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update
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@@ -347,6 +347,8 @@ class FromSingleFileMixin:
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pretrained_model_link_or_path,
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**kwargs,
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)
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if not components:
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continue
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pipeline_components.update(components)
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additional_components = set(pipeline_components.keys() - component_names)
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@@ -1268,7 +1268,7 @@ def create_ldm_bert_config(original_config):
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return config
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def create_unet_model(pipeline_class_name, original_config, checkpoint, checkpoint_path_or_dict, image_size, **kwargs):
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def create_unet_model(pipeline_class_name, original_config, checkpoint, checkpoint_path_or_dict, **kwargs):
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if "num_in_channels" in kwargs:
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num_in_channels = kwargs.get("num_in_channels")
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@@ -1291,7 +1291,7 @@ def create_unet_model(pipeline_class_name, original_config, checkpoint, checkpoi
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extract_ema = kwargs.get("extract_ema", False)
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unet_config = create_unet_diffusers_config(original_config, image_size=image_size)
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unet_config["num_in_channels"] = num_in_channels
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unet_config["in_channels"] = num_in_channels
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unet_config["upcast_attention"] = upcast_attention
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path = checkpoint_path_or_dict if isinstance(checkpoint_path_or_dict, str) else ""
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@@ -1423,6 +1423,7 @@ def create_text_encoders_and_tokenizers(pipeline_class_name, original_config, ch
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try:
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config_name = "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k"
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config_kwargs = {"projection_dim": 1280}
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prefix = "conditioner.embedders.1.model."
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tokenizer_2 = CLIPTokenizer.from_pretrained(config_name, pad_token="!", local_files_only=local_files_only)
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text_encoder_2 = convert_open_clip_checkpoint(
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@@ -1434,7 +1435,6 @@ def create_text_encoders_and_tokenizers(pipeline_class_name, original_config, ch
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**config_kwargs,
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)
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except Exception as e:
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print(e)
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raise ValueError(
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f"With local_files_only set to {local_files_only}, you must first locally save the text_encoder_2 and tokenizer_2 in the following path: {config_name} with `pad_token` set to '!'."
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)
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