Upload app.py

app.py

@@ -94,7 +94,7 @@ def upscale(samples, upscale_method, scale_by):
 
 def check_inputs(prompt: str, control_image: Image.Image):
     if control_image is None:
-        raise gr.Error("Please select or upload an Input Illusion")
+        raise gr.Error("Please select or upload a photo of a person.")
     if prompt is None or prompt == "":
         raise gr.Error("Prompt is required")
 
@@ -186,4 +186,89 @@ def inference(
         },
     )
 
-    return out_image["images"][0], gr.update(visible=True), gr.update(visible=True), my_seed
+    return out_image["images"][0], gr.update(visible=True), gr.update(visible=True), my_seed
+
+with gr.Blocks() as app:
+    gr.Markdown(
+        '''
+        <center><h1>Core Ultra Heroes</h1></span>  
+        <span font-size:16px;">Turn yourself into an AI-powered superhero!</span>  
+        </center>
+ 
+        '''
+    )
+    state_img_input = gr.State()
+    state_img_output = gr.State()
+    with gr.Row():
+        with gr.Column():
+            control_image = gr.Image(label="Provide a photo of yourself", type="pil", elem_id="control_image")
+            # controlnet_conditioning_scale = gr.Slider(minimum=0.0, maximum=5.0, step=0.01, value=0.8, label="Illusion strength", elem_id="illusion_strength", info="ControlNet conditioning scale")
+            prompt = gr.Textbox(label="Prompt", elem_id="prompt", info="Type what you want to generate", placeholder="Medieval village scene with busy streets and castle in the distance")
+            negative_prompt = gr.Textbox(label="Negative Prompt", info="Type what you don't want to see", value="low quality", elem_id="negative_prompt")
+            with gr.Accordion(label="Advanced Options", open=False):
+                guidance_scale = gr.Slider(minimum=0.0, maximum=50.0, step=0.25, value=7.5, label="Guidance Scale")
+                sampler = gr.Dropdown(choices=list(SAMPLER_MAP.keys()), value="Euler")
+                control_start = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=0, label="Start of ControlNet")
+                control_end = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=1, label="End of ControlNet")
+                strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=1, label="Strength of the upscaler")
+                seed = gr.Slider(minimum=-1, maximum=9999999999, step=1, value=-1, label="Seed", info="-1 means random seed")
+                used_seed = gr.Number(label="Last seed used",interactive=False)
+            run_btn = gr.Button("Run")
+        with gr.Column():
+            result_image = gr.Image(label="You're a hero!", interactive=False, elem_id="output")
+
+    controlnet_conditioning_scale = 0.5
+
+    prompt.submit(
+        check_inputs,
+        inputs=[prompt, control_image],
+        queue=False
+    ).success(
+        convert_to_pil,
+        inputs=[control_image],
+        outputs=[state_img_input],
+        queue=False,
+        preprocess=False,
+    ).success(
+        inference,
+        inputs=[state_img_input, prompt, negative_prompt, guidance_scale, control_start, control_end, strength, seed, sampler],
+        outputs=[state_img_output, result_image, used_seed]
+    ).success(
+        convert_to_base64,
+        inputs=[state_img_output],
+        outputs=[result_image],
+        queue=False,
+        postprocess=False
+    )
+    run_btn.click(
+        check_inputs,
+        inputs=[prompt, control_image],
+        queue=False
+    ).success(
+        convert_to_pil,
+        inputs=[control_image],
+        outputs=[state_img_input],
+        queue=False,
+        preprocess=False,
+    ).success(
+        inference,
+        inputs=[state_img_input, prompt, negative_prompt, guidance_scale, controlnet_conditioning_scale, control_start, control_end, strength, seed, sampler],
+        outputs=[state_img_output, result_image, share_group, used_seed]
+    ).success(
+        convert_to_base64,
+        inputs=[state_img_output],
+        outputs=[result_image],
+        queue=False,
+        postprocess=False
+    )
+
+with gr.Blocks(css=css) as app_with_history:
+    with gr.Tab("Demo"):
+        app.render()
+    with gr.Tab("Past generations"):
+        user_history.render()
+
+app_with_history.queue(max_size=20,api_open=False )
+
+if __name__ == "__main__":
+    app_with_history.launch(max_threads=400)