mirror of
https://github.com/invoke-ai/InvokeAI
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Merge branch 'main' into feat/nodes/freeu
This commit is contained in:
Millun Atluri
GitHub
@ -1,7 +1,7 @@
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import copy
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from typing import List, Optional
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from pydantic import BaseModel, Field
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from pydantic import BaseModel, ConfigDict, Field
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from invokeai.app.invocations.shared import FreeUConfig
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@ -26,6 +26,8 @@ class ModelInfo(BaseModel):
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model_type: ModelType = Field(description="Info to load submodel")
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submodel: Optional[SubModelType] = Field(default=None, description="Info to load submodel")
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model_config = ConfigDict(protected_namespaces=())
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class LoraInfo(ModelInfo):
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weight: float = Field(description="Lora's weight which to use when apply to model")
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@ -87,6 +89,8 @@ class MainModelField(BaseModel):
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base_model: BaseModelType = Field(description="Base model")
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model_type: ModelType = Field(description="Model Type")
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model_config = ConfigDict(protected_namespaces=())
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class LoRAModelField(BaseModel):
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"""LoRA model field"""
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@ -94,8 +98,16 @@ class LoRAModelField(BaseModel):
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model_name: str = Field(description="Name of the LoRA model")
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base_model: BaseModelType = Field(description="Base model")
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model_config = ConfigDict(protected_namespaces=())
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@invocation("main_model_loader", title="Main Model", tags=["model"], category="model", version="1.0.0")
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@invocation(
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"main_model_loader",
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title="Main Model",
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tags=["model"],
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category="model",
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version="1.0.0",
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)
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class MainModelLoaderInvocation(BaseInvocation):
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"""Loads a main model, outputting its submodels."""
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@ -202,10 +214,16 @@ class LoraLoaderInvocation(BaseInvocation):
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lora: LoRAModelField = InputField(description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA")
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weight: float = InputField(default=0.75, description=FieldDescriptions.lora_weight)
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unet: Optional[UNetField] = InputField(
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default=None, description=FieldDescriptions.unet, input=Input.Connection, title="UNet"
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default=None,
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description=FieldDescriptions.unet,
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input=Input.Connection,
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title="UNet",
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)
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clip: Optional[ClipField] = InputField(
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default=None, description=FieldDescriptions.clip, input=Input.Connection, title="CLIP"
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default=None,
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description=FieldDescriptions.clip,
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input=Input.Connection,
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title="CLIP",
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)
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def invoke(self, context: InvocationContext) -> LoraLoaderOutput:
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@ -266,20 +284,35 @@ class SDXLLoraLoaderOutput(BaseInvocationOutput):
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clip2: Optional[ClipField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP 2")
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@invocation("sdxl_lora_loader", title="SDXL LoRA", tags=["lora", "model"], category="model", version="1.0.0")
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@invocation(
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"sdxl_lora_loader",
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title="SDXL LoRA",
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tags=["lora", "model"],
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category="model",
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version="1.0.0",
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)
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class SDXLLoraLoaderInvocation(BaseInvocation):
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"""Apply selected lora to unet and text_encoder."""
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lora: LoRAModelField = InputField(description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA")
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weight: float = InputField(default=0.75, description=FieldDescriptions.lora_weight)
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unet: Optional[UNetField] = InputField(
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default=None, description=FieldDescriptions.unet, input=Input.Connection, title="UNet"
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default=None,
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description=FieldDescriptions.unet,
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input=Input.Connection,
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title="UNet",
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)
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clip: Optional[ClipField] = InputField(
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default=None, description=FieldDescriptions.clip, input=Input.Connection, title="CLIP 1"
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default=None,
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description=FieldDescriptions.clip,
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input=Input.Connection,
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title="CLIP 1",
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)
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clip2: Optional[ClipField] = InputField(
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default=None, description=FieldDescriptions.clip, input=Input.Connection, title="CLIP 2"
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default=None,
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description=FieldDescriptions.clip,
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input=Input.Connection,
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title="CLIP 2",
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)
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def invoke(self, context: InvocationContext) -> SDXLLoraLoaderOutput:
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@ -352,13 +385,18 @@ class VAEModelField(BaseModel):
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model_name: str = Field(description="Name of the model")
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base_model: BaseModelType = Field(description="Base model")
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model_config = ConfigDict(protected_namespaces=())
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@invocation("vae_loader", title="VAE", tags=["vae", "model"], category="model", version="1.0.0")
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class VaeLoaderInvocation(BaseInvocation):
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"""Loads a VAE model, outputting a VaeLoaderOutput"""
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vae_model: VAEModelField = InputField(
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description=FieldDescriptions.vae_model, input=Input.Direct, ui_type=UIType.VaeModel, title="VAE"
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description=FieldDescriptions.vae_model,
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input=Input.Direct,
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ui_type=UIType.VaeModel,
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title="VAE",
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)
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def invoke(self, context: InvocationContext) -> VAEOutput:
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@ -387,19 +425,31 @@ class VaeLoaderInvocation(BaseInvocation):
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class SeamlessModeOutput(BaseInvocationOutput):
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"""Modified Seamless Model output"""
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unet: Optional[UNetField] = OutputField(description=FieldDescriptions.unet, title="UNet")
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vae: Optional[VaeField] = OutputField(description=FieldDescriptions.vae, title="VAE")
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unet: Optional[UNetField] = OutputField(default=None, description=FieldDescriptions.unet, title="UNet")
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vae: Optional[VaeField] = OutputField(default=None, description=FieldDescriptions.vae, title="VAE")
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@invocation("seamless", title="Seamless", tags=["seamless", "model"], category="model", version="1.0.0")
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@invocation(
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"seamless",
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title="Seamless",
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tags=["seamless", "model"],
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category="model",
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version="1.0.0",
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)
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class SeamlessModeInvocation(BaseInvocation):
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"""Applies the seamless transformation to the Model UNet and VAE."""
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unet: Optional[UNetField] = InputField(
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default=None, description=FieldDescriptions.unet, input=Input.Connection, title="UNet"
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default=None,
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description=FieldDescriptions.unet,
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input=Input.Connection,
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title="UNet",
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)
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vae: Optional[VaeField] = InputField(
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default=None, description=FieldDescriptions.vae_model, input=Input.Connection, title="VAE"
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default=None,
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description=FieldDescriptions.vae_model,
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input=Input.Connection,
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title="VAE",
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)
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seamless_y: bool = InputField(default=True, input=Input.Any, description="Specify whether Y axis is seamless")
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seamless_x: bool = InputField(default=True, input=Input.Any, description="Specify whether X axis is seamless")
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