Principle:Sgl project Sglang Visual Input Preparation
| Knowledge Sources | |
|---|---|
| Domains | Vision, Multimodal, Data_Processing |
| Last Updated | 2026-02-10 00:00 GMT |
Overview
A data preparation pattern that normalizes diverse visual input formats (URLs, files, base64, PIL images) into a unified representation for vision-language model inference.
Description
Vision-language models (VLMs) require images and videos as input alongside text. Visual input preparation handles the heterogeneity of input sources — images may come as URLs, local file paths, base64-encoded strings, PIL Image objects, or pre-processed tensors. The preparation pipeline normalizes these diverse formats into a unified internal representation that the model's visual processor can consume. For videos, frame extraction (via decord) converts video files into sequences of image frames.
Usage
Prepare visual inputs whenever using VLMs for image understanding, video analysis, or multimodal question answering. The preparation step is required before any multimodal inference call.
Theoretical Basis
Visual input preparation follows a normalization pipeline:
- Format detection — Identify input type (URL, path, base64, PIL, dict)
- Loading — Fetch/decode the raw image data
- Preprocessing — Resize, normalize, and convert to model-expected format
- Batching — Organize multiple images per request
Supported formats:
- URL strings (fetched via HTTP)
- Local file paths
- Base64-encoded strings
- PIL.Image objects
- Pre-processed dicts with format: "processor_output"
- Pre-computed embeddings with format: "precomputed_embedding"