95 lines
3.2 KiB
Python
95 lines
3.2 KiB
Python
import importlib
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from typing import Optional
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from ..model_revisions import revision_for_repo
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__attributes = {
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# Sparse Structure
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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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# SLat Generation
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'SLatFlowModel': 'structured_latent_flow',
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'ElasticSLatFlowModel': 'structured_latent_flow',
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# SC-VAEs
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'SparseUnetVaeEncoder': 'sc_vaes.sparse_unet_vae',
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'SparseUnetVaeDecoder': 'sc_vaes.sparse_unet_vae',
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'FlexiDualGridVaeEncoder': 'sc_vaes.fdg_vae',
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'FlexiDualGridVaeDecoder': 'sc_vaes.fdg_vae'
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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(
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path: str,
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*,
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revision: Optional[str] = None,
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cache_dir: Optional[str] = None,
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local_files_only: bool = False,
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**kwargs,
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):
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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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revision = revision_for_repo(repo_id, revision)
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hub_kwargs = {
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"revision": revision,
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"cache_dir": cache_dir,
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"local_files_only": local_files_only,
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}
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config_file = hf_hub_download(repo_id, f"{model_name}.json", **hub_kwargs)
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model_file = hf_hub_download(repo_id, f"{model_name}.safetensors", **hub_kwargs)
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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), strict=False)
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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_flow import SLatFlowModel, ElasticSLatFlowModel
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from .sc_vaes.sparse_unet_vae import SparseUnetVaeEncoder, SparseUnetVaeDecoder
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from .sc_vaes.fdg_vae import FlexiDualGridVaeEncoder, FlexiDualGridVaeDecoder
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