From 91b9a6fb3485a0e1b80f95b2a1dc97254971a36c Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 12 Aug 2025 22:50:51 -0400
Subject: [PATCH] Revert "Wan2.2 fun control ref image."

This reverts commit c2981b397b3a36e6ea16cffc7dbf828be8399120.
---
 comfy/ldm/wan/model.py   | 19 -------------------
 comfy/model_base.py      |  4 ----
 comfy/model_detection.py |  5 -----
 3 files changed, 28 deletions(-)

diff --git a/comfy/ldm/wan/model.py b/comfy/ldm/wan/model.py
index 4e2d99566..86d0795e9 100644
--- a/comfy/ldm/wan/model.py
+++ b/comfy/ldm/wan/model.py
@@ -391,7 +391,6 @@ class WanModel(torch.nn.Module):
                  cross_attn_norm=True,
                  eps=1e-6,
                  flf_pos_embed_token_number=None,
-                 in_dim_ref_conv=None,
                  image_model=None,
                  device=None,
                  dtype=None,
@@ -485,11 +484,6 @@ class WanModel(torch.nn.Module):
         else:
             self.img_emb = None
 
-        if in_dim_ref_conv is not None:
-            self.ref_conv = operations.Conv2d(in_dim_ref_conv, dim, kernel_size=patch_size[1:], stride=patch_size[1:], device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
-        else:
-            self.ref_conv = None
-
     def forward_orig(
         self,
         x,
@@ -532,13 +526,6 @@ class WanModel(torch.nn.Module):
         e = e.reshape(t.shape[0], -1, e.shape[-1])
         e0 = self.time_projection(e).unflatten(2, (6, self.dim))
 
-        full_ref = None
-        if self.ref_conv is not None:
-            full_ref = kwargs.get("reference_latent", None)
-            if full_ref is not None:
-                full_ref = self.ref_conv(full_ref).flatten(2).transpose(1, 2)
-                x = torch.concat((full_ref, x), dim=1)
-
         # context
         context = self.text_embedding(context)
 
@@ -565,9 +552,6 @@ class WanModel(torch.nn.Module):
         # head
         x = self.head(x, e)
 
-        if full_ref is not None:
-            x = x[:, full_ref.shape[1]:]
-
         # unpatchify
         x = self.unpatchify(x, grid_sizes)
         return x
@@ -586,9 +570,6 @@ class WanModel(torch.nn.Module):
             x = torch.cat([x, time_dim_concat], dim=2)
             t_len = ((x.shape[2] + (patch_size[0] // 2)) // patch_size[0])
 
-        if self.ref_conv is not None and "reference_latent" in kwargs:
-            t_len += 1
-
         img_ids = torch.zeros((t_len, h_len, w_len, 3), device=x.device, dtype=x.dtype)
         img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + torch.linspace(0, t_len - 1, steps=t_len, device=x.device, dtype=x.dtype).reshape(-1, 1, 1)
         img_ids[:, :, :, 1] = img_ids[:, :, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).reshape(1, -1, 1)
diff --git a/comfy/model_base.py b/comfy/model_base.py
index 74def1e60..8a2d9cbe6 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -1140,10 +1140,6 @@ class WAN21(BaseModel):
         if time_dim_concat is not None:
             out['time_dim_concat'] = comfy.conds.CONDRegular(self.process_latent_in(time_dim_concat))
 
-        reference_latents = kwargs.get("reference_latents", None)
-        if reference_latents is not None:
-            out['reference_latent'] = comfy.conds.CONDRegular(self.process_latent_in(reference_latents[-1])[:, :, 0])
-
         return out
 
 
diff --git a/comfy/model_detection.py b/comfy/model_detection.py
index 8acc51e20..8b57ebd2f 100644
--- a/comfy/model_detection.py
+++ b/comfy/model_detection.py
@@ -373,11 +373,6 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
         flf_weight = state_dict.get('{}img_emb.emb_pos'.format(key_prefix))
         if flf_weight is not None:
             dit_config["flf_pos_embed_token_number"] = flf_weight.shape[1]
-
-        ref_conv_weight = state_dict.get('{}ref_conv.weight'.format(key_prefix))
-        if ref_conv_weight is not None:
-            dit_config["in_dim_ref_conv"] = ref_conv_weight.shape[1]
-
         return dit_config
 
     if '{}latent_in.weight'.format(key_prefix) in state_dict_keys:  # Hunyuan 3D