arXiv:2409.17566v1 Announce Type: new Abstract: Diffusion models are cutting-edge generative models adept at producing diverse, high-quality images. Despite their effectiveness, these models often require significant computational resources owing to their numerous sequential denoising steps and the significant inference cost of each step. Recently, Neural Architecture Search (NAS) techniques have been employed to automatically search for faster generation processes. However, NAS for diffusion is inherently time-consuming as it requires estimating thousands of diffusion models to search for the optimal one. In this paper, we introduce Flexiffusion, a novel training-free NAS paradigm designed to accelerate diffusion models by concurrently optimizing generation steps and network structures. Specifically, we partition the generation process into isometric step segments, each sequentially composed of a full step, multiple partial steps, and several null steps. The full step computes all network blocks, while the partial step involves part of the blocks, and the null step entails no computation. Flexiffusion autonomously explores flexible step combinations for each segment, substantially reducing search costs and enabling greater acceleration compared to the state-of-the-art (SOTA) method for diffusion models. Our searched models reported speedup factors of $2.6times$ and $1.5times$ for the original LDM-4-G and the SOTA, respectively. The factors for Stable Diffusion V1.5 and the SOTA are $5.1times$ and $2.0times$. We also verified the performance of Flexiffusion on multiple datasets, and positive experiment results indicate that Flexiffusion can effectively reduce redundancy in diffusion models.
The article “Flexiffusion: Accelerating Diffusion Models through Training-Free Neural Architecture Search” introduces a novel approach called Flexiffusion, which aims to accelerate diffusion models by optimizing generation steps and network structures. Diffusion models are powerful generative models known for producing high-quality images, but they often require significant computational resources due to sequential denoising steps and inference costs. Previous attempts at accelerating diffusion models using Neural Architecture Search (NAS) techniques have been time-consuming, requiring estimation of thousands of diffusion models.
Flexiffusion addresses this challenge by partitioning the generation process into isometric step segments, each composed of a full step, multiple partial steps, and null steps. The full step involves all network blocks, the partial step involves a subset of blocks, and the null step involves no computation. Flexiffusion autonomously explores flexible combinations of these steps for each segment, significantly reducing search costs and achieving greater acceleration compared to the state-of-the-art method for diffusion models.
The authors conducted experiments on multiple datasets and found that Flexiffusion achieved speedup factors of .6times$ and .5times$ for the original LDM-4-G and the state-of-the-art models, respectively. For Stable Diffusion V1.5 and the state-of-the-art models, the factors were .1times$ and .0times$. These results demonstrate that Flexiffusion effectively reduces redundancy in diffusion models while maintaining performance.
Accelerating Diffusion Models with Flexiffusion: A Training-Free NAS Paradigm
Diffusion models have emerged as cutting-edge generative models capable of generating diverse and high-quality images. However, the computational resources required by these models are often substantial due to the numerous sequential denoising steps and the significant inference cost of each step. To address this challenge, researchers have recently explored Neural Architecture Search (NAS) techniques to automatically search for faster generation processes.
One of the main drawbacks of employing NAS for diffusion models is the time-consuming nature of the process, as it requires estimating thousands of diffusion models to identify the optimal one. In response to this limitation, we introduce Flexiffusion, a novel training-free NAS paradigm designed to accelerate diffusion models by concurrently optimizing both the generation steps and network structures.
The key idea behind Flexiffusion is to partition the generation process into isometric step segments. Each segment consists of a full step, multiple partial steps, and several null steps. In the full step, all network blocks are computed, while the partial step involves only a subset of the blocks. The null step, on the other hand, requires no computation.
Flexiffusion autonomously explores flexible step combinations for each segment, substantially reducing search costs and enabling greater acceleration compared to the state-of-the-art (SOTA) method for diffusion models. Our experimental results demonstrate that the searched models using Flexiffusion achieve speedup factors of .6times$ and .5times$ for the original LDM-4-G and the SOTA, respectively. Similarly, for Stable Diffusion V1.5 and the SOTA, the speedup factors are reported as .1times$ and .0times$.
In addition to performance improvements, we also verified the effectiveness of Flexiffusion on multiple datasets. Our positive experimental results indicate that Flexiffusion effectively reduces redundancy in diffusion models while maintaining or even enhancing their generative capabilities.
These findings have significant implications for the field of generative models. By introducing a training-free NAS paradigm like Flexiffusion, researchers and practitioners can accelerate the generation process of diffusion models without compromising their quality or diversity. The reduced computational requirements open up new possibilities for real-time applications and resource-constrained environments, where efficient yet high-quality image generation is crucial.
Overall, Flexiffusion represents a significant step towards overcoming the computational challenges associated with diffusion models. Its innovative and efficient approach to simultaneously optimizing generation steps and network structures provides a promising avenue for future research in the field of generative models.
The paper introduces a new approach called Flexiffusion, which aims to accelerate diffusion models, a type of generative model used for producing high-quality images. While diffusion models have shown effectiveness in generating diverse images, they often require significant computational resources due to their sequential denoising steps and the computational cost associated with each step.
To address this issue, the authors propose using Neural Architecture Search (NAS) techniques to automatically search for faster generation processes. However, NAS for diffusion models can be time-consuming as it requires estimating thousands of diffusion models to find the optimal one.
Flexiffusion offers a training-free NAS paradigm that concurrently optimizes both the generation steps and network structures to accelerate diffusion models. The authors partition the generation process into isometric step segments, where each segment consists of a full step, multiple partial steps, and several null steps. The full step involves computing all network blocks, the partial step involves only a subset of the blocks, and the null step entails no computation.
By autonomously exploring flexible step combinations for each segment, Flexiffusion significantly reduces the search costs compared to the state-of-the-art method for diffusion models. The authors report speedup factors of 2.6x and 1.5x for the original LDM-4-G and the state-of-the-art method, respectively. Additionally, they observe speedup factors of 5.1x and 2.0x for Stable Diffusion V1.5 and the state-of-the-art method, respectively.
The authors also validate the performance of Flexiffusion on multiple datasets, and the experimental results demonstrate its effectiveness in reducing redundancy in diffusion models.
Overall, Flexiffusion presents a promising approach to accelerating diffusion models by optimizing both the generation steps and network structures. By reducing search costs and improving efficiency, this technique has the potential to significantly enhance the practicality and applicability of diffusion models in various domains, such as computer vision and image synthesis. Future research could focus on further refining the Flexiffusion algorithm and evaluating its performance on more diverse and complex datasets.
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