Handle Anima LoRA alpha keys

src/diffusers/loaders/lora_conversion_utils.py

@@ -2411,6 +2411,39 @@ def _convert_non_diffusers_anima_lora_to_diffusers(state_dict):
 
         converted_state_dict[new_key] = value
 
+    for down_key_suffix, up_key_suffix in (
+        (".lora_down.weight", ".lora_up.weight"),
+        (".lora_A.weight", ".lora_B.weight"),
+    ):
+        all_keys = list(converted_state_dict.keys())
+        for key in all_keys:
+            if not key.endswith(down_key_suffix) or key not in converted_state_dict:
+                continue
+
+            up_key = key[: -len(down_key_suffix)] + up_key_suffix
+            alpha_key = key[: -len(down_key_suffix)] + ".alpha"
+            if up_key not in converted_state_dict:
+                continue
+
+            down_weight = converted_state_dict.pop(key)
+            up_weight = converted_state_dict.pop(up_key)
+            alpha = converted_state_dict.pop(alpha_key, None)
+
+            if alpha is None:
+                scale_down, scale_up = 1.0, 1.0
+            else:
+                scale = alpha.item() / down_weight.shape[0]
+                scale_down, scale_up = scale, 1.0
+                while scale_down * 2 < scale_up:
+                    scale_down *= 2
+                    scale_up /= 2
+
+            converted_down_key = key[: -len(down_key_suffix)] + ".lora_A.weight"
+            converted_up_key = key[: -len(down_key_suffix)] + ".lora_B.weight"
+            converted_state_dict[converted_down_key] = down_weight * scale_down
+            converted_state_dict[converted_up_key] = up_weight * scale_up
+
+    converted_state_dict = {k: v for k, v in converted_state_dict.items() if not k.endswith(".alpha")}
     return converted_state_dict
 
 

tests/loaders/test_lora_conversion_utils.py

@@ -0,0 +1,55 @@
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import torch
+
+from diffusers.loaders.lora_conversion_utils import _convert_non_diffusers_anima_lora_to_diffusers
+
+
+class AnimaLoraConversionUtilsTests(unittest.TestCase):
+    def test_comfy_lora_alpha_is_folded_into_converted_weights(self):
+        state_dict = {
+            "diffusion_model.blocks.0.cross_attn.k_proj.lora_down.weight": torch.ones(4, 8),
+            "diffusion_model.blocks.0.cross_attn.k_proj.lora_up.weight": torch.ones(8, 4),
+            "diffusion_model.blocks.0.cross_attn.k_proj.alpha": torch.tensor(2.0),
+        }
+
+        converted_state_dict = _convert_non_diffusers_anima_lora_to_diffusers(state_dict)
+
+        down_key = "transformer.transformer_blocks.0.attn2.to_k.lora_A.weight"
+        up_key = "transformer.transformer_blocks.0.attn2.to_k.lora_B.weight"
+        self.assertEqual(set(converted_state_dict), {down_key, up_key})
+        self.assertTrue(torch.allclose(converted_state_dict[down_key], torch.full((4, 8), 0.5)))
+        self.assertTrue(torch.allclose(converted_state_dict[up_key], torch.ones(8, 4)))
+
+    def test_comfy_diffusers_style_lora_alpha_is_removed(self):
+        state_dict = {
+            "diffusion_model.blocks.0.cross_attn.k_proj.lora_A.weight": torch.ones(4, 8),
+            "diffusion_model.blocks.0.cross_attn.k_proj.lora_B.weight": torch.ones(8, 4),
+            "diffusion_model.blocks.0.cross_attn.k_proj.alpha": torch.tensor(2.0),
+        }
+
+        converted_state_dict = _convert_non_diffusers_anima_lora_to_diffusers(state_dict)
+
+        down_key = "transformer.transformer_blocks.0.attn2.to_k.lora_A.weight"
+        up_key = "transformer.transformer_blocks.0.attn2.to_k.lora_B.weight"
+        self.assertEqual(set(converted_state_dict), {down_key, up_key})
+        self.assertTrue(torch.allclose(converted_state_dict[down_key], torch.full((4, 8), 0.5)))
+        self.assertTrue(torch.allclose(converted_state_dict[up_key], torch.ones(8, 4)))
+
+
+if __name__ == "__main__":
+    unittest.main()