Update app.py

app.py

@@ -598,80 +598,72 @@ def infer_upscale(
 
 
 
-with gr.Blocks(theme="Nymbo/Nymbo_Theme", css=css, delete_cache=(60, 3600)) as app:
-
+with gr.Blocks(theme=gr.themes.Default(), css=css) as app:
     loras_state = gr.State(loras)
     selected_indices = gr.State([])
+
     with gr.Row():
         with gr.Column(scale=3):
             prompt = gr.Textbox(label="Prompt", lines=1, placeholder="Type a prompt after selecting a LoRA")
         with gr.Column(scale=1):
-            generate_button = gr.Button("Generate", variant="primary", elem_classes=["button_total"])
-            
-    with gr.Row(elem_id="loaded_loras"):
+            generate_button = gr.Button("Generate", variant="primary")
+
+    with gr.Row():
         with gr.Column(scale=1, min_width=25):
-            randomize_button = gr.Button("🎲", variant="secondary", scale=1, elem_id="random_btn")
+            randomize_button = gr.Button("🎲", variant="secondary")
         with gr.Column(scale=8):
             with gr.Row():
                 with gr.Column(scale=0, min_width=50):
-                    lora_image_1 = gr.Image(label="LoRA 1 Image", interactive=False, min_width=50, width=50, show_label=False, show_share_button=False, show_download_button=False, show_fullscreen_button=False, height=50)
+                    lora_image_1 = gr.Image(label="LoRA 1 Image", interactive=False, width=50, height=50)
                 with gr.Column(scale=3, min_width=100):
                     selected_info_1 = gr.Markdown("Select a LoRA 1")
                 with gr.Column(scale=5, min_width=50):
                     lora_scale_1 = gr.Slider(label="LoRA 1 Scale", minimum=0, maximum=3, step=0.01, value=1.15)
-            with gr.Row():
-                remove_button_1 = gr.Button("Remove", size="sm")
+            remove_button_1 = gr.Button("Remove", size="sm")
         with gr.Column(scale=8):
             with gr.Row():
                 with gr.Column(scale=0, min_width=50):
-                    lora_image_2 = gr.Image(label="LoRA 2 Image", interactive=False, min_width=50, width=50, show_label=False, show_share_button=False, show_download_button=False, show_fullscreen_button=False, height=50)
+                    lora_image_2 = gr.Image(label="LoRA 2 Image", interactive=False, width=50, height=50)
                 with gr.Column(scale=3, min_width=100):
                     selected_info_2 = gr.Markdown("Select a LoRA 2")
                 with gr.Column(scale=5, min_width=50):
                     lora_scale_2 = gr.Slider(label="LoRA 2 Scale", minimum=0, maximum=3, step=0.01, value=1.15)
-            with gr.Row():
-                remove_button_2 = gr.Button("Remove", size="sm")
+            remove_button_2 = gr.Button("Remove", size="sm")
+
     with gr.Row():
         with gr.Column():
             with gr.Group():
-                with gr.Row(elem_id="custom_lora_structure"):
-                    custom_lora = gr.Textbox(label="Custom LoRA", info="LoRA Hugging Face path or *.safetensors public URL", placeholder="ginipick/flux-lora-eric-cat", scale=3, min_width=150)
-                    add_custom_lora_button = gr.Button("Add Custom LoRA", elem_id="custom_lora_btn", scale=2, min_width=150)
+                with gr.Row():
+                    custom_lora = gr.Textbox(label="Custom LoRA", info="LoRA Hugging Face path or *.safetensors public URL", placeholder="ginipick/flux-lora-eric-cat", scale=3)
+                    add_custom_lora_button = gr.Button("Add Custom LoRA", scale=2)
                 remove_custom_lora_button = gr.Button("Remove Custom LoRA", visible=False)
-                gr.Markdown("[Check the list of FLUX LoRAs](https://huggingface.co/models?other=base_model:adapter:black-forest-labs/FLUX.1-dev)", elem_id="lora_list")
+                gr.Markdown("[Check the list of FLUX LoRAs](https://huggingface.co/models?other=base_model:adapter:black-forest-labs/FLUX.1-dev)")
             gallery = gr.Gallery(
                 [(item["image"], item["title"]) for item in loras],
                 label="Or pick from the LoRA Explorer gallery",
-                allow_preview=False,
                 columns=4,
-                elem_id="gallery"
             )
         with gr.Column():
-            progress_bar = gr.Markdown(elem_id="progress", visible=False)
+            progress_bar = gr.Markdown(visible=False)
             result = gr.Image(label="Generated Image", interactive=False)
             with gr.Accordion("History", open=False):
                 history_gallery = gr.Gallery(label="History", columns=6, object_fit="contain", interactive=False)
 
-    
-    with gr.Row():
-        with gr.Accordion("Advanced Settings", open=False):
-            with gr.Row():
-                input_image = gr.Image(label="Input image", type="filepath")
-                image_strength = gr.Slider(label="Denoise Strength", info="Lower means more image influence", minimum=0.1, maximum=1.0, step=0.01, value=0.75)
-            with gr.Column():
-                with gr.Row():
-                    cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, step=0.5, value=3.5)
-                    steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=28)
-
-                with gr.Row():
-                    width = gr.Slider(label="Width", minimum=256, maximum=1536, step=64, value=1024)
-                    height = gr.Slider(label="Height", minimum=256, maximum=1536, step=64, value=1024)
-
-                with gr.Row():
-                    randomize_seed = gr.Checkbox(True, label="Randomize seed")
-                    seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, randomize=True)
-
-    # 업스케일 관련 UI 추가
+    with gr.Accordion("Advanced Settings", open=False):
+        with gr.Row():
+            input_image = gr.Image(label="Input image", type="filepath")
+            image_strength = gr.Slider(label="Denoise Strength", info="Lower means more image influence", minimum=0.1, maximum=1.0, step=0.01, value=0.75)
+        with gr.Row():
+            cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, step=0.5, value=3.5)
+            steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=28)
+        with gr.Row():
+            width = gr.Slider(label="Width", minimum=256, maximum=1536, step=64, value=1024)
+            height = gr.Slider(label="Height", minimum=256, maximum=1536, step=64, value=1024)
+        with gr.Row():
+            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+            seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, randomize=True)
+
+    # Upscale related UI
     with gr.Row():
         upscale_button = gr.Button("Upscale")
     
@@ -679,44 +671,21 @@ with gr.Blocks(theme="Nymbo/Nymbo_Theme", css=css, delete_cache=(60, 3600)) as a
         with gr.Column(scale=4):
             upscale_input = gr.Image(label="Input Image for Upscaling", type="pil")
         with gr.Column(scale=1):
-            upscale_steps = gr.Slider(
-                label="Number of Inference Steps for Upscaling",
-                minimum=8,
-                maximum=50,
-                step=1,
-                value=28,
-            )
-            upscale_factor = gr.Slider(
-                label="Upscale Factor",
-                minimum=1,
-                maximum=4,
-                step=1,
-                value=4,
-            )
-            controlnet_conditioning_scale = gr.Slider(
-                label="Controlnet Conditioning Scale",
-                minimum=0.1,
-                maximum=1.5,
-                step=0.1,
-                value=0.6,
-            )
-            upscale_seed = gr.Slider(
-                label="Seed for Upscaling",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=42,
-            )
+            upscale_steps = gr.Slider(label="Number of Inference Steps for Upscaling", minimum=8, maximum=50, step=1, value=28)
+            upscale_factor = gr.Slider(label="Upscale Factor", minimum=1, maximum=4, step=1, value=4)
+            controlnet_conditioning_scale = gr.Slider(label="Controlnet Conditioning Scale", minimum=0.1, maximum=1.5, step=0.1, value=0.6)
+            upscale_seed = gr.Slider(label="Seed for Upscaling", minimum=0, maximum=MAX_SEED, step=1, value=42)
             upscale_randomize_seed = gr.Checkbox(label="Randomize seed for Upscaling", value=True)
 
     with gr.Row():
-        upscale_result = ImageSlider(label="Input / Output for Upscaling", type="pil", interactive=True)
-
+        upscale_result = gr.Image(label="Input / Output for Upscaling", type="pil", interactive=True)
 
+    # Event handlers
     gallery.select(
         update_selection,
         inputs=[selected_indices, loras_state, width, height],
-        outputs=[prompt, selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, width, height, lora_image_1, lora_image_2])
+        outputs=[prompt, selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, width, height, lora_image_1, lora_image_2]
+    )
     remove_button_1.click(
         remove_lora_1,
         inputs=[selected_indices, loras_state],
@@ -742,21 +711,17 @@ with gr.Blocks(theme="Nymbo/Nymbo_Theme", css=css, delete_cache=(60, 3600)) as a
         inputs=[selected_indices, loras_state],
         outputs=[loras_state, gallery, selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2]
     )
-
-    gr.on(
-        [upscale_button.click],
-        fn=infer_upscale,
-        inputs=[
-            upscale_seed,
-            upscale_randomize_seed,
-            upscale_input,
-            upscale_steps,
-            upscale_factor,
-            controlnet_conditioning_scale,
-        ],
-        outputs=upscale_result,
+    generate_button.click(
+        run_lora,
+        inputs=[prompt, input_image, image_strength, cfg_scale, steps, selected_indices, lora_scale_1, lora_scale_2, randomize_seed, seed, width, height, loras_state],
+        outputs=[result, seed, progress_bar]
+    )
+    upscale_button.click(
+        infer_upscale,
+        inputs=[upscale_seed, upscale_randomize_seed, upscale_input, upscale_steps, upscale_factor, controlnet_conditioning_scale],
+        outputs=upscale_result
     )
 
-
-app.queue()
-app.launch()
+if __name__ == "__main__":
+    app.queue(concurrency_count=3, max_size=20)
+    app.launch(debug=True)