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--- |
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license: openrail++ |
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language: |
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- en |
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library_name: diffusers |
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pipeline_tag: text-to-image |
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tags: |
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- text-to-image |
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base_model: |
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- stabilityai/stable-diffusion-xl-base-1.0 |
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--- |
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# Anime Stable Diffusion Model |
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A custom Stable Diffusion model fine-tuned for anime-style image generation, trained on a large dataset of anime images. |
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This is the first concept model for the entire series as I am spending more time filtering and processing the |
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larger dataset. Currently the model is still undertrained, while it can reflect certain notions, a lot of additional |
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improvements need to be done. |
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## Prompt |
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Danbooru style tagging. |
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Quality tag: Masterpiece, high quality, normal quality, low quality |
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Aesthetic tag: Very aesthetic, aesthetic, pleasent, unpleasent |
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Additional special tag: High resolution, elegant, artist: |
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| Rating Modifier | Rating Criterion | |
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| --------------- | ---------------- | |
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| - | general | |
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| - | sensitive | |
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| nsfw | questionable | |
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| nsfw | explicit | |
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Recommanded prompt order: Rating tag, quality tag, aesthetic tag, (additional tag), general tag |
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### Dataset Specifications |
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- Total Images: 172k |
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- General Training Set: 160k images |
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- Aesthetic Fine-tuning Set: 12k high-quality images |
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- Resolution: 1024x1024 |
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### Hardware Configuration |
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- GPUs: 2x NVIDIA RTX 6000 Ada |
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- Training Time: 16 days (General), 3 days (Aesthetic fine tune) |
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### Training Configuration |
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| Parameter | Value | Description | |
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|-----------|--------|-------------| |
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| Resolution | 1024x1024 | Training resolution | |
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| Batch Size | 8x2x2 | Effective batch size | |
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| Learning Rate | 5e-5 | Base learning rate | |
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| Text Encoder LR | 1e-5 | Learning rate for text encoder | |
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| Epochs | 10 | Total training epochs | |
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| Mixed Precision | FP16 | Training precision mode | |
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| Optimizer | AdamW8bit | Optimizer type | |
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### Advanced Settings |
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| Feature | Setting | Purpose | |
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|---------|---------|----------| |
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| Gradient Checkpointing | Enabled | Memory optimization | |
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| XFormers | Enabled | Attention optimization | |
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| Memory Efficient Attention | Enabled | Memory optimization | |
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| Bucket Resolution Steps | 128 | Dynamic resolution handling | |
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| Min Bucket Resolution | 512 | Minimum image size | |
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| Max Bucket Resolution | 4096 | Maximum image size | |
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| Noise Offset | 0.035 | Training stability | |
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| Min SNR Gamma | 5 | Signal-to-noise ratio control | |
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