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Diffusion is both a probabilistic hop and a transformer defining the parameters. As such, it's amenable to acceleration on hybrid systems of NN accelerator (Groq, Cerebras, Etched, Positron) + TSU TSUs are synergistic with the existing ecosystem!

"An hour of planning can save you 10 hours of doing." ✨📝 Planned Diffusion 📝 ✨ makes a plan before parallel dLLM generation. Planned Diffusion runs 1.2-1.8× faster than autoregressive and an order of magnitude faster than diffusion, while staying within 0.9–5% AR quality.

Yes, it turns out diffusion models can learn from feedback as effectively as language models do with RL! Tsinghua, NVIDIA, and Stanford introduced Diffusion Negative-aware FineTuning (DiffusionNFT), a new online reinforcement learning paradigm that finally makes RL practical for…

The Diffusion Duality unlock few-step generation in discrete diffusion language models via the underlying Gaussian diffusion

The Diffusion Duality "The arg max operation transforms Gaussian diffusion into Uniform-state diffusion" Adapts consistency distillation to diffusion language models, unlocking few-step generation by accelerating sampling by two orders of magnitude. Introduces a curriculum…

It’s finally happening!!! Diffusion is so much more satisfying than autoregressive for protein & DNA sequences that don’t really have directionality 🥹 Waiting for this to empirically land & replace BERT/one-step discrete diffusion for protein foundation models 👀

I wasnt paying attention on discrete diffusion past three months and shot myself on the foot. Multi-granularity Diffusion Modeling: arxiv.org/abs/2410.14157 Scaling language diffusion models: arxiv.org/abs/2410.18514 Infinity: arxiv.org/abs/2412.04431 MD4: arxiv.org/abs/2406.04329…

i was learning about how diffusion works in video generation...came across this og blog by @lilianweng ....detailed and clear explanation 🤩

Diffusion is annoying because for language model at least we got the objective (kinda) sorted out: just use CE. but its not even close for diffusion models. What you end up wanting is some mixture of CLIP + FID + (some reasoning /factual benchmarks) (that is vaguely aligned…

1/3) Proud to share: Dynamic Negative Guidance of Diffusion Models We found a nearly exact formula to guide the diffusion process away from a given class or prompt. The mixing parameter λ(t,x) is estimated iteratively during generation. Paper: arxiv.org/pdf/2410.14398

Diffusion models are the quintessential example of the asymmetry between going to disorder and coming back from it in a data genaration process. The former is white noise, the latter requires a modeling of every level of the signal from local patterns to compositions to…

Excited to share Diffusion-DPO, a method to directly align diffusion models to user preference. DPO-tuned SDXL obtains a 70% win rate over SDXL on PartiPrompts, a new SOTA for open source models! It is also effective at Learning from AI Feedback. arxiv.org/abs/2311.12908 (1/N)

𝗗𝗶𝗳𝗳𝘂𝘀𝗶𝗼𝗻 𝗺𝗼𝗱𝗲𝗹𝘀 generate data by progressively perturbing data to noise via Gaussian perturbations, then creating samples from noise via sequential denoising steps. 👇Required basics on the stochastic differential equation and its associated Probability Flow ODE.

Attention pépite ! Petite Sale de @MeyLouise vient de remporter le Prix Landerneau Polar 2023. À retrouver en version numérique #ebook sur NUMILOG 👉 tinyurl.com/m2tx8rnf #distributionnumerique #diffusionNumilog #PrixLanderneau #PetiteSale #LouiseMey #LeMasque @EditionsMasque

Au cœur de l'actualité sociale et politique avec la collection Doc en poche de la Documentation française @viepubliquefr, en version numérique #ebook👉 tinyurl.com/tdssmvrd #diffusionumerique #diffusionNumilog #viepublique #documentationfrançaise #docenpoche

Introducing Decision Diffuser, a conditional diffusion model that outperforms offline RL across standard benchmarks – using only generative modeling training! Decision Diffusers can also combine multiple constraints and skills at test-time. Website: anuragajay.github.io/decision-diffu… 1/5

Diffusion is just an easy-to-optimize way to give neural networks adaptive computation time. Makes sense then that diffusion models beat GANs, which only get one forward pass to generate an image. have to wonder what other ways there are to integrate for loops into NNs...

Diffusion models are powerful image generators, but they are built on two simple components: a function that degrades images by adding Gaussian noise, and a simple image restoration network for removing this noise.

Au cœur de l'actualité sociale et politique avec la collection Doc en poche de la Documentation française @viepubliquefr, en version numérique #ebook👉 tinyurl.com/tdssmvrd #diffusionumerique #diffusionNumilog #viepublique #documentationfrançaise #docenpoche

Attention pépite ! Petite Sale de @MeyLouise vient de remporter le Prix Landerneau Polar 2023. À retrouver en version numérique #ebook sur NUMILOG 👉 tinyurl.com/m2tx8rnf #distributionnumerique #diffusionNumilog #PrixLanderneau #PetiteSale #LouiseMey #LeMasque @EditionsMasque