Efficient On-Device Text-to-Image Generation with EdgeFusion

Konsep Inti

EdgeFusion is a method that optimizes Stable Diffusion models for efficient execution on resource-limited devices like Neural Processing Units (NPUs). It employs a compact Stable Diffusion variant, advanced distillation techniques, and specialized deployment optimizations to enable high-quality text-to-image generation in just a few steps and under one second on edge devices.

Abstrak

The paper introduces EdgeFusion, a method that advances the field of text-to-image (T2I) synthesis by optimizing Stable Diffusion (SD) models for efficient execution on resource-limited devices.
Key highlights:

EdgeFusion starts with a compact SD variant, BK-SDM, and significantly improves its generation performance by leveraging high-quality image-text pairs from synthetic datasets.
It refines the step distillation process of Latent Consistency Model (LCM) through empirical practices, achieving high-quality few-step inference.
For deployment, EdgeFusion adopts model-level tiling, quantization, and graph optimization to generate a 512x512 image under one second on Samsung Exynos NPU.
Extensive experiments demonstrate that EdgeFusion can produce photorealistic, text-aligned images in just 2 or 4 denoising steps, with a 10.3x speedup compared to the original SD-v1.4 on GPU.
The paper makes several key contributions:

Developing an advanced distillation approach that leverages a high-quality teacher model and specialized training data to enhance the performance of a compact SD variant.
Introducing techniques to efficiently deploy the optimized SD model on resource-constrained edge devices, including model-level tiling and mixed-precision quantization.
Comprehensive evaluation and benchmarking of EdgeFusion, showcasing its ability to generate high-quality images in just a few steps while achieving real-time inference on NPUs.

Statistik

EdgeFusion can generate 512x512 images in under 1 second on Samsung Exynos NPU.
Compared to SD-v1.4, EdgeFusion achieves a 10.3x speedup on GPU inference.

Kutipan

"EdgeFusion enhances the model efficiency by employing Block-removed Knowledge-distilled SDM (BK-SDM), a foundational effort towards lightweight SD, and significantly improve its generation performance with superior image-text pairs from synthetic datasets."
"Through our thorough exploration of quantization, profiling, and on-device deployment, we achieve rapid generation of photo-realistic, text-aligned images in just two steps, with latency under one second on resource-limited edge devices."

Wawasan Utama Disaring Dari

EdgeFusion: On-Device Text-to-Image Generation

by Thibault Cas... pada arxiv.org 04-19-2024

https://arxiv.org/pdf/2404.11925.pdf

Pertanyaan yang Lebih Dalam

How can the techniques used in EdgeFusion be extended to optimize other types of generative models for deployment on edge devices

The techniques used in EdgeFusion can be extended to optimize other types of generative models for deployment on edge devices by focusing on several key strategies:

Architectural Reduction: Similar to how EdgeFusion employed a compact SD variant (BK-SDM) as a foundational effort towards lightweight SD, other generative models can benefit from architectural optimizations to reduce computational burden and memory requirements. This could involve exploring model pruning, knowledge distillation, and other techniques to streamline the model architecture.

Data Quality Enhancement: Improving the quality of synthetic image-text pairs used for training is crucial for model performance. By employing advanced data preprocessing techniques, such as data deduplication, image cropping optimization, and synthetic caption generation, the training data can be enriched to enhance the model's ability to generate high-quality outputs.

Deployment Optimization: EdgeFusion's deployment optimizations, such as model-level tiling, quantization, and graph optimization, can be applied to other generative models to enhance their efficiency on edge devices. By reducing memory access and optimizing computational resources, models can achieve rapid inference speeds while maintaining high performance.

Fine-tuning and Distillation: Leveraging advanced distillation processes, similar to the approach taken in EdgeFusion with Latent Consistency Model (LCM), can help in training compact models with superior performance. Fine-tuning the student model with high-quality data and utilizing a strong teacher model can lead to improved generation quality with fewer inference steps.

By incorporating these strategies and adapting them to the specific characteristics of different generative models, it is possible to optimize their deployment on edge devices for efficient and rapid text-to-image generation.

What are the potential challenges and trade-offs in further improving the quality of synthetic image-text pairs used for training the compact SD model

Improving the quality of synthetic image-text pairs used for training the compact SD model involves addressing several potential challenges and trade-offs:

Data Relevance and Quality: One challenge is ensuring that the synthetic data accurately represents the real-world scenarios the model will encounter. Balancing the generation of diverse and relevant image-text pairs while maintaining high quality can be a trade-off, as increasing diversity may introduce noise or irrelevant information.

Data Quantity vs. Quality: There is a trade-off between dataset size and data quality. While larger datasets can provide more diverse samples for training, ensuring the quality of each sample becomes challenging. Curating a smaller dataset with high-quality samples may lead to better model performance but could limit the model's exposure to diverse data.

Caption Informativeness: Generating informative and relevant captions for synthetic images is crucial for text-image alignment. However, ensuring that the generated captions accurately describe the visual content without introducing biases or inaccuracies can be a challenge.

Manual Data Curation Effort: Manually curating synthetic data to improve image quality and text-image alignment requires significant human effort. The trade-off lies in determining whether the benefits of manual curation justify the resources and time invested in the process.

By carefully navigating these challenges and trade-offs, it is possible to enhance the quality of synthetic image-text pairs, leading to improved model performance in text-to-image generation tasks.

How can the insights from EdgeFusion's deployment optimizations be applied to enhance the performance of other deep learning models on heterogeneous computing platforms

Insights from EdgeFusion's deployment optimizations can be applied to enhance the performance of other deep learning models on heterogeneous computing platforms by focusing on the following strategies:

Model-Level Tiling: Implementing a method similar to EdgeFusion's model-level tiling can help optimize memory access and computational resources for other deep learning models. By dividing the model into smaller segments and leveraging heterogeneous computing resources, models can achieve efficient inference on edge devices.

Quantization Techniques: Applying mixed-precision post-training quantization, as done in EdgeFusion with FP16 and INT8 quantization, can improve the efficiency of other deep learning models on NPUs. By optimizing the precision of weights and activations, models can achieve faster inference speeds without compromising performance.

Architectural Reduction: Similar to EdgeFusion's focus on reducing the computational burden through architectural optimizations, other deep learning models can benefit from techniques like model pruning, knowledge distillation, and architectural modifications tailored for deployment on heterogeneous computing platforms.

Fine-Tuning and Distillation: Leveraging advanced distillation processes, as demonstrated in EdgeFusion with Latent Consistency Model (LCM), can enhance the performance of other deep learning models. By fine-tuning models with high-quality data and utilizing strong teacher models, models can achieve rapid inference speeds and high-quality outputs on edge devices.

By incorporating these insights and adapting them to the specific requirements of different deep learning models, it is possible to optimize their performance on heterogeneous computing platforms for efficient and effective deployment.