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feat: node editor

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squashed rebase on main after backendd refactor
This commit is contained in:
psychedelicious
2023-08-14 13:23:09 +10:00
parent d6c9bf5b38
commit f49fc7fb55
188 changed files with 8541 additions and 4660 deletions
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139
invokeai/app/invocations/model.py
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@ -4,7 +4,18 @@ from typing import List, Literal, Optional, Union
from pydantic import BaseModel, Field
from ...backend.model_management import BaseModelType, ModelType, SubModelType
from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationConfig, InvocationContext
from .baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
FieldDescriptions,
InputField,
Input,
InvocationContext,
OutputField,
UITypeHint,
tags,
title,
)
class ModelInfo(BaseModel):
@ -39,13 +50,11 @@ class VaeField(BaseModel):
class ModelLoaderOutput(BaseInvocationOutput):
"""Model loader output"""
# fmt: off
type: Literal["model_loader_output"] = "model_loader_output"
unet: UNetField = Field(default=None, description="UNet submodel")
clip: ClipField = Field(default=None, description="Tokenizer and text_encoder submodels")
vae: VaeField = Field(default=None, description="Vae submodel")
# fmt: on
unet: UNetField = OutputField(description=FieldDescriptions.unet, title="UNet")
clip: ClipField = OutputField(description=FieldDescriptions.clip, title="CLIP")
vae: VaeField = OutputField(description=FieldDescriptions.vae, title="VAE")
class MainModelField(BaseModel):
@ -63,24 +72,17 @@ class LoRAModelField(BaseModel):
base_model: BaseModelType = Field(description="Base model")
@title("Main Model Loader")
@tags("model")
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")
# Inputs
model: MainModelField = InputField(description=FieldDescriptions.main_model, input=Input.Direct)
# TODO: precision?
# Schema customisation
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Model Loader",
"tags": ["model", "loader"],
"type_hints": {"model": "model"},
},
}
def invoke(self, context: InvocationContext) -> ModelLoaderOutput:
base_model = self.model.base_model
model_name = self.model.model_name
@ -155,22 +157,6 @@ class MainModelLoaderInvocation(BaseInvocation):
loras=[],
skipped_layers=0,
),
clip2=ClipField(
tokenizer=ModelInfo(
model_name=model_name,
base_model=base_model,
model_type=model_type,
submodel=SubModelType.Tokenizer2,
),
text_encoder=ModelInfo(
model_name=model_name,
base_model=base_model,
model_type=model_type,
submodel=SubModelType.TextEncoder2,
),
loras=[],
skipped_layers=0,
),
vae=VaeField(
vae=ModelInfo(
model_name=model_name,
@ -188,30 +174,27 @@ class LoraLoaderOutput(BaseInvocationOutput):
# fmt: off
type: Literal["lora_loader_output"] = "lora_loader_output"
unet: Optional[UNetField] = Field(default=None, description="UNet submodel")
clip: Optional[ClipField] = Field(default=None, description="Tokenizer and text_encoder submodels")
unet: Optional[UNetField] = OutputField(default=None, description=FieldDescriptions.unet, title="UNet")
clip: Optional[ClipField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")
# fmt: on
@title("LoRA Loader")
@tags("lora", "model")
class LoraLoaderInvocation(BaseInvocation):
"""Apply selected lora to unet and text_encoder."""
type: Literal["lora_loader"] = "lora_loader"
lora: Union[LoRAModelField, None] = Field(default=None, description="Lora model name")
weight: float = Field(default=0.75, description="With what weight to apply lora")
unet: Optional[UNetField] = Field(description="UNet model for applying lora")
clip: Optional[ClipField] = Field(description="Clip model for applying lora")
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Lora Loader",
"tags": ["lora", "loader"],
"type_hints": {"lora": "lora_model"},
},
}
# Inputs
lora: LoRAModelField = InputField(description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA")
weight: float = InputField(default=0.75, description=FieldDescriptions.lora_weight)
unet: Optional[UNetField] = InputField(
default=None, description=FieldDescriptions.unet, input=Input.Connection, title="UNet"
)
clip: Optional[ClipField] = InputField(
default=None, description=FieldDescriptions.clip, input=Input.Connection, title="CLIP"
)
def invoke(self, context: InvocationContext) -> LoraLoaderOutput:
if self.lora is None:
@ -263,37 +246,35 @@ class LoraLoaderInvocation(BaseInvocation):
class SDXLLoraLoaderOutput(BaseInvocationOutput):
"""Model loader output"""
"""SDXL LoRA Loader Output"""
# fmt: off
type: Literal["sdxl_lora_loader_output"] = "sdxl_lora_loader_output"
unet: Optional[UNetField] = Field(default=None, description="UNet submodel")
clip: Optional[ClipField] = Field(default=None, description="Tokenizer and text_encoder submodels")
clip2: Optional[ClipField] = Field(default=None, description="Tokenizer2 and text_encoder2 submodels")
unet: Optional[UNetField] = OutputField(default=None, description=FieldDescriptions.unet, title="UNet")
clip: Optional[ClipField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP 1")
clip2: Optional[ClipField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP 2")
# fmt: on
@title("SDXL LoRA Loader")
@tags("sdxl", "lora", "model")
class SDXLLoraLoaderInvocation(BaseInvocation):
"""Apply selected lora to unet and text_encoder."""
type: Literal["sdxl_lora_loader"] = "sdxl_lora_loader"
lora: Union[LoRAModelField, None] = Field(default=None, description="Lora model name")
weight: float = Field(default=0.75, description="With what weight to apply lora")
unet: Optional[UNetField] = Field(description="UNet model for applying lora")
clip: Optional[ClipField] = Field(description="Clip model for applying lora")
clip2: Optional[ClipField] = Field(description="Clip2 model for applying lora")
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "SDXL Lora Loader",
"tags": ["lora", "loader"],
"type_hints": {"lora": "lora_model"},
},
}
lora: LoRAModelField = InputField(description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA")
weight: float = Field(default=0.75, description=FieldDescriptions.lora_weight)
unet: Optional[UNetField] = Field(
default=None, description=FieldDescriptions.unet, input=Input.Connection, title="UNET"
)
clip: Optional[ClipField] = Field(
default=None, description=FieldDescriptions.clip, input=Input.Connection, title="CLIP 1"
)
clip2: Optional[ClipField] = Field(
default=None, description=FieldDescriptions.clip, input=Input.Connection, title="CLIP 2"
)
def invoke(self, context: InvocationContext) -> SDXLLoraLoaderOutput:
if self.lora is None:
@ -369,29 +350,23 @@ class VAEModelField(BaseModel):
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
# Outputs
vae: VaeField = OutputField(description=FieldDescriptions.vae, title="VAE")
@title("VAE Loader")
@tags("vae", "model")
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"},
},
}
# Inputs
vae_model: VAEModelField = InputField(
description=FieldDescriptions.vae_model, input=Input.Direct, ui_type_hint=UITypeHint.VaeModelField, title="VAE"
)
def invoke(self, context: InvocationContext) -> VaeLoaderOutput:
base_model = self.vae_model.base_model