# VAE

A machine-learning method for variational inference and learning in directed probabilistic models with continuous latent variables.

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## Temporally stable generative illumination with a one-step diffusion model

DevFeed: [Temporally stable generative illumination with a one-step diffusion model](<https://devfeed.tech/articles/temporally-stable-generative-illumination-with-a-one-step-diffusion-model-15050.md>)

Original publisher: [Read original article](<https://gpuopen.com/learn/temporally-stable-generative-illumination/>)

Author: SungYe Kim; Harish Anand; Alexandr Kuznetsov; Wojciech Uss; Wojciech Kaliński; Rama Harihara

Published: 2026-09-09T13:00:00Z

Content type: article

Language: en

Sources: [AMD GPUOpen](<https://devfeed.tech/sources/amd-gpuopen.md>)

Topics: [real-time rendering](<https://devfeed.tech/topics/real-time-rendering.md>), [VAE](<https://devfeed.tech/topics/vae.md>)

Tags: [arr-group](<https://devfeed.tech/tags/arr-group.md>), [article-release](<https://devfeed.tech/tags/article-release.md>), [diffusion](<https://devfeed.tech/tags/diffusion.md>), [generation](<https://devfeed.tech/tags/generation.md>), [gi](<https://devfeed.tech/tags/gi.md>), [inference](<https://devfeed.tech/tags/inference.md>), [lighting](<https://devfeed.tech/tags/lighting.md>), [ml](<https://devfeed.tech/tags/ml.md>), [model](<https://devfeed.tech/tags/model.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [quality](<https://devfeed.tech/tags/quality.md>), [ray-tracing](<https://devfeed.tech/tags/ray-tracing.md>), [raytracing](<https://devfeed.tech/tags/raytracing.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [real-time-rendering](<https://devfeed.tech/tags/real-time-rendering.md>), [research](<https://devfeed.tech/tags/research.md>), [white-paper](<https://devfeed.tech/tags/white-paper.md>)

### AI overview

The article presents a single-step latent diffusion method for real-time global illumination. It conditions image generation on scene signals and lighting hints, and uses a Temporal VAE decoder with motion-vector reprojection to improve temporal stability and reduce flicker.

### Source excerpt

A generative method for real-time global illumination using a single-step latent diffusion model, delivering stable, high-quality lighting without costly iterative processing.

## Remote VAEs for decoding with Inference Endpoints 🤗

DevFeed: [Remote VAEs for decoding with Inference Endpoints 🤗](<https://devfeed.tech/articles/remote-vaes-for-decoding-with-inference-endpoints-7456.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/remote_vae>)

Author: hlky; Sayak Paul

Published: 2025-02-24T00:00:00Z

Content type: tutorial

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [VAE](<https://devfeed.tech/topics/vae.md>), [inference-endpoints](<https://devfeed.tech/topics/inference-endpoints.md>), [diffusers](<https://devfeed.tech/topics/diffusers.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [Inference Performance](<https://devfeed.tech/topics/inference-performance.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [Deadlock](<https://devfeed.tech/topics/deadlock.md>)

Tags: [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [concurrency](<https://devfeed.tech/tags/concurrency.md>), [cpu](<https://devfeed.tech/tags/cpu.md>), [data](<https://devfeed.tech/tags/data.md>), [diffusers](<https://devfeed.tech/tags/diffusers.md>), [diffusion](<https://devfeed.tech/tags/diffusion.md>), [generate](<https://devfeed.tech/tags/generate.md>), [generation](<https://devfeed.tech/tags/generation.md>), [getting-started](<https://devfeed.tech/tags/getting-started.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [hdd](<https://devfeed.tech/tags/hdd.md>), [inference](<https://devfeed.tech/tags/inference.md>), [inference-endpoints](<https://devfeed.tech/tags/inference-endpoints.md>), [nvme](<https://devfeed.tech/tags/nvme.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [queue](<https://devfeed.tech/tags/queue.md>), [remote](<https://devfeed.tech/tags/remote.md>), [tensors](<https://devfeed.tech/tags/tensors.md>), [time](<https://devfeed.tech/tags/time.md>), [vae](<https://devfeed.tech/tags/vae.md>)

### AI overview

This article presents an experimental approach for decoding latent-space diffusion outputs with remote VAEs hosted on Inference Endpoints. It explains how remote decoding can reduce consumer GPU memory pressure, avoid the quality loss associated with tiled decoding, and improve concurrency by queueing generation requests. The article includes setup and usage examples for random tensors and pipelines involving SD v1.5, Flux, and HunyuanVideo.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Deep Probabilistic Modelling with Gaussian Processes #NIPS2017

DevFeed: [Deep Probabilistic Modelling with Gaussian Processes #NIPS2017](<https://devfeed.tech/articles/deep-probabilistic-modelling-with-gaussian-processes-nips2017-40106.md>)

Original publisher: [Read original article](<https://korbonits.com/blog/2017-12-04-nips-tutorials-dgp/>)

Published: 2017-12-04T12:00:00Z

Content type: tutorial

Language: en

Sources: [Alex Korbonits](<https://devfeed.tech/sources/alex-korbonits.md>)

Topics: [Tutorial](<https://devfeed.tech/topics/tutorial.md>), [VAE](<https://devfeed.tech/topics/vae.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Deep learning](<https://devfeed.tech/topics/deep-learning.md>), [Neural Network](<https://devfeed.tech/topics/neural-network.md>), [NeurIPS](<https://devfeed.tech/topics/neurips.md>)

Tags: [analysis](<https://devfeed.tech/tags/analysis.md>), [bias](<https://devfeed.tech/tags/bias.md>), [conference](<https://devfeed.tech/tags/conference.md>), [data](<https://devfeed.tech/tags/data.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [gaussian](<https://devfeed.tech/tags/gaussian.md>), [inference](<https://devfeed.tech/tags/inference.md>), [modelling](<https://devfeed.tech/tags/modelling.md>), [neural-network](<https://devfeed.tech/tags/neural-network.md>), [neurips](<https://devfeed.tech/tags/neurips.md>), [probabilistic](<https://devfeed.tech/tags/probabilistic.md>), [research](<https://devfeed.tech/tags/research.md>), [supervised-learning](<https://devfeed.tech/tags/supervised-learning.md>), [theory](<https://devfeed.tech/tags/theory.md>), [tutorial](<https://devfeed.tech/tags/tutorial.md>), [unsupervised-learning](<https://devfeed.tech/tags/unsupervised-learning.md>), [videos](<https://devfeed.tech/tags/videos.md>)

### AI overview

Lecture notes from a NeurIPS 2017 tutorial introduce deep probabilistic modelling with Gaussian processes, covering probabilistic neural networks, uncertainty, graphical models, and the computational challenge of inference.

### Source excerpt

Lecture notes from Neil Lawrence's NIPS 2017 tutorial on deep probabilistic modelling with Gaussian processes -- from GPs to deep GPs and variational inference.