Update app.py

app.py

@@ -37,37 +37,43 @@ def get_upscale_finegrain(prompt, img_path, upscale_factor):
         return None
 
 async def gen(prompt, basemodel, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model, process_lora):
-    model = enable_lora(lora_model, basemodel) if process_lora else basemodel
-    
-    improved_prompt = await improve_prompt(prompt)
-    combined_prompt = f"{prompt} {improved_prompt}"
+    try:
+        model = enable_lora(lora_model, basemodel) if process_lora else basemodel
+        
+        improved_prompt = await improve_prompt(prompt)
+        combined_prompt = f"{prompt} {improved_prompt}"
 
-    image, seed = await generate_image(combined_prompt, model, "", width, height, scales, steps, seed)
-    
-    if isinstance(image, str) and image.startswith("Error"):
-        return [image, None]
-    
-    image_path = "temp_image.jpg"
-    image.save(image_path, format="JPEG")
-    
-    if process_upscale:
-        upscale_image_path = get_upscale_finegrain(combined_prompt, image_path, upscale_factor)
-        if upscale_image_path is not None:
-            upscale_image = Image.open(upscale_image_path)
-            upscale_image.save("upscale_image.jpg", format="JPEG")
-            return [image_path, "upscale_image.jpg"]
+        image, seed = await generate_image(combined_prompt, model, "", width, height, scales, steps, seed)
+        
+        if isinstance(image, str) and image.startswith("Error"):
+            return [image, None]
+        
+        image_path = "temp_image.jpg"
+        image.save(image_path, format="JPEG")
+        
+        if process_upscale:
+            upscale_image_path = get_upscale_finegrain(combined_prompt, image_path, upscale_factor)
+            if upscale_image_path is not None:
+                upscale_image = Image.open(upscale_image_path)
+                upscale_image.save("upscale_image.jpg", format="JPEG")
+                return [image_path, "upscale_image.jpg"]
+            else:
+                return [image_path, image_path]
         else:
             return [image_path, image_path]
-    else:
-        return [image_path, image_path]
+    except Exception as e:
+        return [f"Error: {e}", None]
+
+def error_handler(err):
+    return f"Error: {err}"
 
 async def improve_prompt(prompt):
     try:
-        instruction = "Take the following text input and expand it into a high quality and descriptive prompt to create a cinematic image, camera angles, lighting, realism atmosphere, and character details. Condense the narrative into two paragraphs (max. 200 words)"
-        formatted_prompt = f"{instruction}: {prompt}"
-        response = llm_client.text_generation(formatted_prompt, max_new_tokens=300)
+        instruction = "With this idea, describe in English a detailed img2vid prompt in a single paragraph of up to 300 characters, developing atmosphere, characters, lighting, and cameras."
+        formatted_prompt = f"{prompt}: {instruction}"
+        response = llm_client.text_generation(formatted_prompt, max_new_tokens=300, language="english")
         improved_text = response['generated_text'].strip() if 'generated_text' in response else response.strip()
-
+        
         return improved_text
     except Exception as e:
         return f"Error mejorando el prompt: {e}"
@@ -90,20 +96,22 @@ with gr.Blocks(css=css, theme="Nymbo/Nymbo_Theme") as demo:
                     process_lora = gr.Checkbox(label="Procesar LORA")
                     process_upscale = gr.Checkbox(label="Procesar Escalador")
 
-                upscale_factor = gr.Radio(label="Factor de Escala", choices=[2, 4, 8], value=2)
-
                 improved_prompt = gr.Textbox(label="Prompt Mejorado", interactive=False)
-
                 improve_btn = gr.Button("Mejora mi prompt")
-                improve_btn.click(fn=improve_prompt, inputs=[prompt], outputs=improved_prompt)
+                improve_btn.click(fn=lambda prompt: improve_prompt(prompt), inputs=[prompt], outputs=[improved_prompt, prompt])
+                reset_btn = gr.Button("Reset")
+                reset_btn.click(fn=lambda: [prompt.update(""), improved_prompt.update("")], inputs=None, outputs=[prompt, improved_prompt])
                 
                 with gr.Accordion(label="Opciones Avanzadas", open=False):
                     width = gr.Slider(label="Ancho", minimum=512, maximum=1280, step=8, value=1280)
                     height = gr.Slider(label="Alto", minimum=512, maximum=1280, step=8, value=768)
+                    upscale_factor = gr.Radio(label="Factor de Escala", choices=[2, 4, 8], value=2)
                     scales = gr.Slider(label="Escalado", minimum=1, maximum=20, step=1, value=10)
                     steps = gr.Slider(label="Pasos", minimum=1, maximum=100, step=1, value=20)
                     seed = gr.Number(label="Semilla", value=-1)
+                    reset_advanced = gr.Button("Reset")
+                    reset_advanced.click(fn=lambda: [width.update(1280), height.update(768), scales.update(10), steps.update(20), seed.update(-1)], inputs=None, outputs=[width, height, scales, steps, seed])
     
                 btn = gr.Button("Generar")
-                btn.click(fn=gen, inputs=[prompt, basemodel_choice, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model_choice, process_lora], outputs=output_res)
+                btn.click(fn=gen, inputs=[prompt, basemodel_choice, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model_choice, process_lora], outputs=[output_res], error_handler=error_handler)
     demo.launch()