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---
license: other
license_name: fair-ai-public-license-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
language:
- en
base_model:
- Laxhar/noobai-XL-1.0
pipeline_tag: text-to-image
library_name: diffusers
tags:
- safetensors
- diffusers
- stable-diffusion
- stable-diffusion-xl
---
# V-Prediction Loss Weighting Test
## Notice
This repository contains personal experimental records. No guarantees are made regarding accuracy or reproducibility.
**These models are for verification purposes only and is not intended for general use.**
## Overview
This repository is a test project comparing different loss weighting schemes for Stable Diffusion v-prediction training.
## Environment
- [sd-scripts](https://github.com/kohya-ss/sd-scripts) dev branch
- Commit hash: [6adb69b] + Modified
## Test Cases
This repository includes test models using different weighting schemes:
1. **test_normal_weight**
- Baseline model using standard weighting
2. **test_edm2_weighting**
- New loss weighting scheme
- implementation by A
3. **test_min_snr_1**
- Baseline model with `--min_snr_gamma = 1`
4. **test_debias_scale-like**
- Baseline model with additional parameters:
- `--debiased_estimation_loss`
- `--scale_v_pred_loss_like_noise_pred`
5. **test_edm2_weight_new**
- New loss weighting scheme
- Implementation by madman404
## Training Parameters
For detailed parameters, please refer to the `.toml` files in each model directory.
Each model uses sdxl_train.py in each model directory
(and sdxl_train.py and t.py for test_edm2_weighting, sdxl_train.py andlossweightMLP.py for test_edm2_weight_new)
Common parameters:
- Samples: 57,373
- Epochs: 3
- U-Net only
- Learning rate: 3.5e-6
- Batch size: 8
- Gradient accumulation steps: 4
- Optimizer: Adafactor (stochastic rounding)
- Training time: 13.5 GPU hours (RTX4090) per trial
## Dataset Information
The dataset used for testing consists of:
- ~53,000 images extracted from danbooru2023 based on specific artist styles (approximately 300 artists)
- ~4,000 carefully selected danbooru images for standardization
**Note**: As this dataset is a subset of my regular training data focused on specific artists, the model's generalization might be limited. A wildcard file (wildcard_style.txt) containing the list of included artists is provided for reference.
### Tag Format
The training follows the tag format from [Kohaku-XL-Epsilon](https://huggingface.co/KBlueLeaf/Kohaku-XL-Epsilon):
`<1girl/1boy/1other/...>, <character>, <series>, <artists>, <general tags>, <quality tags>, <year tags>, <meta tags>, <rating tags>`
### Style Prompts
The following style prompts from Kohaku-XL-Epsilon might be compatible (untested):
```
ask \(askzy\), torino aqua, migolu, (jiu ye sang:1.1), (rumoon:0.9), (mizumi zumi:1.1)
```
```
ciloranko, maccha \(mochancc\), lobelia \(saclia\), migolu,
ask \(askzy\), wanke, (jiu ye sang:1.1), (rumoon:0.9), (mizumi zumi:1.1)
```
```
shiro9jira, ciloranko, ask \(askzy\), (tianliang duohe fangdongye:0.8)
```
```
(azuuru:1.1), (torino aqua:1.2), (azuuru:1.1), kedama milk,
fuzichoco, ask \(askzy\), chen bin, atdan, hito, mignon
```
```
ask \(askzy\), torino aqua, migolu
```
*This model card was written with the assistance of Claude 3.5 Sonnet.* |