debug

app.py

@@ -23,18 +23,13 @@ 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("cpu")
 
-# torch.autocast(device_type="cuda", dtype=torch.bfloat16).__enter__()
-# if torch.cuda.get_device_properties(0).major >= 8:
-#     torch.backends.cuda.matmul.allow_tf32 = True
-#     torch.backends.cudnn.allow_tf32 = True
-
 FLORENCE_MODEL, FLORENCE_PROCESSOR = load_florence_model(device=DEVICE)
 SAM_IMAGE_MODEL = load_sam_image_model(device=DEVICE)
-# FLUX_INPAINTING_PIPELINE = FluxInpaintPipeline.from_pretrained(
-#     "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16).to(DEVICE)
+FLUX_INPAINTING_PIPELINE = FluxInpaintPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-schnell",
+    torch_dtype=torch.bfloat16).to(torch.device("cuda"))
 
 
 def resize_image_dimensions(
@@ -65,7 +60,6 @@ def is_image_empty(image: Image.Image) -> bool:
 
 @spaces.GPU()
 @torch.inference_mode()
-# @torch.autocast(device_type="cuda", dtype=torch.bfloat16)
 def process(
     input_image_editor: dict,
     inpainting_prompt_text: str,
@@ -128,23 +122,23 @@ 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: