description: <p>🌟 Trained specifically for style mixing on Safe For Work (<strong>SFW</strong>) dataset.</p><p><img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/fc81a78b-b565-45cb-80b8-f996963b0410/width=525/fc81a78b-b565-45cb-80b8-f996963b0410.jpeg" /></p><p>🔹 <strong>Text Encoder (strongly reliant on U-Net architecture)</strong>:</p><p><img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/067c1ef3-cf14-49ef-b4d2-152e6c69cc03/width=525/067c1ef3-cf14-49ef-b4d2-152e6c69cc03.jpeg" /></p><p></p><p>🔹 <strong>Lora Model</strong>:</p><ul><li><p>Trained using the CivitAI trainer.</p></li><li><p>Underwent training for 16 epochs.</p></li><li><p>Performance: Lora showcases stable diffusion in XL settings.</p></li></ul><p>🔹 <strong>Lycoris Models</strong>:</p><ul><li><p>Configured using <a target="_new" rel="ugc" href="https://github.com/KohakuBlueleaf/LyCORIS/blob/main/example_configs/preset_configs/example.toml">this preset</a> from LyCORIS, but with our custom tweaks.</p></li><li><p>Trained on 8xA6000 GPUs.</p></li><li><p>Underwent 20-40 epochs with a batch size of 20.</p></li><li><p>Module Type Breakdown:</p><ul><li><p>LohaModule: 176</p></li><li><p>LoConModule: 150</p></li><li><p>FullModule: 26</p></li><li><p>LokrModule: 700</p></li></ul></li></ul><p>🏆 <strong>Comparison</strong>:</p><p><img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/f8d5876e-fa9b-4ad9-9d15-c361e48e0c35/width=525/f8d5876e-fa9b-4ad9-9d15-c361e48e0c35.jpeg" /></p><ul><li><p>CivitAI's Lora stands out in our tests, but we've yet to explore its full potential in style mixing with other models.</p></li></ul><p>Feedback, collaboration, and tests are welcome! 🤟 🥃</p><p></p><p><img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/5abaf673-e7d1-4b5d-a8e2-0c5a042d7405/width=525/5abaf673-e7d1-4b5d-a8e2-0c5a042d7405.jpeg" /></p>
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modelVersions.description: <p>Text Encoder Only<br /></p><pre><code>{
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  }</code></pre>
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modelVersions.description: <p>Mixed training experiment using | lokr lora loha</p><p>More balanced dims (lower size)</p><p>module type table: {'<strong>LohaModule</strong>': 176, '<strong>LoConModule</strong>': 150, '<strong>FullModule</strong>': 26, '<strong>LokrModule</strong>': 700}</p><pre><code> @unet_target_module = [
  
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    "Upsample2D",
  
  ]
  
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  ]
  
  text_encoder_target_module = [
  
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    "CLIPMLP",
  
  ]
  
  [module_algo_map]
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    [module_algo_map.CLIPMLP]         # MLP Layer in TE
      algo = "lora"
  
  </code></pre>
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