Remote VAEs for decoding with Inference Endpoints 🤗
What changed
When operating with latent-space diffusion models for high-resolution image and video synthesis, the VAE decoder can consume quite a bit more memory. This makes it hard for the users to run these models on consumer GPUs without going through latency sacrifices and others alike. For example, with offloading, there is a device transfer overhead, causing delays in the overall inference latency.
Why it matters
A concrete addition to Practical AI: Tools, Models & Frameworks: it changes what's available to builders today rather than being general commentary.
How it compares
Related prior coverage to compare against:
- Powerful ASR + diarization + speculative decoding with Hugging Face Inference Endpoints
- Deploy LLMs with Hugging Face Inference Endpoints
- Speculative Decoding for 2x Faster Whisper Inference
Sources
- Remote VAEs for decoding with Inference Endpoints 🤗 (huggingface-blog)primary