mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
b7938d9ca9
* fix(config): fix typing issues in `config/` `config/invokeai_config.py`: - use `Optional` for things that are optional - fix typing of `ram_cache_size()` and `vram_cache_size()` - remove unused and incorrectly typed method `autoconvert_path` - fix types and logic for `parse_args()`, in which `InvokeAIAppConfig.initconf` *must* be a `DictConfig`, but function would allow it to be set as a `ListConfig`, which presumably would cause issues elsewhere `config/base.py`: - use `cls` for first arg of class methods - use `Optional` for things that are optional - fix minor type issue related to setting of `env_prefix` - remove unused `add_subparser()` method, which calls `add_parser()` on an `ArgumentParser` (method only available on the `_SubParsersAction` object, which is returned from ArgumentParser.add_subparsers()`) * feat: queued generation and batches Due to a very messy branch with broad addition of `isort` on `main` alongside it, some git surgery was needed to get an agreeable git history. This commit represents all of the work on queued generation. See PR for notes. * chore: flake8, isort, black * fix(nodes): fix incorrect service stop() method * fix(nodes): improve names of a few variables * fix(tests): fix up tests after changes to batches/queue * feat(tests): add unit tests for session queue helper functions * feat(ui): dynamic prompts is always enabled * feat(queue): add queue_status_changed event * feat(ui): wip queue graphs * feat(nodes): move cleanup til after invoker startup * feat(nodes): add cancel_by_batch_ids * feat(ui): wip batch graphs & UI * fix(nodes): remove `Batch.batch_id` from required * fix(ui): cleanup and use fixedCacheKey for all mutations * fix(ui): remove orphaned nodes from canvas graphs * fix(nodes): fix cancel_by_batch_ids result count * fix(ui): only show cancel batch tooltip when batches were canceled * chore: isort * fix(api): return `[""]` when dynamic prompts generates no prompts Just a simple fallback so we always have a prompt. * feat(ui): dynamicPrompts.combinatorial is always on There seems to be little purpose in using the combinatorial generation for dynamic prompts. I've disabled it by hiding it from the UI and defaulting combinatorial to true. If we want to enable it again in the future it's straightforward to do so. * feat: add queue_id & support logic * feat(ui): fix upscale button It prepends the upscale operation to queue * feat(nodes): return queue item when enqueuing a single graph This facilitates one-off graph async workflows in the client. * feat(ui): move controlnet autoprocess to queue * fix(ui): fix non-serializable DOMRect in redux state * feat(ui): QueueTable performance tweaks * feat(ui): update queue list Queue items expand to show the full queue item. Just as JSON for now. * wip threaded session_processor * feat(nodes,ui): fully migrate queue to session_processor * feat(nodes,ui): add processor events * feat(ui): ui tweaks * feat(nodes,ui): consolidate events, reduce network requests * feat(ui): cleanup & abstract queue hooks * feat(nodes): optimize batch permutation Use a generator to do only as much work as is needed. Previously, though we only ended up creating exactly as many queue items as was needed, there was still some intermediary work that calculated *all* permutations. When that number was very high, the system had a very hard time and used a lot of memory. The logic has been refactored to use a generator. Additionally, the batch validators are optimized to return early and use less memory. * feat(ui): add seed behaviour parameter This dynamic prompts parameter allows the seed to be randomized per prompt or per iteration: - Per iteration: Use the same seed for all prompts in a single dynamic prompt expansion - Per prompt: Use a different seed for every single prompt "Per iteration" is appropriate for exploring a the latents space with a stable starting noise, while "Per prompt" provides more variation. * fix(ui): remove extraneous random seed nodes from linear graphs * fix(ui): fix controlnet autoprocess not working when queue is running * feat(queue): add timestamps to queue status updates Also show execution time in queue list * feat(queue): change all execution-related events to use the `queue_id` as the room, also include `queue_item_id` in InvocationQueueItem This allows for much simpler handling of queue items. * feat(api): deprecate sessions router * chore(backend): tidy logging in `dependencies.py` * fix(backend): respect `use_memory_db` * feat(backend): add `config.log_sql` (enables sql trace logging) * feat: add invocation cache Supersedes #4574 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. * fix(ui): fix queue list item width * feat(nodes): do not send the whole node on every generator progress * feat(ui): strip out old logic related to sessions Things like `isProcessing` are no longer relevant with queue. Removed them all & updated everything be appropriate for queue. May be a few little quirks I've missed... * feat(ui): fix up param collapse labels * feat(ui): click queue count to go to queue tab * tidy(queue): update comment, query format * feat(ui): fix progress bar when canceling * fix(ui): fix circular dependency * feat(nodes): bail on node caching logic if `node_cache_size == 0` * feat(nodes): handle KeyError on node cache pop * feat(nodes): bypass cache codepath if caches is disabled more better no do thing * fix(ui): reset api cache on connect/disconnect * feat(ui): prevent enqueue when no prompts generated * feat(ui): add queue controls to workflow editor * feat(ui): update floating buttons & other incidental UI tweaks * fix(ui): fix missing/incorrect translation keys * fix(tests): add config service to mock invocation services invoking needs access to `node_cache_size` to occur * optionally remove pause/resume buttons from queue UI * option to disable prepending * chore(ui): remove unused file * feat(queue): remove `order_id` entirely, `item_id` is now an autoinc pk --------- Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
486 lines
19 KiB
Python
486 lines
19 KiB
Python
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team
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from invokeai.app.services.invocation_cache.invocation_cache_memory import MemoryInvocationCache
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from .services.config import InvokeAIAppConfig
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# parse_args() must be called before any other imports. if it is not called first, consumers of the config
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# which are imported/used before parse_args() is called will get the default config values instead of the
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# values from the command line or config file.
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config = InvokeAIAppConfig.get_config()
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config.parse_args()
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if True: # hack to make flake8 happy with imports coming after setting up the config
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import argparse
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import re
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import shlex
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import sqlite3
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import sys
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import time
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from typing import Optional, Union, get_type_hints
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import torch
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from pydantic import BaseModel, ValidationError
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from pydantic.fields import Field
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import invokeai.backend.util.hotfixes # noqa: F401 (monkeypatching on import)
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from invokeai.app.services.board_image_record_storage import SqliteBoardImageRecordStorage
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from invokeai.app.services.board_images import BoardImagesService, BoardImagesServiceDependencies
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from invokeai.app.services.board_record_storage import SqliteBoardRecordStorage
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from invokeai.app.services.boards import BoardService, BoardServiceDependencies
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from invokeai.app.services.image_record_storage import SqliteImageRecordStorage
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from invokeai.app.services.images import ImageService, ImageServiceDependencies
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from invokeai.app.services.invocation_stats import InvocationStatsService
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from invokeai.app.services.resource_name import SimpleNameService
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from invokeai.app.services.urls import LocalUrlService
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from invokeai.backend.util.logging import InvokeAILogger
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from invokeai.version.invokeai_version import __version__
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from .cli.commands import BaseCommand, CliContext, ExitCli, SortedHelpFormatter, add_graph_parsers, add_parsers
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from .cli.completer import set_autocompleter
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from .invocations.baseinvocation import BaseInvocation
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from .services.default_graphs import create_system_graphs, default_text_to_image_graph_id
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from .services.events import EventServiceBase
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from .services.graph import (
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Edge,
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EdgeConnection,
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GraphExecutionState,
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GraphInvocation,
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LibraryGraph,
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are_connection_types_compatible,
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)
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from .services.image_file_storage import DiskImageFileStorage
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from .services.invocation_queue import MemoryInvocationQueue
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from .services.invocation_services import InvocationServices
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from .services.invoker import Invoker
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from .services.latent_storage import DiskLatentsStorage, ForwardCacheLatentsStorage
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from .services.model_manager_service import ModelManagerService
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from .services.processor import DefaultInvocationProcessor
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from .services.sqlite import SqliteItemStorage
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if torch.backends.mps.is_available():
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import invokeai.backend.util.mps_fixes # noqa: F401 (monkeypatching on import)
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logger = InvokeAILogger().getLogger(config=config)
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class CliCommand(BaseModel):
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command: Union[BaseCommand.get_commands() + BaseInvocation.get_invocations()] = Field(discriminator="type") # type: ignore
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class InvalidArgs(Exception):
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pass
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def add_invocation_args(command_parser):
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# Add linking capability
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command_parser.add_argument(
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"--link",
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"-l",
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action="append",
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nargs=3,
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help="A link in the format 'source_node source_field dest_field'. source_node can be relative to history (e.g. -1)",
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)
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command_parser.add_argument(
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"--link_node",
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"-ln",
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action="append",
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help="A link from all fields in the specified node. Node can be relative to history (e.g. -1)",
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)
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def get_command_parser(services: InvocationServices) -> argparse.ArgumentParser:
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# Create invocation parser
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parser = argparse.ArgumentParser(formatter_class=SortedHelpFormatter)
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def exit(*args, **kwargs):
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raise InvalidArgs
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parser.exit = exit
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subparsers = parser.add_subparsers(dest="type")
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# Create subparsers for each invocation
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invocations = BaseInvocation.get_all_subclasses()
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add_parsers(subparsers, invocations, add_arguments=add_invocation_args)
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# Create subparsers for each command
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commands = BaseCommand.get_all_subclasses()
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add_parsers(subparsers, commands, exclude_fields=["type"])
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# Create subparsers for exposed CLI graphs
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# TODO: add a way to identify these graphs
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text_to_image = services.graph_library.get(default_text_to_image_graph_id)
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add_graph_parsers(subparsers, [text_to_image], add_arguments=add_invocation_args)
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return parser
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class NodeField:
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alias: str
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node_path: str
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field: str
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field_type: type
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def __init__(self, alias: str, node_path: str, field: str, field_type: type):
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self.alias = alias
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self.node_path = node_path
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self.field = field
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self.field_type = field_type
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def fields_from_type_hints(hints: dict[str, type], node_path: str) -> dict[str, NodeField]:
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return {k: NodeField(alias=k, node_path=node_path, field=k, field_type=v) for k, v in hints.items()}
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def get_node_input_field(graph: LibraryGraph, field_alias: str, node_id: str) -> NodeField:
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"""Gets the node field for the specified field alias"""
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exposed_input = next(e for e in graph.exposed_inputs if e.alias == field_alias)
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node_type = type(graph.graph.get_node(exposed_input.node_path))
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return NodeField(
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alias=exposed_input.alias,
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node_path=f"{node_id}.{exposed_input.node_path}",
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field=exposed_input.field,
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field_type=get_type_hints(node_type)[exposed_input.field],
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)
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def get_node_output_field(graph: LibraryGraph, field_alias: str, node_id: str) -> NodeField:
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"""Gets the node field for the specified field alias"""
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exposed_output = next(e for e in graph.exposed_outputs if e.alias == field_alias)
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node_type = type(graph.graph.get_node(exposed_output.node_path))
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node_output_type = node_type.get_output_type()
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return NodeField(
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alias=exposed_output.alias,
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node_path=f"{node_id}.{exposed_output.node_path}",
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field=exposed_output.field,
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field_type=get_type_hints(node_output_type)[exposed_output.field],
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)
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def get_node_inputs(invocation: BaseInvocation, context: CliContext) -> dict[str, NodeField]:
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"""Gets the inputs for the specified invocation from the context"""
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node_type = type(invocation)
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if node_type is not GraphInvocation:
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return fields_from_type_hints(get_type_hints(node_type), invocation.id)
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else:
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graph: LibraryGraph = context.invoker.services.graph_library.get(context.graph_nodes[invocation.id])
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return {e.alias: get_node_input_field(graph, e.alias, invocation.id) for e in graph.exposed_inputs}
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def get_node_outputs(invocation: BaseInvocation, context: CliContext) -> dict[str, NodeField]:
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"""Gets the outputs for the specified invocation from the context"""
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node_type = type(invocation)
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if node_type is not GraphInvocation:
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return fields_from_type_hints(get_type_hints(node_type.get_output_type()), invocation.id)
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else:
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graph: LibraryGraph = context.invoker.services.graph_library.get(context.graph_nodes[invocation.id])
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return {e.alias: get_node_output_field(graph, e.alias, invocation.id) for e in graph.exposed_outputs}
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def generate_matching_edges(a: BaseInvocation, b: BaseInvocation, context: CliContext) -> list[Edge]:
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"""Generates all possible edges between two invocations"""
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afields = get_node_outputs(a, context)
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bfields = get_node_inputs(b, context)
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matching_fields = set(afields.keys()).intersection(bfields.keys())
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# Remove invalid fields
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invalid_fields = set(["type", "id"])
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matching_fields = matching_fields.difference(invalid_fields)
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# Validate types
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matching_fields = [
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f for f in matching_fields if are_connection_types_compatible(afields[f].field_type, bfields[f].field_type)
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]
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edges = [
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Edge(
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source=EdgeConnection(node_id=afields[alias].node_path, field=afields[alias].field),
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destination=EdgeConnection(node_id=bfields[alias].node_path, field=bfields[alias].field),
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)
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for alias in matching_fields
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]
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return edges
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class SessionError(Exception):
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"""Raised when a session error has occurred"""
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pass
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def invoke_all(context: CliContext):
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"""Runs all invocations in the specified session"""
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context.invoker.invoke(context.session, invoke_all=True)
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while not context.get_session().is_complete():
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# Wait some time
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time.sleep(0.1)
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# Print any errors
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if context.session.has_error():
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for n in context.session.errors:
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context.invoker.services.logger.error(
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f"Error in node {n} (source node {context.session.prepared_source_mapping[n]}): {context.session.errors[n]}"
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)
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raise SessionError()
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def invoke_cli():
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logger.info(f"InvokeAI version {__version__}")
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# get the optional list of invocations to execute on the command line
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parser = config.get_parser()
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parser.add_argument("commands", nargs="*")
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invocation_commands = parser.parse_args().commands
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# get the optional file to read commands from.
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# Simplest is to use it for STDIN
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if infile := config.from_file:
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sys.stdin = open(infile, "r")
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model_manager = ModelManagerService(config, logger)
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events = EventServiceBase()
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output_folder = config.output_path
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# TODO: build a file/path manager?
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if config.use_memory_db:
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db_location = ":memory:"
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else:
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db_location = config.db_path
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db_location.parent.mkdir(parents=True, exist_ok=True)
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db_conn = sqlite3.connect(db_location, check_same_thread=False) # TODO: figure out a better threading solution
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logger.info(f'InvokeAI database location is "{db_location}"')
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graph_execution_manager = SqliteItemStorage[GraphExecutionState](conn=db_conn, table_name="graph_executions")
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urls = LocalUrlService()
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image_record_storage = SqliteImageRecordStorage(conn=db_conn)
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image_file_storage = DiskImageFileStorage(f"{output_folder}/images")
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names = SimpleNameService()
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board_record_storage = SqliteBoardRecordStorage(conn=db_conn)
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board_image_record_storage = SqliteBoardImageRecordStorage(conn=db_conn)
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boards = BoardService(
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services=BoardServiceDependencies(
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board_image_record_storage=board_image_record_storage,
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board_record_storage=board_record_storage,
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image_record_storage=image_record_storage,
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url=urls,
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logger=logger,
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)
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)
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board_images = BoardImagesService(
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services=BoardImagesServiceDependencies(
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board_image_record_storage=board_image_record_storage,
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board_record_storage=board_record_storage,
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image_record_storage=image_record_storage,
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url=urls,
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logger=logger,
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)
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)
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images = ImageService(
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services=ImageServiceDependencies(
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board_image_record_storage=board_image_record_storage,
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image_record_storage=image_record_storage,
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image_file_storage=image_file_storage,
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url=urls,
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logger=logger,
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names=names,
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graph_execution_manager=graph_execution_manager,
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)
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)
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services = InvocationServices(
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model_manager=model_manager,
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events=events,
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latents=ForwardCacheLatentsStorage(DiskLatentsStorage(f"{output_folder}/latents")),
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images=images,
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boards=boards,
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board_images=board_images,
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queue=MemoryInvocationQueue(),
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graph_library=SqliteItemStorage[LibraryGraph](conn=db_conn, table_name="graphs"),
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graph_execution_manager=graph_execution_manager,
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processor=DefaultInvocationProcessor(),
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performance_statistics=InvocationStatsService(graph_execution_manager),
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logger=logger,
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configuration=config,
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invocation_cache=MemoryInvocationCache(max_cache_size=config.node_cache_size),
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)
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system_graphs = create_system_graphs(services.graph_library)
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system_graph_names = set([g.name for g in system_graphs])
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set_autocompleter(services)
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invoker = Invoker(services)
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session: GraphExecutionState = invoker.create_execution_state()
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parser = get_command_parser(services)
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re_negid = re.compile("^-[0-9]+$")
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# Uncomment to print out previous sessions at startup
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# print(services.session_manager.list())
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context = CliContext(invoker, session, parser)
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set_autocompleter(services)
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command_line_args_exist = len(invocation_commands) > 0
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done = False
|
|
|
|
while not done:
|
|
try:
|
|
if command_line_args_exist:
|
|
cmd_input = invocation_commands.pop(0)
|
|
done = len(invocation_commands) == 0
|
|
else:
|
|
cmd_input = input("invoke> ")
|
|
except (KeyboardInterrupt, EOFError):
|
|
# Ctrl-c exits
|
|
break
|
|
|
|
try:
|
|
# Refresh the state of the session
|
|
# history = list(get_graph_execution_history(context.session))
|
|
history = list(reversed(context.nodes_added))
|
|
|
|
# Split the command for piping
|
|
cmds = cmd_input.split("|")
|
|
start_id = len(context.nodes_added)
|
|
current_id = start_id
|
|
new_invocations = list()
|
|
for cmd in cmds:
|
|
if cmd is None or cmd.strip() == "":
|
|
raise InvalidArgs("Empty command")
|
|
|
|
# Parse args to create invocation
|
|
args = vars(context.parser.parse_args(shlex.split(cmd.strip())))
|
|
|
|
# Override defaults
|
|
for field_name, field_default in context.defaults.items():
|
|
if field_name in args:
|
|
args[field_name] = field_default
|
|
|
|
# Parse invocation
|
|
command: CliCommand = None # type:ignore
|
|
system_graph: Optional[LibraryGraph] = None
|
|
if args["type"] in system_graph_names:
|
|
system_graph = next(filter(lambda g: g.name == args["type"], system_graphs))
|
|
invocation = GraphInvocation(graph=system_graph.graph, id=str(current_id))
|
|
for exposed_input in system_graph.exposed_inputs:
|
|
if exposed_input.alias in args:
|
|
node = invocation.graph.get_node(exposed_input.node_path)
|
|
field = exposed_input.field
|
|
setattr(node, field, args[exposed_input.alias])
|
|
command = CliCommand(command=invocation)
|
|
context.graph_nodes[invocation.id] = system_graph.id
|
|
else:
|
|
args["id"] = current_id
|
|
command = CliCommand(command=args)
|
|
|
|
if command is None:
|
|
continue
|
|
|
|
# Run any CLI commands immediately
|
|
if isinstance(command.command, BaseCommand):
|
|
# Invoke all current nodes to preserve operation order
|
|
invoke_all(context)
|
|
|
|
# Run the command
|
|
command.command.run(context)
|
|
continue
|
|
|
|
# TODO: handle linking with library graphs
|
|
# Pipe previous command output (if there was a previous command)
|
|
edges: list[Edge] = list()
|
|
if len(history) > 0 or current_id != start_id:
|
|
from_id = history[0] if current_id == start_id else str(current_id - 1)
|
|
from_node = (
|
|
next(filter(lambda n: n[0].id == from_id, new_invocations))[0]
|
|
if current_id != start_id
|
|
else context.session.graph.get_node(from_id)
|
|
)
|
|
matching_edges = generate_matching_edges(from_node, command.command, context)
|
|
edges.extend(matching_edges)
|
|
|
|
# Parse provided links
|
|
if "link_node" in args and args["link_node"]:
|
|
for link in args["link_node"]:
|
|
node_id = link
|
|
if re_negid.match(node_id):
|
|
node_id = str(current_id + int(node_id))
|
|
|
|
link_node = context.session.graph.get_node(node_id)
|
|
matching_edges = generate_matching_edges(link_node, command.command, context)
|
|
matching_destinations = [e.destination for e in matching_edges]
|
|
edges = [e for e in edges if e.destination not in matching_destinations]
|
|
edges.extend(matching_edges)
|
|
|
|
if "link" in args and args["link"]:
|
|
for link in args["link"]:
|
|
edges = [
|
|
e
|
|
for e in edges
|
|
if e.destination.node_id != command.command.id or e.destination.field != link[2]
|
|
]
|
|
|
|
node_id = link[0]
|
|
if re_negid.match(node_id):
|
|
node_id = str(current_id + int(node_id))
|
|
|
|
# TODO: handle missing input/output
|
|
node_output = get_node_outputs(context.session.graph.get_node(node_id), context)[link[1]]
|
|
node_input = get_node_inputs(command.command, context)[link[2]]
|
|
|
|
edges.append(
|
|
Edge(
|
|
source=EdgeConnection(node_id=node_output.node_path, field=node_output.field),
|
|
destination=EdgeConnection(node_id=node_input.node_path, field=node_input.field),
|
|
)
|
|
)
|
|
|
|
new_invocations.append((command.command, edges))
|
|
|
|
current_id = current_id + 1
|
|
|
|
# Add the node to the session
|
|
context.add_node(command.command)
|
|
for edge in edges:
|
|
print(edge)
|
|
context.add_edge(edge)
|
|
|
|
# Execute all remaining nodes
|
|
invoke_all(context)
|
|
|
|
except InvalidArgs:
|
|
invoker.services.logger.warning('Invalid command, use "help" to list commands')
|
|
continue
|
|
|
|
except ValidationError:
|
|
invoker.services.logger.warning('Invalid command arguments, run "<command> --help" for summary')
|
|
|
|
except SessionError:
|
|
# Start a new session
|
|
invoker.services.logger.warning("Session error: creating a new session")
|
|
context.reset()
|
|
|
|
except ExitCli:
|
|
break
|
|
|
|
except SystemExit:
|
|
continue
|
|
|
|
invoker.stop()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
if config.version:
|
|
print(f"InvokeAI version {__version__}")
|
|
else:
|
|
invoke_cli()
|