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Initial work to update to pydantic 2 and newer fastapi. Still need to review all fields, fix schema generation (to link output types), and update all Config classes to use model_config.

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This commit is contained in:
Kyle Schouviller 2023-08-12 12:32:23 -07:00
parent a67d8376c7
commit 5a532d00ec
34 changed files with 184 additions and 183 deletions
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6
invokeai/app/api/routers/models.py
View File

@ -105,7 +105,7 @@ async def update_model(
info.path = new_info.get("path") info.path = new_info.get("path")
# replace empty string values with None/null to avoid phenomenon of vae: '' # replace empty string values with None/null to avoid phenomenon of vae: ''
info_dict = info.dict() info_dict = info.model_dump()
info_dict = {x: info_dict[x] if info_dict[x] else None for x in info_dict.keys()} info_dict = {x: info_dict[x] if info_dict[x] else None for x in info_dict.keys()}
ApiDependencies.invoker.services.model_manager.update_model( ApiDependencies.invoker.services.model_manager.update_model(
@ -203,7 +203,7 @@ async def add_model(
try: try:
ApiDependencies.invoker.services.model_manager.add_model( ApiDependencies.invoker.services.model_manager.add_model(
info.model_name, info.base_model, info.model_type, model_attributes=info.dict() info.model_name, info.base_model, info.model_type, model_attributes=info.model_dump()
) )
logger.info(f"Successfully added {info.model_name}") logger.info(f"Successfully added {info.model_name}")
model_raw = ApiDependencies.invoker.services.model_manager.list_model( model_raw = ApiDependencies.invoker.services.model_manager.list_model(
@ -348,7 +348,7 @@ async def sync_to_config() -> bool:
) )
async def merge_models( async def merge_models(
base_model: BaseModelType = Path(description="Base model"), base_model: BaseModelType = Path(description="Base model"),
model_names: List[str] = Body(description="model name", min_items=2, max_items=3), model_names: List[str] = Body(description="model name", min_length=2, max_length=3),
merged_model_name: Optional[str] = Body(description="Name of destination model"), merged_model_name: Optional[str] = Body(description="Name of destination model"),
alpha: Optional[float] = Body(description="Alpha weighting strength to apply to 2d and 3d models", default=0.5), alpha: Optional[float] = Body(description="Alpha weighting strength to apply to 2d and 3d models", default=0.5),
interp: Optional[MergeInterpolationMethod] = Body(description="Interpolation method"), interp: Optional[MergeInterpolationMethod] = Body(description="Interpolation method"),

14
invokeai/app/api_app.py
View File

@ -15,7 +15,7 @@ from fastapi.staticfiles import StaticFiles
from fastapi_events.handlers.local import local_handler from fastapi_events.handlers.local import local_handler
from fastapi_events.middleware import EventHandlerASGIMiddleware from fastapi_events.middleware import EventHandlerASGIMiddleware
from pathlib import Path from pathlib import Path
from pydantic.schema import schema #from pydantic.schema import schema
# This should come early so that modules can log their initialization properly # This should come early so that modules can log their initialization properly
from .services.config import InvokeAIAppConfig from .services.config import InvokeAIAppConfig
@ -126,13 +126,13 @@ def custom_openapi():
output_type = signature(invoker.invoke).return_annotation output_type = signature(invoker.invoke).return_annotation
output_types.add(output_type) output_types.add(output_type)
output_schemas = schema(output_types, ref_prefix="#/components/schemas/") # output_schemas = schema(output_types, ref_prefix="#/components/schemas/")
for schema_key, output_schema in output_schemas["definitions"].items(): # for schema_key, output_schema in output_schemas["definitions"].items():
openapi_schema["components"]["schemas"][schema_key] = output_schema # openapi_schema["components"]["schemas"][schema_key] = output_schema
# TODO: note that we assume the schema_key here is the TYPE.__name__ # # TODO: note that we assume the schema_key here is the TYPE.__name__
# This could break in some cases, figure out a better way to do it # # This could break in some cases, figure out a better way to do it
output_type_titles[schema_key] = output_schema["title"] # output_type_titles[schema_key] = output_schema["title"]
# Add a reference to the output type to additionalProperties of the invoker schema # Add a reference to the output type to additionalProperties of the invoker schema
for invoker in all_invocations: for invoker in all_invocations:

6
invokeai/app/cli/commands.py
View File

@ -67,7 +67,7 @@ def add_parsers(
add_arguments(command_parser) add_arguments(command_parser)
# Convert all fields to arguments # Convert all fields to arguments
fields = command.__fields__ # type: ignore fields = command.model_fields # type: ignore
for name, field in fields.items(): for name, field in fields.items():
if name in exclude_fields: if name in exclude_fields:
continue continue
@ -87,7 +87,7 @@ def add_graph_parsers(
# Add arguments for inputs # Add arguments for inputs
for exposed_input in graph.exposed_inputs: for exposed_input in graph.exposed_inputs:
node = graph.graph.get_node(exposed_input.node_path) node = graph.graph.get_node(exposed_input.node_path)
field = node.__fields__[exposed_input.field] field = node.model_fields[exposed_input.field]
default_override = getattr(node, exposed_input.field) default_override = getattr(node, exposed_input.field)
add_field_argument(command_parser, exposed_input.alias, field, default_override) add_field_argument(command_parser, exposed_input.alias, field, default_override)
@ -194,7 +194,7 @@ def get_graph_execution_history(
def get_invocation_command(invocation) -> str: def get_invocation_command(invocation) -> str:
fields = invocation.__fields__.items() fields = invocation.model_fields.items()
type_hints = get_type_hints(type(invocation)) type_hints = get_type_hints(type(invocation))
command = [invocation.type] command = [invocation.type]
for name, field in fields: for name, field in fields:

6
invokeai/app/invocations/baseinvocation.py
View File

@ -118,7 +118,7 @@ class CustomisedSchemaExtra(TypedDict):
class InvocationConfig(BaseConfig): class InvocationConfig(BaseConfig):
"""Customizes pydantic's BaseModel.Config class for use by Invocations. """Customizes pydantic's BaseModel.Config class for use by Invocations.
Provide `schema_extra` a `ui` dict to add hints for generated UIs. Provide `json_schema_extra` a `ui` dict to add hints for generated UIs.
`tags` `tags`
- A list of strings, used to categorise invocations. - A list of strings, used to categorise invocations.
@ -131,7 +131,7 @@ class InvocationConfig(BaseConfig):
```python ```python
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": { "ui": {
"tags": ["stable-diffusion", "image"], "tags": ["stable-diffusion", "image"],
"type_hints": { "type_hints": {
@ -142,4 +142,4 @@ class InvocationConfig(BaseConfig):
``` ```
""" """
schema_extra: CustomisedSchemaExtra json_schema_extra: CustomisedSchemaExtra

14
invokeai/app/invocations/collections.py
View File

@ -3,7 +3,7 @@
from typing import Literal from typing import Literal
import numpy as np import numpy as np
from pydantic import Field, validator from pydantic import Field, field_field_validator
from invokeai.app.models.image import ImageField from invokeai.app.models.image import ImageField
from invokeai.app.util.misc import SEED_MAX, get_random_seed from invokeai.app.util.misc import SEED_MAX, get_random_seed
@ -38,7 +38,7 @@ class ImageCollectionOutput(BaseInvocationOutput):
collection: list[ImageField] = Field(default=[], description="The output images") collection: list[ImageField] = Field(default=[], description="The output images")
class Config: class Config:
schema_extra = {"required": ["type", "collection"]} json_schema_extra = {"required": ["type", "collection"]}
class RangeInvocation(BaseInvocation): class RangeInvocation(BaseInvocation):
@ -52,11 +52,11 @@ class RangeInvocation(BaseInvocation):
step: int = Field(default=1, description="The step of the range") step: int = Field(default=1, description="The step of the range")
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "Range", "tags": ["range", "integer", "collection"]}, "ui": {"title": "Range", "tags": ["range", "integer", "collection"]},
} }
@validator("stop") @field_validator("stop")
def stop_gt_start(cls, v, values): def stop_gt_start(cls, v, values):
if "start" in values and v <= values["start"]: if "start" in values and v <= values["start"]:
raise ValueError("stop must be greater than start") raise ValueError("stop must be greater than start")
@ -77,7 +77,7 @@ class RangeOfSizeInvocation(BaseInvocation):
step: int = Field(default=1, description="The step of the range") step: int = Field(default=1, description="The step of the range")
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "Sized Range", "tags": ["range", "integer", "size", "collection"]}, "ui": {"title": "Sized Range", "tags": ["range", "integer", "size", "collection"]},
} }
@ -102,7 +102,7 @@ class RandomRangeInvocation(BaseInvocation):
) )
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "Random Range", "tags": ["range", "integer", "random", "collection"]}, "ui": {"title": "Random Range", "tags": ["range", "integer", "random", "collection"]},
} }
@ -127,7 +127,7 @@ class ImageCollectionInvocation(BaseInvocation):
return ImageCollectionOutput(collection=self.images) return ImageCollectionOutput(collection=self.images)
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": { "ui": {
"type_hints": { "type_hints": {
"title": "Image Collection", "title": "Image Collection",

26
invokeai/app/invocations/compel.py
View File

@ -26,7 +26,7 @@ class ConditioningField(BaseModel):
conditioning_name: Optional[str] = Field(default=None, description="The name of conditioning data") conditioning_name: Optional[str] = Field(default=None, description="The name of conditioning data")
class Config: class Config:
schema_extra = {"required": ["conditioning_name"]} json_schema_extra = {"required": ["conditioning_name"]}
@dataclass @dataclass
@ -80,18 +80,18 @@ class CompelInvocation(BaseInvocation):
# Schema customisation # Schema customisation
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "Prompt (Compel)", "tags": ["prompt", "compel"], "type_hints": {"model": "model"}}, "ui": {"title": "Prompt (Compel)", "tags": ["prompt", "compel"], "type_hints": {"model": "model"}},
} }
@torch.no_grad() @torch.no_grad()
def invoke(self, context: InvocationContext) -> CompelOutput: def invoke(self, context: InvocationContext) -> CompelOutput:
tokenizer_info = context.services.model_manager.get_model( tokenizer_info = context.services.model_manager.get_model(
**self.clip.tokenizer.dict(), **self.clip.tokenizer.model_dump(),
context=context, context=context,
) )
text_encoder_info = context.services.model_manager.get_model( text_encoder_info = context.services.model_manager.get_model(
**self.clip.text_encoder.dict(), **self.clip.text_encoder.model_dump(),
context=context, context=context,
) )
@ -178,11 +178,11 @@ class CompelInvocation(BaseInvocation):
class SDXLPromptInvocationBase: class SDXLPromptInvocationBase:
def run_clip_raw(self, context, clip_field, prompt, get_pooled, lora_prefix): def run_clip_raw(self, context, clip_field, prompt, get_pooled, lora_prefix):
tokenizer_info = context.services.model_manager.get_model( tokenizer_info = context.services.model_manager.get_model(
**clip_field.tokenizer.dict(), **clip_field.tokenizer.model_dump(),
context=context, context=context,
) )
text_encoder_info = context.services.model_manager.get_model( text_encoder_info = context.services.model_manager.get_model(
**clip_field.text_encoder.dict(), **clip_field.text_encoder.model_dump(),
context=context, context=context,
) )
@ -255,11 +255,11 @@ class SDXLPromptInvocationBase:
def run_clip_compel(self, context, clip_field, prompt, get_pooled, lora_prefix): def run_clip_compel(self, context, clip_field, prompt, get_pooled, lora_prefix):
tokenizer_info = context.services.model_manager.get_model( tokenizer_info = context.services.model_manager.get_model(
**clip_field.tokenizer.dict(), **clip_field.tokenizer.model_dump(),
context=context, context=context,
) )
text_encoder_info = context.services.model_manager.get_model( text_encoder_info = context.services.model_manager.get_model(
**clip_field.text_encoder.dict(), **clip_field.text_encoder.model_dump(),
context=context, context=context,
) )
@ -360,7 +360,7 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
# Schema customisation # Schema customisation
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "SDXL Prompt (Compel)", "tags": ["prompt", "compel"], "type_hints": {"model": "model"}}, "ui": {"title": "SDXL Prompt (Compel)", "tags": ["prompt", "compel"], "type_hints": {"model": "model"}},
} }
@ -414,7 +414,7 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
# Schema customisation # Schema customisation
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": { "ui": {
"title": "SDXL Refiner Prompt (Compel)", "title": "SDXL Refiner Prompt (Compel)",
"tags": ["prompt", "compel"], "tags": ["prompt", "compel"],
@ -471,7 +471,7 @@ class SDXLRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
# Schema customisation # Schema customisation
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "SDXL Prompt (Raw)", "tags": ["prompt", "compel"], "type_hints": {"model": "model"}}, "ui": {"title": "SDXL Prompt (Raw)", "tags": ["prompt", "compel"], "type_hints": {"model": "model"}},
} }
@ -525,7 +525,7 @@ class SDXLRefinerRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
# Schema customisation # Schema customisation
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": { "ui": {
"title": "SDXL Refiner Prompt (Raw)", "title": "SDXL Refiner Prompt (Raw)",
"tags": ["prompt", "compel"], "tags": ["prompt", "compel"],
@ -580,7 +580,7 @@ class ClipSkipInvocation(BaseInvocation):
skipped_layers: int = Field(0, description="Number of layers to skip in text_encoder") skipped_layers: int = Field(0, description="Number of layers to skip in text_encoder")
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "CLIP Skip", "tags": ["clip", "skip"]}, "ui": {"title": "CLIP Skip", "tags": ["clip", "skip"]},
} }

40
invokeai/app/invocations/controlnet_image_processors.py
View File

@ -24,7 +24,7 @@ from controlnet_aux import (
) )
from controlnet_aux.util import HWC3, ade_palette from controlnet_aux.util import HWC3, ade_palette
from PIL import Image from PIL import Image
from pydantic import BaseModel, Field, validator from pydantic import BaseModel, Field, field_validator
from ...backend.model_management import BaseModelType, ModelType from ...backend.model_management import BaseModelType, ModelType
from ..models.image import ImageCategory, ImageField, ResourceOrigin from ..models.image import ImageCategory, ImageField, ResourceOrigin
@ -123,7 +123,7 @@ class ControlField(BaseModel):
control_mode: CONTROLNET_MODE_VALUES = Field(default="balanced", description="The control mode to use") control_mode: CONTROLNET_MODE_VALUES = Field(default="balanced", description="The control mode to use")
resize_mode: CONTROLNET_RESIZE_VALUES = Field(default="just_resize", description="The resize mode to use") resize_mode: CONTROLNET_RESIZE_VALUES = Field(default="just_resize", description="The resize mode to use")
@validator("control_weight") @field_validator("control_weight")
def validate_control_weight(cls, v): def validate_control_weight(cls, v):
"""Validate that all control weights in the valid range""" """Validate that all control weights in the valid range"""
if isinstance(v, list): if isinstance(v, list):
@ -136,7 +136,7 @@ class ControlField(BaseModel):
return v return v
class Config: class Config:
schema_extra = { json_schema_extra = {
"required": ["image", "control_model", "control_weight", "begin_step_percent", "end_step_percent"], "required": ["image", "control_model", "control_weight", "begin_step_percent", "end_step_percent"],
"ui": { "ui": {
"type_hints": { "type_hints": {
@ -176,7 +176,7 @@ class ControlNetInvocation(BaseInvocation):
# fmt: on # fmt: on
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": { "ui": {
"title": "ControlNet", "title": "ControlNet",
"tags": ["controlnet", "latents"], "tags": ["controlnet", "latents"],
@ -214,7 +214,7 @@ class ImageProcessorInvocation(BaseInvocation, PILInvocationConfig):
# fmt: on # fmt: on
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "Image Processor", "tags": ["image", "processor"]}, "ui": {"title": "Image Processor", "tags": ["image", "processor"]},
} }
@ -266,7 +266,7 @@ class CannyImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfi
# fmt: on # fmt: on
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "Canny Processor", "tags": ["controlnet", "canny", "image", "processor"]}, "ui": {"title": "Canny Processor", "tags": ["controlnet", "canny", "image", "processor"]},
} }
@ -290,7 +290,7 @@ class HedImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig)
# fmt: on # fmt: on
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "Softedge(HED) Processor", "tags": ["controlnet", "softedge", "hed", "image", "processor"]}, "ui": {"title": "Softedge(HED) Processor", "tags": ["controlnet", "softedge", "hed", "image", "processor"]},
} }
@ -319,7 +319,7 @@ class LineartImageProcessorInvocation(ImageProcessorInvocation, PILInvocationCon
# fmt: on # fmt: on
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "Lineart Processor", "tags": ["controlnet", "lineart", "image", "processor"]}, "ui": {"title": "Lineart Processor", "tags": ["controlnet", "lineart", "image", "processor"]},
} }
@ -342,7 +342,7 @@ class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation, PILInvocati
# fmt: on # fmt: on
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": { "ui": {
"title": "Lineart Anime Processor", "title": "Lineart Anime Processor",
"tags": ["controlnet", "lineart", "anime", "image", "processor"], "tags": ["controlnet", "lineart", "anime", "image", "processor"],
@ -371,7 +371,7 @@ class OpenposeImageProcessorInvocation(ImageProcessorInvocation, PILInvocationCo
# fmt: on # fmt: on
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "Openpose Processor", "tags": ["controlnet", "openpose", "image", "processor"]}, "ui": {"title": "Openpose Processor", "tags": ["controlnet", "openpose", "image", "processor"]},
} }
@ -399,7 +399,7 @@ class MidasDepthImageProcessorInvocation(ImageProcessorInvocation, PILInvocation
# fmt: on # fmt: on
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "Midas (Depth) Processor", "tags": ["controlnet", "midas", "depth", "image", "processor"]}, "ui": {"title": "Midas (Depth) Processor", "tags": ["controlnet", "midas", "depth", "image", "processor"]},
} }
@ -426,7 +426,7 @@ class NormalbaeImageProcessorInvocation(ImageProcessorInvocation, PILInvocationC
# fmt: on # fmt: on
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "Normal BAE Processor", "tags": ["controlnet", "normal", "bae", "image", "processor"]}, "ui": {"title": "Normal BAE Processor", "tags": ["controlnet", "normal", "bae", "image", "processor"]},
} }
@ -451,7 +451,7 @@ class MlsdImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig
# fmt: on # fmt: on
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "MLSD Processor", "tags": ["controlnet", "mlsd", "image", "processor"]}, "ui": {"title": "MLSD Processor", "tags": ["controlnet", "mlsd", "image", "processor"]},
} }
@ -480,7 +480,7 @@ class PidiImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig
# fmt: on # fmt: on
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "PIDI Processor", "tags": ["controlnet", "pidi", "image", "processor"]}, "ui": {"title": "PIDI Processor", "tags": ["controlnet", "pidi", "image", "processor"]},
} }
@ -510,7 +510,7 @@ class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation, PILInvoca
# fmt: on # fmt: on
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": { "ui": {
"title": "Content Shuffle Processor", "title": "Content Shuffle Processor",
"tags": ["controlnet", "contentshuffle", "image", "processor"], "tags": ["controlnet", "contentshuffle", "image", "processor"],
@ -539,7 +539,7 @@ class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation, PILInvocationCo
# fmt: on # fmt: on
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "Zoe (Depth) Processor", "tags": ["controlnet", "zoe", "depth", "image", "processor"]}, "ui": {"title": "Zoe (Depth) Processor", "tags": ["controlnet", "zoe", "depth", "image", "processor"]},
} }
@ -560,7 +560,7 @@ class MediapipeFaceProcessorInvocation(ImageProcessorInvocation, PILInvocationCo
# fmt: on # fmt: on
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "Mediapipe Processor", "tags": ["controlnet", "mediapipe", "image", "processor"]}, "ui": {"title": "Mediapipe Processor", "tags": ["controlnet", "mediapipe", "image", "processor"]},
} }
@ -588,7 +588,7 @@ class LeresImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfi
# fmt: on # fmt: on
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "Leres (Depth) Processor", "tags": ["controlnet", "leres", "depth", "image", "processor"]}, "ui": {"title": "Leres (Depth) Processor", "tags": ["controlnet", "leres", "depth", "image", "processor"]},
} }
@ -614,7 +614,7 @@ class TileResamplerProcessorInvocation(ImageProcessorInvocation, PILInvocationCo
# fmt: on # fmt: on
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": { "ui": {
"title": "Tile Resample Processor", "title": "Tile Resample Processor",
"tags": ["controlnet", "tile", "resample", "image", "processor"], "tags": ["controlnet", "tile", "resample", "image", "processor"],
@ -656,7 +656,7 @@ class SegmentAnythingProcessorInvocation(ImageProcessorInvocation, PILInvocation
# fmt: on # fmt: on
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": { "ui": {
"title": "Segment Anything Processor", "title": "Segment Anything Processor",
"tags": ["controlnet", "segment", "anything", "sam", "image", "processor"], "tags": ["controlnet", "segment", "anything", "sam", "image", "processor"],

4
invokeai/app/invocations/cv.py
View File

@ -17,7 +17,7 @@ class CvInvocationConfig(BaseModel):
# Schema customisation # Schema customisation
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": { "ui": {
"tags": ["cv", "image"], "tags": ["cv", "image"],
}, },
@ -36,7 +36,7 @@ class CvInpaintInvocation(BaseInvocation, CvInvocationConfig):
# fmt: on # fmt: on
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "OpenCV Inpaint", "tags": ["opencv", "inpaint"]}, "ui": {"title": "OpenCV Inpaint", "tags": ["opencv", "inpaint"]},
} }

8
invokeai/app/invocations/generate.py
View File

@ -129,7 +129,7 @@ class InpaintInvocation(BaseInvocation):
# Schema customisation # Schema customisation
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"tags": ["stable-diffusion", "image"], "title": "Inpaint"}, "ui": {"tags": ["stable-diffusion", "image"], "title": "Inpaint"},
} }
@ -142,7 +142,7 @@ class InpaintInvocation(BaseInvocation):
stable_diffusion_step_callback( stable_diffusion_step_callback(
context=context, context=context,
intermediate_state=intermediate_state, intermediate_state=intermediate_state,
node=self.dict(), node=self.model_dump(),
source_node_id=source_node_id, source_node_id=source_node_id,
) )
@ -169,11 +169,11 @@ class InpaintInvocation(BaseInvocation):
return return
unet_info = context.services.model_manager.get_model( unet_info = context.services.model_manager.get_model(
**self.unet.unet.dict(), **self.unet.unet.model_dump(),
context=context, context=context,
) )
vae_info = context.services.model_manager.get_model( vae_info = context.services.model_manager.get_model(
**self.vae.vae.dict(), **self.vae.vae.model_dump(),
context=context, context=context,
) )

34
invokeai/app/invocations/image.py
View File

@ -39,7 +39,7 @@ class LoadImageInvocation(BaseInvocation):
# fmt: on # fmt: on
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "Load Image", "tags": ["image", "load"]}, "ui": {"title": "Load Image", "tags": ["image", "load"]},
} }
@ -62,7 +62,7 @@ class ShowImageInvocation(BaseInvocation):
image: Optional[ImageField] = Field(default=None, description="The image to show") image: Optional[ImageField] = Field(default=None, description="The image to show")
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "Show Image", "tags": ["image", "show"]}, "ui": {"title": "Show Image", "tags": ["image", "show"]},
} }
@ -95,7 +95,7 @@ class ImageCropInvocation(BaseInvocation, PILInvocationConfig):
# fmt: on # fmt: on
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "Crop Image", "tags": ["image", "crop"]}, "ui": {"title": "Crop Image", "tags": ["image", "crop"]},
} }
@ -136,7 +136,7 @@ class ImagePasteInvocation(BaseInvocation, PILInvocationConfig):
# fmt: on # fmt: on
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "Paste Image", "tags": ["image", "paste"]}, "ui": {"title": "Paste Image", "tags": ["image", "paste"]},
} }
@ -185,7 +185,7 @@ class MaskFromAlphaInvocation(BaseInvocation, PILInvocationConfig):
# fmt: on # fmt: on
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "Mask From Alpha", "tags": ["image", "mask", "alpha"]}, "ui": {"title": "Mask From Alpha", "tags": ["image", "mask", "alpha"]},
} }
@ -224,7 +224,7 @@ class ImageMultiplyInvocation(BaseInvocation, PILInvocationConfig):
# fmt: on # fmt: on
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "Multiply Images", "tags": ["image", "multiply"]}, "ui": {"title": "Multiply Images", "tags": ["image", "multiply"]},
} }
@ -265,7 +265,7 @@ class ImageChannelInvocation(BaseInvocation, PILInvocationConfig):
# fmt: on # fmt: on
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "Image Channel", "tags": ["image", "channel"]}, "ui": {"title": "Image Channel", "tags": ["image", "channel"]},
} }
@ -305,7 +305,7 @@ class ImageConvertInvocation(BaseInvocation, PILInvocationConfig):
# fmt: on # fmt: on
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "Convert Image", "tags": ["image", "convert"]}, "ui": {"title": "Convert Image", "tags": ["image", "convert"]},
} }
@ -343,7 +343,7 @@ class ImageBlurInvocation(BaseInvocation, PILInvocationConfig):
# fmt: on # fmt: on
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "Blur Image", "tags": ["image", "blur"]}, "ui": {"title": "Blur Image", "tags": ["image", "blur"]},
} }
@ -405,7 +405,7 @@ class ImageResizeInvocation(BaseInvocation, PILInvocationConfig):
# fmt: on # fmt: on
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "Resize Image", "tags": ["image", "resize"]}, "ui": {"title": "Resize Image", "tags": ["image", "resize"]},
} }
@ -448,7 +448,7 @@ class ImageScaleInvocation(BaseInvocation, PILInvocationConfig):
# fmt: on # fmt: on
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "Scale Image", "tags": ["image", "scale"]}, "ui": {"title": "Scale Image", "tags": ["image", "scale"]},
} }
@ -493,7 +493,7 @@ class ImageLerpInvocation(BaseInvocation, PILInvocationConfig):
# fmt: on # fmt: on
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "Image Linear Interpolation", "tags": ["image", "linear", "interpolation", "lerp"]}, "ui": {"title": "Image Linear Interpolation", "tags": ["image", "linear", "interpolation", "lerp"]},
} }
@ -534,7 +534,7 @@ class ImageInverseLerpInvocation(BaseInvocation, PILInvocationConfig):
# fmt: on # fmt: on
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": { "ui": {
"title": "Image Inverse Linear Interpolation", "title": "Image Inverse Linear Interpolation",
"tags": ["image", "linear", "interpolation", "inverse"], "tags": ["image", "linear", "interpolation", "inverse"],
@ -577,7 +577,7 @@ class ImageNSFWBlurInvocation(BaseInvocation, PILInvocationConfig):
# fmt: on # fmt: on
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "Blur NSFW Images", "tags": ["image", "nsfw", "checker"]}, "ui": {"title": "Blur NSFW Images", "tags": ["image", "nsfw", "checker"]},
} }
@ -600,7 +600,7 @@ class ImageNSFWBlurInvocation(BaseInvocation, PILInvocationConfig):
node_id=self.id, node_id=self.id,
session_id=context.graph_execution_state_id, session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate, is_intermediate=self.is_intermediate,
metadata=self.metadata.dict() if self.metadata else None, metadata=self.metadata.model_dump() if self.metadata else None,
) )
return ImageOutput( return ImageOutput(
@ -629,7 +629,7 @@ class ImageWatermarkInvocation(BaseInvocation, PILInvocationConfig):
# fmt: on # fmt: on
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "Add Invisible Watermark", "tags": ["image", "watermark", "invisible"]}, "ui": {"title": "Add Invisible Watermark", "tags": ["image", "watermark", "invisible"]},
} }
@ -643,7 +643,7 @@ class ImageWatermarkInvocation(BaseInvocation, PILInvocationConfig):
node_id=self.id, node_id=self.id,
session_id=context.graph_execution_state_id, session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate, is_intermediate=self.is_intermediate,
metadata=self.metadata.dict() if self.metadata else None, metadata=self.metadata.model_dump() if self.metadata else None,
) )
return ImageOutput( return ImageOutput(

6
invokeai/app/invocations/infill.py
View File

@ -125,7 +125,7 @@ class InfillColorInvocation(BaseInvocation):
) )
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "Color Infill", "tags": ["image", "inpaint", "color", "infill"]}, "ui": {"title": "Color Infill", "tags": ["image", "inpaint", "color", "infill"]},
} }
@ -168,7 +168,7 @@ class InfillTileInvocation(BaseInvocation):
) )
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "Tile Infill", "tags": ["image", "inpaint", "tile", "infill"]}, "ui": {"title": "Tile Infill", "tags": ["image", "inpaint", "tile", "infill"]},
} }
@ -202,7 +202,7 @@ class InfillPatchMatchInvocation(BaseInvocation):
image: Optional[ImageField] = Field(default=None, description="The image to infill") image: Optional[ImageField] = Field(default=None, description="The image to infill")
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "Patch Match Infill", "tags": ["image", "inpaint", "patchmatch", "infill"]}, "ui": {"title": "Patch Match Infill", "tags": ["image", "inpaint", "patchmatch", "infill"]},
} }

34
invokeai/app/invocations/latent.py
View File

@ -13,7 +13,7 @@ from diffusers.models.attention_processor import (
XFormersAttnProcessor, XFormersAttnProcessor,
) )
from diffusers.schedulers import SchedulerMixin as Scheduler from diffusers.schedulers import SchedulerMixin as Scheduler
from pydantic import BaseModel, Field, validator from pydantic import BaseModel, Field, field_validator
from invokeai.app.invocations.metadata import CoreMetadata from invokeai.app.invocations.metadata import CoreMetadata
from invokeai.app.util.controlnet_utils import prepare_control_image from invokeai.app.util.controlnet_utils import prepare_control_image
@ -46,7 +46,7 @@ class LatentsField(BaseModel):
latents_name: Optional[str] = Field(default=None, description="The name of the latents") latents_name: Optional[str] = Field(default=None, description="The name of the latents")
class Config: class Config:
schema_extra = {"required": ["latents_name"]} json_schema_extra = {"required": ["latents_name"]}
class LatentsOutput(BaseInvocationOutput): class LatentsOutput(BaseInvocationOutput):
@ -80,7 +80,7 @@ def get_scheduler(
) -> Scheduler: ) -> Scheduler:
scheduler_class, scheduler_extra_config = SCHEDULER_MAP.get(scheduler_name, SCHEDULER_MAP["ddim"]) scheduler_class, scheduler_extra_config = SCHEDULER_MAP.get(scheduler_name, SCHEDULER_MAP["ddim"])
orig_scheduler_info = context.services.model_manager.get_model( orig_scheduler_info = context.services.model_manager.get_model(
**scheduler_info.dict(), **scheduler_info.model_dump(),
context=context, context=context,
) )
with orig_scheduler_info as orig_scheduler: with orig_scheduler_info as orig_scheduler:
@ -121,7 +121,7 @@ class TextToLatentsInvocation(BaseInvocation):
# seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'") # seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'")
# fmt: on # fmt: on
@validator("cfg_scale") @field_validator("cfg_scale")
def ge_one(cls, v): def ge_one(cls, v):
"""validate that all cfg_scale values are >= 1""" """validate that all cfg_scale values are >= 1"""
if isinstance(v, list): if isinstance(v, list):
@ -135,7 +135,7 @@ class TextToLatentsInvocation(BaseInvocation):
# Schema customisation # Schema customisation
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": { "ui": {
"title": "Text To Latents", "title": "Text To Latents",
"tags": ["latents"], "tags": ["latents"],
@ -158,7 +158,7 @@ class TextToLatentsInvocation(BaseInvocation):
stable_diffusion_step_callback( stable_diffusion_step_callback(
context=context, context=context,
intermediate_state=intermediate_state, intermediate_state=intermediate_state,
node=self.dict(), node=self.model_dump(),
source_node_id=source_node_id, source_node_id=source_node_id,
) )
@ -327,7 +327,7 @@ class TextToLatentsInvocation(BaseInvocation):
return return
unet_info = context.services.model_manager.get_model( unet_info = context.services.model_manager.get_model(
**self.unet.unet.dict(), **self.unet.unet.model_dump(),
context=context, context=context,
) )
with ExitStack() as exit_stack, ModelPatcher.apply_lora_unet( with ExitStack() as exit_stack, ModelPatcher.apply_lora_unet(
@ -384,7 +384,7 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
# Schema customisation # Schema customisation
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": { "ui": {
"title": "Latent To Latents", "title": "Latent To Latents",
"tags": ["latents"], "tags": ["latents"],
@ -420,7 +420,7 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
return return
unet_info = context.services.model_manager.get_model( unet_info = context.services.model_manager.get_model(
**self.unet.unet.dict(), **self.unet.unet.model_dump(),
context=context, context=context,
) )
with ExitStack() as exit_stack, ModelPatcher.apply_lora_unet( with ExitStack() as exit_stack, ModelPatcher.apply_lora_unet(
@ -495,7 +495,7 @@ class LatentsToImageInvocation(BaseInvocation):
# Schema customisation # Schema customisation
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": { "ui": {
"title": "Latents To Image", "title": "Latents To Image",
"tags": ["latents", "image"], "tags": ["latents", "image"],
@ -507,7 +507,7 @@ class LatentsToImageInvocation(BaseInvocation):
latents = context.services.latents.get(self.latents.latents_name) latents = context.services.latents.get(self.latents.latents_name)
vae_info = context.services.model_manager.get_model( vae_info = context.services.model_manager.get_model(
**self.vae.vae.dict(), **self.vae.vae.model_dump(),
context=context, context=context,
) )
@ -565,7 +565,7 @@ class LatentsToImageInvocation(BaseInvocation):
node_id=self.id, node_id=self.id,
session_id=context.graph_execution_state_id, session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate, is_intermediate=self.is_intermediate,
metadata=self.metadata.dict() if self.metadata else None, metadata=self.metadata.model_dump() if self.metadata else None,
) )
return ImageOutput( return ImageOutput(
@ -593,7 +593,7 @@ class ResizeLatentsInvocation(BaseInvocation):
) )
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "Resize Latents", "tags": ["latents", "resize"]}, "ui": {"title": "Resize Latents", "tags": ["latents", "resize"]},
} }
@ -634,7 +634,7 @@ class ScaleLatentsInvocation(BaseInvocation):
) )
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "Scale Latents", "tags": ["latents", "scale"]}, "ui": {"title": "Scale Latents", "tags": ["latents", "scale"]},
} }
@ -675,7 +675,7 @@ class ImageToLatentsInvocation(BaseInvocation):
# Schema customisation # Schema customisation
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "Image To Latents", "tags": ["latents", "image"]}, "ui": {"title": "Image To Latents", "tags": ["latents", "image"]},
} }
@ -686,9 +686,9 @@ class ImageToLatentsInvocation(BaseInvocation):
# ) # )
image = context.services.images.get_pil_image(self.image.image_name) image = context.services.images.get_pil_image(self.image.image_name)
# vae_info = context.services.model_manager.get_model(**self.vae.vae.dict()) # vae_info = context.services.model_manager.get_model(**self.vae.vae.model_dump())
vae_info = context.services.model_manager.get_model( vae_info = context.services.model_manager.get_model(
**self.vae.vae.dict(), **self.vae.vae.model_dump(),
context=context, context=context,
) )

12
invokeai/app/invocations/math.py
View File

@ -18,7 +18,7 @@ class MathInvocationConfig(BaseModel):
# Schema customisation # Schema customisation
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": { "ui": {
"tags": ["math"], "tags": ["math"],
} }
@ -53,7 +53,7 @@ class AddInvocation(BaseInvocation, MathInvocationConfig):
# fmt: on # fmt: on
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "Add", "tags": ["math", "add"]}, "ui": {"title": "Add", "tags": ["math", "add"]},
} }
@ -71,7 +71,7 @@ class SubtractInvocation(BaseInvocation, MathInvocationConfig):
# fmt: on # fmt: on
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "Subtract", "tags": ["math", "subtract"]}, "ui": {"title": "Subtract", "tags": ["math", "subtract"]},
} }
@ -89,7 +89,7 @@ class MultiplyInvocation(BaseInvocation, MathInvocationConfig):
# fmt: on # fmt: on
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "Multiply", "tags": ["math", "multiply"]}, "ui": {"title": "Multiply", "tags": ["math", "multiply"]},
} }
@ -107,7 +107,7 @@ class DivideInvocation(BaseInvocation, MathInvocationConfig):
# fmt: on # fmt: on
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "Divide", "tags": ["math", "divide"]}, "ui": {"title": "Divide", "tags": ["math", "divide"]},
} }
@ -127,7 +127,7 @@ class RandomIntInvocation(BaseInvocation):
# fmt: on # fmt: on
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "Random Integer", "tags": ["math", "random", "integer"]}, "ui": {"title": "Random Integer", "tags": ["math", "random", "integer"]},
} }

4
invokeai/app/invocations/metadata.py
View File

@ -142,7 +142,7 @@ class MetadataAccumulatorInvocation(BaseInvocation):
refiner_start: Union[float, None] = Field(default=None, description="The start value used for refiner denoising") refiner_start: Union[float, None] = Field(default=None, description="The start value used for refiner denoising")
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": { "ui": {
"title": "Metadata Accumulator", "title": "Metadata Accumulator",
"tags": ["image", "metadata", "generation"], "tags": ["image", "metadata", "generation"],
@ -152,4 +152,4 @@ class MetadataAccumulatorInvocation(BaseInvocation):
def invoke(self, context: InvocationContext) -> MetadataAccumulatorOutput: def invoke(self, context: InvocationContext) -> MetadataAccumulatorOutput:
"""Collects and outputs a CoreMetadata object""" """Collects and outputs a CoreMetadata object"""
return MetadataAccumulatorOutput(metadata=CoreMetadata(**self.dict())) return MetadataAccumulatorOutput(metadata=CoreMetadata(**self.model_dump()))

8
invokeai/app/invocations/model.py
View File

@ -73,7 +73,7 @@ class MainModelLoaderInvocation(BaseInvocation):
# Schema customisation # Schema customisation
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": { "ui": {
"title": "Model Loader", "title": "Model Loader",
"tags": ["model", "loader"], "tags": ["model", "loader"],
@ -205,7 +205,7 @@ class LoraLoaderInvocation(BaseInvocation):
clip: Optional[ClipField] = Field(description="Clip model for applying lora") clip: Optional[ClipField] = Field(description="Clip model for applying lora")
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": { "ui": {
"title": "Lora Loader", "title": "Lora Loader",
"tags": ["lora", "loader"], "tags": ["lora", "loader"],
@ -287,7 +287,7 @@ class SDXLLoraLoaderInvocation(BaseInvocation):
clip2: Optional[ClipField] = Field(description="Clip2 model for applying lora") clip2: Optional[ClipField] = Field(description="Clip2 model for applying lora")
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": { "ui": {
"title": "SDXL Lora Loader", "title": "SDXL Lora Loader",
"tags": ["lora", "loader"], "tags": ["lora", "loader"],
@ -385,7 +385,7 @@ class VaeLoaderInvocation(BaseInvocation):
# Schema customisation # Schema customisation
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": { "ui": {
"title": "VAE Loader", "title": "VAE Loader",
"tags": ["vae", "loader"], "tags": ["vae", "loader"],

6
invokeai/app/invocations/noise.py
View File

@ -3,7 +3,7 @@
import math import math
from typing import Literal from typing import Literal
from pydantic import Field, validator from pydantic import Field, field_validator
import torch import torch
from invokeai.app.invocations.latent import LatentsField from invokeai.app.invocations.latent import LatentsField
@ -110,14 +110,14 @@ class NoiseInvocation(BaseInvocation):
# Schema customisation # Schema customisation
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": { "ui": {
"title": "Noise", "title": "Noise",
"tags": ["latents", "noise"], "tags": ["latents", "noise"],
}, },
} }
@validator("seed", pre=True) @field_validator("seed", pre=True)
def modulo_seed(cls, v): def modulo_seed(cls, v):
"""Returns the seed modulo (SEED_MAX + 1) to ensure it is within the valid range.""" """Returns the seed modulo (SEED_MAX + 1) to ensure it is within the valid range."""
return v % (SEED_MAX + 1) return v % (SEED_MAX + 1)

24
invokeai/app/invocations/onnx.py
View File

@ -6,7 +6,7 @@ from typing import List, Literal, Optional, Union
import re import re
import inspect import inspect
from pydantic import BaseModel, Field, validator from pydantic import BaseModel, Field, field_validator
import torch import torch
import numpy as np import numpy as np
from diffusers import ControlNetModel, DPMSolverMultistepScheduler from diffusers import ControlNetModel, DPMSolverMultistepScheduler
@ -59,10 +59,10 @@ class ONNXPromptInvocation(BaseInvocation):
def invoke(self, context: InvocationContext) -> CompelOutput: def invoke(self, context: InvocationContext) -> CompelOutput:
tokenizer_info = context.services.model_manager.get_model( tokenizer_info = context.services.model_manager.get_model(
**self.clip.tokenizer.dict(), **self.clip.tokenizer.model_dump(),
) )
text_encoder_info = context.services.model_manager.get_model( text_encoder_info = context.services.model_manager.get_model(
**self.clip.text_encoder.dict(), **self.clip.text_encoder.model_dump(),
) )
with tokenizer_info as orig_tokenizer, text_encoder_info as text_encoder, ExitStack() as stack: with tokenizer_info as orig_tokenizer, text_encoder_info as text_encoder, ExitStack() as stack:
loras = [ loras = [
@ -154,7 +154,7 @@ class ONNXTextToLatentsInvocation(BaseInvocation):
# seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'") # seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'")
# fmt: on # fmt: on
@validator("cfg_scale") @field_validator("cfg_scale")
def ge_one(cls, v): def ge_one(cls, v):
"""validate that all cfg_scale values are >= 1""" """validate that all cfg_scale values are >= 1"""
if isinstance(v, list): if isinstance(v, list):
@ -168,7 +168,7 @@ class ONNXTextToLatentsInvocation(BaseInvocation):
# Schema customisation # Schema customisation
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": { "ui": {
"tags": ["latents"], "tags": ["latents"],
"type_hints": { "type_hints": {
@ -226,7 +226,7 @@ class ONNXTextToLatentsInvocation(BaseInvocation):
stable_diffusion_step_callback( stable_diffusion_step_callback(
context=context, context=context,
intermediate_state=intermediate_state, intermediate_state=intermediate_state,
node=self.dict(), node=self.model_dump(),
source_node_id=source_node_id, source_node_id=source_node_id,
) )
@ -239,7 +239,7 @@ class ONNXTextToLatentsInvocation(BaseInvocation):
eta=0.0, eta=0.0,
) )
unet_info = context.services.model_manager.get_model(**self.unet.unet.dict()) unet_info = context.services.model_manager.get_model(**self.unet.unet.model_dump())
with unet_info as unet, ExitStack() as stack: with unet_info as unet, ExitStack() as stack:
# loras = [(stack.enter_context(context.services.model_manager.get_model(**lora.dict(exclude={"weight"}))), lora.weight) for lora in self.unet.loras] # loras = [(stack.enter_context(context.services.model_manager.get_model(**lora.dict(exclude={"weight"}))), lora.weight) for lora in self.unet.loras]
@ -314,7 +314,7 @@ class ONNXLatentsToImageInvocation(BaseInvocation):
# Schema customisation # Schema customisation
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": { "ui": {
"tags": ["latents", "image"], "tags": ["latents", "image"],
}, },
@ -327,7 +327,7 @@ class ONNXLatentsToImageInvocation(BaseInvocation):
raise Exception(f"Expected vae_decoder, found: {self.vae.vae.model_type}") raise Exception(f"Expected vae_decoder, found: {self.vae.vae.model_type}")
vae_info = context.services.model_manager.get_model( vae_info = context.services.model_manager.get_model(
**self.vae.vae.dict(), **self.vae.vae.model_dump(),
) )
# clear memory as vae decode can request a lot # clear memory as vae decode can request a lot
@ -356,7 +356,7 @@ class ONNXLatentsToImageInvocation(BaseInvocation):
node_id=self.id, node_id=self.id,
session_id=context.graph_execution_state_id, session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate, is_intermediate=self.is_intermediate,
metadata=self.metadata.dict() if self.metadata else None, metadata=self.metadata.model_dump() if self.metadata else None,
) )
return ImageOutput( return ImageOutput(
@ -389,7 +389,7 @@ class ONNXSD1ModelLoaderInvocation(BaseInvocation):
# Schema customisation # Schema customisation
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"tags": ["model", "loader"], "type_hints": {"model_name": "model"}}, # TODO: rename to model_name? "ui": {"tags": ["model", "loader"], "type_hints": {"model_name": "model"}}, # TODO: rename to model_name?
} }
@ -472,7 +472,7 @@ class OnnxModelLoaderInvocation(BaseInvocation):
# Schema customisation # Schema customisation
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": { "ui": {
"title": "Onnx Model Loader", "title": "Onnx Model Loader",
"tags": ["model", "loader"], "tags": ["model", "loader"],

4
invokeai/app/invocations/param_easing.py
View File

@ -65,7 +65,7 @@ class FloatLinearRangeInvocation(BaseInvocation):
steps: int = Field(default=30, description="number of values to interpolate over (including start and stop)") steps: int = Field(default=30, description="number of values to interpolate over (including start and stop)")
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "Linear Range (Float)", "tags": ["math", "float", "linear", "range"]}, "ui": {"title": "Linear Range (Float)", "tags": ["math", "float", "linear", "range"]},
} }
@ -136,7 +136,7 @@ class StepParamEasingInvocation(BaseInvocation):
# fmt: on # fmt: on
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "Param Easing By Step", "tags": ["param", "step", "easing"]}, "ui": {"title": "Param Easing By Step", "tags": ["param", "step", "easing"]},
} }

8
invokeai/app/invocations/params.py
View File

@ -21,7 +21,7 @@ class ParamIntInvocation(BaseInvocation):
# fmt: on # fmt: on
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"tags": ["param", "integer"], "title": "Integer Parameter"}, "ui": {"tags": ["param", "integer"], "title": "Integer Parameter"},
} }
@ -38,7 +38,7 @@ class ParamFloatInvocation(BaseInvocation):
# fmt: on # fmt: on
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"tags": ["param", "float"], "title": "Float Parameter"}, "ui": {"tags": ["param", "float"], "title": "Float Parameter"},
} }
@ -60,7 +60,7 @@ class ParamStringInvocation(BaseInvocation):
text: str = Field(default="", description="The string value") text: str = Field(default="", description="The string value")
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"tags": ["param", "string"], "title": "String Parameter"}, "ui": {"tags": ["param", "string"], "title": "String Parameter"},
} }
@ -75,7 +75,7 @@ class ParamPromptInvocation(BaseInvocation):
prompt: str = Field(default="", description="The prompt value") prompt: str = Field(default="", description="The prompt value")
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"tags": ["param", "prompt"], "title": "Prompt"}, "ui": {"tags": ["param", "prompt"], "title": "Prompt"},
} }

12
invokeai/app/invocations/prompt.py
View File

@ -2,7 +2,7 @@ from os.path import exists
from typing import Literal, Optional from typing import Literal, Optional
import numpy as np import numpy as np
from pydantic import Field, validator from pydantic import Field, field_validator
from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationConfig, InvocationContext from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationConfig, InvocationContext
from dynamicprompts.generators import RandomPromptGenerator, CombinatorialPromptGenerator from dynamicprompts.generators import RandomPromptGenerator, CombinatorialPromptGenerator
@ -18,7 +18,7 @@ class PromptOutput(BaseInvocationOutput):
# fmt: on # fmt: on
class Config: class Config:
schema_extra = { json_schema_extra = {
"required": [ "required": [
"type", "type",
"prompt", "prompt",
@ -37,7 +37,7 @@ class PromptCollectionOutput(BaseInvocationOutput):
# fmt: on # fmt: on
class Config: class Config:
schema_extra = {"required": ["type", "prompt_collection", "count"]} json_schema_extra = {"required": ["type", "prompt_collection", "count"]}
class DynamicPromptInvocation(BaseInvocation): class DynamicPromptInvocation(BaseInvocation):
@ -49,7 +49,7 @@ class DynamicPromptInvocation(BaseInvocation):
combinatorial: bool = Field(default=False, description="Whether to use the combinatorial generator") combinatorial: bool = Field(default=False, description="Whether to use the combinatorial generator")
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "Dynamic Prompt", "tags": ["prompt", "dynamic"]}, "ui": {"title": "Dynamic Prompt", "tags": ["prompt", "dynamic"]},
} }
@ -79,11 +79,11 @@ class PromptsFromFileInvocation(BaseInvocation):
# fmt: on # fmt: on
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "Prompts From File", "tags": ["prompt", "file"]}, "ui": {"title": "Prompts From File", "tags": ["prompt", "file"]},
} }
@validator("file_path") @field_validator("file_path")
def file_path_exists(cls, v): def file_path_exists(cls, v):
if not exists(v): if not exists(v):
raise ValueError(FileNotFoundError) raise ValueError(FileNotFoundError)

22
invokeai/app/invocations/sdxl.py
View File

@ -3,7 +3,7 @@ import inspect
from tqdm import tqdm from tqdm import tqdm
from typing import List, Literal, Optional, Union from typing import List, Literal, Optional, Union
from pydantic import Field, validator from pydantic import Field, field_validator
from ...backend.model_management import ModelType, SubModelType, ModelPatcher from ...backend.model_management import ModelType, SubModelType, ModelPatcher
from invokeai.app.util.step_callback import stable_diffusion_xl_step_callback from invokeai.app.util.step_callback import stable_diffusion_xl_step_callback
@ -49,7 +49,7 @@ class SDXLModelLoaderInvocation(BaseInvocation):
# Schema customisation # Schema customisation
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": { "ui": {
"title": "SDXL Model Loader", "title": "SDXL Model Loader",
"tags": ["model", "loader", "sdxl"], "tags": ["model", "loader", "sdxl"],
@ -139,7 +139,7 @@ class SDXLRefinerModelLoaderInvocation(BaseInvocation):
# Schema customisation # Schema customisation
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": { "ui": {
"title": "SDXL Refiner Model Loader", "title": "SDXL Refiner Model Loader",
"tags": ["model", "loader", "sdxl_refiner"], "tags": ["model", "loader", "sdxl_refiner"],
@ -224,7 +224,7 @@ class SDXLTextToLatentsInvocation(BaseInvocation):
# seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'") # seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'")
# fmt: on # fmt: on
@validator("cfg_scale") @field_validator("cfg_scale")
def ge_one(cls, v): def ge_one(cls, v):
"""validate that all cfg_scale values are >= 1""" """validate that all cfg_scale values are >= 1"""
if isinstance(v, list): if isinstance(v, list):
@ -238,7 +238,7 @@ class SDXLTextToLatentsInvocation(BaseInvocation):
# Schema customisation # Schema customisation
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": { "ui": {
"title": "SDXL Text To Latents", "title": "SDXL Text To Latents",
"tags": ["latents"], "tags": ["latents"],
@ -260,7 +260,7 @@ class SDXLTextToLatentsInvocation(BaseInvocation):
) -> None: ) -> None:
stable_diffusion_xl_step_callback( stable_diffusion_xl_step_callback(
context=context, context=context,
node=self.dict(), node=self.model_dump(),
source_node_id=source_node_id, source_node_id=source_node_id,
sample=sample, sample=sample,
step=step, step=step,
@ -303,7 +303,7 @@ class SDXLTextToLatentsInvocation(BaseInvocation):
del lora_info del lora_info
return return
unet_info = context.services.model_manager.get_model(**self.unet.unet.dict(), context=context) unet_info = context.services.model_manager.get_model(**self.unet.unet.model_dump(), context=context)
do_classifier_free_guidance = True do_classifier_free_guidance = True
cross_attention_kwargs = None cross_attention_kwargs = None
with ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()), unet_info as unet: with ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()), unet_info as unet:
@ -481,7 +481,7 @@ class SDXLLatentsToLatentsInvocation(BaseInvocation):
# seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'") # seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'")
# fmt: on # fmt: on
@validator("cfg_scale") @field_validator("cfg_scale")
def ge_one(cls, v): def ge_one(cls, v):
"""validate that all cfg_scale values are >= 1""" """validate that all cfg_scale values are >= 1"""
if isinstance(v, list): if isinstance(v, list):
@ -495,7 +495,7 @@ class SDXLLatentsToLatentsInvocation(BaseInvocation):
# Schema customisation # Schema customisation
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": { "ui": {
"title": "SDXL Latents to Latents", "title": "SDXL Latents to Latents",
"tags": ["latents"], "tags": ["latents"],
@ -517,7 +517,7 @@ class SDXLLatentsToLatentsInvocation(BaseInvocation):
) -> None: ) -> None:
stable_diffusion_xl_step_callback( stable_diffusion_xl_step_callback(
context=context, context=context,
node=self.dict(), node=self.model_dump(),
source_node_id=source_node_id, source_node_id=source_node_id,
sample=sample, sample=sample,
step=step, step=step,
@ -549,7 +549,7 @@ class SDXLLatentsToLatentsInvocation(BaseInvocation):
) )
unet_info = context.services.model_manager.get_model( unet_info = context.services.model_manager.get_model(
**self.unet.unet.dict(), **self.unet.unet.model_dump(),
context=context, context=context,
) )

2
invokeai/app/invocations/upscale.py
View File

@ -32,7 +32,7 @@ class ESRGANInvocation(BaseInvocation):
model_name: ESRGAN_MODELS = Field(default="RealESRGAN_x4plus.pth", description="The Real-ESRGAN model to use") model_name: ESRGAN_MODELS = Field(default="RealESRGAN_x4plus.pth", description="The Real-ESRGAN model to use")
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": {"title": "Upscale (RealESRGAN)", "tags": ["image", "upscale", "realesrgan"]}, "ui": {"title": "Upscale (RealESRGAN)", "tags": ["image", "upscale", "realesrgan"]},
} }

8
invokeai/app/models/image.py
View File

@ -15,7 +15,7 @@ class ImageField(BaseModel):
image_name: Optional[str] = Field(default=None, description="The name of the image") image_name: Optional[str] = Field(default=None, description="The name of the image")
class Config: class Config:
schema_extra = {"required": ["image_name"]} json_schema_extra = {"required": ["image_name"]}
class ColorField(BaseModel): class ColorField(BaseModel):
@ -40,7 +40,7 @@ class PILInvocationConfig(BaseModel):
"""Helper class to provide all PIL invocations with additional config""" """Helper class to provide all PIL invocations with additional config"""
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { json_schema_extra = {
"ui": { "ui": {
"tags": ["PIL", "image"], "tags": ["PIL", "image"],
}, },
@ -58,7 +58,7 @@ class ImageOutput(BaseInvocationOutput):
# fmt: on # fmt: on
class Config: class Config:
schema_extra = {"required": ["type", "image", "width", "height"]} json_schema_extra = {"required": ["type", "image", "width", "height"]}
class MaskOutput(BaseInvocationOutput): class MaskOutput(BaseInvocationOutput):
@ -72,7 +72,7 @@ class MaskOutput(BaseInvocationOutput):
# fmt: on # fmt: on
class Config: class Config:
schema_extra = { json_schema_extra = {
"required": [ "required": [
"type", "type",
"mask", "mask",

9
invokeai/app/services/config.py
View File

@ -166,7 +166,8 @@ import sys
from argparse import ArgumentParser from argparse import ArgumentParser
from omegaconf import OmegaConf, DictConfig, ListConfig from omegaconf import OmegaConf, DictConfig, ListConfig
from pathlib import Path from pathlib import Path
from pydantic import BaseSettings, Field, parse_obj_as from pydantic import Field, parse_obj_as
from pydantic_settings import BaseSettings
from typing import ClassVar, Dict, List, Set, Literal, Union, get_origin, get_type_hints, get_args from typing import ClassVar, Dict, List, Set, Literal, Union, get_origin, get_type_hints, get_args
INIT_FILE = Path("invokeai.yaml") INIT_FILE = Path("invokeai.yaml")
@ -187,7 +188,7 @@ class InvokeAISettings(BaseSettings):
def parse_args(self, argv: list = sys.argv[1:]): def parse_args(self, argv: list = sys.argv[1:]):
parser = self.get_parser() parser = self.get_parser()
opt = parser.parse_args(argv) opt = parser.parse_args(argv)
for name in self.__fields__: for name in self.model_fields:
if name not in self._excluded(): if name not in self._excluded():
value = getattr(opt, name) value = getattr(opt, name)
if isinstance(value, ListConfig): if isinstance(value, ListConfig):
@ -204,7 +205,7 @@ class InvokeAISettings(BaseSettings):
cls = self.__class__ cls = self.__class__
type = get_args(get_type_hints(cls)["type"])[0] type = get_args(get_type_hints(cls)["type"])[0]
field_dict = dict({type: dict()}) field_dict = dict({type: dict()})
for name, field in self.__fields__.items(): for name, field in self.model_fields.items():
if name in cls._excluded_from_yaml(): if name in cls._excluded_from_yaml():
continue continue
category = field.field_info.extra.get("category") or "Uncategorized" category = field.field_info.extra.get("category") or "Uncategorized"
@ -238,7 +239,7 @@ class InvokeAISettings(BaseSettings):
for key, value in os.environ.items(): for key, value in os.environ.items():
upcase_environ[key.upper()] = value upcase_environ[key.upper()] = value
fields = cls.__fields__ fields = cls.model_fields
cls.argparse_groups = {} cls.argparse_groups = {}
for name, field in fields.items(): for name, field in fields.items():

2
invokeai/app/services/events.py
View File

@ -44,7 +44,7 @@ class EventServiceBase:
graph_execution_state_id=graph_execution_state_id, graph_execution_state_id=graph_execution_state_id,
node=node, node=node,
source_node_id=source_node_id, source_node_id=source_node_id,
progress_image=progress_image.dict() if progress_image is not None else None, progress_image=progress_image.model_dump() if progress_image is not None else None,
step=step, step=step,
total_steps=total_steps, total_steps=total_steps,
), ),

14
invokeai/app/services/graph.py
View File

@ -15,7 +15,7 @@ from typing import (
) )
import networkx as nx import networkx as nx
from pydantic import BaseModel, root_validator, validator from pydantic import BaseModel, model_validator, field_validator
from pydantic.fields import Field from pydantic.fields import Field
from ..invocations import * from ..invocations import *
@ -156,7 +156,7 @@ class GraphInvocationOutput(BaseInvocationOutput):
type: Literal["graph_output"] = "graph_output" type: Literal["graph_output"] = "graph_output"
class Config: class Config:
schema_extra = { json_schema_extra = {
"required": [ "required": [
"type", "type",
"image", "image",
@ -186,7 +186,7 @@ class IterateInvocationOutput(BaseInvocationOutput):
item: Any = Field(description="The item being iterated over") item: Any = Field(description="The item being iterated over")
class Config: class Config:
schema_extra = { json_schema_extra = {
"required": [ "required": [
"type", "type",
"item", "item",
@ -214,7 +214,7 @@ class CollectInvocationOutput(BaseInvocationOutput):
collection: list[Any] = Field(description="The collection of input items") collection: list[Any] = Field(description="The collection of input items")
class Config: class Config:
schema_extra = { json_schema_extra = {
"required": [ "required": [
"type", "type",
"collection", "collection",
@ -755,7 +755,7 @@ class GraphExecutionState(BaseModel):
) )
class Config: class Config:
schema_extra = { json_schema_extra = {
"required": [ "required": [
"id", "id",
"graph", "graph",
@ -1110,13 +1110,13 @@ class LibraryGraph(BaseModel):
description="The outputs exposed by this graph", default_factory=list description="The outputs exposed by this graph", default_factory=list
) )
@validator("exposed_inputs", "exposed_outputs") @field_validator("exposed_inputs", "exposed_outputs")
def validate_exposed_aliases(cls, v): def validate_exposed_aliases(cls, v):
if len(v) != len(set(i.alias for i in v)): if len(v) != len(set(i.alias for i in v)):
raise ValueError("Duplicate exposed alias") raise ValueError("Duplicate exposed alias")
return v return v
@root_validator @model_validator
def validate_exposed_nodes(cls, values): def validate_exposed_nodes(cls, values):
graph = values["graph"] graph = values["graph"]

3
invokeai/app/services/item_storage.py
View File

@ -2,12 +2,11 @@ from abc import ABC, abstractmethod
from typing import Callable, Generic, Optional, TypeVar from typing import Callable, Generic, Optional, TypeVar
from pydantic import BaseModel, Field from pydantic import BaseModel, Field
from pydantic.generics import GenericModel
T = TypeVar("T", bound=BaseModel) T = TypeVar("T", bound=BaseModel)
class PaginatedResults(GenericModel, Generic[T]): class PaginatedResults(BaseModel, Generic[T]):
"""Paginated results""" """Paginated results"""
# fmt: off # fmt: off

4
invokeai/app/services/model_manager_service.py
View File

@ -238,7 +238,7 @@ class ModelManagerServiceBase(ABC):
def merge_models( def merge_models(
self, self,
model_names: List[str] = Field( model_names: List[str] = Field(
default=None, min_items=2, max_items=3, description="List of model names to merge" default=None, min_length=2, max_length=3, description="List of model names to merge"
), ),
base_model: Union[BaseModelType, str] = Field( base_model: Union[BaseModelType, str] = Field(
default=None, description="Base model shared by all models to be merged" default=None, description="Base model shared by all models to be merged"
@ -568,7 +568,7 @@ class ModelManagerService(ModelManagerServiceBase):
def merge_models( def merge_models(
self, self,
model_names: List[str] = Field( model_names: List[str] = Field(
default=None, min_items=2, max_items=3, description="List of model names to merge" default=None, min_length=2, max_length=3, description="List of model names to merge"
), ),
base_model: Union[BaseModelType, str] = Field( base_model: Union[BaseModelType, str] = Field(
default=None, description="Base model shared by all models to be merged" default=None, description="Base model shared by all models to be merged"

2
invokeai/app/services/models/image_record.py
View File

@ -89,7 +89,7 @@ def image_record_to_dto(
) -> ImageDTO: ) -> ImageDTO:
"""Converts an image record to an image DTO.""" """Converts an image record to an image DTO."""
return ImageDTO( return ImageDTO(
**image_record.dict(), **image_record.model_dump(),
image_url=image_url, image_url=image_url,
thumbnail_url=thumbnail_url, thumbnail_url=thumbnail_url,
board_id=board_id, board_id=board_id,

10
invokeai/app/services/processor.py
View File

@ -80,7 +80,7 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
# Send starting event # Send starting event
self.__invoker.services.events.emit_invocation_started( self.__invoker.services.events.emit_invocation_started(
graph_execution_state_id=graph_execution_state.id, graph_execution_state_id=graph_execution_state.id,
node=invocation.dict(), node=invocation.model_dump(),
source_node_id=source_node_id, source_node_id=source_node_id,
) )
@ -107,9 +107,9 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
# Send complete event # Send complete event
self.__invoker.services.events.emit_invocation_complete( self.__invoker.services.events.emit_invocation_complete(
graph_execution_state_id=graph_execution_state.id, graph_execution_state_id=graph_execution_state.id,
node=invocation.dict(), node=invocation.model_dump(),
source_node_id=source_node_id, source_node_id=source_node_id,
result=outputs.dict(), result=outputs.model_dump(),
) )
statistics.log_stats() statistics.log_stats()
@ -134,7 +134,7 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
# Send error event # Send error event
self.__invoker.services.events.emit_invocation_error( self.__invoker.services.events.emit_invocation_error(
graph_execution_state_id=graph_execution_state.id, graph_execution_state_id=graph_execution_state.id,
node=invocation.dict(), node=invocation.model_dump(),
source_node_id=source_node_id, source_node_id=source_node_id,
error_type=e.__class__.__name__, error_type=e.__class__.__name__,
error=error, error=error,
@ -155,7 +155,7 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
self.__invoker.services.logger.error("Error while invoking:\n%s" % e) self.__invoker.services.logger.error("Error while invoking:\n%s" % e)
self.__invoker.services.events.emit_invocation_error( self.__invoker.services.events.emit_invocation_error(
graph_execution_state_id=graph_execution_state.id, graph_execution_state_id=graph_execution_state.id,
node=invocation.dict(), node=invocation.model_dump(),
source_node_id=source_node_id, source_node_id=source_node_id,
error_type=e.__class__.__name__, error_type=e.__class__.__name__,
error=traceback.format_exc(), error=traceback.format_exc(),

2
invokeai/backend/install/invokeai_configure.py
View File

@ -716,7 +716,7 @@ def migrate_init_file(legacy_format: Path):
old = legacy_parser.parse_args([f"@{str(legacy_format)}"]) old = legacy_parser.parse_args([f"@{str(legacy_format)}"])
new = InvokeAIAppConfig.get_config() new = InvokeAIAppConfig.get_config()
fields = [x for x, y in InvokeAIAppConfig.__fields__.items() if y.field_info.extra.get("category") != "DEPRECATED"] fields = [x for x, y in InvokeAIAppConfig.model_fields.items() if y.field_info.extra.get("category") != "DEPRECATED"]
for attr in fields: for attr in fields:
if hasattr(old, attr): if hasattr(old, attr):
try: try:

2
invokeai/backend/model_management/model_manager.py
View File

@ -897,7 +897,7 @@ class ModelManager(object):
Write current configuration out to the indicated file. Write current configuration out to the indicated file.
""" """
data_to_save = dict() data_to_save = dict()
data_to_save["__metadata__"] = self.config_meta.dict() data_to_save["__metadata__"] = self.config_meta.model_dump()
for model_key, model_config in self.models.items(): for model_key, model_config in self.models.items():
model_name, base_model, model_type = self.parse_key(model_key) model_name, base_model, model_type = self.parse_key(model_key)

5
pyproject.toml
View File

@ -47,7 +47,7 @@ dependencies = [
"einops", "einops",
"eventlet", "eventlet",
"facexlib", "facexlib",
"fastapi==0.88.0", "fastapi==0.101.0",
"fastapi-events==0.8.0", "fastapi-events==0.8.0",
"fastapi-socketio==0.0.10", "fastapi-socketio==0.0.10",
"flask==2.1.3", "flask==2.1.3",
@ -64,7 +64,8 @@ dependencies = [
"onnx", "onnx",
"onnxruntime", "onnxruntime",
"opencv-python", "opencv-python",
"pydantic==1.*", "pydantic==2.1.1",
"pydantic-settings",
"picklescan", "picklescan",
"pillow", "pillow",
"prompt-toolkit", "prompt-toolkit",

6
tests/nodes/test_node_graph.py
View File

@ -584,7 +584,7 @@ def test_graph_can_serialize():
g.add_edge(e) g.add_edge(e)
# Not throwing on this line is sufficient # Not throwing on this line is sufficient
json = g.json() json = g.model_dump_json()
def test_graph_can_deserialize(): def test_graph_can_deserialize():
@ -596,8 +596,8 @@ def test_graph_can_deserialize():
e = create_edge(n1.id, "image", n2.id, "image") e = create_edge(n1.id, "image", n2.id, "image")
g.add_edge(e) g.add_edge(e)
json = g.json() json = g.model_dump_json()
g2 = Graph.parse_raw(json) g2 = Graph.model_validate_json(json)
assert g2 is not None assert g2 is not None
assert g2.nodes["1"] is not None assert g2.nodes["1"] is not None