Initial

app.py

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+import gradio as gr
+
+def infer_segmentation(prompt, negative_prompt, image):
+    # your inference function for segmentation control 
+    return im
+
+def infer_canny(prompt, negative_prompt, image):
+    # your inference function for canny control 
+    return im
+
+with gr.Blocks(theme='gradio/soft') as demo:
+    gr.Markdown("## ControlNet for animal pose")
+    gr.Markdown("Under construction ")
+
+
+    with gr.Tab("ControlNet for dogs"):
+        prompt_input_canny = gr.Textbox(label="Prompt")
+        negative_prompt_canny = gr.Textbox(label="Negative Prompt")
+        canny_input = gr.Image(label="Input Image")
+        canny_output = gr.Image(label="Output Image")
+        submit_btn = gr.Button(value = "Submit")
+        canny_inputs = [prompt_input_canny, negative_prompt_canny, canny_input]
+        submit_btn.click(fn=infer_canny, inputs=canny_inputs, outputs=[canny_output])
+        
+    with gr.Tab("ControlNet for cats and dogs"):
+        prompt_input_seg = gr.Textbox(label="Prompt")
+        negative_prompt_seg = gr.Textbox(label="Negative Prompt")
+        seg_input = gr.Image(label="Image")
+        seg_output = gr.Image(label="Output Image")
+        submit_btn = gr.Button(value = "Submit")
+        seg_inputs = [prompt_input_seg, negative_prompt_seg, seg_input]
+        submit_btn.click(fn=infer_segmentation, inputs=seg_inputs, outputs=[seg_output])
+
+demo.launch()

requirements.txt

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+-f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
+jax[cuda11_cudnn805]
+jaxlib
+git+https://github.com/huggingface/diffusers@main
+opencv-python
+transformers
+flax