merge with main

README.md

@@ -132,8 +132,10 @@ and go to http://localhost:9090.
 
 ### Command-Line Installation (for developers and users familiar with Terminals)
 
-You must have Python 3.9 or 3.10 installed on your machine. Earlier or later versions are
-not supported.
+You must have Python 3.9 or 3.10 installed on your machine. Earlier or
+later versions are not supported.
+Node.js also needs to be installed along with yarn (can be installed with
+the command `npm install -g yarn` if needed)
 
 1. Open a command-line window on your machine. The PowerShell is recommended for Windows.
 2. Create a directory to install InvokeAI into. You'll need at least 15 GB of free space:
@@ -197,11 +199,18 @@ not supported.
 7. Launch the web server (do it every time you run InvokeAI):
 
     ```terminal
-    invokeai --web
+    invokeai-web
     ```
 
-8. Point your browser to http://localhost:9090 to bring up the web interface.
-9. Type `banana sushi` in the box on the top left and click `Invoke`.
+8. Build Node.js assets
+
+  ```terminal
+  cd invokeai/frontend/web/
+  yarn vite build
+  ```
+
+9. Point your browser to http://localhost:9090 to bring up the web interface.
+10. Type `banana sushi` in the box on the top left and click `Invoke`.
 
 Be sure to activate the virtual environment each time before re-launching InvokeAI,
 using `source .venv/bin/activate` or `.venv\Scripts\activate`.

docs/contributing/LOCAL_DEVELOPMENT.md

@@ -81,3 +81,193 @@ pytest --cov; open ./coverage/html/index.html
 <!--#TODO: get input from blessedcoolant here, for the moment inserted the frontend README via snippets extension.-->
 
 --8<-- "invokeai/frontend/web/README.md"
+
+## Developing InvokeAI in VSCode
+
+VSCode offers some nice tools:
+
+- python debugger
+- automatic `venv` activation
+- remote dev (e.g. run InvokeAI on a beefy linux desktop while you type in
+  comfort on your macbook)
+
+### Setup
+
+You'll need the
+[Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python)
+and
+[Pylance](https://marketplace.visualstudio.com/items?itemName=ms-python.vscode-pylance)
+extensions installed first.
+
+It's also really handy to install the `Jupyter` extensions:
+
+- [Jupyter](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter)
+- [Jupyter Cell Tags](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.vscode-jupyter-cell-tags)
+- [Jupyter Notebook Renderers](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter-renderers)
+- [Jupyter Slide Show](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.vscode-jupyter-slideshow)
+
+#### InvokeAI workspace
+
+Creating a VSCode workspace for working on InvokeAI is highly recommended. It
+can hold InvokeAI-specific settings and configs.
+
+To make a workspace:
+
+- Open the InvokeAI repo dir in VSCode
+- `File` > `Save Workspace As` > save it _outside_ the repo
+
+#### Default python interpreter (i.e. automatic virtual environment activation)
+
+- Use command palette to run command
+  `Preferences: Open Workspace Settings (JSON)`
+- Add `python.defaultInterpreterPath` to `settings`, pointing to your `venv`'s
+  python
+
+Should look something like this:
+
+```jsonc
+{
+  // I like to have all InvokeAI-related folders in my workspace
+  "folders": [
+    {
+      // repo root
+      "path": "InvokeAI"
+    },
+    {
+      // InvokeAI root dir, where `invokeai.yaml` lives
+      "path": "/path/to/invokeai_root"
+    }
+  ],
+  "settings": {
+    // Where your InvokeAI `venv`'s python executable lives
+    "python.defaultInterpreterPath": "/path/to/invokeai_root/.venv/bin/python"
+  }
+}
+```
+
+Now when you open the VSCode integrated terminal, or do anything that needs to
+run python, it will automatically be in your InvokeAI virtual environment.
+
+Bonus: When you create a Jupyter notebook, when you run it, you'll be prompted
+for the python interpreter to run in. This will default to your `venv` python,
+and so you'll have access to the same python environment as the InvokeAI app.
+
+This is _super_ handy.
+
+#### Debugging configs with `launch.json`
+
+Debugging configs are managed in a `launch.json` file. Like most VSCode configs,
+these can be scoped to a workspace or folder.
+
+Follow the [official guide](https://code.visualstudio.com/docs/python/debugging)
+to set up your `launch.json` and try it out.
+
+Now we can create the InvokeAI debugging configs:
+
+```jsonc
+{
+  // Use IntelliSense to learn about possible attributes.
+  // Hover to view descriptions of existing attributes.
+  // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+  "version": "0.2.0",
+  "configurations": [
+    {
+      // Run the InvokeAI backend & serve the pre-built UI
+      "name": "InvokeAI Web",
+      "type": "python",
+      "request": "launch",
+      "program": "scripts/invokeai-web.py",
+      "args": [
+        // Your InvokeAI root dir (where `invokeai.yaml` lives)
+        "--root",
+        "/path/to/invokeai_root",
+        // Access the app from anywhere on your local network
+        "--host",
+        "0.0.0.0"
+      ],
+      "justMyCode": true
+    },
+    {
+      // Run the nodes-based CLI
+      "name": "InvokeAI CLI",
+      "type": "python",
+      "request": "launch",
+      "program": "scripts/invokeai-cli.py",
+      "justMyCode": true
+    },
+    {
+      // Run tests
+      "name": "InvokeAI Test",
+      "type": "python",
+      "request": "launch",
+      "module": "pytest",
+      "args": ["--capture=no"],
+      "justMyCode": true
+    },
+    {
+      // Run a single test
+      "name": "InvokeAI Single Test",
+      "type": "python",
+      "request": "launch",
+      "module": "pytest",
+      "args": [
+        // Change this to point to the specific test you are working on
+        "tests/nodes/test_invoker.py"
+      ],
+      "justMyCode": true
+    },
+    {
+      // This is the default, useful to just run a single file
+      "name": "Python: File",
+      "type": "python",
+      "request": "launch",
+      "program": "${file}",
+      "justMyCode": true
+    }
+  ]
+}
+```
+
+You'll see these configs in the debugging configs drop down. Running them will
+start InvokeAI with attached debugger, in the correct environment, and work just
+like the normal app.
+
+Enjoy debugging InvokeAI with ease (not that we have any bugs of course).
+
+#### Remote dev
+
+This is very easy to set up and provides the same very smooth experience as
+local development. Environments and debugging, as set up above, just work,
+though you'd need to recreate the workspace and debugging configs on the remote.
+
+Consult the
+[official guide](https://code.visualstudio.com/docs/remote/remote-overview) to
+get it set up.
+
+Suggest using VSCode's included settings sync so that your remote dev host has
+all the same app settings and extensions automagically.
+
+##### One remote dev gotcha
+
+I've found the automatic port forwarding to be very flakey. You can disable it
+in `Preferences: Open Remote Settings (ssh: hostname)`. Search for
+`remote.autoForwardPorts` and untick the box.
+
+To forward ports very reliably, use SSH on the remote dev client (e.g. your
+macbook). Here's how to forward both backend API port (`9090`) and the frontend
+live dev server port (`5173`):
+
+```bash
+ssh \
+    -L 9090:localhost:9090 \
+    -L 5173:localhost:5173 \
+    user@remote-dev-host
+```
+
+The forwarding stops when you close the terminal window, so suggest to do this
+_outside_ the VSCode integrated terminal in case you need to restart VSCode for
+an extension update or something
+
+Now, on your remote dev client, you can open `localhost:9090` and access the UI,
+now served from the remote dev host, just the same as if it was running on the
+client.

docs/features/NODES.md

@@ -1,4 +1,8 @@
-# Nodes Editor (Experimental Beta)
+# Nodes Editor (Experimental)
+
+🚨
+*The node editor is experimental. We've made it accessible because we use it to develop the application, but we have not addressed the many known rough edges. It's very easy to shoot yourself in the foot, and we cannot offer support for it until it sees full release (ETA v3.1). Everything is subject to change without warning.* 
+🚨
 
 The nodes editor is a blank canvas allowing for the use of individual functions and image transformations to control the image generation workflow. The node processing flow is usually done from left (inputs) to right (outputs), though linearity can become abstracted the more complex the node graph becomes. Nodes inputs and outputs are connected by dragging connectors from node to node.
 

invokeai/app/api/dependencies.py

@@ -11,6 +11,7 @@ from invokeai.app.services.board_images import (
 )
 from invokeai.app.services.board_record_storage import SqliteBoardRecordStorage
 from invokeai.app.services.boards import BoardService, BoardServiceDependencies
+from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.app.services.image_record_storage import SqliteImageRecordStorage
 from invokeai.app.services.images import ImageService, ImageServiceDependencies
 from invokeai.app.services.resource_name import SimpleNameService
@@ -56,7 +57,7 @@ class ApiDependencies:
     invoker: Invoker = None
 
     @staticmethod
-    def initialize(config, event_handler_id: int, logger: Logger = logger):
+    def initialize(config: InvokeAIAppConfig, event_handler_id: int, logger: Logger = logger):
         logger.debug(f"InvokeAI version {__version__}")
         logger.debug(f"Internet connectivity is {config.internet_available}")
 

invokeai/app/api/routers/models.py

@@ -13,8 +13,11 @@ from invokeai.backend import BaseModelType, ModelType
 from invokeai.backend.model_management.models import (
     OPENAPI_MODEL_CONFIGS,
     SchedulerPredictionType,
+    ModelNotFoundException,
+    InvalidModelException,
 )
 from invokeai.backend.model_management import MergeInterpolationMethod
+
 from ..dependencies import ApiDependencies
 
 models_router = APIRouter(prefix="/v1/models", tags=["models"])
@@ -34,11 +37,16 @@ class ModelsList(BaseModel):
     responses={200: {"model": ModelsList }},
 )
 async def list_models(
-    base_model: Optional[BaseModelType] = Query(default=None, description="Base model"),
+    base_models: Optional[List[BaseModelType]] = Query(default=None, description="Base models to include"),
     model_type: Optional[ModelType] = Query(default=None, description="The type of model to get"),
 ) -> ModelsList:
     """Gets a list of models"""
-    models_raw = ApiDependencies.invoker.services.model_manager.list_models(base_model, model_type)
+    if base_models and len(base_models)>0:
+        models_raw = list()
+        for base_model in base_models:
+            models_raw.extend(ApiDependencies.invoker.services.model_manager.list_models(base_model, model_type))
+    else:
+        models_raw = ApiDependencies.invoker.services.model_manager.list_models(None, model_type)
     models = parse_obj_as(ModelsList, { "models": models_raw })
     return models
 
@@ -46,8 +54,9 @@ async def list_models(
     "/{base_model}/{model_type}/{model_name}",
     operation_id="update_model",
     responses={200: {"description" : "The model was updated successfully"},
+               400: {"description" : "Bad request"},
                404: {"description" : "The model could not be found"},
-               400: {"description" : "Bad request"}
+               409: {"description" : "There is already a model corresponding to the new name"},
                },
     status_code = 200,
     response_model = UpdateModelResponse,
@@ -58,23 +67,58 @@ async def update_model(
         model_name: str = Path(description="model name"),
         info: Union[tuple(OPENAPI_MODEL_CONFIGS)] = Body(description="Model configuration"),
 ) -> UpdateModelResponse:
-    """ Add Model """
+    """ Update model contents with a new config. If the model name or base fields are changed, then the model is renamed. """
+    logger = ApiDependencies.invoker.services.logger
+
+    
     try:
+        previous_info = ApiDependencies.invoker.services.model_manager.list_model(
+            model_name=model_name,
+            base_model=base_model,
+            model_type=model_type,
+        )
+
+        # rename operation requested
+        if info.model_name != model_name or info.base_model != base_model:
+            ApiDependencies.invoker.services.model_manager.rename_model(
+                base_model = base_model,
+                model_type = model_type,
+                model_name = model_name,
+                new_name = info.model_name,
+                new_base = info.base_model,
+            )
+            logger.info(f'Successfully renamed {base_model}/{model_name}=>{info.base_model}/{info.model_name}')
+            # update information to support an update of attributes
+            model_name = info.model_name
+            base_model = info.base_model
+            new_info = ApiDependencies.invoker.services.model_manager.list_model(
+                model_name=model_name,
+                base_model=base_model,
+                model_type=model_type,
+            )
+            if new_info.get('path') != previous_info.get('path'):  # model manager moved model path during rename - don't overwrite it
+                info.path = new_info.get('path')
+            
         ApiDependencies.invoker.services.model_manager.update_model(
             model_name=model_name,
             base_model=base_model,
             model_type=model_type,
             model_attributes=info.dict()
         )
+            
         model_raw = ApiDependencies.invoker.services.model_manager.list_model(
             model_name=model_name,
             base_model=base_model,
             model_type=model_type,
         )
         model_response = parse_obj_as(UpdateModelResponse, model_raw)
-    except KeyError as e:
+    except ModelNotFoundException as e:
         raise HTTPException(status_code=404, detail=str(e))
     except ValueError as e:
+        logger.error(str(e))
+        raise HTTPException(status_code=409, detail=str(e))
+    except Exception as e:
+        logger.error(str(e))
         raise HTTPException(status_code=400, detail=str(e))
 
     return model_response
@@ -85,6 +129,7 @@ async def update_model(
     responses= {
         201: {"description" : "The model imported successfully"},
         404: {"description" : "The model could not be found"},
+        415: {"description" : "Unrecognized file/folder format"},
         424: {"description" : "The model appeared to import successfully, but could not be found in the model manager"},
         409: {"description" : "There is already a model corresponding to this path or repo_id"},
     },
@@ -111,7 +156,7 @@ async def import_model(
 
         if not info:
             logger.error("Import failed")
-            raise HTTPException(status_code=424)
+            raise HTTPException(status_code=415)
         
         logger.info(f'Successfully imported {location}, got {info}')
         model_raw = ApiDependencies.invoker.services.model_manager.list_model(
@@ -121,9 +166,12 @@ async def import_model(
         )
         return parse_obj_as(ImportModelResponse, model_raw)
     
-    except KeyError as e:
+    except ModelNotFoundException as e:
         logger.error(str(e))
         raise HTTPException(status_code=404, detail=str(e))
+    except InvalidModelException as e:
+        logger.error(str(e))
+        raise HTTPException(status_code=415)
     except ValueError as e:
         logger.error(str(e))
         raise HTTPException(status_code=409, detail=str(e))
@@ -161,57 +209,13 @@ async def add_model(
             model_type=info.model_type
         )
         return parse_obj_as(ImportModelResponse, model_raw)
-    except KeyError as e:
+    except ModelNotFoundException as e:
         logger.error(str(e))
         raise HTTPException(status_code=404, detail=str(e))
     except ValueError as e:
         logger.error(str(e))
         raise HTTPException(status_code=409, detail=str(e))
 
-@models_router.post(
-    "/rename/{base_model}/{model_type}/{model_name}",
-    operation_id="rename_model",
-    responses= {
-        201: {"description" : "The model was renamed successfully"},
-        404: {"description" : "The model could not be found"},
-        409: {"description" : "There is already a model corresponding to the new name"},
-    },
-    status_code=201,
-    response_model=ImportModelResponse
-)
-async def rename_model(
-        base_model: BaseModelType = Path(description="Base model"),
-        model_type: ModelType = Path(description="The type of model"),
-        model_name: str = Path(description="current model name"),
-        new_name: Optional[str] = Query(description="new model name", default=None),
-        new_base: Optional[BaseModelType] = Query(description="new model base", default=None),
-) -> ImportModelResponse:
-    """ Rename a model"""
-    
-    logger = ApiDependencies.invoker.services.logger
-
-    try:
-        result = ApiDependencies.invoker.services.model_manager.rename_model(
-            base_model = base_model,
-            model_type = model_type,
-            model_name = model_name,
-            new_name = new_name,
-            new_base = new_base,
-        )
-        logger.debug(result)
-        logger.info(f'Successfully renamed {model_name}=>{new_name}')
-        model_raw = ApiDependencies.invoker.services.model_manager.list_model(
-            model_name=new_name or model_name,
-            base_model=new_base or base_model,
-            model_type=model_type
-        )
-        return parse_obj_as(ImportModelResponse, model_raw)
-    except KeyError as e:
-        logger.error(str(e))
-        raise HTTPException(status_code=404, detail=str(e))
-    except ValueError as e:
-        logger.error(str(e))
-        raise HTTPException(status_code=409, detail=str(e))
     
 @models_router.delete(
     "/{base_model}/{model_type}/{model_name}",
@@ -238,9 +242,9 @@ async def delete_model(
                                                                  )
         logger.info(f"Deleted model: {model_name}")
         return Response(status_code=204)
-    except KeyError:
-        logger.error(f"Model not found: {model_name}")
-        raise HTTPException(status_code=404, detail=f"Model '{model_name}' not found")
+    except ModelNotFoundException as e:
+        logger.error(str(e))
+        raise HTTPException(status_code=404, detail=str(e))
 
 @models_router.put(
     "/convert/{base_model}/{model_type}/{model_name}",
@@ -273,8 +277,8 @@ async def convert_model(
                                                                               base_model = base_model,
                                                                               model_type = model_type)
         response = parse_obj_as(ConvertModelResponse, model_raw)
-    except KeyError:
-        raise HTTPException(status_code=404, detail=f"Model '{model_name}' not found")
+    except ModelNotFoundException as e:
+        raise HTTPException(status_code=404, detail=f"Model '{model_name}' not found: {str(e)}")
     except ValueError as e:
         raise HTTPException(status_code=400, detail=str(e))
     return response
@@ -364,8 +368,55 @@ async def merge_models(
                                                                               model_type = ModelType.Main,
                                                                               )
         response = parse_obj_as(ConvertModelResponse, model_raw)
-    except KeyError:
+    except ModelNotFoundException:
         raise HTTPException(status_code=404, detail=f"One or more of the models '{model_names}' not found")
     except ValueError as e:
         raise HTTPException(status_code=400, detail=str(e))
     return response
+
+# The rename operation is now supported by update_model and no longer needs to be
+# a standalone route.
+# @models_router.post(
+#     "/rename/{base_model}/{model_type}/{model_name}",
+#     operation_id="rename_model",
+#     responses= {
+#         201: {"description" : "The model was renamed successfully"},
+#         404: {"description" : "The model could not be found"},
+#         409: {"description" : "There is already a model corresponding to the new name"},
+#     },
+#     status_code=201,
+#     response_model=ImportModelResponse
+# )
+# async def rename_model(
+#         base_model: BaseModelType = Path(description="Base model"),
+#         model_type: ModelType = Path(description="The type of model"),
+#         model_name: str = Path(description="current model name"),
+#         new_name: Optional[str] = Query(description="new model name", default=None),
+#         new_base: Optional[BaseModelType] = Query(description="new model base", default=None),
+# ) -> ImportModelResponse:
+#     """ Rename a model"""
+    
+#     logger = ApiDependencies.invoker.services.logger
+
+#     try:
+#         result = ApiDependencies.invoker.services.model_manager.rename_model(
+#             base_model = base_model,
+#             model_type = model_type,
+#             model_name = model_name,
+#             new_name = new_name,
+#             new_base = new_base,
+#         )
+#         logger.debug(result)
+#         logger.info(f'Successfully renamed {model_name}=>{new_name}')
+#         model_raw = ApiDependencies.invoker.services.model_manager.list_model(
+#             model_name=new_name or model_name,
+#             base_model=new_base or base_model,
+#             model_type=model_type
+#         )
+#         return parse_obj_as(ImportModelResponse, model_raw)
+#     except ModelNotFoundException as e:
+#         logger.error(str(e))
+#         raise HTTPException(status_code=404, detail=str(e))
+#     except ValueError as e:
+#         logger.error(str(e))
+#         raise HTTPException(status_code=409, detail=str(e))

invokeai/app/api_app.py

@@ -39,6 +39,7 @@ from .invocations.baseinvocation import BaseInvocation
     
 
 import torch
+import invokeai.backend.util.hotfixes
 if torch.backends.mps.is_available():
     import invokeai.backend.util.mps_fixes
 

invokeai/app/cli_app.py

@@ -57,6 +57,7 @@ from .services.processor import DefaultInvocationProcessor
 from .services.sqlite import SqliteItemStorage
 
 import torch
+import invokeai.backend.util.hotfixes
 if torch.backends.mps.is_available():
     import invokeai.backend.util.mps_fixes
 

invokeai/app/invocations/compel.py

@@ -1,8 +1,8 @@
-from typing import Literal, Optional, Union, List
+from typing import Literal, Optional, Union, List, Annotated
 from pydantic import BaseModel, Field
 import re
 import torch
-from compel import Compel
+from compel import Compel, ReturnedEmbeddingsType
 from compel.prompt_parser import (Blend, Conjunction,
                                   CrossAttentionControlSubstitute,
                                   FlattenedPrompt, Fragment)
@@ -14,6 +14,7 @@ from ...backend.stable_diffusion.diffusion import InvokeAIDiffuserComponent
 from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
                              InvocationConfig, InvocationContext)
 from .model import ClipField
+from dataclasses import dataclass
 
 
 class ConditioningField(BaseModel):
@@ -23,6 +24,34 @@ class ConditioningField(BaseModel):
     class Config:
         schema_extra = {"required": ["conditioning_name"]}
 
+@dataclass
+class BasicConditioningInfo:
+    #type: Literal["basic_conditioning"] = "basic_conditioning"
+    embeds: torch.Tensor
+    extra_conditioning: Optional[InvokeAIDiffuserComponent.ExtraConditioningInfo]
+    # weight: float
+    # mode: ConditioningAlgo
+
+@dataclass
+class SDXLConditioningInfo(BasicConditioningInfo):
+    #type: Literal["sdxl_conditioning"] = "sdxl_conditioning"
+    pooled_embeds: torch.Tensor
+    add_time_ids: torch.Tensor
+
+ConditioningInfoType = Annotated[
+    Union[BasicConditioningInfo, SDXLConditioningInfo],
+    Field(discriminator="type")
+]
+
+@dataclass
+class ConditioningFieldData:
+    conditionings: List[Union[BasicConditioningInfo, SDXLConditioningInfo]]
+    #unconditioned: Optional[torch.Tensor]
+
+#class ConditioningAlgo(str, Enum):
+#    Compose = "compose"
+#    ComposeEx = "compose_ex"
+#    PerpNeg = "perp_neg"
 
 class CompelOutput(BaseInvocationOutput):
     """Compel parser output"""
@@ -57,10 +86,10 @@ class CompelInvocation(BaseInvocation):
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> CompelOutput:
         tokenizer_info = context.services.model_manager.get_model(
-            **self.clip.tokenizer.dict(),
+            **self.clip.tokenizer.dict(), context=context,
         )
         text_encoder_info = context.services.model_manager.get_model(
-            **self.clip.text_encoder.dict(),
+            **self.clip.text_encoder.dict(), context=context,
         )
 
         def _lora_loader():
@@ -82,6 +111,7 @@ class CompelInvocation(BaseInvocation):
                         model_name=name,
                         base_model=self.clip.text_encoder.base_model,
                         model_type=ModelType.TextualInversion,
+                        context=context,
                     ).context.model
                 )
             except ModelNotFoundException:
@@ -118,10 +148,17 @@ class CompelInvocation(BaseInvocation):
                 cross_attention_control_args=options.get(
                     "cross_attention_control", None),)
 
-        conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
+        conditioning_data = ConditioningFieldData(
+            conditionings=[
+                BasicConditioningInfo(
+                    embeds=c,
+                    extra_conditioning=ec,
+                )
+            ]
+        )
 
-        # TODO: hacky but works ;D maybe rename latents somehow?
-        context.services.latents.save(conditioning_name, (c, ec))
+        conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
+        context.services.latents.save(conditioning_name, conditioning_data)
 
         return CompelOutput(
             conditioning=ConditioningField(
@@ -129,6 +166,389 @@ class CompelInvocation(BaseInvocation):
             ),
         )
 
+class SDXLPromptInvocationBase:
+    def run_clip_raw(self, context, clip_field, prompt, get_pooled):
+        tokenizer_info = context.services.model_manager.get_model(
+            **clip_field.tokenizer.dict(),
+        )
+        text_encoder_info = context.services.model_manager.get_model(
+            **clip_field.text_encoder.dict(),
+        )
+
+        def _lora_loader():
+            for lora in clip_field.loras:
+                lora_info = context.services.model_manager.get_model(
+                    **lora.dict(exclude={"weight"}))
+                yield (lora_info.context.model, lora.weight)
+                del lora_info
+            return
+
+        #loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
+
+        ti_list = []
+        for trigger in re.findall(r"<[a-zA-Z0-9., _-]+>", prompt):
+            name = trigger[1:-1]
+            try:
+                ti_list.append(
+                    context.services.model_manager.get_model(
+                        model_name=name,
+                        base_model=clip_field.text_encoder.base_model,
+                        model_type=ModelType.TextualInversion,
+                    ).context.model
+                )
+            except ModelNotFoundException:
+                # print(e)
+                #import traceback
+                #print(traceback.format_exc())
+                print(f"Warn: trigger: \"{trigger}\" not found")
+
+        with ModelPatcher.apply_lora_text_encoder(text_encoder_info.context.model, _lora_loader()),\
+                ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (tokenizer, ti_manager),\
+                ModelPatcher.apply_clip_skip(text_encoder_info.context.model, clip_field.skipped_layers),\
+                text_encoder_info as text_encoder:
+
+            text_inputs = tokenizer(
+                prompt,
+                padding="max_length",
+                max_length=tokenizer.model_max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            text_input_ids = text_inputs.input_ids
+            prompt_embeds = text_encoder(
+                text_input_ids.to(text_encoder.device),
+                output_hidden_states=True,
+            )
+            if get_pooled:
+                c_pooled = prompt_embeds[0]
+            else:
+                c_pooled = None
+            c = prompt_embeds.hidden_states[-2]
+
+        del tokenizer
+        del text_encoder
+        del tokenizer_info
+        del text_encoder_info
+
+        return c, c_pooled, None
+
+    def run_clip_compel(self, context, clip_field, prompt, get_pooled):
+        tokenizer_info = context.services.model_manager.get_model(
+            **clip_field.tokenizer.dict(),
+        )
+        text_encoder_info = context.services.model_manager.get_model(
+            **clip_field.text_encoder.dict(),
+        )
+
+        def _lora_loader():
+            for lora in clip_field.loras:
+                lora_info = context.services.model_manager.get_model(
+                    **lora.dict(exclude={"weight"}))
+                yield (lora_info.context.model, lora.weight)
+                del lora_info
+            return
+
+        #loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
+
+        ti_list = []
+        for trigger in re.findall(r"<[a-zA-Z0-9., _-]+>", prompt):
+            name = trigger[1:-1]
+            try:
+                ti_list.append(
+                    context.services.model_manager.get_model(
+                        model_name=name,
+                        base_model=clip_field.text_encoder.base_model,
+                        model_type=ModelType.TextualInversion,
+                    ).context.model
+                )
+            except ModelNotFoundException:
+                # print(e)
+                #import traceback
+                #print(traceback.format_exc())
+                print(f"Warn: trigger: \"{trigger}\" not found")
+
+        with ModelPatcher.apply_lora_text_encoder(text_encoder_info.context.model, _lora_loader()),\
+                ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (tokenizer, ti_manager),\
+                ModelPatcher.apply_clip_skip(text_encoder_info.context.model, clip_field.skipped_layers),\
+                text_encoder_info as text_encoder:
+
+            compel = Compel(
+                tokenizer=tokenizer,
+                text_encoder=text_encoder,
+                textual_inversion_manager=ti_manager,
+                dtype_for_device_getter=torch_dtype,
+                truncate_long_prompts=True,  # TODO:
+                returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED, # TODO: clip skip
+                requires_pooled=True,
+            )
+
+            conjunction = Compel.parse_prompt_string(prompt)
+
+            if context.services.configuration.log_tokenization:
+                # TODO: better logging for and syntax
+                for prompt_obj in conjunction.prompts:
+                    log_tokenization_for_prompt_object(prompt_obj, tokenizer)
+
+            # TODO: ask for optimizations? to not run text_encoder twice
+            c, options = compel.build_conditioning_tensor_for_conjunction(conjunction)
+            if get_pooled:
+                c_pooled = compel.conditioning_provider.get_pooled_embeddings([prompt])
+            else:
+                c_pooled = None
+
+            ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(
+                tokens_count_including_eos_bos=get_max_token_count(tokenizer, conjunction),
+                cross_attention_control_args=options.get("cross_attention_control", None),
+            )
+
+        del tokenizer
+        del text_encoder
+        del tokenizer_info
+        del text_encoder_info
+
+        return c, c_pooled, ec
+
+class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
+    """Parse prompt using compel package to conditioning."""
+
+    type: Literal["sdxl_compel_prompt"] = "sdxl_compel_prompt"
+
+    prompt: str = Field(default="", description="Prompt")
+    style: str = Field(default="", description="Style prompt")
+    original_width: int = Field(1024, description="")
+    original_height: int = Field(1024, description="")
+    crop_top: int = Field(0, description="")
+    crop_left: int = Field(0, description="")
+    target_width: int = Field(1024, description="")
+    target_height: int = Field(1024, description="")
+    clip1: ClipField = Field(None, description="Clip to use")
+    clip2: ClipField = Field(None, description="Clip to use")
+
+    # Schema customisation
+    class Config(InvocationConfig):
+        schema_extra = {
+            "ui": {
+                "title": "SDXL Prompt (Compel)",
+                "tags": ["prompt", "compel"],
+                "type_hints": {
+                    "model": "model"
+                }
+            },
+        }
+
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> CompelOutput:
+        c1, c1_pooled, ec1 = self.run_clip_compel(context, self.clip1, self.prompt, False)
+        if self.style.strip() == "":
+            c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.prompt, True)
+        else:
+            c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.style, True)
+
+        original_size = (self.original_height, self.original_width)
+        crop_coords = (self.crop_top, self.crop_left)
+        target_size = (self.target_height, self.target_width)
+
+        add_time_ids = torch.tensor([
+            original_size + crop_coords + target_size
+        ])
+
+        conditioning_data = ConditioningFieldData(
+            conditionings=[
+                SDXLConditioningInfo(
+                    embeds=torch.cat([c1, c2], dim=-1),
+                    pooled_embeds=c2_pooled,
+                    add_time_ids=add_time_ids,
+                    extra_conditioning=ec1,
+                )
+            ]
+        )
+
+        conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
+        context.services.latents.save(conditioning_name, conditioning_data)
+
+        return CompelOutput(
+            conditioning=ConditioningField(
+                conditioning_name=conditioning_name,
+            ),
+        )
+
+class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
+    """Parse prompt using compel package to conditioning."""
+
+    type: Literal["sdxl_refiner_compel_prompt"] = "sdxl_refiner_compel_prompt"
+
+    style: str = Field(default="", description="Style prompt") # TODO: ?
+    original_width: int = Field(1024, description="")
+    original_height: int = Field(1024, description="")
+    crop_top: int = Field(0, description="")
+    crop_left: int = Field(0, description="")
+    aesthetic_score: float = Field(6.0, description="")
+    clip2: ClipField = Field(None, description="Clip to use")
+
+    # Schema customisation
+    class Config(InvocationConfig):
+        schema_extra = {
+            "ui": {
+                "title": "SDXL Refiner Prompt (Compel)",
+                "tags": ["prompt", "compel"],
+                "type_hints": {
+                    "model": "model"
+                }
+            },
+        }
+
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> CompelOutput:
+        c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.style, True)
+
+        original_size = (self.original_height, self.original_width)
+        crop_coords = (self.crop_top, self.crop_left)
+
+        add_time_ids = torch.tensor([
+            original_size + crop_coords + (self.aesthetic_score,)
+        ])
+
+        conditioning_data = ConditioningFieldData(
+            conditionings=[
+                SDXLConditioningInfo(
+                    embeds=c2,
+                    pooled_embeds=c2_pooled,
+                    add_time_ids=add_time_ids,
+                    extra_conditioning=ec2, # or None
+                )
+            ]
+        )
+
+        conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
+        context.services.latents.save(conditioning_name, conditioning_data)
+
+        return CompelOutput(
+            conditioning=ConditioningField(
+                conditioning_name=conditioning_name,
+            ),
+        )
+
+class SDXLRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
+    """Pass unmodified prompt to conditioning without compel processing."""
+
+    type: Literal["sdxl_raw_prompt"] = "sdxl_raw_prompt"
+
+    prompt: str = Field(default="", description="Prompt")
+    style: str = Field(default="", description="Style prompt")
+    original_width: int = Field(1024, description="")
+    original_height: int = Field(1024, description="")
+    crop_top: int = Field(0, description="")
+    crop_left: int = Field(0, description="")
+    target_width: int = Field(1024, description="")
+    target_height: int = Field(1024, description="")
+    clip1: ClipField = Field(None, description="Clip to use")
+    clip2: ClipField = Field(None, description="Clip to use")
+
+    # Schema customisation
+    class Config(InvocationConfig):
+        schema_extra = {
+            "ui": {
+                "title": "SDXL Prompt (Raw)",
+                "tags": ["prompt", "compel"],
+                "type_hints": {
+                    "model": "model"
+                }
+            },
+        }
+
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> CompelOutput:
+        c1, c1_pooled, ec1 = self.run_clip_raw(context, self.clip1, self.prompt, False)
+        if self.style.strip() == "":
+            c2, c2_pooled, ec2 = self.run_clip_raw(context, self.clip2, self.prompt, True)
+        else:
+            c2, c2_pooled, ec2 = self.run_clip_raw(context, self.clip2, self.style, True)
+
+        original_size = (self.original_height, self.original_width)
+        crop_coords = (self.crop_top, self.crop_left)
+        target_size = (self.target_height, self.target_width)
+
+        add_time_ids = torch.tensor([
+            original_size + crop_coords + target_size
+        ])
+
+        conditioning_data = ConditioningFieldData(
+            conditionings=[
+                SDXLConditioningInfo(
+                    embeds=torch.cat([c1, c2], dim=-1),
+                    pooled_embeds=c2_pooled,
+                    add_time_ids=add_time_ids,
+                    extra_conditioning=ec1,
+                )
+            ]
+        )
+
+        conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
+        context.services.latents.save(conditioning_name, conditioning_data)
+
+        return CompelOutput(
+            conditioning=ConditioningField(
+                conditioning_name=conditioning_name,
+            ),
+        )
+
+class SDXLRefinerRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
+    """Parse prompt using compel package to conditioning."""
+
+    type: Literal["sdxl_refiner_raw_prompt"] = "sdxl_refiner_raw_prompt"
+
+    style: str = Field(default="", description="Style prompt") # TODO: ?
+    original_width: int = Field(1024, description="")
+    original_height: int = Field(1024, description="")
+    crop_top: int = Field(0, description="")
+    crop_left: int = Field(0, description="")
+    aesthetic_score: float = Field(6.0, description="")
+    clip2: ClipField = Field(None, description="Clip to use")
+
+    # Schema customisation
+    class Config(InvocationConfig):
+        schema_extra = {
+            "ui": {
+                "title": "SDXL Refiner Prompt (Raw)",
+                "tags": ["prompt", "compel"],
+                "type_hints": {
+                    "model": "model"
+                }
+            },
+        }
+
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> CompelOutput:
+        c2, c2_pooled, ec2 = self.run_clip_raw(context, self.clip2, self.style, True)
+
+        original_size = (self.original_height, self.original_width)
+        crop_coords = (self.crop_top, self.crop_left)
+
+        add_time_ids = torch.tensor([
+            original_size + crop_coords + (self.aesthetic_score,)
+        ])
+
+        conditioning_data = ConditioningFieldData(
+            conditionings=[
+                SDXLConditioningInfo(
+                    embeds=c2,
+                    pooled_embeds=c2_pooled,
+                    add_time_ids=add_time_ids,
+                    extra_conditioning=ec2, # or None
+                )
+            ]
+        )
+
+        conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
+        context.services.latents.save(conditioning_name, conditioning_data)
+
+        return CompelOutput(
+            conditioning=ConditioningField(
+                conditioning_name=conditioning_name,
+            ),
+        )
+
+
 class ClipSkipInvocationOutput(BaseInvocationOutput):
     """Clip skip node output"""
     type: Literal["clip_skip_output"] = "clip_skip_output"

invokeai/app/invocations/generate.py

@@ -157,13 +157,13 @@ class InpaintInvocation(BaseInvocation):
         def _lora_loader():
             for lora in self.unet.loras:
                 lora_info = context.services.model_manager.get_model(
-                    **lora.dict(exclude={"weight"}))
+                    **lora.dict(exclude={"weight"}), context=context,)
                 yield (lora_info.context.model, lora.weight)
                 del lora_info
             return
         
-        unet_info = context.services.model_manager.get_model(**self.unet.unet.dict())
-        vae_info = context.services.model_manager.get_model(**self.vae.vae.dict())
+        unet_info = context.services.model_manager.get_model(**self.unet.unet.dict(), context=context,)
+        vae_info = context.services.model_manager.get_model(**self.vae.vae.dict(), context=context,)
 
         with vae_info as vae,\
                 ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()),\

invokeai/app/invocations/latent.py

@@ -31,6 +31,13 @@ from .controlnet_image_processors import ControlField
 from .image import ImageOutput
 from .model import ModelInfo, UNetField, VaeField
 
+from diffusers.models.attention_processor import (
+    AttnProcessor2_0,
+    LoRAAttnProcessor2_0,
+    LoRAXFormersAttnProcessor,
+    XFormersAttnProcessor,
+)
+
 
 class LatentsField(BaseModel):
     """A latents field used for passing latents between invocations"""
@@ -76,7 +83,7 @@ def get_scheduler(
         scheduler_name, SCHEDULER_MAP['ddim']
     )
     orig_scheduler_info = context.services.model_manager.get_model(
-        **scheduler_info.dict()
+        **scheduler_info.dict(), context=context,
     )
     with orig_scheduler_info as orig_scheduler:
         scheduler_config = orig_scheduler.config
@@ -161,12 +168,12 @@ class TextToLatentsInvocation(BaseInvocation):
         context: InvocationContext,
         scheduler,
     ) -> ConditioningData:
-        c, extra_conditioning_info = context.services.latents.get(
-            self.positive_conditioning.conditioning_name
-        )
-        uc, _ = context.services.latents.get(
-            self.negative_conditioning.conditioning_name
-        )
+        positive_cond_data = context.services.latents.get(self.positive_conditioning.conditioning_name)
+        c = positive_cond_data.conditionings[0].embeds
+        extra_conditioning_info = positive_cond_data.conditionings[0].extra_conditioning
+
+        negative_cond_data = context.services.latents.get(self.negative_conditioning.conditioning_name)
+        uc = negative_cond_data.conditionings[0].embeds
 
         conditioning_data = ConditioningData(
             unconditioned_embeddings=uc,
@@ -262,6 +269,7 @@ class TextToLatentsInvocation(BaseInvocation):
                         model_name=control_info.control_model.model_name,
                         model_type=ModelType.ControlNet,
                         base_model=control_info.control_model.base_model,
+                        context=context,
                     )
                 )
 
@@ -313,14 +321,14 @@ class TextToLatentsInvocation(BaseInvocation):
         def _lora_loader():
             for lora in self.unet.loras:
                 lora_info = context.services.model_manager.get_model(
-                    **lora.dict(exclude={"weight"})
+                    **lora.dict(exclude={"weight"}), context=context,
                 )
                 yield (lora_info.context.model, lora.weight)
                 del lora_info
             return
 
         unet_info = context.services.model_manager.get_model(
-            **self.unet.unet.dict()
+            **self.unet.unet.dict(), context=context,
         )
         with ExitStack() as exit_stack,\
                 ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()),\
@@ -403,14 +411,14 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
         def _lora_loader():
             for lora in self.unet.loras:
                 lora_info = context.services.model_manager.get_model(
-                    **lora.dict(exclude={"weight"})
+                    **lora.dict(exclude={"weight"}), context=context,
                 )
                 yield (lora_info.context.model, lora.weight)
                 del lora_info
             return
 
         unet_info = context.services.model_manager.get_model(
-            **self.unet.unet.dict()
+            **self.unet.unet.dict(), context=context,
         )
         with ExitStack() as exit_stack,\
                 ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()),\
@@ -475,8 +483,8 @@ class LatentsToImageInvocation(BaseInvocation):
     tiled: bool = Field(
         default=False,
         description="Decode latents by overlaping tiles(less memory consumption)")
+    fp32: bool = Field(False, description="Decode in full precision")
     metadata: Optional[CoreMetadata] = Field(default=None, description="Optional core metadata to be written to the image")
-    
 
     # Schema customisation
     class Config(InvocationConfig):
@@ -491,10 +499,35 @@ class LatentsToImageInvocation(BaseInvocation):
         latents = context.services.latents.get(self.latents.latents_name)
 
         vae_info = context.services.model_manager.get_model(
-            **self.vae.vae.dict(),
+            **self.vae.vae.dict(), context=context,
         )
 
         with vae_info as vae:
+            if self.fp32:
+                vae.to(dtype=torch.float32)
+
+                use_torch_2_0_or_xformers = isinstance(
+                    vae.decoder.mid_block.attentions[0].processor,
+                    (
+                        AttnProcessor2_0,
+                        XFormersAttnProcessor,
+                        LoRAXFormersAttnProcessor,
+                        LoRAAttnProcessor2_0,
+                    ),
+                )
+                # if xformers or torch_2_0 is used attention block does not need
+                # to be in float32 which can save lots of memory
+                if use_torch_2_0_or_xformers:
+                    vae.post_quant_conv.to(latents.dtype)
+                    vae.decoder.conv_in.to(latents.dtype)
+                    vae.decoder.mid_block.to(latents.dtype)
+                else:
+                    latents = latents.float()
+
+            else:
+                vae.to(dtype=torch.float16)
+                latents = latents.half()
+
             if self.tiled or context.services.configuration.tiled_decode:
                 vae.enable_tiling()
             else:
@@ -618,6 +651,8 @@ class ImageToLatentsInvocation(BaseInvocation):
     tiled: bool = Field(
         default=False,
         description="Encode latents by overlaping tiles(less memory consumption)")
+    fp32: bool = Field(False, description="Decode in full precision")
+
 
     # Schema customisation
     class Config(InvocationConfig):
@@ -636,7 +671,7 @@ class ImageToLatentsInvocation(BaseInvocation):
 
         #vae_info = context.services.model_manager.get_model(**self.vae.vae.dict())
         vae_info = context.services.model_manager.get_model(
-            **self.vae.vae.dict(),
+            **self.vae.vae.dict(), context=context,
         )
 
         image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB"))
@@ -644,6 +679,32 @@ class ImageToLatentsInvocation(BaseInvocation):
             image_tensor = einops.rearrange(image_tensor, "c h w -> 1 c h w")
 
         with vae_info as vae:
+            orig_dtype = vae.dtype
+            if self.fp32:
+                vae.to(dtype=torch.float32)
+
+                use_torch_2_0_or_xformers = isinstance(
+                    vae.decoder.mid_block.attentions[0].processor,
+                    (
+                        AttnProcessor2_0,
+                        XFormersAttnProcessor,
+                        LoRAXFormersAttnProcessor,
+                        LoRAAttnProcessor2_0,
+                    ),
+                )
+                # if xformers or torch_2_0 is used attention block does not need
+                # to be in float32 which can save lots of memory
+                if use_torch_2_0_or_xformers:
+                    vae.post_quant_conv.to(orig_dtype)
+                    vae.decoder.conv_in.to(orig_dtype)
+                    vae.decoder.mid_block.to(orig_dtype)
+                #else:
+                #    latents = latents.float()
+
+            else:
+                vae.to(dtype=torch.float16)
+                #latents = latents.half()
+
             if self.tiled:
                 vae.enable_tiling()
             else:
@@ -658,6 +719,7 @@ class ImageToLatentsInvocation(BaseInvocation):
                 )  # FIXME: uses torch.randn. make reproducible!
 
             latents = 0.18215 * latents
+            latents = latents.to(dtype=orig_dtype)
 
         name = f"{context.graph_execution_state_id}__{self.id}"
         # context.services.latents.set(name, latents)

invokeai/app/invocations/model.py

@@ -33,7 +33,6 @@ class ClipField(BaseModel):
     skipped_layers: int = Field(description="Number of skipped layers in text_encoder")
     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")
@@ -50,7 +49,6 @@ class ModelLoaderOutput(BaseInvocationOutput):
     vae: VaeField = Field(default=None, description="Vae submodel")
     # fmt: on
 
-
 class MainModelField(BaseModel):
     """Main model field"""
 
@@ -64,7 +62,6 @@ class LoRAModelField(BaseModel):
     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."""
 
@@ -157,6 +154,22 @@ class MainModelLoaderInvocation(BaseInvocation):
                 loras=[],
                 skipped_layers=0,
             ),
+            clip2=ClipField(
+                tokenizer=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.Tokenizer2,
+                ),
+                text_encoder=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.TextEncoder2,
+                ),
+                loras=[],
+                skipped_layers=0,
+            ),
             vae=VaeField(
                 vae=ModelInfo(
                     model_name=model_name,
@@ -167,7 +180,7 @@ class MainModelLoaderInvocation(BaseInvocation):
             ),
         )
 
-
+    
 class LoraLoaderOutput(BaseInvocationOutput):
     """Model loader output"""
 

invokeai/app/invocations/prompt.py

@@ -1,6 +1,8 @@
-from typing import Literal
+from os.path import exists
+from typing import Literal, Optional
 
-from pydantic.fields import Field
+import numpy as np
+from pydantic import Field, validator
 
 from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext
 from dynamicprompts.generators import RandomPromptGenerator, CombinatorialPromptGenerator
@@ -55,3 +57,41 @@ class DynamicPromptInvocation(BaseInvocation):
             prompts = generator.generate(self.prompt, num_images=self.max_prompts)
 
         return PromptCollectionOutput(prompt_collection=prompts, count=len(prompts))
+    
+
+class PromptsFromFileInvocation(BaseInvocation):
+    '''Loads prompts from a text file'''
+    # fmt: off
+    type: Literal['prompt_from_file'] = 'prompt_from_file'
+
+    # Inputs
+    file_path: str = Field(description="Path to prompt text file")
+    pre_prompt: Optional[str] = Field(description="String to prepend to each prompt")
+    post_prompt: Optional[str] = Field(description="String to append to each prompt")
+    start_line: int = Field(default=1, ge=1, description="Line in the file to start start from")
+    max_prompts: int = Field(default=1, ge=0, description="Max lines to read from file (0=all)")
+    #fmt: on
+
+    @validator("file_path")
+    def file_path_exists(cls, v):
+        if not exists(v):
+            raise ValueError(FileNotFoundError)
+        return v
+
+    def promptsFromFile(self, file_path: str, pre_prompt: str, post_prompt: str, start_line: int, max_prompts: int):
+        prompts = []
+        start_line -= 1
+        end_line = start_line + max_prompts
+        if max_prompts <= 0:
+            end_line = np.iinfo(np.int32).max
+        with open(file_path) as f:
+            for i, line in enumerate(f):
+                if i >= start_line and i < end_line:
+                    prompts.append((pre_prompt or '') + line.strip() + (post_prompt or ''))
+                if i >= end_line:
+                    break
+        return prompts
+
+    def invoke(self, context: InvocationContext) -> PromptCollectionOutput:
+        prompts = self.promptsFromFile(self.file_path, self.pre_prompt, self.post_prompt, self.start_line, self.max_prompts)
+        return PromptCollectionOutput(prompt_collection=prompts, count=len(prompts))

invokeai/app/invocations/sdxl.py

@@ -0,0 +1,658 @@
+import torch
+import inspect
+from tqdm import tqdm
+from typing import List, Literal, Optional, Union
+
+from pydantic import Field, validator
+
+from ...backend.model_management import ModelType, SubModelType
+from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
+                             InvocationConfig, InvocationContext)
+
+from .model import UNetField, ClipField, VaeField, MainModelField, ModelInfo
+from .compel import ConditioningField
+from .latent import LatentsField, SAMPLER_NAME_VALUES, LatentsOutput, get_scheduler, build_latents_output
+
+class SDXLModelLoaderOutput(BaseInvocationOutput):
+    """SDXL base model loader output"""
+
+    # fmt: off
+    type: Literal["sdxl_model_loader_output"] = "sdxl_model_loader_output"
+
+    unet: UNetField = Field(default=None, description="UNet submodel")
+    clip: ClipField = Field(default=None, description="Tokenizer and text_encoder submodels")
+    clip2: ClipField = Field(default=None, description="Tokenizer and text_encoder submodels")
+    vae: VaeField = Field(default=None, description="Vae submodel")
+    # fmt: on
+
+class SDXLRefinerModelLoaderOutput(BaseInvocationOutput):
+    """SDXL refiner model loader output"""
+    # fmt: off
+    type: Literal["sdxl_refiner_model_loader_output"] = "sdxl_refiner_model_loader_output"
+    unet: UNetField = Field(default=None, description="UNet submodel")
+    clip2: ClipField = Field(default=None, description="Tokenizer and text_encoder submodels")
+    vae: VaeField = Field(default=None, description="Vae submodel")
+    # fmt: on
+    #fmt: on
+    
+class SDXLModelLoaderInvocation(BaseInvocation):
+    """Loads an sdxl base model, outputting its submodels."""
+
+    type: Literal["sdxl_model_loader"] = "sdxl_model_loader"
+
+    model: MainModelField = Field(description="The model to load")
+    # TODO: precision?
+
+    # Schema customisation
+    class Config(InvocationConfig):
+        schema_extra = {
+            "ui": {
+                "title": "SDXL Model Loader",
+                "tags": ["model", "loader", "sdxl"],
+                "type_hints": {"model": "model"},
+            },
+        }
+
+    def invoke(self, context: InvocationContext) -> SDXLModelLoaderOutput:
+        base_model = self.model.base_model
+        model_name = self.model.model_name
+        model_type = ModelType.Main
+
+        # TODO: not found exceptions
+        if not context.services.model_manager.model_exists(
+            model_name=model_name,
+            base_model=base_model,
+            model_type=model_type,
+        ):
+            raise Exception(f"Unknown {base_model} {model_type} model: {model_name}")
+
+        return SDXLModelLoaderOutput(
+            unet=UNetField(
+                unet=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.UNet,
+                ),
+                scheduler=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.Scheduler,
+                ),
+                loras=[],
+            ),
+            clip=ClipField(
+                tokenizer=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.Tokenizer,
+                ),
+                text_encoder=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.TextEncoder,
+                ),
+                loras=[],
+                skipped_layers=0,
+            ),
+            clip2=ClipField(
+                tokenizer=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.Tokenizer2,
+                ),
+                text_encoder=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.TextEncoder2,
+                ),
+                loras=[],
+                skipped_layers=0,
+            ),
+            vae=VaeField(
+                vae=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.Vae,
+                ),
+            ),
+        )
+
+class SDXLRefinerModelLoaderInvocation(BaseInvocation):
+    """Loads an sdxl refiner model, outputting its submodels."""
+    type: Literal["sdxl_refiner_model_loader"] = "sdxl_refiner_model_loader"
+
+    model: MainModelField = Field(description="The model to load")
+    # TODO: precision?
+
+    # Schema customisation
+    class Config(InvocationConfig):
+        schema_extra = {
+            "ui": {
+                "title": "SDXL Refiner Model Loader",
+                "tags": ["model", "loader", "sdxl_refiner"],
+                "type_hints": {"model": "model"},
+            },
+        }
+
+    def invoke(self, context: InvocationContext) -> SDXLRefinerModelLoaderOutput:
+        base_model = self.model.base_model
+        model_name = self.model.model_name
+        model_type = ModelType.Main
+
+        # TODO: not found exceptions
+        if not context.services.model_manager.model_exists(
+            model_name=model_name,
+            base_model=base_model,
+            model_type=model_type,
+        ):
+            raise Exception(f"Unknown {base_model} {model_type} model: {model_name}")
+
+        return SDXLRefinerModelLoaderOutput(
+            unet=UNetField(
+                unet=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.UNet,
+                ),
+                scheduler=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.Scheduler,
+                ),
+                loras=[],
+            ),
+            clip2=ClipField(
+                tokenizer=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.Tokenizer2,
+                ),
+                text_encoder=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.TextEncoder2,
+                ),
+                loras=[],
+                skipped_layers=0,
+            ),
+            vae=VaeField(
+                vae=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.Vae,
+                ),
+            ),
+        )
+    
+# Text to image
+class SDXLTextToLatentsInvocation(BaseInvocation):
+    """Generates latents from conditionings."""
+
+    type: Literal["t2l_sdxl"] = "t2l_sdxl"
+
+    # Inputs
+    # fmt: off
+    positive_conditioning: Optional[ConditioningField] = Field(description="Positive conditioning for generation")
+    negative_conditioning: Optional[ConditioningField] = Field(description="Negative conditioning for generation")
+    noise: Optional[LatentsField] = Field(description="The noise to use")
+    steps:       int = Field(default=10, gt=0, description="The number of steps to use to generate the image")
+    cfg_scale: Union[float, List[float]] = Field(default=7.5, ge=1, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", )
+    scheduler: SAMPLER_NAME_VALUES = Field(default="euler", description="The scheduler to use" )
+    unet: UNetField = Field(default=None, description="UNet submodel")
+    denoising_end: float = Field(default=1.0, gt=0, le=1, description="")
+    #control: Union[ControlField, list[ControlField]] = Field(default=None, description="The control to use")
+    #seamless:   bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
+    #seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'")
+    # fmt: on
+
+    @validator("cfg_scale")
+    def ge_one(cls, v):
+        """validate that all cfg_scale values are >= 1"""
+        if isinstance(v, list):
+            for i in v:
+                if i < 1:
+                    raise ValueError('cfg_scale must be greater than 1')
+        else:
+            if v < 1:
+                raise ValueError('cfg_scale must be greater than 1')
+        return v
+
+    # Schema customisation
+    class Config(InvocationConfig):
+        schema_extra = {
+            "ui": {
+                "tags": ["latents"],
+                "type_hints": {
+                  "model": "model",
+                  # "cfg_scale": "float",
+                  "cfg_scale": "number"
+                }
+            },
+        }
+
+    # based on
+    # https://github.com/huggingface/diffusers/blob/3ebbaf7c96801271f9e6c21400033b6aa5ffcf29/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py#L375
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> LatentsOutput:
+        latents = context.services.latents.get(self.noise.latents_name)
+
+        positive_cond_data = context.services.latents.get(self.positive_conditioning.conditioning_name)
+        prompt_embeds = positive_cond_data.conditionings[0].embeds
+        pooled_prompt_embeds = positive_cond_data.conditionings[0].pooled_embeds
+        add_time_ids = positive_cond_data.conditionings[0].add_time_ids
+
+        negative_cond_data = context.services.latents.get(self.negative_conditioning.conditioning_name)
+        negative_prompt_embeds = negative_cond_data.conditionings[0].embeds
+        negative_pooled_prompt_embeds = negative_cond_data.conditionings[0].pooled_embeds
+        add_neg_time_ids = negative_cond_data.conditionings[0].add_time_ids
+
+        scheduler = get_scheduler(
+            context=context,
+            scheduler_info=self.unet.scheduler,
+            scheduler_name=self.scheduler,
+        )
+
+        num_inference_steps = self.steps
+        scheduler.set_timesteps(num_inference_steps)
+        timesteps = scheduler.timesteps
+
+        latents = latents * scheduler.init_noise_sigma
+
+
+        unet_info = context.services.model_manager.get_model(
+            **self.unet.unet.dict()
+        )
+        do_classifier_free_guidance = True
+        cross_attention_kwargs = None
+        with unet_info as unet:
+
+            extra_step_kwargs = dict()
+            if "eta" in set(inspect.signature(scheduler.step).parameters.keys()):
+                extra_step_kwargs.update(
+                    eta=0.0,
+                )
+            if "generator" in set(inspect.signature(scheduler.step).parameters.keys()):
+                extra_step_kwargs.update(
+                    generator=torch.Generator(device=unet.device).manual_seed(0),
+                )
+
+            num_warmup_steps = len(timesteps) - self.steps * scheduler.order
+
+            # apply denoising_end
+            skipped_final_steps = int(round((1 - self.denoising_end) * self.steps))
+            num_inference_steps = num_inference_steps - skipped_final_steps
+            timesteps = timesteps[: num_warmup_steps + scheduler.order * num_inference_steps]
+
+            if not context.services.configuration.sequential_guidance:
+                prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
+                add_text_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds], dim=0)
+                add_time_ids = torch.cat([add_neg_time_ids, add_time_ids], dim=0)
+
+                prompt_embeds = prompt_embeds.to(device=unet.device, dtype=unet.dtype)
+                add_text_embeds = add_text_embeds.to(device=unet.device, dtype=unet.dtype)
+                add_time_ids = add_time_ids.to(device=unet.device, dtype=unet.dtype)
+                latents = latents.to(device=unet.device, dtype=unet.dtype)
+
+                with tqdm(total=self.steps) as progress_bar:
+                    for i, t in enumerate(timesteps):
+                        # expand the latents if we are doing classifier free guidance
+                        latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+
+                        latent_model_input = scheduler.scale_model_input(latent_model_input, t)
+
+                        # predict the noise residual
+                        added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
+                        noise_pred = unet(
+                            latent_model_input,
+                            t,
+                            encoder_hidden_states=prompt_embeds,
+                            cross_attention_kwargs=cross_attention_kwargs,
+                            added_cond_kwargs=added_cond_kwargs,
+                            return_dict=False,
+                        )[0]
+
+                        # perform guidance
+                        if do_classifier_free_guidance:
+                            noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                            noise_pred = noise_pred_uncond + self.cfg_scale * (noise_pred_text - noise_pred_uncond)
+                            #del noise_pred_uncond
+                            #del noise_pred_text
+
+                        #if do_classifier_free_guidance and guidance_rescale > 0.0:
+                        #    # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
+                        #    noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
+
+                        # compute the previous noisy sample x_t -> x_t-1
+                        latents = scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
+
+                        # call the callback, if provided
+                        if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % scheduler.order == 0):
+                            progress_bar.update()
+                            #if callback is not None and i % callback_steps == 0:
+                            #    callback(i, t, latents)
+            else:
+                negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.to(device=unet.device, dtype=unet.dtype)
+                negative_prompt_embeds = negative_prompt_embeds.to(device=unet.device, dtype=unet.dtype)
+                add_neg_time_ids = add_neg_time_ids.to(device=unet.device, dtype=unet.dtype)
+                pooled_prompt_embeds = pooled_prompt_embeds.to(device=unet.device, dtype=unet.dtype)
+                prompt_embeds = prompt_embeds.to(device=unet.device, dtype=unet.dtype)
+                add_time_ids = add_time_ids.to(device=unet.device, dtype=unet.dtype)
+                latents = latents.to(device=unet.device, dtype=unet.dtype)
+
+                with tqdm(total=self.steps) as progress_bar:
+                    for i, t in enumerate(timesteps):
+                        # expand the latents if we are doing classifier free guidance
+                        #latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+
+                        latent_model_input = scheduler.scale_model_input(latents, t)
+
+                        #import gc
+                        #gc.collect()
+                        #torch.cuda.empty_cache()
+
+                        # predict the noise residual
+
+                        added_cond_kwargs = {"text_embeds": negative_pooled_prompt_embeds, "time_ids": add_neg_time_ids}
+                        noise_pred_uncond = unet(
+                            latent_model_input,
+                            t,
+                            encoder_hidden_states=negative_prompt_embeds,
+                            cross_attention_kwargs=cross_attention_kwargs,
+                            added_cond_kwargs=added_cond_kwargs,
+                            return_dict=False,
+                        )[0]
+
+                        added_cond_kwargs = {"text_embeds": pooled_prompt_embeds, "time_ids": add_time_ids}
+                        noise_pred_text = unet(
+                            latent_model_input,
+                            t,
+                            encoder_hidden_states=prompt_embeds,
+                            cross_attention_kwargs=cross_attention_kwargs,
+                            added_cond_kwargs=added_cond_kwargs,
+                            return_dict=False,
+                        )[0]
+
+                        # perform guidance
+                        noise_pred = noise_pred_uncond + self.cfg_scale * (noise_pred_text - noise_pred_uncond)
+
+                        #del noise_pred_text
+                        #del noise_pred_uncond
+                        #import gc
+                        #gc.collect()
+                        #torch.cuda.empty_cache()
+
+                        #if do_classifier_free_guidance and guidance_rescale > 0.0:
+                        #    # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
+                        #    noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
+
+                        # compute the previous noisy sample x_t -> x_t-1
+                        latents = scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
+
+                        #del noise_pred
+                        #import gc
+                        #gc.collect()
+                        #torch.cuda.empty_cache()
+
+                        # call the callback, if provided
+                        if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % scheduler.order == 0):
+                            progress_bar.update()
+                            #if callback is not None and i % callback_steps == 0:
+                            #    callback(i, t, latents)
+
+
+
+        #################
+
+        torch.cuda.empty_cache()
+
+        name = f'{context.graph_execution_state_id}__{self.id}'
+        context.services.latents.save(name, latents)
+        return build_latents_output(latents_name=name, latents=latents)
+
+class SDXLLatentsToLatentsInvocation(BaseInvocation):
+    """Generates latents from conditionings."""
+
+    type: Literal["l2l_sdxl"] = "l2l_sdxl"
+
+    # Inputs
+    # fmt: off
+    positive_conditioning: Optional[ConditioningField] = Field(description="Positive conditioning for generation")
+    negative_conditioning: Optional[ConditioningField] = Field(description="Negative conditioning for generation")
+    noise: Optional[LatentsField] = Field(description="The noise to use")
+    steps:       int = Field(default=10, gt=0, description="The number of steps to use to generate the image")
+    cfg_scale: Union[float, List[float]] = Field(default=7.5, ge=1, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", )
+    scheduler: SAMPLER_NAME_VALUES = Field(default="euler", description="The scheduler to use" )
+    unet: UNetField = Field(default=None, description="UNet submodel")
+    latents: Optional[LatentsField] = Field(description="Initial latents")
+
+    denoising_start: float = Field(default=0.0, ge=0, lt=1, description="")
+    denoising_end: float = Field(default=1.0, gt=0, le=1, description="")
+
+    #control: Union[ControlField, list[ControlField]] = Field(default=None, description="The control to use")
+    #seamless:   bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
+    #seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'")
+    # fmt: on
+
+    @validator("cfg_scale")
+    def ge_one(cls, v):
+        """validate that all cfg_scale values are >= 1"""
+        if isinstance(v, list):
+            for i in v:
+                if i < 1:
+                    raise ValueError('cfg_scale must be greater than 1')
+        else:
+            if v < 1:
+                raise ValueError('cfg_scale must be greater than 1')
+        return v
+
+    # Schema customisation
+    class Config(InvocationConfig):
+        schema_extra = {
+            "ui": {
+                "tags": ["latents"],
+                "type_hints": {
+                  "model": "model",
+                  # "cfg_scale": "float",
+                  "cfg_scale": "number"
+                }
+            },
+        }
+
+    # based on
+    # https://github.com/huggingface/diffusers/blob/3ebbaf7c96801271f9e6c21400033b6aa5ffcf29/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py#L375
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> LatentsOutput:
+        latents = context.services.latents.get(self.latents.latents_name)
+
+        positive_cond_data = context.services.latents.get(self.positive_conditioning.conditioning_name)
+        prompt_embeds = positive_cond_data.conditionings[0].embeds
+        pooled_prompt_embeds = positive_cond_data.conditionings[0].pooled_embeds
+        add_time_ids = positive_cond_data.conditionings[0].add_time_ids
+
+        negative_cond_data = context.services.latents.get(self.negative_conditioning.conditioning_name)
+        negative_prompt_embeds = negative_cond_data.conditionings[0].embeds
+        negative_pooled_prompt_embeds = negative_cond_data.conditionings[0].pooled_embeds
+        add_neg_time_ids = negative_cond_data.conditionings[0].add_time_ids
+
+        scheduler = get_scheduler(
+            context=context,
+            scheduler_info=self.unet.scheduler,
+            scheduler_name=self.scheduler,
+        )
+
+        # apply denoising_start
+        num_inference_steps = self.steps
+        scheduler.set_timesteps(num_inference_steps)
+
+        t_start = int(round(self.denoising_start * num_inference_steps))
+        timesteps = scheduler.timesteps[t_start * scheduler.order:]
+        num_inference_steps = num_inference_steps - t_start
+
+        # apply noise(if provided)
+        if self.noise is not None:
+            noise = context.services.latents.get(self.noise.latents_name)
+            latents = scheduler.add_noise(latents, noise, timesteps[:1])
+            del noise
+
+        unet_info = context.services.model_manager.get_model(
+            **self.unet.unet.dict()
+        )
+        do_classifier_free_guidance = True
+        cross_attention_kwargs = None
+        with unet_info as unet:
+
+            # apply scheduler extra args
+            extra_step_kwargs = dict()
+            if "eta" in set(inspect.signature(scheduler.step).parameters.keys()):
+                extra_step_kwargs.update(
+                    eta=0.0,
+                )
+            if "generator" in set(inspect.signature(scheduler.step).parameters.keys()):
+                extra_step_kwargs.update(
+                    generator=torch.Generator(device=unet.device).manual_seed(0),
+                )
+
+            num_warmup_steps = max(len(timesteps) - num_inference_steps * scheduler.order, 0)
+
+            # apply denoising_end
+            skipped_final_steps = int(round((1 - self.denoising_end) * self.steps))
+            num_inference_steps = num_inference_steps - skipped_final_steps
+            timesteps = timesteps[: num_warmup_steps + scheduler.order * num_inference_steps]
+
+            if not context.services.configuration.sequential_guidance:
+                prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
+                add_text_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds], dim=0)
+                add_time_ids = torch.cat([add_neg_time_ids, add_time_ids], dim=0)
+
+                prompt_embeds = prompt_embeds.to(device=unet.device, dtype=unet.dtype)
+                add_text_embeds = add_text_embeds.to(device=unet.device, dtype=unet.dtype)
+                add_time_ids = add_time_ids.to(device=unet.device, dtype=unet.dtype)
+                latents = latents.to(device=unet.device, dtype=unet.dtype)
+
+                with tqdm(total=num_inference_steps) as progress_bar:
+                    for i, t in enumerate(timesteps):
+                        # expand the latents if we are doing classifier free guidance
+                        latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+
+                        latent_model_input = scheduler.scale_model_input(latent_model_input, t)
+
+                        # predict the noise residual
+                        added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
+                        noise_pred = unet(
+                            latent_model_input,
+                            t,
+                            encoder_hidden_states=prompt_embeds,
+                            cross_attention_kwargs=cross_attention_kwargs,
+                            added_cond_kwargs=added_cond_kwargs,
+                            return_dict=False,
+                        )[0]
+
+                        # perform guidance
+                        if do_classifier_free_guidance:
+                            noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                            noise_pred = noise_pred_uncond + self.cfg_scale * (noise_pred_text - noise_pred_uncond)
+                            #del noise_pred_uncond
+                            #del noise_pred_text
+
+                        #if do_classifier_free_guidance and guidance_rescale > 0.0:
+                        #    # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
+                        #    noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
+
+                        # compute the previous noisy sample x_t -> x_t-1
+                        latents = scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
+
+                        # call the callback, if provided
+                        if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % scheduler.order == 0):
+                            progress_bar.update()
+                            #if callback is not None and i % callback_steps == 0:
+                            #    callback(i, t, latents)
+            else:
+                negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.to(device=unet.device, dtype=unet.dtype)
+                negative_prompt_embeds = negative_prompt_embeds.to(device=unet.device, dtype=unet.dtype)
+                add_neg_time_ids = add_neg_time_ids.to(device=unet.device, dtype=unet.dtype)
+                pooled_prompt_embeds = pooled_prompt_embeds.to(device=unet.device, dtype=unet.dtype)
+                prompt_embeds = prompt_embeds.to(device=unet.device, dtype=unet.dtype)
+                add_time_ids = add_time_ids.to(device=unet.device, dtype=unet.dtype)
+                latents = latents.to(device=unet.device, dtype=unet.dtype)
+
+                with tqdm(total=num_inference_steps) as progress_bar:
+                    for i, t in enumerate(timesteps):
+                        # expand the latents if we are doing classifier free guidance
+                        #latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+
+                        latent_model_input = scheduler.scale_model_input(latents, t)
+
+                        #import gc
+                        #gc.collect()
+                        #torch.cuda.empty_cache()
+
+                        # predict the noise residual
+
+                        added_cond_kwargs = {"text_embeds": negative_pooled_prompt_embeds, "time_ids": add_time_ids}
+                        noise_pred_uncond = unet(
+                            latent_model_input,
+                            t,
+                            encoder_hidden_states=negative_prompt_embeds,
+                            cross_attention_kwargs=cross_attention_kwargs,
+                            added_cond_kwargs=added_cond_kwargs,
+                            return_dict=False,
+                        )[0]
+
+                        added_cond_kwargs = {"text_embeds": pooled_prompt_embeds, "time_ids": add_time_ids}
+                        noise_pred_text = unet(
+                            latent_model_input,
+                            t,
+                            encoder_hidden_states=prompt_embeds,
+                            cross_attention_kwargs=cross_attention_kwargs,
+                            added_cond_kwargs=added_cond_kwargs,
+                            return_dict=False,
+                        )[0]
+
+                        # perform guidance
+                        noise_pred = noise_pred_uncond + self.cfg_scale * (noise_pred_text - noise_pred_uncond)
+
+                        #del noise_pred_text
+                        #del noise_pred_uncond
+                        #import gc
+                        #gc.collect()
+                        #torch.cuda.empty_cache()
+
+                        #if do_classifier_free_guidance and guidance_rescale > 0.0:
+                        #    # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
+                        #    noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
+
+                        # compute the previous noisy sample x_t -> x_t-1
+                        latents = scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
+
+                        #del noise_pred
+                        #import gc
+                        #gc.collect()
+                        #torch.cuda.empty_cache()
+
+                        # call the callback, if provided
+                        if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % scheduler.order == 0):
+                            progress_bar.update()
+                            #if callback is not None and i % callback_steps == 0:
+                            #    callback(i, t, latents)
+
+
+
+        #################
+
+        torch.cuda.empty_cache()
+
+        name = f'{context.graph_execution_state_id}__{self.id}'
+        context.services.latents.save(name, latents)
+        return build_latents_output(latents_name=name, latents=latents)

invokeai/app/invocations/upscale.py

@@ -1,5 +1,5 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) & the InvokeAI Team
-from pathlib import Path
+from pathlib import Path, PosixPath
 from typing import Literal, Union, cast
 
 import cv2 as cv
@@ -33,12 +33,12 @@ class RealESRGANInvocation(BaseInvocation):
     )
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get_pil_image(self.image.image_name)  # type: ignore
-        models_dir = cast(Path, context.services.configuration.root_dir) / Path("models/")  # type: ignore
+        image = context.services.images.get_pil_image(self.image.image_name)
+        models_path = context.services.configuration.models_path
 
         rrdbnet_model = None
         netscale = None
-        model_path = None
+        esrgan_model_path = None
 
         if self.model_name in [
             "RealESRGAN_x4plus.pth",
@@ -54,13 +54,13 @@ class RealESRGANInvocation(BaseInvocation):
                 scale=4,
             )
             netscale = 4
-        elif self.model_name == "RealESRGAN_x4plus_anime_6B.pth":
+        elif self.model_name in ["RealESRGAN_x4plus_anime_6B.pth"]:
             # x4 RRDBNet model, 6 blocks
             rrdbnet_model = RRDBNet(
                 num_in_ch=3,
                 num_out_ch=3,
                 num_feat=64,
-                num_block=6, # 6 blocks
+                num_block=6,  # 6 blocks
                 num_grow_ch=32,
                 scale=4,
             )
@@ -83,11 +83,11 @@ class RealESRGANInvocation(BaseInvocation):
             context.services.logger.error(msg)
             raise ValueError(msg)
 
-        model_path = Path(f"core/upscaling/realesrgan/{self.model_name}")
+        esrgan_model_path = Path(f"core/upscaling/realesrgan/{self.model_name}")
 
         upsampler = RealESRGANer(
             scale=netscale,
-            model_path=str(models_dir / model_path),
+            model_path=str(models_path / esrgan_model_path),
             model=rrdbnet_model,
             half=False,
         )

invokeai/app/services/events.py

@@ -105,8 +105,6 @@ class EventServiceBase:
     def emit_model_load_started (
             self,
             graph_execution_state_id: str,
-            node: dict,
-            source_node_id: str,
             model_name: str,
             base_model: BaseModelType,
             model_type: ModelType,
@@ -117,8 +115,6 @@ class EventServiceBase:
             event_name="model_load_started",
             payload=dict(
                 graph_execution_state_id=graph_execution_state_id,
-                node=node,
-                source_node_id=source_node_id,
                 model_name=model_name,
                 base_model=base_model,
                 model_type=model_type,
@@ -129,8 +125,6 @@ class EventServiceBase:
     def emit_model_load_completed(
             self,
             graph_execution_state_id: str,
-            node: dict,
-            source_node_id: str,
             model_name: str,
             base_model: BaseModelType,
             model_type: ModelType,
@@ -142,12 +136,12 @@ class EventServiceBase:
             event_name="model_load_completed",
             payload=dict(
                 graph_execution_state_id=graph_execution_state_id,
-                node=node,
-                source_node_id=source_node_id,
                 model_name=model_name,
                 base_model=base_model,
                 model_type=model_type,
                 submodel=submodel,
-                model_info=model_info,
+                hash=model_info.hash,
+                location=model_info.location,
+                precision=str(model_info.precision),
             ),
         )

invokeai/app/services/invocation_services.py

@@ -12,7 +12,7 @@ if TYPE_CHECKING:
     from invokeai.app.services.latent_storage import LatentsStorageBase
     from invokeai.app.services.invocation_queue import InvocationQueueABC
     from invokeai.app.services.item_storage import ItemStorageABC
-    from invokeai.app.services.config import InvokeAISettings
+    from invokeai.app.services.config import InvokeAIAppConfig
     from invokeai.app.services.graph import GraphExecutionState, LibraryGraph
     from invokeai.app.services.invoker import InvocationProcessorABC
 
@@ -23,7 +23,7 @@ class InvocationServices:
     # TODO: Just forward-declared everything due to circular dependencies. Fix structure.
     board_images: "BoardImagesServiceABC"
     boards: "BoardServiceABC"
-    configuration: "InvokeAISettings"
+    configuration: "InvokeAIAppConfig"
     events: "EventServiceBase"
     graph_execution_manager: "ItemStorageABC"["GraphExecutionState"]
     graph_library: "ItemStorageABC"["LibraryGraph"]
@@ -38,7 +38,7 @@ class InvocationServices:
         self,
         board_images: "BoardImagesServiceABC",
         boards: "BoardServiceABC",
-        configuration: "InvokeAISettings",
+        configuration: "InvokeAIAppConfig",
         events: "EventServiceBase",
         graph_execution_manager: "ItemStorageABC"["GraphExecutionState"],
         graph_library: "ItemStorageABC"["LibraryGraph"],

invokeai/app/services/model_manager_service.py

@@ -18,6 +18,7 @@ from invokeai.backend.model_management import (
     SchedulerPredictionType,
     ModelMerger,
     MergeInterpolationMethod,
+    ModelNotFoundException,
 )
 from invokeai.backend.model_management.model_search import FindModels
 
@@ -145,7 +146,7 @@ class ModelManagerServiceBase(ABC):
     ) -> AddModelResult:
         """
         Update the named model with a dictionary of attributes. Will fail with a
-        KeyErrorException if the name does not already exist.
+        ModelNotFoundException if the name does not already exist.
 
         On a successful update, the config will be changed in memory. Will fail 
         with an assertion error if provided attributes are incorrect or 
@@ -338,7 +339,6 @@ class ModelManagerService(ModelManagerServiceBase):
         base_model: BaseModelType,
         model_type: ModelType,
         submodel: Optional[SubModelType] = None,
-        node: Optional[BaseInvocation] = None,
         context: Optional[InvocationContext] = None,
     ) -> ModelInfo:
         """
@@ -346,11 +346,9 @@ class ModelManagerService(ModelManagerServiceBase):
         part (such as the vae) of a diffusers mode.
         """
 
-        # if we are called from within a node, then we get to emit
-        # load start and complete events
-        if node and context:
+        # we can emit model loading events if we are executing with access to the invocation context
+        if context:
             self._emit_load_event(
-                node=node,
                 context=context,
                 model_name=model_name,
                 base_model=base_model,
@@ -365,9 +363,8 @@ class ModelManagerService(ModelManagerServiceBase):
             submodel,
         )
 
-        if node and context:
+        if context:
             self._emit_load_event(
-                node=node,
                 context=context,
                 model_name=model_name,
                 base_model=base_model,
@@ -451,14 +448,14 @@ class ModelManagerService(ModelManagerServiceBase):
     ) -> AddModelResult:
         """
         Update the named model with a dictionary of attributes. Will fail with a
-        KeyError exception if the name does not already exist.
+        ModelNotFoundException exception if the name does not already exist.
         On a successful update, the config will be changed in memory. Will fail 
         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'update model {model_name}')
         if not self.model_exists(model_name, base_model, model_type):
-            raise KeyError(f"Unknown model {model_name}")
+            raise ModelNotFoundException(f"Unknown model {model_name}")
         return self.add_model(model_name, base_model, model_type, model_attributes, clobber=True)
     
     def del_model(
@@ -509,23 +506,19 @@ class ModelManagerService(ModelManagerServiceBase):
 
     def _emit_load_event(
         self,
-        node,
         context,
         model_name: str,
         base_model: BaseModelType,
         model_type: ModelType,
-        submodel: SubModelType,
+        submodel: Optional[SubModelType] = None,
         model_info: Optional[ModelInfo] = None,
     ):
         if context.services.queue.is_canceled(context.graph_execution_state_id):
             raise CanceledException()
-        graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
-        source_node_id = graph_execution_state.prepared_source_mapping[node.id]
+
         if model_info:
             context.services.events.emit_model_load_completed(
                 graph_execution_state_id=context.graph_execution_state_id,
-                node=node.dict(),
-                source_node_id=source_node_id,
                 model_name=model_name,
                 base_model=base_model,
                 model_type=model_type,
@@ -535,8 +528,6 @@ class ModelManagerService(ModelManagerServiceBase):
         else:
             context.services.events.emit_model_load_started(
                 graph_execution_state_id=context.graph_execution_state_id,
-                node=node.dict(),
-                source_node_id=source_node_id,
                 model_name=model_name,
                 base_model=base_model,
                 model_type=model_type,

invokeai/backend/install/model_install_backend.py

@@ -212,7 +212,7 @@ class ModelInstall(object):
                                     {'config.json','model_index.json','learned_embeds.bin','pytorch_lora_weights.bin'}
                                     ]
                                    ):
-            models_installed.update(self._install_path(path))
+            models_installed.update({str(model_path_id_or_url): self._install_path(path)})
 
         # recursive scan
         elif path.is_dir():

invokeai/backend/model_management/__init__.py

@@ -3,6 +3,6 @@ Initialization file for invokeai.backend.model_management
 """
 from .model_manager import ModelManager, ModelInfo, AddModelResult, SchedulerPredictionType
 from .model_cache import ModelCache
-from .models import BaseModelType, ModelType, SubModelType, ModelVariantType
+from .models import BaseModelType, ModelType, SubModelType, ModelVariantType, ModelNotFoundException
 from .model_merge import ModelMerger, MergeInterpolationMethod
 

invokeai/backend/model_management/model_manager.py

@@ -552,7 +552,7 @@ class ModelManager(object):
             model_config = self.models.get(model_key)
             if not model_config:
                 self.logger.error(f'Unknown model {model_name}')
-                raise KeyError(f'Unknown model {model_name}')
+                raise ModelNotFoundException(f'Unknown model {model_name}')
 
             cur_model_name, cur_base_model, cur_model_type = self.parse_key(model_key)
             if base_model is not None and cur_base_model != base_model:
@@ -568,6 +568,9 @@ class ModelManager(object):
                 model_type=cur_model_type,
             )
 
+            # expose paths as absolute to help web UI
+            if path := model_dict.get('path'):
+                model_dict['path'] = str(self.app_config.root_path / path)
             models.append(model_dict)
 
         return models
@@ -596,7 +599,7 @@ class ModelManager(object):
         model_cfg = self.models.pop(model_key, None)
 
         if model_cfg is None:
-            raise KeyError(f"Unknown model {model_key}")
+            raise ModelNotFoundException(f"Unknown model {model_key}")
 
         # note: it not garantie to release memory(model can has other references)
         cache_ids = self.cache_keys.pop(model_key, [])
@@ -635,6 +638,10 @@ class ModelManager(object):
         The returned dict has the same format as the dict returned by
         model_info().
         """
+        # relativize paths as they go in - this makes it easier to move the root directory around
+        if path := model_attributes.get('path'):
+            if Path(path).is_relative_to(self.app_config.root_path):
+                model_attributes['path'] = str(Path(path).relative_to(self.app_config.root_path))
 
         model_class = MODEL_CLASSES[base_model][model_type]
         model_config = model_class.create_config(**model_attributes)
@@ -689,7 +696,7 @@ class ModelManager(object):
         model_key = self.create_key(model_name, base_model, model_type)
         model_cfg = self.models.get(model_key, None)
         if not model_cfg:
-            raise KeyError(f"Unknown model: {model_key}")
+            raise ModelNotFoundException(f"Unknown model: {model_key}")
         
         old_path = self.app_config.root_path / model_cfg.path
         new_name = new_name or model_name
@@ -700,7 +707,7 @@ class ModelManager(object):
 
         # if this is a model file/directory that we manage ourselves, we need to move it
         if old_path.is_relative_to(self.app_config.models_path):
-            new_path = self.app_config.root_path / 'models' / new_base.value / model_type.value / new_name
+            new_path = self.app_config.root_path / 'models' / BaseModelType(new_base).value / ModelType(model_type).value / new_name
             move(old_path, new_path)
             model_cfg.path = str(new_path.relative_to(self.app_config.root_path))
 
@@ -964,7 +971,7 @@ class ModelManager(object):
         that model.
 
         May return the following exceptions:
-        - KeyError   - one or more of the items to import is not a valid path, repo_id or URL
+        - ModelNotFoundException   - one or more of the items to import is not a valid path, repo_id or URL
         - ValueError - a corresponding model already exists
         '''
         # avoid circular import here

invokeai/backend/model_management/model_probe.py

@@ -12,6 +12,7 @@ from picklescan.scanner import scan_file_path
 from .models import (
     BaseModelType, ModelType, ModelVariantType,
     SchedulerPredictionType, SilenceWarnings,
+    InvalidModelException
 )
 from .models.base import read_checkpoint_meta
 
@@ -38,6 +39,8 @@ class ModelProbe(object):
 
     CLASS2TYPE = {
         'StableDiffusionPipeline' : ModelType.Main,
+        'StableDiffusionXLPipeline' : ModelType.Main,
+        'StableDiffusionXLImg2ImgPipeline' : ModelType.Main,
         'AutoencoderKL' : ModelType.Vae,
         'ControlNetModel' : ModelType.ControlNet,
     }
@@ -59,7 +62,7 @@ class ModelProbe(object):
         elif isinstance(model,(dict,ModelMixin,ConfigMixin)):
             return cls.probe(model_path=None, model=model, prediction_type_helper=prediction_type_helper)
         else:
-            raise ValueError("model parameter {model} is neither a Path, nor a model")
+            raise InvalidModelException("model parameter {model} is neither a Path, nor a model")
 
     @classmethod
     def probe(cls,
@@ -99,9 +102,10 @@ class ModelProbe(object):
                 upcast_attention = (base_type==BaseModelType.StableDiffusion2 \
                                      and prediction_type==SchedulerPredictionType.VPrediction),
                 format = format,
-                image_size = 768 if (base_type==BaseModelType.StableDiffusion2 \
-                                     and prediction_type==SchedulerPredictionType.VPrediction \
-                                     ) else 512,
+                image_size = 1024 if (base_type in {BaseModelType.StableDiffusionXL,BaseModelType.StableDiffusionXLRefiner}) else \
+                              768 if (base_type==BaseModelType.StableDiffusion2 \
+                                     and prediction_type==SchedulerPredictionType.VPrediction ) else \
+                              512
             )
         except Exception:
             raise
@@ -138,7 +142,7 @@ class ModelProbe(object):
             if len(ckpt) < 10 and all(isinstance(v, torch.Tensor) for v in ckpt.values()):
                 return ModelType.TextualInversion
         
-        raise ValueError(f"Unable to determine model type for {model_path}")
+        raise InvalidModelException(f"Unable to determine model type for {model_path}")
 
     @classmethod
     def get_model_type_from_folder(cls, folder_path: Path, model: ModelMixin)->ModelType:
@@ -168,7 +172,7 @@ class ModelProbe(object):
             return type
 
         # give up
-        raise ValueError(f"Unable to determine model type for {folder_path}")
+        raise InvalidModelException(f"Unable to determine model type for {folder_path}")
 
     @classmethod
     def _scan_and_load_checkpoint(cls,model_path: Path)->dict:
@@ -237,7 +241,7 @@ class CheckpointProbeBase(ProbeBase):
         elif in_channels == 4:
             return ModelVariantType.Normal
         else:
-            raise ValueError(f"Cannot determine variant type (in_channels={in_channels}) at {self.checkpoint_path}")
+            raise InvalidModelException(f"Cannot determine variant type (in_channels={in_channels}) at {self.checkpoint_path}")
 
 class PipelineCheckpointProbe(CheckpointProbeBase):
     def get_base_type(self)->BaseModelType:
@@ -248,7 +252,10 @@ class PipelineCheckpointProbe(CheckpointProbeBase):
             return BaseModelType.StableDiffusion1
         if key_name in state_dict and state_dict[key_name].shape[-1] == 1024:
             return BaseModelType.StableDiffusion2
-        raise ValueError("Cannot determine base type")
+        # TODO: Verify that this is correct! Need an XL checkpoint file for this.
+        if key_name in state_dict and state_dict[key_name].shape[-1] == 2048:
+            return BaseModelType.StableDiffusionXL
+        raise InvalidModelException("Cannot determine base type")
 
     def get_scheduler_prediction_type(self)->SchedulerPredictionType:
         type = self.get_base_type()
@@ -329,7 +336,7 @@ class ControlNetCheckpointProbe(CheckpointProbeBase):
                 return BaseModelType.StableDiffusion2
             elif self.checkpoint_path and self.helper:
                 return self.helper(self.checkpoint_path)
-        raise ValueError("Unable to determine base type for {self.checkpoint_path}")
+        raise InvalidModelException("Unable to determine base type for {self.checkpoint_path}")
 
 ########################################################
 # classes for probing folders
@@ -360,8 +367,12 @@ class PipelineFolderProbe(FolderProbeBase):
             return BaseModelType.StableDiffusion1  
         elif unet_conf['cross_attention_dim'] == 1024:
             return BaseModelType.StableDiffusion2
+        elif unet_conf['cross_attention_dim'] == 1280:
+            return BaseModelType.StableDiffusionXLRefiner
+        elif unet_conf['cross_attention_dim'] == 2048:
+            return BaseModelType.StableDiffusionXL
         else:
-            raise ValueError(f'Unknown base model for {self.folder_path}')
+            raise InvalidModelException(f'Unknown base model for {self.folder_path}')
 
     def get_scheduler_prediction_type(self)->SchedulerPredictionType:
         if self.model:
@@ -418,7 +429,7 @@ class ControlNetFolderProbe(FolderProbeBase):
     def get_base_type(self)->BaseModelType:
         config_file = self.folder_path / 'config.json'
         if not config_file.exists():
-            raise ValueError(f"Cannot determine base type for {self.folder_path}")
+            raise InvalidModelException(f"Cannot determine base type for {self.folder_path}")
         with open(config_file,'r') as file:
             config = json.load(file)
         # no obvious way to distinguish between sd2-base and sd2-768
@@ -435,7 +446,7 @@ class LoRAFolderProbe(FolderProbeBase):
                 model_file = base_file
                 break
         if not model_file:
-            raise ValueError('Unknown LoRA format encountered')
+            raise InvalidModelException('Unknown LoRA format encountered')
         return LoRACheckpointProbe(model_file,None).get_base_type()
 
 ############## register probe classes ######

invokeai/backend/model_management/models/__init__.py

@@ -4,6 +4,7 @@ from pydantic import BaseModel
 from typing import Literal, get_origin
 from .base import BaseModelType, ModelType, SubModelType, ModelBase, ModelConfigBase, ModelVariantType, SchedulerPredictionType, ModelError, SilenceWarnings, ModelNotFoundException, InvalidModelException
 from .stable_diffusion import StableDiffusion1Model, StableDiffusion2Model
+from .sdxl import StableDiffusionXLModel
 from .vae import VaeModel
 from .lora import LoRAModel
 from .controlnet import ControlNetModel # TODO:
@@ -24,6 +25,22 @@ MODEL_CLASSES = {
         ModelType.ControlNet: ControlNetModel,
         ModelType.TextualInversion: TextualInversionModel,
     },
+    BaseModelType.StableDiffusionXL: {
+        ModelType.Main: StableDiffusionXLModel,
+        ModelType.Vae: VaeModel,
+        # will not work until support written
+        ModelType.Lora: LoRAModel,
+        ModelType.ControlNet: ControlNetModel,
+        ModelType.TextualInversion: TextualInversionModel,
+    },
+    BaseModelType.StableDiffusionXLRefiner: {
+        ModelType.Main: StableDiffusionXLModel,
+        ModelType.Vae: VaeModel,
+        # will not work until support written
+        ModelType.Lora: LoRAModel,
+        ModelType.ControlNet: ControlNetModel,
+        ModelType.TextualInversion: TextualInversionModel,
+    },
     #BaseModelType.Kandinsky2_1: {
     #    ModelType.Main: Kandinsky2_1Model,
     #    ModelType.MoVQ: MoVQModel,

invokeai/backend/model_management/models/base.py

@@ -24,6 +24,8 @@ class ModelNotFoundException(Exception):
 class BaseModelType(str, Enum):
     StableDiffusion1 = "sd-1"
     StableDiffusion2 = "sd-2"
+    StableDiffusionXL = "sdxl"
+    StableDiffusionXLRefiner = "sdxl-refiner"
     #Kandinsky2_1 = "kandinsky-2.1"
 
 class ModelType(str, Enum):
@@ -36,7 +38,9 @@ class ModelType(str, Enum):
 class SubModelType(str, Enum):
     UNet = "unet"
     TextEncoder = "text_encoder"
+    TextEncoder2 = "text_encoder_2"
     Tokenizer = "tokenizer"
+    Tokenizer2 = "tokenizer_2"
     Vae = "vae"
     Scheduler = "scheduler"
     SafetyChecker = "safety_checker"

invokeai/backend/model_management/models/sdxl.py

@@ -0,0 +1,114 @@
+import os
+import json
+from enum import Enum
+from pydantic import Field
+from typing import Literal, Optional
+from .base import (
+    ModelConfigBase,
+    BaseModelType,
+    ModelType,
+    ModelVariantType,
+    DiffusersModel,
+    read_checkpoint_meta,
+    classproperty,
+)
+from omegaconf import OmegaConf
+
+class StableDiffusionXLModelFormat(str, Enum):
+    Checkpoint = "checkpoint"
+    Diffusers = "diffusers"
+    
+class StableDiffusionXLModel(DiffusersModel):
+
+    # TODO: check that configs overwriten properly
+    class DiffusersConfig(ModelConfigBase):
+        model_format: Literal[StableDiffusionXLModelFormat.Diffusers]
+        vae: Optional[str] = Field(None)
+        variant: ModelVariantType
+
+    class CheckpointConfig(ModelConfigBase):
+        model_format: Literal[StableDiffusionXLModelFormat.Checkpoint]
+        vae: Optional[str] = Field(None)
+        config: str
+        variant: ModelVariantType
+
+    def __init__(self, model_path: str, base_model: BaseModelType, model_type: ModelType):
+        assert base_model in {BaseModelType.StableDiffusionXL, BaseModelType.StableDiffusionXLRefiner}
+        assert model_type == ModelType.Main
+        super().__init__(
+            model_path=model_path,
+            base_model=BaseModelType.StableDiffusionXL,
+            model_type=ModelType.Main,
+        )
+
+    @classmethod
+    def probe_config(cls, path: str, **kwargs):
+        model_format = cls.detect_format(path)
+        ckpt_config_path = kwargs.get("config", None)
+        if model_format == StableDiffusionXLModelFormat.Checkpoint:
+            if ckpt_config_path:
+                ckpt_config = OmegaConf.load(ckpt_config_path)
+                ckpt_config["model"]["params"]["unet_config"]["params"]["in_channels"]
+
+            else:
+                checkpoint = read_checkpoint_meta(path)
+                checkpoint = checkpoint.get('state_dict', checkpoint)
+                in_channels = checkpoint["model.diffusion_model.input_blocks.0.0.weight"].shape[1]
+
+        elif model_format == StableDiffusionXLModelFormat.Diffusers:
+            unet_config_path = os.path.join(path, "unet", "config.json")
+            if os.path.exists(unet_config_path):
+                with open(unet_config_path, "r") as f:
+                    unet_config = json.loads(f.read())
+                in_channels = unet_config['in_channels']
+
+            else:
+                raise Exception("Not supported stable diffusion diffusers format(possibly onnx?)")
+
+        else:
+            raise NotImplementedError(f"Unknown stable diffusion 2.* format: {model_format}")
+
+        if in_channels == 9:
+            variant = ModelVariantType.Inpaint
+        elif in_channels == 5:
+            variant = ModelVariantType.Depth
+        elif in_channels == 4:
+            variant = ModelVariantType.Normal
+        else:
+            raise Exception("Unkown stable diffusion 2.* model format")
+
+        if ckpt_config_path is None:
+            # TO DO: implement picking
+            pass
+        
+        return cls.create_config(
+            path=path,
+            model_format=model_format,
+
+            config=ckpt_config_path,
+            variant=variant,
+        )
+
+    @classproperty
+    def save_to_config(cls) -> bool:
+        return True
+
+    @classmethod
+    def detect_format(cls, model_path: str):
+        if os.path.isdir(model_path):
+            return StableDiffusionXLModelFormat.Diffusers
+        else:
+            return StableDiffusionXLModelFormat.Checkpoint
+
+    @classmethod
+    def convert_if_required(
+        cls,
+        model_path: str,
+        output_path: str,
+        config: ModelConfigBase,
+        base_model: BaseModelType,
+    ) -> str:
+        if isinstance(config, cls.CheckpointConfig):
+            raise NotImplementedError('conversion of SDXL checkpoint models to diffusers format is not yet supported')
+        else:
+            return model_path

invokeai/backend/model_management/models/stable_diffusion.py

@@ -5,14 +5,11 @@ from pydantic import Field
 from pathlib import Path
 from typing import Literal, Optional, Union
 from .base import (
-    ModelBase,
     ModelConfigBase,
     BaseModelType,
     ModelType,
-    SubModelType,
     ModelVariantType,
     DiffusersModel,
-    SchedulerPredictionType,
     SilenceWarnings,
     read_checkpoint_meta,
     classproperty,
@@ -248,6 +245,12 @@ def _select_ckpt_config(version: BaseModelType, variant: ModelVariantType):
             ModelVariantType.Normal: "v2-inference-v.yaml", # best guess, as we can't differentiate with base(512)
             ModelVariantType.Inpaint: "v2-inpainting-inference.yaml",
             ModelVariantType.Depth: "v2-midas-inference.yaml",
+        },
+        # note that these .yaml files don't yet exist!
+        BaseModelType.StableDiffusionXL: {
+            ModelVariantType.Normal: "xl-inference-v.yaml",
+            ModelVariantType.Inpaint: "xl-inpainting-inference.yaml",
+            ModelVariantType.Depth: "xl-midas-inference.yaml",
         }
     }
 
@@ -263,6 +266,7 @@ def _select_ckpt_config(version: BaseModelType, variant: ModelVariantType):
 
 
 # TODO: rework
+# Note that convert_ckpt_to_diffuses does not currently support conversion of SDXL models
 def _convert_ckpt_and_cache(
     version: BaseModelType,
     model_config: Union[StableDiffusion1Model.CheckpointConfig, StableDiffusion2Model.CheckpointConfig],

invokeai/backend/model_management/models/textual_inversion.py

@@ -68,7 +68,7 @@ class TextualInversionModel(ModelBase):
                 return None # diffusers-ti
 
         if os.path.isfile(path):
-            if any([path.endswith(f".{ext}") for ext in ["safetensors", "ckpt", "pt"]]):
+            if any([path.endswith(f".{ext}") for ext in ["safetensors", "ckpt", "pt", "bin"]]):
                 return None
 
         raise InvalidModelException(f"Not a valid model: {path}")

invokeai/backend/model_management/models/vae.py

@@ -16,6 +16,7 @@ from .base import (
     calc_model_size_by_data,
     classproperty,
     InvalidModelException,
+    ModelNotFoundException,
 )
 from invokeai.app.services.config import InvokeAIAppConfig
 from diffusers.utils import is_safetensors_available

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -221,7 +221,7 @@ class ControlNetData:
     control_mode: str = Field(default="balanced")
 
 
-@dataclass(frozen=True)
+@dataclass
 class ConditioningData:
     unconditioned_embeddings: torch.Tensor
     text_embeddings: torch.Tensor
@@ -422,7 +422,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         noise: torch.Tensor,
         callback: Callable[[PipelineIntermediateState], None] = None,
         run_id=None,
-        **kwargs,
     ) -> InvokeAIStableDiffusionPipelineOutput:
         r"""
         Function invoked when calling the pipeline for generation.
@@ -443,7 +442,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             noise=noise,
             run_id=run_id,
             callback=callback,
-            **kwargs,
         )
         # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
         torch.cuda.empty_cache()
@@ -469,7 +467,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         run_id=None,
         callback: Callable[[PipelineIntermediateState], None] = None,
         control_data: List[ControlNetData] = None,
-        **kwargs,
     ) -> tuple[torch.Tensor, Optional[AttentionMapSaver]]:
         if self.scheduler.config.get("cpu_only", False):
             scheduler_device = torch.device('cpu')
@@ -487,11 +484,11 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             timesteps,
             conditioning_data,
             noise=noise,
-            additional_guidance=additional_guidance,
             run_id=run_id,
-            callback=callback,
+            additional_guidance=additional_guidance,
             control_data=control_data,
-            **kwargs,
+
+            callback=callback,
         )
         return result.latents, result.attention_map_saver
 
@@ -505,7 +502,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         run_id: str = None,
         additional_guidance: List[Callable] = None,
         control_data: List[ControlNetData] = None,
-        **kwargs,
     ):
         self._adjust_memory_efficient_attention(latents)
         if run_id is None:
@@ -546,7 +542,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
                     total_step_count=len(timesteps),
                     additional_guidance=additional_guidance,
                     control_data=control_data,
-                    **kwargs,
                 )
                 latents = step_output.prev_sample
 
@@ -588,7 +583,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         total_step_count: int,
         additional_guidance: List[Callable] = None,
         control_data: List[ControlNetData] = None,
-        **kwargs,
     ):
         # invokeai_diffuser has batched timesteps, but diffusers schedulers expect a single value
         timestep = t[0]
@@ -632,9 +626,12 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
 
                     if cfg_injection:  # only applying ControlNet to conditional instead of in unconditioned
                         encoder_hidden_states = conditioning_data.text_embeddings
+                        encoder_attention_mask = None
                     else:
-                        encoder_hidden_states = torch.cat([conditioning_data.unconditioned_embeddings,
-                                                           conditioning_data.text_embeddings])
+                        encoder_hidden_states, encoder_attention_mask = self.invokeai_diffuser._concat_conditionings_for_batch(
+                            conditioning_data.unconditioned_embeddings,
+                            conditioning_data.text_embeddings,
+                        )
                     if isinstance(control_datum.weight, list):
                         # if controlnet has multiple weights, use the weight for the current step
                         controlnet_weight = control_datum.weight[step_index]
@@ -649,6 +646,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
                         encoder_hidden_states=encoder_hidden_states,
                         controlnet_cond=control_datum.image_tensor,
                         conditioning_scale=controlnet_weight, # controlnet specific, NOT the guidance scale
+                        encoder_attention_mask=encoder_attention_mask,
                         guess_mode=soft_injection, # this is still called guess_mode in diffusers ControlNetModel
                         return_dict=False,
                     )

invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py

@@ -237,11 +237,7 @@ class InvokeAIDiffuserComponent:
             )
         return latents
 
-    # methods below are called from do_diffusion_step and should be considered private to this class.
-
-    def _apply_standard_conditioning(self, x, sigma, unconditioning, conditioning, **kwargs):
-        # fast batched path
-
+    def _concat_conditionings_for_batch(self, unconditioning, conditioning):
         def _pad_conditioning(cond, target_len, encoder_attention_mask):
             conditioning_attention_mask = torch.ones((cond.shape[0], cond.shape[1]), device=cond.device, dtype=cond.dtype)
 
@@ -266,16 +262,24 @@ class InvokeAIDiffuserComponent:
             
             return cond, encoder_attention_mask
 
-        x_twice = torch.cat([x] * 2)
-        sigma_twice = torch.cat([sigma] * 2)
-
         encoder_attention_mask = None
         if unconditioning.shape[1] != conditioning.shape[1]:
             max_len = max(unconditioning.shape[1], conditioning.shape[1])
             unconditioning, encoder_attention_mask = _pad_conditioning(unconditioning, max_len, encoder_attention_mask)
             conditioning, encoder_attention_mask = _pad_conditioning(conditioning, max_len, encoder_attention_mask)
 
-        both_conditionings = torch.cat([unconditioning, conditioning])
+        return torch.cat([unconditioning, conditioning]), encoder_attention_mask
+
+    # methods below are called from do_diffusion_step and should be considered private to this class.
+
+    def _apply_standard_conditioning(self, x, sigma, unconditioning, conditioning, **kwargs):
+        # fast batched path
+        x_twice = torch.cat([x] * 2)
+        sigma_twice = torch.cat([sigma] * 2)
+
+        both_conditionings, encoder_attention_mask = self._concat_conditionings_for_batch(
+            unconditioning, conditioning
+        )
         both_results = self.model_forward_callback(
             x_twice, sigma_twice, both_conditionings,
             encoder_attention_mask=encoder_attention_mask,
@@ -293,8 +297,32 @@ class InvokeAIDiffuserComponent:
         **kwargs,
     ):
         # low-memory sequential path
-        unconditioned_next_x = self.model_forward_callback(x, sigma, unconditioning, **kwargs)
-        conditioned_next_x = self.model_forward_callback(x, sigma, conditioning, **kwargs)
+        uncond_down_block, cond_down_block = None, None
+        down_block_additional_residuals = kwargs.pop("down_block_additional_residuals", None)
+        if down_block_additional_residuals is not None:
+            uncond_down_block, cond_down_block = [], []
+            for down_block in down_block_additional_residuals:
+                _uncond_down, _cond_down = down_block.chunk(2)
+                uncond_down_block.append(_uncond_down)
+                cond_down_block.append(_cond_down)
+
+        uncond_mid_block, cond_mid_block = None, None
+        mid_block_additional_residual = kwargs.pop("mid_block_additional_residual", None)
+        if mid_block_additional_residual is not None:
+            uncond_mid_block, cond_mid_block = mid_block_additional_residual.chunk(2)
+
+        unconditioned_next_x = self.model_forward_callback(
+            x, sigma, unconditioning,
+            down_block_additional_residuals=uncond_down_block,
+            mid_block_additional_residual=uncond_mid_block,
+            **kwargs,
+        )
+        conditioned_next_x = self.model_forward_callback(
+            x, sigma, conditioning,
+            down_block_additional_residuals=cond_down_block,
+            mid_block_additional_residual=cond_mid_block,
+            **kwargs,
+        )
         return unconditioned_next_x, conditioned_next_x
 
     # TODO: looks unused
@@ -328,6 +356,20 @@ class InvokeAIDiffuserComponent:
     ):
         context: Context = self.cross_attention_control_context
 
+        uncond_down_block, cond_down_block = None, None
+        down_block_additional_residuals = kwargs.pop("down_block_additional_residuals", None)
+        if down_block_additional_residuals is not None:
+            uncond_down_block, cond_down_block = [], []
+            for down_block in down_block_additional_residuals:
+                _uncond_down, _cond_down = down_block.chunk(2)
+                uncond_down_block.append(_uncond_down)
+                cond_down_block.append(_cond_down)
+
+        uncond_mid_block, cond_mid_block = None, None
+        mid_block_additional_residual = kwargs.pop("mid_block_additional_residual", None)
+        if mid_block_additional_residual is not None:
+            uncond_mid_block, cond_mid_block = mid_block_additional_residual.chunk(2)
+
         cross_attn_processor_context = SwapCrossAttnContext(
             modified_text_embeddings=context.arguments.edited_conditioning,
             index_map=context.cross_attention_index_map,
@@ -340,6 +382,8 @@ class InvokeAIDiffuserComponent:
             sigma,
             unconditioning,
             {"swap_cross_attn_context": cross_attn_processor_context},
+            down_block_additional_residuals=uncond_down_block,
+            mid_block_additional_residual=uncond_mid_block,
             **kwargs,
         )
 
@@ -352,6 +396,8 @@ class InvokeAIDiffuserComponent:
             sigma,
             conditioning,
             {"swap_cross_attn_context": cross_attn_processor_context},
+            down_block_additional_residuals=cond_down_block,
+            mid_block_additional_residual=cond_mid_block,
             **kwargs,
         )
         return unconditioned_next_x, conditioned_next_x

invokeai/backend/util/hotfixes.py

@@ -0,0 +1,634 @@
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import torch
+from torch import nn
+
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.models.attention_processor import AttentionProcessor, AttnProcessor
+from diffusers.models.embeddings import TimestepEmbedding, Timesteps
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.models.unet_2d_blocks import (
+    CrossAttnDownBlock2D,
+    DownBlock2D,
+    UNetMidBlock2DCrossAttn,
+    get_down_block,
+)
+from diffusers.models.unet_2d_condition import UNet2DConditionModel
+
+import diffusers
+from diffusers.models.controlnet import ControlNetConditioningEmbedding, ControlNetOutput, zero_module
+
+# Modified ControlNetModel with encoder_attention_mask argument added
+
+class ControlNetModel(ModelMixin, ConfigMixin):
+    """
+    A ControlNet model.
+
+    Args:
+        in_channels (`int`, defaults to 4):
+            The number of channels in the input sample.
+        flip_sin_to_cos (`bool`, defaults to `True`):
+            Whether to flip the sin to cos in the time embedding.
+        freq_shift (`int`, defaults to 0):
+            The frequency shift to apply to the time embedding.
+        down_block_types (`tuple[str]`, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
+            The tuple of downsample blocks to use.
+        only_cross_attention (`Union[bool, Tuple[bool]]`, defaults to `False`):
+        block_out_channels (`tuple[int]`, defaults to `(320, 640, 1280, 1280)`):
+            The tuple of output channels for each block.
+        layers_per_block (`int`, defaults to 2):
+            The number of layers per block.
+        downsample_padding (`int`, defaults to 1):
+            The padding to use for the downsampling convolution.
+        mid_block_scale_factor (`float`, defaults to 1):
+            The scale factor to use for the mid block.
+        act_fn (`str`, defaults to "silu"):
+            The activation function to use.
+        norm_num_groups (`int`, *optional*, defaults to 32):
+            The number of groups to use for the normalization. If None, normalization and activation layers is skipped
+            in post-processing.
+        norm_eps (`float`, defaults to 1e-5):
+            The epsilon to use for the normalization.
+        cross_attention_dim (`int`, defaults to 1280):
+            The dimension of the cross attention features.
+        attention_head_dim (`Union[int, Tuple[int]]`, defaults to 8):
+            The dimension of the attention heads.
+        use_linear_projection (`bool`, defaults to `False`):
+        class_embed_type (`str`, *optional*, defaults to `None`):
+            The type of class embedding to use which is ultimately summed with the time embeddings. Choose from None,
+            `"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`.
+        num_class_embeds (`int`, *optional*, defaults to 0):
+            Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing
+            class conditioning with `class_embed_type` equal to `None`.
+        upcast_attention (`bool`, defaults to `False`):
+        resnet_time_scale_shift (`str`, defaults to `"default"`):
+            Time scale shift config for ResNet blocks (see `ResnetBlock2D`). Choose from `default` or `scale_shift`.
+        projection_class_embeddings_input_dim (`int`, *optional*, defaults to `None`):
+            The dimension of the `class_labels` input when `class_embed_type="projection"`. Required when
+            `class_embed_type="projection"`.
+        controlnet_conditioning_channel_order (`str`, defaults to `"rgb"`):
+            The channel order of conditional image. Will convert to `rgb` if it's `bgr`.
+        conditioning_embedding_out_channels (`tuple[int]`, *optional*, defaults to `(16, 32, 96, 256)`):
+            The tuple of output channel for each block in the `conditioning_embedding` layer.
+        global_pool_conditions (`bool`, defaults to `False`):
+    """
+
+    _supports_gradient_checkpointing = True
+
+    @register_to_config
+    def __init__(
+        self,
+        in_channels: int = 4,
+        conditioning_channels: int = 3,
+        flip_sin_to_cos: bool = True,
+        freq_shift: int = 0,
+        down_block_types: Tuple[str] = (
+            "CrossAttnDownBlock2D",
+            "CrossAttnDownBlock2D",
+            "CrossAttnDownBlock2D",
+            "DownBlock2D",
+        ),
+        only_cross_attention: Union[bool, Tuple[bool]] = False,
+        block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
+        layers_per_block: int = 2,
+        downsample_padding: int = 1,
+        mid_block_scale_factor: float = 1,
+        act_fn: str = "silu",
+        norm_num_groups: Optional[int] = 32,
+        norm_eps: float = 1e-5,
+        cross_attention_dim: int = 1280,
+        attention_head_dim: Union[int, Tuple[int]] = 8,
+        num_attention_heads: Optional[Union[int, Tuple[int]]] = None,
+        use_linear_projection: bool = False,
+        class_embed_type: Optional[str] = None,
+        num_class_embeds: Optional[int] = None,
+        upcast_attention: bool = False,
+        resnet_time_scale_shift: str = "default",
+        projection_class_embeddings_input_dim: Optional[int] = None,
+        controlnet_conditioning_channel_order: str = "rgb",
+        conditioning_embedding_out_channels: Optional[Tuple[int]] = (16, 32, 96, 256),
+        global_pool_conditions: bool = False,
+    ):
+        super().__init__()
+
+        # If `num_attention_heads` is not defined (which is the case for most models)
+        # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
+        # The reason for this behavior is to correct for incorrectly named variables that were introduced
+        # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
+        # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
+        # which is why we correct for the naming here.
+        num_attention_heads = num_attention_heads or attention_head_dim
+
+        # Check inputs
+        if len(block_out_channels) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
+            )
+
+        # input
+        conv_in_kernel = 3
+        conv_in_padding = (conv_in_kernel - 1) // 2
+        self.conv_in = nn.Conv2d(
+            in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding
+        )
+
+        # time
+        time_embed_dim = block_out_channels[0] * 4
+
+        self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
+        timestep_input_dim = block_out_channels[0]
+
+        self.time_embedding = TimestepEmbedding(
+            timestep_input_dim,
+            time_embed_dim,
+            act_fn=act_fn,
+        )
+
+        # class embedding
+        if class_embed_type is None and num_class_embeds is not None:
+            self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
+        elif class_embed_type == "timestep":
+            self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
+        elif class_embed_type == "identity":
+            self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
+        elif class_embed_type == "projection":
+            if projection_class_embeddings_input_dim is None:
+                raise ValueError(
+                    "`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set"
+                )
+            # The projection `class_embed_type` is the same as the timestep `class_embed_type` except
+            # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings
+            # 2. it projects from an arbitrary input dimension.
+            #
+            # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.
+            # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.
+            # As a result, `TimestepEmbedding` can be passed arbitrary vectors.
+            self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
+        else:
+            self.class_embedding = None
+
+        # control net conditioning embedding
+        self.controlnet_cond_embedding = ControlNetConditioningEmbedding(
+            conditioning_embedding_channels=block_out_channels[0],
+            block_out_channels=conditioning_embedding_out_channels,
+            conditioning_channels=conditioning_channels,
+        )
+
+        self.down_blocks = nn.ModuleList([])
+        self.controlnet_down_blocks = nn.ModuleList([])
+
+        if isinstance(only_cross_attention, bool):
+            only_cross_attention = [only_cross_attention] * len(down_block_types)
+
+        if isinstance(attention_head_dim, int):
+            attention_head_dim = (attention_head_dim,) * len(down_block_types)
+
+        if isinstance(num_attention_heads, int):
+            num_attention_heads = (num_attention_heads,) * len(down_block_types)
+
+        # down
+        output_channel = block_out_channels[0]
+
+        controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
+        controlnet_block = zero_module(controlnet_block)
+        self.controlnet_down_blocks.append(controlnet_block)
+
+        for i, down_block_type in enumerate(down_block_types):
+            input_channel = output_channel
+            output_channel = block_out_channels[i]
+            is_final_block = i == len(block_out_channels) - 1
+
+            down_block = get_down_block(
+                down_block_type,
+                num_layers=layers_per_block,
+                in_channels=input_channel,
+                out_channels=output_channel,
+                temb_channels=time_embed_dim,
+                add_downsample=not is_final_block,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                resnet_groups=norm_num_groups,
+                cross_attention_dim=cross_attention_dim,
+                num_attention_heads=num_attention_heads[i],
+                attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
+                downsample_padding=downsample_padding,
+                use_linear_projection=use_linear_projection,
+                only_cross_attention=only_cross_attention[i],
+                upcast_attention=upcast_attention,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+            )
+            self.down_blocks.append(down_block)
+
+            for _ in range(layers_per_block):
+                controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
+                controlnet_block = zero_module(controlnet_block)
+                self.controlnet_down_blocks.append(controlnet_block)
+
+            if not is_final_block:
+                controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
+                controlnet_block = zero_module(controlnet_block)
+                self.controlnet_down_blocks.append(controlnet_block)
+
+        # mid
+        mid_block_channel = block_out_channels[-1]
+
+        controlnet_block = nn.Conv2d(mid_block_channel, mid_block_channel, kernel_size=1)
+        controlnet_block = zero_module(controlnet_block)
+        self.controlnet_mid_block = controlnet_block
+
+        self.mid_block = UNetMidBlock2DCrossAttn(
+            in_channels=mid_block_channel,
+            temb_channels=time_embed_dim,
+            resnet_eps=norm_eps,
+            resnet_act_fn=act_fn,
+            output_scale_factor=mid_block_scale_factor,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+            cross_attention_dim=cross_attention_dim,
+            num_attention_heads=num_attention_heads[-1],
+            resnet_groups=norm_num_groups,
+            use_linear_projection=use_linear_projection,
+            upcast_attention=upcast_attention,
+        )
+
+    @classmethod
+    def from_unet(
+        cls,
+        unet: UNet2DConditionModel,
+        controlnet_conditioning_channel_order: str = "rgb",
+        conditioning_embedding_out_channels: Optional[Tuple[int]] = (16, 32, 96, 256),
+        load_weights_from_unet: bool = True,
+    ):
+        r"""
+        Instantiate a [`ControlNetModel`] from [`UNet2DConditionModel`].
+
+        Parameters:
+            unet (`UNet2DConditionModel`):
+                The UNet model weights to copy to the [`ControlNetModel`]. All configuration options are also copied
+                where applicable.
+        """
+        controlnet = cls(
+            in_channels=unet.config.in_channels,
+            flip_sin_to_cos=unet.config.flip_sin_to_cos,
+            freq_shift=unet.config.freq_shift,
+            down_block_types=unet.config.down_block_types,
+            only_cross_attention=unet.config.only_cross_attention,
+            block_out_channels=unet.config.block_out_channels,
+            layers_per_block=unet.config.layers_per_block,
+            downsample_padding=unet.config.downsample_padding,
+            mid_block_scale_factor=unet.config.mid_block_scale_factor,
+            act_fn=unet.config.act_fn,
+            norm_num_groups=unet.config.norm_num_groups,
+            norm_eps=unet.config.norm_eps,
+            cross_attention_dim=unet.config.cross_attention_dim,
+            attention_head_dim=unet.config.attention_head_dim,
+            num_attention_heads=unet.config.num_attention_heads,
+            use_linear_projection=unet.config.use_linear_projection,
+            class_embed_type=unet.config.class_embed_type,
+            num_class_embeds=unet.config.num_class_embeds,
+            upcast_attention=unet.config.upcast_attention,
+            resnet_time_scale_shift=unet.config.resnet_time_scale_shift,
+            projection_class_embeddings_input_dim=unet.config.projection_class_embeddings_input_dim,
+            controlnet_conditioning_channel_order=controlnet_conditioning_channel_order,
+            conditioning_embedding_out_channels=conditioning_embedding_out_channels,
+        )
+
+        if load_weights_from_unet:
+            controlnet.conv_in.load_state_dict(unet.conv_in.state_dict())
+            controlnet.time_proj.load_state_dict(unet.time_proj.state_dict())
+            controlnet.time_embedding.load_state_dict(unet.time_embedding.state_dict())
+
+            if controlnet.class_embedding:
+                controlnet.class_embedding.load_state_dict(unet.class_embedding.state_dict())
+
+            controlnet.down_blocks.load_state_dict(unet.down_blocks.state_dict())
+            controlnet.mid_block.load_state_dict(unet.mid_block.state_dict())
+
+        return controlnet
+
+    @property
+    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
+    def attn_processors(self) -> Dict[str, AttentionProcessor]:
+        r"""
+        Returns:
+            `dict` of attention processors: A dictionary containing all attention processors used in the model with
+            indexed by its weight name.
+        """
+        # set recursively
+        processors = {}
+
+        def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
+            if hasattr(module, "set_processor"):
+                processors[f"{name}.processor"] = module.processor
+
+            for sub_name, child in module.named_children():
+                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
+
+            return processors
+
+        for name, module in self.named_children():
+            fn_recursive_add_processors(name, module, processors)
+
+        return processors
+
+    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attn_processor
+    def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
+        r"""
+        Sets the attention processor to use to compute attention.
+
+        Parameters:
+            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
+                The instantiated processor class or a dictionary of processor classes that will be set as the processor
+                for **all** `Attention` layers.
+
+                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
+                processor. This is strongly recommended when setting trainable attention processors.
+
+        """
+        count = len(self.attn_processors.keys())
+
+        if isinstance(processor, dict) and len(processor) != count:
+            raise ValueError(
+                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
+                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
+            )
+
+        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
+            if hasattr(module, "set_processor"):
+                if not isinstance(processor, dict):
+                    module.set_processor(processor)
+                else:
+                    module.set_processor(processor.pop(f"{name}.processor"))
+
+            for sub_name, child in module.named_children():
+                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
+
+        for name, module in self.named_children():
+            fn_recursive_attn_processor(name, module, processor)
+
+    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor
+    def set_default_attn_processor(self):
+        """
+        Disables custom attention processors and sets the default attention implementation.
+        """
+        self.set_attn_processor(AttnProcessor())
+
+    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attention_slice
+    def set_attention_slice(self, slice_size):
+        r"""
+        Enable sliced attention computation.
+
+        When this option is enabled, the attention module splits the input tensor in slices to compute attention in
+        several steps. This is useful for saving some memory in exchange for a small decrease in speed.
+
+        Args:
+            slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
+                When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If
+                `"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is
+                provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
+                must be a multiple of `slice_size`.
+        """
+        sliceable_head_dims = []
+
+        def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):
+            if hasattr(module, "set_attention_slice"):
+                sliceable_head_dims.append(module.sliceable_head_dim)
+
+            for child in module.children():
+                fn_recursive_retrieve_sliceable_dims(child)
+
+        # retrieve number of attention layers
+        for module in self.children():
+            fn_recursive_retrieve_sliceable_dims(module)
+
+        num_sliceable_layers = len(sliceable_head_dims)
+
+        if slice_size == "auto":
+            # half the attention head size is usually a good trade-off between
+            # speed and memory
+            slice_size = [dim // 2 for dim in sliceable_head_dims]
+        elif slice_size == "max":
+            # make smallest slice possible
+            slice_size = num_sliceable_layers * [1]
+
+        slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
+
+        if len(slice_size) != len(sliceable_head_dims):
+            raise ValueError(
+                f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
+                f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
+            )
+
+        for i in range(len(slice_size)):
+            size = slice_size[i]
+            dim = sliceable_head_dims[i]
+            if size is not None and size > dim:
+                raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
+
+        # Recursively walk through all the children.
+        # Any children which exposes the set_attention_slice method
+        # gets the message
+        def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
+            if hasattr(module, "set_attention_slice"):
+                module.set_attention_slice(slice_size.pop())
+
+            for child in module.children():
+                fn_recursive_set_attention_slice(child, slice_size)
+
+        reversed_slice_size = list(reversed(slice_size))
+        for module in self.children():
+            fn_recursive_set_attention_slice(module, reversed_slice_size)
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, (CrossAttnDownBlock2D, DownBlock2D)):
+            module.gradient_checkpointing = value
+
+    def forward(
+        self,
+        sample: torch.FloatTensor,
+        timestep: Union[torch.Tensor, float, int],
+        encoder_hidden_states: torch.Tensor,
+        controlnet_cond: torch.FloatTensor,
+        conditioning_scale: float = 1.0,
+        class_labels: Optional[torch.Tensor] = None,
+        timestep_cond: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        encoder_attention_mask: Optional[torch.Tensor] = None,
+        guess_mode: bool = False,
+        return_dict: bool = True,
+    ) -> Union[ControlNetOutput, Tuple]:
+        """
+        The [`ControlNetModel`] forward method.
+
+        Args:
+            sample (`torch.FloatTensor`):
+                The noisy input tensor.
+            timestep (`Union[torch.Tensor, float, int]`):
+                The number of timesteps to denoise an input.
+            encoder_hidden_states (`torch.Tensor`):
+                The encoder hidden states.
+            controlnet_cond (`torch.FloatTensor`):
+                The conditional input tensor of shape `(batch_size, sequence_length, hidden_size)`.
+            conditioning_scale (`float`, defaults to `1.0`):
+                The scale factor for ControlNet outputs.
+            class_labels (`torch.Tensor`, *optional*, defaults to `None`):
+                Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.
+            timestep_cond (`torch.Tensor`, *optional*, defaults to `None`):
+            attention_mask (`torch.Tensor`, *optional*, defaults to `None`):
+            cross_attention_kwargs(`dict[str]`, *optional*, defaults to `None`):
+                A kwargs dictionary that if specified is passed along to the `AttnProcessor`.
+            encoder_attention_mask (`torch.Tensor`):
+                A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If
+                `True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias,
+                which adds large negative values to the attention scores corresponding to "discard" tokens.
+            guess_mode (`bool`, defaults to `False`):
+                In this mode, the ControlNet encoder tries its best to recognize the input content of the input even if
+                you remove all prompts. A `guidance_scale` between 3.0 and 5.0 is recommended.
+            return_dict (`bool`, defaults to `True`):
+                Whether or not to return a [`~models.controlnet.ControlNetOutput`] instead of a plain tuple.
+
+        Returns:
+            [`~models.controlnet.ControlNetOutput`] **or** `tuple`:
+                If `return_dict` is `True`, a [`~models.controlnet.ControlNetOutput`] is returned, otherwise a tuple is
+                returned where the first element is the sample tensor.
+        """
+        # check channel order
+        channel_order = self.config.controlnet_conditioning_channel_order
+
+        if channel_order == "rgb":
+            # in rgb order by default
+            ...
+        elif channel_order == "bgr":
+            controlnet_cond = torch.flip(controlnet_cond, dims=[1])
+        else:
+            raise ValueError(f"unknown `controlnet_conditioning_channel_order`: {channel_order}")
+
+        # prepare attention_mask
+        if attention_mask is not None:
+            attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
+            attention_mask = attention_mask.unsqueeze(1)
+
+        # convert encoder_attention_mask to a bias the same way we do for attention_mask
+        if encoder_attention_mask is not None:
+            encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0
+            encoder_attention_mask = encoder_attention_mask.unsqueeze(1)
+
+        # 1. time
+        timesteps = timestep
+        if not torch.is_tensor(timesteps):
+            # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
+            # This would be a good case for the `match` statement (Python 3.10+)
+            is_mps = sample.device.type == "mps"
+            if isinstance(timestep, float):
+                dtype = torch.float32 if is_mps else torch.float64
+            else:
+                dtype = torch.int32 if is_mps else torch.int64
+            timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
+        elif len(timesteps.shape) == 0:
+            timesteps = timesteps[None].to(sample.device)
+
+        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+        timesteps = timesteps.expand(sample.shape[0])
+
+        t_emb = self.time_proj(timesteps)
+
+        # timesteps does not contain any weights and will always return f32 tensors
+        # but time_embedding might actually be running in fp16. so we need to cast here.
+        # there might be better ways to encapsulate this.
+        t_emb = t_emb.to(dtype=sample.dtype)
+
+        emb = self.time_embedding(t_emb, timestep_cond)
+
+        if self.class_embedding is not None:
+            if class_labels is None:
+                raise ValueError("class_labels should be provided when num_class_embeds > 0")
+
+            if self.config.class_embed_type == "timestep":
+                class_labels = self.time_proj(class_labels)
+
+            class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)
+            emb = emb + class_emb
+
+        # 2. pre-process
+        sample = self.conv_in(sample)
+
+        controlnet_cond = self.controlnet_cond_embedding(controlnet_cond)
+
+        sample = sample + controlnet_cond
+
+        # 3. down
+        down_block_res_samples = (sample,)
+        for downsample_block in self.down_blocks:
+            if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
+                sample, res_samples = downsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    encoder_hidden_states=encoder_hidden_states,
+                    attention_mask=attention_mask,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    encoder_attention_mask=encoder_attention_mask,
+                )
+            else:
+                sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
+
+            down_block_res_samples += res_samples
+
+        # 4. mid
+        if self.mid_block is not None:
+            sample = self.mid_block(
+                sample,
+                emb,
+                encoder_hidden_states=encoder_hidden_states,
+                attention_mask=attention_mask,
+                cross_attention_kwargs=cross_attention_kwargs,
+                encoder_attention_mask=encoder_attention_mask,
+            )
+
+        # 5. Control net blocks
+
+        controlnet_down_block_res_samples = ()
+
+        for down_block_res_sample, controlnet_block in zip(down_block_res_samples, self.controlnet_down_blocks):
+            down_block_res_sample = controlnet_block(down_block_res_sample)
+            controlnet_down_block_res_samples = controlnet_down_block_res_samples + (down_block_res_sample,)
+
+        down_block_res_samples = controlnet_down_block_res_samples
+
+        mid_block_res_sample = self.controlnet_mid_block(sample)
+
+        # 6. scaling
+        if guess_mode and not self.config.global_pool_conditions:
+            scales = torch.logspace(-1, 0, len(down_block_res_samples) + 1, device=sample.device)  # 0.1 to 1.0
+
+            scales = scales * conditioning_scale
+            down_block_res_samples = [sample * scale for sample, scale in zip(down_block_res_samples, scales)]
+            mid_block_res_sample = mid_block_res_sample * scales[-1]  # last one
+        else:
+            down_block_res_samples = [sample * conditioning_scale for sample in down_block_res_samples]
+            mid_block_res_sample = mid_block_res_sample * conditioning_scale
+
+        if self.config.global_pool_conditions:
+            down_block_res_samples = [
+                torch.mean(sample, dim=(2, 3), keepdim=True) for sample in down_block_res_samples
+            ]
+            mid_block_res_sample = torch.mean(mid_block_res_sample, dim=(2, 3), keepdim=True)
+
+        if not return_dict:
+            return (down_block_res_samples, mid_block_res_sample)
+
+        return ControlNetOutput(
+            down_block_res_samples=down_block_res_samples, mid_block_res_sample=mid_block_res_sample
+        )
+
+diffusers.ControlNetModel = ControlNetModel
+diffusers.models.controlnet.ControlNetModel = ControlNetModel

invokeai/configs/INITIAL_MODELS.yaml

@@ -1,4 +1,10 @@
 # This file predefines a few models that the user may want to install.
+sd-1/main/stable-diffusion-xdl-base:
+   description: Stable Diffusion XL base model - NOT YET RELEASED!! (70 GB)
+   repo_id: stabilityai/stable-diffusion-xl-base
+sd-1/main/stable-diffusion-xdl-refiner:
+   description: Stable Diffusion XL refiner model - NOT YET RELEASED!! (60 GB)
+   repo_id: stabilityai/stable-diffusion-xl-refiner
 sd-1/main/stable-diffusion-v1-5:
    description: Stable Diffusion version 1.5 diffusers model (4.27 GB)
    repo_id: runwayml/stable-diffusion-v1-5

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

@@ -399,6 +399,8 @@
         "deleteModel": "Delete Model",
         "deleteConfig": "Delete Config",
         "deleteMsg1": "Are you sure you want to delete this model from InvokeAI?",
+        "modelDeleted": "Model Deleted",
+        "modelDeleteFailed": "Failed to delete model",
         "deleteMsg2": "This WILL delete the model from disk if it is in the InvokeAI root folder. If you are using a custom location, then the model WILL NOT be deleted from disk.",
         "formMessageDiffusersModelLocation": "Diffusers Model Location",
         "formMessageDiffusersModelLocationDesc": "Please enter at least one.",
@@ -408,11 +410,13 @@
         "convertToDiffusers": "Convert To Diffusers",
         "convertToDiffusersHelpText1": "This model will be converted to the 🧨 Diffusers format.",
         "convertToDiffusersHelpText2": "This process will replace your Model Manager entry with the Diffusers version of the same model.",
-        "convertToDiffusersHelpText3": "Your checkpoint file on the disk will NOT be deleted or modified in anyway. You can add your checkpoint to the Model Manager again if you want to.",
+        "convertToDiffusersHelpText3": "Your checkpoint file on disk WILL be deleted if it is in InvokeAI root folder. If it is in a custom location, then it WILL NOT be deleted.",
         "convertToDiffusersHelpText4": "This is a one time process only. It might take around 30s-60s depending on the specifications of your computer.",
         "convertToDiffusersHelpText5": "Please make sure you have enough disk space. Models generally vary between 2GB-7GB in size.",
         "convertToDiffusersHelpText6": "Do you wish to convert this model?",
         "convertToDiffusersSaveLocation": "Save Location",
+        "noCustomLocationProvided": "No Custom Location Provided",
+        "convertingModelBegin": "Converting Model. Please wait.",
         "v1": "v1",
         "v2_base": "v2 (512px)",
         "v2_768": "v2 (768px)",
@@ -450,7 +454,8 @@
         "none": "none",
         "addDifference": "Add Difference",
         "pickModelType": "Pick Model Type",
-        "selectModel": "Select Model"
+        "selectModel": "Select Model",
+        "importModels": "Import Models"
     },
     "parameters": {
         "general": "General",
@@ -572,6 +577,7 @@
         "uploadFailedInvalidUploadDesc": "Must be single PNG or JPEG image",
         "downloadImageStarted": "Image Download Started",
         "imageCopied": "Image Copied",
+        "problemCopyingImage": "Unable to Copy Image",
         "imageLinkCopied": "Image Link Copied",
         "problemCopyingImageLink": "Unable to Copy Image Link",
         "imageNotLoaded": "No Image Loaded",
@@ -688,6 +694,15 @@
         "reloadSchema": "Reload Schema",
         "saveNodes": "Save Nodes",
         "loadNodes": "Load Nodes",
-        "clearNodes": "Clear Nodes"
+        "clearNodes": "Clear Nodes",
+        "zoomInNodes": "Zoom In",
+        "zoomOutNodes": "Zoom Out",
+        "fitViewportNodes": "Fit View",
+        "hideGraphNodes": "Hide Graph Overlay",
+        "showGraphNodes": "Show Graph Overlay",
+        "hideLegendNodes": "Hide Field Type Legend",
+        "showLegendNodes": "Show Field Type Legend",
+        "hideMinimapnodes": "Hide MiniMap",
+        "showMinimapnodes": "Show MiniMap"
     }
 }

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/index.ts

@@ -88,6 +88,8 @@ import { addUserInvokedCanvasListener } from './listeners/userInvokedCanvas';
 import { addUserInvokedImageToImageListener } from './listeners/userInvokedImageToImage';
 import { addUserInvokedNodesListener } from './listeners/userInvokedNodes';
 import { addUserInvokedTextToImageListener } from './listeners/userInvokedTextToImage';
+import { addModelLoadStartedEventListener } from './listeners/socketio/socketModelLoadStarted';
+import { addModelLoadCompletedEventListener } from './listeners/socketio/socketModelLoadCompleted';
 
 export const listenerMiddleware = createListenerMiddleware();
 
@@ -177,6 +179,8 @@ addSocketConnectedListener();
 addSocketDisconnectedListener();
 addSocketSubscribedListener();
 addSocketUnsubscribedListener();
+addModelLoadStartedEventListener();
+addModelLoadCompletedEventListener();
 
 // Session Created
 addSessionCreatedPendingListener();

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

@@ -0,0 +1,28 @@
+import { log } from 'app/logging/useLogger';
+import {
+  appSocketModelLoadCompleted,
+  socketModelLoadCompleted,
+} from 'services/events/actions';
+import { startAppListening } from '../..';
+
+const moduleLog = log.child({ namespace: 'socketio' });
+
+export const addModelLoadCompletedEventListener = () => {
+  startAppListening({
+    actionCreator: socketModelLoadCompleted,
+    effect: (action, { dispatch, getState }) => {
+      const { model_name, model_type, submodel } = action.payload.data;
+
+      let modelString = `${model_type} model: ${model_name}`;
+
+      if (submodel) {
+        modelString = modelString.concat(`, submodel: ${submodel}`);
+      }
+
+      moduleLog.debug(action.payload, `Model load completed (${modelString})`);
+
+      // pass along the socket event as an application action
+      dispatch(appSocketModelLoadCompleted(action.payload));
+    },
+  });
+};

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

@@ -0,0 +1,28 @@
+import { log } from 'app/logging/useLogger';
+import {
+  appSocketModelLoadStarted,
+  socketModelLoadStarted,
+} from 'services/events/actions';
+import { startAppListening } from '../..';
+
+const moduleLog = log.child({ namespace: 'socketio' });
+
+export const addModelLoadStartedEventListener = () => {
+  startAppListening({
+    actionCreator: socketModelLoadStarted,
+    effect: (action, { dispatch, getState }) => {
+      const { model_name, model_type, submodel } = action.payload.data;
+
+      let modelString = `${model_type} model: ${model_name}`;
+
+      if (submodel) {
+        modelString = modelString.concat(`, submodel: ${submodel}`);
+      }
+
+      moduleLog.debug(action.payload, `Model load started (${modelString})`);
+
+      // pass along the socket event as an application action
+      dispatch(appSocketModelLoadStarted(action.payload));
+    },
+  });
+};

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

@@ -21,6 +21,7 @@ import generationReducer from 'features/parameters/store/generationSlice';
 import postprocessingReducer from 'features/parameters/store/postprocessingSlice';
 import configReducer from 'features/system/store/configSlice';
 import systemReducer from 'features/system/store/systemSlice';
+import modelmanagerReducer from 'features/ui/components/tabs/ModelManager/store/modelManagerSlice';
 import hotkeysReducer from 'features/ui/store/hotkeysSlice';
 import uiReducer from 'features/ui/store/uiSlice';
 
@@ -49,6 +50,7 @@ const allReducers = {
   dynamicPrompts: dynamicPromptsReducer,
   imageDeletion: imageDeletionReducer,
   lora: loraReducer,
+  modelmanager: modelmanagerReducer,
   [api.reducerPath]: api.reducer,
 };
 
@@ -67,6 +69,7 @@ const rememberedKeys: (keyof typeof allReducers)[] = [
   'controlNet',
   'dynamicPrompts',
   'lora',
+  'modelmanager',
 ];
 
 export const store = configureStore({

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

@@ -21,6 +21,7 @@ import { ImageDTO } from 'services/api/types';
 import { mode } from 'theme/util/mode';
 import IAIDraggable from './IAIDraggable';
 import IAIDroppable from './IAIDroppable';
+import ImageContextMenu from 'features/gallery/components/ImageContextMenu/ImageContextMenu';
 
 type IAIDndImageProps = {
   imageDTO: ImageDTO | undefined;
@@ -96,119 +97,124 @@ const IAIDndImage = (props: IAIDndImageProps) => {
       };
 
   return (
-    <Flex
-      sx={{
-        width: 'full',
-        height: 'full',
-        alignItems: 'center',
-        justifyContent: 'center',
-        position: 'relative',
-        minW: minSize ? minSize : undefined,
-        minH: minSize ? minSize : undefined,
-        userSelect: 'none',
-        cursor: isDragDisabled || !imageDTO ? 'default' : 'pointer',
-      }}
-    >
-      {imageDTO && (
+    <ImageContextMenu imageDTO={imageDTO}>
+      {(ref) => (
         <Flex
+          ref={ref}
           sx={{
-            w: 'full',
-            h: 'full',
-            position: fitContainer ? 'absolute' : 'relative',
+            width: 'full',
+            height: 'full',
             alignItems: 'center',
             justifyContent: 'center',
+            position: 'relative',
+            minW: minSize ? minSize : undefined,
+            minH: minSize ? minSize : undefined,
+            userSelect: 'none',
+            cursor: isDragDisabled || !imageDTO ? 'default' : 'pointer',
           }}
         >
-          <Image
-            src={thumbnail ? imageDTO.thumbnail_url : imageDTO.image_url}
-            fallbackStrategy="beforeLoadOrError"
-            // If we fall back to thumbnail, it feels much snappier than the skeleton...
-            fallbackSrc={imageDTO.thumbnail_url}
-            // fallback={<IAILoadingImageFallback image={imageDTO} />}
-            width={imageDTO.width}
-            height={imageDTO.height}
-            onError={onError}
-            draggable={false}
-            sx={{
-              objectFit: 'contain',
-              maxW: 'full',
-              maxH: 'full',
-              borderRadius: 'base',
-              shadow: isSelected ? 'selected.light' : undefined,
-              _dark: { shadow: isSelected ? 'selected.dark' : undefined },
-              ...imageSx,
-            }}
-          />
-          {withMetadataOverlay && <ImageMetadataOverlay image={imageDTO} />}
-        </Flex>
-      )}
-      {!imageDTO && !isUploadDisabled && (
-        <>
-          <Flex
-            sx={{
-              minH: minSize,
-              w: 'full',
-              h: 'full',
-              alignItems: 'center',
-              justifyContent: 'center',
-              borderRadius: 'base',
-              transitionProperty: 'common',
-              transitionDuration: '0.1s',
-              color: mode('base.500', 'base.500')(colorMode),
-              ...uploadButtonStyles,
-            }}
-            {...getUploadButtonProps()}
-          >
-            <input {...getUploadInputProps()} />
-            <Icon
-              as={FaUpload}
+          {imageDTO && (
+            <Flex
               sx={{
-                boxSize: 16,
+                w: 'full',
+                h: 'full',
+                position: fitContainer ? 'absolute' : 'relative',
+                alignItems: 'center',
+                justifyContent: 'center',
+              }}
+            >
+              <Image
+                src={thumbnail ? imageDTO.thumbnail_url : imageDTO.image_url}
+                fallbackStrategy="beforeLoadOrError"
+                // If we fall back to thumbnail, it feels much snappier than the skeleton...
+                fallbackSrc={imageDTO.thumbnail_url}
+                // fallback={<IAILoadingImageFallback image={imageDTO} />}
+                width={imageDTO.width}
+                height={imageDTO.height}
+                onError={onError}
+                draggable={false}
+                sx={{
+                  objectFit: 'contain',
+                  maxW: 'full',
+                  maxH: 'full',
+                  borderRadius: 'base',
+                  shadow: isSelected ? 'selected.light' : undefined,
+                  _dark: { shadow: isSelected ? 'selected.dark' : undefined },
+                  ...imageSx,
+                }}
+              />
+              {withMetadataOverlay && <ImageMetadataOverlay image={imageDTO} />}
+            </Flex>
+          )}
+          {!imageDTO && !isUploadDisabled && (
+            <>
+              <Flex
+                sx={{
+                  minH: minSize,
+                  w: 'full',
+                  h: 'full',
+                  alignItems: 'center',
+                  justifyContent: 'center',
+                  borderRadius: 'base',
+                  transitionProperty: 'common',
+                  transitionDuration: '0.1s',
+                  color: mode('base.500', 'base.500')(colorMode),
+                  ...uploadButtonStyles,
+                }}
+                {...getUploadButtonProps()}
+              >
+                <input {...getUploadInputProps()} />
+                <Icon
+                  as={FaUpload}
+                  sx={{
+                    boxSize: 16,
+                  }}
+                />
+              </Flex>
+            </>
+          )}
+          {!imageDTO && isUploadDisabled && noContentFallback}
+          {!isDropDisabled && (
+            <IAIDroppable
+              data={droppableData}
+              disabled={isDropDisabled}
+              dropLabel={dropLabel}
+            />
+          )}
+          {imageDTO && !isDragDisabled && (
+            <IAIDraggable
+              data={draggableData}
+              disabled={isDragDisabled || !imageDTO}
+              onClick={onClick}
+            />
+          )}
+          {onClickReset && withResetIcon && imageDTO && (
+            <IAIIconButton
+              onClick={onClickReset}
+              aria-label={resetTooltip}
+              tooltip={resetTooltip}
+              icon={resetIcon}
+              size="sm"
+              variant="link"
+              sx={{
+                position: 'absolute',
+                top: 1,
+                insetInlineEnd: 1,
+                p: 0,
+                minW: 0,
+                svg: {
+                  transitionProperty: 'common',
+                  transitionDuration: 'normal',
+                  fill: 'base.100',
+                  _hover: { fill: 'base.50' },
+                  filter: resetIconShadow,
+                },
               }}
             />
-          </Flex>
-        </>
+          )}
+        </Flex>
       )}
-      {!imageDTO && isUploadDisabled && noContentFallback}
-      {!isDropDisabled && (
-        <IAIDroppable
-          data={droppableData}
-          disabled={isDropDisabled}
-          dropLabel={dropLabel}
-        />
-      )}
-      {imageDTO && !isDragDisabled && (
-        <IAIDraggable
-          data={draggableData}
-          disabled={isDragDisabled || !imageDTO}
-          onClick={onClick}
-        />
-      )}
-      {onClickReset && withResetIcon && imageDTO && (
-        <IAIIconButton
-          onClick={onClickReset}
-          aria-label={resetTooltip}
-          tooltip={resetTooltip}
-          icon={resetIcon}
-          size="sm"
-          variant="link"
-          sx={{
-            position: 'absolute',
-            top: 1,
-            insetInlineEnd: 1,
-            p: 0,
-            minW: 0,
-            svg: {
-              transitionProperty: 'common',
-              transitionDuration: 'normal',
-              fill: 'base.100',
-              _hover: { fill: 'base.50' },
-              filter: resetIconShadow,
-            },
-          }}
-        />
-      )}
-    </Flex>
+    </ImageContextMenu>
   );
 };
 

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

@@ -8,19 +8,34 @@ import {
 import { useAppDispatch } from 'app/store/storeHooks';
 import { stopPastePropagation } from 'common/util/stopPastePropagation';
 import { shiftKeyPressed } from 'features/ui/store/hotkeysSlice';
-import { ChangeEvent, KeyboardEvent, memo, useCallback } from 'react';
+import {
+  CSSProperties,
+  ChangeEvent,
+  KeyboardEvent,
+  memo,
+  useCallback,
+} from 'react';
 
 interface IAIInputProps extends InputProps {
   label?: string;
+  labelPos?: 'top' | 'side';
   value?: string;
   size?: string;
   onChange?: (e: ChangeEvent<HTMLInputElement>) => void;
   formControlProps?: Omit<FormControlProps, 'isInvalid' | 'isDisabled'>;
 }
 
+const labelPosVerticalStyle: CSSProperties = {
+  display: 'flex',
+  flexDirection: 'row',
+  alignItems: 'center',
+  gap: 10,
+};
+
 const IAIInput = (props: IAIInputProps) => {
   const {
     label = '',
+    labelPos = 'top',
     isDisabled = false,
     isInvalid,
     formControlProps,
@@ -51,6 +66,7 @@ const IAIInput = (props: IAIInputProps) => {
       isInvalid={isInvalid}
       isDisabled={isDisabled}
       {...formControlProps}
+      style={labelPos === 'side' ? labelPosVerticalStyle : undefined}
     >
       {label !== '' && <FormLabel>{label}</FormLabel>}
       <Input

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

@@ -36,6 +36,7 @@ export default function IAIMantineTextInput(props: IAIMantineTextInputProps) {
         label: {
           color: mode(base700, base300)(colorMode),
           fontWeight: 'normal',
+          marginBottom: 4,
         },
       })}
       {...rest}

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

@@ -9,14 +9,14 @@ export type IAISelectDataType = {
   tooltip?: string;
 };
 
-type IAISelectProps = Omit<SelectProps, 'label'> & {
+export type IAISelectProps = Omit<SelectProps, 'label'> & {
   tooltip?: string;
   inputRef?: RefObject<HTMLInputElement>;
   label?: string;
 };
 
 const IAIMantineSelect = (props: IAISelectProps) => {
-  const { tooltip, inputRef, label, disabled, ...rest } = props;
+  const { tooltip, inputRef, label, disabled, required, ...rest } = props;
 
   const styles = useMantineSelectStyles();
 
@@ -25,7 +25,7 @@ const IAIMantineSelect = (props: IAISelectProps) => {
       <Select
         label={
           label ? (
-            <FormControl isDisabled={disabled}>
+            <FormControl isRequired={required} isDisabled={disabled}>
               <FormLabel>{label}</FormLabel>
             </FormControl>
           ) : undefined

invokeai/frontend/web/src/common/util/getTimestamp.ts

@@ -3,4 +3,5 @@ import dateFormat from 'dateformat';
 /**
  * Get a `now` timestamp with 1s precision, formatted as ISO datetime.
  */
-export const getTimestamp = () => dateFormat(new Date(), 'isoDateTime');
+export const getTimestamp = () =>
+  dateFormat(new Date(), `yyyy-mm-dd'T'HH:MM:ss:lo`);

invokeai/frontend/web/src/features/canvas/components/IAICanvasToolbar/IAICanvasToolbar.tsx

@@ -48,6 +48,7 @@ import IAICanvasRedoButton from './IAICanvasRedoButton';
 import IAICanvasSettingsButtonPopover from './IAICanvasSettingsButtonPopover';
 import IAICanvasToolChooserOptions from './IAICanvasToolChooserOptions';
 import IAICanvasUndoButton from './IAICanvasUndoButton';
+import { useCopyImageToClipboard } from 'features/ui/hooks/useCopyImageToClipboard';
 
 export const selector = createSelector(
   [systemSelector, canvasSelector, isStagingSelector],
@@ -79,6 +80,7 @@ const IAICanvasToolbar = () => {
   const canvasBaseLayer = getCanvasBaseLayer();
 
   const { t } = useTranslation();
+  const { isClipboardAPIAvailable } = useCopyImageToClipboard();
 
   const { openUploader } = useImageUploader();
 
@@ -136,10 +138,10 @@ const IAICanvasToolbar = () => {
       handleCopyImageToClipboard();
     },
     {
-      enabled: () => !isStaging,
+      enabled: () => !isStaging && isClipboardAPIAvailable,
       preventDefault: true,
     },
-    [canvasBaseLayer, isProcessing]
+    [canvasBaseLayer, isProcessing, isClipboardAPIAvailable]
   );
 
   useHotkeys(
@@ -189,6 +191,9 @@ const IAICanvasToolbar = () => {
   };
 
   const handleCopyImageToClipboard = () => {
+    if (!isClipboardAPIAvailable) {
+      return;
+    }
     dispatch(canvasCopiedToClipboard());
   };
 
@@ -256,13 +261,15 @@ const IAICanvasToolbar = () => {
           onClick={handleSaveToGallery}
           isDisabled={isStaging}
         />
-        <IAIIconButton
-          aria-label={`${t('unifiedCanvas.copyToClipboard')} (Cmd/Ctrl+C)`}
-          tooltip={`${t('unifiedCanvas.copyToClipboard')} (Cmd/Ctrl+C)`}
-          icon={<FaCopy />}
-          onClick={handleCopyImageToClipboard}
-          isDisabled={isStaging}
-        />
+        {isClipboardAPIAvailable && (
+          <IAIIconButton
+            aria-label={`${t('unifiedCanvas.copyToClipboard')} (Cmd/Ctrl+C)`}
+            tooltip={`${t('unifiedCanvas.copyToClipboard')} (Cmd/Ctrl+C)`}
+            icon={<FaCopy />}
+            onClick={handleCopyImageToClipboard}
+            isDisabled={isStaging}
+          />
+        )}
         <IAIIconButton
           aria-label={`${t('unifiedCanvas.downloadAsImage')} (Shift+D)`}
           tooltip={`${t('unifiedCanvas.downloadAsImage')} (Shift+D)`}

invokeai/frontend/web/src/features/gallery/components/CurrentImage/CurrentImageButtons.tsx

@@ -1,7 +1,16 @@
 import { createSelector } from '@reduxjs/toolkit';
 import { isEqual } from 'lodash-es';
 
-import { ButtonGroup, Flex, FlexProps, Link } from '@chakra-ui/react';
+import {
+  ButtonGroup,
+  Flex,
+  FlexProps,
+  Link,
+  Menu,
+  MenuButton,
+  MenuItem,
+  MenuList,
+} from '@chakra-ui/react';
 // import { runESRGAN, runFacetool } from 'app/socketio/actions';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import IAIButton from 'common/components/IAIButton';
@@ -20,6 +29,7 @@ import UpscaleSettings from 'features/parameters/components/Parameters/Upscale/U
 import { useRecallParameters } from 'features/parameters/hooks/useRecallParameters';
 import { initialImageSelected } from 'features/parameters/store/actions';
 import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus';
+import { useCopyImageToClipboard } from 'features/ui/hooks/useCopyImageToClipboard';
 import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
 import {
   setActiveTab,
@@ -48,6 +58,8 @@ import {
 } from 'services/api/endpoints/images';
 import { useDebounce } from 'use-debounce';
 import { sentImageToCanvas, sentImageToImg2Img } from '../../store/actions';
+import { menuListMotionProps } from 'theme/components/menu';
+import SingleSelectionMenuItems from '../ImageContextMenu/SingleSelectionMenuItems';
 
 const currentImageButtonsSelector = createSelector(
   [stateSelector, activeTabNameSelector],
@@ -120,6 +132,9 @@ const CurrentImageButtons = (props: CurrentImageButtonsProps) => {
   const toaster = useAppToaster();
   const { t } = useTranslation();
 
+  const { isClipboardAPIAvailable, copyImageToClipboard } =
+    useCopyImageToClipboard();
+
   const { recallBothPrompts, recallSeed, recallAllParameters } =
     useRecallParameters();
 
@@ -128,7 +143,7 @@ const CurrentImageButtons = (props: CurrentImageButtonsProps) => {
     500
   );
 
-  const { currentData: image, isFetching } = useGetImageDTOQuery(
+  const { currentData: imageDTO, isFetching } = useGetImageDTOQuery(
     lastSelectedImage ?? skipToken
   );
 
@@ -142,15 +157,15 @@ const CurrentImageButtons = (props: CurrentImageButtonsProps) => {
 
   const handleCopyImageLink = useCallback(() => {
     const getImageUrl = () => {
-      if (!image) {
+      if (!imageDTO) {
         return;
       }
 
-      if (image.image_url.startsWith('http')) {
-        return image.image_url;
+      if (imageDTO.image_url.startsWith('http')) {
+        return imageDTO.image_url;
       }
 
-      return window.location.toString() + image.image_url;
+      return window.location.toString() + imageDTO.image_url;
     };
 
     const url = getImageUrl();
@@ -174,7 +189,7 @@ const CurrentImageButtons = (props: CurrentImageButtonsProps) => {
         isClosable: true,
       });
     });
-  }, [toaster, t, image]);
+  }, [toaster, t, imageDTO]);
 
   const handleClickUseAllParameters = useCallback(() => {
     recallAllParameters(metadata);
@@ -192,31 +207,31 @@ const CurrentImageButtons = (props: CurrentImageButtonsProps) => {
     recallSeed(metadata?.seed);
   }, [metadata?.seed, recallSeed]);
 
-  useHotkeys('s', handleUseSeed, [image]);
+  useHotkeys('s', handleUseSeed, [imageDTO]);
 
   const handleUsePrompt = useCallback(() => {
     recallBothPrompts(metadata?.positive_prompt, metadata?.negative_prompt);
   }, [metadata?.negative_prompt, metadata?.positive_prompt, recallBothPrompts]);
 
-  useHotkeys('p', handleUsePrompt, [image]);
+  useHotkeys('p', handleUsePrompt, [imageDTO]);
 
   const handleSendToImageToImage = useCallback(() => {
     dispatch(sentImageToImg2Img());
-    dispatch(initialImageSelected(image));
-  }, [dispatch, image]);
+    dispatch(initialImageSelected(imageDTO));
+  }, [dispatch, imageDTO]);
 
-  useHotkeys('shift+i', handleSendToImageToImage, [image]);
+  useHotkeys('shift+i', handleSendToImageToImage, [imageDTO]);
 
   const handleClickUpscale = useCallback(() => {
     // selectedImage && dispatch(runESRGAN(selectedImage));
   }, []);
 
   const handleDelete = useCallback(() => {
-    if (!image) {
+    if (!imageDTO) {
       return;
     }
-    dispatch(imageToDeleteSelected(image));
-  }, [dispatch, image]);
+    dispatch(imageToDeleteSelected(imageDTO));
+  }, [dispatch, imageDTO]);
 
   useHotkeys(
     'Shift+U',
@@ -236,7 +251,7 @@ const CurrentImageButtons = (props: CurrentImageButtonsProps) => {
     },
     [
       isUpscalingEnabled,
-      image,
+      imageDTO,
       isESRGANAvailable,
       shouldDisableToolbarButtons,
       isConnected,
@@ -268,7 +283,7 @@ const CurrentImageButtons = (props: CurrentImageButtonsProps) => {
 
     [
       isFaceRestoreEnabled,
-      image,
+      imageDTO,
       isGFPGANAvailable,
       shouldDisableToolbarButtons,
       isConnected,
@@ -283,10 +298,10 @@ const CurrentImageButtons = (props: CurrentImageButtonsProps) => {
   );
 
   const handleSendToCanvas = useCallback(() => {
-    if (!image) return;
+    if (!imageDTO) return;
     dispatch(sentImageToCanvas());
 
-    dispatch(setInitialCanvasImage(image));
+    dispatch(setInitialCanvasImage(imageDTO));
     dispatch(requestCanvasRescale());
 
     if (activeTabName !== 'unifiedCanvas') {
@@ -299,12 +314,12 @@ const CurrentImageButtons = (props: CurrentImageButtonsProps) => {
       duration: 2500,
       isClosable: true,
     });
-  }, [image, dispatch, activeTabName, toaster, t]);
+  }, [imageDTO, dispatch, activeTabName, toaster, t]);
 
   useHotkeys(
     'i',
     () => {
-      if (image) {
+      if (imageDTO) {
         handleClickShowImageDetails();
       } else {
         toaster({
@@ -315,13 +330,20 @@ const CurrentImageButtons = (props: CurrentImageButtonsProps) => {
         });
       }
     },
-    [image, shouldShowImageDetails, toaster]
+    [imageDTO, shouldShowImageDetails, toaster]
   );
 
   const handleClickProgressImagesToggle = useCallback(() => {
     dispatch(setShouldShowProgressInViewer(!shouldShowProgressInViewer));
   }, [dispatch, shouldShowProgressInViewer]);
 
+  const handleCopyImage = useCallback(() => {
+    if (!imageDTO) {
+      return;
+    }
+    copyImageToClipboard(imageDTO.image_url);
+  }, [copyImageToClipboard, imageDTO]);
+
   return (
     <>
       <Flex
@@ -334,63 +356,18 @@ const CurrentImageButtons = (props: CurrentImageButtonsProps) => {
         {...props}
       >
         <ButtonGroup isAttached={true} isDisabled={shouldDisableToolbarButtons}>
-          <IAIPopover
-            triggerComponent={
-              <IAIIconButton
-                aria-label={`${t('parameters.sendTo')}...`}
-                tooltip={`${t('parameters.sendTo')}...`}
-                isDisabled={!image}
-                icon={<FaShareAlt />}
-              />
-            }
-          >
-            <Flex
-              sx={{
-                flexDirection: 'column',
-                rowGap: 2,
-              }}
-            >
-              <IAIButton
-                size="sm"
-                onClick={handleSendToImageToImage}
-                leftIcon={<FaShare />}
-                id="send-to-img2img"
-              >
-                {t('parameters.sendToImg2Img')}
-              </IAIButton>
-              {isCanvasEnabled && (
-                <IAIButton
-                  size="sm"
-                  onClick={handleSendToCanvas}
-                  leftIcon={<FaShare />}
-                  id="send-to-canvas"
-                >
-                  {t('parameters.sendToUnifiedCanvas')}
-                </IAIButton>
-              )}
-
-              {/* <IAIButton
-                size="sm"
-                onClick={handleCopyImage}
-                leftIcon={<FaCopy />}
-              >
-                {t('parameters.copyImage')}
-              </IAIButton> */}
-              <IAIButton
-                size="sm"
-                onClick={handleCopyImageLink}
-                leftIcon={<FaCopy />}
-              >
-                {t('parameters.copyImageToLink')}
-              </IAIButton>
-
-              <Link download={true} href={image?.image_url} target="_blank">
-                <IAIButton leftIcon={<FaDownload />} size="sm" w="100%">
-                  {t('parameters.downloadImage')}
-                </IAIButton>
-              </Link>
-            </Flex>
-          </IAIPopover>
+          <Menu>
+            <MenuButton
+              as={IAIIconButton}
+              aria-label={`${t('parameters.sendTo')}...`}
+              tooltip={`${t('parameters.sendTo')}...`}
+              isDisabled={!imageDTO}
+              icon={<FaShareAlt />}
+            />
+            <MenuList motionProps={menuListMotionProps}>
+              {imageDTO && <SingleSelectionMenuItems imageDTO={imageDTO} />}
+            </MenuList>
+          </Menu>
         </ButtonGroup>
 
         <ButtonGroup isAttached={true} isDisabled={shouldDisableToolbarButtons}>
@@ -443,7 +420,7 @@ const CurrentImageButtons = (props: CurrentImageButtonsProps) => {
                   <IAIButton
                     isDisabled={
                       !isGFPGANAvailable ||
-                      !image ||
+                      !imageDTO ||
                       !(isConnected && !isProcessing) ||
                       !facetoolStrength
                     }
@@ -474,7 +451,7 @@ const CurrentImageButtons = (props: CurrentImageButtonsProps) => {
                   <IAIButton
                     isDisabled={
                       !isESRGANAvailable ||
-                      !image ||
+                      !imageDTO ||
                       !(isConnected && !isProcessing) ||
                       !upscalingLevel
                     }

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

@@ -4,13 +4,14 @@ import { stateSelector } from 'app/store/store';
 import { useAppSelector } from 'app/store/storeHooks';
 import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
 import { ContextMenu, ContextMenuProps } from 'chakra-ui-contextmenu';
-import { memo, useMemo } from 'react';
+import { MouseEvent, memo, useCallback, useMemo } from 'react';
 import { ImageDTO } from 'services/api/types';
+import { menuListMotionProps } from 'theme/components/menu';
 import MultipleSelectionMenuItems from './MultipleSelectionMenuItems';
 import SingleSelectionMenuItems from './SingleSelectionMenuItems';
 
 type Props = {
-  imageDTO: ImageDTO;
+  imageDTO: ImageDTO | undefined;
   children: ContextMenuProps<HTMLDivElement>['children'];
 };
 
@@ -31,18 +32,32 @@ const ImageContextMenu = ({ imageDTO, children }: Props) => {
 
   const { selectionCount } = useAppSelector(selector);
 
+  const handleContextMenu = useCallback((e: MouseEvent<HTMLDivElement>) => {
+    e.preventDefault();
+  }, []);
+
   return (
     <ContextMenu<HTMLDivElement>
       menuProps={{ size: 'sm', isLazy: true }}
-      renderMenu={() => (
-        <MenuList sx={{ visibility: 'visible !important' }}>
-          {selectionCount === 1 ? (
-            <SingleSelectionMenuItems imageDTO={imageDTO} />
-          ) : (
-            <MultipleSelectionMenuItems />
-          )}
-        </MenuList>
-      )}
+      menuButtonProps={{
+        bg: 'transparent',
+        _hover: { bg: 'transparent' },
+      }}
+      renderMenu={() =>
+        imageDTO ? (
+          <MenuList
+            sx={{ visibility: 'visible !important' }}
+            motionProps={menuListMotionProps}
+            onContextMenu={handleContextMenu}
+          >
+            {selectionCount === 1 ? (
+              <SingleSelectionMenuItems imageDTO={imageDTO} />
+            ) : (
+              <MultipleSelectionMenuItems />
+            )}
+          </MenuList>
+        ) : null
+      }
     >
       {children}
     </ContextMenu>

invokeai/frontend/web/src/features/gallery/components/ImageContextMenu/SingleSelectionMenuItems.tsx

@@ -1,5 +1,4 @@
-import { ExternalLinkIcon } from '@chakra-ui/icons';
-import { MenuItem } from '@chakra-ui/react';
+import { Link, MenuItem } from '@chakra-ui/react';
 import { createSelector } from '@reduxjs/toolkit';
 import { useAppToaster } from 'app/components/Toaster';
 import { stateSelector } from 'app/store/store';
@@ -14,11 +13,21 @@ import { imageToDeleteSelected } from 'features/imageDeletion/store/imageDeletio
 import { useRecallParameters } from 'features/parameters/hooks/useRecallParameters';
 import { initialImageSelected } from 'features/parameters/store/actions';
 import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus';
+import { useCopyImageToClipboard } from 'features/ui/hooks/useCopyImageToClipboard';
 import { setActiveTab } from 'features/ui/store/uiSlice';
 import { memo, useCallback, useContext, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
-import { FaFolder, FaShare, FaTrash } from 'react-icons/fa';
-import { IoArrowUndoCircleOutline } from 'react-icons/io5';
+import {
+  FaAsterisk,
+  FaCopy,
+  FaDownload,
+  FaExternalLinkAlt,
+  FaFolder,
+  FaQuoteRight,
+  FaSeedling,
+  FaShare,
+  FaTrash,
+} from 'react-icons/fa';
 import { useRemoveImageFromBoardMutation } from 'services/api/endpoints/boardImages';
 import { useGetImageMetadataQuery } from 'services/api/endpoints/images';
 import { ImageDTO } from 'services/api/types';
@@ -61,6 +70,9 @@ const SingleSelectionMenuItems = (props: SingleSelectionMenuItemsProps) => {
 
   const { currentData } = useGetImageMetadataQuery(imageDTO.image_name);
 
+  const { isClipboardAPIAvailable, copyImageToClipboard } =
+    useCopyImageToClipboard();
+
   const metadata = currentData?.metadata;
 
   const handleDelete = useCallback(() => {
@@ -130,13 +142,27 @@ const SingleSelectionMenuItems = (props: SingleSelectionMenuItemsProps) => {
     dispatch(imagesAddedToBatch([imageDTO.image_name]));
   }, [dispatch, imageDTO.image_name]);
 
+  const handleCopyImage = useCallback(() => {
+    copyImageToClipboard(imageDTO.image_url);
+  }, [copyImageToClipboard, imageDTO.image_url]);
+
   return (
     <>
-      <MenuItem icon={<ExternalLinkIcon />} onClickCapture={handleOpenInNewTab}>
-        {t('common.openInNewTab')}
-      </MenuItem>
+      <Link href={imageDTO.image_url} target="_blank">
+        <MenuItem
+          icon={<FaExternalLinkAlt />}
+          onClickCapture={handleOpenInNewTab}
+        >
+          {t('common.openInNewTab')}
+        </MenuItem>
+      </Link>
+      {isClipboardAPIAvailable && (
+        <MenuItem icon={<FaCopy />} onClickCapture={handleCopyImage}>
+          {t('parameters.copyImage')}
+        </MenuItem>
+      )}
       <MenuItem
-        icon={<IoArrowUndoCircleOutline />}
+        icon={<FaQuoteRight />}
         onClickCapture={handleRecallPrompt}
         isDisabled={
           metadata?.positive_prompt === undefined &&
@@ -147,14 +173,14 @@ const SingleSelectionMenuItems = (props: SingleSelectionMenuItemsProps) => {
       </MenuItem>
 
       <MenuItem
-        icon={<IoArrowUndoCircleOutline />}
+        icon={<FaSeedling />}
         onClickCapture={handleRecallSeed}
         isDisabled={metadata?.seed === undefined}
       >
         {t('parameters.useSeed')}
       </MenuItem>
       <MenuItem
-        icon={<IoArrowUndoCircleOutline />}
+        icon={<FaAsterisk />}
         onClickCapture={handleUseAllParameters}
         isDisabled={!metadata}
       >
@@ -193,6 +219,11 @@ const SingleSelectionMenuItems = (props: SingleSelectionMenuItemsProps) => {
           Remove from Board
         </MenuItem>
       )}
+      <Link download={true} href={imageDTO.image_url} target="_blank">
+        <MenuItem icon={<FaDownload />} w="100%">
+          {t('parameters.downloadImage')}
+        </MenuItem>
+      </Link>
       <MenuItem
         sx={{ color: 'error.600', _dark: { color: 'error.300' } }}
         icon={<FaTrash />}

invokeai/frontend/web/src/features/gallery/components/ImageGrid/GalleryImageGrid.tsx

@@ -16,14 +16,13 @@ import {
   ASSETS_CATEGORIES,
   IMAGE_CATEGORIES,
   IMAGE_LIMIT,
-  selectImagesAll,
 } from 'features/gallery//store/gallerySlice';
 import { selectFilteredImages } from 'features/gallery/store/gallerySelectors';
 import { VirtuosoGrid } from 'react-virtuoso';
 import { receivedPageOfImages } from 'services/api/thunks/image';
+import { useListBoardImagesQuery } from '../../../../services/api/endpoints/boardImages';
 import ImageGridItemContainer from './ImageGridItemContainer';
 import ImageGridListContainer from './ImageGridListContainer';
-import { useListBoardImagesQuery } from '../../../../services/api/endpoints/boardImages';
 
 const selector = createSelector(
   [stateSelector, selectFilteredImages],
@@ -180,7 +179,6 @@ const GalleryImageGrid = () => {
       </Box>
     );
   }
-  console.log({ selectedBoardId });
 
   if (status !== 'rejected') {
     return (

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

@@ -1,17 +1,36 @@
-import { ButtonGroup } from '@chakra-ui/react';
+import { ButtonGroup, Tooltip } from '@chakra-ui/react';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import IAIIconButton from 'common/components/IAIIconButton';
 import { memo, useCallback } from 'react';
-import { FaCode, FaExpand, FaMinus, FaPlus } from 'react-icons/fa';
+import {
+  FaCode,
+  FaExpand,
+  FaMinus,
+  FaPlus,
+  FaInfo,
+  FaMapMarkerAlt,
+} from 'react-icons/fa';
 import { useReactFlow } from 'reactflow';
-import { shouldShowGraphOverlayChanged } from '../store/nodesSlice';
+import { useTranslation } from 'react-i18next';
+import {
+  shouldShowGraphOverlayChanged,
+  shouldShowFieldTypeLegendChanged,
+  shouldShowMinimapPanelChanged,
+} from '../store/nodesSlice';
 
 const ViewportControls = () => {
+  const { t } = useTranslation();
   const { zoomIn, zoomOut, fitView } = useReactFlow();
   const dispatch = useAppDispatch();
   const shouldShowGraphOverlay = useAppSelector(
     (state) => state.nodes.shouldShowGraphOverlay
   );
+  const shouldShowFieldTypeLegend = useAppSelector(
+    (state) => state.nodes.shouldShowFieldTypeLegend
+  );
+  const shouldShowMinimapPanel = useAppSelector(
+    (state) => state.nodes.shouldShowMinimapPanel
+  );
 
   const handleClickedZoomIn = useCallback(() => {
     zoomIn();
@@ -29,29 +48,64 @@ const ViewportControls = () => {
     dispatch(shouldShowGraphOverlayChanged(!shouldShowGraphOverlay));
   }, [shouldShowGraphOverlay, dispatch]);
 
+  const handleClickedToggleFieldTypeLegend = useCallback(() => {
+    dispatch(shouldShowFieldTypeLegendChanged(!shouldShowFieldTypeLegend));
+  }, [shouldShowFieldTypeLegend, dispatch]);
+
+  const handleClickedToggleMiniMapPanel = useCallback(() => {
+    dispatch(shouldShowMinimapPanelChanged(!shouldShowMinimapPanel));
+  }, [shouldShowMinimapPanel, dispatch]);
+
   return (
     <ButtonGroup isAttached orientation="vertical">
-      <IAIIconButton
-        onClick={handleClickedZoomIn}
-        aria-label="Zoom In"
-        icon={<FaPlus />}
-      />
-      <IAIIconButton
-        onClick={handleClickedZoomOut}
-        aria-label="Zoom Out"
-        icon={<FaMinus />}
-      />
-      <IAIIconButton
-        onClick={handleClickedFitView}
-        aria-label="Fit to Viewport"
-        icon={<FaExpand />}
-      />
-      <IAIIconButton
-        isChecked={shouldShowGraphOverlay}
-        onClick={handleClickedToggleGraphOverlay}
-        aria-label="Show/Hide Graph"
-        icon={<FaCode />}
-      />
+      <Tooltip label={t('nodes.zoomInNodes')}>
+        <IAIIconButton onClick={handleClickedZoomIn} icon={<FaPlus />} />
+      </Tooltip>
+      <Tooltip label={t('nodes.zoomOutNodes')}>
+        <IAIIconButton onClick={handleClickedZoomOut} icon={<FaMinus />} />
+      </Tooltip>
+      <Tooltip label={t('nodes.fitViewportNodes')}>
+        <IAIIconButton onClick={handleClickedFitView} icon={<FaExpand />} />
+      </Tooltip>
+      <Tooltip
+        label={
+          shouldShowGraphOverlay
+            ? t('nodes.hideGraphNodes')
+            : t('nodes.showGraphNodes')
+        }
+      >
+        <IAIIconButton
+          isChecked={shouldShowGraphOverlay}
+          onClick={handleClickedToggleGraphOverlay}
+          icon={<FaCode />}
+        />
+      </Tooltip>
+      <Tooltip
+        label={
+          shouldShowFieldTypeLegend
+            ? t('nodes.hideLegendNodes')
+            : t('nodes.showLegendNodes')
+        }
+      >
+        <IAIIconButton
+          isChecked={shouldShowFieldTypeLegend}
+          onClick={handleClickedToggleFieldTypeLegend}
+          icon={<FaInfo />}
+        />
+      </Tooltip>
+      <Tooltip
+        label={
+          shouldShowMinimapPanel
+            ? t('nodes.hideMinimapnodes')
+            : t('nodes.showMinimapnodes')
+        }
+      >
+        <IAIIconButton
+          isChecked={shouldShowMinimapPanel}
+          onClick={handleClickedToggleMiniMapPanel}
+          icon={<FaMapMarkerAlt />}
+        />
+      </Tooltip>
     </ButtonGroup>
   );
 };

invokeai/frontend/web/src/features/nodes/components/panels/MinimapPanel.tsx

@@ -1,3 +1,5 @@
+import { RootState } from 'app/store/store';
+import { useAppSelector } from 'app/store/storeHooks';
 import { useColorModeValue } from '@chakra-ui/react';
 import { memo } from 'react';
 import { MiniMap } from 'reactflow';
@@ -12,6 +14,10 @@ const MinimapPanel = () => {
     }
   );
 
+  const shouldShowMinimapPanel = useAppSelector(
+    (state: RootState) => state.nodes.shouldShowMinimapPanel
+  );
+
   const nodeColor = useColorModeValue(
     'var(--invokeai-colors-accent-300)',
     'var(--invokeai-colors-accent-700)'
@@ -23,15 +29,19 @@ const MinimapPanel = () => {
   );
 
   return (
-    <MiniMap
-      nodeStrokeWidth={3}
-      pannable
-      zoomable
-      nodeBorderRadius={30}
-      style={miniMapStyle}
-      nodeColor={nodeColor}
-      maskColor={maskColor}
-    />
+    <>
+      {shouldShowMinimapPanel && (
+        <MiniMap
+          nodeStrokeWidth={3}
+          pannable
+          zoomable
+          nodeBorderRadius={30}
+          style={miniMapStyle}
+          nodeColor={nodeColor}
+          maskColor={maskColor}
+        />
+      )}
+    </>
   );
 };
 

invokeai/frontend/web/src/features/nodes/components/panels/TopRightPanel.tsx

@@ -9,10 +9,13 @@ const TopRightPanel = () => {
   const shouldShowGraphOverlay = useAppSelector(
     (state: RootState) => state.nodes.shouldShowGraphOverlay
   );
+  const shouldShowFieldTypeLegend = useAppSelector(
+    (state: RootState) => state.nodes.shouldShowFieldTypeLegend
+  );
 
   return (
     <Panel position="top-right">
-      <FieldTypeLegend />
+      {shouldShowFieldTypeLegend && <FieldTypeLegend />}
       {shouldShowGraphOverlay && <NodeGraphOverlay />}
     </Panel>
   );

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

@@ -32,6 +32,8 @@ export type NodesState = {
   invocationTemplates: Record<string, InvocationTemplate>;
   connectionStartParams: OnConnectStartParams | null;
   shouldShowGraphOverlay: boolean;
+  shouldShowFieldTypeLegend: boolean;
+  shouldShowMinimapPanel: boolean;
   editorInstance: ReactFlowInstance | undefined;
 };
 
@@ -42,6 +44,8 @@ export const initialNodesState: NodesState = {
   invocationTemplates: {},
   connectionStartParams: null,
   shouldShowGraphOverlay: false,
+  shouldShowFieldTypeLegend: false,
+  shouldShowMinimapPanel: true,
   editorInstance: undefined,
 };
 
@@ -125,6 +129,15 @@ const nodesSlice = createSlice({
     shouldShowGraphOverlayChanged: (state, action: PayloadAction<boolean>) => {
       state.shouldShowGraphOverlay = action.payload;
     },
+    shouldShowFieldTypeLegendChanged: (
+      state,
+      action: PayloadAction<boolean>
+    ) => {
+      state.shouldShowFieldTypeLegend = action.payload;
+    },
+    shouldShowMinimapPanelChanged: (state, action: PayloadAction<boolean>) => {
+      state.shouldShowMinimapPanel = action.payload;
+    },
     nodeTemplatesBuilt: (
       state,
       action: PayloadAction<Record<string, InvocationTemplate>>
@@ -161,6 +174,8 @@ export const {
   connectionStarted,
   connectionEnded,
   shouldShowGraphOverlayChanged,
+  shouldShowFieldTypeLegendChanged,
+  shouldShowMinimapPanelChanged,
   nodeTemplatesBuilt,
   nodeEditorReset,
   imageCollectionFieldValueChanged,

invokeai/frontend/web/src/features/parameters/components/Parameters/Advanced/ParamClipSkip.tsx

@@ -14,6 +14,14 @@ export const clipSkipMap = {
     maxClip: 24,
     markers: [0, 1, 2, 3, 5, 10, 15, 20, 24],
   },
+  sdxl: {
+    maxClip: 24,
+    markers: [0, 1, 2, 3, 5, 10, 15, 20, 24],
+  },
+  'sdxl-refiner': {
+    maxClip: 24,
+    markers: [0, 1, 2, 3, 5, 10, 15, 20, 24],
+  },
 };
 
 export default function ParamClipSkip() {

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

@@ -1,4 +1,6 @@
 export const MODEL_TYPE_MAP = {
   'sd-1': 'Stable Diffusion 1.x',
   'sd-2': 'Stable Diffusion 2.x',
+  sdxl: 'Stable Diffusion XL',
+  'sdxl-refiner': 'Stable Diffusion XL Refiner',
 };

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

@@ -126,7 +126,7 @@ export type HeightParam = z.infer<typeof zHeight>;
 export const isValidHeight = (val: unknown): val is HeightParam =>
   zHeight.safeParse(val).success;
 
-const zBaseModel = z.enum(['sd-1', 'sd-2']);
+const zBaseModel = z.enum(['sd-1', 'sd-2', 'sdxl', 'sdxl-refiner']);
 
 export type BaseModelParam = z.infer<typeof zBaseModel>;
 

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

@@ -1,11 +1,10 @@
 import { UseToastOptions } from '@chakra-ui/react';
 import { PayloadAction, createSlice } from '@reduxjs/toolkit';
-import * as InvokeAI from 'app/types/invokeai';
 
 import { InvokeLogLevel } from 'app/logging/useLogger';
 import { userInvoked } from 'app/store/actions';
 import { nodeTemplatesBuilt } from 'features/nodes/store/nodesSlice';
-import { TFuncKey, t } from 'i18next';
+import { t } from 'i18next';
 import { LogLevelName } from 'roarr';
 import { imageUploaded } from 'services/api/thunks/image';
 import {
@@ -44,8 +43,6 @@ export interface SystemState {
   isCancelable: boolean;
   enableImageDebugging: boolean;
   toastQueue: UseToastOptions[];
-  searchFolder: string | null;
-  foundModels: InvokeAI.FoundModel[] | null;
   /**
    * The current progress image
    */
@@ -79,7 +76,7 @@ export interface SystemState {
    */
   consoleLogLevel: InvokeLogLevel;
   shouldLogToConsole: boolean;
-  statusTranslationKey: TFuncKey;
+  statusTranslationKey: any;
   /**
    * When a session is canceled, its ID is stored here until a new session is created.
    */
@@ -106,8 +103,6 @@ export const initialSystemState: SystemState = {
   isCancelable: true,
   enableImageDebugging: false,
   toastQueue: [],
-  searchFolder: null,
-  foundModels: null,
   progressImage: null,
   shouldAntialiasProgressImage: false,
   sessionId: null,
@@ -132,7 +127,7 @@ export const systemSlice = createSlice({
     setIsProcessing: (state, action: PayloadAction<boolean>) => {
       state.isProcessing = action.payload;
     },
-    setCurrentStatus: (state, action: PayloadAction<TFuncKey>) => {
+    setCurrentStatus: (state, action: any) => {
       state.statusTranslationKey = action.payload;
     },
     setShouldConfirmOnDelete: (state, action: PayloadAction<boolean>) => {
@@ -153,15 +148,6 @@ export const systemSlice = createSlice({
     clearToastQueue: (state) => {
       state.toastQueue = [];
     },
-    setSearchFolder: (state, action: PayloadAction<string | null>) => {
-      state.searchFolder = action.payload;
-    },
-    setFoundModels: (
-      state,
-      action: PayloadAction<InvokeAI.FoundModel[] | null>
-    ) => {
-      state.foundModels = action.payload;
-    },
     /**
      * A cancel was scheduled
      */
@@ -426,8 +412,6 @@ export const {
   setEnableImageDebugging,
   addToast,
   clearToastQueue,
-  setSearchFolder,
-  setFoundModels,
   cancelScheduled,
   scheduledCancelAborted,
   cancelTypeChanged,

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

@@ -1,11 +1,11 @@
 import { Tab, TabList, TabPanel, TabPanels, Tabs } from '@chakra-ui/react';
 import i18n from 'i18n';
 import { ReactNode, memo } from 'react';
-import AddModelsPanel from './subpanels/AddModelsPanel';
+import ImportModelsPanel from './subpanels/ImportModelsPanel';
 import MergeModelsPanel from './subpanels/MergeModelsPanel';
 import ModelManagerPanel from './subpanels/ModelManagerPanel';
 
-type ModelManagerTabName = 'modelManager' | 'addModels' | 'mergeModels';
+type ModelManagerTabName = 'modelManager' | 'importModels' | 'mergeModels';
 
 type ModelManagerTabInfo = {
   id: ModelManagerTabName;
@@ -20,9 +20,9 @@ const tabs: ModelManagerTabInfo[] = [
     content: <ModelManagerPanel />,
   },
   {
-    id: 'addModels',
-    label: i18n.t('modelManager.addModel'),
-    content: <AddModelsPanel />,
+    id: 'importModels',
+    label: i18n.t('modelManager.importModels'),
+    content: <ImportModelsPanel />,
   },
   {
     id: 'mergeModels',
@@ -46,7 +46,7 @@ const ModelManagerTab = () => {
           </Tab>
         ))}
       </TabList>
-      <TabPanels sx={{ w: 'full', h: 'full', p: 4 }}>
+      <TabPanels sx={{ w: 'full', h: 'full' }}>
         {tabs.map((tab) => (
           <TabPanel sx={{ w: 'full', h: 'full' }} key={tab.id}>
             {tab.content}

invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/store/modelManagerSlice.ts

@@ -0,0 +1,29 @@
+import { PayloadAction, createSlice } from '@reduxjs/toolkit';
+
+type ModelManagerState = {
+  searchFolder: string | null;
+  advancedAddScanModel: string | null;
+};
+
+const initialModelManagerState: ModelManagerState = {
+  searchFolder: null,
+  advancedAddScanModel: null,
+};
+
+export const modelManagerSlice = createSlice({
+  name: 'modelmanager',
+  initialState: initialModelManagerState,
+  reducers: {
+    setSearchFolder: (state, action: PayloadAction<string | null>) => {
+      state.searchFolder = action.payload;
+    },
+    setAdvancedAddScanModel: (state, action: PayloadAction<string | null>) => {
+      state.advancedAddScanModel = action.payload;
+    },
+  },
+});
+
+export const { setSearchFolder, setAdvancedAddScanModel } =
+  modelManagerSlice.actions;
+
+export default modelManagerSlice.reducer;

invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/store/modelmanagerSelectors.tsx

@@ -0,0 +1,3 @@
+import { RootState } from 'app/store/store';
+
+export const modelmanagerSelector = (state: RootState) => state.modelmanager;

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

@@ -1,43 +0,0 @@
-import { Divider, Flex, useColorMode } from '@chakra-ui/react';
-import { RootState } from 'app/store/store';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import IAIButton from 'common/components/IAIButton';
-import { setAddNewModelUIOption } from 'features/ui/store/uiSlice';
-import { useTranslation } from 'react-i18next';
-import AddCheckpointModel from './AddModelsPanel/AddCheckpointModel';
-import AddDiffusersModel from './AddModelsPanel/AddDiffusersModel';
-
-export default function AddModelsPanel() {
-  const addNewModelUIOption = useAppSelector(
-    (state: RootState) => state.ui.addNewModelUIOption
-  );
-
-  const { colorMode } = useColorMode();
-
-  const dispatch = useAppDispatch();
-  const { t } = useTranslation();
-
-  return (
-    <Flex flexDirection="column" gap={4}>
-      <Flex columnGap={4}>
-        <IAIButton
-          onClick={() => dispatch(setAddNewModelUIOption('ckpt'))}
-          isChecked={addNewModelUIOption == 'ckpt'}
-        >
-          {t('modelManager.addCheckpointModel')}
-        </IAIButton>
-        <IAIButton
-          onClick={() => dispatch(setAddNewModelUIOption('diffusers'))}
-          isChecked={addNewModelUIOption == 'diffusers'}
-        >
-          {t('modelManager.addDiffuserModel')}
-        </IAIButton>
-      </Flex>
-
-      <Divider />
-
-      {addNewModelUIOption == 'ckpt' && <AddCheckpointModel />}
-      {addNewModelUIOption == 'diffusers' && <AddDiffusersModel />}
-    </Flex>
-  );
-}

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

@@ -1,337 +0,0 @@
-import {
-  Flex,
-  FormControl,
-  FormErrorMessage,
-  FormHelperText,
-  FormLabel,
-  HStack,
-  Text,
-  VStack,
-} from '@chakra-ui/react';
-
-import IAIButton from 'common/components/IAIButton';
-import IAIInput from 'common/components/IAIInput';
-import IAINumberInput from 'common/components/IAINumberInput';
-import IAISimpleCheckbox from 'common/components/IAISimpleCheckbox';
-import React from 'react';
-
-// import { addNewModel } from 'app/socketio/actions';
-
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-
-import { Field, Formik } from 'formik';
-import { useTranslation } from 'react-i18next';
-
-import type { RootState } from 'app/store/store';
-import type { InvokeModelConfigProps } from 'app/types/invokeai';
-import IAIForm from 'common/components/IAIForm';
-import { IAIFormItemWrapper } from 'common/components/IAIForms/IAIFormItemWrapper';
-import { setAddNewModelUIOption } from 'features/ui/store/uiSlice';
-import type { FieldInputProps, FormikProps } from 'formik';
-import SearchModels from './SearchModels';
-
-const MIN_MODEL_SIZE = 64;
-const MAX_MODEL_SIZE = 2048;
-
-export default function AddCheckpointModel() {
-  const dispatch = useAppDispatch();
-  const { t } = useTranslation();
-
-  const isProcessing = useAppSelector(
-    (state: RootState) => state.system.isProcessing
-  );
-
-  function hasWhiteSpace(s: string) {
-    return /\s/.test(s);
-  }
-
-  function baseValidation(value: string) {
-    let error;
-    if (hasWhiteSpace(value)) error = t('modelManager.cannotUseSpaces');
-    return error;
-  }
-
-  const addModelFormValues: InvokeModelConfigProps = {
-    name: '',
-    description: '',
-    config: 'configs/stable-diffusion/v1-inference.yaml',
-    weights: '',
-    vae: '',
-    width: 512,
-    height: 512,
-    format: 'ckpt',
-    default: false,
-  };
-
-  const addModelFormSubmitHandler = (values: InvokeModelConfigProps) => {
-    dispatch(addNewModel(values));
-    dispatch(setAddNewModelUIOption(null));
-  };
-
-  const [addManually, setAddmanually] = React.useState<boolean>(false);
-
-  return (
-    <VStack gap={2} alignItems="flex-start">
-      <Flex columnGap={4}>
-        <IAISimpleCheckbox
-          isChecked={!addManually}
-          label={t('modelManager.scanForModels')}
-          onChange={() => setAddmanually(!addManually)}
-        />
-        <IAISimpleCheckbox
-          label={t('modelManager.addManually')}
-          isChecked={addManually}
-          onChange={() => setAddmanually(!addManually)}
-        />
-      </Flex>
-
-      {addManually ? (
-        <Formik
-          initialValues={addModelFormValues}
-          onSubmit={addModelFormSubmitHandler}
-        >
-          {({ handleSubmit, errors, touched }) => (
-            <IAIForm onSubmit={handleSubmit} sx={{ w: 'full' }}>
-              <VStack rowGap={2}>
-                <Text fontSize={20} fontWeight="bold" alignSelf="start">
-                  {t('modelManager.manual')}
-                </Text>
-                {/* Name */}
-                <IAIFormItemWrapper>
-                  <FormControl
-                    isInvalid={!!errors.name && touched.name}
-                    isRequired
-                  >
-                    <FormLabel htmlFor="name" fontSize="sm">
-                      {t('modelManager.name')}
-                    </FormLabel>
-                    <VStack alignItems="start">
-                      <Field
-                        as={IAIInput}
-                        id="name"
-                        name="name"
-                        type="text"
-                        validate={baseValidation}
-                        width="full"
-                      />
-                      {!!errors.name && touched.name ? (
-                        <FormErrorMessage>{errors.name}</FormErrorMessage>
-                      ) : (
-                        <FormHelperText margin={0}>
-                          {t('modelManager.nameValidationMsg')}
-                        </FormHelperText>
-                      )}
-                    </VStack>
-                  </FormControl>
-                </IAIFormItemWrapper>
-
-                {/* Description */}
-                <IAIFormItemWrapper>
-                  <FormControl
-                    isInvalid={!!errors.description && touched.description}
-                    isRequired
-                  >
-                    <FormLabel htmlFor="description" fontSize="sm">
-                      {t('modelManager.description')}
-                    </FormLabel>
-                    <VStack alignItems="start">
-                      <Field
-                        as={IAIInput}
-                        id="description"
-                        name="description"
-                        type="text"
-                        width="full"
-                      />
-                      {!!errors.description && touched.description ? (
-                        <FormErrorMessage>
-                          {errors.description}
-                        </FormErrorMessage>
-                      ) : (
-                        <FormHelperText margin={0}>
-                          {t('modelManager.descriptionValidationMsg')}
-                        </FormHelperText>
-                      )}
-                    </VStack>
-                  </FormControl>
-                </IAIFormItemWrapper>
-
-                {/* Config */}
-                <IAIFormItemWrapper>
-                  <FormControl
-                    isInvalid={!!errors.config && touched.config}
-                    isRequired
-                  >
-                    <FormLabel htmlFor="config" fontSize="sm">
-                      {t('modelManager.config')}
-                    </FormLabel>
-                    <VStack alignItems="start">
-                      <Field
-                        as={IAIInput}
-                        id="config"
-                        name="config"
-                        type="text"
-                        width="full"
-                      />
-                      {!!errors.config && touched.config ? (
-                        <FormErrorMessage>{errors.config}</FormErrorMessage>
-                      ) : (
-                        <FormHelperText margin={0}>
-                          {t('modelManager.configValidationMsg')}
-                        </FormHelperText>
-                      )}
-                    </VStack>
-                  </FormControl>
-                </IAIFormItemWrapper>
-
-                {/* Weights */}
-                <IAIFormItemWrapper>
-                  <FormControl
-                    isInvalid={!!errors.weights && touched.weights}
-                    isRequired
-                  >
-                    <FormLabel htmlFor="config" fontSize="sm">
-                      {t('modelManager.modelLocation')}
-                    </FormLabel>
-                    <VStack alignItems="start">
-                      <Field
-                        as={IAIInput}
-                        id="weights"
-                        name="weights"
-                        type="text"
-                        width="full"
-                      />
-                      {!!errors.weights && touched.weights ? (
-                        <FormErrorMessage>{errors.weights}</FormErrorMessage>
-                      ) : (
-                        <FormHelperText margin={0}>
-                          {t('modelManager.modelLocationValidationMsg')}
-                        </FormHelperText>
-                      )}
-                    </VStack>
-                  </FormControl>
-                </IAIFormItemWrapper>
-
-                {/* VAE */}
-                <IAIFormItemWrapper>
-                  <FormControl isInvalid={!!errors.vae && touched.vae}>
-                    <FormLabel htmlFor="vae" fontSize="sm">
-                      {t('modelManager.vaeLocation')}
-                    </FormLabel>
-                    <VStack alignItems="start">
-                      <Field
-                        as={IAIInput}
-                        id="vae"
-                        name="vae"
-                        type="text"
-                        width="full"
-                      />
-                      {!!errors.vae && touched.vae ? (
-                        <FormErrorMessage>{errors.vae}</FormErrorMessage>
-                      ) : (
-                        <FormHelperText margin={0}>
-                          {t('modelManager.vaeLocationValidationMsg')}
-                        </FormHelperText>
-                      )}
-                    </VStack>
-                  </FormControl>
-                </IAIFormItemWrapper>
-
-                <HStack width="100%">
-                  {/* Width */}
-                  <IAIFormItemWrapper>
-                    <FormControl isInvalid={!!errors.width && touched.width}>
-                      <FormLabel htmlFor="width" fontSize="sm">
-                        {t('modelManager.width')}
-                      </FormLabel>
-                      <VStack alignItems="start">
-                        <Field id="width" name="width">
-                          {({
-                            field,
-                            form,
-                          }: {
-                            field: FieldInputProps<number>;
-                            form: FormikProps<InvokeModelConfigProps>;
-                          }) => (
-                            <IAINumberInput
-                              id="width"
-                              name="width"
-                              min={MIN_MODEL_SIZE}
-                              max={MAX_MODEL_SIZE}
-                              step={64}
-                              value={form.values.width}
-                              onChange={(value) =>
-                                form.setFieldValue(field.name, Number(value))
-                              }
-                            />
-                          )}
-                        </Field>
-
-                        {!!errors.width && touched.width ? (
-                          <FormErrorMessage>{errors.width}</FormErrorMessage>
-                        ) : (
-                          <FormHelperText margin={0}>
-                            {t('modelManager.widthValidationMsg')}
-                          </FormHelperText>
-                        )}
-                      </VStack>
-                    </FormControl>
-                  </IAIFormItemWrapper>
-
-                  {/* Height */}
-                  <IAIFormItemWrapper>
-                    <FormControl isInvalid={!!errors.height && touched.height}>
-                      <FormLabel htmlFor="height" fontSize="sm">
-                        {t('modelManager.height')}
-                      </FormLabel>
-                      <VStack alignItems="start">
-                        <Field id="height" name="height">
-                          {({
-                            field,
-                            form,
-                          }: {
-                            field: FieldInputProps<number>;
-                            form: FormikProps<InvokeModelConfigProps>;
-                          }) => (
-                            <IAINumberInput
-                              id="height"
-                              name="height"
-                              min={MIN_MODEL_SIZE}
-                              max={MAX_MODEL_SIZE}
-                              step={64}
-                              value={form.values.height}
-                              onChange={(value) =>
-                                form.setFieldValue(field.name, Number(value))
-                              }
-                            />
-                          )}
-                        </Field>
-
-                        {!!errors.height && touched.height ? (
-                          <FormErrorMessage>{errors.height}</FormErrorMessage>
-                        ) : (
-                          <FormHelperText margin={0}>
-                            {t('modelManager.heightValidationMsg')}
-                          </FormHelperText>
-                        )}
-                      </VStack>
-                    </FormControl>
-                  </IAIFormItemWrapper>
-                </HStack>
-
-                <IAIButton
-                  type="submit"
-                  className="modal-close-btn"
-                  isLoading={isProcessing}
-                >
-                  {t('modelManager.addModel')}
-                </IAIButton>
-              </VStack>
-            </IAIForm>
-          )}
-        </Formik>
-      ) : (
-        <SearchModels />
-      )}
-    </VStack>
-  );
-}

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

@@ -1,259 +0,0 @@
-import {
-  Flex,
-  FormControl,
-  FormErrorMessage,
-  FormHelperText,
-  FormLabel,
-  Text,
-  VStack,
-} from '@chakra-ui/react';
-import { InvokeDiffusersModelConfigProps } from 'app/types/invokeai';
-// import { addNewModel } from 'app/socketio/actions';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import IAIButton from 'common/components/IAIButton';
-import IAIInput from 'common/components/IAIInput';
-import { setAddNewModelUIOption } from 'features/ui/store/uiSlice';
-import { Field, Formik } from 'formik';
-import { useTranslation } from 'react-i18next';
-
-import type { RootState } from 'app/store/store';
-import IAIForm from 'common/components/IAIForm';
-import { IAIFormItemWrapper } from 'common/components/IAIForms/IAIFormItemWrapper';
-
-export default function AddDiffusersModel() {
-  const dispatch = useAppDispatch();
-  const { t } = useTranslation();
-
-  const isProcessing = useAppSelector(
-    (state: RootState) => state.system.isProcessing
-  );
-
-  function hasWhiteSpace(s: string) {
-    return /\s/.test(s);
-  }
-
-  function baseValidation(value: string) {
-    let error;
-    if (hasWhiteSpace(value)) error = t('modelManager.cannotUseSpaces');
-    return error;
-  }
-
-  const addModelFormValues: InvokeDiffusersModelConfigProps = {
-    name: '',
-    description: '',
-    repo_id: '',
-    path: '',
-    format: 'diffusers',
-    default: false,
-    vae: {
-      repo_id: '',
-      path: '',
-    },
-  };
-
-  const addModelFormSubmitHandler = (
-    values: InvokeDiffusersModelConfigProps
-  ) => {
-    const diffusersModelToAdd = values;
-
-    if (values.path === '') delete diffusersModelToAdd.path;
-    if (values.repo_id === '') delete diffusersModelToAdd.repo_id;
-    if (values.vae.path === '') delete diffusersModelToAdd.vae.path;
-    if (values.vae.repo_id === '') delete diffusersModelToAdd.vae.repo_id;
-
-    dispatch(addNewModel(diffusersModelToAdd));
-    dispatch(setAddNewModelUIOption(null));
-  };
-
-  return (
-    <Flex overflow="scroll" maxHeight={window.innerHeight - 270} width="100%">
-      <Formik
-        initialValues={addModelFormValues}
-        onSubmit={addModelFormSubmitHandler}
-      >
-        {({ handleSubmit, errors, touched }) => (
-          <IAIForm onSubmit={handleSubmit} w="full">
-            <VStack rowGap={2}>
-              <IAIFormItemWrapper>
-                {/* Name */}
-                <FormControl
-                  isInvalid={!!errors.name && touched.name}
-                  isRequired
-                >
-                  <FormLabel htmlFor="name" fontSize="sm">
-                    {t('modelManager.name')}
-                  </FormLabel>
-                  <VStack alignItems="start">
-                    <Field
-                      as={IAIInput}
-                      id="name"
-                      name="name"
-                      type="text"
-                      validate={baseValidation}
-                      isRequired
-                    />
-                    {!!errors.name && touched.name ? (
-                      <FormErrorMessage>{errors.name}</FormErrorMessage>
-                    ) : (
-                      <FormHelperText margin={0}>
-                        {t('modelManager.nameValidationMsg')}
-                      </FormHelperText>
-                    )}
-                  </VStack>
-                </FormControl>
-              </IAIFormItemWrapper>
-
-              <IAIFormItemWrapper>
-                {/* Description */}
-                <FormControl
-                  isInvalid={!!errors.description && touched.description}
-                  isRequired
-                >
-                  <FormLabel htmlFor="description" fontSize="sm">
-                    {t('modelManager.description')}
-                  </FormLabel>
-                  <VStack alignItems="start">
-                    <Field
-                      as={IAIInput}
-                      id="description"
-                      name="description"
-                      type="text"
-                      isRequired
-                    />
-                    {!!errors.description && touched.description ? (
-                      <FormErrorMessage>{errors.description}</FormErrorMessage>
-                    ) : (
-                      <FormHelperText margin={0}>
-                        {t('modelManager.descriptionValidationMsg')}
-                      </FormHelperText>
-                    )}
-                  </VStack>
-                </FormControl>
-              </IAIFormItemWrapper>
-
-              <IAIFormItemWrapper>
-                <Text fontWeight="bold" fontSize="sm">
-                  {t('modelManager.formMessageDiffusersModelLocation')}
-                </Text>
-                <Text
-                  sx={{
-                    fontSize: 'sm',
-                    fontStyle: 'italic',
-                  }}
-                  variant="subtext"
-                >
-                  {t('modelManager.formMessageDiffusersModelLocationDesc')}
-                </Text>
-
-                {/* Path */}
-                <FormControl isInvalid={!!errors.path && touched.path}>
-                  <FormLabel htmlFor="path" fontSize="sm">
-                    {t('modelManager.modelLocation')}
-                  </FormLabel>
-                  <VStack alignItems="start">
-                    <Field as={IAIInput} id="path" name="path" type="text" />
-                    {!!errors.path && touched.path ? (
-                      <FormErrorMessage>{errors.path}</FormErrorMessage>
-                    ) : (
-                      <FormHelperText margin={0}>
-                        {t('modelManager.modelLocationValidationMsg')}
-                      </FormHelperText>
-                    )}
-                  </VStack>
-                </FormControl>
-
-                {/* Repo ID */}
-                <FormControl isInvalid={!!errors.repo_id && touched.repo_id}>
-                  <FormLabel htmlFor="repo_id" fontSize="sm">
-                    {t('modelManager.repo_id')}
-                  </FormLabel>
-                  <VStack alignItems="start">
-                    <Field
-                      as={IAIInput}
-                      id="repo_id"
-                      name="repo_id"
-                      type="text"
-                    />
-                    {!!errors.repo_id && touched.repo_id ? (
-                      <FormErrorMessage>{errors.repo_id}</FormErrorMessage>
-                    ) : (
-                      <FormHelperText margin={0}>
-                        {t('modelManager.repoIDValidationMsg')}
-                      </FormHelperText>
-                    )}
-                  </VStack>
-                </FormControl>
-              </IAIFormItemWrapper>
-
-              <IAIFormItemWrapper>
-                {/* VAE Path */}
-                <Text fontWeight="bold">
-                  {t('modelManager.formMessageDiffusersVAELocation')}
-                </Text>
-                <Text
-                  sx={{
-                    fontSize: 'sm',
-                    fontStyle: 'italic',
-                  }}
-                  variant="subtext"
-                >
-                  {t('modelManager.formMessageDiffusersVAELocationDesc')}
-                </Text>
-                <FormControl
-                  isInvalid={!!errors.vae?.path && touched.vae?.path}
-                >
-                  <FormLabel htmlFor="vae.path" fontSize="sm">
-                    {t('modelManager.vaeLocation')}
-                  </FormLabel>
-                  <VStack alignItems="start">
-                    <Field
-                      as={IAIInput}
-                      id="vae.path"
-                      name="vae.path"
-                      type="text"
-                    />
-                    {!!errors.vae?.path && touched.vae?.path ? (
-                      <FormErrorMessage>{errors.vae?.path}</FormErrorMessage>
-                    ) : (
-                      <FormHelperText margin={0}>
-                        {t('modelManager.vaeLocationValidationMsg')}
-                      </FormHelperText>
-                    )}
-                  </VStack>
-                </FormControl>
-
-                {/* VAE Repo ID */}
-                <FormControl
-                  isInvalid={!!errors.vae?.repo_id && touched.vae?.repo_id}
-                >
-                  <FormLabel htmlFor="vae.repo_id" fontSize="sm">
-                    {t('modelManager.vaeRepoID')}
-                  </FormLabel>
-                  <VStack alignItems="start">
-                    <Field
-                      as={IAIInput}
-                      id="vae.repo_id"
-                      name="vae.repo_id"
-                      type="text"
-                    />
-                    {!!errors.vae?.repo_id && touched.vae?.repo_id ? (
-                      <FormErrorMessage>{errors.vae?.repo_id}</FormErrorMessage>
-                    ) : (
-                      <FormHelperText margin={0}>
-                        {t('modelManager.vaeRepoIDValidationMsg')}
-                      </FormHelperText>
-                    )}
-                  </VStack>
-                </FormControl>
-              </IAIFormItemWrapper>
-
-              <IAIButton type="submit" isLoading={isProcessing}>
-                {t('modelManager.addModel')}
-              </IAIButton>
-            </VStack>
-          </IAIForm>
-        )}
-      </Formik>
-    </Flex>
-  );
-}

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

@@ -0,0 +1,48 @@
+import { ButtonGroup, Flex } from '@chakra-ui/react';
+import IAIButton from 'common/components/IAIButton';
+import { useState } from 'react';
+import AdvancedAddModels from './AdvancedAddModels';
+import SimpleAddModels from './SimpleAddModels';
+
+export default function AddModels() {
+  const [addModelMode, setAddModelMode] = useState<'simple' | 'advanced'>(
+    'simple'
+  );
+  return (
+    <Flex
+      flexDirection="column"
+      width="100%"
+      overflow="scroll"
+      maxHeight={window.innerHeight - 250}
+      gap={4}
+    >
+      <ButtonGroup isAttached>
+        <IAIButton
+          size="sm"
+          isChecked={addModelMode == 'simple'}
+          onClick={() => setAddModelMode('simple')}
+        >
+          Simple
+        </IAIButton>
+        <IAIButton
+          size="sm"
+          isChecked={addModelMode == 'advanced'}
+          onClick={() => setAddModelMode('advanced')}
+        >
+          Advanced
+        </IAIButton>
+      </ButtonGroup>
+      <Flex
+        sx={{
+          p: 4,
+          borderRadius: 4,
+          background: 'base.200',
+          _dark: { background: 'base.800' },
+        }}
+      >
+        {addModelMode === 'simple' && <SimpleAddModels />}
+        {addModelMode === 'advanced' && <AdvancedAddModels />}
+      </Flex>
+    </Flex>
+  );
+}

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

@@ -0,0 +1,143 @@
+import { Flex } from '@chakra-ui/react';
+import { useForm } from '@mantine/form';
+import { makeToast } from 'app/components/Toaster';
+import { useAppDispatch } from 'app/store/storeHooks';
+import IAIButton from 'common/components/IAIButton';
+import IAIMantineTextInput from 'common/components/IAIMantineInput';
+import IAISimpleCheckbox from 'common/components/IAISimpleCheckbox';
+import { addToast } from 'features/system/store/systemSlice';
+import { useState } from 'react';
+import { useTranslation } from 'react-i18next';
+import { useAddMainModelsMutation } from 'services/api/endpoints/models';
+import { CheckpointModelConfig } from 'services/api/types';
+import { setAdvancedAddScanModel } from '../../store/modelManagerSlice';
+import BaseModelSelect from '../shared/BaseModelSelect';
+import CheckpointConfigsSelect from '../shared/CheckpointConfigsSelect';
+import ModelVariantSelect from '../shared/ModelVariantSelect';
+
+type AdvancedAddCheckpointProps = {
+  model_path?: string;
+};
+
+export default function AdvancedAddCheckpoint(
+  props: AdvancedAddCheckpointProps
+) {
+  const { t } = useTranslation();
+  const dispatch = useAppDispatch();
+  const { model_path } = props;
+
+  const advancedAddCheckpointForm = useForm<CheckpointModelConfig>({
+    initialValues: {
+      model_name: model_path
+        ? model_path.split('\\').splice(-1)[0].split('.')[0]
+        : '',
+      base_model: 'sd-1',
+      model_type: 'main',
+      path: model_path ? model_path : '',
+      description: '',
+      model_format: 'checkpoint',
+      error: undefined,
+      vae: '',
+      variant: 'normal',
+      config: 'configs\\stable-diffusion\\v1-inference.yaml',
+    },
+  });
+
+  const [addMainModel] = useAddMainModelsMutation();
+
+  const [useCustomConfig, setUseCustomConfig] = useState<boolean>(false);
+
+  const advancedAddCheckpointFormHandler = (values: CheckpointModelConfig) => {
+    addMainModel({
+      body: values,
+    })
+      .unwrap()
+      .then((_) => {
+        dispatch(
+          addToast(
+            makeToast({
+              title: `Model Added: ${values.model_name}`,
+              status: 'success',
+            })
+          )
+        );
+        advancedAddCheckpointForm.reset();
+
+        // Close Advanced Panel in Scan Models tab
+        if (model_path) {
+          dispatch(setAdvancedAddScanModel(null));
+        }
+      })
+      .catch((error) => {
+        if (error) {
+          dispatch(
+            addToast(
+              makeToast({
+                title: 'Model Add Failed',
+                status: 'error',
+              })
+            )
+          );
+        }
+      });
+  };
+
+  return (
+    <form
+      onSubmit={advancedAddCheckpointForm.onSubmit((v) =>
+        advancedAddCheckpointFormHandler(v)
+      )}
+      style={{ width: '100%' }}
+    >
+      <Flex flexDirection="column" gap={2}>
+        <IAIMantineTextInput
+          label="Model Name"
+          required
+          {...advancedAddCheckpointForm.getInputProps('model_name')}
+        />
+        <BaseModelSelect
+          {...advancedAddCheckpointForm.getInputProps('base_model')}
+        />
+        <IAIMantineTextInput
+          label="Model Location"
+          required
+          {...advancedAddCheckpointForm.getInputProps('path')}
+        />
+        <IAIMantineTextInput
+          label="Description"
+          {...advancedAddCheckpointForm.getInputProps('description')}
+        />
+        <IAIMantineTextInput
+          label="VAE Location"
+          {...advancedAddCheckpointForm.getInputProps('vae')}
+        />
+        <ModelVariantSelect
+          {...advancedAddCheckpointForm.getInputProps('variant')}
+        />
+        <Flex flexDirection="column" width="100%" gap={2}>
+          {!useCustomConfig ? (
+            <CheckpointConfigsSelect
+              required
+              width="100%"
+              {...advancedAddCheckpointForm.getInputProps('config')}
+            />
+          ) : (
+            <IAIMantineTextInput
+              required
+              label="Custom Config File Location"
+              {...advancedAddCheckpointForm.getInputProps('config')}
+            />
+          )}
+          <IAISimpleCheckbox
+            isChecked={useCustomConfig}
+            onChange={() => setUseCustomConfig(!useCustomConfig)}
+            label="Use Custom Config"
+          />
+          <IAIButton mt={2} type="submit">
+            {t('modelManager.addModel')}
+          </IAIButton>
+        </Flex>
+      </Flex>
+    </form>
+  );
+}

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

@@ -0,0 +1,113 @@
+import { Flex } from '@chakra-ui/react';
+import { useForm } from '@mantine/form';
+import { makeToast } from 'app/components/Toaster';
+import { useAppDispatch } from 'app/store/storeHooks';
+import IAIButton from 'common/components/IAIButton';
+import IAIMantineTextInput from 'common/components/IAIMantineInput';
+import { addToast } from 'features/system/store/systemSlice';
+import { useTranslation } from 'react-i18next';
+import { useAddMainModelsMutation } from 'services/api/endpoints/models';
+import { DiffusersModelConfig } from 'services/api/types';
+import { setAdvancedAddScanModel } from '../../store/modelManagerSlice';
+import BaseModelSelect from '../shared/BaseModelSelect';
+import ModelVariantSelect from '../shared/ModelVariantSelect';
+
+type AdvancedAddDiffusersProps = {
+  model_path?: string;
+};
+
+export default function AdvancedAddDiffusers(props: AdvancedAddDiffusersProps) {
+  const { t } = useTranslation();
+  const dispatch = useAppDispatch();
+  const { model_path } = props;
+
+  const [addMainModel] = useAddMainModelsMutation();
+
+  const advancedAddDiffusersForm = useForm<DiffusersModelConfig>({
+    initialValues: {
+      model_name: model_path ? model_path.split('\\').splice(-1)[0] : '',
+      base_model: 'sd-1',
+      model_type: 'main',
+      path: model_path ? model_path : '',
+      description: '',
+      model_format: 'diffusers',
+      error: undefined,
+      vae: '',
+      variant: 'normal',
+    },
+  });
+
+  const advancedAddDiffusersFormHandler = (values: DiffusersModelConfig) => {
+    addMainModel({
+      body: values,
+    })
+      .unwrap()
+      .then((_) => {
+        dispatch(
+          addToast(
+            makeToast({
+              title: `Model Added: ${values.model_name}`,
+              status: 'success',
+            })
+          )
+        );
+        advancedAddDiffusersForm.reset();
+        // Close Advanced Panel in Scan Models tab
+        if (model_path) {
+          dispatch(setAdvancedAddScanModel(null));
+        }
+      })
+      .catch((error) => {
+        if (error) {
+          dispatch(
+            addToast(
+              makeToast({
+                title: 'Model Add Failed',
+                status: 'error',
+              })
+            )
+          );
+        }
+      });
+  };
+
+  return (
+    <form
+      onSubmit={advancedAddDiffusersForm.onSubmit((v) =>
+        advancedAddDiffusersFormHandler(v)
+      )}
+      style={{ width: '100%' }}
+    >
+      <Flex flexDirection="column" gap={2}>
+        <IAIMantineTextInput
+          required
+          label="Model Name"
+          {...advancedAddDiffusersForm.getInputProps('model_name')}
+        />
+        <BaseModelSelect
+          {...advancedAddDiffusersForm.getInputProps('base_model')}
+        />
+        <IAIMantineTextInput
+          required
+          label="Model Location"
+          placeholder="Provide the path to a local folder where your Diffusers Model is stored"
+          {...advancedAddDiffusersForm.getInputProps('path')}
+        />
+        <IAIMantineTextInput
+          label="Description"
+          {...advancedAddDiffusersForm.getInputProps('description')}
+        />
+        <IAIMantineTextInput
+          label="VAE Location"
+          {...advancedAddDiffusersForm.getInputProps('vae')}
+        />
+        <ModelVariantSelect
+          {...advancedAddDiffusersForm.getInputProps('variant')}
+        />
+        <IAIButton mt={2} type="submit">
+          {t('modelManager.addModel')}
+        </IAIButton>
+      </Flex>
+    </form>
+  );
+}

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

@@ -0,0 +1,46 @@
+import { Flex } from '@chakra-ui/react';
+import { SelectItem } from '@mantine/core';
+import IAIMantineSelect from 'common/components/IAIMantineSelect';
+import { useState } from 'react';
+import AdvancedAddCheckpoint from './AdvancedAddCheckpoint';
+import AdvancedAddDiffusers from './AdvancedAddDiffusers';
+
+export const advancedAddModeData: SelectItem[] = [
+  { label: 'Diffusers', value: 'diffusers' },
+  { label: 'Checkpoint / Safetensors', value: 'checkpoint' },
+];
+
+export type ManualAddMode = 'diffusers' | 'checkpoint';
+
+export default function AdvancedAddModels() {
+  const [advancedAddMode, setAdvancedAddMode] =
+    useState<ManualAddMode>('diffusers');
+
+  return (
+    <Flex flexDirection="column" gap={4} width="100%">
+      <IAIMantineSelect
+        label="Model Type"
+        value={advancedAddMode}
+        data={advancedAddModeData}
+        onChange={(v) => {
+          if (!v) return;
+          setAdvancedAddMode(v as ManualAddMode);
+        }}
+      />
+
+      <Flex
+        sx={{
+          p: 4,
+          borderRadius: 4,
+          bg: 'base.300',
+          _dark: {
+            bg: 'base.850',
+          },
+        }}
+      >
+        {advancedAddMode === 'diffusers' && <AdvancedAddDiffusers />}
+        {advancedAddMode === 'checkpoint' && <AdvancedAddCheckpoint />}
+      </Flex>
+    </Flex>
+  );
+}

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

@@ -0,0 +1,253 @@
+import { Flex, Text } from '@chakra-ui/react';
+import { makeToast } from 'app/components/Toaster';
+import { RootState } from 'app/store/store';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import IAIButton from 'common/components/IAIButton';
+import IAIInput from 'common/components/IAIInput';
+import IAIScrollArea from 'common/components/IAIScrollArea';
+import { addToast } from 'features/system/store/systemSlice';
+import { difference, forEach, intersection, map, values } from 'lodash-es';
+import { ChangeEvent, MouseEvent, useCallback, useState } from 'react';
+import { useTranslation } from 'react-i18next';
+import {
+  SearchFolderResponse,
+  useGetMainModelsQuery,
+  useGetModelsInFolderQuery,
+  useImportMainModelsMutation,
+} from 'services/api/endpoints/models';
+import { setAdvancedAddScanModel } from '../../store/modelManagerSlice';
+
+export default function FoundModelsList() {
+  const searchFolder = useAppSelector(
+    (state: RootState) => state.modelmanager.searchFolder
+  );
+  const [nameFilter, setNameFilter] = useState<string>('');
+
+  // Get paths of models that are already installed
+  const { data: installedModels } = useGetMainModelsQuery();
+
+  // Get all model paths from a given directory
+  const { foundModels, alreadyInstalled, filteredModels } =
+    useGetModelsInFolderQuery(
+      {
+        search_path: searchFolder ? searchFolder : '',
+      },
+      {
+        selectFromResult: ({ data }) => {
+          const installedModelValues = values(installedModels?.entities);
+          const installedModelPaths = map(installedModelValues, 'path');
+          // Only select models those that aren't already installed to Invoke
+          const notInstalledModels = difference(data, installedModelPaths);
+          const alreadyInstalled = intersection(data, installedModelPaths);
+          return {
+            foundModels: data,
+            alreadyInstalled: foundModelsFilter(alreadyInstalled, nameFilter),
+            filteredModels: foundModelsFilter(notInstalledModels, nameFilter),
+          };
+        },
+      }
+    );
+
+  const [importMainModel, { isLoading }] = useImportMainModelsMutation();
+  const dispatch = useAppDispatch();
+  const { t } = useTranslation();
+
+  const quickAddHandler = useCallback(
+    (e: MouseEvent<HTMLButtonElement>) => {
+      const model_name = e.currentTarget.id.split('\\').splice(-1)[0];
+      importMainModel({
+        body: {
+          location: e.currentTarget.id,
+        },
+      })
+        .unwrap()
+        .then((_) => {
+          dispatch(
+            addToast(
+              makeToast({
+                title: `Added Model: ${model_name}`,
+                status: 'success',
+              })
+            )
+          );
+        })
+        .catch((error) => {
+          if (error) {
+            dispatch(
+              addToast(
+                makeToast({
+                  title: 'Faile To Add Model',
+                  status: 'error',
+                })
+              )
+            );
+          }
+        });
+    },
+    [dispatch, importMainModel]
+  );
+
+  const handleSearchFilter = useCallback((e: ChangeEvent<HTMLInputElement>) => {
+    setNameFilter(e.target.value);
+  }, []);
+
+  const renderModels = ({
+    models,
+    showActions = true,
+  }: {
+    models: string[];
+    showActions?: boolean;
+  }) => {
+    return models.map((model) => {
+      return (
+        <Flex
+          sx={{
+            p: 4,
+            gap: 4,
+            alignItems: 'center',
+            borderRadius: 4,
+            bg: 'base.200',
+            _dark: {
+              bg: 'base.800',
+            },
+          }}
+          key={model}
+        >
+          <Flex w="100%" sx={{ flexDirection: 'column', minW: '25%' }}>
+            <Text sx={{ fontWeight: 600 }}>
+              {model.split('\\').slice(-1)[0]}
+            </Text>
+            <Text
+              sx={{
+                fontSize: 'sm',
+                color: 'base.600',
+                _dark: {
+                  color: 'base.400',
+                },
+              }}
+            >
+              {model}
+            </Text>
+          </Flex>
+          {showActions ? (
+            <Flex gap={2}>
+              <IAIButton
+                id={model}
+                onClick={quickAddHandler}
+                isLoading={isLoading}
+              >
+                Quick Add
+              </IAIButton>
+              <IAIButton
+                onClick={() => dispatch(setAdvancedAddScanModel(model))}
+                isLoading={isLoading}
+              >
+                Advanced
+              </IAIButton>
+            </Flex>
+          ) : (
+            <Text
+              sx={{
+                fontWeight: 600,
+                p: 2,
+                borderRadius: 4,
+                color: 'accent.50',
+                bg: 'accent.400',
+                _dark: {
+                  color: 'accent.100',
+                  bg: 'accent.600',
+                },
+              }}
+            >
+              Installed
+            </Text>
+          )}
+        </Flex>
+      );
+    });
+  };
+
+  const renderFoundModels = () => {
+    if (!searchFolder) return;
+
+    if (!foundModels || foundModels.length === 0) {
+      return (
+        <Flex
+          sx={{
+            w: 'full',
+            h: 'full',
+            justifyContent: 'center',
+            alignItems: 'center',
+            height: 96,
+            userSelect: 'none',
+            bg: 'base.200',
+            _dark: {
+              bg: 'base.900',
+            },
+          }}
+        >
+          <Text variant="subtext">No Models Found</Text>
+        </Flex>
+      );
+    }
+
+    return (
+      <Flex
+        sx={{
+          flexDirection: 'column',
+          gap: 2,
+          w: '100%',
+          minW: '50%',
+        }}
+      >
+        <IAIInput
+          onChange={handleSearchFilter}
+          label={t('modelManager.search')}
+          labelPos="side"
+        />
+        <Flex p={2} gap={2}>
+          <Text sx={{ fontWeight: 600 }}>
+            Models Found: {foundModels.length}
+          </Text>
+          <Text
+            sx={{
+              fontWeight: 600,
+              color: 'accent.500',
+              _dark: {
+                color: 'accent.200',
+              },
+            }}
+          >
+            Not Installed: {filteredModels.length}
+          </Text>
+        </Flex>
+
+        <IAIScrollArea offsetScrollbars>
+          <Flex gap={2} flexDirection="column">
+            {renderModels({ models: filteredModels })}
+            {renderModels({ models: alreadyInstalled, showActions: false })}
+          </Flex>
+        </IAIScrollArea>
+      </Flex>
+    );
+  };
+
+  return renderFoundModels();
+}
+
+const foundModelsFilter = (
+  data: SearchFolderResponse | undefined,
+  nameFilter: string
+) => {
+  const filteredModels: SearchFolderResponse = [];
+  forEach(data, (model) => {
+    if (!model) {
+      return;
+    }
+
+    if (model.includes(nameFilter)) {
+      filteredModels.push(model);
+    }
+  });
+  return filteredModels;
+};

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

@@ -0,0 +1,97 @@
+import { Box, Flex, Text } from '@chakra-ui/react';
+import { RootState } from 'app/store/store';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import IAIIconButton from 'common/components/IAIIconButton';
+import IAIMantineSelect from 'common/components/IAIMantineSelect';
+import { motion } from 'framer-motion';
+import { useEffect, useState } from 'react';
+import { FaTimes } from 'react-icons/fa';
+import { setAdvancedAddScanModel } from '../../store/modelManagerSlice';
+import AdvancedAddCheckpoint from './AdvancedAddCheckpoint';
+import AdvancedAddDiffusers from './AdvancedAddDiffusers';
+import { ManualAddMode, advancedAddModeData } from './AdvancedAddModels';
+
+export default function ScanAdvancedAddModels() {
+  const advancedAddScanModel = useAppSelector(
+    (state: RootState) => state.modelmanager.advancedAddScanModel
+  );
+
+  const [advancedAddMode, setAdvancedAddMode] =
+    useState<ManualAddMode>('diffusers');
+
+  const [isCheckpoint, setIsCheckpoint] = useState<boolean>(true);
+
+  useEffect(() => {
+    advancedAddScanModel &&
+    ['.ckpt', '.safetensors', '.pth', '.pt'].some((ext) =>
+      advancedAddScanModel.endsWith(ext)
+    )
+      ? setAdvancedAddMode('checkpoint')
+      : setAdvancedAddMode('diffusers');
+  }, [advancedAddScanModel, setAdvancedAddMode, isCheckpoint]);
+
+  const dispatch = useAppDispatch();
+
+  return (
+    advancedAddScanModel && (
+      <Box
+        as={motion.div}
+        initial={{ x: -100, opacity: 0 }}
+        animate={{ x: 0, opacity: 1, transition: { duration: 0.2 } }}
+        sx={{
+          display: 'flex',
+          flexDirection: 'column',
+          minWidth: '40%',
+          maxHeight: window.innerHeight - 300,
+          overflow: 'scroll',
+          p: 4,
+          gap: 4,
+          borderRadius: 4,
+          bg: 'base.200',
+          _dark: {
+            bg: 'base.800',
+          },
+        }}
+      >
+        <Flex justifyContent="space-between" alignItems="center">
+          <Text size="xl" fontWeight={600}>
+            {isCheckpoint || advancedAddMode === 'checkpoint'
+              ? 'Add Checkpoint Model'
+              : 'Add Diffusers Model'}
+          </Text>
+          <IAIIconButton
+            icon={<FaTimes />}
+            aria-label="Close Advanced"
+            onClick={() => dispatch(setAdvancedAddScanModel(null))}
+            size="sm"
+          />
+        </Flex>
+        <IAIMantineSelect
+          label="Model Type"
+          value={advancedAddMode}
+          data={advancedAddModeData}
+          onChange={(v) => {
+            if (!v) return;
+            setAdvancedAddMode(v as ManualAddMode);
+            if (v === 'checkpoint') {
+              setIsCheckpoint(true);
+            } else {
+              setIsCheckpoint(false);
+            }
+          }}
+        />
+        {isCheckpoint ? (
+          <AdvancedAddCheckpoint
+            key={advancedAddScanModel}
+            model_path={advancedAddScanModel}
+          />
+        ) : (
+          <AdvancedAddDiffusers
+            key={advancedAddScanModel}
+            model_path={advancedAddScanModel}
+          />
+        )}
+      </Box>
+    )
+  );
+}

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

@@ -0,0 +1,25 @@
+import { Flex } from '@chakra-ui/react';
+import FoundModelsList from './FoundModelsList';
+import ScanAdvancedAddModels from './ScanAdvancedAddModels';
+import SearchFolderForm from './SearchFolderForm';
+
+export default function ScanModels() {
+  return (
+    <Flex flexDirection="column" w="100%" gap={4}>
+      <SearchFolderForm />
+      <Flex gap={4}>
+        <Flex
+          sx={{
+            maxHeight: window.innerHeight - 300,
+            overflow: 'scroll',
+            gap: 4,
+            w: '100%',
+          }}
+        >
+          <FoundModelsList />
+        </Flex>
+        <ScanAdvancedAddModels />
+      </Flex>
+    </Flex>
+  );
+}

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

@@ -0,0 +1,139 @@
+import { Flex, Text } from '@chakra-ui/react';
+import { useForm } from '@mantine/form';
+import { RootState } from 'app/store/store';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import IAIIconButton from 'common/components/IAIIconButton';
+import IAIInput from 'common/components/IAIInput';
+import { memo, useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+import { FaSearch, FaSync, FaTrash } from 'react-icons/fa';
+import { useGetModelsInFolderQuery } from 'services/api/endpoints/models';
+import {
+  setAdvancedAddScanModel,
+  setSearchFolder,
+} from '../../store/modelManagerSlice';
+
+type SearchFolderForm = {
+  folder: string;
+};
+
+function SearchFolderForm() {
+  const dispatch = useAppDispatch();
+  const { t } = useTranslation();
+
+  const searchFolder = useAppSelector(
+    (state: RootState) => state.modelmanager.searchFolder
+  );
+
+  const { refetch: refetchFoundModels } = useGetModelsInFolderQuery({
+    search_path: searchFolder ? searchFolder : '',
+  });
+
+  const searchFolderForm = useForm<SearchFolderForm>({
+    initialValues: {
+      folder: '',
+    },
+  });
+
+  const searchFolderFormSubmitHandler = useCallback(
+    (values: SearchFolderForm) => {
+      dispatch(setSearchFolder(values.folder));
+    },
+    [dispatch]
+  );
+
+  const scanAgainHandler = () => {
+    refetchFoundModels();
+  };
+
+  return (
+    <form
+      onSubmit={searchFolderForm.onSubmit((values) =>
+        searchFolderFormSubmitHandler(values)
+      )}
+      style={{ width: '100%' }}
+    >
+      <Flex
+        sx={{
+          w: '100%',
+          gap: 2,
+          borderRadius: 4,
+          alignItems: 'center',
+        }}
+      >
+        <Flex w="100%" alignItems="center" gap={4} minH={12}>
+          <Text
+            sx={{
+              fontSize: 'sm',
+              fontWeight: 600,
+              color: 'base.700',
+              minW: 'max-content',
+              _dark: { color: 'base.300' },
+            }}
+          >
+            Folder
+          </Text>
+          {!searchFolder ? (
+            <IAIInput
+              w="100%"
+              size="md"
+              {...searchFolderForm.getInputProps('folder')}
+            />
+          ) : (
+            <Flex
+              sx={{
+                w: '100%',
+                p: 2,
+                px: 4,
+                bg: 'base.300',
+                borderRadius: 4,
+                fontSize: 'sm',
+                fontWeight: 'bold',
+                _dark: { bg: 'base.700' },
+              }}
+            >
+              {searchFolder}
+            </Flex>
+          )}
+        </Flex>
+
+        <Flex gap={2}>
+          {!searchFolder ? (
+            <IAIIconButton
+              aria-label={t('modelManager.findModels')}
+              tooltip={t('modelManager.findModels')}
+              icon={<FaSearch />}
+              fontSize={18}
+              size="sm"
+              type="submit"
+            />
+          ) : (
+            <IAIIconButton
+              aria-label={t('modelManager.scanAgain')}
+              tooltip={t('modelManager.scanAgain')}
+              icon={<FaSync />}
+              onClick={scanAgainHandler}
+              fontSize={18}
+              size="sm"
+            />
+          )}
+
+          <IAIIconButton
+            aria-label={t('modelManager.clearCheckpointFolder')}
+            tooltip={t('modelManager.clearCheckpointFolder')}
+            icon={<FaTrash />}
+            size="sm"
+            onClick={() => {
+              dispatch(setSearchFolder(null));
+              dispatch(setAdvancedAddScanModel(null));
+            }}
+            isDisabled={!searchFolder}
+            colorScheme="red"
+          />
+        </Flex>
+      </Flex>
+    </form>
+  );
+}
+
+export default memo(SearchFolderForm);

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

@@ -0,0 +1,108 @@
+import { Flex } from '@chakra-ui/react';
+// import { addNewModel } from 'app/socketio/actions';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { useTranslation } from 'react-i18next';
+
+import { SelectItem } from '@mantine/core';
+import { useForm } from '@mantine/form';
+import { makeToast } from 'app/components/Toaster';
+import { RootState } from 'app/store/store';
+import IAIButton from 'common/components/IAIButton';
+import IAIMantineTextInput from 'common/components/IAIMantineInput';
+import IAIMantineSelect from 'common/components/IAIMantineSelect';
+import { addToast } from 'features/system/store/systemSlice';
+import { useImportMainModelsMutation } from 'services/api/endpoints/models';
+
+const predictionSelectData: SelectItem[] = [
+  { label: 'None', value: 'none' },
+  { label: 'v_prediction', value: 'v_prediction' },
+  { label: 'epsilon', value: 'epsilon' },
+  { label: 'sample', value: 'sample' },
+];
+
+type ExtendedImportModelConfig = {
+  location: string;
+  prediction_type?: 'v_prediction' | 'epsilon' | 'sample' | 'none' | undefined;
+};
+
+export default function SimpleAddModels() {
+  const dispatch = useAppDispatch();
+  const { t } = useTranslation();
+
+  const isProcessing = useAppSelector(
+    (state: RootState) => state.system.isProcessing
+  );
+
+  const [importMainModel, { isLoading }] = useImportMainModelsMutation();
+
+  const addModelForm = useForm<ExtendedImportModelConfig>({
+    initialValues: {
+      location: '',
+      prediction_type: undefined,
+    },
+  });
+
+  const handleAddModelSubmit = (values: ExtendedImportModelConfig) => {
+    const importModelResponseBody = {
+      location: values.location,
+      prediction_type:
+        values.prediction_type === 'none' ? undefined : values.prediction_type,
+    };
+
+    importMainModel({ body: importModelResponseBody })
+      .unwrap()
+      .then((_) => {
+        dispatch(
+          addToast(
+            makeToast({
+              title: 'Model Added',
+              status: 'success',
+            })
+          )
+        );
+        addModelForm.reset();
+      })
+      .catch((error) => {
+        if (error) {
+          console.log(error);
+          dispatch(
+            addToast(
+              makeToast({
+                title: `${error.data.detail} `,
+                status: 'error',
+              })
+            )
+          );
+        }
+      });
+  };
+
+  return (
+    <form
+      onSubmit={addModelForm.onSubmit((v) => handleAddModelSubmit(v))}
+      style={{ width: '100%' }}
+    >
+      <Flex flexDirection="column" width="100%" gap={4}>
+        <IAIMantineTextInput
+          label="Model Location"
+          placeholder="Provide a path to a local Diffusers model, local checkpoint / safetensors model or a HuggingFace Repo ID"
+          w="100%"
+          {...addModelForm.getInputProps('location')}
+        />
+        <IAIMantineSelect
+          label="Prediction Type (for Stable Diffusion 2.x Models only)"
+          data={predictionSelectData}
+          defaultValue="none"
+          {...addModelForm.getInputProps('prediction_type')}
+        />
+        <IAIButton
+          type="submit"
+          isLoading={isLoading}
+          isDisabled={isLoading || isProcessing}
+        >
+          {t('modelManager.addModel')}
+        </IAIButton>
+      </Flex>
+    </form>
+  );
+}

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

@@ -0,0 +1,39 @@
+import { ButtonGroup, Flex } from '@chakra-ui/react';
+import IAIButton from 'common/components/IAIButton';
+import { useState } from 'react';
+import { useTranslation } from 'react-i18next';
+import AddModels from './AddModelsPanel/AddModels';
+import ScanModels from './AddModelsPanel/ScanModels';
+
+type AddModelTabs = 'add' | 'scan';
+
+export default function ImportModelsPanel() {
+  const [addModelTab, setAddModelTab] = useState<AddModelTabs>('add');
+  const { t } = useTranslation();
+
+  return (
+    <Flex flexDirection="column" gap={4}>
+      <ButtonGroup isAttached>
+        <IAIButton
+          onClick={() => setAddModelTab('add')}
+          isChecked={addModelTab == 'add'}
+          size="sm"
+          width="100%"
+        >
+          {t('modelManager.addModel')}
+        </IAIButton>
+        <IAIButton
+          onClick={() => setAddModelTab('scan')}
+          isChecked={addModelTab == 'scan'}
+          size="sm"
+          width="100%"
+        >
+          {t('modelManager.scanForModels')}
+        </IAIButton>
+      </ButtonGroup>
+
+      {addModelTab == 'add' && <AddModels />}
+      {addModelTab == 'scan' && <ScanModels />}
+    </Flex>
+  );
+}

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

@@ -1,11 +1,4 @@
-import {
-  Flex,
-  Radio,
-  RadioGroup,
-  Text,
-  Tooltip,
-  useColorMode,
-} from '@chakra-ui/react';
+import { Flex, Radio, RadioGroup, Text, Tooltip } from '@chakra-ui/react';
 import { makeToast } from 'app/components/Toaster';
 import { useAppDispatch } from 'app/store/storeHooks';
 import IAIButton from 'common/components/IAIButton';
@@ -23,7 +16,6 @@ import {
   useMergeMainModelsMutation,
 } from 'services/api/endpoints/models';
 import { BaseModelType, MergeModelConfig } from 'services/api/types';
-import { mode } from 'theme/util/mode';
 
 const baseModelTypeSelectData = [
   { label: 'Stable Diffusion 1', value: 'sd-1' },
@@ -38,7 +30,6 @@ type MergeInterpolationMethods =
 
 export default function MergeModelsPanel() {
   const { t } = useTranslation();
-  const { colorMode } = useColorMode();
   const dispatch = useAppDispatch();
 
   const { data } = useGetMainModelsQuery();
@@ -67,10 +58,10 @@ export default function MergeModelsPanel() {
   }, [sd1DiffusersModels, sd2DiffusersModels]);
 
   const [modelOne, setModelOne] = useState<string | null>(
-    Object.keys(modelsMap[baseModel])[0]
+    Object.keys(modelsMap[baseModel as keyof typeof modelsMap])[0]
   );
   const [modelTwo, setModelTwo] = useState<string | null>(
-    Object.keys(modelsMap[baseModel])[1]
+    Object.keys(modelsMap[baseModel as keyof typeof modelsMap])[1]
   );
 
   const [modelThree, setModelThree] = useState<string | null>(null);
@@ -98,9 +89,9 @@ export default function MergeModelsPanel() {
     modelsMap[baseModel as keyof typeof modelsMap]
   ).filter((model) => model !== modelOne && model !== modelThree);
 
-  const modelThreeList = Object.keys(modelsMap[baseModel]).filter(
-    (model) => model !== modelOne && model !== modelTwo
-  );
+  const modelThreeList = Object.keys(
+    modelsMap[baseModel as keyof typeof modelsMap]
+  ).filter((model) => model !== modelOne && model !== modelTwo);
 
   const handleBaseModelChange = (v: string) => {
     setBaseModel(v as BaseModelType);
@@ -125,9 +116,9 @@ export default function MergeModelsPanel() {
         mergedModelName !== '' ? mergedModelName : models_names.join('-'),
       alpha: modelMergeAlpha,
       interp: modelMergeInterp,
-      // model_merge_save_path:
-      //   modelMergeSaveLocType === 'root' ? null : modelMergeCustomSaveLoc,
       force: modelMergeForce,
+      merge_dest_directory:
+        modelMergeSaveLocType === 'root' ? undefined : modelMergeCustomSaveLoc,
     };
 
     mergeModels({
@@ -227,7 +218,10 @@ export default function MergeModelsPanel() {
           padding: 4,
           borderRadius: 'base',
           gap: 4,
-          bg: mode('base.100', 'base.800')(colorMode),
+          bg: 'base.200',
+          _dark: {
+            bg: 'base.800',
+          },
         }}
       >
         <IAISlider
@@ -252,7 +246,10 @@ export default function MergeModelsPanel() {
           padding: 4,
           borderRadius: 'base',
           gap: 4,
-          bg: mode('base.100', 'base.800')(colorMode),
+          bg: 'base.200',
+          _dark: {
+            bg: 'base.800',
+          },
         }}
       >
         <Text fontWeight={500} fontSize="sm" variant="subtext">
@@ -288,13 +285,16 @@ export default function MergeModelsPanel() {
         </RadioGroup>
       </Flex>
 
-      {/* <Flex
+      <Flex
         sx={{
           flexDirection: 'column',
           padding: 4,
           borderRadius: 'base',
           gap: 4,
-          bg: 'base.900',
+          bg: 'base.200',
+          _dark: {
+            bg: 'base.900',
+          },
         }}
       >
         <Flex columnGap={4}>
@@ -324,7 +324,7 @@ export default function MergeModelsPanel() {
             onChange={(e) => setModelMergeCustomSaveLoc(e.target.value)}
           />
         )}
-      </Flex> */}
+      </Flex>
 
       <IAISimpleCheckbox
         label={t('modelManager.ignoreMismatch')}

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

@@ -1,33 +1,26 @@
-import { Divider, Flex, Text } from '@chakra-ui/react';
+import { Badge, Divider, Flex, Text } from '@chakra-ui/react';
 import { useForm } from '@mantine/form';
 import { makeToast } from 'app/components/Toaster';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import IAIButton from 'common/components/IAIButton';
 import IAIMantineTextInput from 'common/components/IAIMantineInput';
-import IAIMantineSelect from 'common/components/IAIMantineSelect';
+import IAISimpleCheckbox from 'common/components/IAISimpleCheckbox';
 import { MODEL_TYPE_MAP } from 'features/parameters/types/constants';
 import { selectIsBusy } from 'features/system/store/systemSelectors';
 import { addToast } from 'features/system/store/systemSlice';
-import { useCallback } from 'react';
+import { useCallback, useEffect, useState } from 'react';
 import { useTranslation } from 'react-i18next';
 import {
   CheckpointModelConfigEntity,
+  useGetCheckpointConfigsQuery,
   useUpdateMainModelsMutation,
 } from 'services/api/endpoints/models';
 import { CheckpointModelConfig } from 'services/api/types';
+import BaseModelSelect from '../shared/BaseModelSelect';
+import CheckpointConfigsSelect from '../shared/CheckpointConfigsSelect';
+import ModelVariantSelect from '../shared/ModelVariantSelect';
 import ModelConvert from './ModelConvert';
 
-const baseModelSelectData = [
-  { value: 'sd-1', label: MODEL_TYPE_MAP['sd-1'] },
-  { value: 'sd-2', label: MODEL_TYPE_MAP['sd-2'] },
-];
-
-const variantSelectData = [
-  { value: 'normal', label: 'Normal' },
-  { value: 'inpaint', label: 'Inpaint' },
-  { value: 'depth', label: 'Depth' },
-];
-
 type CheckpointModelEditProps = {
   model: CheckpointModelConfigEntity;
 };
@@ -38,6 +31,15 @@ export default function CheckpointModelEdit(props: CheckpointModelEditProps) {
   const { model } = props;
 
   const [updateMainModel, { isLoading }] = useUpdateMainModelsMutation();
+  const { data: availableCheckpointConfigs } = useGetCheckpointConfigsQuery();
+
+  const [useCustomConfig, setUseCustomConfig] = useState<boolean>(false);
+
+  useEffect(() => {
+    if (!availableCheckpointConfigs?.includes(model.config)) {
+      setUseCustomConfig(true);
+    }
+  }, [availableCheckpointConfigs, model.config]);
 
   const dispatch = useAppDispatch();
   const { t } = useTranslation();
@@ -80,7 +82,7 @@ export default function CheckpointModelEdit(props: CheckpointModelEditProps) {
             )
           );
         })
-        .catch((error) => {
+        .catch((_) => {
           checkpointEditForm.reset();
           dispatch(
             addToast(
@@ -113,7 +115,20 @@ export default function CheckpointModelEdit(props: CheckpointModelEditProps) {
             {MODEL_TYPE_MAP[model.base_model]} Model
           </Text>
         </Flex>
-        <ModelConvert model={model} />
+        {!['sdxl', 'sdxl-refiner'].includes(model.base_model) ? (
+          <ModelConvert model={model} />
+        ) : (
+          <Badge
+            sx={{
+              p: 2,
+              borderRadius: 4,
+              bg: 'error.200',
+              _dark: { bg: 'error.400' },
+            }}
+          >
+            Conversion Not Supported
+          </Badge>
+        )}
       </Flex>
       <Divider />
 
@@ -128,21 +143,24 @@ export default function CheckpointModelEdit(props: CheckpointModelEditProps) {
           )}
         >
           <Flex flexDirection="column" overflowY="scroll" gap={4}>
+            <IAIMantineTextInput
+              label={t('modelManager.name')}
+              {...checkpointEditForm.getInputProps('model_name')}
+            />
             <IAIMantineTextInput
               label={t('modelManager.description')}
               {...checkpointEditForm.getInputProps('description')}
             />
-            <IAIMantineSelect
-              label={t('modelManager.baseModel')}
-              data={baseModelSelectData}
+            <BaseModelSelect
+              required
               {...checkpointEditForm.getInputProps('base_model')}
             />
-            <IAIMantineSelect
-              label={t('modelManager.variant')}
-              data={variantSelectData}
+            <ModelVariantSelect
+              required
               {...checkpointEditForm.getInputProps('variant')}
             />
             <IAIMantineTextInput
+              required
               label={t('modelManager.modelLocation')}
               {...checkpointEditForm.getInputProps('path')}
             />
@@ -150,10 +168,27 @@ export default function CheckpointModelEdit(props: CheckpointModelEditProps) {
               label={t('modelManager.vaeLocation')}
               {...checkpointEditForm.getInputProps('vae')}
             />
-            <IAIMantineTextInput
-              label={t('modelManager.config')}
-              {...checkpointEditForm.getInputProps('config')}
-            />
+
+            <Flex flexDirection="column" gap={2}>
+              {!useCustomConfig ? (
+                <CheckpointConfigsSelect
+                  required
+                  {...checkpointEditForm.getInputProps('config')}
+                />
+              ) : (
+                <IAIMantineTextInput
+                  required
+                  label={t('modelManager.config')}
+                  {...checkpointEditForm.getInputProps('config')}
+                />
+              )}
+              <IAISimpleCheckbox
+                isChecked={useCustomConfig}
+                onChange={() => setUseCustomConfig(!useCustomConfig)}
+                label="Use Custom Config"
+              />
+            </Flex>
+
             <IAIButton
               type="submit"
               isDisabled={isBusy || isLoading}

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

@@ -4,7 +4,6 @@ import { makeToast } from 'app/components/Toaster';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import IAIButton from 'common/components/IAIButton';
 import IAIMantineTextInput from 'common/components/IAIMantineInput';
-import IAIMantineSelect from 'common/components/IAIMantineSelect';
 import { MODEL_TYPE_MAP } from 'features/parameters/types/constants';
 import { selectIsBusy } from 'features/system/store/systemSelectors';
 import { addToast } from 'features/system/store/systemSlice';
@@ -15,22 +14,13 @@ import {
   useUpdateMainModelsMutation,
 } from 'services/api/endpoints/models';
 import { DiffusersModelConfig } from 'services/api/types';
+import BaseModelSelect from '../shared/BaseModelSelect';
+import ModelVariantSelect from '../shared/ModelVariantSelect';
 
 type DiffusersModelEditProps = {
   model: DiffusersModelConfigEntity;
 };
 
-const baseModelSelectData = [
-  { value: 'sd-1', label: MODEL_TYPE_MAP['sd-1'] },
-  { value: 'sd-2', label: MODEL_TYPE_MAP['sd-2'] },
-];
-
-const variantSelectData = [
-  { value: 'normal', label: 'Normal' },
-  { value: 'inpaint', label: 'Inpaint' },
-  { value: 'depth', label: 'Depth' },
-];
-
 export default function DiffusersModelEdit(props: DiffusersModelEditProps) {
   const isBusy = useAppSelector(selectIsBusy);
 
@@ -65,6 +55,7 @@ export default function DiffusersModelEdit(props: DiffusersModelEditProps) {
         model_name: model.model_name,
         body: values,
       };
+
       updateMainModel(responseBody)
         .unwrap()
         .then((payload) => {
@@ -78,7 +69,7 @@ export default function DiffusersModelEdit(props: DiffusersModelEditProps) {
             )
           );
         })
-        .catch((error) => {
+        .catch((_) => {
           diffusersEditForm.reset();
           dispatch(
             addToast(
@@ -118,21 +109,24 @@ export default function DiffusersModelEdit(props: DiffusersModelEditProps) {
         )}
       >
         <Flex flexDirection="column" overflowY="scroll" gap={4}>
+          <IAIMantineTextInput
+            label={t('modelManager.name')}
+            {...diffusersEditForm.getInputProps('model_name')}
+          />
           <IAIMantineTextInput
             label={t('modelManager.description')}
             {...diffusersEditForm.getInputProps('description')}
           />
-          <IAIMantineSelect
-            label={t('modelManager.baseModel')}
-            data={baseModelSelectData}
+          <BaseModelSelect
+            required
             {...diffusersEditForm.getInputProps('base_model')}
           />
-          <IAIMantineSelect
-            label={t('modelManager.variant')}
-            data={variantSelectData}
+          <ModelVariantSelect
+            required
             {...diffusersEditForm.getInputProps('variant')}
           />
           <IAIMantineTextInput
+            required
             label={t('modelManager.modelLocation')}
             {...diffusersEditForm.getInputProps('path')}
           />

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

@@ -1,9 +1,18 @@
-import { Flex, ListItem, Text, UnorderedList } from '@chakra-ui/react';
-// import { convertToDiffusers } from 'app/socketio/actions';
+import {
+  Flex,
+  ListItem,
+  Radio,
+  RadioGroup,
+  Text,
+  Tooltip,
+  UnorderedList,
+} from '@chakra-ui/react';
 import { makeToast } from 'app/components/Toaster';
+// import { convertToDiffusers } from 'app/socketio/actions';
 import { useAppDispatch } from 'app/store/storeHooks';
 import IAIAlertDialog from 'common/components/IAIAlertDialog';
 import IAIButton from 'common/components/IAIButton';
+import IAIInput from 'common/components/IAIInput';
 import { addToast } from 'features/system/store/systemSlice';
 import { useEffect, useState } from 'react';
 import { useTranslation } from 'react-i18next';
@@ -15,6 +24,8 @@ interface ModelConvertProps {
   model: CheckpointModelConfig;
 }
 
+type SaveLocation = 'InvokeAIRoot' | 'Custom';
+
 export default function ModelConvert(props: ModelConvertProps) {
   const { model } = props;
 
@@ -23,22 +34,51 @@ export default function ModelConvert(props: ModelConvertProps) {
 
   const [convertModel, { isLoading }] = useConvertMainModelsMutation();
 
-  const [saveLocation, setSaveLocation] = useState<string>('same');
+  const [saveLocation, setSaveLocation] =
+    useState<SaveLocation>('InvokeAIRoot');
   const [customSaveLocation, setCustomSaveLocation] = useState<string>('');
 
   useEffect(() => {
-    setSaveLocation('same');
+    setSaveLocation('InvokeAIRoot');
   }, [model]);
 
   const modelConvertCancelHandler = () => {
-    setSaveLocation('same');
+    setSaveLocation('InvokeAIRoot');
   };
 
   const modelConvertHandler = () => {
     const responseBody = {
       base_model: model.base_model,
       model_name: model.model_name,
+      params: {
+        convert_dest_directory:
+          saveLocation === 'Custom' ? customSaveLocation : undefined,
+      },
     };
+
+    if (saveLocation === 'Custom' && customSaveLocation === '') {
+      dispatch(
+        addToast(
+          makeToast({
+            title: t('modelManager.noCustomLocationProvided'),
+            status: 'error',
+          })
+        )
+      );
+      return;
+    }
+
+    dispatch(
+      addToast(
+        makeToast({
+          title: `${t('modelManager.convertingModelBegin')}: ${
+            model.model_name
+          }`,
+          status: 'success',
+        })
+      )
+    );
+
     convertModel(responseBody)
       .unwrap()
       .then((_) => {
@@ -94,35 +134,30 @@ export default function ModelConvert(props: ModelConvertProps) {
         <Text>{t('modelManager.convertToDiffusersHelpText6')}</Text>
       </Flex>
 
-      {/* <Flex flexDir="column" gap={4}>
+      <Flex flexDir="column" gap={2}>
         <Flex marginTop={4} flexDir="column" gap={2}>
           <Text fontWeight="600">
             {t('modelManager.convertToDiffusersSaveLocation')}
           </Text>
-          <RadioGroup value={saveLocation} onChange={(v) => setSaveLocation(v)}>
+          <RadioGroup
+            value={saveLocation}
+            onChange={(v) => setSaveLocation(v as SaveLocation)}
+          >
             <Flex gap={4}>
-              <Radio value="same">
-                <Tooltip label="Save converted model in the same folder">
-                  {t('modelManager.sameFolder')}
-                </Tooltip>
-              </Radio>
-
-              <Radio value="root">
+              <Radio value="InvokeAIRoot">
                 <Tooltip label="Save converted model in the InvokeAI root folder">
                   {t('modelManager.invokeRoot')}
                 </Tooltip>
               </Radio>
-
-              <Radio value="custom">
+              <Radio value="Custom">
                 <Tooltip label="Save converted model in a custom folder">
                   {t('modelManager.custom')}
                 </Tooltip>
               </Radio>
             </Flex>
           </RadioGroup>
-        </Flex> */}
-
-      {/* {saveLocation === 'custom' && (
+        </Flex>
+        {saveLocation === 'Custom' && (
           <Flex flexDirection="column" rowGap={2}>
             <Text fontWeight="500" fontSize="sm" variant="subtext">
               {t('modelManager.customSaveLocation')}
@@ -130,13 +165,13 @@ export default function ModelConvert(props: ModelConvertProps) {
             <IAIInput
               value={customSaveLocation}
               onChange={(e) => {
-                if (e.target.value !== '')
-                  setCustomSaveLocation(e.target.value);
+                setCustomSaveLocation(e.target.value);
               }}
               width="full"
             />
           </Flex>
-        )} */}
+        )}
+      </Flex>
     </IAIAlertDialog>
   );
 }

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

@@ -44,10 +44,6 @@ const ModelList = (props: ModelListProps) => {
 
   return (
     <Flex flexDirection="column" rowGap={4} width="50%" minWidth="50%">
-      <IAIInput
-        onChange={handleSearchFilter}
-        label={t('modelManager.search')}
-      />
       <Flex
         flexDirection="column"
         gap={4}
@@ -79,6 +75,12 @@ const ModelList = (props: ModelListProps) => {
           </IAIButton>
         </ButtonGroup>
 
+        <IAIInput
+          onChange={handleSearchFilter}
+          label={t('modelManager.search')}
+          labelPos="side"
+        />
+
         {['all', 'diffusers'].includes(modelFormatFilter) &&
           filteredDiffusersModels.length > 0 && (
             <Flex sx={{ gap: 2, flexDir: 'column' }}>

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

@@ -1,10 +1,12 @@
 import { DeleteIcon } from '@chakra-ui/icons';
 import { Flex, Text, Tooltip } from '@chakra-ui/react';
-import { useAppSelector } from 'app/store/storeHooks';
+import { makeToast } from 'app/components/Toaster';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import IAIAlertDialog from 'common/components/IAIAlertDialog';
 import IAIButton from 'common/components/IAIButton';
 import IAIIconButton from 'common/components/IAIIconButton';
 import { selectIsBusy } from 'features/system/store/systemSelectors';
+import { addToast } from 'features/system/store/systemSlice';
 import { useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
 import {
@@ -21,6 +23,7 @@ type ModelListItemProps = {
 export default function ModelListItem(props: ModelListItemProps) {
   const isBusy = useAppSelector(selectIsBusy);
   const { t } = useTranslation();
+  const dispatch = useAppDispatch();
   const [deleteMainModel] = useDeleteMainModelsMutation();
 
   const { model, isSelected, setSelectedModelId } = props;
@@ -30,9 +33,34 @@ export default function ModelListItem(props: ModelListItemProps) {
   }, [model.id, setSelectedModelId]);
 
   const handleModelDelete = useCallback(() => {
-    deleteMainModel(model);
+    deleteMainModel(model)
+      .unwrap()
+      .then((_) => {
+        dispatch(
+          addToast(
+            makeToast({
+              title: `${t('modelManager.modelDeleted')}: ${model.model_name}`,
+              status: 'success',
+            })
+          )
+        );
+      })
+      .catch((error) => {
+        if (error) {
+          dispatch(
+            addToast(
+              makeToast({
+                title: `${t('modelManager.modelDeleteFailed')}: ${
+                  model.model_name
+                }`,
+                status: 'success',
+              })
+            )
+          );
+        }
+      });
     setSelectedModelId(undefined);
-  }, [deleteMainModel, model, setSelectedModelId]);
+  }, [deleteMainModel, model, setSelectedModelId, dispatch, t]);
 
   return (
     <Flex sx={{ gap: 2, alignItems: 'center', w: 'full' }}>

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

@@ -0,0 +1,28 @@
+import IAIMantineSelect, {
+  IAISelectDataType,
+  IAISelectProps,
+} from 'common/components/IAIMantineSelect';
+import { MODEL_TYPE_MAP } from 'features/parameters/types/constants';
+import { useTranslation } from 'react-i18next';
+
+const baseModelSelectData: IAISelectDataType[] = [
+  { value: 'sd-1', label: MODEL_TYPE_MAP['sd-1'] },
+  { value: 'sd-2', label: MODEL_TYPE_MAP['sd-2'] },
+  { value: 'sdxl', label: MODEL_TYPE_MAP['sdxl'] },
+  { value: 'sdxl-refiner', label: MODEL_TYPE_MAP['sdxl-refiner'] },
+];
+
+type BaseModelSelectProps = Omit<IAISelectProps, 'data'>;
+
+export default function BaseModelSelect(props: BaseModelSelectProps) {
+  const { ...rest } = props;
+  const { t } = useTranslation();
+
+  return (
+    <IAIMantineSelect
+      label={t('modelManager.baseModel')}
+      data={baseModelSelectData}
+      {...rest}
+    />
+  );
+}

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

@@ -0,0 +1,22 @@
+import IAIMantineSelect, {
+  IAISelectProps,
+} from 'common/components/IAIMantineSelect';
+import { useGetCheckpointConfigsQuery } from 'services/api/endpoints/models';
+
+type CheckpointConfigSelectProps = Omit<IAISelectProps, 'data'>;
+
+export default function CheckpointConfigsSelect(
+  props: CheckpointConfigSelectProps
+) {
+  const { data: availableCheckpointConfigs } = useGetCheckpointConfigsQuery();
+  const { ...rest } = props;
+
+  return (
+    <IAIMantineSelect
+      label="Config File"
+      placeholder="Select A Config File"
+      data={availableCheckpointConfigs ? availableCheckpointConfigs : []}
+      {...rest}
+    />
+  );
+}

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

@@ -0,0 +1,26 @@
+import IAIMantineSelect, {
+  IAISelectDataType,
+  IAISelectProps,
+} from 'common/components/IAIMantineSelect';
+import { useTranslation } from 'react-i18next';
+
+const variantSelectData: IAISelectDataType[] = [
+  { value: 'normal', label: 'Normal' },
+  { value: 'inpaint', label: 'Inpaint' },
+  { value: 'depth', label: 'Depth' },
+];
+
+type VariantSelectProps = Omit<IAISelectProps, 'data'>;
+
+export default function ModelVariantSelect(props: VariantSelectProps) {
+  const { ...rest } = props;
+  const { t } = useTranslation();
+
+  return (
+    <IAIMantineSelect
+      label={t('modelManager.variant')}
+      data={variantSelectData}
+      {...rest}
+    />
+  );
+}

invokeai/frontend/web/src/features/ui/components/tabs/UnifiedCanvas/UnifiedCanvasBeta/UnifiedCanvasToolbar/UnifiedCanvasCopyToClipboard.tsx

@@ -4,6 +4,8 @@ import IAIIconButton from 'common/components/IAIIconButton';
 import { canvasCopiedToClipboard } from 'features/canvas/store/actions';
 import { isStagingSelector } from 'features/canvas/store/canvasSelectors';
 import { getCanvasBaseLayer } from 'features/canvas/util/konvaInstanceProvider';
+import { useCopyImageToClipboard } from 'features/ui/hooks/useCopyImageToClipboard';
+import { useCallback } from 'react';
 import { useHotkeys } from 'react-hotkeys-hook';
 import { useTranslation } from 'react-i18next';
 import { FaCopy } from 'react-icons/fa';
@@ -11,6 +13,7 @@ import { FaCopy } from 'react-icons/fa';
 export default function UnifiedCanvasCopyToClipboard() {
   const isStaging = useAppSelector(isStagingSelector);
   const canvasBaseLayer = getCanvasBaseLayer();
+  const { isClipboardAPIAvailable } = useCopyImageToClipboard();
 
   const isProcessing = useAppSelector(
     (state: RootState) => state.system.isProcessing
@@ -25,15 +28,22 @@ export default function UnifiedCanvasCopyToClipboard() {
       handleCopyImageToClipboard();
     },
     {
-      enabled: () => !isStaging,
+      enabled: () => !isStaging && isClipboardAPIAvailable,
       preventDefault: true,
     },
-    [canvasBaseLayer, isProcessing]
+    [canvasBaseLayer, isProcessing, isClipboardAPIAvailable]
   );
 
-  const handleCopyImageToClipboard = () => {
+  const handleCopyImageToClipboard = useCallback(() => {
+    if (!isClipboardAPIAvailable) {
+      return;
+    }
     dispatch(canvasCopiedToClipboard());
-  };
+  }, [dispatch, isClipboardAPIAvailable]);
+
+  if (!isClipboardAPIAvailable) {
+    return null;
+  }
 
   return (
     <IAIIconButton

invokeai/frontend/web/src/features/ui/hooks/useCopyImageToClipboard.ts

@@ -0,0 +1,52 @@
+import { useAppToaster } from 'app/components/Toaster';
+import { useCallback, useMemo } from 'react';
+import { useTranslation } from 'react-i18next';
+
+export const useCopyImageToClipboard = () => {
+  const toaster = useAppToaster();
+  const { t } = useTranslation();
+
+  const isClipboardAPIAvailable = useMemo(() => {
+    return Boolean(navigator.clipboard) && Boolean(window.ClipboardItem);
+  }, []);
+
+  const copyImageToClipboard = useCallback(
+    async (image_url: string) => {
+      if (!isClipboardAPIAvailable) {
+        toaster({
+          title: t('toast.problemCopyingImage'),
+          description: "Your browser doesn't support the Clipboard API.",
+          status: 'error',
+          duration: 2500,
+          isClosable: true,
+        });
+      }
+      try {
+        const response = await fetch(image_url);
+        const blob = await response.blob();
+        await navigator.clipboard.write([
+          new ClipboardItem({
+            [blob.type]: blob,
+          }),
+        ]);
+        toaster({
+          title: t('toast.imageCopied'),
+          status: 'success',
+          duration: 2500,
+          isClosable: true,
+        });
+      } catch (err) {
+        toaster({
+          title: t('toast.problemCopyingImage'),
+          description: String(err),
+          status: 'error',
+          duration: 2500,
+          isClosable: true,
+        });
+      }
+    },
+    [isClipboardAPIAvailable, t, toaster]
+  );
+
+  return { isClipboardAPIAvailable, copyImageToClipboard };
+};

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

@@ -1,7 +1,5 @@
 import { SchedulerParam } from 'features/parameters/types/parameterSchemas';
 
-export type AddNewModelType = 'ckpt' | 'diffusers' | null;
-
 export type Coordinates = {
   x: number;
   y: number;
@@ -22,7 +20,6 @@ export interface UIState {
   shouldUseCanvasBetaLayout: boolean;
   shouldShowExistingModelsInSearch: boolean;
   shouldUseSliders: boolean;
-  addNewModelUIOption: AddNewModelType;
   shouldHidePreview: boolean;
   shouldPinGallery: boolean;
   shouldShowGallery: boolean;

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

@@ -5,7 +5,9 @@ import {
   BaseModelType,
   CheckpointModelConfig,
   ControlNetModelConfig,
+  ConvertModelConfig,
   DiffusersModelConfig,
+  ImportModelConfig,
   LoRAModelConfig,
   MainModelConfig,
   MergeModelConfig,
@@ -13,8 +15,9 @@ import {
   VaeModelConfig,
 } from 'services/api/types';
 
+import queryString from 'query-string';
 import { ApiFullTagDescription, LIST_TAG, api } from '..';
-import { paths } from '../schema';
+import { operations, paths } from '../schema';
 
 export type DiffusersModelConfigEntity = DiffusersModelConfig & { id: string };
 export type CheckpointModelConfigEntity = CheckpointModelConfig & {
@@ -62,6 +65,7 @@ type DeleteMainModelResponse = void;
 type ConvertMainModelArg = {
   base_model: BaseModelType;
   model_name: string;
+  params: ConvertModelConfig;
 };
 
 type ConvertMainModelResponse =
@@ -75,6 +79,28 @@ type MergeMainModelArg = {
 type MergeMainModelResponse =
   paths['/api/v1/models/merge/{base_model}']['put']['responses']['200']['content']['application/json'];
 
+type ImportMainModelArg = {
+  body: ImportModelConfig;
+};
+
+type ImportMainModelResponse =
+  paths['/api/v1/models/import']['post']['responses']['201']['content']['application/json'];
+
+type AddMainModelArg = {
+  body: MainModelConfig;
+};
+
+type AddMainModelResponse =
+  paths['/api/v1/models/add']['post']['responses']['201']['content']['application/json'];
+
+export type SearchFolderResponse =
+  paths['/api/v1/models/search']['get']['responses']['200']['content']['application/json'];
+
+type CheckpointConfigsResponse =
+  paths['/api/v1/models/ckpt_confs']['get']['responses']['200']['content']['application/json'];
+
+type SearchFolderArg = operations['search_for_models']['parameters']['query'];
+
 const mainModelsAdapter = createEntityAdapter<MainModelConfigEntity>({
   sortComparer: (a, b) => a.model_name.localeCompare(b.model_name),
 });
@@ -160,6 +186,29 @@ export const modelsApi = api.injectEndpoints({
       },
       invalidatesTags: [{ type: 'MainModel', id: LIST_TAG }],
     }),
+    importMainModels: build.mutation<
+      ImportMainModelResponse,
+      ImportMainModelArg
+    >({
+      query: ({ body }) => {
+        return {
+          url: `models/import`,
+          method: 'POST',
+          body: body,
+        };
+      },
+      invalidatesTags: [{ type: 'MainModel', id: LIST_TAG }],
+    }),
+    addMainModels: build.mutation<AddMainModelResponse, AddMainModelArg>({
+      query: ({ body }) => {
+        return {
+          url: `models/add`,
+          method: 'POST',
+          body: body,
+        };
+      },
+      invalidatesTags: [{ type: 'MainModel', id: LIST_TAG }],
+    }),
     deleteMainModels: build.mutation<
       DeleteMainModelResponse,
       DeleteMainModelArg
@@ -176,10 +225,11 @@ export const modelsApi = api.injectEndpoints({
       ConvertMainModelResponse,
       ConvertMainModelArg
     >({
-      query: ({ base_model, model_name }) => {
+      query: ({ base_model, model_name, params }) => {
         return {
           url: `models/convert/${base_model}/main/${model_name}`,
           method: 'PUT',
+          params: params,
         };
       },
       invalidatesTags: [{ type: 'MainModel', id: LIST_TAG }],
@@ -328,6 +378,36 @@ export const modelsApi = api.injectEndpoints({
         );
       },
     }),
+    getModelsInFolder: build.query<SearchFolderResponse, SearchFolderArg>({
+      query: (arg) => {
+        const folderQueryStr = queryString.stringify(arg, {});
+        return {
+          url: `/models/search?${folderQueryStr}`,
+        };
+      },
+      providesTags: (result, error, arg) => {
+        const tags: ApiFullTagDescription[] = [
+          { type: 'ScannedModels', id: LIST_TAG },
+        ];
+
+        if (result) {
+          tags.push(
+            ...result.map((id) => ({
+              type: 'ScannedModels' as const,
+              id,
+            }))
+          );
+        }
+        return tags;
+      },
+    }),
+    getCheckpointConfigs: build.query<CheckpointConfigsResponse, void>({
+      query: () => {
+        return {
+          url: `/models/ckpt_confs`,
+        };
+      },
+    }),
   }),
 });
 
@@ -339,6 +419,10 @@ export const {
   useGetVaeModelsQuery,
   useUpdateMainModelsMutation,
   useDeleteMainModelsMutation,
+  useImportMainModelsMutation,
+  useAddMainModelsMutation,
   useConvertMainModelsMutation,
   useMergeMainModelsMutation,
+  useGetModelsInFolderQuery,
+  useGetCheckpointConfigsQuery,
 } = modelsApi;

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

@@ -84,7 +84,7 @@ export type paths = {
     delete: operations["del_model"];
     /**
      * Update Model 
-     * @description Add Model
+     * @description Update model contents with a new config. If the model name or base fields are changed, then the model is renamed.
      */
     patch: operations["update_model"];
   };
@@ -102,13 +102,6 @@ export type paths = {
      */
     post: operations["add_model"];
   };
-  "/api/v1/models/rename/{base_model}/{model_type}/{model_name}": {
-    /**
-     * Rename Model 
-     * @description Rename a model
-     */
-    post: operations["rename_model"];
-  };
   "/api/v1/models/convert/{base_model}/{model_type}/{model_name}": {
     /**
      * Convert Model 
@@ -323,7 +316,7 @@ export type components = {
      * @description An enumeration. 
      * @enum {string}
      */
-    BaseModelType: "sd-1" | "sd-2";
+    BaseModelType: "sd-1" | "sd-2" | "sdxl" | "sdxl-refiner";
     /** BoardChanges */
     BoardChanges: {
       /**
@@ -1226,7 +1219,7 @@ export type components = {
        * @description The nodes in this graph
        */
       nodes?: {
-        [key: string]: (components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["RealESRGANInvocation"] | components["schemas"]["RestoreFaceInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"]) | undefined;
+        [key: string]: (components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["RealESRGANInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"]) | undefined;
       };
       /**
        * Edges 
@@ -1269,7 +1262,7 @@ export type components = {
        * @description The results of node executions
        */
       results: {
-        [key: string]: (components["schemas"]["ImageOutput"] | components["schemas"]["MaskOutput"] | components["schemas"]["ControlOutput"] | components["schemas"]["ModelLoaderOutput"] | components["schemas"]["LoraLoaderOutput"] | components["schemas"]["VaeLoaderOutput"] | components["schemas"]["MetadataAccumulatorOutput"] | components["schemas"]["PromptOutput"] | components["schemas"]["PromptCollectionOutput"] | components["schemas"]["CompelOutput"] | components["schemas"]["ClipSkipInvocationOutput"] | components["schemas"]["IntOutput"] | components["schemas"]["FloatOutput"] | components["schemas"]["LatentsOutput"] | components["schemas"]["IntCollectionOutput"] | components["schemas"]["FloatCollectionOutput"] | components["schemas"]["ImageCollectionOutput"] | components["schemas"]["NoiseOutput"] | components["schemas"]["GraphInvocationOutput"] | components["schemas"]["IterateInvocationOutput"] | components["schemas"]["CollectInvocationOutput"]) | undefined;
+        [key: string]: (components["schemas"]["ImageOutput"] | components["schemas"]["MaskOutput"] | components["schemas"]["ControlOutput"] | components["schemas"]["ModelLoaderOutput"] | components["schemas"]["LoraLoaderOutput"] | components["schemas"]["VaeLoaderOutput"] | components["schemas"]["MetadataAccumulatorOutput"] | components["schemas"]["IntCollectionOutput"] | components["schemas"]["FloatCollectionOutput"] | components["schemas"]["ImageCollectionOutput"] | components["schemas"]["CompelOutput"] | components["schemas"]["ClipSkipInvocationOutput"] | components["schemas"]["LatentsOutput"] | components["schemas"]["IntOutput"] | components["schemas"]["FloatOutput"] | components["schemas"]["NoiseOutput"] | components["schemas"]["PromptOutput"] | components["schemas"]["PromptCollectionOutput"] | components["schemas"]["SDXLModelLoaderOutput"] | components["schemas"]["SDXLRefinerModelLoaderOutput"] | components["schemas"]["GraphInvocationOutput"] | components["schemas"]["IterateInvocationOutput"] | components["schemas"]["CollectInvocationOutput"]) | undefined;
       };
       /**
        * Errors 
@@ -2067,6 +2060,12 @@ export type components = {
        * @default false
        */
       tiled?: boolean;
+      /**
+       * Fp32 
+       * @description Decode in full precision 
+       * @default false
+       */
+      fp32?: boolean;
     };
     /**
      * ImageUrlsDTO 
@@ -2522,6 +2521,12 @@ export type components = {
        * @default false
        */
       tiled?: boolean;
+      /**
+       * Fp32 
+       * @description Decode in full precision 
+       * @default false
+       */
+      fp32?: boolean;
       /**
        * Metadata 
        * @description Optional core metadata to be written to the image
@@ -3342,7 +3347,7 @@ 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"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"])[];
+      models: (components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusionXLModelCheckpointConfig"] | components["schemas"]["StableDiffusionXLModelDiffusersConfig"])[];
     };
     /**
      * MultiplyInvocation 
@@ -3770,6 +3775,56 @@ export type components = {
        */
       prompt: string;
     };
+    /**
+     * PromptsFromFileInvocation 
+     * @description Loads prompts from a text file
+     */
+    PromptsFromFileInvocation: {
+      /**
+       * Id 
+       * @description The id of this node. Must be unique among all nodes.
+       */
+      id: string;
+      /**
+       * Is Intermediate 
+       * @description Whether or not this node is an intermediate node. 
+       * @default false
+       */
+      is_intermediate?: boolean;
+      /**
+       * Type 
+       * @default prompt_from_file 
+       * @enum {string}
+       */
+      type?: "prompt_from_file";
+      /**
+       * File Path 
+       * @description Path to prompt text file
+       */
+      file_path: string;
+      /**
+       * Pre Prompt 
+       * @description String to prepend to each prompt
+       */
+      pre_prompt?: string;
+      /**
+       * Post Prompt 
+       * @description String to append to each prompt
+       */
+      post_prompt?: string;
+      /**
+       * Start Line 
+       * @description Line in the file to start start from 
+       * @default 1
+       */
+      start_line?: number;
+      /**
+       * Max Prompts 
+       * @description Max lines to read from file (0=all) 
+       * @default 1
+       */
+      max_prompts?: number;
+    };
     /**
      * RandomIntInvocation 
      * @description Outputs a single random integer.
@@ -4032,10 +4087,10 @@ export type components = {
      */
     ResourceOrigin: "internal" | "external";
     /**
-     * RestoreFaceInvocation 
-     * @description Restores faces in an image.
+     * SDXLCompelPromptInvocation 
+     * @description Parse prompt using compel package to conditioning.
      */
-    RestoreFaceInvocation: {
+    SDXLCompelPromptInvocation: {
       /**
        * Id 
        * @description The id of this node. Must be unique among all nodes.
@@ -4049,21 +4104,514 @@ export type components = {
       is_intermediate?: boolean;
       /**
        * Type 
-       * @default restore_face 
+       * @default sdxl_compel_prompt 
        * @enum {string}
        */
-      type?: "restore_face";
+      type?: "sdxl_compel_prompt";
       /**
-       * Image 
-       * @description The input image
+       * Prompt 
+       * @description Prompt 
+       * @default
        */
-      image?: components["schemas"]["ImageField"];
+      prompt?: string;
       /**
-       * Strength 
-       * @description The strength of the restoration 
-       * @default 0.75
+       * Style 
+       * @description Style prompt 
+       * @default
        */
-      strength?: number;
+      style?: string;
+      /**
+       * Original Width 
+       * @default 1024
+       */
+      original_width?: number;
+      /**
+       * Original Height 
+       * @default 1024
+       */
+      original_height?: number;
+      /**
+       * Crop Top 
+       * @default 0
+       */
+      crop_top?: number;
+      /**
+       * Crop Left 
+       * @default 0
+       */
+      crop_left?: number;
+      /**
+       * Target Width 
+       * @default 1024
+       */
+      target_width?: number;
+      /**
+       * Target Height 
+       * @default 1024
+       */
+      target_height?: number;
+      /**
+       * Clip1 
+       * @description Clip to use
+       */
+      clip1?: components["schemas"]["ClipField"];
+      /**
+       * Clip2 
+       * @description Clip to use
+       */
+      clip2?: components["schemas"]["ClipField"];
+    };
+    /**
+     * SDXLLatentsToLatentsInvocation 
+     * @description Generates latents from conditionings.
+     */
+    SDXLLatentsToLatentsInvocation: {
+      /**
+       * Id 
+       * @description The id of this node. Must be unique among all nodes.
+       */
+      id: string;
+      /**
+       * Is Intermediate 
+       * @description Whether or not this node is an intermediate node. 
+       * @default false
+       */
+      is_intermediate?: boolean;
+      /**
+       * Type 
+       * @default l2l_sdxl 
+       * @enum {string}
+       */
+      type?: "l2l_sdxl";
+      /**
+       * Positive Conditioning 
+       * @description Positive conditioning for generation
+       */
+      positive_conditioning?: components["schemas"]["ConditioningField"];
+      /**
+       * Negative Conditioning 
+       * @description Negative conditioning for generation
+       */
+      negative_conditioning?: components["schemas"]["ConditioningField"];
+      /**
+       * Noise 
+       * @description The noise to use
+       */
+      noise?: components["schemas"]["LatentsField"];
+      /**
+       * Steps 
+       * @description The number of steps to use to generate the image 
+       * @default 10
+       */
+      steps?: number;
+      /**
+       * Cfg Scale 
+       * @description The Classifier-Free Guidance, higher values may result in a result closer to the prompt 
+       * @default 7.5
+       */
+      cfg_scale?: number | (number)[];
+      /**
+       * Scheduler 
+       * @description The scheduler to use 
+       * @default euler 
+       * @enum {string}
+       */
+      scheduler?: "ddim" | "ddpm" | "deis" | "lms" | "lms_k" | "pndm" | "heun" | "heun_k" | "euler" | "euler_k" | "euler_a" | "kdpm_2" | "kdpm_2_a" | "dpmpp_2s" | "dpmpp_2s_k" | "dpmpp_2m" | "dpmpp_2m_k" | "dpmpp_2m_sde" | "dpmpp_2m_sde_k" | "dpmpp_sde" | "dpmpp_sde_k" | "unipc";
+      /**
+       * Unet 
+       * @description UNet submodel
+       */
+      unet?: components["schemas"]["UNetField"];
+      /**
+       * Latents 
+       * @description Initial latents
+       */
+      latents?: components["schemas"]["LatentsField"];
+      /**
+       * Denoising Start 
+       * @default 0
+       */
+      denoising_start?: number;
+      /**
+       * Denoising End 
+       * @default 1
+       */
+      denoising_end?: number;
+    };
+    /**
+     * SDXLModelLoaderInvocation 
+     * @description Loads an sdxl base model, outputting its submodels.
+     */
+    SDXLModelLoaderInvocation: {
+      /**
+       * Id 
+       * @description The id of this node. Must be unique among all nodes.
+       */
+      id: string;
+      /**
+       * Is Intermediate 
+       * @description Whether or not this node is an intermediate node. 
+       * @default false
+       */
+      is_intermediate?: boolean;
+      /**
+       * Type 
+       * @default sdxl_model_loader 
+       * @enum {string}
+       */
+      type?: "sdxl_model_loader";
+      /**
+       * Model 
+       * @description The model to load
+       */
+      model: components["schemas"]["MainModelField"];
+    };
+    /**
+     * SDXLModelLoaderOutput 
+     * @description SDXL base model loader output
+     */
+    SDXLModelLoaderOutput: {
+      /**
+       * Type 
+       * @default sdxl_model_loader_output 
+       * @enum {string}
+       */
+      type?: "sdxl_model_loader_output";
+      /**
+       * Unet 
+       * @description UNet submodel
+       */
+      unet?: components["schemas"]["UNetField"];
+      /**
+       * Clip 
+       * @description Tokenizer and text_encoder submodels
+       */
+      clip?: components["schemas"]["ClipField"];
+      /**
+       * Clip2 
+       * @description Tokenizer and text_encoder submodels
+       */
+      clip2?: components["schemas"]["ClipField"];
+      /**
+       * Vae 
+       * @description Vae submodel
+       */
+      vae?: components["schemas"]["VaeField"];
+    };
+    /**
+     * SDXLRawPromptInvocation 
+     * @description Parse prompt using compel package to conditioning.
+     */
+    SDXLRawPromptInvocation: {
+      /**
+       * Id 
+       * @description The id of this node. Must be unique among all nodes.
+       */
+      id: string;
+      /**
+       * Is Intermediate 
+       * @description Whether or not this node is an intermediate node. 
+       * @default false
+       */
+      is_intermediate?: boolean;
+      /**
+       * Type 
+       * @default sdxl_raw_prompt 
+       * @enum {string}
+       */
+      type?: "sdxl_raw_prompt";
+      /**
+       * Prompt 
+       * @description Prompt 
+       * @default
+       */
+      prompt?: string;
+      /**
+       * Style 
+       * @description Style prompt 
+       * @default
+       */
+      style?: string;
+      /**
+       * Original Width 
+       * @default 1024
+       */
+      original_width?: number;
+      /**
+       * Original Height 
+       * @default 1024
+       */
+      original_height?: number;
+      /**
+       * Crop Top 
+       * @default 0
+       */
+      crop_top?: number;
+      /**
+       * Crop Left 
+       * @default 0
+       */
+      crop_left?: number;
+      /**
+       * Target Width 
+       * @default 1024
+       */
+      target_width?: number;
+      /**
+       * Target Height 
+       * @default 1024
+       */
+      target_height?: number;
+      /**
+       * Clip1 
+       * @description Clip to use
+       */
+      clip1?: components["schemas"]["ClipField"];
+      /**
+       * Clip2 
+       * @description Clip to use
+       */
+      clip2?: components["schemas"]["ClipField"];
+    };
+    /**
+     * SDXLRefinerCompelPromptInvocation 
+     * @description Parse prompt using compel package to conditioning.
+     */
+    SDXLRefinerCompelPromptInvocation: {
+      /**
+       * Id 
+       * @description The id of this node. Must be unique among all nodes.
+       */
+      id: string;
+      /**
+       * Is Intermediate 
+       * @description Whether or not this node is an intermediate node. 
+       * @default false
+       */
+      is_intermediate?: boolean;
+      /**
+       * Type 
+       * @default sdxl_refiner_compel_prompt 
+       * @enum {string}
+       */
+      type?: "sdxl_refiner_compel_prompt";
+      /**
+       * Style 
+       * @description Style prompt 
+       * @default
+       */
+      style?: string;
+      /**
+       * Original Width 
+       * @default 1024
+       */
+      original_width?: number;
+      /**
+       * Original Height 
+       * @default 1024
+       */
+      original_height?: number;
+      /**
+       * Crop Top 
+       * @default 0
+       */
+      crop_top?: number;
+      /**
+       * Crop Left 
+       * @default 0
+       */
+      crop_left?: number;
+      /**
+       * Aesthetic Score 
+       * @default 6
+       */
+      aesthetic_score?: number;
+      /**
+       * Clip2 
+       * @description Clip to use
+       */
+      clip2?: components["schemas"]["ClipField"];
+    };
+    /**
+     * SDXLRefinerModelLoaderInvocation 
+     * @description Loads an sdxl refiner model, outputting its submodels.
+     */
+    SDXLRefinerModelLoaderInvocation: {
+      /**
+       * Id 
+       * @description The id of this node. Must be unique among all nodes.
+       */
+      id: string;
+      /**
+       * Is Intermediate 
+       * @description Whether or not this node is an intermediate node. 
+       * @default false
+       */
+      is_intermediate?: boolean;
+      /**
+       * Type 
+       * @default sdxl_refiner_model_loader 
+       * @enum {string}
+       */
+      type?: "sdxl_refiner_model_loader";
+      /**
+       * Model 
+       * @description The model to load
+       */
+      model: components["schemas"]["MainModelField"];
+    };
+    /**
+     * SDXLRefinerModelLoaderOutput 
+     * @description SDXL refiner model loader output
+     */
+    SDXLRefinerModelLoaderOutput: {
+      /**
+       * Type 
+       * @default sdxl_refiner_model_loader_output 
+       * @enum {string}
+       */
+      type?: "sdxl_refiner_model_loader_output";
+      /**
+       * Unet 
+       * @description UNet submodel
+       */
+      unet?: components["schemas"]["UNetField"];
+      /**
+       * Clip2 
+       * @description Tokenizer and text_encoder submodels
+       */
+      clip2?: components["schemas"]["ClipField"];
+      /**
+       * Vae 
+       * @description Vae submodel
+       */
+      vae?: components["schemas"]["VaeField"];
+    };
+    /**
+     * SDXLRefinerRawPromptInvocation 
+     * @description Parse prompt using compel package to conditioning.
+     */
+    SDXLRefinerRawPromptInvocation: {
+      /**
+       * Id 
+       * @description The id of this node. Must be unique among all nodes.
+       */
+      id: string;
+      /**
+       * Is Intermediate 
+       * @description Whether or not this node is an intermediate node. 
+       * @default false
+       */
+      is_intermediate?: boolean;
+      /**
+       * Type 
+       * @default sdxl_refiner_raw_prompt 
+       * @enum {string}
+       */
+      type?: "sdxl_refiner_raw_prompt";
+      /**
+       * Style 
+       * @description Style prompt 
+       * @default
+       */
+      style?: string;
+      /**
+       * Original Width 
+       * @default 1024
+       */
+      original_width?: number;
+      /**
+       * Original Height 
+       * @default 1024
+       */
+      original_height?: number;
+      /**
+       * Crop Top 
+       * @default 0
+       */
+      crop_top?: number;
+      /**
+       * Crop Left 
+       * @default 0
+       */
+      crop_left?: number;
+      /**
+       * Aesthetic Score 
+       * @default 6
+       */
+      aesthetic_score?: number;
+      /**
+       * Clip2 
+       * @description Clip to use
+       */
+      clip2?: components["schemas"]["ClipField"];
+    };
+    /**
+     * SDXLTextToLatentsInvocation 
+     * @description Generates latents from conditionings.
+     */
+    SDXLTextToLatentsInvocation: {
+      /**
+       * Id 
+       * @description The id of this node. Must be unique among all nodes.
+       */
+      id: string;
+      /**
+       * Is Intermediate 
+       * @description Whether or not this node is an intermediate node. 
+       * @default false
+       */
+      is_intermediate?: boolean;
+      /**
+       * Type 
+       * @default t2l_sdxl 
+       * @enum {string}
+       */
+      type?: "t2l_sdxl";
+      /**
+       * Positive Conditioning 
+       * @description Positive conditioning for generation
+       */
+      positive_conditioning?: components["schemas"]["ConditioningField"];
+      /**
+       * Negative Conditioning 
+       * @description Negative conditioning for generation
+       */
+      negative_conditioning?: components["schemas"]["ConditioningField"];
+      /**
+       * Noise 
+       * @description The noise to use
+       */
+      noise?: components["schemas"]["LatentsField"];
+      /**
+       * Steps 
+       * @description The number of steps to use to generate the image 
+       * @default 10
+       */
+      steps?: number;
+      /**
+       * Cfg Scale 
+       * @description The Classifier-Free Guidance, higher values may result in a result closer to the prompt 
+       * @default 7.5
+       */
+      cfg_scale?: number | (number)[];
+      /**
+       * Scheduler 
+       * @description The scheduler to use 
+       * @default euler 
+       * @enum {string}
+       */
+      scheduler?: "ddim" | "ddpm" | "deis" | "lms" | "lms_k" | "pndm" | "heun" | "heun_k" | "euler" | "euler_k" | "euler_a" | "kdpm_2" | "kdpm_2_a" | "dpmpp_2s" | "dpmpp_2s_k" | "dpmpp_2m" | "dpmpp_2m_k" | "dpmpp_2m_sde" | "dpmpp_2m_sde_k" | "dpmpp_sde" | "dpmpp_sde_k" | "unipc";
+      /**
+       * Unet 
+       * @description UNet submodel
+       */
+      unet?: components["schemas"]["UNetField"];
+      /**
+       * Denoising End 
+       * @default 1
+       */
+      denoising_end?: number;
     };
     /**
      * ScaleLatentsInvocation 
@@ -4267,6 +4815,56 @@ export type components = {
       vae?: string;
       variant: components["schemas"]["ModelVariantType"];
     };
+    /** StableDiffusionXLModelCheckpointConfig */
+    StableDiffusionXLModelCheckpointConfig: {
+      /** Model Name */
+      model_name: string;
+      base_model: components["schemas"]["BaseModelType"];
+      /**
+       * Model Type 
+       * @enum {string}
+       */
+      model_type: "main";
+      /** Path */
+      path: string;
+      /** Description */
+      description?: string;
+      /**
+       * Model Format 
+       * @enum {string}
+       */
+      model_format: "checkpoint";
+      error?: components["schemas"]["ModelError"];
+      /** Vae */
+      vae?: string;
+      /** Config */
+      config: string;
+      variant: components["schemas"]["ModelVariantType"];
+    };
+    /** StableDiffusionXLModelDiffusersConfig */
+    StableDiffusionXLModelDiffusersConfig: {
+      /** Model Name */
+      model_name: string;
+      base_model: components["schemas"]["BaseModelType"];
+      /**
+       * Model Type 
+       * @enum {string}
+       */
+      model_type: "main";
+      /** Path */
+      path: string;
+      /** Description */
+      description?: string;
+      /**
+       * Model Format 
+       * @enum {string}
+       */
+      model_format: "diffusers";
+      error?: components["schemas"]["ModelError"];
+      /** Vae */
+      vae?: string;
+      variant: components["schemas"]["ModelVariantType"];
+    };
     /**
      * StepParamEasingInvocation 
      * @description Experimental per-step parameter easing for denoising steps
@@ -4354,7 +4952,7 @@ export type components = {
      * @description An enumeration. 
      * @enum {string}
      */
-    SubModelType: "unet" | "text_encoder" | "tokenizer" | "vae" | "scheduler" | "safety_checker";
+    SubModelType: "unet" | "text_encoder" | "text_encoder_2" | "tokenizer" | "tokenizer_2" | "vae" | "scheduler" | "safety_checker";
     /**
      * SubtractInvocation 
      * @description Subtracts two numbers
@@ -4659,6 +5257,12 @@ export type components = {
      * @enum {string}
      */
     StableDiffusion2ModelFormat: "checkpoint" | "diffusers";
+    /**
+     * StableDiffusionXLModelFormat 
+     * @description An enumeration. 
+     * @enum {string}
+     */
+    StableDiffusionXLModelFormat: "checkpoint" | "diffusers";
     /**
      * StableDiffusion1ModelFormat 
      * @description An enumeration. 
@@ -4775,7 +5379,7 @@ export type operations = {
     };
     requestBody: {
       content: {
-        "application/json": components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["RealESRGANInvocation"] | components["schemas"]["RestoreFaceInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
+        "application/json": components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["RealESRGANInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
       };
     };
     responses: {
@@ -4812,7 +5416,7 @@ export type operations = {
     };
     requestBody: {
       content: {
-        "application/json": components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["RealESRGANInvocation"] | components["schemas"]["RestoreFaceInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
+        "application/json": components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["RealESRGANInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
       };
     };
     responses: {
@@ -5010,8 +5614,8 @@ export type operations = {
   list_models: {
     parameters: {
       query?: {
-        /** @description Base model */
-        base_model?: components["schemas"]["BaseModelType"];
+        /** @description Base models to include */
+        base_models?: (components["schemas"]["BaseModelType"])[];
         /** @description The type of model to get */
         model_type?: components["schemas"]["ModelType"];
       };
@@ -5061,7 +5665,7 @@ export type operations = {
   };
   /**
    * Update Model 
-   * @description Add Model
+   * @description Update model contents with a new config. If the model name or base fields are changed, then the model is renamed.
    */
   update_model: {
     parameters: {
@@ -5076,20 +5680,22 @@ export type operations = {
     };
     requestBody: {
       content: {
-        "application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"];
+        "application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusionXLModelCheckpointConfig"] | components["schemas"]["StableDiffusionXLModelDiffusersConfig"];
       };
     };
     responses: {
       /** @description The model was updated successfully */
       200: {
         content: {
-          "application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"];
+          "application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusionXLModelCheckpointConfig"] | components["schemas"]["StableDiffusionXLModelDiffusersConfig"];
         };
       };
       /** @description Bad request */
       400: never;
       /** @description The model could not be found */
       404: never;
+      /** @description There is already a model corresponding to the new name */
+      409: never;
       /** @description Validation Error */
       422: {
         content: {
@@ -5112,13 +5718,15 @@ export type operations = {
       /** @description The model imported successfully */
       201: {
         content: {
-          "application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"];
+          "application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusionXLModelCheckpointConfig"] | components["schemas"]["StableDiffusionXLModelDiffusersConfig"];
         };
       };
       /** @description The model could not be found */
       404: never;
       /** @description There is already a model corresponding to this path or repo_id */
       409: never;
+      /** @description Unrecognized file/folder format */
+      415: never;
       /** @description Validation Error */
       422: {
         content: {
@@ -5136,14 +5744,14 @@ export type operations = {
   add_model: {
     requestBody: {
       content: {
-        "application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"];
+        "application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusionXLModelCheckpointConfig"] | components["schemas"]["StableDiffusionXLModelDiffusersConfig"];
       };
     };
     responses: {
       /** @description The model added successfully */
       201: {
         content: {
-          "application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"];
+          "application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusionXLModelCheckpointConfig"] | components["schemas"]["StableDiffusionXLModelDiffusersConfig"];
         };
       };
       /** @description The model could not be found */
@@ -5160,46 +5768,6 @@ export type operations = {
       424: never;
     };
   };
-  /**
-   * Rename Model 
-   * @description Rename a model
-   */
-  rename_model: {
-    parameters: {
-      query?: {
-        /** @description new model name */
-        new_name?: string;
-        /** @description new model base */
-        new_base?: components["schemas"]["BaseModelType"];
-      };
-      path: {
-        /** @description Base model */
-        base_model: components["schemas"]["BaseModelType"];
-        /** @description The type of model */
-        model_type: components["schemas"]["ModelType"];
-        /** @description current model name */
-        model_name: string;
-      };
-    };
-    responses: {
-      /** @description The model was renamed successfully */
-      201: {
-        content: {
-          "application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"];
-        };
-      };
-      /** @description The model could not be found */
-      404: never;
-      /** @description There is already a model corresponding to the new name */
-      409: never;
-      /** @description Validation Error */
-      422: {
-        content: {
-          "application/json": components["schemas"]["HTTPValidationError"];
-        };
-      };
-    };
-  };
   /**
    * Convert Model 
    * @description Convert a checkpoint model into a diffusers model, optionally saving to the indicated destination directory, or `models` if none.
@@ -5223,7 +5791,7 @@ export type operations = {
       /** @description Model converted successfully */
       200: {
         content: {
-          "application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"];
+          "application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusionXLModelCheckpointConfig"] | components["schemas"]["StableDiffusionXLModelDiffusersConfig"];
         };
       };
       /** @description Bad request */
@@ -5312,7 +5880,7 @@ export type operations = {
       /** @description Model converted successfully */
       200: {
         content: {
-          "application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"];
+          "application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusionXLModelCheckpointConfig"] | components["schemas"]["StableDiffusionXLModelDiffusersConfig"];
         };
       };
       /** @description Incompatible models */

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

@@ -28,6 +28,7 @@ export type OffsetPaginatedResults_ImageDTO_ =
 
 // Models
 export type ModelType = components['schemas']['ModelType'];
+export type SubModelType = components['schemas']['SubModelType'];
 export type BaseModelType = components['schemas']['BaseModelType'];
 export type MainModelField = components['schemas']['MainModelField'];
 export type VAEModelField = components['schemas']['VAEModelField'];
@@ -46,10 +47,12 @@ export type TextualInversionModelConfig =
   components['schemas']['TextualInversionModelConfig'];
 export type DiffusersModelConfig =
   | components['schemas']['StableDiffusion1ModelDiffusersConfig']
-  | components['schemas']['StableDiffusion2ModelDiffusersConfig'];
+  | components['schemas']['StableDiffusion2ModelDiffusersConfig']
+  | components['schemas']['StableDiffusionXLModelDiffusersConfig'];
 export type CheckpointModelConfig =
   | components['schemas']['StableDiffusion1ModelCheckpointConfig']
-  | components['schemas']['StableDiffusion2ModelCheckpointConfig'];
+  | components['schemas']['StableDiffusion2ModelCheckpointConfig']
+  | components['schemas']['StableDiffusionXLModelCheckpointConfig'];
 export type MainModelConfig = DiffusersModelConfig | CheckpointModelConfig;
 export type AnyModelConfig =
   | LoRAModelConfig
@@ -57,7 +60,10 @@ export type AnyModelConfig =
   | ControlNetModelConfig
   | TextualInversionModelConfig
   | MainModelConfig;
+
 export type MergeModelConfig = components['schemas']['Body_merge_models'];
+export type ConvertModelConfig = components['schemas']['Body_convert_model'];
+export type ImportModelConfig = components['schemas']['Body_import_model'];
 
 // Graphs
 export type Graph = components['schemas']['Graph'];

invokeai/frontend/web/src/services/events/actions.ts

@@ -5,6 +5,8 @@ import {
   InvocationCompleteEvent,
   InvocationErrorEvent,
   InvocationStartedEvent,
+  ModelLoadCompletedEvent,
+  ModelLoadStartedEvent,
 } from 'services/events/types';
 
 // Common socket action payload data
@@ -162,3 +164,35 @@ export const socketGeneratorProgress = createAction<
 export const appSocketGeneratorProgress = createAction<
   BaseSocketPayload & { data: GeneratorProgressEvent }
 >('socket/appSocketGeneratorProgress');
+
+/**
+ * Socket.IO Model Load Started
+ *
+ * Do not use. Only for use in middleware.
+ */
+export const socketModelLoadStarted = createAction<
+  BaseSocketPayload & { data: ModelLoadStartedEvent }
+>('socket/socketModelLoadStarted');
+
+/**
+ * App-level Model Load Started
+ */
+export const appSocketModelLoadStarted = createAction<
+  BaseSocketPayload & { data: ModelLoadStartedEvent }
+>('socket/appSocketModelLoadStarted');
+
+/**
+ * Socket.IO Model Load Started
+ *
+ * Do not use. Only for use in middleware.
+ */
+export const socketModelLoadCompleted = createAction<
+  BaseSocketPayload & { data: ModelLoadCompletedEvent }
+>('socket/socketModelLoadCompleted');
+
+/**
+ * App-level Model Load Completed
+ */
+export const appSocketModelLoadCompleted = createAction<
+  BaseSocketPayload & { data: ModelLoadCompletedEvent }
+>('socket/appSocketModelLoadCompleted');

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

@@ -1,5 +1,11 @@
 import { O } from 'ts-toolbelt';
-import { Graph, GraphExecutionState } from '../api/types';
+import {
+  BaseModelType,
+  Graph,
+  GraphExecutionState,
+  ModelType,
+  SubModelType,
+} from '../api/types';
 
 /**
  * A progress image, we get one for each step in the generation
@@ -25,6 +31,25 @@ export type BaseNode = {
   [key: string]: AnyInvocation[keyof AnyInvocation];
 };
 
+export type ModelLoadStartedEvent = {
+  graph_execution_state_id: string;
+  model_name: string;
+  base_model: BaseModelType;
+  model_type: ModelType;
+  submodel: SubModelType;
+};
+
+export type ModelLoadCompletedEvent = {
+  graph_execution_state_id: string;
+  model_name: string;
+  base_model: BaseModelType;
+  model_type: ModelType;
+  submodel: SubModelType;
+  hash?: string;
+  location: string;
+  precision: string;
+};
+
 /**
  * A `generator_progress` socket.io event.
  *
@@ -101,6 +126,8 @@ export type ServerToClientEvents = {
   graph_execution_state_complete: (
     payload: GraphExecutionStateCompleteEvent
   ) => void;
+  model_load_started: (payload: ModelLoadStartedEvent) => void;
+  model_load_completed: (payload: ModelLoadCompletedEvent) => void;
 };
 
 export type ClientToServerEvents = {

invokeai/frontend/web/src/services/events/util/setEventListeners.ts

@@ -11,6 +11,8 @@ import {
   socketConnected,
   socketDisconnected,
   socketSubscribed,
+  socketModelLoadStarted,
+  socketModelLoadCompleted,
 } from '../actions';
 import { ClientToServerEvents, ServerToClientEvents } from '../types';
 import { Logger } from 'roarr';
@@ -44,7 +46,7 @@ export const setEventListeners = (arg: SetEventListenersArg) => {
         socketSubscribed({
           sessionId,
           timestamp: getTimestamp(),
-          boardId: getState().boards.selectedBoardId,
+          boardId: getState().gallery.selectedBoardId,
         })
       );
     }
@@ -118,4 +120,28 @@ export const setEventListeners = (arg: SetEventListenersArg) => {
       })
     );
   });
+
+  /**
+   * Model load started
+   */
+  socket.on('model_load_started', (data) => {
+    dispatch(
+      socketModelLoadStarted({
+        data,
+        timestamp: getTimestamp(),
+      })
+    );
+  });
+
+  /**
+   * Model load completed
+   */
+  socket.on('model_load_completed', (data) => {
+    dispatch(
+      socketModelLoadCompleted({
+        data,
+        timestamp: getTimestamp(),
+      })
+    );
+  });
 };

invokeai/frontend/web/src/theme/components/button.ts

@@ -16,7 +16,7 @@ const invokeAI = defineStyle((props) => {
     };
 
     return {
-      bg: mode('base.200', 'base.600')(props),
+      bg: mode('base.250', 'base.600')(props),
       color: mode('base.850', 'base.100')(props),
       borderRadius: 'base',
       svg: {

invokeai/frontend/web/src/theme/components/menu.ts

@@ -1,6 +1,7 @@
 import { menuAnatomy } from '@chakra-ui/anatomy';
 import { createMultiStyleConfigHelpers } from '@chakra-ui/react';
 import { mode } from '@chakra-ui/theme-tools';
+import { MotionProps } from 'framer-motion';
 
 const { definePartsStyle, defineMultiStyleConfig } =
   createMultiStyleConfigHelpers(menuAnatomy.keys);
@@ -21,6 +22,7 @@ const invokeAI = definePartsStyle((props) => ({
   },
   list: {
     zIndex: 9999,
+    color: mode('base.900', 'base.150')(props),
     bg: mode('base.200', 'base.800')(props),
     shadow: 'dark-lg',
     border: 'none',
@@ -35,6 +37,9 @@ const invokeAI = definePartsStyle((props) => ({
     _focus: {
       bg: mode('base.400', 'base.600')(props),
     },
+    svg: {
+      opacity: 0.5,
+    },
   },
 }));
 
@@ -46,3 +51,28 @@ export const menuTheme = defineMultiStyleConfig({
     variant: 'invokeAI',
   },
 });
+
+export const menuListMotionProps: MotionProps = {
+  variants: {
+    enter: {
+      visibility: 'visible',
+      opacity: 1,
+      scale: 1,
+      transition: {
+        duration: 0.07,
+        ease: [0.4, 0, 0.2, 1],
+      },
+    },
+    exit: {
+      transitionEnd: {
+        visibility: 'hidden',
+      },
+      opacity: 0,
+      scale: 0.8,
+      transition: {
+        duration: 0.07,
+        easings: 'easeOut',
+      },
+    },
+  },
+};

pull_request_template.md

@@ -0,0 +1,45 @@
+## What type of PR is this? (check all applicable)
+
+- [ ] Refactor
+- [ ] Feature
+- [ ] Bug Fix
+- [ ] Optimization
+- [ ] Documentation Update
+
+
+## Have you discussed this change with the InvokeAI team?
+- [ ] Yes
+- [ ] No, because:
+
+      
+## Description
+
+
+## Related Tickets & Documents
+
+<!--
+For pull requests that relate or close an issue, please include them
+below. 
+
+For example having the text: "closes #1234" would connect the current pull
+request to issue 1234.  And when we merge the pull request, Github will
+automatically close the issue.
+-->
+
+- Related Issue #
+- Closes #
+
+## QA Instructions, Screenshots, Recordings
+
+<!-- 
+Please provide steps on how to test changes, any hardware or 
+software specifications as well as any other pertinent information. 
+-->
+
+## Added/updated tests?
+
+- [ ] Yes
+- [ ] No : _please replace this line with details on why tests
+      have not been included_
+
+## [optional] Are there any post deployment tasks we need to perform?

pyproject.toml

@@ -38,7 +38,7 @@ dependencies = [
   "albumentations",
   "click",
   "clip_anytorch",          # replacing "clip @ https://github.com/openai/CLIP/archive/eaa22acb90a5876642d0507623e859909230a52d.zip",
-  "compel>=1.2.1",
+  "compel==2.0.0rc2",
   "controlnet-aux>=0.0.6",
   "timm==0.6.13",           # needed to override timm latest in controlnet_aux, see  https://github.com/isl-org/ZoeDepth/issues/26
   "datasets",