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tidy(nodes): use canonical capitalizations

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psychedelicious 2024-03-06 19:42:47 +11:00
parent c57f6ee885
commit 132790eebe
8 changed files with 70 additions and 71 deletions
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26
invokeai/app/invocations/compel.py
View File

@ -20,7 +20,7 @@ from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
from invokeai.backend.util.devices import torch_dtype from invokeai.backend.util.devices import torch_dtype
from .baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output from .baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
from .model import ClipField from .model import CLIPField
# unconditioned: Optional[torch.Tensor] # unconditioned: Optional[torch.Tensor]
@ -46,7 +46,7 @@ class CompelInvocation(BaseInvocation):
description=FieldDescriptions.compel_prompt, description=FieldDescriptions.compel_prompt,
ui_component=UIComponent.Textarea, ui_component=UIComponent.Textarea,
) )
clip: ClipField = InputField( clip: CLIPField = InputField(
title="CLIP", title="CLIP",
description=FieldDescriptions.clip, description=FieldDescriptions.clip,
input=Input.Connection, input=Input.Connection,
@ -127,7 +127,7 @@ class SDXLPromptInvocationBase:
def run_clip_compel( def run_clip_compel(
self, self,
context: InvocationContext, context: InvocationContext,
clip_field: ClipField, clip_field: CLIPField,
prompt: str, prompt: str,
get_pooled: bool, get_pooled: bool,
lora_prefix: str, lora_prefix: str,
@ -253,8 +253,8 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
crop_left: int = InputField(default=0, description="") crop_left: int = InputField(default=0, description="")
target_width: int = InputField(default=1024, description="") target_width: int = InputField(default=1024, description="")
target_height: int = InputField(default=1024, description="") target_height: int = InputField(default=1024, description="")
clip: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection, title="CLIP 1") clip: CLIPField = InputField(description=FieldDescriptions.clip, input=Input.Connection, title="CLIP 1")
clip2: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection, title="CLIP 2") clip2: CLIPField = InputField(description=FieldDescriptions.clip, input=Input.Connection, title="CLIP 2")
@torch.no_grad() @torch.no_grad()
def invoke(self, context: InvocationContext) -> ConditioningOutput: def invoke(self, context: InvocationContext) -> ConditioningOutput:
@ -340,7 +340,7 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
crop_top: int = InputField(default=0, description="") crop_top: int = InputField(default=0, description="")
crop_left: int = InputField(default=0, description="") crop_left: int = InputField(default=0, description="")
aesthetic_score: float = InputField(default=6.0, description=FieldDescriptions.sdxl_aesthetic) aesthetic_score: float = InputField(default=6.0, description=FieldDescriptions.sdxl_aesthetic)
clip2: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection) clip2: CLIPField = InputField(description=FieldDescriptions.clip, input=Input.Connection)
@torch.no_grad() @torch.no_grad()
def invoke(self, context: InvocationContext) -> ConditioningOutput: def invoke(self, context: InvocationContext) -> ConditioningOutput:
@ -370,10 +370,10 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
@invocation_output("clip_skip_output") @invocation_output("clip_skip_output")
class ClipSkipInvocationOutput(BaseInvocationOutput): class CLIPSkipInvocationOutput(BaseInvocationOutput):
"""Clip skip node output""" """CLIP skip node output"""
clip: Optional[ClipField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP") clip: Optional[CLIPField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")
@invocation( @invocation(
@ -383,15 +383,15 @@ class ClipSkipInvocationOutput(BaseInvocationOutput):
category="conditioning", category="conditioning",
version="1.0.0", version="1.0.0",
) )
class ClipSkipInvocation(BaseInvocation): class CLIPSkipInvocation(BaseInvocation):
"""Skip layers in clip text_encoder model.""" """Skip layers in clip text_encoder model."""
clip: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection, title="CLIP") clip: CLIPField = InputField(description=FieldDescriptions.clip, input=Input.Connection, title="CLIP")
skipped_layers: int = InputField(default=0, ge=0, description=FieldDescriptions.skipped_layers) skipped_layers: int = InputField(default=0, ge=0, description=FieldDescriptions.skipped_layers)
def invoke(self, context: InvocationContext) -> ClipSkipInvocationOutput: def invoke(self, context: InvocationContext) -> CLIPSkipInvocationOutput:
self.clip.skipped_layers += self.skipped_layers self.clip.skipped_layers += self.skipped_layers
return ClipSkipInvocationOutput( return CLIPSkipInvocationOutput(
clip=self.clip, clip=self.clip,
) )

8
invokeai/app/invocations/latent.py
View File

@ -76,7 +76,7 @@ from .baseinvocation import (
invocation_output, invocation_output,
) )
from .controlnet_image_processors import ControlField from .controlnet_image_processors import ControlField
from .model import ModelField, UNetField, VaeField from .model import ModelField, UNetField, VAEField
if choose_torch_device() == torch.device("mps"): if choose_torch_device() == torch.device("mps"):
from torch import mps from torch import mps
@ -119,7 +119,7 @@ class SchedulerInvocation(BaseInvocation):
class CreateDenoiseMaskInvocation(BaseInvocation): class CreateDenoiseMaskInvocation(BaseInvocation):
"""Creates mask for denoising model run.""" """Creates mask for denoising model run."""
vae: VaeField = InputField(description=FieldDescriptions.vae, input=Input.Connection, ui_order=0) vae: VAEField = InputField(description=FieldDescriptions.vae, input=Input.Connection, ui_order=0)
image: Optional[ImageField] = InputField(default=None, description="Image which will be masked", ui_order=1) image: Optional[ImageField] = InputField(default=None, description="Image which will be masked", ui_order=1)
mask: ImageField = InputField(description="The mask to use when pasting", ui_order=2) mask: ImageField = InputField(description="The mask to use when pasting", ui_order=2)
tiled: bool = InputField(default=False, description=FieldDescriptions.tiled, ui_order=3) tiled: bool = InputField(default=False, description=FieldDescriptions.tiled, ui_order=3)
@ -832,7 +832,7 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
description=FieldDescriptions.latents, description=FieldDescriptions.latents,
input=Input.Connection, input=Input.Connection,
) )
vae: VaeField = InputField( vae: VAEField = InputField(
description=FieldDescriptions.vae, description=FieldDescriptions.vae,
input=Input.Connection, input=Input.Connection,
) )
@ -1010,7 +1010,7 @@ class ImageToLatentsInvocation(BaseInvocation):
image: ImageField = InputField( image: ImageField = InputField(
description="The image to encode", description="The image to encode",
) )
vae: VaeField = InputField( vae: VAEField = InputField(
description=FieldDescriptions.vae, description=FieldDescriptions.vae,
input=Input.Connection, input=Input.Connection,
) )

63
invokeai/app/invocations/model.py
View File

@ -29,20 +29,19 @@ class LoRAField(BaseModel):
class UNetField(BaseModel): class UNetField(BaseModel):
unet: ModelField = Field(description="Info to load unet submodel") unet: ModelField = Field(description="Info to load unet submodel")
scheduler: ModelField = Field(description="Info to load scheduler submodel") scheduler: ModelField = Field(description="Info to load scheduler submodel")
loras: List[LoRAField] = Field(description="Loras to apply on model loading") loras: List[LoRAField] = Field(description="LoRAs to apply on model loading")
seamless_axes: List[str] = Field(default_factory=list, description='Axes("x" and "y") to which apply seamless') seamless_axes: List[str] = Field(default_factory=list, description='Axes("x" and "y") to which apply seamless')
freeu_config: Optional[FreeUConfig] = Field(default=None, description="FreeU configuration") freeu_config: Optional[FreeUConfig] = Field(default=None, description="FreeU configuration")
class ClipField(BaseModel): class CLIPField(BaseModel):
tokenizer: ModelField = Field(description="Info to load tokenizer submodel") tokenizer: ModelField = Field(description="Info to load tokenizer submodel")
text_encoder: ModelField = Field(description="Info to load text_encoder submodel") text_encoder: ModelField = Field(description="Info to load text_encoder submodel")
skipped_layers: int = Field(description="Number of skipped layers in text_encoder") skipped_layers: int = Field(description="Number of skipped layers in text_encoder")
loras: List[LoRAField] = Field(description="Loras to apply on model loading") loras: List[LoRAField] = Field(description="LoRAs to apply on model loading")
class VaeField(BaseModel): class VAEField(BaseModel):
# TODO: better naming?
vae: ModelField = Field(description="Info to load vae submodel") vae: ModelField = Field(description="Info to load vae submodel")
seamless_axes: List[str] = Field(default_factory=list, description='Axes("x" and "y") to which apply seamless') seamless_axes: List[str] = Field(default_factory=list, description='Axes("x" and "y") to which apply seamless')
@ -58,14 +57,14 @@ class UNetOutput(BaseInvocationOutput):
class VAEOutput(BaseInvocationOutput): class VAEOutput(BaseInvocationOutput):
"""Base class for invocations that output a VAE field""" """Base class for invocations that output a VAE field"""
vae: VaeField = OutputField(description=FieldDescriptions.vae, title="VAE") vae: VAEField = OutputField(description=FieldDescriptions.vae, title="VAE")
@invocation_output("clip_output") @invocation_output("clip_output")
class CLIPOutput(BaseInvocationOutput): class CLIPOutput(BaseInvocationOutput):
"""Base class for invocations that output a CLIP field""" """Base class for invocations that output a CLIP field"""
clip: ClipField = OutputField(description=FieldDescriptions.clip, title="CLIP") clip: CLIPField = OutputField(description=FieldDescriptions.clip, title="CLIP")
@invocation_output("model_loader_output") @invocation_output("model_loader_output")
@ -101,21 +100,21 @@ class MainModelLoaderInvocation(BaseInvocation):
return ModelLoaderOutput( return ModelLoaderOutput(
unet=UNetField(unet=unet, scheduler=scheduler, loras=[]), unet=UNetField(unet=unet, scheduler=scheduler, loras=[]),
clip=ClipField(tokenizer=tokenizer, text_encoder=text_encoder, loras=[], skipped_layers=0), clip=CLIPField(tokenizer=tokenizer, text_encoder=text_encoder, loras=[], skipped_layers=0),
vae=VaeField(vae=vae), vae=VAEField(vae=vae),
) )
@invocation_output("lora_loader_output") @invocation_output("lora_loader_output")
class LoraLoaderOutput(BaseInvocationOutput): class LoRALoaderOutput(BaseInvocationOutput):
"""Model loader output""" """Model loader output"""
unet: Optional[UNetField] = OutputField(default=None, description=FieldDescriptions.unet, title="UNet") unet: Optional[UNetField] = OutputField(default=None, description=FieldDescriptions.unet, title="UNet")
clip: Optional[ClipField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP") clip: Optional[CLIPField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")
@invocation("lora_loader", title="LoRA", tags=["model"], category="model", version="1.0.1") @invocation("lora_loader", title="LoRA", tags=["model"], category="model", version="1.0.1")
class LoraLoaderInvocation(BaseInvocation): class LoRALoaderInvocation(BaseInvocation):
"""Apply selected lora to unet and text_encoder.""" """Apply selected lora to unet and text_encoder."""
lora: ModelField = InputField(description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA") lora: ModelField = InputField(description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA")
@ -126,26 +125,26 @@ class LoraLoaderInvocation(BaseInvocation):
input=Input.Connection, input=Input.Connection,
title="UNet", title="UNet",
) )
clip: Optional[ClipField] = InputField( clip: Optional[CLIPField] = InputField(
default=None, default=None,
description=FieldDescriptions.clip, description=FieldDescriptions.clip,
input=Input.Connection, input=Input.Connection,
title="CLIP", title="CLIP",
) )
def invoke(self, context: InvocationContext) -> LoraLoaderOutput: def invoke(self, context: InvocationContext) -> LoRALoaderOutput:
lora_key = self.lora.key lora_key = self.lora.key
if not context.models.exists(lora_key): if not context.models.exists(lora_key):
raise Exception(f"Unkown lora: {lora_key}!") raise Exception(f"Unkown lora: {lora_key}!")
if self.unet is not None and any(lora.lora.key == lora_key for lora in self.unet.loras): if self.unet is not None and any(lora.lora.key == lora_key for lora in self.unet.loras):
raise Exception(f'Lora "{lora_key}" already applied to unet') raise Exception(f'LoRA "{lora_key}" already applied to unet')
if self.clip is not None and any(lora.lora.key == lora_key for lora in self.clip.loras): if self.clip is not None and any(lora.lora.key == lora_key for lora in self.clip.loras):
raise Exception(f'Lora "{lora_key}" already applied to clip') raise Exception(f'LoRA "{lora_key}" already applied to clip')
output = LoraLoaderOutput() output = LoRALoaderOutput()
if self.unet is not None: if self.unet is not None:
output.unet = self.unet.model_copy(deep=True) output.unet = self.unet.model_copy(deep=True)
@ -169,12 +168,12 @@ class LoraLoaderInvocation(BaseInvocation):
@invocation_output("sdxl_lora_loader_output") @invocation_output("sdxl_lora_loader_output")
class SDXLLoraLoaderOutput(BaseInvocationOutput): class SDXLLoRALoaderOutput(BaseInvocationOutput):
"""SDXL LoRA Loader Output""" """SDXL LoRA Loader Output"""
unet: Optional[UNetField] = OutputField(default=None, description=FieldDescriptions.unet, title="UNet") unet: Optional[UNetField] = OutputField(default=None, description=FieldDescriptions.unet, title="UNet")
clip: Optional[ClipField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP 1") clip: Optional[CLIPField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP 1")
clip2: Optional[ClipField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP 2") clip2: Optional[CLIPField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP 2")
@invocation( @invocation(
@ -184,7 +183,7 @@ class SDXLLoraLoaderOutput(BaseInvocationOutput):
category="model", category="model",
version="1.0.1", version="1.0.1",
) )
class SDXLLoraLoaderInvocation(BaseInvocation): class SDXLLoRALoaderInvocation(BaseInvocation):
"""Apply selected lora to unet and text_encoder.""" """Apply selected lora to unet and text_encoder."""
lora: ModelField = InputField(description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA") lora: ModelField = InputField(description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA")
@ -195,35 +194,35 @@ class SDXLLoraLoaderInvocation(BaseInvocation):
input=Input.Connection, input=Input.Connection,
title="UNet", title="UNet",
) )
clip: Optional[ClipField] = InputField( clip: Optional[CLIPField] = InputField(
default=None, default=None,
description=FieldDescriptions.clip, description=FieldDescriptions.clip,
input=Input.Connection, input=Input.Connection,
title="CLIP 1", title="CLIP 1",
) )
clip2: Optional[ClipField] = InputField( clip2: Optional[CLIPField] = InputField(
default=None, default=None,
description=FieldDescriptions.clip, description=FieldDescriptions.clip,
input=Input.Connection, input=Input.Connection,
title="CLIP 2", title="CLIP 2",
) )
def invoke(self, context: InvocationContext) -> SDXLLoraLoaderOutput: def invoke(self, context: InvocationContext) -> SDXLLoRALoaderOutput:
lora_key = self.lora.key lora_key = self.lora.key
if not context.models.exists(lora_key): if not context.models.exists(lora_key):
raise Exception(f"Unknown lora: {lora_key}!") raise Exception(f"Unknown lora: {lora_key}!")
if self.unet is not None and any(lora.lora.key == lora_key for lora in self.unet.loras): if self.unet is not None and any(lora.lora.key == lora_key for lora in self.unet.loras):
raise Exception(f'Lora "{lora_key}" already applied to unet') raise Exception(f'LoRA "{lora_key}" already applied to unet')
if self.clip is not None and any(lora.lora.key == lora_key for lora in self.clip.loras): if self.clip is not None and any(lora.lora.key == lora_key for lora in self.clip.loras):
raise Exception(f'Lora "{lora_key}" already applied to clip') raise Exception(f'LoRA "{lora_key}" already applied to clip')
if self.clip2 is not None and any(lora.lora.key == lora_key for lora in self.clip2.loras): if self.clip2 is not None and any(lora.lora.key == lora_key for lora in self.clip2.loras):
raise Exception(f'Lora "{lora_key}" already applied to clip2') raise Exception(f'LoRA "{lora_key}" already applied to clip2')
output = SDXLLoraLoaderOutput() output = SDXLLoRALoaderOutput()
if self.unet is not None: if self.unet is not None:
output.unet = self.unet.model_copy(deep=True) output.unet = self.unet.model_copy(deep=True)
@ -256,7 +255,7 @@ class SDXLLoraLoaderInvocation(BaseInvocation):
@invocation("vae_loader", title="VAE", tags=["vae", "model"], category="model", version="1.0.1") @invocation("vae_loader", title="VAE", tags=["vae", "model"], category="model", version="1.0.1")
class VaeLoaderInvocation(BaseInvocation): class VAELoaderInvocation(BaseInvocation):
"""Loads a VAE model, outputting a VaeLoaderOutput""" """Loads a VAE model, outputting a VaeLoaderOutput"""
vae_model: ModelField = InputField( vae_model: ModelField = InputField(
@ -271,7 +270,7 @@ class VaeLoaderInvocation(BaseInvocation):
if not context.models.exists(key): if not context.models.exists(key):
raise Exception(f"Unkown vae: {key}!") raise Exception(f"Unkown vae: {key}!")
return VAEOutput(vae=VaeField(vae=self.vae_model)) return VAEOutput(vae=VAEField(vae=self.vae_model))
@invocation_output("seamless_output") @invocation_output("seamless_output")
@ -279,7 +278,7 @@ class SeamlessModeOutput(BaseInvocationOutput):
"""Modified Seamless Model output""" """Modified Seamless Model output"""
unet: Optional[UNetField] = OutputField(default=None, description=FieldDescriptions.unet, title="UNet") unet: Optional[UNetField] = OutputField(default=None, description=FieldDescriptions.unet, title="UNet")
vae: Optional[VaeField] = OutputField(default=None, description=FieldDescriptions.vae, title="VAE") vae: Optional[VAEField] = OutputField(default=None, description=FieldDescriptions.vae, title="VAE")
@invocation( @invocation(
@ -298,7 +297,7 @@ class SeamlessModeInvocation(BaseInvocation):
input=Input.Connection, input=Input.Connection,
title="UNet", title="UNet",
) )
vae: Optional[VaeField] = InputField( vae: Optional[VAEField] = InputField(
default=None, default=None,
description=FieldDescriptions.vae_model, description=FieldDescriptions.vae_model,
input=Input.Connection, input=Input.Connection,

22
invokeai/app/invocations/sdxl.py
View File

@ -8,7 +8,7 @@ from .baseinvocation import (
invocation, invocation,
invocation_output, invocation_output,
) )
from .model import ClipField, ModelField, UNetField, VaeField from .model import CLIPField, ModelField, UNetField, VAEField
@invocation_output("sdxl_model_loader_output") @invocation_output("sdxl_model_loader_output")
@ -16,9 +16,9 @@ class SDXLModelLoaderOutput(BaseInvocationOutput):
"""SDXL base model loader output""" """SDXL base model loader output"""
unet: UNetField = OutputField(description=FieldDescriptions.unet, title="UNet") unet: UNetField = OutputField(description=FieldDescriptions.unet, title="UNet")
clip: ClipField = OutputField(description=FieldDescriptions.clip, title="CLIP 1") clip: CLIPField = OutputField(description=FieldDescriptions.clip, title="CLIP 1")
clip2: ClipField = OutputField(description=FieldDescriptions.clip, title="CLIP 2") clip2: CLIPField = OutputField(description=FieldDescriptions.clip, title="CLIP 2")
vae: VaeField = OutputField(description=FieldDescriptions.vae, title="VAE") vae: VAEField = OutputField(description=FieldDescriptions.vae, title="VAE")
@invocation_output("sdxl_refiner_model_loader_output") @invocation_output("sdxl_refiner_model_loader_output")
@ -26,8 +26,8 @@ class SDXLRefinerModelLoaderOutput(BaseInvocationOutput):
"""SDXL refiner model loader output""" """SDXL refiner model loader output"""
unet: UNetField = OutputField(description=FieldDescriptions.unet, title="UNet") unet: UNetField = OutputField(description=FieldDescriptions.unet, title="UNet")
clip2: ClipField = OutputField(description=FieldDescriptions.clip, title="CLIP 2") clip2: CLIPField = OutputField(description=FieldDescriptions.clip, title="CLIP 2")
vae: VaeField = OutputField(description=FieldDescriptions.vae, title="VAE") vae: VAEField = OutputField(description=FieldDescriptions.vae, title="VAE")
@invocation("sdxl_model_loader", title="SDXL Main Model", tags=["model", "sdxl"], category="model", version="1.0.1") @invocation("sdxl_model_loader", title="SDXL Main Model", tags=["model", "sdxl"], category="model", version="1.0.1")
@ -56,9 +56,9 @@ class SDXLModelLoaderInvocation(BaseInvocation):
return SDXLModelLoaderOutput( return SDXLModelLoaderOutput(
unet=UNetField(unet=unet, scheduler=scheduler, loras=[]), unet=UNetField(unet=unet, scheduler=scheduler, loras=[]),
clip=ClipField(tokenizer=tokenizer, text_encoder=text_encoder, loras=[], skipped_layers=0), clip=CLIPField(tokenizer=tokenizer, text_encoder=text_encoder, loras=[], skipped_layers=0),
clip2=ClipField(tokenizer=tokenizer2, text_encoder=text_encoder2, loras=[], skipped_layers=0), clip2=CLIPField(tokenizer=tokenizer2, text_encoder=text_encoder2, loras=[], skipped_layers=0),
vae=VaeField(vae=vae), vae=VAEField(vae=vae),
) )
@ -92,6 +92,6 @@ class SDXLRefinerModelLoaderInvocation(BaseInvocation):
return SDXLRefinerModelLoaderOutput( return SDXLRefinerModelLoaderOutput(
unet=UNetField(unet=unet, scheduler=scheduler, loras=[]), unet=UNetField(unet=unet, scheduler=scheduler, loras=[]),
clip2=ClipField(tokenizer=tokenizer2, text_encoder=text_encoder2, loras=[], skipped_layers=0), clip2=CLIPField(tokenizer=tokenizer2, text_encoder=text_encoder2, loras=[], skipped_layers=0),
vae=VaeField(vae=vae), vae=VAEField(vae=vae),
) )

2
invokeai/backend/model_manager/config.py
View File

@ -310,7 +310,7 @@ class IPAdapterConfig(ModelConfigBase):
class CLIPVisionDiffusersConfig(ModelConfigBase): class CLIPVisionDiffusersConfig(ModelConfigBase):
"""Model config for ClipVision.""" """Model config for CLIPVision."""
type: Literal[ModelType.CLIPVision] = ModelType.CLIPVision type: Literal[ModelType.CLIPVision] = ModelType.CLIPVision
format: Literal[ModelFormat.Diffusers] format: Literal[ModelFormat.Diffusers]

2
invokeai/backend/model_manager/load/model_loaders/lora.py
View File

@ -24,7 +24,7 @@ from .. import ModelLoader, ModelLoaderRegistry
@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.LoRA, format=ModelFormat.Diffusers) @ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.LoRA, format=ModelFormat.Diffusers)
@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.LoRA, format=ModelFormat.LyCORIS) @ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.LoRA, format=ModelFormat.LyCORIS)
class LoraLoader(ModelLoader): class LoRALoader(ModelLoader):
"""Class to load LoRA models.""" """Class to load LoRA models."""
# We cheat a little bit to get access to the model base # We cheat a little bit to get access to the model base

2
invokeai/backend/model_manager/load/model_loaders/vae.py
View File

@ -23,7 +23,7 @@ from .generic_diffusers import GenericDiffusersLoader
@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.VAE, format=ModelFormat.Diffusers) @ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.VAE, format=ModelFormat.Diffusers)
@ModelLoaderRegistry.register(base=BaseModelType.StableDiffusion1, type=ModelType.VAE, format=ModelFormat.Checkpoint) @ModelLoaderRegistry.register(base=BaseModelType.StableDiffusion1, type=ModelType.VAE, format=ModelFormat.Checkpoint)
@ModelLoaderRegistry.register(base=BaseModelType.StableDiffusion2, type=ModelType.VAE, format=ModelFormat.Checkpoint) @ModelLoaderRegistry.register(base=BaseModelType.StableDiffusion2, type=ModelType.VAE, format=ModelFormat.Checkpoint)
class VaeLoader(GenericDiffusersLoader): class VAELoader(GenericDiffusersLoader):
"""Class to load VAE models.""" """Class to load VAE models."""
def _needs_conversion(self, config: AnyModelConfig, model_path: Path, dest_path: Path) -> bool: def _needs_conversion(self, config: AnyModelConfig, model_path: Path, dest_path: Path) -> bool:

16
invokeai/invocation_api/__init__.py
View File

@ -33,15 +33,15 @@ from invokeai.app.invocations.fields import (
from invokeai.app.invocations.latent import SchedulerOutput from invokeai.app.invocations.latent import SchedulerOutput
from invokeai.app.invocations.metadata import MetadataItemField, MetadataItemOutput, MetadataOutput from invokeai.app.invocations.metadata import MetadataItemField, MetadataItemOutput, MetadataOutput
from invokeai.app.invocations.model import ( from invokeai.app.invocations.model import (
ClipField, CLIPField,
CLIPOutput, CLIPOutput,
LoraLoaderOutput, LoRALoaderOutput,
ModelField, ModelField,
ModelLoaderOutput, ModelLoaderOutput,
SDXLLoraLoaderOutput, SDXLLoRALoaderOutput,
UNetField, UNetField,
UNetOutput, UNetOutput,
VaeField, VAEField,
VAEOutput, VAEOutput,
) )
from invokeai.app.invocations.primitives import ( from invokeai.app.invocations.primitives import (
@ -116,14 +116,14 @@ __all__ = [
# invokeai.app.invocations.model # invokeai.app.invocations.model
"ModelField", "ModelField",
"UNetField", "UNetField",
"ClipField", "CLIPField",
"VaeField", "VAEField",
"UNetOutput", "UNetOutput",
"VAEOutput", "VAEOutput",
"CLIPOutput", "CLIPOutput",
"ModelLoaderOutput", "ModelLoaderOutput",
"LoraLoaderOutput", "LoRALoaderOutput",
"SDXLLoraLoaderOutput", "SDXLLoRALoaderOutput",
# invokeai.app.invocations.primitives # invokeai.app.invocations.primitives
"BooleanCollectionOutput", "BooleanCollectionOutput",
"BooleanOutput", "BooleanOutput",