[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

infer_vae.py

@@ -112,7 +112,9 @@
 parser.add_argument("--lr", type=float, default=1e-4, help="Learning Rate.")
 parser.add_argument("--vq_codebook_size", type=int, default=256, help="Image Size.")
 parser.add_argument("--vq_codebook_dim", type=int, default=256, help="VQ Codebook dimensions.")
-parser.add_argument("--channels", type=int, default=3, help="Number of channels for the VAE. Use 3 for RGB or 4 for RGBA.")
+parser.add_argument(
+    "--channels", type=int, default=3, help="Number of channels for the VAE. Use 3 for RGB or 4 for RGBA."
+)
 parser.add_argument("--layers", type=int, default=4, help="Number of layers for the VAE.")
 parser.add_argument("--discr_layers", type=int, default=4, help="Number of layers for the VAE discriminator.")
 parser.add_argument(

muse_maskgit_pytorch/dataset.py

@@ -45,14 +45,20 @@ def __init__(
         stream=False,
         using_taming=False,
         random_crop=False,
-        alpha_channel=True
+        alpha_channel=True,
     ):
         super().__init__()
         self.dataset = dataset
         self.image_column = image_column
         self.stream = stream
         transform_list = [
-            T.Lambda(lambda img: img.convert("RGBA") if img.mode != "RGBA" and alpha_channel else img if img.mode == "RGB" and not alpha_channel else img.convert("RGB")),
+            T.Lambda(
+                lambda img: img.convert("RGBA")
+                if img.mode != "RGBA" and alpha_channel
+                else img
+                if img.mode == "RGB" and not alpha_channel
+                else img.convert("RGB")
+            ),
             T.Resize(image_size),
         ]
 
@@ -201,7 +207,15 @@ def __getitem__(self, index):
 
 class LocalTextImageDataset(Dataset):
     def __init__(
-        self, path, image_size, tokenizer, flip=True, center_crop=True, using_taming=False, random_crop=False, alpha_channel=False
+        self,
+        path,
+        image_size,
+        tokenizer,
+        flip=True,
+        center_crop=True,
+        using_taming=False,
+        random_crop=False,
+        alpha_channel=False,
     ):
         super().__init__()
         self.tokenizer = tokenizer
@@ -231,7 +245,13 @@ def __init__(
             self.caption_pair.append(captions)
 
         transform_list = [
-            T.Lambda(lambda img: img.convert("RGBA") if img.mode != "RGBA" and alpha_channel else img if img.mode == "RGB" and not alpha_channel else img.convert("RGB")),
+            T.Lambda(
+                lambda img: img.convert("RGBA")
+                if img.mode != "RGBA" and alpha_channel
+                else img
+                if img.mode == "RGB" and not alpha_channel
+                else img.convert("RGB")
+            ),
             T.Resize(image_size),
         ]
         if flip:

muse_maskgit_pytorch/vqgan_vae.py

@@ -413,7 +413,6 @@ def vgg(self):
         self._vgg = vgg.to(self.device)
         return self._vgg
 
-
     @property
     def encoded_dim(self):
         return self.enc_dec.encoded_dim

train_muse_maskgit.py

@@ -298,7 +298,9 @@
     help="Image Size.",
 )
 parser.add_argument("--vq_codebook_dim", type=int, default=256, help="VQ Codebook dimensions.")
-parser.add_argument("--channels", type=int, default=3, help="Number of channels for the VAE. Use 3 for RGB or 4 for RGBA.")
+parser.add_argument(
+    "--channels", type=int, default=3, help="Number of channels for the VAE. Use 3 for RGB or 4 for RGBA."
+)
 parser.add_argument("--layers", type=int, default=4, help="Number of layers for the VAE.")
 parser.add_argument("--discr_layers", type=int, default=4, help="Number of layers for the VAE discriminator.")
 parser.add_argument(

train_muse_vae.py

@@ -222,7 +222,9 @@
 )
 parser.add_argument("--vq_codebook_size", type=int, default=256, help="Image Size.")
 parser.add_argument("--vq_codebook_dim", type=int, default=256, help="VQ Codebook dimensions.")
-parser.add_argument("--channels", type=int, default=3, help="Number of channels for the VAE. Use 3 for RGB or 4 for RGBA.")
+parser.add_argument(
+    "--channels", type=int, default=3, help="Number of channels for the VAE. Use 3 for RGB or 4 for RGBA."
+)
 parser.add_argument("--layers", type=int, default=4, help="Number of layers for the VAE.")
 parser.add_argument("--discr_layers", type=int, default=4, help="Number of layers for the VAE discriminator.")
 parser.add_argument(