mask

app.py

@@ -70,8 +70,8 @@ def image_inpainting(prompt, image_path, mask_image_path, num_samples=4, is_orig
         guidance_scale = 7.5
         generator = torch.Generator(device=device).manual_seed(0)  # change the seed to get different results
 
-        images = pipe(prompt=prompt, image=image, mask_image=mask_image, guidance_scale=guidance_scale, generator=generator,
-                      num_images_per_prompt=num_samples).images
+        images = pipe(prompt=prompt, image=image, mask_image=mask_image, guidance_scale=guidance_scale,
+                      generator=generator, num_images_per_prompt=num_samples).images
     else:
         images = pipe(prompt=prompt, image=image, mask_image=mask_image, num_images_per_prompt=num_samples).images
 
@@ -83,9 +83,8 @@ def image_inpainting(prompt, image_path, mask_image_path, num_samples=4, is_orig
 title = "Person Matting & Stable Diffusion In-Painting"
 description = "Inpainting Stable Diffusion <br/><b>mediapipe + Stable Diffusion<b/><br/>"
 
-
-examples = [
-    [os.path.join(os.path.dirname(__file__), 'example1.png'), 'a bench in a field', 2],
+examples = [[{'image': os.path.join(os.path.dirname(__file__), 'example1.png'),
+              'mask': os.path.join(os.path.dirname(__file__), 'example1_mask.png')}, 'a bench in a field', 2],
     # [os.path.join(os.path.dirname(__file__), "example2.png"), 'a building with many steps', 2],
     # [os.path.join(os.path.dirname(__file__), "example3.png"), 'a big ship parked on the shore', 2]
 ]
@@ -95,7 +94,8 @@ def predict1(dict, prompt, num_samples):
     dict['image'].save('image.png')
     # dict['mask'].save('mask.png')
     mediapipe_segmentation('image.png', 'm_mask.png')
-    image = image_inpainting(prompt, num_samples=num_samples, image_path='image.png', mask_image_path='m_mask.png', is_origin=False)
+    image = image_inpainting(prompt, num_samples=num_samples, image_path='image.png', mask_image_path='m_mask.png',
+                             is_origin=False)
     return image
 
 
@@ -103,7 +103,8 @@ def predict2(dict, prompt, num_samples):
     dict['image'].save('image.png')
     if 'mask' in dict:
         dict['mask'].save('mask.png')
-    image = image_inpainting(prompt, num_samples=num_samples, image_path='image.png', mask_image_path='mask.png', is_origin=True)
+    image = image_inpainting(prompt, num_samples=num_samples, image_path='image.png', mask_image_path='mask.png',
+                             is_origin=True)
     return image
 
 
@@ -115,6 +116,5 @@ greeter_1 = gr.Interface(predict1, inputs=[image_input, prompt, number], outputs
 greeter_2 = gr.Interface(predict2, inputs=[image_input, prompt, number], outputs=gr.Image(label='paint'))
 demo = gr.Parallel(greeter_1, greeter_2, examples=examples)
 
-
 if __name__ == "__main__":
     demo.launch(enable_queue=True)