diff --git a/DiT/model.py b/DiT/model.py
index 327b86b..74103ee 100644
--- a/DiT/model.py
+++ b/DiT/model.py
@@ -230,51 +230,46 @@ class DiT(nn.Module):
 		imgs = x.reshape(shape=(x.shape[0], c, h * p, h * p))
 		return imgs
 
-	def forward(self, x, timesteps, context, y, **kwargs):
+	def forward_raw(self, x, t, y):
 		"""
 		Forward pass of DiT.
 		x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
 		t: (N,) tensor of diffusion timesteps
 		y: (N,) tensor of class labels
 		"""
-		# adapt to comfy inputs
-		t = timesteps
-		y = context.to(torch.int)
-		y = y[:, 0]
-
-		x = self.x_embedder(x) + self.pos_embed # (N, T, D), where T = H * W / patch_size ** 2
-		t = self.t_embedder(t)                  # (N, D) + change to model dtype
-		y = self.y_embedder(y, self.training)   # (N, D)
-		c = t + y                               # (N, D)
+		x = self.x_embedder(x) + self.pos_embed  # (N, T, D), where T = H * W / patch_size ** 2
+		t = self.t_embedder(t)                   # (N, D)
+		y = self.y_embedder(y, self.training)    # (N, D)
+		c = t + y                                # (N, D)
 		for block in self.blocks:
-			x = block(x, c)                     # (N, T, D)
-		x = self.final_layer(x, c)              # (N, T, patch_size ** 2 * out_channels)
-		x = self.unpatchify(x)                  # (N, out_channels, H, W)
+			x = block(x, c)                      # (N, T, D)
+		x = self.final_layer(x, c)                # (N, T, patch_size ** 2 * out_channels)
+		x = self.unpatchify(x)                   # (N, out_channels, H, W)
+		return x
 
-		# adapt to comfy outputs
-		x = x.to(torch.float)
-		eps, rest = x[:, :self.in_channels], x[:, self.in_channels:]
+	def forward(self, x, timesteps, context, y=None, **kwargs):
+		"""
+		Forward pass that adapts comfy input to original forward function
+		x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
+		timesteps: (N,) tensor of diffusion timesteps
+		context: (N, [LabelID]) conditioning
+		y: extra conditioning.
+		"""
+		## Remove outer array from cond
+		context = context[:, 0]
+
+		## run original forward pass
+		out = self.forward_raw(
+			x = x.to(self.dtype),
+			t = timesteps.to(self.dtype),
+			y = context.to(torch.int),
+		)
+
+		## only return EPS
+		out = out.to(torch.float)
+		eps, rest = out[:, :self.in_channels], out[:, self.in_channels:]
 		return eps
 
-	def forward_with_cfg(self, x, t, y, cfg_scale):
-		"""
-		Forward pass of DiT, but also batches the unconditional forward pass for classifier-free guidance.
-		"""
-		# https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb
-		half = x[: len(x) // 2]
-		combined = torch.cat([half, half], dim=0)
-		model_out = self.forward(combined, t, y)
-		# For exact reproducibility reasons, we apply classifier-free guidance on only
-		# three channels by default. The standard approach to cfg applies it to all channels.
-		# This can be done by uncommenting the following line and commenting-out the line following that.
-		eps, rest = model_out[:, :self.in_channels], model_out[:, self.in_channels:]
-		# eps, rest = model_out[:, :3], model_out[:, 3:]
-		cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0)
-		half_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps)
-		eps = torch.cat([half_eps, half_eps], dim=0)
-		return torch.cat([eps, rest], dim=1)
-
-
 #################################################################################
 #				   Sine/Cosine Positional Embedding Functions				  #
 #################################################################################
diff --git a/DiT/nodes.py b/DiT/nodes.py
index c4e6316..1b05f79 100644
--- a/DiT/nodes.py
+++ b/DiT/nodes.py
@@ -78,7 +78,7 @@ class DiTCondLabelSelect:
 		global label_data
 		class_labels = [int(k) for k,v in label_data.items() if v == label_name]
 		y = torch.tensor([[class_labels[0]]]).to(torch.int)
-		return ([[y, {"pooled_output": []}]], )
+		return ([[y, {}]], )
 
 class DiTCondLabelEmpty:
 	@classmethod
@@ -100,7 +100,7 @@ class DiTCondLabelEmpty:
 		# [ID of last class + 1] == [num_classes]
 		y_null = model.model.dit_config["num_classes"]
 		y = torch.tensor([[y_null]]).to(torch.int)
-		return ([[y, {"pooled_output": []}]], )
+		return ([[y, {}]], )
 
 NODE_CLASS_MAPPINGS = {
 	"DitCheckpointLoader" : DitCheckpointLoader,