Laurent Erignoux
144fb65246
git-subtree-dir: Stable3DGen git-subtree-mainline:846d6cfad8git-subtree-split:c29f668ece
99 lines
4.0 KiB
Python
Executable File
99 lines
4.0 KiB
Python
Executable File
# MIT License
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# Copyright (c) Microsoft
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# Permission is hereby granted, free of charge, to any person obtaining a copy
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# of this software and associated documentation files (the "Software"), to deal
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# in the Software without restriction, including without limitation the rights
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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# copies of the Software, and to permit persons to whom the Software is
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# furnished to do so, subject to the following conditions:
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# The above copyright notice and this permission notice shall be included in all
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# copies or substantial portions of the Software.
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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# SOFTWARE.
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# Copyright (c) [2025] [Microsoft]
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# Copyright (c) [2025] [Chongjie Ye]
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# SPDX-License-Identifier: MIT
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# This file has been modified by Chongjie Ye on 2025/04/10
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# Original file was released under MIT, with the full license text # available at https://github.com/atong01/conditional-flow-matching/blob/1.0.7/LICENSE.
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# This modified file is released under the same license.
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import importlib
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__attributes = {
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'SparseStructureEncoder': 'sparse_structure_vae',
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'SparseStructureDecoder': 'sparse_structure_vae',
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'SparseStructureFlowModel': 'sparse_structure_flow',
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'SLatEncoder': 'structured_latent_vae',
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'SLatGaussianDecoder': 'structured_latent_vae',
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'SLatRadianceFieldDecoder': 'structured_latent_vae',
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'SLatMeshDecoder': 'structured_latent_vae',
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'SLatFlowModel': 'structured_latent_flow',
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}
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__submodules = []
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__all__ = list(__attributes.keys()) + __submodules
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def __getattr__(name):
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if name not in globals():
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if name in __attributes:
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module_name = __attributes[name]
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module = importlib.import_module(f".{module_name}", __name__)
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globals()[name] = getattr(module, name)
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elif name in __submodules:
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module = importlib.import_module(f".{name}", __name__)
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globals()[name] = module
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else:
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raise AttributeError(f"module {__name__} has no attribute {name}")
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return globals()[name]
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def from_pretrained(path: str, **kwargs):
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"""
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Load a model from a pretrained checkpoint.
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Args:
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path: The path to the checkpoint. Can be either local path or a Hugging Face model name.
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NOTE: config file and model file should take the name f'{path}.json' and f'{path}.safetensors' respectively.
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**kwargs: Additional arguments for the model constructor.
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"""
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import os
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import json
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from safetensors.torch import load_file
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is_local = os.path.exists(f"{path}.json") and os.path.exists(f"{path}.safetensors")
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if is_local:
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config_file = f"{path}.json"
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model_file = f"{path}.safetensors"
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else:
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from huggingface_hub import hf_hub_download
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path_parts = path.split('/')
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repo_id = f'{path_parts[0]}/{path_parts[1]}'
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model_name = '/'.join(path_parts[2:])
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config_file = hf_hub_download(repo_id, f"{model_name}.json")
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model_file = hf_hub_download(repo_id, f"{model_name}.safetensors")
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with open(config_file, 'r') as f:
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config = json.load(f)
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model = __getattr__(config['name'])(**config['args'], **kwargs)
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model.load_state_dict(load_file(model_file))
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return model
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# For Pylance
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if __name__ == '__main__':
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from .sparse_structure_vae import SparseStructureEncoder, SparseStructureDecoder
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from .sparse_structure_flow import SparseStructureFlowModel
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from .structured_latent_vae import SLatEncoder, SLatGaussianDecoder, SLatRadianceFieldDecoder, SLatMeshDecoder
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from .structured_latent_flow import SLatFlowModel
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