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AI visionaries tend to be. A dreamer who can not dream. They are utterly engulfed within their own doctrine that their daring stabs at the truth amount to moving numbers on a plot.
Excited to introduce DiffuseNNX, a comprehensive JAX/Flax NNX-based library for diffusion and flow matching! It supports multiple diffusion / flow-matching frameworks, Autoencoders, DiT variants, and sampling algorithms. Repo: github.com/willisma/diffu… Delve into details below!…
Introducing RND1, the most powerful base diffusion language model (DLM) to date. RND1 (Radical Numerics Diffusion) is an experimental DLM with 30B params (3B active) with a sparse MoE architecture. We are making it open source, releasing weights, training details, and code to…
Introducing Paris - world's first decentralized trained open-weight diffusion model. We named it Paris after the city that has always been a refuge for those creating without permission. Paris is open for research and commercial use.
addition of SARA structural loss only (adversarial proved unstable) yields further improvements: 6.39 FID @ 100k! New SOTA set. Have started writing a paper/technical report. Will further experiment with my own version of CFM, TREAD, and dispersive loss.
INVAE + REG = 7.15 FID @ 100k steps Original SiT-XL/2 gets 8.3 FID @ 7M steps. So something like 70+ times faster training?
some ramtorch progress, now backward pass is also interleaved. using full duplex RX-TX of your PCI-E. now training model is no longer limited to VRAM. you can simply upgrade your system RAM to train big model locally now. next stuff, zero2-grad reduce github.com/lodestone-rock…
We found that visual foundation encoder can be aligned to serve as tokenizers for latent diffusion models in image generation! Our new paper introduces a new tokenizer training paradigm that produces a semantically rich latent space, improving diffusion model performance🚀🚀.
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