InvokeAI/invokeai/app/invocations/math.py

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# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
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from typing import Literal
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import numpy as np
from pydantic import ValidationInfo, field_validator
from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
from invokeai.app.invocations.fields import FieldDescriptions, InputField
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from invokeai.app.invocations.primitives import FloatOutput, IntegerOutput
from invokeai.app.services.shared.invocation_context import InvocationContext
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@invocation("add", title="Add Integers", tags=["math", "add"], category="math", version="1.0.1")
class AddInvocation(BaseInvocation):
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"""Adds two numbers"""
a: int = InputField(default=0, description=FieldDescriptions.num_1)
b: int = InputField(default=0, description=FieldDescriptions.num_2)
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def invoke(self, context: InvocationContext) -> IntegerOutput:
return IntegerOutput(value=self.a + self.b)
@invocation("sub", title="Subtract Integers", tags=["math", "subtract"], category="math", version="1.0.1")
class SubtractInvocation(BaseInvocation):
"""Subtracts two numbers"""
a: int = InputField(default=0, description=FieldDescriptions.num_1)
b: int = InputField(default=0, description=FieldDescriptions.num_2)
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def invoke(self, context: InvocationContext) -> IntegerOutput:
return IntegerOutput(value=self.a - self.b)
@invocation("mul", title="Multiply Integers", tags=["math", "multiply"], category="math", version="1.0.1")
class MultiplyInvocation(BaseInvocation):
"""Multiplies two numbers"""
a: int = InputField(default=0, description=FieldDescriptions.num_1)
b: int = InputField(default=0, description=FieldDescriptions.num_2)
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def invoke(self, context: InvocationContext) -> IntegerOutput:
return IntegerOutput(value=self.a * self.b)
@invocation("div", title="Divide Integers", tags=["math", "divide"], category="math", version="1.0.1")
class DivideInvocation(BaseInvocation):
"""Divides two numbers"""
a: int = InputField(default=0, description=FieldDescriptions.num_1)
b: int = InputField(default=0, description=FieldDescriptions.num_2)
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def invoke(self, context: InvocationContext) -> IntegerOutput:
return IntegerOutput(value=int(self.a / self.b))
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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@invocation(
"rand_int",
title="Random Integer",
tags=["math", "random"],
category="math",
version="1.0.1",
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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use_cache=False,
)
class RandomIntInvocation(BaseInvocation):
"""Outputs a single random integer."""
low: int = InputField(default=0, description=FieldDescriptions.inclusive_low)
high: int = InputField(default=np.iinfo(np.int32).max, description=FieldDescriptions.exclusive_high)
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def invoke(self, context: InvocationContext) -> IntegerOutput:
return IntegerOutput(value=np.random.randint(self.low, self.high))
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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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@invocation(
"rand_float",
title="Random Float",
tags=["math", "float", "random"],
category="math",
version="1.0.1",
use_cache=False,
)
class RandomFloatInvocation(BaseInvocation):
"""Outputs a single random float"""
low: float = InputField(default=0.0, description=FieldDescriptions.inclusive_low)
high: float = InputField(default=1.0, description=FieldDescriptions.exclusive_high)
decimals: int = InputField(default=2, description=FieldDescriptions.decimal_places)
def invoke(self, context: InvocationContext) -> FloatOutput:
random_float = np.random.uniform(self.low, self.high)
rounded_float = round(random_float, self.decimals)
return FloatOutput(value=rounded_float)
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@invocation(
"float_to_int",
title="Float To Integer",
tags=["math", "round", "integer", "float", "convert"],
category="math",
version="1.0.1",
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)
class FloatToIntegerInvocation(BaseInvocation):
"""Rounds a float number to (a multiple of) an integer."""
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value: float = InputField(default=0, description="The value to round")
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multiple: int = InputField(default=1, ge=1, title="Multiple of", description="The multiple to round to")
method: Literal["Nearest", "Floor", "Ceiling", "Truncate"] = InputField(
default="Nearest", description="The method to use for rounding"
)
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def invoke(self, context: InvocationContext) -> IntegerOutput:
if self.method == "Nearest":
return IntegerOutput(value=round(self.value / self.multiple) * self.multiple)
elif self.method == "Floor":
return IntegerOutput(value=np.floor(self.value / self.multiple) * self.multiple)
elif self.method == "Ceiling":
return IntegerOutput(value=np.ceil(self.value / self.multiple) * self.multiple)
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else: # self.method == "Truncate"
return IntegerOutput(value=int(self.value / self.multiple) * self.multiple)
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@invocation("round_float", title="Round Float", tags=["math", "round"], category="math", version="1.0.1")
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class RoundInvocation(BaseInvocation):
"""Rounds a float to a specified number of decimal places."""
value: float = InputField(default=0, description="The float value")
decimals: int = InputField(default=0, description="The number of decimal places")
def invoke(self, context: InvocationContext) -> FloatOutput:
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return FloatOutput(value=round(self.value, self.decimals))
INTEGER_OPERATIONS = Literal[
"ADD",
"SUB",
"MUL",
"DIV",
"EXP",
"MOD",
"ABS",
"MIN",
"MAX",
]
INTEGER_OPERATIONS_LABELS = {
"ADD": "Add A+B",
"SUB": "Subtract A-B",
"MUL": "Multiply A*B",
"DIV": "Divide A/B",
"EXP": "Exponentiate A^B",
"MOD": "Modulus A%B",
"ABS": "Absolute Value of A",
"MIN": "Minimum(A,B)",
"MAX": "Maximum(A,B)",
}
@invocation(
"integer_math",
title="Integer Math",
tags=[
"math",
"integer",
"add",
"subtract",
"multiply",
"divide",
"modulus",
"power",
"absolute value",
"min",
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"max",
],
category="math",
version="1.0.1",
)
class IntegerMathInvocation(BaseInvocation):
"""Performs integer math."""
operation: INTEGER_OPERATIONS = InputField(
default="ADD", description="The operation to perform", ui_choice_labels=INTEGER_OPERATIONS_LABELS
)
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a: int = InputField(default=1, description=FieldDescriptions.num_1)
b: int = InputField(default=1, description=FieldDescriptions.num_2)
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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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@field_validator("b")
def no_unrepresentable_results(cls, v: int, info: ValidationInfo):
if info.data["operation"] == "DIV" and v == 0:
raise ValueError("Cannot divide by zero")
elif info.data["operation"] == "MOD" and v == 0:
raise ValueError("Cannot divide by zero")
elif info.data["operation"] == "EXP" and v < 0:
raise ValueError("Result of exponentiation is not an integer")
return v
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def invoke(self, context: InvocationContext) -> IntegerOutput:
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# Python doesn't support switch statements until 3.10, but InvokeAI supports back to 3.9
if self.operation == "ADD":
return IntegerOutput(value=self.a + self.b)
elif self.operation == "SUB":
return IntegerOutput(value=self.a - self.b)
elif self.operation == "MUL":
return IntegerOutput(value=self.a * self.b)
elif self.operation == "DIV":
return IntegerOutput(value=int(self.a / self.b))
elif self.operation == "EXP":
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return IntegerOutput(value=self.a**self.b)
elif self.operation == "MOD":
return IntegerOutput(value=self.a % self.b)
elif self.operation == "ABS":
return IntegerOutput(value=abs(self.a))
elif self.operation == "MIN":
return IntegerOutput(value=min(self.a, self.b))
else: # self.operation == "MAX":
return IntegerOutput(value=max(self.a, self.b))
FLOAT_OPERATIONS = Literal[
"ADD",
"SUB",
"MUL",
"DIV",
"EXP",
"ABS",
"SQRT",
"MIN",
"MAX",
]
FLOAT_OPERATIONS_LABELS = {
"ADD": "Add A+B",
"SUB": "Subtract A-B",
"MUL": "Multiply A*B",
"DIV": "Divide A/B",
"EXP": "Exponentiate A^B",
"ABS": "Absolute Value of A",
"SQRT": "Square Root of A",
"MIN": "Minimum(A,B)",
"MAX": "Maximum(A,B)",
}
@invocation(
"float_math",
title="Float Math",
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tags=["math", "float", "add", "subtract", "multiply", "divide", "power", "root", "absolute value", "min", "max"],
category="math",
version="1.0.1",
)
class FloatMathInvocation(BaseInvocation):
"""Performs floating point math."""
operation: FLOAT_OPERATIONS = InputField(
default="ADD", description="The operation to perform", ui_choice_labels=FLOAT_OPERATIONS_LABELS
)
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a: float = InputField(default=1, description=FieldDescriptions.num_1)
b: float = InputField(default=1, description=FieldDescriptions.num_2)
2023-09-06 13:39:47 +00:00
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.
2023-09-24 08:11:07 +00:00
@field_validator("b")
def no_unrepresentable_results(cls, v: float, info: ValidationInfo):
if info.data["operation"] == "DIV" and v == 0:
raise ValueError("Cannot divide by zero")
elif info.data["operation"] == "EXP" and info.data["a"] == 0 and v < 0:
raise ValueError("Cannot raise zero to a negative power")
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elif info.data["operation"] == "EXP" and isinstance(info.data["a"] ** v, complex):
raise ValueError("Root operation resulted in a complex number")
return v
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def invoke(self, context: InvocationContext) -> FloatOutput:
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# Python doesn't support switch statements until 3.10, but InvokeAI supports back to 3.9
if self.operation == "ADD":
return FloatOutput(value=self.a + self.b)
elif self.operation == "SUB":
return FloatOutput(value=self.a - self.b)
elif self.operation == "MUL":
return FloatOutput(value=self.a * self.b)
elif self.operation == "DIV":
return FloatOutput(value=self.a / self.b)
elif self.operation == "EXP":
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return FloatOutput(value=self.a**self.b)
elif self.operation == "SQRT":
return FloatOutput(value=np.sqrt(self.a))
elif self.operation == "ABS":
return FloatOutput(value=abs(self.a))
elif self.operation == "MIN":
return FloatOutput(value=min(self.a, self.b))
else: # self.operation == "MAX":
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return FloatOutput(value=max(self.a, self.b))