Update app.py

app.py

@@ -6,7 +6,6 @@ from huggingface_hub import AsyncInferenceClient, InferenceClient
 from PIL import Image
 from gradio_client import Client, handle_file
 from gradio_imageslider import ImageSlider
-import asyncio
 
 MAX_SEED = np.iinfo(np.int32).max
 HF_TOKEN = os.environ.get("HF_TOKEN")
@@ -38,40 +37,37 @@ 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):
-    try:
-        model = enable_lora(lora_model, basemodel) if process_lora else basemodel
-        
-        improved_prompt = await improve_prompt(prompt)
-        combined_prompt = f"{prompt} {improved_prompt}"
+    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, "", "No generando"]
-        
-        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", "", "Generando..."]
-            else:
-                return [image_path, image_path, "", "Generando..."]
+    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, "", "Generando..."]
-    except Exception as e:
-        return [f"Error: {e}", None, "", "No generando"]
+            return [image_path, image_path]
+    else:
+        return [image_path, image_path]
 
 async def improve_prompt(prompt):
     try:
-        instruction = "improve this idea and describe in English a detailed img2vid prompt in a single paragraph of up to 200 characters, developing atmosphere, characters, lighting, and cameras."
-        formatted_prompt = f"{prompt}: {instruction}"
-        response = llm_client.text_generation(formatted_prompt, max_new_tokens=200)
+        instruction = "Improve and translate this prompt into English, adding detailed descriptions of style, cinematography, cameras, atmosphere, and lighting for the best quality, up to 200 words."
+        formatted_prompt = f"{instruction}: {prompt}"
+        response = llm_client.text_generation(formatted_prompt, max_new_tokens=300)  # Allowing more tokens for detailed description
         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}"
@@ -80,10 +76,6 @@ css = """
 #col-container{ margin: 0 auto; max-width: 1024px;}
 """
 
-def improve_prompt_wrapper(prompt):
-    improved_text = asyncio.run(improve_prompt(prompt))
-    return prompt, improved_text
-
 with gr.Blocks(css=css, theme="Nymbo/Nymbo_Theme") as demo:
     with gr.Column(elem_id="col-container"):
         with gr.Row():
@@ -98,25 +90,20 @@ 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_wrapper, inputs=[prompt], outputs=[prompt, improved_prompt])
+                improve_btn = gr.Button("Mejora mi prompt")
+                improve_btn.click(fn=improve_prompt, inputs=[prompt], outputs=improved_prompt)
                 
-                generating_label = gr.Label(label="No generando")
+                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)
+                    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)
+    
                 btn = gr.Button("Generar")
-
-                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)
-                
-                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, prompt, generating_label]
-                )
-                    
-    demo.launch()
+                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)
+    demo.launch()