<h2 id="heading-4">FFAI 🦁🐶🐱 AnimAI Planet Demonstrations</h2><img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/0f916dda-a201-4286-a6e5-115b6f027463/width=525/0f916dda-a201-4286-a6e5-115b6f027463.jpeg" /> <img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/b6218a1b-acf8-43bb-9af8-070b0717943f/width=525/b6218a1b-acf8-43bb-9af8-070b0717943f.jpeg" /><img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/81b8f1e0-4a6e-4271-994a-26a82dfe94f7/width=525/81b8f1e0-4a6e-4271-994a-26a82dfe94f7.jpeg" />A normal LyCORIS - algo: LoRA was used for the training.<h2 id="heading-947">🎯 Models at a Glance:</h2><h3 id="heading-950">1. General LyCORIS Training</h3>📌 Highlights:<pre><code>'algo': 'lora'</code></pre>📊 Specifications:<ul><li>Date: 2023-08-26T23:08:56</li><li>Resolution: 1024x1024</li><li>Architecture: stable-diffusion-xl-v1-base/lora</li><li>Network Dim/Rank: 16.0</li><li>Alpha: 64.0</li></ul><h3 id="heading-950">2. Text Encoder LyCORIS</h3>📌 Highlights:TEXT ENCODER ONLY 'algo': 'lora'📊 Specifications:<ul><li>Date: 2023-10-01T07:31:55</li><li>Resolution: 1024x1024</li><li>Architecture: stable-diffusion-xl-v1-base/lora</li><li>Network Dim/Rank: 16.0</li><li>Alpha: 32.0</li></ul><h3 id="heading-950">3. General LyCORIS Training</h3>📌 Highlights:<pre><code>{'conv_dim': '32', 'conv_alpha': '64', 'algo': 'lora'}</code></pre>📊 Specifications:<ul><li>Date: 2023-10-02T08:37:35</li><li>Resolution: 1024x1024</li><li>Architecture: stable-diffusion-xl-v1-base/lora</li><li>Network Dim/Rank: 32.0</li><li>Alpha: 64.0</li></ul><h3 id="heading-950">4. General LyCORIS Training</h3>📌 Highlights:<pre><code>{'conv_dim': '64', 'conv_alpha': '64', 'algo': 'lora'}</code></pre>📊 Specifications:<ul><li>Date: 2023-10-02T11:40:06</li><li>Resolution: 1024x1024</li><li>Architecture: stable-diffusion-xl-v1-base/lora</li><li>Network Dim/Rank: 64.0</li><li>Alpha: 64.0</li></ul><img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/d40e6ed4-0ab7-4faf-b94b-4fd2658b365a/width=525/d40e6ed4-0ab7-4faf-b94b-4fd2658b365a.jpeg" /><pre><code>1.AnimAl P FFusion.safetensors
Date: 2023-10-01T05:08:45 Title: AnimAl P FFusion
Resolution: 1024x1024 Architecture: stable-diffusion-xl-v1-base/lora
Network Dim/Rank: 16.0 Alpha: 64.0
Module: lycoris.kohya : {'conv_dim': '16', 'conv_alpha': '64', 'algo': 'lora'}

UNet weight average magnitude: 1.5612285654851892
UNet weight average strength: 0.00855100617083033
UNet Conv weight average magnitude: 2.3661269530966935
UNet Conv weight average strength: 0.005806554066645475
Text Encoder (1) weight average magnitude: 1.4098021807770307
Text Encoder (1) weight average strength: 0.009792171967200777
Text Encoder (2) weight average magnitude: 1.5160455204089474
Text Encoder (2) weight average strength: 0.008057038737473559

----------------------


2.AnimAl P FFusion (TX Encoder).safetensors
Date: 2023-10-01T07:31:55 Title: AnimAl P FFusion
Resolution: 1024x1024 Architecture: stable-diffusion-xl-v1-base/lora
Network Dim/Rank: 16.0 Alpha: 32.0
Module: lycoris.kohya : {'conv_dim': '16', 'conv_alpha': '32', 'algo': 'lora'}

Text Encoder (1) weight average magnitude: 2.5523402023430064
Text Encoder (1) weight average strength: 0.016584716454285692
Text Encoder (2) weight average magnitude: 3.2502294766067283
Text Encoder (2) weight average strength: 0.01608869545658079
No UNet found in this LoRA

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3.AnimAl P FFusion v2-ep2.safetensors
Date: 2023-10-02T08:37:35 Title: AnimAl P FFusion v2-ep2
Resolution: 1024x1024 Architecture: stable-diffusion-xl-v1-base/lora
Network Dim/Rank: 32.0 Alpha: 64.0
Module: lycoris.kohya : {'conv_dim': '32', 'conv_alpha': '64', 'algo': 'lora'}




UNet weight average magnitude: 2.0946387136587896
UNet weight average strength: 0.008093249212950576
UNet Conv weight average magnitude: 3.5111623881034153
UNet Conv weight average strength: 0.006158271377234372
Text Encoder (1) weight average magnitude: 1.9438021141701276
Text Encoder (1) weight average strength: 0.009529127702349835
Text Encoder (2) weight average magnitude: 2.0788744162733246
Text Encoder (2) weight average strength: 0.007869903389610624

----------------------
4.AnimAl P FFusion v2-ep2.4.safetensors
Date: 2023-10-02T11:40:06 Title: AnimAl P FFusion v2-ep2.4
Resolution: 1024x1024 Architecture: stable-diffusion-xl-v1-base/lora
Network Dim/Rank: 64.0 Alpha: 64.0
Module: lycoris.kohya : {'conv_dim': '64', 'conv_alpha': '64', 'algo': 'lora'}

UNet weight average magnitude: 3.58799512097297
UNet weight average strength: 0.00944065170718396
UNet Conv weight average magnitude: 7.605353630925971
UNet Conv weight average strength: 0.008435387484832527
Text Encoder (1) weight average magnitude: 3.167086568188093
Text Encoder (1) weight average strength: 0.01076880155542553
Text Encoder (2) weight average magnitude: 3.525044115169337
Text Encoder (2) weight average strength: 0.009155737913150608</code></pre><h2 id="heading-951">🎨 Readme Crafted by: 🤖 &amp; FFusionAI 🚀</h2><h3 id="heading-952">🌐 Contact Information</h3>The <a target="_blank" rel="ugc" href="http://FFusion.ai">FFusion.ai</a> project is proudly maintained by Source Code Bulgaria Ltd &amp; Black Swan Technologies.📧 Reach us at <a target="_blank" rel="ugc" href="mailto:di@ffusion.ai">di@ffusion.ai</a> for any inquiries or support.🌌 Find us on:<ul><li>🐙 <a target="_blank" rel="ugc" href="https://github.com/1e-2">GitHub</a></li><li>😊 <a target="_blank" rel="ugc" href="https://huggingface.co/FFusion/">Hugging Face</a></li><li>💡 <a target="_blank" rel="ugc" href="https://civarchive.com/user/idle/models">Civitai</a></li></ul><a target="_blank" rel="ugc" href="mailto:enquiries@ffusion.ai"><img src="https://img.shields.io/badge/Email-enquiries%40ffusion.ai-blue?style=for-the-badge&amp;logo=gmail" alt="Email" /></a>🌍 Sofia Istanbul London

FFusion - AnimAI Planet XL

further training and new dataset images<pre><code>Date: 2023-10-02T08:37:35 Title: AnimAl P FFusion v2-ep2
Resolution: 1024x1024 Architecture: stable-diffusion-xl-v1-base/lora
Network Dim/Rank: 32.0 Alpha: 64.0
Module: lycoris.kohya : {'conv_dim': '32', 'conv_alpha': '64', 'algo': 'lora'}



UNet weight average magnitude: 2.0946387136587896
UNet weight average strength: 0.008093249212950576
UNet Conv weight average magnitude: 3.5111623881034153
UNet Conv weight average strength: 0.006158271377234372
Text Encoder (1) weight average magnitude: 1.9438021141701276
Text Encoder (1) weight average strength: 0.009529127702349835
Text Encoder (2) weight average magnitude: 2.0788744162733246
Text Encoder (2) weight average strength: 0.007869903389610624</code></pre>

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