finalize

app.py

@@ -128,7 +128,8 @@ def inpaint(input_img, input_img_with_mask, prompt):
     input_img_with_mask_converted = input_img.convert('RGBA').getchannel( 'A' )  # Mode 'L'
 
     input_img_with_mask_64 = input_img_with_mask_converted.resize((64, 64), resample=Image.BICUBIC)
-    input_img_with_mask_256 = input_img_with_mask_converted.resize((256, 256), resample=Image.BICUBIC)
+    # TODO: make 256x256 mask more accurate when upscaling?
+    # input_img_with_mask_256 = input_img_with_mask_converted.resize((256, 256), resample=Image.BICUBIC)
     # return input_img, input_img_with_mask_64
     # Source image we are inpainting
     source_image_256 = pil_to_numpy(input_img_256)
@@ -138,18 +139,18 @@ def inpaint(input_img, input_img_with_mask, prompt):
     # Assuming that all black pixels are meant for inpainting.
     # input_img_with_mask_64 = input_img_with_mask.convert('L').resize((64, 64), resample=Image.BICUBIC)
     gray_scale_source_image_64 = image_to_tensor(input_img_with_mask_64)
-    gray_scale_source_image_256 = image_to_tensor(input_img_with_mask_256)
+    # gray_scale_source_image_256 = image_to_tensor(input_img_with_mask_256)
 
     source_mask_64 = (gray_scale_source_image_64!=0).float()
-    source_mask_256 = (gray_scale_source_image_256!=0).float()
+    # source_mask_256 = (gray_scale_source_image_256!=0).float()
 
     source_mask_64_img = tensor_to_image(source_mask_64)
 
     # The mask should always be a boolean 64x64 mask, and then we
     # can upsample it for the second stage.
     source_mask_64 = source_mask_64.unsqueeze(0)
-    source_mask_256 = source_mask_256.unsqueeze(0)
-    # source_mask_256 = F.interpolate(source_mask_64, (256, 256), mode='nearest')
+    # source_mask_256 = source_mask_256.unsqueeze(0)
+    source_mask_256 = F.interpolate(source_mask_64, (256, 256), mode='nearest')
 
 
     ##############################
@@ -249,19 +250,16 @@ def inpaint(input_img, input_img_with_mask, prompt):
 gradio_inputs = [gr.inputs.Image(type='pil',
                                  label="Input Image",
                                  image_mode="RGBA"),
-                 gr.inputs.Image(type='pil',
-                                 label="Input Image With Mask"),
                  gr.inputs.Textbox(label='Conditional Text to Inpaint')]
 
 # gradio_outputs = [gr.outputs.Image(label='Auto-Detected Mask (From drawn black pixels)')]
 
-gradio_outputs = [gr.outputs.Image(label='Auto-Detected Mask (From drawn black pixels)'),
-                 gr.outputs.Image(label='Inpainted Image')]
-examples = [['grass.png', 'grass_with_mask.png', 'a corgi in a field']]
+gradio_outputs = [gr.outputs.Image(label='Inpainted Image')]
+examples = [['desert.png', 'a car in the desert']]
 
 title = "GLIDE Inpaint"
 description = "[WARNING: Queue times may take 4-6 minutes per person if there's no GPU! If there is a GPU, it'll take around 60 seconds] Using GLIDE to inpaint black regions of an input image! Instructions: 1) For the 'Input Image', upload an image. 2) For the 'Input Image with Mask', draw a black-colored mask (either manually with something like Paint, or by using gradio's built-in image editor & add a black-colored shape) IT MUST BE BLACK COLOR, but doesn't have to be rectangular! This is because it auto-detects the mask based on 0 (black) pixel values! 3) For the Conditional Text, type something you'd like to see the black region get filled in with :)"
-article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2112.10741' target='_blank'>GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models</a> | <a href='https://github.com/openai/glide-text2im' target='_blank'>Github Repo</a> | <img src='https://visitor-badge.glitch.me/badge?page_id=epoching_glide_inpaint' alt='visitor badge'></p>"
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2112.10741' target='_blank'>GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models</a> | <a href='https://github.com/openai/glide-text2im' target='_blank'>Github Repo</a></p>"
 iface = gr.Interface(fn=inpaint, inputs=gradio_inputs,
                      outputs=gradio_outputs,
                      examples=examples, title=title,