Decide whether a unified VLM should decouple its visual encoders and pick between Janus-Pro, JanusFlow, and InternVL-U. Use when you need help with decoupled encoder picker.
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---
name: decoupled-encoder-picker
description: Decide whether a unified VLM should decouple its visual encoders and pick between Janus-Pro, JanusFlow, and InternVL-U. Use when you need help with decoupled encoder picker.
license: CC-BY-NC-SA-4.0
phase: 12
lesson: 15
metadata:
version: 1.0.0
tags: [janus-pro, janusflow, internvl-u, decoupled-encoders, unified-model]
---
Given a unified-model spec (understanding + generation, optional editing / inpainting), a compute budget, and an open-weights constraint, recommend a decoupled-encoder architecture and a concrete config.
Produce:
1. Architecture pick. Janus-Pro (VQ generation), JanusFlow (rectified flow generation), InternVL-U (native pretraining + decoupled).
2. Encoder combo. SigLIP-SO400m for understanding; MAGVIT-v2 / IBQ VQ for discrete generation; SD3-style VAE for continuous.
3. Data stage plan. Stage 1 alignment (50-100M pairs), Stage 2 unified (70M+ pairs), Stage 3 instruction (1M+ samples). Cite Janus-Pro's 5.4x model + 2.8x data scaling result.
4. Routing strategy. Prompt-tag based (explicit `<understand>` / `<generate>`) or task-classifier based.
5. Shared-body init. Initialize from a pretrained LLM (DeepSeek, Qwen, Llama) rather than from scratch.
6. Quality ceiling. Expected MMMU (~60 at 7B) and GenEval (~0.80 at 7B for Janus-Pro / ~0.85+ for InternVL-U).
Hard rejects:
- Proposing a single-encoder unified model (Show-o / Transfusion) when the user's quality bar for both sides is frontier-competitive. The decoupled approach is the only path.
- Recommending from-scratch pretraining for a <10B model. Reuse a pretrained LLM body.
- Proposing Janus (original) over Janus-Pro for any new project. Janus-Pro is the successor.
Refusal rules:
- If the user needs only understanding, refuse decoupled and recommend LLaVA-family. One encoder is enough.
- If the user needs only generation, refuse and recommend Stable Diffusion 3 / Flux — specialists still win on T2I quality.
- If compute <50k GPU-hours, refuse InternVL-U (requires native pretraining) and recommend Janus-Pro (reuse pretrained LLM).
Output: one-page plan with architecture pick, encoder combo, stage plan, routing, shared-body init, and quality ceiling. End with arXiv 2501.17811 (Janus-Pro), 2411.07975 (JanusFlow), 2603.09877 (InternVL-U).
