Merge pull request #57 from ZeroCool940711/dev

Added support for both 3 and 4 channels images.

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.")
+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(
@@ -435,6 +437,7 @@ def main():
         center_crop=True if not args.no_center_crop and not args.random_crop else False,
         flip=not args.no_flip,
         random_crop=args.random_crop if args.random_crop else False,
+        alpha_channel=False if args.channels == 3 else True,
     )
 
     if args.input_image and not args.input_folder:

muse_maskgit_pytorch/dataset.py

@@ -45,15 +45,23 @@ def __init__(
         stream=False,
         using_taming=False,
         random_crop=False,
+        alpha_channel=True,
     ):
         super().__init__()
         self.dataset = dataset
         self.image_column = image_column
         self.stream = stream
         transform_list = [
-            T.Lambda(lambda img: img.convert("RGB") if img.mode != "RGB" else img),
+            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:
             transform_list.append(T.RandomHorizontalFlip())
         if center_crop and not random_crop:
@@ -199,7 +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
+        self,
+        path,
+        image_size,
+        tokenizer,
+        flip=True,
+        center_crop=True,
+        using_taming=False,
+        random_crop=False,
+        alpha_channel=False,
     ):
         super().__init__()
         self.tokenizer = tokenizer
@@ -229,7 +245,13 @@ def __init__(
             self.caption_pair.append(captions)
 
         transform_list = [
-            T.Lambda(lambda img: img.convert("RGB") if img.mode != "RGB" else img),
+            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

@@ -156,7 +156,7 @@ def forward(self, x):
 
 # discriminator
 class Discriminator(nn.Module):
-    def __init__(self, dims, channels=3, groups=16, init_kernel_size=5):
+    def __init__(self, dims, channels=4, groups=16, init_kernel_size=5):
         super().__init__()
         dim_pairs = zip(dims[:-1], dims[1:])
 
@@ -194,7 +194,7 @@ def __init__(
         self,
         dim: int,
         *,
-        channels=3,
+        channels=4,
         layers=4,
         layer_mults=None,
         num_resnet_blocks=1,
@@ -337,7 +337,7 @@ def __init__(
         *,
         dim: int,
         accelerator: Accelerator = None,
-        channels=3,
+        channels=4,
         layers=4,
         l2_recon_loss=False,
         use_hinge_loss=True,
@@ -407,7 +407,8 @@ def vgg(self):
         if exists(self._vgg):
             return self._vgg
 
-        vgg = torchvision.models.vgg16(weights=torchvision.models.VGG16_Weights.DEFAULT)
+        vgg = torchvision.models.vgg16(pretrained=True)
+        vgg.features[0] = nn.Conv2d(self.channels, 64, kernel_size=3, stride=1, padding=1)
         vgg.classifier = nn.Sequential(*vgg.classifier[:-2])
         self._vgg = vgg.to(self.device)
         return self._vgg

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.")
+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(
@@ -812,6 +814,7 @@ def main():
                 flip=False if args.no_flip else True,
                 using_taming=False if not args.taming_model_path else True,
                 random_crop=args.random_crop if args.random_crop else False,
+                alpha_channel=False if args.channels == 3 else True,
             )
         elif args.link:
             if not args.dataset_name:

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.")
+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(
@@ -563,6 +565,7 @@ def main():
         flip=not args.no_flip,
         stream=args.streaming,
         random_crop=args.random_crop,
+        alpha_channel=False if args.channels == 3 else True,
     )
     # dataloader