add: faster color-canny-controlnet

app.py

@@ -144,6 +144,10 @@ with block:
     We trained this model on the cleaned laion-art dataset that contains 2.6 million images with 2 epochs, using the Canny edge and color mosaic of the images as input. The processed dataset and pretrained model can be found in [ghoskno/laion-art-en-colorcanny](https://huggingface.co/datasets/ghoskno/laion-art-en-colorcanny) and [ghoskno/Color-Canny-Controlnet-model](https://huggingface.co/ghoskno/Color-Canny-Controlnet-model).
     
     This allows generated images to maintain the same color composition as the original images. If you are looking to control both the contours and colors of the original image while using ControlNet to generate images, then this is the best option for you! You can try out this model or test the examples provided below 🤗.
+
+    ## Update
+    Hi, everyone, We have added a Color-Canny-ControlNet accelerated version of our implementation based on Nvidia Triton and operator optimization. This faster ControlNet is deployed on a Nvidia A10 machine, and for a 512-pixel image, the inference takes about 1.2s, which is more faster than general implementation. 
+    Welcome to try this [faster Color-Canny-ControlNet](http://121.40.118.209:7860/).
     """)
     with gr.Row():
         with gr.Column():