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![AINativeF Avatar](https://lunarcrush.com/gi/w:24/cr:twitter::1795402815298486272.png) AI Native Foundation [@AINativeF](/creator/twitter/AINativeF) on x 2018 followers
Created: 2025-07-24 00:51:11 UTC

X. Upsample What Matters: Region-Adaptive Latent Sampling for Accelerated Diffusion Transformers

🔑 Keywords: Diffusion transformers, Image and video generation, Region-Adaptive Latent Upsampling, Scalability, Temporal acceleration

💡 Category: Generative Models
 
🌟 Research Objective:
   - To propose Region-Adaptive Latent Upsampling (RALU) as a framework to accelerate inference in diffusion transformers for high-fidelity image and video generation without degrading image quality.

🛠️ Research Methods:
   - Implementation of a training-free, three-stage mixed-resolution sampling process involving low-resolution denoising, region-adaptive upsampling for artifact-prone areas, and full-resolution latent upsampling for detailed refinement.

💬 Research Conclusions:
   - RALU significantly reduces computation by achieving up to 7.0x speed-up on FLUX and 3.0x on Stable Diffusion X while maintaining image quality. It is also complementary to existing temporal acceleration methods, allowing for further reduction in inference latency. 

👉 Paper link:

![](https://pbs.twimg.com/media/GwlXKPoaUAAmPTu.png)

XX engagements

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**Related Topics**
[inference](/topic/inference)
[generative](/topic/generative)
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[Post Link](https://x.com/AINativeF/status/1948184149531218107)