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Merge branch 'set-timestep-mps-fix' of ssh://github.com/ZachNagengast/InvokeAI into set-timestep-mps-fix

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This commit is contained in:
ZachNagengast
2023-07-27 23:40:44 -07:00
parent e6d14c708c 7f81a95b20
commit ddaf753f7b
26 changed files with 518 additions and 210 deletions
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2
invokeai/app/api/routers/models.py
View File

@@ -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

19
invokeai/app/api_app.py
View File

@@ -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)
# Use access_log to turn off logging
config = uvicorn.Config(
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())

242
invokeai/app/invocations/latent.py
View File

@@ -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)
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]
# 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)
source_node_id = graph_execution_state.prepared_source_mapping[self.id]
def step_callback(state: PipelineIntermediateState):
self.dispatch_progress(context, source_node_id, state)
def step_callback(state: PipelineIntermediateState):
self.dispatch_progress(context, source_node_id, state)
def _lora_loader():
for lora in self.unet.loras:
lora_info = context.services.model_manager.get_model(
**lora.dict(exclude={"weight"}),
def _lora_loader():
for lora in self.unet.loras:
lora_info = context.services.model_manager.get_model(
**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(
**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)
pipeline = self.create_pipeline(unet, scheduler)
conditioning_data = self.get_conditioning_data(context, scheduler, unet)
scheduler = get_scheduler(
context=context,
scheduler_info=self.unet.scheduler,
scheduler_name=self.scheduler,
)
control_data = self.prep_control_data(
model=pipeline,
context=context,
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)
conditioning_data = self.get_conditioning_data(context, scheduler, unet)
# TODO: Verify the noise is the right size
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(
model=pipeline,
context=context,
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,
)
# https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
result_latents = result_latents.to("cpu")
torch.cuda.empty_cache()
# TODO: Verify the noise is the right size
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,
)
# 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)
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)
latent = context.services.latents.get(self.latents.latents_name)
with SilenceWarnings(): # this quenches NSFW nag from diffusers
noise = context.services.latents.get(self.noise.latents_name)
latent = context.services.latents.get(self.latents.latents_name)
# Get the source node id (we are invoking the prepared node)
graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
source_node_id = graph_execution_state.prepared_source_mapping[self.id]
# 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)
source_node_id = graph_execution_state.prepared_source_mapping[self.id]
def step_callback(state: PipelineIntermediateState):
self.dispatch_progress(context, source_node_id, state)
def step_callback(state: PipelineIntermediateState):
self.dispatch_progress(context, source_node_id, state)
def _lora_loader():
for lora in self.unet.loras:
lora_info = context.services.model_manager.get_model(
**lora.dict(exclude={"weight"}),
def _lora_loader():
for lora in self.unet.loras:
lora_info = context.services.model_manager.get_model(
**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(
**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)
pipeline = self.create_pipeline(unet, scheduler)
conditioning_data = self.get_conditioning_data(context, scheduler, unet)
scheduler = get_scheduler(
context=context,
scheduler_info=self.unet.scheduler,
scheduler_name=self.scheduler,
)
control_data = self.prep_control_data(
model=pipeline,
context=context,
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)
conditioning_data = self.get_conditioning_data(context, scheduler, unet)
# TODO: Verify the noise is the right size
initial_latents = (
latent if self.strength < 1.0 else torch.zeros_like(latent, device=unet.device, dtype=latent.dtype)
)
control_data = self.prep_control_data(
model=pipeline,
context=context,
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,
)
timesteps, _ = pipeline.get_img2img_timesteps(
self.steps,
self.strength,
device=unet.device,
)
# TODO: Verify the noise is the right size
initial_latents = (
latent if self.strength < 1.0 else torch.zeros_like(latent, device=unet.device, dtype=latent.dtype)
)
result_latents, result_attention_map_saver = pipeline.latents_from_embeddings(
latents=initial_latents,
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(
self.steps,
self.strength,
device=unet.device,
)
# https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
result_latents = result_latents.to("cpu")
torch.cuda.empty_cache()
result_latents, result_attention_map_saver = pipeline.latents_from_embeddings(
latents=initial_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)
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"

6
invokeai/backend/model_management/model_manager.py
View File

@@ -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]:

2
invokeai/backend/model_management/models/lora.py
View File

@@ -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):

2
invokeai/frontend/web/dist/assets/App-69e5ea36.js → invokeai/frontend/web/dist/assets/App-58b095d3.js vendored
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File diff suppressed because one or more lines are too long

2
invokeai/frontend/web/dist/assets/MantineProvider-8184f020.js → invokeai/frontend/web/dist/assets/MantineProvider-ea42d3d1.js vendored
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File diff suppressed because one or more lines are too long

2
invokeai/frontend/web/dist/assets/ThemeLocaleProvider-9ac72450.js → invokeai/frontend/web/dist/assets/ThemeLocaleProvider-13e3db3d.js vendored
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@@ -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;

24
invokeai/frontend/web/dist/assets/index-89941396.js → invokeai/frontend/web/dist/assets/index-5a784cdd.js vendored
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File diff suppressed because one or more lines are too long

2
invokeai/frontend/web/dist/index.html vendored
View File

@@ -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">

1
invokeai/frontend/web/public/locales/en.json
View File

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

18
invokeai/frontend/web/src/features/parameters/types/constants.ts
View File

@@ -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',
};

28
invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/ModelManagerPanel.tsx
View File

@@ -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 (

137
invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/ModelManagerPanel/LoRAModelEdit.tsx Normal file
View File

@@ -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>
);
}

68
invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/ModelManagerPanel/ModelList.tsx
View File

@@ -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 = (
data: EntityState<MainModelConfigEntity> | undefined,
model_format: ModelFormat,
const modelsFilter = <T extends MainModelConfigEntity | LoRAModelConfigEntity>(
data: EntityState<T> | undefined,
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);
}
});

33
invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/ModelManagerPanel/ModelListItem.tsx
View File

@@ -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.base_model as keyof typeof modelBaseTypeMap
MODEL_TYPE_SHORT_MAP[
model.base_model as keyof typeof MODEL_TYPE_SHORT_MAP
]
}
</Badge>

39
invokeai/frontend/web/src/services/api/endpoints/models.ts
View File

@@ -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,

12
invokeai/frontend/web/src/services/api/schema.d.ts vendored
View File

@@ -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;

7
invokeai/frontend/web/src/services/api/types.d.ts vendored
View File

@@ -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 =

2
invokeai/version/invokeai_version.py
View File

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