diff --git a/comfy/controlnet.py b/comfy/controlnet.py
index 356d5c0e..1352d141 100644
--- a/comfy/controlnet.py
+++ b/comfy/controlnet.py
@@ -527,6 +527,10 @@ def load_t2i_adapter(t2i_data):
         model_ad = comfy.ldm.cascade.controlnet.ControlNet(c_in=t2i_data['backbone.0.0.weight'].shape[1], proj_blocks=[0, 4, 8, 12, 51, 55, 59, 63])
         compression_ratio = 32
         upscale_algorithm = 'bilinear'
+    elif "backbone.10.blocks.0.weight" in keys:
+        model_ad = comfy.ldm.cascade.controlnet.ControlNet(c_in=t2i_data['backbone.0.weight'].shape[1], bottleneck_mode="large", proj_blocks=[0, 4, 8, 12, 51, 55, 59, 63])
+        compression_ratio = 1
+        upscale_algorithm = 'nearest-exact'
     else:
         return None
 
diff --git a/comfy/ldm/cascade/controlnet.py b/comfy/ldm/cascade/controlnet.py
index c5757308..5dac5939 100644
--- a/comfy/ldm/cascade/controlnet.py
+++ b/comfy/ldm/cascade/controlnet.py
@@ -67,7 +67,7 @@ class ControlNet(nn.Module):
                 operations.Conv2d(c_in, 4096 * 4, kernel_size=1, dtype=dtype, device=device),
                 nn.LeakyReLU(0.2, inplace=True),
                 operations.Conv2d(4096 * 4, 1024, kernel_size=1, dtype=dtype, device=device),
-                *[CNetResBlock(1024) for _ in range(8)],
+                *[CNetResBlock(1024, dtype=dtype, device=device, operations=operations) for _ in range(8)],
                 operations.Conv2d(1024, 1280, kernel_size=1, dtype=dtype, device=device),
             )
             embd_channels = 1280