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update

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This commit is contained in:
Dhruv Nair
2024-01-19 04:06:15 +00:00
parent a076513fd2
commit db3eb06ae4
1 changed files with 9 additions and 10 deletions
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19
src/diffusers/loaders/single_file_utils.py
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@@ -301,7 +301,7 @@ def set_image_size(pipeline_class_name, original_config, checkpoint, image_size=
model_type = infer_model_type(original_config, model_type)
if pipeline_class_name == "StableDiffusionUpscalePipeline":
image_size = original_config["model"]["params"].unet_config.params.image_size
image_size = original_config["model"]["params"]["unet_config"]["params"]["image_size"]
return image_size
elif model_type in ["SDXL", "SDXL-Refiner"]:
@@ -771,11 +771,6 @@ def create_diffusers_controlnet_model_from_ldm(
# import here to avoid circular imports
from ..models import ControlNetModel
# NOTE: this while loop isn't great but this controlnet checkpoint has one additional
# "state_dict" key https://huggingface.co/thibaud/controlnet-canny-sd21
while "state_dict" in checkpoint:
checkpoint = checkpoint["state_dict"]
image_size = set_image_size(pipeline_class_name, original_config, checkpoint, image_size=image_size)
diffusers_config = create_controlnet_diffusers_config(original_config, image_size=image_size)
@@ -1033,8 +1028,11 @@ def create_text_encoder_from_open_clip_checkpoint(
text_model_dict[diffusers_key] = checkpoint[key]
if is_accelerate_available():
for param_name, param in text_model_dict.items():
set_module_tensor_to_device(text_model, param_name, "cpu", value=param)
try:
for param_name, param in text_model_dict.items():
set_module_tensor_to_device(text_model, param_name, "cpu", value=param)
except Exception as e:
raise e
else:
if not (hasattr(text_model, "embeddings") and hasattr(text_model.embeddings.position_ids)):
text_model_dict.pop("text_model.embeddings.position_ids", None)
@@ -1127,12 +1125,13 @@ def create_text_encoders_and_tokenizers_from_ldm(
try:
text_encoder = create_text_encoder_from_open_clip_checkpoint(
checkpoint, config_name, local_files_only=local_files_only, **config_kwargs
checkpoint, config_name, has_projection=True, local_files_only=local_files_only, **config_kwargs
)
tokenizer = CLIPTokenizer.from_pretrained(
config_name, subfolder="tokenizer", local_files_only=local_files_only
)
except Exception:
except Exception as e:
raise e
raise ValueError(
f"With local_files_only set to {local_files_only}, you must first locally save the text_encoder in the following path: '{config_name}'."
)