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60 lines
2.3 KiB
Python
60 lines
2.3 KiB
Python
from pathlib import Path
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from typing import Optional
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import torch
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from invokeai.backend.model_manager.configs.base import Checkpoint_Config_Base, Diffusers_Config_Base
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from invokeai.backend.model_manager.configs.factory import AnyModelConfig
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from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
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from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import GenericDiffusersLoader
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from invokeai.backend.model_manager.taxonomy import (
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AnyModel,
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BaseModelType,
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ModelFormat,
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ModelType,
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SubModelType,
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)
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@ModelLoaderRegistry.register(base=BaseModelType.CogView4, type=ModelType.Main, format=ModelFormat.Diffusers)
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class CogView4DiffusersModel(GenericDiffusersLoader):
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"""Class to load CogView4 main models."""
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def _load_model(
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self,
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config: AnyModelConfig,
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submodel_type: Optional[SubModelType] = None,
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) -> AnyModel:
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if isinstance(config, Checkpoint_Config_Base):
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raise NotImplementedError("CheckpointConfigBase is not implemented for CogView4 models.")
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if submodel_type is None:
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raise Exception("A submodel type must be provided when loading main pipelines.")
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model_path = Path(config.path)
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load_class = self.get_hf_load_class(model_path, submodel_type)
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repo_variant = config.repo_variant if isinstance(config, Diffusers_Config_Base) else None
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variant = repo_variant.value if repo_variant else None
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model_path = model_path / submodel_type.value
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# We force bfloat16 for CogView4 models. It produces black images with float16. I haven't tracked down
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# specifically which model(s) is/are responsible.
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dtype = torch.bfloat16
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try:
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result: AnyModel = load_class.from_pretrained(
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model_path,
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torch_dtype=dtype,
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variant=variant,
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local_files_only=True,
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)
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except OSError as e:
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if variant and "no file named" in str(
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e
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): # try without the variant, just in case user's preferences changed
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result = load_class.from_pretrained(model_path, torch_dtype=dtype, local_files_only=True)
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else:
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raise e
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result = self._apply_fp8_layerwise_casting(result, config, submodel_type)
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return result
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