Remove bogus file

src/diffusers/models/unet_grad_tts.py

@@ -5,8 +5,7 @@ from ..modeling_utils import ModelMixin
 from .attention import LinearAttention
 from .embeddings import get_timestep_embedding
 from .resnet import Downsample
-from .resnet import ResnetBlock as ResnetBlockNew
-from .resnet import ResnetBlockGradTTS as ResnetBlock
+from .resnet import ResnetBlock
 from .resnet import Upsample
 
 
@@ -82,20 +81,13 @@ class UNetGradTTSModel(ModelMixin, ConfigMixin):
         self.ups = torch.nn.ModuleList([])
         num_resolutions = len(in_out)
 
-#        num_groups = 8
-#        self.pre_norm = False
-#        eps = 1e-5
-#        non_linearity = "mish"
-
         for ind, (dim_in, dim_out) in enumerate(in_out):
             is_last = ind >= (num_resolutions - 1)
             self.downs.append(
                 torch.nn.ModuleList(
                     [
-#                        ResnetBlock(dim_in, dim_out, time_emb_dim=dim),
-#                        ResnetBlock(dim_out, dim_out, time_emb_dim=dim),
-                        ResnetBlockNew(in_channels=dim_in, out_channels=dim_out, temb_channels=dim, groups=8, pre_norm=False, eps=1e-5, non_linearity="mish", overwrite_for_grad_tts=True),
-                        ResnetBlockNew(in_channels=dim_out, out_channels=dim_out, temb_channels=dim, groups=8, pre_norm=False, eps=1e-5, non_linearity="mish", overwrite_for_grad_tts=True),
+                        ResnetBlock(in_channels=dim_in, out_channels=dim_out, temb_channels=dim, groups=8, pre_norm=False, eps=1e-5, non_linearity="mish", overwrite_for_grad_tts=True),
+                        ResnetBlock(in_channels=dim_out, out_channels=dim_out, temb_channels=dim, groups=8, pre_norm=False, eps=1e-5, non_linearity="mish", overwrite_for_grad_tts=True),
                         Residual(Rezero(LinearAttention(dim_out))),
                         Downsample(dim_out, use_conv=True, padding=1) if not is_last else torch.nn.Identity(),
                     ]
@@ -103,20 +95,16 @@ class UNetGradTTSModel(ModelMixin, ConfigMixin):
             )
 
         mid_dim = dims[-1]
-#        self.mid_block1 = ResnetBlock(mid_dim, mid_dim, time_emb_dim=dim)
-#        self.mid_block2 = ResnetBlock(mid_dim, mid_dim, time_emb_dim=dim)
-        self.mid_block1 = ResnetBlockNew(in_channels=mid_dim, out_channels=mid_dim, temb_channels=dim, groups=8, pre_norm=False, eps=1e-5, non_linearity="mish", overwrite_for_grad_tts=True)
+        self.mid_block1 = ResnetBlock(in_channels=mid_dim, out_channels=mid_dim, temb_channels=dim, groups=8, pre_norm=False, eps=1e-5, non_linearity="mish", overwrite_for_grad_tts=True)
         self.mid_attn = Residual(Rezero(LinearAttention(mid_dim)))
-        self.mid_block2 = ResnetBlockNew(in_channels=mid_dim, out_channels=mid_dim, temb_channels=dim, groups=8, pre_norm=False, eps=1e-5, non_linearity="mish", overwrite_for_grad_tts=True)
+        self.mid_block2 = ResnetBlock(in_channels=mid_dim, out_channels=mid_dim, temb_channels=dim, groups=8, pre_norm=False, eps=1e-5, non_linearity="mish", overwrite_for_grad_tts=True)
 
         for ind, (dim_in, dim_out) in enumerate(reversed(in_out[1:])):
             self.ups.append(
                 torch.nn.ModuleList(
                     [
-#                        ResnetBlock(dim_out * 2, dim_in, time_emb_dim=dim),
-#                        ResnetBlock(dim_in, dim_in, time_emb_dim=dim),
-                        ResnetBlockNew(in_channels=dim_out * 2, out_channels=dim_in, temb_channels=dim, groups=8, pre_norm=False, eps=1e-5, non_linearity="mish", overwrite_for_grad_tts=True),
-                        ResnetBlockNew(in_channels=dim_in, out_channels=dim_in, temb_channels=dim, groups=8, pre_norm=False, eps=1e-5, non_linearity="mish", overwrite_for_grad_tts=True),
+                        ResnetBlock(in_channels=dim_out * 2, out_channels=dim_in, temb_channels=dim, groups=8, pre_norm=False, eps=1e-5, non_linearity="mish", overwrite_for_grad_tts=True),
+                        ResnetBlock(in_channels=dim_in, out_channels=dim_in, temb_channels=dim, groups=8, pre_norm=False, eps=1e-5, non_linearity="mish", overwrite_for_grad_tts=True),
                         Residual(Rezero(LinearAttention(dim_in))),
                         Upsample(dim_in, use_conv_transpose=True),
                     ]