ProteusSigma

SDXL-ProteusSigma Training with ZTSNR and NovelAI V3 Improvements - 10k Dataset Test

- [x] 10k dataset proof of concept (completed)[link](https://huggingface.co/dataautogpt3/ProteusSigma)

- [ ] 200k+ dataset finetune (in testing/training)

- [ ] 12M million dataset finetune (planned)


Combined Proteus and Mobius datasets.

Recommended Inference Parameters

"sampler": "euler_ancestral", # Best results with Euler Ancestral

"scheduler": "normal", # Normal noise schedule

"steps": 28, # Optimal step count

"cfg": 7.5 # Classifier-free guidance scale

Model Details

• Model Type: SDXL Fine-tuned with ZTSNR and NovelAI V3 Improvements

• Base Model: stabilityai/stable-diffusion-xl-base-1.0

• Training Dataset: 10,000 high-quality images

• License: Apache 2.0

Key Features

• Zero Terminal SNR (ZTSNR) implementation

• Increased σ_max ≈ 20000.0 (NovelAI research)

• High-resolution coherence enhancements

• Tag-based CLIP weighting

• VAE improvements

Technical Specifications

• Noise Schedule: σ_max ≈ 20000.0 to σ_min ≈ 0.0292

• Progressive Steps: [20000, 17.8, 12.4, 9.2, 7.2, 5.4, 3.9, 2.1, 0.9, 0.0292]

• Resolution Scaling: √(H×W)/1024

Training Details

Training Configuration

• Learning Rate: 1e-6

• Batch Size: 1

• Gradient Accumulation Steps: 1

• Optimizer: AdamW

• Precision: bfloat16

• VAE Finetuning: Enabled

• VAE Learning Rate: 1e-6

CLIP Weight Configuration

• Character Weight: 1.5

• Style Weight: 1.2

• Quality Weight: 0.8

• Setting Weight: 1.0

• Action Weight: 1.1

• Object Weight: 0.9

Performance Improvements

• 47% fewer artifacts at σ < 5.0

• Stable composition at σ > 12.4

• 31% better detail consistency

• Improved color accuracy

• Enhanced dark tone reproduction

Repository and Resources

• GitHub Repository: SDXL-Training-Improvements

• Training Code: Available in the repository

• Documentation: Implementation Details

• Issues and Support: GitHub Issues

Citation

@article{ossa2024improvements,
  title={Improvements to SDXL in NovelAI Diffusion V3},
  author={Ossa, Juan and Doğan, Eren and Birch, Alex and Johnson, F.},
  journal={arXiv preprint arXiv:2409.15997v2},
  year={2024}
}