Update app.py

app.py

@@ -1,35 +1,34 @@
 import gradio as gr
-from transformers import FlaxAutoModel, AutoTokenizer
+from transformers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline, AutoTokenizer
 
-def load_model_and_tokenizer(model_name):
-    model = FlaxAutoModel.from_pretrained(model_name)
+def load_model(model_name):
     tokenizer = AutoTokenizer.from_pretrained(model_name)
-    return model, tokenizer
+    controlnet = FlaxControlNetModel.from_pretrained(model_name)
+    pipeline = FlaxStableDiffusionControlNetPipeline.from_pretrained(model_name)
+    return tokenizer, controlnet, pipeline
 
-def generate_image(model, tokenizer, prompt):
-    # Process the input prompt and generate the output image using the model and tokenizer
-    # ...
-    # output_image = ... (your implementation here)
-    return output_image
+model_name = "Ryukijano/controlnet-fill-circle"
+tokenizer, controlnet, pipeline = load_model(model_name)
 
-def infer(prompt):
-    model_name = "Ryukijano/controlnet-fill-circle"
-    model, tokenizer = load_model_and_tokenizer(model_name)
-    output_image = generate_image(model, tokenizer, prompt)
-    return output_image
+def infer_fill_circle(prompt, negative_prompt, image):
+    # Your inference function for fill circle control
+    inputs = tokenizer(prompt, return_tensors="jax")
+    # Implement your image preprocessing here
+    outputs = pipeline.generate(inputs, image)
+    return outputs
 
-iface = gr.Interface(
-    fn=infer,
-    inputs=["text"],
-    outputs="image",
-    title="ControlNet Fill Circle",
-    description="This is a demo of ControlNet Fill Circle.",
-    examples=[
-        ["red circle with blue background"],
-        ["cyan circle with brown floral background"]
-    ],
-    theme="gradio/soft",
-)
+with gr.Blocks(theme='gradio/soft') as demo:
+    gr.Markdown("## Stable Diffusion with Fill Circle Control")
+    gr.Markdown("In this app, you can find the ControlNet with Fill Circle control.")
 
-iface.launch()
+    with gr.Tab("ControlNet Fill Circle"):
+        prompt_input_fill_circle = gr.Textbox(label="Prompt")
+        negative_prompt_fill_circle = gr.Textbox(label="Negative Prompt")
+        fill_circle_input = gr.Image(label="Input Image")
+        fill_circle_output = gr.Image(label="Output Image")
+        submit_btn = gr.Button(value="Submit")
+        fill_circle_inputs = [prompt_input_fill_circle, negative_prompt_fill_circle, fill_circle_input]
+        submit_btn.click(fn=infer_fill_circle, inputs=fill_circle_inputs, outputs=[fill_circle_output])
+
+demo.launch()