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