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[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>
This commit is contained in:
Dhruv Nair
2024-06-18 21:09:30 +05:30
committed by YiYi Xu
parent 7fada49e29
commit 10ada4dae6
2 changed files with 20 additions and 23 deletions
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18
src/diffusers/loaders/single_file_model.py
View File

@@ -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)

25
src/diffusers/loaders/single_file_utils.py
View File

@@ -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]
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)