?? What is Trending in AI Research?: InstaFlow + ReLU vs. Softmax in Vision Transformers + OmnimatteRF + DeciDiffusion 1.0...
Asif Razzaq
AI Research Editor | CEO @ Marktechpost | 1 Million Monthly Readers and 56k+ ML Subreddit
Hey Folks!
This newsletter will discuss some cool AI research papers and AI tools. But before we start, we have included a small message from our sponsor.
?? Meet InstaFlow: A Novel One-Step Generative AI Model Derived from the Open-Source StableDiffusion (SD)
How can we generate high-quality text-to-image outputs without the computational overhead of multi-step sampling in diffusion models? This paper addresses this problem by introducing a novel method called "InstaFlow," which leverages Rectified Flow—a technique previously only used on small datasets. At the heart of Rectified Flow is the 'reflow' procedure that straightens probability flows and improves the noise-image relationship. Using this approach, the paper successfully transforms Stable Diffusion (SD) into an ultra-fast, one-step text-to-image generation model while maintaining high-quality outputs. The model achieves an FID (Frechet Inception Distance) score of 23.3 on MS COCO 2017-5k, significantly surpassing the previous state-of-the-art. With a larger 1.7B parameter network, the FID further improves to 22.4. The model is not only more accurate but also more time-efficient, producing an FID of 13.1 in just 0.09 seconds on MS COCO 2014-30k, outperforming competitors while being computationally less expensive.
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?? ReLU vs. Softmax in Vision Transformers: Does Sequence Length Matter? Insights from a Google DeepMind Research Paper
How can one maintain accuracy when substituting the attention softmax with a point-wise activation in vision transformers? This study reveals that the previously observed accuracy degradation can be alleviated by dividing by sequence length. By training vision transformers of varying sizes on ImageNet-21k, the researchers demonstrate that ReLU-attention, when adjusted this way, can match or even rival the performance of softmax-attention in scalability relative to compute resources.
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?? ?Researchers from the University of Maryland and Meta AI Propose OmnimatteRF: A Novel Video Matting Method that Combines Dynamic 2D Foreground Layers and a 3D Background Model
How can video matting methods better represent complicated, real-world scenes, especially when traditional techniques are limited to 2D background layers? This paper proposes OmnimatteRF, an innovative video matting approach that combines dynamic 2D foreground layers with a 3D background model. Unlike existing methods, which primarily focus on 2D background representations, OmnimatteRF leverages the power of 3D modeling to reconstruct complex scenes. The 2D layers are dedicated to capturing detailed information of foreground objects, while the 3D background model handles the intricacies of real-world environments. Through extensive experiments on various videos, the paper demonstrates that OmnimatteRF outperforms existing methods in terms of scene reconstruction quality.
?? ?Researchers from UCI and Zhejiang University Introduce Lossless Large Language Model Acceleration via Self-Speculative Decoding Using Drafting And Verifying Stages
How can Large Language Models (LLMs) be accelerated without compromising on output quality or requiring auxiliary models? This paper introduces "self-speculative decoding" as a novel inference scheme to address this. The method comprises two stages: drafting and verification. In the drafting phase, tokens are rapidly produced by skipping certain model layers, resulting in slightly lower-quality outputs. The verification phase then uses the full LLM to validate these tokens, ensuring the final result matches the original model's. Remarkably, this approach doesn't demand extra training or memory, making it an efficient plug-and-play solution. Testing with LLaMA-2 models showed up to 1.73× speed improvements.
?? Deci AI Unveils DeciDiffusion 1.0: A 820 Million Parameter Text-to-Image Latent Diffusion Model and 3x the Speed of Stable Diffusion
Deci AI introduces DeciDiffusion 1.0 – A New Approach To solve the text-to-image generation problem; a research team introduced DeciDiffusion 1.0, a groundbreaking model representing a significant leap forward in this domain. DeciDiffusion 1.0 builds upon the foundations of previous models but introduces several key innovations that set it apart. One of the key innovations is the substitution of the traditional U-Net architecture with the more efficient U-Net-NAS. This architectural change reduces the number of parameters while maintaining or even improving performance. The result is a model that is not only capable of generating high-quality images but also does so more efficiently in terms of computation. The model’s training process is also noteworthy. It undergoes a four-phase training procedure to optimize sample efficiency and computational speed. This approach is crucial for ensuring the model can generate images with fewer iterations, making it more practical for real-world applications.
What is Trending in AI Tools?
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