Merge branch 'main' into set-timestep-mps-fix

README.md

@@ -123,7 +123,7 @@ 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
+You must have Python 3.9 through 3.11 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)

docs/installation/010_INSTALL_AUTOMATED.md

@@ -40,10 +40,8 @@ experimental versions later.
     this, open up a command-line window ("Terminal" on Linux and
     Macintosh, "Command" or "Powershell" on Windows) and type `python
     --version`. If Python is installed, it will print out the version
-    number. If it is version `3.9.*` or `3.10.*`, you meet
+    number. If it is version `3.9.*`, `3.10.*` or `3.11.*` you meet
-    requirements. We do not recommend using Python 3.11 or higher,
+    requirements.
-    as not all the libraries that InvokeAI depends on work properly
-    with this version.
 
     !!! warning "What to do if you have an unsupported version"
 

docs/installation/020_INSTALL_MANUAL.md

@@ -32,7 +32,7 @@ gaming):
 
 * **Python**
 
-    version 3.9 or 3.10 (3.11 is not recommended).
+    version 3.9 through 3.11
 
 * **CUDA Tools**
 
@@ -65,7 +65,7 @@ gaming):
 To install InvokeAI with virtual environments and the PIP package
 manager, please follow these steps:
 
-1.  Please make sure you are using Python 3.9 or 3.10. The rest of the install
+1.  Please make sure you are using Python 3.9 through 3.11. The rest of the install
     procedure depends on this and will not work with other versions:
 
     ```bash

installer/install.sh.in

@@ -9,16 +9,20 @@ cd $scriptdir
 function version { echo "$@" | awk -F. '{ printf("%d%03d%03d%03d\n", $1,$2,$3,$4); }'; }
 
 MINIMUM_PYTHON_VERSION=3.9.0
-MAXIMUM_PYTHON_VERSION=3.11.0
+MAXIMUM_PYTHON_VERSION=3.11.100
 PYTHON=""
-for candidate in python3.10 python3.9 python3 python ; do
+for candidate in python3.11 python3.10 python3.9 python3 python ; do
     if ppath=`which $candidate`; then
+        # when using `pyenv`, the executable for an inactive Python version will exist but will not be operational
+        # we check that this found executable can actually run
+        if [ $($candidate --version &>/dev/null; echo ${PIPESTATUS}) -gt 0 ]; then continue; fi
+
         python_version=$($ppath -V | awk '{ print $2 }')
         if [ $(version $python_version) -ge $(version "$MINIMUM_PYTHON_VERSION") ]; then
-	    if [ $(version $python_version) -lt $(version "$MAXIMUM_PYTHON_VERSION") ]; then
+            if [ $(version $python_version) -le $(version "$MAXIMUM_PYTHON_VERSION") ]; then
-		PYTHON=$ppath
+                PYTHON=$ppath
-		break
+                break
-	    fi
+            fi
         fi
     fi
 done

invokeai/app/api/routers/models.py

@@ -90,7 +90,7 @@ async def update_model(
                 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}")
+            logger.info(f"Successfully renamed {base_model.value}/{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

invokeai/app/api_app.py

@@ -3,6 +3,7 @@ import asyncio
 import sys
 from inspect import signature
 
+import logging
 import uvicorn
 import socket
 
@@ -210,11 +211,25 @@ def invoke_api():
     port = find_port(app_config.port)
     if port != app_config.port:
         logger.warn(f"Port {app_config.port} in use, using port {port}")
+
     # Start our own event loop for eventing usage
     loop = asyncio.new_event_loop()
-    config = uvicorn.Config(app=app, host=app_config.host, port=port, loop=loop)
+    config = uvicorn.Config(
-    # Use access_log to turn off logging
+        app=app,
+        host=app_config.host,
+        port=port,
+        loop=loop,
+        log_level=app_config.log_level,
+    )
     server = uvicorn.Server(config)
+
+    # replace uvicorn's loggers with InvokeAI's for consistent appearance
+    for logname in ["uvicorn.access", "uvicorn"]:
+        l = logging.getLogger(logname)
+        l.handlers.clear()
+        for ch in logger.handlers:
+            l.addHandler(ch)
+
     loop.run_until_complete(server.serve())
 
 

invokeai/app/invocations/latent.py

@@ -12,7 +12,7 @@ from pydantic import BaseModel, Field, validator
 
 from invokeai.app.invocations.metadata import CoreMetadata
 from invokeai.app.util.step_callback import stable_diffusion_step_callback
-from invokeai.backend.model_management.models.base import ModelType
+from invokeai.backend.model_management.models import ModelType, SilenceWarnings
 
 from ...backend.model_management.lora import ModelPatcher
 from ...backend.stable_diffusion import PipelineIntermediateState
@@ -311,70 +311,71 @@ class TextToLatentsInvocation(BaseInvocation):
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> LatentsOutput:
-        noise = context.services.latents.get(self.noise.latents_name)
+        with SilenceWarnings():
+            noise = context.services.latents.get(self.noise.latents_name)
 
-        # Get the source node id (we are invoking the prepared node)
+            # Get the source node id (we are invoking the prepared node)
-        graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
+            graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
-        source_node_id = graph_execution_state.prepared_source_mapping[self.id]
+            source_node_id = graph_execution_state.prepared_source_mapping[self.id]
 
-        def step_callback(state: PipelineIntermediateState):
+            def step_callback(state: PipelineIntermediateState):
-            self.dispatch_progress(context, source_node_id, state)
+                self.dispatch_progress(context, source_node_id, state)
 
-        def _lora_loader():
+            def _lora_loader():
-            for lora in self.unet.loras:
+                for lora in self.unet.loras:
-                lora_info = context.services.model_manager.get_model(
+                    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(),
+                context=context,
+            )
+            with ExitStack() as exit_stack, ModelPatcher.apply_lora_unet(
+                unet_info.context.model, _lora_loader()
+            ), unet_info as unet:
+                noise = noise.to(device=unet.device, dtype=unet.dtype)
+
+                scheduler = get_scheduler(
                     context=context,
+                    scheduler_info=self.unet.scheduler,
+                    scheduler_name=self.scheduler,
                 )
-                yield (lora_info.context.model, lora.weight)
-                del lora_info
-            return
 
-        unet_info = context.services.model_manager.get_model(
+                pipeline = self.create_pipeline(unet, scheduler)
-            **self.unet.unet.dict(),
+                conditioning_data = self.get_conditioning_data(context, scheduler, unet)
-            context=context,
-        )
-        with ExitStack() as exit_stack, ModelPatcher.apply_lora_unet(
-            unet_info.context.model, _lora_loader()
-        ), unet_info as unet:
-            noise = noise.to(device=unet.device, dtype=unet.dtype)
 
-            scheduler = get_scheduler(
+                control_data = self.prep_control_data(
-                context=context,
+                    model=pipeline,
-                scheduler_info=self.unet.scheduler,
+                    context=context,
-                scheduler_name=self.scheduler,
+                    control_input=self.control,
-            )
+                    latents_shape=noise.shape,
+                    # do_classifier_free_guidance=(self.cfg_scale >= 1.0))
+                    do_classifier_free_guidance=True,
+                    exit_stack=exit_stack,
+                )
 
-            pipeline = self.create_pipeline(unet, scheduler)
+                # TODO: Verify the noise is the right size
-            conditioning_data = self.get_conditioning_data(context, scheduler, unet)
+                result_latents, result_attention_map_saver = pipeline.latents_from_embeddings(
+                    latents=torch.zeros_like(noise, dtype=torch_dtype(unet.device)),
+                    noise=noise,
+                    num_inference_steps=self.steps,
+                    conditioning_data=conditioning_data,
+                    control_data=control_data,  # list[ControlNetData]
+                    callback=step_callback,
+                )
 
-            control_data = self.prep_control_data(
+            # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
-                model=pipeline,
+            result_latents = result_latents.to("cpu")
-                context=context,
+            torch.cuda.empty_cache()
-                control_input=self.control,
-                latents_shape=noise.shape,
-                # do_classifier_free_guidance=(self.cfg_scale >= 1.0))
-                do_classifier_free_guidance=True,
-                exit_stack=exit_stack,
-            )
 
-            # TODO: Verify the noise is the right size
+            name = f"{context.graph_execution_state_id}__{self.id}"
-            result_latents, result_attention_map_saver = pipeline.latents_from_embeddings(
+            context.services.latents.save(name, result_latents)
-                latents=torch.zeros_like(noise, dtype=torch_dtype(unet.device)),
+            return build_latents_output(latents_name=name, latents=result_latents)
-                noise=noise,
-                num_inference_steps=self.steps,
-                conditioning_data=conditioning_data,
-                control_data=control_data,  # list[ControlNetData]
-                callback=step_callback,
-            )
-
-        # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
-        result_latents = result_latents.to("cpu")
-        torch.cuda.empty_cache()
-
-        name = f"{context.graph_execution_state_id}__{self.id}"
-        context.services.latents.save(name, result_latents)
-        return build_latents_output(latents_name=name, latents=result_latents)
 
 
 class LatentsToLatentsInvocation(TextToLatentsInvocation):
@@ -402,82 +403,83 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> LatentsOutput:
-        noise = context.services.latents.get(self.noise.latents_name)
+        with SilenceWarnings():  # this quenches NSFW nag from diffusers
-        latent = context.services.latents.get(self.latents.latents_name)
+            noise = context.services.latents.get(self.noise.latents_name)
+            latent = context.services.latents.get(self.latents.latents_name)
 
-        # Get the source node id (we are invoking the prepared node)
+            # Get the source node id (we are invoking the prepared node)
-        graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
+            graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
-        source_node_id = graph_execution_state.prepared_source_mapping[self.id]
+            source_node_id = graph_execution_state.prepared_source_mapping[self.id]
 
-        def step_callback(state: PipelineIntermediateState):
+            def step_callback(state: PipelineIntermediateState):
-            self.dispatch_progress(context, source_node_id, state)
+                self.dispatch_progress(context, source_node_id, state)
 
-        def _lora_loader():
+            def _lora_loader():
-            for lora in self.unet.loras:
+                for lora in self.unet.loras:
-                lora_info = context.services.model_manager.get_model(
+                    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(),
+                context=context,
+            )
+            with ExitStack() as exit_stack, ModelPatcher.apply_lora_unet(
+                unet_info.context.model, _lora_loader()
+            ), unet_info as unet:
+                noise = noise.to(device=unet.device, dtype=unet.dtype)
+                latent = latent.to(device=unet.device, dtype=unet.dtype)
+
+                scheduler = get_scheduler(
                     context=context,
+                    scheduler_info=self.unet.scheduler,
+                    scheduler_name=self.scheduler,
                 )
-                yield (lora_info.context.model, lora.weight)
-                del lora_info
-            return
 
-        unet_info = context.services.model_manager.get_model(
+                pipeline = self.create_pipeline(unet, scheduler)
-            **self.unet.unet.dict(),
+                conditioning_data = self.get_conditioning_data(context, scheduler, unet)
-            context=context,
-        )
-        with ExitStack() as exit_stack, ModelPatcher.apply_lora_unet(
-            unet_info.context.model, _lora_loader()
-        ), unet_info as unet:
-            noise = noise.to(device=unet.device, dtype=unet.dtype)
-            latent = latent.to(device=unet.device, dtype=unet.dtype)
 
-            scheduler = get_scheduler(
+                control_data = self.prep_control_data(
-                context=context,
+                    model=pipeline,
-                scheduler_info=self.unet.scheduler,
+                    context=context,
-                scheduler_name=self.scheduler,
+                    control_input=self.control,
-            )
+                    latents_shape=noise.shape,
+                    # do_classifier_free_guidance=(self.cfg_scale >= 1.0))
+                    do_classifier_free_guidance=True,
+                    exit_stack=exit_stack,
+                )
 
-            pipeline = self.create_pipeline(unet, scheduler)
+                # TODO: Verify the noise is the right size
-            conditioning_data = self.get_conditioning_data(context, scheduler, unet)
+                initial_latents = (
+                    latent if self.strength < 1.0 else torch.zeros_like(latent, device=unet.device, dtype=latent.dtype)
+                )
 
-            control_data = self.prep_control_data(
+                timesteps, _ = pipeline.get_img2img_timesteps(
-                model=pipeline,
+                    self.steps,
-                context=context,
+                    self.strength,
-                control_input=self.control,
+                    device=unet.device,
-                latents_shape=noise.shape,
+                )
-                # do_classifier_free_guidance=(self.cfg_scale >= 1.0))
-                do_classifier_free_guidance=True,
-                exit_stack=exit_stack,
-            )
 
-            # TODO: Verify the noise is the right size
+                result_latents, result_attention_map_saver = pipeline.latents_from_embeddings(
-            initial_latents = (
+                    latents=initial_latents,
-                latent if self.strength < 1.0 else torch.zeros_like(latent, device=unet.device, dtype=latent.dtype)
+                    timesteps=timesteps,
-            )
+                    noise=noise,
+                    num_inference_steps=self.steps,
+                    conditioning_data=conditioning_data,
+                    control_data=control_data,  # list[ControlNetData]
+                    callback=step_callback,
+                )
 
-            timesteps, _ = pipeline.get_img2img_timesteps(
+            # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
-                self.steps,
+            result_latents = result_latents.to("cpu")
-                self.strength,
+            torch.cuda.empty_cache()
-                device=unet.device,
-            )
 
-            result_latents, result_attention_map_saver = pipeline.latents_from_embeddings(
+            name = f"{context.graph_execution_state_id}__{self.id}"
-                latents=initial_latents,
+            context.services.latents.save(name, result_latents)
-                timesteps=timesteps,
-                noise=noise,
-                num_inference_steps=self.steps,
-                conditioning_data=conditioning_data,
-                control_data=control_data,  # list[ControlNetData]
-                callback=step_callback,
-            )
-
-        # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
-        result_latents = result_latents.to("cpu")
-        torch.cuda.empty_cache()
-
-        name = f"{context.graph_execution_state_id}__{self.id}"
-        context.services.latents.save(name, result_latents)
         return build_latents_output(latents_name=name, latents=result_latents)
 
 
@@ -490,7 +492,7 @@ class LatentsToImageInvocation(BaseInvocation):
     # Inputs
     latents: Optional[LatentsField] = Field(description="The latents to generate an image from")
     vae: VaeField = Field(default=None, description="Vae submodel")
-    tiled: bool = Field(default=False, description="Decode latents by overlapping tiles(less memory consumption)")
+    tiled: bool = Field(default=False, description="Decode latents by overlaping tiles (less memory consumption)")
     fp32: bool = Field(DEFAULT_PRECISION == "float32", description="Decode in full precision")
     metadata: Optional[CoreMetadata] = Field(
         default=None, description="Optional core metadata to be written to the image"

invokeai/backend/model_management/model_manager.py

@@ -401,7 +401,11 @@ class ModelManager(object):
         base_model: BaseModelType,
         model_type: ModelType,
     ) -> str:
-        return f"{base_model}/{model_type}/{model_name}"
+        # In 3.11, the behavior of (str,enum) when interpolated into a
+        # string has changed. The next two lines are defensive.
+        base_model = BaseModelType(base_model)
+        model_type = ModelType(model_type)
+        return f"{base_model.value}/{model_type.value}/{model_name}"
 
     @classmethod
     def parse_key(cls, model_key: str) -> Tuple[str, BaseModelType, ModelType]:

invokeai/backend/model_management/models/lora.py

@@ -57,7 +57,7 @@ class LoRAModel(ModelBase):
 
     @classproperty
     def save_to_config(cls) -> bool:
-        return False
+        return True
 
     @classmethod
     def detect_format(cls, path: str):

invokeai/frontend/web/dist/assets/ThemeLocaleProvider-13e3db3d.js

@@ -1,4 +1,4 @@
-import{A as g,fS as Xe,z as x,a4 as Ba,fT as Ea,af as ca,aj as c,fU as b,al as Da,fV as t,fW as Ra,fX as h,fY as ba,fZ as ja,f_ as Ha,aI as Wa,f$ as Va,ad as La,g0 as qa}from"./index-89941396.js";import{n,o as Sr,p as Oa,T as Na,q as Ga,s as Ua,t as Ya,v as Xa,w as Ka,x as Za,y as Ja,z as Qa,A as et,B as rt,D as at,E as tt,F as ot,G as nt,e as it,M as lt}from"./MantineProvider-8184f020.js";var va=String.raw,ua=va`
+import{A as g,fS as Xe,z as x,a4 as Ba,fT as Ea,af as ca,aj as c,fU as b,al as Da,fV as t,fW as Ra,fX as h,fY as ba,fZ as ja,f_ as Ha,aI as Wa,f$ as Va,ad as La,g0 as qa}from"./index-5a784cdd.js";import{n,o as Sr,p as Oa,T as Na,q as Ga,s as Ua,t as Ya,v as Xa,w as Ka,x as Za,y as Ja,z as Qa,A as et,B as rt,D as at,E as tt,F as ot,G as nt,e as it,M as lt}from"./MantineProvider-ea42d3d1.js";var va=String.raw,ua=va`
   :root,
   :host {
     --chakra-vh: 100vh;

invokeai/frontend/web/dist/index.html

@@ -12,7 +12,7 @@
         margin: 0;
       }
     </style>
-    <script type="module" crossorigin src="./assets/index-89941396.js"></script>
+    <script type="module" crossorigin src="./assets/index-5a784cdd.js"></script>
   </head>
 
   <body dir="ltr">

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

@@ -340,6 +340,7 @@
         "allModels": "All Models",
         "checkpointModels": "Checkpoints",
         "diffusersModels": "Diffusers",
+        "loraModels": "LoRAs",
         "safetensorModels": "SafeTensors",
         "modelAdded": "Model Added",
         "modelUpdated": "Model Updated",

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

@@ -1,3 +1,5 @@
+import { components } from 'services/api/schema';
+
 export const MODEL_TYPE_MAP = {
   'sd-1': 'Stable Diffusion 1.x',
   'sd-2': 'Stable Diffusion 2.x',
@@ -5,6 +7,13 @@ export const MODEL_TYPE_MAP = {
   'sdxl-refiner': 'Stable Diffusion XL Refiner',
 };
 
+export const MODEL_TYPE_SHORT_MAP = {
+  'sd-1': 'SD1',
+  'sd-2': 'SD2',
+  sdxl: 'SDXL',
+  'sdxl-refiner': 'SDXLR',
+};
+
 export const clipSkipMap = {
   'sd-1': {
     maxClip: 12,
@@ -23,3 +32,12 @@ export const clipSkipMap = {
     markers: [0, 1, 2, 3, 5, 10, 15, 20, 24],
   },
 };
+
+type LoRAModelFormatMap = {
+  [key in components['schemas']['LoRAModelFormat']]: string;
+};
+
+export const LORA_MODEL_FORMAT_MAP: LoRAModelFormatMap = {
+  lycoris: 'LyCORIS',
+  diffusers: 'Diffusers',
+};

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

@@ -3,20 +3,31 @@ import { Flex, Text } from '@chakra-ui/react';
 import { useState } from 'react';
 import {
   MainModelConfigEntity,
+  DiffusersModelConfigEntity,
+  LoRAModelConfigEntity,
   useGetMainModelsQuery,
+  useGetLoRAModelsQuery,
 } from 'services/api/endpoints/models';
 import CheckpointModelEdit from './ModelManagerPanel/CheckpointModelEdit';
 import DiffusersModelEdit from './ModelManagerPanel/DiffusersModelEdit';
+import LoRAModelEdit from './ModelManagerPanel/LoRAModelEdit';
 import ModelList from './ModelManagerPanel/ModelList';
 import { ALL_BASE_MODELS } from 'services/api/constants';
 
 export default function ModelManagerPanel() {
   const [selectedModelId, setSelectedModelId] = useState<string>();
-  const { model } = useGetMainModelsQuery(ALL_BASE_MODELS, {
+  const { mainModel } = useGetMainModelsQuery(ALL_BASE_MODELS, {
     selectFromResult: ({ data }) => ({
-      model: selectedModelId ? data?.entities[selectedModelId] : undefined,
+      mainModel: selectedModelId ? data?.entities[selectedModelId] : undefined,
     }),
   });
+  const { loraModel } = useGetLoRAModelsQuery(undefined, {
+    selectFromResult: ({ data }) => ({
+      loraModel: selectedModelId ? data?.entities[selectedModelId] : undefined,
+    }),
+  });
+
+  const model = mainModel ? mainModel : loraModel;
 
   return (
     <Flex sx={{ gap: 8, w: 'full', h: 'full' }}>
@@ -30,7 +41,7 @@ export default function ModelManagerPanel() {
 }
 
 type ModelEditProps = {
-  model: MainModelConfigEntity | undefined;
+  model: MainModelConfigEntity | LoRAModelConfigEntity | undefined;
 };
 
 const ModelEdit = (props: ModelEditProps) => {
@@ -41,7 +52,16 @@ const ModelEdit = (props: ModelEditProps) => {
   }
 
   if (model?.model_format === 'diffusers') {
-    return <DiffusersModelEdit key={model.id} model={model} />;
+    return (
+      <DiffusersModelEdit
+        key={model.id}
+        model={model as DiffusersModelConfigEntity}
+      />
+    );
+  }
+
+  if (model?.model_type === 'lora') {
+    return <LoRAModelEdit key={model.id} model={model} />;
   }
 
   return (

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

@@ -0,0 +1,137 @@
+import { Divider, Flex, Text } from '@chakra-ui/react';
+import { useForm } from '@mantine/form';
+import { makeToast } from 'features/system/util/makeToast';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import IAIButton from 'common/components/IAIButton';
+import IAIMantineTextInput from 'common/components/IAIMantineInput';
+import { selectIsBusy } from 'features/system/store/systemSelectors';
+import { addToast } from 'features/system/store/systemSlice';
+import { useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+import {
+  LORA_MODEL_FORMAT_MAP,
+  MODEL_TYPE_MAP,
+} from 'features/parameters/types/constants';
+import {
+  LoRAModelConfigEntity,
+  useUpdateLoRAModelsMutation,
+} from 'services/api/endpoints/models';
+import { LoRAModelConfig } from 'services/api/types';
+import BaseModelSelect from '../shared/BaseModelSelect';
+
+type LoRAModelEditProps = {
+  model: LoRAModelConfigEntity;
+};
+
+export default function LoRAModelEdit(props: LoRAModelEditProps) {
+  const isBusy = useAppSelector(selectIsBusy);
+
+  const { model } = props;
+
+  const [updateLoRAModel, { isLoading }] = useUpdateLoRAModelsMutation();
+
+  const dispatch = useAppDispatch();
+  const { t } = useTranslation();
+
+  const loraEditForm = useForm<LoRAModelConfig>({
+    initialValues: {
+      model_name: model.model_name ? model.model_name : '',
+      base_model: model.base_model,
+      model_type: 'lora',
+      path: model.path ? model.path : '',
+      description: model.description ? model.description : '',
+      model_format: model.model_format,
+    },
+    validate: {
+      path: (value) =>
+        value.trim().length === 0 ? 'Must provide a path' : null,
+    },
+  });
+
+  const editModelFormSubmitHandler = useCallback(
+    (values: LoRAModelConfig) => {
+      const responseBody = {
+        base_model: model.base_model,
+        model_name: model.model_name,
+        body: values,
+      };
+
+      updateLoRAModel(responseBody)
+        .unwrap()
+        .then((payload) => {
+          loraEditForm.setValues(payload as LoRAModelConfig);
+          dispatch(
+            addToast(
+              makeToast({
+                title: t('modelManager.modelUpdated'),
+                status: 'success',
+              })
+            )
+          );
+        })
+        .catch((_) => {
+          loraEditForm.reset();
+          dispatch(
+            addToast(
+              makeToast({
+                title: t('modelManager.modelUpdateFailed'),
+                status: 'error',
+              })
+            )
+          );
+        });
+    },
+    [
+      dispatch,
+      loraEditForm,
+      model.base_model,
+      model.model_name,
+      t,
+      updateLoRAModel,
+    ]
+  );
+
+  return (
+    <Flex flexDirection="column" rowGap={4} width="100%">
+      <Flex flexDirection="column">
+        <Text fontSize="lg" fontWeight="bold">
+          {model.model_name}
+        </Text>
+        <Text fontSize="sm" color="base.400">
+          {MODEL_TYPE_MAP[model.base_model]} Model ⋅{' '}
+          {LORA_MODEL_FORMAT_MAP[model.model_format]} format
+        </Text>
+      </Flex>
+      <Divider />
+
+      <form
+        onSubmit={loraEditForm.onSubmit((values) =>
+          editModelFormSubmitHandler(values)
+        )}
+      >
+        <Flex flexDirection="column" overflowY="scroll" gap={4}>
+          <IAIMantineTextInput
+            label={t('modelManager.name')}
+            {...loraEditForm.getInputProps('model_name')}
+          />
+          <IAIMantineTextInput
+            label={t('modelManager.description')}
+            {...loraEditForm.getInputProps('description')}
+          />
+          <BaseModelSelect {...loraEditForm.getInputProps('base_model')} />
+          <IAIMantineTextInput
+            label={t('modelManager.modelLocation')}
+            {...loraEditForm.getInputProps('path')}
+          />
+          <IAIButton
+            type="submit"
+            isDisabled={isBusy || isLoading}
+            isLoading={isLoading}
+          >
+            {t('modelManager.updateModel')}
+          </IAIButton>
+        </Flex>
+      </form>
+    </Flex>
+  );
+}

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

@@ -9,6 +9,8 @@ import { useTranslation } from 'react-i18next';
 import {
   MainModelConfigEntity,
   useGetMainModelsQuery,
+  useGetLoRAModelsQuery,
+  LoRAModelConfigEntity,
 } from 'services/api/endpoints/models';
 import ModelListItem from './ModelListItem';
 import { ALL_BASE_MODELS } from 'services/api/constants';
@@ -20,22 +22,42 @@ type ModelListProps = {
 
 type ModelFormat = 'images' | 'checkpoint' | 'diffusers';
 
+type ModelType = 'main' | 'lora';
+
+type CombinedModelFormat = ModelFormat | 'lora';
+
 const ModelList = (props: ModelListProps) => {
   const { selectedModelId, setSelectedModelId } = props;
   const { t } = useTranslation();
   const [nameFilter, setNameFilter] = useState<string>('');
   const [modelFormatFilter, setModelFormatFilter] =
-    useState<ModelFormat>('images');
+    useState<CombinedModelFormat>('images');
 
   const { filteredDiffusersModels } = useGetMainModelsQuery(ALL_BASE_MODELS, {
     selectFromResult: ({ data }) => ({
-      filteredDiffusersModels: modelsFilter(data, 'diffusers', nameFilter),
+      filteredDiffusersModels: modelsFilter(
+        data,
+        'main',
+        'diffusers',
+        nameFilter
+      ),
     }),
   });
 
   const { filteredCheckpointModels } = useGetMainModelsQuery(ALL_BASE_MODELS, {
     selectFromResult: ({ data }) => ({
-      filteredCheckpointModels: modelsFilter(data, 'checkpoint', nameFilter),
+      filteredCheckpointModels: modelsFilter(
+        data,
+        'main',
+        'checkpoint',
+        nameFilter
+      ),
+    }),
+  });
+
+  const { filteredLoraModels } = useGetLoRAModelsQuery(undefined, {
+    selectFromResult: ({ data }) => ({
+      filteredLoraModels: modelsFilter(data, 'lora', undefined, nameFilter),
     }),
   });
 
@@ -68,6 +90,13 @@ const ModelList = (props: ModelListProps) => {
           >
             {t('modelManager.checkpointModels')}
           </IAIButton>
+          <IAIButton
+            size="sm"
+            onClick={() => setModelFormatFilter('lora')}
+            isChecked={modelFormatFilter === 'lora'}
+          >
+            {t('modelManager.loraModels')}
+          </IAIButton>
         </ButtonGroup>
 
         <IAIInput
@@ -118,6 +147,24 @@ const ModelList = (props: ModelListProps) => {
                 </Flex>
               </StyledModelContainer>
             )}
+          {['images', 'lora'].includes(modelFormatFilter) &&
+            filteredLoraModels.length > 0 && (
+              <StyledModelContainer>
+                <Flex sx={{ gap: 2, flexDir: 'column' }}>
+                  <Text variant="subtext" fontSize="sm">
+                    LoRAs
+                  </Text>
+                  {filteredLoraModels.map((model) => (
+                    <ModelListItem
+                      key={model.id}
+                      model={model}
+                      isSelected={selectedModelId === model.id}
+                      setSelectedModelId={setSelectedModelId}
+                    />
+                  ))}
+                </Flex>
+              </StyledModelContainer>
+            )}
         </Flex>
       </Flex>
     </Flex>
@@ -126,12 +173,13 @@ const ModelList = (props: ModelListProps) => {
 
 export default ModelList;
 
-const modelsFilter = (
+const modelsFilter = <T extends MainModelConfigEntity | LoRAModelConfigEntity>(
-  data: EntityState<MainModelConfigEntity> | undefined,
+  data: EntityState<T> | undefined,
-  model_format: ModelFormat,
+  model_type: ModelType,
+  model_format: ModelFormat | undefined,
   nameFilter: string
 ) => {
-  const filteredModels: MainModelConfigEntity[] = [];
+  const filteredModels: T[] = [];
   forEach(data?.entities, (model) => {
     if (!model) {
       return;
@@ -141,9 +189,11 @@ const modelsFilter = (
       .toLowerCase()
       .includes(nameFilter.toLowerCase());
 
-    const matchesFormat = model.model_format === model_format;
+    const matchesFormat =
+      model_format === undefined || model.model_format === model_format;
+    const matchesType = model.model_type === model_type;
 
-    if (matchesFilter && matchesFormat) {
+    if (matchesFilter && matchesFormat && matchesType) {
       filteredModels.push(model);
     }
   });

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

@@ -9,29 +9,26 @@ import { selectIsBusy } from 'features/system/store/systemSelectors';
 import { addToast } from 'features/system/store/systemSlice';
 import { useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
+import { MODEL_TYPE_SHORT_MAP } from 'features/parameters/types/constants';
 import {
   MainModelConfigEntity,
+  LoRAModelConfigEntity,
   useDeleteMainModelsMutation,
+  useDeleteLoRAModelsMutation,
 } from 'services/api/endpoints/models';
 
 type ModelListItemProps = {
-  model: MainModelConfigEntity;
+  model: MainModelConfigEntity | LoRAModelConfigEntity;
   isSelected: boolean;
   setSelectedModelId: (v: string | undefined) => void;
 };
 
-const modelBaseTypeMap = {
-  'sd-1': 'SD1',
-  'sd-2': 'SD2',
-  sdxl: 'SDXL',
-  'sdxl-refiner': 'SDXLR',
-};
-
 export default function ModelListItem(props: ModelListItemProps) {
   const isBusy = useAppSelector(selectIsBusy);
   const { t } = useTranslation();
   const dispatch = useAppDispatch();
   const [deleteMainModel] = useDeleteMainModelsMutation();
+  const [deleteLoRAModel] = useDeleteLoRAModelsMutation();
 
   const { model, isSelected, setSelectedModelId } = props;
 
@@ -40,7 +37,10 @@ export default function ModelListItem(props: ModelListItemProps) {
   }, [model.id, setSelectedModelId]);
 
   const handleModelDelete = useCallback(() => {
-    deleteMainModel(model)
+    const method = { main: deleteMainModel, lora: deleteLoRAModel }[
+      model.model_type
+    ];
+    method(model)
       .unwrap()
       .then((_) => {
         dispatch(
@@ -60,14 +60,21 @@ export default function ModelListItem(props: ModelListItemProps) {
                 title: `${t('modelManager.modelDeleteFailed')}: ${
                   model.model_name
                 }`,
-                status: 'success',
+                status: 'error',
               })
             )
           );
         }
       });
     setSelectedModelId(undefined);
-  }, [deleteMainModel, model, setSelectedModelId, dispatch, t]);
+  }, [
+    deleteMainModel,
+    deleteLoRAModel,
+    model,
+    setSelectedModelId,
+    dispatch,
+    t,
+  ]);
 
   return (
     <Flex sx={{ gap: 2, alignItems: 'center', w: 'full' }}>
@@ -100,8 +107,8 @@ export default function ModelListItem(props: ModelListItemProps) {
         <Flex gap={4} alignItems="center">
           <Badge minWidth={14} p={0.5} fontSize="sm" variant="solid">
             {
-              modelBaseTypeMap[
+              MODEL_TYPE_SHORT_MAP[
-                model.base_model as keyof typeof modelBaseTypeMap
+                model.base_model as keyof typeof MODEL_TYPE_SHORT_MAP
               ]
             }
           </Badge>

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

@@ -52,9 +52,17 @@ type UpdateMainModelArg = {
   body: MainModelConfig;
 };
 
+type UpdateLoRAModelArg = {
+  base_model: BaseModelType;
+  model_name: string;
+  body: LoRAModelConfig;
+};
+
 type UpdateMainModelResponse =
   paths['/api/v1/models/{base_model}/{model_type}/{model_name}']['patch']['responses']['200']['content']['application/json'];
 
+type UpdateLoRAModelResponse = UpdateMainModelResponse;
+
 type DeleteMainModelArg = {
   base_model: BaseModelType;
   model_name: string;
@@ -62,6 +70,10 @@ type DeleteMainModelArg = {
 
 type DeleteMainModelResponse = void;
 
+type DeleteLoRAModelArg = DeleteMainModelArg;
+
+type DeleteLoRAModelResponse = void;
+
 type ConvertMainModelArg = {
   base_model: BaseModelType;
   model_name: string;
@@ -320,6 +332,31 @@ export const modelsApi = api.injectEndpoints({
         );
       },
     }),
+    updateLoRAModels: build.mutation<
+      UpdateLoRAModelResponse,
+      UpdateLoRAModelArg
+    >({
+      query: ({ base_model, model_name, body }) => {
+        return {
+          url: `models/${base_model}/lora/${model_name}`,
+          method: 'PATCH',
+          body: body,
+        };
+      },
+      invalidatesTags: [{ type: 'LoRAModel', id: LIST_TAG }],
+    }),
+    deleteLoRAModels: build.mutation<
+      DeleteLoRAModelResponse,
+      DeleteLoRAModelArg
+    >({
+      query: ({ base_model, model_name }) => {
+        return {
+          url: `models/${base_model}/lora/${model_name}`,
+          method: 'DELETE',
+        };
+      },
+      invalidatesTags: [{ type: 'LoRAModel', id: LIST_TAG }],
+    }),
     getControlNetModels: build.query<
       EntityState<ControlNetModelConfigEntity>,
       void
@@ -467,6 +504,8 @@ export const {
   useAddMainModelsMutation,
   useConvertMainModelsMutation,
   useMergeMainModelsMutation,
+  useDeleteLoRAModelsMutation,
+  useUpdateLoRAModelsMutation,
   useSyncModelsMutation,
   useGetModelsInFolderQuery,
   useGetCheckpointConfigsQuery,

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

@@ -5562,12 +5562,6 @@ export type components = {
      * @enum {string}
      */
     StableDiffusion2ModelFormat: "checkpoint" | "diffusers";
-    /**
-     * StableDiffusion1ModelFormat 
-     * @description An enumeration. 
-     * @enum {string}
-     */
-    StableDiffusion1ModelFormat: "checkpoint" | "diffusers";
     /**
      * StableDiffusionXLModelFormat 
      * @description An enumeration. 
@@ -5580,6 +5574,12 @@ export type components = {
      * @enum {string}
      */
     ControlNetModelFormat: "checkpoint" | "diffusers";
+    /**
+     * StableDiffusion1ModelFormat 
+     * @description An enumeration. 
+     * @enum {string}
+     */
+    StableDiffusion1ModelFormat: "checkpoint" | "diffusers";
   };
   responses: never;
   parameters: never;

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

@@ -42,8 +42,13 @@ export type ControlField = components['schemas']['ControlField'];
 // Model Configs
 export type LoRAModelConfig = components['schemas']['LoRAModelConfig'];
 export type VaeModelConfig = components['schemas']['VaeModelConfig'];
+export type ControlNetModelCheckpointConfig =
+  components['schemas']['ControlNetModelCheckpointConfig'];
+export type ControlNetModelDiffusersConfig =
+  components['schemas']['ControlNetModelDiffusersConfig'];
 export type ControlNetModelConfig =
-  components['schemas']['ControlNetModelConfig'];
+  | ControlNetModelCheckpointConfig
+  | ControlNetModelDiffusersConfig;
 export type TextualInversionModelConfig =
   components['schemas']['TextualInversionModelConfig'];
 export type DiffusersModelConfig =

invokeai/version/invokeai_version.py

@@ -1 +1 @@
-__version__ = "3.0.1rc1"
+__version__ = "3.0.1rc2"

pyproject.toml

@@ -5,7 +5,7 @@ build-backend = "setuptools.build_meta"
 [project]
 name = "InvokeAI"
 description = "An implementation of Stable Diffusion which provides various new features and options to aid the image generation process"
-requires-python = ">=3.9, <3.11"
+requires-python = ">=3.9, <3.12"
 readme = { content-type = "text/markdown", file = "README.md" }
 keywords = ["stable-diffusion", "AI"]
 dynamic = ["version"]
@@ -32,16 +32,16 @@ classifiers = [
   'Topic :: Scientific/Engineering :: Image Processing',
 ]
 dependencies = [
-  "accelerate~=0.16",
+  "accelerate~=0.21.0",
   "albumentations",
   "click",
-  "clip_anytorch",          # replacing "clip @ https://github.com/openai/CLIP/archive/eaa22acb90a5876642d0507623e859909230a52d.zip",
+  "clip_anytorch",  # replacing "clip @ https://github.com/openai/CLIP/archive/eaa22acb90a5876642d0507623e859909230a52d.zip",
-  "compel==2.0.0",
+  "compel~=2.0.0",
   "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
+  "timm==0.6.13",   # needed to override timm latest in controlnet_aux, see  https://github.com/isl-org/ZoeDepth/issues/26
   "datasets",
-  "diffusers[torch]~=0.18.1",
+  "diffusers[torch]~=0.19.0",
-  "dnspython==2.2.1",
+  "dnspython~=2.4.0",
   "dynamicprompts",
   "easing-functions",
   "einops",
@@ -54,37 +54,37 @@ dependencies = [
   "flask_cors==3.0.10",
   "flask_socketio==5.3.0",
   "flaskwebgui==1.0.3",
-  "gfpgan==1.3.8",
   "huggingface-hub>=0.11.1",
-  "invisible-watermark>=0.2.0", # needed to install SDXL base and refiner using their repo_ids
+  "invisible-watermark~=0.2.0", # needed to install SDXL base and refiner using their repo_ids
   "matplotlib",                 # needed for plotting of Penner easing functions
   "mediapipe",                  # needed for "mediapipeface" controlnet model
   "npyscreen",
-  "numpy<1.24",
+  "numpy==1.24.4",
   "omegaconf",
   "opencv-python",
   "picklescan",
   "pillow",
   "prompt-toolkit",
-  "pympler==1.0.1",
+  "pydantic==1.10.10",
+  "pympler~=1.0.1",
   "pypatchmatch",
   'pyperclip',
   "pyreadline3",
-  "python-multipart==0.0.6",
+  "python-multipart",
-  "pytorch-lightning==1.7.7",
+  "pytorch-lightning",
   "realesrgan",
-  "requests==2.28.2",
+  "requests~=2.28.2",
   "rich~=13.3",
   "safetensors~=0.3.0",
-  "scikit-image>=0.19",
+  "scikit-image~=0.21.0",
   "send2trash",
-  "test-tube>=0.7.5",
+  "test-tube~=0.7.5",
-  "torch~=2.0.0",
+  "torch~=2.0.1",
-  "torchvision>=0.14.1",
+  "torchvision~=0.15.2",
-  "torchmetrics==0.11.4",
+  "torchmetrics~=1.0.1",
-  "torchsde==0.2.5",
+  "torchsde~=0.2.5",
   "transformers~=4.31.0",
-  "uvicorn[standard]==0.21.1",
+  "uvicorn[standard]~=0.21.1",
   "windows-curses; sys_platform=='win32'",
 ]
 

scripts/probe-model.py

@@ -1,8 +1,16 @@
 #!/bin/env python
 
+import argparse
 import sys
 from pathlib import Path
 from invokeai.backend.model_management.model_probe import ModelProbe
 
-info = ModelProbe().probe(Path(sys.argv[1]))
+parser = argparse.ArgumentParser(description="Probe model type")
+parser.add_argument(
+    "model_path",
+    type=Path,
+)
+args = parser.parse_args()
+
+info = ModelProbe().probe(args.model_path)
 print(info)