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@@ -6,6 +6,7 @@ language:
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  - en
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  ---
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  ## Using HunyuanDiT ControlNet
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@@ -20,7 +21,9 @@ language:
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  ```bash
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  cd HunyuanDiT
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  # Use the huggingface-cli tool to download the model.
 
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  huggingface-cli download Tencent-Hunyuan/HYDiT-ControlNet --local-dir ./ckpts/t2i/controlnet
 
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  # Quick start
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  python3 sample_controlnet.py --no-enhance --load-key distill --infer-steps 50 --control_type canny --prompt "在夜晚的酒店门前,一座古老的中国风格的狮子雕像矗立着,它的眼睛闪烁着光芒,仿佛在守护着这座建筑。背景是夜晚的酒店前,构图方式是特写,平视,居中构图。这张照片呈现了真实摄影风格,蕴含了中国雕塑文化,同时展现了神秘氛围" --condition_image_path controlnet/asset/input/canny.jpg --control_weight 1.0
@@ -67,10 +70,17 @@ Examples of condition input and ControlNet results are as follows:
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  ### Training
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- We utilize [**DWPose**](https://github.com/IDEA-Research/DWPose) for pose extraction. Please follow their guidelines to download the checkpoints and save them to `hydit/annotator/ckpts` directory. Additionally, ensure that you install the related dependencies.
 
 
 
 
 
 
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  ```bash
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- pip install matplotlib
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- pip install onnxruntime_gpu
 
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  ```
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@@ -162,3 +172,4 @@ c. Using depth ControlNet during inference
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  python3 sample_controlnet.py --no-enhance --load-key distill --infer-steps 50 --control_type pose --prompt "一位亚洲女性,身穿绿色上衣,戴着紫色头巾和紫色围巾,站在黑板前。背景是黑板。照片采用近景、平视和居中构图的方式呈现真实摄影风格" --condition_image_path controlnet/asset/input/pose.jpg --control_weight 1.0
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  ```
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  ---
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+
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  ## Using HunyuanDiT ControlNet
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  ```bash
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  cd HunyuanDiT
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  # Use the huggingface-cli tool to download the model.
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+ # We recommend using distilled weights as the base model for ControlNet inference, as our provided pretrained weights are trained on them.
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  huggingface-cli download Tencent-Hunyuan/HYDiT-ControlNet --local-dir ./ckpts/t2i/controlnet
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+ huggingface-cli download Tencent-Hunyuan/Distillation-v1.1 ./pytorch_model_distill.pt --local-dir ./ckpts/t2i/model
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  # Quick start
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  python3 sample_controlnet.py --no-enhance --load-key distill --infer-steps 50 --control_type canny --prompt "在夜晚的酒店门前,一座古老的中国风格的狮子雕像矗立着,它的眼睛闪烁着光芒,仿佛在守护着这座建筑。背景是夜晚的酒店前,构图方式是特写,平视,居中构图。这张照片呈现了真实摄影风格,蕴含了中国雕塑文化,同时展现了神秘氛围" --condition_image_path controlnet/asset/input/canny.jpg --control_weight 1.0
 
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  ### Training
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+ We utilize [**DWPose**](https://github.com/IDEA-Research/DWPose) for pose extraction. Please follow their guidelines to download the checkpoints and save them to `hydit/annotator/ckpts` directory. We provide serveral commands to quick install:
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+ ```bash
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+ mkdir ./hydit/annotator/ckpts
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+ wget -O ./hydit/annotator/ckpts/dwpose.zip https://dit.hunyuan.tencent.com/download/HunyuanDiT/dwpose.zip
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+ unzip ./hydit/annotator/ckpts/dwpose.zip -d ./hydit/annotator/ckpts/
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+ ```
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+ Additionally, ensure that you install the related dependencies.
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  ```bash
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+ pip install matplotlib==3.7.5
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+ pip install onnxruntime_gpu==1.16.3
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+ pip install opencv-python==4.8.1.78
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  ```
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  python3 sample_controlnet.py --no-enhance --load-key distill --infer-steps 50 --control_type pose --prompt "一位亚洲女性,身穿绿色上衣,戴着紫色头巾和紫色围巾,站在黑板前。背景是黑板。照片采用近景、平视和居中构图的方式呈现真实摄影风格" --condition_image_path controlnet/asset/input/pose.jpg --control_weight 1.0
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  ```
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+