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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")
# 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()}
ApiDependencies.invoker.services.model_manager.update_model(
@ -203,7 +203,7 @@ async def add_model(
try:
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}")
model_raw = ApiDependencies.invoker.services.model_manager.list_model(
@ -348,7 +348,7 @@ async def sync_to_config() -> bool:
)
async def merge_models(
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"),
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"),

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.middleware import EventHandlerASGIMiddleware
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
from .services.config import InvokeAIAppConfig
@ -126,13 +126,13 @@ def custom_openapi():
output_type = signature(invoker.invoke).return_annotation
output_types.add(output_type)
output_schemas = schema(output_types, ref_prefix="#/components/schemas/")
for schema_key, output_schema in output_schemas["definitions"].items():
openapi_schema["components"]["schemas"][schema_key] = output_schema
# output_schemas = schema(output_types, ref_prefix="#/components/schemas/")
# for schema_key, output_schema in output_schemas["definitions"].items():
# openapi_schema["components"]["schemas"][schema_key] = output_schema
# 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
output_type_titles[schema_key] = output_schema["title"]
# # 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
# output_type_titles[schema_key] = output_schema["title"]
# Add a reference to the output type to additionalProperties of the invoker schema
for invoker in all_invocations:

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

@ -67,7 +67,7 @@ def add_parsers(
add_arguments(command_parser)
# Convert all fields to arguments
fields = command.__fields__ # type: ignore
fields = command.model_fields # type: ignore
for name, field in fields.items():
if name in exclude_fields:
continue
@ -87,7 +87,7 @@ def add_graph_parsers(
# Add arguments for inputs
for exposed_input in graph.exposed_inputs:
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)
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:
fields = invocation.__fields__.items()
fields = invocation.model_fields.items()
type_hints = get_type_hints(type(invocation))
command = [invocation.type]
for name, field in fields:

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

@ -118,7 +118,7 @@ class CustomisedSchemaExtra(TypedDict):
class InvocationConfig(BaseConfig):
"""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`
- A list of strings, used to categorise invocations.
@ -131,7 +131,7 @@ class InvocationConfig(BaseConfig):
```python
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {
"tags": ["stable-diffusion", "image"],
"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
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.util.misc import SEED_MAX, get_random_seed
@ -38,7 +38,7 @@ class ImageCollectionOutput(BaseInvocationOutput):
collection: list[ImageField] = Field(default=[], description="The output images")
class Config:
schema_extra = {"required": ["type", "collection"]}
json_schema_extra = {"required": ["type", "collection"]}
class RangeInvocation(BaseInvocation):
@ -52,11 +52,11 @@ class RangeInvocation(BaseInvocation):
step: int = Field(default=1, description="The step of the range")
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"title": "Range", "tags": ["range", "integer", "collection"]},
}
@validator("stop")
@field_validator("stop")
def stop_gt_start(cls, v, values):
if "start" in values and v <= values["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")
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"title": "Sized Range", "tags": ["range", "integer", "size", "collection"]},
}
@ -102,7 +102,7 @@ class RandomRangeInvocation(BaseInvocation):
)
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"title": "Random Range", "tags": ["range", "integer", "random", "collection"]},
}
@ -127,7 +127,7 @@ class ImageCollectionInvocation(BaseInvocation):
return ImageCollectionOutput(collection=self.images)
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {
"type_hints": {
"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")
class Config:
schema_extra = {"required": ["conditioning_name"]}
json_schema_extra = {"required": ["conditioning_name"]}
@dataclass
@ -80,18 +80,18 @@ class CompelInvocation(BaseInvocation):
# Schema customisation
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"title": "Prompt (Compel)", "tags": ["prompt", "compel"], "type_hints": {"model": "model"}},
}
@torch.no_grad()
def invoke(self, context: InvocationContext) -> CompelOutput:
tokenizer_info = context.services.model_manager.get_model(
**self.clip.tokenizer.dict(),
**self.clip.tokenizer.model_dump(),
context=context,
)
text_encoder_info = context.services.model_manager.get_model(
**self.clip.text_encoder.dict(),
**self.clip.text_encoder.model_dump(),
context=context,
)
@ -178,11 +178,11 @@ class CompelInvocation(BaseInvocation):
class SDXLPromptInvocationBase:
def run_clip_raw(self, context, clip_field, prompt, get_pooled, lora_prefix):
tokenizer_info = context.services.model_manager.get_model(
**clip_field.tokenizer.dict(),
**clip_field.tokenizer.model_dump(),
context=context,
)
text_encoder_info = context.services.model_manager.get_model(
**clip_field.text_encoder.dict(),
**clip_field.text_encoder.model_dump(),
context=context,
)
@ -255,11 +255,11 @@ class SDXLPromptInvocationBase:
def run_clip_compel(self, context, clip_field, prompt, get_pooled, lora_prefix):
tokenizer_info = context.services.model_manager.get_model(
**clip_field.tokenizer.dict(),
**clip_field.tokenizer.model_dump(),
context=context,
)
text_encoder_info = context.services.model_manager.get_model(
**clip_field.text_encoder.dict(),
**clip_field.text_encoder.model_dump(),
context=context,
)
@ -360,7 +360,7 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
# Schema customisation
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"title": "SDXL Prompt (Compel)", "tags": ["prompt", "compel"], "type_hints": {"model": "model"}},
}
@ -414,7 +414,7 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
# Schema customisation
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {
"title": "SDXL Refiner Prompt (Compel)",
"tags": ["prompt", "compel"],
@ -471,7 +471,7 @@ class SDXLRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
# Schema customisation
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"title": "SDXL Prompt (Raw)", "tags": ["prompt", "compel"], "type_hints": {"model": "model"}},
}
@ -525,7 +525,7 @@ class SDXLRefinerRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
# Schema customisation
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {
"title": "SDXL Refiner Prompt (Raw)",
"tags": ["prompt", "compel"],
@ -580,7 +580,7 @@ class ClipSkipInvocation(BaseInvocation):
skipped_layers: int = Field(0, description="Number of layers to skip in text_encoder")
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"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 PIL import Image
from pydantic import BaseModel, Field, validator
from pydantic import BaseModel, Field, field_validator
from ...backend.model_management import BaseModelType, ModelType
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")
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):
"""Validate that all control weights in the valid range"""
if isinstance(v, list):
@ -136,7 +136,7 @@ class ControlField(BaseModel):
return v
class Config:
schema_extra = {
json_schema_extra = {
"required": ["image", "control_model", "control_weight", "begin_step_percent", "end_step_percent"],
"ui": {
"type_hints": {
@ -176,7 +176,7 @@ class ControlNetInvocation(BaseInvocation):
# fmt: on
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {
"title": "ControlNet",
"tags": ["controlnet", "latents"],
@ -214,7 +214,7 @@ class ImageProcessorInvocation(BaseInvocation, PILInvocationConfig):
# fmt: on
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"title": "Image Processor", "tags": ["image", "processor"]},
}
@ -266,7 +266,7 @@ class CannyImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfi
# fmt: on
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"title": "Canny Processor", "tags": ["controlnet", "canny", "image", "processor"]},
}
@ -290,7 +290,7 @@ class HedImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig)
# fmt: on
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"title": "Softedge(HED) Processor", "tags": ["controlnet", "softedge", "hed", "image", "processor"]},
}
@ -319,7 +319,7 @@ class LineartImageProcessorInvocation(ImageProcessorInvocation, PILInvocationCon
# fmt: on
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"title": "Lineart Processor", "tags": ["controlnet", "lineart", "image", "processor"]},
}
@ -342,7 +342,7 @@ class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation, PILInvocati
# fmt: on
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {
"title": "Lineart Anime Processor",
"tags": ["controlnet", "lineart", "anime", "image", "processor"],
@ -371,7 +371,7 @@ class OpenposeImageProcessorInvocation(ImageProcessorInvocation, PILInvocationCo
# fmt: on
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"title": "Openpose Processor", "tags": ["controlnet", "openpose", "image", "processor"]},
}
@ -399,7 +399,7 @@ class MidasDepthImageProcessorInvocation(ImageProcessorInvocation, PILInvocation
# fmt: on
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"title": "Midas (Depth) Processor", "tags": ["controlnet", "midas", "depth", "image", "processor"]},
}
@ -426,7 +426,7 @@ class NormalbaeImageProcessorInvocation(ImageProcessorInvocation, PILInvocationC
# fmt: on
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"title": "Normal BAE Processor", "tags": ["controlnet", "normal", "bae", "image", "processor"]},
}
@ -451,7 +451,7 @@ class MlsdImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig
# fmt: on
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"title": "MLSD Processor", "tags": ["controlnet", "mlsd", "image", "processor"]},
}
@ -480,7 +480,7 @@ class PidiImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig
# fmt: on
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"title": "PIDI Processor", "tags": ["controlnet", "pidi", "image", "processor"]},
}
@ -510,7 +510,7 @@ class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation, PILInvoca
# fmt: on
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {
"title": "Content Shuffle Processor",
"tags": ["controlnet", "contentshuffle", "image", "processor"],
@ -539,7 +539,7 @@ class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation, PILInvocationCo
# fmt: on
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"title": "Zoe (Depth) Processor", "tags": ["controlnet", "zoe", "depth", "image", "processor"]},
}
@ -560,7 +560,7 @@ class MediapipeFaceProcessorInvocation(ImageProcessorInvocation, PILInvocationCo
# fmt: on
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"title": "Mediapipe Processor", "tags": ["controlnet", "mediapipe", "image", "processor"]},
}
@ -588,7 +588,7 @@ class LeresImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfi
# fmt: on
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"title": "Leres (Depth) Processor", "tags": ["controlnet", "leres", "depth", "image", "processor"]},
}
@ -614,7 +614,7 @@ class TileResamplerProcessorInvocation(ImageProcessorInvocation, PILInvocationCo
# fmt: on
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {
"title": "Tile Resample Processor",
"tags": ["controlnet", "tile", "resample", "image", "processor"],
@ -656,7 +656,7 @@ class SegmentAnythingProcessorInvocation(ImageProcessorInvocation, PILInvocation
# fmt: on
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {
"title": "Segment Anything 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
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {
"tags": ["cv", "image"],
},
@ -36,7 +36,7 @@ class CvInpaintInvocation(BaseInvocation, CvInvocationConfig):
# fmt: on
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"title": "OpenCV Inpaint", "tags": ["opencv", "inpaint"]},
}

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

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

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

@ -39,7 +39,7 @@ class LoadImageInvocation(BaseInvocation):
# fmt: on
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"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")
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"title": "Show Image", "tags": ["image", "show"]},
}
@ -95,7 +95,7 @@ class ImageCropInvocation(BaseInvocation, PILInvocationConfig):
# fmt: on
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"title": "Crop Image", "tags": ["image", "crop"]},
}
@ -136,7 +136,7 @@ class ImagePasteInvocation(BaseInvocation, PILInvocationConfig):
# fmt: on
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"title": "Paste Image", "tags": ["image", "paste"]},
}
@ -185,7 +185,7 @@ class MaskFromAlphaInvocation(BaseInvocation, PILInvocationConfig):
# fmt: on
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"title": "Mask From Alpha", "tags": ["image", "mask", "alpha"]},
}
@ -224,7 +224,7 @@ class ImageMultiplyInvocation(BaseInvocation, PILInvocationConfig):
# fmt: on
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"title": "Multiply Images", "tags": ["image", "multiply"]},
}
@ -265,7 +265,7 @@ class ImageChannelInvocation(BaseInvocation, PILInvocationConfig):
# fmt: on
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"title": "Image Channel", "tags": ["image", "channel"]},
}
@ -305,7 +305,7 @@ class ImageConvertInvocation(BaseInvocation, PILInvocationConfig):
# fmt: on
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"title": "Convert Image", "tags": ["image", "convert"]},
}
@ -343,7 +343,7 @@ class ImageBlurInvocation(BaseInvocation, PILInvocationConfig):
# fmt: on
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"title": "Blur Image", "tags": ["image", "blur"]},
}
@ -405,7 +405,7 @@ class ImageResizeInvocation(BaseInvocation, PILInvocationConfig):
# fmt: on
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"title": "Resize Image", "tags": ["image", "resize"]},
}
@ -448,7 +448,7 @@ class ImageScaleInvocation(BaseInvocation, PILInvocationConfig):
# fmt: on
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"title": "Scale Image", "tags": ["image", "scale"]},
}
@ -493,7 +493,7 @@ class ImageLerpInvocation(BaseInvocation, PILInvocationConfig):
# fmt: on
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"title": "Image Linear Interpolation", "tags": ["image", "linear", "interpolation", "lerp"]},
}
@ -534,7 +534,7 @@ class ImageInverseLerpInvocation(BaseInvocation, PILInvocationConfig):
# fmt: on
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {
"title": "Image Inverse Linear Interpolation",
"tags": ["image", "linear", "interpolation", "inverse"],
@ -577,7 +577,7 @@ class ImageNSFWBlurInvocation(BaseInvocation, PILInvocationConfig):
# fmt: on
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"title": "Blur NSFW Images", "tags": ["image", "nsfw", "checker"]},
}
@ -600,7 +600,7 @@ class ImageNSFWBlurInvocation(BaseInvocation, PILInvocationConfig):
node_id=self.id,
session_id=context.graph_execution_state_id,
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(
@ -629,7 +629,7 @@ class ImageWatermarkInvocation(BaseInvocation, PILInvocationConfig):
# fmt: on
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"title": "Add Invisible Watermark", "tags": ["image", "watermark", "invisible"]},
}
@ -643,7 +643,7 @@ class ImageWatermarkInvocation(BaseInvocation, PILInvocationConfig):
node_id=self.id,
session_id=context.graph_execution_state_id,
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(

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

@ -125,7 +125,7 @@ class InfillColorInvocation(BaseInvocation):
)
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"title": "Color Infill", "tags": ["image", "inpaint", "color", "infill"]},
}
@ -168,7 +168,7 @@ class InfillTileInvocation(BaseInvocation):
)
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"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")
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"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,
)
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.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")
class Config:
schema_extra = {"required": ["latents_name"]}
json_schema_extra = {"required": ["latents_name"]}
class LatentsOutput(BaseInvocationOutput):
@ -80,7 +80,7 @@ def get_scheduler(
) -> Scheduler:
scheduler_class, scheduler_extra_config = SCHEDULER_MAP.get(scheduler_name, SCHEDULER_MAP["ddim"])
orig_scheduler_info = context.services.model_manager.get_model(
**scheduler_info.dict(),
**scheduler_info.model_dump(),
context=context,
)
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'")
# fmt: on
@validator("cfg_scale")
@field_validator("cfg_scale")
def ge_one(cls, v):
"""validate that all cfg_scale values are >= 1"""
if isinstance(v, list):
@ -135,7 +135,7 @@ class TextToLatentsInvocation(BaseInvocation):
# Schema customisation
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {
"title": "Text To Latents",
"tags": ["latents"],
@ -158,7 +158,7 @@ class TextToLatentsInvocation(BaseInvocation):
stable_diffusion_step_callback(
context=context,
intermediate_state=intermediate_state,
node=self.dict(),
node=self.model_dump(),
source_node_id=source_node_id,
)
@ -327,7 +327,7 @@ class TextToLatentsInvocation(BaseInvocation):
return
unet_info = context.services.model_manager.get_model(
**self.unet.unet.dict(),
**self.unet.unet.model_dump(),
context=context,
)
with ExitStack() as exit_stack, ModelPatcher.apply_lora_unet(
@ -384,7 +384,7 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
# Schema customisation
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {
"title": "Latent To Latents",
"tags": ["latents"],
@ -420,7 +420,7 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
return
unet_info = context.services.model_manager.get_model(
**self.unet.unet.dict(),
**self.unet.unet.model_dump(),
context=context,
)
with ExitStack() as exit_stack, ModelPatcher.apply_lora_unet(
@ -495,7 +495,7 @@ class LatentsToImageInvocation(BaseInvocation):
# Schema customisation
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {
"title": "Latents To Image",
"tags": ["latents", "image"],
@ -507,7 +507,7 @@ class LatentsToImageInvocation(BaseInvocation):
latents = context.services.latents.get(self.latents.latents_name)
vae_info = context.services.model_manager.get_model(
**self.vae.vae.dict(),
**self.vae.vae.model_dump(),
context=context,
)
@ -565,7 +565,7 @@ class LatentsToImageInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
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(
@ -593,7 +593,7 @@ class ResizeLatentsInvocation(BaseInvocation):
)
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"title": "Resize Latents", "tags": ["latents", "resize"]},
}
@ -634,7 +634,7 @@ class ScaleLatentsInvocation(BaseInvocation):
)
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"title": "Scale Latents", "tags": ["latents", "scale"]},
}
@ -675,7 +675,7 @@ class ImageToLatentsInvocation(BaseInvocation):
# Schema customisation
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"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)
# 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(
**self.vae.vae.dict(),
**self.vae.vae.model_dump(),
context=context,
)

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

@ -18,7 +18,7 @@ class MathInvocationConfig(BaseModel):
# Schema customisation
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {
"tags": ["math"],
}
@ -53,7 +53,7 @@ class AddInvocation(BaseInvocation, MathInvocationConfig):
# fmt: on
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"title": "Add", "tags": ["math", "add"]},
}
@ -71,7 +71,7 @@ class SubtractInvocation(BaseInvocation, MathInvocationConfig):
# fmt: on
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"title": "Subtract", "tags": ["math", "subtract"]},
}
@ -89,7 +89,7 @@ class MultiplyInvocation(BaseInvocation, MathInvocationConfig):
# fmt: on
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"title": "Multiply", "tags": ["math", "multiply"]},
}
@ -107,7 +107,7 @@ class DivideInvocation(BaseInvocation, MathInvocationConfig):
# fmt: on
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"title": "Divide", "tags": ["math", "divide"]},
}
@ -127,7 +127,7 @@ class RandomIntInvocation(BaseInvocation):
# fmt: on
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"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")
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {
"title": "Metadata Accumulator",
"tags": ["image", "metadata", "generation"],
@ -152,4 +152,4 @@ class MetadataAccumulatorInvocation(BaseInvocation):
def invoke(self, context: InvocationContext) -> MetadataAccumulatorOutput:
"""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
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {
"title": "Model Loader",
"tags": ["model", "loader"],
@ -205,7 +205,7 @@ class LoraLoaderInvocation(BaseInvocation):
clip: Optional[ClipField] = Field(description="Clip model for applying lora")
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {
"title": "Lora Loader",
"tags": ["lora", "loader"],
@ -287,7 +287,7 @@ class SDXLLoraLoaderInvocation(BaseInvocation):
clip2: Optional[ClipField] = Field(description="Clip2 model for applying lora")
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {
"title": "SDXL Lora Loader",
"tags": ["lora", "loader"],
@ -385,7 +385,7 @@ class VaeLoaderInvocation(BaseInvocation):
# Schema customisation
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {
"title": "VAE Loader",
"tags": ["vae", "loader"],

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

@ -3,7 +3,7 @@
import math
from typing import Literal
from pydantic import Field, validator
from pydantic import Field, field_validator
import torch
from invokeai.app.invocations.latent import LatentsField
@ -110,14 +110,14 @@ class NoiseInvocation(BaseInvocation):
# Schema customisation
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {
"title": "Noise",
"tags": ["latents", "noise"],
},
}
@validator("seed", pre=True)
@field_validator("seed", pre=True)
def modulo_seed(cls, v):
"""Returns the seed modulo (SEED_MAX + 1) to ensure it is within the valid range."""
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 inspect
from pydantic import BaseModel, Field, validator
from pydantic import BaseModel, Field, field_validator
import torch
import numpy as np
from diffusers import ControlNetModel, DPMSolverMultistepScheduler
@ -59,10 +59,10 @@ class ONNXPromptInvocation(BaseInvocation):
def invoke(self, context: InvocationContext) -> CompelOutput:
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(
**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:
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'")
# fmt: on
@validator("cfg_scale")
@field_validator("cfg_scale")
def ge_one(cls, v):
"""validate that all cfg_scale values are >= 1"""
if isinstance(v, list):
@ -168,7 +168,7 @@ class ONNXTextToLatentsInvocation(BaseInvocation):
# Schema customisation
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {
"tags": ["latents"],
"type_hints": {
@ -226,7 +226,7 @@ class ONNXTextToLatentsInvocation(BaseInvocation):
stable_diffusion_step_callback(
context=context,
intermediate_state=intermediate_state,
node=self.dict(),
node=self.model_dump(),
source_node_id=source_node_id,
)
@ -239,7 +239,7 @@ class ONNXTextToLatentsInvocation(BaseInvocation):
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:
# 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
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {
"tags": ["latents", "image"],
},
@ -327,7 +327,7 @@ class ONNXLatentsToImageInvocation(BaseInvocation):
raise Exception(f"Expected vae_decoder, found: {self.vae.vae.model_type}")
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
@ -356,7 +356,7 @@ class ONNXLatentsToImageInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
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(
@ -389,7 +389,7 @@ class ONNXSD1ModelLoaderInvocation(BaseInvocation):
# Schema customisation
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"tags": ["model", "loader"], "type_hints": {"model_name": "model"}}, # TODO: rename to model_name?
}
@ -472,7 +472,7 @@ class OnnxModelLoaderInvocation(BaseInvocation):
# Schema customisation
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {
"title": "Onnx 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)")
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"title": "Linear Range (Float)", "tags": ["math", "float", "linear", "range"]},
}
@ -136,7 +136,7 @@ class StepParamEasingInvocation(BaseInvocation):
# fmt: on
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"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
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"tags": ["param", "integer"], "title": "Integer Parameter"},
}
@ -38,7 +38,7 @@ class ParamFloatInvocation(BaseInvocation):
# fmt: on
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"tags": ["param", "float"], "title": "Float Parameter"},
}
@ -60,7 +60,7 @@ class ParamStringInvocation(BaseInvocation):
text: str = Field(default="", description="The string value")
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"tags": ["param", "string"], "title": "String Parameter"},
}
@ -75,7 +75,7 @@ class ParamPromptInvocation(BaseInvocation):
prompt: str = Field(default="", description="The prompt value")
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"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
import numpy as np
from pydantic import Field, validator
from pydantic import Field, field_validator
from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationConfig, InvocationContext
from dynamicprompts.generators import RandomPromptGenerator, CombinatorialPromptGenerator
@ -18,7 +18,7 @@ class PromptOutput(BaseInvocationOutput):
# fmt: on
class Config:
schema_extra = {
json_schema_extra = {
"required": [
"type",
"prompt",
@ -37,7 +37,7 @@ class PromptCollectionOutput(BaseInvocationOutput):
# fmt: on
class Config:
schema_extra = {"required": ["type", "prompt_collection", "count"]}
json_schema_extra = {"required": ["type", "prompt_collection", "count"]}
class DynamicPromptInvocation(BaseInvocation):
@ -49,7 +49,7 @@ class DynamicPromptInvocation(BaseInvocation):
combinatorial: bool = Field(default=False, description="Whether to use the combinatorial generator")
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"title": "Dynamic Prompt", "tags": ["prompt", "dynamic"]},
}
@ -79,11 +79,11 @@ class PromptsFromFileInvocation(BaseInvocation):
# fmt: on
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {"title": "Prompts From File", "tags": ["prompt", "file"]},
}
@validator("file_path")
@field_validator("file_path")
def file_path_exists(cls, v):
if not exists(v):
raise ValueError(FileNotFoundError)

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

@ -3,7 +3,7 @@ import inspect
from tqdm import tqdm
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 invokeai.app.util.step_callback import stable_diffusion_xl_step_callback
@ -49,7 +49,7 @@ class SDXLModelLoaderInvocation(BaseInvocation):
# Schema customisation
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {
"title": "SDXL Model Loader",
"tags": ["model", "loader", "sdxl"],
@ -139,7 +139,7 @@ class SDXLRefinerModelLoaderInvocation(BaseInvocation):
# Schema customisation
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {
"title": "SDXL Refiner Model Loader",
"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'")
# fmt: on
@validator("cfg_scale")
@field_validator("cfg_scale")
def ge_one(cls, v):
"""validate that all cfg_scale values are >= 1"""
if isinstance(v, list):
@ -238,7 +238,7 @@ class SDXLTextToLatentsInvocation(BaseInvocation):
# Schema customisation
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {
"title": "SDXL Text To Latents",
"tags": ["latents"],
@ -260,7 +260,7 @@ class SDXLTextToLatentsInvocation(BaseInvocation):
) -> None:
stable_diffusion_xl_step_callback(
context=context,
node=self.dict(),
node=self.model_dump(),
source_node_id=source_node_id,
sample=sample,
step=step,
@ -303,7 +303,7 @@ class SDXLTextToLatentsInvocation(BaseInvocation):
del lora_info
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
cross_attention_kwargs = None
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'")
# fmt: on
@validator("cfg_scale")
@field_validator("cfg_scale")
def ge_one(cls, v):
"""validate that all cfg_scale values are >= 1"""
if isinstance(v, list):
@ -495,7 +495,7 @@ class SDXLLatentsToLatentsInvocation(BaseInvocation):
# Schema customisation
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {
"title": "SDXL Latents to Latents",
"tags": ["latents"],
@ -517,7 +517,7 @@ class SDXLLatentsToLatentsInvocation(BaseInvocation):
) -> None:
stable_diffusion_xl_step_callback(
context=context,
node=self.dict(),
node=self.model_dump(),
source_node_id=source_node_id,
sample=sample,
step=step,
@ -549,7 +549,7 @@ class SDXLLatentsToLatentsInvocation(BaseInvocation):
)
unet_info = context.services.model_manager.get_model(
**self.unet.unet.dict(),
**self.unet.unet.model_dump(),
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")
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"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")
class Config:
schema_extra = {"required": ["image_name"]}
json_schema_extra = {"required": ["image_name"]}
class ColorField(BaseModel):
@ -40,7 +40,7 @@ class PILInvocationConfig(BaseModel):
"""Helper class to provide all PIL invocations with additional config"""
class Config(InvocationConfig):
schema_extra = {
json_schema_extra = {
"ui": {
"tags": ["PIL", "image"],
},
@ -58,7 +58,7 @@ class ImageOutput(BaseInvocationOutput):
# fmt: on
class Config:
schema_extra = {"required": ["type", "image", "width", "height"]}
json_schema_extra = {"required": ["type", "image", "width", "height"]}
class MaskOutput(BaseInvocationOutput):
@ -72,7 +72,7 @@ class MaskOutput(BaseInvocationOutput):
# fmt: on
class Config:
schema_extra = {
json_schema_extra = {
"required": [
"type",
"mask",

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

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

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

@ -15,7 +15,7 @@ from typing import (
)
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 ..invocations import *
@ -156,7 +156,7 @@ class GraphInvocationOutput(BaseInvocationOutput):
type: Literal["graph_output"] = "graph_output"
class Config:
schema_extra = {
json_schema_extra = {
"required": [
"type",
"image",
@ -186,7 +186,7 @@ class IterateInvocationOutput(BaseInvocationOutput):
item: Any = Field(description="The item being iterated over")
class Config:
schema_extra = {
json_schema_extra = {
"required": [
"type",
"item",
@ -214,7 +214,7 @@ class CollectInvocationOutput(BaseInvocationOutput):
collection: list[Any] = Field(description="The collection of input items")
class Config:
schema_extra = {
json_schema_extra = {
"required": [
"type",
"collection",
@ -755,7 +755,7 @@ class GraphExecutionState(BaseModel):
)
class Config:
schema_extra = {
json_schema_extra = {
"required": [
"id",
"graph",
@ -1110,13 +1110,13 @@ class LibraryGraph(BaseModel):
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):
if len(v) != len(set(i.alias for i in v)):
raise ValueError("Duplicate exposed alias")
return v
@root_validator
@model_validator
def validate_exposed_nodes(cls, values):
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 pydantic import BaseModel, Field
from pydantic.generics import GenericModel
T = TypeVar("T", bound=BaseModel)
class PaginatedResults(GenericModel, Generic[T]):
class PaginatedResults(BaseModel, Generic[T]):
"""Paginated results"""
# fmt: off

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

@ -238,7 +238,7 @@ class ModelManagerServiceBase(ABC):
def merge_models(
self,
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(
default=None, description="Base model shared by all models to be merged"
@ -568,7 +568,7 @@ class ModelManagerService(ModelManagerServiceBase):
def merge_models(
self,
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(
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:
"""Converts an image record to an image DTO."""
return ImageDTO(
**image_record.dict(),
**image_record.model_dump(),
image_url=image_url,
thumbnail_url=thumbnail_url,
board_id=board_id,

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

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

5
pyproject.toml
View File

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

6
tests/nodes/test_node_graph.py
View File

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