for effective mitigation of improper binding, while still generating visually appealing images.

B.3 Setting the Scale Factor

The scale factor affects the update step size. Recall the update step stated in Section 2 z ′ t = z t -α · ∇ z t L . Here, α is the scale-factor.

To determine a good selection for the scale-factor, we generate 100 randomly sampled prompts from the DVMP dataset, with a scale-factor value from 1 to 40, in increments of 10.

As can be seen in Fig. 10, we observe that merely updating the latent using a scale-factor of 1 yields relatively good results in terms of improper binding, which confirms the utility of our loss function. However, such a low scale-factor also consistently leads to missing objects.

Every step

'a furry

spotted dog

and a black

apple'

25 steps

(SynGen)

15

steps

"a sliced banana

and a teal spotted

skateboard and an

orange elephant"

"a spotted

baby rabbit

and a baby

panda"

No

intervention

"a spotted

sleepy horse

and a sliced

apple"

"a red

turtle and a

pink apple"

Interestingly, for greater scale-factor values, the generations become alike in their overall look, but are nonetheless very different. As an example, for both values, 10 and 30, the sliced banana is missing from the image in column 2, but the 30-value does result in a spotted teal skateboard. In column 3, values below 20 lead to images that contain two pandas (none of which are spotted), which indicates the proper binding process, and that the latent was not updated enough. On the other hand, a value greater than 20 leads to an image of a striped rabbit, instead of a spotted rabbit.

One interesting conclusion from this experiment is that the greater the scale-factor, the stronger the concept separation. However, this is only true to a point. For a great enough value, generations become too blurred or simply lose their visual appeal.

C Additional Quantitative Analyses

To study the efficacy of SynGen relative to the baselines in improper binding setting, we analyze the results under three perspectives. (1) as a function of repeating entities and modifiers; (2) as a function of the number of modifiers; and (3) degree of entanglement. Samples of generations are shown in Fig. 14.

Number of repeating modifiers and entities. In this analysis, we examine the performance of all methods for prompts containing recurring modifiers (e.g., 'a sliced strawberry and a sliced tomato) or entities (e.g., 'a sliced tomato and a skewered tomato '). Aggregated results are illustrated in Fig. 11. Our observations reveal a decrease in performance across all methods when modifiers are repeated. However, the relative success between SynGen and the baselines in performance remains the same. Moreover, there is no substantial decline in results when entities are repeated.

Number of modifiers in prompt. We hypothesize that since our method is specifically designed to tackle improper binding, it handles prompts containing many modifiers with more success. This is

Scale

Factor = 30

'a furry

spotted dog

and a black

apple'

Scale

Factor = 20

Scale

Factor = 10

"a sliced banana

and a teal spotted

skateboard and an

orange elephant"

"a spotted

baby rabbit

and a baby

panda"

Scale

Factor = 1

"a spotted sleepy

horse and a

sliced apple"

"a red turtle and

a pink apple"

Scale

Factor = 40

confirmed in Fig. 12, which shows the gap between SynGen and the baselines widens as the number of modifiers in a prompt increases.

Entangled entities. As we describe in Section 4.2, entangled entities are strongly associated with their most common attribute. For instance, a tomato is typically red, and thus, it is common for images to depict red tomatoes.

We categorize the prompts into three groups: (1) entangled prompts, which contain entangled entities with a modifier that overrides a common modifier (e.g., a purple strawberry); (2) common entangled prompts, which contain entangled entities with their common modifiers; and (3) neutral prompts, which do not contain entangled entities at all. Performance as a function of these groups is demonstrated in Fig. 13.

D Additional Qualitative Results

D.1 Qualitative analysis by number of modifiers

In Fig. 15, examples from the DVMP challenge set include 2 to 6 modifiers.

While errors of all types are prevalent regardless of the number of modifiers, their frequency tends to rise as more modifiers are added.

As for SynGen, although it does not display semantic leakages at an increased rate compared to the baselines (as quantitatively demonstrated in Fig. 12), it does show a tendency to generate more than the specified number of entities as the modifier count increases. This behavior is observable in rows 8 and 10 for SynGen, and in rows 7 through 10 for the baselines.

(a)

(b)

Distribution of Method byMaxRepeatingModifiers inPrompt

DistributionofMethodbyMaxRepeatingEntitiesinPrompt

SynGen (ours)

25%

SynGen(ours)

25%

Attend-and-Excite

Attend-and-Excite

Structured

Structured

Vanilla-SD

20%

Vanilla-SD

20%

-36%

-46%

15%

-47%

15%

10%

10%

-44%

5%

-82%

-87%

-66%

5%

-81%

-84%

-81%

-83%

-90%

0%

0%

1

2

1

2

MaxRepeatingModifiersinPrompt

MaxRepeatingEntitiesinPrompt

DistributionofMethodby#Modifiers

ISelected asBest

35%

SynGen (ours)

Attend-and-Excite

30%

25%

ModelS

20%

-46%

-42%

-36%

Times

15%

-59%

Percent of'

10%

-66%

5%

0%

2

3

4

5

6+

NumberofModifiers

D.2 Comparison to Spatial-Temporal Diffusion

As described in Section 5, concurrent to this work, [24] developed a method to optimize the latents. While they primarily attend spatial and temporal relations, they too report on improper binding, namely, attribute mismatch. Thus, we extend the tables from Section 4, to include Spatial-Temporal Diffusion, see Fig. 16, Fig. 17, Fig. 18.

Based on these 18 images, we observe that Spatial-Temporal Diffusion consistently misses at least one entity from the prompt. As an example, see Fig. 16. The images in columns 1 and 2 miss a crown (but include 'wooden' objects), and columns 3 and 4 miss a lion and exhibit semantic leakage.

In other cases, we note many cases of semantic leakage in and out of the prompt. For instance, in Fig. 18, in column 2 the clock is brown and the wall is pink, and in column 3, the chair is pink.

DistributionofMethodbyEntangledGroup

SynGen (ours)

Best

Attend-and-Excite

TimesModel Selected as

50%

Structured

Vanilla-SD

40%

30%

-50%

-45%

60%

20%

-64%-64%

Number of

-80%

-80%-80%

10%

-91%

0%

entangled

entangled

neutral

without

with

commonmodifiers

commonmodifiers

Entangled Group

Structure

Diffusion

SynGen

(ours)

'a furry zebra

and a spotted

metal

bicycle'

'a skewered

apple and a

spiky

wooden

chair'

'a pink

cat and a

gray

wooden

camera'

Attend-and

Excite

'a blue

bear and a

beige

rabbit'

(c)

'a beige

modern

balloon and

a purple

strawberry'

(d)

'a black

apple

and a

black

sliced

apple'

(b)

'a skewered

and a yellow

strawberry'

(a)

(e)

(f)

(g)

'a wooden crown and a furry

baby rabbit'

Spatial-Temporal

Diffusion

SynGen

(ours)

'a red chair and a purple

camera and a baby lion'

'a spiky bowl and a green cat'

2 modifiers

3 modifiers

4 modifiers

5 modifiers

Structured Diffusion Attend-and Excite SynGen (ours)

6 modifiers

a pink crown and a furry elephant

a white bicycle and a spiky bow

a green bicycle and a red baby bird

a spotted gorilla and

a beige modern car

a sliced strawberry and

a wooden bench and

a baby furry rabbit

a red spiky spotted clock and

a baby horse

a spiky wooden clock and

a modern checkered camera and

a purple apple

a baby lion and

a green sliced banana and

a white skewered apple

a purple metal wooden car and

a sliced tomato and

a blue sliced apple

an orange wooden spiky camera and

a gray checkered spotted hat

D.3 Stable Diffusion and Structured Diffusion Comparison

A comparison between Stable Diffusion and Structured Diffusion is depicted in Fig. 19. The findings from the study by [11] suggest that the generated images from Structured Diffusion are often similar to those generated by Stable Diffusion, with limited improvements in addressing semantic flaws and enhancing image quality. This is further supported by the comparable results presented in our findings Table 1. Therefore, while we include all baselines in our evaluations, our qualitative analysis only showcases images produced by the slightly superior Structured Diffusion.

'a brown brush glides through

beautiful blue hair'

Spatial-Temporal

Diffusion

SynGen

(ours)

'a blue and white dog sleeps in

front of a black door'

'a white fire hydrant sitting in a

field next to a red building'

'a monkey and a black bow'

Spatial-Temporal

Diffusion

SynGen

(ours)

'a pink clock and a brown

chair'

'a frog and a brown apple'

'a wooden

crown and

a furry

baby

rabbit'

Structure

Diffusion

Stable

Diffusion

'a red chair

and a

purple

camera and

a baby

lion'

'a spiky

bowl and

a green

cat'

'a brown

brush

glides

through

beautiful

blue hair'

'a blue and

white dog

sleeps in front

of a black

door'

'a white fire

hydrant

sitting in a

field next to

a red

building'

'a

monkey

and a

black

bow'

E SynGen Failures

We observe three recurring types of failure SynGen displays Fig. 20. First, when there are many modifiers and entities in the prompt, despite the results in Fig. 12, we note that sometimes the negative loss component becomes exceedingly large, and thus, pushes the latent out of the distribution the decoder was trained on. Consequently, images become blurred, or contain concepts which are successfully separated, but are incoherent. This is likely because our method over-fixates on incorporating all elements described in the prompt.

Second, while SynGen typically successfully addresses the possible error cases described in Section 4.2, at times it can neglect generating all objects, unify separate entities, or neglect generating attributes. We conjecture that it is because the cross-attention maps of the modifier and its corresponding entity do not overlap enough. We note it usually occurs when there are many modifiers that refer to the same entity.

Finally, as common with many diffusion models, we report a recurring issue with faithfulness to the number of units specified in the prompt, for a certain entity. For instance, upon receiving prompts containing 'a strawberry', SynGen generates images with multiple strawberries, instead of just one. One explanation to this problem is that the representation of a certain entity begins 'scattered', and is never quite formed into a single cluster. Interestingly, the opposite problem, where multiple units are 'merged' into one, occurs far less in the generations of SynGen. Possibly, because of the inherent objective function of our loss, which 'pushes away' foreign concepts from one another.

'a red spotted

dog and a blue

striped cat are

playing with a

yellow spiky

'a brown

skewered

baby furry

zebra and a

yellow modern

skateboard'

'a pink baby

zebra and a

brown lion and

a checkered

wooden

camera'

"a baby sleepy

penguin and a

and a sleepy

lion"

'a sliced

strawberry and

a yellow furry

zebra'

'a curved

modern car and

an orange baby

sleepy elephant

and a brown

bench'

(b)

(a)

(c)

(d)

(e)

(f)

F The Diverse Multiple Modifiers Prompts (DVMP) dataset

In Section 3.2 we describe DVMP, a new dataset containing rich and challenging combinations, for the purpose of evaluating improper binding.

In total, DVMP has 18 types of objects, 16 types of animals, and 4 types of fruit. There are four animal modifiers, 7 object modifiers, two fruit modifiers, and 13 colors. A comprehensive account of the entities and their possible modifiers is shown in Table 4.

G Extended Evaluation

G.1 Additional Details on Human Evaluation Experiments

In the manual evaluation procedure detailed in Section 3.3 the evaluator is tasked with comparing various image generations and selecting the optimal image based on multiple criteria. The guidelines and examples given to the evaluators are presented in Fig. 21 and Fig. 22. Fig. 23 provides a screenshot of the rating interface. The full results of the human evaluation are given in Table 5

Rater Compensation. Raters were selected based on their performance history, requiring a minimum of 5,000 approved HITs with an approval rate exceeding 98%. They were required to pass a qualification exam with a perfect score before given access to the task. The hourly compensation was $10, ensuring fair renumeration for their contributions.

Instructions

·Please read thefollowinginstructions carefully:

·2.VisualAppeal:Whichimagelooksoverallbetterormorenatural?

·ConceptSeparation:Foreachimage,askyourself:

-Do you see all objects from the description?

-Are allobjects'details correct?

-Arethereanydetailsonobjectsthatshouldnotbethere?

Choose theimage that best matches the description.If they are equally good, choose'equally good'and if they are equally bad, choose 'equallybad'.

·VisualAppeal:AfterevaluatingConceptSeparation,decidewhichimagelooksbetterornaturaltoyou.

·Pleasechoose theimagesbased onConceptSeparationfirst,and thenconsidertheirVisualAppeal.

'a blue balloon and an orange bench'

Good match

Bad match

This is a good match because the image clearly shows a BLUE balloon and an ORANGE bench (even though we only see a part of the bench), which matches the description.

This is a bad match because the image shows a BLUE bench, which does not match the description.

This is a good match because the the image clearly shows an ORANGE car and a BLUE suitcase (an image that would show a clearer orange car or that would not have an orange fruit would be better)

"a blue suitcase and an orange car"

This is a bad match. While there's an orange object, it does not look more like a car than the first option.

Description:"thetanteddybearhasaredbowtiedaroundits

neck"

Image 1

Image 2

Image 3

Image 4

Equally

Equally

Good

Bad

TAILINXI

Which image matchesbetter the given

description?

Q

Which image looks overall better or

more natural?

Submit

G.2 Phrases-to-Image Similarity

A common approach to automatically assess text-based image generation is by computing the cosine similarity between an image and prompt, using a vision-language model like CLIP [9]. However, the very challenge we tackle here is rooted in CLIP's failure in establishing correct mapping between syntactic bindings and visual bindings, functioning like a bag-of-words model [10]. As an example, suppose CLIP is prompted with 'a blue room with a yellow window'. If we present CLIP with an image of a yellow room with a blue window, it may yield a similar score to an image that accurately depicts a blue room with a yellow window.

In an attempt to address this flaw, we segment prompts to phrases containing entity-nouns and their corresponding modifiers (e.g., 'a blue room' and 'a yellow window'), and compute the similarity between these segmented phrases and the image. We then aggregate the result to a single score by computing the mean. With this approach, we expect CLIP to properly associate the modifiers (e.g., 'blue' and 'yellow') with the correct entity-noun (i.e., 'room' and 'window') as there is only one entity-noun in each segment. Unfortunately, this metric achieves relatively low agreement with the majority selection of human evaluation, only 43.5% of the time, where 25% is random selection. Despite the low agreement, we note the overall trend of selections of this automatic metric is very similar to the human majority selection. Table 6 shows the results of our automatic evaluation.

Task

"awhitebicycleandaspikybow"

Howmanyobjectsaredescribed inthetext?

How many objects which are described in the text appearin the image?

Looking only at the objects that appear in the text and image,howmany attributes are correctlyassigned to their object?

I'mnotconfident inmyanswer

Feel free toleavefeedbackhere(optional)

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents an approach called “SynGen” to improve attribute binding between nouns and their modifiers for text-to-image latent diffusion models, specifically Stable Diffusion. They propose a two-part loss on attention maps that (1) encourages attention maps of nouns and their modifiers to be similar, and (2) encourages attention maps between the noun/modifier to be different from those of other tokens in the prompt. \n\nDatasets and baselines: They run experiments on three datasets: the attribute binding contrast set (ABC-6K) from the Structured Diffusion (baseline 1) paper, data from the Attend-and-Excite (baseline 2), and a newly proposed challenge set called Diverse Visual Modifier Prompts.\n\nEvaluation:  \n(1) Human evals for concept separation and visual appeal. They find that SynGen outperforms baselines.  \n(2) Qualitative analysis with sample prompts and generations for Syngen and the two baselines, demonstrating that SynGen overcomes failure cases of the other two models.  \n(3) Ablations on the loss components and loss weighting.\n\nSTRENGTHS:\n1. This paper tackles an important and challenging drawback of text-to-image diffusion models struggling to faithfully generate objects with the right attributes. \n2. This paper proposes an interesting two-part loss that imposes localized pairwise constraints on the attention maps between image patches and token embeddings. The first constraint enforces that the same image patches attend to both the noun and its modifiers. The second constraint enforces that image patches attending to these nouns/modifiers do not attend to any other tokens in the prompt. \n3. This paper attempts to evaluate on a large set of prompts including those from prior work as well as proposing their own challenge set.\n4. This paper attempts to better understand the effects and side-effects of the proposed losses via ablations.\n\nWEAKNESSES:\n1. The proposed method appears to be strongly grounded in the framework of the Attend-and-Excite paper. The writing doesn’t highlight this connection while introducing the approach in Section 2, beyond a minor footnote. Section 2 would probably need some revision to better draw this connection.\n2. Considering that the paper is tackling a very specific problem, the general “content separation” rating collected in the human eval seems pretty weak. Why not collect finer-grained scores for: number of objects in the prompt, number of objects from prompt generated, number of objects from prompt generated with the correct attributes? Wouldn’t this give a much better sense of task success and could potentially allow computing recall?\n3. The loss function ablation mentions that sometimes objects are omitted from the generated image. Is combining the proposed losses with global constraint defined in the Attend-and-Excite paper a viable option? Since the framework is identical, wouldn’t combining the losses have mitigated this issue? The Attend-and-Excite paper talks about attribute binding as well, so this appears to be a variant to try. \n\nNits:\n1. Maybe more explicitly state that this is an inference-only guidance procedure?\n2. There’s a typo on line 112 for the equation: $\\\\nabla_{z_t}\\\\mathcal{L}$ not $\\\\nabla z_t\\\\mathcal{L}$. Also $z’_t$ not $z’t$.\n\nQUESTIONS:\n1. Was there a reason to not include numerical modifiers in the study? \n2. Are there unexpected side-effects of such constraints on model generalization?\n\nLIMITATIONS:\nOne limitation that was perhaps overlooked is the lack of standardized evaluation. Having to rely on different crowdsourcing tasks and qualitative comparisons makes it much harder to track progress.", "TITLE": "Official Review", "RECOMMENDATION": 7, "REVIEWER_CONFIDENCE": 3, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2023-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nA frequent issue in text-prompted image generation (and in many other grounded language scenarios) is that a model will treat text akin to a bag of words and ignore syntactic relationships, such as which adjective attaches to which noun -- this is referred to as a problem with lingusitic binding. The paper proposes addressing this problem in diffusion models by adding an extra step to the diffusion process that nudges the image in the process of being generated so as to respect modifier relationships extracted from a syntactic parse of the prompt. This intervention operates in terms of cross-attention maps (i.e. attention relationships established between tokens and parts of the image), with the intuition that related tokens should map to the same region of the image, and unrelated tokens to different regions.\n\nThe paper also introduces a new challenge dataset aimed at diagnosing problems with linguistic binding in image generation (the proposed method performs well on this challenge set).\n\nSTRENGTHS:\nThe issue of improper linguistic binding is one that plagues not only attempts at image generation, but many other model uses of grounded language. It's an important problem that has not been adequately resolved by prior work, whereas this paper has demonstrated substantial steps to overcoming this issue without sacrificing on generated image quality. All of this speaks to the significance of this work, which has the potential to see broad and immediate application without the need to undertake costly new model training efforts.\n\nThe paper is clearly written, and makes effective use of figures to illustrate the problem and how the proposed method resolves it.\n\nThere is thorough and convincing human evaluation on both existing data, and a new challenge dataset designed specifically to reveal instances of problems with binding.\n\nWEAKNESSES:\nThere are not many weaknesses to point to. This is not a weakness of the paper per se, but when the method my main remaining (aesthetic) dissatisfaction is the reliance on an external parser, and the possibility of cascading failure that any such pipeline-based system entails.\n\nQUESTIONS:\nnone\n\nLIMITATIONS:\nLimitations are well addressed in the paper.", "TITLE": "Official Review", "RECOMMENDATION": 8, "REVIEWER_CONFIDENCE": 3, "SOUNDNESS_CORRECTNESS": 4, "DATE": "2023-07-06"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper focuses on attribute binding/leak/neglect problems in text-to-image models. The authors propose, SynGen, a method that utilizes dependency trees combined with cross-attention map optimization to achieve better attribute binding results. Extensive experiments are conducted to show the effectiveness of SynGen compared to previous methods.\n\nSTRENGTHS:\n- The paper addresses an important compositional problem in text-to-image generation. \n\n- The method is intuitive and effective. SynGen can obtain stronger attribute-object association using dependency trees compared to Structure Diffusion, the positive loss design is well-motivated for solving the problem, and the negative loss design enforces the cross-attention maps over A&E.  \n\n- The experiment looks comprehensive by considering all sources of datasets and including even more challenging prompts DVMP. The prompts in DVMP are more challenging and realistic than previous \"A and B\" format prompts.\n\nWEAKNESSES:\n- I am concerned with the efficiency of incorporating so many negative losses in the diffusion process. What's the speed of SynGen compared to the original SD?\n\n- Using tree-based methods for binding may fail for more complicated and practical prompts. In reality, SD users write much longer prompts that describe the components at different levels. However, I think the contribution of SynGen still matters a lot as the community needs time to develop from methods for short prompts to more generalized methods.\n\nQUESTIONS:\nApart from the question in weaknesses:\n\n1. Would SynGen still work for prompts like \"an apple that is blue\" or \"a red apple on the left and another on the right\"?\n\n2. I may have missed this part but how do you deal with the padding tokens?\n\n3. Table 2 shows that positive only and positive+negative have lower visual appeal percentages than negative only. Does that imply that the positive loss will harm the visual appealing of the images?\n\nLIMITATIONS:\nSee weaknesses.", "TITLE": "Official Review", "RECOMMENDATION": 7, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2023-07-07"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper approaches the binding problem in text to image diffusion models that is marked by the inability of the models to appropriately identify which modifiers are attached to which nouns in the input text. This often results either in images that mix up the attributes of the different nouns that are mentioned in the text or in images that completely disregard some of the attributes or fall back to statistically likely combinations that are not mentioned in the text. The paper proposes an inference time optimization based on first identifying which modifiers are associated with which nouns, and then optimizing a loss function that explicitly enforces the cross attention matrices of corresponding (i.e. related by modifier-noun relationship) tokens in the text to be similar, while those of every other pair in the sentence to be dissimilar. This results in a diffusion model that adheres more faithfully to the input text and produces images that are deemed better more often than competing baseline approaches that attack the same problem.\n\nSTRENGTHS:\n## Originality, Quality, Significance\n* The paper provides a novel lightweight method using off the shelf syntactic parsers to enforce better binding of modifiers to nouns in text to image diffusion models. The qualitative results seem promising and the human evaluation results suggest that the proposed method outperforms previous approaches to mitigate the binding issue.  \n\n## Clarity\nThe paper is easy to follow. The qualitative examples demonstrate the different kind of issues faced by the baselines and the improvements brought by the proposed approach.\n\nWEAKNESSES:\n* Some qualitative examples on the collected DVMP dataset with some categorization on failure cases based on having uncommon modifiers vs different number (performance on 1,2,3 and more modifiers) of modifiers would have been good to see.\n\nQUESTIONS:\n## Clarifications\n* The metric used in the human evaluation in Table 1 is not sufficiently clear. Aren't the values in each column supposed to sum to 100? \n* What is the Complex Concepts Prompts dataset mentioned in Fig 5?\n\n## Suggestions\n* It would be easier for the reader to follow if the examples in Figure 4,5,6 are reorganized such that the references to them in the text are chronological. Currently it requires the reader to keep going back and forth across the figures.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 3, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2023-07-09"}