Merge branch 'main' into lstein/model-manager-router-api

.gitignore

@@ -201,8 +201,6 @@ checkpoints
 # If it's a Mac
 .DS_Store
 
-invokeai/frontend/web/dist/*
-
 # Let the frontend manage its own gitignore
 !invokeai/frontend/web/*
 

invokeai/app/invocations/baseinvocation.py

@@ -4,9 +4,10 @@ from __future__ import annotations
 
 from abc import ABC, abstractmethod
 from inspect import signature
-from typing import get_args, get_type_hints, Dict, List, Literal, TypedDict, TYPE_CHECKING
+from typing import (TYPE_CHECKING, Dict, List, Literal, TypedDict, get_args,
+                    get_type_hints)
 
-from pydantic import BaseModel, Field
+from pydantic import BaseConfig, BaseModel, Field
 
 if TYPE_CHECKING:
     from ..services.invocation_services import InvocationServices
@@ -65,8 +66,13 @@ class BaseInvocation(ABC, BaseModel):
     @classmethod
     def get_invocations_map(cls):
         # Get the type strings out of the literals and into a dictionary
-        return dict(map(lambda t: (get_args(get_type_hints(t)['type'])[0], t),BaseInvocation.get_all_subclasses()))
+        return dict(
-    
+            map(
+                lambda t: (get_args(get_type_hints(t)["type"])[0], t),
+                BaseInvocation.get_all_subclasses(),
+            )
+        )
+
     @classmethod
     def get_output_type(cls):
         return signature(cls.invoke).return_annotation
@@ -75,11 +81,11 @@ class BaseInvocation(ABC, BaseModel):
     def invoke(self, context: InvocationContext) -> BaseInvocationOutput:
         """Invoke with provided context and return outputs."""
         pass
-    
+
-    #fmt: off
+    # fmt: off
     id: str = Field(description="The id of this node. Must be unique among all nodes.")
     is_intermediate: bool = Field(default=False, description="Whether or not this node is an intermediate node.")
-    #fmt: on
+    # fmt: on
 
 
 # TODO: figure out a better way to provide these hints
@@ -98,16 +104,19 @@ class UIConfig(TypedDict, total=False):
             "model",
             "control",
             "image_collection",
+            "vae_model",
+            "lora_model",
         ],
     ]
     tags: List[str]
     title: str
 
+
 class CustomisedSchemaExtra(TypedDict):
     ui: UIConfig
 
 
-class InvocationConfig(BaseModel.Config):
+class InvocationConfig(BaseConfig):
     """Customizes pydantic's BaseModel.Config class for use by Invocations.
 
     Provide `schema_extra` a `ui` dict to add hints for generated UIs.

invokeai/app/invocations/compel.py

@@ -1,28 +1,28 @@
-from typing import Literal, Optional, Union
-from pydantic import BaseModel, Field
-from contextlib import ExitStack
 import re
+from contextlib import ExitStack
+from typing import List, Literal, Optional, Union
+
 import torch
+from compel import Compel
+from compel.prompt_parser import (Blend, Conjunction,
+                                  CrossAttentionControlSubstitute,
+                                  FlattenedPrompt, Fragment)
+from pydantic import BaseModel, Field
 
-from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext, InvocationConfig
+from ...backend.model_management.models import ModelNotFoundException
-from .model import ClipField
-
-from ...backend.util.devices import torch_dtype
-from ...backend.stable_diffusion.diffusion import InvokeAIDiffuserComponent
 from ...backend.model_management import BaseModelType, ModelType, SubModelType
 from ...backend.model_management.lora import ModelPatcher
-
+from ...backend.stable_diffusion.diffusion import InvokeAIDiffuserComponent
-from compel import Compel
+from ...backend.util.devices import torch_dtype
-from compel.prompt_parser import (
+from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
-    Blend,
+                             InvocationConfig, InvocationContext)
-    CrossAttentionControlSubstitute,
+from .model import ClipField
-    FlattenedPrompt,
-    Fragment, Conjunction,
-)
 
 
 class ConditioningField(BaseModel):
-    conditioning_name: Optional[str] = Field(default=None, description="The name of conditioning data")
+    conditioning_name: Optional[str] = Field(
+        default=None, description="The name of conditioning data")
+
     class Config:
         schema_extra = {"required": ["conditioning_name"]}
 
@@ -52,84 +52,92 @@ class CompelInvocation(BaseInvocation):
                 "title": "Prompt (Compel)",
                 "tags": ["prompt", "compel"],
                 "type_hints": {
-                  "model": "model"
+                    "model": "model"
                 }
             },
         }
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> CompelOutput:
-
         tokenizer_info = context.services.model_manager.get_model(
             **self.clip.tokenizer.dict(),
         )
         text_encoder_info = context.services.model_manager.get_model(
             **self.clip.text_encoder.dict(),
         )
-        with tokenizer_info as orig_tokenizer,\
-             text_encoder_info as text_encoder:
 
-            loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
+        def _lora_loader():
+            for lora in self.clip.loras:
+                lora_info = context.services.model_manager.get_model(
+                    **lora.dict(exclude={"weight"}))
+                yield (lora_info.context.model, lora.weight)
+                del lora_info
+            return
 
-            ti_list = []
+        #loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
-            for trigger in re.findall(r"<[a-zA-Z0-9., _-]+>", self.prompt):
-                name = trigger[1:-1]
-                try:
-                    ti_list.append(
-                        context.services.model_manager.get_model(
-                            model_name=name,
-                            base_model=self.clip.text_encoder.base_model,
-                            model_type=ModelType.TextualInversion,
-                        ).context.model
-                    )
-                except Exception:
-                    #print(e)
-                    #import traceback
-                    #print(traceback.format_exc())
-                    print(f"Warn: trigger: \"{trigger}\" not found")
 
-            with ModelPatcher.apply_lora_text_encoder(text_encoder, loras),\
+        ti_list = []
-                 ModelPatcher.apply_ti(orig_tokenizer, text_encoder, ti_list) as (tokenizer, ti_manager):
+        for trigger in re.findall(r"<[a-zA-Z0-9., _-]+>", self.prompt):
-
+            name = trigger[1:-1]
-                compel = Compel(
+            try:
-                    tokenizer=tokenizer,
+                ti_list.append(
-                    text_encoder=text_encoder,
+                    context.services.model_manager.get_model(
-                    textual_inversion_manager=ti_manager,
+                        model_name=name,
-                    dtype_for_device_getter=torch_dtype,
+                        base_model=self.clip.text_encoder.base_model,
-                    truncate_long_prompts=True, # TODO:
+                        model_type=ModelType.TextualInversion,
+                    ).context.model
                 )
-                
+            except ModelNotFoundException:
-                conjunction = Compel.parse_prompt_string(self.prompt)
+                # print(e)
-                prompt: Union[FlattenedPrompt, Blend] = conjunction.prompts[0]
+                #import traceback
+                #print(traceback.format_exc())
+                print(f"Warn: trigger: \"{trigger}\" not found")
 
-                if context.services.configuration.log_tokenization:
+        with ModelPatcher.apply_lora_text_encoder(text_encoder_info.context.model, _lora_loader()),\
-                    log_tokenization_for_prompt_object(prompt, tokenizer)
+                ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (tokenizer, ti_manager),\
+                text_encoder_info as text_encoder:
 
-                c, options = compel.build_conditioning_tensor_for_prompt_object(prompt)
+            compel = Compel(
-                
+                tokenizer=tokenizer,
-                # TODO: long prompt support
+                text_encoder=text_encoder,
-                #if not self.truncate_long_prompts:
+                textual_inversion_manager=ti_manager,
-                #    [c, uc] = compel.pad_conditioning_tensors_to_same_length([c, uc])
+                dtype_for_device_getter=torch_dtype,
-                ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(
+                truncate_long_prompts=True,  # TODO:
-                    tokens_count_including_eos_bos=get_max_token_count(tokenizer, conjunction),
-                    cross_attention_control_args=options.get("cross_attention_control", None),
-                )
-                
-            conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
-
-            # TODO: hacky but works ;D maybe rename latents somehow?
-            context.services.latents.save(conditioning_name, (c, ec))
-
-            return CompelOutput(
-                conditioning=ConditioningField(
-                    conditioning_name=conditioning_name,
-                ),
             )
 
+            conjunction = Compel.parse_prompt_string(self.prompt)
+            prompt: Union[FlattenedPrompt, Blend] = conjunction.prompts[0]
+
+            if context.services.configuration.log_tokenization:
+                log_tokenization_for_prompt_object(prompt, tokenizer)
+
+            c, options = compel.build_conditioning_tensor_for_prompt_object(
+                prompt)
+
+            # TODO: long prompt support
+            # if not self.truncate_long_prompts:
+            #    [c, uc] = compel.pad_conditioning_tensors_to_same_length([c, uc])
+            ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(
+                tokens_count_including_eos_bos=get_max_token_count(
+                    tokenizer, conjunction),
+                cross_attention_control_args=options.get(
+                    "cross_attention_control", None),)
+
+        conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
+
+        # TODO: hacky but works ;D maybe rename latents somehow?
+        context.services.latents.save(conditioning_name, (c, ec))
+
+        return CompelOutput(
+            conditioning=ConditioningField(
+                conditioning_name=conditioning_name,
+            ),
+        )
+
 
 def get_max_token_count(
-    tokenizer, prompt: Union[FlattenedPrompt, Blend, Conjunction], truncate_if_too_long=False
+        tokenizer, prompt: Union[FlattenedPrompt, Blend, Conjunction],
-) -> int:
+        truncate_if_too_long=False) -> int:
     if type(prompt) is Blend:
         blend: Blend = prompt
         return max(
@@ -148,13 +156,13 @@ def get_max_token_count(
         )
     else:
         return len(
-            get_tokens_for_prompt_object(tokenizer, prompt, truncate_if_too_long)
+            get_tokens_for_prompt_object(
-        )
+                tokenizer, prompt, truncate_if_too_long))
 
 
 def get_tokens_for_prompt_object(
     tokenizer, parsed_prompt: FlattenedPrompt, truncate_if_too_long=True
-) -> [str]:
+) -> List[str]:
     if type(parsed_prompt) is Blend:
         raise ValueError(
             "Blend is not supported here - you need to get tokens for each of its .children"
@@ -183,7 +191,7 @@ def log_tokenization_for_conjunction(
 ):
     display_label_prefix = display_label_prefix or ""
     for i, p in enumerate(c.prompts):
-        if len(c.prompts)>1:
+        if len(c.prompts) > 1:
             this_display_label_prefix = f"{display_label_prefix}(conjunction part {i + 1}, weight={c.weights[i]})"
         else:
             this_display_label_prefix = display_label_prefix
@@ -238,7 +246,8 @@ def log_tokenization_for_prompt_object(
             )
 
 
-def log_tokenization_for_text(text, tokenizer, display_label=None, truncate_if_too_long=False):
+def log_tokenization_for_text(
+        text, tokenizer, display_label=None, truncate_if_too_long=False):
     """shows how the prompt is tokenized
     # usually tokens have '</w>' to indicate end-of-word,
     # but for readability it has been replaced with ' '

invokeai/app/invocations/latent.py

@@ -4,18 +4,17 @@ from contextlib import ExitStack
 from typing import List, Literal, Optional, Union
 
 import einops
-
-from pydantic import BaseModel, Field, validator
 import torch
 from diffusers import ControlNetModel, DPMSolverMultistepScheduler
 from diffusers.image_processor import VaeImageProcessor
 from diffusers.schedulers import SchedulerMixin as Scheduler
+from pydantic import BaseModel, Field, validator
 
 from invokeai.app.util.misc import SEED_MAX, get_random_seed
 from invokeai.app.util.step_callback import stable_diffusion_step_callback
 
-from ..models.image import ImageCategory, ImageField, ResourceOrigin
 from ...backend.image_util.seamless import configure_model_padding
+from ...backend.model_management.lora import ModelPatcher
 from ...backend.stable_diffusion import PipelineIntermediateState
 from ...backend.stable_diffusion.diffusers_pipeline import (
     ConditioningData, ControlNetData, StableDiffusionGeneratorPipeline,
@@ -24,7 +23,7 @@ from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import \
     PostprocessingSettings
 from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP
 from ...backend.util.devices import torch_dtype
-from ...backend.model_management.lora import ModelPatcher
+from ..models.image import ImageCategory, ImageField, ResourceOrigin
 from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
                              InvocationConfig, InvocationContext)
 from .compel import ConditioningField
@@ -32,14 +31,17 @@ from .controlnet_image_processors import ControlField
 from .image import ImageOutput
 from .model import ModelInfo, UNetField, VaeField
 
+
 class LatentsField(BaseModel):
     """A latents field used for passing latents between invocations"""
 
-    latents_name: Optional[str] = Field(default=None, description="The name of the latents")
+    latents_name: Optional[str] = Field(
+        default=None, description="The name of the latents")
 
     class Config:
         schema_extra = {"required": ["latents_name"]}
 
+
 class LatentsOutput(BaseInvocationOutput):
     """Base class for invocations that output latents"""
     #fmt: off
@@ -53,11 +55,11 @@ class LatentsOutput(BaseInvocationOutput):
 
 
 def build_latents_output(latents_name: str, latents: torch.Tensor):
-      return LatentsOutput(
+    return LatentsOutput(
-          latents=LatentsField(latents_name=latents_name),
+        latents=LatentsField(latents_name=latents_name),
-          width=latents.size()[3] * 8,
+        width=latents.size()[3] * 8,
-          height=latents.size()[2] * 8,
+        height=latents.size()[2] * 8,
-      )
+    )
 
 
 SAMPLER_NAME_VALUES = Literal[
@@ -70,16 +72,19 @@ def get_scheduler(
     scheduler_info: ModelInfo,
     scheduler_name: str,
 ) -> Scheduler:
-    scheduler_class, scheduler_extra_config = SCHEDULER_MAP.get(scheduler_name, SCHEDULER_MAP['ddim'])
+    scheduler_class, scheduler_extra_config = SCHEDULER_MAP.get(
-    orig_scheduler_info = context.services.model_manager.get_model(**scheduler_info.dict())
+        scheduler_name, SCHEDULER_MAP['ddim'])
+    orig_scheduler_info = context.services.model_manager.get_model(
+        **scheduler_info.dict())
     with orig_scheduler_info as orig_scheduler:
         scheduler_config = orig_scheduler.config
-        
+
     if "_backup" in scheduler_config:
         scheduler_config = scheduler_config["_backup"]
-    scheduler_config = {**scheduler_config, **scheduler_extra_config, "_backup": scheduler_config}
+    scheduler_config = {**scheduler_config, **
+                        scheduler_extra_config, "_backup": scheduler_config}
     scheduler = scheduler_class.from_config(scheduler_config)
-    
+
     # hack copied over from generate.py
     if not hasattr(scheduler, 'uses_inpainting_model'):
         scheduler.uses_inpainting_model = lambda: False
@@ -124,18 +129,18 @@ class TextToLatentsInvocation(BaseInvocation):
             "ui": {
                 "tags": ["latents"],
                 "type_hints": {
-                  "model": "model",
+                    "model": "model",
-                  "control": "control",
+                    "control": "control",
-                  # "cfg_scale": "float",
+                    # "cfg_scale": "float",
-                  "cfg_scale": "number"
+                    "cfg_scale": "number"
                 }
             },
         }
 
     # TODO: pass this an emitter method or something? or a session for dispatching?
     def dispatch_progress(
-        self, context: InvocationContext, source_node_id: str, intermediate_state: PipelineIntermediateState
+            self, context: InvocationContext, source_node_id: str,
-    ) -> None:
+            intermediate_state: PipelineIntermediateState) -> None:
         stable_diffusion_step_callback(
             context=context,
             intermediate_state=intermediate_state,
@@ -143,9 +148,12 @@ class TextToLatentsInvocation(BaseInvocation):
             source_node_id=source_node_id,
         )
 
-    def get_conditioning_data(self, context: InvocationContext, scheduler) -> ConditioningData:
+    def get_conditioning_data(
-        c, extra_conditioning_info = context.services.latents.get(self.positive_conditioning.conditioning_name)
+            self, context: InvocationContext, scheduler) -> ConditioningData:
-        uc, _ = context.services.latents.get(self.negative_conditioning.conditioning_name)
+        c, extra_conditioning_info = context.services.latents.get(
+            self.positive_conditioning.conditioning_name)
+        uc, _ = context.services.latents.get(
+            self.negative_conditioning.conditioning_name)
 
         conditioning_data = ConditioningData(
             unconditioned_embeddings=uc,
@@ -153,10 +161,10 @@ class TextToLatentsInvocation(BaseInvocation):
             guidance_scale=self.cfg_scale,
             extra=extra_conditioning_info,
             postprocessing_settings=PostprocessingSettings(
-                threshold=0.0,#threshold,
+                threshold=0.0,  # threshold,
-                warmup=0.2,#warmup,
+                warmup=0.2,  # warmup,
-                h_symmetry_time_pct=None,#h_symmetry_time_pct,
+                h_symmetry_time_pct=None,  # h_symmetry_time_pct,
-                v_symmetry_time_pct=None#v_symmetry_time_pct,
+                v_symmetry_time_pct=None  # v_symmetry_time_pct,
             ),
         )
 
@@ -164,31 +172,32 @@ class TextToLatentsInvocation(BaseInvocation):
             scheduler,
 
             # for ddim scheduler
-            eta=0.0, #ddim_eta
+            eta=0.0,  # ddim_eta
 
             # for ancestral and sde schedulers
             generator=torch.Generator(device=uc.device).manual_seed(0),
         )
         return conditioning_data
 
-    def create_pipeline(self, unet, scheduler) -> StableDiffusionGeneratorPipeline:
+    def create_pipeline(
+            self, unet, scheduler) -> StableDiffusionGeneratorPipeline:
         # TODO:
-        #configure_model_padding(
+        # configure_model_padding(
         #    unet,
         #    self.seamless,
         #    self.seamless_axes,
-        #)
+        # )
 
         class FakeVae:
             class FakeVaeConfig:
                 def __init__(self):
                     self.block_out_channels = [0]
-            
+
             def __init__(self):
                 self.config = FakeVae.FakeVaeConfig()
 
         return StableDiffusionGeneratorPipeline(
-            vae=FakeVae(), # TODO: oh...
+            vae=FakeVae(),  # TODO: oh...
             text_encoder=None,
             tokenizer=None,
             unet=unet,
@@ -198,11 +207,12 @@ class TextToLatentsInvocation(BaseInvocation):
             requires_safety_checker=False,
             precision="float16" if unet.dtype == torch.float16 else "float32",
         )
-    
+
     def prep_control_data(
         self,
         context: InvocationContext,
-        model: StableDiffusionGeneratorPipeline, # really only need model for dtype and device
+        # really only need model for dtype and device
+        model: StableDiffusionGeneratorPipeline,
         control_input: List[ControlField],
         latents_shape: List[int],
         do_classifier_free_guidance: bool = True,
@@ -238,15 +248,17 @@ class TextToLatentsInvocation(BaseInvocation):
                     print("Using HF model subfolders")
                     print("    control_name: ", control_name)
                     print("    control_subfolder: ", control_subfolder)
-                    control_model = ControlNetModel.from_pretrained(control_name,
+                    control_model = ControlNetModel.from_pretrained(
-                                                                    subfolder=control_subfolder,
+                        control_name, subfolder=control_subfolder,
-                                                                    torch_dtype=model.unet.dtype).to(model.device)
+                        torch_dtype=model.unet.dtype).to(
+                        model.device)
                 else:
-                    control_model = ControlNetModel.from_pretrained(control_info.control_model,
+                    control_model = ControlNetModel.from_pretrained(
-                                                                    torch_dtype=model.unet.dtype).to(model.device)
+                        control_info.control_model, torch_dtype=model.unet.dtype).to(model.device)
                 control_models.append(control_model)
                 control_image_field = control_info.image
-                input_image = context.services.images.get_pil_image(control_image_field.image_name)
+                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?
@@ -263,41 +275,50 @@ class TextToLatentsInvocation(BaseInvocation):
                     dtype=control_model.dtype,
                     control_mode=control_info.control_mode,
                 )
-                control_item = ControlNetData(model=control_model,
+                control_item = ControlNetData(
-                                              image_tensor=control_image,
+                    model=control_model, image_tensor=control_image,
-                                              weight=control_info.control_weight,
+                    weight=control_info.control_weight,
-                                              begin_step_percent=control_info.begin_step_percent,
+                    begin_step_percent=control_info.begin_step_percent,
-                                              end_step_percent=control_info.end_step_percent,
+                    end_step_percent=control_info.end_step_percent,
-                                              control_mode=control_info.control_mode,
+                    control_mode=control_info.control_mode,)
-                                              )
                 control_data.append(control_item)
                 # MultiControlNetModel has been refactored out, just need list[ControlNetData]
         return control_data
 
+    @torch.no_grad()
     def invoke(self, context: InvocationContext) -> LatentsOutput:
         noise = context.services.latents.get(self.noise.latents_name)
 
         # Get the source node id (we are invoking the prepared node)
-        graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
+        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]
 
         def step_callback(state: PipelineIntermediateState):
             self.dispatch_progress(context, source_node_id, state)
 
-        unet_info = context.services.model_manager.get_model(**self.unet.unet.dict())
+        def _lora_loader():
-        with unet_info as unet:
+            for lora in self.unet.loras:
+                lora_info = context.services.model_manager.get_model(
+                    **lora.dict(exclude={"weight"}))
+                yield (lora_info.context.model, lora.weight)
+                del lora_info
+            return
+
+        unet_info = context.services.model_manager.get_model(
+            **self.unet.unet.dict())
+        with ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()),\
+                unet_info as unet:
 
             scheduler = get_scheduler(
                 context=context,
                 scheduler_info=self.unet.scheduler,
                 scheduler_name=self.scheduler,
             )
-            
+
             pipeline = self.create_pipeline(unet, scheduler)
             conditioning_data = self.get_conditioning_data(context, scheduler)
 
-            loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.unet.loras]
-
             control_data = self.prep_control_data(
                 model=pipeline, context=context, control_input=self.control,
                 latents_shape=noise.shape,
@@ -305,16 +326,15 @@ class TextToLatentsInvocation(BaseInvocation):
                 do_classifier_free_guidance=True,
             )
 
-            with ModelPatcher.apply_lora_unet(pipeline.unet, loras):
+            # TODO: Verify the noise is the right size
-                # TODO: Verify the noise is the right size
+            result_latents, result_attention_map_saver = pipeline.latents_from_embeddings(
-                result_latents, result_attention_map_saver = pipeline.latents_from_embeddings(
+                latents=torch.zeros_like(noise, dtype=torch_dtype(unet.device)),
-                    latents=torch.zeros_like(noise, dtype=torch_dtype(unet.device)),
+                noise=noise,
-                    noise=noise,
+                num_inference_steps=self.steps,
-                    num_inference_steps=self.steps,
+                conditioning_data=conditioning_data,
-                    conditioning_data=conditioning_data,
+                control_data=control_data,  # list[ControlNetData]
-                    control_data=control_data, # list[ControlNetData]
+                callback=step_callback,
-                    callback=step_callback,
+            )
-                )
 
         # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
         torch.cuda.empty_cache()
@@ -323,14 +343,18 @@ class TextToLatentsInvocation(BaseInvocation):
         context.services.latents.save(name, result_latents)
         return build_latents_output(latents_name=name, latents=result_latents)
 
+
 class LatentsToLatentsInvocation(TextToLatentsInvocation):
     """Generates latents using latents as base image."""
 
     type: Literal["l2l"] = "l2l"
 
     # Inputs
-    latents: Optional[LatentsField] = Field(description="The latents to use as a base image")
+    latents: Optional[LatentsField] = Field(
-    strength: float = Field(default=0.7, ge=0, le=1, description="The strength of the latents to use")
+        description="The latents to use as a base image")
+    strength: float = Field(
+        default=0.7, ge=0, le=1,
+        description="The strength of the latents to use")
 
     # Schema customisation
     class Config(InvocationConfig):
@@ -345,22 +369,31 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
             },
         }
 
+    @torch.no_grad()
     def invoke(self, context: InvocationContext) -> LatentsOutput:
         noise = context.services.latents.get(self.noise.latents_name)
         latent = context.services.latents.get(self.latents.latents_name)
 
         # Get the source node id (we are invoking the prepared node)
-        graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
+        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]
 
         def step_callback(state: PipelineIntermediateState):
             self.dispatch_progress(context, source_node_id, state)
 
-        unet_info = context.services.model_manager.get_model(
+        def _lora_loader():
-            **self.unet.unet.dict(),
+            for lora in self.unet.loras:
-        )
+                lora_info = context.services.model_manager.get_model(
+                    **lora.dict(exclude={"weight"}))
+                yield (lora_info.context.model, lora.weight)
+                del lora_info
+            return
 
-        with unet_info as unet:
+        unet_info = context.services.model_manager.get_model(
+            **self.unet.unet.dict())
+        with ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()),\
+                unet_info as unet:
 
             scheduler = get_scheduler(
                 context=context,
@@ -370,7 +403,7 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
 
             pipeline = self.create_pipeline(unet, scheduler)
             conditioning_data = self.get_conditioning_data(context, scheduler)
-            
+
             control_data = self.prep_control_data(
                 model=pipeline, context=context, control_input=self.control,
                 latents_shape=noise.shape,
@@ -380,8 +413,7 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
 
             # TODO: Verify the noise is the right size
             initial_latents = latent if self.strength < 1.0 else torch.zeros_like(
-                latent, device=unet.device, dtype=latent.dtype
+                latent, device=unet.device, dtype=latent.dtype)
-            )
 
             timesteps, _ = pipeline.get_img2img_timesteps(
                 self.steps,
@@ -389,18 +421,15 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
                 device=unet.device,
             )
 
-            loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.unet.loras]
+            result_latents, result_attention_map_saver = pipeline.latents_from_embeddings(
-
+                latents=initial_latents,
-            with ModelPatcher.apply_lora_unet(pipeline.unet, loras):
+                timesteps=timesteps,
-                result_latents, result_attention_map_saver = pipeline.latents_from_embeddings(
+                noise=noise,
-                    latents=initial_latents,
+                num_inference_steps=self.steps,
-                    timesteps=timesteps,
+                conditioning_data=conditioning_data,
-                    noise=noise,
+                control_data=control_data,  # list[ControlNetData]
-                    num_inference_steps=self.steps,
+                callback=step_callback
-                    conditioning_data=conditioning_data,
+            )
-                    control_data=control_data,  # list[ControlNetData]
-                    callback=step_callback
-                )
 
         # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
         torch.cuda.empty_cache()
@@ -417,9 +446,12 @@ class LatentsToImageInvocation(BaseInvocation):
     type: Literal["l2i"] = "l2i"
 
     # Inputs
-    latents: Optional[LatentsField] = Field(description="The latents to generate an image from")
+    latents: Optional[LatentsField] = Field(
+        description="The latents to generate an image from")
     vae: VaeField = Field(default=None, description="Vae submodel")
-    tiled: bool = Field(default=False, description="Decode latents by overlaping tiles(less memory consumption)")
+    tiled: bool = Field(
+        default=False,
+        description="Decode latents by overlaping tiles(less memory consumption)")
 
     # Schema customisation
     class Config(InvocationConfig):
@@ -450,7 +482,7 @@ class LatentsToImageInvocation(BaseInvocation):
                 # copied from diffusers pipeline
                 latents = latents / vae.config.scaling_factor
                 image = vae.decode(latents, return_dict=False)[0]
-                image = (image / 2 + 0.5).clamp(0, 1) # denormalize
+                image = (image / 2 + 0.5).clamp(0, 1)  # denormalize
                 # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
                 np_image = image.cpu().permute(0, 2, 3, 1).float().numpy()
 
@@ -473,9 +505,9 @@ class LatentsToImageInvocation(BaseInvocation):
             height=image_dto.height,
         )
 
-LATENTS_INTERPOLATION_MODE = Literal[
+
-    "nearest", "linear", "bilinear", "bicubic", "trilinear", "area", "nearest-exact"
+LATENTS_INTERPOLATION_MODE = Literal["nearest", "linear",
-]
+                                     "bilinear", "bicubic", "trilinear", "area", "nearest-exact"]
 
 
 class ResizeLatentsInvocation(BaseInvocation):
@@ -484,21 +516,25 @@ class ResizeLatentsInvocation(BaseInvocation):
     type: Literal["lresize"] = "lresize"
 
     # Inputs
-    latents:    Optional[LatentsField] = Field(description="The latents to resize")
+    latents: Optional[LatentsField] = Field(
-    width:                         int = Field(ge=64, multiple_of=8, description="The width to resize to (px)")
+        description="The latents to resize")
-    height:                        int = Field(ge=64, multiple_of=8, description="The height to resize to (px)")
+    width:                         int = Field(
-    mode:   LATENTS_INTERPOLATION_MODE = Field(default="bilinear", description="The interpolation mode")
+        ge=64, multiple_of=8, description="The width to resize to (px)")
-    antialias:                    bool = Field(default=False, description="Whether or not to antialias (applied in bilinear and bicubic modes only)")
+    height:                        int = Field(
+        ge=64, multiple_of=8, description="The height to resize to (px)")
+    mode:   LATENTS_INTERPOLATION_MODE = Field(
+        default="bilinear", description="The interpolation mode")
+    antialias: bool = Field(
+        default=False,
+        description="Whether or not to antialias (applied in bilinear and bicubic modes only)")
 
     def invoke(self, context: InvocationContext) -> LatentsOutput:
         latents = context.services.latents.get(self.latents.latents_name)
 
         resized_latents = torch.nn.functional.interpolate(
-            latents,
+            latents, size=(self.height // 8, self.width // 8),
-            size=(self.height // 8, self.width // 8),
+            mode=self.mode, antialias=self.antialias
-            mode=self.mode,
+            if self.mode in ["bilinear", "bicubic"] else False,)
-            antialias=self.antialias if self.mode in ["bilinear", "bicubic"] else False,
-        )
 
         # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
         torch.cuda.empty_cache()
@@ -515,21 +551,24 @@ class ScaleLatentsInvocation(BaseInvocation):
     type: Literal["lscale"] = "lscale"
 
     # Inputs
-    latents:   Optional[LatentsField] = Field(description="The latents to scale")
+    latents: Optional[LatentsField] = Field(
-    scale_factor:               float = Field(gt=0, description="The factor by which to scale the latents")
+        description="The latents to scale")
-    mode:  LATENTS_INTERPOLATION_MODE = Field(default="bilinear", description="The interpolation mode")
+    scale_factor:               float = Field(
-    antialias:                   bool = Field(default=False, description="Whether or not to antialias (applied in bilinear and bicubic modes only)")
+        gt=0, description="The factor by which to scale the latents")
+    mode:  LATENTS_INTERPOLATION_MODE = Field(
+        default="bilinear", description="The interpolation mode")
+    antialias: bool = Field(
+        default=False,
+        description="Whether or not to antialias (applied in bilinear and bicubic modes only)")
 
     def invoke(self, context: InvocationContext) -> LatentsOutput:
         latents = context.services.latents.get(self.latents.latents_name)
 
         # resizing
         resized_latents = torch.nn.functional.interpolate(
-            latents,
+            latents, scale_factor=self.scale_factor, mode=self.mode,
-            scale_factor=self.scale_factor,
+            antialias=self.antialias
-            mode=self.mode,
+            if self.mode in ["bilinear", "bicubic"] else False,)
-            antialias=self.antialias if self.mode in ["bilinear", "bicubic"] else False,
-        )
 
         # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
         torch.cuda.empty_cache()
@@ -548,7 +587,9 @@ class ImageToLatentsInvocation(BaseInvocation):
     # Inputs
     image: Union[ImageField, None] = Field(description="The image to encode")
     vae: VaeField = Field(default=None, description="Vae submodel")
-    tiled: bool = Field(default=False, description="Encode latents by overlaping tiles(less memory consumption)")
+    tiled: bool = Field(
+        default=False,
+        description="Encode latents by overlaping tiles(less memory consumption)")
 
     # Schema customisation
     class Config(InvocationConfig):

invokeai/app/invocations/model.py

@@ -1,5 +1,5 @@
 import copy
-from typing import List, Literal, Optional
+from typing import List, Literal, Optional, Union
 
 from pydantic import BaseModel, Field
 
@@ -12,35 +12,42 @@ class ModelInfo(BaseModel):
     model_name: str = Field(description="Info to load submodel")
     base_model: BaseModelType = Field(description="Base model")
     model_type: ModelType = Field(description="Info to load submodel")
-    submodel: Optional[SubModelType] = Field(description="Info to load submodel")
+    submodel: Optional[SubModelType] = Field(
+        default=None, description="Info to load submodel"
+    )
+
 
 class LoraInfo(ModelInfo):
     weight: float = Field(description="Lora's weight which to use when apply to model")
 
+
 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")
 
+
 class ClipField(BaseModel):
     tokenizer: ModelInfo = Field(description="Info to load tokenizer submodel")
     text_encoder: ModelInfo = Field(description="Info to load text_encoder submodel")
     loras: List[LoraInfo] = Field(description="Loras to apply on model loading")
 
+
 class VaeField(BaseModel):
     # TODO: better naming?
     vae: ModelInfo = Field(description="Info to load vae submodel")
 
+
 class ModelLoaderOutput(BaseInvocationOutput):
     """Model loader output"""
 
-    #fmt: off
+    # fmt: off
     type: Literal["model_loader_output"] = "model_loader_output"
 
     unet: UNetField = Field(default=None, description="UNet submodel")
     clip: ClipField = Field(default=None, description="Tokenizer and text_encoder submodels")
     vae: VaeField = Field(default=None, description="Vae submodel")
-    #fmt: on
+    # fmt: on
 
 
 class MainModelField(BaseModel):
@@ -50,6 +57,13 @@ class MainModelField(BaseModel):
     base_model: BaseModelType = Field(description="Base model")
 
 
+class LoRAModelField(BaseModel):
+    """LoRA model field"""
+
+    model_name: str = Field(description="Name of the LoRA model")
+    base_model: BaseModelType = Field(description="Base model")
+
+
 class MainModelLoaderInvocation(BaseInvocation):
     """Loads a main model, outputting its submodels."""
 
@@ -64,14 +78,11 @@ class MainModelLoaderInvocation(BaseInvocation):
             "ui": {
                 "title": "Model Loader",
                 "tags": ["model", "loader"],
-                "type_hints": {
+                "type_hints": {"model": "model"},
-                  "model": "model"
-                }
             },
         }
 
     def invoke(self, context: InvocationContext) -> ModelLoaderOutput:
-
         base_model = self.model.base_model
         model_name = self.model.model_name
         model_type = ModelType.Main
@@ -113,7 +124,6 @@ class MainModelLoaderInvocation(BaseInvocation):
             )
         """
 
-
         return ModelLoaderOutput(
             unet=UNetField(
                 unet=ModelInfo(
@@ -152,25 +162,29 @@ class MainModelLoaderInvocation(BaseInvocation):
                     model_type=model_type,
                     submodel=SubModelType.Vae,
                 ),
-            )
+            ),
         )
 
+
 class LoraLoaderOutput(BaseInvocationOutput):
     """Model loader output"""
 
-    #fmt: off
+    # fmt: off
     type: Literal["lora_loader_output"] = "lora_loader_output"
 
     unet: Optional[UNetField] = Field(default=None, description="UNet submodel")
     clip: Optional[ClipField] = Field(default=None, description="Tokenizer and text_encoder submodels")
-    #fmt: on
+    # fmt: on
+
 
 class LoraLoaderInvocation(BaseInvocation):
     """Apply selected lora to unet and text_encoder."""
 
     type: Literal["lora_loader"] = "lora_loader"
 
-    lora_name: str = Field(description="Lora model name")
+    lora: Union[LoRAModelField, None] = Field(
+        default=None, description="Lora model name"
+    )
     weight: float = Field(default=0.75, description="With what weight to apply lora")
 
     unet: Optional[UNetField] = Field(description="UNet model for applying lora")
@@ -181,26 +195,33 @@ class LoraLoaderInvocation(BaseInvocation):
             "ui": {
                 "title": "Lora Loader",
                 "tags": ["lora", "loader"],
+                "type_hints": {"lora": "lora_model"},
             },
         }
 
     def invoke(self, context: InvocationContext) -> LoraLoaderOutput:
+        if self.lora is None:
+            raise Exception("No LoRA provided")
 
-        # TODO: ui rewrite
+        base_model = self.lora.base_model
-        base_model = BaseModelType.StableDiffusion1
+        lora_name = self.lora.model_name
 
         if not context.services.model_manager.model_exists(
             base_model=base_model,
-            model_name=self.lora_name,
+            model_name=lora_name,
             model_type=ModelType.Lora,
         ):
-            raise Exception(f"Unkown lora name: {self.lora_name}!")
+            raise Exception(f"Unkown lora name: {lora_name}!")
 
-        if self.unet is not None and any(lora.model_name == self.lora_name for lora in self.unet.loras):
+        if self.unet is not None and any(
-            raise Exception(f"Lora \"{self.lora_name}\" already applied to unet")
+            lora.model_name == lora_name for lora in self.unet.loras
+        ):
+            raise Exception(f'Lora "{lora_name}" already applied to unet')
 
-        if self.clip is not None and any(lora.model_name == self.lora_name for lora in self.clip.loras):
+        if self.clip is not None and any(
-            raise Exception(f"Lora \"{self.lora_name}\" already applied to clip")
+            lora.model_name == lora_name for lora in self.clip.loras
+        ):
+            raise Exception(f'Lora "{lora_name}" already applied to clip')
 
         output = LoraLoaderOutput()
 
@@ -209,7 +230,7 @@ class LoraLoaderInvocation(BaseInvocation):
             output.unet.loras.append(
                 LoraInfo(
                     base_model=base_model,
-                    model_name=self.lora_name,
+                    model_name=lora_name,
                     model_type=ModelType.Lora,
                     submodel=None,
                     weight=self.weight,
@@ -221,7 +242,7 @@ class LoraLoaderInvocation(BaseInvocation):
             output.clip.loras.append(
                 LoraInfo(
                     base_model=base_model,
-                    model_name=self.lora_name,
+                    model_name=lora_name,
                     model_type=ModelType.Lora,
                     submodel=None,
                     weight=self.weight,
@@ -230,25 +251,29 @@ class LoraLoaderInvocation(BaseInvocation):
 
         return output
 
+
 class VAEModelField(BaseModel):
     """Vae model field"""
 
     model_name: str = Field(description="Name of the model")
     base_model: BaseModelType = Field(description="Base model")
 
+
 class VaeLoaderOutput(BaseInvocationOutput):
     """Model loader output"""
 
-    #fmt: off
+    # fmt: off
     type: Literal["vae_loader_output"] = "vae_loader_output"
 
     vae: VaeField = Field(default=None, description="Vae model")
-    #fmt: on
+    # fmt: on
+
 
 class VaeLoaderInvocation(BaseInvocation):
     """Loads a VAE model, outputting a VaeLoaderOutput"""
+
     type: Literal["vae_loader"] = "vae_loader"
-    
+
     vae_model: VAEModelField = Field(description="The VAE to load")
 
     # Schema customisation
@@ -257,29 +282,27 @@ class VaeLoaderInvocation(BaseInvocation):
             "ui": {
                 "title": "VAE Loader",
                 "tags": ["vae", "loader"],
-                "type_hints": {
+                "type_hints": {"vae_model": "vae_model"},
-                  "vae_model": "vae_model"
-                }
             },
         }
-        
+
     def invoke(self, context: InvocationContext) -> VaeLoaderOutput:
         base_model = self.vae_model.base_model
         model_name = self.vae_model.model_name
         model_type = ModelType.Vae
 
         if not context.services.model_manager.model_exists(
-                base_model=base_model,
+            base_model=base_model,
-                model_name=model_name,
+            model_name=model_name,
-                model_type=model_type,
+            model_type=model_type,
         ):
             raise Exception(f"Unkown vae name: {model_name}!")
         return VaeLoaderOutput(
             vae=VaeField(
-                vae = ModelInfo(
+                vae=ModelInfo(
-                    model_name = model_name,
+                    model_name=model_name,
-                    base_model = base_model,
+                    base_model=base_model,
-                    model_type = model_type,
+                    model_type=model_type,
                 )
             )
         )

invokeai/app/services/config.py

@@ -228,10 +228,10 @@ class InvokeAISettings(BaseSettings):
         upcase_environ = dict()
         for key,value in os.environ.items():
             upcase_environ[key.upper()] = value
-        
+
         fields = cls.__fields__
         cls.argparse_groups = {}
-        
+
         for name, field in fields.items():
             if name not in cls._excluded():
                 current_default = field.default
@@ -348,7 +348,7 @@ setting environment variables INVOKEAI_<setting>.
      '''
     singleton_config: ClassVar[InvokeAIAppConfig] = None
     singleton_init: ClassVar[Dict] = None
-    
+
     #fmt: off
     type: Literal["InvokeAI"] = "InvokeAI"
     host                : str = Field(default="127.0.0.1", description="IP address to bind to", category='Web Server')
@@ -367,7 +367,8 @@ setting environment variables INVOKEAI_<setting>.
 
     always_use_cpu      : bool = Field(default=False, description="If true, use the CPU for rendering even if a GPU is available.", category='Memory/Performance')
     free_gpu_mem        : bool = Field(default=False, description="If true, purge model from GPU after each generation.", category='Memory/Performance')
-    max_loaded_models   : int = Field(default=3, gt=0, description="Maximum number of models to keep in memory for rapid switching", category='Memory/Performance')
+    max_loaded_models   : int = Field(default=3, gt=0, description="(DEPRECATED: use max_cache_size) Maximum number of models to keep in memory for rapid switching", category='Memory/Performance')
+    max_cache_size      : float = Field(default=6.0, gt=0, description="Maximum memory amount used by model cache for rapid switching", category='Memory/Performance')
     precision           : Literal[tuple(['auto','float16','float32','autocast'])] = Field(default='float16',description='Floating point precision', category='Memory/Performance')
     sequential_guidance : bool = Field(default=False, description="Whether to calculate guidance in serial instead of in parallel, lowering memory requirements", category='Memory/Performance')
     xformers_enabled    : bool = Field(default=True, description="Enable/disable memory-efficient attention", category='Memory/Performance')
@@ -385,9 +386,9 @@ setting environment variables INVOKEAI_<setting>.
     outdir              : Path = Field(default='outputs', description='Default folder for output images', category='Paths')
     from_file           : Path = Field(default=None, description='Take command input from the indicated file (command-line client only)', category='Paths')
     use_memory_db       : bool = Field(default=False, description='Use in-memory database for storing image metadata', category='Paths')
-    
+
     model               : str = Field(default='stable-diffusion-1.5', description='Initial model name', category='Models')
-    
+
     log_handlers        : List[str] = Field(default=["console"], description='Log handler. Valid options are "console", "file=<path>", "syslog=path|address:host:port", "http=<url>"', category="Logging")
     # note - would be better to read the log_format values from logging.py, but this creates circular dependencies issues
     log_format          : Literal[tuple(['plain','color','syslog','legacy'])] = Field(default="color", description='Log format. Use "plain" for text-only, "color" for colorized output, "legacy" for 2.3-style logging and "syslog" for syslog-style', category="Logging")
@@ -396,7 +397,7 @@ setting environment variables INVOKEAI_<setting>.
 
     def parse_args(self, argv: List[str]=None, conf: DictConfig = None, clobber=False):
         '''
-        Update settings with contents of init file, environment, and 
+        Update settings with contents of init file, environment, and
         command-line settings.
         :param conf: alternate Omegaconf dictionary object
         :param argv: aternate sys.argv list
@@ -411,7 +412,7 @@ setting environment variables INVOKEAI_<setting>.
             except:
                 pass
         InvokeAISettings.initconf = conf
-        
+
         # parse args again in order to pick up settings in configuration file
         super().parse_args(argv)
 
@@ -431,7 +432,7 @@ setting environment variables INVOKEAI_<setting>.
             cls.singleton_config = cls(**kwargs)
             cls.singleton_init = kwargs
         return cls.singleton_config
-        
+
     @property
     def root_path(self)->Path:
         '''

invokeai/app/services/model_manager_service.py

@@ -36,13 +36,13 @@ class ModelManagerServiceBase(ABC):
         logger: ModuleType,
     ):
         """
-        Initialize with the path to the models.yaml config file. 
+        Initialize with the path to the models.yaml config file.
         Optional parameters are the torch device type, precision, max_models,
         and sequential_offload boolean. Note that the default device
         type and precision are set up for a CUDA system running at half precision.
         """
         pass
-    
+
     @abstractmethod
     def get_model(
         self,
@@ -53,8 +53,8 @@ class ModelManagerServiceBase(ABC):
         node: Optional[BaseInvocation] = None,
         context: Optional[InvocationContext] = None,
     ) -> ModelInfo:
-        """Retrieve the indicated model with name and type. 
+        """Retrieve the indicated model with name and type.
-        submodel can be used to get a part (such as the vae) 
+        submodel can be used to get a part (such as the vae)
         of a diffusers pipeline."""
         pass
 
@@ -125,8 +125,8 @@ class ModelManagerServiceBase(ABC):
         """
         Update the named model with a dictionary of attributes. Will fail with an
         assertion error if the name already exists. Pass clobber=True to overwrite.
-        On a successful update, the config will be changed in memory. Will fail 
+        On a successful update, the config will be changed in memory. Will fail
-        with an assertion error if provided attributes are incorrect or 
+        with an assertion error if provided attributes are incorrect or
         the model name is missing. Call commit() to write changes to disk.
         """
         pass
@@ -157,8 +157,8 @@ class ModelManagerServiceBase(ABC):
         model_type: ModelType,
     ):
         """
-        Delete the named model from configuration. If delete_files is true, 
+        Delete the named model from configuration. If delete_files is true,
-        then the underlying weight file or diffusers directory will be deleted 
+        then the underlying weight file or diffusers directory will be deleted
         as well. Call commit() to write to disk.
         """
         pass
@@ -225,7 +225,7 @@ class ModelManagerService(ModelManagerServiceBase):
         logger: ModuleType,
     ):
         """
-        Initialize with the path to the models.yaml config file. 
+        Initialize with the path to the models.yaml config file.
         Optional parameters are the torch device type, precision, max_models,
         and sequential_offload boolean. Note that the default device
         type and precision are set up for a CUDA system running at half precision.
@@ -255,6 +255,8 @@ class ModelManagerService(ModelManagerServiceBase):
             if hasattr(config,'max_cache_size') \
                else config.max_loaded_models * 2.5
 
+        logger.debug(f"Maximum RAM cache size: {max_cache_size} GiB")
+
         sequential_offload = config.sequential_guidance
 
         self.mgr = ModelManager(
@@ -310,7 +312,7 @@ class ModelManagerService(ModelManagerServiceBase):
                 submodel=submodel,
                 model_info=model_info
             )
-            
+
         return model_info
 
     def model_exists(
@@ -370,8 +372,8 @@ class ModelManagerService(ModelManagerServiceBase):
         """
         Update the named model with a dictionary of attributes. Will fail with an
         assertion error if the name already exists. Pass clobber=True to overwrite.
-        On a successful update, the config will be changed in memory. Will fail 
+        On a successful update, the config will be changed in memory. Will fail
-        with an assertion error if provided attributes are incorrect or 
+        with an assertion error if provided attributes are incorrect or
         the model name is missing. Call commit() to write changes to disk.
         """
         self.logger.debug(f'add/update model {model_name}')        
@@ -403,8 +405,8 @@ class ModelManagerService(ModelManagerServiceBase):
         model_type: ModelType,
     ):
         """
-        Delete the named model from configuration. If delete_files is true, 
+        Delete the named model from configuration. If delete_files is true,
-        then the underlying weight file or diffusers directory will be deleted 
+        then the underlying weight file or diffusers directory will be deleted
         as well. Call commit() to write to disk.
         """
         self.logger.debug(f'delete model {model_name}')
@@ -479,7 +481,7 @@ class ModelManagerService(ModelManagerServiceBase):
     @property
     def logger(self):
         return self.mgr.logger
-        
+
     def heuristic_import(self,
                          items_to_import: set[str],
                          prediction_type_helper: Optional[Callable[[Path],SchedulerPredictionType]]=None,
@@ -500,4 +502,4 @@ class ModelManagerService(ModelManagerServiceBase):
         of the set is a dict corresponding to the newly-created OmegaConf stanza for
         that model.
         '''
-        return self.mgr.heuristic_import(items_to_import, prediction_type_helper)        
+        return self.mgr.heuristic_import(items_to_import, prediction_type_helper)

invokeai/backend/install/migrate_to_3.py

@@ -76,6 +76,10 @@ class MigrateTo3(object):
         Create a unique name for a model for use within models.yaml.
         '''
         done = False
+        
+        # some model names have slashes in them, which really screws things up
+        name = name.replace('/','_')
+        
         key = ModelManager.create_key(name,info.base_type,info.model_type)
         unique_name = key
         counter = 1
@@ -219,11 +223,12 @@ class MigrateTo3(object):
             repo_id = 'openai/clip-vit-large-patch14'
             self._migrate_pretrained(CLIPTokenizer,
                                      repo_id= repo_id,
-                                     dest= target_dir / 'clip-vit-large-patch14' / 'tokenizer',
+                                     dest= target_dir / 'clip-vit-large-patch14',
                                      **kwargs)
             self._migrate_pretrained(CLIPTextModel,
                                      repo_id = repo_id,
-                                     dest = target_dir / 'clip-vit-large-patch14' / 'text_encoder',
+                                     dest = target_dir / 'clip-vit-large-patch14',
+                                     force = True,
                                      **kwargs)
 
             # sd-2
@@ -287,21 +292,21 @@ class MigrateTo3(object):
     def _model_probe_to_path(self, info: ModelProbeInfo)->Path:
         return Path(self.dest_models, info.base_type.value, info.model_type.value)
 
-    def _migrate_pretrained(self, model_class, repo_id: str, dest: Path, **kwargs):
+    def _migrate_pretrained(self, model_class, repo_id: str, dest: Path, force:bool=False, **kwargs):
-        if dest.exists():
+        if dest.exists() and not force:
             logger.info(f'Skipping existing {dest}')
             return
         model = model_class.from_pretrained(repo_id, **kwargs)
-        self._save_pretrained(model, dest)
+        self._save_pretrained(model, dest, overwrite=force)
 
-    def _save_pretrained(self, model, dest: Path):
+    def _save_pretrained(self, model, dest: Path, overwrite: bool=False):
-        if dest.exists():
-            logger.info(f'Skipping existing {dest}')
-            return
         model_name = dest.name
-        download_path = dest.with_name(f'{model_name}.downloading')
+        if overwrite:
-        model.save_pretrained(download_path, safe_serialization=True)
+            model.save_pretrained(dest, safe_serialization=True)
-        download_path.replace(dest)
+        else:
+            download_path = dest.with_name(f'{model_name}.downloading')
+            model.save_pretrained(download_path, safe_serialization=True)
+            download_path.replace(dest)
 
     def _download_vae(self, repo_id: str, subfolder:str=None)->Path:
         vae = AutoencoderKL.from_pretrained(repo_id, cache_dir=self.root_directory / 'models/hub', subfolder=subfolder)
@@ -569,8 +574,10 @@ script, which will perform a full upgrade in place."""
 
     dest_directory = args.dest_directory
     assert dest_directory.is_dir(), f"{dest_directory} is not a valid directory"
-    assert (dest_directory / 'models').is_dir(), f"{dest_directory} does not contain a 'models' subdirectory"
+
-    assert (dest_directory / 'invokeai.yaml').exists(), f"{dest_directory} does not contain an InvokeAI init file."
+    # TODO: revisit
+    # assert (dest_directory / 'models').is_dir(), f"{dest_directory} does not contain a 'models' subdirectory"
+    # assert (dest_directory / 'invokeai.yaml').exists(), f"{dest_directory} does not contain an InvokeAI init file."
 
     do_migrate(root_directory,dest_directory)
 

invokeai/backend/install/model_install_backend.py

@@ -236,7 +236,6 @@ class ModelInstall(object):
                                   )
 
     def _install_url(self, url: str)->AddModelResult:
-        # copy to a staging area, probe, import and delete
         with TemporaryDirectory(dir=self.config.models_path) as staging:
             location = download_with_resume(url,Path(staging))
             if not location:

invokeai/backend/model_management/convert_ckpt_to_diffusers.py

@@ -29,7 +29,7 @@ import invokeai.backend.util.logging as logger
 from invokeai.app.services.config import InvokeAIAppConfig
 
 from .model_manager import ModelManager
-from .model_cache import ModelCache
+from picklescan.scanner import scan_file_path
 from .models import BaseModelType, ModelVariantType
 
 try:
@@ -1014,7 +1014,10 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
             checkpoint = load_file(checkpoint_path)
         else:
             if scan_needed:
-                ModelCache.scan_model(checkpoint_path, checkpoint_path)
+                # scan model
+                scan_result = scan_file_path(checkpoint_path)
+                if scan_result.infected_files != 0:
+                    raise "The model {checkpoint_path} is potentially infected by malware. Aborting import."
             checkpoint = torch.load(checkpoint_path)
 
         # sometimes there is a state_dict key and sometimes not

invokeai/backend/model_management/lora.py

@@ -1,16 +1,17 @@
 from __future__ import annotations
 
 import copy
-from pathlib import Path
 from contextlib import contextmanager
 from typing import Optional, Dict, Tuple, Any, Union, List
-import torch
+from pathlib import Path
-from safetensors.torch import load_file
 
+import torch
+from compel.embeddings_provider import BaseTextualInversionManager
+from diffusers.models import UNet2DConditionModel
+from safetensors.torch import load_file
 from diffusers.models import UNet2DConditionModel
 from transformers import CLIPTextModel, CLIPTokenizer
-
+from torch.utils.hooks import RemovableHandle
-from compel.embeddings_provider import BaseTextualInversionManager
 
 class LoRALayerBase:
     #rank: Optional[int]
@@ -537,9 +538,10 @@ class ModelPatcher:
                             original_weights[module_key] = module.weight.detach().to(device="cpu", copy=True)
 
                         # enable autocast to calc fp16 loras on cpu
-                        with torch.autocast(device_type="cpu"):
+                        #with torch.autocast(device_type="cpu"):
-                            layer_scale = layer.alpha / layer.rank if (layer.alpha and layer.rank) else 1.0
+                        layer.to(dtype=torch.float32)
-                            layer_weight = layer.get_weight() * lora_weight * layer_scale
+                        layer_scale = layer.alpha / layer.rank if (layer.alpha and layer.rank) else 1.0
+                        layer_weight = layer.get_weight() * lora_weight * layer_scale
 
                         if module.weight.shape != layer_weight.shape:
                             # TODO: debug on lycoris
@@ -653,6 +655,9 @@ class TextualInversionModel:
         else:
             result.embedding = next(iter(state_dict.values()))
 
+            if len(result.embedding.shape) == 1:
+                result.embedding = result.embedding.unsqueeze(0)
+
             if not isinstance(result.embedding, torch.Tensor):
                 raise ValueError(f"Invalid embeddings file: {file_path.name}")
 

invokeai/backend/model_management/model_cache.py

@@ -100,8 +100,6 @@ class ModelCache(object):
         :param sha_chunksize: Chunksize to use when calculating sha256 model hash
         '''
         #max_cache_size = 9999
-        execution_device = torch.device('cuda')
-
         self.model_infos: Dict[str, ModelBase] = dict()
         self.lazy_offloading = lazy_offloading
         #self.sequential_offload: bool=sequential_offload

invokeai/backend/model_management/model_manager.py

@@ -249,7 +249,7 @@ from .model_cache import ModelCache, ModelLocker
 from .models import (
     BaseModelType, ModelType, SubModelType,
     ModelError, SchedulerPredictionType, MODEL_CLASSES,
-    ModelConfigBase,
+    ModelConfigBase, ModelNotFoundException,
     )
 
 # We are only starting to number the config file with release 3.
@@ -409,7 +409,7 @@ class ModelManager(object):
         if model_key not in self.models:
             self.scan_models_directory(base_model=base_model, model_type=model_type)
             if model_key not in self.models:
-                raise Exception(f"Model not found - {model_key}")
+                raise ModelNotFoundException(f"Model not found - {model_key}")
 
         model_config = self.models[model_key]
         model_path = self.app_config.root_path / model_config.path
@@ -421,7 +421,7 @@ class ModelManager(object):
 
             else:
                 self.models.pop(model_key, None)
-                raise Exception(f"Model not found - {model_key}")
+                raise ModelNotFoundException(f"Model not found - {model_key}")
 
         # vae/movq override
         # TODO: 
@@ -798,12 +798,12 @@ class ModelManager(object):
 
                             if model_path.is_relative_to(self.app_config.root_path):
                                 model_path = model_path.relative_to(self.app_config.root_path)
-                                try:
+                            try:
-                                    model_config: ModelConfigBase = model_class.probe_config(str(model_path))
+                                model_config: ModelConfigBase = model_class.probe_config(str(model_path))
-                                    self.models[model_key] = model_config
+                                self.models[model_key] = model_config
-                                    new_models_found = True
+                                new_models_found = True
-                                except NotImplementedError as e:
+                            except NotImplementedError as e:
-                                    self.logger.warning(e)
+                                self.logger.warning(e)
 
         imported_models = self.autoimport()
 

invokeai/backend/model_management/models/__init__.py

@@ -2,7 +2,7 @@ import inspect
 from enum import Enum
 from pydantic import BaseModel
 from typing import Literal, get_origin
-from .base import BaseModelType, ModelType, SubModelType, ModelBase, ModelConfigBase, ModelVariantType, SchedulerPredictionType, ModelError, SilenceWarnings
+from .base import BaseModelType, ModelType, SubModelType, ModelBase, ModelConfigBase, ModelVariantType, SchedulerPredictionType, ModelError, SilenceWarnings, ModelNotFoundException
 from .stable_diffusion import StableDiffusion1Model, StableDiffusion2Model
 from .vae import VaeModel
 from .lora import LoRAModel

invokeai/backend/model_management/models/base.py

@@ -15,6 +15,9 @@ from contextlib import suppress
 from pydantic import BaseModel, Field
 from typing import List, Dict, Optional, Type, Literal, TypeVar, Generic, Callable, Any, Union
 
+class ModelNotFoundException(Exception):
+    pass
+
 class BaseModelType(str, Enum):
     StableDiffusion1 = "sd-1"
     StableDiffusion2 = "sd-2"

invokeai/backend/model_management/models/textual_inversion.py

@@ -8,6 +8,7 @@ from .base import (
     ModelType,
     SubModelType,
     classproperty,
+    ModelNotFoundException,
 )
 # TODO: naming
 from ..lora import TextualInversionModel as TextualInversionModelRaw
@@ -37,8 +38,15 @@ class TextualInversionModel(ModelBase):
         if child_type is not None:
             raise Exception("There is no child models in textual inversion")
 
+        checkpoint_path = self.model_path
+        if os.path.isdir(checkpoint_path):
+            checkpoint_path = os.path.join(checkpoint_path, "learned_embeds.bin")
+
+        if not os.path.exists(checkpoint_path):
+            raise ModelNotFoundException()
+
         model = TextualInversionModelRaw.from_checkpoint(
-            file_path=self.model_path,
+            file_path=checkpoint_path,
             dtype=torch_dtype,
         )
 

invokeai/frontend/web/src/app/components/ImageDnd/DragPreview.tsx

@@ -1,4 +1,8 @@
 import { Box, ChakraProps, Flex, Heading, Image } from '@chakra-ui/react';
+import { createSelector } from '@reduxjs/toolkit';
+import { stateSelector } from 'app/store/store';
+import { useAppSelector } from 'app/store/storeHooks';
+import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
 import { memo } from 'react';
 import { TypesafeDraggableData } from './typesafeDnd';
 
@@ -28,7 +32,24 @@ const STYLES: ChakraProps['sx'] = {
   },
 };
 
+const selector = createSelector(
+  stateSelector,
+  (state) => {
+    const gallerySelectionCount = state.gallery.selection.length;
+    const batchSelectionCount = state.batch.selection.length;
+
+    return {
+      gallerySelectionCount,
+      batchSelectionCount,
+    };
+  },
+  defaultSelectorOptions
+);
+
 const DragPreview = (props: OverlayDragImageProps) => {
+  const { gallerySelectionCount, batchSelectionCount } =
+    useAppSelector(selector);
+
   if (!props.dragData) {
     return;
   }
@@ -57,7 +78,7 @@ const DragPreview = (props: OverlayDragImageProps) => {
     );
   }
 
-  if (props.dragData.payloadType === 'IMAGE_NAMES') {
+  if (props.dragData.payloadType === 'BATCH_SELECTION') {
     return (
       <Flex
         sx={{
@@ -70,7 +91,26 @@ const DragPreview = (props: OverlayDragImageProps) => {
           ...STYLES,
         }}
       >
-        <Heading>{props.dragData.payload.imageNames.length}</Heading>
+        <Heading>{batchSelectionCount}</Heading>
+        <Heading size="sm">Images</Heading>
+      </Flex>
+    );
+  }
+
+  if (props.dragData.payloadType === 'GALLERY_SELECTION') {
+    return (
+      <Flex
+        sx={{
+          cursor: 'none',
+          userSelect: 'none',
+          position: 'relative',
+          alignItems: 'center',
+          justifyContent: 'center',
+          flexDir: 'column',
+          ...STYLES,
+        }}
+      >
+        <Heading>{gallerySelectionCount}</Heading>
         <Heading size="sm">Images</Heading>
       </Flex>
     );

invokeai/frontend/web/src/app/components/ImageDnd/typesafeDnd.tsx

@@ -77,14 +77,18 @@ export type ImageDraggableData = BaseDragData & {
   payload: { imageDTO: ImageDTO };
 };
 
-export type ImageNamesDraggableData = BaseDragData & {
+export type GallerySelectionDraggableData = BaseDragData & {
-  payloadType: 'IMAGE_NAMES';
+  payloadType: 'GALLERY_SELECTION';
-  payload: { imageNames: string[] };
+};
+
+export type BatchSelectionDraggableData = BaseDragData & {
+  payloadType: 'BATCH_SELECTION';
 };
 
 export type TypesafeDraggableData =
   | ImageDraggableData
-  | ImageNamesDraggableData;
+  | GallerySelectionDraggableData
+  | BatchSelectionDraggableData;
 
 interface UseDroppableTypesafeArguments
   extends Omit<UseDroppableArguments, 'data'> {
@@ -155,11 +159,13 @@ export const isValidDrop = (
     case 'SET_NODES_IMAGE':
       return payloadType === 'IMAGE_DTO';
     case 'SET_MULTI_NODES_IMAGE':
-      return payloadType === 'IMAGE_DTO' || 'IMAGE_NAMES';
+      return payloadType === 'IMAGE_DTO' || 'GALLERY_SELECTION';
     case 'ADD_TO_BATCH':
-      return payloadType === 'IMAGE_DTO' || 'IMAGE_NAMES';
+      return payloadType === 'IMAGE_DTO' || 'GALLERY_SELECTION';
     case 'MOVE_BOARD':
-      return payloadType === 'IMAGE_DTO' || 'IMAGE_NAMES';
+      return (
+        payloadType === 'IMAGE_DTO' || 'GALLERY_SELECTION' || 'BATCH_SELECTION'
+      );
     default:
       return false;
   }

invokeai/frontend/web/src/app/store/enhancers/reduxRemember/serialize.ts

@@ -20,10 +20,8 @@ const serializationDenylist: {
   nodes: nodesPersistDenylist,
   postprocessing: postprocessingPersistDenylist,
   system: systemPersistDenylist,
-  // config: configPersistDenyList,
   ui: uiPersistDenylist,
   controlNet: controlNetDenylist,
-  // hotkeys: hotkeysPersistDenylist,
 };
 
 export const serialize: SerializeFunction = (data, key) => {

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

@@ -1,21 +1,21 @@
-import { startAppListening } from '..';
-import { imageDeleted } from 'services/api/thunks/image';
 import { log } from 'app/logging/useLogger';
-import { clamp } from 'lodash-es';
-import {
-  imageSelected,
-  imageRemoved,
-  selectImagesIds,
-} from 'features/gallery/store/gallerySlice';
 import { resetCanvas } from 'features/canvas/store/canvasSlice';
 import { controlNetReset } from 'features/controlNet/store/controlNetSlice';
-import { clearInitialImage } from 'features/parameters/store/generationSlice';
+import {
-import { nodeEditorReset } from 'features/nodes/store/nodesSlice';
+  imageRemoved,
-import { api } from 'services/api';
+  imageSelected,
+  selectFilteredImages,
+} from 'features/gallery/store/gallerySlice';
 import {
   imageDeletionConfirmed,
   isModalOpenChanged,
 } from 'features/imageDeletion/store/imageDeletionSlice';
+import { nodeEditorReset } from 'features/nodes/store/nodesSlice';
+import { clearInitialImage } from 'features/parameters/store/generationSlice';
+import { clamp } from 'lodash-es';
+import { api } from 'services/api';
+import { imageDeleted } from 'services/api/thunks/image';
+import { startAppListening } from '..';
 
 const moduleLog = log.child({ namespace: 'image' });
 
@@ -37,7 +37,9 @@ export const addRequestedImageDeletionListener = () => {
         state.gallery.selection[state.gallery.selection.length - 1];
 
       if (lastSelectedImage === image_name) {
-        const ids = selectImagesIds(state);
+        const filteredImages = selectFilteredImages(state);
+
+        const ids = filteredImages.map((i) => i.image_name);
 
         const deletedImageIndex = ids.findIndex(
           (result) => result.toString() === image_name

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

@@ -1,24 +1,23 @@
 import { createAction } from '@reduxjs/toolkit';
-import { startAppListening } from '../';
-import { log } from 'app/logging/useLogger';
 import {
   TypesafeDraggableData,
   TypesafeDroppableData,
 } from 'app/components/ImageDnd/typesafeDnd';
-import { imageSelected } from 'features/gallery/store/gallerySlice';
+import { log } from 'app/logging/useLogger';
-import { initialImageChanged } from 'features/parameters/store/generationSlice';
 import {
   imageAddedToBatch,
   imagesAddedToBatch,
 } from 'features/batch/store/batchSlice';
-import { controlNetImageChanged } from 'features/controlNet/store/controlNetSlice';
 import { setInitialCanvasImage } from 'features/canvas/store/canvasSlice';
+import { controlNetImageChanged } from 'features/controlNet/store/controlNetSlice';
+import { imageSelected } from 'features/gallery/store/gallerySlice';
 import {
   fieldValueChanged,
   imageCollectionFieldValueChanged,
 } from 'features/nodes/store/nodesSlice';
-import { boardsApi } from 'services/api/endpoints/boards';
+import { initialImageChanged } from 'features/parameters/store/generationSlice';
 import { boardImagesApi } from 'services/api/endpoints/boardImages';
+import { startAppListening } from '../';
 
 const moduleLog = log.child({ namespace: 'dnd' });
 
@@ -33,6 +32,7 @@ export const addImageDroppedListener = () => {
     effect: (action, { dispatch, getState }) => {
       const { activeData, overData } = action.payload;
       const { actionType } = overData;
+      const state = getState();
 
       // set current image
       if (
@@ -64,9 +64,9 @@ export const addImageDroppedListener = () => {
       // add multiple images to batch
       if (
         actionType === 'ADD_TO_BATCH' &&
-        activeData.payloadType === 'IMAGE_NAMES'
+        activeData.payloadType === 'GALLERY_SELECTION'
       ) {
-        dispatch(imagesAddedToBatch(activeData.payload.imageNames));
+        dispatch(imagesAddedToBatch(state.gallery.selection));
       }
 
       // set control image
@@ -128,14 +128,14 @@ export const addImageDroppedListener = () => {
       // set multiple nodes images (multiple images handler)
       if (
         actionType === 'SET_MULTI_NODES_IMAGE' &&
-        activeData.payloadType === 'IMAGE_NAMES'
+        activeData.payloadType === 'GALLERY_SELECTION'
       ) {
         const { fieldName, nodeId } = overData.context;
         dispatch(
           imageCollectionFieldValueChanged({
             nodeId,
             fieldName,
-            value: activeData.payload.imageNames.map((image_name) => ({
+            value: state.gallery.selection.map((image_name) => ({
               image_name,
             })),
           })

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

@@ -8,31 +8,32 @@ import {
 import dynamicMiddlewares from 'redux-dynamic-middlewares';
 import { rememberEnhancer, rememberReducer } from 'redux-remember';
 
+import batchReducer from 'features/batch/store/batchSlice';
 import canvasReducer from 'features/canvas/store/canvasSlice';
 import controlNetReducer from 'features/controlNet/store/controlNetSlice';
+import dynamicPromptsReducer from 'features/dynamicPrompts/store/slice';
+import boardsReducer from 'features/gallery/store/boardSlice';
 import galleryReducer from 'features/gallery/store/gallerySlice';
+import imageDeletionReducer from 'features/imageDeletion/store/imageDeletionSlice';
 import lightboxReducer from 'features/lightbox/store/lightboxSlice';
+import loraReducer from 'features/lora/store/loraSlice';
+import nodesReducer from 'features/nodes/store/nodesSlice';
 import generationReducer from 'features/parameters/store/generationSlice';
 import postprocessingReducer from 'features/parameters/store/postprocessingSlice';
-import systemReducer from 'features/system/store/systemSlice';
-import nodesReducer from 'features/nodes/store/nodesSlice';
-import boardsReducer from 'features/gallery/store/boardSlice';
 import configReducer from 'features/system/store/configSlice';
+import systemReducer from 'features/system/store/systemSlice';
 import hotkeysReducer from 'features/ui/store/hotkeysSlice';
 import uiReducer from 'features/ui/store/uiSlice';
-import dynamicPromptsReducer from 'features/dynamicPrompts/store/slice';
-import batchReducer from 'features/batch/store/batchSlice';
-import imageDeletionReducer from 'features/imageDeletion/store/imageDeletionSlice';
 
 import { listenerMiddleware } from './middleware/listenerMiddleware';
 
-import { actionSanitizer } from './middleware/devtools/actionSanitizer';
+import { api } from 'services/api';
-import { actionsDenylist } from './middleware/devtools/actionsDenylist';
-import { stateSanitizer } from './middleware/devtools/stateSanitizer';
 import { LOCALSTORAGE_PREFIX } from './constants';
 import { serialize } from './enhancers/reduxRemember/serialize';
 import { unserialize } from './enhancers/reduxRemember/unserialize';
-import { api } from 'services/api';
+import { actionSanitizer } from './middleware/devtools/actionSanitizer';
+import { actionsDenylist } from './middleware/devtools/actionsDenylist';
+import { stateSanitizer } from './middleware/devtools/stateSanitizer';
 
 const allReducers = {
   canvas: canvasReducer,
@@ -50,6 +51,7 @@ const allReducers = {
   dynamicPrompts: dynamicPromptsReducer,
   batch: batchReducer,
   imageDeletion: imageDeletionReducer,
+  lora: loraReducer,
   [api.reducerPath]: api.reducer,
 };
 
@@ -69,6 +71,7 @@ const rememberedKeys: (keyof typeof allReducers)[] = [
   'controlNet',
   'dynamicPrompts',
   'batch',
+  'lora',
   // 'boards',
   // 'hotkeys',
   // 'config',

invokeai/frontend/web/src/common/components/IAICollapse.tsx

@@ -4,22 +4,25 @@ import {
   Collapse,
   Flex,
   Spacer,
-  Switch,
+  Text,
   useColorMode,
+  useDisclosure,
 } from '@chakra-ui/react';
+import { AnimatePresence, motion } from 'framer-motion';
 import { PropsWithChildren, memo } from 'react';
 import { mode } from 'theme/util/mode';
 
 export type IAIToggleCollapseProps = PropsWithChildren & {
   label: string;
-  isOpen: boolean;
+  activeLabel?: string;
-  onToggle: () => void;
+  defaultIsOpen?: boolean;
-  withSwitch?: boolean;
 };
 
 const IAICollapse = (props: IAIToggleCollapseProps) => {
-  const { label, isOpen, onToggle, children, withSwitch = false } = props;
+  const { label, activeLabel, children, defaultIsOpen = false } = props;
+  const { isOpen, onToggle } = useDisclosure({ defaultIsOpen });
   const { colorMode } = useColorMode();
+
   return (
     <Box>
       <Flex
@@ -28,6 +31,7 @@ const IAICollapse = (props: IAIToggleCollapseProps) => {
           alignItems: 'center',
           p: 2,
           px: 4,
+          gap: 2,
           borderTopRadius: 'base',
           borderBottomRadius: isOpen ? 0 : 'base',
           bg: isOpen
@@ -48,19 +52,40 @@ const IAICollapse = (props: IAIToggleCollapseProps) => {
         }}
       >
         {label}
+        <AnimatePresence>
+          {activeLabel && (
+            <motion.div
+              key="statusText"
+              initial={{
+                opacity: 0,
+              }}
+              animate={{
+                opacity: 1,
+                transition: { duration: 0.1 },
+              }}
+              exit={{
+                opacity: 0,
+                transition: { duration: 0.1 },
+              }}
+            >
+              <Text
+                sx={{ color: 'accent.500', _dark: { color: 'accent.300' } }}
+              >
+                {activeLabel}
+              </Text>
+            </motion.div>
+          )}
+        </AnimatePresence>
         <Spacer />
-        {withSwitch && <Switch isChecked={isOpen} pointerEvents="none" />}
+        <ChevronUpIcon
-        {!withSwitch && (
+          sx={{
-          <ChevronUpIcon
+            w: '1rem',
-            sx={{
+            h: '1rem',
-              w: '1rem',
+            transform: isOpen ? 'rotate(0deg)' : 'rotate(180deg)',
-              h: '1rem',
+            transitionProperty: 'common',
-              transform: isOpen ? 'rotate(0deg)' : 'rotate(180deg)',
+            transitionDuration: 'normal',
-              transitionProperty: 'common',
+          }}
-              transitionDuration: 'normal',
+        />
-            }}
-          />
-        )}
       </Flex>
       <Collapse in={isOpen} animateOpacity style={{ overflow: 'unset' }}>
         <Box

invokeai/frontend/web/src/common/components/IAIMantineMultiSelect.tsx

@@ -61,7 +61,7 @@ const IAIMantineMultiSelect = (props: IAIMultiSelectProps) => {
             '&:focus-within': {
               borderColor: mode(accent200, accent600)(colorMode),
             },
-            '&:disabled': {
+            '&[data-disabled]': {
               backgroundColor: mode(base300, base700)(colorMode),
               color: mode(base600, base400)(colorMode),
             },

invokeai/frontend/web/src/common/components/IAIMantineSelect.tsx

@@ -64,7 +64,7 @@ const IAIMantineSelect = (props: IAISelectProps) => {
             '&:focus-within': {
               borderColor: mode(accent200, accent600)(colorMode),
             },
-            '&:disabled': {
+            '&[data-disabled]': {
               backgroundColor: mode(base300, base700)(colorMode),
               color: mode(base600, base400)(colorMode),
             },

invokeai/frontend/web/src/common/components/IAISwitch.tsx

@@ -36,7 +36,6 @@ const IAISwitch = (props: Props) => {
         isDisabled={isDisabled}
         width={width}
         display="flex"
-        gap={4}
         alignItems="center"
         {...formControlProps}
       >
@@ -47,6 +46,7 @@ const IAISwitch = (props: Props) => {
             sx={{
               cursor: isDisabled ? 'not-allowed' : 'pointer',
               ...formLabelProps?.sx,
+              pe: 4,
             }}
             {...formLabelProps}
           >

invokeai/frontend/web/src/features/batch/components/BatchImage.tsx

@@ -1,28 +1,29 @@
 import { Box, Icon, Skeleton } from '@chakra-ui/react';
+import { createSelector } from '@reduxjs/toolkit';
+import { TypesafeDraggableData } from 'app/components/ImageDnd/typesafeDnd';
+import { stateSelector } from 'app/store/store';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { FaExclamationCircle } from 'react-icons/fa';
+import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
-import { useGetImageDTOQuery } from 'services/api/endpoints/images';
+import IAIDndImage from 'common/components/IAIDndImage';
-import { MouseEvent, memo, useCallback, useMemo } from 'react';
 import {
   batchImageRangeEndSelected,
   batchImageSelected,
   batchImageSelectionToggled,
   imageRemovedFromBatch,
 } from 'features/batch/store/batchSlice';
-import IAIDndImage from 'common/components/IAIDndImage';
+import { MouseEvent, memo, useCallback, useMemo } from 'react';
-import { createSelector } from '@reduxjs/toolkit';
+import { FaExclamationCircle } from 'react-icons/fa';
-import { RootState, stateSelector } from 'app/store/store';
+import { useGetImageDTOQuery } from 'services/api/endpoints/images';
-import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
-import { TypesafeDraggableData } from 'app/components/ImageDnd/typesafeDnd';
 
-const isSelectedSelector = createSelector(
+const makeSelector = (image_name: string) =>
-  [stateSelector, (state: RootState, imageName: string) => imageName],
+  createSelector(
-  (state, imageName) => ({
+    [stateSelector],
-    selection: state.batch.selection,
+    (state) => ({
-    isSelected: state.batch.selection.includes(imageName),
+      selectionCount: state.batch.selection.length,
-  }),
+      isSelected: state.batch.selection.includes(image_name),
-  defaultSelectorOptions
+    }),
-);
+    defaultSelectorOptions
+  );
 
 type BatchImageProps = {
   imageName: string;
@@ -37,10 +38,13 @@ const BatchImage = (props: BatchImageProps) => {
   } = useGetImageDTOQuery(props.imageName);
   const dispatch = useAppDispatch();
 
-  const { isSelected, selection } = useAppSelector((state) =>
+  const selector = useMemo(
-    isSelectedSelector(state, props.imageName)
+    () => makeSelector(props.imageName),
+    [props.imageName]
   );
 
+  const { isSelected, selectionCount } = useAppSelector(selector);
+
   const handleClickRemove = useCallback(() => {
     dispatch(imageRemovedFromBatch(props.imageName));
   }, [dispatch, props.imageName]);
@@ -59,13 +63,10 @@ const BatchImage = (props: BatchImageProps) => {
   );
 
   const draggableData = useMemo<TypesafeDraggableData | undefined>(() => {
-    if (selection.length > 1) {
+    if (selectionCount > 1) {
       return {
         id: 'batch',
-        payloadType: 'IMAGE_NAMES',
+        payloadType: 'BATCH_SELECTION',
-        payload: {
-          imageNames: selection,
-        },
       };
     }
 
@@ -76,7 +77,7 @@ const BatchImage = (props: BatchImageProps) => {
         payload: { imageDTO },
       };
     }
-  }, [imageDTO, selection]);
+  }, [imageDTO, selectionCount]);
 
   if (isError) {
     return <Icon as={FaExclamationCircle} />;

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

@@ -1,25 +1,22 @@
-import { memo, useCallback, useMemo, useState } from 'react';
-import { ImageDTO } from 'services/api/types';
-import {
-  ControlNetConfig,
-  controlNetImageChanged,
-  controlNetSelector,
-} from '../store/controlNetSlice';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { Box, Flex, SystemStyleObject } from '@chakra-ui/react';
-import IAIDndImage from 'common/components/IAIDndImage';
 import { createSelector } from '@reduxjs/toolkit';
-import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
-import { IAILoadingImageFallback } from 'common/components/IAIImageFallback';
-import IAIIconButton from 'common/components/IAIIconButton';
-import { FaUndo } from 'react-icons/fa';
-import { useGetImageDTOQuery } from 'services/api/endpoints/images';
 import { skipToken } from '@reduxjs/toolkit/dist/query';
 import {
   TypesafeDraggableData,
   TypesafeDroppableData,
 } from 'app/components/ImageDnd/typesafeDnd';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
+import IAIDndImage from 'common/components/IAIDndImage';
+import { IAILoadingImageFallback } from 'common/components/IAIImageFallback';
+import { memo, useCallback, useMemo, useState } from 'react';
+import { useGetImageDTOQuery } from 'services/api/endpoints/images';
 import { PostUploadAction } from 'services/api/thunks/image';
+import {
+  ControlNetConfig,
+  controlNetImageChanged,
+  controlNetSelector,
+} from '../store/controlNetSlice';
 
 const selector = createSelector(
   controlNetSelector,
@@ -83,15 +80,14 @@ const ControlNetImagePreview = (props: Props) => {
     }
   }, [controlImage, controlNetId]);
 
-  const droppableData = useMemo<TypesafeDroppableData | undefined>(() => {
+  const droppableData = useMemo<TypesafeDroppableData | undefined>(
-    if (controlNetId) {
+    () => ({
-      return {
+      id: controlNetId,
-        id: controlNetId,
+      actionType: 'SET_CONTROLNET_IMAGE',
-        actionType: 'SET_CONTROLNET_IMAGE',
+      context: { controlNetId },
-        context: { controlNetId },
+    }),
-      };
+    [controlNetId]
-    }
+  );
-  }, [controlNetId]);
 
   const postUploadAction = useMemo<PostUploadAction>(
     () => ({ type: 'SET_CONTROLNET_IMAGE', controlNetId }),

invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetFeatureToggle.tsx

@@ -0,0 +1,36 @@
+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 { isControlNetEnabledToggled } from 'features/controlNet/store/controlNetSlice';
+import { useCallback } from 'react';
+
+const selector = createSelector(
+  stateSelector,
+  (state) => {
+    const { isEnabled } = state.controlNet;
+
+    return { isEnabled };
+  },
+  defaultSelectorOptions
+);
+
+const ParamControlNetFeatureToggle = () => {
+  const { isEnabled } = useAppSelector(selector);
+  const dispatch = useAppDispatch();
+
+  const handleChange = useCallback(() => {
+    dispatch(isControlNetEnabledToggled());
+  }, [dispatch]);
+
+  return (
+    <IAISwitch
+      label="Enable ControlNet"
+      isChecked={isEnabled}
+      onChange={handleChange}
+    />
+  );
+};
+
+export default ParamControlNetFeatureToggle;

invokeai/frontend/web/src/features/controlNet/util/getValidControlNets.ts

@@ -0,0 +1,15 @@
+import { filter } from 'lodash-es';
+import { ControlNetConfig } from '../store/controlNetSlice';
+
+export const getValidControlNets = (
+  controlNets: Record<string, ControlNetConfig>
+) => {
+  const validControlNets = filter(
+    controlNets,
+    (c) =>
+      c.isEnabled &&
+      (Boolean(c.processedControlImage) ||
+        (c.processorType === 'none' && Boolean(c.controlImage)))
+  );
+  return validControlNets;
+};

invokeai/frontend/web/src/features/dynamicPrompts/components/ParamDynamicPromptsCollapse.tsx

@@ -1,40 +1,30 @@
+import { Flex } from '@chakra-ui/react';
 import { createSelector } from '@reduxjs/toolkit';
 import { stateSelector } from 'app/store/store';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { useAppSelector } from 'app/store/storeHooks';
 import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
 import IAICollapse from 'common/components/IAICollapse';
-import { useCallback } from 'react';
-import { isEnabledToggled } from '../store/slice';
-import ParamDynamicPromptsMaxPrompts from './ParamDynamicPromptsMaxPrompts';
 import ParamDynamicPromptsCombinatorial from './ParamDynamicPromptsCombinatorial';
-import { Flex } from '@chakra-ui/react';
+import ParamDynamicPromptsToggle from './ParamDynamicPromptsEnabled';
+import ParamDynamicPromptsMaxPrompts from './ParamDynamicPromptsMaxPrompts';
 
 const selector = createSelector(
   stateSelector,
   (state) => {
     const { isEnabled } = state.dynamicPrompts;
 
-    return { isEnabled };
+    return { activeLabel: isEnabled ? 'Enabled' : undefined };
   },
   defaultSelectorOptions
 );
 
 const ParamDynamicPromptsCollapse = () => {
-  const dispatch = useAppDispatch();
+  const { activeLabel } = useAppSelector(selector);
-  const { isEnabled } = useAppSelector(selector);
-
-  const handleToggleIsEnabled = useCallback(() => {
-    dispatch(isEnabledToggled());
-  }, [dispatch]);
 
   return (
-    <IAICollapse
+    <IAICollapse label="Dynamic Prompts" activeLabel={activeLabel}>
-      isOpen={isEnabled}
-      onToggle={handleToggleIsEnabled}
-      label="Dynamic Prompts"
-      withSwitch
-    >
       <Flex sx={{ gap: 2, flexDir: 'column' }}>
+        <ParamDynamicPromptsToggle />
         <ParamDynamicPromptsCombinatorial />
         <ParamDynamicPromptsMaxPrompts />
       </Flex>

invokeai/frontend/web/src/features/dynamicPrompts/components/ParamDynamicPromptsCombinatorial.tsx

@@ -1,23 +1,23 @@
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { combinatorialToggled } from '../store/slice';
 import { createSelector } from '@reduxjs/toolkit';
-import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
-import { useCallback } from 'react';
 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 { useCallback } from 'react';
+import { combinatorialToggled } from '../store/slice';
 
 const selector = createSelector(
   stateSelector,
   (state) => {
-    const { combinatorial } = state.dynamicPrompts;
+    const { combinatorial, isEnabled } = state.dynamicPrompts;
 
-    return { combinatorial };
+    return { combinatorial, isDisabled: !isEnabled };
   },
   defaultSelectorOptions
 );
 
 const ParamDynamicPromptsCombinatorial = () => {
-  const { combinatorial } = useAppSelector(selector);
+  const { combinatorial, isDisabled } = useAppSelector(selector);
   const dispatch = useAppDispatch();
 
   const handleChange = useCallback(() => {
@@ -26,6 +26,7 @@ const ParamDynamicPromptsCombinatorial = () => {
 
   return (
     <IAISwitch
+      isDisabled={isDisabled}
       label="Combinatorial Generation"
       isChecked={combinatorial}
       onChange={handleChange}

invokeai/frontend/web/src/features/dynamicPrompts/components/ParamDynamicPromptsEnabled.tsx

@@ -0,0 +1,36 @@
+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 { useCallback } from 'react';
+import { isEnabledToggled } from '../store/slice';
+
+const selector = createSelector(
+  stateSelector,
+  (state) => {
+    const { isEnabled } = state.dynamicPrompts;
+
+    return { isEnabled };
+  },
+  defaultSelectorOptions
+);
+
+const ParamDynamicPromptsToggle = () => {
+  const dispatch = useAppDispatch();
+  const { isEnabled } = useAppSelector(selector);
+
+  const handleToggleIsEnabled = useCallback(() => {
+    dispatch(isEnabledToggled());
+  }, [dispatch]);
+
+  return (
+    <IAISwitch
+      label="Enable Dynamic Prompts"
+      isChecked={isEnabled}
+      onChange={handleToggleIsEnabled}
+    />
+  );
+};
+
+export default ParamDynamicPromptsToggle;

invokeai/frontend/web/src/features/dynamicPrompts/components/ParamDynamicPromptsMaxPrompts.tsx

@@ -1,25 +1,31 @@
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import IAISlider from 'common/components/IAISlider';
-import { maxPromptsChanged, maxPromptsReset } from '../store/slice';
 import { createSelector } from '@reduxjs/toolkit';
-import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
-import { useCallback } from 'react';
 import { stateSelector } from 'app/store/store';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
+import IAISlider from 'common/components/IAISlider';
+import { useCallback } from 'react';
+import { maxPromptsChanged, maxPromptsReset } from '../store/slice';
 
 const selector = createSelector(
   stateSelector,
   (state) => {
-    const { maxPrompts, combinatorial } = state.dynamicPrompts;
+    const { maxPrompts, combinatorial, isEnabled } = state.dynamicPrompts;
     const { min, sliderMax, inputMax } =
       state.config.sd.dynamicPrompts.maxPrompts;
 
-    return { maxPrompts, min, sliderMax, inputMax, combinatorial };
+    return {
+      maxPrompts,
+      min,
+      sliderMax,
+      inputMax,
+      isDisabled: !isEnabled || !combinatorial,
+    };
   },
   defaultSelectorOptions
 );
 
 const ParamDynamicPromptsMaxPrompts = () => {
-  const { maxPrompts, min, sliderMax, inputMax, combinatorial } =
+  const { maxPrompts, min, sliderMax, inputMax, isDisabled } =
     useAppSelector(selector);
   const dispatch = useAppDispatch();
 
@@ -37,7 +43,7 @@ const ParamDynamicPromptsMaxPrompts = () => {
   return (
     <IAISlider
       label="Max Prompts"
-      isDisabled={!combinatorial}
+      isDisabled={isDisabled}
       min={min}
       max={sliderMax}
       value={maxPrompts}

invokeai/frontend/web/src/features/gallery/components/CurrentImagePreview.tsx

@@ -1,19 +1,19 @@
 import { Box, Flex, Image } from '@chakra-ui/react';
 import { createSelector } from '@reduxjs/toolkit';
-import { useAppSelector } from 'app/store/storeHooks';
-import { isEqual } from 'lodash-es';
-import ImageMetadataViewer from './ImageMetaDataViewer/ImageMetadataViewer';
-import NextPrevImageButtons from './NextPrevImageButtons';
-import { memo, useMemo } from 'react';
-import IAIDndImage from 'common/components/IAIDndImage';
-import { useGetImageDTOQuery } from 'services/api/endpoints/images';
 import { skipToken } from '@reduxjs/toolkit/dist/query';
-import { stateSelector } from 'app/store/store';
-import { selectLastSelectedImage } from 'features/gallery/store/gallerySlice';
 import {
   TypesafeDraggableData,
   TypesafeDroppableData,
 } from 'app/components/ImageDnd/typesafeDnd';
+import { stateSelector } from 'app/store/store';
+import { useAppSelector } from 'app/store/storeHooks';
+import IAIDndImage from 'common/components/IAIDndImage';
+import { selectLastSelectedImage } from 'features/gallery/store/gallerySlice';
+import { isEqual } from 'lodash-es';
+import { memo, useMemo } from 'react';
+import { useGetImageDTOQuery } from 'services/api/endpoints/images';
+import ImageMetadataViewer from './ImageMetaDataViewer/ImageMetadataViewer';
+import NextPrevImageButtons from './NextPrevImageButtons';
 
 export const imagesSelector = createSelector(
   [stateSelector, selectLastSelectedImage],

invokeai/frontend/web/src/features/gallery/components/GalleryImage.tsx

@@ -1,34 +1,35 @@
 import { Box } from '@chakra-ui/react';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { MouseEvent, memo, useCallback, useMemo } from 'react';
-import { FaTrash } from 'react-icons/fa';
-import { useTranslation } from 'react-i18next';
 import { createSelector } from '@reduxjs/toolkit';
-import { ImageDTO } from 'services/api/types';
 import { TypesafeDraggableData } from 'app/components/ImageDnd/typesafeDnd';
 import { stateSelector } from 'app/store/store';
-import ImageContextMenu from './ImageContextMenu';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
 import IAIDndImage from 'common/components/IAIDndImage';
+import { imageToDeleteSelected } from 'features/imageDeletion/store/imageDeletionSlice';
+import { MouseEvent, memo, useCallback, useMemo } from 'react';
+import { useTranslation } from 'react-i18next';
+import { FaTrash } from 'react-icons/fa';
+import { ImageDTO } from 'services/api/types';
 import {
   imageRangeEndSelected,
   imageSelected,
   imageSelectionToggled,
 } from '../store/gallerySlice';
-import { imageToDeleteSelected } from 'features/imageDeletion/store/imageDeletionSlice';
+import ImageContextMenu from './ImageContextMenu';
 
-export const selector = createSelector(
+export const makeSelector = (image_name: string) =>
-  [stateSelector, (state, { image_name }: ImageDTO) => image_name],
+  createSelector(
-  ({ gallery }, image_name) => {
+    [stateSelector],
-    const isSelected = gallery.selection.includes(image_name);
+    ({ gallery }) => {
-    const selection = gallery.selection;
+      const isSelected = gallery.selection.includes(image_name);
-    return {
+      const selectionCount = gallery.selection.length;
-      isSelected,
+      return {
-      selection,
+        isSelected,
-    };
+        selectionCount,
-  },
+      };
-  defaultSelectorOptions
+    },
-);
+    defaultSelectorOptions
+  );
 
 interface HoverableImageProps {
   imageDTO: ImageDTO;
@@ -38,13 +39,13 @@ interface HoverableImageProps {
  * Gallery image component with delete/use all/use seed buttons on hover.
  */
 const GalleryImage = (props: HoverableImageProps) => {
-  const { isSelected, selection } = useAppSelector((state) =>
-    selector(state, props.imageDTO)
-  );
-
   const { imageDTO } = props;
   const { image_url, thumbnail_url, image_name } = imageDTO;
 
+  const localSelector = useMemo(() => makeSelector(image_name), [image_name]);
+
+  const { isSelected, selectionCount } = useAppSelector(localSelector);
+
   const dispatch = useAppDispatch();
 
   const { t } = useTranslation();
@@ -74,11 +75,10 @@ const GalleryImage = (props: HoverableImageProps) => {
   );
 
   const draggableData = useMemo<TypesafeDraggableData | undefined>(() => {
-    if (selection.length > 1) {
+    if (selectionCount > 1) {
       return {
         id: 'gallery-image',
-        payloadType: 'IMAGE_NAMES',
+        payloadType: 'GALLERY_SELECTION',
-        payload: { imageNames: selection },
       };
     }
 
@@ -89,7 +89,7 @@ const GalleryImage = (props: HoverableImageProps) => {
         payload: { imageDTO },
       };
     }
-  }, [imageDTO, selection]);
+  }, [imageDTO, selectionCount]);
 
   return (
     <Box sx={{ w: 'full', h: 'full', touchAction: 'none' }}>

invokeai/frontend/web/src/features/gallery/store/gallerySlice.ts

@@ -7,7 +7,6 @@ import {
 import { RootState } from 'app/store/store';
 import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
 import { dateComparator } from 'common/util/dateComparator';
-import { imageDeletionConfirmed } from 'features/imageDeletion/store/imageDeletionSlice';
 import { keyBy, uniq } from 'lodash-es';
 import { boardsApi } from 'services/api/endpoints/boards';
 import {
@@ -174,11 +173,6 @@ export const gallerySlice = createSlice({
       state.limit = limit;
       state.total = total;
     });
-    builder.addCase(imageDeletionConfirmed, (state, action) => {
-      // Image deleted
-      const { image_name } = action.payload.imageDTO;
-      imagesAdapter.removeOne(state, image_name);
-    });
     builder.addCase(imageUrlsReceived.fulfilled, (state, action) => {
       const { image_name, image_url, thumbnail_url } = action.payload;
 

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

@@ -23,6 +23,7 @@ import { stateSelector } from 'app/store/store';
 import {
   imageDeletionConfirmed,
   imageToDeleteCleared,
+  isModalOpenChanged,
   selectImageUsage,
 } from '../store/imageDeletionSlice';
 
@@ -63,6 +64,7 @@ const DeleteImageModal = () => {
 
   const handleClose = useCallback(() => {
     dispatch(imageToDeleteCleared());
+    dispatch(isModalOpenChanged(false));
   }, [dispatch]);
 
   const handleDelete = useCallback(() => {

invokeai/frontend/web/src/features/imageDeletion/store/imageDeletionSlice.ts

@@ -31,6 +31,7 @@ const imageDeletion = createSlice({
     },
     imageToDeleteCleared: (state) => {
       state.imageToDelete = null;
+      state.isModalOpen = false;
     },
   },
 });

invokeai/frontend/web/src/features/lora/components/ParamLora.tsx

@@ -0,0 +1,59 @@
+import { Flex } from '@chakra-ui/react';
+import { useAppDispatch } from 'app/store/storeHooks';
+import IAIIconButton from 'common/components/IAIIconButton';
+import IAISlider from 'common/components/IAISlider';
+import { memo, useCallback } from 'react';
+import { FaTrash } from 'react-icons/fa';
+import { Lora, loraRemoved, loraWeightChanged } from '../store/loraSlice';
+
+type Props = {
+  lora: Lora;
+};
+
+const ParamLora = (props: Props) => {
+  const dispatch = useAppDispatch();
+  const { lora } = props;
+
+  const handleChange = useCallback(
+    (v: number) => {
+      dispatch(loraWeightChanged({ id: lora.id, weight: v }));
+    },
+    [dispatch, lora.id]
+  );
+
+  const handleReset = useCallback(() => {
+    dispatch(loraWeightChanged({ id: lora.id, weight: 1 }));
+  }, [dispatch, lora.id]);
+
+  const handleRemoveLora = useCallback(() => {
+    dispatch(loraRemoved(lora.id));
+  }, [dispatch, lora.id]);
+
+  return (
+    <Flex sx={{ gap: 2.5, alignItems: 'flex-end' }}>
+      <IAISlider
+        label={lora.name}
+        value={lora.weight}
+        onChange={handleChange}
+        min={-1}
+        max={2}
+        step={0.01}
+        withInput
+        withReset
+        handleReset={handleReset}
+        withSliderMarks
+        sliderMarks={[-1, 0, 1, 2]}
+      />
+      <IAIIconButton
+        size="sm"
+        onClick={handleRemoveLora}
+        tooltip="Remove LoRA"
+        aria-label="Remove LoRA"
+        icon={<FaTrash />}
+        colorScheme="error"
+      />
+    </Flex>
+  );
+};
+
+export default memo(ParamLora);

invokeai/frontend/web/src/features/lora/components/ParamLoraCollapse.tsx

@@ -0,0 +1,36 @@
+import { Flex } from '@chakra-ui/react';
+import { createSelector } from '@reduxjs/toolkit';
+import { stateSelector } from 'app/store/store';
+import { useAppSelector } from 'app/store/storeHooks';
+import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
+import IAICollapse from 'common/components/IAICollapse';
+import { size } from 'lodash-es';
+import { memo } from 'react';
+import ParamLoraList from './ParamLoraList';
+import ParamLoraSelect from './ParamLoraSelect';
+
+const selector = createSelector(
+  stateSelector,
+  (state) => {
+    const loraCount = size(state.lora.loras);
+    return {
+      activeLabel: loraCount > 0 ? `${loraCount} Active` : undefined,
+    };
+  },
+  defaultSelectorOptions
+);
+
+const ParamLoraCollapse = () => {
+  const { activeLabel } = useAppSelector(selector);
+
+  return (
+    <IAICollapse label={'LoRA'} activeLabel={activeLabel}>
+      <Flex sx={{ flexDir: 'column', gap: 2 }}>
+        <ParamLoraSelect />
+        <ParamLoraList />
+      </Flex>
+    </IAICollapse>
+  );
+};
+
+export default memo(ParamLoraCollapse);

invokeai/frontend/web/src/features/lora/components/ParamLoraList.tsx

@@ -0,0 +1,24 @@
+import { createSelector } from '@reduxjs/toolkit';
+import { stateSelector } from 'app/store/store';
+import { useAppSelector } from 'app/store/storeHooks';
+import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
+import { map } from 'lodash-es';
+import ParamLora from './ParamLora';
+
+const selector = createSelector(
+  stateSelector,
+  ({ lora }) => {
+    const { loras } = lora;
+
+    return { loras };
+  },
+  defaultSelectorOptions
+);
+
+const ParamLoraList = () => {
+  const { loras } = useAppSelector(selector);
+
+  return map(loras, (lora) => <ParamLora key={lora.name} lora={lora} />);
+};
+
+export default ParamLoraList;

invokeai/frontend/web/src/features/lora/components/ParamLoraSelect.tsx

@@ -0,0 +1,107 @@
+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 IAIMantineMultiSelect from 'common/components/IAIMantineMultiSelect';
+import { forEach } from 'lodash-es';
+import { forwardRef, useCallback, useMemo } from 'react';
+import { useGetLoRAModelsQuery } from 'services/api/endpoints/models';
+import { loraAdded } from '../store/loraSlice';
+
+type LoraSelectItem = {
+  label: string;
+  value: string;
+  description?: string;
+};
+
+const selector = createSelector(
+  stateSelector,
+  ({ lora }) => ({
+    loras: lora.loras,
+  }),
+  defaultSelectorOptions
+);
+
+const ParamLoraSelect = () => {
+  const dispatch = useAppDispatch();
+  const { loras } = useAppSelector(selector);
+  const { data: lorasQueryData } = useGetLoRAModelsQuery();
+
+  const data = useMemo(() => {
+    if (!lorasQueryData) {
+      return [];
+    }
+
+    const data: LoraSelectItem[] = [];
+
+    forEach(lorasQueryData.entities, (lora, id) => {
+      if (!lora || Boolean(id in loras)) {
+        return;
+      }
+
+      data.push({
+        value: id,
+        label: lora.name,
+        description: lora.description,
+      });
+    });
+
+    return data;
+  }, [loras, lorasQueryData]);
+
+  const handleChange = useCallback(
+    (v: string[]) => {
+      const loraEntity = lorasQueryData?.entities[v[0]];
+      if (!loraEntity) {
+        return;
+      }
+      v[0] && dispatch(loraAdded(loraEntity));
+    },
+    [dispatch, lorasQueryData?.entities]
+  );
+
+  return (
+    <IAIMantineMultiSelect
+      placeholder={data.length === 0 ? 'All LoRAs added' : 'Add LoRA'}
+      value={[]}
+      data={data}
+      maxDropdownHeight={400}
+      nothingFound="No matching LoRAs"
+      itemComponent={SelectItem}
+      disabled={data.length === 0}
+      filter={(value, selected, item: LoraSelectItem) =>
+        item.label.toLowerCase().includes(value.toLowerCase().trim()) ||
+        item.value.toLowerCase().includes(value.toLowerCase().trim())
+      }
+      onChange={handleChange}
+    />
+  );
+};
+
+interface ItemProps extends React.ComponentPropsWithoutRef<'div'> {
+  value: string;
+  label: string;
+  description?: string;
+}
+
+const SelectItem = forwardRef<HTMLDivElement, ItemProps>(
+  ({ label, description, ...others }: ItemProps, ref) => {
+    return (
+      <div ref={ref} {...others}>
+        <div>
+          <Text>{label}</Text>
+          {description && (
+            <Text size="xs" color="base.600">
+              {description}
+            </Text>
+          )}
+        </div>
+      </div>
+    );
+  }
+);
+
+SelectItem.displayName = 'SelectItem';
+
+export default ParamLoraSelect;

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

@@ -0,0 +1,46 @@
+import { PayloadAction, createSlice } from '@reduxjs/toolkit';
+import { LoRAModelConfigEntity } from 'services/api/endpoints/models';
+
+export type Lora = {
+  id: string;
+  name: string;
+  weight: number;
+};
+
+export const defaultLoRAConfig: Omit<Lora, 'id' | 'name'> = {
+  weight: 1,
+};
+
+export type LoraState = {
+  loras: Record<string, Lora>;
+};
+
+export const intialLoraState: LoraState = {
+  loras: {},
+};
+
+export const loraSlice = createSlice({
+  name: 'lora',
+  initialState: intialLoraState,
+  reducers: {
+    loraAdded: (state, action: PayloadAction<LoRAModelConfigEntity>) => {
+      const { name, id } = action.payload;
+      state.loras[id] = { id, name, ...defaultLoRAConfig };
+    },
+    loraRemoved: (state, action: PayloadAction<string>) => {
+      const id = action.payload;
+      delete state.loras[id];
+    },
+    loraWeightChanged: (
+      state,
+      action: PayloadAction<{ id: string; weight: number }>
+    ) => {
+      const { id, weight } = action.payload;
+      state.loras[id].weight = weight;
+    },
+  },
+});
+
+export const { loraAdded, loraRemoved, loraWeightChanged } = loraSlice.actions;
+
+export default loraSlice.reducer;

invokeai/frontend/web/src/features/nodes/components/InputFieldComponent.tsx

@@ -12,6 +12,7 @@ import ImageCollectionInputFieldComponent from './fields/ImageCollectionInputFie
 import ImageInputFieldComponent from './fields/ImageInputFieldComponent';
 import ItemInputFieldComponent from './fields/ItemInputFieldComponent';
 import LatentsInputFieldComponent from './fields/LatentsInputFieldComponent';
+import LoRAModelInputFieldComponent from './fields/LoRAModelInputFieldComponent';
 import ModelInputFieldComponent from './fields/ModelInputFieldComponent';
 import NumberInputFieldComponent from './fields/NumberInputFieldComponent';
 import StringInputFieldComponent from './fields/StringInputFieldComponent';
@@ -163,6 +164,16 @@ const InputFieldComponent = (props: InputFieldComponentProps) => {
     );
   }
 
+  if (type === 'lora_model' && template.type === 'lora_model') {
+    return (
+      <LoRAModelInputFieldComponent
+        nodeId={nodeId}
+        field={field}
+        template={template}
+      />
+    );
+  }
+
   if (type === 'array' && template.type === 'array') {
     return (
       <ArrayInputFieldComponent

invokeai/frontend/web/src/features/nodes/components/fields/ImageInputFieldComponent.tsx

@@ -7,18 +7,16 @@ import {
 } from 'features/nodes/types/types';
 import { memo, useCallback, useMemo } from 'react';
 
-import { FieldComponentProps } from './types';
-import IAIDndImage from 'common/components/IAIDndImage';
-import { ImageDTO } from 'services/api/types';
 import { Flex } from '@chakra-ui/react';
-import { useGetImageDTOQuery } from 'services/api/endpoints/images';
 import { skipToken } from '@reduxjs/toolkit/dist/query';
 import {
-  NodesImageDropData,
   TypesafeDraggableData,
   TypesafeDroppableData,
 } from 'app/components/ImageDnd/typesafeDnd';
+import IAIDndImage from 'common/components/IAIDndImage';
+import { useGetImageDTOQuery } from 'services/api/endpoints/images';
 import { PostUploadAction } from 'services/api/thunks/image';
+import { FieldComponentProps } from './types';
 
 const ImageInputFieldComponent = (
   props: FieldComponentProps<ImageInputFieldValue, ImageInputFieldTemplate>
@@ -34,23 +32,6 @@ const ImageInputFieldComponent = (
     isSuccess,
   } = useGetImageDTOQuery(field.value?.image_name ?? skipToken);
 
-  const handleDrop = useCallback(
-    ({ image_name }: ImageDTO) => {
-      if (field.value?.image_name === image_name) {
-        return;
-      }
-
-      dispatch(
-        fieldValueChanged({
-          nodeId,
-          fieldName: field.name,
-          value: { image_name },
-        })
-      );
-    },
-    [dispatch, field.name, field.value, nodeId]
-  );
-
   const handleReset = useCallback(() => {
     dispatch(
       fieldValueChanged({
@@ -71,15 +52,14 @@ const ImageInputFieldComponent = (
     }
   }, [field.name, imageDTO, nodeId]);
 
-  const droppableData = useMemo<TypesafeDroppableData | undefined>(() => {
+  const droppableData = useMemo<TypesafeDroppableData | undefined>(
-    if (imageDTO) {
+    () => ({
-      return {
+      id: `node-${nodeId}-${field.name}`,
-        id: `node-${nodeId}-${field.name}`,
+      actionType: 'SET_NODES_IMAGE',
-        actionType: 'SET_NODES_IMAGE',
+      context: { nodeId, fieldName: field.name },
-        context: { nodeId, fieldName: field.name },
+    }),
-      };
+    [field.name, nodeId]
-    }
+  );
-  }, [field.name, imageDTO, nodeId]);
 
   const postUploadAction = useMemo<PostUploadAction>(
     () => ({

invokeai/frontend/web/src/features/nodes/components/fields/LoRAModelInputFieldComponent.tsx

@@ -0,0 +1,102 @@
+import { SelectItem } from '@mantine/core';
+import { useAppDispatch } from 'app/store/storeHooks';
+import IAIMantineSelect from 'common/components/IAIMantineSelect';
+import { fieldValueChanged } from 'features/nodes/store/nodesSlice';
+import {
+  VaeModelInputFieldTemplate,
+  VaeModelInputFieldValue,
+} from 'features/nodes/types/types';
+import { MODEL_TYPE_MAP as BASE_MODEL_NAME_MAP } from 'features/system/components/ModelSelect';
+import { forEach, isString } from 'lodash-es';
+import { memo, useCallback, useEffect, useMemo } from 'react';
+import { useTranslation } from 'react-i18next';
+import { useGetLoRAModelsQuery } from 'services/api/endpoints/models';
+import { FieldComponentProps } from './types';
+
+const LoRAModelInputFieldComponent = (
+  props: FieldComponentProps<
+    VaeModelInputFieldValue,
+    VaeModelInputFieldTemplate
+  >
+) => {
+  const { nodeId, field } = props;
+
+  const dispatch = useAppDispatch();
+  const { t } = useTranslation();
+
+  const { data: loraModels } = useGetLoRAModelsQuery();
+
+  const selectedModel = useMemo(
+    () => loraModels?.entities[field.value ?? loraModels.ids[0]],
+    [loraModels?.entities, loraModels?.ids, field.value]
+  );
+
+  const data = useMemo(() => {
+    if (!loraModels) {
+      return [];
+    }
+
+    const data: SelectItem[] = [];
+
+    forEach(loraModels.entities, (model, id) => {
+      if (!model) {
+        return;
+      }
+
+      data.push({
+        value: id,
+        label: model.name,
+        group: BASE_MODEL_NAME_MAP[model.base_model],
+      });
+    });
+
+    return data;
+  }, [loraModels]);
+
+  const handleValueChanged = useCallback(
+    (v: string | null) => {
+      if (!v) {
+        return;
+      }
+
+      dispatch(
+        fieldValueChanged({
+          nodeId,
+          fieldName: field.name,
+          value: v,
+        })
+      );
+    },
+    [dispatch, field.name, nodeId]
+  );
+
+  useEffect(() => {
+    if (field.value && loraModels?.ids.includes(field.value)) {
+      return;
+    }
+
+    const firstLora = loraModels?.ids[0];
+
+    if (!isString(firstLora)) {
+      return;
+    }
+
+    handleValueChanged(firstLora);
+  }, [field.value, handleValueChanged, loraModels?.ids]);
+
+  return (
+    <IAIMantineSelect
+      tooltip={selectedModel?.description}
+      label={
+        selectedModel?.base_model &&
+        BASE_MODEL_NAME_MAP[selectedModel?.base_model]
+      }
+      value={field.value}
+      placeholder="Pick one"
+      data={data}
+      onChange={handleValueChanged}
+    />
+  );
+};
+
+export default memo(LoRAModelInputFieldComponent);

invokeai/frontend/web/src/features/nodes/components/fields/ModelInputFieldComponent.tsx

@@ -11,7 +11,7 @@ import { MODEL_TYPE_MAP as BASE_MODEL_NAME_MAP } from 'features/system/component
 import { forEach, isString } from 'lodash-es';
 import { memo, useCallback, useEffect, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
-import { useListModelsQuery } from 'services/api/endpoints/models';
+import { useGetMainModelsQuery } from 'services/api/endpoints/models';
 import { FieldComponentProps } from './types';
 
 const ModelInputFieldComponent = (
@@ -22,9 +22,7 @@ const ModelInputFieldComponent = (
   const dispatch = useAppDispatch();
   const { t } = useTranslation();
 
-  const { data: mainModels } = useListModelsQuery({
+  const { data: mainModels } = useGetMainModelsQuery();
-    model_type: 'main',
-  });
 
   const data = useMemo(() => {
     if (!mainModels) {

invokeai/frontend/web/src/features/nodes/components/fields/VaeModelInputFieldComponent.tsx

@@ -10,7 +10,7 @@ import { MODEL_TYPE_MAP as BASE_MODEL_NAME_MAP } from 'features/system/component
 import { forEach } from 'lodash-es';
 import { memo, useCallback, useEffect, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
-import { useListModelsQuery } from 'services/api/endpoints/models';
+import { useGetVaeModelsQuery } from 'services/api/endpoints/models';
 import { FieldComponentProps } from './types';
 
 const VaeModelInputFieldComponent = (
@@ -24,9 +24,7 @@ const VaeModelInputFieldComponent = (
   const dispatch = useAppDispatch();
   const { t } = useTranslation();
 
-  const { data: vaeModels } = useListModelsQuery({
+  const { data: vaeModels } = useGetVaeModelsQuery();
-    model_type: 'vae',
-  });
 
   const selectedModel = useMemo(
     () => vaeModels?.entities[field.value ?? vaeModels.ids[0]],

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

@@ -1,5 +1,8 @@
 import { createSlice, PayloadAction } from '@reduxjs/toolkit';
+import { RootState } from 'app/store/store';
+import { cloneDeep, uniqBy } from 'lodash-es';
 import { OpenAPIV3 } from 'openapi-types';
+import { RgbaColor } from 'react-colorful';
 import {
   addEdge,
   applyEdgeChanges,
@@ -11,12 +14,9 @@ import {
   NodeChange,
   OnConnectStartParams,
 } from 'reactflow';
-import { ImageField } from 'services/api/types';
 import { receivedOpenAPISchema } from 'services/api/thunks/schema';
+import { ImageField } from 'services/api/types';
 import { InvocationTemplate, InvocationValue } from '../types/types';
-import { RgbaColor } from 'react-colorful';
-import { RootState } from 'app/store/store';
-import { cloneDeep, isArray, uniq, uniqBy } from 'lodash-es';
 
 export type NodesState = {
   nodes: Node<InvocationValue>[];

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

@@ -18,6 +18,7 @@ export const FIELD_TYPE_MAP: Record<string, FieldType> = {
   VaeField: 'vae',
   model: 'model',
   vae_model: 'vae_model',
+  lora_model: 'lora_model',
   array: 'array',
   item: 'item',
   ColorField: 'color',
@@ -120,7 +121,13 @@ export const FIELDS: Record<FieldType, FieldUIConfig> = {
   vae_model: {
     color: 'teal',
     colorCssVar: getColorTokenCssVariable('teal'),
-    title: 'Model',
+    title: 'VAE',
+    description: 'Models are models.',
+  },
+  lora_model: {
+    color: 'teal',
+    colorCssVar: getColorTokenCssVariable('teal'),
+    title: 'LoRA',
     description: 'Models are models.',
   },
   array: {

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

@@ -65,6 +65,7 @@ export type FieldType =
   | 'control'
   | 'model'
   | 'vae_model'
+  | 'lora_model'
   | 'array'
   | 'item'
   | 'color'
@@ -93,6 +94,7 @@ export type InputFieldValue =
   | EnumInputFieldValue
   | ModelInputFieldValue
   | VaeModelInputFieldValue
+  | LoRAModelInputFieldValue
   | ArrayInputFieldValue
   | ItemInputFieldValue
   | ColorInputFieldValue
@@ -119,6 +121,7 @@ export type InputFieldTemplate =
   | EnumInputFieldTemplate
   | ModelInputFieldTemplate
   | VaeModelInputFieldTemplate
+  | LoRAModelInputFieldTemplate
   | ArrayInputFieldTemplate
   | ItemInputFieldTemplate
   | ColorInputFieldTemplate
@@ -236,6 +239,11 @@ export type VaeModelInputFieldValue = FieldValueBase & {
   value?: string;
 };
 
+export type LoRAModelInputFieldValue = FieldValueBase & {
+  type: 'lora_model';
+  value?: string;
+};
+
 export type ArrayInputFieldValue = FieldValueBase & {
   type: 'array';
   value?: (string | number)[];
@@ -350,6 +358,11 @@ export type VaeModelInputFieldTemplate = InputFieldTemplateBase & {
   type: 'vae_model';
 };
 
+export type LoRAModelInputFieldTemplate = InputFieldTemplateBase & {
+  default: string;
+  type: 'lora_model';
+};
+
 export type ArrayInputFieldTemplate = InputFieldTemplateBase & {
   default: [];
   type: 'array';

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

@@ -1,5 +1,5 @@
 import { RootState } from 'app/store/store';
-import { filter } from 'lodash-es';
+import { getValidControlNets } from 'features/controlNet/util/getValidControlNets';
 import { CollectInvocation, ControlNetInvocation } from 'services/api/types';
 import { NonNullableGraph } from '../types/types';
 import { CONTROL_NET_COLLECT } from './graphBuilders/constants';
@@ -11,13 +11,7 @@ export const addControlNetToLinearGraph = (
 ): void => {
   const { isEnabled: isControlNetEnabled, controlNets } = state.controlNet;
 
-  const validControlNets = filter(
+  const validControlNets = getValidControlNets(controlNets);
-    controlNets,
-    (c) =>
-      c.isEnabled &&
-      (Boolean(c.processedControlImage) ||
-        (c.processorType === 'none' && Boolean(c.controlImage)))
-  );
 
   if (isControlNetEnabled && Boolean(validControlNets.length)) {
     if (validControlNets.length > 1) {

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

@@ -18,6 +18,7 @@ import {
   IntegerInputFieldTemplate,
   ItemInputFieldTemplate,
   LatentsInputFieldTemplate,
+  LoRAModelInputFieldTemplate,
   ModelInputFieldTemplate,
   OutputFieldTemplate,
   StringInputFieldTemplate,
@@ -191,6 +192,21 @@ const buildVaeModelInputFieldTemplate = ({
   return template;
 };
 
+const buildLoRAModelInputFieldTemplate = ({
+  schemaObject,
+  baseField,
+}: BuildInputFieldArg): LoRAModelInputFieldTemplate => {
+  const template: LoRAModelInputFieldTemplate = {
+    ...baseField,
+    type: 'lora_model',
+    inputRequirement: 'always',
+    inputKind: 'direct',
+    default: schemaObject.default ?? undefined,
+  };
+
+  return template;
+};
+
 const buildImageInputFieldTemplate = ({
   schemaObject,
   baseField,
@@ -460,6 +476,9 @@ export const buildInputFieldTemplate = (
   if (['vae_model'].includes(fieldType)) {
     return buildVaeModelInputFieldTemplate({ schemaObject, baseField });
   }
+  if (['lora_model'].includes(fieldType)) {
+    return buildLoRAModelInputFieldTemplate({ schemaObject, baseField });
+  }
   if (['enum'].includes(fieldType)) {
     return buildEnumInputFieldTemplate({ schemaObject, baseField });
   }

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

@@ -79,6 +79,10 @@ export const buildInputFieldValue = (
     if (template.type === 'vae_model') {
       fieldValue.value = undefined;
     }
+
+    if (template.type === 'lora_model') {
+      fieldValue.value = undefined;
+    }
   }
 
   return fieldValue;

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

@@ -0,0 +1,148 @@
+import { RootState } from 'app/store/store';
+import { NonNullableGraph } from 'features/nodes/types/types';
+import { forEach, size } from 'lodash-es';
+import { LoraLoaderInvocation } from 'services/api/types';
+import { modelIdToLoRAModelField } from '../modelIdToLoRAName';
+import {
+  LORA_LOADER,
+  MAIN_MODEL_LOADER,
+  NEGATIVE_CONDITIONING,
+  POSITIVE_CONDITIONING,
+} from './constants';
+
+export const addLoRAsToGraph = (
+  graph: NonNullableGraph,
+  state: RootState,
+  baseNodeId: string
+): void => {
+  /**
+   * LoRA nodes get the UNet and CLIP models from the main model loader and apply the LoRA to them.
+   * They then output the UNet and CLIP models references on to either the next LoRA in the chain,
+   * or to the inference/conditioning nodes.
+   *
+   * So we need to inject a LoRA chain into the graph.
+   */
+
+  const { loras } = state.lora;
+  const loraCount = size(loras);
+
+  if (loraCount > 0) {
+    // remove any existing connections from main model loader, we need to insert the lora nodes
+    graph.edges = graph.edges.filter(
+      (e) =>
+        !(
+          e.source.node_id === MAIN_MODEL_LOADER &&
+          ['unet', 'clip'].includes(e.source.field)
+        )
+    );
+  }
+
+  // we need to remember the last lora so we can chain from it
+  let lastLoraNodeId = '';
+  let currentLoraIndex = 0;
+
+  forEach(loras, (lora) => {
+    const { id, name, weight } = lora;
+    const loraField = modelIdToLoRAModelField(id);
+    const currentLoraNodeId = `${LORA_LOADER}_${loraField.model_name.replace(
+      '.',
+      '_'
+    )}`;
+
+    const loraLoaderNode: LoraLoaderInvocation = {
+      type: 'lora_loader',
+      id: currentLoraNodeId,
+      lora: loraField,
+      weight,
+    };
+
+    graph.nodes[currentLoraNodeId] = loraLoaderNode;
+
+    if (currentLoraIndex === 0) {
+      // first lora = start the lora chain, attach directly to model loader
+      graph.edges.push({
+        source: {
+          node_id: MAIN_MODEL_LOADER,
+          field: 'unet',
+        },
+        destination: {
+          node_id: currentLoraNodeId,
+          field: 'unet',
+        },
+      });
+
+      graph.edges.push({
+        source: {
+          node_id: MAIN_MODEL_LOADER,
+          field: 'clip',
+        },
+        destination: {
+          node_id: currentLoraNodeId,
+          field: 'clip',
+        },
+      });
+    } else {
+      // we are in the middle of the lora chain, instead connect to the previous lora
+      graph.edges.push({
+        source: {
+          node_id: lastLoraNodeId,
+          field: 'unet',
+        },
+        destination: {
+          node_id: currentLoraNodeId,
+          field: 'unet',
+        },
+      });
+      graph.edges.push({
+        source: {
+          node_id: lastLoraNodeId,
+          field: 'clip',
+        },
+        destination: {
+          node_id: currentLoraNodeId,
+          field: 'clip',
+        },
+      });
+    }
+
+    if (currentLoraIndex === loraCount - 1) {
+      // final lora, end the lora chain - we need to connect up to inference and conditioning nodes
+      graph.edges.push({
+        source: {
+          node_id: currentLoraNodeId,
+          field: 'unet',
+        },
+        destination: {
+          node_id: baseNodeId,
+          field: 'unet',
+        },
+      });
+
+      graph.edges.push({
+        source: {
+          node_id: currentLoraNodeId,
+          field: 'clip',
+        },
+        destination: {
+          node_id: POSITIVE_CONDITIONING,
+          field: 'clip',
+        },
+      });
+
+      graph.edges.push({
+        source: {
+          node_id: currentLoraNodeId,
+          field: 'clip',
+        },
+        destination: {
+          node_id: NEGATIVE_CONDITIONING,
+          field: 'clip',
+        },
+      });
+    }
+
+    // increment the lora for the next one in the chain
+    lastLoraNodeId = currentLoraNodeId;
+    currentLoraIndex += 1;
+  });
+};

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

@@ -9,6 +9,7 @@ import {
 import { addControlNetToLinearGraph } from '../addControlNetToLinearGraph';
 import { modelIdToMainModelField } from '../modelIdToMainModelField';
 import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
+import { addLoRAsToGraph } from './addLoRAsToGraph';
 import { addVAEToGraph } from './addVAEToGraph';
 import {
   IMAGE_TO_IMAGE_GRAPH,
@@ -252,6 +253,8 @@ export const buildCanvasImageToImageGraph = (
     });
   }
 
+  addLoRAsToGraph(graph, state, LATENTS_TO_LATENTS);
+
   // Add VAE
   addVAEToGraph(graph, state);
 

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

@@ -8,6 +8,7 @@ import {
   RangeOfSizeInvocation,
 } from 'services/api/types';
 import { modelIdToMainModelField } from '../modelIdToMainModelField';
+import { addLoRAsToGraph } from './addLoRAsToGraph';
 import { addVAEToGraph } from './addVAEToGraph';
 import {
   INPAINT,
@@ -194,6 +195,8 @@ export const buildCanvasInpaintGraph = (
     ],
   };
 
+  addLoRAsToGraph(graph, state, INPAINT);
+
   // Add VAE
   addVAEToGraph(graph, state);
 

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

@@ -3,6 +3,7 @@ import { NonNullableGraph } from 'features/nodes/types/types';
 import { addControlNetToLinearGraph } from '../addControlNetToLinearGraph';
 import { modelIdToMainModelField } from '../modelIdToMainModelField';
 import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
+import { addLoRAsToGraph } from './addLoRAsToGraph';
 import { addVAEToGraph } from './addVAEToGraph';
 import {
   LATENTS_TO_IMAGE,
@@ -157,6 +158,8 @@ export const buildCanvasTextToImageGraph = (
     ],
   };
 
+  addLoRAsToGraph(graph, state, TEXT_TO_LATENTS);
+
   // Add VAE
   addVAEToGraph(graph, state);
 

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

@@ -10,6 +10,7 @@ import {
 import { addControlNetToLinearGraph } from '../addControlNetToLinearGraph';
 import { modelIdToMainModelField } from '../modelIdToMainModelField';
 import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
+import { addLoRAsToGraph } from './addLoRAsToGraph';
 import { addVAEToGraph } from './addVAEToGraph';
 import {
   IMAGE_COLLECTION,
@@ -304,6 +305,9 @@ export const buildLinearImageToImageGraph = (
       },
     });
   }
+
+  addLoRAsToGraph(graph, state, LATENTS_TO_LATENTS);
+
   // Add VAE
   addVAEToGraph(graph, state);
 

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

@@ -3,6 +3,7 @@ import { NonNullableGraph } from 'features/nodes/types/types';
 import { addControlNetToLinearGraph } from '../addControlNetToLinearGraph';
 import { modelIdToMainModelField } from '../modelIdToMainModelField';
 import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
+import { addLoRAsToGraph } from './addLoRAsToGraph';
 import { addVAEToGraph } from './addVAEToGraph';
 import {
   LATENTS_TO_IMAGE,
@@ -150,6 +151,8 @@ export const buildLinearTextToImageGraph = (
     ],
   };
 
+  addLoRAsToGraph(graph, state, TEXT_TO_LATENTS);
+
   // Add Custom VAE Support
   addVAEToGraph(graph, state);
 

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

@@ -4,6 +4,7 @@ import { cloneDeep, omit, reduce } from 'lodash-es';
 import { Graph } from 'services/api/types';
 import { AnyInvocation } from 'services/events/types';
 import { v4 as uuidv4 } from 'uuid';
+import { modelIdToLoRAModelField } from '../modelIdToLoRAName';
 import { modelIdToMainModelField } from '../modelIdToMainModelField';
 import { modelIdToVAEModelField } from '../modelIdToVAEModelField';
 
@@ -38,6 +39,12 @@ export const parseFieldValue = (field: InputFieldValue) => {
     }
   }
 
+  if (field.type === 'lora_model') {
+    if (field.value) {
+      return modelIdToLoRAModelField(field.value);
+    }
+  }
+
   return field.value;
 };
 

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

@@ -9,6 +9,7 @@ export const RANGE_OF_SIZE = 'range_of_size';
 export const ITERATE = 'iterate';
 export const MAIN_MODEL_LOADER = 'main_model_loader';
 export const VAE_LOADER = 'vae_loader';
+export const LORA_LOADER = 'lora_loader';
 export const IMAGE_TO_LATENTS = 'image_to_latents';
 export const LATENTS_TO_LATENTS = 'latents_to_latents';
 export const RESIZE = 'resize_image';

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

@@ -0,0 +1,12 @@
+import { BaseModelType, LoRAModelField } from 'services/api/types';
+
+export const modelIdToLoRAModelField = (loraId: string): LoRAModelField => {
+  const [base_model, model_type, model_name] = loraId.split('/');
+
+  const field: LoRAModelField = {
+    base_model: base_model as BaseModelType,
+    model_name,
+  };
+
+  return field;
+};

invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/BoundingBox/ParamBoundingBoxCollapse.tsx

@@ -1,20 +1,15 @@
-import { Flex, useDisclosure } from '@chakra-ui/react';
+import { Flex } from '@chakra-ui/react';
-import { useTranslation } from 'react-i18next';
 import IAICollapse from 'common/components/IAICollapse';
 import { memo } from 'react';
-import ParamBoundingBoxWidth from './ParamBoundingBoxWidth';
+import { useTranslation } from 'react-i18next';
 import ParamBoundingBoxHeight from './ParamBoundingBoxHeight';
+import ParamBoundingBoxWidth from './ParamBoundingBoxWidth';
 
 const ParamBoundingBoxCollapse = () => {
   const { t } = useTranslation();
-  const { isOpen, onToggle } = useDisclosure();
 
   return (
-    <IAICollapse
+    <IAICollapse label={t('parameters.boundingBoxHeader')}>
-      label={t('parameters.boundingBoxHeader')}
-      isOpen={isOpen}
-      onToggle={onToggle}
-    >
       <Flex sx={{ gap: 2, flexDirection: 'column' }}>
         <ParamBoundingBoxWidth />
         <ParamBoundingBoxHeight />

invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/InfillAndScaling/ParamInfillAndScalingCollapse.tsx

@@ -1,4 +1,4 @@
-import { Flex, useDisclosure } from '@chakra-ui/react';
+import { Flex } from '@chakra-ui/react';
 import { memo } from 'react';
 import { useTranslation } from 'react-i18next';
 
@@ -6,19 +6,14 @@ import IAICollapse from 'common/components/IAICollapse';
 import ParamInfillMethod from './ParamInfillMethod';
 import ParamInfillTilesize from './ParamInfillTilesize';
 import ParamScaleBeforeProcessing from './ParamScaleBeforeProcessing';
-import ParamScaledWidth from './ParamScaledWidth';
 import ParamScaledHeight from './ParamScaledHeight';
+import ParamScaledWidth from './ParamScaledWidth';
 
 const ParamInfillCollapse = () => {
   const { t } = useTranslation();
-  const { isOpen, onToggle } = useDisclosure();
 
   return (
-    <IAICollapse
+    <IAICollapse label={t('parameters.infillScalingHeader')}>
-      label={t('parameters.infillScalingHeader')}
-      isOpen={isOpen}
-      onToggle={onToggle}
-    >
       <Flex sx={{ gap: 2, flexDirection: 'column' }}>
         <ParamInfillMethod />
         <ParamInfillTilesize />

invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/SeamCorrection/ParamSeamCorrectionCollapse.tsx

@@ -1,22 +1,16 @@
+import IAICollapse from 'common/components/IAICollapse';
+import { memo } from 'react';
+import { useTranslation } from 'react-i18next';
 import ParamSeamBlur from './ParamSeamBlur';
 import ParamSeamSize from './ParamSeamSize';
 import ParamSeamSteps from './ParamSeamSteps';
 import ParamSeamStrength from './ParamSeamStrength';
-import { useDisclosure } from '@chakra-ui/react';
-import { useTranslation } from 'react-i18next';
-import IAICollapse from 'common/components/IAICollapse';
-import { memo } from 'react';
 
 const ParamSeamCorrectionCollapse = () => {
   const { t } = useTranslation();
-  const { isOpen, onToggle } = useDisclosure();
 
   return (
-    <IAICollapse
+    <IAICollapse label={t('parameters.seamCorrectionHeader')}>
-      label={t('parameters.seamCorrectionHeader')}
-      isOpen={isOpen}
-      onToggle={onToggle}
-    >
       <ParamSeamSize />
       <ParamSeamBlur />
       <ParamSeamStrength />

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

@@ -1,41 +1,45 @@
 import { Divider, Flex } from '@chakra-ui/react';
-import { useTranslation } from 'react-i18next';
-import IAICollapse from 'common/components/IAICollapse';
-import { Fragment, memo, useCallback } from 'react';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { createSelector } from '@reduxjs/toolkit';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
+import IAIButton from 'common/components/IAIButton';
+import IAICollapse from 'common/components/IAICollapse';
+import ControlNet from 'features/controlNet/components/ControlNet';
+import ParamControlNetFeatureToggle from 'features/controlNet/components/parameters/ParamControlNetFeatureToggle';
 import {
   controlNetAdded,
   controlNetSelector,
-  isControlNetEnabledToggled,
 } from 'features/controlNet/store/controlNetSlice';
-import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
+import { getValidControlNets } from 'features/controlNet/util/getValidControlNets';
-import { map } from 'lodash-es';
-import { v4 as uuidv4 } from 'uuid';
 import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus';
-import IAIButton from 'common/components/IAIButton';
+import { map } from 'lodash-es';
-import ControlNet from 'features/controlNet/components/ControlNet';
+import { Fragment, memo, useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+import { v4 as uuidv4 } from 'uuid';
 
 const selector = createSelector(
   controlNetSelector,
   (controlNet) => {
     const { controlNets, isEnabled } = controlNet;
 
-    return { controlNetsArray: map(controlNets), isEnabled };
+    const validControlNets = getValidControlNets(controlNets);
+
+    const activeLabel =
+      isEnabled && validControlNets.length > 0
+        ? `${validControlNets.length} Active`
+        : undefined;
+
+    return { controlNetsArray: map(controlNets), activeLabel };
   },
   defaultSelectorOptions
 );
 
 const ParamControlNetCollapse = () => {
   const { t } = useTranslation();
-  const { controlNetsArray, isEnabled } = useAppSelector(selector);
+  const { controlNetsArray, activeLabel } = useAppSelector(selector);
   const isControlNetDisabled = useFeatureStatus('controlNet').isFeatureDisabled;
   const dispatch = useAppDispatch();
 
-  const handleClickControlNetToggle = useCallback(() => {
-    dispatch(isControlNetEnabledToggled());
-  }, [dispatch]);
-
   const handleClickedAddControlNet = useCallback(() => {
     dispatch(controlNetAdded({ controlNetId: uuidv4() }));
   }, [dispatch]);
@@ -45,13 +49,9 @@ const ParamControlNetCollapse = () => {
   }
 
   return (
-    <IAICollapse
+    <IAICollapse label="ControlNet" activeLabel={activeLabel}>
-      label={'ControlNet'}
-      isOpen={isEnabled}
-      onToggle={handleClickControlNetToggle}
-      withSwitch
-    >
       <Flex sx={{ flexDir: 'column', gap: 3 }}>
+        <ParamControlNetFeatureToggle />
         {controlNetsArray.map((c, i) => (
           <Fragment key={c.controlNetId}>
             {i > 0 && <Divider />}

invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamCFGScale.tsx

@@ -1,5 +1,6 @@
 import { createSelector } from '@reduxjs/toolkit';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
 import IAINumberInput from 'common/components/IAINumberInput';
 import IAISlider from 'common/components/IAISlider';
 import { generationSelector } from 'features/parameters/store/generationSelectors';
@@ -27,7 +28,8 @@ const selector = createSelector(
       shouldUseSliders,
       shift,
     };
-  }
+  },
+  defaultSelectorOptions
 );
 
 const ParamCFGScale = () => {

invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamHeight.tsx

@@ -1,5 +1,6 @@
 import { createSelector } from '@reduxjs/toolkit';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
 import IAISlider, { IAIFullSliderProps } from 'common/components/IAISlider';
 import { generationSelector } from 'features/parameters/store/generationSelectors';
 import { setHeight } from 'features/parameters/store/generationSlice';
@@ -25,7 +26,8 @@ const selector = createSelector(
       inputMax,
       step,
     };
-  }
+  },
+  defaultSelectorOptions
 );
 
 type ParamHeightProps = Omit<

invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamIterations.tsx

@@ -1,37 +1,38 @@
 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 IAINumberInput from 'common/components/IAINumberInput';
 import IAISlider from 'common/components/IAISlider';
-import { generationSelector } from 'features/parameters/store/generationSelectors';
 import { setIterations } from 'features/parameters/store/generationSlice';
-import { configSelector } from 'features/system/store/configSelectors';
-import { hotkeysSelector } from 'features/ui/store/hotkeysSlice';
-import { uiSelector } from 'features/ui/store/uiSelectors';
 import { memo, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
 
-const selector = createSelector([stateSelector], (state) => {
+const selector = createSelector(
-  const { initial, min, sliderMax, inputMax, fineStep, coarseStep } =
+  [stateSelector],
-    state.config.sd.iterations;
+  (state) => {
-  const { iterations } = state.generation;
+    const { initial, min, sliderMax, inputMax, fineStep, coarseStep } =
-  const { shouldUseSliders } = state.ui;
+      state.config.sd.iterations;
-  const isDisabled =
+    const { iterations } = state.generation;
-    state.dynamicPrompts.isEnabled && state.dynamicPrompts.combinatorial;
+    const { shouldUseSliders } = state.ui;
+    const isDisabled =
+      state.dynamicPrompts.isEnabled && state.dynamicPrompts.combinatorial;
 
-  const step = state.hotkeys.shift ? fineStep : coarseStep;
+    const step = state.hotkeys.shift ? fineStep : coarseStep;
 
-  return {
+    return {
-    iterations,
+      iterations,
-    initial,
+      initial,
-    min,
+      min,
-    sliderMax,
+      sliderMax,
-    inputMax,
+      inputMax,
-    step,
+      step,
-    shouldUseSliders,
+      shouldUseSliders,
-    isDisabled,
+      isDisabled,
-  };
+    };
-});
+  },
+  defaultSelectorOptions
+);
 
 const ParamIterations = () => {
   const {

invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamSteps.tsx

@@ -1,5 +1,6 @@
 import { createSelector } from '@reduxjs/toolkit';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
 import IAINumberInput from 'common/components/IAINumberInput';
 
 import IAISlider from 'common/components/IAISlider';
@@ -33,7 +34,8 @@ const selector = createSelector(
       step,
       shouldUseSliders,
     };
-  }
+  },
+  defaultSelectorOptions
 );
 
 const ParamSteps = () => {

invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamWidth.tsx

@@ -1,7 +1,7 @@
 import { createSelector } from '@reduxjs/toolkit';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import IAISlider from 'common/components/IAISlider';
+import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
-import { IAIFullSliderProps } from 'common/components/IAISlider';
+import IAISlider, { IAIFullSliderProps } from 'common/components/IAISlider';
 import { generationSelector } from 'features/parameters/store/generationSelectors';
 import { setWidth } from 'features/parameters/store/generationSlice';
 import { configSelector } from 'features/system/store/configSelectors';
@@ -26,7 +26,8 @@ const selector = createSelector(
       inputMax,
       step,
     };
-  }
+  },
+  defaultSelectorOptions
 );
 
 type ParamWidthProps = Omit<IAIFullSliderProps, 'label' | 'value' | 'onChange'>;

invokeai/frontend/web/src/features/parameters/components/Parameters/Hires/ParamHiresCollapse.tsx

@@ -1,37 +1,39 @@
 import { Flex } from '@chakra-ui/react';
-import { useTranslation } from 'react-i18next';
+import { createSelector } from '@reduxjs/toolkit';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { stateSelector } from 'app/store/store';
-import { RootState } from 'app/store/store';
+import { useAppSelector } from 'app/store/storeHooks';
+import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
 import IAICollapse from 'common/components/IAICollapse';
-import { memo } from 'react';
-import { ParamHiresStrength } from './ParamHiresStrength';
-import { setHiresFix } from 'features/parameters/store/postprocessingSlice';
 import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus';
+import { memo } from 'react';
+import { useTranslation } from 'react-i18next';
+import { ParamHiresStrength } from './ParamHiresStrength';
+import { ParamHiresToggle } from './ParamHiresToggle';
+
+const selector = createSelector(
+  stateSelector,
+  (state) => {
+    const activeLabel = state.postprocessing.hiresFix ? 'Enabled' : undefined;
+
+    return { activeLabel };
+  },
+  defaultSelectorOptions
+);
 
 const ParamHiresCollapse = () => {
   const { t } = useTranslation();
-  const hiresFix = useAppSelector(
+  const { activeLabel } = useAppSelector(selector);
-    (state: RootState) => state.postprocessing.hiresFix
-  );
 
   const isHiresEnabled = useFeatureStatus('hires').isFeatureEnabled;
 
-  const dispatch = useAppDispatch();
-
-  const handleToggle = () => dispatch(setHiresFix(!hiresFix));
-
   if (!isHiresEnabled) {
     return null;
   }
 
   return (
-    <IAICollapse
+    <IAICollapse label={t('parameters.hiresOptim')} activeLabel={activeLabel}>
-      label={t('parameters.hiresOptim')}
-      isOpen={hiresFix}
-      onToggle={handleToggle}
-      withSwitch
-    >
       <Flex sx={{ gap: 2, flexDirection: 'column' }}>
+        <ParamHiresToggle />
         <ParamHiresStrength />
       </Flex>
     </IAICollapse>

invokeai/frontend/web/src/features/parameters/components/Parameters/Hires/ParamHiresToggle.tsx

@@ -23,7 +23,6 @@ export const ParamHiresToggle = () => {
   return (
     <IAISwitch
       label={t('parameters.hiresOptim')}
-      fontSize="md"
       isChecked={hiresFix}
       onChange={handleChangeHiresFix}
     />

invokeai/frontend/web/src/features/parameters/components/Parameters/Noise/ParamNoiseCollapse.tsx

@@ -1,27 +1,33 @@
-import { useTranslation } from 'react-i18next';
 import { Flex } from '@chakra-ui/react';
+import { createSelector } from '@reduxjs/toolkit';
+import { stateSelector } from 'app/store/store';
+import { useAppSelector } from 'app/store/storeHooks';
+import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
 import IAICollapse from 'common/components/IAICollapse';
-import ParamPerlinNoise from './ParamPerlinNoise';
-import ParamNoiseThreshold from './ParamNoiseThreshold';
-import { RootState } from 'app/store/store';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { setShouldUseNoiseSettings } from 'features/parameters/store/generationSlice';
-import { memo } from 'react';
 import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus';
+import { memo } from 'react';
+import { useTranslation } from 'react-i18next';
+import ParamNoiseThreshold from './ParamNoiseThreshold';
+import { ParamNoiseToggle } from './ParamNoiseToggle';
+import ParamPerlinNoise from './ParamPerlinNoise';
+
+const selector = createSelector(
+  stateSelector,
+  (state) => {
+    const { shouldUseNoiseSettings } = state.generation;
+    return {
+      activeLabel: shouldUseNoiseSettings ? 'Enabled' : undefined,
+    };
+  },
+  defaultSelectorOptions
+);
 
 const ParamNoiseCollapse = () => {
   const { t } = useTranslation();
 
   const isNoiseEnabled = useFeatureStatus('noise').isFeatureEnabled;
 
-  const shouldUseNoiseSettings = useAppSelector(
+  const { activeLabel } = useAppSelector(selector);
-    (state: RootState) => state.generation.shouldUseNoiseSettings
-  );
-
-  const dispatch = useAppDispatch();
-
-  const handleToggle = () =>
-    dispatch(setShouldUseNoiseSettings(!shouldUseNoiseSettings));
 
   if (!isNoiseEnabled) {
     return null;
@@ -30,11 +36,10 @@ const ParamNoiseCollapse = () => {
   return (
     <IAICollapse
       label={t('parameters.noiseSettings')}
-      isOpen={shouldUseNoiseSettings}
+      activeLabel={activeLabel}
-      onToggle={handleToggle}
-      withSwitch
     >
       <Flex sx={{ gap: 2, flexDirection: 'column' }}>
+        <ParamNoiseToggle />
         <ParamPerlinNoise />
         <ParamNoiseThreshold />
       </Flex>

invokeai/frontend/web/src/features/parameters/components/Parameters/Noise/ParamNoiseThreshold.tsx

@@ -1,18 +1,31 @@
-import { RootState } from 'app/store/store';
+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 IAISlider from 'common/components/IAISlider';
 import { setThreshold } from 'features/parameters/store/generationSlice';
 import { useTranslation } from 'react-i18next';
 
+const selector = createSelector(
+  stateSelector,
+  (state) => {
+    const { shouldUseNoiseSettings, threshold } = state.generation;
+    return {
+      isDisabled: !shouldUseNoiseSettings,
+      threshold,
+    };
+  },
+  defaultSelectorOptions
+);
+
 export default function ParamNoiseThreshold() {
   const dispatch = useAppDispatch();
-  const threshold = useAppSelector(
+  const { threshold, isDisabled } = useAppSelector(selector);
-    (state: RootState) => state.generation.threshold
-  );
   const { t } = useTranslation();
 
   return (
     <IAISlider
+      isDisabled={isDisabled}
       label={t('parameters.noiseThreshold')}
       min={0}
       max={20}

invokeai/frontend/web/src/features/parameters/components/Parameters/Noise/ParamNoiseToggle.tsx

@@ -0,0 +1,27 @@
+import type { RootState } from 'app/store/store';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import IAISwitch from 'common/components/IAISwitch';
+import { setShouldUseNoiseSettings } from 'features/parameters/store/generationSlice';
+import { ChangeEvent } from 'react';
+import { useTranslation } from 'react-i18next';
+
+export const ParamNoiseToggle = () => {
+  const dispatch = useAppDispatch();
+
+  const shouldUseNoiseSettings = useAppSelector(
+    (state: RootState) => state.generation.shouldUseNoiseSettings
+  );
+
+  const { t } = useTranslation();
+
+  const handleChange = (e: ChangeEvent<HTMLInputElement>) =>
+    dispatch(setShouldUseNoiseSettings(e.target.checked));
+
+  return (
+    <IAISwitch
+      label="Enable Noise Settings"
+      isChecked={shouldUseNoiseSettings}
+      onChange={handleChange}
+    />
+  );
+};

invokeai/frontend/web/src/features/parameters/components/Parameters/Noise/ParamPerlinNoise.tsx

@@ -1,16 +1,31 @@
-import { RootState } from 'app/store/store';
+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 IAISlider from 'common/components/IAISlider';
 import { setPerlin } from 'features/parameters/store/generationSlice';
 import { useTranslation } from 'react-i18next';
 
+const selector = createSelector(
+  stateSelector,
+  (state) => {
+    const { shouldUseNoiseSettings, perlin } = state.generation;
+    return {
+      isDisabled: !shouldUseNoiseSettings,
+      perlin,
+    };
+  },
+  defaultSelectorOptions
+);
+
 export default function ParamPerlinNoise() {
   const dispatch = useAppDispatch();
-  const perlin = useAppSelector((state: RootState) => state.generation.perlin);
+  const { perlin, isDisabled } = useAppSelector(selector);
   const { t } = useTranslation();
 
   return (
     <IAISlider
+      isDisabled={isDisabled}
       label={t('parameters.perlinNoise')}
       min={0}
       max={1}

invokeai/frontend/web/src/features/parameters/components/Parameters/Seamless/ParamSeamlessCollapse.tsx

@@ -1,36 +1,46 @@
-import { useTranslation } from 'react-i18next';
 import { Box, Flex } from '@chakra-ui/react';
-import IAICollapse from 'common/components/IAICollapse';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { setSeamless } from 'features/parameters/store/generationSlice';
-import { memo } from 'react';
 import { createSelector } from '@reduxjs/toolkit';
-import { generationSelector } from 'features/parameters/store/generationSelectors';
+import { useAppSelector } from 'app/store/storeHooks';
 import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
+import IAICollapse from 'common/components/IAICollapse';
+import { generationSelector } from 'features/parameters/store/generationSelectors';
+import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus';
+import { memo } from 'react';
+import { useTranslation } from 'react-i18next';
 import ParamSeamlessXAxis from './ParamSeamlessXAxis';
 import ParamSeamlessYAxis from './ParamSeamlessYAxis';
-import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus';
+
+const getActiveLabel = (seamlessXAxis: boolean, seamlessYAxis: boolean) => {
+  if (seamlessXAxis && seamlessYAxis) {
+    return 'X & Y';
+  }
+
+  if (seamlessXAxis) {
+    return 'X';
+  }
+
+  if (seamlessYAxis) {
+    return 'Y';
+  }
+};
 
 const selector = createSelector(
   generationSelector,
   (generation) => {
-    const { shouldUseSeamless, seamlessXAxis, seamlessYAxis } = generation;
+    const { seamlessXAxis, seamlessYAxis } = generation;
 
-    return { shouldUseSeamless, seamlessXAxis, seamlessYAxis };
+    const activeLabel = getActiveLabel(seamlessXAxis, seamlessYAxis);
+    return { activeLabel };
   },
   defaultSelectorOptions
 );
 
 const ParamSeamlessCollapse = () => {
   const { t } = useTranslation();
-  const { shouldUseSeamless } = useAppSelector(selector);
+  const { activeLabel } = useAppSelector(selector);
 
   const isSeamlessEnabled = useFeatureStatus('seamless').isFeatureEnabled;
 
-  const dispatch = useAppDispatch();
-
-  const handleToggle = () => dispatch(setSeamless(!shouldUseSeamless));
-
   if (!isSeamlessEnabled) {
     return null;
   }
@@ -38,9 +48,7 @@ const ParamSeamlessCollapse = () => {
   return (
     <IAICollapse
       label={t('parameters.seamlessTiling')}
-      isOpen={shouldUseSeamless}
+      activeLabel={activeLabel}
-      onToggle={handleToggle}
-      withSwitch
     >
       <Flex sx={{ gap: 5 }}>
         <Box flexGrow={1}>

invokeai/frontend/web/src/features/parameters/components/Parameters/Symmetry/ParamSymmetryCollapse.tsx

@@ -1,39 +1,39 @@
-import { memo } from 'react';
 import { Flex } from '@chakra-ui/react';
+import { memo } from 'react';
 import ParamSymmetryHorizontal from './ParamSymmetryHorizontal';
 import ParamSymmetryVertical from './ParamSymmetryVertical';
 
-import { useTranslation } from 'react-i18next';
+import { createSelector } from '@reduxjs/toolkit';
+import { stateSelector } from 'app/store/store';
+import { useAppSelector } from 'app/store/storeHooks';
+import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
 import IAICollapse from 'common/components/IAICollapse';
-import { RootState } from 'app/store/store';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { setShouldUseSymmetry } from 'features/parameters/store/generationSlice';
 import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus';
+import { useTranslation } from 'react-i18next';
+import ParamSymmetryToggle from './ParamSymmetryToggle';
+
+const selector = createSelector(
+  stateSelector,
+  (state) => ({
+    activeLabel: state.generation.shouldUseSymmetry ? 'Enabled' : undefined,
+  }),
+  defaultSelectorOptions
+);
 
 const ParamSymmetryCollapse = () => {
   const { t } = useTranslation();
-  const shouldUseSymmetry = useAppSelector(
+  const { activeLabel } = useAppSelector(selector);
-    (state: RootState) => state.generation.shouldUseSymmetry
-  );
 
   const isSymmetryEnabled = useFeatureStatus('symmetry').isFeatureEnabled;
 
-  const dispatch = useAppDispatch();
-
-  const handleToggle = () => dispatch(setShouldUseSymmetry(!shouldUseSymmetry));
-
   if (!isSymmetryEnabled) {
     return null;
   }
 
   return (
-    <IAICollapse
+    <IAICollapse label={t('parameters.symmetry')} activeLabel={activeLabel}>
-      label={t('parameters.symmetry')}
-      isOpen={shouldUseSymmetry}
-      onToggle={handleToggle}
-      withSwitch
-    >
       <Flex sx={{ gap: 2, flexDirection: 'column' }}>
+        <ParamSymmetryToggle />
         <ParamSymmetryHorizontal />
         <ParamSymmetryVertical />
       </Flex>

invokeai/frontend/web/src/features/parameters/components/Parameters/Symmetry/ParamSymmetryToggle.tsx

@@ -12,6 +12,7 @@ export default function ParamSymmetryToggle() {
 
   return (
     <IAISwitch
+      label="Enable Symmetry"
       isChecked={shouldUseSymmetry}
       onChange={(e) => dispatch(setShouldUseSymmetry(e.target.checked))}
     />

invokeai/frontend/web/src/features/parameters/components/Parameters/Variations/ParamVariationCollapse.tsx

@@ -1,39 +1,42 @@
-import ParamVariationWeights from './ParamVariationWeights';
-import ParamVariationAmount from './ParamVariationAmount';
-import { useTranslation } from 'react-i18next';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { RootState } from 'app/store/store';
-import { setShouldGenerateVariations } from 'features/parameters/store/generationSlice';
 import { Flex } from '@chakra-ui/react';
+import { createSelector } from '@reduxjs/toolkit';
+import { stateSelector } from 'app/store/store';
+import { useAppSelector } from 'app/store/storeHooks';
+import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
 import IAICollapse from 'common/components/IAICollapse';
-import { memo } from 'react';
 import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus';
+import { memo } from 'react';
+import { useTranslation } from 'react-i18next';
+import ParamVariationAmount from './ParamVariationAmount';
+import { ParamVariationToggle } from './ParamVariationToggle';
+import ParamVariationWeights from './ParamVariationWeights';
+
+const selector = createSelector(
+  stateSelector,
+  (state) => {
+    const activeLabel = state.generation.shouldGenerateVariations
+      ? 'Enabled'
+      : undefined;
+
+    return { activeLabel };
+  },
+  defaultSelectorOptions
+);
 
 const ParamVariationCollapse = () => {
   const { t } = useTranslation();
-  const shouldGenerateVariations = useAppSelector(
+  const { activeLabel } = useAppSelector(selector);
-    (state: RootState) => state.generation.shouldGenerateVariations
-  );
 
   const isVariationEnabled = useFeatureStatus('variation').isFeatureEnabled;
 
-  const dispatch = useAppDispatch();
-
-  const handleToggle = () =>
-    dispatch(setShouldGenerateVariations(!shouldGenerateVariations));
-
   if (!isVariationEnabled) {
     return null;
   }
 
   return (
-    <IAICollapse
+    <IAICollapse label={t('parameters.variations')} activeLabel={activeLabel}>
-      label={t('parameters.variations')}
-      isOpen={shouldGenerateVariations}
-      onToggle={handleToggle}
-      withSwitch
-    >
       <Flex sx={{ gap: 2, flexDirection: 'column' }}>
+        <ParamVariationToggle />
         <ParamVariationAmount />
         <ParamVariationWeights />
       </Flex>

invokeai/frontend/web/src/features/parameters/components/Parameters/Variations/ParamVariationToggle.tsx

@@ -0,0 +1,27 @@
+import type { RootState } from 'app/store/store';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import IAISwitch from 'common/components/IAISwitch';
+import { setShouldGenerateVariations } from 'features/parameters/store/generationSlice';
+import { ChangeEvent } from 'react';
+import { useTranslation } from 'react-i18next';
+
+export const ParamVariationToggle = () => {
+  const dispatch = useAppDispatch();
+
+  const shouldGenerateVariations = useAppSelector(
+    (state: RootState) => state.generation.shouldGenerateVariations
+  );
+
+  const { t } = useTranslation();
+
+  const handleChange = (e: ChangeEvent<HTMLInputElement>) =>
+    dispatch(setShouldGenerateVariations(e.target.checked));
+
+  return (
+    <IAISwitch
+      label="Enable Variations"
+      isChecked={shouldGenerateVariations}
+      onChange={handleChange}
+    />
+  );
+};

invokeai/frontend/web/src/features/parameters/store/generationSlice.ts

@@ -49,7 +49,6 @@ export interface GenerationState {
   verticalSymmetrySteps: number;
   model: ModelParam;
   vae: VAEParam;
-  shouldUseSeamless: boolean;
   seamlessXAxis: boolean;
   seamlessYAxis: boolean;
 }
@@ -84,9 +83,8 @@ export const initialGenerationState: GenerationState = {
   verticalSymmetrySteps: 0,
   model: '',
   vae: '',
-  shouldUseSeamless: false,
+  seamlessXAxis: false,
-  seamlessXAxis: true,
+  seamlessYAxis: false,
-  seamlessYAxis: true,
 };
 
 const initialState: GenerationState = initialGenerationState;
@@ -144,9 +142,6 @@ export const generationSlice = createSlice({
     setImg2imgStrength: (state, action: PayloadAction<number>) => {
       state.img2imgStrength = action.payload;
     },
-    setSeamless: (state, action: PayloadAction<boolean>) => {
-      state.shouldUseSeamless = action.payload;
-    },
     setSeamlessXAxis: (state, action: PayloadAction<boolean>) => {
       state.seamlessXAxis = action.payload;
     },
@@ -268,7 +263,6 @@ export const {
   modelSelected,
   vaeSelected,
   setShouldUseNoiseSettings,
-  setSeamless,
   setSeamlessXAxis,
   setSeamlessYAxis,
 } = generationSlice.actions;

invokeai/frontend/web/src/features/system/components/ModelSelect.tsx

@@ -8,7 +8,7 @@ import { modelSelected } from 'features/parameters/store/generationSlice';
 import { SelectItem } from '@mantine/core';
 import { RootState } from 'app/store/store';
 import { forEach, isString } from 'lodash-es';
-import { useListModelsQuery } from 'services/api/endpoints/models';
+import { useGetMainModelsQuery } from 'services/api/endpoints/models';
 
 export const MODEL_TYPE_MAP = {
   'sd-1': 'Stable Diffusion 1.x',
@@ -23,9 +23,7 @@ const ModelSelect = () => {
     (state: RootState) => state.generation.model
   );
 
-  const { data: mainModels, isLoading } = useListModelsQuery({
+  const { data: mainModels, isLoading } = useGetMainModelsQuery();
-    model_type: 'main',
-  });
 
   const data = useMemo(() => {
     if (!mainModels) {

invokeai/frontend/web/src/features/system/components/VAESelect.tsx

@@ -6,7 +6,7 @@ import IAIMantineSelect from 'common/components/IAIMantineSelect';
 
 import { SelectItem } from '@mantine/core';
 import { forEach } from 'lodash-es';
-import { useListModelsQuery } from 'services/api/endpoints/models';
+import { useGetVaeModelsQuery } from 'services/api/endpoints/models';
 
 import { RootState } from 'app/store/store';
 import { vaeSelected } from 'features/parameters/store/generationSlice';
@@ -16,9 +16,7 @@ const VAESelect = () => {
   const dispatch = useAppDispatch();
   const { t } = useTranslation();
 
-  const { data: vaeModels } = useListModelsQuery({
+  const { data: vaeModels } = useGetVaeModelsQuery();
-    model_type: 'vae',
-  });
 
   const selectedModelId = useAppSelector(
     (state: RootState) => state.generation.vae

invokeai/frontend/web/src/features/ui/components/InvokeTabs.tsx

@@ -66,16 +66,16 @@ const tabs: InvokeTabInfo[] = [
     icon: <Icon as={MdDeviceHub} sx={{ boxSize: 6, pointerEvents: 'none' }} />,
     content: <NodesTab />,
   },
-  // {
-  //   id: 'batch',
-  //   icon: <Icon as={FaLayerGroup} sx={{ boxSize: 6, pointerEvents: 'none' }} />,
-  //   content: <BatchTab />,
-  // },
   {
     id: 'modelManager',
     icon: <Icon as={FaCube} sx={{ boxSize: 6, pointerEvents: 'none' }} />,
     content: <ModelManagerTab />,
   },
+  // {
+  //   id: 'batch',
+  //   icon: <Icon as={FaLayerGroup} sx={{ boxSize: 6, pointerEvents: 'none' }} />,
+  //   content: <BatchTab />,
+  // },
 ];
 
 const enabledTabsSelector = createSelector(

invokeai/frontend/web/src/features/ui/components/tabs/ImageToImage/ImageToImageTabCoreParameters.tsx

@@ -1,4 +1,4 @@
-import { Box, Flex, useDisclosure } from '@chakra-ui/react';
+import { Box, Flex } from '@chakra-ui/react';
 import { createSelector } from '@reduxjs/toolkit';
 import { useAppSelector } from 'app/store/storeHooks';
 import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
@@ -21,19 +21,25 @@ const selector = createSelector(
   [uiSelector, generationSelector],
   (ui, generation) => {
     const { shouldUseSliders } = ui;
-    const { shouldFitToWidthHeight } = generation;
+    const { shouldFitToWidthHeight, shouldRandomizeSeed } = generation;
 
-    return { shouldUseSliders, shouldFitToWidthHeight };
+    const activeLabel = !shouldRandomizeSeed ? 'Manual Seed' : undefined;
+
+    return { shouldUseSliders, shouldFitToWidthHeight, activeLabel };
   },
   defaultSelectorOptions
 );
 
 const ImageToImageTabCoreParameters = () => {
-  const { shouldUseSliders, shouldFitToWidthHeight } = useAppSelector(selector);
+  const { shouldUseSliders, shouldFitToWidthHeight, activeLabel } =
-  const { isOpen, onToggle } = useDisclosure({ defaultIsOpen: true });
+    useAppSelector(selector);
 
   return (
-    <IAICollapse label={'General'} isOpen={isOpen} onToggle={onToggle}>
+    <IAICollapse
+      label={'General'}
+      activeLabel={activeLabel}
+      defaultIsOpen={true}
+    >
       <Flex
         sx={{
           flexDirection: 'column',

invokeai/frontend/web/src/features/ui/components/tabs/ImageToImage/ImageToImageTabParameters.tsx

@@ -1,14 +1,15 @@
-import { memo } from 'react';
-import ProcessButtons from 'features/parameters/components/ProcessButtons/ProcessButtons';
-import ParamPositiveConditioning from 'features/parameters/components/Parameters/Core/ParamPositiveConditioning';
-import ParamNegativeConditioning from 'features/parameters/components/Parameters/Core/ParamNegativeConditioning';
-import ParamVariationCollapse from 'features/parameters/components/Parameters/Variations/ParamVariationCollapse';
-import ParamNoiseCollapse from 'features/parameters/components/Parameters/Noise/ParamNoiseCollapse';
-import ParamSymmetryCollapse from 'features/parameters/components/Parameters/Symmetry/ParamSymmetryCollapse';
-import ParamSeamlessCollapse from 'features/parameters/components/Parameters/Seamless/ParamSeamlessCollapse';
-import ImageToImageTabCoreParameters from './ImageToImageTabCoreParameters';
-import ParamControlNetCollapse from 'features/parameters/components/Parameters/ControlNet/ParamControlNetCollapse';
 import ParamDynamicPromptsCollapse from 'features/dynamicPrompts/components/ParamDynamicPromptsCollapse';
+import ParamLoraCollapse from 'features/lora/components/ParamLoraCollapse';
+import ParamControlNetCollapse from 'features/parameters/components/Parameters/ControlNet/ParamControlNetCollapse';
+import ParamNegativeConditioning from 'features/parameters/components/Parameters/Core/ParamNegativeConditioning';
+import ParamPositiveConditioning from 'features/parameters/components/Parameters/Core/ParamPositiveConditioning';
+import ParamNoiseCollapse from 'features/parameters/components/Parameters/Noise/ParamNoiseCollapse';
+import ParamSeamlessCollapse from 'features/parameters/components/Parameters/Seamless/ParamSeamlessCollapse';
+import ParamSymmetryCollapse from 'features/parameters/components/Parameters/Symmetry/ParamSymmetryCollapse';
+import ParamVariationCollapse from 'features/parameters/components/Parameters/Variations/ParamVariationCollapse';
+import ProcessButtons from 'features/parameters/components/ProcessButtons/ProcessButtons';
+import { memo } from 'react';
+import ImageToImageTabCoreParameters from './ImageToImageTabCoreParameters';
 
 const ImageToImageTabParameters = () => {
   return (
@@ -17,6 +18,7 @@ const ImageToImageTabParameters = () => {
       <ParamNegativeConditioning />
       <ProcessButtons />
       <ImageToImageTabCoreParameters />
+      <ParamLoraCollapse />
       <ParamDynamicPromptsCollapse />
       <ParamControlNetCollapse />
       <ParamVariationCollapse />

invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/MergeModelsPanel.tsx

@@ -9,16 +9,14 @@ import IAISlider from 'common/components/IAISlider';
 import { pickBy } from 'lodash-es';
 import { useState } from 'react';
 import { useTranslation } from 'react-i18next';
-import { useListModelsQuery } from 'services/api/endpoints/models';
+import { useGetMainModelsQuery } from 'services/api/endpoints/models';
 
 export default function MergeModelsPanel() {
   const { t } = useTranslation();
 
   const dispatch = useAppDispatch();
 
-  const { data } = useListModelsQuery({
+  const { data } = useGetMainModelsQuery();
-    model_type: 'main',
-  });
 
   const diffusersModels = pickBy(
     data?.entities,

invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/ModelManagerPanel.tsx

@@ -2,15 +2,13 @@ import { Flex } from '@chakra-ui/react';
 import { RootState } from 'app/store/store';
 import { useAppSelector } from 'app/store/storeHooks';
 
-import { useListModelsQuery } from 'services/api/endpoints/models';
+import { useGetMainModelsQuery } from 'services/api/endpoints/models';
 import CheckpointModelEdit from './ModelManagerPanel/CheckpointModelEdit';
 import DiffusersModelEdit from './ModelManagerPanel/DiffusersModelEdit';
 import ModelList from './ModelManagerPanel/ModelList';
 
 export default function ModelManagerPanel() {
-  const { data: mainModels } = useListModelsQuery({
+  const { data: mainModels } = useGetMainModelsQuery();
-    model_type: 'main',
-  });
 
   const openModel = useAppSelector(
     (state: RootState) => state.system.openModel

invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/ModelManagerPanel/ModelList.tsx

@@ -8,7 +8,7 @@ import { useTranslation } from 'react-i18next';
 
 import type { ChangeEvent, ReactNode } from 'react';
 import React, { useMemo, useState, useTransition } from 'react';
-import { useListModelsQuery } from 'services/api/endpoints/models';
+import { useGetMainModelsQuery } from 'services/api/endpoints/models';
 
 function ModelFilterButton({
   label,
@@ -36,9 +36,7 @@ function ModelFilterButton({
 }
 
 const ModelList = () => {
-  const { data: mainModels } = useListModelsQuery({
+  const { data: mainModels } = useGetMainModelsQuery();
-    model_type: 'main',
-  });
 
   const [renderModelList, setRenderModelList] = React.useState<boolean>(false);
 

invokeai/frontend/web/src/features/ui/components/tabs/TextToImage/TextToImageTabCoreParameters.tsx

@@ -1,5 +1,6 @@
-import { Box, Flex, useDisclosure } from '@chakra-ui/react';
+import { Box, Flex } from '@chakra-ui/react';
 import { createSelector } from '@reduxjs/toolkit';
+import { stateSelector } from 'app/store/store';
 import { useAppSelector } from 'app/store/storeHooks';
 import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
 import IAICollapse from 'common/components/IAICollapse';
@@ -11,25 +12,30 @@ import ParamScheduler from 'features/parameters/components/Parameters/Core/Param
 import ParamSteps from 'features/parameters/components/Parameters/Core/ParamSteps';
 import ParamWidth from 'features/parameters/components/Parameters/Core/ParamWidth';
 import ParamSeedFull from 'features/parameters/components/Parameters/Seed/ParamSeedFull';
-import { uiSelector } from 'features/ui/store/uiSelectors';
 import { memo } from 'react';
 
 const selector = createSelector(
-  uiSelector,
+  stateSelector,
-  (ui) => {
+  ({ ui, generation }) => {
     const { shouldUseSliders } = ui;
+    const { shouldRandomizeSeed } = generation;
 
-    return { shouldUseSliders };
+    const activeLabel = !shouldRandomizeSeed ? 'Manual Seed' : undefined;
+
+    return { shouldUseSliders, activeLabel };
   },
   defaultSelectorOptions
 );
 
 const TextToImageTabCoreParameters = () => {
-  const { shouldUseSliders } = useAppSelector(selector);
+  const { shouldUseSliders, activeLabel } = useAppSelector(selector);
-  const { isOpen, onToggle } = useDisclosure({ defaultIsOpen: true });
 
   return (
-    <IAICollapse label={'General'} isOpen={isOpen} onToggle={onToggle}>
+    <IAICollapse
+      label={'General'}
+      activeLabel={activeLabel}
+      defaultIsOpen={true}
+    >
       <Flex
         sx={{
           flexDirection: 'column',

invokeai/frontend/web/src/features/ui/components/tabs/TextToImage/TextToImageTabParameters.tsx

@@ -1,15 +1,16 @@
+import ParamDynamicPromptsCollapse from 'features/dynamicPrompts/components/ParamDynamicPromptsCollapse';
+import ParamLoraCollapse from 'features/lora/components/ParamLoraCollapse';
+import ParamControlNetCollapse from 'features/parameters/components/Parameters/ControlNet/ParamControlNetCollapse';
+import ParamNegativeConditioning from 'features/parameters/components/Parameters/Core/ParamNegativeConditioning';
+import ParamPositiveConditioning from 'features/parameters/components/Parameters/Core/ParamPositiveConditioning';
+import ParamHiresCollapse from 'features/parameters/components/Parameters/Hires/ParamHiresCollapse';
+import ParamNoiseCollapse from 'features/parameters/components/Parameters/Noise/ParamNoiseCollapse';
+import ParamSeamlessCollapse from 'features/parameters/components/Parameters/Seamless/ParamSeamlessCollapse';
+import ParamSymmetryCollapse from 'features/parameters/components/Parameters/Symmetry/ParamSymmetryCollapse';
+import ParamVariationCollapse from 'features/parameters/components/Parameters/Variations/ParamVariationCollapse';
 import ProcessButtons from 'features/parameters/components/ProcessButtons/ProcessButtons';
 import { memo } from 'react';
-import ParamPositiveConditioning from 'features/parameters/components/Parameters/Core/ParamPositiveConditioning';
-import ParamNegativeConditioning from 'features/parameters/components/Parameters/Core/ParamNegativeConditioning';
-import ParamVariationCollapse from 'features/parameters/components/Parameters/Variations/ParamVariationCollapse';
-import ParamNoiseCollapse from 'features/parameters/components/Parameters/Noise/ParamNoiseCollapse';
-import ParamSymmetryCollapse from 'features/parameters/components/Parameters/Symmetry/ParamSymmetryCollapse';
-import ParamHiresCollapse from 'features/parameters/components/Parameters/Hires/ParamHiresCollapse';
-import ParamSeamlessCollapse from 'features/parameters/components/Parameters/Seamless/ParamSeamlessCollapse';
 import TextToImageTabCoreParameters from './TextToImageTabCoreParameters';
-import ParamControlNetCollapse from 'features/parameters/components/Parameters/ControlNet/ParamControlNetCollapse';
-import ParamDynamicPromptsCollapse from 'features/dynamicPrompts/components/ParamDynamicPromptsCollapse';
 
 const TextToImageTabParameters = () => {
   return (
@@ -18,6 +19,7 @@ const TextToImageTabParameters = () => {
       <ParamNegativeConditioning />
       <ProcessButtons />
       <TextToImageTabCoreParameters />
+      <ParamLoraCollapse />
       <ParamDynamicPromptsCollapse />
       <ParamControlNetCollapse />
       <ParamVariationCollapse />

invokeai/frontend/web/src/features/ui/components/tabs/UnifiedCanvas/UnifiedCanvasCoreParameters.tsx

@@ -1,5 +1,6 @@
-import { Box, Flex, useDisclosure } from '@chakra-ui/react';
+import { Box, Flex } from '@chakra-ui/react';
 import { createSelector } from '@reduxjs/toolkit';
+import { stateSelector } from 'app/store/store';
 import { useAppSelector } from 'app/store/storeHooks';
 import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
 import IAICollapse from 'common/components/IAICollapse';
@@ -12,25 +13,30 @@ import ParamScheduler from 'features/parameters/components/Parameters/Core/Param
 import ParamSteps from 'features/parameters/components/Parameters/Core/ParamSteps';
 import ImageToImageStrength from 'features/parameters/components/Parameters/ImageToImage/ImageToImageStrength';
 import ParamSeedFull from 'features/parameters/components/Parameters/Seed/ParamSeedFull';
-import { uiSelector } from 'features/ui/store/uiSelectors';
 import { memo } from 'react';
 
 const selector = createSelector(
-  uiSelector,
+  stateSelector,
-  (ui) => {
+  ({ ui, generation }) => {
     const { shouldUseSliders } = ui;
+    const { shouldRandomizeSeed } = generation;
 
-    return { shouldUseSliders };
+    const activeLabel = !shouldRandomizeSeed ? 'Manual Seed' : undefined;
+
+    return { shouldUseSliders, activeLabel };
   },
   defaultSelectorOptions
 );
 
 const UnifiedCanvasCoreParameters = () => {
-  const { shouldUseSliders } = useAppSelector(selector);
+  const { shouldUseSliders, activeLabel } = useAppSelector(selector);
-  const { isOpen, onToggle } = useDisclosure({ defaultIsOpen: true });
 
   return (
-    <IAICollapse label={'General'} isOpen={isOpen} onToggle={onToggle}>
+    <IAICollapse
+      label={'General'}
+      activeLabel={activeLabel}
+      defaultIsOpen={true}
+    >
       <Flex
         sx={{
           flexDirection: 'column',

invokeai/frontend/web/src/features/ui/components/tabs/UnifiedCanvas/UnifiedCanvasParameters.tsx

@@ -1,14 +1,15 @@
-import ProcessButtons from 'features/parameters/components/ProcessButtons/ProcessButtons';
+import ParamDynamicPromptsCollapse from 'features/dynamicPrompts/components/ParamDynamicPromptsCollapse';
-import ParamVariationCollapse from 'features/parameters/components/Parameters/Variations/ParamVariationCollapse';
+import ParamLoraCollapse from 'features/lora/components/ParamLoraCollapse';
-import ParamSymmetryCollapse from 'features/parameters/components/Parameters/Symmetry/ParamSymmetryCollapse';
 import ParamInfillAndScalingCollapse from 'features/parameters/components/Parameters/Canvas/InfillAndScaling/ParamInfillAndScalingCollapse';
 import ParamSeamCorrectionCollapse from 'features/parameters/components/Parameters/Canvas/SeamCorrection/ParamSeamCorrectionCollapse';
-import UnifiedCanvasCoreParameters from './UnifiedCanvasCoreParameters';
-import { memo } from 'react';
-import ParamPositiveConditioning from 'features/parameters/components/Parameters/Core/ParamPositiveConditioning';
-import ParamNegativeConditioning from 'features/parameters/components/Parameters/Core/ParamNegativeConditioning';
 import ParamControlNetCollapse from 'features/parameters/components/Parameters/ControlNet/ParamControlNetCollapse';
-import ParamDynamicPromptsCollapse from 'features/dynamicPrompts/components/ParamDynamicPromptsCollapse';
+import ParamNegativeConditioning from 'features/parameters/components/Parameters/Core/ParamNegativeConditioning';
+import ParamPositiveConditioning from 'features/parameters/components/Parameters/Core/ParamPositiveConditioning';
+import ParamSymmetryCollapse from 'features/parameters/components/Parameters/Symmetry/ParamSymmetryCollapse';
+import ParamVariationCollapse from 'features/parameters/components/Parameters/Variations/ParamVariationCollapse';
+import ProcessButtons from 'features/parameters/components/ProcessButtons/ProcessButtons';
+import { memo } from 'react';
+import UnifiedCanvasCoreParameters from './UnifiedCanvasCoreParameters';
 
 const UnifiedCanvasParameters = () => {
   return (
@@ -17,6 +18,7 @@ const UnifiedCanvasParameters = () => {
       <ParamNegativeConditioning />
       <ProcessButtons />
       <UnifiedCanvasCoreParameters />
+      <ParamLoraCollapse />
       <ParamDynamicPromptsCollapse />
       <ParamControlNetCollapse />
       <ParamVariationCollapse />

invokeai/frontend/web/src/features/ui/store/tabMap.ts

@@ -1,13 +1,10 @@
 export const tabMap = [
   'txt2img',
   'img2img',
-  // 'generate',
   'unifiedCanvas',
   'nodes',
-  'batch',
-  // 'postprocessing',
-  // 'training',
   'modelManager',
+  'batch',
 ] as const;
 
 export type InvokeTabName = (typeof tabMap)[number];

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

@@ -1,37 +1,85 @@
-import { ModelsList } from 'services/api/types';
 import { EntityState, createEntityAdapter } from '@reduxjs/toolkit';
-import { keyBy } from 'lodash-es';
+import { cloneDeep } from 'lodash-es';
+import {
+  AnyModelConfig,
+  ControlNetModelConfig,
+  LoRAModelConfig,
+  MainModelConfig,
+  TextualInversionModelConfig,
+  VaeModelConfig,
+} from 'services/api/types';
 
 import { ApiFullTagDescription, LIST_TAG, api } from '..';
-import { paths } from '../schema';
 
-type ModelConfig = ModelsList['models'][number];
+export type MainModelConfigEntity = MainModelConfig & { id: string };
 
-type ListModelsArg = NonNullable<
+export type LoRAModelConfigEntity = LoRAModelConfig & { id: string };
-  paths['/api/v1/models/']['get']['parameters']['query']
->;
 
-const modelsAdapter = createEntityAdapter<ModelConfig>({
+export type ControlNetModelConfigEntity = ControlNetModelConfig & {
-  selectId: (model) => getModelId(model),
+  id: string;
+};
+
+export type TextualInversionModelConfigEntity = TextualInversionModelConfig & {
+  id: string;
+};
+
+export type VaeModelConfigEntity = VaeModelConfig & { id: string };
+
+type AnyModelConfigEntity =
+  | MainModelConfigEntity
+  | LoRAModelConfigEntity
+  | ControlNetModelConfigEntity
+  | TextualInversionModelConfigEntity
+  | VaeModelConfigEntity;
+
+const mainModelsAdapter = createEntityAdapter<MainModelConfigEntity>({
+  sortComparer: (a, b) => a.name.localeCompare(b.name),
+});
+const loraModelsAdapter = createEntityAdapter<LoRAModelConfigEntity>({
+  sortComparer: (a, b) => a.name.localeCompare(b.name),
+});
+const controlNetModelsAdapter =
+  createEntityAdapter<ControlNetModelConfigEntity>({
+    sortComparer: (a, b) => a.name.localeCompare(b.name),
+  });
+const textualInversionModelsAdapter =
+  createEntityAdapter<TextualInversionModelConfigEntity>({
+    sortComparer: (a, b) => a.name.localeCompare(b.name),
+  });
+const vaeModelsAdapter = createEntityAdapter<VaeModelConfigEntity>({
   sortComparer: (a, b) => a.name.localeCompare(b.name),
 });
 
-const getModelId = ({ base_model, type, name }: ModelConfig) =>
+export const getModelId = ({ base_model, type, name }: AnyModelConfig) =>
   `${base_model}/${type}/${name}`;
 
+const createModelEntities = <T extends AnyModelConfigEntity>(
+  models: AnyModelConfig[]
+): T[] => {
+  const entityArray: T[] = [];
+  models.forEach((model) => {
+    const entity = {
+      ...cloneDeep(model),
+      id: getModelId(model),
+    } as T;
+    entityArray.push(entity);
+  });
+  return entityArray;
+};
+
 export const modelsApi = api.injectEndpoints({
   endpoints: (build) => ({
-    listModels: build.query<EntityState<ModelConfig>, ListModelsArg>({
+    getMainModels: build.query<EntityState<MainModelConfigEntity>, void>({
-      query: (arg) => ({ url: 'models/', params: arg }),
+      query: () => ({ url: 'models/', params: { model_type: 'main' } }),
       providesTags: (result, error, arg) => {
-        // any list of boards
+        const tags: ApiFullTagDescription[] = [
-        const tags: ApiFullTagDescription[] = [{ id: 'Model', type: LIST_TAG }];
+          { id: 'MainModel', type: LIST_TAG },
+        ];
 
         if (result) {
-          // and individual tags for each board
           tags.push(
             ...result.ids.map((id) => ({
-              type: 'Model' as const,
+              type: 'MainModel' as const,
               id,
             }))
           );
@@ -39,14 +87,161 @@ export const modelsApi = api.injectEndpoints({
 
         return tags;
       },
-      transformResponse: (response: ModelsList, meta, arg) => {
+      transformResponse: (
-        return modelsAdapter.setAll(
+        response: { models: MainModelConfig[] },
-          modelsAdapter.getInitialState(),
+        meta,
-          keyBy(response.models, getModelId)
+        arg
+      ) => {
+        const entities = createModelEntities<MainModelConfigEntity>(
+          response.models
+        );
+        return mainModelsAdapter.setAll(
+          mainModelsAdapter.getInitialState(),
+          entities
+        );
+      },
+    }),
+    getLoRAModels: build.query<EntityState<LoRAModelConfigEntity>, void>({
+      query: () => ({ url: 'models/', params: { model_type: 'lora' } }),
+      providesTags: (result, error, arg) => {
+        const tags: ApiFullTagDescription[] = [
+          { id: 'LoRAModel', type: LIST_TAG },
+        ];
+
+        if (result) {
+          tags.push(
+            ...result.ids.map((id) => ({
+              type: 'LoRAModel' as const,
+              id,
+            }))
+          );
+        }
+
+        return tags;
+      },
+      transformResponse: (
+        response: { models: LoRAModelConfig[] },
+        meta,
+        arg
+      ) => {
+        const entities = createModelEntities<LoRAModelConfigEntity>(
+          response.models
+        );
+        return loraModelsAdapter.setAll(
+          loraModelsAdapter.getInitialState(),
+          entities
+        );
+      },
+    }),
+    getControlNetModels: build.query<
+      EntityState<ControlNetModelConfigEntity>,
+      void
+    >({
+      query: () => ({ url: 'models/', params: { model_type: 'controlnet' } }),
+      providesTags: (result, error, arg) => {
+        const tags: ApiFullTagDescription[] = [
+          { id: 'ControlNetModel', type: LIST_TAG },
+        ];
+
+        if (result) {
+          tags.push(
+            ...result.ids.map((id) => ({
+              type: 'ControlNetModel' as const,
+              id,
+            }))
+          );
+        }
+
+        return tags;
+      },
+      transformResponse: (
+        response: { models: ControlNetModelConfig[] },
+        meta,
+        arg
+      ) => {
+        const entities = createModelEntities<ControlNetModelConfigEntity>(
+          response.models
+        );
+        return controlNetModelsAdapter.setAll(
+          controlNetModelsAdapter.getInitialState(),
+          entities
+        );
+      },
+    }),
+    getVaeModels: build.query<EntityState<VaeModelConfigEntity>, void>({
+      query: () => ({ url: 'models/', params: { model_type: 'vae' } }),
+      providesTags: (result, error, arg) => {
+        const tags: ApiFullTagDescription[] = [
+          { id: 'VaeModel', type: LIST_TAG },
+        ];
+
+        if (result) {
+          tags.push(
+            ...result.ids.map((id) => ({
+              type: 'VaeModel' as const,
+              id,
+            }))
+          );
+        }
+
+        return tags;
+      },
+      transformResponse: (
+        response: { models: VaeModelConfig[] },
+        meta,
+        arg
+      ) => {
+        const entities = createModelEntities<VaeModelConfigEntity>(
+          response.models
+        );
+        return vaeModelsAdapter.setAll(
+          vaeModelsAdapter.getInitialState(),
+          entities
+        );
+      },
+    }),
+    getTextualInversionModels: build.query<
+      EntityState<TextualInversionModelConfigEntity>,
+      void
+    >({
+      query: () => ({ url: 'models/', params: { model_type: 'embedding' } }),
+      providesTags: (result, error, arg) => {
+        const tags: ApiFullTagDescription[] = [
+          { id: 'TextualInversionModel', type: LIST_TAG },
+        ];
+
+        if (result) {
+          tags.push(
+            ...result.ids.map((id) => ({
+              type: 'TextualInversionModel' as const,
+              id,
+            }))
+          );
+        }
+
+        return tags;
+      },
+      transformResponse: (
+        response: { models: TextualInversionModelConfig[] },
+        meta,
+        arg
+      ) => {
+        const entities = createModelEntities<TextualInversionModelConfigEntity>(
+          response.models
+        );
+        return textualInversionModelsAdapter.setAll(
+          textualInversionModelsAdapter.getInitialState(),
+          entities
         );
       },
     }),
   }),
 });
 
-export const { useListModelsQuery } = modelsApi;
+export const {
+  useGetMainModelsQuery,
+  useGetControlNetModelsQuery,
+  useGetLoRAModelsQuery,
+  useGetTextualInversionModelsQuery,
+  useGetVaeModelsQuery,
+} = modelsApi;

invokeai/frontend/web/src/services/api/schema.d.ts

@@ -2690,6 +2690,19 @@ export type components = {
       model_format: components["schemas"]["LoRAModelFormat"];
       error?: components["schemas"]["ModelError"];
     };
+    /**
+     * LoRAModelField 
+     * @description LoRA model field
+     */
+    LoRAModelField: {
+      /**
+       * Model Name 
+       * @description Name of the LoRA model
+       */
+      model_name: string;
+      /** @description Base model */
+      base_model: components["schemas"]["BaseModelType"];
+    };
     /**
      * LoRAModelFormat 
      * @description An enumeration. 
@@ -2766,10 +2779,10 @@ export type components = {
        */
       type?: "lora_loader";
       /**
-       * Lora Name 
+       * Lora 
        * @description Lora model name
        */
-      lora_name: string;
+      lora?: components["schemas"]["LoRAModelField"];
       /**
        * Weight 
        * @description With what weight to apply lora 
@@ -3115,7 +3128,7 @@ export type components = {
     /** ModelsList */
     ModelsList: {
       /** Models */
-      models: (components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"])[];
+      models: (components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"])[];
     };
     /**
      * MultiplyInvocation 
@@ -4448,18 +4461,18 @@ export type components = {
        */
       image?: components["schemas"]["ImageField"];
     };
-    /**
-     * StableDiffusion2ModelFormat 
-     * @description An enumeration. 
-     * @enum {string}
-     */
-    StableDiffusion2ModelFormat: "checkpoint" | "diffusers";
     /**
      * StableDiffusion1ModelFormat 
      * @description An enumeration. 
      * @enum {string}
      */
     StableDiffusion1ModelFormat: "checkpoint" | "diffusers";
+    /**
+     * StableDiffusion2ModelFormat 
+     * @description An enumeration. 
+     * @enum {string}
+     */
+    StableDiffusion2ModelFormat: "checkpoint" | "diffusers";
   };
   responses: never;
   parameters: never;