Diffusion models
Source reports covering Diffusion models. Stories will appear when analysis and community reactions are ready.
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Source reports
How to Train a Gen AI Kick Drum Model on Your Old Linux Desktop with 6GB VRAM
tool
1 min read
Zhinit has developed a kick drum diffusion model that utilizes advanced training techniques to enhance audio generation. The model aims to produce high-quality kick drum sounds for music production.
DiffusionGemma: 4x Faster Text Generation
tool
5 min read
DiffusionGemma is a 26B Mixture of Experts (MoE) model that utilizes text diffusion for text generation. It can generate entire blocks of text simultaneously, achieving up to 4x faster performance on GPUs compared to traditional autoregressive Large Language Models.
1-Bit Bonsai Image 4B Image Generation for Local Devices
tool
6 min read
Bonsai Image 4B is a family of compact image-generation models designed for high-quality diffusion inference on local devices, including laptops and phones. The 1-bit variant utilizes binary transformer weights with an FP16 scaling factor, achieving maximum compression with 1.125 effective bits per weight.
Learning the Integral of a Diffusion Model
research
83 min read
Sampling from a diffusion model involves an iterative process where a denoiser estimates the tangent direction to a path through input space. Neural networks can be trained to directly predict the integral that transforms samples from a simple noise distribution into samples from a target distribution.
Introspective Diffusion Language Models
research
4 min read
Diffusion language models (DLMs) enable parallel token generation, potentially overcoming the sequential limitations of autoregressive (AR) decoding. However, DLMs currently underperform AR models in quality due to a lack of introspective consistency, where AR models align with their generated outputs.
Hamilton-Jacobi-Bellman Equation: Reinforcement Learning and Diffusion Models
research
16 min read
Richard Bellman's 1952 paper established the foundation for optimal control and reinforcement learning. His later work in the 1950s connected continuous-time systems to a previously published physical result from the 1840s, formulating the optimal condition as a partial differential equation (PDE).