debug

app.py

@@ -94,6 +94,7 @@ def process(
     image = image.resize((width, height), Image.LANCZOS)
 
     if segmentation_prompt_text:
+        print('FLORENCE INFERENCE STARTED')
         _, result = run_florence_inference(
             model=FLORENCE_MODEL,
             processor=FLORENCE_PROCESSOR,
@@ -107,7 +108,10 @@ def process(
             result=result,
             resolution_wh=image.size
         )
+        print('FLORENCE INFERENCE DONE')
+        print('SAM INFERENCE STARTED')
         detections = run_sam_inference(SAM_IMAGE_MODEL, image, detections)
+        print('SAM INFERENCE DONE')
 
         if len(detections) == 0:
             gr.Info(f"{segmentation_prompt_text} prompt did not return any detections.")
@@ -118,21 +122,23 @@ def process(
     mask = mask.resize((width, height), Image.LANCZOS)
     mask = mask.filter(ImageFilter.GaussianBlur(radius=10))
 
-    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: