---
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
- art
---
# V-Prediction Loss Weighting Test

## Notice
This repository contains personal experimental records. No guarantees are made regarding accuracy or reproducibility.

## Overview
This repository is a test project comparing different loss weighting schemes for Stable Diffusion v-prediction training, examining the effects of static weighting curves versus adaptive neural network-based weighting.

## Environment
- [sd-scripts](https://github.com/kohya-ss/sd-scripts) dev branch
  - Commit hash: [6adb69b] + Modified

## Test Cases

This repository includes test models using four different weighting schemes:

1. **test_normal_weight**
   - Baseline model using standard weighting

2. **test_edm2_weighting**
   - New loss weighting scheme

3. **test_min_snr_1(incomplete)**
   - Baseline model with `--min_snr_gamma = 1`

4. **test_debias_scale-like(incomplete)**
   - Baseline model with additional parameters:
     - `--debiased_estimation_loss`
     - `--scale_v_pred_loss_like_noise_pred`

## Training Parameters
For detailed parameters, please refer to the `.toml` files in each model directory.
Each model directory uses sdxl_train.py (and sdxl_train.py and t.py for edm2).

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