Update app_inference.py

app_inference.py

@@ -226,138 +226,137 @@ button#load_model_btn{
     font-size: 0.9em;
 }
 """
-def create_inference_demo() -> gr.Blocks:
+# def create_inference_demo() -> gr.Blocks:
   
-  with gr.Blocks(css=css) as demo:
-      with gr.Column(elem_id="col-container"):
-          if is_shared_ui:
-              top_description = gr.HTML(f'''
-                  <div class="gr-prose">
-                      <h2><svg xmlns="http://www.w3.org/2000/svg" width="18px" height="18px" style="margin-right: 0px;display: inline-block;"fill="none"><path fill="#fff" d="M7 13.2a6.3 6.3 0 0 0 4.4-10.7A6.3 6.3 0 0 0 .6 6.9 6.3 6.3 0 0 0 7 13.2Z"/><path fill="#fff" fill-rule="evenodd" d="M7 0a6.9 6.9 0 0 1 4.8 11.8A6.9 6.9 0 0 1 0 7 6.9 6.9 0 0 1 7 0Zm0 0v.7V0ZM0 7h.6H0Zm7 6.8v-.6.6ZM13.7 7h-.6.6ZM9.1 1.7c-.7-.3-1.4-.4-2.2-.4a5.6 5.6 0 0 0-4 1.6 5.6 5.6 0 0 0-1.6 4 5.6 5.6 0 0 0 1.6 4 5.6 5.6 0 0 0 4 1.7 5.6 5.6 0 0 0 4-1.7 5.6 5.6 0 0 0 1.7-4 5.6 5.6 0 0 0-1.7-4c-.5-.5-1.1-.9-1.8-1.2Z" clip-rule="evenodd"/><path fill="#000" fill-rule="evenodd" d="M7 2.9a.8.8 0 1 1 0 1.5A.8.8 0 0 1 7 3ZM5.8 5.7c0-.4.3-.6.6-.6h.7c.3 0 .6.2.6.6v3.7h.5a.6.6 0 0 1 0 1.3H6a.6.6 0 0 1 0-1.3h.4v-3a.6.6 0 0 1-.6-.7Z" clip-rule="evenodd"/></svg>
-                      Note: you might want to use a <strong>private</strong> custom LoRa model</h2>
-                      <p class="main-message">
-                          To do so, <strong>duplicate the Space</strong> and run it on your own profile using <strong>your own access token</strong> and eventually a GPU (T4-small or A10G-small) for faster inference without waiting in the queue.<br />
-                      </p>
-                      <p class="actions">
-                          <a href="https://huggingface.co/spaces/{os.environ['SPACE_ID']}?duplicate=true">
-                              <img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/duplicate-this-space-lg-dark.svg" alt="Duplicate this Space" />
-                          </a>
-                          to start using private models and skip the queue
-                      </p>
-                  </div>
-              ''', elem_id="warning-duplicate")
-          gr.HTML("""
-  <h2 style="text-align: center;">SD-XL Control LoRas</h2>
-  <p style="text-align: center;">Use StableDiffusion XL with <a href="https://huggingface.co/collections/diffusers/sdxl-controlnets-64f9c35846f3f06f5abe351f">Diffusers' SDXL ControlNets</a></p>
-
-          """)
-
-          use_custom_model = gr.Checkbox(label="Use a custom pre-trained LoRa model ? (optional)", value=False, info="To use a private model, you'll need to duplicate the space with your own access token.")
-          
-          with gr.Box(visible=False) as custom_model_box:
-              with gr.Row():
-                  with gr.Column():
-                      if not is_shared_ui:
-                          your_username = api.whoami()["name"]
-                          my_models = api.list_models(author=your_username, filter=["diffusers", "stable-diffusion-xl", 'lora'])
-                          model_names = [item.modelId for item in my_models]
-      
-                      if not is_shared_ui:
-                          custom_model = gr.Dropdown(
-                              label = "Your custom model ID",
-                              info="You can pick one of your private models",
-                              choices = model_names,
-                              allow_custom_value = True
-                              #placeholder = "username/model_id"
-                          )
-                      else:
-                          custom_model = gr.Textbox(
-                              label="Your custom model ID", 
-                              placeholder="your_username/your_trained_model_name", 
-                              info="Make sure your model is set to PUBLIC"
-                          )
-                      
-                      weight_name = gr.Dropdown(
-                          label="Safetensors file", 
-                          #value="pytorch_lora_weights.safetensors", 
-                          info="specify which one if model has several .safetensors files",
-                          allow_custom_value=True,
-                          visible = False
-                      )
-                  with gr.Column():
-                      with gr.Group():
-                          load_model_btn = gr.Button("Load my model", elem_id="load_model_btn")
-                          previous_model = gr.Textbox(
-                              visible = False
-                          )
-                          model_status = gr.Textbox(
-                              label = "model status",
-                              show_label = False,
-                              elem_id = "status_info"
-                          )
-                      trigger_word = gr.Textbox(label="Trigger word", interactive=False, visible=False)
-          
-          image_in = gr.Image(source="upload", type="filepath")
-          
-          with gr.Row():
-              
-              with gr.Column():
-                  with gr.Group():
-                      prompt = gr.Textbox(label="Prompt")
-                      negative_prompt = gr.Textbox(label="Negative prompt", value="extra digit, fewer digits, cropped, worst quality, low quality, glitch, deformed, mutated, ugly, disfigured")
-                  with gr.Group():
-                      guidance_scale = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=10.0, step=0.1, value=7.5)
-                      inf_steps = gr.Slider(label="Inference Steps", minimum="25", maximum="50", step=1, value=25)
-                      custom_lora_weight = gr.Slider(label="Custom model weights", minimum=0.1, maximum=0.9, step=0.1, value=0.9)
-
-              with gr.Column():
-                  with gr.Group():
-                      preprocessor = gr.Dropdown(label="Preprocessor", choices=["canny"], value="canny", interactive=False, info="For the moment, only canny is available")  
-                      controlnet_conditioning_scale = gr.Slider(label="Controlnet conditioning Scale", minimum=0.1, maximum=0.9, step=0.01, value=0.5)
-                  with gr.Group():
-                      seed = gr.Slider(
-                          label="Seed",
-                          info = "-1 denotes a random seed",
-                          minimum=-1,
-                          maximum=423538377342,
-                          step=1,
-                          value=-1
-                      )
-                      last_used_seed = gr.Number(
-                          label = "Last used seed",
-                          info = "the seed used in the last generation",
-                      )
-          
-          
-          submit_btn = gr.Button("Submit")
-          
-          result = gr.Image(label="Result")
-      
-      use_custom_model.change(
-          fn = check_use_custom_or_no,
-          inputs =[use_custom_model],
-          outputs = [custom_model_box],
-          queue = False
-      )
-      custom_model.blur(
-          fn=custom_model_changed,
-          inputs = [custom_model, previous_model],
-          outputs = [model_status],
-          queue = False
-      )
-      load_model_btn.click(
-          fn = load_model,
-          inputs=[custom_model],
-          outputs = [previous_model, model_status, weight_name, trigger_word],
-          queue = False
-      )
-      submit_btn.click(
-          fn = infer,
-          inputs = [use_custom_model, custom_model, weight_name, custom_lora_weight, image_in, prompt, negative_prompt, preprocessor, controlnet_conditioning_scale, guidance_scale, inf_steps, seed],
-          outputs = [result, last_used_seed]
-      )
-
-  return demo 
+with gr.Blocks(css=css) as demo:
+    with gr.Column(elem_id="col-container"):
+        if is_shared_ui:
+            top_description = gr.HTML(f'''
+                <div class="gr-prose">
+                    <h2><svg xmlns="http://www.w3.org/2000/svg" width="18px" height="18px" style="margin-right: 0px;display: inline-block;"fill="none"><path fill="#fff" d="M7 13.2a6.3 6.3 0 0 0 4.4-10.7A6.3 6.3 0 0 0 .6 6.9 6.3 6.3 0 0 0 7 13.2Z"/><path fill="#fff" fill-rule="evenodd" d="M7 0a6.9 6.9 0 0 1 4.8 11.8A6.9 6.9 0 0 1 0 7 6.9 6.9 0 0 1 7 0Zm0 0v.7V0ZM0 7h.6H0Zm7 6.8v-.6.6ZM13.7 7h-.6.6ZM9.1 1.7c-.7-.3-1.4-.4-2.2-.4a5.6 5.6 0 0 0-4 1.6 5.6 5.6 0 0 0-1.6 4 5.6 5.6 0 0 0 1.6 4 5.6 5.6 0 0 0 4 1.7 5.6 5.6 0 0 0 4-1.7 5.6 5.6 0 0 0 1.7-4 5.6 5.6 0 0 0-1.7-4c-.5-.5-1.1-.9-1.8-1.2Z" clip-rule="evenodd"/><path fill="#000" fill-rule="evenodd" d="M7 2.9a.8.8 0 1 1 0 1.5A.8.8 0 0 1 7 3ZM5.8 5.7c0-.4.3-.6.6-.6h.7c.3 0 .6.2.6.6v3.7h.5a.6.6 0 0 1 0 1.3H6a.6.6 0 0 1 0-1.3h.4v-3a.6.6 0 0 1-.6-.7Z" clip-rule="evenodd"/></svg>
+                    Note: you might want to use a <strong>private</strong> custom LoRa model</h2>
+                    <p class="main-message">
+                        To do so, <strong>duplicate the Space</strong> and run it on your own profile using <strong>your own access token</strong> and eventually a GPU (T4-small or A10G-small) for faster inference without waiting in the queue.<br />
+                    </p>
+                    <p class="actions">
+                        <a href="https://huggingface.co/spaces/{os.environ['SPACE_ID']}?duplicate=true">
+                            <img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/duplicate-this-space-lg-dark.svg" alt="Duplicate this Space" />
+                        </a>
+                        to start using private models and skip the queue
+                    </p>
+                </div>
+            ''', elem_id="warning-duplicate")
+        gr.HTML("""
+<h2 style="text-align: center;">SD-XL Control LoRas</h2>
+<p style="text-align: center;">Use StableDiffusion XL with <a href="https://huggingface.co/collections/diffusers/sdxl-controlnets-64f9c35846f3f06f5abe351f">Diffusers' SDXL ControlNets</a></p>
+        """)
+
+        use_custom_model = gr.Checkbox(label="Use a custom pre-trained LoRa model ? (optional)", value=False, info="To use a private model, you'll need to duplicate the space with your own access token.")
+        
+        with gr.Blocks(visible=False) as custom_model_box:
+            with gr.Row():
+                with gr.Column():
+                    if not is_shared_ui:
+                        your_username = api.whoami()["name"]
+                        my_models = api.list_models(author=your_username, filter=["diffusers", "stable-diffusion-xl", 'lora'])
+                        model_names = [item.modelId for item in my_models]
+    
+                    if not is_shared_ui:
+                        custom_model = gr.Dropdown(
+                            label = "Your custom model ID",
+                            info="You can pick one of your private models",
+                            choices = model_names,
+                            allow_custom_value = True
+                            #placeholder = "username/model_id"
+                        )
+                    else:
+                        custom_model = gr.Textbox(
+                            label="Your custom model ID", 
+                            placeholder="your_username/your_trained_model_name", 
+                            info="Make sure your model is set to PUBLIC"
+                        )
+                    
+                    weight_name = gr.Dropdown(
+                        label="Safetensors file", 
+                        #value="pytorch_lora_weights.safetensors", 
+                        info="specify which one if model has several .safetensors files",
+                        allow_custom_value=True,
+                        visible = False
+                    )
+                with gr.Column():
+                    with gr.Group():
+                        load_model_btn = gr.Button("Load my model", elem_id="load_model_btn")
+                        previous_model = gr.Textbox(
+                            visible = False
+                        )
+                        model_status = gr.Textbox(
+                            label = "model status",
+                            show_label = False,
+                            elem_id = "status_info"
+                        )
+                    trigger_word = gr.Textbox(label="Trigger word", interactive=False, visible=False)
+        
+        image_in = gr.Image(sources="upload", type="filepath")
+        
+        with gr.Row():
+            
+            with gr.Column():
+                with gr.Group():
+                    prompt = gr.Textbox(label="Prompt")
+                    negative_prompt = gr.Textbox(label="Negative prompt", value="extra digit, fewer digits, cropped, worst quality, low quality, glitch, deformed, mutated, ugly, disfigured")
+                with gr.Group():
+                    guidance_scale = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=10.0, step=0.1, value=7.5)
+                    inf_steps = gr.Slider(label="Inference Steps", minimum="25", maximum="50", step=1, value=25)
+                    custom_lora_weight = gr.Slider(label="Custom model weights", minimum=0.1, maximum=0.9, step=0.1, value=0.9)
+
+            with gr.Column():
+                with gr.Group():
+                    preprocessor = gr.Dropdown(label="Preprocessor", choices=["canny"], value="canny", interactive=False, info="For the moment, only canny is available")  
+                    controlnet_conditioning_scale = gr.Slider(label="Controlnet conditioning Scale", minimum=0.1, maximum=0.9, step=0.01, value=0.5)
+                with gr.Group():
+                    seed = gr.Slider(
+                        label="Seed",
+                        info = "-1 denotes a random seed",
+                        minimum=-1,
+                        maximum=423538377342,
+                        step=1,
+                        value=-1
+                    )
+                    last_used_seed = gr.Number(
+                        label = "Last used seed",
+                        info = "the seed used in the last generation",
+                    )
+        
+        
+        submit_btn = gr.Button("Submit")
+        
+        result = gr.Image(label="Result")
+    
+    use_custom_model.change(
+        fn = check_use_custom_or_no,
+        inputs =[use_custom_model],
+        outputs = [custom_model_box],
+        queue = False
+    )
+    custom_model.blur(
+        fn=custom_model_changed,
+        inputs = [custom_model, previous_model],
+        outputs = [model_status],
+        queue = False
+    )
+    load_model_btn.click(
+        fn = load_model,
+        inputs=[custom_model],
+        outputs = [previous_model, model_status, weight_name, trigger_word],
+        queue = False
+    )
+    submit_btn.click(
+        fn = infer,
+        inputs = [use_custom_model, custom_model, weight_name, custom_lora_weight, image_in, prompt, negative_prompt, preprocessor, controlnet_conditioning_scale, guidance_scale, inf_steps, seed],
+        outputs = [result, last_used_seed]
+    )
+
+  # return demo 
   
 
-#demo.queue(max_size=12).launch(share=True)
+demo.queue(max_size=12).launch(share=True)