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https://github.com/invoke-ai/InvokeAI
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The invocation cache provides simple node memoization functionality. Nodes that use the cache are memoized and not re-executed if their inputs haven't changed. Instead, the stored output is returned. ## Results This feature provides anywhere some significant to massive performance improvement. The improvement is most marked on large batches of generations where you only change a couple things (e.g. different seed or prompt for each iteration) and low-VRAM systems, where skipping an extraneous model load is a big deal. ## Overview A new `invocation_cache` service is added to handle the caching. There's not much to it. All nodes now inherit a boolean `use_cache` field from `BaseInvocation`. This is a node field and not a class attribute, because specific instances of nodes may want to opt in or out of caching. The recently-added `invoke_internal()` method on `BaseInvocation` is used as an entrypoint for the cache logic. To create a cache key, the invocation is first serialized using pydantic's provided `json()` method, skipping the unique `id` field. Then python's very fast builtin `hash()` is used to create an integer key. All implementations of `InvocationCacheBase` must provide a class method `create_key()` which accepts an invocation and outputs a string or integer key. ## In-Memory Implementation An in-memory implementation is provided. In this implementation, the node outputs are stored in memory as python classes. The in-memory cache does not persist application restarts. Max node cache size is added as `node_cache_size` under the `Generation` config category. It defaults to 512 - this number is up for discussion, but given that these are relatively lightweight pydantic models, I think it's safe to up this even higher. Note that the cache isn't storing the big stuff - tensors and images are store on disk, and outputs include only references to them. ## Node Definition The default for all nodes is to use the cache. The `@invocation` decorator now accepts an optional `use_cache: bool` argument to override the default of `True`. Non-deterministic nodes, however, should set this to `False`. Currently, all random-stuff nodes, including `dynamic_prompt`, are set to `False`. The field name `use_cache` is now effectively a reserved field name and possibly a breaking change if any community nodes use this as a field name. In hindsight, all our reserved field names should have been prefixed with underscores or something. ## One Gotcha Leaf nodes probably want to opt out of the cache, because if they are not cached, their outputs are not saved again. If you run the same graph multiple times, you only end up with a single image output, because the image storage side-effects are in the `invoke()` method, which is bypassed if we have a cache hit. ## Linear UI The linear graphs _almost_ just work, but due to the gotcha, we need to be careful about the final image-outputting node. To resolve this, a `SaveImageInvocation` node is added and used in the linear graphs. This node is similar to `ImagePrimitive`, except it saves a copy of its input image, and has `use_cache` set to `False` by default. This is now the leaf node in all linear graphs, and is the only node in those graphs with `use_cache == False` _and_ the only node with `is_intermedate == False`. ## Workflow Editor All nodes now have a footer with a new `Use Cache [ ]` checkbox. It defaults to the value set by the invocation in its python definition, but can be changed by the user. The workflow/node validation logic has been updated to migrate old workflows to use the new default values for `use_cache`. Users may still want to review the settings that have been chosen. In the event of catastrophic failure when running this migration, the default value of `True` is applied, as this is correct for most nodes. Users should consider saving their workflows after loading them in and having them updated. ## Future Enhancements - Callback A future enhancement would be to provide a callback to the `use_cache` flag that would be run as the node is executed to determine, based on its own internal state, if the cache should be used or not. This would be useful for `DynamicPromptInvocation`, where the deterministic behaviour is determined by the `combinatorial: bool` field. ## Future Enhancements - Persisted Cache Similar to how the latents storage is backed by disk, the invocation cache could be persisted to the database or disk. We'd need to be very careful about deserializing outputs, but it's perhaps worth exploring in the future.
75 lines
3.1 KiB
Python
75 lines
3.1 KiB
Python
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team
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from __future__ import annotations
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from typing import TYPE_CHECKING
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if TYPE_CHECKING:
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from logging import Logger
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from invokeai.app.services.board_images import BoardImagesServiceABC
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from invokeai.app.services.boards import BoardServiceABC
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from invokeai.app.services.config import InvokeAIAppConfig
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from invokeai.app.services.events import EventServiceBase
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from invokeai.app.services.graph import GraphExecutionState, LibraryGraph
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from invokeai.app.services.images import ImageServiceABC
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from invokeai.app.services.invocation_cache.invocation_cache_base import InvocationCacheBase
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from invokeai.app.services.invocation_queue import InvocationQueueABC
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from invokeai.app.services.invocation_stats import InvocationStatsServiceBase
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from invokeai.app.services.invoker import InvocationProcessorABC
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from invokeai.app.services.item_storage import ItemStorageABC
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from invokeai.app.services.latent_storage import LatentsStorageBase
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from invokeai.app.services.model_manager_service import ModelManagerServiceBase
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class InvocationServices:
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"""Services that can be used by invocations"""
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# TODO: Just forward-declared everything due to circular dependencies. Fix structure.
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board_images: "BoardImagesServiceABC"
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boards: "BoardServiceABC"
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configuration: "InvokeAIAppConfig"
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events: "EventServiceBase"
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graph_execution_manager: "ItemStorageABC"["GraphExecutionState"]
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graph_library: "ItemStorageABC"["LibraryGraph"]
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images: "ImageServiceABC"
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latents: "LatentsStorageBase"
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logger: "Logger"
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model_manager: "ModelManagerServiceBase"
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processor: "InvocationProcessorABC"
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performance_statistics: "InvocationStatsServiceBase"
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queue: "InvocationQueueABC"
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invocation_cache: "InvocationCacheBase"
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def __init__(
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self,
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board_images: "BoardImagesServiceABC",
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boards: "BoardServiceABC",
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configuration: "InvokeAIAppConfig",
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events: "EventServiceBase",
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graph_execution_manager: "ItemStorageABC"["GraphExecutionState"],
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graph_library: "ItemStorageABC"["LibraryGraph"],
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images: "ImageServiceABC",
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latents: "LatentsStorageBase",
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logger: "Logger",
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model_manager: "ModelManagerServiceBase",
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processor: "InvocationProcessorABC",
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performance_statistics: "InvocationStatsServiceBase",
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queue: "InvocationQueueABC",
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invocation_cache: "InvocationCacheBase",
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):
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self.board_images = board_images
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self.boards = boards
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self.boards = boards
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self.configuration = configuration
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self.events = events
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self.graph_execution_manager = graph_execution_manager
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self.graph_library = graph_library
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self.images = images
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self.latents = latents
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self.logger = logger
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self.model_manager = model_manager
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self.processor = processor
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self.performance_statistics = performance_statistics
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self.queue = queue
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self.invocation_cache = invocation_cache
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