Update README.md

README.md

@@ -2,113 +2,281 @@
 license: creativeml-openrail-m
 ---
 
-<img src="https://huggingface.co/darkstorm2150/Protogen_Nova_Official_Release/resolve/main/Protogen%20Nova-512.png" alt="Protogen-Nova" style="border: 3px solid purple" />
-
-## Table of contents
-* [General info](#general-info)
-* [Granular Adaptive Learning](#granular-adaptive-learning)
-* [Checkpoint Merging Data Reference](#checkpoint-merging-data-reference)
-* [Setup](#setup)
-
-## General info
-The Protogen x2.2 model is a cutting-edge machine learning algorithm that leverages the power of granular adaptive learning through the utilization of the revolutionary Stable Diffusion v1-5 algorithm. By fine-tuning the model with a vast and diverse array of data sourced from some of the most contemporary and comprehensive datasets available on Civitai.com and Huggingface.co, the Protogen x2.2 model has the ability to adapt to specific patterns and features in the data, unlocking a new level of performance and accuracy in the field of machine learning.
-
-## Granular Adaptive Learning
-
-Granular adaptive learning is a machine learning technique that focuses on adjusting the learning process at a fine-grained level, rather than making global adjustments to the model. This approach allows the model to adapt to specific patterns or features in the data, rather than making assumptions based on general trends.
+## CONSTRUCTION ZONE, MIND THE GAP...
 
-Granular adaptive learning can be achieved through techniques such as active learning, which allows the model to select the data it wants to learn from, or through the use of reinforcement learning, where the model receives feedback on its performance and adapts based on that feedback. It can also be achieved through techniques such as online learning where the model adjust itself as it receives more data.
-
-Granular adaptive learning is often used in situations where the data is highly diverse or non-stationary and where the model needs to adapt quickly to changing patterns. This is often the case in dynamic environments such as robotics, financial markets, and natural language processing.
-	
-## Checkpoint Merging Data Reference
-Work In Progress...
-
-<table>
-<thead>
-  <tr>
-    <th>Models</th>
-    <th>Protogen v2.2 (Anime)</th>
-    <th>Protogen x3.4 (Photo)</th>
-    <th>Protogen x5.3 (Photo)</th>
-    <th>Protogen x5.8 (Sci-fi/Anime)</th>
-    <th>Protogen x5.9 (Dragon)</th>
-    <th>Protogen x7.4 (Eclipse)</th>
-    <th>Protogen x8.0 (Nova)</th>
-    <th>Protogen x8.6 (Infinity)</th>
-  </tr>
-</thead>
-<tbody>
-  <tr>
-    <td>Seek_Art_Mega_v1</td>
-    <td>52.50%</td>
-    <td rowspan="1">$100</td>
-  </tr>
-  <tr>
-    <td>Modelshoot_v1</td>
-    <td>30.00%</td>
-  </tr>
-    <tr>
-    <td>elldreth_v1</td>
-    <td>12.64%</td>
-  </tr>
-    <tr>
-    <td>photoreal_v2</td>
-    <td></td>
-  </tr>
-</tbody>
-</table>
-
-
-<title>Model Weights</title>
 <style>
 .myTable {
 border-collapse:collapse; 
 }
 .myTable th { 
-background-color:#51087E;
+background-color:#BDB76B;
 color:white; 
 }
 .myTable td, .myTable th { 
 padding:5px;
-border:1px solid #D7A1F9; 
+border:1px solid #BDB76B; 
 }
 </style>
 <table class="myTable">
 <tr>
 <th>Models</th>
-<th>Protogen v2.2</th>
-<th>Protogen x3.4</th>
+<th>Protogen v2.2 (Anime)</th>
+<th>Protogen x3.4 (Photo)</th>
+<th>Protogen x5.3 (Photo)</th>
+<th>Protogen x5.8 (Sci-fi/Anime)</th>
+<th>Protogen x5.9 (Dragon)</th>
+<th>Protogen x7.4 (Eclipse)</th>
+<th>Protogen x8.0 (Nova)</th>
+<th>Protogen x8.6 (Infinity)</th>
 </tr>
 <tr>
-<td>a1</td>
-<td>b1</td>
-<td>c1</td>
-<td>d1</td>
-<td>e1</td>
+<td>Seek_Art_Mega_v1</td>
+<td>Table 1b</td>
+<td>Table 1c</td>
+<td>Table 1d</td>
+<td>Table 1e</td>
+<td>Table 1f</td>
+<td>Table 1g</td>
+<td>Table 1h</td>
+<td>Table 1i</td>
 </tr>
 <tr>
-<td>a2</td>
-<td>b2</td>
-<td>c2</td>
-<td>d2</td>
-<td>e2</td>
+<td>Modelshoot_v1</td>
+<td>Table 1b</td>
+<td>Table 1c</td>
+<td>Table 1d</td>
+<td>Table 1e</td>
+<td>Table 1f</td>
+<td>Table 1g</td>
+<td>Table 1h</td>
+<td>Table 1i</td>
 </tr>
 <tr>
-<td>a3</td>
-<td>b3</td>
-<td>c3</td>
-<td>d3</td>
-<td>e3</td>
+<td>elldreth_v1</td>
+<td>Table 1b</td>
+<td>Table 1c</td>
+<td>Table 1d</td>
+<td>Table 1e</td>
+<td>Table 1f</td>
+<td>Table 1g</td>
+<td>Table 1h</td>
+<td>Table 1i</td>
 </tr>
 <tr>
-<td>a4</td>
-<td>b4</td>
-<td>c4</td>
-<td>d4</td>
-<td>e4</td>
+<td>photoreal_v2</td>
+<td>Table 1b</td>
+<td>Table 1c</td>
+<td>Table 1d</td>
+<td>Table 1e</td>
+<td>Table 1f</td>
+<td>Table 1g</td>
+<td>Table 1h</td>
+<td>Table 1i</td>
+</tr>
+<tr>
+<td>analogdiffusion_v1</td>
+<td>Table 1b</td>
+<td>Table 1c</td>
+<td>Table 1d</td>
+<td>Table 1e</td>
+<td>Table 1f</td>
+<td>Table 1g</td>
+<td>Table 1h</td>
+<td>Table 1i</td>
+</tr>
+<tr>
+<td>openjourney_v2</td>
+<td>Table 1b</td>
+<td>Table 1c</td>
+<td>Table 1d</td>
+<td>Table 1e</td>
+<td>Table 1f</td>
+<td>Table 1g</td>
+<td>Table 1h</td>
+<td>Table 1i</td>
+</tr>
+  <tr>
+<td>hassan1.4</td>
+<td>Table 1b</td>
+<td>Table 1c</td>
+<td>Table 1d</td>
+<td>Table 1e</td>
+<td>Table 1f</td>
+<td>Table 1g</td>
+<td>Table 1h</td>
+<td>Table 1i</td>
+</tr>
+  <tr>
+<td>f222</td>
+<td>Table 1b</td>
+<td>Table 1c</td>
+<td>Table 1d</td>
+<td>Table 1e</td>
+<td>Table 1f</td>
+<td>Table 1g</td>
+<td>Table 1h</td>
+<td>Table 1i</td>
+</tr>
+  <tr>
+<td>hasdx</td>
+<td>Table 1b</td>
+<td>Table 1c</td>
+<td>Table 1d</td>
+<td>Table 1e</td>
+<td>Table 1f</td>
+<td>Table 1g</td>
+<td>Table 1h</td>
+<td>Table 1i</td>
+</tr>
+  <tr>
+<td>moistmix</td>
+<td>Table 1b</td>
+<td>Table 1c</td>
+<td>Table 1d</td>
+<td>Table 1e</td>
+<td>Table 1f</td>
+<td>Table 1g</td>
+<td>Table 1h</td>
+<td>Table 1i</td>
+</tr>
+  <tr>
+<td>robodiffusion_v1</td>
+<td>Table 1b</td>
+<td>Table 1c</td>
+<td>Table 1d</td>
+<td>Table 1e</td>
+<td>Table 1f</td>
+<td>Table 1g</td>
+<td>Table 1h</td>
+<td>Table 1i</td>
+</tr>
+  <tr>
+<td>rpg_v3</td>
+<td>Table 1b</td>
+<td>Table 1c</td>
+<td>Table 1d</td>
+<td>Table 1e</td>
+<td>Table 1f</td>
+<td>Table 1g</td>
+<td>Table 1h</td>
+<td>Table 1i</td>
+</tr>
+  <tr>
+<td>anything&everything</td>
+<td>Table 1b</td>
+<td>Table 1c</td>
+<td>Table 1d</td>
+<td>Table 1e</td>
+<td>Table 1f</td>
+<td>Table 1g</td>
+<td>Table 1h</td>
+<td>Table 1i</td>
+</tr>
+  <tr>
+<td>dreamlikediffusion</td>
+<td>Table 1b</td>
+<td>Table 1c</td>
+<td>Table 1d</td>
+<td>Table 1e</td>
+<td>Table 1f</td>
+<td>Table 1g</td>
+<td>Table 1h</td>
+<td>Table 1i</td>
+</tr>
+  <tr>
+<td>sci-fidiffi_v1</td>
+<td>Table 1b</td>
+<td>Table 1c</td>
+<td>Table 1d</td>
+<td>Table 1e</td>
+<td>Table 1f</td>
+<td>Table 1g</td>
+<td>Table 1h</td>
+<td>Table 1i</td>
+</tr>
+  <tr>
+<td>synthwavepunk_v2</td>
+<td>Table 1b</td>
+<td>Table 1c</td>
+<td>Table 1d</td>
+<td>Table 1e</td>
+<td>Table 1f</td>
+<td>Table 1g</td>
+<td>Table 1h</td>
+<td>Table 1i</td>
+</tr>
+  <tr>
+<td>mashup_v2</td>
+<td>Table 1b</td>
+<td>Table 1c</td>
+<td>Table 1d</td>
+<td>Table 1e</td>
+<td>Table 1f</td>
+<td>Table 1g</td>
+<td>Table 1h</td>
+<td>Table 1i</td>
+</tr>
+  <tr>
+<td>dreamshaper_252</td>
+<td>Table 1b</td>
+<td>Table 1c</td>
+<td>Table 1d</td>
+<td>Table 1e</td>
+<td>Table 1f</td>
+<td>Table 1g</td>
+<td>Table 1h</td>
+<td>Table 1i</td>
+</tr>
+  <tr>
+<td>comicdiff_v2</td>
+<td>Table 1b</td>
+<td>Table 1c</td>
+<td>Table 1d</td>
+<td>Table 1e</td>
+<td>Table 1f</td>
+<td>Table 1g</td>
+<td>Table 1h</td>
+<td>Table 1i</td>
+</tr>
+  <tr>
+<td>artEros</td>
+<td>Table 1b</td>
+<td>Table 1c</td>
+<td>Table 1d</td>
+<td>Table 1e</td>
+<td>Table 1f</td>
+<td>Table 1g</td>
+<td>Table 1h</td>
+<td>Table 1i</td>
 </tr>
 </table>
+
+
+
+
+<img src="https://huggingface.co/darkstorm2150/Protogen_Nova_Official_Release/resolve/main/Protogen%20Nova-512.png" alt="Protogen-Nova" style="border: 3px solid purple" />
+
+## Table of contents
+* [General info](#general-info)
+* [Granular Adaptive Learning](#granular-adaptive-learning)
+* [Checkpoint Merging Data Reference](#checkpoint-merging-data-reference)
+* [Setup](#setup)
+
+## General info
+The Protogen x2.2 model is a cutting-edge machine learning algorithm that leverages the power of granular adaptive learning through the utilization of the revolutionary Stable Diffusion v1-5 algorithm. By fine-tuning the model with a vast and diverse array of data sourced from some of the most contemporary and comprehensive datasets available on Civitai.com and Huggingface.co, the Protogen x2.2 model has the ability to adapt to specific patterns and features in the data, unlocking a new level of performance and accuracy in the field of machine learning.
+
+## Granular Adaptive Learning
+
+Granular adaptive learning is a machine learning technique that focuses on adjusting the learning process at a fine-grained level, rather than making global adjustments to the model. This approach allows the model to adapt to specific patterns or features in the data, rather than making assumptions based on general trends.
+
+Granular adaptive learning can be achieved through techniques such as active learning, which allows the model to select the data it wants to learn from, or through the use of reinforcement learning, where the model receives feedback on its performance and adapts based on that feedback. It can also be achieved through techniques such as online learning where the model adjust itself as it receives more data.
+
+Granular adaptive learning is often used in situations where the data is highly diverse or non-stationary and where the model needs to adapt quickly to changing patterns. This is often the case in dynamic environments such as robotics, financial markets, and natural language processing.
+	
+## Checkpoint Merging Data Reference
+Work In Progress...
+
+
 	
 ## Setup
 To run this model, download the model.ckpt and install it in your "stable-diffusion-webui\models\Stable-diffusion" directory