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.

installer/readme.txt

@@ -8,16 +8,13 @@ Preparations:
   to work. Instructions are given here:
   https://invoke-ai.github.io/InvokeAI/installation/INSTALL_AUTOMATED/
 
-  NOTE: At this time we do not recommend Python 3.11. We recommend 
-  Version 3.10.9, which has been extensively tested with InvokeAI.
-  
   Before you start the installer, please open up your system's command
   line window (Terminal or Command) and type the commands:
 
      python --version
 
   If all is well, it will print "Python 3.X.X", where the version number
-  is at least 3.9.1, and less than 3.11.
+  is at least 3.9.*, and not higher than 3.11.*.
 
   If this works, check the version of the Python package manager, pip:
 

invokeai/app/api/routers/models.py

@@ -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"),

invokeai/app/api_app.py

@@ -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:

invokeai/app/cli/commands.py

@@ -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:

invokeai/app/invocations/baseinvocation.py

@@ -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

invokeai/app/invocations/collections.py

@@ -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",

invokeai/app/invocations/compel.py

@@ -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"]},
         }
 

invokeai/app/invocations/controlnet_image_processors.py

@@ -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"],

invokeai/app/invocations/cv.py

@@ -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"]},
         }
 

invokeai/app/invocations/generate.py

@@ -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,
         )
 

invokeai/app/invocations/image.py

@@ -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(

invokeai/app/invocations/infill.py

@@ -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"]},
         }
 

invokeai/app/invocations/latent.py

@@ -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,
         )
 

invokeai/app/invocations/math.py

@@ -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"]},
         }
 

invokeai/app/invocations/metadata.py

@@ -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()))

invokeai/app/invocations/model.py

@@ -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"],

invokeai/app/invocations/noise.py

@@ -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)

invokeai/app/invocations/onnx.py

@@ -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"],

invokeai/app/invocations/param_easing.py

@@ -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"]},
         }
 

invokeai/app/invocations/params.py

@@ -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"},
         }
 

invokeai/app/invocations/prompt.py

@@ -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)

invokeai/app/invocations/sdxl.py

@@ -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,
         )
 

invokeai/app/invocations/upscale.py

@@ -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"]},
         }
 

invokeai/app/models/image.py

@@ -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",

invokeai/app/services/config.py

@@ -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():

invokeai/app/services/events.py

@@ -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,
             ),

invokeai/app/services/graph.py

@@ -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"]
 

invokeai/app/services/item_storage.py

@@ -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

invokeai/app/services/model_manager_service.py

@@ -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"

invokeai/app/services/models/image_record.py

@@ -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,

invokeai/app/services/processor.py

@@ -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(),

invokeai/backend/install/invokeai_configure.py

@@ -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:

invokeai/backend/model_management/model_manager.py

@@ -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)

invokeai/backend/model_management/models/lora.py

@@ -1,18 +1,21 @@
+import bisect
 import os
-import torch
 from enum import Enum
-from typing import Optional, Dict, Union, Literal, Any
 from pathlib import Path
+from typing import Dict, Optional, Union
+
+import torch
 from safetensors.torch import load_file
+
 from .base import (
+    BaseModelType,
+    InvalidModelException,
     ModelBase,
     ModelConfigBase,
-    BaseModelType,
+    ModelNotFoundException,
     ModelType,
     SubModelType,
     classproperty,
-    InvalidModelException,
-    ModelNotFoundException,
 )
 
 
@@ -482,30 +485,61 @@ class LoRAModelRaw:  # (torch.nn.Module):
         return model_size
 
     @classmethod
-    def _convert_sdxl_compvis_keys(cls, state_dict):
+    def _convert_sdxl_keys_to_diffusers_format(cls, state_dict):
+        """Convert the keys of an SDXL LoRA state_dict to diffusers format.
+
+        The input state_dict can be in either Stability AI format or diffusers format. If the state_dict is already in
+        diffusers format, then this function will have no effect.
+
+        This function is adapted from:
+        https://github.com/bmaltais/kohya_ss/blob/2accb1305979ba62f5077a23aabac23b4c37e935/networks/lora_diffusers.py#L385-L409
+
+        Args:
+            state_dict (Dict[str, Tensor]): The SDXL LoRA state_dict.
+
+        Raises:
+            ValueError: If state_dict contains an unrecognized key, or not all keys could be converted.
+
+        Returns:
+            Dict[str, Tensor]: The diffusers-format state_dict.
+        """
+        converted_count = 0  # The number of Stability AI keys converted to diffusers format.
+        not_converted_count = 0  # The number of keys that were not converted.
+
+        # Get a sorted list of Stability AI UNet keys so that we can efficiently search for keys with matching prefixes.
+        # For example, we want to efficiently find `input_blocks_4_1` in the list when searching for
+        # `input_blocks_4_1_proj_in`.
+        stability_unet_keys = list(SDXL_UNET_STABILITY_TO_DIFFUSERS_MAP)
+        stability_unet_keys.sort()
+
         new_state_dict = dict()
         for full_key, value in state_dict.items():
-            if full_key.startswith("lora_te1_") or full_key.startswith("lora_te2_"):
-                continue  # clip same
+            if full_key.startswith("lora_unet_"):
+                search_key = full_key.replace("lora_unet_", "")
+                # Use bisect to find the key in stability_unet_keys that *may* match the search_key's prefix.
+                position = bisect.bisect_right(stability_unet_keys, search_key)
+                map_key = stability_unet_keys[position - 1]
+                # Now, check if the map_key *actually* matches the search_key.
+                if search_key.startswith(map_key):
+                    new_key = full_key.replace(map_key, SDXL_UNET_STABILITY_TO_DIFFUSERS_MAP[map_key])
+                    new_state_dict[new_key] = value
+                    converted_count += 1
+                else:
+                    new_state_dict[full_key] = value
+                    not_converted_count += 1
+            elif full_key.startswith("lora_te1_") or full_key.startswith("lora_te2_"):
+                # The CLIP text encoders have the same keys in both Stability AI and diffusers formats.
+                new_state_dict[full_key] = value
+                continue
+            else:
+                raise ValueError(f"Unrecognized SDXL LoRA key prefix: '{full_key}'.")
 
-            if not full_key.startswith("lora_unet_"):
-                raise NotImplementedError(f"Unknown prefix for sdxl lora key - {full_key}")
-            src_key = full_key.replace("lora_unet_", "")
-            try:
-                dst_key = None
-                while "_" in src_key:
-                    if src_key in SDXL_UNET_COMPVIS_MAP:
-                        dst_key = SDXL_UNET_COMPVIS_MAP[src_key]
-                        break
-                    src_key = "_".join(src_key.split("_")[:-1])
+        if converted_count > 0 and not_converted_count > 0:
+            raise ValueError(
+                f"The SDXL LoRA could only be partially converted to diffusers format. converted={converted_count},"
+                f" not_converted={not_converted_count}"
+            )
 
-                if dst_key is None:
-                    raise Exception(f"Unknown sdxl lora key - {full_key}")
-                new_key = full_key.replace(src_key, dst_key)
-            except:
-                print(SDXL_UNET_COMPVIS_MAP)
-                raise
-            new_state_dict[new_key] = value
         return new_state_dict
 
     @classmethod
@@ -537,7 +571,7 @@ class LoRAModelRaw:  # (torch.nn.Module):
         state_dict = cls._group_state(state_dict)
 
         if base_model == BaseModelType.StableDiffusionXL:
-            state_dict = cls._convert_sdxl_compvis_keys(state_dict)
+            state_dict = cls._convert_sdxl_keys_to_diffusers_format(state_dict)
 
         for layer_key, values in state_dict.items():
             # lora and locon
@@ -588,6 +622,7 @@ class LoRAModelRaw:  # (torch.nn.Module):
 # code from
 # https://github.com/bmaltais/kohya_ss/blob/2accb1305979ba62f5077a23aabac23b4c37e935/networks/lora_diffusers.py#L15C1-L97C32
 def make_sdxl_unet_conversion_map():
+    """Create a dict mapping state_dict keys from Stability AI SDXL format to diffusers SDXL format."""
     unet_conversion_map_layer = []
 
     for i in range(3):  # num_blocks is 3 in sdxl
@@ -671,7 +706,6 @@ def make_sdxl_unet_conversion_map():
     return unet_conversion_map
 
 
-SDXL_UNET_COMPVIS_MAP = {
-    f"{sd}".rstrip(".").replace(".", "_"): f"{hf}".rstrip(".").replace(".", "_")
-    for sd, hf in make_sdxl_unet_conversion_map()
+SDXL_UNET_STABILITY_TO_DIFFUSERS_MAP = {
+    sd.rstrip(".").replace(".", "_"): hf.rstrip(".").replace(".", "_") for sd, hf in make_sdxl_unet_conversion_map()
 }

invokeai/backend/model_management/models/sdxl.py

@@ -1,6 +1,5 @@
 import os
 import json
-import invokeai.backend.util.logging as logger
 from enum import Enum
 from pydantic import Field
 from typing import Literal, Optional
@@ -12,6 +11,7 @@ from .base import (
     DiffusersModel,
     read_checkpoint_meta,
     classproperty,
+    InvalidModelException,
 )
 from omegaconf import OmegaConf
 
@@ -65,7 +65,7 @@ class StableDiffusionXLModel(DiffusersModel):
                 in_channels = unet_config["in_channels"]
 
             else:
-                raise Exception("Not supported stable diffusion diffusers format(possibly onnx?)")
+                raise InvalidModelException(f"{path} is not a recognized Stable Diffusion diffusers model")
 
         else:
             raise NotImplementedError(f"Unknown stable diffusion 2.* format: {model_format}")

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/stagingAreaImageSaved.ts

@@ -20,7 +20,7 @@ export const addStagingAreaImageSavedListener = () => {
         // we may need to add it to the autoadd board
         const { autoAddBoardId } = getState().gallery;
 
-        if (autoAddBoardId) {
+        if (autoAddBoardId && autoAddBoardId !== 'none') {
           await dispatch(
             imagesApi.endpoints.addImageToBoard.initiate({
               imageDTO: newImageDTO,

invokeai/frontend/web/src/features/system/store/systemSlice.ts

@@ -365,12 +365,19 @@ export const systemSlice = createSlice({
       state.statusTranslationKey = 'common.statusConnected';
       state.progressImage = null;
 
+      let errorDescription = undefined;
+
+      if (action.payload?.status === 422) {
+        errorDescription = 'Validation Error';
+      } else if (action.payload?.error) {
+        errorDescription = action.payload?.error as string;
+      }
+
       state.toastQueue.push(
         makeToast({
           title: t('toast.serverError'),
           status: 'error',
-          description:
-            action.payload?.status === 422 ? 'Validation Error' : undefined,
+          description: errorDescription,
         })
       );
     });

invokeai/frontend/web/src/services/api/thunks/session.ts

@@ -60,6 +60,9 @@ type InvokedSessionThunkConfig = {
 const isErrorWithStatus = (error: unknown): error is { status: number } =>
   isObject(error) && 'status' in error;
 
+const isErrorWithDetail = (error: unknown): error is { detail: string } =>
+  isObject(error) && 'detail' in error;
+
 /**
  * `SessionsService.invokeSession()` thunk
  */
@@ -85,7 +88,15 @@ export const sessionInvoked = createAsyncThunk<
         error: (error as any).body.detail,
       });
     }
-    return rejectWithValue({ arg, status: response.status, error });
+    if (isErrorWithDetail(error) && response.status === 403) {
+      return rejectWithValue({
+        arg,
+        status: response.status,
+        error: error.detail
+      });
+    }
+    if (error)
+      return rejectWithValue({ arg, status: response.status, error });
   }
 });
 

pyproject.toml

@@ -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",

tests/nodes/test_node_graph.py

@@ -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