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feat(nodes): make all ModelIdentifierField inputs accept connections

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psychedelicious
2024-05-17 20:15:04 +10:00
parent 85a5a7c47a
commit fe7ed72c9c
5 changed files with 21 additions and 26 deletions
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22
invokeai/app/invocations/model.py
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@ -98,14 +98,12 @@ class ModelLoaderOutput(UNetOutput, CLIPOutput, VAEOutput):
title="Main Model",
tags=["model"],
category="model",
version="1.0.2",
version="1.0.3",
)
class MainModelLoaderInvocation(BaseInvocation):
"""Loads a main model, outputting its submodels."""
model: ModelIdentifierField = InputField(
description=FieldDescriptions.main_model, input=Input.Direct, ui_type=UIType.MainModel
)
model: ModelIdentifierField = InputField(description=FieldDescriptions.main_model, ui_type=UIType.MainModel)
# TODO: precision?
def invoke(self, context: InvocationContext) -> ModelLoaderOutput:
@ -134,12 +132,12 @@ class LoRALoaderOutput(BaseInvocationOutput):
clip: Optional[CLIPField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")
@invocation("lora_loader", title="LoRA", tags=["model"], category="model", version="1.0.2")
@invocation("lora_loader", title="LoRA", tags=["model"], category="model", version="1.0.3")
class LoRALoaderInvocation(BaseInvocation):
"""Apply selected lora to unet and text_encoder."""
lora: ModelIdentifierField = InputField(
description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA", ui_type=UIType.LoRAModel
description=FieldDescriptions.lora_model, title="LoRA", ui_type=UIType.LoRAModel
)
weight: float = InputField(default=0.75, description=FieldDescriptions.lora_weight)
unet: Optional[UNetField] = InputField(
@ -197,12 +195,12 @@ class LoRASelectorOutput(BaseInvocationOutput):
lora: LoRAField = OutputField(description="LoRA model and weight", title="LoRA")
@invocation("lora_selector", title="LoRA Selector", tags=["model"], category="model", version="1.0.0")
@invocation("lora_selector", title="LoRA Selector", tags=["model"], category="model", version="1.0.1")
class LoRASelectorInvocation(BaseInvocation):
"""Selects a LoRA model and weight."""
lora: ModelIdentifierField = InputField(
description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA", ui_type=UIType.LoRAModel
description=FieldDescriptions.lora_model, title="LoRA", ui_type=UIType.LoRAModel
)
weight: float = InputField(default=0.75, description=FieldDescriptions.lora_weight)
@ -273,13 +271,13 @@ class SDXLLoRALoaderOutput(BaseInvocationOutput):
title="SDXL LoRA",
tags=["lora", "model"],
category="model",
version="1.0.2",
version="1.0.3",
)
class SDXLLoRALoaderInvocation(BaseInvocation):
"""Apply selected lora to unet and text_encoder."""
lora: ModelIdentifierField = InputField(
description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA", ui_type=UIType.LoRAModel
description=FieldDescriptions.lora_model, title="LoRA", ui_type=UIType.LoRAModel
)
weight: float = InputField(default=0.75, description=FieldDescriptions.lora_weight)
unet: Optional[UNetField] = InputField(
@ -414,12 +412,12 @@ class SDXLLoRACollectionLoader(BaseInvocation):
return output
@invocation("vae_loader", title="VAE", tags=["vae", "model"], category="model", version="1.0.2")
@invocation("vae_loader", title="VAE", tags=["vae", "model"], category="model", version="1.0.3")
class VAELoaderInvocation(BaseInvocation):
"""Loads a VAE model, outputting a VaeLoaderOutput"""
vae_model: ModelIdentifierField = InputField(
description=FieldDescriptions.vae_model, input=Input.Direct, title="VAE", ui_type=UIType.VAEModel
description=FieldDescriptions.vae_model, title="VAE", ui_type=UIType.VAEModel
)
def invoke(self, context: InvocationContext) -> VAEOutput: