fixes to transparency handling to fix artifacting

app.py

@@ -105,9 +105,10 @@ def read_mask(pil_img_full, size: int = 256) -> Tuple[th.Tensor, th.Tensor]:
     pil_img_full = pil_img_full.convert('RGBA')
 
     pil_img = pil_img_full.getchannel( 'A' ) # Mode 'L'
-    pil_img = pil_img.resize((size, size), resample=PIL.Image.BICUBIC)
+    pil_img = pil_img.resize((size, size), resample=PIL.Image.BILINEAR)
 
     img = np.array(pil_img)[..., np.newaxis]
+    img[img < 255] = 0 # extremely aggressive transparency removal. maybe too aggressive. slider?
     return th.from_numpy(img)[None].permute(0, 3, 1, 2).float() / 255.0
 
 def pil_to_numpy(pil_img: Image) -> Tuple[th.Tensor, th.Tensor]:
@@ -134,11 +135,15 @@ def inpaint(input_img, prompt):
     source_image_64 = read_image(input_img, size=64)
 
     source_mask_64 = read_mask(input_img, size=64)
-    source_mask_64 = (source_mask_64>0.5).float()
+    source_mask_256 = F.interpolate(source_mask_64, (256, 256), mode='nearest')
+    # source_mask_64 = (source_mask_64>0.5).float()
+    # 0.75 = transparency threshold
+    # pixels below 75% opacity will be deleted
+    # add as an option in the future? 
 
     # these are better but will leave a "mark"
-    source_mask_256 = read_mask(input_img, size=256)
-    source_mask_256 = (source_mask_256>0.5).float()
+    # source_mask_256 = read_mask(input_img, size=256)
+    # source_mask_256 = (source_mask_256>0.5).float()
     # source_mask_256 = F.interpolate(source_mask_64, (256, 256), mode='nearest')
     # source_image_256 = pil_to_numpy(input_img_256)
     # source_image_64 = pil_to_numpy(input_img_64)