Hugging Face 推出 Remote VAE 功能：優化 Inference Endpoints 的圖像解碼與 VRAM 佔用
Original: Remote VAEs for decoding with Inference Endpoints 🤗
In the generative AI domain, latent diffusion models (such as Stable Diffusion, FLUX.1, etc.) operate in two main stages: first, denoising…
Hugging Face 宣布在 Inference Endpoints 中支援「Remote VAE」解碼功能。在運行 FLUX.1 或 Stable Diffusion 等大型圖像生成模型時，VAE 解碼通常會消耗大量 GPU 顯存（VRAM）。透過將 VAE 解碼步驟與潛在空間生成解耦並進行遠端處理，開發者可以在較小、較便宜的 GPU 上部署大型擴散模型，同時優化整體的推論吞吐量與頻寬傳輸。
In the generative AI domain, latent diffusion models (such as Stable Diffusion, FLUX.1, etc.) operate in two main stages: first, denoising and generation take place in the latent space, and then a Variational Autoencoder (VAE) decodes those latent features into the final pixel image. However, as model resolution and size increase, the VAE decoding step becomes extremely demanding on GPU VRAM, and can even cause out-of-memory (OOM) errors at the very last stage of generation.
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