From 361134133918fcac3dd15e54b3ed6307e6455952 Mon Sep 17 00:00:00 2001
From: kijai <40791699+kijai@users.noreply.github.com>
Date: Sun, 14 Dec 2025 16:48:55 +0200
Subject: [PATCH] Fix uni3c reloading

---
 nodes_model_loading.py    | 2 +-
 nodes_sampler.py          | 2 +-
 wanvideo/modules/model.py | 8 ++++----
 3 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/nodes_model_loading.py b/nodes_model_loading.py
index 3366a8e..0517a6b 100644
--- a/nodes_model_loading.py
+++ b/nodes_model_loading.py
@@ -863,7 +863,7 @@ def load_weights(transformer, sd=None, weight_dtype=None, base_dtype=None,
             except Exception:
                 block_idx = None
 
-        if "loras" in name:
+        if "loras" in name or "uni3c" in name:
             continue
 
         # GGUF: skip GGUFParameter params
diff --git a/nodes_sampler.py b/nodes_sampler.py
index 88fece9..0329db3 100644
--- a/nodes_sampler.py
+++ b/nodes_sampler.py
@@ -890,7 +890,7 @@ class WanVideoSampler:
         #uni3c
         uni3c_data = uni3c_data_input = None
         if uni3c_embeds is not None:
-            transformer.controlnet = uni3c_embeds["controlnet"]
+            transformer.uni3c_controlnet = uni3c_embeds["controlnet"]
             render_latent = uni3c_embeds["render_latent"].to(device)
             if render_latent.shape != noise.shape:
                 render_latent = torch.nn.functional.interpolate(render_latent, size=(noise.shape[1], noise.shape[2], noise.shape[3]), mode='trilinear', align_corners=False)
diff --git a/wanvideo/modules/model.py b/wanvideo/modules/model.py
index 90c5628..7d4687a 100644
--- a/wanvideo/modules/model.py
+++ b/wanvideo/modules/model.py
@@ -2939,15 +2939,15 @@ class WanModel(torch.nn.Module):
             if uni3c_data is not None:
                 if (uni3c_data["start"] <= current_step_percentage <= uni3c_data["end"]) or \
                             (uni3c_data["end"] > 0 and current_step == 0 and current_step_percentage >= uni3c_data["start"]):
-                    self.controlnet.to(self.main_device)
+                    self.uni3c_controlnet.to(self.main_device)
                     with torch.autocast(device_type=mm.get_autocast_device(device), dtype=self.base_dtype, enabled=True):
-                        uni3c_controlnet_states = self.controlnet(
-                            render_latent=render_latent.to(self.main_device, self.controlnet.dtype), 
+                        uni3c_controlnet_states = self.uni3c_controlnet(
+                            render_latent=render_latent.to(self.main_device, self.uni3c_controlnet.dtype),
                             render_mask=uni3c_data["render_mask"], 
                             camera_embedding=uni3c_data["camera_embedding"], 
                             temb=e.to(self.main_device),
                             device=self.offload_device)
-                    self.controlnet.to(self.offload_device)
+                    self.uni3c_controlnet.to(self.offload_device)
 
             # Asynchronous block offloading with CUDA streams and events
             if torch.cuda.is_available():