Expose seamless variables to node

## What type of PR is this? (check all applicable)

- [ ] Refactor
- [x] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission


## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:

      
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No


## Description (edit by @blessedcoolant , @RyanJDick )

This PR adds support for IP-Adapters (a technique for image-based
prompts) in Invoke AI. Currently only available in the Node UI.

IP-Adapter Paper: [IP-Adapter: Text Compatible Image Prompt Adapter for
Text-to-Image Diffusion Models](https://arxiv.org/abs/2308.06721)
IP-Adapter reference code: https://github.com/tencent-ailab/IP-Adapter

On order to test, install the following models via the InvokeAI UI:

Image Encoders:

[InvokeAI/ip_adapter_sd_image_encoder](https://huggingface.co/InvokeAI/ip_adapter_sd_image_encoder)

[InvokeAI/ip_adapter_sdxl_image_encoder](https://huggingface.co/InvokeAI/ip_adapter_sdxl_image_encoder)

IP-Adapters:

[InvokeAI/ip_adapter_sd15](https://huggingface.co/InvokeAI/ip_adapter_sd15)

[InvokeAI/ip_adapter_plus_sd15](https://huggingface.co/InvokeAI/ip_adapter_plus_sd15)

[InvokeAI/ip_adapter_plus_face_sd15](https://huggingface.co/InvokeAI/ip_adapter_plus_face_sd15)

[InvokeAI/ip_adapter_sdxl](https://huggingface.co/InvokeAI/ip_adapter_sdxl)

Old instructions (for reference only):

> In order to test, you need to download and place the following models
in your InvokeAI models directory.
> 
> - SD 1.5 - https://huggingface.co/h94/IP-Adapter/tree/main/models -->
Download the models and the `image_encoder` folder to
`models/core/ip_adapters/sd-1`
> - SDXL - https://huggingface.co/h94/IP-Adapter/tree/main/sdxl_models
-Download the models and the `image_encoder` folder to
`models/core/ip_adapaters/sdxl`
> 
> This is only temporary. This needs to be handled differently. I
outlined them here.
https://github.com/invoke-ai/InvokeAI/pull/4429#issuecomment-1705776570

## Examples using this PR

### Image variations, no text prompt
Leftmost image in each row is original image used for input to
IP-Adapter. The other rows are example outputs with different seeds,
other parameters identical.

![ipadapter_invokai_example1](https://github.com/invoke-ai/InvokeAI/assets/303100/cae18b97-14a9-4499-8d87-f07faa8ad13a)







## Related Tickets & Documents

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- Related Issue #
- Closes #

## QA Instructions, Screenshots, Recordings

<!-- 
Please provide steps on how to test changes, any hardware or 
software specifications as well as any other pertinent information. 
-->

## Added/updated tests?

- [ ] Yes
- [ ] No : _please replace this line with details on why tests
      have not been included_

## [optional] Are there any post deployment tasks we need to perform?

invokeai/app/invocations/baseinvocation.py

@@ -67,6 +67,7 @@ class FieldDescriptions:
     width = "Width of output (px)"
     height = "Height of output (px)"
     control = "ControlNet(s) to apply"
+    ip_adapter = "IP-Adapter to apply"
     denoised_latents = "Denoised latents tensor"
     latents = "Latents tensor"
     strength = "Strength of denoising (proportional to steps)"
@@ -155,6 +156,7 @@ class UIType(str, Enum):
     VaeModel = "VaeModelField"
     LoRAModel = "LoRAModelField"
     ControlNetModel = "ControlNetModelField"
+    IPAdapterModel = "IPAdapterModelField"
     UNet = "UNetField"
     Vae = "VaeField"
     CLIP = "ClipField"

invokeai/app/invocations/compel.py

@@ -7,14 +7,14 @@ from compel import Compel, ReturnedEmbeddingsType
 from compel.prompt_parser import Blend, Conjunction, CrossAttentionControlSubstitute, FlattenedPrompt, Fragment
 
 from invokeai.app.invocations.primitives import ConditioningField, ConditioningOutput
-from invokeai.backend.stable_diffusion.diffusion.shared_invokeai_diffusion import (
+from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
     BasicConditioningInfo,
+    ExtraConditioningInfo,
     SDXLConditioningInfo,
 )
 
 from ...backend.model_management.lora import ModelPatcher
 from ...backend.model_management.models import ModelNotFoundException, ModelType
-from ...backend.stable_diffusion.diffusion import InvokeAIDiffuserComponent
 from ...backend.util.devices import torch_dtype
 from .baseinvocation import (
     BaseInvocation,
@@ -99,14 +99,15 @@ class CompelInvocation(BaseInvocation):
                 # print(traceback.format_exc())
                 print(f'Warn: trigger: "{trigger}" not found')
 
-        with ModelPatcher.apply_lora_text_encoder(
-            text_encoder_info.context.model, _lora_loader()
-        ), ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
-            tokenizer,
-            ti_manager,
-        ), ModelPatcher.apply_clip_skip(
-            text_encoder_info.context.model, self.clip.skipped_layers
-        ), text_encoder_info as text_encoder:
+        with (
+            ModelPatcher.apply_lora_text_encoder(text_encoder_info.context.model, _lora_loader()),
+            ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
+                tokenizer,
+                ti_manager,
+            ),
+            ModelPatcher.apply_clip_skip(text_encoder_info.context.model, self.clip.skipped_layers),
+            text_encoder_info as text_encoder,
+        ):
             compel = Compel(
                 tokenizer=tokenizer,
                 text_encoder=text_encoder,
@@ -122,7 +123,7 @@ class CompelInvocation(BaseInvocation):
 
             c, options = compel.build_conditioning_tensor_for_conjunction(conjunction)
 
-            ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(
+            ec = ExtraConditioningInfo(
                 tokens_count_including_eos_bos=get_max_token_count(tokenizer, conjunction),
                 cross_attention_control_args=options.get("cross_attention_control", None),
             )
@@ -213,14 +214,15 @@ class SDXLPromptInvocationBase:
                 # print(traceback.format_exc())
                 print(f'Warn: trigger: "{trigger}" not found')
 
-        with ModelPatcher.apply_lora(
-            text_encoder_info.context.model, _lora_loader(), lora_prefix
-        ), ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
-            tokenizer,
-            ti_manager,
-        ), ModelPatcher.apply_clip_skip(
-            text_encoder_info.context.model, clip_field.skipped_layers
-        ), text_encoder_info as text_encoder:
+        with (
+            ModelPatcher.apply_lora(text_encoder_info.context.model, _lora_loader(), lora_prefix),
+            ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
+                tokenizer,
+                ti_manager,
+            ),
+            ModelPatcher.apply_clip_skip(text_encoder_info.context.model, clip_field.skipped_layers),
+            text_encoder_info as text_encoder,
+        ):
             compel = Compel(
                 tokenizer=tokenizer,
                 text_encoder=text_encoder,
@@ -244,7 +246,7 @@ class SDXLPromptInvocationBase:
             else:
                 c_pooled = None
 
-            ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(
+            ec = ExtraConditioningInfo(
                 tokens_count_including_eos_bos=get_max_token_count(tokenizer, conjunction),
                 cross_attention_control_args=options.get("cross_attention_control", None),
             )
@@ -436,9 +438,11 @@ def get_tokens_for_prompt_object(tokenizer, parsed_prompt: FlattenedPrompt, trun
         raise ValueError("Blend is not supported here - you need to get tokens for each of its .children")
 
     text_fragments = [
-        x.text
-        if type(x) is Fragment
-        else (" ".join([f.text for f in x.original]) if type(x) is CrossAttentionControlSubstitute else str(x))
+        (
+            x.text
+            if type(x) is Fragment
+            else (" ".join([f.text for f in x.original]) if type(x) is CrossAttentionControlSubstitute else str(x))
+        )
         for x in parsed_prompt.children
     ]
     text = " ".join(text_fragments)

invokeai/app/invocations/ip_adapter.py

@@ -0,0 +1,105 @@
+import os
+from builtins import float
+from typing import List, Union
+
+from pydantic import BaseModel, Field
+
+from invokeai.app.invocations.baseinvocation import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    FieldDescriptions,
+    Input,
+    InputField,
+    InvocationContext,
+    OutputField,
+    UIType,
+    invocation,
+    invocation_output,
+)
+from invokeai.app.invocations.primitives import ImageField
+from invokeai.backend.model_management.models.base import BaseModelType, ModelType
+from invokeai.backend.model_management.models.ip_adapter import get_ip_adapter_image_encoder_model_id
+
+
+class IPAdapterModelField(BaseModel):
+    model_name: str = Field(description="Name of the IP-Adapter model")
+    base_model: BaseModelType = Field(description="Base model")
+
+
+class CLIPVisionModelField(BaseModel):
+    model_name: str = Field(description="Name of the CLIP Vision image encoder model")
+    base_model: BaseModelType = Field(description="Base model (usually 'Any')")
+
+
+class IPAdapterField(BaseModel):
+    image: ImageField = Field(description="The IP-Adapter image prompt.")
+    ip_adapter_model: IPAdapterModelField = Field(description="The IP-Adapter model to use.")
+    image_encoder_model: CLIPVisionModelField = Field(description="The name of the CLIP image encoder model.")
+    weight: Union[float, List[float]] = Field(default=1, description="The weight given to the ControlNet")
+    # weight: float = Field(default=1.0, ge=0, description="The weight of the IP-Adapter.")
+    begin_step_percent: float = Field(
+        default=0, ge=0, le=1, description="When the IP-Adapter is first applied (% of total steps)"
+    )
+    end_step_percent: float = Field(
+        default=1, ge=0, le=1, description="When the IP-Adapter is last applied (% of total steps)"
+    )
+
+
+@invocation_output("ip_adapter_output")
+class IPAdapterOutput(BaseInvocationOutput):
+    # Outputs
+    ip_adapter: IPAdapterField = OutputField(description=FieldDescriptions.ip_adapter, title="IP-Adapter")
+
+
+@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.0.0")
+class IPAdapterInvocation(BaseInvocation):
+    """Collects IP-Adapter info to pass to other nodes."""
+
+    # Inputs
+    image: ImageField = InputField(description="The IP-Adapter image prompt.")
+    ip_adapter_model: IPAdapterModelField = InputField(
+        description="The IP-Adapter model.",
+        title="IP-Adapter Model",
+        input=Input.Direct,
+    )
+
+    # weight: float = InputField(default=1.0, description="The weight of the IP-Adapter.", ui_type=UIType.Float)
+    weight: Union[float, List[float]] = InputField(
+        default=1, ge=0, description="The weight given to the IP-Adapter", ui_type=UIType.Float, title="Weight"
+    )
+
+    begin_step_percent: float = InputField(
+        default=0, ge=-1, le=2, description="When the IP-Adapter is first applied (% of total steps)"
+    )
+    end_step_percent: float = InputField(
+        default=1, ge=0, le=1, description="When the IP-Adapter is last applied (% of total steps)"
+    )
+
+    def invoke(self, context: InvocationContext) -> IPAdapterOutput:
+        # Lookup the CLIP Vision encoder that is intended to be used with the IP-Adapter model.
+        ip_adapter_info = context.services.model_manager.model_info(
+            self.ip_adapter_model.model_name, self.ip_adapter_model.base_model, ModelType.IPAdapter
+        )
+        # HACK(ryand): This is bad for a couple of reasons: 1) we are bypassing the model manager to read the model
+        # directly, and 2) we are reading from disk every time this invocation is called without caching the result.
+        # A better solution would be to store the image encoder model reference in the IP-Adapter model info, but this
+        # is currently messy due to differences between how the model info is generated when installing a model from
+        # disk vs. downloading the model.
+        image_encoder_model_id = get_ip_adapter_image_encoder_model_id(
+            os.path.join(context.services.configuration.get_config().models_path, ip_adapter_info["path"])
+        )
+        image_encoder_model_name = image_encoder_model_id.split("/")[-1].strip()
+        image_encoder_model = CLIPVisionModelField(
+            model_name=image_encoder_model_name,
+            base_model=BaseModelType.Any,
+        )
+        return IPAdapterOutput(
+            ip_adapter=IPAdapterField(
+                image=self.image,
+                ip_adapter_model=self.ip_adapter_model,
+                image_encoder_model=image_encoder_model,
+                weight=self.weight,
+                begin_step_percent=self.begin_step_percent,
+                end_step_percent=self.end_step_percent,
+            ),
+        )

invokeai/app/invocations/latent.py

@@ -8,6 +8,7 @@ import numpy as np
 import torch
 import torchvision.transforms as T
 from diffusers.image_processor import VaeImageProcessor
+from diffusers.models import UNet2DConditionModel
 from diffusers.models.attention_processor import (
     AttnProcessor2_0,
     LoRAAttnProcessor2_0,
@@ -19,6 +20,7 @@ from diffusers.schedulers import SchedulerMixin as Scheduler
 from pydantic import validator
 from torchvision.transforms.functional import resize as tv_resize
 
+from invokeai.app.invocations.ip_adapter import IPAdapterField
 from invokeai.app.invocations.metadata import CoreMetadata
 from invokeai.app.invocations.primitives import (
     DenoiseMaskField,
@@ -31,15 +33,17 @@ from invokeai.app.invocations.primitives import (
 )
 from invokeai.app.util.controlnet_utils import prepare_control_image
 from invokeai.app.util.step_callback import stable_diffusion_step_callback
+from invokeai.backend.ip_adapter.ip_adapter import IPAdapter, IPAdapterPlus
 from invokeai.backend.model_management.models import ModelType, SilenceWarnings
+from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningData, IPAdapterConditioningInfo
 
 from ...backend.model_management.lora import ModelPatcher
 from ...backend.model_management.models import BaseModelType
 from ...backend.model_management.seamless import set_seamless
 from ...backend.stable_diffusion import PipelineIntermediateState
 from ...backend.stable_diffusion.diffusers_pipeline import (
-    ConditioningData,
     ControlNetData,
+    IPAdapterData,
     StableDiffusionGeneratorPipeline,
     image_resized_to_grid_as_tensor,
 )
@@ -68,7 +72,6 @@ if choose_torch_device() == torch.device("mps"):
 
 DEFAULT_PRECISION = choose_precision(choose_torch_device())
 
-
 SAMPLER_NAME_VALUES = Literal[tuple(list(SCHEDULER_MAP.keys()))]
 
 
@@ -191,7 +194,7 @@ def get_scheduler(
     title="Denoise Latents",
     tags=["latents", "denoise", "txt2img", "t2i", "t2l", "img2img", "i2i", "l2l"],
     category="latents",
-    version="1.0.0",
+    version="1.1.0",
 )
 class DenoiseLatentsInvocation(BaseInvocation):
     """Denoises noisy latents to decodable images"""
@@ -219,9 +222,12 @@ class DenoiseLatentsInvocation(BaseInvocation):
         input=Input.Connection,
         ui_order=5,
     )
+    ip_adapter: Optional[IPAdapterField] = InputField(
+        description=FieldDescriptions.ip_adapter, title="IP-Adapter", default=None, input=Input.Connection, ui_order=6
+    )
     latents: Optional[LatentsField] = InputField(description=FieldDescriptions.latents, input=Input.Connection)
     denoise_mask: Optional[DenoiseMaskField] = InputField(
-        default=None, description=FieldDescriptions.mask, input=Input.Connection, ui_order=6
+        default=None, description=FieldDescriptions.mask, input=Input.Connection, ui_order=7
     )
 
     @validator("cfg_scale")
@@ -323,8 +329,6 @@ class DenoiseLatentsInvocation(BaseInvocation):
     def prep_control_data(
         self,
         context: InvocationContext,
-        # really only need model for dtype and device
-        model: StableDiffusionGeneratorPipeline,
         control_input: Union[ControlField, List[ControlField]],
         latents_shape: List[int],
         exit_stack: ExitStack,
@@ -344,57 +348,107 @@ class DenoiseLatentsInvocation(BaseInvocation):
         else:
             control_list = None
         if control_list is None:
-            control_data = None
-            # from above handling, any control that is not None should now be of type list[ControlField]
-        else:
-            # FIXME: add checks to skip entry if model or image is None
-            #        and if weight is None, populate with default 1.0?
-            control_data = []
-            control_models = []
-            for control_info in control_list:
-                control_model = exit_stack.enter_context(
-                    context.services.model_manager.get_model(
-                        model_name=control_info.control_model.model_name,
-                        model_type=ModelType.ControlNet,
-                        base_model=control_info.control_model.base_model,
-                        context=context,
-                    )
-                )
+            return None
+        # After above handling, any control that is not None should now be of type list[ControlField].
 
-                control_models.append(control_model)
-                control_image_field = control_info.image
-                input_image = context.services.images.get_pil_image(control_image_field.image_name)
-                # self.image.image_type, self.image.image_name
-                # FIXME: still need to test with different widths, heights, devices, dtypes
-                #        and add in batch_size, num_images_per_prompt?
-                #        and do real check for classifier_free_guidance?
-                # prepare_control_image should return torch.Tensor of shape(batch_size, 3, height, width)
-                control_image = prepare_control_image(
-                    image=input_image,
-                    do_classifier_free_guidance=do_classifier_free_guidance,
-                    width=control_width_resize,
-                    height=control_height_resize,
-                    # batch_size=batch_size * num_images_per_prompt,
-                    # num_images_per_prompt=num_images_per_prompt,
-                    device=control_model.device,
-                    dtype=control_model.dtype,
-                    control_mode=control_info.control_mode,
-                    resize_mode=control_info.resize_mode,
+        # FIXME: add checks to skip entry if model or image is None
+        #        and if weight is None, populate with default 1.0?
+        controlnet_data = []
+        for control_info in control_list:
+            control_model = exit_stack.enter_context(
+                context.services.model_manager.get_model(
+                    model_name=control_info.control_model.model_name,
+                    model_type=ModelType.ControlNet,
+                    base_model=control_info.control_model.base_model,
+                    context=context,
                 )
-                control_item = ControlNetData(
-                    model=control_model,
-                    image_tensor=control_image,
-                    weight=control_info.control_weight,
-                    begin_step_percent=control_info.begin_step_percent,
-                    end_step_percent=control_info.end_step_percent,
-                    control_mode=control_info.control_mode,
-                    # any resizing needed should currently be happening in prepare_control_image(),
-                    #    but adding resize_mode to ControlNetData in case needed in the future
-                    resize_mode=control_info.resize_mode,
-                )
-                control_data.append(control_item)
-                # MultiControlNetModel has been refactored out, just need list[ControlNetData]
-        return control_data
+            )
+
+            # control_models.append(control_model)
+            control_image_field = control_info.image
+            input_image = context.services.images.get_pil_image(control_image_field.image_name)
+            # self.image.image_type, self.image.image_name
+            # FIXME: still need to test with different widths, heights, devices, dtypes
+            #        and add in batch_size, num_images_per_prompt?
+            #        and do real check for classifier_free_guidance?
+            # prepare_control_image should return torch.Tensor of shape(batch_size, 3, height, width)
+            control_image = prepare_control_image(
+                image=input_image,
+                do_classifier_free_guidance=do_classifier_free_guidance,
+                width=control_width_resize,
+                height=control_height_resize,
+                # batch_size=batch_size * num_images_per_prompt,
+                # num_images_per_prompt=num_images_per_prompt,
+                device=control_model.device,
+                dtype=control_model.dtype,
+                control_mode=control_info.control_mode,
+                resize_mode=control_info.resize_mode,
+            )
+            control_item = ControlNetData(
+                model=control_model,  # model object
+                image_tensor=control_image,
+                weight=control_info.control_weight,
+                begin_step_percent=control_info.begin_step_percent,
+                end_step_percent=control_info.end_step_percent,
+                control_mode=control_info.control_mode,
+                # any resizing needed should currently be happening in prepare_control_image(),
+                #    but adding resize_mode to ControlNetData in case needed in the future
+                resize_mode=control_info.resize_mode,
+            )
+            controlnet_data.append(control_item)
+            # MultiControlNetModel has been refactored out, just need list[ControlNetData]
+
+        return controlnet_data
+
+    def prep_ip_adapter_data(
+        self,
+        context: InvocationContext,
+        ip_adapter: Optional[IPAdapterField],
+        conditioning_data: ConditioningData,
+        unet: UNet2DConditionModel,
+        exit_stack: ExitStack,
+    ) -> Optional[IPAdapterData]:
+        """If IP-Adapter is enabled, then this function loads the requisite models, and adds the image prompt embeddings
+        to the `conditioning_data` (in-place).
+        """
+        if ip_adapter is None:
+            return None
+
+        image_encoder_model_info = context.services.model_manager.get_model(
+            model_name=ip_adapter.image_encoder_model.model_name,
+            model_type=ModelType.CLIPVision,
+            base_model=ip_adapter.image_encoder_model.base_model,
+            context=context,
+        )
+
+        ip_adapter_model: Union[IPAdapter, IPAdapterPlus] = exit_stack.enter_context(
+            context.services.model_manager.get_model(
+                model_name=ip_adapter.ip_adapter_model.model_name,
+                model_type=ModelType.IPAdapter,
+                base_model=ip_adapter.ip_adapter_model.base_model,
+                context=context,
+            )
+        )
+
+        input_image = context.services.images.get_pil_image(ip_adapter.image.image_name)
+
+        # TODO(ryand): With some effort, the step of running the CLIP Vision encoder could be done before any other
+        # models are needed in memory. This would help to reduce peak memory utilization in low-memory environments.
+        with image_encoder_model_info as image_encoder_model:
+            # Get image embeddings from CLIP and ImageProjModel.
+            image_prompt_embeds, uncond_image_prompt_embeds = ip_adapter_model.get_image_embeds(
+                input_image, image_encoder_model
+            )
+            conditioning_data.ip_adapter_conditioning = IPAdapterConditioningInfo(
+                image_prompt_embeds, uncond_image_prompt_embeds
+            )
+
+        return IPAdapterData(
+            ip_adapter_model=ip_adapter_model,
+            weight=ip_adapter.weight,
+            begin_step_percent=ip_adapter.begin_step_percent,
+            end_step_percent=ip_adapter.end_step_percent,
+        )
 
     # original idea by https://github.com/AmericanPresidentJimmyCarter
     # TODO: research more for second order schedulers timesteps
@@ -488,9 +542,12 @@ class DenoiseLatentsInvocation(BaseInvocation):
                 **self.unet.unet.dict(),
                 context=context,
             )
-            with ExitStack() as exit_stack, ModelPatcher.apply_lora_unet(
-                unet_info.context.model, _lora_loader()
-            ), set_seamless(unet_info.context.model, self.unet.seamless_axes), unet_info as unet:
+            with (
+                ExitStack() as exit_stack,
+                ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()),
+                set_seamless(unet_info.context.model, **self.unet.seamless.dict()),
+                unet_info as unet,
+            ):
                 latents = latents.to(device=unet.device, dtype=unet.dtype)
                 if noise is not None:
                     noise = noise.to(device=unet.device, dtype=unet.dtype)
@@ -509,8 +566,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
                 pipeline = self.create_pipeline(unet, scheduler)
                 conditioning_data = self.get_conditioning_data(context, scheduler, unet, seed)
 
-                control_data = self.prep_control_data(
-                    model=pipeline,
+                controlnet_data = self.prep_control_data(
                     context=context,
                     control_input=self.control,
                     latents_shape=latents.shape,
@@ -519,6 +575,14 @@ class DenoiseLatentsInvocation(BaseInvocation):
                     exit_stack=exit_stack,
                 )
 
+                ip_adapter_data = self.prep_ip_adapter_data(
+                    context=context,
+                    ip_adapter=self.ip_adapter,
+                    conditioning_data=conditioning_data,
+                    unet=unet,
+                    exit_stack=exit_stack,
+                )
+
                 num_inference_steps, timesteps, init_timestep = self.init_scheduler(
                     scheduler,
                     device=unet.device,
@@ -537,7 +601,8 @@ class DenoiseLatentsInvocation(BaseInvocation):
                     masked_latents=masked_latents,
                     num_inference_steps=num_inference_steps,
                     conditioning_data=conditioning_data,
-                    control_data=control_data,  # list[ControlNetData]
+                    control_data=controlnet_data,  # list[ControlNetData],
+                    ip_adapter_data=ip_adapter_data,  # IPAdapterData,
                     callback=step_callback,
                 )
 
@@ -583,7 +648,7 @@ class LatentsToImageInvocation(BaseInvocation):
             context=context,
         )
 
-        with set_seamless(vae_info.context.model, self.vae.seamless_axes), vae_info as vae:
+        with set_seamless(vae_info.context.model, **self.vae.seamless.dict()), vae_info as vae:
             latents = latents.to(vae.device)
             if self.fp32:
                 vae.to(dtype=torch.float32)

invokeai/app/invocations/model.py

@@ -18,6 +18,13 @@ from .baseinvocation import (
 )
 
 
+class SeamlessSettings(BaseModel):
+    axes: List[str] = Field(description="Axes('x' and 'y') to which apply seamless")
+    skipped_layers: int = Field(description="How much down layers skip when applying seamless")
+    skip_second_resnet: bool = Field(description="Skip or not second resnet in down blocks when applying seamless")
+    skip_conv2: bool = Field(description="Skip or not conv2 in down blocks when applying seamless")
+
+
 class ModelInfo(BaseModel):
     model_name: str = Field(description="Info to load submodel")
     base_model: BaseModelType = Field(description="Base model")
@@ -33,7 +40,7 @@ class UNetField(BaseModel):
     unet: ModelInfo = Field(description="Info to load unet submodel")
     scheduler: ModelInfo = Field(description="Info to load scheduler submodel")
     loras: List[LoraInfo] = Field(description="Loras to apply on model loading")
-    seamless_axes: List[str] = Field(default_factory=list, description='Axes("x" and "y") to which apply seamless')
+    seamless: Optional[SeamlessSettings] = Field(default=None, description="Seamless settings applied to model")
 
 
 class ClipField(BaseModel):
@@ -46,7 +53,7 @@ class ClipField(BaseModel):
 class VaeField(BaseModel):
     # TODO: better naming?
     vae: ModelInfo = Field(description="Info to load vae submodel")
-    seamless_axes: List[str] = Field(default_factory=list, description='Axes("x" and "y") to which apply seamless')
+    seamless: Optional[SeamlessSettings] = Field(default=None, description="Seamless settings applied to model")
 
 
 @invocation_output("model_loader_output")
@@ -388,6 +395,11 @@ class SeamlessModeInvocation(BaseInvocation):
     )
     seamless_y: bool = InputField(default=True, input=Input.Any, description="Specify whether Y axis is seamless")
     seamless_x: bool = InputField(default=True, input=Input.Any, description="Specify whether X axis is seamless")
+    skipped_layers: int = InputField(default=0, input=Input.Any, description="How much model's down layers to skip")
+    skip_second_resnet: bool = InputField(
+        default=True, input=Input.Any, description="Skip or not second resnet in down layers"
+    )
+    skip_conv2: bool = InputField(default=True, input=Input.Any, description="Skip or not conv2 in down layers")
 
     def invoke(self, context: InvocationContext) -> SeamlessModeOutput:
         # Conditionally append 'x' and 'y' based on seamless_x and seamless_y
@@ -402,8 +414,18 @@ class SeamlessModeInvocation(BaseInvocation):
             seamless_axes_list.append("y")
 
         if unet is not None:
-            unet.seamless_axes = seamless_axes_list
+            unet.seamless = SeamlessSettings(
+                axes=seamless_axes_list,
+                skipped_layers=self.skipped_layers,
+                skip_second_resnet=self.skip_second_resnet,
+                skip_conv2=self.skip_conv2,
+            )
         if vae is not None:
-            vae.seamless_axes = seamless_axes_list
+            vae.seamless = SeamlessSettings(
+                axes=seamless_axes_list,
+                skipped_layers=self.skipped_layers,
+                skip_second_resnet=self.skip_second_resnet,
+                skip_conv2=self.skip_conv2,
+            )
 
         return SeamlessModeOutput(unet=unet, vae=vae)

invokeai/app/invocations/onnx.py

@@ -95,9 +95,10 @@ class ONNXPromptInvocation(BaseInvocation):
                     print(f'Warn: trigger: "{trigger}" not found')
             if loras or ti_list:
                 text_encoder.release_session()
-            with ONNXModelPatcher.apply_lora_text_encoder(text_encoder, loras), ONNXModelPatcher.apply_ti(
-                orig_tokenizer, text_encoder, ti_list
-            ) as (tokenizer, ti_manager):
+            with (
+                ONNXModelPatcher.apply_lora_text_encoder(text_encoder, loras),
+                ONNXModelPatcher.apply_ti(orig_tokenizer, text_encoder, ti_list) as (tokenizer, ti_manager),
+            ):
                 text_encoder.create_session()
 
                 # copy from

invokeai/app/invocations/upscale_sdx4.py

@@ -1,173 +0,0 @@
-from typing import List, Union
-
-import torch
-from diffusers import StableDiffusionUpscalePipeline
-
-from invokeai.app.invocations.baseinvocation import (
-    BaseInvocation,
-    FieldDescriptions,
-    Input,
-    InputField,
-    InvocationContext,
-    UIType,
-    invocation,
-)
-from invokeai.app.invocations.image import ImageOutput
-from invokeai.app.invocations.latent import SAMPLER_NAME_VALUES, get_scheduler
-from invokeai.app.invocations.metadata import CoreMetadata
-from invokeai.app.invocations.model import UNetField, VaeField
-from invokeai.app.invocations.primitives import ConditioningField, ImageField
-from invokeai.app.models.image import ImageCategory, ResourceOrigin
-from invokeai.app.util.step_callback import stable_diffusion_step_callback
-from invokeai.backend import BaseModelType
-from invokeai.backend.stable_diffusion import ConditioningData, PipelineIntermediateState, PostprocessingSettings
-
-
-@invocation("upscale_sdx4", title="Upscale (Stable Diffusion x4)", tags=["upscale"], version="0.1.0")
-class UpscaleLatentsInvocation(BaseInvocation):
-    """Upscales an image using an upscaling diffusion model.
-
-    https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler
-
-    The upscaling model is its own thing, independent of other Stable Diffusion text-to-image
-    models. We don't have ControlNet or LoRA support for it. It has its own VAE.
-    """
-
-    # Inputs
-    image: ImageField = InputField(description="The image to upscale")
-
-    positive_conditioning: ConditioningField = InputField(
-        description=FieldDescriptions.positive_cond, input=Input.Connection
-    )
-    negative_conditioning: ConditioningField = InputField(
-        description=FieldDescriptions.negative_cond, input=Input.Connection
-    )
-    steps: int = InputField(default=10, gt=0, description=FieldDescriptions.steps)
-    cfg_scale: Union[float, List[float]] = InputField(
-        default=7.5, ge=1, description=FieldDescriptions.cfg_scale, ui_type=UIType.Float
-    )
-    scheduler: SAMPLER_NAME_VALUES = InputField(default="euler", description=FieldDescriptions.scheduler)
-    seed: int = InputField(default=0, description=FieldDescriptions.seed)
-
-    unet: UNetField = InputField(description=FieldDescriptions.unet, input=Input.Connection)
-    vae: VaeField = InputField(description=FieldDescriptions.vae, input=Input.Connection)
-    metadata: CoreMetadata = InputField(default=None, description=FieldDescriptions.core_metadata)
-    tiled: bool = InputField(default=False, description=FieldDescriptions.tiled)
-
-    @torch.inference_mode()
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get_pil_image(self.image.image_name)
-
-        model_manager = context.services.model_manager
-        unet_info = model_manager.get_model(**self.unet.unet.dict(), context=context)
-        vae_info = model_manager.get_model(**self.vae.vae.dict(), context=context)
-
-        with unet_info as unet, vae_info as vae:
-            # don't re-use the same scheduler instance for both fields
-            low_res_scheduler = get_scheduler(context, self.unet.scheduler, self.scheduler, self.seed ^ 0xFFFFFFFF)
-            scheduler = get_scheduler(context, self.unet.scheduler, self.scheduler, self.seed ^ 0xF7F7F7F7)
-
-            conditioning_data = self.get_conditioning_data(context, scheduler, unet, self.seed)
-
-            pipeline = StableDiffusionUpscalePipeline(
-                vae=vae,
-                text_encoder=None,
-                tokenizer=None,
-                unet=unet,
-                low_res_scheduler=low_res_scheduler,
-                scheduler=scheduler,
-            )
-
-            if self.tiled or context.services.configuration.tiled_decode:
-                vae.enable_tiling()
-            else:
-                vae.disable_tiling()
-
-            generator = torch.Generator().manual_seed(self.seed)
-
-            output = pipeline(
-                image=image,
-                # latents=noise,
-                num_inference_steps=self.steps,
-                guidance_scale=self.cfg_scale,
-                # noise_level =
-                generator=generator,
-                prompt_embeds=conditioning_data.text_embeddings.embeds.data,
-                negative_prompt_embeds=conditioning_data.unconditioned_embeddings.embeds.data,
-                output_type="pil",
-                callback=lambda *args: self.dispatch_upscale_progress(context, *args),
-            )
-            result_image = output.images[0]
-
-        image_dto = context.services.images.create(
-            image=result_image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            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,
-            workflow=self.workflow,
-        )
-
-        return ImageOutput(
-            image=ImageField(image_name=image_dto.image_name),
-            width=image_dto.width,
-            height=image_dto.height,
-        )
-
-    def get_conditioning_data(
-        self,
-        context: InvocationContext,
-        scheduler,
-        unet,
-        seed,
-    ) -> ConditioningData:
-        # FIXME: duplicated from DenoiseLatentsInvocation.get_conditoning_data
-        positive_cond_data = context.services.latents.get(self.positive_conditioning.conditioning_name)
-        c = positive_cond_data.conditionings[0].to(device=unet.device, dtype=unet.dtype)
-        extra_conditioning_info = c.extra_conditioning
-
-        negative_cond_data = context.services.latents.get(self.negative_conditioning.conditioning_name)
-        uc = negative_cond_data.conditionings[0].to(device=unet.device, dtype=unet.dtype)
-
-        conditioning_data = ConditioningData(
-            unconditioned_embeddings=uc,
-            text_embeddings=c,
-            guidance_scale=self.cfg_scale,
-            extra=extra_conditioning_info,
-            postprocessing_settings=PostprocessingSettings(
-                threshold=0.0,  # threshold,
-                warmup=0.2,  # warmup,
-                h_symmetry_time_pct=None,  # h_symmetry_time_pct,
-                v_symmetry_time_pct=None,  # v_symmetry_time_pct,
-            ),
-        )
-
-        conditioning_data = conditioning_data.add_scheduler_args_if_applicable(
-            scheduler,
-            # for ddim scheduler
-            eta=0.0,  # ddim_eta
-            # for ancestral and sde schedulers
-            # FIXME: why do we need both a generator here and a seed argument to get_scheduler?
-            generator=torch.Generator(device=unet.device).manual_seed(seed ^ 0xFFFFFFFF),
-        )
-        return conditioning_data
-
-    def dispatch_upscale_progress(self, context, step, timestep, latents):
-        graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
-        source_node_id = graph_execution_state.prepared_source_mapping[self.id]
-        intermediate_state = PipelineIntermediateState(
-            step=step,
-            order=1,  # FIXME: fudging this, but why does it need both order and total-steps anyway?
-            total_steps=self.steps,
-            timestep=timestep,
-            latents=latents,
-        )
-        stable_diffusion_step_callback(
-            context=context,
-            intermediate_state=intermediate_state,
-            node=self.dict(),
-            source_node_id=source_node_id,
-            base_model=BaseModelType.StableDiffusionXLRefiner,  # FIXME: this upscaler needs its own model type
-        )

invokeai/backend/install/model_install_backend.py

@@ -326,6 +326,16 @@ class ModelInstall(object):
                     elif f"learned_embeds.{suffix}" in files:
                         location = self._download_hf_model(repo_id, [f"learned_embeds.{suffix}"], staging)
                         break
+                    elif "image_encoder.txt" in files and f"ip_adapter.{suffix}" in files:  # IP-Adapter
+                        files = ["image_encoder.txt", f"ip_adapter.{suffix}"]
+                        location = self._download_hf_model(repo_id, files, staging)
+                        break
+                    elif f"model.{suffix}" in files and "config.json" in files:
+                        # This elif-condition is pretty fragile, but it is intended to handle CLIP Vision models hosted
+                        # by InvokeAI for use with IP-Adapters.
+                        files = ["config.json", f"model.{suffix}"]
+                        location = self._download_hf_model(repo_id, files, staging)
+                        break
             if not location:
                 logger.warning(f"Could not determine type of repo {repo_id}. Skipping install.")
                 return {}
@@ -534,14 +544,17 @@ def hf_download_with_resume(
         logger.info(f"{model_name}: Downloading...")
 
     try:
-        with open(model_dest, open_mode) as file, tqdm(
-            desc=model_name,
-            initial=exist_size,
-            total=total + exist_size,
-            unit="iB",
-            unit_scale=True,
-            unit_divisor=1000,
-        ) as bar:
+        with (
+            open(model_dest, open_mode) as file,
+            tqdm(
+                desc=model_name,
+                initial=exist_size,
+                total=total + exist_size,
+                unit="iB",
+                unit_scale=True,
+                unit_divisor=1000,
+            ) as bar,
+        ):
             for data in resp.iter_content(chunk_size=1024):
                 size = file.write(data)
                 bar.update(size)

invokeai/backend/ip_adapter/README.md

@@ -0,0 +1,45 @@
+# IP-Adapter Model Formats
+
+The official IP-Adapter models are released here: [h94/IP-Adapter](https://huggingface.co/h94/IP-Adapter)
+
+This official model repo does not integrate well with InvokeAI's current approach to model management, so we have defined a new file structure for IP-Adapter models. The InvokeAI format is described below.
+
+## CLIP Vision Models
+
+CLIP Vision models are organized in `diffusers`` format. The expected directory structure is:
+
+```bash
+ip_adapter_sd_image_encoder/
+├── config.json
+└── model.safetensors
+```
+
+## IP-Adapter Models
+
+IP-Adapter models are stored in a directory containing two files
+- `image_encoder.txt`: A text file containing the model identifier for the CLIP Vision encoder that is intended to be used with this IP-Adapter model.
+- `ip_adapter.bin`: The IP-Adapter weights.
+
+Sample directory structure:
+```bash
+ip_adapter_sd15/
+├── image_encoder.txt
+└── ip_adapter.bin
+```
+
+### Why save the weights in a .safetensors file?
+
+The weights in `ip_adapter.bin` are stored in a nested dict, which is not supported by `safetensors`. This could be solved by splitting `ip_adapter.bin` into multiple files, but for now we have decided to maintain consistency with the checkpoint structure used in the official [h94/IP-Adapter](https://huggingface.co/h94/IP-Adapter) repo.
+
+## InvokeAI Hosted IP-Adapters
+
+Image Encoders:
+- [InvokeAI/ip_adapter_sd_image_encoder](https://huggingface.co/InvokeAI/ip_adapter_sd_image_encoder)
+- [InvokeAI/ip_adapter_sdxl_image_encoder](https://huggingface.co/InvokeAI/ip_adapter_sdxl_image_encoder)
+
+IP-Adapters:
+- [InvokeAI/ip_adapter_sd15](https://huggingface.co/InvokeAI/ip_adapter_sd15)
+- [InvokeAI/ip_adapter_plus_sd15](https://huggingface.co/InvokeAI/ip_adapter_plus_sd15)
+- [InvokeAI/ip_adapter_plus_face_sd15](https://huggingface.co/InvokeAI/ip_adapter_plus_face_sd15)
+- [InvokeAI/ip_adapter_sdxl](https://huggingface.co/InvokeAI/ip_adapter_sdxl)
+- Not yet supported: [InvokeAI/ip_adapter_sdxl_vit_h](https://huggingface.co/InvokeAI/ip_adapter_sdxl_vit_h)

invokeai/backend/ip_adapter/attention_processor.py

@@ -0,0 +1,162 @@
+# copied from https://github.com/tencent-ailab/IP-Adapter (Apache License 2.0)
+#   and modified as needed
+
+# tencent-ailab comment:
+# modified from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from diffusers.models.attention_processor import AttnProcessor2_0 as DiffusersAttnProcessor2_0
+
+
+# Create a version of AttnProcessor2_0 that is a sub-class of nn.Module. This is required for IP-Adapter state_dict
+# loading.
+class AttnProcessor2_0(DiffusersAttnProcessor2_0, nn.Module):
+    def __init__(self):
+        DiffusersAttnProcessor2_0.__init__(self)
+        nn.Module.__init__(self)
+
+    def __call__(
+        self,
+        attn,
+        hidden_states,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        temb=None,
+        ip_adapter_image_prompt_embeds=None,
+    ):
+        """Re-definition of DiffusersAttnProcessor2_0.__call__(...) that accepts and ignores the
+        ip_adapter_image_prompt_embeds parameter.
+        """
+        return DiffusersAttnProcessor2_0.__call__(
+            self, attn, hidden_states, encoder_hidden_states, attention_mask, temb
+        )
+
+
+class IPAttnProcessor2_0(torch.nn.Module):
+    r"""
+    Attention processor for IP-Adapater for PyTorch 2.0.
+    Args:
+        hidden_size (`int`):
+            The hidden size of the attention layer.
+        cross_attention_dim (`int`):
+            The number of channels in the `encoder_hidden_states`.
+        scale (`float`, defaults to 1.0):
+            the weight scale of image prompt.
+    """
+
+    def __init__(self, hidden_size, cross_attention_dim=None, scale=1.0):
+        super().__init__()
+
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
+
+        self.hidden_size = hidden_size
+        self.cross_attention_dim = cross_attention_dim
+        self.scale = scale
+
+        self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
+        self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
+
+    def __call__(
+        self,
+        attn,
+        hidden_states,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        temb=None,
+        ip_adapter_image_prompt_embeds=None,
+    ):
+        if encoder_hidden_states is not None:
+            # If encoder_hidden_states is not None, then we are doing cross-attention, not self-attention. In this case,
+            # we will apply IP-Adapter conditioning. We validate the inputs for IP-Adapter conditioning here.
+            assert ip_adapter_image_prompt_embeds is not None
+            # The batch dimensions should match.
+            assert ip_adapter_image_prompt_embeds.shape[0] == encoder_hidden_states.shape[0]
+            # The channel dimensions should match.
+            assert ip_adapter_image_prompt_embeds.shape[2] == encoder_hidden_states.shape[2]
+            ip_hidden_states = ip_adapter_image_prompt_embeds
+
+        residual = hidden_states
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+            # scaled_dot_product_attention expects attention_mask shape to be
+            # (batch, heads, source_length, target_length)
+            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        # the output of sdp = (batch, num_heads, seq_len, head_dim)
+        # TODO: add support for attn.scale when we move to Torch 2.1
+        hidden_states = F.scaled_dot_product_attention(
+            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+        )
+
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        hidden_states = hidden_states.to(query.dtype)
+
+        if ip_hidden_states is not None:
+            ip_key = self.to_k_ip(ip_hidden_states)
+            ip_value = self.to_v_ip(ip_hidden_states)
+
+            ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+            ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+            # the output of sdp = (batch, num_heads, seq_len, head_dim)
+            # TODO: add support for attn.scale when we move to Torch 2.1
+            ip_hidden_states = F.scaled_dot_product_attention(
+                query, ip_key, ip_value, attn_mask=None, dropout_p=0.0, is_causal=False
+            )
+
+            ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+            ip_hidden_states = ip_hidden_states.to(query.dtype)
+
+            hidden_states = hidden_states + self.scale * ip_hidden_states
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states

invokeai/backend/ip_adapter/ip_adapter.py

@@ -0,0 +1,217 @@
+# copied from https://github.com/tencent-ailab/IP-Adapter (Apache License 2.0)
+#   and modified as needed
+
+from contextlib import contextmanager
+from typing import Optional, Union
+
+import torch
+from diffusers.models import UNet2DConditionModel
+from PIL import Image
+from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
+
+from .attention_processor import AttnProcessor2_0, IPAttnProcessor2_0
+from .resampler import Resampler
+
+
+class ImageProjModel(torch.nn.Module):
+    """Image Projection Model"""
+
+    def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024, clip_extra_context_tokens=4):
+        super().__init__()
+
+        self.cross_attention_dim = cross_attention_dim
+        self.clip_extra_context_tokens = clip_extra_context_tokens
+        self.proj = torch.nn.Linear(clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim)
+        self.norm = torch.nn.LayerNorm(cross_attention_dim)
+
+    @classmethod
+    def from_state_dict(cls, state_dict: dict[torch.Tensor], clip_extra_context_tokens=4):
+        """Initialize an ImageProjModel from a state_dict.
+
+        The cross_attention_dim and clip_embeddings_dim are inferred from the shape of the tensors in the state_dict.
+
+        Args:
+            state_dict (dict[torch.Tensor]): The state_dict of model weights.
+            clip_extra_context_tokens (int, optional): Defaults to 4.
+
+        Returns:
+            ImageProjModel
+        """
+        cross_attention_dim = state_dict["norm.weight"].shape[0]
+        clip_embeddings_dim = state_dict["proj.weight"].shape[-1]
+
+        model = cls(cross_attention_dim, clip_embeddings_dim, clip_extra_context_tokens)
+
+        model.load_state_dict(state_dict)
+        return model
+
+    def forward(self, image_embeds):
+        embeds = image_embeds
+        clip_extra_context_tokens = self.proj(embeds).reshape(
+            -1, self.clip_extra_context_tokens, self.cross_attention_dim
+        )
+        clip_extra_context_tokens = self.norm(clip_extra_context_tokens)
+        return clip_extra_context_tokens
+
+
+class IPAdapter:
+    """IP-Adapter: https://arxiv.org/pdf/2308.06721.pdf"""
+
+    def __init__(
+        self,
+        state_dict: dict[torch.Tensor],
+        device: torch.device,
+        dtype: torch.dtype = torch.float16,
+        num_tokens: int = 4,
+    ):
+        self.device = device
+        self.dtype = dtype
+
+        self._num_tokens = num_tokens
+
+        self._clip_image_processor = CLIPImageProcessor()
+
+        self._state_dict = state_dict
+
+        self._image_proj_model = self._init_image_proj_model(self._state_dict["image_proj"])
+
+        # The _attn_processors will be initialized later when we have access to the UNet.
+        self._attn_processors = None
+
+    def to(self, device: torch.device, dtype: Optional[torch.dtype] = None):
+        self.device = device
+        if dtype is not None:
+            self.dtype = dtype
+
+        self._image_proj_model.to(device=self.device, dtype=self.dtype)
+        if self._attn_processors is not None:
+            torch.nn.ModuleList(self._attn_processors.values()).to(device=self.device, dtype=self.dtype)
+
+    def _init_image_proj_model(self, state_dict):
+        return ImageProjModel.from_state_dict(state_dict, self._num_tokens).to(self.device, dtype=self.dtype)
+
+    def _prepare_attention_processors(self, unet: UNet2DConditionModel):
+        """Prepare a dict of attention processors that can later be injected into a unet, and load the IP-Adapter
+        attention weights into them.
+
+        Note that the `unet` param is only used to determine attention block dimensions and naming.
+        TODO(ryand): As a future improvement, this could all be inferred from the state_dict when the IPAdapter is
+        intialized.
+        """
+        attn_procs = {}
+        for name in unet.attn_processors.keys():
+            cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
+            if name.startswith("mid_block"):
+                hidden_size = unet.config.block_out_channels[-1]
+            elif name.startswith("up_blocks"):
+                block_id = int(name[len("up_blocks.")])
+                hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
+            elif name.startswith("down_blocks"):
+                block_id = int(name[len("down_blocks.")])
+                hidden_size = unet.config.block_out_channels[block_id]
+            if cross_attention_dim is None:
+                attn_procs[name] = AttnProcessor2_0()
+            else:
+                attn_procs[name] = IPAttnProcessor2_0(
+                    hidden_size=hidden_size,
+                    cross_attention_dim=cross_attention_dim,
+                    scale=1.0,
+                ).to(self.device, dtype=self.dtype)
+
+        ip_layers = torch.nn.ModuleList(attn_procs.values())
+        ip_layers.load_state_dict(self._state_dict["ip_adapter"])
+        self._attn_processors = attn_procs
+        self._state_dict = None
+
+    # @genomancer: pushed scaling back out into its own method (like original Tencent implementation)
+    #      which makes implementing begin_step_percent and end_step_percent easier
+    #  but based on self._attn_processors (ala @Ryan) instead of original Tencent unet.attn_processors,
+    #      which should make it easier to implement multiple IPAdapters
+    def set_scale(self, scale):
+        if self._attn_processors is not None:
+            for attn_processor in self._attn_processors.values():
+                if isinstance(attn_processor, IPAttnProcessor2_0):
+                    attn_processor.scale = scale
+
+    @contextmanager
+    def apply_ip_adapter_attention(self, unet: UNet2DConditionModel, scale: float):
+        """A context manager that patches `unet` with this IP-Adapter's attention processors while it is active.
+
+        Yields:
+            None
+        """
+        if self._attn_processors is None:
+            # We only have to call _prepare_attention_processors(...) once, and then the result is cached and can be
+            # used on any UNet model (with the same dimensions).
+            self._prepare_attention_processors(unet)
+
+        # Set scale
+        self.set_scale(scale)
+        # for attn_processor in self._attn_processors.values():
+        #     if isinstance(attn_processor, IPAttnProcessor2_0):
+        #         attn_processor.scale = scale
+
+        orig_attn_processors = unet.attn_processors
+
+        # Make a (moderately-) shallow copy of the self._attn_processors dict, because unet.set_attn_processor(...)
+        # actually pops elements from the passed dict.
+        ip_adapter_attn_processors = {k: v for k, v in self._attn_processors.items()}
+
+        try:
+            unet.set_attn_processor(ip_adapter_attn_processors)
+            yield None
+        finally:
+            unet.set_attn_processor(orig_attn_processors)
+
+    @torch.inference_mode()
+    def get_image_embeds(self, pil_image, image_encoder: CLIPVisionModelWithProjection):
+        if isinstance(pil_image, Image.Image):
+            pil_image = [pil_image]
+        clip_image = self._clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
+        clip_image_embeds = image_encoder(clip_image.to(self.device, dtype=self.dtype)).image_embeds
+        image_prompt_embeds = self._image_proj_model(clip_image_embeds)
+        uncond_image_prompt_embeds = self._image_proj_model(torch.zeros_like(clip_image_embeds))
+        return image_prompt_embeds, uncond_image_prompt_embeds
+
+
+class IPAdapterPlus(IPAdapter):
+    """IP-Adapter with fine-grained features"""
+
+    def _init_image_proj_model(self, state_dict):
+        return Resampler.from_state_dict(
+            state_dict=state_dict,
+            depth=4,
+            dim_head=64,
+            heads=12,
+            num_queries=self._num_tokens,
+            ff_mult=4,
+        ).to(self.device, dtype=self.dtype)
+
+    @torch.inference_mode()
+    def get_image_embeds(self, pil_image, image_encoder: CLIPVisionModelWithProjection):
+        if isinstance(pil_image, Image.Image):
+            pil_image = [pil_image]
+        clip_image = self._clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
+        clip_image = clip_image.to(self.device, dtype=self.dtype)
+        clip_image_embeds = image_encoder(clip_image, output_hidden_states=True).hidden_states[-2]
+        image_prompt_embeds = self._image_proj_model(clip_image_embeds)
+        uncond_clip_image_embeds = image_encoder(torch.zeros_like(clip_image), output_hidden_states=True).hidden_states[
+            -2
+        ]
+        uncond_image_prompt_embeds = self._image_proj_model(uncond_clip_image_embeds)
+        return image_prompt_embeds, uncond_image_prompt_embeds
+
+
+def build_ip_adapter(
+    ip_adapter_ckpt_path: str, device: torch.device, dtype: torch.dtype = torch.float16
+) -> Union[IPAdapter, IPAdapterPlus]:
+    state_dict = torch.load(ip_adapter_ckpt_path, map_location="cpu")
+
+    # Determine if the state_dict is from an IPAdapter or IPAdapterPlus based on the image_proj weights that it
+    # contains.
+    is_plus = "proj.weight" not in state_dict["image_proj"]
+
+    if is_plus:
+        return IPAdapterPlus(state_dict, device=device, dtype=dtype)
+    else:
+        return IPAdapter(state_dict, device=device, dtype=dtype)

invokeai/backend/ip_adapter/resampler.py

@@ -0,0 +1,158 @@
+# copied from https://github.com/tencent-ailab/IP-Adapter (Apache License 2.0)
+
+# tencent ailab comment: modified from
+# https://github.com/mlfoundations/open_flamingo/blob/main/open_flamingo/src/helpers.py
+import math
+
+import torch
+import torch.nn as nn
+
+
+# FFN
+def FeedForward(dim, mult=4):
+    inner_dim = int(dim * mult)
+    return nn.Sequential(
+        nn.LayerNorm(dim),
+        nn.Linear(dim, inner_dim, bias=False),
+        nn.GELU(),
+        nn.Linear(inner_dim, dim, bias=False),
+    )
+
+
+def reshape_tensor(x, heads):
+    bs, length, width = x.shape
+    # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
+    x = x.view(bs, length, heads, -1)
+    # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
+    x = x.transpose(1, 2)
+    # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
+    x = x.reshape(bs, heads, length, -1)
+    return x
+
+
+class PerceiverAttention(nn.Module):
+    def __init__(self, *, dim, dim_head=64, heads=8):
+        super().__init__()
+        self.scale = dim_head**-0.5
+        self.dim_head = dim_head
+        self.heads = heads
+        inner_dim = dim_head * heads
+
+        self.norm1 = nn.LayerNorm(dim)
+        self.norm2 = nn.LayerNorm(dim)
+
+        self.to_q = nn.Linear(dim, inner_dim, bias=False)
+        self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)
+        self.to_out = nn.Linear(inner_dim, dim, bias=False)
+
+    def forward(self, x, latents):
+        """
+        Args:
+            x (torch.Tensor): image features
+                shape (b, n1, D)
+            latent (torch.Tensor): latent features
+                shape (b, n2, D)
+        """
+        x = self.norm1(x)
+        latents = self.norm2(latents)
+
+        b, l, _ = latents.shape
+
+        q = self.to_q(latents)
+        kv_input = torch.cat((x, latents), dim=-2)
+        k, v = self.to_kv(kv_input).chunk(2, dim=-1)
+
+        q = reshape_tensor(q, self.heads)
+        k = reshape_tensor(k, self.heads)
+        v = reshape_tensor(v, self.heads)
+
+        # attention
+        scale = 1 / math.sqrt(math.sqrt(self.dim_head))
+        weight = (q * scale) @ (k * scale).transpose(-2, -1)  # More stable with f16 than dividing afterwards
+        weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
+        out = weight @ v
+
+        out = out.permute(0, 2, 1, 3).reshape(b, l, -1)
+
+        return self.to_out(out)
+
+
+class Resampler(nn.Module):
+    def __init__(
+        self,
+        dim=1024,
+        depth=8,
+        dim_head=64,
+        heads=16,
+        num_queries=8,
+        embedding_dim=768,
+        output_dim=1024,
+        ff_mult=4,
+    ):
+        super().__init__()
+
+        self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5)
+
+        self.proj_in = nn.Linear(embedding_dim, dim)
+
+        self.proj_out = nn.Linear(dim, output_dim)
+        self.norm_out = nn.LayerNorm(output_dim)
+
+        self.layers = nn.ModuleList([])
+        for _ in range(depth):
+            self.layers.append(
+                nn.ModuleList(
+                    [
+                        PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
+                        FeedForward(dim=dim, mult=ff_mult),
+                    ]
+                )
+            )
+
+    @classmethod
+    def from_state_dict(cls, state_dict: dict[torch.Tensor], depth=8, dim_head=64, heads=16, num_queries=8, ff_mult=4):
+        """A convenience function that initializes a Resampler from a state_dict.
+
+        Some of the shape parameters are inferred from the state_dict (e.g. dim, embedding_dim, etc.). At the time of
+        writing, we did not have a need for inferring ALL of the shape parameters from the state_dict, but this would be
+        possible if needed in the future.
+
+        Args:
+            state_dict (dict[torch.Tensor]): The state_dict to load.
+            depth (int, optional):
+            dim_head (int, optional):
+            heads (int, optional):
+            ff_mult (int, optional):
+
+        Returns:
+            Resampler
+        """
+        dim = state_dict["latents"].shape[2]
+        num_queries = state_dict["latents"].shape[1]
+        embedding_dim = state_dict["proj_in.weight"].shape[-1]
+        output_dim = state_dict["norm_out.weight"].shape[0]
+
+        model = cls(
+            dim=dim,
+            depth=depth,
+            dim_head=dim_head,
+            heads=heads,
+            num_queries=num_queries,
+            embedding_dim=embedding_dim,
+            output_dim=output_dim,
+            ff_mult=ff_mult,
+        )
+        model.load_state_dict(state_dict)
+        return model
+
+    def forward(self, x):
+        latents = self.latents.repeat(x.size(0), 1, 1)
+
+        x = self.proj_in(x)
+
+        for attn, ff in self.layers:
+            latents = attn(x, latents) + latents
+            latents = ff(latents) + latents
+
+        latents = self.proj_out(latents)
+        return self.norm_out(latents)

invokeai/backend/model_management/model_manager.py

@@ -25,6 +25,7 @@ Models are described using four attributes:
          ModelType.Lora -- a LoRA or LyCORIS fine-tune
          ModelType.TextualInversion -- a textual inversion embedding
          ModelType.ControlNet -- a ControlNet model
+         ModelType.IPAdapter -- an IPAdapter model
 
   3) BaseModelType -- an enum indicating the stable diffusion base model, one of:
          BaseModelType.StableDiffusion1
@@ -1000,8 +1001,8 @@ class ModelManager(object):
                                 new_models_found = True
                             except DuplicateModelException as e:
                                 self.logger.warning(e)
-                            except InvalidModelException:
-                                self.logger.warning(f"Not a valid model: {model_path}")
+                            except InvalidModelException as e:
+                                self.logger.warning(f"Not a valid model: {model_path}. {e}")
                             except NotImplementedError as e:
                                 self.logger.warning(e)
 

invokeai/backend/model_management/model_probe.py

@@ -8,6 +8,8 @@ import torch
 from diffusers import ConfigMixin, ModelMixin
 from picklescan.scanner import scan_file_path
 
+from invokeai.backend.model_management.models.ip_adapter import IPAdapterModelFormat
+
 from .models import (
     BaseModelType,
     InvalidModelException,
@@ -47,12 +49,12 @@ class ModelProbe(object):
     CLASS2TYPE = {
         "StableDiffusionPipeline": ModelType.Main,
         "StableDiffusionInpaintPipeline": ModelType.Main,
-        "StableDiffusionUpscalePipeline": ModelType.Main,
         "StableDiffusionXLPipeline": ModelType.Main,
         "StableDiffusionXLImg2ImgPipeline": ModelType.Main,
         "StableDiffusionXLInpaintPipeline": ModelType.Main,
         "AutoencoderKL": ModelType.Vae,
         "ControlNetModel": ModelType.ControlNet,
+        "CLIPVisionModelWithProjection": ModelType.CLIPVision,
     }
 
     @classmethod
@@ -119,14 +121,18 @@ class ModelProbe(object):
                     and prediction_type == SchedulerPredictionType.VPrediction
                 ),
                 format=format,
-                image_size=1024
-                if (base_type in {BaseModelType.StableDiffusionXL, BaseModelType.StableDiffusionXLRefiner})
-                else 768
-                if (
-                    base_type == BaseModelType.StableDiffusion2
-                    and prediction_type == SchedulerPredictionType.VPrediction
-                )
-                else 512,
+                image_size=(
+                    1024
+                    if (base_type in {BaseModelType.StableDiffusionXL, BaseModelType.StableDiffusionXLRefiner})
+                    else (
+                        768
+                        if (
+                            base_type == BaseModelType.StableDiffusion2
+                            and prediction_type == SchedulerPredictionType.VPrediction
+                        )
+                        else 512
+                    )
+                ),
             )
         except Exception:
             raise
@@ -178,9 +184,10 @@ class ModelProbe(object):
                 return ModelType.ONNX
             if (folder_path / "learned_embeds.bin").exists():
                 return ModelType.TextualInversion
-
             if (folder_path / "pytorch_lora_weights.bin").exists():
                 return ModelType.Lora
+            if (folder_path / "image_encoder.txt").exists():
+                return ModelType.IPAdapter
 
             i = folder_path / "model_index.json"
             c = folder_path / "config.json"
@@ -189,7 +196,12 @@ class ModelProbe(object):
             if config_path:
                 with open(config_path, "r") as file:
                     conf = json.load(file)
-                class_name = conf["_class_name"]
+                if "_class_name" in conf:
+                    class_name = conf["_class_name"]
+                elif "architectures" in conf:
+                    class_name = conf["architectures"][0]
+                else:
+                    class_name = None
 
         if class_name and (type := cls.CLASS2TYPE.get(class_name)):
             return type
@@ -367,6 +379,16 @@ class ControlNetCheckpointProbe(CheckpointProbeBase):
         raise InvalidModelException("Unable to determine base type for {self.checkpoint_path}")
 
 
+class IPAdapterCheckpointProbe(CheckpointProbeBase):
+    def get_base_type(self) -> BaseModelType:
+        raise NotImplementedError()
+
+
+class CLIPVisionCheckpointProbe(CheckpointProbeBase):
+    def get_base_type(self) -> BaseModelType:
+        raise NotImplementedError()
+
+
 ########################################################
 # classes for probing folders
 #######################################################
@@ -486,11 +508,13 @@ class ControlNetFolderProbe(FolderProbeBase):
         base_model = (
             BaseModelType.StableDiffusion1
             if dimension == 768
-            else BaseModelType.StableDiffusion2
-            if dimension == 1024
-            else BaseModelType.StableDiffusionXL
-            if dimension == 2048
-            else None
+            else (
+                BaseModelType.StableDiffusion2
+                if dimension == 1024
+                else BaseModelType.StableDiffusionXL
+                if dimension == 2048
+                else None
+            )
         )
         if not base_model:
             raise InvalidModelException(f"Unable to determine model base for {self.folder_path}")
@@ -510,15 +534,47 @@ class LoRAFolderProbe(FolderProbeBase):
         return LoRACheckpointProbe(model_file, None).get_base_type()
 
 
+class IPAdapterFolderProbe(FolderProbeBase):
+    def get_format(self) -> str:
+        return IPAdapterModelFormat.InvokeAI.value
+
+    def get_base_type(self) -> BaseModelType:
+        model_file = self.folder_path / "ip_adapter.bin"
+        if not model_file.exists():
+            raise InvalidModelException("Unknown IP-Adapter model format.")
+
+        state_dict = torch.load(model_file, map_location="cpu")
+        cross_attention_dim = state_dict["ip_adapter"]["1.to_k_ip.weight"].shape[-1]
+        if cross_attention_dim == 768:
+            return BaseModelType.StableDiffusion1
+        elif cross_attention_dim == 1024:
+            return BaseModelType.StableDiffusion2
+        elif cross_attention_dim == 2048:
+            return BaseModelType.StableDiffusionXL
+        else:
+            raise InvalidModelException(f"IP-Adapter had unexpected cross-attention dimension: {cross_attention_dim}.")
+
+
+class CLIPVisionFolderProbe(FolderProbeBase):
+    def get_base_type(self) -> BaseModelType:
+        return BaseModelType.Any
+
+
 ############## register probe classes ######
 ModelProbe.register_probe("diffusers", ModelType.Main, PipelineFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.Vae, VaeFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.Lora, LoRAFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.TextualInversion, TextualInversionFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.ControlNet, ControlNetFolderProbe)
+ModelProbe.register_probe("diffusers", ModelType.IPAdapter, IPAdapterFolderProbe)
+ModelProbe.register_probe("diffusers", ModelType.CLIPVision, CLIPVisionFolderProbe)
+
 ModelProbe.register_probe("checkpoint", ModelType.Main, PipelineCheckpointProbe)
 ModelProbe.register_probe("checkpoint", ModelType.Vae, VaeCheckpointProbe)
 ModelProbe.register_probe("checkpoint", ModelType.Lora, LoRACheckpointProbe)
 ModelProbe.register_probe("checkpoint", ModelType.TextualInversion, TextualInversionCheckpointProbe)
 ModelProbe.register_probe("checkpoint", ModelType.ControlNet, ControlNetCheckpointProbe)
+ModelProbe.register_probe("checkpoint", ModelType.IPAdapter, IPAdapterCheckpointProbe)
+ModelProbe.register_probe("checkpoint", ModelType.CLIPVision, CLIPVisionCheckpointProbe)
+
 ModelProbe.register_probe("onnx", ModelType.ONNX, ONNXFolderProbe)

invokeai/backend/model_management/model_search.py

@@ -79,7 +79,7 @@ class ModelSearch(ABC):
                         self._models_found += 1
                         self._scanned_dirs.add(path)
                     except Exception as e:
-                        self.logger.warning(str(e))
+                        self.logger.warning(f"Failed to process '{path}': {e}")
 
             for f in files:
                 path = Path(root) / f
@@ -90,7 +90,7 @@ class ModelSearch(ABC):
                         self.on_model_found(path)
                         self._models_found += 1
                     except Exception as e:
-                        self.logger.warning(str(e))
+                        self.logger.warning(f"Failed to process '{path}': {e}")
 
 
 class FindModels(ModelSearch):

invokeai/backend/model_management/models/__init__.py

@@ -18,7 +18,9 @@ from .base import (  # noqa: F401
     SilenceWarnings,
     SubModelType,
 )
+from .clip_vision import CLIPVisionModel
 from .controlnet import ControlNetModel  # TODO:
+from .ip_adapter import IPAdapterModel
 from .lora import LoRAModel
 from .sdxl import StableDiffusionXLModel
 from .stable_diffusion import StableDiffusion1Model, StableDiffusion2Model
@@ -34,6 +36,8 @@ MODEL_CLASSES = {
         ModelType.Lora: LoRAModel,
         ModelType.ControlNet: ControlNetModel,
         ModelType.TextualInversion: TextualInversionModel,
+        ModelType.IPAdapter: IPAdapterModel,
+        ModelType.CLIPVision: CLIPVisionModel,
     },
     BaseModelType.StableDiffusion2: {
         ModelType.ONNX: ONNXStableDiffusion2Model,
@@ -42,6 +46,8 @@ MODEL_CLASSES = {
         ModelType.Lora: LoRAModel,
         ModelType.ControlNet: ControlNetModel,
         ModelType.TextualInversion: TextualInversionModel,
+        ModelType.IPAdapter: IPAdapterModel,
+        ModelType.CLIPVision: CLIPVisionModel,
     },
     BaseModelType.StableDiffusionXL: {
         ModelType.Main: StableDiffusionXLModel,
@@ -51,6 +57,8 @@ MODEL_CLASSES = {
         ModelType.ControlNet: ControlNetModel,
         ModelType.TextualInversion: TextualInversionModel,
         ModelType.ONNX: ONNXStableDiffusion2Model,
+        ModelType.IPAdapter: IPAdapterModel,
+        ModelType.CLIPVision: CLIPVisionModel,
     },
     BaseModelType.StableDiffusionXLRefiner: {
         ModelType.Main: StableDiffusionXLModel,
@@ -60,6 +68,19 @@ MODEL_CLASSES = {
         ModelType.ControlNet: ControlNetModel,
         ModelType.TextualInversion: TextualInversionModel,
         ModelType.ONNX: ONNXStableDiffusion2Model,
+        ModelType.IPAdapter: IPAdapterModel,
+        ModelType.CLIPVision: CLIPVisionModel,
+    },
+    BaseModelType.Any: {
+        ModelType.CLIPVision: CLIPVisionModel,
+        # The following model types are not expected to be used with BaseModelType.Any.
+        ModelType.ONNX: ONNXStableDiffusion2Model,
+        ModelType.Main: StableDiffusion2Model,
+        ModelType.Vae: VaeModel,
+        ModelType.Lora: LoRAModel,
+        ModelType.ControlNet: ControlNetModel,
+        ModelType.TextualInversion: TextualInversionModel,
+        ModelType.IPAdapter: IPAdapterModel,
     },
     # BaseModelType.Kandinsky2_1: {
     #    ModelType.Main: Kandinsky2_1Model,

invokeai/backend/model_management/models/base.py

@@ -36,6 +36,7 @@ class ModelNotFoundException(Exception):
 
 
 class BaseModelType(str, Enum):
+    Any = "any"  # For models that are not associated with any particular base model.
     StableDiffusion1 = "sd-1"
     StableDiffusion2 = "sd-2"
     StableDiffusionXL = "sdxl"
@@ -50,6 +51,8 @@ class ModelType(str, Enum):
     Lora = "lora"
     ControlNet = "controlnet"  # used by model_probe
     TextualInversion = "embedding"
+    IPAdapter = "ip_adapter"
+    CLIPVision = "clip_vision"
 
 
 class SubModelType(str, Enum):

invokeai/backend/model_management/models/clip_vision.py

@@ -0,0 +1,82 @@
+import os
+from enum import Enum
+from typing import Literal, Optional
+
+import torch
+from transformers import CLIPVisionModelWithProjection
+
+from invokeai.backend.model_management.models.base import (
+    BaseModelType,
+    InvalidModelException,
+    ModelBase,
+    ModelConfigBase,
+    ModelType,
+    SubModelType,
+    calc_model_size_by_data,
+    calc_model_size_by_fs,
+    classproperty,
+)
+
+
+class CLIPVisionModelFormat(str, Enum):
+    Diffusers = "diffusers"
+
+
+class CLIPVisionModel(ModelBase):
+    class DiffusersConfig(ModelConfigBase):
+        model_format: Literal[CLIPVisionModelFormat.Diffusers]
+
+    def __init__(self, model_path: str, base_model: BaseModelType, model_type: ModelType):
+        assert model_type == ModelType.CLIPVision
+        super().__init__(model_path, base_model, model_type)
+
+        self.model_size = calc_model_size_by_fs(self.model_path)
+
+    @classmethod
+    def detect_format(cls, path: str) -> str:
+        if not os.path.exists(path):
+            raise ModuleNotFoundError(f"No CLIP Vision model at path '{path}'.")
+
+        if os.path.isdir(path) and os.path.exists(os.path.join(path, "config.json")):
+            return CLIPVisionModelFormat.Diffusers
+
+        raise InvalidModelException(f"Unexpected CLIP Vision model format: {path}")
+
+    @classproperty
+    def save_to_config(cls) -> bool:
+        return True
+
+    def get_size(self, child_type: Optional[SubModelType] = None) -> int:
+        if child_type is not None:
+            raise ValueError("There are no child models in a CLIP Vision model.")
+
+        return self.model_size
+
+    def get_model(
+        self,
+        torch_dtype: Optional[torch.dtype],
+        child_type: Optional[SubModelType] = None,
+    ) -> CLIPVisionModelWithProjection:
+        if child_type is not None:
+            raise ValueError("There are no child models in a CLIP Vision model.")
+
+        model = CLIPVisionModelWithProjection.from_pretrained(self.model_path, torch_dtype=torch_dtype)
+
+        # Calculate a more accurate model size.
+        self.model_size = calc_model_size_by_data(model)
+
+        return model
+
+    @classmethod
+    def convert_if_required(
+        cls,
+        model_path: str,
+        output_path: str,
+        config: ModelConfigBase,
+        base_model: BaseModelType,
+    ) -> str:
+        format = cls.detect_format(model_path)
+        if format == CLIPVisionModelFormat.Diffusers:
+            return model_path
+        else:
+            raise ValueError(f"Unsupported format: '{format}'.")

invokeai/backend/model_management/models/ip_adapter.py

@@ -0,0 +1,92 @@
+import os
+import typing
+from enum import Enum
+from typing import Literal, Optional
+
+import torch
+
+from invokeai.backend.ip_adapter.ip_adapter import IPAdapter, IPAdapterPlus, build_ip_adapter
+from invokeai.backend.model_management.models.base import (
+    BaseModelType,
+    InvalidModelException,
+    ModelBase,
+    ModelConfigBase,
+    ModelType,
+    SubModelType,
+    classproperty,
+)
+
+
+class IPAdapterModelFormat(str, Enum):
+    # The custom IP-Adapter model format defined by InvokeAI.
+    InvokeAI = "invokeai"
+
+
+class IPAdapterModel(ModelBase):
+    class InvokeAIConfig(ModelConfigBase):
+        model_format: Literal[IPAdapterModelFormat.InvokeAI]
+
+    def __init__(self, model_path: str, base_model: BaseModelType, model_type: ModelType):
+        assert model_type == ModelType.IPAdapter
+        super().__init__(model_path, base_model, model_type)
+
+        self.model_size = os.path.getsize(self.model_path)
+
+    @classmethod
+    def detect_format(cls, path: str) -> str:
+        if not os.path.exists(path):
+            raise ModuleNotFoundError(f"No IP-Adapter model at path '{path}'.")
+
+        if os.path.isdir(path):
+            model_file = os.path.join(path, "ip_adapter.bin")
+            image_encoder_config_file = os.path.join(path, "image_encoder.txt")
+            if os.path.exists(model_file) and os.path.exists(image_encoder_config_file):
+                return IPAdapterModelFormat.InvokeAI
+
+        raise InvalidModelException(f"Unexpected IP-Adapter model format: {path}")
+
+    @classproperty
+    def save_to_config(cls) -> bool:
+        return True
+
+    def get_size(self, child_type: Optional[SubModelType] = None) -> int:
+        if child_type is not None:
+            raise ValueError("There are no child models in an IP-Adapter model.")
+
+        return self.model_size
+
+    def get_model(
+        self,
+        torch_dtype: Optional[torch.dtype],
+        child_type: Optional[SubModelType] = None,
+    ) -> typing.Union[IPAdapter, IPAdapterPlus]:
+        if child_type is not None:
+            raise ValueError("There are no child models in an IP-Adapter model.")
+
+        return build_ip_adapter(
+            ip_adapter_ckpt_path=os.path.join(self.model_path, "ip_adapter.bin"), device="cpu", dtype=torch_dtype
+        )
+
+    @classmethod
+    def convert_if_required(
+        cls,
+        model_path: str,
+        output_path: str,
+        config: ModelConfigBase,
+        base_model: BaseModelType,
+    ) -> str:
+        format = cls.detect_format(model_path)
+        if format == IPAdapterModelFormat.InvokeAI:
+            return model_path
+        else:
+            raise ValueError(f"Unsupported format: '{format}'.")
+
+
+def get_ip_adapter_image_encoder_model_id(model_path: str):
+    """Read the ID of the image encoder associated with the IP-Adapter at `model_path`."""
+    image_encoder_config_file = os.path.join(model_path, "image_encoder.txt")
+
+    with open(image_encoder_config_file, "r") as f:
+        image_encoder_model = f.readline().strip()
+
+    return image_encoder_model

invokeai/backend/model_management/models/stable_diffusion.py

@@ -194,8 +194,6 @@ class StableDiffusion2Model(DiffusersModel):
             variant = ModelVariantType.Depth
         elif in_channels == 4:
             variant = ModelVariantType.Normal
-        elif in_channels == 7:
-            variant = ModelVariantType.Normal  # FIXME: temp kludge for 4x upscaler
         else:
             raise Exception("Unkown stable diffusion 2.* model format")
 

invokeai/backend/model_management/seamless.py

@@ -25,71 +25,55 @@ def _conv_forward_asymmetric(self, input, weight, bias):
 
 
 @contextmanager
-def set_seamless(model: Union[UNet2DConditionModel, AutoencoderKL], seamless_axes: List[str]):
+def set_seamless(
+    model: Union[UNet2DConditionModel, AutoencoderKL],
+    axes: List[str],
+    skipped_layers: int,
+    skip_second_resnet: bool,
+    skip_conv2: bool,
+):
     try:
         to_restore = []
 
         for m_name, m in model.named_modules():
-            if isinstance(model, UNet2DConditionModel):
-                if ".attentions." in m_name:
+            if not isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
+                continue
+
+            if isinstance(model, UNet2DConditionModel) and m_name.startswith("down_blocks.") and ".resnets." in m_name:
+                # down_blocks.1.resnets.1.conv1
+                _, block_num, _, resnet_num, submodule_name = m_name.split(".")
+                block_num = int(block_num)
+                resnet_num = int(resnet_num)
+
+                # if block_num >= seamless_down_blocks:
+                if block_num >= len(model.down_blocks) - skipped_layers:
                     continue
 
-                if ".resnets." in m_name:
-                    if ".conv2" in m_name:
-                        continue
-                    if ".conv_shortcut" in m_name:
-                        continue
-
-            """
-            if isinstance(model, UNet2DConditionModel):
-                if False and ".upsamplers." in m_name:
+                if resnet_num > 0 and skip_second_resnet:
                     continue
 
-                if False and ".downsamplers." in m_name:
+                if submodule_name == "conv2" and skip_conv2:
                     continue
 
-                if True and ".resnets." in m_name:
-                    if True and ".conv1" in m_name:
-                        if False and "down_blocks" in m_name:
-                            continue
-                        if False and "mid_block" in m_name:
-                            continue
-                        if False and "up_blocks" in m_name:
-                            continue
+            m.asymmetric_padding_mode = {}
+            m.asymmetric_padding = {}
+            m.asymmetric_padding_mode["x"] = "circular" if ("x" in axes) else "constant"
+            m.asymmetric_padding["x"] = (
+                m._reversed_padding_repeated_twice[0],
+                m._reversed_padding_repeated_twice[1],
+                0,
+                0,
+            )
+            m.asymmetric_padding_mode["y"] = "circular" if ("y" in axes) else "constant"
+            m.asymmetric_padding["y"] = (
+                0,
+                0,
+                m._reversed_padding_repeated_twice[2],
+                m._reversed_padding_repeated_twice[3],
+            )
 
-                    if True and ".conv2" in m_name:
-                        continue
-
-                    if True and ".conv_shortcut" in m_name:
-                        continue
-
-                if True and ".attentions." in m_name:
-                    continue
-
-                if False and m_name in ["conv_in", "conv_out"]:
-                    continue
-            """
-
-            if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
-                m.asymmetric_padding_mode = {}
-                m.asymmetric_padding = {}
-                m.asymmetric_padding_mode["x"] = "circular" if ("x" in seamless_axes) else "constant"
-                m.asymmetric_padding["x"] = (
-                    m._reversed_padding_repeated_twice[0],
-                    m._reversed_padding_repeated_twice[1],
-                    0,
-                    0,
-                )
-                m.asymmetric_padding_mode["y"] = "circular" if ("y" in seamless_axes) else "constant"
-                m.asymmetric_padding["y"] = (
-                    0,
-                    0,
-                    m._reversed_padding_repeated_twice[2],
-                    m._reversed_padding_repeated_twice[3],
-                )
-
-                to_restore.append((m, m._conv_forward))
-                m._conv_forward = _conv_forward_asymmetric.__get__(m, nn.Conv2d)
+            to_restore.append((m, m._conv_forward))
+            m._conv_forward = _conv_forward_asymmetric.__get__(m, nn.Conv2d)
 
         yield
 

invokeai/backend/stable_diffusion/__init__.py

@@ -1,15 +1,6 @@
 """
 Initialization file for the invokeai.backend.stable_diffusion package
 """
-from .diffusers_pipeline import (  # noqa: F401
-    ConditioningData,
-    PipelineIntermediateState,
-    StableDiffusionGeneratorPipeline,
-)
+from .diffusers_pipeline import PipelineIntermediateState, StableDiffusionGeneratorPipeline  # noqa: F401
 from .diffusion import InvokeAIDiffuserComponent  # noqa: F401
 from .diffusion.cross_attention_map_saving import AttentionMapSaver  # noqa: F401
-from .diffusion.shared_invokeai_diffusion import (  # noqa: F401
-    BasicConditioningInfo,
-    PostprocessingSettings,
-    SDXLConditioningInfo,
-)

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -1,8 +1,8 @@
 from __future__ import annotations
 
-import dataclasses
-import inspect
-from dataclasses import dataclass, field
+import math
+from contextlib import nullcontext
+from dataclasses import dataclass
 from typing import Any, Callable, List, Optional, Union
 
 import einops
@@ -23,9 +23,11 @@ from pydantic import Field
 from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 
 from invokeai.app.services.config import InvokeAIAppConfig
+from invokeai.backend.ip_adapter.ip_adapter import IPAdapter
+from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningData
 
 from ..util import auto_detect_slice_size, normalize_device
-from .diffusion import AttentionMapSaver, BasicConditioningInfo, InvokeAIDiffuserComponent, PostprocessingSettings
+from .diffusion import AttentionMapSaver, InvokeAIDiffuserComponent
 
 
 @dataclass
@@ -95,7 +97,7 @@ class AddsMaskGuidance:
         # Mask anything that has the same shape as prev_sample, return others as-is.
         return output_class(
             {
-                k: (self.apply_mask(v, self._t_for_field(k, t)) if are_like_tensors(prev_sample, v) else v)
+                k: self.apply_mask(v, self._t_for_field(k, t)) if are_like_tensors(prev_sample, v) else v
                 for k, v in step_output.items()
             }
         )
@@ -162,39 +164,13 @@ class ControlNetData:
 
 
 @dataclass
-class ConditioningData:
-    unconditioned_embeddings: BasicConditioningInfo
-    text_embeddings: BasicConditioningInfo
-    guidance_scale: Union[float, List[float]]
-    """
-    Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-    `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf).
-    Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate
-    images that are closely linked to the text `prompt`, usually at the expense of lower image quality.
-    """
-    extra: Optional[InvokeAIDiffuserComponent.ExtraConditioningInfo] = None
-    scheduler_args: dict[str, Any] = field(default_factory=dict)
-    """
-    Additional arguments to pass to invokeai_diffuser.do_latent_postprocessing().
-    """
-    postprocessing_settings: Optional[PostprocessingSettings] = None
-
-    @property
-    def dtype(self):
-        return self.text_embeddings.dtype
-
-    def add_scheduler_args_if_applicable(self, scheduler, **kwargs):
-        scheduler_args = dict(self.scheduler_args)
-        step_method = inspect.signature(scheduler.step)
-        for name, value in kwargs.items():
-            try:
-                step_method.bind_partial(**{name: value})
-            except TypeError:
-                # FIXME: don't silently discard arguments
-                pass  # debug("%s does not accept argument named %r", scheduler, name)
-            else:
-                scheduler_args[name] = value
-        return dataclasses.replace(self, scheduler_args=scheduler_args)
+class IPAdapterData:
+    ip_adapter_model: IPAdapter = Field(default=None)
+    # TODO: change to polymorphic so can do different weights per step (once implemented...)
+    weight: Union[float, List[float]] = Field(default=1.0)
+    # weight: float = Field(default=1.0)
+    begin_step_percent: float = Field(default=0.0)
+    end_step_percent: float = Field(default=1.0)
 
 
 @dataclass
@@ -277,6 +253,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         )
         self.invokeai_diffuser = InvokeAIDiffuserComponent(self.unet, self._unet_forward)
         self.control_model = control_model
+        self.use_ip_adapter = False
 
     def _adjust_memory_efficient_attention(self, latents: torch.Tensor):
         """
@@ -349,6 +326,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         additional_guidance: List[Callable] = None,
         callback: Callable[[PipelineIntermediateState], None] = None,
         control_data: List[ControlNetData] = None,
+        ip_adapter_data: Optional[IPAdapterData] = None,
         mask: Optional[torch.Tensor] = None,
         masked_latents: Optional[torch.Tensor] = None,
         seed: Optional[int] = None,
@@ -400,6 +378,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
                 conditioning_data,
                 additional_guidance=additional_guidance,
                 control_data=control_data,
+                ip_adapter_data=ip_adapter_data,
                 callback=callback,
             )
         finally:
@@ -419,6 +398,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         *,
         additional_guidance: List[Callable] = None,
         control_data: List[ControlNetData] = None,
+        ip_adapter_data: Optional[IPAdapterData] = None,
         callback: Callable[[PipelineIntermediateState], None] = None,
     ):
         self._adjust_memory_efficient_attention(latents)
@@ -431,12 +411,26 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         if timesteps.shape[0] == 0:
             return latents, attention_map_saver
 
-        extra_conditioning_info = conditioning_data.extra
-        with self.invokeai_diffuser.custom_attention_context(
-            self.invokeai_diffuser.model,
-            extra_conditioning_info=extra_conditioning_info,
-            step_count=len(self.scheduler.timesteps),
-        ):
+        if conditioning_data.extra is not None and conditioning_data.extra.wants_cross_attention_control:
+            attn_ctx = self.invokeai_diffuser.custom_attention_context(
+                self.invokeai_diffuser.model,
+                extra_conditioning_info=conditioning_data.extra,
+                step_count=len(self.scheduler.timesteps),
+            )
+            self.use_ip_adapter = False
+        elif ip_adapter_data is not None:
+            # TODO(ryand): Should we raise an exception if both custom attention and IP-Adapter attention are active?
+            # As it is now, the IP-Adapter will silently be skipped.
+            weight = ip_adapter_data.weight[0] if isinstance(ip_adapter_data.weight, List) else ip_adapter_data.weight
+            attn_ctx = ip_adapter_data.ip_adapter_model.apply_ip_adapter_attention(
+                unet=self.invokeai_diffuser.model,
+                scale=weight,
+            )
+            self.use_ip_adapter = True
+        else:
+            attn_ctx = nullcontext()
+
+        with attn_ctx:
             if callback is not None:
                 callback(
                     PipelineIntermediateState(
@@ -459,6 +453,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
                     total_step_count=len(timesteps),
                     additional_guidance=additional_guidance,
                     control_data=control_data,
+                    ip_adapter_data=ip_adapter_data,
                 )
                 latents = step_output.prev_sample
 
@@ -504,6 +499,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         total_step_count: int,
         additional_guidance: List[Callable] = None,
         control_data: List[ControlNetData] = None,
+        ip_adapter_data: Optional[IPAdapterData] = None,
     ):
         # invokeai_diffuser has batched timesteps, but diffusers schedulers expect a single value
         timestep = t[0]
@@ -514,6 +510,24 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         #     i.e. before or after passing it to InvokeAIDiffuserComponent
         latent_model_input = self.scheduler.scale_model_input(latents, timestep)
 
+        # handle IP-Adapter
+        if self.use_ip_adapter and ip_adapter_data is not None:  # somewhat redundant but logic is clearer
+            first_adapter_step = math.floor(ip_adapter_data.begin_step_percent * total_step_count)
+            last_adapter_step = math.ceil(ip_adapter_data.end_step_percent * total_step_count)
+            weight = (
+                ip_adapter_data.weight[step_index]
+                if isinstance(ip_adapter_data.weight, List)
+                else ip_adapter_data.weight
+            )
+            if step_index >= first_adapter_step and step_index <= last_adapter_step:
+                # only apply IP-Adapter if current step is within the IP-Adapter's begin/end step range
+                # ip_adapter_data.ip_adapter_model.set_scale(ip_adapter_data.weight)
+                ip_adapter_data.ip_adapter_model.set_scale(weight)
+            else:
+                # otherwise, set IP-Adapter scale to 0, so it has no effect
+                ip_adapter_data.ip_adapter_model.set_scale(0.0)
+
+        # handle ControlNet(s)
         # default is no controlnet, so set controlnet processing output to None
         controlnet_down_block_samples, controlnet_mid_block_sample = None, None
         if control_data is not None:

invokeai/backend/stable_diffusion/diffusion/__init__.py

@@ -3,9 +3,4 @@ Initialization file for invokeai.models.diffusion
 """
 from .cross_attention_control import InvokeAICrossAttentionMixin  # noqa: F401
 from .cross_attention_map_saving import AttentionMapSaver  # noqa: F401
-from .shared_invokeai_diffusion import (  # noqa: F401
-    BasicConditioningInfo,
-    InvokeAIDiffuserComponent,
-    PostprocessingSettings,
-    SDXLConditioningInfo,
-)
+from .shared_invokeai_diffusion import InvokeAIDiffuserComponent  # noqa: F401

invokeai/backend/stable_diffusion/diffusion/conditioning_data.py

@@ -0,0 +1,101 @@
+import dataclasses
+import inspect
+from dataclasses import dataclass, field
+from typing import Any, List, Optional, Union
+
+import torch
+
+from .cross_attention_control import Arguments
+
+
+@dataclass
+class ExtraConditioningInfo:
+    tokens_count_including_eos_bos: int
+    cross_attention_control_args: Optional[Arguments] = None
+
+    @property
+    def wants_cross_attention_control(self):
+        return self.cross_attention_control_args is not None
+
+
+@dataclass
+class BasicConditioningInfo:
+    embeds: torch.Tensor
+    # TODO(ryand): Right now we awkwardly copy the extra conditioning info from here up to `ConditioningData`. This
+    # should only be stored in one place.
+    extra_conditioning: Optional[ExtraConditioningInfo]
+    # weight: float
+    # mode: ConditioningAlgo
+
+    def to(self, device, dtype=None):
+        self.embeds = self.embeds.to(device=device, dtype=dtype)
+        return self
+
+
+@dataclass
+class SDXLConditioningInfo(BasicConditioningInfo):
+    pooled_embeds: torch.Tensor
+    add_time_ids: torch.Tensor
+
+    def to(self, device, dtype=None):
+        self.pooled_embeds = self.pooled_embeds.to(device=device, dtype=dtype)
+        self.add_time_ids = self.add_time_ids.to(device=device, dtype=dtype)
+        return super().to(device=device, dtype=dtype)
+
+
+@dataclass(frozen=True)
+class PostprocessingSettings:
+    threshold: float
+    warmup: float
+    h_symmetry_time_pct: Optional[float]
+    v_symmetry_time_pct: Optional[float]
+
+
+@dataclass
+class IPAdapterConditioningInfo:
+    cond_image_prompt_embeds: torch.Tensor
+    """IP-Adapter image encoder conditioning embeddings.
+    Shape: (batch_size, num_tokens, encoding_dim).
+    """
+    uncond_image_prompt_embeds: torch.Tensor
+    """IP-Adapter image encoding embeddings to use for unconditional generation.
+    Shape: (batch_size, num_tokens, encoding_dim).
+    """
+
+
+@dataclass
+class ConditioningData:
+    unconditioned_embeddings: BasicConditioningInfo
+    text_embeddings: BasicConditioningInfo
+    guidance_scale: Union[float, List[float]]
+    """
+    Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+    `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf).
+    Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate
+    images that are closely linked to the text `prompt`, usually at the expense of lower image quality.
+    """
+    extra: Optional[ExtraConditioningInfo] = None
+    scheduler_args: dict[str, Any] = field(default_factory=dict)
+    """
+    Additional arguments to pass to invokeai_diffuser.do_latent_postprocessing().
+    """
+    postprocessing_settings: Optional[PostprocessingSettings] = None
+
+    ip_adapter_conditioning: Optional[IPAdapterConditioningInfo] = None
+
+    @property
+    def dtype(self):
+        return self.text_embeddings.dtype
+
+    def add_scheduler_args_if_applicable(self, scheduler, **kwargs):
+        scheduler_args = dict(self.scheduler_args)
+        step_method = inspect.signature(scheduler.step)
+        for name, value in kwargs.items():
+            try:
+                step_method.bind_partial(**{name: value})
+            except TypeError:
+                # FIXME: don't silently discard arguments
+                pass  # debug("%s does not accept argument named %r", scheduler, name)
+            else:
+                scheduler_args[name] = value
+        return dataclasses.replace(self, scheduler_args=scheduler_args)

invokeai/backend/stable_diffusion/diffusion/cross_attention_control.py

@@ -376,11 +376,11 @@ def get_cross_attention_modules(model, which: CrossAttentionType) -> list[tuple[
         # non-fatal error but .swap() won't work.
         logger.error(
             f"Error! CrossAttentionControl found an unexpected number of {cross_attention_class} modules in the model "
-            + f"(expected {expected_count}, found {cross_attention_modules_in_model_count}). Either monkey-patching failed "
-            + "or some assumption has changed about the structure of the model itself. Please fix the monkey-patching, "
-            + f"and/or update the {expected_count} above to an appropriate number, and/or find and inform someone who knows "
-            + "what it means. This error is non-fatal, but it is likely that .swap() and attention map display will not "
-            + "work properly until it is fixed."
+            f"(expected {expected_count}, found {cross_attention_modules_in_model_count}). Either monkey-patching "
+            "failed or some assumption has changed about the structure of the model itself. Please fix the "
+            f"monkey-patching, and/or update the {expected_count} above to an appropriate number, and/or find and "
+            "inform someone who knows what it means. This error is non-fatal, but it is likely that .swap() and "
+            "attention map display will not work properly until it is fixed."
         )
     return attention_module_tuples
 
@@ -577,6 +577,7 @@ class SlicedSwapCrossAttnProcesser(SlicedAttnProcessor):
         attention_mask=None,
         # kwargs
         swap_cross_attn_context: SwapCrossAttnContext = None,
+        **kwargs,
     ):
         attention_type = CrossAttentionType.SELF if encoder_hidden_states is None else CrossAttentionType.TOKENS
 

invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py

@@ -2,7 +2,6 @@ from __future__ import annotations
 
 import math
 from contextlib import contextmanager
-from dataclasses import dataclass
 from typing import Any, Callable, Optional, Union
 
 import torch
@@ -10,9 +9,14 @@ from diffusers import UNet2DConditionModel
 from typing_extensions import TypeAlias
 
 from invokeai.app.services.config import InvokeAIAppConfig
+from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
+    ConditioningData,
+    ExtraConditioningInfo,
+    PostprocessingSettings,
+    SDXLConditioningInfo,
+)
 
 from .cross_attention_control import (
-    Arguments,
     Context,
     CrossAttentionType,
     SwapCrossAttnContext,
@@ -31,37 +35,6 @@ ModelForwardCallback: TypeAlias = Union[
 ]
 
 
-@dataclass
-class BasicConditioningInfo:
-    embeds: torch.Tensor
-    extra_conditioning: Optional[InvokeAIDiffuserComponent.ExtraConditioningInfo]
-    # weight: float
-    # mode: ConditioningAlgo
-
-    def to(self, device, dtype=None):
-        self.embeds = self.embeds.to(device=device, dtype=dtype)
-        return self
-
-
-@dataclass
-class SDXLConditioningInfo(BasicConditioningInfo):
-    pooled_embeds: torch.Tensor
-    add_time_ids: torch.Tensor
-
-    def to(self, device, dtype=None):
-        self.pooled_embeds = self.pooled_embeds.to(device=device, dtype=dtype)
-        self.add_time_ids = self.add_time_ids.to(device=device, dtype=dtype)
-        return super().to(device=device, dtype=dtype)
-
-
-@dataclass(frozen=True)
-class PostprocessingSettings:
-    threshold: float
-    warmup: float
-    h_symmetry_time_pct: Optional[float]
-    v_symmetry_time_pct: Optional[float]
-
-
 class InvokeAIDiffuserComponent:
     """
     The aim of this component is to provide a single place for code that can be applied identically to
@@ -75,15 +48,6 @@ class InvokeAIDiffuserComponent:
     debug_thresholding = False
     sequential_guidance = False
 
-    @dataclass
-    class ExtraConditioningInfo:
-        tokens_count_including_eos_bos: int
-        cross_attention_control_args: Optional[Arguments] = None
-
-        @property
-        def wants_cross_attention_control(self):
-            return self.cross_attention_control_args is not None
-
     def __init__(
         self,
         model,
@@ -103,30 +67,26 @@ class InvokeAIDiffuserComponent:
     @contextmanager
     def custom_attention_context(
         self,
-        unet: UNet2DConditionModel,  # note: also may futz with the text encoder depending on requested LoRAs
+        unet: UNet2DConditionModel,
         extra_conditioning_info: Optional[ExtraConditioningInfo],
         step_count: int,
     ):
-        old_attn_processors = None
-        if extra_conditioning_info and (extra_conditioning_info.wants_cross_attention_control):
-            old_attn_processors = unet.attn_processors
-            # Load lora conditions into the model
-            if extra_conditioning_info.wants_cross_attention_control:
-                self.cross_attention_control_context = Context(
-                    arguments=extra_conditioning_info.cross_attention_control_args,
-                    step_count=step_count,
-                )
-                setup_cross_attention_control_attention_processors(
-                    unet,
-                    self.cross_attention_control_context,
-                )
+        old_attn_processors = unet.attn_processors
 
         try:
+            self.cross_attention_control_context = Context(
+                arguments=extra_conditioning_info.cross_attention_control_args,
+                step_count=step_count,
+            )
+            setup_cross_attention_control_attention_processors(
+                unet,
+                self.cross_attention_control_context,
+            )
+
             yield None
         finally:
             self.cross_attention_control_context = None
-            if old_attn_processors is not None:
-                unet.set_attn_processor(old_attn_processors)
+            unet.set_attn_processor(old_attn_processors)
             # TODO resuscitate attention map saving
             # self.remove_attention_map_saving()
 
@@ -376,11 +336,24 @@ class InvokeAIDiffuserComponent:
 
     # methods below are called from do_diffusion_step and should be considered private to this class.
 
-    def _apply_standard_conditioning(self, x, sigma, conditioning_data, **kwargs):
-        # fast batched path
+    def _apply_standard_conditioning(self, x, sigma, conditioning_data: ConditioningData, **kwargs):
+        """Runs the conditioned and unconditioned UNet forward passes in a single batch for faster inference speed at
+        the cost of higher memory usage.
+        """
         x_twice = torch.cat([x] * 2)
         sigma_twice = torch.cat([sigma] * 2)
 
+        cross_attention_kwargs = None
+        if conditioning_data.ip_adapter_conditioning is not None:
+            cross_attention_kwargs = {
+                "ip_adapter_image_prompt_embeds": torch.cat(
+                    [
+                        conditioning_data.ip_adapter_conditioning.uncond_image_prompt_embeds,
+                        conditioning_data.ip_adapter_conditioning.cond_image_prompt_embeds,
+                    ]
+                )
+            }
+
         added_cond_kwargs = None
         if type(conditioning_data.text_embeddings) is SDXLConditioningInfo:
             added_cond_kwargs = {
@@ -408,6 +381,7 @@ class InvokeAIDiffuserComponent:
             x_twice,
             sigma_twice,
             both_conditionings,
+            cross_attention_kwargs=cross_attention_kwargs,
             encoder_attention_mask=encoder_attention_mask,
             added_cond_kwargs=added_cond_kwargs,
             **kwargs,
@@ -419,9 +393,12 @@ class InvokeAIDiffuserComponent:
         self,
         x: torch.Tensor,
         sigma,
-        conditioning_data,
+        conditioning_data: ConditioningData,
         **kwargs,
     ):
+        """Runs the conditioned and unconditioned UNet forward passes sequentially for lower memory usage at the cost of
+        slower execution speed.
+        """
         # low-memory sequential path
         uncond_down_block, cond_down_block = None, None
         down_block_additional_residuals = kwargs.pop("down_block_additional_residuals", None)
@@ -437,6 +414,13 @@ class InvokeAIDiffuserComponent:
         if mid_block_additional_residual is not None:
             uncond_mid_block, cond_mid_block = mid_block_additional_residual.chunk(2)
 
+        # Run unconditional UNet denoising.
+        cross_attention_kwargs = None
+        if conditioning_data.ip_adapter_conditioning is not None:
+            cross_attention_kwargs = {
+                "ip_adapter_image_prompt_embeds": conditioning_data.ip_adapter_conditioning.uncond_image_prompt_embeds
+            }
+
         added_cond_kwargs = None
         is_sdxl = type(conditioning_data.text_embeddings) is SDXLConditioningInfo
         if is_sdxl:
@@ -449,12 +433,21 @@ class InvokeAIDiffuserComponent:
             x,
             sigma,
             conditioning_data.unconditioned_embeddings.embeds,
+            cross_attention_kwargs=cross_attention_kwargs,
             down_block_additional_residuals=uncond_down_block,
             mid_block_additional_residual=uncond_mid_block,
             added_cond_kwargs=added_cond_kwargs,
             **kwargs,
         )
 
+        # Run conditional UNet denoising.
+        cross_attention_kwargs = None
+        if conditioning_data.ip_adapter_conditioning is not None:
+            cross_attention_kwargs = {
+                "ip_adapter_image_prompt_embeds": conditioning_data.ip_adapter_conditioning.cond_image_prompt_embeds
+            }
+
+        added_cond_kwargs = None
         if is_sdxl:
             added_cond_kwargs = {
                 "text_embeds": conditioning_data.text_embeddings.pooled_embeds,
@@ -465,6 +458,7 @@ class InvokeAIDiffuserComponent:
             x,
             sigma,
             conditioning_data.text_embeddings.embeds,
+            cross_attention_kwargs=cross_attention_kwargs,
             down_block_additional_residuals=cond_down_block,
             mid_block_additional_residual=cond_mid_block,
             added_cond_kwargs=added_cond_kwargs,

invokeai/frontend/web/dist/assets/ThemeLocaleProvider-a3380d0c.js

@@ -1,4 +1,4 @@
-import{v as m,h5 as Je,u as y,Y as Xa,h6 as Ja,a7 as ua,ab as d,h7 as b,h8 as o,h9 as Qa,ha as h,hb as fa,hc as Za,hd as eo,aE as ro,he as ao,a4 as oo,hf as to}from"./index-f83c2c5c.js";import{s as ha,n as t,t as io,o as ma,p as no,q as ga,v as ya,w as pa,x as lo,y as Sa,z as xa,A as xr,B as so,D as co,E as bo,F as $a,G as ka,H as _a,J as vo,K as wa,L as uo,M as fo,N as ho,O as mo,Q as za,R as go,S as yo,T as po,U as So,V as xo,W as $o,e as ko,X as _o}from"./menu-31376327.js";var Ca=String.raw,Aa=Ca`
+import{v as m,hj as Je,u as y,Y as Xa,hk as Ja,a7 as ua,ab as d,hl as b,hm as o,hn as Qa,ho as h,hp as fa,hq as Za,hr as eo,aE as ro,hs as ao,a4 as oo,ht as to}from"./index-f6c3f475.js";import{s as ha,n as t,t as io,o as ma,p as no,q as ga,v as ya,w as pa,x as lo,y as Sa,z as xa,A as xr,B as so,D as co,E as bo,F as $a,G as ka,H as _a,J as vo,K as wa,L as uo,M as fo,N as ho,O as mo,Q as za,R as go,S as yo,T as po,U as So,V as xo,W as $o,e as ko,X as _o}from"./menu-c9cc8c3d.js";var Ca=String.raw,Aa=Ca`
   :root,
   :host {
     --chakra-vh: 100vh;

invokeai/frontend/web/dist/index.html

@@ -12,7 +12,7 @@
         margin: 0;
       }
     </style>
-    <script type="module" crossorigin src="./assets/index-f83c2c5c.js"></script>
+    <script type="module" crossorigin src="./assets/index-f6c3f475.js"></script>
   </head>
 
   <body dir="ltr">

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

@@ -49,6 +49,7 @@
         "close": "Close",
         "communityLabel": "Community",
         "controlNet": "Controlnet",
+        "ipAdapter": "IP Adapter",
         "darkMode": "Dark Mode",
         "discordLabel": "Discord",
         "dontAskMeAgain": "Don't ask me again",
@@ -191,7 +192,11 @@
         "showAdvanced": "Show Advanced",
         "toggleControlNet": "Toggle this ControlNet",
         "w": "W",
-        "weight": "Weight"
+        "weight": "Weight",
+        "enableIPAdapter": "Enable IP Adapter",
+        "ipAdapterModel": "Adapter Model",
+        "resetIPAdapterImage": "Reset IP Adapter Image",
+        "ipAdapterImageFallback": "No IP Adapter Image Selected"
     },
     "embedding": {
         "addEmbedding": "Add Embedding",
@@ -1036,6 +1041,7 @@
         "serverError": "Server Error",
         "setCanvasInitialImage": "Set as canvas initial image",
         "setControlImage": "Set as control image",
+        "setIPAdapterImage": "Set as IP Adapter Image",
         "setInitialImage": "Set as initial image",
         "setNodeField": "Set as node field",
         "tempFoldersEmptied": "Temp Folder Emptied",

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

@@ -1,5 +1,8 @@
 import { resetCanvas } from 'features/canvas/store/canvasSlice';
-import { controlNetReset } from 'features/controlNet/store/controlNetSlice';
+import {
+  controlNetReset,
+  ipAdapterStateReset,
+} from 'features/controlNet/store/controlNetSlice';
 import { getImageUsage } from 'features/deleteImageModal/store/selectors';
 import { nodeEditorReset } from 'features/nodes/store/nodesSlice';
 import { clearInitialImage } from 'features/parameters/store/generationSlice';
@@ -18,6 +21,7 @@ export const addDeleteBoardAndImagesFulfilledListener = () => {
       let wasCanvasReset = false;
       let wasNodeEditorReset = false;
       let wasControlNetReset = false;
+      let wasIPAdapterReset = false;
 
       const state = getState();
       deleted_images.forEach((image_name) => {
@@ -42,6 +46,11 @@ export const addDeleteBoardAndImagesFulfilledListener = () => {
           dispatch(controlNetReset());
           wasControlNetReset = true;
         }
+
+        if (imageUsage.isIPAdapterImage && !wasIPAdapterReset) {
+          dispatch(ipAdapterStateReset());
+          wasIPAdapterReset = true;
+        }
       });
     },
   });

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

@@ -3,6 +3,7 @@ import { resetCanvas } from 'features/canvas/store/canvasSlice';
 import {
   controlNetImageChanged,
   controlNetProcessedImageChanged,
+  ipAdapterImageChanged,
 } from 'features/controlNet/store/controlNetSlice';
 import { imageDeletionConfirmed } from 'features/deleteImageModal/store/actions';
 import { isModalOpenChanged } from 'features/deleteImageModal/store/slice';
@@ -110,6 +111,14 @@ export const addRequestedSingleImageDeletionListener = () => {
           }
         });
 
+        // Remove IP Adapter Set Image if image is deleted.
+        if (
+          getState().controlNet.ipAdapterInfo.adapterImage?.image_name ===
+          imageDTO.image_name
+        ) {
+          dispatch(ipAdapterImageChanged(null));
+        }
+
         // reset nodes that use the deleted images
         getState().nodes.nodes.forEach((node) => {
           if (!isInvocationNode(node)) {
@@ -227,6 +236,14 @@ export const addRequestedMultipleImageDeletionListener = () => {
             }
           });
 
+          // Remove IP Adapter Set Image if image is deleted.
+          if (
+            getState().controlNet.ipAdapterInfo.adapterImage?.image_name ===
+            imageDTO.image_name
+          ) {
+            dispatch(ipAdapterImageChanged(null));
+          }
+
           // reset nodes that use the deleted images
           getState().nodes.nodes.forEach((node) => {
             if (!isInvocationNode(node)) {

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

@@ -1,7 +1,11 @@
 import { createAction } from '@reduxjs/toolkit';
 import { logger } from 'app/logging/logger';
+import { parseify } from 'common/util/serialize';
 import { setInitialCanvasImage } from 'features/canvas/store/canvasSlice';
-import { controlNetImageChanged } from 'features/controlNet/store/controlNetSlice';
+import {
+  controlNetImageChanged,
+  ipAdapterImageChanged,
+} from 'features/controlNet/store/controlNetSlice';
 import {
   TypesafeDraggableData,
   TypesafeDroppableData,
@@ -14,7 +18,6 @@ import {
 import { initialImageChanged } from 'features/parameters/store/generationSlice';
 import { imagesApi } from 'services/api/endpoints/images';
 import { startAppListening } from '../';
-import { parseify } from 'common/util/serialize';
 
 export const dndDropped = createAction<{
   overData: TypesafeDroppableData;
@@ -99,6 +102,18 @@ export const addImageDroppedListener = () => {
         return;
       }
 
+      /**
+       * Image dropped on IP Adapter image
+       */
+      if (
+        overData.actionType === 'SET_IP_ADAPTER_IMAGE' &&
+        activeData.payloadType === 'IMAGE_DTO' &&
+        activeData.payload.imageDTO
+      ) {
+        dispatch(ipAdapterImageChanged(activeData.payload.imageDTO));
+        return;
+      }
+
       /**
        * Image dropped on Canvas
        */

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

@@ -19,6 +19,7 @@ export const addImageToDeleteSelectedListener = () => {
         imagesUsage.some((i) => i.isCanvasImage) ||
         imagesUsage.some((i) => i.isInitialImage) ||
         imagesUsage.some((i) => i.isControlNetImage) ||
+        imagesUsage.some((i) => i.isIPAdapterImage) ||
         imagesUsage.some((i) => i.isNodesImage);
 
       if (shouldConfirmOnDelete || isImageInUse) {

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

@@ -1,15 +1,18 @@
 import { UseToastOptions } from '@chakra-ui/react';
 import { logger } from 'app/logging/logger';
 import { setInitialCanvasImage } from 'features/canvas/store/canvasSlice';
-import { controlNetImageChanged } from 'features/controlNet/store/controlNetSlice';
+import {
+  controlNetImageChanged,
+  ipAdapterImageChanged,
+} from 'features/controlNet/store/controlNetSlice';
 import { fieldImageValueChanged } from 'features/nodes/store/nodesSlice';
 import { initialImageChanged } from 'features/parameters/store/generationSlice';
 import { addToast } from 'features/system/store/systemSlice';
+import { t } from 'i18next';
 import { omit } from 'lodash-es';
 import { boardsApi } from 'services/api/endpoints/boards';
 import { startAppListening } from '..';
 import { imagesApi } from '../../../../../services/api/endpoints/images';
-import { t } from 'i18next';
 
 const DEFAULT_UPLOADED_TOAST: UseToastOptions = {
   title: t('toast.imageUploaded'),
@@ -99,6 +102,17 @@ export const addImageUploadedFulfilledListener = () => {
         return;
       }
 
+      if (postUploadAction?.type === 'SET_IP_ADAPTER_IMAGE') {
+        dispatch(ipAdapterImageChanged(imageDTO));
+        dispatch(
+          addToast({
+            ...DEFAULT_UPLOADED_TOAST,
+            description: t('toast.setIPAdapterImage'),
+          })
+        );
+        return;
+      }
+
       if (postUploadAction?.type === 'SET_INITIAL_IMAGE') {
         dispatch(initialImageChanged(imageDTO));
         dispatch(

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

@@ -1,6 +1,9 @@
 import { logger } from 'app/logging/logger';
 import { setBoundingBoxDimensions } from 'features/canvas/store/canvasSlice';
-import { controlNetRemoved } from 'features/controlNet/store/controlNetSlice';
+import {
+  controlNetRemoved,
+  ipAdapterStateReset,
+} from 'features/controlNet/store/controlNetSlice';
 import { loraRemoved } from 'features/lora/store/loraSlice';
 import { modelSelected } from 'features/parameters/store/actions';
 import {
@@ -56,6 +59,7 @@ export const addModelSelectedListener = () => {
           modelsCleared += 1;
         }
 
+        // handle incompatible controlnets
         const { controlNets } = state.controlNet;
         forEach(controlNets, (controlNet, controlNetId) => {
           if (controlNet.model?.base_model !== base_model) {
@@ -64,6 +68,16 @@ export const addModelSelectedListener = () => {
           }
         });
 
+        // handle incompatible IP-Adapter
+        const { ipAdapterInfo } = state.controlNet;
+        if (
+          ipAdapterInfo.model &&
+          ipAdapterInfo.model.base_model !== base_model
+        ) {
+          dispatch(ipAdapterStateReset());
+          modelsCleared += 1;
+        }
+
         if (modelsCleared > 0) {
           dispatch(
             addToast(

invokeai/frontend/web/src/app/store/store.ts

@@ -86,7 +86,10 @@ export const store = configureStore({
       .concat(autoBatchEnhancer());
   },
   middleware: (getDefaultMiddleware) =>
-    getDefaultMiddleware({ immutableCheck: false })
+    getDefaultMiddleware({
+      serializableCheck: false,
+      immutableCheck: false,
+    })
       .concat(api.middleware)
       .concat(dynamicMiddlewares)
       .prepend(listenerMiddleware.middleware),

invokeai/frontend/web/src/features/controlNet/components/ControlNet.tsx

@@ -18,6 +18,7 @@ import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
 import IAIIconButton from 'common/components/IAIIconButton';
 import IAISwitch from 'common/components/IAISwitch';
 import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
+import { useTranslation } from 'react-i18next';
 import { useToggle } from 'react-use';
 import { v4 as uuidv4 } from 'uuid';
 import ControlNetImagePreview from './ControlNetImagePreview';
@@ -28,7 +29,6 @@ import ParamControlNetBeginEnd from './parameters/ParamControlNetBeginEnd';
 import ParamControlNetControlMode from './parameters/ParamControlNetControlMode';
 import ParamControlNetProcessorSelect from './parameters/ParamControlNetProcessorSelect';
 import ParamControlNetResizeMode from './parameters/ParamControlNetResizeMode';
-import { useTranslation } from 'react-i18next';
 
 type ControlNetProps = {
   controlNet: ControlNetConfig;

invokeai/frontend/web/src/features/controlNet/components/ipAdapter/IPAdapterPanel.tsx

@@ -0,0 +1,35 @@
+import { Flex } from '@chakra-ui/react';
+import { memo } from 'react';
+import ParamIPAdapterBeginEnd from './ParamIPAdapterBeginEnd';
+import ParamIPAdapterFeatureToggle from './ParamIPAdapterFeatureToggle';
+import ParamIPAdapterImage from './ParamIPAdapterImage';
+import ParamIPAdapterModelSelect from './ParamIPAdapterModelSelect';
+import ParamIPAdapterWeight from './ParamIPAdapterWeight';
+
+const IPAdapterPanel = () => {
+  return (
+    <Flex
+      sx={{
+        flexDir: 'column',
+        gap: 3,
+        paddingInline: 3,
+        paddingBlock: 2,
+        paddingBottom: 5,
+        borderRadius: 'base',
+        position: 'relative',
+        bg: 'base.250',
+        _dark: {
+          bg: 'base.750',
+        },
+      }}
+    >
+      <ParamIPAdapterFeatureToggle />
+      <ParamIPAdapterImage />
+      <ParamIPAdapterModelSelect />
+      <ParamIPAdapterWeight />
+      <ParamIPAdapterBeginEnd />
+    </Flex>
+  );
+};
+
+export default memo(IPAdapterPanel);

invokeai/frontend/web/src/features/controlNet/components/ipAdapter/ParamIPAdapterBeginEnd.tsx

@@ -0,0 +1,100 @@
+import {
+  FormControl,
+  FormLabel,
+  HStack,
+  RangeSlider,
+  RangeSliderFilledTrack,
+  RangeSliderMark,
+  RangeSliderThumb,
+  RangeSliderTrack,
+  Tooltip,
+} from '@chakra-ui/react';
+import { RootState } from 'app/store/store';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import {
+  ipAdapterBeginStepPctChanged,
+  ipAdapterEndStepPctChanged,
+} from 'features/controlNet/store/controlNetSlice';
+import { memo, useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+
+const formatPct = (v: number) => `${Math.round(v * 100)}%`;
+
+const ParamIPAdapterBeginEnd = () => {
+  const isEnabled = useAppSelector(
+    (state: RootState) => state.controlNet.isIPAdapterEnabled
+  );
+  const beginStepPct = useAppSelector(
+    (state: RootState) => state.controlNet.ipAdapterInfo.beginStepPct
+  );
+  const endStepPct = useAppSelector(
+    (state: RootState) => state.controlNet.ipAdapterInfo.endStepPct
+  );
+  const dispatch = useAppDispatch();
+  const { t } = useTranslation();
+
+  const handleStepPctChanged = useCallback(
+    (v: number[]) => {
+      dispatch(ipAdapterBeginStepPctChanged(v[0] as number));
+      dispatch(ipAdapterEndStepPctChanged(v[1] as number));
+    },
+    [dispatch]
+  );
+
+  return (
+    <FormControl isDisabled={!isEnabled}>
+      <FormLabel>{t('controlnet.beginEndStepPercent')}</FormLabel>
+      <HStack w="100%" gap={2} alignItems="center">
+        <RangeSlider
+          aria-label={['Begin Step %', 'End Step %!']}
+          value={[beginStepPct, endStepPct]}
+          onChange={handleStepPctChanged}
+          min={0}
+          max={1}
+          step={0.01}
+          minStepsBetweenThumbs={5}
+          isDisabled={!isEnabled}
+        >
+          <RangeSliderTrack>
+            <RangeSliderFilledTrack />
+          </RangeSliderTrack>
+          <Tooltip label={formatPct(beginStepPct)} placement="top" hasArrow>
+            <RangeSliderThumb index={0} />
+          </Tooltip>
+          <Tooltip label={formatPct(endStepPct)} placement="top" hasArrow>
+            <RangeSliderThumb index={1} />
+          </Tooltip>
+          <RangeSliderMark
+            value={0}
+            sx={{
+              insetInlineStart: '0 !important',
+              insetInlineEnd: 'unset !important',
+            }}
+          >
+            0%
+          </RangeSliderMark>
+          <RangeSliderMark
+            value={0.5}
+            sx={{
+              insetInlineStart: '50% !important',
+              transform: 'translateX(-50%)',
+            }}
+          >
+            50%
+          </RangeSliderMark>
+          <RangeSliderMark
+            value={1}
+            sx={{
+              insetInlineStart: 'unset !important',
+              insetInlineEnd: '0 !important',
+            }}
+          >
+            100%
+          </RangeSliderMark>
+        </RangeSlider>
+      </HStack>
+    </FormControl>
+  );
+};
+
+export default memo(ParamIPAdapterBeginEnd);

invokeai/frontend/web/src/features/controlNet/components/ipAdapter/ParamIPAdapterFeatureToggle.tsx

@@ -0,0 +1,41 @@
+import { createSelector } from '@reduxjs/toolkit';
+import { stateSelector } from 'app/store/store';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
+import IAISwitch from 'common/components/IAISwitch';
+import { isIPAdapterEnableToggled } from 'features/controlNet/store/controlNetSlice';
+import { memo, useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+
+const selector = createSelector(
+  stateSelector,
+  (state) => {
+    const { isIPAdapterEnabled } = state.controlNet;
+
+    return { isIPAdapterEnabled };
+  },
+  defaultSelectorOptions
+);
+
+const ParamIPAdapterFeatureToggle = () => {
+  const { isIPAdapterEnabled } = useAppSelector(selector);
+  const dispatch = useAppDispatch();
+  const { t } = useTranslation();
+
+  const handleChange = useCallback(() => {
+    dispatch(isIPAdapterEnableToggled());
+  }, [dispatch]);
+
+  return (
+    <IAISwitch
+      label={t('controlnet.enableIPAdapter')}
+      isChecked={isIPAdapterEnabled}
+      onChange={handleChange}
+      formControlProps={{
+        width: '100%',
+      }}
+    />
+  );
+};
+
+export default memo(ParamIPAdapterFeatureToggle);

invokeai/frontend/web/src/features/controlNet/components/ipAdapter/ParamIPAdapterImage.tsx

@@ -0,0 +1,93 @@
+import { Flex } from '@chakra-ui/react';
+import { skipToken } from '@reduxjs/toolkit/dist/query';
+import { RootState } from 'app/store/store';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import IAIDndImage from 'common/components/IAIDndImage';
+import IAIDndImageIcon from 'common/components/IAIDndImageIcon';
+import { IAINoContentFallback } from 'common/components/IAIImageFallback';
+import { ipAdapterImageChanged } from 'features/controlNet/store/controlNetSlice';
+import {
+  TypesafeDraggableData,
+  TypesafeDroppableData,
+} from 'features/dnd/types';
+import { memo, useMemo } from 'react';
+import { useTranslation } from 'react-i18next';
+import { FaUndo } from 'react-icons/fa';
+import { useGetImageDTOQuery } from 'services/api/endpoints/images';
+import { PostUploadAction } from 'services/api/types';
+
+const ParamIPAdapterImage = () => {
+  const ipAdapterInfo = useAppSelector(
+    (state: RootState) => state.controlNet.ipAdapterInfo
+  );
+
+  const isIPAdapterEnabled = useAppSelector(
+    (state: RootState) => state.controlNet.isIPAdapterEnabled
+  );
+
+  const dispatch = useAppDispatch();
+  const { t } = useTranslation();
+
+  const { currentData: imageDTO } = useGetImageDTOQuery(
+    ipAdapterInfo.adapterImage?.image_name ?? skipToken
+  );
+
+  const draggableData = useMemo<TypesafeDraggableData | undefined>(() => {
+    if (imageDTO) {
+      return {
+        id: 'ip-adapter-image',
+        payloadType: 'IMAGE_DTO',
+        payload: { imageDTO },
+      };
+    }
+  }, [imageDTO]);
+
+  const droppableData = useMemo<TypesafeDroppableData | undefined>(
+    () => ({
+      id: 'ip-adapter-image',
+      actionType: 'SET_IP_ADAPTER_IMAGE',
+    }),
+    []
+  );
+
+  const postUploadAction = useMemo<PostUploadAction>(
+    () => ({
+      type: 'SET_IP_ADAPTER_IMAGE',
+    }),
+    []
+  );
+
+  return (
+    <Flex
+      sx={{
+        position: 'relative',
+        w: 'full',
+        alignItems: 'center',
+        justifyContent: 'center',
+      }}
+    >
+      <IAIDndImage
+        imageDTO={imageDTO}
+        droppableData={droppableData}
+        draggableData={draggableData}
+        postUploadAction={postUploadAction}
+        isUploadDisabled={!isIPAdapterEnabled}
+        isDropDisabled={!isIPAdapterEnabled}
+        dropLabel={t('toast.setIPAdapterImage')}
+        noContentFallback={
+          <IAINoContentFallback
+            label={t('controlnet.ipAdapterImageFallback')}
+          />
+        }
+      />
+
+      <IAIDndImageIcon
+        onClick={() => dispatch(ipAdapterImageChanged(null))}
+        icon={ipAdapterInfo.adapterImage ? <FaUndo /> : undefined}
+        tooltip={t('controlnet.resetIPAdapterImage')}
+      />
+    </Flex>
+  );
+};
+
+export default memo(ParamIPAdapterImage);

invokeai/frontend/web/src/features/controlNet/components/ipAdapter/ParamIPAdapterModelSelect.tsx

@@ -0,0 +1,97 @@
+import { SelectItem } from '@mantine/core';
+import { RootState } from 'app/store/store';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import IAIMantineSelect from 'common/components/IAIMantineSelect';
+import { ipAdapterModelChanged } from 'features/controlNet/store/controlNetSlice';
+import { MODEL_TYPE_MAP } from 'features/parameters/types/constants';
+import { modelIdToIPAdapterModelParam } from 'features/parameters/util/modelIdToIPAdapterModelParams';
+import { forEach } from 'lodash-es';
+import { memo, useCallback, useMemo } from 'react';
+import { useTranslation } from 'react-i18next';
+import { useGetIPAdapterModelsQuery } from 'services/api/endpoints/models';
+
+const ParamIPAdapterModelSelect = () => {
+  const ipAdapterModel = useAppSelector(
+    (state: RootState) => state.controlNet.ipAdapterInfo.model
+  );
+  const model = useAppSelector((state: RootState) => state.generation.model);
+
+  const dispatch = useAppDispatch();
+  const { t } = useTranslation();
+
+  const { data: ipAdapterModels } = useGetIPAdapterModelsQuery();
+
+  // grab the full model entity from the RTK Query cache
+  const selectedModel = useMemo(
+    () =>
+      ipAdapterModels?.entities[
+        `${ipAdapterModel?.base_model}/ip_adapter/${ipAdapterModel?.model_name}`
+      ] ?? null,
+    [
+      ipAdapterModel?.base_model,
+      ipAdapterModel?.model_name,
+      ipAdapterModels?.entities,
+    ]
+  );
+
+  const data = useMemo(() => {
+    if (!ipAdapterModels) {
+      return [];
+    }
+
+    const data: SelectItem[] = [];
+
+    forEach(ipAdapterModels.entities, (ipAdapterModel, id) => {
+      if (!ipAdapterModel) {
+        return;
+      }
+
+      const disabled = model?.base_model !== ipAdapterModel.base_model;
+
+      data.push({
+        value: id,
+        label: ipAdapterModel.model_name,
+        group: MODEL_TYPE_MAP[ipAdapterModel.base_model],
+        disabled,
+        tooltip: disabled
+          ? `Incompatible base model: ${ipAdapterModel.base_model}`
+          : undefined,
+      });
+    });
+
+    return data.sort((a, b) => (a.disabled && !b.disabled ? 1 : -1));
+  }, [ipAdapterModels, model?.base_model]);
+
+  const handleValueChanged = useCallback(
+    (v: string | null) => {
+      if (!v) {
+        return;
+      }
+
+      const newIPAdapterModel = modelIdToIPAdapterModelParam(v);
+
+      if (!newIPAdapterModel) {
+        return;
+      }
+
+      dispatch(ipAdapterModelChanged(newIPAdapterModel));
+    },
+    [dispatch]
+  );
+
+  return (
+    <IAIMantineSelect
+      label={t('controlnet.ipAdapterModel')}
+      className="nowheel nodrag"
+      tooltip={selectedModel?.description}
+      value={selectedModel?.id ?? null}
+      placeholder="Pick one"
+      error={!selectedModel}
+      data={data}
+      onChange={handleValueChanged}
+      sx={{ width: '100%' }}
+    />
+  );
+};
+
+export default memo(ParamIPAdapterModelSelect);

invokeai/frontend/web/src/features/controlNet/components/ipAdapter/ParamIPAdapterWeight.tsx

@@ -0,0 +1,46 @@
+import { RootState } from 'app/store/store';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import IAISlider from 'common/components/IAISlider';
+import { ipAdapterWeightChanged } from 'features/controlNet/store/controlNetSlice';
+import { memo, useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+
+const ParamIPAdapterWeight = () => {
+  const isIpAdapterEnabled = useAppSelector(
+    (state: RootState) => state.controlNet.isIPAdapterEnabled
+  );
+  const ipAdapterWeight = useAppSelector(
+    (state: RootState) => state.controlNet.ipAdapterInfo.weight
+  );
+  const dispatch = useAppDispatch();
+  const { t } = useTranslation();
+
+  const handleWeightChanged = useCallback(
+    (weight: number) => {
+      dispatch(ipAdapterWeightChanged(weight));
+    },
+    [dispatch]
+  );
+
+  const handleWeightReset = useCallback(() => {
+    dispatch(ipAdapterWeightChanged(1));
+  }, [dispatch]);
+
+  return (
+    <IAISlider
+      isDisabled={!isIpAdapterEnabled}
+      label={t('controlnet.weight')}
+      value={ipAdapterWeight}
+      onChange={handleWeightChanged}
+      min={0}
+      max={2}
+      step={0.01}
+      withSliderMarks
+      sliderMarks={[0, 1, 2]}
+      withReset
+      handleReset={handleWeightReset}
+    />
+  );
+};
+
+export default memo(ParamIPAdapterWeight);

invokeai/frontend/web/src/features/controlNet/store/controlNetSlice.ts

@@ -1,9 +1,13 @@
 import { PayloadAction, createSlice } from '@reduxjs/toolkit';
-import { ControlNetModelParam } from 'features/parameters/types/parameterSchemas';
+import {
+  ControlNetModelParam,
+  IPAdapterModelParam,
+} from 'features/parameters/types/parameterSchemas';
 import { cloneDeep, forEach } from 'lodash-es';
 import { imagesApi } from 'services/api/endpoints/images';
 import { components } from 'services/api/schema';
 import { isAnySessionRejected } from 'services/api/thunks/session';
+import { ImageDTO } from 'services/api/types';
 import { appSocketInvocationError } from 'services/events/actions';
 import { controlNetImageProcessed } from './actions';
 import {
@@ -56,16 +60,36 @@ export type ControlNetConfig = {
   shouldAutoConfig: boolean;
 };
 
+export type IPAdapterConfig = {
+  adapterImage: ImageDTO | null;
+  model: IPAdapterModelParam | null;
+  weight: number;
+  beginStepPct: number;
+  endStepPct: number;
+};
+
 export type ControlNetState = {
   controlNets: Record<string, ControlNetConfig>;
   isEnabled: boolean;
   pendingControlImages: string[];
+  isIPAdapterEnabled: boolean;
+  ipAdapterInfo: IPAdapterConfig;
+};
+
+export const initialIPAdapterState: IPAdapterConfig = {
+  adapterImage: null,
+  model: null,
+  weight: 1,
+  beginStepPct: 0,
+  endStepPct: 1,
 };
 
 export const initialControlNetState: ControlNetState = {
   controlNets: {},
   isEnabled: false,
   pendingControlImages: [],
+  isIPAdapterEnabled: false,
+  ipAdapterInfo: { ...initialIPAdapterState },
 };
 
 export const controlNetSlice = createSlice({
@@ -353,6 +377,31 @@ export const controlNetSlice = createSlice({
     controlNetReset: () => {
       return { ...initialControlNetState };
     },
+    isIPAdapterEnableToggled: (state) => {
+      state.isIPAdapterEnabled = !state.isIPAdapterEnabled;
+    },
+    ipAdapterImageChanged: (state, action: PayloadAction<ImageDTO | null>) => {
+      state.ipAdapterInfo.adapterImage = action.payload;
+    },
+    ipAdapterWeightChanged: (state, action: PayloadAction<number>) => {
+      state.ipAdapterInfo.weight = action.payload;
+    },
+    ipAdapterModelChanged: (
+      state,
+      action: PayloadAction<IPAdapterModelParam | null>
+    ) => {
+      state.ipAdapterInfo.model = action.payload;
+    },
+    ipAdapterBeginStepPctChanged: (state, action: PayloadAction<number>) => {
+      state.ipAdapterInfo.beginStepPct = action.payload;
+    },
+    ipAdapterEndStepPctChanged: (state, action: PayloadAction<number>) => {
+      state.ipAdapterInfo.endStepPct = action.payload;
+    },
+    ipAdapterStateReset: (state) => {
+      state.isIPAdapterEnabled = false;
+      state.ipAdapterInfo = { ...initialIPAdapterState };
+    },
   },
   extraReducers: (builder) => {
     builder.addCase(controlNetImageProcessed, (state, action) => {
@@ -412,6 +461,13 @@ export const {
   controlNetProcessorTypeChanged,
   controlNetReset,
   controlNetAutoConfigToggled,
+  isIPAdapterEnableToggled,
+  ipAdapterImageChanged,
+  ipAdapterWeightChanged,
+  ipAdapterModelChanged,
+  ipAdapterBeginStepPctChanged,
+  ipAdapterEndStepPctChanged,
+  ipAdapterStateReset,
 } = controlNetSlice.actions;
 
 export default controlNetSlice.reducer;

invokeai/frontend/web/src/features/deleteImageModal/components/DeleteImageModal.tsx

@@ -10,20 +10,20 @@ import {
   Text,
 } from '@chakra-ui/react';
 import { createSelector } from '@reduxjs/toolkit';
+import { stateSelector } from 'app/store/store';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
 import IAIButton from 'common/components/IAIButton';
 import IAISwitch from 'common/components/IAISwitch';
 import { setShouldConfirmOnDelete } from 'features/system/store/systemSlice';
-import { stateSelector } from 'app/store/store';
 import { some } from 'lodash-es';
 import { ChangeEvent, memo, useCallback, useRef } from 'react';
 import { useTranslation } from 'react-i18next';
 import { imageDeletionConfirmed } from '../store/actions';
 import { getImageUsage, selectImageUsage } from '../store/selectors';
 import { imageDeletionCanceled, isModalOpenChanged } from '../store/slice';
-import ImageUsageMessage from './ImageUsageMessage';
 import { ImageUsage } from '../store/types';
+import ImageUsageMessage from './ImageUsageMessage';
 
 const selector = createSelector(
   [stateSelector, selectImageUsage],
@@ -42,6 +42,7 @@ const selector = createSelector(
       isCanvasImage: some(allImageUsage, (i) => i.isCanvasImage),
       isNodesImage: some(allImageUsage, (i) => i.isNodesImage),
       isControlNetImage: some(allImageUsage, (i) => i.isControlNetImage),
+      isIPAdapterImage: some(allImageUsage, (i) => i.isIPAdapterImage),
     };
 
     return {

invokeai/frontend/web/src/features/deleteImageModal/components/ImageUsageMessage.tsx

@@ -1,8 +1,8 @@
 import { ListItem, Text, UnorderedList } from '@chakra-ui/react';
 import { some } from 'lodash-es';
 import { memo } from 'react';
-import { ImageUsage } from '../store/types';
 import { useTranslation } from 'react-i18next';
+import { ImageUsage } from '../store/types';
 
 type Props = {
   imageUsage?: ImageUsage;
@@ -38,6 +38,9 @@ const ImageUsageMessage = (props: Props) => {
         {imageUsage.isControlNetImage && (
           <ListItem>{t('common.controlNet')}</ListItem>
         )}
+        {imageUsage.isIPAdapterImage && (
+          <ListItem>{t('common.ipAdapter')}</ListItem>
+        )}
         {imageUsage.isNodesImage && (
           <ListItem>{t('common.nodeEditor')}</ListItem>
         )}

invokeai/frontend/web/src/features/deleteImageModal/store/selectors.ts

@@ -1,9 +1,9 @@
 import { createSelector } from '@reduxjs/toolkit';
 import { RootState } from 'app/store/store';
 import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
+import { isInvocationNode } from 'features/nodes/types/types';
 import { some } from 'lodash-es';
 import { ImageUsage } from './types';
-import { isInvocationNode } from 'features/nodes/types/types';
 
 export const getImageUsage = (state: RootState, image_name: string) => {
   const { generation, canvas, nodes, controlNet } = state;
@@ -27,11 +27,15 @@ export const getImageUsage = (state: RootState, image_name: string) => {
       c.controlImage === image_name || c.processedControlImage === image_name
   );
 
+  const isIPAdapterImage =
+    controlNet.ipAdapterInfo.adapterImage?.image_name === image_name;
+
   const imageUsage: ImageUsage = {
     isInitialImage,
     isCanvasImage,
     isNodesImage,
     isControlNetImage,
+    isIPAdapterImage,
   };
 
   return imageUsage;

invokeai/frontend/web/src/features/deleteImageModal/store/types.ts

@@ -10,4 +10,5 @@ export type ImageUsage = {
   isCanvasImage: boolean;
   isNodesImage: boolean;
   isControlNetImage: boolean;
+  isIPAdapterImage: boolean;
 };

invokeai/frontend/web/src/features/dnd/types/index.ts

@@ -35,6 +35,10 @@ export type ControlNetDropData = BaseDropData & {
   };
 };
 
+export type IPAdapterImageDropData = BaseDropData & {
+  actionType: 'SET_IP_ADAPTER_IMAGE';
+};
+
 export type CanvasInitialImageDropData = BaseDropData & {
   actionType: 'SET_CANVAS_INITIAL_IMAGE';
 };
@@ -73,6 +77,7 @@ export type TypesafeDroppableData =
   | CurrentImageDropData
   | InitialImageDropData
   | ControlNetDropData
+  | IPAdapterImageDropData
   | CanvasInitialImageDropData
   | NodesImageDropData
   | AddToBatchDropData

invokeai/frontend/web/src/features/dnd/util/isValidDrop.ts

@@ -24,6 +24,8 @@ export const isValidDrop = (
       return payloadType === 'IMAGE_DTO';
     case 'SET_CONTROLNET_IMAGE':
       return payloadType === 'IMAGE_DTO';
+    case 'SET_IP_ADAPTER_IMAGE':
+      return payloadType === 'IMAGE_DTO';
     case 'SET_CANVAS_INITIAL_IMAGE':
       return payloadType === 'IMAGE_DTO';
     case 'SET_NODES_IMAGE':

invokeai/frontend/web/src/features/gallery/components/Boards/DeleteBoardModal.tsx

@@ -53,6 +53,7 @@ const DeleteBoardModal = (props: Props) => {
           isCanvasImage: some(allImageUsage, (i) => i.isCanvasImage),
           isNodesImage: some(allImageUsage, (i) => i.isNodesImage),
           isControlNetImage: some(allImageUsage, (i) => i.isControlNetImage),
+          isIPAdapterImage: some(allImageUsage, (i) => i.isIPAdapterImage),
         };
         return { imageUsageSummary };
       }),

invokeai/frontend/web/src/features/gallery/components/ImageMetadataViewer/ImageMetadataActions.tsx

@@ -1,8 +1,9 @@
-import { CoreMetadata } from 'features/nodes/types/types';
+import { CoreMetadata, LoRAMetadataItem } from 'features/nodes/types/types';
 import { useRecallParameters } from 'features/parameters/hooks/useRecallParameters';
 import { memo, useCallback } from 'react';
-import ImageMetadataItem from './ImageMetadataItem';
 import { useTranslation } from 'react-i18next';
+import { isValidLoRAModel } from '../../../parameters/types/parameterSchemas';
+import ImageMetadataItem from './ImageMetadataItem';
 
 type Props = {
   metadata?: CoreMetadata;
@@ -24,6 +25,7 @@ const ImageMetadataActions = (props: Props) => {
     recallWidth,
     recallHeight,
     recallStrength,
+    recallLoRA,
   } = useRecallParameters();
 
   const handleRecallPositivePrompt = useCallback(() => {
@@ -66,6 +68,13 @@ const ImageMetadataActions = (props: Props) => {
     recallStrength(metadata?.strength);
   }, [metadata?.strength, recallStrength]);
 
+  const handleRecallLoRA = useCallback(
+    (lora: LoRAMetadataItem) => {
+      recallLoRA(lora);
+    },
+    [recallLoRA]
+  );
+
   if (!metadata || Object.keys(metadata).length === 0) {
     return null;
   }
@@ -130,20 +139,6 @@ const ImageMetadataActions = (props: Props) => {
           onClick={handleRecallHeight}
         />
       )}
-      {/* {metadata.threshold !== undefined && (
-          <MetadataItem
-            label={t('metadata.threshold')}
-            value={metadata.threshold}
-            onClick={() => dispatch(setThreshold(Number(metadata.threshold)))}
-          />
-        )}
-        {metadata.perlin !== undefined && (
-          <MetadataItem
-            label={t('metadata.perlin')}
-            value={metadata.perlin}
-            onClick={() => dispatch(setPerlin(Number(metadata.perlin)))}
-          />
-        )} */}
       {metadata.scheduler && (
         <ImageMetadataItem
           label={t('metadata.scheduler')}
@@ -165,40 +160,6 @@ const ImageMetadataActions = (props: Props) => {
           onClick={handleRecallCfgScale}
         />
       )}
-      {/* {metadata.variations && metadata.variations.length > 0 && (
-          <MetadataItem
-            label="{t('metadata.variations')}
-            value={seedWeightsToString(metadata.variations)}
-            onClick={() =>
-              dispatch(
-                setSeedWeights(seedWeightsToString(metadata.variations))
-              )
-            }
-          />
-        )}
-        {metadata.seamless && (
-          <MetadataItem
-            label={t('metadata.seamless')}
-            value={metadata.seamless}
-            onClick={() => dispatch(setSeamless(metadata.seamless))}
-          />
-        )}
-        {metadata.hires_fix && (
-          <MetadataItem
-            label={t('metadata.hiresFix')}
-            value={metadata.hires_fix}
-            onClick={() => dispatch(setHiresFix(metadata.hires_fix))}
-          />
-        )} */}
-
-      {/* {init_image_path && (
-          <MetadataItem
-            label={t('metadata.initImage')}
-            value={init_image_path}
-            isLink
-            onClick={() => dispatch(setInitialImage(init_image_path))}
-          />
-        )} */}
       {metadata.strength && (
         <ImageMetadataItem
           label={t('metadata.strength')}
@@ -206,13 +167,19 @@ const ImageMetadataActions = (props: Props) => {
           onClick={handleRecallStrength}
         />
       )}
-      {/* {metadata.fit && (
-          <MetadataItem
-            label={t('metadata.fit')}
-            value={metadata.fit}
-            onClick={() => dispatch(setShouldFitToWidthHeight(metadata.fit))}
-          />
-        )} */}
+      {metadata.loras &&
+        metadata.loras.map((lora, index) => {
+          if (isValidLoRAModel(lora.lora)) {
+            return (
+              <ImageMetadataItem
+                key={index}
+                label="LoRA"
+                value={`${lora.lora.model_name} - ${lora.weight}`}
+                onClick={() => handleRecallLoRA(lora)}
+              />
+            );
+          }
+        })}
     </>
   );
 };

invokeai/frontend/web/src/features/lora/store/loraSlice.ts

@@ -27,6 +27,13 @@ export const loraSlice = createSlice({
       const { model_name, id, base_model } = action.payload;
       state.loras[id] = { id, model_name, base_model, ...defaultLoRAConfig };
     },
+    loraRecalled: (
+      state,
+      action: PayloadAction<LoRAModelConfigEntity & { weight: number }>
+    ) => {
+      const { model_name, id, base_model, weight } = action.payload;
+      state.loras[id] = { id, model_name, base_model, weight };
+    },
     loraRemoved: (state, action: PayloadAction<string>) => {
       const id = action.payload;
       delete state.loras[id];
@@ -62,6 +69,7 @@ export const {
   loraWeightChanged,
   loraWeightReset,
   lorasCleared,
+  loraRecalled,
 } = loraSlice.actions;
 
 export default loraSlice.reducer;

invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/fields/InputFieldRenderer.tsx

@@ -15,6 +15,7 @@ import SDXLMainModelInputField from './inputs/SDXLMainModelInputField';
 import SchedulerInputField from './inputs/SchedulerInputField';
 import StringInputField from './inputs/StringInputField';
 import VaeModelInputField from './inputs/VaeModelInputField';
+import IPAdapterModelInputField from './inputs/IPAdapterModelInputField';
 
 type InputFieldProps = {
   nodeId: string;
@@ -147,6 +148,19 @@ const InputFieldRenderer = ({ nodeId, fieldName }: InputFieldProps) => {
     );
   }
 
+  if (
+    field?.type === 'IPAdapterModelField' &&
+    fieldTemplate?.type === 'IPAdapterModelField'
+  ) {
+    return (
+      <IPAdapterModelInputField
+        nodeId={nodeId}
+        field={field}
+        fieldTemplate={fieldTemplate}
+      />
+    );
+  }
+
   if (field?.type === 'ColorField' && fieldTemplate?.type === 'ColorField') {
     return (
       <ColorInputField

invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/fields/inputs/IPAdapterInputField.tsx

@@ -0,0 +1,17 @@
+import {
+  IPAdapterInputFieldTemplate,
+  IPAdapterInputFieldValue,
+  FieldComponentProps,
+} from 'features/nodes/types/types';
+import { memo } from 'react';
+
+const IPAdapterInputFieldComponent = (
+  _props: FieldComponentProps<
+    IPAdapterInputFieldValue,
+    IPAdapterInputFieldTemplate
+  >
+) => {
+  return null;
+};
+
+export default memo(IPAdapterInputFieldComponent);

invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/fields/inputs/IPAdapterModelInputField.tsx

@@ -0,0 +1,100 @@
+import { SelectItem } from '@mantine/core';
+import { useAppDispatch } from 'app/store/storeHooks';
+import IAIMantineSelect from 'common/components/IAIMantineSelect';
+import { fieldIPAdapterModelValueChanged } from 'features/nodes/store/nodesSlice';
+import {
+  IPAdapterModelInputFieldTemplate,
+  IPAdapterModelInputFieldValue,
+  FieldComponentProps,
+} from 'features/nodes/types/types';
+import { MODEL_TYPE_MAP } from 'features/parameters/types/constants';
+import { modelIdToIPAdapterModelParam } from 'features/parameters/util/modelIdToIPAdapterModelParams';
+import { forEach } from 'lodash-es';
+import { memo, useCallback, useMemo } from 'react';
+import { useGetIPAdapterModelsQuery } from 'services/api/endpoints/models';
+
+const IPAdapterModelInputFieldComponent = (
+  props: FieldComponentProps<
+    IPAdapterModelInputFieldValue,
+    IPAdapterModelInputFieldTemplate
+  >
+) => {
+  const { nodeId, field } = props;
+  const ipAdapterModel = field.value;
+  const dispatch = useAppDispatch();
+
+  const { data: ipAdapterModels } = useGetIPAdapterModelsQuery();
+
+  // grab the full model entity from the RTK Query cache
+  const selectedModel = useMemo(
+    () =>
+      ipAdapterModels?.entities[
+        `${ipAdapterModel?.base_model}/ip_adapter/${ipAdapterModel?.model_name}`
+      ] ?? null,
+    [
+      ipAdapterModel?.base_model,
+      ipAdapterModel?.model_name,
+      ipAdapterModels?.entities,
+    ]
+  );
+
+  const data = useMemo(() => {
+    if (!ipAdapterModels) {
+      return [];
+    }
+
+    const data: SelectItem[] = [];
+
+    forEach(ipAdapterModels.entities, (model, id) => {
+      if (!model) {
+        return;
+      }
+
+      data.push({
+        value: id,
+        label: model.model_name,
+        group: MODEL_TYPE_MAP[model.base_model],
+      });
+    });
+
+    return data;
+  }, [ipAdapterModels]);
+
+  const handleValueChanged = useCallback(
+    (v: string | null) => {
+      if (!v) {
+        return;
+      }
+
+      const newIPAdapterModel = modelIdToIPAdapterModelParam(v);
+
+      if (!newIPAdapterModel) {
+        return;
+      }
+
+      dispatch(
+        fieldIPAdapterModelValueChanged({
+          nodeId,
+          fieldName: field.name,
+          value: newIPAdapterModel,
+        })
+      );
+    },
+    [dispatch, field.name, nodeId]
+  );
+
+  return (
+    <IAIMantineSelect
+      className="nowheel nodrag"
+      tooltip={selectedModel?.description}
+      value={selectedModel?.id ?? null}
+      placeholder="Pick one"
+      error={!selectedModel}
+      data={data}
+      onChange={handleValueChanged}
+      sx={{ width: '100%' }}
+    />
+  );
+};
+
+export default memo(IPAdapterModelInputFieldComponent);

invokeai/frontend/web/src/features/nodes/store/nodesSlice.ts

@@ -41,6 +41,7 @@ import {
   IntegerInputFieldValue,
   InvocationNodeData,
   InvocationTemplate,
+  IPAdapterModelInputFieldValue,
   isInvocationNode,
   isNotesNode,
   LoRAModelInputFieldValue,
@@ -520,6 +521,12 @@ const nodesSlice = createSlice({
     ) => {
       fieldValueReducer(state, action);
     },
+    fieldIPAdapterModelValueChanged: (
+      state,
+      action: FieldValueAction<IPAdapterModelInputFieldValue>
+    ) => {
+      fieldValueReducer(state, action);
+    },
     fieldEnumModelValueChanged: (
       state,
       action: FieldValueAction<EnumInputFieldValue>
@@ -866,6 +873,7 @@ export const {
   fieldLoRAModelValueChanged,
   fieldEnumModelValueChanged,
   fieldControlNetModelValueChanged,
+  fieldIPAdapterModelValueChanged,
   fieldRefinerModelValueChanged,
   fieldSchedulerValueChanged,
   nodeIsOpenChanged,

invokeai/frontend/web/src/features/nodes/types/constants.ts

@@ -41,6 +41,7 @@ export const POLYMORPHIC_TYPES = [
 ];
 
 export const MODEL_TYPES = [
+  'IPAdapterModelField',
   'ControlNetModelField',
   'LoRAModelField',
   'MainModelField',
@@ -236,6 +237,16 @@ export const FIELDS: Record<FieldType, FieldUIConfig> = {
     description: t('nodes.integerPolymorphicDescription'),
     title: t('nodes.integerPolymorphic'),
   },
+  IPAdapterField: {
+    color: 'green.300',
+    description: 'IP-Adapter info passed between nodes.',
+    title: 'IP-Adapter',
+  },
+  IPAdapterModelField: {
+    color: 'teal.500',
+    description: 'IP-Adapter model',
+    title: 'IP-Adapter Model',
+  },
   LatentsCollection: {
     color: 'pink.500',
     description: t('nodes.latentsCollectionDescription'),

invokeai/frontend/web/src/features/nodes/types/types.ts

@@ -94,6 +94,8 @@ export const zFieldType = z.enum([
   'integer',
   'IntegerCollection',
   'IntegerPolymorphic',
+  'IPAdapterField',
+  'IPAdapterModelField',
   'LatentsCollection',
   'LatentsField',
   'LatentsPolymorphic',
@@ -389,6 +391,25 @@ export type ControlCollectionInputFieldValue = z.infer<
   typeof zControlCollectionInputFieldValue
 >;
 
+export const zIPAdapterModel = zModelIdentifier;
+export type IPAdapterModel = z.infer<typeof zIPAdapterModel>;
+
+export const zIPAdapterField = z.object({
+  image: zImageField,
+  ip_adapter_model: zIPAdapterModel,
+  image_encoder_model: z.string().trim().min(1),
+  weight: z.number(),
+});
+export type IPAdapterField = z.infer<typeof zIPAdapterField>;
+
+export const zIPAdapterInputFieldValue = zInputFieldValueBase.extend({
+  type: z.literal('IPAdapterField'),
+  value: zIPAdapterField.optional(),
+});
+export type IPAdapterInputFieldValue = z.infer<
+  typeof zIPAdapterInputFieldValue
+>;
+
 export const zModelType = z.enum([
   'onnx',
   'main',
@@ -538,6 +559,17 @@ export type ControlNetModelInputFieldValue = z.infer<
   typeof zControlNetModelInputFieldValue
 >;
 
+export const zIPAdapterModelField = zModelIdentifier;
+export type IPAdapterModelField = z.infer<typeof zIPAdapterModelField>;
+
+export const zIPAdapterModelInputFieldValue = zInputFieldValueBase.extend({
+  type: z.literal('IPAdapterModelField'),
+  value: zIPAdapterModelField.optional(),
+});
+export type IPAdapterModelInputFieldValue = z.infer<
+  typeof zIPAdapterModelInputFieldValue
+>;
+
 export const zCollectionInputFieldValue = zInputFieldValueBase.extend({
   type: z.literal('Collection'),
   value: z.array(z.any()).optional(), // TODO: should this field ever have a value?
@@ -620,6 +652,8 @@ export const zInputFieldValue = z.discriminatedUnion('type', [
   zIntegerCollectionInputFieldValue,
   zIntegerPolymorphicInputFieldValue,
   zIntegerInputFieldValue,
+  zIPAdapterInputFieldValue,
+  zIPAdapterModelInputFieldValue,
   zLatentsInputFieldValue,
   zLatentsCollectionInputFieldValue,
   zLatentsPolymorphicInputFieldValue,
@@ -822,6 +856,11 @@ export type ControlPolymorphicInputFieldTemplate = Omit<
   type: 'ControlPolymorphic';
 };
 
+export type IPAdapterInputFieldTemplate = InputFieldTemplateBase & {
+  default: undefined;
+  type: 'IPAdapterField';
+};
+
 export type EnumInputFieldTemplate = InputFieldTemplateBase & {
   default: string;
   type: 'enum';
@@ -859,6 +898,11 @@ export type ControlNetModelInputFieldTemplate = InputFieldTemplateBase & {
   type: 'ControlNetModelField';
 };
 
+export type IPAdapterModelInputFieldTemplate = InputFieldTemplateBase & {
+  default: string;
+  type: 'IPAdapterModelField';
+};
+
 export type CollectionInputFieldTemplate = InputFieldTemplateBase & {
   default: [];
   type: 'Collection';
@@ -930,6 +974,8 @@ export type InputFieldTemplate =
   | IntegerCollectionInputFieldTemplate
   | IntegerPolymorphicInputFieldTemplate
   | IntegerInputFieldTemplate
+  | IPAdapterInputFieldTemplate
+  | IPAdapterModelInputFieldTemplate
   | LatentsInputFieldTemplate
   | LatentsCollectionInputFieldTemplate
   | LatentsPolymorphicInputFieldTemplate
@@ -1057,6 +1103,13 @@ export const isInvocationFieldSchema = (
 
 export type InvocationEdgeExtra = { type: 'default' | 'collapsed' };
 
+const zLoRAMetadataItem = z.object({
+  lora: zLoRAModelField.deepPartial(),
+  weight: z.number(),
+});
+
+export type LoRAMetadataItem = z.infer<typeof zLoRAMetadataItem>;
+
 export const zCoreMetadata = z
   .object({
     app_version: z.string().nullish(),
@@ -1076,14 +1129,7 @@ export const zCoreMetadata = z
       .union([zMainModel.deepPartial(), zOnnxModel.deepPartial()])
       .nullish(),
     controlnets: z.array(zControlField.deepPartial()).nullish(),
-    loras: z
-      .array(
-        z.object({
-          lora: zLoRAModelField.deepPartial(),
-          weight: z.number(),
-        })
-      )
-      .nullish(),
+    loras: z.array(zLoRAMetadataItem).nullish(),
     vae: zVaeModelField.nullish(),
     strength: z.number().nullish(),
     init_image: z.string().nullish(),

invokeai/frontend/web/src/features/nodes/util/fieldTemplateBuilders.ts

@@ -60,6 +60,8 @@ import {
   ImageField,
   LatentsField,
   ConditioningField,
+  IPAdapterInputFieldTemplate,
+  IPAdapterModelInputFieldTemplate,
 } from '../types/types';
 import { ControlField } from 'services/api/types';
 
@@ -435,6 +437,19 @@ const buildControlNetModelInputFieldTemplate = ({
   return template;
 };
 
+const buildIPAdapterModelInputFieldTemplate = ({
+  schemaObject,
+  baseField,
+}: BuildInputFieldArg): IPAdapterModelInputFieldTemplate => {
+  const template: IPAdapterModelInputFieldTemplate = {
+    ...baseField,
+    type: 'IPAdapterModelField',
+    default: schemaObject.default ?? undefined,
+  };
+
+  return template;
+};
+
 const buildImageInputFieldTemplate = ({
   schemaObject,
   baseField,
@@ -648,6 +663,19 @@ const buildControlCollectionInputFieldTemplate = ({
   return template;
 };
 
+const buildIPAdapterInputFieldTemplate = ({
+  schemaObject,
+  baseField,
+}: BuildInputFieldArg): IPAdapterInputFieldTemplate => {
+  const template: IPAdapterInputFieldTemplate = {
+    ...baseField,
+    type: 'IPAdapterField',
+    default: schemaObject.default ?? undefined,
+  };
+
+  return template;
+};
+
 const buildEnumInputFieldTemplate = ({
   schemaObject,
   baseField,
@@ -851,6 +879,8 @@ const TEMPLATE_BUILDER_MAP = {
   integer: buildIntegerInputFieldTemplate,
   IntegerCollection: buildIntegerCollectionInputFieldTemplate,
   IntegerPolymorphic: buildIntegerPolymorphicInputFieldTemplate,
+  IPAdapterField: buildIPAdapterInputFieldTemplate,
+  IPAdapterModelField: buildIPAdapterModelInputFieldTemplate,
   LatentsCollection: buildLatentsCollectionInputFieldTemplate,
   LatentsField: buildLatentsInputFieldTemplate,
   LatentsPolymorphic: buildLatentsPolymorphicInputFieldTemplate,

invokeai/frontend/web/src/features/nodes/util/fieldValueBuilders.ts

@@ -28,6 +28,8 @@ const FIELD_VALUE_FALLBACK_MAP = {
   integer: 0,
   IntegerCollection: [],
   IntegerPolymorphic: 0,
+  IPAdapterField: undefined,
+  IPAdapterModelField: undefined,
   LatentsCollection: [],
   LatentsField: undefined,
   LatentsPolymorphic: undefined,

invokeai/frontend/web/src/features/nodes/util/graphBuilders/addIPAdapterToLinearGraph.ts

@@ -0,0 +1,59 @@
+import { RootState } from 'app/store/store';
+import { IPAdapterInvocation } from 'services/api/types';
+import { NonNullableGraph } from '../../types/types';
+import { IP_ADAPTER } from './constants';
+
+export const addIPAdapterToLinearGraph = (
+  state: RootState,
+  graph: NonNullableGraph,
+  baseNodeId: string
+): void => {
+  const { isIPAdapterEnabled, ipAdapterInfo } = state.controlNet;
+
+  // const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
+  //   | MetadataAccumulatorInvocation
+  //   | undefined;
+
+  if (isIPAdapterEnabled && ipAdapterInfo.model) {
+    const ipAdapterNode: IPAdapterInvocation = {
+      id: IP_ADAPTER,
+      type: 'ip_adapter',
+      is_intermediate: true,
+      weight: ipAdapterInfo.weight,
+      ip_adapter_model: {
+        base_model: ipAdapterInfo.model?.base_model,
+        model_name: ipAdapterInfo.model?.model_name,
+      },
+      begin_step_percent: ipAdapterInfo.beginStepPct,
+      end_step_percent: ipAdapterInfo.endStepPct,
+    };
+
+    if (ipAdapterInfo.adapterImage) {
+      ipAdapterNode.image = {
+        image_name: ipAdapterInfo.adapterImage.image_name,
+      };
+    } else {
+      return;
+    }
+
+    graph.nodes[ipAdapterNode.id] = ipAdapterNode as IPAdapterInvocation;
+
+    // if (metadataAccumulator?.ip_adapters) {
+    //   // metadata accumulator only needs the ip_adapter field - not the whole node
+    //   // extract what we need and add to the accumulator
+    //   const ipAdapterField = omit(ipAdapterNode, [
+    //     'id',
+    //     'type',
+    //   ]) as IPAdapterField;
+    //   metadataAccumulator.ip_adapters.push(ipAdapterField);
+    // }
+
+    graph.edges.push({
+      source: { node_id: ipAdapterNode.id, field: 'ip_adapter' },
+      destination: {
+        node_id: baseNodeId,
+        field: 'ip_adapter',
+      },
+    });
+  }
+};

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasImageToImageGraph.ts

@@ -5,6 +5,7 @@ import { initialGenerationState } from 'features/parameters/store/generationSlic
 import { ImageDTO, ImageToLatentsInvocation } from 'services/api/types';
 import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
+import { addIPAdapterToLinearGraph } from './addIPAdapterToLinearGraph';
 import { addLoRAsToGraph } from './addLoRAsToGraph';
 import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
 import { addSeamlessToLinearGraph } from './addSeamlessToLinearGraph';
@@ -366,6 +367,9 @@ export const buildCanvasImageToImageGraph = (
   // add controlnet, mutating `graph`
   addControlNetToLinearGraph(state, graph, DENOISE_LATENTS);
 
+  // Add IP Adapter
+  addIPAdapterToLinearGraph(state, graph, DENOISE_LATENTS);
+
   // NSFW & watermark - must be last thing added to graph
   if (state.system.shouldUseNSFWChecker) {
     // must add before watermarker!

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasInpaintGraph.ts

@@ -12,6 +12,7 @@ import {
   RangeOfSizeInvocation,
 } from 'services/api/types';
 import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
+import { addIPAdapterToLinearGraph } from './addIPAdapterToLinearGraph';
 import { addLoRAsToGraph } from './addLoRAsToGraph';
 import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
 import { addSeamlessToLinearGraph } from './addSeamlessToLinearGraph';
@@ -736,6 +737,9 @@ export const buildCanvasInpaintGraph = (
   // add controlnet, mutating `graph`
   addControlNetToLinearGraph(state, graph, DENOISE_LATENTS);
 
+  // Add IP Adapter
+  addIPAdapterToLinearGraph(state, graph, DENOISE_LATENTS);
+
   // NSFW & watermark - must be last thing added to graph
   if (state.system.shouldUseNSFWChecker) {
     // must add before watermarker!

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasOutpaintGraph.ts

@@ -11,6 +11,7 @@ import {
   RangeOfSizeInvocation,
 } from 'services/api/types';
 import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
+import { addIPAdapterToLinearGraph } from './addIPAdapterToLinearGraph';
 import { addLoRAsToGraph } from './addLoRAsToGraph';
 import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
 import { addSeamlessToLinearGraph } from './addSeamlessToLinearGraph';
@@ -838,6 +839,9 @@ export const buildCanvasOutpaintGraph = (
   // add controlnet, mutating `graph`
   addControlNetToLinearGraph(state, graph, DENOISE_LATENTS);
 
+  // Add IP Adapter
+  addIPAdapterToLinearGraph(state, graph, DENOISE_LATENTS);
+
   // NSFW & watermark - must be last thing added to graph
   if (state.system.shouldUseNSFWChecker) {
     // must add before watermarker!

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasSDXLImageToImageGraph.ts

@@ -5,6 +5,7 @@ import { initialGenerationState } from 'features/parameters/store/generationSlic
 import { ImageDTO, ImageToLatentsInvocation } from 'services/api/types';
 import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
+import { addIPAdapterToLinearGraph } from './addIPAdapterToLinearGraph';
 import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
 import { addSDXLLoRAsToGraph } from './addSDXLLoRAstoGraph';
 import { addSDXLRefinerToGraph } from './addSDXLRefinerToGraph';
@@ -392,6 +393,9 @@ export const buildCanvasSDXLImageToImageGraph = (
   // add controlnet, mutating `graph`
   addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
 
+  // Add IP Adapter
+  addIPAdapterToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
+
   // NSFW & watermark - must be last thing added to graph
   if (state.system.shouldUseNSFWChecker) {
     // must add before watermarker!

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasSDXLInpaintGraph.ts

@@ -46,6 +46,7 @@ import {
   SEAMLESS,
 } from './constants';
 import { craftSDXLStylePrompt } from './helpers/craftSDXLStylePrompt';
+import { addIPAdapterToLinearGraph } from './addIPAdapterToLinearGraph';
 
 /**
  * Builds the Canvas tab's Inpaint graph.
@@ -765,6 +766,9 @@ export const buildCanvasSDXLInpaintGraph = (
   // add controlnet, mutating `graph`
   addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
 
+  // Add IP Adapter
+  addIPAdapterToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
+
   // NSFW & watermark - must be last thing added to graph
   if (state.system.shouldUseNSFWChecker) {
     // must add before watermarker!

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasSDXLOutpaintGraph.ts

@@ -11,6 +11,7 @@ import {
   RangeOfSizeInvocation,
 } from 'services/api/types';
 import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
+import { addIPAdapterToLinearGraph } from './addIPAdapterToLinearGraph';
 import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
 import { addSDXLLoRAsToGraph } from './addSDXLLoRAstoGraph';
 import { addSDXLRefinerToGraph } from './addSDXLRefinerToGraph';
@@ -868,6 +869,9 @@ export const buildCanvasSDXLOutpaintGraph = (
   // add controlnet, mutating `graph`
   addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
 
+  // Add IP Adapter
+  addIPAdapterToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
+
   // NSFW & watermark - must be last thing added to graph
   if (state.system.shouldUseNSFWChecker) {
     // must add before watermarker!

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasSDXLTextToImageGraph.ts

@@ -8,6 +8,7 @@ import {
 } from 'services/api/types';
 import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
+import { addIPAdapterToLinearGraph } from './addIPAdapterToLinearGraph';
 import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
 import { addSDXLLoRAsToGraph } from './addSDXLLoRAstoGraph';
 import { addSDXLRefinerToGraph } from './addSDXLRefinerToGraph';
@@ -372,6 +373,9 @@ export const buildCanvasSDXLTextToImageGraph = (
   // add controlnet, mutating `graph`
   addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
 
+  // Add IP Adapter
+  addIPAdapterToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
+
   // NSFW & watermark - must be last thing added to graph
   if (state.system.shouldUseNSFWChecker) {
     // must add before watermarker!

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasTextToImageGraph.ts

@@ -8,6 +8,7 @@ import {
 } from 'services/api/types';
 import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
+import { addIPAdapterToLinearGraph } from './addIPAdapterToLinearGraph';
 import { addLoRAsToGraph } from './addLoRAsToGraph';
 import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
 import { addSeamlessToLinearGraph } from './addSeamlessToLinearGraph';
@@ -345,6 +346,9 @@ export const buildCanvasTextToImageGraph = (
   // add controlnet, mutating `graph`
   addControlNetToLinearGraph(state, graph, DENOISE_LATENTS);
 
+  // Add IP Adapter
+  addIPAdapterToLinearGraph(state, graph, DENOISE_LATENTS);
+
   // NSFW & watermark - must be last thing added to graph
   if (state.system.shouldUseNSFWChecker) {
     // must add before watermarker!

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearImageToImageGraph.ts

@@ -8,6 +8,7 @@ import {
 } from 'services/api/types';
 import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
+import { addIPAdapterToLinearGraph } from './addIPAdapterToLinearGraph';
 import { addLoRAsToGraph } from './addLoRAsToGraph';
 import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
 import { addSeamlessToLinearGraph } from './addSeamlessToLinearGraph';
@@ -364,6 +365,9 @@ export const buildLinearImageToImageGraph = (
   // add controlnet, mutating `graph`
   addControlNetToLinearGraph(state, graph, DENOISE_LATENTS);
 
+  // Add IP Adapter
+  addIPAdapterToLinearGraph(state, graph, DENOISE_LATENTS);
+
   // NSFW & watermark - must be last thing added to graph
   if (state.system.shouldUseNSFWChecker) {
     // must add before watermarker!

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearSDXLImageToImageGraph.ts

@@ -8,6 +8,7 @@ import {
 } from 'services/api/types';
 import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
+import { addIPAdapterToLinearGraph } from './addIPAdapterToLinearGraph';
 import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
 import { addSDXLLoRAsToGraph } from './addSDXLLoRAstoGraph';
 import { addSDXLRefinerToGraph } from './addSDXLRefinerToGraph';
@@ -384,6 +385,9 @@ export const buildLinearSDXLImageToImageGraph = (
   // add controlnet, mutating `graph`
   addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
 
+  // Add IP Adapter
+  addIPAdapterToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
+
   // add dynamic prompts - also sets up core iteration and seed
   addDynamicPromptsToGraph(state, graph);
 

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearSDXLTextToImageGraph.ts

@@ -4,6 +4,7 @@ import { NonNullableGraph } from 'features/nodes/types/types';
 import { initialGenerationState } from 'features/parameters/store/generationSlice';
 import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
+import { addIPAdapterToLinearGraph } from './addIPAdapterToLinearGraph';
 import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
 import { addSDXLLoRAsToGraph } from './addSDXLLoRAstoGraph';
 import { addSDXLRefinerToGraph } from './addSDXLRefinerToGraph';
@@ -277,6 +278,9 @@ export const buildLinearSDXLTextToImageGraph = (
   // add controlnet, mutating `graph`
   addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
 
+  // add IP Adapter
+  addIPAdapterToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
+
   // add dynamic prompts - also sets up core iteration and seed
   addDynamicPromptsToGraph(state, graph);
 

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearTextToImageGraph.ts

@@ -8,6 +8,7 @@ import {
 } from 'services/api/types';
 import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
+import { addIPAdapterToLinearGraph } from './addIPAdapterToLinearGraph';
 import { addLoRAsToGraph } from './addLoRAsToGraph';
 import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
 import { addSeamlessToLinearGraph } from './addSeamlessToLinearGraph';
@@ -282,6 +283,9 @@ export const buildLinearTextToImageGraph = (
   // add controlnet, mutating `graph`
   addControlNetToLinearGraph(state, graph, DENOISE_LATENTS);
 
+  // add IP Adapter
+  addIPAdapterToLinearGraph(state, graph, DENOISE_LATENTS);
+
   // NSFW & watermark - must be last thing added to graph
   if (state.system.shouldUseNSFWChecker) {
     // must add before watermarker!

invokeai/frontend/web/src/features/nodes/util/graphBuilders/constants.ts

@@ -45,6 +45,7 @@ export const MASK_RESIZE_DOWN = 'mask_resize_down';
 export const COLOR_CORRECT = 'color_correct';
 export const PASTE_IMAGE = 'img_paste';
 export const CONTROL_NET_COLLECT = 'control_net_collect';
+export const IP_ADAPTER = 'ip_adapter';
 export const DYNAMIC_PROMPT = 'dynamic_prompt';
 export const IMAGE_COLLECTION = 'image_collection';
 export const IMAGE_COLLECTION_ITERATE = 'image_collection_iterate';

invokeai/frontend/web/src/features/parameters/components/Parameters/ControlNet/ParamControlNetCollapse.tsx

@@ -6,6 +6,7 @@ import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
 import IAICollapse from 'common/components/IAICollapse';
 import IAIIconButton from 'common/components/IAIIconButton';
 import ControlNet from 'features/controlNet/components/ControlNet';
+import IPAdapterPanel from 'features/controlNet/components/ipAdapter/IPAdapterPanel';
 import ParamControlNetFeatureToggle from 'features/controlNet/components/parameters/ParamControlNetFeatureToggle';
 import {
   controlNetAdded,
@@ -25,14 +26,23 @@ import { v4 as uuidv4 } from 'uuid';
 const selector = createSelector(
   [stateSelector],
   ({ controlNet }) => {
-    const { controlNets, isEnabled } = controlNet;
+    const { controlNets, isEnabled, isIPAdapterEnabled } = controlNet;
 
     const validControlNets = getValidControlNets(controlNets);
 
-    const activeLabel =
-      isEnabled && validControlNets.length > 0
-        ? `${validControlNets.length} Active`
-        : undefined;
+    let activeLabel = undefined;
+
+    if (isEnabled && validControlNets.length > 0) {
+      activeLabel = `${validControlNets.length} ControlNet`;
+    }
+
+    if (isIPAdapterEnabled) {
+      if (activeLabel) {
+        activeLabel = `${activeLabel}, IP Adapter`;
+      } else {
+        activeLabel = 'IP Adapter';
+      }
+    }
 
     return { controlNetsArray: map(controlNets), activeLabel };
   },
@@ -101,6 +111,7 @@ const ParamControlNetCollapse = () => {
             <ControlNet controlNet={c} />
           </Fragment>
         ))}
+        <IPAdapterPanel />
       </Flex>
     </IAICollapse>
   );

invokeai/frontend/web/src/features/parameters/hooks/useRecallParameters.ts

@@ -1,6 +1,8 @@
+import { createSelector } from '@reduxjs/toolkit';
 import { useAppToaster } from 'app/components/Toaster';
-import { useAppDispatch } from 'app/store/storeHooks';
-import { CoreMetadata } from 'features/nodes/types/types';
+import { stateSelector } from 'app/store/store';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { CoreMetadata, LoRAMetadataItem } from 'features/nodes/types/types';
 import {
   refinerModelChanged,
   setNegativeStylePromptSDXL,
@@ -15,6 +17,11 @@ import {
 import { useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
 import { ImageDTO } from 'services/api/types';
+import {
+  loraModelsAdapter,
+  useGetLoRAModelsQuery,
+} from '../../../services/api/endpoints/models';
+import { loraRecalled } from '../../lora/store/loraSlice';
 import { initialImageSelected, modelSelected } from '../store/actions';
 import {
   setCfgScale,
@@ -30,6 +37,7 @@ import {
 import {
   isValidCfgScale,
   isValidHeight,
+  isValidLoRAModel,
   isValidMainModel,
   isValidNegativePrompt,
   isValidPositivePrompt,
@@ -46,10 +54,16 @@ import {
   isValidWidth,
 } from '../types/parameterSchemas';
 
+const selector = createSelector(stateSelector, ({ generation }) => {
+  const { model } = generation;
+  return { model };
+});
+
 export const useRecallParameters = () => {
   const dispatch = useAppDispatch();
   const toaster = useAppToaster();
   const { t } = useTranslation();
+  const { model } = useAppSelector(selector);
 
   const parameterSetToast = useCallback(() => {
     toaster({
@@ -60,14 +74,18 @@ export const useRecallParameters = () => {
     });
   }, [t, toaster]);
 
-  const parameterNotSetToast = useCallback(() => {
-    toaster({
-      title: t('toast.parameterNotSet'),
-      status: 'warning',
-      duration: 2500,
-      isClosable: true,
-    });
-  }, [t, toaster]);
+  const parameterNotSetToast = useCallback(
+    (description?: string) => {
+      toaster({
+        title: t('toast.parameterNotSet'),
+        description,
+        status: 'warning',
+        duration: 2500,
+        isClosable: true,
+      });
+    },
+    [t, toaster]
+  );
 
   const allParameterSetToast = useCallback(() => {
     toaster({
@@ -78,14 +96,18 @@ export const useRecallParameters = () => {
     });
   }, [t, toaster]);
 
-  const allParameterNotSetToast = useCallback(() => {
-    toaster({
-      title: t('toast.parametersNotSet'),
-      status: 'warning',
-      duration: 2500,
-      isClosable: true,
-    });
-  }, [t, toaster]);
+  const allParameterNotSetToast = useCallback(
+    (description?: string) => {
+      toaster({
+        title: t('toast.parametersNotSet'),
+        status: 'warning',
+        description,
+        duration: 2500,
+        isClosable: true,
+      });
+    },
+    [t, toaster]
+  );
 
   /**
    * Recall both prompts with toast
@@ -307,6 +329,67 @@ export const useRecallParameters = () => {
     [dispatch, parameterSetToast, parameterNotSetToast]
   );
 
+  /**
+   * Recall LoRA with toast
+   */
+
+  const { loras } = useGetLoRAModelsQuery(undefined, {
+    selectFromResult: (result) => ({
+      loras: result.data
+        ? loraModelsAdapter.getSelectors().selectAll(result.data)
+        : [],
+    }),
+  });
+
+  const prepareLoRAMetadataItem = useCallback(
+    (loraMetadataItem: LoRAMetadataItem) => {
+      if (!isValidLoRAModel(loraMetadataItem.lora)) {
+        return { lora: null, error: 'Invalid LoRA model' };
+      }
+
+      const { base_model, model_name } = loraMetadataItem.lora;
+
+      const matchingLoRA = loras.find(
+        (l) => l.base_model === base_model && l.model_name === model_name
+      );
+
+      if (!matchingLoRA) {
+        return { lora: null, error: 'LoRA model is not installed' };
+      }
+
+      const isCompatibleBaseModel =
+        matchingLoRA?.base_model === model?.base_model;
+
+      if (!isCompatibleBaseModel) {
+        return {
+          lora: null,
+          error: 'LoRA incompatible with currently-selected model',
+        };
+      }
+
+      return { lora: matchingLoRA, error: null };
+    },
+    [loras, model?.base_model]
+  );
+
+  const recallLoRA = useCallback(
+    (loraMetadataItem: LoRAMetadataItem) => {
+      const result = prepareLoRAMetadataItem(loraMetadataItem);
+
+      if (!result.lora) {
+        parameterNotSetToast(result.error);
+        return;
+      }
+
+      dispatch(
+        loraRecalled({ ...result.lora, weight: loraMetadataItem.weight })
+      );
+
+      parameterSetToast();
+    },
+    [prepareLoRAMetadataItem, dispatch, parameterSetToast, parameterNotSetToast]
+  );
+
   /*
    * Sets image as initial image with toast
    */
@@ -344,6 +427,7 @@ export const useRecallParameters = () => {
         refiner_positive_aesthetic_score,
         refiner_negative_aesthetic_score,
         refiner_start,
+        loras,
       } = metadata;
 
       if (isValidCfgScale(cfg_scale)) {
@@ -425,9 +509,21 @@ export const useRecallParameters = () => {
         dispatch(setRefinerStart(refiner_start));
       }
 
+      loras?.forEach((lora) => {
+        const result = prepareLoRAMetadataItem(lora);
+        if (result.lora) {
+          dispatch(loraRecalled({ ...result.lora, weight: lora.weight }));
+        }
+      });
+
       allParameterSetToast();
     },
-    [allParameterNotSetToast, allParameterSetToast, dispatch]
+    [
+      allParameterNotSetToast,
+      allParameterSetToast,
+      dispatch,
+      prepareLoRAMetadataItem,
+    ]
   );
 
   return {
@@ -444,6 +540,7 @@ export const useRecallParameters = () => {
     recallWidth,
     recallHeight,
     recallStrength,
+    recallLoRA,
     recallAllParameters,
     sendToImageToImage,
   };

invokeai/frontend/web/src/features/parameters/types/constants.ts

@@ -1,6 +1,7 @@
 import { components } from 'services/api/schema';
 
 export const MODEL_TYPE_MAP = {
+  any: 'Any',
   'sd-1': 'Stable Diffusion 1.x',
   'sd-2': 'Stable Diffusion 2.x',
   sdxl: 'Stable Diffusion XL',
@@ -8,6 +9,7 @@ export const MODEL_TYPE_MAP = {
 };
 
 export const MODEL_TYPE_SHORT_MAP = {
+  any: 'Any',
   'sd-1': 'SD1',
   'sd-2': 'SD2',
   sdxl: 'SDXL',
@@ -15,6 +17,10 @@ export const MODEL_TYPE_SHORT_MAP = {
 };
 
 export const clipSkipMap = {
+  any: {
+    maxClip: 0,
+    markers: [],
+  },
   'sd-1': {
     maxClip: 12,
     markers: [0, 1, 2, 3, 4, 8, 12],

invokeai/frontend/web/src/features/parameters/types/parameterSchemas.ts

@@ -210,7 +210,13 @@ export type HeightParam = z.infer<typeof zHeight>;
 export const isValidHeight = (val: unknown): val is HeightParam =>
   zHeight.safeParse(val).success;
 
-export const zBaseModel = z.enum(['sd-1', 'sd-2', 'sdxl', 'sdxl-refiner']);
+export const zBaseModel = z.enum([
+  'any',
+  'sd-1',
+  'sd-2',
+  'sdxl',
+  'sdxl-refiner',
+]);
 
 export type BaseModelParam = z.infer<typeof zBaseModel>;
 
@@ -323,7 +329,17 @@ export type ControlNetModelParam = z.infer<typeof zLoRAModel>;
 export const isValidControlNetModel = (
   val: unknown
 ): val is ControlNetModelParam => zControlNetModel.safeParse(val).success;
-
+/**
+ * Zod schema for IP-Adapter models
+ */
+export const zIPAdapterModel = z.object({
+  model_name: z.string().min(1),
+  base_model: zBaseModel,
+});
+/**
+ * Type alias for model parameter, inferred from its zod schema
+ */
+export type IPAdapterModelParam = z.infer<typeof zIPAdapterModel>;
 /**
  * Zod schema for l2l strength parameter
  */

invokeai/frontend/web/src/features/parameters/util/modelIdToIPAdapterModelParams.ts

@@ -0,0 +1,29 @@
+import { logger } from 'app/logging/logger';
+import { zIPAdapterModel } from 'features/parameters/types/parameterSchemas';
+import { IPAdapterModelField } from 'services/api/types';
+
+export const modelIdToIPAdapterModelParam = (
+  ipAdapterModelId: string
+): IPAdapterModelField | undefined => {
+  const log = logger('models');
+  const [base_model, _model_type, model_name] = ipAdapterModelId.split('/');
+
+  const result = zIPAdapterModel.safeParse({
+    base_model,
+    model_name,
+  });
+
+  if (!result.success) {
+    log.error(
+      {
+        ipAdapterModelId,
+        errors: result.error.format(),
+      },
+      'Failed to parse IP-Adapter model id'
+    );
+
+    return;
+  }
+
+  return result.data;
+};

invokeai/frontend/web/src/services/api/endpoints/models.ts

@@ -5,6 +5,7 @@ import {
   BaseModelType,
   CheckpointModelConfig,
   ControlNetModelConfig,
+  IPAdapterModelConfig,
   DiffusersModelConfig,
   ImportModelConfig,
   LoRAModelConfig,
@@ -36,6 +37,10 @@ export type ControlNetModelConfigEntity = ControlNetModelConfig & {
   id: string;
 };
 
+export type IPAdapterModelConfigEntity = IPAdapterModelConfig & {
+  id: string;
+};
+
 export type TextualInversionModelConfigEntity = TextualInversionModelConfig & {
   id: string;
 };
@@ -47,6 +52,7 @@ type AnyModelConfigEntity =
   | OnnxModelConfigEntity
   | LoRAModelConfigEntity
   | ControlNetModelConfigEntity
+  | IPAdapterModelConfigEntity
   | TextualInversionModelConfigEntity
   | VaeModelConfigEntity;
 
@@ -128,13 +134,17 @@ export const mainModelsAdapter = createEntityAdapter<MainModelConfigEntity>({
 const onnxModelsAdapter = createEntityAdapter<OnnxModelConfigEntity>({
   sortComparer: (a, b) => a.model_name.localeCompare(b.model_name),
 });
-const loraModelsAdapter = createEntityAdapter<LoRAModelConfigEntity>({
+export const loraModelsAdapter = createEntityAdapter<LoRAModelConfigEntity>({
   sortComparer: (a, b) => a.model_name.localeCompare(b.model_name),
 });
 export const controlNetModelsAdapter =
   createEntityAdapter<ControlNetModelConfigEntity>({
     sortComparer: (a, b) => a.model_name.localeCompare(b.model_name),
   });
+export const ipAdapterModelsAdapter =
+  createEntityAdapter<IPAdapterModelConfigEntity>({
+    sortComparer: (a, b) => a.model_name.localeCompare(b.model_name),
+  });
 export const textualInversionModelsAdapter =
   createEntityAdapter<TextualInversionModelConfigEntity>({
     sortComparer: (a, b) => a.model_name.localeCompare(b.model_name),
@@ -435,6 +445,37 @@ export const modelsApi = api.injectEndpoints({
         );
       },
     }),
+    getIPAdapterModels: build.query<
+      EntityState<IPAdapterModelConfigEntity>,
+      void
+    >({
+      query: () => ({ url: 'models/', params: { model_type: 'ip_adapter' } }),
+      providesTags: (result) => {
+        const tags: ApiFullTagDescription[] = [
+          { type: 'IPAdapterModel', id: LIST_TAG },
+        ];
+
+        if (result) {
+          tags.push(
+            ...result.ids.map((id) => ({
+              type: 'IPAdapterModel' as const,
+              id,
+            }))
+          );
+        }
+
+        return tags;
+      },
+      transformResponse: (response: { models: IPAdapterModelConfig[] }) => {
+        const entities = createModelEntities<IPAdapterModelConfigEntity>(
+          response.models
+        );
+        return ipAdapterModelsAdapter.setAll(
+          ipAdapterModelsAdapter.getInitialState(),
+          entities
+        );
+      },
+    }),
     getVaeModels: build.query<EntityState<VaeModelConfigEntity>, void>({
       query: () => ({ url: 'models/', params: { model_type: 'vae' } }),
       providesTags: (result) => {
@@ -533,6 +574,7 @@ export const {
   useGetMainModelsQuery,
   useGetOnnxModelsQuery,
   useGetControlNetModelsQuery,
+  useGetIPAdapterModelsQuery,
   useGetLoRAModelsQuery,
   useGetTextualInversionModelsQuery,
   useGetVaeModelsQuery,

invokeai/frontend/web/src/services/api/types.ts

@@ -60,8 +60,10 @@ export type OnnxModelField = s['OnnxModelField'];
 export type VAEModelField = s['VAEModelField'];
 export type LoRAModelField = s['LoRAModelField'];
 export type ControlNetModelField = s['ControlNetModelField'];
+export type IPAdapterModelField = s['IPAdapterModelField'];
 export type ModelsList = s['ModelsList'];
 export type ControlField = s['ControlField'];
+export type IPAdapterField = s['IPAdapterField'];
 
 // Model Configs
 export type LoRAModelConfig = s['LoRAModelConfig'];
@@ -73,6 +75,8 @@ export type ControlNetModelDiffusersConfig =
 export type ControlNetModelConfig =
   | ControlNetModelCheckpointConfig
   | ControlNetModelDiffusersConfig;
+export type IPAdapterModelInvokeAIConfig = s['IPAdapterModelInvokeAIConfig'];
+export type IPAdapterModelConfig = IPAdapterModelInvokeAIConfig;
 export type TextualInversionModelConfig = s['TextualInversionModelConfig'];
 export type DiffusersModelConfig =
   | s['StableDiffusion1ModelDiffusersConfig']
@@ -88,6 +92,7 @@ export type AnyModelConfig =
   | LoRAModelConfig
   | VaeModelConfig
   | ControlNetModelConfig
+  | IPAdapterModelConfig
   | TextualInversionModelConfig
   | MainModelConfig
   | OnnxModelConfig;
@@ -135,6 +140,7 @@ export type SeamlessModeInvocation = s['SeamlessModeInvocation'];
 
 // ControlNet Nodes
 export type ControlNetInvocation = s['ControlNetInvocation'];
+export type IPAdapterInvocation = s['IPAdapterInvocation'];
 export type CannyImageProcessorInvocation = s['CannyImageProcessorInvocation'];
 export type ContentShuffleImageProcessorInvocation =
   s['ContentShuffleImageProcessorInvocation'];
@@ -173,6 +179,10 @@ export type ControlNetAction = {
   controlNetId: string;
 };
 
+export type IPAdapterAction = {
+  type: 'SET_IP_ADAPTER_IMAGE';
+};
+
 export type InitialImageAction = {
   type: 'SET_INITIAL_IMAGE';
 };
@@ -198,6 +208,7 @@ export type AddToBatchAction = {
 
 export type PostUploadAction =
   | ControlNetAction
+  | IPAdapterAction
   | InitialImageAction
   | NodesAction
   | CanvasInitialImageAction

pyproject.toml

@@ -198,6 +198,13 @@ output = "coverage/index.xml"
 max-line-length = 120
 ignore = ["E203", "E266", "E501", "W503"]
 select = ["B", "C", "E", "F", "W", "T4"]
+exclude = [
+  ".git",
+  "__pycache__",
+  "build",
+  "dist",
+  "invokeai/frontend/web/node_modules/"
+]
 
 [tool.black]
 line-length = 120