fix

app.py

@@ -60,6 +60,33 @@ def set_client_for_session(request: gr.Request):
 
 
 @spaces.GPU(duration=100)
+def run_flux(
+    image: Image.Image,
+    mask: Image.Image,
+    inpainting_prompt_text: str,
+    seed_slicer: int,
+    randomize_seed_checkbox: bool,
+    strength_slider: float,
+    num_inference_steps_slider: int,
+    width: int,
+    height: int,
+    progress=gr.Progress(track_tqdm=True)
+) -> Image.Image:
+    if randomize_seed_checkbox:
+        seed_slicer = random.randint(0, MAX_SEED)
+    generator = torch.Generator().manual_seed(seed_slicer)
+    return pipe(
+        prompt=inpainting_prompt_text,
+        image=image,
+        mask_image=mask,
+        width=width,
+        height=height,
+        strength=strength_slider,
+        generator=generator,
+        num_inference_steps=num_inference_steps_slider
+    ).images[0]
+
+
 def process(
     client,
     input_image_editor: dict,
@@ -102,24 +129,21 @@ def process(
 
     mask = mask.filter(ImageFilter.GaussianBlur(radius=5))
     width, height = resize_image_dimensions(original_resolution_wh=image.size)
-    resized_image = image.resize((width, height), Image.LANCZOS)
-    resized_mask = mask.resize((width, height), Image.LANCZOS)
-
-    if randomize_seed_checkbox:
-        seed_slicer = random.randint(0, MAX_SEED)
-    generator = torch.Generator().manual_seed(seed_slicer)
-    result = pipe(
-        prompt=inpainting_prompt_text,
-        image=resized_image,
-        mask_image=resized_mask,
+    image = image.resize((width, height), Image.LANCZOS)
+    mask = mask.resize((width, height), Image.LANCZOS)
+    result = run_flux(
+        image=image,
+        mask=mask,
+        inpainting_prompt_text=inpainting_prompt_text,
+        seed_slicer=seed_slicer,
+        randomize_seed_checkbox=randomize_seed_checkbox,
+        strength_slider=strength_slider,
+        num_inference_steps_slider=num_inference_steps_slider,
         width=width,
         height=height,
-        strength=strength_slider,
-        generator=generator,
-        num_inference_steps=num_inference_steps_slider
-    ).images[0]
-    print('INFERENCE DONE')
-    return result, resized_mask
+        progress=progress
+    )
+    return result, mask
 
 
 with gr.Blocks() as demo: