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license: bigscience-openrail-m
base_model:
- MiniMaxAI/MiniMax-H3-Image
---
**NOTE Encoder was Frozen Using this VAE for Training should not effect outcome**
## Overview
This repository contains evaluation results for a **MiniMax Image HDR VAE (High Dynamic Range Variational Autoencoder)** designed for MiniMax-H3 image reconstruction and enhancement workflows.
The HDR VAE is evaluated against the **BASE MiniMax Image VAE** using a multi-domain reconstruction analysis covering:
* perceptual similarity
* structural energy enhancement
* color distribution changes
* saturation enhancement
* sharpness and high-frequency detail
* channel-level color differences
The objective is not only to measure pixel reconstruction accuracy, but to evaluate how the VAE enhances image characteristics through the encode/decode pipeline.
### Image Enhancement
Measured through reconstruction comparisons:
* RGB pixel deviation
* saturation changes
* gradient energy
* sharpness retention
* contrast gain
* high-frequency energy
* perceptual color difference (DeltaE)
These evaluate how reconstruction changes image detail, color intensity, sharpness, and structural information.
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# Results Summary
## BASE MiniMax Image VAE vs HDR VAE
| Metric | BASE MiniMax VAE | HDR VAE |
| ----------------------------- | ---------------: | ------: |
| PSNR ↑ | **32.4389** | 28.4966 |
| SSIM ↑ | 0.87133 | **0.87810** |
| RGB MAE ↓ | **0.015623** | 0.028214 |
| Saturation MAE ↓ | **0.034885** | 0.091786 |
| Gradient Energy Retention | 0.98014 | **1.05029** |
| Sharpness Retention | **0.96952** | 0.91151 |
| Contrast Retention | 1.01329 | 1.00179 |
| High-Frequency Retention | 0.96159 | **1.04110** |
| DeltaE ↓ | **2.7502** | 7.0100 |
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# Interpretation
The **BASE MiniMax Image VAE** achieves lower pixel reconstruction error and DeltaE, indicating closer numerical agreement with the original input.
The **HDR VAE** demonstrates a different reconstruction profile:
* increased gradient energy
* increased high-frequency energy
* higher structural similarity
* stronger saturation changes
* enhancement-oriented reconstruction behavior
The HDR VAE behaves as an enhancement-oriented reconstruction operator rather than a pixel-preservation model, producing measurable changes to image characteristics during reconstruction.
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# Design Goal
The HDR VAE is designed to improve image characteristics that standard VAEs commonly soften or reduce:
* fine texture
* high-frequency detail
* local contrast
* color variation
* edge definition
* perceived sharpness
Rather than optimizing only for pixel-level similarity, the HDR VAE emphasizes image enhancement through decoder fine-tuning.
---
# Conclusion
The evaluation demonstrates two different reconstruction profiles:
**BASE MiniMax Image VAE**
* lower PSNR error
* lower RGB reconstruction error
* lower DeltaE
* stronger numerical color preservation
* more faithful pixel reconstruction
**HDR MiniMax Image VAE**
* higher SSIM
* increased gradient energy
* increased high-frequency energy
* enhancement-oriented reconstruction
* greater image characteristic variation
The HDR VAE provides an enhancement-oriented reconstruction profile for users seeking increased structural information, richer image characteristics, and enhanced visual detail rather than strict pixel-perfect reconstruction. |