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Editing Implicit Assumptions in Text-to-Image Diffusion Models

Hadas Orgad, Bahjat Kawar, Yonatan Belinkov

arXiv:2303.08084v2cs.CV

TL;DR

Text-to-image diffusion models encode implicit, potentially outdated or biased assumptions that are difficult to change without user intervention or retraining. TIME edits cross-attention projections to replace a source prompt’s assumption with a destination attribute, and experiments show generalization to related prompts with limited effects on unrelated generations. The method is evaluated with TIMED and also reduces gender bias, while remaining limited by the model’s existing generative capabilities and a generality–specificity tradeoff.

  • Problem

    Text-to-image models encode implicit assumptions that may be outdated, incorrect, or socially biased, motivating methods to edit them without user input or costly retraining.

  • Method

    TIME edits cross-attention projection matrices so an under-specified source prompt maps toward a destination prompt with the desired attribute.

  • Results

    TIME generalizes to related prompts, leaves unrelated generations mostly intact, preserves generative capabilities, and reduces gender bias across many professions.

  • Takeaways & Limitations

    TIMED provides 147 source–destination pairs and related prompts for evaluating text-to-image model editing across efficacy, generality, and specificity.

  • Takeaways & Limitations

    TIME cannot teach visual concepts the original model cannot generate and sometimes edits too mildly or too aggressively, limiting the generality–specificity tradeoff.

Abstract

from arXiv · show

Text-to-image diffusion models often make implicit assumptions about the world when generating images. While some assumptions are useful (e.g., the sky is blue), they can also be outdated, incorrect, or reflective of social biases present in the training data. Thus, there is a need to control these assumptions without requiring explicit user input or costly re-training. In this work, we aim to edit a given implicit assumption in a pre-trained diffusion model. Our Text-to-Image Model Editing method, TIME for short, receives a pair of inputs: a "source" under-specified prompt for which the model makes an implicit assumption (e.g., "a pack of roses"), and a "destination" prompt that describes the same setting, but with a specified desired attribute (e.g., "a pack of blue roses"). TIME then updates the model's cross-attention layers, as these layers assign visual meaning to textual tokens. We edit the projection matrices in these layers such that the source prompt is projected close to the destination prompt. Our method is highly efficient, as it modifies a mere 2.2% of the model's parameters in under one second. To evaluate model editing approaches, we introduce TIMED (TIME Dataset), containing 147 source and destination prompt pairs from various domains. Our experiments (using Stable Diffusion) show that TIME is successful in model editing, generalizes well for related prompts unseen during editing, and imposes minimal effect on unrelated generations.

1. Introduction

TIME edits implicit assumptions in text-to-image diffusion models by changing cross-attention projections, aiming to alter specified concepts while preserving unrelated generations. The method is evaluated with TIMED and applied to gender-bias mitigation.

  • Motivation and approach: TIME is intended to update outdated information, mitigate harmful social biases, and support alternate-reality scenarios without explicit user requests or costly retraining.The motivation includes avoiding data recollection, filtering, retraining, or finetuning.
  • Motivation and approach: TIME replaces an under-specified source prompt’s implicit attribute with an explicitly specified destination attribute by editing model weights.The edit targets the model’s perception of a concept rather than only one prompt or image.
  • Motivation and approach: Cross-attention projection matrices are edited because they connect textual inputs to visual representations, while remaining independent of diffusion steps and image contents.TIME avoids training or finetuning, edits layers in parallel, and leaves the language model unchanged.
  • Evaluation: TIMED contains 147 source–destination prompt pairs from varied domains, with related prompts for evaluating editing quality.The dataset supports assessment of whether edits generalize to related prompts and spare unrelated ones.
  • Results: TIME generalizes to related prompts, leaves unrelated generations mostly intact, and preserves generative capabilities as measured by FID.For example, editing roses to blue leaves poppies red in a separate prompt.
  • Results: For the prompt “A photo of a CEO”, only 4% of generated images contain female figures before editing, and TIME improves gender representation across many professions.The application targets gender stereotypes encoded in text-to-image generations.

2. Related Work

Related work includes image editing with diffusion models and model editing across several neural-network types. TIME instead edits knowledge embedded in a text-to-image model.

  • Image editing: Diffusion-based image-editing methods modify object properties such as color, style, or pose in a given image using a textual prompt.These approaches edit images rather than directly changing the model’s embedded associations.
  • Model editing: Prior model-editing techniques have addressed language models, generative adversarial networks, and image classifiers.Like some of these methods, TIME focuses its edits on a concise portion of the neural network.

3. Background

Text-to-image diffusion models condition iterative denoising on text embeddings through cross-attention. TIME targets the text-to-visual projections in these layers as the editable representation.

  • Diffusion models: Diffusion models generate images through iterative stochastic noise removal, transforming an initial noisy sample into a final synthesized output.The denoising process follows a predefined noise schedule and update rule.
  • Diffusion models: Additional conditioning signals, including text prompts, are supplied to the denoising model to control the generated output.Text prompts describe desired images and guide the diffusion process.
  • TIME’s target: TIME edits the text-based cross-attention layers because their keys and values are computed independently of the current diffusion step and image data.This independence makes them suitable targets for editing textual knowledge.
  • Cross-attention: Text encoders produce token embeddings that are injected into cross-attention layers and projected into keys and values by learned matrices.The matrices are W_K and W_V, mapping text embeddings into representations consumed by visual features.
  • Cross-attention: Queries representing visual features interact with textual keys to form an attention map encoding each token’s relevance to visual features.The attention map connects the current intermediate image representation with textual information.
  • Cross-attention: The cross-attention output is a weighted average of textual values for each visual query and then propagates through later diffusion layers.This mechanism provides the text-to-image interface targeted by TIME.

4. TIME: Text-to-Image Model Editing

TIME edits implicit visual associations by modifying cross-attention projections so an under-specified source prompt behaves like a specified destination prompt. The method optimizes these edits while preserving proximity to the original model parameters.

  • TIME shifts an under-specified source prompt’s visual association toward a more specific destination prompt.For example, “a pack of roses” can be edited toward “a pack of blue roses.”
  • TIME targets cross-attention layers, where W_K and W_V map text embeddings into keys and values attended to by visual data.Because these keys and values are independent of the current diffusion step and image data, the matrices serve as editing targets.
  • The method aligns source and destination token representations while discarding destination-only tokens from direct correspondence.Destination-only tokens still influence other token representations through the text encoder.
  • TIME optimizes new projection matrices with a loss that encourages destination-like source generations while remaining close to the original matrices.The optimization can be performed independently and in parallel for each cross-attention layer, with a closed-form global minimum.
  • The edited projection matrices are used to generate images expected to follow the user-requested assumption.The paper also evaluates variants that edit only W′_V or change λ.

5. Experiments

Experiments evaluate TIME with the TIMED benchmark across editing efficacy, generalization, specificity, and overall image quality. TIME edits source prompts successfully, generalizes to related prompts, largely preserves unrelated generations and generative quality, while exposing a generality–specificity trade-off.

  • Implementation: TIME edits Stable Diffusion’s key and value projections, modifying 2.2% of parameters in around 0.4 seconds.The implementation uses Stable Diffusion v1.4 with 16 cross-attention layers and does not alter the language model.
  • TIME Dataset: TIMED contains 147 source–destination pairs spanning varied implicit assumptions, with positive prompts testing generalization and negative prompts testing specificity.Each entry includes five positive and five negative prompts associated with destination prompts.
  • Qualitative Evaluation: TIME’s edited source generations mimic the destination prompt’s oracle behavior and generalize to related prompts with minimal effect on unrelated ones.The benchmark evaluates efficacy on the edited source, generality on related prompts, and specificity on unrelated prompts.
  • Qualitative Evaluation: Increasing λ trades generality for specificity, producing broader related-prompt effects at lower values and more targeted edits at higher values.The paper identifies this as a natural trade-off and notes that λ should be tuned for the editing scenario.
  • Quantitative Evaluation: TIME generates the desired concept substantially more often than the baseline while maintaining comparable FID and CLIP Score to the unedited model.The results are reported on 104 TIMED test entries, with generative quality measured on MS-COCO.
  • Limitations: TIME’s evaluation is limited to editing a single assumption at a time, with multiple-assumption editing reported only through preliminary experiments.Different scenarios may also require tuning λ because the fixed Table 2 setting does not suit every generality–specificity trade-off.

6. TIME for Gender Bias Mitigation

TIME is applied to gender bias in profession prompts, targeting more equal male–female representation while acknowledging a binary-gender scope. It reduces aggregate bias and generalizes across prompts, but editing professions jointly is harder to control.

  • Scope: The analysis considers only binary genders and may exclude or marginalize non-binary individuals, with potential unwanted stereotypes in their visual features.The authors identify broader gender coverage and carefully defined data as future research needs.
  • Setup: The study targets stereotypical gender associations in profession prompts, using a dataset of 35 professions and five test prompts per profession.The destination prompt adds the non-stereotypical gender, and evaluation uses professions and prompts unseen during editing.
  • Method: TIME targets 50% female and 50% male generations for each profession by tuning a profession-specific λ.Smaller λ values steer toward the non-stereotypical gender, while larger values preserve existing assumptions.
  • Evaluation: The bias metric ∆ is the normalized absolute difference between generated female representation Fp and the 50% equality target.For each test prompt, 24 images are generated and CLIP classifies gender; ∆ is averaged across professions.
  • Results: TIME reduces ∆ to less than half the baseline bias, with 14 professions near equality compared with 8 for the baseline.Some professions become more balanced, while others become biased toward the previously non-stereotypical gender.
  • Results: Single-profession debiasing generalizes to related prompts, whereas multi-profession editing is less effective because edits affect other professions.Professions sharing a stereotypical gender show stronger cross-effects, complicating control.

7. Limitations

TIME edits existing model assumptions but does not add visual concepts the underlying model cannot generate. Its edits can also trade off generality and specificity, sometimes becoming too weak or too strong.

  • Model dependence: TIME inherits the base model’s generative limitations and is not designed to teach concepts it cannot already generate.Its success correlates with oracle generative performance at ρ = 0.73.
  • Edit strength: TIME sometimes applies edits too mildly, hindering generality, or too aggressively, hindering specificity.The paper suggests per-edit λ adjustment or alternative regularization as possible remedies.

8. Conclusion

The paper asks how specific implicit assumptions in text-to-image models can be edited after training and presents TIME and TIMED to study that problem. It positions efficient editing and bias reduction as foundations for further model-editing research.

  • Conclusion: TIME addresses the question of how to edit specific implicit assumptions in a text-to-image model after training.The method is presented as an efficient approach to this task.
  • Conclusion: TIMED provides a dataset for evaluating text-to-image model editing methods.The conclusion presents the method and dataset as resources for future advances.
  • Future work: Future work includes analyzing cross-attention components and prompt tokens, editing multiple facts in bulk, and pursuing more comprehensive debiasing.These directions aim to expand the scope of model editing while maintaining model performance.

A. Additional Results

Additional results illustrate TIME’s edits across object, person, and scene prompts, while the appendix also presents the optimization procedure and its closed-form solution. Related prompts change after editing, whereas unrelated prompts remain largely unaffected.

  • Additional results: Additional examples edit attributes or concepts including cats, dogs, houses, Hagrid, Shaquille O’Neal, and chairs.The examples include color, breed, identity attributes, activities, object types, and object functions.
  • Additional results: The examples include related and unrelated prompts such as “A chair in the park,” “A yellow chair,” and “A beach chair.”These prompts accompany edits involving chairs, illustrating the evaluation of behavioral transfer across related settings.
  • Additional results: After editing, related prompts change their behavior accordingly, whereas unrelated prompts remain unaffected.The figure summarizes the intended contrast between generalization and specificity.
  • Optimization: The optimized projection matrices have a unique, well-defined solution because the relevant summed matrices are positive definite and invertible.The appendix derives the solution for W′K and obtains W′V analogously.

C. Implementation Details

The implementation evaluates TIME on Stable Diffusion with standardized generation settings, prompt augmentations, and CLIP-based metrics across edited models and captions.

  • Stable Diffusion v1.4 generates 512×512 images using 50 diffusion timesteps, classifier-free guidance scale 7.5, and a 77-token maximum.
  • TIME applies three simple augmentations to each source and destination prompt before constructing the corresponding embedding lists.
  • CLIP ViT-B/32 measures efficacy, generality, and specificity using randomly sampled MS-COCO captions and independently edited TIMED models.
  • The source code and datasets are provided in the supplementary material and were planned for public release upon acceptance.
  • Table 4 reports ablation results using the harmonic mean of generality and specificity.

D. Filtering TIMED for Quantitative Evaluation

The quantitative evaluation filters TIMED to cases where Stable Diffusion can already generate the desired distribution from explicit destination prompts, isolating assumption editing from failures to create novel concepts.

  • TIME assumes the base diffusion model can generate the desired image distribution when the desired attribute is explicitly specified.
  • When the base model cannot generate the specified destination distribution, text-only editing is expected to fail because the needed generative capability is absent.
  • The oracle test generates 24 images for each positive destination prompt and expects at least 80% to be classified as the destination prompt by CLIP.
  • The released TIMED data include 147 test-set entries, 8 validation entries, and a filtered 104-entry test set for Stable Diffusion v1.4 comparisons.

E. Ablation Study

The ablation and supplementary experiments examine TIME’s design choices, tradeoffs, bulk editing, finetuning comparison, and sensitivity of gender debiasing to prompt context.

  • Ablation Study: The main TIME configuration uses textual augmentations, optimizes both WV and WK, and sets λ = 0.1 based on harmonic mean performance.
  • Ablation Study: The harmonic-mean choice reflects a generality–specificity tradeoff, so other configurations may perform better on either individual criterion.
  • Multiple Edits: Bulk TIME editing sums the requested edits in the loss and solution and is evaluated on 82 filtered TIMED assumptions with λ = 1000 and 24 random seeds.
  • Comparison to Text Encoder Finetuning: Text-encoder finetuning takes 10 minutes instead of a fraction of a second, fails to achieve a good generality–specificity tradeoff, and often produces incoherent images.
  • Gender Debiasing: The debiasing data use professions aligned with stereotypes observed in the tested model rather than always following an external stereotype list.
  • Gender Debiasing: Gender-debiasing evaluation uses profession-specific λp values and unseen testing prompts, with distributions computed across repeated random seeds.
  • Variance Across Prompts: Prompt wording substantially changes gender representation, including 36% versus 76% female figures for baker prompts and 76% versus 16% for designer prompts.
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