description: <h1 id="heading-6"><strong>CivitAI Style Fusion</strong><span style="color:rgb(144, 146, 150)">🏆</span>LoRAs</h1><p><img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/70a8fd48-f04d-4fdd-a75f-4cba89031c41/width=525/70a8fd48-f04d-4fdd-a75f-4cba89031c41.jpeg" /></p><h1 id="heading-6"><img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/6b9e5fb0-0f44-4d32-8675-2b04b555762d/width=525/6b9e5fb0-0f44-4d32-8675-2b04b555762d.jpeg" /></h1><p><strong>Last update: </strong>🚀 <strong>CivitAI Lora5 32DIM Notebook with dataset </strong></p><p><img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/710152cc-d028-456c-ac17-db4a102a243a/width=525/710152cc-d028-456c-ac17-db4a102a243a.jpeg" /></p><p></p><p><strong>Last update: </strong>🚀 <strong>CivitAI Lora3 Configuration - Trained with CivitAI Trainer</strong></p><p>🚀 <em>Date: 2023-11-10 | Title: CivitAI_64_ALL</em></p><p></p><p>🔍 <strong>Key Specifications</strong>:</p><ul><li><p><strong>Resolution</strong>: 1024x1024</p></li><li><p><strong>Architecture</strong>: stable-diffusion-xl-v1-base/lora</p></li><li><p><strong>Network Dim/Rank</strong>: 64.0, <strong>Alpha</strong>: 1.0</p></li><li><p><strong>Module</strong>: networks.lora</p></li><li><p><strong>Learning Rates</strong>: UNet LR &amp; TE LR set to optimal levels</p></li><li><p><strong>Optimizer</strong>: Advanced AdamW8bit</p></li><li><p><strong>Epochs &amp; Training</strong>: Intensive 10 epochs with 576 batches</p></li></ul><p>📊 <strong>Model Stats</strong>:</p><ul><li><p><strong>UNet Weight</strong>: Mag - 7.602, Str - 0.0187</p></li></ul><p></p><pre><code>Resolution: 1024x1024 Architecture: stable-diffusion-xl-v1-base/lora
  Network Dim/Rank: 64.0 Alpha: 1.0
  Module: networks.lora
  Learning Rate (LR): 0.0005 UNet LR: 0.0005 TE LR: 5e-05
  Optimizer: bitsandbytes.optim.adamw.AdamW8bit(weight_decay=0.1)
  Scheduler: constant  Warmup steps: 0
  Epoch: 10 Batches per epoch: 576 Gradient accumulation steps: 1
  Train images: 2304 Regularization images: 0
  Multires noise iterations: 6.0 Multires noise discount: 0.3
  Min SNR gamma: 5.0 Zero terminal SNR: True Max grad norm: 1.0  Clip skip: 1
  Dataset dirs: 1
          [img] 576 images
  UNet weight average magnitude: 7.602270778898858
  UNet weight average strength: 0.018722912685324843
  Text Encoder (1) weight average magnitude: 2.7649271326702607
  Text Encoder (1) weight average strength: 0.009535635958680934
  Text Encoder (2) weight average magnitude: 2.6905091182810352
  Text Encoder (2) weight average strength: 0.007233532415344915</code></pre><p><img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/ffa14b04-02d8-482b-9172-92962a5fabba/width=525/ffa14b04-02d8-482b-9172-92962a5fabba.jpeg" /></p><p>Delve into FFusionAI's approach to AI-driven style synthesis with our newly released LoRA models. Each model has been developed using CivitAI's official trainer, ensuring precision and quality.</p><h3 id="heading-135"><strong>🛠️ LoRA Model Overview:</strong></h3><ul><li><p><strong>LoRA 1 - Lite Version:</strong> Designed for quick testing, this model utilizes a small dataset for swift style generation, operating with a 32-dimension capacity.</p></li><li><p><strong>LoRA 2 - Community Fusion:</strong> A robust model developed from over 500+ images, submitted by various users for the CivitAI contest. This iteration also features a 32-dimension capacity.</p></li><li><p><strong>LoRA 3 - Enhanced Fidelity:</strong> Building upon LoRA 2, this model is further trained with higher dimensions, focusing on improving the overall image quality.</p></li><li><p><strong>LoRA 4 - Comprehensive Style Mash:</strong> Our expansive dataset of 1400 images represents a confluence of all FFusionAI submissions. This model undergoes additional UNet training to refine and diversify the generated styles.</p></li></ul><p></p><h3 id="heading-81">1. FFusionAI <span style="color:rgb(144, 146, 150)">Style Capture &amp; Fusion Showdown </span>LoRA</h3><p><img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/e8e7e0cc-5e5a-4284-8743-7c5f16401f9c/width=525/e8e7e0cc-5e5a-4284-8743-7c5f16401f9c.jpeg" /></p><h3 id="heading-136"><strong>🎨 Dataset and Training:</strong></h3><p>Included within the package are curated collections accessible at <a target="_new" rel="ugc" href="https://civitai.com/collections/96636">CivitAI Collections</a>. The training prompts have been crafted with BLIP-2, FLAN-T5-XL, and ViT-H-14.</p><p>Please note, original prompts were not utilized for training. Instead, intentional modifications were made using blip2-flan-t5-xl &amp; ViT-H-14/laion2b_s32b_b79k to adjust and enhance the training dataset, which can be reviewed <a target="_new" rel="ugc" href="https://civitai.com/articles/2755">here</a>.</p><h3 id="heading-137"><strong>🔄 Further Information:</strong></h3><p>For a detailed examination of the training datasets, parameters, and model specifications, professionals and enthusiasts are encouraged to explore the metadata provided within the collection.</p><p><img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/6a13a9ce-d408-4a3f-b0cc-6000942f32bc/width=525/6a13a9ce-d408-4a3f-b0cc-6000942f32bc.jpeg" /></p><p></p><ul><li><p><strong>LORA 2</strong><br /><br />🚀 <strong>CivitAI Configuration Overview - 2023-11-10</strong></p></li></ul><p>🚀 <em>Trained with the Official CivitAI Trainer</em></p><p></p><p>📅 <strong>Date</strong>: 2023-11-10</p><p>🖼️ <strong>Title</strong>: CivitAI_ALL</p><p>🔍 <strong>Resolution</strong>: 1024x1024</p><p>🏗️ <strong>Architecture</strong>: stable-diffusion-xl-v1-base/lora</p><p></p><p>⚙️ <strong>Key Settings</strong>:</p><ul><li><p><strong>Network Dim/Rank</strong>: 32.0</p></li><li><p><strong>Alpha</strong>: 1.0</p></li><li><p><strong>Module</strong>: networks.lora</p></li><li><p><strong>Learning Rates</strong>: UNet LR - 0.0005, TE LR - 5e-05</p></li><li><p><strong>Optimizer</strong>: AdamW8bit (weight_decay=0.1)</p></li><li><p><strong>Epochs &amp; Batches</strong>: 10 epochs, 167 batches/epoch</p></li><li><p><strong>Train Images</strong>: 576</p><p></p></li></ul><p>📊 <strong>Model Stats</strong>:</p><ul><li><p><strong>UNet Weight</strong>: Mag - 3.755, Str - 0.0135</p></li><li><p><strong>Text Encoder (1)</strong>: Mag - 1.833, Str - 0.0091</p></li><li><p><strong>Text Encoder (2)</strong>: Mag - 1.836, Str - 0.0071</p></li></ul><p>🏷️ <strong>Prominent Tags</strong>:</p><ul><li><p>Fusion styles, Artgerm, Beeple</p></li><li><p>Dark fantasy, Official artwork, Pinup art</p></li><li><p>Digital illustration, Fantasy &amp; Sci-fi</p></li><li><p>...and over 4500 more!</p></li></ul><p></p><p></p><h3 id="heading-47">🌐 <a target="_blank" rel="ugc" href="http://FFusion.ai"><strong><u>FFusion.ai</u></strong></a><strong> Contact Information</strong></h3><p><em>Proudly maintained by Source Code Bulgaria Ltd &amp; Black Swan Technologies.</em></p><ul><li><p><span style="color:rgb(209, 213, 219)">📧 For collaborations, inquiries, or support: </span><a target="_blank" rel="ugc" href="mailto:di@ffusion.ai">di@ffusion.ai</a></p></li><li><p>🌍 <strong>Locations</strong>: Sofia | Istanbul | London</p></li></ul><p><strong>Connect with Us:</strong></p><ul><li><p>🐙 <a target="_blank" rel="ugc" href="https://github.com/1e-2">GitHub</a></p></li><li><p>😊 <a target="_blank" rel="ugc" href="https://huggingface.co/FFusion">Hugging </a><a target="_new" rel="ugc" href="https://chat.openai.com/c/4440256e-8c09-4f2e-ab25-1c9abfbf67f9#">Face</a></p></li><li><p>💡 <a target="_blank" rel="ugc" href="https://civitai.com/user/idle/models">Civitai</a></p></li></ul><p><strong>Our Websites:</strong></p><ul><li><p>🌐 <a target="_blank" rel="ugc" href="http://FFusion.ai">FFusion.ai</a></p></li><li><p>🌌 <a target="_blank" rel="ugc" href="http://FFAI.eu">FFAI.eu</a></p></li><li><p>🔗 <a target="_blank" rel="ugc" href="http://1e-2.com">1e-2.com</a><br /><br /></p></li></ul>
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