fix: cleanup across various ip adapter files

invokeai/app/invocations/ip_adapter.py

@@ -91,7 +91,7 @@ class IPAdapterInvocation(BaseInvocation):
         image_encoder_model_id = (
             ip_adapter_info.image_encoder_model_id
             if isinstance(ip_adapter_info, IPAdapterDiffusersConfig)
-            else "InvokeAI/ip_adapter_sd_image_encoder"
+            else "ip_adapter_sd_image_encoder"
         )
         image_encoder_model_name = image_encoder_model_id.split("/")[-1].strip()
         image_encoder_model = self._get_image_encoder(context, image_encoder_model_name)

invokeai/app/invocations/latent.py

@@ -14,10 +14,12 @@ from diffusers import AutoencoderKL, AutoencoderTiny
 from diffusers.configuration_utils import ConfigMixin
 from diffusers.image_processor import VaeImageProcessor
 from diffusers.models.adapter import T2IAdapter
-from diffusers.models.attention_processor import (AttnProcessor2_0,
-                                                  LoRAAttnProcessor2_0,
-                                                  LoRAXFormersAttnProcessor,
-                                                  XFormersAttnProcessor)
+from diffusers.models.attention_processor import (
+    AttnProcessor2_0,
+    LoRAAttnProcessor2_0,
+    LoRAXFormersAttnProcessor,
+    XFormersAttnProcessor,
+)
 from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
 from diffusers.schedulers import DPMSolverSDEScheduler
 from diffusers.schedulers import SchedulerMixin as Scheduler
@@ -26,17 +28,22 @@ from pydantic import field_validator
 from torchvision.transforms.functional import resize as tv_resize
 from transformers import CLIPVisionModelWithProjection
 
-from invokeai.app.invocations.constants import (LATENT_SCALE_FACTOR,
-                                                SCHEDULER_NAME_VALUES)
-from invokeai.app.invocations.fields import (ConditioningField,
-                                             DenoiseMaskField,
-                                             FieldDescriptions, ImageField,
-                                             Input, InputField, LatentsField,
-                                             OutputField, UIType, WithBoard,
-                                             WithMetadata)
+from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR, SCHEDULER_NAME_VALUES
+from invokeai.app.invocations.fields import (
+    ConditioningField,
+    DenoiseMaskField,
+    FieldDescriptions,
+    ImageField,
+    Input,
+    InputField,
+    LatentsField,
+    OutputField,
+    UIType,
+    WithBoard,
+    WithMetadata,
+)
 from invokeai.app.invocations.ip_adapter import IPAdapterField
-from invokeai.app.invocations.primitives import (DenoiseMaskOutput,
-                                                 ImageOutput, LatentsOutput)
+from invokeai.app.invocations.primitives import DenoiseMaskOutput, ImageOutput, LatentsOutput
 from invokeai.app.invocations.t2i_adapter import T2IAdapterField
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.util.controlnet_utils import prepare_control_image
@@ -44,19 +51,20 @@ from invokeai.backend.ip_adapter.ip_adapter import IPAdapter, IPAdapterPlus
 from invokeai.backend.lora import LoRAModelRaw
 from invokeai.backend.model_manager import BaseModelType, LoadedModel
 from invokeai.backend.model_patcher import ModelPatcher
-from invokeai.backend.stable_diffusion import (PipelineIntermediateState,
-                                               set_seamless)
-from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
-    ConditioningData, IPAdapterConditioningInfo)
+from invokeai.backend.stable_diffusion import PipelineIntermediateState, set_seamless
+from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningData, IPAdapterConditioningInfo
 from invokeai.backend.util.silence_warnings import SilenceWarnings
 
 from ...backend.stable_diffusion.diffusers_pipeline import (
-    ControlNetData, IPAdapterData, StableDiffusionGeneratorPipeline,
-    T2IAdapterData, image_resized_to_grid_as_tensor)
+    ControlNetData,
+    IPAdapterData,
+    StableDiffusionGeneratorPipeline,
+    T2IAdapterData,
+    image_resized_to_grid_as_tensor,
+)
 from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP
 from ...backend.util.devices import choose_precision, choose_torch_device
-from .baseinvocation import (BaseInvocation, BaseInvocationOutput, invocation,
-                             invocation_output)
+from .baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
 from .controlnet_image_processors import ControlField
 from .model import ModelIdentifierField, UNetField, VAEField
 

invokeai/backend/model_manager/probe.py

@@ -9,23 +9,16 @@ from picklescan.scanner import scan_file_path
 
 import invokeai.backend.util.logging as logger
 from invokeai.app.util.misc import uuid_string
-from invokeai.backend.model_hash.model_hash import HASHING_ALGORITHMS, ModelHash
+from invokeai.backend.model_hash.model_hash import (HASHING_ALGORITHMS,
+                                                    ModelHash)
 from invokeai.backend.util.util import SilenceWarnings
 
-from .config import (
-    AnyModelConfig,
-    BaseModelType,
-    ControlAdapterDefaultSettings,
-    InvalidModelConfigException,
-    MainModelDefaultSettings,
-    ModelConfigFactory,
-    ModelFormat,
-    ModelRepoVariant,
-    ModelSourceType,
-    ModelType,
-    ModelVariantType,
-    SchedulerPredictionType,
-)
+from .config import (AnyModelConfig, BaseModelType,
+                     ControlAdapterDefaultSettings,
+                     InvalidModelConfigException, MainModelDefaultSettings,
+                     ModelConfigFactory, ModelFormat, ModelRepoVariant,
+                     ModelSourceType, ModelType, ModelVariantType,
+                     SchedulerPredictionType)
 from .util.model_util import lora_token_vector_length, read_checkpoint_meta
 
 CkptType = Dict[str | int, Any]
@@ -536,7 +529,6 @@ class IPAdapterCheckpointProbe(CheckpointProbeBase):
             if not key.startswith(("image_proj.", "ip_adapter.")):
                 continue
             cross_attention_dim = checkpoint["ip_adapter.1.to_k_ip.weight"].shape[-1]
-            print(cross_attention_dim)
             if cross_attention_dim == 768:
                 return BaseModelType.StableDiffusion1
             elif cross_attention_dim == 1024:

invokeai/frontend/web/public/locales/en.json

@@ -655,6 +655,7 @@
         "install": "Install",
         "installAll": "Install All",
         "installRepo": "Install Repo",
+        "ipAdapters": "IP Adapters",
         "load": "Load",
         "localOnly": "local only",
         "manual": "Manual",