diff --git a/comfy/weight_adapter/boft.py b/comfy/weight_adapter/boft.py
index c85adc7a..b2a2f1bd 100644
--- a/comfy/weight_adapter/boft.py
+++ b/comfy/weight_adapter/boft.py
@@ -24,7 +24,7 @@ class BOFTAdapter(WeightAdapterBase):
     ) -> Optional["BOFTAdapter"]:
         if loaded_keys is None:
             loaded_keys = set()
-        blocks_name = "{}.boft_blocks".format(x)
+        blocks_name = "{}.oft_blocks".format(x)
         rescale_name = "{}.rescale".format(x)
 
         blocks = None
@@ -32,17 +32,18 @@ class BOFTAdapter(WeightAdapterBase):
             blocks = lora[blocks_name]
             if blocks.ndim == 4:
                 loaded_keys.add(blocks_name)
+            else:
+                blocks = None
+        if blocks is None:
+            return None
 
         rescale = None
         if rescale_name in lora.keys():
             rescale = lora[rescale_name]
             loaded_keys.add(rescale_name)
 
-        if blocks is not None:
-            weights = (blocks, rescale, alpha, dora_scale)
-            return cls(loaded_keys, weights)
-        else:
-            return None
+        weights = (blocks, rescale, alpha, dora_scale)
+        return cls(loaded_keys, weights)
 
     def calculate_weight(
         self,
@@ -71,7 +72,7 @@ class BOFTAdapter(WeightAdapterBase):
             # Get r
             I = torch.eye(boft_b, device=blocks.device, dtype=blocks.dtype)
             # for Q = -Q^T
-            q = blocks - blocks.transpose(1, 2)
+            q = blocks - blocks.transpose(-1, -2)
             normed_q = q
             if alpha > 0: # alpha in boft/bboft is for constraint
                 q_norm = torch.norm(q) + 1e-8
@@ -79,9 +80,8 @@ class BOFTAdapter(WeightAdapterBase):
                     normed_q = q * alpha / q_norm
             # use float() to prevent unsupported type in .inverse()
             r = (I + normed_q) @ (I - normed_q).float().inverse()
-            r = r.to(original_weight)
-
-            inp = org = original_weight
+            r = r.to(weight)
+            inp = org = weight
 
             r_b = boft_b//2
             for i in range(boft_m):
@@ -91,14 +91,14 @@ class BOFTAdapter(WeightAdapterBase):
                 if strength != 1:
                     bi = bi * strength + (1-strength) * I
                 inp = (
-                    inp.unflatten(-1, (-1, g, k))
-                    .transpose(-2, -1)
-                    .flatten(-3)
-                    .unflatten(-1, (-1, boft_b))
+                    inp.unflatten(0, (-1, g, k))
+                    .transpose(1, 2)
+                    .flatten(0, 2)
+                    .unflatten(0, (-1, boft_b))
                 )
-                inp = torch.einsum("b n m, b n ... -> b m ...", inp, bi)
+                inp = torch.einsum("b i j, b j ...-> b i ...", bi, inp)
                 inp = (
-                    inp.flatten(-2).unflatten(-1, (-1, k, g)).transpose(-2, -1).flatten(-3)
+                    inp.flatten(0, 1).unflatten(0, (-1, k, g)).transpose(1, 2).flatten(0, 2)
                 )
 
             if rescale is not None:
@@ -109,7 +109,7 @@ class BOFTAdapter(WeightAdapterBase):
             if dora_scale is not None:
                 weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function)
             else:
-                weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
+                weight += function((strength * lora_diff).type(weight.dtype))
         except Exception as e:
             logging.error("ERROR {} {} {}".format(self.name, key, e))
         return weight
diff --git a/comfy/weight_adapter/oft.py b/comfy/weight_adapter/oft.py
index 0ea229b7..25009eca 100644
--- a/comfy/weight_adapter/oft.py
+++ b/comfy/weight_adapter/oft.py
@@ -32,17 +32,18 @@ class OFTAdapter(WeightAdapterBase):
             blocks = lora[blocks_name]
             if blocks.ndim == 3:
                 loaded_keys.add(blocks_name)
+            else:
+                blocks = None
+        if blocks is None:
+            return None
 
         rescale = None
         if rescale_name in lora.keys():
             rescale = lora[rescale_name]
             loaded_keys.add(rescale_name)
 
-        if blocks is not None:
-            weights = (blocks, rescale, alpha, dora_scale)
-            return cls(loaded_keys, weights)
-        else:
-            return None
+        weights = (blocks, rescale, alpha, dora_scale)
+        return cls(loaded_keys, weights)
 
     def calculate_weight(
         self,
@@ -79,16 +80,17 @@ class OFTAdapter(WeightAdapterBase):
                     normed_q = q * alpha / q_norm
             # use float() to prevent unsupported type in .inverse()
             r = (I + normed_q) @ (I - normed_q).float().inverse()
-            r = r.to(original_weight)
+            r = r.to(weight)
+            _, *shape = weight.shape
             lora_diff = torch.einsum(
                 "k n m, k n ... -> k m ...",
                 (r * strength) - strength * I,
-                original_weight,
-            )
+                weight.view(block_num, block_size, *shape),
+            ).view(-1, *shape)
             if dora_scale is not None:
                 weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function)
             else:
-                weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
+                weight += function((strength * lora_diff).type(weight.dtype))
         except Exception as e:
             logging.error("ERROR {} {} {}".format(self.name, key, e))
         return weight