# Q-Align — Qwen-based Visual Scorer

## Model Info

- **GitHub:** <https://github.com/Q-Future/Q-Align>
- **Paper:** ICML 2024 — "Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels"
- **HuggingFace:** Multiple checkpoints available
- **License:** Apache 2.0
- **Latest:** Q-ReAlign (Qwen3.5-VL backbone) — June 2026 update in IQA-PyTorch

## Architecture

- **Type:** Vision-Language Model (VLM) fine-tuned for visual scoring
- **Backbone:** Qwen LLM + vision encoder
- **Output:** Discrete score levels (text-defined)
- **Innovation:** Uses reinforcement learning to align model scoring with human perception

## Hardware Requirements

- **Mini (0.8B):** ~3 GB VRAM
- **Lite (4B):** ~10 GB VRAM
- **Pro (9B):** ~20 GB VRAM
- **Batch size:** 1-4 (VLMs are memory-intensive)

## What It's Good At

- **Human-aligned scoring:** Trained to match human subjective ratings
- **Explainable:** Can generate text descriptions of why an image scored high/low
- **Multi-task:** Can do IQA, IAA (Image Aesthetic Assessment), and VQA
- **Fine-tunable:** Can be adapted to specific domains (e.g., wedding photos)

## What It's Bad At

- **Slower inference:** VLM architecture is significantly slower than CLIP-based models
- **Higher VRAM:** Even the mini variant needs 3 GB; not ideal for batch processing thousands of images
- **Still no wedding-specific training:** General-purpose model
- **Complex setup:** Requires more dependencies and configuration than LAION predictors