wehub-resource-sync
47 lines
1.8 KiB
Python
47 lines
1.8 KiB
Python
#
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# SPDX-FileCopyrightText: Copyright (c) 1993-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import os
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from transformers import (
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CLIPTokenizer,
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T5TokenizerFast,
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)
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from demo_diffusion.model import load
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def make_tokenizer(version, pipeline, hf_token, framework_model_dir, subfolder="tokenizer", tokenizer_type="clip"):
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if tokenizer_type == "clip":
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tokenizer_class = CLIPTokenizer
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elif tokenizer_type == "t5":
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tokenizer_class = T5TokenizerFast
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else:
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raise ValueError(
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f"Unsupported tokenizer_type {tokenizer_type}. Only tokenizer_type clip and t5 are currently supported"
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)
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tokenizer_model_dir = load.get_checkpoint_dir(framework_model_dir, version, pipeline.name, subfolder)
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if not os.path.exists(tokenizer_model_dir):
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model = tokenizer_class.from_pretrained(
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load.get_path(version, pipeline), subfolder=subfolder, use_safetensors=pipeline.is_sd_xl(), token=hf_token
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)
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model.save_pretrained(tokenizer_model_dir)
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else:
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print(f"[I] Load {tokenizer_class.__name__} model from: {tokenizer_model_dir}")
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model = tokenizer_class.from_pretrained(tokenizer_model_dir)
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return model
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