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Home»ADOPTION NEWS»Chipmunk introduces an acceleration without training for the diffusion transformer.
ADOPTION NEWS

Chipmunk introduces an acceleration without training for the diffusion transformer.

By Crypto FlexsApril 23, 20252 Mins Read
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Chipmunk introduces an acceleration without training for the diffusion transformer.
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Ted Hirokawa
April 22, 2025 02:14

Chipmunk uses dynamic scarcity to accelerate the diffusion transformer to achieve significant speeds in video and images without further education.





Chipmunk, a new approach to accelerate the diffusion transformer, has been introduced together. This method uses dynamic heat sparse deltas without further training.

Dynamic scarcity for faster processing

Chipmunk dynamically calculates Spasdelta for these cache weights using the technology that caches the attention weight and MLP activation in the previous stage. This method allows Chipmunk to achieve up to 3.7 times faster than traditional methods on platforms like HUNYUANVIDEO. This method shows a 2.16x speed improvement in a particular configuration and a speed of up to 1.6 times faster image creation in flux.

Solve the diffusion transformer problem

The diffusion transformer (DIT) is widely used for video production, but high time and cost requirements have limited accessibility. Chipmunk solves these tasks by focusing on two major insights, a slowly changing characteristic of model activation and a unique scarcity. By reconstructing these activation to calculate cross -step delta, this method improves scarcity and efficiency.

Hardware recognition optimization

Chipmunk’s design includes a hardware recognition Sparsity pattern that optimizes dense shared memory tiles using non -continuous columns in global memory. Combined with a fast kernel, this approach enables significant calculation efficiency and speed improvement. This method uses the preference of the GPU for calculating a large block that is aligned with the default tile size for optimal performance.

Kernel optimization

To further improve performance, Chipmunk integrates some kernel optimization. This includes fast rare identification through custom CUDA kernels, efficient cache writing bags using the CUDA driver API, and continuous kernels of WARP SRECIALIZED. This innovation contributes to more efficient implementation, reducing the use of calculations and resources.

Participation in open source and community

Together, the.ai has released the OPEN-SOURCE community by revealing the resources of Chipmunk in Github to invite developers to explore and utilize these developments. This initiative is part of a wide range of efforts to accelerate model performance in a variety of architectures such as Flux-1.dev and DeepSeek R1.

To see more insights and technical documents, interesting readers can access the entire blog post together.

Image Source: Shutter Stock


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