[Single File Loading] Handle unexpected keys in CLIP models when `accelerate`...

[Single File Loading] Handle unexpected keys in CLIP models when `accelerate` isn't installed.  (#8462)

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Co-authored-by: YiYi Xu <yixu310@gmail.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

src/diffusers/loaders/single_file_model.py

@@ -276,16 +276,18 @@ class FromOriginalModelMixin:
 
         if is_accelerate_available():
             unexpected_keys = load_model_dict_into_meta(model, diffusers_format_checkpoint, dtype=torch_dtype)
-            if model._keys_to_ignore_on_load_unexpected is not None:
-                for pat in model._keys_to_ignore_on_load_unexpected:
-                    unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]
 
-            if len(unexpected_keys) > 0:
-                logger.warning(
-                    f"Some weights of the model checkpoint were not used when initializing {cls.__name__}: \n {[', '.join(unexpected_keys)]}"
-                )
         else:
-            model.load_state_dict(diffusers_format_checkpoint)
+            _, unexpected_keys = model.load_state_dict(diffusers_format_checkpoint, strict=False)
+
+        if model._keys_to_ignore_on_load_unexpected is not None:
+            for pat in model._keys_to_ignore_on_load_unexpected:
+                unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]
+
+        if len(unexpected_keys) > 0:
+            logger.warning(
+                f"Some weights of the model checkpoint were not used when initializing {cls.__name__}: \n {[', '.join(unexpected_keys)]}"
+            )
 
         if torch_dtype is not None:
             model.to(torch_dtype)

src/diffusers/loaders/single_file_utils.py

@@ -1268,8 +1268,6 @@ def convert_open_clip_checkpoint(
     else:
         text_proj_dim = LDM_OPEN_CLIP_TEXT_PROJECTION_DIM
 
-    text_model_dict["text_model.embeddings.position_ids"] = text_model.text_model.embeddings.get_buffer("position_ids")
-
     keys = list(checkpoint.keys())
     keys_to_ignore = SD_2_TEXT_ENCODER_KEYS_TO_IGNORE
 
@@ -1318,9 +1316,6 @@ def convert_open_clip_checkpoint(
         else:
             text_model_dict[diffusers_key] = checkpoint.get(key)
 
-    if not (hasattr(text_model, "embeddings") and hasattr(text_model.embeddings.position_ids)):
-        text_model_dict.pop("text_model.embeddings.position_ids", None)
-
     return text_model_dict
 
 
@@ -1414,17 +1409,17 @@ def create_diffusers_clip_model_from_ldm(
 
     if is_accelerate_available():
         unexpected_keys = load_model_dict_into_meta(model, diffusers_format_checkpoint, dtype=torch_dtype)
-        if model._keys_to_ignore_on_load_unexpected is not None:
-            for pat in model._keys_to_ignore_on_load_unexpected:
-                unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]
+    else:
+        _, unexpected_keys = model.load_state_dict(diffusers_format_checkpoint, strict=False)
 
-        if len(unexpected_keys) > 0:
-            logger.warning(
-                f"Some weights of the model checkpoint were not used when initializing {cls.__name__}: \n {[', '.join(unexpected_keys)]}"
-            )
+    if model._keys_to_ignore_on_load_unexpected is not None:
+        for pat in model._keys_to_ignore_on_load_unexpected:
+            unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]
 
-    else:
-        model.load_state_dict(diffusers_format_checkpoint)
+    if len(unexpected_keys) > 0:
+        logger.warning(
+            f"Some weights of the model checkpoint were not used when initializing {cls.__name__}: \n {[', '.join(unexpected_keys)]}"
+        )
 
     if torch_dtype is not None:
         model.to(torch_dtype)