Instructions to use Felldude/SDXL_HDR_VAE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Felldude/SDXL_HDR_VAE with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Felldude/SDXL_HDR_VAE", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
NOTE Encoder was not trained this VAE will likely not effect training in anyway
Overview
This repository contains evaluation results for an HDR VAE (High Dynamic Range Variational Autoencoder) designed for SDXL image reconstruction workflows.
The HDR VAE is evaluated against the BASE SDXL VAE using a multi-domain reconstruction analysis covering:
- perceptual similarity
- structural energy preservation
- color distribution retention
- photometric stability
- channel-level color drift
The objective is not only to measure pixel reconstruction accuracy, but to evaluate how well the VAE preserves the complete visual signal of an image through the encode/decode pipeline.
Photometric Stability
Measured through calibrated marker regions:
- brightness bias
- contrast gain
- RGB channel drift
These evaluate whether reconstruction maintains luminance and color relationships.
Results Summary
BASE SDXL VAE vs HDR VAE
| Metric | BASE SDXL VAE | HDR VAE |
|---|---|---|
| LPIPS โ | 0.0321 | 0.0593 |
| Gradient Energy Ratio | 0.792 | 1.167 |
| Color Support Ratio | 0.951 | 0.968 |
| Brightness Bias โ | 0.00101 | 0.00030 |
| Contrast Error โ | 0.00928 | 0.00439 |
| RGB Drift โ | 0.1608 | 0.1035 |
Interpretation
The BASE SDXL VAE achieves lower LPIPS error, indicating stronger agreement in learned perceptual feature space. Its reconstruction behavior favors smooth perceptual similarity.
The HDR VAE preserves more measurable image information:
- higher structural energy retention
- improved color support preservation
- stronger contrast stability
- reduced channel drift
- improved photometric consistency
The HDR VAE behaves as a more information-preserving reconstruction operator, maintaining image characteristics that are often reduced during standard VAE compression.
Design Goal
The HDR VAE is designed to improve reconstruction fidelity in areas where standard VAEs commonly lose information:
- fine texture
- high-frequency detail
- local contrast
- color variation
- HDR-like tonal relationships
Rather than optimizing only for perceptual closeness, the HDR VAE emphasizes preservation of the original image signal through the latent representation.
Conclusion
The evaluation demonstrates two different reconstruction profiles:
BASE SDXL VAE
- lower LPIPS
- smoother reconstruction behavior
- stronger perceptual averaging
HDR VAE
- improved structural retention
- improved color preservation
- improved photometric accuracy
- greater signal preservation
The HDR VAE provides a reconstruction profile optimized for users requiring higher information retention, improved detail preservation, and more stable image characteristics through SDXL latent encoding and decoding.
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