feat: Refactorizar función de refinamiento de imágenes en app.py

app.py

@@ -134,6 +134,24 @@ def save_image(image):
     image.save(unique_filename)
     return unique_filename
 
+def refine_image(apply_refiner, image, model ,prompt, negative_prompt, num_inference_steps, guidance_scale, seed, strength):
+    if (not apply_refiner):
+        return image
+    
+    client = InferenceClient()
+    refined_image = client.image_to_image(
+        handle_file(image), 
+        prompt=prompt, 
+        negative_prompt=negative_prompt, 
+        num_inference_steps=num_inference_steps, 
+        guidance_scale=guidance_scale, 
+        seed=seed, 
+        model=model,
+        strength=strength
+    )
+    return refined_image
+
+
 
 def upscale_image(image, resolution, num_inference_steps, strength, hdr, guidance_scale, controlnet_strength, scheduler_name, reduce_factor):
     global client_tile_upscaler
@@ -166,7 +184,7 @@ def upscale_image(image, resolution, num_inference_steps, strength, hdr, guidanc
         print(f"Reduce Factor: {reduce_factor}")
 
         job = client_tile_upscaler.submit(
-            param_0=image,
+            param_0=handle_file(image),
             param_1=resolution,
             param_2=num_inference_steps,
             param_3=strength,
@@ -183,24 +201,6 @@ def upscale_image(image, resolution, num_inference_steps, strength, hdr, guidanc
     return [image, result]
 
 
-def refine_image(apply_refiner, image, model ,prompt, negative_prompt, num_inference_steps, guidance_scale, seed, strength):
-    if (not apply_refiner):
-        return image
-    
-    client = InferenceClient()
-    refined_image = client.image_to_image(
-        handle_file(image), 
-        prompt=prompt, 
-        negative_prompt=negative_prompt, 
-        num_inference_steps=num_inference_steps, 
-        guidance_scale=guidance_scale, 
-        seed=seed, 
-        model=model,
-        strength=strength
-    )
-    return refined_image
-
-
 css="""
 """