init: project structure with LAION scorer, conda setup, and research docs

2026-08-23 15:31:47 +00:00
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+# 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