Update weights

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README.md

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 ---
 license: apache-2.0
 ---
+# Z-Image-Turbo-Fun-Controlnet-Union
+
+## Model Features
+- This ControlNet is added on 6 blocks.
+- The model was trained from scratch for 10,000 steps on a dataset of 1 million high-quality images covering both general and human-centric content. Training was performed at 1328 resolution using BFloat16 precision, with a batch size of 64, a learning rate of 2e-5, and a text dropout ratio of 0.10.
+- It supports multiple control conditions—including Canny, HED, depth maps, pose estimation, and MLSD—and can be used like a standard ControlNet.
+- You can adjust controlnet_conditioning_scale and control_guidance_end for stronger control and better detail preservation. For better stability, we highly recommend using a detailed prompt. The optimal range for controlnet_conditioning_scale is from 0.65 to 0.80.
+
+## TODO 
+- [ ] Train on more data and for more steps.
+- [ ] Support inpaint mode.
+
+## Results
+
+<table border="0" style="width: 100%; text-align: left; margin-top: 20px;">
+  <tr>
+    <td>Pose</td>
+    <td>Output</td>
+  </tr>
+  <tr>
+    <td><img src="asset/pose2.jpg" width="100%" /></td>
+    <td><img src="results/pose2.png" width="100%" /></td>
+  </tr>
+</table>
+
+<table border="0" style="width: 100%; text-align: left; margin-top: 20px;">
+  <tr>
+    <td>Pose</td>
+    <td>Output</td>
+  </tr>
+  <tr>
+    <td><img src="asset/pose.jpg" width="100%" /></td>
+    <td><img src="results/pose.png" width="100%" /></td>
+  </tr>
+</table>
+
+<table border="0" style="width: 100%; text-align: left; margin-top: 20px;">
+  <tr>
+    <td>Canny</td>
+    <td>Output</td>
+  </tr>
+  <tr>
+    <td><img src="asset/canny.jpg" width="100%" /></td>
+    <td><img src="results/canny.png" width="100%" /></td>
+  </tr>
+</table>
+
+<table border="0" style="width: 100%; text-align: left; margin-top: 20px;">
+  <tr>
+    <td>HED</td>
+    <td>Output</td>
+  </tr>
+  <tr>
+    <td><img src="asset/hed.jpg" width="100%" /></td>
+    <td><img src="results/hed.png" width="100%" /></td>
+  </tr>
+</table>
+
+<table border="0" style="width: 100%; text-align: left; margin-top: 20px;">
+  <tr>
+    <td>Depth</td>
+    <td>Output</td>
+  </tr>
+  <tr>
+    <td><img src="asset/depth.jpg" width="100%" /></td>
+    <td><img src="results/depth.png" width="100%" /></td>
+  </tr>
+</table>
+
+## Inference
+Go to the VideoX-Fun repository for more details.
+
+Please clone the VideoX-Fun repository and create the required directories:
+
+```sh
+# Clone the code
+git clone https://github.com/aigc-apps/VideoX-Fun.git
+
+# Enter VideoX-Fun's directory
+cd VideoX-Fun
+
+# Create model directories
+mkdir -p models/Diffusion_Transformer
+mkdir -p models/Personalized_Model
+```
+
+Then download the weights into models/Diffusion_Transformer and models/Personalized_Model.
+
+```
+📦 models/
+├── 📂 Diffusion_Transformer/
+│   └── 📂 Z-Image-Turbo/
+├── 📂 Personalized_Model/
+│   └── 📦 Z-Image-Turbo-Fun-Controlnet-Union.safetensors
+```
+
+Then run the file `examples/z_image_fun/predict_t2i_control.py`.

Z-Image-Turbo-Fun-Controlnet-Union.safetensors

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