Update app.py

app.py

@@ -63,29 +63,31 @@ with gr.Blocks(css=CSS, js=JS, theme="Nymbo/Nymbo_Theme") as demo:
             with gr.Column(scale=1.5):
                 output_res = ImageSlider(label="Flux / Upscaled")
             with gr.Column(scale=0.8):
-                with gr.Group():
-                    prompt = gr.Textbox(label="Prompt")
-                    basemodel_choice = gr.Dropdown(label="Base Model", choices=["black-forest-labs/FLUX.1-schnell", "black-forest-labs/FLUX.1-DEV"], value="black-forest-labs/FLUX.1-schnell")
-                    lora_model_choice = gr.Dropdown(label="LORA Model", choices=["Shakker-Labs/FLUX.1-dev-LoRA-add-details", "XLabs-AI/flux-RealismLora"])
-                    process_lora = gr.Checkbox(label="Process LORA", value=False)
-                    upscale_factor = gr.Radio(label="UpScale Factor", choices=[2, 4, 8], value=2, scale=2)
-                    process_upscale = gr.Checkbox(label="Process Upscale", value=False)
+                prompt = gr.Textbox(label="Prompt")
+                basemodel_choice = gr.Dropdown(label="Base Model", choices=["black-forest-labs/FLUX.1-schnell", "black-forest-labs/FLUX.1-DEV"], value="black-forest-labs/FLUX.1-schnell")
+                lora_model_choice = gr.Dropdown(label="LORA Model", choices=["Shakker-Labs/FLUX.1-dev-LoRA-add-details", "Otro modelo LORA"])
+                process_lora = gr.Checkbox(label="Process LORA", value=True)
+                upscale_factor = gr.Radio(label="UpScale Factor", choices=[2, 4, 8], value=2, scale=2)
+                process_upscale = gr.Checkbox(label="Process Upscale", value=False)
+                
+                with gr.Accordion(label="Advanced Options", open=False):
+                    width = gr.Slider(label="Width", minimum=512, maximum=1280, step=8, value=512)
+                    height = gr.Slider(label="Height", minimum=512, maximum=1280, step=8, value=512)
+                    scales = gr.Slider(label="Guidance", minimum=3.5, maximum=7, step=0.1, value=3.5)
+                    steps = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=24)
+                    seed = gr.Slider(label="Seeds", minimum=-1, maximum=MAX_SEED, step=1, value=-1)
+                    lora_add = gr.Textbox(label="Add Flux LoRA", info="Modelo Lora", lines=1, value="XLabs-AI/flux-RealismLora")
+                    lora_word = gr.Textbox(label="Add Flux LoRA Trigger Word", info="Add the Trigger Word", lines=1, value="")
                     
-                    with gr.Accordion(label="Advanced Options", open=False):
-                        width = gr.Slider(label="Width", minimum=512, maximum=1280, step=8, value=512)
-                        height = gr.Slider(label="Height", minimum=512, maximum=1280, step=8, value=512)
-                        scales = gr.Slider(label="Guidance", minimum=3.5, maximum=7, step=0.1, value=3.5)
-                        steps = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=24)
-                        seed = gr.Slider(label="Seeds", minimum=-1, maximum=MAX_SEED, step=1, value=-1)
-                        submit_btn = gr.Button("Submit", scale=1)
-                        submit_btn.click(
-                        fn=lambda: None,
-                        inputs=None,
-                        outputs=[output_res],
-                        queue=False
-                        ).then(
-                        fn=gen,
-                        inputs=[prompt, basemodel_choice, lora_add, lora_word, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model_choice, process_lora],
-                        outputs=[output_res]
-                        )
-                        demo.launch()
+                submit_btn = gr.Button("Submit", scale=1)
+                submit_btn.click(
+                    fn=lambda: None,
+                    inputs=None,
+                    outputs=[output_res],
+                    queue=False
+                ).then(
+                    fn=gen,
+                    inputs=[prompt, basemodel_choice, lora_add, lora_word, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model_choice, process_lora],
+                    outputs=[output_res]
+                )
+demo.launch()