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Accelerating Text-to-Image Editing via Cache-Enabled Sparse Diffusion Inference (2305.17423v3)

Published 27 May 2023 in cs.CV

Abstract: Due to the recent success of diffusion models, text-to-image generation is becoming increasingly popular and achieves a wide range of applications. Among them, text-to-image editing, or continuous text-to-image generation, attracts lots of attention and can potentially improve the quality of generated images. It's common to see that users may want to slightly edit the generated image by making minor modifications to their input textual descriptions for several rounds of diffusion inference. However, such an image editing process suffers from the low inference efficiency of many existing diffusion models even using GPU accelerators. To solve this problem, we introduce Fast Image Semantically Edit (FISEdit), a cached-enabled sparse diffusion model inference engine for efficient text-to-image editing. The key intuition behind our approach is to utilize the semantic mapping between the minor modifications on the input text and the affected regions on the output image. For each text editing step, FISEdit can automatically identify the affected image regions and utilize the cached unchanged regions' feature map to accelerate the inference process. Extensive empirical results show that FISEdit can be $3.4\times$ and $4.4\times$ faster than existing methods on NVIDIA TITAN RTX and A100 GPUs respectively, and even generates more satisfactory images.

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Accelerating Text-to-Image Editing via Cache-Enabled Sparse Diffusion Inference

The paper "Accelerating Text-to-Image Editing via Cache-Enabled Sparse Diffusion Inference" addresses the computational inefficiencies inherent in text-to-image editing using diffusion models. The proposed system, named Fast Image Semantically Edit (FISEdit), innovates by employing a novel cache-enabled sparse diffusion inference engine to expedite the text-to-image editing process. This paper is especially relevant given the widespread adoption and computational demands of diffusion models for realistic image generation, which require significant computing resources, even with the assistance of GPU accelerators.

Contributions and Methodology

The primary contribution of this paper is the development of FISEdit, a framework specifically designed for efficient minor image editing tasks. Central to the method is an intuitive understanding of the semantic relationships between textual modifications and alterations in the generated imagery. Two main technical challenges are addressed: detecting the regions in the image that are affected by textual changes and optimizing computational resources by focusing only on these regions.

FISEdit's architecture incorporates a distinct mask generation mechanism to identify areas within images that necessitate updates. This is achieved by quantifying the correspondence between modifications in textual inputs and the corresponding spatial changes in the image, thereby generating a mask that captures regions with significant updates. These insights are then used to develop a sparse inference engine that recalculates only the feature maps related to affected regions, while cached data optimizes the rest of the image generation process. This technique not only cuts down computational overhead but also accelerates the editing process substantially.

Empirical Evaluation

Through comprehensive empirical evaluations, FISEdit demonstrates a performance improvement of 3.4×3.4\times on NVIDIA A100 GPUs and 4.4×4.4\times on NVIDIA TITAN RTX GPUs as compared to existing text-to-image editing methodologies. The paper underscores that this accelerated performance does not compromise the quality of the generated images, achieving high fidelity to the text prompts while ensuring minimal computational requirements for unaffected image regions.

Practical and Theoretical Implications

Practically, FISEdit offers significant improvements in the speed and efficiency of image editing tasks, making real-time applications more feasible while reducing the operational cost of model deployments. Theoretically, this work extends the understanding of semantic modification implications in image generation, suggesting a path towards more intelligent and resource-efficient generative models.

Future Considerations

The potential for future work includes extending FISEdit's applicability to higher-resolution images, which were identified as a limitation in low-resolution contexts. Moreover, the exploration of integrating FISEdit with larger-scale text-to-image services, where semantic changes could dynamically update pre-existing cached data for rapid inference, represents an exciting avenue for broadening the system's deployed impact. Researchers may also investigate further optimizations in diffusion model architectures or adopt similar sparse computation strategies in related generative models like GANs and VAEs.

In conclusion, this paper provides a strategically important contribution to the field of text-to-image generation, offering an efficient solution that balances computational efficiency with the semantic accuracy of image edits. These advances could serve as a foundation for subsequent innovations in diffusion model optimizations and broader AI applications.

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Authors (5)
  1. Zihao Yu (24 papers)
  2. Haoyang Li (95 papers)
  3. Fangcheng Fu (31 papers)
  4. Xupeng Miao (37 papers)
  5. Bin Cui (165 papers)
Citations (4)
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