Spaces:
Running
on
Zero
Running
on
Zero
File size: 2,609 Bytes
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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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