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