upsampling method changed + face model added

README.md

@@ -1,7 +1,13 @@
 ## PyTorch Implementation of [AnimeGANv2](https://github.com/TachibanaYoshino/AnimeGANv2)
 
 
-**Weight Conversion (Optional, requires TensorFlow 1.x)**
+**Face Model**: Distilled from [this model](https://github.com/bryandlee/naver-webtoon-faces/blob/master/README.md#face2webtoon) with L2 + VGG + GAN Loss and CelebA-HQ images. See `test_faces.ipynb` for the inference. Model file can be downloaded from [here](https://drive.google.com/file/d/10T6F3-_RFOCJn6lMb-6mRmcISuYWJXGc/view?usp=sharing). Enjoy!
+
+
+<img src="./samples/face_results.jpg" width="512"> &nbsp; 
+
+
+**Weight Conversion from the Original Repo (Requires TensorFlow 1.x)**
 ```
 git clone https://github.com/TachibanaYoshino/AnimeGANv2
 python convert_weights.py
@@ -12,6 +18,8 @@ python convert_weights.py
 python test.py --input_dir [image_folder_path]
 ```
 
+
+
 **Results from converted [[Paprika](https://drive.google.com/file/d/1K_xN32uoQKI8XmNYNLTX5gDn1UnQVe5I/view?usp=sharing)] style model**
 
 (input image, original tensorflow result, pytorch result from left to right)
@@ -20,4 +28,4 @@ python test.py --input_dir [image_folder_path]
 <img src="./samples/compare/2.jpg" width="960"> &nbsp; 
 <img src="./samples/compare/3.jpg" width="960"> &nbsp; 
 
-**Note:** Training code not included / Tested on RTX3090 + PyTorch1.7.1 / Results slightly different due to the [bilinear upsample issue](https://github.com/pytorch/pytorch/issues/10604)
+**Note:** Tested on RTX3090 + PyTorch1.7.1 / Results from converted weights slightly different due to the [bilinear upsample issue](https://github.com/pytorch/pytorch/issues/10604)

model.py

@@ -93,12 +93,12 @@ class Generator(nn.Module):
         out = self.block_b(out)
         out = self.block_c(out)
         
-#         out = F.interpolate(out, half_size, mode="bilinear", align_corners=True)
-        out = F.interpolate(out, scale_factor=2, mode="bilinear", align_corners=False)
+        out = F.interpolate(out, half_size, mode="bilinear", align_corners=True)
+#         out = F.interpolate(out, scale_factor=2, mode="bilinear", align_corners=False)
         out = self.block_d(out)
 
-#         out = F.interpolate(out, input.size()[-2:], mode="bilinear", align_corners=True)
-        out = F.interpolate(out, scale_factor=2, mode="bilinear", align_corners=False)
+        out = F.interpolate(out, input.size()[-2:], mode="bilinear", align_corners=True)
+#         out = F.interpolate(out, scale_factor=2, mode="bilinear", align_corners=False)
         out = self.block_e(out)
 
         out = self.out_layer(out)