diff --git a/nodes.py b/nodes.py
index c3df7fe..a10ce7d 100644
--- a/nodes.py
+++ b/nodes.py
@@ -57,6 +57,7 @@ def teacache_flux_forward(
         img_mod1, _ = self.double_blocks[0].img_mod(vec_)
         modulated_inp = self.double_blocks[0].img_norm1(inp)
         modulated_inp = (1 + img_mod1.scale) * modulated_inp + img_mod1.shift
+        ca_idx = 0
 
         if not hasattr(self, 'accumulated_rel_l1_distance'):
             should_calc = True
@@ -113,6 +114,16 @@ def teacache_flux_forward(
                         if add is not None:
                             img += add
 
+                # PuLID attention
+                if getattr(self, "pulid_data", {}):
+                    if i % self.pulid_double_interval == 0:
+                        # Will calculate influence of all pulid nodes at once
+                        for _, node_data in self.pulid_data.items():
+                            if torch.any((node_data['sigma_start'] >= timesteps)
+                                        & (timesteps >= node_data['sigma_end'])):
+                                img = img + node_data['weight'] * self.pulid_ca[ca_idx](node_data['embedding'], img)
+                        ca_idx += 1
+
             img = torch.cat((txt, img), 1)
 
             for i, block in enumerate(self.single_blocks):
@@ -141,6 +152,18 @@ def teacache_flux_forward(
                         if add is not None:
                             img[:, txt.shape[1] :, ...] += add
 
+                # PuLID attention
+                if getattr(self, "pulid_data", {}):
+                    real_img, txt = img[:, txt.shape[1]:, ...], img[:, :txt.shape[1], ...]
+                    if i % self.pulid_single_interval == 0:
+                        # Will calculate influence of all nodes at once
+                        for _, node_data in self.pulid_data.items():
+                            if torch.any((node_data['sigma_start'] >= timesteps)
+                                        & (timesteps >= node_data['sigma_end'])):
+                                real_img = real_img + node_data['weight'] * self.pulid_ca[ca_idx](node_data['embedding'], real_img)
+                        ca_idx += 1
+                    img = torch.cat((txt, real_img), 1)
+
             img = img[:, txt.shape[1] :, ...]
             self.previous_residual = img - ori_img