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Update README.md
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README.md
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license: creativeml-openrail-m
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---
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## CONSTRUCTION ZONE, MIND THE GAP...
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<style>
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.myTable {
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border-collapse:collapse;
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<td>2.26%</td>
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<td>1.33%</td>
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</tr>
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<td>hassan1.4</td>
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<td>2.63%</td>
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<td>2.14%</td>
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<td>1.26%</td>
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<td>0.74%</td>
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</tr>
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<td>f222</td>
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<td>2.23%</td>
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<td>1.82%</td>
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<td>1.07%</td>
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<td>0.63%</td>
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</tr>
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<td>hasdx</td>
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<td></td>
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<td></td>
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<td>5.01%</td>
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<td>2.95%</td>
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</tr>
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<td>moistmix</td>
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<td></td>
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<td></td>
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<td>4.08%</td>
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<td>2.40%</td>
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</tr>
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<td>roboDiffusion v1</td>
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<td></td>
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<td>4.29%</td>
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<td>4.41%</td>
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<td>2.60%</td>
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</tr>
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<td>RPG v3</td>
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<td></td>
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<td>5.00%</td>
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<td>4.29%</td>
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<td>2.52%</td>
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</tr>
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<td>anything&everything</td>
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<td></td>
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<td></td>
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<td>0.56%</td>
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<td>0.33%</td>
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</tr>
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<td>
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<td></td>
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<td></td>
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<td></td>
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<td>0.63%</td>
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<td>0.37%</td>
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</tr>
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<td>sci-fidiff v1</td>
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<td></td>
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<td></td>
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<td></td>
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<td>3.10%</td>
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</tr>
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<td>synthwavepunk v2</td>
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<td></td>
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<td></td>
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<td></td>
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<td>3.26%</td>
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</tr>
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<td>mashupv2</td>
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<td></td>
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<td></td>
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<td></td>
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<td>11.51%</td>
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</tr>
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<td>dreamshaper 252</td>
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<td></td>
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<td></td>
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<td></td>
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<td>4.04%</td>
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</tr>
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<td>comicdiff v2</td>
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<td></td>
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<td></td>
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<td></td>
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<td>4.25%</td>
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</tr>
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<td>artEros</td>
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<td></td>
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<td></td>
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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.
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## Checkpoint Merging Data Reference
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license: creativeml-openrail-m
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---
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<style>
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.myTable {
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border-collapse:collapse;
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<td>2.26%</td>
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<td>1.33%</td>
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</tr>
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<tr>
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<td>hassan1.4</td>
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<td>2.63%</td>
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<td>2.14%</td>
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<td>1.26%</td>
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<td>0.74%</td>
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</tr>
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<tr>
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<td>f222</td>
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<td>2.23%</td>
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<td>1.82%</td>
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<td>1.07%</td>
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<td>0.63%</td>
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</tr>
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<tr>
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<td>hasdx</td>
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<td></td>
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<td></td>
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<td>5.01%</td>
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<td>2.95%</td>
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</tr>
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<tr>
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<td>moistmix</td>
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<td></td>
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<td></td>
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<td>4.08%</td>
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<td>2.40%</td>
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</tr>
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<tr>
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<td>roboDiffusion v1</td>
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<td></td>
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<td>4.29%</td>
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<td>4.41%</td>
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<td>2.60%</td>
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</tr>
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<tr>
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<td>RPG v3</td>
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<td></td>
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<td>5.00%</td>
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<td>4.29%</td>
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<td>2.52%</td>
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</tr>
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<tr>
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<td>anything&everything</td>
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<td></td>
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<td></td>
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<td>0.56%</td>
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<td>0.33%</td>
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</tr>
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<tr>
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<td>dreamlikediff v1</td>
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<td></td>
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<td></td>
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<td></td>
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<td>0.63%</td>
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<td>0.37%</td>
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</tr>
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<tr>
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<td>sci-fidiff v1</td>
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<td></td>
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<td></td>
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<td></td>
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<td>3.10%</td>
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</tr>
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<tr>
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<td>synthwavepunk v2</td>
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<td></td>
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<td></td>
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<td></td>
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<td>3.26%</td>
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</tr>
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<tr>
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<td>mashupv2</td>
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<td></td>
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<td></td>
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<td></td>
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<td>11.51%</td>
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</tr>
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<tr>
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<td>dreamshaper 252</td>
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<td></td>
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<td></td>
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<td></td>
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<td>4.04%</td>
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</tr>
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<tr>
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<td>comicdiff v2</td>
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<td></td>
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<td></td>
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<td></td>
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<td>4.25%</td>
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</tr>
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<tr>
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<td>artEros</td>
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<td></td>
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<td></td>
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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.
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## Checkpoint Merging Data Reference
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<style>
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.myTable {
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border-collapse:collapse;
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}
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.myTable th {
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background-color:#663380;
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color:white;
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}
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.myTable td, .myTable th {
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padding:5px;
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border:1px solid #663380;
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}
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</style>
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<table class="myTable">
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<tr>
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<th>Models</th>
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<th>Protogen v2.2 (Anime)</th>
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<th>Protogen x3.4 (Photo)</th>
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<th>Protogen x5.3 (Photo)</th>
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<th>Protogen x5.8 (Sci-fi/Anime)</th>
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<th>Protogen x5.9 (Dragon)</th>
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<th>Protogen x7.4 (Eclipse)</th>
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<th>Protogen x8.0 (Nova)</th>
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<th>Protogen x8.6 (Infinity)</th>
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</tr>
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<tr>
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<td>seek_art_mega v1</td>
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<td>52.50%</td>
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<td>42.76%</td>
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<td>42.63%</td>
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<td></td>
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<td></td>
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<td></td>
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<td>25.21%</td>
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<td>14.83%</td>
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</tr>
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<tr>
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<td>modelshoot v1</td>
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<td>30.00%</td>
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<td>24.44%</td>
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<td>24.37%</td>
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<td>2.56%</td>
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<td>2.05%</td>
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<td>3.48%</td>
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<td>22.91%</td>
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<td>13.48%</td>
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</tr>
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<tr>
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<td>elldreth v1</td>
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<td>12.64%</td>
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<td>10.30%</td>
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<td>10.23%</td>
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<td></td>
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<td></td>
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<td></td>
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<td>6.06%</td>
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<td>3.57%</td>
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</tr>
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<tr>
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<td>photoreal v2</td>
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<td></td>
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<td></td>
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<td>10.00%</td>
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<td>48.64%</td>
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<td>38.91%</td>
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<td>66.33%</td>
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<td>20.49%</td>
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<td>12.06%</td>
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</tr>
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<tr>
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<td>analogdiffusion v1</td>
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<td></td>
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<td>4.75%</td>
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<td>4.50%</td>
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<td></td>
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<td></td>
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<td></td>
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<td>1.75%</td>
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<td>1.03%</td>
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</tr>
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<tr>
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<td>openjourney v2</td>
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<td></td>
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<td>4.51%</td>
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<td>4.28%</td>
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<td></td>
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<td></td>
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<td>4.75%</td>
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<td>2.26%</td>
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<td>1.33%</td>
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</tr>
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<tr>
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<td>hassan1.4</td>
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<td>2.63%</td>
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<td>2.14%</td>
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<td>2.13%</td>
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<td></td>
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<td></td>
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<td></td>
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<td>1.26%</td>
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<td>0.74%</td>
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</tr>
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<tr>
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<td>f222</td>
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<td>2.23%</td>
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<td>1.82%</td>
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<td>1.81%</td>
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<td></td>
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<td></td>
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<td></td>
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<td>1.07%</td>
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<td>0.63%</td>
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</tr>
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<tr>
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<td>hasdx</td>
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<td></td>
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<td></td>
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<td></td>
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<td>20.00%</td>
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<td>16.00%</td>
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<td>4.07%</td>
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<td>5.01%</td>
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<td>2.95%</td>
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</tr>
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<tr>
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<td>moistmix</td>
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<td></td>
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<td></td>
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<td></td>
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<td>16.00%</td>
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<td>12.80%</td>
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407 |
+
<td>3.86%</td>
|
408 |
+
<td>4.08%</td>
|
409 |
+
<td>2.40%</td>
|
410 |
+
</tr>
|
411 |
+
<tr>
|
412 |
+
<td>roboDiffusion v1</td>
|
413 |
+
<td></td>
|
414 |
+
<td>4.29%</td>
|
415 |
+
<td></td>
|
416 |
+
<td>12.80%</td>
|
417 |
+
<td>10.24%</td>
|
418 |
+
<td>3.67%</td>
|
419 |
+
<td>4.41%</td>
|
420 |
+
<td>2.60%</td>
|
421 |
+
</tr>
|
422 |
+
<tr>
|
423 |
+
<td>RPG v3</td>
|
424 |
+
<td></td>
|
425 |
+
<td>5.00%</td>
|
426 |
+
<td></td>
|
427 |
+
<td></td>
|
428 |
+
<td>20.00%</td>
|
429 |
+
<td>4.29%</td>
|
430 |
+
<td>4.29%</td>
|
431 |
+
<td>2.52%</td>
|
432 |
+
</tr>
|
433 |
+
<tr>
|
434 |
+
<td>anything&everything</td>
|
435 |
+
<td></td>
|
436 |
+
<td></td>
|
437 |
+
<td></td>
|
438 |
+
<td></td>
|
439 |
+
<td></td>
|
440 |
+
<td>4.51%</td>
|
441 |
+
<td>0.56%</td>
|
442 |
+
<td>0.33%</td>
|
443 |
+
</tr>
|
444 |
+
<tr>
|
445 |
+
<td>dreamlikediff v1</td>
|
446 |
+
<td></td>
|
447 |
+
<td></td>
|
448 |
+
<td></td>
|
449 |
+
<td></td>
|
450 |
+
<td></td>
|
451 |
+
<td>5.0%</td>
|
452 |
+
<td>0.63%</td>
|
453 |
+
<td>0.37%</td>
|
454 |
+
</tr>
|
455 |
+
<tr>
|
456 |
+
<td>sci-fidiff v1</td>
|
457 |
+
<td></td>
|
458 |
+
<td></td>
|
459 |
+
<td></td>
|
460 |
+
<td></td>
|
461 |
+
<td></td>
|
462 |
+
<td></td>
|
463 |
+
<td></td>
|
464 |
+
<td>3.10%</td>
|
465 |
+
</tr>
|
466 |
+
<tr>
|
467 |
+
<td>synthwavepunk v2</td>
|
468 |
+
<td></td>
|
469 |
+
<td></td>
|
470 |
+
<td></td>
|
471 |
+
<td></td>
|
472 |
+
<td></td>
|
473 |
+
<td></td>
|
474 |
+
<td></td>
|
475 |
+
<td>3.26%</td>
|
476 |
+
</tr>
|
477 |
+
<tr>
|
478 |
+
<td>mashupv2</td>
|
479 |
+
<td></td>
|
480 |
+
<td></td>
|
481 |
+
<td></td>
|
482 |
+
<td></td>
|
483 |
+
<td></td>
|
484 |
+
<td></td>
|
485 |
+
<td></td>
|
486 |
+
<td>11.51%</td>
|
487 |
+
</tr>
|
488 |
+
<tr>
|
489 |
+
<td>dreamshaper 252</td>
|
490 |
+
<td></td>
|
491 |
+
<td></td>
|
492 |
+
<td></td>
|
493 |
+
<td></td>
|
494 |
+
<td></td>
|
495 |
+
<td></td>
|
496 |
+
<td></td>
|
497 |
+
<td>4.04%</td>
|
498 |
+
</tr>
|
499 |
+
<tr>
|
500 |
+
<td>comicdiff v2</td>
|
501 |
+
<td></td>
|
502 |
+
<td></td>
|
503 |
+
<td></td>
|
504 |
+
<td></td>
|
505 |
+
<td></td>
|
506 |
+
<td></td>
|
507 |
+
<td></td>
|
508 |
+
<td>4.25%</td>
|
509 |
+
</tr>
|
510 |
+
<tr>
|
511 |
+
<td>artEros</td>
|
512 |
+
<td></td>
|
513 |
+
<td></td>
|
514 |
+
<td></td>
|
515 |
+
<td></td>
|
516 |
+
<td></td>
|
517 |
+
<td></td>
|
518 |
+
<td></td>
|
519 |
+
<td>15.00%</td>
|
520 |
+
</tr>
|
521 |
+
</table>
|
522 |
|
523 |
|
524 |
|