debug

app.py

@@ -23,8 +23,8 @@ for taking it to the next level by enabling inpainting with the FLUX.
 
 MAX_SEED = np.iinfo(np.int32).max
 IMAGE_SIZE = 1024
-# DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-DEVICE = torch.device("cuda")
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+# DEVICE = torch.device("cpu")
 
 torch.autocast(device_type="cuda", dtype=torch.bfloat16).__enter__()
 if torch.cuda.get_device_properties(0).major >= 8:
@@ -57,6 +57,12 @@ def resize_image_dimensions(
     return new_width, new_height
 
 
+def is_image_empty(image: Image.Image) -> bool:
+    gray_img = image.convert("L")
+    pixels = list(gray_img.getdata())
+    return all(pixel == 0 for pixel in pixels)
+
+
 @spaces.GPU(duration=150)
 @torch.inference_mode()
 @torch.autocast(device_type="cuda", dtype=torch.bfloat16)
@@ -81,11 +87,11 @@ def process(
         gr.Info("Please upload an image.")
         return None, None
 
-    if not mask and not segmentation_prompt_text:
+    if is_image_empty(mask) and not segmentation_prompt_text:
         gr.Info("Please draw a mask or enter a segmentation prompt.")
         return None, None
 
-    if mask and segmentation_prompt_text:
+    if not is_image_empty(mask) and segmentation_prompt_text:
         gr.Info("Both mask and segmentation prompt are provided. Please provide only "
                 "one.")
         return None, None
@@ -122,23 +128,23 @@ def process(
     mask = mask.resize((width, height), Image.LANCZOS)
     mask = mask.filter(ImageFilter.GaussianBlur(radius=10))
 
-    return image, mask
-
-    # if randomize_seed_checkbox:
-    #     seed_slicer = random.randint(0, MAX_SEED)
-    # generator = torch.Generator().manual_seed(seed_slicer)
-    # result = FLUX_INPAINTING_PIPELINE(
-    #     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]
-    # print('INFERENCE DONE')
-    # return result, mask
+    # return image, mask
+
+    if randomize_seed_checkbox:
+        seed_slicer = random.randint(0, MAX_SEED)
+    generator = torch.Generator().manual_seed(seed_slicer)
+    result = FLUX_INPAINTING_PIPELINE(
+        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]
+    print('INFERENCE DONE')
+    return result, mask
 
 
 with gr.Blocks() as demo: