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Merge branch 'main' into lstein/remove-hardcoded-cuda-device

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Lincoln Stein 2023-07-05 13:38:33 -04:00 committed by GitHub
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133 changed files with 3999 additions and 4024 deletions
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2
.gitignore vendored
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@ -201,8 +201,6 @@ checkpoints
# If it's a Mac # If it's a Mac
.DS_Store .DS_Store
invokeai/frontend/web/dist/*
# Let the frontend manage its own gitignore # Let the frontend manage its own gitignore
!invokeai/frontend/web/* !invokeai/frontend/web/*

47
invokeai/app/api/routers/models.py
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@ -2,17 +2,17 @@
from typing import Literal, Optional, Union from typing import Literal, Optional, Union
from fastapi import Query from fastapi import Query, Body
from fastapi.routing import APIRouter, HTTPException from fastapi.routing import APIRouter, HTTPException
from pydantic import BaseModel, Field, parse_obj_as from pydantic import BaseModel, Field, parse_obj_as
from ..dependencies import ApiDependencies from ..dependencies import ApiDependencies
from invokeai.backend import BaseModelType, ModelType from invokeai.backend import BaseModelType, ModelType
from invokeai.backend.model_management import AddModelResult
from invokeai.backend.model_management.models import OPENAPI_MODEL_CONFIGS, SchedulerPredictionType from invokeai.backend.model_management.models import OPENAPI_MODEL_CONFIGS, SchedulerPredictionType
MODEL_CONFIGS = Union[tuple(OPENAPI_MODEL_CONFIGS)] MODEL_CONFIGS = Union[tuple(OPENAPI_MODEL_CONFIGS)]
models_router = APIRouter(prefix="/v1/models", tags=["models"]) models_router = APIRouter(prefix="/v1/models", tags=["models"])
class VaeRepo(BaseModel): class VaeRepo(BaseModel):
repo_id: str = Field(description="The repo ID to use for this VAE") repo_id: str = Field(description="The repo ID to use for this VAE")
path: Optional[str] = Field(description="The path to the VAE") path: Optional[str] = Field(description="The path to the VAE")
@ -51,9 +51,12 @@ class CreateModelResponse(BaseModel):
info: Union[CkptModelInfo, DiffusersModelInfo] = Field(discriminator="format", description="The model info") info: Union[CkptModelInfo, DiffusersModelInfo] = Field(discriminator="format", description="The model info")
status: str = Field(description="The status of the API response") status: str = Field(description="The status of the API response")
class ImportModelRequest(BaseModel): class ImportModelResponse(BaseModel):
name: str = Field(description="A model path, repo_id or URL to import") name: str = Field(description="The name of the imported model")
prediction_type: Optional[Literal['epsilon','v_prediction','sample']] = Field(description='Prediction type for SDv2 checkpoint files') # base_model: str = Field(description="The base model")
# model_type: str = Field(description="The model type")
info: AddModelResult = Field(description="The model info")
status: str = Field(description="The status of the API response")
class ConversionRequest(BaseModel): class ConversionRequest(BaseModel):
name: str = Field(description="The name of the new model") name: str = Field(description="The name of the new model")
@ -86,7 +89,6 @@ async def list_models(
models = parse_obj_as(ModelsList, { "models": models_raw }) models = parse_obj_as(ModelsList, { "models": models_raw })
return models return models
@models_router.post( @models_router.post(
"/", "/",
operation_id="update_model", operation_id="update_model",
@ -109,27 +111,38 @@ async def update_model(
return model_response return model_response
@models_router.post( @models_router.post(
"/", "/import",
operation_id="import_model", operation_id="import_model",
responses={200: {"status": "success"}}, responses= {
201: {"description" : "The model imported successfully"},
404: {"description" : "The model could not be found"},
},
status_code=201,
response_model=ImportModelResponse
) )
async def import_model( async def import_model(
model_request: ImportModelRequest name: str = Query(description="A model path, repo_id or URL to import"),
) -> None: prediction_type: Optional[Literal['v_prediction','epsilon','sample']] = Query(description='Prediction type for SDv2 checkpoint files', default="v_prediction"),
""" Add Model """ ) -> ImportModelResponse:
items_to_import = set([model_request.name]) """ Add a model using its local path, repo_id, or remote URL """
items_to_import = {name}
prediction_types = { x.value: x for x in SchedulerPredictionType } prediction_types = { x.value: x for x in SchedulerPredictionType }
logger = ApiDependencies.invoker.services.logger logger = ApiDependencies.invoker.services.logger
installed_models = ApiDependencies.invoker.services.model_manager.heuristic_import( installed_models = ApiDependencies.invoker.services.model_manager.heuristic_import(
items_to_import = items_to_import, items_to_import = items_to_import,
prediction_type_helper = lambda x: prediction_types.get(model_request.prediction_type) prediction_type_helper = lambda x: prediction_types.get(prediction_type)
) )
if len(installed_models) > 0: if info := installed_models.get(name):
logger.info(f'Successfully imported {model_request.name}') logger.info(f'Successfully imported {name}, got {info}')
return ImportModelResponse(
name = name,
info = info,
status = "success",
)
else: else:
logger.error(f'Model {model_request.name} not imported') logger.error(f'Model {name} not imported')
raise HTTPException(status_code=500, detail=f'Model {model_request.name} not imported') raise HTTPException(status_code=404, detail=f'Model {name} not found')
@models_router.delete( @models_router.delete(
"/{model_name}", "/{model_name}",

25
invokeai/app/invocations/baseinvocation.py
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@ -4,9 +4,10 @@ from __future__ import annotations
from abc import ABC, abstractmethod from abc import ABC, abstractmethod
from inspect import signature from inspect import signature
from typing import get_args, get_type_hints, Dict, List, Literal, TypedDict, TYPE_CHECKING from typing import (TYPE_CHECKING, Dict, List, Literal, TypedDict, get_args,
get_type_hints)
from pydantic import BaseModel, Field from pydantic import BaseConfig, BaseModel, Field
if TYPE_CHECKING: if TYPE_CHECKING:
from ..services.invocation_services import InvocationServices from ..services.invocation_services import InvocationServices
@ -65,8 +66,13 @@ class BaseInvocation(ABC, BaseModel):
@classmethod @classmethod
def get_invocations_map(cls): def get_invocations_map(cls):
# Get the type strings out of the literals and into a dictionary # Get the type strings out of the literals and into a dictionary
return dict(map(lambda t: (get_args(get_type_hints(t)['type'])[0], t),BaseInvocation.get_all_subclasses())) return dict(
map(
lambda t: (get_args(get_type_hints(t)["type"])[0], t),
BaseInvocation.get_all_subclasses(),
)
)
@classmethod @classmethod
def get_output_type(cls): def get_output_type(cls):
return signature(cls.invoke).return_annotation return signature(cls.invoke).return_annotation
@ -75,11 +81,11 @@ class BaseInvocation(ABC, BaseModel):
def invoke(self, context: InvocationContext) -> BaseInvocationOutput: def invoke(self, context: InvocationContext) -> BaseInvocationOutput:
"""Invoke with provided context and return outputs.""" """Invoke with provided context and return outputs."""
pass pass
#fmt: off # fmt: off
id: str = Field(description="The id of this node. Must be unique among all nodes.") id: str = Field(description="The id of this node. Must be unique among all nodes.")
is_intermediate: bool = Field(default=False, description="Whether or not this node is an intermediate node.") is_intermediate: bool = Field(default=False, description="Whether or not this node is an intermediate node.")
#fmt: on # fmt: on
# TODO: figure out a better way to provide these hints # TODO: figure out a better way to provide these hints
@ -98,16 +104,19 @@ class UIConfig(TypedDict, total=False):
"model", "model",
"control", "control",
"image_collection", "image_collection",
"vae_model",
"lora_model",
], ],
] ]
tags: List[str] tags: List[str]
title: str title: str
class CustomisedSchemaExtra(TypedDict): class CustomisedSchemaExtra(TypedDict):
ui: UIConfig ui: UIConfig
class InvocationConfig(BaseModel.Config): class InvocationConfig(BaseConfig):
"""Customizes pydantic's BaseModel.Config class for use by Invocations. """Customizes pydantic's BaseModel.Config class for use by Invocations.
Provide `schema_extra` a `ui` dict to add hints for generated UIs. Provide `schema_extra` a `ui` dict to add hints for generated UIs.

165
invokeai/app/invocations/compel.py
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@ -1,27 +1,28 @@
from typing import Literal, Optional, Union
from pydantic import BaseModel, Field
from contextlib import ExitStack
import re import re
from contextlib import ExitStack
from typing import List, Literal, Optional, Union
from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext, InvocationConfig import torch
from .model import ClipField from compel import Compel
from compel.prompt_parser import (Blend, Conjunction,
CrossAttentionControlSubstitute,
FlattenedPrompt, Fragment)
from pydantic import BaseModel, Field
from ...backend.util.devices import torch_dtype from ...backend.model_management.models import ModelNotFoundException
from ...backend.stable_diffusion.diffusion import InvokeAIDiffuserComponent
from ...backend.model_management import BaseModelType, ModelType, SubModelType from ...backend.model_management import BaseModelType, ModelType, SubModelType
from ...backend.model_management.lora import ModelPatcher from ...backend.model_management.lora import ModelPatcher
from ...backend.stable_diffusion.diffusion import InvokeAIDiffuserComponent
from compel import Compel from ...backend.util.devices import torch_dtype
from compel.prompt_parser import ( from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
Blend, InvocationConfig, InvocationContext)
CrossAttentionControlSubstitute, from .model import ClipField
FlattenedPrompt,
Fragment, Conjunction,
)
class ConditioningField(BaseModel): class ConditioningField(BaseModel):
conditioning_name: Optional[str] = Field(default=None, description="The name of conditioning data") conditioning_name: Optional[str] = Field(
default=None, description="The name of conditioning data")
class Config: class Config:
schema_extra = {"required": ["conditioning_name"]} schema_extra = {"required": ["conditioning_name"]}
@ -51,83 +52,92 @@ class CompelInvocation(BaseInvocation):
"title": "Prompt (Compel)", "title": "Prompt (Compel)",
"tags": ["prompt", "compel"], "tags": ["prompt", "compel"],
"type_hints": { "type_hints": {
"model": "model" "model": "model"
} }
}, },
} }
@torch.no_grad()
def invoke(self, context: InvocationContext) -> CompelOutput: def invoke(self, context: InvocationContext) -> CompelOutput:
tokenizer_info = context.services.model_manager.get_model( tokenizer_info = context.services.model_manager.get_model(
**self.clip.tokenizer.dict(), **self.clip.tokenizer.dict(),
) )
text_encoder_info = context.services.model_manager.get_model( text_encoder_info = context.services.model_manager.get_model(
**self.clip.text_encoder.dict(), **self.clip.text_encoder.dict(),
) )
with tokenizer_info as orig_tokenizer,\
text_encoder_info as text_encoder:
loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras] def _lora_loader():
for lora in self.clip.loras:
lora_info = context.services.model_manager.get_model(
**lora.dict(exclude={"weight"}))
yield (lora_info.context.model, lora.weight)
del lora_info
return
ti_list = [] #loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
for trigger in re.findall(r"<[a-zA-Z0-9., _-]+>", self.prompt):
name = trigger[1:-1]
try:
ti_list.append(
context.services.model_manager.get_model(
model_name=name,
base_model=self.clip.text_encoder.base_model,
model_type=ModelType.TextualInversion,
).context.model
)
except Exception:
#print(e)
#import traceback
#print(traceback.format_exc())
print(f"Warn: trigger: \"{trigger}\" not found")
with ModelPatcher.apply_lora_text_encoder(text_encoder, loras),\ ti_list = []
ModelPatcher.apply_ti(orig_tokenizer, text_encoder, ti_list) as (tokenizer, ti_manager): for trigger in re.findall(r"<[a-zA-Z0-9., _-]+>", self.prompt):
name = trigger[1:-1]
compel = Compel( try:
tokenizer=tokenizer, ti_list.append(
text_encoder=text_encoder, context.services.model_manager.get_model(
textual_inversion_manager=ti_manager, model_name=name,
dtype_for_device_getter=torch_dtype, base_model=self.clip.text_encoder.base_model,
truncate_long_prompts=True, # TODO: model_type=ModelType.TextualInversion,
).context.model
) )
except ModelNotFoundException:
conjunction = Compel.parse_prompt_string(self.prompt) # print(e)
prompt: Union[FlattenedPrompt, Blend] = conjunction.prompts[0] #import traceback
#print(traceback.format_exc())
print(f"Warn: trigger: \"{trigger}\" not found")
if context.services.configuration.log_tokenization: with ModelPatcher.apply_lora_text_encoder(text_encoder_info.context.model, _lora_loader()),\
log_tokenization_for_prompt_object(prompt, tokenizer) ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (tokenizer, ti_manager),\
text_encoder_info as text_encoder:
c, options = compel.build_conditioning_tensor_for_prompt_object(prompt) compel = Compel(
tokenizer=tokenizer,
# TODO: long prompt support text_encoder=text_encoder,
#if not self.truncate_long_prompts: textual_inversion_manager=ti_manager,
# [c, uc] = compel.pad_conditioning_tensors_to_same_length([c, uc]) dtype_for_device_getter=torch_dtype,
ec = InvokeAIDiffuserComponent.ExtraConditioningInfo( truncate_long_prompts=True, # TODO:
tokens_count_including_eos_bos=get_max_token_count(tokenizer, conjunction),
cross_attention_control_args=options.get("cross_attention_control", None),
)
conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
# TODO: hacky but works ;D maybe rename latents somehow?
context.services.latents.save(conditioning_name, (c, ec))
return CompelOutput(
conditioning=ConditioningField(
conditioning_name=conditioning_name,
),
) )
conjunction = Compel.parse_prompt_string(self.prompt)
prompt: Union[FlattenedPrompt, Blend] = conjunction.prompts[0]
if context.services.configuration.log_tokenization:
log_tokenization_for_prompt_object(prompt, tokenizer)
c, options = compel.build_conditioning_tensor_for_prompt_object(
prompt)
# TODO: long prompt support
# if not self.truncate_long_prompts:
# [c, uc] = compel.pad_conditioning_tensors_to_same_length([c, uc])
ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(
tokens_count_including_eos_bos=get_max_token_count(
tokenizer, conjunction),
cross_attention_control_args=options.get(
"cross_attention_control", None),)
conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
# TODO: hacky but works ;D maybe rename latents somehow?
context.services.latents.save(conditioning_name, (c, ec))
return CompelOutput(
conditioning=ConditioningField(
conditioning_name=conditioning_name,
),
)
def get_max_token_count( def get_max_token_count(
tokenizer, prompt: Union[FlattenedPrompt, Blend, Conjunction], truncate_if_too_long=False tokenizer, prompt: Union[FlattenedPrompt, Blend, Conjunction],
) -> int: truncate_if_too_long=False) -> int:
if type(prompt) is Blend: if type(prompt) is Blend:
blend: Blend = prompt blend: Blend = prompt
return max( return max(
@ -146,13 +156,13 @@ def get_max_token_count(
) )
else: else:
return len( return len(
get_tokens_for_prompt_object(tokenizer, prompt, truncate_if_too_long) get_tokens_for_prompt_object(
) tokenizer, prompt, truncate_if_too_long))
def get_tokens_for_prompt_object( def get_tokens_for_prompt_object(
tokenizer, parsed_prompt: FlattenedPrompt, truncate_if_too_long=True tokenizer, parsed_prompt: FlattenedPrompt, truncate_if_too_long=True
) -> [str]: ) -> List[str]:
if type(parsed_prompt) is Blend: if type(parsed_prompt) is Blend:
raise ValueError( raise ValueError(
"Blend is not supported here - you need to get tokens for each of its .children" "Blend is not supported here - you need to get tokens for each of its .children"
@ -181,7 +191,7 @@ def log_tokenization_for_conjunction(
): ):
display_label_prefix = display_label_prefix or "" display_label_prefix = display_label_prefix or ""
for i, p in enumerate(c.prompts): for i, p in enumerate(c.prompts):
if len(c.prompts)>1: if len(c.prompts) > 1:
this_display_label_prefix = f"{display_label_prefix}(conjunction part {i + 1}, weight={c.weights[i]})" this_display_label_prefix = f"{display_label_prefix}(conjunction part {i + 1}, weight={c.weights[i]})"
else: else:
this_display_label_prefix = display_label_prefix this_display_label_prefix = display_label_prefix
@ -236,7 +246,8 @@ def log_tokenization_for_prompt_object(
) )
def log_tokenization_for_text(text, tokenizer, display_label=None, truncate_if_too_long=False): def log_tokenization_for_text(
text, tokenizer, display_label=None, truncate_if_too_long=False):
"""shows how the prompt is tokenized """shows how the prompt is tokenized
# usually tokens have '</w>' to indicate end-of-word, # usually tokens have '</w>' to indicate end-of-word,
# but for readability it has been replaced with ' ' # but for readability it has been replaced with ' '

267
invokeai/app/invocations/latent.py
View File

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

151
invokeai/app/invocations/model.py
View File

@ -1,31 +1,38 @@
from typing import Literal, Optional, Union, List
from pydantic import BaseModel, Field
import copy import copy
from typing import List, Literal, Optional, Union
from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext, InvocationConfig from pydantic import BaseModel, Field
from ...backend.util.devices import choose_torch_device, torch_dtype
from ...backend.model_management import BaseModelType, ModelType, SubModelType from ...backend.model_management import BaseModelType, ModelType, SubModelType
from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
InvocationConfig, InvocationContext)
class ModelInfo(BaseModel): class ModelInfo(BaseModel):
model_name: str = Field(description="Info to load submodel") model_name: str = Field(description="Info to load submodel")
base_model: BaseModelType = Field(description="Base model") base_model: BaseModelType = Field(description="Base model")
model_type: ModelType = Field(description="Info to load submodel") model_type: ModelType = Field(description="Info to load submodel")
submodel: Optional[SubModelType] = Field(description="Info to load submodel") submodel: Optional[SubModelType] = Field(
default=None, description="Info to load submodel"
)
class LoraInfo(ModelInfo): class LoraInfo(ModelInfo):
weight: float = Field(description="Lora's weight which to use when apply to model") weight: float = Field(description="Lora's weight which to use when apply to model")
class UNetField(BaseModel): class UNetField(BaseModel):
unet: ModelInfo = Field(description="Info to load unet submodel") unet: ModelInfo = Field(description="Info to load unet submodel")
scheduler: ModelInfo = Field(description="Info to load scheduler submodel") scheduler: ModelInfo = Field(description="Info to load scheduler submodel")
loras: List[LoraInfo] = Field(description="Loras to apply on model loading") loras: List[LoraInfo] = Field(description="Loras to apply on model loading")
class ClipField(BaseModel): class ClipField(BaseModel):
tokenizer: ModelInfo = Field(description="Info to load tokenizer submodel") tokenizer: ModelInfo = Field(description="Info to load tokenizer submodel")
text_encoder: ModelInfo = Field(description="Info to load text_encoder submodel") text_encoder: ModelInfo = Field(description="Info to load text_encoder submodel")
loras: List[LoraInfo] = Field(description="Loras to apply on model loading") loras: List[LoraInfo] = Field(description="Loras to apply on model loading")
class VaeField(BaseModel): class VaeField(BaseModel):
# TODO: better naming? # TODO: better naming?
vae: ModelInfo = Field(description="Info to load vae submodel") vae: ModelInfo = Field(description="Info to load vae submodel")
@ -34,43 +41,48 @@ class VaeField(BaseModel):
class ModelLoaderOutput(BaseInvocationOutput): class ModelLoaderOutput(BaseInvocationOutput):
"""Model loader output""" """Model loader output"""
#fmt: off # fmt: off
type: Literal["model_loader_output"] = "model_loader_output" type: Literal["model_loader_output"] = "model_loader_output"
unet: UNetField = Field(default=None, description="UNet submodel") unet: UNetField = Field(default=None, description="UNet submodel")
clip: ClipField = Field(default=None, description="Tokenizer and text_encoder submodels") clip: ClipField = Field(default=None, description="Tokenizer and text_encoder submodels")
vae: VaeField = Field(default=None, description="Vae submodel") vae: VaeField = Field(default=None, description="Vae submodel")
#fmt: on # fmt: on
class PipelineModelField(BaseModel): class MainModelField(BaseModel):
"""Pipeline model field""" """Main model field"""
model_name: str = Field(description="Name of the model") model_name: str = Field(description="Name of the model")
base_model: BaseModelType = Field(description="Base model") base_model: BaseModelType = Field(description="Base model")
class PipelineModelLoaderInvocation(BaseInvocation): class LoRAModelField(BaseModel):
"""Loads a pipeline model, outputting its submodels.""" """LoRA model field"""
type: Literal["pipeline_model_loader"] = "pipeline_model_loader" model_name: str = Field(description="Name of the LoRA model")
base_model: BaseModelType = Field(description="Base model")
model: PipelineModelField = Field(description="The model to load")
class MainModelLoaderInvocation(BaseInvocation):
"""Loads a main model, outputting its submodels."""
type: Literal["main_model_loader"] = "main_model_loader"
model: MainModelField = Field(description="The model to load")
# TODO: precision? # TODO: precision?
# Schema customisation # Schema customisation
class Config(InvocationConfig): class Config(InvocationConfig):
schema_extra = { schema_extra = {
"ui": { "ui": {
"title": "Model Loader",
"tags": ["model", "loader"], "tags": ["model", "loader"],
"type_hints": { "type_hints": {"model": "model"},
"model": "model"
}
}, },
} }
def invoke(self, context: InvocationContext) -> ModelLoaderOutput: def invoke(self, context: InvocationContext) -> ModelLoaderOutput:
base_model = self.model.base_model base_model = self.model.base_model
model_name = self.model.model_name model_name = self.model.model_name
model_type = ModelType.Main model_type = ModelType.Main
@ -112,7 +124,6 @@ class PipelineModelLoaderInvocation(BaseInvocation):
) )
""" """
return ModelLoaderOutput( return ModelLoaderOutput(
unet=UNetField( unet=UNetField(
unet=ModelInfo( unet=ModelInfo(
@ -151,47 +162,66 @@ class PipelineModelLoaderInvocation(BaseInvocation):
model_type=model_type, model_type=model_type,
submodel=SubModelType.Vae, submodel=SubModelType.Vae,
), ),
) ),
) )
class LoraLoaderOutput(BaseInvocationOutput): class LoraLoaderOutput(BaseInvocationOutput):
"""Model loader output""" """Model loader output"""
#fmt: off # fmt: off
type: Literal["lora_loader_output"] = "lora_loader_output" type: Literal["lora_loader_output"] = "lora_loader_output"
unet: Optional[UNetField] = Field(default=None, description="UNet submodel") unet: Optional[UNetField] = Field(default=None, description="UNet submodel")
clip: Optional[ClipField] = Field(default=None, description="Tokenizer and text_encoder submodels") clip: Optional[ClipField] = Field(default=None, description="Tokenizer and text_encoder submodels")
#fmt: on # fmt: on
class LoraLoaderInvocation(BaseInvocation): class LoraLoaderInvocation(BaseInvocation):
"""Apply selected lora to unet and text_encoder.""" """Apply selected lora to unet and text_encoder."""
type: Literal["lora_loader"] = "lora_loader" type: Literal["lora_loader"] = "lora_loader"
lora_name: str = Field(description="Lora model name") lora: Union[LoRAModelField, None] = Field(
default=None, description="Lora model name"
)
weight: float = Field(default=0.75, description="With what weight to apply lora") weight: float = Field(default=0.75, description="With what weight to apply lora")
unet: Optional[UNetField] = Field(description="UNet model for applying lora") unet: Optional[UNetField] = Field(description="UNet model for applying lora")
clip: Optional[ClipField] = Field(description="Clip model for applying lora") clip: Optional[ClipField] = Field(description="Clip model for applying lora")
def invoke(self, context: InvocationContext) -> LoraLoaderOutput: class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Lora Loader",
"tags": ["lora", "loader"],
"type_hints": {"lora": "lora_model"},
},
}
# TODO: ui rewrite def invoke(self, context: InvocationContext) -> LoraLoaderOutput:
base_model = BaseModelType.StableDiffusion1 if self.lora is None:
raise Exception("No LoRA provided")
base_model = self.lora.base_model
lora_name = self.lora.model_name
if not context.services.model_manager.model_exists( if not context.services.model_manager.model_exists(
base_model=base_model, base_model=base_model,
model_name=self.lora_name, model_name=lora_name,
model_type=ModelType.Lora, model_type=ModelType.Lora,
): ):
raise Exception(f"Unkown lora name: {self.lora_name}!") raise Exception(f"Unkown lora name: {lora_name}!")
if self.unet is not None and any(lora.model_name == self.lora_name for lora in self.unet.loras): if self.unet is not None and any(
raise Exception(f"Lora \"{self.lora_name}\" already applied to unet") lora.model_name == lora_name for lora in self.unet.loras
):
raise Exception(f'Lora "{lora_name}" already applied to unet')
if self.clip is not None and any(lora.model_name == self.lora_name for lora in self.clip.loras): if self.clip is not None and any(
raise Exception(f"Lora \"{self.lora_name}\" already applied to clip") lora.model_name == lora_name for lora in self.clip.loras
):
raise Exception(f'Lora "{lora_name}" already applied to clip')
output = LoraLoaderOutput() output = LoraLoaderOutput()
@ -200,7 +230,7 @@ class LoraLoaderInvocation(BaseInvocation):
output.unet.loras.append( output.unet.loras.append(
LoraInfo( LoraInfo(
base_model=base_model, base_model=base_model,
model_name=self.lora_name, model_name=lora_name,
model_type=ModelType.Lora, model_type=ModelType.Lora,
submodel=None, submodel=None,
weight=self.weight, weight=self.weight,
@ -212,7 +242,7 @@ class LoraLoaderInvocation(BaseInvocation):
output.clip.loras.append( output.clip.loras.append(
LoraInfo( LoraInfo(
base_model=base_model, base_model=base_model,
model_name=self.lora_name, model_name=lora_name,
model_type=ModelType.Lora, model_type=ModelType.Lora,
submodel=None, submodel=None,
weight=self.weight, weight=self.weight,
@ -221,3 +251,58 @@ class LoraLoaderInvocation(BaseInvocation):
return output return output
class VAEModelField(BaseModel):
"""Vae model field"""
model_name: str = Field(description="Name of the model")
base_model: BaseModelType = Field(description="Base model")
class VaeLoaderOutput(BaseInvocationOutput):
"""Model loader output"""
# fmt: off
type: Literal["vae_loader_output"] = "vae_loader_output"
vae: VaeField = Field(default=None, description="Vae model")
# fmt: on
class VaeLoaderInvocation(BaseInvocation):
"""Loads a VAE model, outputting a VaeLoaderOutput"""
type: Literal["vae_loader"] = "vae_loader"
vae_model: VAEModelField = Field(description="The VAE to load")
# Schema customisation
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "VAE Loader",
"tags": ["vae", "loader"],
"type_hints": {"vae_model": "vae_model"},
},
}
def invoke(self, context: InvocationContext) -> VaeLoaderOutput:
base_model = self.vae_model.base_model
model_name = self.vae_model.model_name
model_type = ModelType.Vae
if not context.services.model_manager.model_exists(
base_model=base_model,
model_name=model_name,
model_type=model_type,
):
raise Exception(f"Unkown vae name: {model_name}!")
return VaeLoaderOutput(
vae=VaeField(
vae=ModelInfo(
model_name=model_name,
base_model=base_model,
model_type=model_type,
)
)
)

19
invokeai/app/services/config.py
View File

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

76
invokeai/app/services/model_manager_service.py
View File

@ -33,13 +33,13 @@ class ModelManagerServiceBase(ABC):
logger: types.ModuleType, logger: types.ModuleType,
): ):
""" """
Initialize with the path to the models.yaml config file. Initialize with the path to the models.yaml config file.
Optional parameters are the torch device type, precision, max_models, Optional parameters are the torch device type, precision, max_models,
and sequential_offload boolean. Note that the default device and sequential_offload boolean. Note that the default device
type and precision are set up for a CUDA system running at half precision. type and precision are set up for a CUDA system running at half precision.
""" """
pass pass
@abstractmethod @abstractmethod
def get_model( def get_model(
self, self,
@ -50,8 +50,8 @@ class ModelManagerServiceBase(ABC):
node: Optional[BaseInvocation] = None, node: Optional[BaseInvocation] = None,
context: Optional[InvocationContext] = None, context: Optional[InvocationContext] = None,
) -> ModelInfo: ) -> ModelInfo:
"""Retrieve the indicated model with name and type. """Retrieve the indicated model with name and type.
submodel can be used to get a part (such as the vae) submodel can be used to get a part (such as the vae)
of a diffusers pipeline.""" of a diffusers pipeline."""
pass pass
@ -115,8 +115,8 @@ class ModelManagerServiceBase(ABC):
""" """
Update the named model with a dictionary of attributes. Will fail with an Update the named model with a dictionary of attributes. Will fail with an
assertion error if the name already exists. Pass clobber=True to overwrite. assertion error if the name already exists. Pass clobber=True to overwrite.
On a successful update, the config will be changed in memory. Will fail On a successful update, the config will be changed in memory. Will fail
with an assertion error if provided attributes are incorrect or with an assertion error if provided attributes are incorrect or
the model name is missing. Call commit() to write changes to disk. the model name is missing. Call commit() to write changes to disk.
""" """
pass pass
@ -129,12 +129,35 @@ class ModelManagerServiceBase(ABC):
model_type: ModelType, model_type: ModelType,
): ):
""" """
Delete the named model from configuration. If delete_files is true, Delete the named model from configuration. If delete_files is true,
then the underlying weight file or diffusers directory will be deleted then the underlying weight file or diffusers directory will be deleted
as well. Call commit() to write to disk. as well. Call commit() to write to disk.
""" """
pass pass
@abstractmethod
def heuristic_import(self,
items_to_import: Set[str],
prediction_type_helper: Callable[[Path],SchedulerPredictionType]=None,
)->Dict[str, AddModelResult]:
'''Import a list of paths, repo_ids or URLs. Returns the set of
successfully imported items.
:param items_to_import: Set of strings corresponding to models to be imported.
:param prediction_type_helper: A callback that receives the Path of a Stable Diffusion 2 checkpoint model and returns a SchedulerPredictionType.
The prediction type helper is necessary to distinguish between
models based on Stable Diffusion 2 Base (requiring
SchedulerPredictionType.Epsilson) and Stable Diffusion 768
(requiring SchedulerPredictionType.VPrediction). It is
generally impossible to do this programmatically, so the
prediction_type_helper usually asks the user to choose.
The result is a set of successfully installed models. Each element
of the set is a dict corresponding to the newly-created OmegaConf stanza for
that model.
'''
pass
@abstractmethod @abstractmethod
def commit(self, conf_file: Path = None) -> None: def commit(self, conf_file: Path = None) -> None:
""" """
@ -153,7 +176,7 @@ class ModelManagerService(ModelManagerServiceBase):
logger: types.ModuleType, logger: types.ModuleType,
): ):
""" """
Initialize with the path to the models.yaml config file. Initialize with the path to the models.yaml config file.
Optional parameters are the torch device type, precision, max_models, Optional parameters are the torch device type, precision, max_models,
and sequential_offload boolean. Note that the default device and sequential_offload boolean. Note that the default device
type and precision are set up for a CUDA system running at half precision. type and precision are set up for a CUDA system running at half precision.
@ -183,6 +206,8 @@ class ModelManagerService(ModelManagerServiceBase):
if hasattr(config,'max_cache_size') \ if hasattr(config,'max_cache_size') \
else config.max_loaded_models * 2.5 else config.max_loaded_models * 2.5
logger.debug(f"Maximum RAM cache size: {max_cache_size} GiB")
sequential_offload = config.sequential_guidance sequential_offload = config.sequential_guidance
self.mgr = ModelManager( self.mgr = ModelManager(
@ -238,7 +263,7 @@ class ModelManagerService(ModelManagerServiceBase):
submodel=submodel, submodel=submodel,
model_info=model_info model_info=model_info
) )
return model_info return model_info
def model_exists( def model_exists(
@ -291,8 +316,8 @@ class ModelManagerService(ModelManagerServiceBase):
""" """
Update the named model with a dictionary of attributes. Will fail with an Update the named model with a dictionary of attributes. Will fail with an
assertion error if the name already exists. Pass clobber=True to overwrite. assertion error if the name already exists. Pass clobber=True to overwrite.
On a successful update, the config will be changed in memory. Will fail On a successful update, the config will be changed in memory. Will fail
with an assertion error if provided attributes are incorrect or with an assertion error if provided attributes are incorrect or
the model name is missing. Call commit() to write changes to disk. the model name is missing. Call commit() to write changes to disk.
""" """
return self.mgr.add_model(model_name, base_model, model_type, model_attributes, clobber) return self.mgr.add_model(model_name, base_model, model_type, model_attributes, clobber)
@ -305,8 +330,8 @@ class ModelManagerService(ModelManagerServiceBase):
model_type: ModelType, model_type: ModelType,
): ):
""" """
Delete the named model from configuration. If delete_files is true, Delete the named model from configuration. If delete_files is true,
then the underlying weight file or diffusers directory will be deleted then the underlying weight file or diffusers directory will be deleted
as well. Call commit() to write to disk. as well. Call commit() to write to disk.
""" """
self.mgr.del_model(model_name, base_model, model_type) self.mgr.del_model(model_name, base_model, model_type)
@ -360,4 +385,25 @@ class ModelManagerService(ModelManagerServiceBase):
@property @property
def logger(self): def logger(self):
return self.mgr.logger return self.mgr.logger
def heuristic_import(self,
items_to_import: Set[str],
prediction_type_helper: Callable[[Path],SchedulerPredictionType]=None,
)->Dict[str, AddModelResult]:
'''Import a list of paths, repo_ids or URLs. Returns the set of
successfully imported items.
:param items_to_import: Set of strings corresponding to models to be imported.
:param prediction_type_helper: A callback that receives the Path of a Stable Diffusion 2 checkpoint model and returns a SchedulerPredictionType.
The prediction type helper is necessary to distinguish between
models based on Stable Diffusion 2 Base (requiring
SchedulerPredictionType.Epsilson) and Stable Diffusion 768
(requiring SchedulerPredictionType.VPrediction). It is
generally impossible to do this programmatically, so the
prediction_type_helper usually asks the user to choose.
The result is a set of successfully installed models. Each element
of the set is a dict corresponding to the newly-created OmegaConf stanza for
that model.
'''
return self.mgr.heuristic_import(items_to_import, prediction_type_helper)

8
invokeai/backend/install/migrate_to_3.py
View File

@ -76,6 +76,10 @@ class MigrateTo3(object):
Create a unique name for a model for use within models.yaml. Create a unique name for a model for use within models.yaml.
''' '''
done = False done = False
# some model names have slashes in them, which really screws things up
name = name.replace('/','_')
key = ModelManager.create_key(name,info.base_type,info.model_type) key = ModelManager.create_key(name,info.base_type,info.model_type)
unique_name = key unique_name = key
counter = 1 counter = 1
@ -219,11 +223,11 @@ class MigrateTo3(object):
repo_id = 'openai/clip-vit-large-patch14' repo_id = 'openai/clip-vit-large-patch14'
self._migrate_pretrained(CLIPTokenizer, self._migrate_pretrained(CLIPTokenizer,
repo_id= repo_id, repo_id= repo_id,
dest= target_dir / 'clip-vit-large-patch14' / 'tokenizer', dest= target_dir / 'clip-vit-large-patch14',
**kwargs) **kwargs)
self._migrate_pretrained(CLIPTextModel, self._migrate_pretrained(CLIPTextModel,
repo_id = repo_id, repo_id = repo_id,
dest = target_dir / 'clip-vit-large-patch14' / 'text_encoder', dest = target_dir / 'clip-vit-large-patch14',
**kwargs) **kwargs)
# sd-2 # sd-2

48
invokeai/backend/install/model_install_backend.py
View File

@ -18,7 +18,7 @@ from tqdm import tqdm
import invokeai.configs as configs import invokeai.configs as configs
from invokeai.app.services.config import InvokeAIAppConfig from invokeai.app.services.config import InvokeAIAppConfig
from invokeai.backend.model_management import ModelManager, ModelType, BaseModelType, ModelVariantType from invokeai.backend.model_management import ModelManager, ModelType, BaseModelType, ModelVariantType, AddModelResult
from invokeai.backend.model_management.model_probe import ModelProbe, SchedulerPredictionType, ModelProbeInfo from invokeai.backend.model_management.model_probe import ModelProbe, SchedulerPredictionType, ModelProbeInfo
from invokeai.backend.util import download_with_resume from invokeai.backend.util import download_with_resume
from ..util.logging import InvokeAILogger from ..util.logging import InvokeAILogger
@ -166,17 +166,22 @@ class ModelInstall(object):
# add requested models # add requested models
for path in selections.install_models: for path in selections.install_models:
logger.info(f'Installing {path} [{job}/{jobs}]') logger.info(f'Installing {path} [{job}/{jobs}]')
self.heuristic_install(path) self.heuristic_import(path)
job += 1 job += 1
self.mgr.commit() self.mgr.commit()
def heuristic_install(self, def heuristic_import(self,
model_path_id_or_url: Union[str,Path], model_path_id_or_url: Union[str,Path],
models_installed: Set[Path]=None)->Set[Path]: models_installed: Set[Path]=None)->Dict[str, AddModelResult]:
'''
:param model_path_id_or_url: A Path to a local model to import, or a string representing its repo_id or URL
:param models_installed: Set of installed models, used for recursive invocation
Returns a set of dict objects corresponding to newly-created stanzas in models.yaml.
'''
if not models_installed: if not models_installed:
models_installed = set() models_installed = dict()
# A little hack to allow nested routines to retrieve info on the requested ID # A little hack to allow nested routines to retrieve info on the requested ID
self.current_id = model_path_id_or_url self.current_id = model_path_id_or_url
@ -185,24 +190,24 @@ class ModelInstall(object):
try: try:
# checkpoint file, or similar # checkpoint file, or similar
if path.is_file(): if path.is_file():
models_installed.add(self._install_path(path)) models_installed.update(self._install_path(path))
# folders style or similar # folders style or similar
elif path.is_dir() and any([(path/x).exists() for x in {'config.json','model_index.json','learned_embeds.bin'}]): elif path.is_dir() and any([(path/x).exists() for x in {'config.json','model_index.json','learned_embeds.bin'}]):
models_installed.add(self._install_path(path)) models_installed.update(self._install_path(path))
# recursive scan # recursive scan
elif path.is_dir(): elif path.is_dir():
for child in path.iterdir(): for child in path.iterdir():
self.heuristic_install(child, models_installed=models_installed) self.heuristic_import(child, models_installed=models_installed)
# huggingface repo # huggingface repo
elif len(str(path).split('/')) == 2: elif len(str(path).split('/')) == 2:
models_installed.add(self._install_repo(str(path))) models_installed.update(self._install_repo(str(path)))
# a URL # a URL
elif model_path_id_or_url.startswith(("http:", "https:", "ftp:")): elif model_path_id_or_url.startswith(("http:", "https:", "ftp:")):
models_installed.add(self._install_url(model_path_id_or_url)) models_installed.update(self._install_url(model_path_id_or_url))
else: else:
logger.warning(f'{str(model_path_id_or_url)} is not recognized as a local path, repo ID or URL. Skipping') logger.warning(f'{str(model_path_id_or_url)} is not recognized as a local path, repo ID or URL. Skipping')
@ -214,24 +219,25 @@ class ModelInstall(object):
# install a model from a local path. The optional info parameter is there to prevent # install a model from a local path. The optional info parameter is there to prevent
# the model from being probed twice in the event that it has already been probed. # the model from being probed twice in the event that it has already been probed.
def _install_path(self, path: Path, info: ModelProbeInfo=None)->Path: def _install_path(self, path: Path, info: ModelProbeInfo=None)->Dict[str, AddModelResult]:
try: try:
# logger.debug(f'Probing {path}') model_result = None
info = info or ModelProbe().heuristic_probe(path,self.prediction_helper) info = info or ModelProbe().heuristic_probe(path,self.prediction_helper)
model_name = path.stem if info.format=='checkpoint' else path.name model_name = path.stem if path.is_file() else path.name
if self.mgr.model_exists(model_name, info.base_type, info.model_type): if self.mgr.model_exists(model_name, info.base_type, info.model_type):
raise ValueError(f'A model named "{model_name}" is already installed.') raise ValueError(f'A model named "{model_name}" is already installed.')
attributes = self._make_attributes(path,info) attributes = self._make_attributes(path,info)
self.mgr.add_model(model_name = model_name, model_result = self.mgr.add_model(model_name = model_name,
base_model = info.base_type, base_model = info.base_type,
model_type = info.model_type, model_type = info.model_type,
model_attributes = attributes, model_attributes = attributes,
) )
except Exception as e: except Exception as e:
logger.warning(f'{str(e)} Skipping registration.') logger.warning(f'{str(e)} Skipping registration.')
return path return {}
return {str(path): model_result}
def _install_url(self, url: str)->Path: def _install_url(self, url: str)->dict:
# copy to a staging area, probe, import and delete # copy to a staging area, probe, import and delete
with TemporaryDirectory(dir=self.config.models_path) as staging: with TemporaryDirectory(dir=self.config.models_path) as staging:
location = download_with_resume(url,Path(staging)) location = download_with_resume(url,Path(staging))
@ -244,7 +250,7 @@ class ModelInstall(object):
# staged version will be garbage-collected at this time # staged version will be garbage-collected at this time
return self._install_path(Path(models_path), info) return self._install_path(Path(models_path), info)
def _install_repo(self, repo_id: str)->Path: def _install_repo(self, repo_id: str)->dict:
hinfo = HfApi().model_info(repo_id) hinfo = HfApi().model_info(repo_id)
# we try to figure out how to download this most economically # we try to figure out how to download this most economically

2
invokeai/backend/model_management/__init__.py
View File

@ -1,7 +1,7 @@
""" """
Initialization file for invokeai.backend.model_management Initialization file for invokeai.backend.model_management
""" """
from .model_manager import ModelManager, ModelInfo from .model_manager import ModelManager, ModelInfo, AddModelResult
from .model_cache import ModelCache from .model_cache import ModelCache
from .models import BaseModelType, ModelType, SubModelType, ModelVariantType from .models import BaseModelType, ModelType, SubModelType, ModelVariantType

7
invokeai/backend/model_management/convert_ckpt_to_diffusers.py
View File

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

19
invokeai/backend/model_management/lora.py
View File

@ -1,18 +1,17 @@
from __future__ import annotations from __future__ import annotations
import copy import copy
from pathlib import Path
from contextlib import contextmanager from contextlib import contextmanager
from typing import Optional, Dict, Tuple, Any from pathlib import Path
from typing import Any, Dict, Optional, Tuple
import torch import torch
from compel.embeddings_provider import BaseTextualInversionManager
from diffusers.models import UNet2DConditionModel
from safetensors.torch import load_file from safetensors.torch import load_file
from torch.utils.hooks import RemovableHandle from torch.utils.hooks import RemovableHandle
from diffusers.models import UNet2DConditionModel
from transformers import CLIPTextModel from transformers import CLIPTextModel
from compel.embeddings_provider import BaseTextualInversionManager
class LoRALayerBase: class LoRALayerBase:
#rank: Optional[int] #rank: Optional[int]
@ -539,9 +538,10 @@ class ModelPatcher:
original_weights[module_key] = module.weight.detach().to(device="cpu", copy=True) original_weights[module_key] = module.weight.detach().to(device="cpu", copy=True)
# enable autocast to calc fp16 loras on cpu # enable autocast to calc fp16 loras on cpu
with torch.autocast(device_type="cpu"): #with torch.autocast(device_type="cpu"):
layer_scale = layer.alpha / layer.rank if (layer.alpha and layer.rank) else 1.0 layer.to(dtype=torch.float32)
layer_weight = layer.get_weight() * lora_weight * layer_scale layer_scale = layer.alpha / layer.rank if (layer.alpha and layer.rank) else 1.0
layer_weight = layer.get_weight() * lora_weight * layer_scale
if module.weight.shape != layer_weight.shape: if module.weight.shape != layer_weight.shape:
# TODO: debug on lycoris # TODO: debug on lycoris
@ -655,6 +655,9 @@ class TextualInversionModel:
else: else:
result.embedding = next(iter(state_dict.values())) result.embedding = next(iter(state_dict.values()))
if len(result.embedding.shape) == 1:
result.embedding = result.embedding.unsqueeze(0)
if not isinstance(result.embedding, torch.Tensor): if not isinstance(result.embedding, torch.Tensor):
raise ValueError(f"Invalid embeddings file: {file_path.name}") raise ValueError(f"Invalid embeddings file: {file_path.name}")

61
invokeai/backend/model_management/model_manager.py
View File

@ -233,14 +233,14 @@ import hashlib
import textwrap import textwrap
from dataclasses import dataclass from dataclasses import dataclass
from pathlib import Path from pathlib import Path
from typing import Optional, List, Tuple, Union, Set, Callable, types from typing import Optional, List, Tuple, Union, Dict, Set, Callable, types
from shutil import rmtree from shutil import rmtree
import torch import torch
from omegaconf import OmegaConf from omegaconf import OmegaConf
from omegaconf.dictconfig import DictConfig from omegaconf.dictconfig import DictConfig
from pydantic import BaseModel from pydantic import BaseModel, Field
import invokeai.backend.util.logging as logger import invokeai.backend.util.logging as logger
from invokeai.app.services.config import InvokeAIAppConfig from invokeai.app.services.config import InvokeAIAppConfig
@ -249,7 +249,7 @@ from .model_cache import ModelCache, ModelLocker
from .models import ( from .models import (
BaseModelType, ModelType, SubModelType, BaseModelType, ModelType, SubModelType,
ModelError, SchedulerPredictionType, MODEL_CLASSES, ModelError, SchedulerPredictionType, MODEL_CLASSES,
ModelConfigBase, ModelConfigBase, ModelNotFoundException,
) )
# We are only starting to number the config file with release 3. # We are only starting to number the config file with release 3.
@ -278,8 +278,13 @@ class InvalidModelError(Exception):
"Raised when an invalid model is requested" "Raised when an invalid model is requested"
pass pass
MAX_CACHE_SIZE = 6.0 # GB class AddModelResult(BaseModel):
name: str = Field(description="The name of the model after import")
model_type: ModelType = Field(description="The type of model")
base_model: BaseModelType = Field(description="The base model")
config: ModelConfigBase = Field(description="The configuration of the model")
MAX_CACHE_SIZE = 6.0 # GB
class ConfigMeta(BaseModel): class ConfigMeta(BaseModel):
version: str version: str
@ -404,7 +409,7 @@ class ModelManager(object):
if model_key not in self.models: if model_key not in self.models:
self.scan_models_directory(base_model=base_model, model_type=model_type) self.scan_models_directory(base_model=base_model, model_type=model_type)
if model_key not in self.models: if model_key not in self.models:
raise Exception(f"Model not found - {model_key}") raise ModelNotFoundException(f"Model not found - {model_key}")
model_config = self.models[model_key] model_config = self.models[model_key]
model_path = self.app_config.root_path / model_config.path model_path = self.app_config.root_path / model_config.path
@ -416,7 +421,7 @@ class ModelManager(object):
else: else:
self.models.pop(model_key, None) self.models.pop(model_key, None)
raise Exception(f"Model not found - {model_key}") raise ModelNotFoundException(f"Model not found - {model_key}")
# vae/movq override # vae/movq override
# TODO: # TODO:
@ -571,13 +576,16 @@ class ModelManager(object):
model_type: ModelType, model_type: ModelType,
model_attributes: dict, model_attributes: dict,
clobber: bool = False, clobber: bool = False,
) -> None: ) -> AddModelResult:
""" """
Update the named model with a dictionary of attributes. Will fail with an Update the named model with a dictionary of attributes. Will fail with an
assertion error if the name already exists. Pass clobber=True to overwrite. assertion error if the name already exists. Pass clobber=True to overwrite.
On a successful update, the config will be changed in memory and the On a successful update, the config will be changed in memory and the
method will return True. Will fail with an assertion error if provided method will return True. Will fail with an assertion error if provided
attributes are incorrect or the model name is missing. attributes are incorrect or the model name is missing.
The returned dict has the same format as the dict returned by
model_info().
""" """
model_class = MODEL_CLASSES[base_model][model_type] model_class = MODEL_CLASSES[base_model][model_type]
@ -601,12 +609,18 @@ class ModelManager(object):
old_model_cache.unlink() old_model_cache.unlink()
# remove in-memory cache # remove in-memory cache
# note: it not garantie to release memory(model can has other references) # note: it not guaranteed to release memory(model can has other references)
cache_ids = self.cache_keys.pop(model_key, []) cache_ids = self.cache_keys.pop(model_key, [])
for cache_id in cache_ids: for cache_id in cache_ids:
self.cache.uncache_model(cache_id) self.cache.uncache_model(cache_id)
self.models[model_key] = model_config self.models[model_key] = model_config
return AddModelResult(
name = model_name,
model_type = model_type,
base_model = base_model,
config = model_config,
)
def search_models(self, search_folder): def search_models(self, search_folder):
self.logger.info(f"Finding Models In: {search_folder}") self.logger.info(f"Finding Models In: {search_folder}")
@ -717,19 +731,19 @@ class ModelManager(object):
if model_path.is_relative_to(self.app_config.root_path): if model_path.is_relative_to(self.app_config.root_path):
model_path = model_path.relative_to(self.app_config.root_path) model_path = model_path.relative_to(self.app_config.root_path)
try: try:
model_config: ModelConfigBase = model_class.probe_config(str(model_path)) model_config: ModelConfigBase = model_class.probe_config(str(model_path))
self.models[model_key] = model_config self.models[model_key] = model_config
new_models_found = True new_models_found = True
except NotImplementedError as e: except NotImplementedError as e:
self.logger.warning(e) self.logger.warning(e)
imported_models = self.autoimport() imported_models = self.autoimport()
if (new_models_found or imported_models) and self.config_path: if (new_models_found or imported_models) and self.config_path:
self.commit() self.commit()
def autoimport(self)->set[Path]: def autoimport(self)->Dict[str, AddModelResult]:
''' '''
Scan the autoimport directory (if defined) and import new models, delete defunct models. Scan the autoimport directory (if defined) and import new models, delete defunct models.
''' '''
@ -742,7 +756,6 @@ class ModelManager(object):
prediction_type_helper = ask_user_for_prediction_type, prediction_type_helper = ask_user_for_prediction_type,
) )
installed = set()
scanned_dirs = set() scanned_dirs = set()
config = self.app_config config = self.app_config
@ -756,13 +769,14 @@ class ModelManager(object):
continue continue
self.logger.info(f'Scanning {autodir} for models to import') self.logger.info(f'Scanning {autodir} for models to import')
installed = dict()
autodir = self.app_config.root_path / autodir autodir = self.app_config.root_path / autodir
if not autodir.exists(): if not autodir.exists():
continue continue
items_scanned = 0 items_scanned = 0
new_models_found = set() new_models_found = dict()
for root, dirs, files in os.walk(autodir): for root, dirs, files in os.walk(autodir):
items_scanned += len(dirs) + len(files) items_scanned += len(dirs) + len(files)
@ -772,7 +786,7 @@ class ModelManager(object):
scanned_dirs.add(path) scanned_dirs.add(path)
continue continue
if any([(path/x).exists() for x in {'config.json','model_index.json','learned_embeds.bin'}]): if any([(path/x).exists() for x in {'config.json','model_index.json','learned_embeds.bin'}]):
new_models_found.update(installer.heuristic_install(path)) new_models_found.update(installer.heuristic_import(path))
scanned_dirs.add(path) scanned_dirs.add(path)
for f in files: for f in files:
@ -780,7 +794,7 @@ class ModelManager(object):
if path in known_paths or path.parent in scanned_dirs: if path in known_paths or path.parent in scanned_dirs:
continue continue
if path.suffix in {'.ckpt','.bin','.pth','.safetensors','.pt'}: if path.suffix in {'.ckpt','.bin','.pth','.safetensors','.pt'}:
new_models_found.update(installer.heuristic_install(path)) new_models_found.update(installer.heuristic_import(path))
self.logger.info(f'Scanned {items_scanned} files and directories, imported {len(new_models_found)} models') self.logger.info(f'Scanned {items_scanned} files and directories, imported {len(new_models_found)} models')
installed.update(new_models_found) installed.update(new_models_found)
@ -790,7 +804,7 @@ class ModelManager(object):
def heuristic_import(self, def heuristic_import(self,
items_to_import: Set[str], items_to_import: Set[str],
prediction_type_helper: Callable[[Path],SchedulerPredictionType]=None, prediction_type_helper: Callable[[Path],SchedulerPredictionType]=None,
)->Set[str]: )->Dict[str, AddModelResult]:
'''Import a list of paths, repo_ids or URLs. Returns the set of '''Import a list of paths, repo_ids or URLs. Returns the set of
successfully imported items. successfully imported items.
:param items_to_import: Set of strings corresponding to models to be imported. :param items_to_import: Set of strings corresponding to models to be imported.
@ -803,17 +817,20 @@ class ModelManager(object):
generally impossible to do this programmatically, so the generally impossible to do this programmatically, so the
prediction_type_helper usually asks the user to choose. prediction_type_helper usually asks the user to choose.
The result is a set of successfully installed models. Each element
of the set is a dict corresponding to the newly-created OmegaConf stanza for
that model.
''' '''
# avoid circular import here # avoid circular import here
from invokeai.backend.install.model_install_backend import ModelInstall from invokeai.backend.install.model_install_backend import ModelInstall
successfully_installed = set() successfully_installed = dict()
installer = ModelInstall(config = self.app_config, installer = ModelInstall(config = self.app_config,
prediction_type_helper = prediction_type_helper, prediction_type_helper = prediction_type_helper,
model_manager = self) model_manager = self)
for thing in items_to_import: for thing in items_to_import:
try: try:
installed = installer.heuristic_install(thing) installed = installer.heuristic_import(thing)
successfully_installed.update(installed) successfully_installed.update(installed)
except Exception as e: except Exception as e:
self.logger.warning(f'{thing} could not be imported: {str(e)}') self.logger.warning(f'{thing} could not be imported: {str(e)}')

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

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

3
invokeai/backend/model_management/models/base.py
View File

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

10
invokeai/backend/model_management/models/textual_inversion.py
View File

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

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

@ -12,7 +12,7 @@
margin: 0; margin: 0;
} }
</style> </style>
<script type="module" crossorigin src="./assets/index-8a3e9251.js"></script> <script type="module" crossorigin src="./assets/index-c0367e37.js"></script>
</head> </head>
<body dir="ltr"> <body dir="ltr">

17
invokeai/frontend/web/dist/locales/en.json vendored
View File

@ -24,16 +24,13 @@
}, },
"common": { "common": {
"hotkeysLabel": "Hotkeys", "hotkeysLabel": "Hotkeys",
"themeLabel": "Theme", "darkMode": "Dark Mode",
"lightMode": "Light Mode",
"languagePickerLabel": "Language", "languagePickerLabel": "Language",
"reportBugLabel": "Report Bug", "reportBugLabel": "Report Bug",
"githubLabel": "Github", "githubLabel": "Github",
"discordLabel": "Discord", "discordLabel": "Discord",
"settingsLabel": "Settings", "settingsLabel": "Settings",
"darkTheme": "Dark",
"lightTheme": "Light",
"greenTheme": "Green",
"oceanTheme": "Ocean",
"langArabic": "العربية", "langArabic": "العربية",
"langEnglish": "English", "langEnglish": "English",
"langDutch": "Nederlands", "langDutch": "Nederlands",
@ -55,6 +52,7 @@
"unifiedCanvas": "Unified Canvas", "unifiedCanvas": "Unified Canvas",
"linear": "Linear", "linear": "Linear",
"nodes": "Node Editor", "nodes": "Node Editor",
"modelmanager": "Model Manager",
"postprocessing": "Post Processing", "postprocessing": "Post Processing",
"nodesDesc": "A node based system for the generation of images is under development currently. Stay tuned for updates about this amazing feature.", "nodesDesc": "A node based system for the generation of images is under development currently. Stay tuned for updates about this amazing feature.",
"postProcessing": "Post Processing", "postProcessing": "Post Processing",
@ -336,6 +334,7 @@
"modelManager": { "modelManager": {
"modelManager": "Model Manager", "modelManager": "Model Manager",
"model": "Model", "model": "Model",
"vae": "VAE",
"allModels": "All Models", "allModels": "All Models",
"checkpointModels": "Checkpoints", "checkpointModels": "Checkpoints",
"diffusersModels": "Diffusers", "diffusersModels": "Diffusers",
@ -351,6 +350,7 @@
"scanForModels": "Scan For Models", "scanForModels": "Scan For Models",
"addManually": "Add Manually", "addManually": "Add Manually",
"manual": "Manual", "manual": "Manual",
"baseModel": "Base Model",
"name": "Name", "name": "Name",
"nameValidationMsg": "Enter a name for your model", "nameValidationMsg": "Enter a name for your model",
"description": "Description", "description": "Description",
@ -363,6 +363,7 @@
"repoIDValidationMsg": "Online repository of your model", "repoIDValidationMsg": "Online repository of your model",
"vaeLocation": "VAE Location", "vaeLocation": "VAE Location",
"vaeLocationValidationMsg": "Path to where your VAE is located.", "vaeLocationValidationMsg": "Path to where your VAE is located.",
"variant": "Variant",
"vaeRepoID": "VAE Repo ID", "vaeRepoID": "VAE Repo ID",
"vaeRepoIDValidationMsg": "Online repository of your VAE", "vaeRepoIDValidationMsg": "Online repository of your VAE",
"width": "Width", "width": "Width",
@ -524,7 +525,8 @@
"initialImage": "Initial Image", "initialImage": "Initial Image",
"showOptionsPanel": "Show Options Panel", "showOptionsPanel": "Show Options Panel",
"hidePreview": "Hide Preview", "hidePreview": "Hide Preview",
"showPreview": "Show Preview" "showPreview": "Show Preview",
"controlNetControlMode": "Control Mode"
}, },
"settings": { "settings": {
"models": "Models", "models": "Models",
@ -547,7 +549,8 @@
"general": "General", "general": "General",
"generation": "Generation", "generation": "Generation",
"ui": "User Interface", "ui": "User Interface",
"availableSchedulers": "Available Schedulers" "favoriteSchedulers": "Favorite Schedulers",
"favoriteSchedulersPlaceholder": "No schedulers favorited"
}, },
"toast": { "toast": {
"serverError": "Server Error", "serverError": "Server Error",

1
invokeai/frontend/web/package.json
View File

@ -67,6 +67,7 @@
"@fontsource-variable/inter": "^5.0.3", "@fontsource-variable/inter": "^5.0.3",
"@fontsource/inter": "^5.0.3", "@fontsource/inter": "^5.0.3",
"@mantine/core": "^6.0.14", "@mantine/core": "^6.0.14",
"@mantine/form": "^6.0.15",
"@mantine/hooks": "^6.0.14", "@mantine/hooks": "^6.0.14",
"@reduxjs/toolkit": "^1.9.5", "@reduxjs/toolkit": "^1.9.5",
"@roarr/browser-log-writer": "^1.1.5", "@roarr/browser-log-writer": "^1.1.5",

4
invokeai/frontend/web/public/locales/en.json
View File

@ -53,6 +53,7 @@
"linear": "Linear", "linear": "Linear",
"nodes": "Node Editor", "nodes": "Node Editor",
"batch": "Batch Manager", "batch": "Batch Manager",
"modelmanager": "Model Manager",
"postprocessing": "Post Processing", "postprocessing": "Post Processing",
"nodesDesc": "A node based system for the generation of images is under development currently. Stay tuned for updates about this amazing feature.", "nodesDesc": "A node based system for the generation of images is under development currently. Stay tuned for updates about this amazing feature.",
"postProcessing": "Post Processing", "postProcessing": "Post Processing",
@ -334,6 +335,7 @@
"modelManager": { "modelManager": {
"modelManager": "Model Manager", "modelManager": "Model Manager",
"model": "Model", "model": "Model",
"vae": "VAE",
"allModels": "All Models", "allModels": "All Models",
"checkpointModels": "Checkpoints", "checkpointModels": "Checkpoints",
"diffusersModels": "Diffusers", "diffusersModels": "Diffusers",
@ -349,6 +351,7 @@
"scanForModels": "Scan For Models", "scanForModels": "Scan For Models",
"addManually": "Add Manually", "addManually": "Add Manually",
"manual": "Manual", "manual": "Manual",
"baseModel": "Base Model",
"name": "Name", "name": "Name",
"nameValidationMsg": "Enter a name for your model", "nameValidationMsg": "Enter a name for your model",
"description": "Description", "description": "Description",
@ -361,6 +364,7 @@
"repoIDValidationMsg": "Online repository of your model", "repoIDValidationMsg": "Online repository of your model",
"vaeLocation": "VAE Location", "vaeLocation": "VAE Location",
"vaeLocationValidationMsg": "Path to where your VAE is located.", "vaeLocationValidationMsg": "Path to where your VAE is located.",
"variant": "Variant",
"vaeRepoID": "VAE Repo ID", "vaeRepoID": "VAE Repo ID",
"vaeRepoIDValidationMsg": "Online repository of your VAE", "vaeRepoIDValidationMsg": "Online repository of your VAE",
"width": "Width", "width": "Width",

6
invokeai/frontend/web/src/app/components/App.tsx
View File

@ -4,6 +4,7 @@ import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
import { PartialAppConfig } from 'app/types/invokeai'; import { PartialAppConfig } from 'app/types/invokeai';
import ImageUploader from 'common/components/ImageUploader'; import ImageUploader from 'common/components/ImageUploader';
import GalleryDrawer from 'features/gallery/components/GalleryPanel'; import GalleryDrawer from 'features/gallery/components/GalleryPanel';
import DeleteImageModal from 'features/imageDeletion/components/DeleteImageModal';
import Lightbox from 'features/lightbox/components/Lightbox'; import Lightbox from 'features/lightbox/components/Lightbox';
import SiteHeader from 'features/system/components/SiteHeader'; import SiteHeader from 'features/system/components/SiteHeader';
import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus'; import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus';
@ -15,11 +16,10 @@ import InvokeTabs from 'features/ui/components/InvokeTabs';
import ParametersDrawer from 'features/ui/components/ParametersDrawer'; import ParametersDrawer from 'features/ui/components/ParametersDrawer';
import i18n from 'i18n'; import i18n from 'i18n';
import { ReactNode, memo, useEffect } from 'react'; import { ReactNode, memo, useEffect } from 'react';
import DeleteBoardImagesModal from '../../features/gallery/components/Boards/DeleteBoardImagesModal';
import UpdateImageBoardModal from '../../features/gallery/components/Boards/UpdateImageBoardModal';
import GlobalHotkeys from './GlobalHotkeys'; import GlobalHotkeys from './GlobalHotkeys';
import Toaster from './Toaster'; import Toaster from './Toaster';
import UpdateImageBoardModal from '../../features/gallery/components/Boards/UpdateImageBoardModal';
import DeleteBoardImagesModal from '../../features/gallery/components/Boards/DeleteBoardImagesModal';
import DeleteImageModal from 'features/imageDeletion/components/DeleteImageModal';
const DEFAULT_CONFIG = {}; const DEFAULT_CONFIG = {};

44
invokeai/frontend/web/src/app/components/ImageDnd/DragPreview.tsx
View File

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

20
invokeai/frontend/web/src/app/components/ImageDnd/typesafeDnd.tsx
View File

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

2
invokeai/frontend/web/src/app/store/enhancers/reduxRemember/serialize.ts
View File

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

26
invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/imageDeleted.ts
View File

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

20
invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/imageDropped.ts
View File

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

23
invokeai/frontend/web/src/app/store/store.ts
View File

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

59
invokeai/frontend/web/src/common/components/IAICollapse.tsx
View File

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

2
invokeai/frontend/web/src/common/components/IAIMantineMultiSelect.tsx
View File

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

2
invokeai/frontend/web/src/common/components/IAIMantineSelect.tsx
View File

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

2
invokeai/frontend/web/src/common/components/IAISwitch.tsx
View File

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

49
invokeai/frontend/web/src/features/batch/components/BatchImage.tsx
View File

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

42
invokeai/frontend/web/src/features/controlNet/components/ControlNetImagePreview.tsx
View File

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

36
invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetFeatureToggle.tsx Normal file
View File

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

15
invokeai/frontend/web/src/features/controlNet/util/getValidControlNets.ts Normal file
View File

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

26
invokeai/frontend/web/src/features/dynamicPrompts/components/ParamDynamicPromptsCollapse.tsx
View File

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

15
invokeai/frontend/web/src/features/dynamicPrompts/components/ParamDynamicPromptsCombinatorial.tsx
View File

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

36
invokeai/frontend/web/src/features/dynamicPrompts/components/ParamDynamicPromptsEnabled.tsx Normal file
View File

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

24
invokeai/frontend/web/src/features/dynamicPrompts/components/ParamDynamicPromptsMaxPrompts.tsx
View File

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

18
invokeai/frontend/web/src/features/gallery/components/CurrentImagePreview.tsx
View File

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

54
invokeai/frontend/web/src/features/gallery/components/GalleryImage.tsx
View File

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

6
invokeai/frontend/web/src/features/gallery/store/gallerySlice.ts
View File

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

2
invokeai/frontend/web/src/features/imageDeletion/components/DeleteImageModal.tsx
View File

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

1
invokeai/frontend/web/src/features/imageDeletion/store/imageDeletionSlice.ts
View File

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

59
invokeai/frontend/web/src/features/lora/components/ParamLora.tsx Normal file
View File

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

36
invokeai/frontend/web/src/features/lora/components/ParamLoraCollapse.tsx Normal file
View File

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

24
invokeai/frontend/web/src/features/lora/components/ParamLoraList.tsx Normal file
View File

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

107
invokeai/frontend/web/src/features/lora/components/ParamLoraSelect.tsx Normal file
View File

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

46
invokeai/frontend/web/src/features/lora/store/loraSlice.ts Normal file
View File

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

40
invokeai/frontend/web/src/features/nodes/components/InputFieldComponent.tsx
View File

@ -3,20 +3,22 @@ import { memo } from 'react';
import { InputFieldTemplate, InputFieldValue } from '../types/types'; import { InputFieldTemplate, InputFieldValue } from '../types/types';
import ArrayInputFieldComponent from './fields/ArrayInputFieldComponent'; import ArrayInputFieldComponent from './fields/ArrayInputFieldComponent';
import BooleanInputFieldComponent from './fields/BooleanInputFieldComponent'; import BooleanInputFieldComponent from './fields/BooleanInputFieldComponent';
import EnumInputFieldComponent from './fields/EnumInputFieldComponent';
import ImageInputFieldComponent from './fields/ImageInputFieldComponent';
import LatentsInputFieldComponent from './fields/LatentsInputFieldComponent';
import ConditioningInputFieldComponent from './fields/ConditioningInputFieldComponent';
import UNetInputFieldComponent from './fields/UNetInputFieldComponent';
import ClipInputFieldComponent from './fields/ClipInputFieldComponent'; import ClipInputFieldComponent from './fields/ClipInputFieldComponent';
import VaeInputFieldComponent from './fields/VaeInputFieldComponent'; import ColorInputFieldComponent from './fields/ColorInputFieldComponent';
import ConditioningInputFieldComponent from './fields/ConditioningInputFieldComponent';
import ControlInputFieldComponent from './fields/ControlInputFieldComponent'; import ControlInputFieldComponent from './fields/ControlInputFieldComponent';
import EnumInputFieldComponent from './fields/EnumInputFieldComponent';
import ImageCollectionInputFieldComponent from './fields/ImageCollectionInputFieldComponent';
import ImageInputFieldComponent from './fields/ImageInputFieldComponent';
import ItemInputFieldComponent from './fields/ItemInputFieldComponent';
import LatentsInputFieldComponent from './fields/LatentsInputFieldComponent';
import LoRAModelInputFieldComponent from './fields/LoRAModelInputFieldComponent';
import ModelInputFieldComponent from './fields/ModelInputFieldComponent'; import ModelInputFieldComponent from './fields/ModelInputFieldComponent';
import NumberInputFieldComponent from './fields/NumberInputFieldComponent'; import NumberInputFieldComponent from './fields/NumberInputFieldComponent';
import StringInputFieldComponent from './fields/StringInputFieldComponent'; import StringInputFieldComponent from './fields/StringInputFieldComponent';
import ColorInputFieldComponent from './fields/ColorInputFieldComponent'; import UNetInputFieldComponent from './fields/UNetInputFieldComponent';
import ItemInputFieldComponent from './fields/ItemInputFieldComponent'; import VaeInputFieldComponent from './fields/VaeInputFieldComponent';
import ImageCollectionInputFieldComponent from './fields/ImageCollectionInputFieldComponent'; import VaeModelInputFieldComponent from './fields/VaeModelInputFieldComponent';
type InputFieldComponentProps = { type InputFieldComponentProps = {
nodeId: string; nodeId: string;
@ -152,6 +154,26 @@ const InputFieldComponent = (props: InputFieldComponentProps) => {
); );
} }
if (type === 'vae_model' && template.type === 'vae_model') {
return (
<VaeModelInputFieldComponent
nodeId={nodeId}
field={field}
template={template}
/>
);
}
if (type === 'lora_model' && template.type === 'lora_model') {
return (
<LoRAModelInputFieldComponent
nodeId={nodeId}
field={field}
template={template}
/>
);
}
if (type === 'array' && template.type === 'array') { if (type === 'array' && template.type === 'array') {
return ( return (
<ArrayInputFieldComponent <ArrayInputFieldComponent

42
invokeai/frontend/web/src/features/nodes/components/fields/ImageInputFieldComponent.tsx
View File

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

102
invokeai/frontend/web/src/features/nodes/components/fields/LoRAModelInputFieldComponent.tsx Normal file
View File

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

30
invokeai/frontend/web/src/features/nodes/components/fields/ModelInputFieldComponent.tsx
View File

@ -6,13 +6,13 @@ import {
ModelInputFieldValue, ModelInputFieldValue,
} from 'features/nodes/types/types'; } from 'features/nodes/types/types';
import { memo, useCallback, useEffect, useMemo } from 'react';
import { FieldComponentProps } from './types';
import { forEach, isString } from 'lodash-es';
import { MODEL_TYPE_MAP as BASE_MODEL_NAME_MAP } from 'features/system/components/ModelSelect';
import IAIMantineSelect from 'common/components/IAIMantineSelect'; import IAIMantineSelect from 'common/components/IAIMantineSelect';
import { MODEL_TYPE_MAP as BASE_MODEL_NAME_MAP } from 'features/system/components/ModelSelect';
import { forEach, isString } from 'lodash-es';
import { memo, useCallback, useEffect, useMemo } from 'react';
import { useTranslation } from 'react-i18next'; import { useTranslation } from 'react-i18next';
import { useListModelsQuery } from 'services/api/endpoints/models'; import { useGetMainModelsQuery } from 'services/api/endpoints/models';
import { FieldComponentProps } from './types';
const ModelInputFieldComponent = ( const ModelInputFieldComponent = (
props: FieldComponentProps<ModelInputFieldValue, ModelInputFieldTemplate> props: FieldComponentProps<ModelInputFieldValue, ModelInputFieldTemplate>
@ -22,18 +22,16 @@ const ModelInputFieldComponent = (
const dispatch = useAppDispatch(); const dispatch = useAppDispatch();
const { t } = useTranslation(); const { t } = useTranslation();
const { data: pipelineModels } = useListModelsQuery({ const { data: mainModels } = useGetMainModelsQuery();
model_type: 'main',
});
const data = useMemo(() => { const data = useMemo(() => {
if (!pipelineModels) { if (!mainModels) {
return []; return [];
} }
const data: SelectItem[] = []; const data: SelectItem[] = [];
forEach(pipelineModels.entities, (model, id) => { forEach(mainModels.entities, (model, id) => {
if (!model) { if (!model) {
return; return;
} }
@ -46,11 +44,11 @@ const ModelInputFieldComponent = (
}); });
return data; return data;
}, [pipelineModels]); }, [mainModels]);
const selectedModel = useMemo( const selectedModel = useMemo(
() => pipelineModels?.entities[field.value ?? pipelineModels.ids[0]], () => mainModels?.entities[field.value ?? mainModels.ids[0]],
[pipelineModels?.entities, pipelineModels?.ids, field.value] [mainModels?.entities, mainModels?.ids, field.value]
); );
const handleValueChanged = useCallback( const handleValueChanged = useCallback(
@ -71,18 +69,18 @@ const ModelInputFieldComponent = (
); );
useEffect(() => { useEffect(() => {
if (field.value && pipelineModels?.ids.includes(field.value)) { if (field.value && mainModels?.ids.includes(field.value)) {
return; return;
} }
const firstModel = pipelineModels?.ids[0]; const firstModel = mainModels?.ids[0];
if (!isString(firstModel)) { if (!isString(firstModel)) {
return; return;
} }
handleValueChanged(firstModel); handleValueChanged(firstModel);
}, [field.value, handleValueChanged, pipelineModels?.ids]); }, [field.value, handleValueChanged, mainModels?.ids]);
return ( return (
<IAIMantineSelect <IAIMantineSelect

95
invokeai/frontend/web/src/features/nodes/components/fields/VaeModelInputFieldComponent.tsx Normal file
View File

@ -0,0 +1,95 @@
import { SelectItem } from '@mantine/core';
import { useAppDispatch } from 'app/store/storeHooks';
import IAIMantineSelect from 'common/components/IAIMantineSelect';
import { fieldValueChanged } from 'features/nodes/store/nodesSlice';
import {
VaeModelInputFieldTemplate,
VaeModelInputFieldValue,
} from 'features/nodes/types/types';
import { MODEL_TYPE_MAP as BASE_MODEL_NAME_MAP } from 'features/system/components/ModelSelect';
import { forEach } from 'lodash-es';
import { memo, useCallback, useEffect, useMemo } from 'react';
import { useTranslation } from 'react-i18next';
import { useGetVaeModelsQuery } from 'services/api/endpoints/models';
import { FieldComponentProps } from './types';
const VaeModelInputFieldComponent = (
props: FieldComponentProps<
VaeModelInputFieldValue,
VaeModelInputFieldTemplate
>
) => {
const { nodeId, field } = props;
const dispatch = useAppDispatch();
const { t } = useTranslation();
const { data: vaeModels } = useGetVaeModelsQuery();
const selectedModel = useMemo(
() => vaeModels?.entities[field.value ?? vaeModels.ids[0]],
[vaeModels?.entities, vaeModels?.ids, field.value]
);
const data = useMemo(() => {
if (!vaeModels) {
return [];
}
const data: SelectItem[] = [];
forEach(vaeModels.entities, (model, id) => {
if (!model) {
return;
}
data.push({
value: id,
label: model.name,
group: BASE_MODEL_NAME_MAP[model.base_model],
});
});
return data;
}, [vaeModels]);
const handleValueChanged = useCallback(
(v: string | null) => {
if (!v) {
return;
}
dispatch(
fieldValueChanged({
nodeId,
fieldName: field.name,
value: v,
})
);
},
[dispatch, field.name, nodeId]
);
useEffect(() => {
if (field.value && vaeModels?.ids.includes(field.value)) {
return;
}
handleValueChanged('auto');
}, [field.value, handleValueChanged, vaeModels?.ids]);
return (
<IAIMantineSelect
tooltip={selectedModel?.description}
label={
selectedModel?.base_model &&
BASE_MODEL_NAME_MAP[selectedModel?.base_model]
}
value={field.value}
placeholder="Pick one"
data={data}
onChange={handleValueChanged}
/>
);
};
export default memo(VaeModelInputFieldComponent);

8
invokeai/frontend/web/src/features/nodes/store/nodesSlice.ts
View File

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

14
invokeai/frontend/web/src/features/nodes/types/constants.ts
View File

@ -17,6 +17,8 @@ export const FIELD_TYPE_MAP: Record<string, FieldType> = {
ClipField: 'clip', ClipField: 'clip',
VaeField: 'vae', VaeField: 'vae',
model: 'model', model: 'model',
vae_model: 'vae_model',
lora_model: 'lora_model',
array: 'array', array: 'array',
item: 'item', item: 'item',
ColorField: 'color', ColorField: 'color',
@ -116,6 +118,18 @@ export const FIELDS: Record<FieldType, FieldUIConfig> = {
title: 'Model', title: 'Model',
description: 'Models are models.', description: 'Models are models.',
}, },
vae_model: {
color: 'teal',
colorCssVar: getColorTokenCssVariable('teal'),
title: 'VAE',
description: 'Models are models.',
},
lora_model: {
color: 'teal',
colorCssVar: getColorTokenCssVariable('teal'),
title: 'LoRA',
description: 'Models are models.',
},
array: { array: {
color: 'gray', color: 'gray',
colorCssVar: getColorTokenCssVariable('gray'), colorCssVar: getColorTokenCssVariable('gray'),

41
invokeai/frontend/web/src/features/nodes/types/types.ts
View File

@ -64,6 +64,8 @@ export type FieldType =
| 'vae' | 'vae'
| 'control' | 'control'
| 'model' | 'model'
| 'vae_model'
| 'lora_model'
| 'array' | 'array'
| 'item' | 'item'
| 'color' | 'color'
@ -91,6 +93,8 @@ export type InputFieldValue =
| ControlInputFieldValue | ControlInputFieldValue
| EnumInputFieldValue | EnumInputFieldValue
| ModelInputFieldValue | ModelInputFieldValue
| VaeModelInputFieldValue
| LoRAModelInputFieldValue
| ArrayInputFieldValue | ArrayInputFieldValue
| ItemInputFieldValue | ItemInputFieldValue
| ColorInputFieldValue | ColorInputFieldValue
@ -116,6 +120,8 @@ export type InputFieldTemplate =
| ControlInputFieldTemplate | ControlInputFieldTemplate
| EnumInputFieldTemplate | EnumInputFieldTemplate
| ModelInputFieldTemplate | ModelInputFieldTemplate
| VaeModelInputFieldTemplate
| LoRAModelInputFieldTemplate
| ArrayInputFieldTemplate | ArrayInputFieldTemplate
| ItemInputFieldTemplate | ItemInputFieldTemplate
| ColorInputFieldTemplate | ColorInputFieldTemplate
@ -228,6 +234,16 @@ export type ModelInputFieldValue = FieldValueBase & {
value?: string; value?: string;
}; };
export type VaeModelInputFieldValue = FieldValueBase & {
type: 'vae_model';
value?: string;
};
export type LoRAModelInputFieldValue = FieldValueBase & {
type: 'lora_model';
value?: string;
};
export type ArrayInputFieldValue = FieldValueBase & { export type ArrayInputFieldValue = FieldValueBase & {
type: 'array'; type: 'array';
value?: (string | number)[]; value?: (string | number)[];
@ -305,6 +321,21 @@ export type ConditioningInputFieldTemplate = InputFieldTemplateBase & {
type: 'conditioning'; type: 'conditioning';
}; };
export type UNetInputFieldTemplate = InputFieldTemplateBase & {
default: undefined;
type: 'unet';
};
export type ClipInputFieldTemplate = InputFieldTemplateBase & {
default: undefined;
type: 'clip';
};
export type VaeInputFieldTemplate = InputFieldTemplateBase & {
default: undefined;
type: 'vae';
};
export type ControlInputFieldTemplate = InputFieldTemplateBase & { export type ControlInputFieldTemplate = InputFieldTemplateBase & {
default: undefined; default: undefined;
type: 'control'; type: 'control';
@ -322,6 +353,16 @@ export type ModelInputFieldTemplate = InputFieldTemplateBase & {
type: 'model'; type: 'model';
}; };
export type VaeModelInputFieldTemplate = InputFieldTemplateBase & {
default: string;
type: 'vae_model';
};
export type LoRAModelInputFieldTemplate = InputFieldTemplateBase & {
default: string;
type: 'lora_model';
};
export type ArrayInputFieldTemplate = InputFieldTemplateBase & { export type ArrayInputFieldTemplate = InputFieldTemplateBase & {
default: []; default: [];
type: 'array'; type: 'array';

10
invokeai/frontend/web/src/features/nodes/util/addControlNetToLinearGraph.ts
View File

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

72
invokeai/frontend/web/src/features/nodes/util/fieldTemplateBuilders.ts
View File

@ -3,27 +3,29 @@ import { OpenAPIV3 } from 'openapi-types';
import { FIELD_TYPE_MAP } from '../types/constants'; import { FIELD_TYPE_MAP } from '../types/constants';
import { isSchemaObject } from '../types/typeGuards'; import { isSchemaObject } from '../types/typeGuards';
import { import {
BooleanInputFieldTemplate,
EnumInputFieldTemplate,
FloatInputFieldTemplate,
ImageInputFieldTemplate,
IntegerInputFieldTemplate,
LatentsInputFieldTemplate,
ConditioningInputFieldTemplate,
UNetInputFieldTemplate,
ClipInputFieldTemplate,
VaeInputFieldTemplate,
ControlInputFieldTemplate,
StringInputFieldTemplate,
ModelInputFieldTemplate,
ArrayInputFieldTemplate, ArrayInputFieldTemplate,
ItemInputFieldTemplate, BooleanInputFieldTemplate,
ClipInputFieldTemplate,
ColorInputFieldTemplate, ColorInputFieldTemplate,
InputFieldTemplateBase, ConditioningInputFieldTemplate,
OutputFieldTemplate, ControlInputFieldTemplate,
TypeHints, EnumInputFieldTemplate,
FieldType, FieldType,
FloatInputFieldTemplate,
ImageCollectionInputFieldTemplate, ImageCollectionInputFieldTemplate,
ImageInputFieldTemplate,
InputFieldTemplateBase,
IntegerInputFieldTemplate,
ItemInputFieldTemplate,
LatentsInputFieldTemplate,
LoRAModelInputFieldTemplate,
ModelInputFieldTemplate,
OutputFieldTemplate,
StringInputFieldTemplate,
TypeHints,
UNetInputFieldTemplate,
VaeInputFieldTemplate,
VaeModelInputFieldTemplate,
} from '../types/types'; } from '../types/types';
export type BaseFieldProperties = 'name' | 'title' | 'description'; export type BaseFieldProperties = 'name' | 'title' | 'description';
@ -175,6 +177,36 @@ const buildModelInputFieldTemplate = ({
return template; return template;
}; };
const buildVaeModelInputFieldTemplate = ({
schemaObject,
baseField,
}: BuildInputFieldArg): VaeModelInputFieldTemplate => {
const template: VaeModelInputFieldTemplate = {
...baseField,
type: 'vae_model',
inputRequirement: 'always',
inputKind: 'direct',
default: schemaObject.default ?? undefined,
};
return template;
};
const buildLoRAModelInputFieldTemplate = ({
schemaObject,
baseField,
}: BuildInputFieldArg): LoRAModelInputFieldTemplate => {
const template: LoRAModelInputFieldTemplate = {
...baseField,
type: 'lora_model',
inputRequirement: 'always',
inputKind: 'direct',
default: schemaObject.default ?? undefined,
};
return template;
};
const buildImageInputFieldTemplate = ({ const buildImageInputFieldTemplate = ({
schemaObject, schemaObject,
baseField, baseField,
@ -441,6 +473,12 @@ export const buildInputFieldTemplate = (
if (['model'].includes(fieldType)) { if (['model'].includes(fieldType)) {
return buildModelInputFieldTemplate({ schemaObject, baseField }); return buildModelInputFieldTemplate({ schemaObject, baseField });
} }
if (['vae_model'].includes(fieldType)) {
return buildVaeModelInputFieldTemplate({ schemaObject, baseField });
}
if (['lora_model'].includes(fieldType)) {
return buildLoRAModelInputFieldTemplate({ schemaObject, baseField });
}
if (['enum'].includes(fieldType)) { if (['enum'].includes(fieldType)) {
return buildEnumInputFieldTemplate({ schemaObject, baseField }); return buildEnumInputFieldTemplate({ schemaObject, baseField });
} }

8
invokeai/frontend/web/src/features/nodes/util/fieldValueBuilders.ts
View File

@ -75,6 +75,14 @@ export const buildInputFieldValue = (
if (template.type === 'model') { if (template.type === 'model') {
fieldValue.value = undefined; fieldValue.value = undefined;
} }
if (template.type === 'vae_model') {
fieldValue.value = undefined;
}
if (template.type === 'lora_model') {
fieldValue.value = undefined;
}
} }
return fieldValue; return fieldValue;

148
invokeai/frontend/web/src/features/nodes/util/graphBuilders/addLoRAsToGraph.ts Normal file
View File

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

68
invokeai/frontend/web/src/features/nodes/util/graphBuilders/addVAEToGraph.ts Normal file
View File

@ -0,0 +1,68 @@
import { RootState } from 'app/store/store';
import { NonNullableGraph } from 'features/nodes/types/types';
import { modelIdToVAEModelField } from '../modelIdToVAEModelField';
import {
IMAGE_TO_IMAGE_GRAPH,
IMAGE_TO_LATENTS,
INPAINT,
INPAINT_GRAPH,
LATENTS_TO_IMAGE,
MAIN_MODEL_LOADER,
TEXT_TO_IMAGE_GRAPH,
VAE_LOADER,
} from './constants';
export const addVAEToGraph = (
graph: NonNullableGraph,
state: RootState
): void => {
const { vae: vaeId } = state.generation;
const vae_model = modelIdToVAEModelField(vaeId);
if (vaeId !== 'auto') {
graph.nodes[VAE_LOADER] = {
type: 'vae_loader',
id: VAE_LOADER,
vae_model,
};
}
if (graph.id === TEXT_TO_IMAGE_GRAPH || graph.id === IMAGE_TO_IMAGE_GRAPH) {
graph.edges.push({
source: {
node_id: vaeId === 'auto' ? MAIN_MODEL_LOADER : VAE_LOADER,
field: 'vae',
},
destination: {
node_id: LATENTS_TO_IMAGE,
field: 'vae',
},
});
}
if (graph.id === IMAGE_TO_IMAGE_GRAPH) {
graph.edges.push({
source: {
node_id: vaeId === 'auto' ? MAIN_MODEL_LOADER : VAE_LOADER,
field: 'vae',
},
destination: {
node_id: IMAGE_TO_LATENTS,
field: 'vae',
},
});
}
if (graph.id === INPAINT_GRAPH) {
graph.edges.push({
source: {
node_id: vaeId === 'auto' ? MAIN_MODEL_LOADER : VAE_LOADER,
field: 'vae',
},
destination: {
node_id: INPAINT,
field: 'vae',
},
});
}
};

65
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasImageToImageGraph.ts
View File

@ -1,31 +1,27 @@
import { log } from 'app/logging/useLogger';
import { RootState } from 'app/store/store'; import { RootState } from 'app/store/store';
import { NonNullableGraph } from 'features/nodes/types/types';
import { import {
ImageDTO, ImageDTO,
ImageResizeInvocation, ImageResizeInvocation,
ImageToLatentsInvocation, ImageToLatentsInvocation,
RandomIntInvocation,
RangeOfSizeInvocation,
} from 'services/api/types'; } from 'services/api/types';
import { NonNullableGraph } from 'features/nodes/types/types'; import { addControlNetToLinearGraph } from '../addControlNetToLinearGraph';
import { log } from 'app/logging/useLogger'; import { modelIdToMainModelField } from '../modelIdToMainModelField';
import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
import { addLoRAsToGraph } from './addLoRAsToGraph';
import { addVAEToGraph } from './addVAEToGraph';
import { import {
ITERATE, IMAGE_TO_IMAGE_GRAPH,
IMAGE_TO_LATENTS,
LATENTS_TO_IMAGE, LATENTS_TO_IMAGE,
PIPELINE_MODEL_LOADER, LATENTS_TO_LATENTS,
MAIN_MODEL_LOADER,
NEGATIVE_CONDITIONING, NEGATIVE_CONDITIONING,
NOISE, NOISE,
POSITIVE_CONDITIONING, POSITIVE_CONDITIONING,
RANDOM_INT,
RANGE_OF_SIZE,
IMAGE_TO_IMAGE_GRAPH,
IMAGE_TO_LATENTS,
LATENTS_TO_LATENTS,
RESIZE, RESIZE,
} from './constants'; } from './constants';
import { set } from 'lodash-es';
import { addControlNetToLinearGraph } from '../addControlNetToLinearGraph';
import { modelIdToPipelineModelField } from '../modelIdToPipelineModelField';
import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
const moduleLog = log.child({ namespace: 'nodes' }); const moduleLog = log.child({ namespace: 'nodes' });
@ -52,7 +48,7 @@ export const buildCanvasImageToImageGraph = (
// The bounding box determines width and height, not the width and height params // The bounding box determines width and height, not the width and height params
const { width, height } = state.canvas.boundingBoxDimensions; const { width, height } = state.canvas.boundingBoxDimensions;
const model = modelIdToPipelineModelField(modelId); const model = modelIdToMainModelField(modelId);
/** /**
* The easiest way to build linear graphs is to do it in the node editor, then copy and paste the * The easiest way to build linear graphs is to do it in the node editor, then copy and paste the
@ -81,9 +77,9 @@ export const buildCanvasImageToImageGraph = (
type: 'noise', type: 'noise',
id: NOISE, id: NOISE,
}, },
[PIPELINE_MODEL_LOADER]: { [MAIN_MODEL_LOADER]: {
type: 'pipeline_model_loader', type: 'main_model_loader',
id: PIPELINE_MODEL_LOADER, id: MAIN_MODEL_LOADER,
model, model,
}, },
[LATENTS_TO_IMAGE]: { [LATENTS_TO_IMAGE]: {
@ -110,7 +106,7 @@ export const buildCanvasImageToImageGraph = (
edges: [ edges: [
{ {
source: { source: {
node_id: PIPELINE_MODEL_LOADER, node_id: MAIN_MODEL_LOADER,
field: 'clip', field: 'clip',
}, },
destination: { destination: {
@ -120,7 +116,7 @@ export const buildCanvasImageToImageGraph = (
}, },
{ {
source: { source: {
node_id: PIPELINE_MODEL_LOADER, node_id: MAIN_MODEL_LOADER,
field: 'clip', field: 'clip',
}, },
destination: { destination: {
@ -128,16 +124,6 @@ export const buildCanvasImageToImageGraph = (
field: 'clip', field: 'clip',
}, },
}, },
{
source: {
node_id: PIPELINE_MODEL_LOADER,
field: 'vae',
},
destination: {
node_id: LATENTS_TO_IMAGE,
field: 'vae',
},
},
{ {
source: { source: {
node_id: LATENTS_TO_LATENTS, node_id: LATENTS_TO_LATENTS,
@ -170,17 +156,7 @@ export const buildCanvasImageToImageGraph = (
}, },
{ {
source: { source: {
node_id: PIPELINE_MODEL_LOADER, node_id: MAIN_MODEL_LOADER,
field: 'vae',
},
destination: {
node_id: IMAGE_TO_LATENTS,
field: 'vae',
},
},
{
source: {
node_id: PIPELINE_MODEL_LOADER,
field: 'unet', field: 'unet',
}, },
destination: { destination: {
@ -277,6 +253,11 @@ export const buildCanvasImageToImageGraph = (
}); });
} }
addLoRAsToGraph(graph, state, LATENTS_TO_LATENTS);
// Add VAE
addVAEToGraph(graph, state);
// add dynamic prompts, mutating `graph` // add dynamic prompts, mutating `graph`
addDynamicPromptsToGraph(graph, state); addDynamicPromptsToGraph(graph, state);

43
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasInpaintGraph.ts
View File

@ -1,23 +1,25 @@
import { log } from 'app/logging/useLogger';
import { RootState } from 'app/store/store'; import { RootState } from 'app/store/store';
import { NonNullableGraph } from 'features/nodes/types/types';
import { import {
ImageDTO, ImageDTO,
InpaintInvocation, InpaintInvocation,
RandomIntInvocation, RandomIntInvocation,
RangeOfSizeInvocation, RangeOfSizeInvocation,
} from 'services/api/types'; } from 'services/api/types';
import { NonNullableGraph } from 'features/nodes/types/types'; import { modelIdToMainModelField } from '../modelIdToMainModelField';
import { log } from 'app/logging/useLogger'; import { addLoRAsToGraph } from './addLoRAsToGraph';
import { addVAEToGraph } from './addVAEToGraph';
import { import {
INPAINT,
INPAINT_GRAPH,
ITERATE, ITERATE,
PIPELINE_MODEL_LOADER, MAIN_MODEL_LOADER,
NEGATIVE_CONDITIONING, NEGATIVE_CONDITIONING,
POSITIVE_CONDITIONING, POSITIVE_CONDITIONING,
RANDOM_INT, RANDOM_INT,
RANGE_OF_SIZE, RANGE_OF_SIZE,
INPAINT_GRAPH,
INPAINT,
} from './constants'; } from './constants';
import { modelIdToPipelineModelField } from '../modelIdToPipelineModelField';
const moduleLog = log.child({ namespace: 'nodes' }); const moduleLog = log.child({ namespace: 'nodes' });
@ -55,7 +57,7 @@ export const buildCanvasInpaintGraph = (
// We may need to set the inpaint width and height to scale the image // We may need to set the inpaint width and height to scale the image
const { scaledBoundingBoxDimensions, boundingBoxScaleMethod } = state.canvas; const { scaledBoundingBoxDimensions, boundingBoxScaleMethod } = state.canvas;
const model = modelIdToPipelineModelField(modelId); const model = modelIdToMainModelField(modelId);
const graph: NonNullableGraph = { const graph: NonNullableGraph = {
id: INPAINT_GRAPH, id: INPAINT_GRAPH,
@ -101,9 +103,9 @@ export const buildCanvasInpaintGraph = (
id: NEGATIVE_CONDITIONING, id: NEGATIVE_CONDITIONING,
prompt: negativePrompt, prompt: negativePrompt,
}, },
[PIPELINE_MODEL_LOADER]: { [MAIN_MODEL_LOADER]: {
type: 'pipeline_model_loader', type: 'main_model_loader',
id: PIPELINE_MODEL_LOADER, id: MAIN_MODEL_LOADER,
model, model,
}, },
[RANGE_OF_SIZE]: { [RANGE_OF_SIZE]: {
@ -142,7 +144,7 @@ export const buildCanvasInpaintGraph = (
}, },
{ {
source: { source: {
node_id: PIPELINE_MODEL_LOADER, node_id: MAIN_MODEL_LOADER,
field: 'clip', field: 'clip',
}, },
destination: { destination: {
@ -152,7 +154,7 @@ export const buildCanvasInpaintGraph = (
}, },
{ {
source: { source: {
node_id: PIPELINE_MODEL_LOADER, node_id: MAIN_MODEL_LOADER,
field: 'clip', field: 'clip',
}, },
destination: { destination: {
@ -162,7 +164,7 @@ export const buildCanvasInpaintGraph = (
}, },
{ {
source: { source: {
node_id: PIPELINE_MODEL_LOADER, node_id: MAIN_MODEL_LOADER,
field: 'unet', field: 'unet',
}, },
destination: { destination: {
@ -170,16 +172,6 @@ export const buildCanvasInpaintGraph = (
field: 'unet', field: 'unet',
}, },
}, },
{
source: {
node_id: PIPELINE_MODEL_LOADER,
field: 'vae',
},
destination: {
node_id: INPAINT,
field: 'vae',
},
},
{ {
source: { source: {
node_id: RANGE_OF_SIZE, node_id: RANGE_OF_SIZE,
@ -203,6 +195,11 @@ export const buildCanvasInpaintGraph = (
], ],
}; };
addLoRAsToGraph(graph, state, INPAINT);
// Add VAE
addVAEToGraph(graph, state);
// handle seed // handle seed
if (shouldRandomizeSeed) { if (shouldRandomizeSeed) {
// Random int node to generate the starting seed // Random int node to generate the starting seed

43
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasTextToImageGraph.ts
View File

@ -1,21 +1,19 @@
import { RootState } from 'app/store/store'; import { RootState } from 'app/store/store';
import { NonNullableGraph } from 'features/nodes/types/types'; import { NonNullableGraph } from 'features/nodes/types/types';
import { RandomIntInvocation, RangeOfSizeInvocation } from 'services/api/types'; import { addControlNetToLinearGraph } from '../addControlNetToLinearGraph';
import { modelIdToMainModelField } from '../modelIdToMainModelField';
import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
import { addLoRAsToGraph } from './addLoRAsToGraph';
import { addVAEToGraph } from './addVAEToGraph';
import { import {
ITERATE,
LATENTS_TO_IMAGE, LATENTS_TO_IMAGE,
PIPELINE_MODEL_LOADER, MAIN_MODEL_LOADER,
NEGATIVE_CONDITIONING, NEGATIVE_CONDITIONING,
NOISE, NOISE,
POSITIVE_CONDITIONING, POSITIVE_CONDITIONING,
RANDOM_INT,
RANGE_OF_SIZE,
TEXT_TO_IMAGE_GRAPH, TEXT_TO_IMAGE_GRAPH,
TEXT_TO_LATENTS, TEXT_TO_LATENTS,
} from './constants'; } from './constants';
import { addControlNetToLinearGraph } from '../addControlNetToLinearGraph';
import { modelIdToPipelineModelField } from '../modelIdToPipelineModelField';
import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
/** /**
* Builds the Canvas tab's Text to Image graph. * Builds the Canvas tab's Text to Image graph.
@ -38,7 +36,7 @@ export const buildCanvasTextToImageGraph = (
// The bounding box determines width and height, not the width and height params // The bounding box determines width and height, not the width and height params
const { width, height } = state.canvas.boundingBoxDimensions; const { width, height } = state.canvas.boundingBoxDimensions;
const model = modelIdToPipelineModelField(modelId); const model = modelIdToMainModelField(modelId);
/** /**
* The easiest way to build linear graphs is to do it in the node editor, then copy and paste the * The easiest way to build linear graphs is to do it in the node editor, then copy and paste the
@ -76,9 +74,9 @@ export const buildCanvasTextToImageGraph = (
scheduler, scheduler,
steps, steps,
}, },
[PIPELINE_MODEL_LOADER]: { [MAIN_MODEL_LOADER]: {
type: 'pipeline_model_loader', type: 'main_model_loader',
id: PIPELINE_MODEL_LOADER, id: MAIN_MODEL_LOADER,
model, model,
}, },
[LATENTS_TO_IMAGE]: { [LATENTS_TO_IMAGE]: {
@ -109,7 +107,7 @@ export const buildCanvasTextToImageGraph = (
}, },
{ {
source: { source: {
node_id: PIPELINE_MODEL_LOADER, node_id: MAIN_MODEL_LOADER,
field: 'clip', field: 'clip',
}, },
destination: { destination: {
@ -119,7 +117,7 @@ export const buildCanvasTextToImageGraph = (
}, },
{ {
source: { source: {
node_id: PIPELINE_MODEL_LOADER, node_id: MAIN_MODEL_LOADER,
field: 'clip', field: 'clip',
}, },
destination: { destination: {
@ -129,7 +127,7 @@ export const buildCanvasTextToImageGraph = (
}, },
{ {
source: { source: {
node_id: PIPELINE_MODEL_LOADER, node_id: MAIN_MODEL_LOADER,
field: 'unet', field: 'unet',
}, },
destination: { destination: {
@ -147,16 +145,6 @@ export const buildCanvasTextToImageGraph = (
field: 'latents', field: 'latents',
}, },
}, },
{
source: {
node_id: PIPELINE_MODEL_LOADER,
field: 'vae',
},
destination: {
node_id: LATENTS_TO_IMAGE,
field: 'vae',
},
},
{ {
source: { source: {
node_id: NOISE, node_id: NOISE,
@ -170,6 +158,11 @@ export const buildCanvasTextToImageGraph = (
], ],
}; };
addLoRAsToGraph(graph, state, TEXT_TO_LATENTS);
// Add VAE
addVAEToGraph(graph, state);
// add dynamic prompts, mutating `graph` // add dynamic prompts, mutating `graph`
addDynamicPromptsToGraph(graph, state); addDynamicPromptsToGraph(graph, state);

64
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearImageToImageGraph.ts
View File

@ -1,28 +1,30 @@
import { log } from 'app/logging/useLogger';
import { RootState } from 'app/store/store'; import { RootState } from 'app/store/store';
import { NonNullableGraph } from 'features/nodes/types/types';
import { import {
ImageCollectionInvocation, ImageCollectionInvocation,
ImageResizeInvocation, ImageResizeInvocation,
ImageToLatentsInvocation, ImageToLatentsInvocation,
IterateInvocation, IterateInvocation,
} from 'services/api/types'; } from 'services/api/types';
import { NonNullableGraph } from 'features/nodes/types/types'; import { addControlNetToLinearGraph } from '../addControlNetToLinearGraph';
import { log } from 'app/logging/useLogger'; import { modelIdToMainModelField } from '../modelIdToMainModelField';
import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
import { addLoRAsToGraph } from './addLoRAsToGraph';
import { addVAEToGraph } from './addVAEToGraph';
import { import {
IMAGE_COLLECTION,
IMAGE_COLLECTION_ITERATE,
IMAGE_TO_IMAGE_GRAPH,
IMAGE_TO_LATENTS,
LATENTS_TO_IMAGE, LATENTS_TO_IMAGE,
PIPELINE_MODEL_LOADER, LATENTS_TO_LATENTS,
MAIN_MODEL_LOADER,
NEGATIVE_CONDITIONING, NEGATIVE_CONDITIONING,
NOISE, NOISE,
POSITIVE_CONDITIONING, POSITIVE_CONDITIONING,
IMAGE_TO_IMAGE_GRAPH,
IMAGE_TO_LATENTS,
LATENTS_TO_LATENTS,
RESIZE, RESIZE,
IMAGE_COLLECTION,
IMAGE_COLLECTION_ITERATE,
} from './constants'; } from './constants';
import { addControlNetToLinearGraph } from '../addControlNetToLinearGraph';
import { modelIdToPipelineModelField } from '../modelIdToPipelineModelField';
import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
const moduleLog = log.child({ namespace: 'nodes' }); const moduleLog = log.child({ namespace: 'nodes' });
@ -69,7 +71,7 @@ export const buildLinearImageToImageGraph = (
throw new Error('No initial image found in state'); throw new Error('No initial image found in state');
} }
const model = modelIdToPipelineModelField(modelId); const model = modelIdToMainModelField(modelId);
// copy-pasted graph from node editor, filled in with state values & friendly node ids // copy-pasted graph from node editor, filled in with state values & friendly node ids
const graph: NonNullableGraph = { const graph: NonNullableGraph = {
@ -89,9 +91,9 @@ export const buildLinearImageToImageGraph = (
type: 'noise', type: 'noise',
id: NOISE, id: NOISE,
}, },
[PIPELINE_MODEL_LOADER]: { [MAIN_MODEL_LOADER]: {
type: 'pipeline_model_loader', type: 'main_model_loader',
id: PIPELINE_MODEL_LOADER, id: MAIN_MODEL_LOADER,
model, model,
}, },
[LATENTS_TO_IMAGE]: { [LATENTS_TO_IMAGE]: {
@ -118,7 +120,7 @@ export const buildLinearImageToImageGraph = (
edges: [ edges: [
{ {
source: { source: {
node_id: PIPELINE_MODEL_LOADER, node_id: MAIN_MODEL_LOADER,
field: 'clip', field: 'clip',
}, },
destination: { destination: {
@ -128,7 +130,7 @@ export const buildLinearImageToImageGraph = (
}, },
{ {
source: { source: {
node_id: PIPELINE_MODEL_LOADER, node_id: MAIN_MODEL_LOADER,
field: 'clip', field: 'clip',
}, },
destination: { destination: {
@ -136,16 +138,6 @@ export const buildLinearImageToImageGraph = (
field: 'clip', field: 'clip',
}, },
}, },
{
source: {
node_id: PIPELINE_MODEL_LOADER,
field: 'vae',
},
destination: {
node_id: LATENTS_TO_IMAGE,
field: 'vae',
},
},
{ {
source: { source: {
node_id: LATENTS_TO_LATENTS, node_id: LATENTS_TO_LATENTS,
@ -176,19 +168,10 @@ export const buildLinearImageToImageGraph = (
field: 'noise', field: 'noise',
}, },
}, },
{ {
source: { source: {
node_id: PIPELINE_MODEL_LOADER, node_id: MAIN_MODEL_LOADER,
field: 'vae',
},
destination: {
node_id: IMAGE_TO_LATENTS,
field: 'vae',
},
},
{
source: {
node_id: PIPELINE_MODEL_LOADER,
field: 'unet', field: 'unet',
}, },
destination: { destination: {
@ -323,6 +306,11 @@ export const buildLinearImageToImageGraph = (
}); });
} }
addLoRAsToGraph(graph, state, LATENTS_TO_LATENTS);
// Add VAE
addVAEToGraph(graph, state);
// add dynamic prompts, mutating `graph` // add dynamic prompts, mutating `graph`
addDynamicPromptsToGraph(graph, state); addDynamicPromptsToGraph(graph, state);

39
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearTextToImageGraph.ts
View File

@ -1,17 +1,19 @@
import { RootState } from 'app/store/store'; import { RootState } from 'app/store/store';
import { NonNullableGraph } from 'features/nodes/types/types'; import { NonNullableGraph } from 'features/nodes/types/types';
import { addControlNetToLinearGraph } from '../addControlNetToLinearGraph';
import { modelIdToMainModelField } from '../modelIdToMainModelField';
import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
import { addLoRAsToGraph } from './addLoRAsToGraph';
import { addVAEToGraph } from './addVAEToGraph';
import { import {
LATENTS_TO_IMAGE, LATENTS_TO_IMAGE,
PIPELINE_MODEL_LOADER, MAIN_MODEL_LOADER,
NEGATIVE_CONDITIONING, NEGATIVE_CONDITIONING,
NOISE, NOISE,
POSITIVE_CONDITIONING, POSITIVE_CONDITIONING,
TEXT_TO_IMAGE_GRAPH, TEXT_TO_IMAGE_GRAPH,
TEXT_TO_LATENTS, TEXT_TO_LATENTS,
} from './constants'; } from './constants';
import { addControlNetToLinearGraph } from '../addControlNetToLinearGraph';
import { modelIdToPipelineModelField } from '../modelIdToPipelineModelField';
import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
export const buildLinearTextToImageGraph = ( export const buildLinearTextToImageGraph = (
state: RootState state: RootState
@ -27,7 +29,7 @@ export const buildLinearTextToImageGraph = (
height, height,
} = state.generation; } = state.generation;
const model = modelIdToPipelineModelField(modelId); const model = modelIdToMainModelField(modelId);
/** /**
* The easiest way to build linear graphs is to do it in the node editor, then copy and paste the * The easiest way to build linear graphs is to do it in the node editor, then copy and paste the
@ -65,9 +67,9 @@ export const buildLinearTextToImageGraph = (
scheduler, scheduler,
steps, steps,
}, },
[PIPELINE_MODEL_LOADER]: { [MAIN_MODEL_LOADER]: {
type: 'pipeline_model_loader', type: 'main_model_loader',
id: PIPELINE_MODEL_LOADER, id: MAIN_MODEL_LOADER,
model, model,
}, },
[LATENTS_TO_IMAGE]: { [LATENTS_TO_IMAGE]: {
@ -98,7 +100,7 @@ export const buildLinearTextToImageGraph = (
}, },
{ {
source: { source: {
node_id: PIPELINE_MODEL_LOADER, node_id: MAIN_MODEL_LOADER,
field: 'clip', field: 'clip',
}, },
destination: { destination: {
@ -108,7 +110,7 @@ export const buildLinearTextToImageGraph = (
}, },
{ {
source: { source: {
node_id: PIPELINE_MODEL_LOADER, node_id: MAIN_MODEL_LOADER,
field: 'clip', field: 'clip',
}, },
destination: { destination: {
@ -118,7 +120,7 @@ export const buildLinearTextToImageGraph = (
}, },
{ {
source: { source: {
node_id: PIPELINE_MODEL_LOADER, node_id: MAIN_MODEL_LOADER,
field: 'unet', field: 'unet',
}, },
destination: { destination: {
@ -136,16 +138,6 @@ export const buildLinearTextToImageGraph = (
field: 'latents', field: 'latents',
}, },
}, },
{
source: {
node_id: PIPELINE_MODEL_LOADER,
field: 'vae',
},
destination: {
node_id: LATENTS_TO_IMAGE,
field: 'vae',
},
},
{ {
source: { source: {
node_id: NOISE, node_id: NOISE,
@ -159,6 +151,11 @@ export const buildLinearTextToImageGraph = (
], ],
}; };
addLoRAsToGraph(graph, state, TEXT_TO_LATENTS);
// Add Custom VAE Support
addVAEToGraph(graph, state);
// add dynamic prompts, mutating `graph` // add dynamic prompts, mutating `graph`
addDynamicPromptsToGraph(graph, state); addDynamicPromptsToGraph(graph, state);

24
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildNodesGraph.ts
View File

@ -1,10 +1,12 @@
import { Graph } from 'services/api/types';
import { v4 as uuidv4 } from 'uuid';
import { cloneDeep, omit, reduce } from 'lodash-es';
import { RootState } from 'app/store/store'; import { RootState } from 'app/store/store';
import { InputFieldValue } from 'features/nodes/types/types'; import { InputFieldValue } from 'features/nodes/types/types';
import { cloneDeep, omit, reduce } from 'lodash-es';
import { Graph } from 'services/api/types';
import { AnyInvocation } from 'services/events/types'; import { AnyInvocation } from 'services/events/types';
import { modelIdToPipelineModelField } from '../modelIdToPipelineModelField'; import { v4 as uuidv4 } from 'uuid';
import { modelIdToLoRAModelField } from '../modelIdToLoRAName';
import { modelIdToMainModelField } from '../modelIdToMainModelField';
import { modelIdToVAEModelField } from '../modelIdToVAEModelField';
/** /**
* We need to do special handling for some fields * We need to do special handling for some fields
@ -27,7 +29,19 @@ export const parseFieldValue = (field: InputFieldValue) => {
if (field.type === 'model') { if (field.type === 'model') {
if (field.value) { if (field.value) {
return modelIdToPipelineModelField(field.value); return modelIdToMainModelField(field.value);
}
}
if (field.type === 'vae_model') {
if (field.value) {
return modelIdToVAEModelField(field.value);
}
}
if (field.type === 'lora_model') {
if (field.value) {
return modelIdToLoRAModelField(field.value);
} }
} }

4
invokeai/frontend/web/src/features/nodes/util/graphBuilders/constants.ts
View File

@ -7,7 +7,9 @@ export const NOISE = 'noise';
export const RANDOM_INT = 'rand_int'; export const RANDOM_INT = 'rand_int';
export const RANGE_OF_SIZE = 'range_of_size'; export const RANGE_OF_SIZE = 'range_of_size';
export const ITERATE = 'iterate'; export const ITERATE = 'iterate';
export const PIPELINE_MODEL_LOADER = 'pipeline_model_loader'; export const MAIN_MODEL_LOADER = 'main_model_loader';
export const VAE_LOADER = 'vae_loader';
export const LORA_LOADER = 'lora_loader';
export const IMAGE_TO_LATENTS = 'image_to_latents'; export const IMAGE_TO_LATENTS = 'image_to_latents';
export const LATENTS_TO_LATENTS = 'latents_to_latents'; export const LATENTS_TO_LATENTS = 'latents_to_latents';
export const RESIZE = 'resize_image'; export const RESIZE = 'resize_image';

12
invokeai/frontend/web/src/features/nodes/util/modelIdToLoRAName.ts Normal file
View File

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

16
invokeai/frontend/web/src/features/nodes/util/modelIdToMainModelField.ts Normal file
View File

@ -0,0 +1,16 @@
import { BaseModelType, MainModelField } from 'services/api/types';
/**
* Crudely converts a model id to a main model field
* TODO: Make better
*/
export const modelIdToMainModelField = (modelId: string): MainModelField => {
const [base_model, model_type, model_name] = modelId.split('/');
const field: MainModelField = {
base_model: base_model as BaseModelType,
model_name,
};
return field;
};

18
invokeai/frontend/web/src/features/nodes/util/modelIdToPipelineModelField.ts
View File

@ -1,18 +0,0 @@
import { BaseModelType, PipelineModelField } from 'services/api/types';
/**
* Crudely converts a model id to a pipeline model field
* TODO: Make better
*/
export const modelIdToPipelineModelField = (
modelId: string
): PipelineModelField => {
const [base_model, model_type, model_name] = modelId.split('/');
const field: PipelineModelField = {
base_model: base_model as BaseModelType,
model_name,
};
return field;
};

16
invokeai/frontend/web/src/features/nodes/util/modelIdToVAEModelField.ts Normal file
View File

@ -0,0 +1,16 @@
import { BaseModelType, VAEModelField } from 'services/api/types';
/**
* Crudely converts a model id to a main model field
* TODO: Make better
*/
export const modelIdToVAEModelField = (modelId: string): VAEModelField => {
const [base_model, model_type, model_name] = modelId.split('/');
const field: VAEModelField = {
base_model: base_model as BaseModelType,
model_name,
};
return field;
};

13
invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/BoundingBox/ParamBoundingBoxCollapse.tsx
View File

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

11
invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/InfillAndScaling/ParamInfillAndScalingCollapse.tsx
View File

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

14
invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/SeamCorrection/ParamSeamCorrectionCollapse.tsx
View File

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

44
invokeai/frontend/web/src/features/parameters/components/Parameters/ControlNet/ParamControlNetCollapse.tsx
View File

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

4
invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamCFGScale.tsx
View File

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

4
invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamHeight.tsx
View File

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

47
invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamIterations.tsx
View File

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

12
invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamSchedulerAndModel.tsx → invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamModelandVAE.tsx
View File

@ -1,19 +1,19 @@
import { Box, Flex } from '@chakra-ui/react'; import { Box, Flex } from '@chakra-ui/react';
import ModelSelect from 'features/system/components/ModelSelect'; import ModelSelect from 'features/system/components/ModelSelect';
import VAESelect from 'features/system/components/VAESelect';
import { memo } from 'react'; import { memo } from 'react';
import ParamScheduler from './ParamScheduler';
const ParamSchedulerAndModel = () => { const ParamModelandVAE = () => {
return ( return (
<Flex gap={3} w="full"> <Flex gap={3} w="full">
<Box w="25rem">
<ParamScheduler />
</Box>
<Box w="full"> <Box w="full">
<ModelSelect /> <ModelSelect />
</Box> </Box>
<Box w="full">
<VAESelect />
</Box>
</Flex> </Flex>
); );
}; };
export default memo(ParamSchedulerAndModel); export default memo(ParamModelandVAE);

4
invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamSteps.tsx
View File

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

7
invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamWidth.tsx
View File

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

40
invokeai/frontend/web/src/features/parameters/components/Parameters/Hires/ParamHiresCollapse.tsx
View File

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

1
invokeai/frontend/web/src/features/parameters/components/Parameters/Hires/ParamHiresToggle.tsx
View File

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

41
invokeai/frontend/web/src/features/parameters/components/Parameters/Noise/ParamNoiseCollapse.tsx
View File

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

21
invokeai/frontend/web/src/features/parameters/components/Parameters/Noise/ParamNoiseThreshold.tsx
View File

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

27
invokeai/frontend/web/src/features/parameters/components/Parameters/Noise/ParamNoiseToggle.tsx Normal file
View File

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

19
invokeai/frontend/web/src/features/parameters/components/Parameters/Noise/ParamPerlinNoise.tsx
View File

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

42
invokeai/frontend/web/src/features/parameters/components/Parameters/Seamless/ParamSeamlessCollapse.tsx
View File

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

36
invokeai/frontend/web/src/features/parameters/components/Parameters/Symmetry/ParamSymmetryCollapse.tsx
View File

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

1
invokeai/frontend/web/src/features/parameters/components/Parameters/Symmetry/ParamSymmetryToggle.tsx
View File

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

45
invokeai/frontend/web/src/features/parameters/components/Parameters/Variations/ParamVariationCollapse.tsx
View File

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

27
invokeai/frontend/web/src/features/parameters/components/Parameters/Variations/ParamVariationToggle.tsx Normal file
View File

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

17
invokeai/frontend/web/src/features/parameters/store/generationSlice.ts
View File

@ -14,6 +14,7 @@ import {
SeedParam, SeedParam,
StepsParam, StepsParam,
StrengthParam, StrengthParam,
VAEParam,
WidthParam, WidthParam,
} from './parameterZodSchemas'; } from './parameterZodSchemas';
@ -47,7 +48,7 @@ export interface GenerationState {
horizontalSymmetrySteps: number; horizontalSymmetrySteps: number;
verticalSymmetrySteps: number; verticalSymmetrySteps: number;
model: ModelParam; model: ModelParam;
shouldUseSeamless: boolean; vae: VAEParam;
seamlessXAxis: boolean; seamlessXAxis: boolean;
seamlessYAxis: boolean; seamlessYAxis: boolean;
} }
@ -81,9 +82,9 @@ export const initialGenerationState: GenerationState = {
horizontalSymmetrySteps: 0, horizontalSymmetrySteps: 0,
verticalSymmetrySteps: 0, verticalSymmetrySteps: 0,
model: '', model: '',
shouldUseSeamless: false, vae: '',
seamlessXAxis: true, seamlessXAxis: false,
seamlessYAxis: true, seamlessYAxis: false,
}; };
const initialState: GenerationState = initialGenerationState; const initialState: GenerationState = initialGenerationState;
@ -141,9 +142,6 @@ export const generationSlice = createSlice({
setImg2imgStrength: (state, action: PayloadAction<number>) => { setImg2imgStrength: (state, action: PayloadAction<number>) => {
state.img2imgStrength = action.payload; state.img2imgStrength = action.payload;
}, },
setSeamless: (state, action: PayloadAction<boolean>) => {
state.shouldUseSeamless = action.payload;
},
setSeamlessXAxis: (state, action: PayloadAction<boolean>) => { setSeamlessXAxis: (state, action: PayloadAction<boolean>) => {
state.seamlessXAxis = action.payload; state.seamlessXAxis = action.payload;
}, },
@ -216,6 +214,9 @@ export const generationSlice = createSlice({
modelSelected: (state, action: PayloadAction<string>) => { modelSelected: (state, action: PayloadAction<string>) => {
state.model = action.payload; state.model = action.payload;
}, },
vaeSelected: (state, action: PayloadAction<string>) => {
state.vae = action.payload;
},
}, },
extraReducers: (builder) => { extraReducers: (builder) => {
builder.addCase(configChanged, (state, action) => { builder.addCase(configChanged, (state, action) => {
@ -260,8 +261,8 @@ export const {
setVerticalSymmetrySteps, setVerticalSymmetrySteps,
initialImageChanged, initialImageChanged,
modelSelected, modelSelected,
vaeSelected,
setShouldUseNoiseSettings, setShouldUseNoiseSettings,
setSeamless,
setSeamlessXAxis, setSeamlessXAxis,
setSeamlessYAxis, setSeamlessYAxis,
} = generationSlice.actions; } = generationSlice.actions;

9
invokeai/frontend/web/src/features/parameters/store/parameterZodSchemas.ts
View File

@ -135,6 +135,15 @@ export const zModel = z.string();
* Type alias for model parameter, inferred from its zod schema * Type alias for model parameter, inferred from its zod schema
*/ */
export type ModelParam = z.infer<typeof zModel>; export type ModelParam = z.infer<typeof zModel>;
/**
* Zod schema for VAE parameter
* TODO: Make this a dynamically generated enum?
*/
export const zVAE = z.string();
/**
* Type alias for model parameter, inferred from its zod schema
*/
export type VAEParam = z.infer<typeof zVAE>;
/** /**
* Validates/type-guards a value as a model parameter * Validates/type-guards a value as a model parameter
*/ */

125
invokeai/frontend/web/src/features/system/components/ModelManager/AddModel.tsx
View File

@ -1,125 +0,0 @@
import {
Button,
Flex,
Modal,
ModalBody,
ModalCloseButton,
ModalContent,
ModalFooter,
ModalHeader,
ModalOverlay,
Text,
useDisclosure,
} from '@chakra-ui/react';
import IAIButton from 'common/components/IAIButton';
import { FaArrowLeft, FaPlus } from 'react-icons/fa';
import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
import { useTranslation } from 'react-i18next';
import type { RootState } from 'app/store/store';
import { setAddNewModelUIOption } from 'features/ui/store/uiSlice';
import AddCheckpointModel from './AddCheckpointModel';
import AddDiffusersModel from './AddDiffusersModel';
import IAIIconButton from 'common/components/IAIIconButton';
function AddModelBox({
text,
onClick,
}: {
text: string;
onClick?: () => void;
}) {
return (
<Flex
position="relative"
width="50%"
height={40}
justifyContent="center"
alignItems="center"
onClick={onClick}
as={Button}
>
<Text fontWeight="bold">{text}</Text>
</Flex>
);
}
export default function AddModel() {
const { isOpen, onOpen, onClose } = useDisclosure();
const addNewModelUIOption = useAppSelector(
(state: RootState) => state.ui.addNewModelUIOption
);
const dispatch = useAppDispatch();
const { t } = useTranslation();
const addModelModalClose = () => {
onClose();
dispatch(setAddNewModelUIOption(null));
};
return (
<>
<IAIButton
aria-label={t('modelManager.addNewModel')}
tooltip={t('modelManager.addNewModel')}
onClick={onOpen}
size="sm"
>
<Flex columnGap={2} alignItems="center">
<FaPlus />
{t('modelManager.addNew')}
</Flex>
</IAIButton>
<Modal
isOpen={isOpen}
onClose={addModelModalClose}
size="3xl"
closeOnOverlayClick={false}
>
<ModalOverlay />
<ModalContent margin="auto">
<ModalHeader>{t('modelManager.addNewModel')} </ModalHeader>
{addNewModelUIOption !== null && (
<IAIIconButton
aria-label={t('common.back')}
tooltip={t('common.back')}
onClick={() => dispatch(setAddNewModelUIOption(null))}
position="absolute"
variant="ghost"
zIndex={1}
size="sm"
insetInlineEnd={12}
top={2}
icon={<FaArrowLeft />}
/>
)}
<ModalCloseButton />
<ModalBody>
{addNewModelUIOption == null && (
<Flex columnGap={4}>
<AddModelBox
text={t('modelManager.addCheckpointModel')}
onClick={() => dispatch(setAddNewModelUIOption('ckpt'))}
/>
<AddModelBox
text={t('modelManager.addDiffuserModel')}
onClick={() => dispatch(setAddNewModelUIOption('diffusers'))}
/>
</Flex>
)}
{addNewModelUIOption == 'ckpt' && <AddCheckpointModel />}
{addNewModelUIOption == 'diffusers' && <AddDiffusersModel />}
</ModalBody>
<ModalFooter />
</ModalContent>
</Modal>
</>
);
}

339
invokeai/frontend/web/src/features/system/components/ModelManager/CheckpointModelEdit.tsx
View File

@ -1,339 +0,0 @@
import { createSelector } from '@reduxjs/toolkit';
import IAIButton from 'common/components/IAIButton';
import IAIInput from 'common/components/IAIInput';
import IAINumberInput from 'common/components/IAINumberInput';
import { useEffect, useState } from 'react';
import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
import { systemSelector } from 'features/system/store/systemSelectors';
import {
Flex,
FormControl,
FormLabel,
HStack,
Text,
VStack,
} from '@chakra-ui/react';
// import { addNewModel } from 'app/socketio/actions';
import { Field, Formik } from 'formik';
import { useTranslation } from 'react-i18next';
import type { InvokeModelConfigProps } from 'app/types/invokeai';
import type { RootState } from 'app/store/store';
import type { FieldInputProps, FormikProps } from 'formik';
import { isEqual, pickBy } from 'lodash-es';
import ModelConvert from './ModelConvert';
import IAIFormHelperText from 'common/components/IAIForms/IAIFormHelperText';
import IAIFormErrorMessage from 'common/components/IAIForms/IAIFormErrorMessage';
import IAIForm from 'common/components/IAIForm';
const selector = createSelector(
[systemSelector],
(system) => {
const { openModel, model_list } = system;
return {
model_list,
openModel,
};
},
{
memoizeOptions: {
resultEqualityCheck: isEqual,
},
}
);
const MIN_MODEL_SIZE = 64;
const MAX_MODEL_SIZE = 2048;
export default function CheckpointModelEdit() {
const { openModel, model_list } = useAppSelector(selector);
const isProcessing = useAppSelector(
(state: RootState) => state.system.isProcessing
);
const dispatch = useAppDispatch();
const { t } = useTranslation();
const [editModelFormValues, setEditModelFormValues] =
useState<InvokeModelConfigProps>({
name: '',
description: '',
config: 'configs/stable-diffusion/v1-inference.yaml',
weights: '',
vae: '',
width: 512,
height: 512,
default: false,
format: 'ckpt',
});
useEffect(() => {
if (openModel) {
const retrievedModel = pickBy(model_list, (_val, key) => {
return isEqual(key, openModel);
});
setEditModelFormValues({
name: openModel,
description: retrievedModel[openModel]?.description,
config: retrievedModel[openModel]?.config,
weights: retrievedModel[openModel]?.weights,
vae: retrievedModel[openModel]?.vae,
width: retrievedModel[openModel]?.width,
height: retrievedModel[openModel]?.height,
default: retrievedModel[openModel]?.default,
format: 'ckpt',
});
}
}, [model_list, openModel]);
const editModelFormSubmitHandler = (values: InvokeModelConfigProps) => {
dispatch(
addNewModel({
...values,
width: Number(values.width),
height: Number(values.height),
})
);
};
return openModel ? (
<Flex flexDirection="column" rowGap={4} width="100%">
<Flex alignItems="center" gap={4} justifyContent="space-between">
<Text fontSize="lg" fontWeight="bold">
{openModel}
</Text>
<ModelConvert model={openModel} />
</Flex>
<Flex
flexDirection="column"
maxHeight={window.innerHeight - 270}
overflowY="scroll"
paddingInlineEnd={8}
>
<Formik
enableReinitialize={true}
initialValues={editModelFormValues}
onSubmit={editModelFormSubmitHandler}
>
{({ handleSubmit, errors, touched }) => (
<IAIForm onSubmit={handleSubmit}>
<VStack rowGap={2} alignItems="start">
{/* 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"
width="full"
/>
{!!errors.description && touched.description ? (
<IAIFormErrorMessage>
{errors.description}
</IAIFormErrorMessage>
) : (
<IAIFormHelperText>
{t('modelManager.descriptionValidationMsg')}
</IAIFormHelperText>
)}
</VStack>
</FormControl>
{/* Config */}
<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 ? (
<IAIFormErrorMessage>{errors.config}</IAIFormErrorMessage>
) : (
<IAIFormHelperText>
{t('modelManager.configValidationMsg')}
</IAIFormHelperText>
)}
</VStack>
</FormControl>
{/* Weights */}
<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 ? (
<IAIFormErrorMessage>
{errors.weights}
</IAIFormErrorMessage>
) : (
<IAIFormHelperText>
{t('modelManager.modelLocationValidationMsg')}
</IAIFormHelperText>
)}
</VStack>
</FormControl>
{/* VAE */}
<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 ? (
<IAIFormErrorMessage>{errors.vae}</IAIFormErrorMessage>
) : (
<IAIFormHelperText>
{t('modelManager.vaeLocationValidationMsg')}
</IAIFormHelperText>
)}
</VStack>
</FormControl>
<HStack width="100%">
{/* Width */}
<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 ? (
<IAIFormErrorMessage>
{errors.width}
</IAIFormErrorMessage>
) : (
<IAIFormHelperText>
{t('modelManager.widthValidationMsg')}
</IAIFormHelperText>
)}
</VStack>
</FormControl>
{/* Height */}
<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 ? (
<IAIFormErrorMessage>
{errors.height}
</IAIFormErrorMessage>
) : (
<IAIFormHelperText>
{t('modelManager.heightValidationMsg')}
</IAIFormHelperText>
)}
</VStack>
</FormControl>
</HStack>
<IAIButton
type="submit"
className="modal-close-btn"
isLoading={isProcessing}
>
{t('modelManager.updateModel')}
</IAIButton>
</VStack>
</IAIForm>
)}
</Formik>
</Flex>
</Flex>
) : (
<Flex
sx={{
width: '100%',
justifyContent: 'center',
alignItems: 'center',
borderRadius: 'base',
bg: 'base.900',
}}
>
<Text fontWeight={500}>Pick A Model To Edit</Text>
</Flex>
);
}

281
invokeai/frontend/web/src/features/system/components/ModelManager/DiffusersModelEdit.tsx
View File

@ -1,281 +0,0 @@
import { createSelector } from '@reduxjs/toolkit';
import IAIButton from 'common/components/IAIButton';
import IAIInput from 'common/components/IAIInput';
import { useEffect, useState } from 'react';
import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
import { systemSelector } from 'features/system/store/systemSelectors';
import { Flex, FormControl, FormLabel, Text, VStack } from '@chakra-ui/react';
// import { addNewModel } from 'app/socketio/actions';
import { Field, Formik } from 'formik';
import { useTranslation } from 'react-i18next';
import type { InvokeDiffusersModelConfigProps } from 'app/types/invokeai';
import type { RootState } from 'app/store/store';
import { isEqual, pickBy } from 'lodash-es';
import IAIFormHelperText from 'common/components/IAIForms/IAIFormHelperText';
import IAIFormErrorMessage from 'common/components/IAIForms/IAIFormErrorMessage';
import IAIForm from 'common/components/IAIForm';
const selector = createSelector(
[systemSelector],
(system) => {
const { openModel, model_list } = system;
return {
model_list,
openModel,
};
},
{
memoizeOptions: {
resultEqualityCheck: isEqual,
},
}
);
export default function DiffusersModelEdit() {
const { openModel, model_list } = useAppSelector(selector);
const isProcessing = useAppSelector(
(state: RootState) => state.system.isProcessing
);
const dispatch = useAppDispatch();
const { t } = useTranslation();
const [editModelFormValues, setEditModelFormValues] =
useState<InvokeDiffusersModelConfigProps>({
name: '',
description: '',
repo_id: '',
path: '',
vae: { repo_id: '', path: '' },
default: false,
format: 'diffusers',
});
useEffect(() => {
if (openModel) {
const retrievedModel = pickBy(model_list, (_val, key) => {
return isEqual(key, openModel);
});
setEditModelFormValues({
name: openModel,
description: retrievedModel[openModel]?.description,
path:
retrievedModel[openModel]?.path &&
retrievedModel[openModel]?.path !== 'None'
? retrievedModel[openModel]?.path
: '',
repo_id:
retrievedModel[openModel]?.repo_id &&
retrievedModel[openModel]?.repo_id !== 'None'
? retrievedModel[openModel]?.repo_id
: '',
vae: {
repo_id: retrievedModel[openModel]?.vae?.repo_id
? retrievedModel[openModel]?.vae?.repo_id
: '',
path: retrievedModel[openModel]?.vae?.path
? retrievedModel[openModel]?.vae?.path
: '',
},
default: retrievedModel[openModel]?.default,
format: 'diffusers',
});
}
}, [model_list, openModel]);
const editModelFormSubmitHandler = (
values: InvokeDiffusersModelConfigProps
) => {
const diffusersModelToEdit = values;
if (values.path === '') delete diffusersModelToEdit.path;
if (values.repo_id === '') delete diffusersModelToEdit.repo_id;
if (values.vae.path === '') delete diffusersModelToEdit.vae.path;
if (values.vae.repo_id === '') delete diffusersModelToEdit.vae.repo_id;
dispatch(addNewModel(values));
};
return openModel ? (
<Flex flexDirection="column" rowGap={4} width="100%">
<Flex alignItems="center">
<Text fontSize="lg" fontWeight="bold">
{openModel}
</Text>
</Flex>
<Flex flexDirection="column" overflowY="scroll" paddingInlineEnd={8}>
<Formik
enableReinitialize={true}
initialValues={editModelFormValues}
onSubmit={editModelFormSubmitHandler}
>
{({ handleSubmit, errors, touched }) => (
<IAIForm onSubmit={handleSubmit}>
<VStack rowGap={2} alignItems="start">
{/* 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"
width="full"
/>
{!!errors.description && touched.description ? (
<IAIFormErrorMessage>
{errors.description}
</IAIFormErrorMessage>
) : (
<IAIFormHelperText>
{t('modelManager.descriptionValidationMsg')}
</IAIFormHelperText>
)}
</VStack>
</FormControl>
{/* Path */}
<FormControl
isInvalid={!!errors.path && touched.path}
isRequired
>
<FormLabel htmlFor="path" fontSize="sm">
{t('modelManager.modelLocation')}
</FormLabel>
<VStack alignItems="start">
<Field
as={IAIInput}
id="path"
name="path"
type="text"
width="full"
/>
{!!errors.path && touched.path ? (
<IAIFormErrorMessage>{errors.path}</IAIFormErrorMessage>
) : (
<IAIFormHelperText>
{t('modelManager.modelLocationValidationMsg')}
</IAIFormHelperText>
)}
</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"
width="full"
/>
{!!errors.repo_id && touched.repo_id ? (
<IAIFormErrorMessage>
{errors.repo_id}
</IAIFormErrorMessage>
) : (
<IAIFormHelperText>
{t('modelManager.repoIDValidationMsg')}
</IAIFormHelperText>
)}
</VStack>
</FormControl>
{/* VAE Path */}
<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"
width="full"
/>
{!!errors.vae?.path && touched.vae?.path ? (
<IAIFormErrorMessage>
{errors.vae?.path}
</IAIFormErrorMessage>
) : (
<IAIFormHelperText>
{t('modelManager.vaeLocationValidationMsg')}
</IAIFormHelperText>
)}
</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"
width="full"
/>
{!!errors.vae?.repo_id && touched.vae?.repo_id ? (
<IAIFormErrorMessage>
{errors.vae?.repo_id}
</IAIFormErrorMessage>
) : (
<IAIFormHelperText>
{t('modelManager.vaeRepoIDValidationMsg')}
</IAIFormHelperText>
)}
</VStack>
</FormControl>
<IAIButton
type="submit"
className="modal-close-btn"
isLoading={isProcessing}
>
{t('modelManager.updateModel')}
</IAIButton>
</VStack>
</IAIForm>
)}
</Formik>
</Flex>
</Flex>
) : (
<Flex
sx={{
width: '100%',
justifyContent: 'center',
alignItems: 'center',
borderRadius: 'base',
bg: 'base.900',
}}
>
<Text fontWeight={'500'}>Pick A Model To Edit</Text>
</Flex>
);
}

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