awkward workaround for double-Annotated in model_record route

invokeai/app/api/routers/model_records.py

@@ -2,14 +2,13 @@
 """FastAPI route for model configuration records."""
 
 
-from hashlib import sha1
-from random import randbytes
 from typing import List, Optional
 
 from fastapi import Body, Path, Query, Response
 from fastapi.routing import APIRouter
 from pydantic import BaseModel, ConfigDict, TypeAdapter
 from starlette.exceptions import HTTPException
+from typing_extensions import Annotated
 
 from invokeai.app.services.model_records import DuplicateModelException, InvalidModelException, UnknownModelException
 from invokeai.backend.model_manager.config import AnyModelConfig, BaseModelType, ModelType
@@ -85,8 +84,9 @@ async def get_model_record(
     response_model=AnyModelConfig,
 )
 async def update_model_record(
-    key: str = Path(description="Unique key of model"),
+    key: Annotated[str, Path(description="Unique key of model")],
-    info: AnyModelConfig = Body(description="Model configuration"),
+    # info: Annotated[AnyModelConfig, Body(description="Model configuration")],
+    info: AnyModelConfig,
 ) -> AnyModelConfig:
     """Update model contents with a new config. If the model name or base fields are changed, then the model is renamed."""
     logger = ApiDependencies.invoker.services.logger
@@ -134,7 +134,7 @@ async def del_model_record(
     status_code=201,
 )
 async def add_model_record(
-    config: AnyModelConfig = Body(description="Model configuration"),
+    config: AnyModelConfig,
 ) -> AnyModelConfig:
     """
     Add a model using the configuration information appropriate for its type.

invokeai/app/invocations/latent.py

@@ -755,10 +755,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
                     denoising_end=self.denoising_end,
                 )
 
-                (
+                (result_latents, result_attention_map_saver,) = pipeline.latents_from_embeddings(
-                    result_latents,
-                    result_attention_map_saver,
-                ) = pipeline.latents_from_embeddings(
                     latents=latents,
                     timesteps=timesteps,
                     init_timestep=init_timestep,

invokeai/app/invocations/math.py

@@ -207,7 +207,7 @@ class IntegerMathInvocation(BaseInvocation):
         elif self.operation == "DIV":
             return IntegerOutput(value=int(self.a / self.b))
         elif self.operation == "EXP":
-            return IntegerOutput(value=self.a**self.b)
+            return IntegerOutput(value=self.a ** self.b)
         elif self.operation == "MOD":
             return IntegerOutput(value=self.a % self.b)
         elif self.operation == "ABS":
@@ -281,7 +281,7 @@ class FloatMathInvocation(BaseInvocation):
         elif self.operation == "DIV":
             return FloatOutput(value=self.a / self.b)
         elif self.operation == "EXP":
-            return FloatOutput(value=self.a**self.b)
+            return FloatOutput(value=self.a ** self.b)
         elif self.operation == "SQRT":
             return FloatOutput(value=np.sqrt(self.a))
         elif self.operation == "ABS":

invokeai/backend/ip_adapter/resampler.py

@@ -33,7 +33,7 @@ def reshape_tensor(x, heads):
 class PerceiverAttention(nn.Module):
     def __init__(self, *, dim, dim_head=64, heads=8):
         super().__init__()
-        self.scale = dim_head**-0.5
+        self.scale = dim_head ** -0.5
         self.dim_head = dim_head
         self.heads = heads
         inner_dim = dim_head * heads
@@ -91,7 +91,7 @@ class Resampler(nn.Module):
     ):
         super().__init__()
 
-        self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5)
+        self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim ** 0.5)
 
         self.proj_in = nn.Linear(embedding_dim, dim)
 

invokeai/backend/model_management/memory_snapshot.py

@@ -6,7 +6,7 @@ import torch
 
 from invokeai.backend.model_management.libc_util import LibcUtil, Struct_mallinfo2
 
-GB = 2**30  # 1 GB
+GB = 2 ** 30  # 1 GB
 
 
 class MemorySnapshot:

invokeai/backend/model_management/model_cache.py

@@ -49,7 +49,7 @@ DEFAULT_MAX_VRAM_CACHE_SIZE = 2.75
 # actual size of a gig
 GIG = 1073741824
 # Size of a MB in bytes.
-MB = 2**20
+MB = 2 ** 20
 
 
 @dataclass

invokeai/backend/model_manager/config.py

@@ -22,6 +22,7 @@ Validation errors will raise an InvalidModelConfigException error.
 from enum import Enum
 from typing import Literal, Optional, Type, Union
 
+from fastapi import Body
 from pydantic import BaseModel, ConfigDict, Field, TypeAdapter
 from typing_extensions import Annotated
 
@@ -268,7 +269,7 @@ AnyModelConfig = Annotated[
         CLIPVisionDiffusersConfig,
         T2IConfig,
     ],
-    Field(discriminator="type"),
+    Body(discriminator="type"),
 ]
 
 AnyModelConfigValidator = TypeAdapter(AnyModelConfig)

invokeai/backend/stable_diffusion/diffusion/cross_attention_control.py

@@ -261,7 +261,7 @@ class InvokeAICrossAttentionMixin:
         if q.shape[1] <= 4096:  # (512x512) max q.shape[1]: 4096
             return self.einsum_lowest_level(q, k, v, None, None, None)
         else:
-            slice_size = math.floor(2**30 / (q.shape[0] * q.shape[1]))
+            slice_size = math.floor(2 ** 30 / (q.shape[0] * q.shape[1]))
             return self.einsum_op_slice_dim1(q, k, v, slice_size)
 
     def einsum_op_mps_v2(self, q, k, v):

invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py

@@ -175,10 +175,7 @@ class InvokeAIDiffuserComponent:
                                 dim=0,
                             ),
                         }
-                    (
+                    (encoder_hidden_states, encoder_attention_mask,) = self._concat_conditionings_for_batch(
-                        encoder_hidden_states,
-                        encoder_attention_mask,
-                    ) = self._concat_conditionings_for_batch(
                         conditioning_data.unconditioned_embeddings.embeds,
                         conditioning_data.text_embeddings.embeds,
                     )
@@ -240,10 +237,7 @@ class InvokeAIDiffuserComponent:
         wants_cross_attention_control = len(cross_attention_control_types_to_do) > 0
 
         if wants_cross_attention_control:
-            (
+            (unconditioned_next_x, conditioned_next_x,) = self._apply_cross_attention_controlled_conditioning(
-                unconditioned_next_x,
-                conditioned_next_x,
-            ) = self._apply_cross_attention_controlled_conditioning(
                 sample,
                 timestep,
                 conditioning_data,
@@ -251,20 +245,14 @@ class InvokeAIDiffuserComponent:
                 **kwargs,
             )
         elif self.sequential_guidance:
-            (
+            (unconditioned_next_x, conditioned_next_x,) = self._apply_standard_conditioning_sequentially(
-                unconditioned_next_x,
-                conditioned_next_x,
-            ) = self._apply_standard_conditioning_sequentially(
                 sample,
                 timestep,
                 conditioning_data,
                 **kwargs,
             )
         else:
-            (
+            (unconditioned_next_x, conditioned_next_x,) = self._apply_standard_conditioning(
-                unconditioned_next_x,
-                conditioned_next_x,
-            ) = self._apply_standard_conditioning(
                 sample,
                 timestep,
                 conditioning_data,

invokeai/backend/training/textual_inversion_training.py

@@ -470,10 +470,7 @@ class TextualInversionDataset(Dataset):
 
         if self.center_crop:
             crop = min(img.shape[0], img.shape[1])
-            (
+            (h, w,) = (
-                h,
-                w,
-            ) = (
                 img.shape[0],
                 img.shape[1],
             )

invokeai/backend/util/mps_fixes.py

@@ -203,7 +203,7 @@ class ChunkedSlicedAttnProcessor:
         if attn.upcast_attention:
             out_item_size = 4
 
-        chunk_size = 2**29
+        chunk_size = 2 ** 29
 
         out_size = query.shape[1] * key.shape[1] * out_item_size
         chunks_count = min(query.shape[1], math.ceil((out_size - 1) / chunk_size))

invokeai/backend/util/util.py

@@ -210,7 +210,7 @@ def parallel_data_prefetch(
         return gather_res
 
 
-def rand_perlin_2d(shape, res, device, fade=lambda t: 6 * t**5 - 15 * t**4 + 10 * t**3):
+def rand_perlin_2d(shape, res, device, fade=lambda t: 6 * t ** 5 - 15 * t ** 4 + 10 * t ** 3):
     delta = (res[0] / shape[0], res[1] / shape[1])
     d = (shape[0] // res[0], shape[1] // res[1])
 

tests/app/services/model_records/test_model_records_sql.py

@@ -16,6 +16,7 @@ from invokeai.app.services.model_records import (
 from invokeai.app.services.shared.sqlite import SqliteDatabase
 from invokeai.backend.model_manager.config import (
     BaseModelType,
+    MainCheckpointConfig,
     MainDiffusersConfig,
     ModelType,
     TextualInversionConfig,
@@ -57,6 +58,7 @@ def test_add(store: ModelRecordServiceBase):
     store.add_model("key1", raw)
     config1 = store.get_model("key1")
     assert config1 is not None
+    assert type(config1) == MainCheckpointConfig
     assert config1.base == BaseModelType("sd-1")
     assert config1.name == "model1"
     assert config1.original_hash == "111222333444"