Update app.py

app.py

@@ -23,18 +23,15 @@ def enable_lora(lora_add, basemodel):
     return basemodel if not lora_add else lora_add
 
 async def generate_image(prompt, model, lora_word, width, height, scales, steps, seed):
-    try:
-        if seed == -1: 
-            seed = random.randint(0, MAX_SEED)
-        seed = int(seed)
-        text = str(translator.translate(prompt, 'English')) + "," + lora_word
-        client = AsyncInferenceClient()
-        image = await client.text_to_image(prompt=text, height=height, width=width, guidance_scale=scales, num_inference_steps=steps, model=model)
-        return image, seed
-    except Exception as e: 
-        raise gr.Error(f"Error en {e}")
+    if seed == -1: 
+        seed = random.randint(0, MAX_SEED)
+    seed = int(seed)
+    text = str(translator.translate(prompt, 'English')) + "," + lora_word
+    client = AsyncInferenceClient()
+    image = await client.text_to_image(prompt=text, height=height, width=width, guidance_scale=scales, num_inference_steps=steps, model=model)
+    return image, seed
 
-async def gen(prompt, basemodel, lora_add, lora_word, width, height, scales, steps, seed, upscale_factor, process_upscale):
+async def gen(prompt, basemodel, lora_add, lora_word, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model, process_lora):
     model = enable_lora(lora_add, basemodel)
     image, seed = await generate_image(prompt, model, lora_word, width, height, scales, steps, seed)
     image_path = "temp_image.png"
@@ -71,24 +68,26 @@ with gr.Blocks(css=CSS, js=JS, theme="Nymbo/Nymbo_Theme") as demo:
                     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_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="")
-                    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_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)
-                    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],
-            outputs=[output_res]
-        )
-
-demo.launch()
+                    
+                    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()