address #282

setup.py

@@ -8,38 +8,41 @@
   name = 'stylegan2_pytorch',
   packages = find_packages(),
   entry_points={
-      'console_scripts': [
-          'stylegan2_pytorch = stylegan2_pytorch.cli:main',
-      ],
+    'console_scripts': [
+      'stylegan2_pytorch = stylegan2_pytorch.cli:main',
+    ],
   },
   version = __version__,
-  license='GPLv3+',
+  license = 'MIT',
   description = 'StyleGan2 in Pytorch',
   long_description_content_type = 'text/markdown',
   author = 'Phil Wang',
   author_email = 'lucidrains@gmail.com',
   url = 'https://github.com/lucidrains/stylegan2-pytorch',
   download_url = 'https://github.com/lucidrains/stylegan2-pytorch/archive/v_036.tar.gz',
-  keywords = ['generative adversarial networks', 'artificial intelligence'],
+  keywords = [
+    'generative adversarial networks',
+    'artificial intelligence'
+  ],
   install_requires=[
-      'aim',
-      'einops',
-      'contrastive_learner>=0.1.0',
-      'fire',
-      'kornia>=0.5.4',
-      'numpy',
-      'retry',
-      'tqdm',
-      'torch',
-      'torchvision',
-      'pillow',
-      'vector-quantize-pytorch==0.1.0'
+    'aim',
+    'einops>=0.7.0',
+    'contrastive_learner>=0.1.0',
+    'fire',
+    'kornia>=0.5.4',
+    'numpy',
+    'retry',
+    'tqdm',
+    'torch',
+    'torchvision',
+    'pillow',
+    'vector-quantize-pytorch==0.1.0'
   ],
   classifiers=[
-      'Development Status :: 4 - Beta',
-      'Intended Audience :: Developers',
-      'Topic :: Scientific/Engineering :: Artificial Intelligence',
-      'License :: OSI Approved :: MIT License',
-      'Programming Language :: Python :: 3.6',
+    'Development Status :: 4 - Beta',
+    'Intended Audience :: Developers',
+    'Topic :: Scientific/Engineering :: Artificial Intelligence',
+    'License :: OSI Approved :: MIT License',
+    'Programming Language :: Python :: 3.6',
   ],
-)
+)

stylegan2_pytorch/stylegan2_pytorch.py

@@ -1251,8 +1251,8 @@ def calculate_fid(self, num_batches):
         return fid_score.calculate_fid_given_paths([str(real_path), str(fake_path)], 256, noise.device, 2048)
 
     @torch.no_grad()
-    def truncate_style(self, tensor, trunc_psi = 0.75):
-        S = self.GAN.S
+    def truncate_style(self, tensor, S = None, trunc_psi = 0.75):
+        S = default(S, self.GAN.S)
         batch_size = self.batch_size
         latent_dim = self.GAN.G.latent_dim
 
@@ -1267,17 +1267,17 @@ def truncate_style(self, tensor, trunc_psi = 0.75):
         return tensor
 
     @torch.no_grad()
-    def truncate_style_defs(self, w, trunc_psi = 0.75):
+    def truncate_style_defs(self, w, S = None, trunc_psi = 0.75):
         w_space = []
         for tensor, num_layers in w:
-            tensor = self.truncate_style(tensor, trunc_psi = trunc_psi)            
+            tensor = self.truncate_style(tensor, S = S, trunc_psi = trunc_psi)
             w_space.append((tensor, num_layers))
         return w_space
 
     @torch.no_grad()
     def generate_truncated(self, S, G, style, noi, trunc_psi = 0.75, num_image_tiles = 8):
         w = map(lambda t: (S(t[0]), t[1]), style)
-        w_truncated = self.truncate_style_defs(w, trunc_psi = trunc_psi)
+        w_truncated = self.truncate_style_defs(w, S = S, trunc_psi = trunc_psi)
         w_styles = styles_def_to_tensor(w_truncated)
         generated_images = evaluate_in_chunks(self.batch_size, G, w_styles, noi)
         return generated_images.clamp_(0., 1.)

stylegan2_pytorch/version.py

@@ -1 +1 @@
-__version__ = '1.8.9'
+__version__ = '1.8.10'