mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
94 lines
2.9 KiB
Python
94 lines
2.9 KiB
Python
# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
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from abc import ABC, abstractmethod
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from pathlib import Path
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from queue import Queue
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from typing import Dict, Optional, Union
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import torch
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class LatentsStorageBase(ABC):
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"""Responsible for storing and retrieving latents."""
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@abstractmethod
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def get(self, name: str) -> torch.Tensor:
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pass
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@abstractmethod
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def save(self, name: str, data: torch.Tensor) -> None:
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pass
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@abstractmethod
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def delete(self, name: str) -> None:
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pass
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class ForwardCacheLatentsStorage(LatentsStorageBase):
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"""Caches the latest N latents in memory, writing-thorugh to and reading from underlying storage"""
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__cache: Dict[str, torch.Tensor]
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__cache_ids: Queue
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__max_cache_size: int
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__underlying_storage: LatentsStorageBase
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def __init__(self, underlying_storage: LatentsStorageBase, max_cache_size: int = 20):
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self.__underlying_storage = underlying_storage
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self.__cache = dict()
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self.__cache_ids = Queue()
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self.__max_cache_size = max_cache_size
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def get(self, name: str) -> torch.Tensor:
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cache_item = self.__get_cache(name)
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if cache_item is not None:
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return cache_item
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latent = self.__underlying_storage.get(name)
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self.__set_cache(name, latent)
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return latent
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def save(self, name: str, data: torch.Tensor) -> None:
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self.__underlying_storage.save(name, data)
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self.__set_cache(name, data)
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def delete(self, name: str) -> None:
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self.__underlying_storage.delete(name)
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if name in self.__cache:
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del self.__cache[name]
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def __get_cache(self, name: str) -> Optional[torch.Tensor]:
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return None if name not in self.__cache else self.__cache[name]
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def __set_cache(self, name: str, data: torch.Tensor):
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if name not in self.__cache:
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self.__cache[name] = data
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self.__cache_ids.put(name)
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if self.__cache_ids.qsize() > self.__max_cache_size:
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self.__cache.pop(self.__cache_ids.get())
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class DiskLatentsStorage(LatentsStorageBase):
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"""Stores latents in a folder on disk without caching"""
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__output_folder: Union[str, Path]
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def __init__(self, output_folder: Union[str, Path]):
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self.__output_folder = output_folder if isinstance(output_folder, Path) else Path(output_folder)
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self.__output_folder.mkdir(parents=True, exist_ok=True)
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def get(self, name: str) -> torch.Tensor:
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latent_path = self.get_path(name)
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return torch.load(latent_path)
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def save(self, name: str, data: torch.Tensor) -> None:
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self.__output_folder.mkdir(parents=True, exist_ok=True)
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latent_path = self.get_path(name)
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torch.save(data, latent_path)
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def delete(self, name: str) -> None:
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latent_path = self.get_path(name)
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latent_path.unlink()
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def get_path(self, name: str) -> Path:
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return self.__output_folder / name
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