# Alternatives Comparison & Summary

## Comparison Table

| Model | Type | Score Scale | Technical Quality | Aesthetic Quality | GPU Required | Speed (4K images) | Self-Hosted | License | Wedding-Specific |
|-------|------|-------------|-------------------|-------------------|--------------|-------------------|-------------|---------|------------------|
| **LAION V2 (CLIP)** | CLIP+Linear | 0-10 | No | Basic | Yes (~2GB) | ~5-10 min | Yes | MIT | No |
| **SigLIP V2.5** | SigLIP+Linear | 0-10 | No | Better | Yes (~3GB) | ~5-10 min | Yes | MIT | No |
| **MUSIQ** | Transformer | 0-100 | Yes | Combined | Yes (~4GB) | ~15-20 min | Yes | Apache 2.0 | No |
| **Q-Align Mini** | VLM (0.8B) | Text levels | Yes | Human-aligned | Yes (~3GB) | ~30-60 min | Yes | Apache 2.0 | No |
| **BRISQUE** | Handcrafted | Inverted | Yes | No | No (CPU) | ~10-20 min | Yes | Academic | No |
| **NIQE** | Handcrafted | Direct | Yes | No | No (CPU) | ~10-20 min | Yes | Academic | No |
| **FilterPixel DeepCull** | Cloud AI | 10 params | Yes | Genre-aware | N/A | ~5 min | No | Commercial | Yes |
| **Aftershoot** | Desktop AI | Stars | Yes | Genre-aware | Yes | ~10 min | Yes (local) | Commercial | Yes |

## Key Takeaways for Wedding Photo Sorting

1. **LAION Aesthetic Predictor V2 is the simplest starting point** — one-line Python API, MIT license, ~2 GB VRAM, fast inference. But it scores "aesthetic appeal," not "technical quality." It will NOT detect blur, out-of-focus shots, or bad exposure.

2. **Best approach: Combine models.** Use BRISQUE/NIQE for technical quality (blur/noise detection) + LAION V2 or MUSIQ for aesthetic scoring. A photo should be kept only if it passes BOTH thresholds.

3. **MUSIQ is the best single-model option** if you want one model that captures both aesthetic and technical quality. It's from Google Research, handles full-resolution images, and has Apache 2.0 license.

4. **SigLIP V2.5 is a modest improvement over LAION V2** — better on illustrations/art, but same fundamental limitations for real photography. Worth trying if you have BF16 GPU support.

5. **No open-source model understands wedding context.** None of these can distinguish a "peak moment" from a "transitional shot" or detect emotional content. For that, you need commercial tools like FilterPixel DeepCull (genre-specific AI) or Aftershoot (learning-based).

6. **For 4,000 wedding photos on a consumer GPU (RTX 4090):**
   - LAION V2 batch: ~5-10 minutes
   - MUSIQ batch: ~15-20 minutes
   - BRISQUE (CPU): ~10-20 minutes
   - Combined approach (LAION + BRISQUE): ~15-25 minutes total

7. **Bias warning:** LAION V2 has documented Western/cultural bias. For culturally diverse weddings (e.g., Indonesian, South Asian, African), scores may not accurately reflect the quality of ceremony shots, cultural attire, or traditional poses.

8. **Practical recommendation:** Start with LAION V2 + BRISQUE as a first-pass filter. Score all 4,000 images, set thresholds (e.g., LAION > 5.5 AND BRISQUE < 30), and manually review the borderline cases. This reduces 4,000 images to ~500-800 for manual review.

## Update Log

- **2026-08-23:** Initial research for wedding photo sorting use case (~4,000 images). Covered LAION V1/V2, SigLIP V2.5, MUSIQ, Q-Align, BRISQUE, NIQE, commercial tools (FilterPixel, Aftershoot, Imagen AI), and IQA-PyTorch toolbox.