Source-linked AI summary
Break-A-Scene: Extracting Multiple Concepts from a Single Image
Omri Avrahami, Kfir Aberman, Ohad Fried, Daniel Cohen-Or, Dani Lischinski
TL;DR
Existing personalization methods mainly learn one concept from multiple images, leaving the extraction of several concepts from a single image insufficiently addressed. Break-A-Scene introduces masked textual scene decomposition with two-phase handle and model optimization, masked diffusion and cross-attention losses, and union-sampling; comparisons and a user study report effective identity preservation and prompt following. The method is limited by lighting and pose entanglement, difficulty with more than four concepts, and substantial computation.
Problem
Existing personalization methods primarily learn a single concept from multiple images, while extracting multiple concepts from one image remains an addressed challenge.
Method
The method uses masks, two-phase optimization of concept handles and model weights, masked diffusion and cross-attention losses, and union-sampling for concept combinations.
Results
The method generates images that preserve concept identities while following text prompts, including combinations of up to four concepts, and is preferred by human evaluators.
Takeaways & Limitations
The paper provides a framework for extracting multiple concepts from a single scene and remixing them into new contexts.
Takeaways & Limitations
The method can entangle learned identities with lighting or pose, struggles beyond four concepts, and takes about 4.5 minutes while fine-tuning the entire model.
Abstract
from arXiv · showhide
Text-to-image model personalization aims to introduce a user-provided concept to the model, allowing its synthesis in diverse contexts. However, current methods primarily focus on the case of learning a single concept from multiple images with variations in backgrounds and poses, and struggle when adapted to a different scenario. In this work, we introduce the task of textual scene decomposition: given a single image of a scene that may contain several concepts, we aim to extract a distinct text token for each concept, enabling fine-grained control over the generated scenes. To this end, we propose augmenting the input image with masks that indicate the presence of target concepts. These masks can be provided by the user or generated automatically by a pre-trained segmentation model. We then present a novel two-phase customization process that optimizes a set of dedicated textual embeddings (handles), as well as the model weights, striking a delicate balance between accurately capturing the concepts and avoiding overfitting. We employ a masked diffusion loss to enable handles to generate their assigned concepts, complemented by a novel loss on cross-attention maps to prevent entanglement. We also introduce union-sampling, a training strategy aimed to improve the ability of combining multiple concepts in generated images. We use several automatic metrics to quantitatively compare our method against several baselines, and further affirm the results using a user study. Finally, we showcase several applications of our method. Project page is available at: https://omriavrahami.com/break-a-scene/
1 INTRODUCTION
The paper introduces textual scene decomposition for extracting multiple concepts from one masked image, addressing the tradeoff between preserving concept identity and maintaining scene editability. Its two-phase customization, disentanglement losses, and union-sampling strategy support combining extracted concepts in new scenes.
- Task: Textual scene decomposition extracts multiple concepts from a single image using masks that specify the concepts to learn.Masks may be provided by users or generated automatically by segmentation models.
- Method: The two-phase pipeline first optimizes dedicated handles with frozen model weights, then jointly optimizes handles and model weights.This design aims to balance concept fidelity with avoidance of overfitting.
- Motivation: Adapting Textual Inversion and DreamBooth exposes a reconstruction-editability tradeoff: the former loses identity, whereas the latter overfits and loses context control.Textual Inversion fails to reconstruct concepts accurately in new contexts, while DreamBooth preserves identity but cannot reliably follow prompts.
- Method: Union-sampling trains concepts together rather than separately, improving the model’s ability to generate combinations of extracted concepts.The strategy addresses failures when customized concepts must appear together in generated images.
- Method: A masked diffusion loss assigns each handle to its target concept, while cross-attention supervision prevents handles from attending to other concepts.The cross-attention loss uses attention maps as a proxy for scene layout to reduce concept entanglement.
- Evaluation: Automatic metrics and a user study are used to compare the method with baselines, and human evaluators prefer the proposed method.The paper also presents several applications.
2 RELATED WORK
The related work spans text-to-image synthesis, text-driven editing, cross-attention manipulation, inversion, and personalization. The paper distinguishes its setting by extracting multiple textual handles from a single image and remixing them into novel scenes.
- Text-to-image synthesis: Text-to-image synthesis progressed from RNNs, GANs, and transformers toward diffusion-based models, which became superior for generation.The passage situates the paper within the broader development of text-to-image models.
- Text-to-image synthesis: Text-driven image editing supports both global and local manipulations, while diffusion models have also been applied to related editing settings.The supplied passage lists representative approaches for both manipulation scopes.
- Cross-attention: Cross-attention maps have been used for image manipulation and explainability, whereas this work uses them to disentangle handles for remixing concepts into novel scenes.Unlike prior editing applications, the paper focuses on extracting textual handles from a scene rather than editing the input image.
- Inversion: Inversion seeks latent codes that reconstruct a given image and can optimize latent representations, train encoders, or refine model weights.This study similarly uses two stages: textual-embedding optimization followed by joint embedding and model-weight training.
- Personalization: Textual Inversion and DreamBooth personalize text-to-image models from several images of one concept, learning to generate it in varied contexts.Textual Inversion learns a text token with the standard diffusion loss, while the passage introduces DreamBooth as another seminal personalization method.
- Personalization: SVDiff also supports combining concepts but requires several images per concept, whereas this paper operates on one image containing multiple concepts.SVDiff’s augmentation places two objects side by side, while the proposed method enables arbitrary placement of up to four objects.
- Personalization: Among recent single-image personalization methods, the paper identifies its setting as the first to address multiple concepts within one image.The comparison includes publicly available ELITE and distinguishes the proposed task from existing personalization scenarios.
3 METHOD
The method extracts masked concepts into dedicated textual handles through a two-phase customization pipeline, combining reconstruction, disentanglement, and support for concept combinations. It balances concept fidelity with editability by jointly optimizing handles and model weights in stages.
- Pipeline overview: The pipeline assigns a dedicated textual handle v_i to each concept indicated by mask M_i, enabling synthesis of individual concepts or novel combinations.The handles are used in prompts to guide generation of new instances and combinations of several extracted concepts.
- Balancing reconstruction and editability: Two-phase training first optimizes handles with frozen model weights and a high learning rate, then also trains model weights with a lower learning rate.This design balances reconstruction of learned concepts against overfitting and loss of generality.
- Union-sampling: Union-sampling randomly selects subsets of concepts during training and computes losses over their union masks, improving support for generated concept combinations.The strategy is applied in both optimization phases rather than customizing each concept separately.
- Masked diffusion loss: The masked diffusion loss penalizes denoising errors only over pixels covered by the selected concept masks, reconstructing the desired concepts.It uses the union of the corresponding masks when multiple concepts are sampled together.
- Cross-attention loss: The cross-attention loss penalizes deviation between each learned handle’s attention map and its corresponding mask, preventing handles from attending to multiple concepts.Without this penalty, both handles can attend to the union of both concept regions; adding it yields distinct attention regions and disentangled generation.
4 EXPERIMENTS
The experiments adapt existing personalization methods to single-image scenes with concept masks, then compare them with Break-A-Scene qualitatively and quantitatively. Break-A-Scene preserves concept identities while following prompts, supports multiple concepts, and enables several scene-editing applications.
- Baseline adaptation: Existing methods are adapted by converting one masked image into image-text pairs containing randomly selected concept subsets, random backgrounds, and augmentations.The adapted baselines are called DB-m and TI-m; CD is adapted using the same procedure, while ELITE natively supports input masks.
- Qualitative comparisons: Break-A-Scene preserves identities and follows guiding prompts more effectively than the adapted baselines, including scenes with up to four concepts.TI-m and CD-m follow prompts but lose identity, DB-m preserves identity but fails prompt compliance, and ELITE remains less faithful for multiple concepts.
- Quantitative comparisons: Break-A-Scene lies on the Pareto front between identity similarity and prompt similarity, whereas DB-m favors identity and TI-m/CD-m favor prompt adherence.The human-ranking scatter plot exhibits similar trends to the automatic comparison.
- Ablation study: Removing either training phase, the masked diffusion loss, the cross-attention loss, or union-sampling produces distinct failures in prompt following, background separation, concept disentanglement, or multi-concept generation.Without union-sampling, identity preservation also significantly decreases when generating multiple concepts.
- Applications: Extracted concepts support image variation, separation of entangled objects, background extraction, and local image editing.Generated variations can change object arrangements and backgrounds, while extracted concepts can be inserted into indicated regions of other images.
5 LIMITATIONS AND CONCLUSIONS
The method has limitations stemming from learning multiple concepts from a single image, including lighting and pose entanglement, difficulty beyond four concepts, and substantial computational cost. Despite these constraints, it preserves concept identities while following prompts and supports several downstream applications.
- Limitations: Single-image input can entangle learned identities with lighting and object pose, limiting control over environments and requested poses.The authors report daylight persistence across differently prompted environments and fixation to a dog's upward-looking, open-mouth pose.
- Limitations: The method works best with up to four concepts and struggles when learning six identities from one image.This limitation is attributed to underfitting of multiple concepts.
- Limitations: Extracting concepts and fine-tuning the entire model takes about 4.5 minutes, limiting applicability in time-sensitive scenarios.The authors state that recent faster, more parameter-efficient approaches did not work and identify reducing model cost as future work.
- Conclusions: The paper addresses extracting multiple concepts from a single image as a new personalization scenario.The authors position the work as a building block for future generative-AI creative applications.
- Conclusions: Compared with baselines, the method better preserves concept identities and follows text prompts, including when learning four concepts.TI-m and CD-m struggle with identity preservation, DB-m ignores prompts, and ELITE remains insufficiently recognizable for multiple concepts.
- Applications: The method supports image variations, concept decomposition, background extraction, and localized editing with off-the-shelf tools.These applications are presented as downstream uses of the extracted concepts.
A ADDITIONAL EXPERIMENTS
The appendix adds further experimental material, including additional generated results, ablation comparisons, and naïve applications of existing personalization methods.
- A.1 Additional Results: Appendix A.1 provides additional results generated by the proposed method.
- A.2 Qualitative Ablation Study Results: Appendix A.2 adds qualitative comparisons from the ablation study.
- A.3 Baseline Applications: Appendix A.3 evaluates naïve applications of Textual Inversion and DreamBooth to multiple concepts from one image without the paper’s adaptation.
A.1 Additional Results
The appendix presents additional examples of scene decomposition, localized editing, entangled-scene decomposition, and image variations, illustrating the method’s application range.
- Scene Decomposition: Figure 11 provides additional results for breaking scenes into components and using them to synthesize novel images.
- Localized Editing: Figure 12 shows additional examples of localized image editing.
- Additional Applications: Figure 13 provides more examples of entangled scene decomposition, while Figure 14 provides more examples of image variations.
A.2 Qualitative Ablation Study Results
The qualitative ablation study removes components of the training pipeline and compares the resulting behavior, while supplementary material also examines baseline personalization methods and comparison tables.
- Training-Phase Ablations: Removing the first training phase causes generated images to stop corresponding to the target text prompt.
- Loss Ablations: Removing masked diffusion loss causes the model to learn the original background, which overrides the target text prompt.
- Loss and Sampling Ablations: Removing cross-attention loss mixes or replicates concepts, while removing union-sampling degrades multiple-concept generation.Increasing the probability of only one concept during union-sampling has a similar effect.
- Baseline Comparisons: The appendix reports naïve single-image applications of Textual Inversion and DreamBooth and extends the personalization-baseline comparison with concurrent works.Only the first four methods in the extended table have an open-source implementation.
B IMPLEMENTATION DETAILS
This section outlines the organization of the implementation-details material and the accompanying evaluation procedures.
- The section introduces implementation details before describing automatic comparison-dataset creation and evaluation metrics.
- It concludes by presenting the full details of the conducted user study.
- The implementation discussion covers the method, automatic comparisons, metrics, and user-study design.
B.1 Method Implementation Details
The method implementation uses Stable Diffusion-based components, staged optimization, cross-attention analysis, and extracted concepts for image editing and resynthesis.
- The method and most baselines use Stable Diffusion V2.1 implementations, while ELITE uses Stable Diffusion V1.4.ELITE uses the authors’ official implementation because its encoders were trained on Stable Diffusion V1.4 embeddings.
- The two-stage training first optimizes text embeddings, then jointly trains UNet and text-encoder weights.The first stage uses learning rate 5e−4, the second 2e−6, and each stage runs for 400 training steps.
- Learned handles can guide synthesis of individual concepts or novel combinations, and support iterative regional image editing with text prompts.The editing application uses Blended Latent Diffusion and can apply different prompts to different regions.
B.2 Automatic Dataset Creation
The automatic comparison dataset pairs segmented COCO scenes with fixed contextual prompts, while evaluation measures prompt correspondence and concept identity.
- The dataset is built from COCO images and instance-segmentation masks, cropped into square shapes and paired with fixed text prompts.Prompts place the extracted tokens in contexts such as beaches, jungles, snow, streets, and city or mountain backgrounds.
- 5400 generations per baseline are produced by iterating over combinations from the powerset of the input tokens.Figure 17 qualitatively compares the baselines and the proposed method on this automatically generated dataset.
- Prompt similarity uses normalized cosine similarity between CLIP embeddings of the prompt and generated image.Specialized tokens are replaced by their ground-truth class descriptions before calculating prompt similarity.
- Identity similarity compares masked input and generated images using input masks and segmentation from a pretrained MaskFormer model.DINO image embeddings are used because they better encompass object identity.
- Image-variation examples show the method generating many variations from a single input scene.
B.3 User Study Details
The user study evaluates prompt correspondence and identity similarity against baselines, while additional examples illustrate editing, societal applications, limitations, and ablations.
- B.3 User Study Details: AMT workers rate generated images for correspondence to modified text prompts and for whether generated objects match masked input objects.Prompt correspondence and identity similarity are both assessed on a 1-to-5 scale.
- B.3 User Study Details: p < 10−213 establishes statistically significant differences across user-study conditions using the Kruskal-Wallis test.Tukey’s honestly significant difference procedure also tests the proposed method against all baselines.
- Applications: Iterative Blended Latent Diffusion enables region-by-region editing with different text prompts for different regions.
- Societal Impact: The method is presented as potentially supporting content creation for people with limited artistic skills or resources and visuals for rare cultural contexts.
- Societal Impact: Using copyrighted reference content may create intellectual-property and copyright issues, and malicious users may create deceptive fabricated images.
- Qualitative Comparisons: Qualitative comparisons report that the proposed method preserves concept identities and follows text prompts better than the displayed baselines.The comparisons include TI-m, CD-m, DB-m, and ELITE.
- Ablation Study: Ablations show that removing training phases, masked loss, cross-attention loss, or union sampling harms prompt adherence, concept isolation, or multi-concept generation.Without masked loss, background elements are extracted; without cross-attention loss, concepts become entangled.