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10 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
Lincoln Stein
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3e330d7d9d | fix a number of typechecking errors | ||
psychedelicious
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c238a7f18b |
feat(api): chore: pydantic & fastapi upgrade
Upgrade pydantic and fastapi to latest.
- pydantic~=2.4.2
- fastapi~=103.2
- fastapi-events~=0.9.1
**Big Changes**
There are a number of logic changes needed to support pydantic v2. Most changes are very simple, like using the new methods to serialized and deserialize models, but there are a few more complex changes.
**Invocations**
The biggest change relates to invocation creation, instantiation and validation.
Because pydantic v2 moves all validation logic into the rust pydantic-core, we may no longer directly stick our fingers into the validation pie.
Previously, we (ab)used models and fields to allow invocation fields to be optional at instantiation, but required when `invoke()` is called. We directly manipulated the fields and invocation models when calling `invoke()`.
With pydantic v2, this is much more involved. Changes to the python wrapper do not propagate down to the rust validation logic - you have to rebuild the model. This causes problem with concurrent access to the invocation classes and is not a free operation.
This logic has been totally refactored and we do not need to change the model any more. The details are in `baseinvocation.py`, in the `InputField` function and `BaseInvocation.invoke_internal()` method.
In the end, this implementation is cleaner.
**Invocation Fields**
In pydantic v2, you can no longer directly add or remove fields from a model.
Previously, we did this to add the `type` field to invocations.
**Invocation Decorators**
With pydantic v2, we instead use the imperative `create_model()` API to create a new model with the additional field. This is done in `baseinvocation.py` in the `invocation()` wrapper.
A similar technique is used for `invocation_output()`.
**Minor Changes**
There are a number of minor changes around the pydantic v2 models API.
**Protected `model_` Namespace**
All models' pydantic-provided methods and attributes are prefixed with `model_` and this is considered a protected namespace. This causes some conflict, because "model" means something to us, and we have a ton of pydantic models with attributes starting with "model_".
Forunately, there are no direct conflicts. However, in any pydantic model where we define an attribute or method that starts with "model_", we must tell set the protected namespaces to an empty tuple.
```py
class IPAdapterModelField(BaseModel):
model_name: str = Field(description="Name of the IP-Adapter model")
base_model: BaseModelType = Field(description="Base model")
model_config = ConfigDict(protected_namespaces=())
```
**Model Serialization**
Pydantic models no longer have `Model.dict()` or `Model.json()`.
Instead, we use `Model.model_dump()` or `Model.model_dump_json()`.
**Model Deserialization**
Pydantic models no longer have `Model.parse_obj()` or `Model.parse_raw()`, and there are no `parse_raw_as()` or `parse_obj_as()` functions.
Instead, you need to create a `TypeAdapter` object to parse python objects or JSON into a model.
```py
adapter_graph = TypeAdapter(Graph)
deserialized_graph_from_json = adapter_graph.validate_json(graph_json)
deserialized_graph_from_dict = adapter_graph.validate_python(graph_dict)
```
**Field Customisation**
Pydantic `Field`s no longer accept arbitrary args.
Now, you must put all additional arbitrary args in a `json_schema_extra` arg on the field.
**Schema Customisation**
FastAPI and pydantic schema generation now follows the OpenAPI version 3.1 spec.
This necessitates two changes:
- Our schema customization logic has been revised
- Schema parsing to build node templates has been revised
The specific aren't important, but this does present additional surface area for bugs.
**Performance Improvements**
Pydantic v2 is a full rewrite with a rust backend. This offers a substantial performance improvement (pydantic claims 5x to 50x depending on the task). We'll notice this the most during serialization and deserialization of sessions/graphs, which happens very very often - a couple times per node.
I haven't done any benchmarks, but anecdotally, graph execution is much faster. Also, very larges graphs - like with massive iterators - are much, much faster.
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psychedelicious
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b7938d9ca9
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feat: queued generation (#4502)
* 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> |
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Martin Kristiansen
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5615c31799 | isort wip | ||
Jonathan
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dc96a3e79d |
Fix random number generator
Passing in seed=0 is not equivalent to seed=None. The latter will get a new seed from entropy in the OS, and that's what we should be using. |
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blessedcoolant
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e766ddbcf4 | fix: Generate random seed using the generator instead of RandomState | ||
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psychedelicious
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d5a75eb833 |
feat: increase seed from int32 to uint32
At some point I typo'd this and set the max seed to signed int32 max. It should be *un*signed int32 max. This restored the seed range to what it was in v2.3. |
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psychedelicious
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9c89d3452c |
feat(nodes): add high-level images service
feat(nodes): add ResultsServiceABC & SqliteResultsService **Doesn't actually work bc of circular imports. Can't even test it.** - add a base class for ResultsService and SQLite implementation - use `graph_execution_manager` `on_changed` callback to keep `results` table in sync fix(nodes): fix results service bugs chore(ui): regen api fix(ui): fix type guards feat(nodes): add `result_type` to results table, fix types fix(nodes): do not shadow `list` builtin feat(nodes): add results router It doesn't work due to circular imports still fix(nodes): Result class should use outputs classes, not fields feat(ui): crude results router fix(ui): send to canvas in currentimagebuttons not working feat(nodes): add core metadata builder feat(nodes): add design doc feat(nodes): wip latents db stuff feat(nodes): images_db_service and resources router feat(nodes): wip images db & router feat(nodes): update image related names feat(nodes): update urlservice feat(nodes): add high-level images service |
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psychedelicious
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a1079e455a | feat(nodes): cleanup unused params, seed generation | ||
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psychedelicious
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5f498e10bd
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Partial migration of UI to nodes API (#3195)
* feat(ui): add axios client generator and simple example * fix(ui): update client & nodes test code w/ new Edge type * chore(ui): organize generated files * chore(ui): update .eslintignore, .prettierignore * chore(ui): update openapi.json * feat(backend): fixes for nodes/generator * feat(ui): generate object args for api client * feat(ui): more nodes api prototyping * feat(ui): nodes cancel * chore(ui): regenerate api client * fix(ui): disable OG web server socket connection * fix(ui): fix scrollbar styles typing and prop just noticed the typo, and made the types stronger. * feat(ui): add socketio types * feat(ui): wip nodes - extract api client method arg types instead of manually declaring them - update example to display images - general tidy up * start building out node translations from frontend state and add notes about missing features * use reference to sampler_name * use reference to sampler_name * add optional apiUrl prop * feat(ui): start hooking up dynamic txt2img node generation, create middleware for session invocation * feat(ui): write separate nodes socket layer, txt2img generating and rendering w single node * feat(ui): img2img implementation * feat(ui): get intermediate images working but types are stubbed out * chore(ui): add support for package mode * feat(ui): add nodes mode script * feat(ui): handle random seeds * fix(ui): fix middleware types * feat(ui): add rtk action type guard * feat(ui): disable NodeAPITest This was polluting the network/socket logs. * feat(ui): fix parameters panel border color This commit should be elsewhere but I don't want to break my flow * feat(ui): make thunk types more consistent * feat(ui): add type guards for outputs * feat(ui): load images on socket connect Rudimentary * chore(ui): bump redux-toolkit * docs(ui): update readme * chore(ui): regenerate api client * chore(ui): add typescript as dev dependency I am having trouble with TS versions after vscode updated and now uses TS 5. `madge` has installed 3.9.10 and for whatever reason my vscode wants to use that. Manually specifying 4.9.5 and then setting vscode to use that as the workspace TS fixes the issue. * feat(ui): begin migrating gallery to nodes Along the way, migrate to use RTK `createEntityAdapter` for gallery images, and separate `results` and `uploads` into separate slices. Much cleaner this way. * feat(ui): clean up & comment results slice * fix(ui): separate thunk for initial gallery load so it properly gets index 0 * feat(ui): POST upload working * fix(ui): restore removed type * feat(ui): patch api generation for headers access * chore(ui): regenerate api * feat(ui): wip gallery migration * feat(ui): wip gallery migration * chore(ui): regenerate api * feat(ui): wip refactor socket events * feat(ui): disable panels based on app props * feat(ui): invert logic to be disabled * disable panels when app mounts * feat(ui): add support to disableTabs * docs(ui): organise and update docs * lang(ui): add toast strings * feat(ui): wip events, comments, and general refactoring * feat(ui): add optional token for auth * feat(ui): export StatusIndicator and ModelSelect for header use * feat(ui) working on making socket URL dynamic * feat(ui): dynamic middleware loading * feat(ui): prep for socket jwt * feat(ui): migrate cancelation also updated action names to be event-like instead of declaration-like sorry, i was scattered and this commit has a lot of unrelated stuff in it. * fix(ui): fix img2img type * chore(ui): regenerate api client * feat(ui): improve InvocationCompleteEvent types * feat(ui): increase StatusIndicator font size * fix(ui): fix middleware order for multi-node graphs * feat(ui): add exampleGraphs object w/ iterations example * feat(ui): generate iterations graph * feat(ui): update ModelSelect for nodes API * feat(ui): add hi-res functionality for txt2img generations * feat(ui): "subscribe" to particular nodes feels like a dirty hack but oh well it works * feat(ui): first steps to node editor ui * fix(ui): disable event subscription it is not fully baked just yet * feat(ui): wip node editor * feat(ui): remove extraneous field types * feat(ui): nodes before deleting stuff * feat(ui): cleanup nodes ui stuff * feat(ui): hook up nodes to redux * fix(ui): fix handle * fix(ui): add basic node edges & connection validation * feat(ui): add connection validation styling * feat(ui): increase edge width * feat(ui): it blends * feat(ui): wip model handling and graph topology validation * feat(ui): validation connections w/ graphlib * docs(ui): update nodes doc * feat(ui): wip node editor * chore(ui): rebuild api, update types * add redux-dynamic-middlewares as a dependency * feat(ui): add url host transformation * feat(ui): handle already-connected fields * feat(ui): rewrite SqliteItemStore in sqlalchemy * fix(ui): fix sqlalchemy dynamic model instantiation * feat(ui, nodes): metadata wip * feat(ui, nodes): models * feat(ui, nodes): more metadata wip * feat(ui): wip range/iterate * fix(nodes): fix sqlite typing * feat(ui): export new type for invoke component * tests(nodes): fix test instantiation of ImageField * feat(nodes): fix LoadImageInvocation * feat(nodes): add `title` ui hint * feat(nodes): make ImageField attrs optional * feat(ui): wip nodes etc * feat(nodes): roll back sqlalchemy * fix(nodes): partially address feedback * fix(backend): roll back changes to pngwriter * feat(nodes): wip address metadata feedback * feat(nodes): add seeded rng to RandomRange * feat(nodes): address feedback * feat(nodes): move GET images error handling to DiskImageStorage * feat(nodes): move GET images error handling to DiskImageStorage * fix(nodes): fix image output schema customization * feat(ui): img2img/txt2img -> linear - remove txt2img and img2img tabs - add linear tab - add initial image selection to linear parameters accordion * feat(ui): tidy graph builders * feat(ui): tidy misc * feat(ui): improve invocation union types * feat(ui): wip metadata viewer recall * feat(ui): move fonts to normal deps * feat(nodes): fix broken upload * feat(nodes): add metadata module + tests, thumbnails - `MetadataModule` is stateless and needed in places where the `InvocationContext` is not available, so have not made it a `service` - Handles loading/parsing/building metadata, and creating png info objects - added tests for MetadataModule - Lifted thumbnail stuff to util * fix(nodes): revert change to RandomRangeInvocation * feat(nodes): address feedback - make metadata a service - rip out pydantic validation, implement metadata parsing as simple functions - update tests - address other minor feedback items * fix(nodes): fix other tests * fix(nodes): add metadata service to cli * fix(nodes): fix latents/image field parsing * feat(nodes): customise LatentsField schema * feat(nodes): move metadata parsing to frontend * fix(nodes): fix metadata test --------- Co-authored-by: maryhipp <maryhipp@gmail.com> Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local> |