diff --git a/animatediff/motion_module_ad.py b/animatediff/motion_module_ad.py
index dc1dfec..da861b8 100644
--- a/animatediff/motion_module_ad.py
+++ b/animatediff/motion_module_ad.py
@@ -136,6 +136,11 @@ def is_animatelcm(mm_state_dict: dict[str, Tensor]) -> bool:
             return False
     return True
 
+def is_hellomeme(mm_state_dict: dict[str, Tensor]) -> bool:
+    for key in mm_state_dict.keys():
+        if "pos_embed" in key:
+            return True
+    return False
 
 def has_conv_in(mm_state_dict: dict[str, Tensor]) -> bool:
     # check if conv_in.weight and .bias are present
@@ -192,6 +197,14 @@ def find_hotshot_module_num(key: str) -> Union[int, None]:
     return None
 
 
+_regex_hellomeme_module_num = re.compile(r'motion_modules\.(\d+)\.')
+def find_hellomeme_module_num(key: str) -> Union[int, None]:
+    found = _regex_hellomeme_module_num.search(key)
+    if found:
+        return int(found.group(1))
+    return None
+
+
 def has_img_encoder(mm_state_dict: dict[str, Tensor]):
     for key in mm_state_dict.keys():
         if key.startswith("img_encoder."):
@@ -239,6 +252,8 @@ def normalize_ad_state_dict(mm_state_dict: dict[str, Tensor], mm_name: str) -> T
         raise ValueError(f"'{mm_name}' is not a valid SD1.5 nor SDXL motion module - contained {down_block_max} downblocks.")
     # determine the model's format
     mm_format = AnimateDiffFormat.ANIMATEDIFF
+    if is_hellomeme(mm_state_dict):
+        convert_hellomeme_state_dict(mm_state_dict)
     if is_hotshotxl(mm_state_dict):
         mm_format = AnimateDiffFormat.HOTSHOTXL
     if is_animatelcm(mm_state_dict):
@@ -260,6 +275,7 @@ def normalize_ad_state_dict(mm_state_dict: dict[str, Tensor], mm_name: str) -> T
             if mm_format == AnimateDiffFormat.FANCYVIDEO and key in FancyVideoKeys:
                 continue
             del mm_state_dict[key]
+
     # determine the model's version
     mm_version = AnimateDiffVersion.V1
     if has_mid_block(mm_state_dict):
@@ -269,26 +285,58 @@ def normalize_ad_state_dict(mm_state_dict: dict[str, Tensor], mm_name: str) -> T
     info = AnimateDiffInfo(sd_type=sd_type, mm_format=mm_format, mm_version=mm_version, mm_name=mm_name)
     # convert to AnimateDiff format, if needed
     if mm_format == AnimateDiffFormat.HOTSHOTXL:
-        # HotshotXL is AD-based architecture applied to SDXL instead of SD1.5
-        # By renaming the keys, no code needs to be adapted at all
-        #
-        # reformat temporal_attentions:
-        # HSXL: temporal_attentions.#.
-        #   AD: motion_modules.#.temporal_transformer.
-        # HSXL: pos_encoder.positional_encoding
-        #   AD: pos_encoder.pe
-        for key in list(mm_state_dict.keys()):
-            module_num = find_hotshot_module_num(key)
-            if module_num is not None:
-                new_key = key.replace(f"temporal_attentions.{module_num}",
-                                      f"motion_modules.{module_num}.temporal_transformer", 1)
-                new_key = new_key.replace("pos_encoder.positional_encoding", "pos_encoder.pe")
-                mm_state_dict[new_key] = mm_state_dict[key]
-                del mm_state_dict[key]
+        convert_hotshot_state_dict(mm_state_dict)
     # return adjusted mm_state_dict and info
     return mm_state_dict, info
 
 
+def convert_hotshot_state_dict(mm_state_dict: dict[str, Tensor]):
+    # HotshotXL is AD-based architecture applied to SDXL instead of SD1.5
+    # By renaming the keys, no code needs to be adapted at all
+    ################################
+    # reformat temporal_attentions:
+    # HSXL: temporal_attentions.#.
+    #   AD: motion_modules.#.temporal_transformer.
+    # HSXL: pos_encoder.positional_encoding
+    #   AD: pos_encoder.pe
+    for key in list(mm_state_dict.keys()):
+        module_num = find_hotshot_module_num(key)
+        if module_num is not None:
+            new_key = key.replace(f"temporal_attentions.{module_num}",
+                                    f"motion_modules.{module_num}.temporal_transformer", 1)
+            new_key = new_key.replace("pos_encoder.positional_encoding", "pos_encoder.pe")
+            mm_state_dict[new_key] = mm_state_dict[key]
+            del mm_state_dict[key]
+
+
+def convert_hellomeme_state_dict(mm_state_dict: dict[str, Tensor]):
+    # HelloMeme is AD-based architecture
+    for key in list(mm_state_dict.keys()):
+        module_num = find_hellomeme_module_num(key)
+        if module_num is not None:
+            # first, add temporal_transformer everywhere as suffix after motion_modules.#.
+            new_key = key.replace(f"motion_modules.{module_num}",
+                                  f"motion_modules.{module_num}.temporal_transformer")
+            if "pos_embed" in new_key:
+                new_key1 = new_key.replace("pos_embed.pe", "attention_blocks.0.pos_encoder.pe")
+                new_key2 = new_key.replace("pos_embed.pe", "attention_blocks.1.pos_encoder.pe")
+                mm_state_dict[new_key1] = mm_state_dict[key].clone()
+                mm_state_dict[new_key2] = mm_state_dict[key].clone()
+            else:
+                if "attn1" in new_key:
+                    new_key = new_key.replace("attn1.", "attention_blocks.0.")
+                elif "attn2" in new_key:
+                    new_key = new_key.replace("attn2.", "attention_blocks.1.")
+                elif "norm1" in new_key:
+                    new_key = new_key.replace("norm1.", "norms.0.")
+                elif "norm2" in new_key:
+                    new_key = new_key.replace("norm2.", "norms.1.")
+                elif "norm3" in new_key:
+                    new_key = new_key.replace("norm3.", "ff_norm.")
+                mm_state_dict[new_key] = mm_state_dict[key]
+            del mm_state_dict[key]
+
+
 class BlockType:
     UP = "up"
     DOWN = "down"