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merge with main and resolve conflicts

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This commit is contained in:
Lincoln Stein
2024-05-27 22:20:34 -04:00
parent 987ee704a1 ddff9b4584
commit 34e1eb19f9
256 changed files with 9360 additions and 6061 deletions
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45
invokeai/app/invocations/compel.py
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@ -65,11 +65,7 @@ class CompelInvocation(BaseInvocation):
@torch.no_grad()
def invoke(self, context: InvocationContext) -> ConditioningOutput:
tokenizer_info = context.models.load(self.clip.tokenizer)
tokenizer_model = tokenizer_info.model
assert isinstance(tokenizer_model, CLIPTokenizer)
text_encoder_info = context.models.load(self.clip.text_encoder)
text_encoder_model = text_encoder_info.model
assert isinstance(text_encoder_model, CLIPTextModel)
def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
for lora in self.clip.loras:
@ -84,19 +80,21 @@ class CompelInvocation(BaseInvocation):
ti_list = generate_ti_list(self.prompt, text_encoder_info.config.base, context)
with (
ModelPatcher.apply_ti(tokenizer_model, text_encoder_model, ti_list) as (
tokenizer,
ti_manager,
),
# apply all patches while the model is on the target device
text_encoder_info as text_encoder,
# Apply the LoRA after text_encoder has been moved to its target device for faster patching.
tokenizer_info as tokenizer,
ModelPatcher.apply_lora_text_encoder(text_encoder, _lora_loader()),
# Apply CLIP Skip after LoRA to prevent LoRA application from failing on skipped layers.
ModelPatcher.apply_clip_skip(text_encoder_model, self.clip.skipped_layers),
ModelPatcher.apply_clip_skip(text_encoder, self.clip.skipped_layers),
ModelPatcher.apply_ti(tokenizer, text_encoder, ti_list) as (
patched_tokenizer,
ti_manager,
),
):
assert isinstance(text_encoder, CLIPTextModel)
assert isinstance(tokenizer, CLIPTokenizer)
compel = Compel(
tokenizer=tokenizer,
tokenizer=patched_tokenizer,
text_encoder=text_encoder,
textual_inversion_manager=ti_manager,
dtype_for_device_getter=TorchDevice.choose_torch_dtype,
@ -106,7 +104,7 @@ class CompelInvocation(BaseInvocation):
conjunction = Compel.parse_prompt_string(self.prompt)
if context.config.get().log_tokenization:
log_tokenization_for_conjunction(conjunction, tokenizer)
log_tokenization_for_conjunction(conjunction, patched_tokenizer)
c, _options = compel.build_conditioning_tensor_for_conjunction(conjunction)
@ -136,11 +134,7 @@ class SDXLPromptInvocationBase:
zero_on_empty: bool,
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
tokenizer_info = context.models.load(clip_field.tokenizer)
tokenizer_model = tokenizer_info.model
assert isinstance(tokenizer_model, CLIPTokenizer)
text_encoder_info = context.models.load(clip_field.text_encoder)
text_encoder_model = text_encoder_info.model
assert isinstance(text_encoder_model, (CLIPTextModel, CLIPTextModelWithProjection))
# return zero on empty
if prompt == "" and zero_on_empty:
@ -177,20 +171,23 @@ class SDXLPromptInvocationBase:
ti_list = generate_ti_list(prompt, text_encoder_info.config.base, context)
with (
ModelPatcher.apply_ti(tokenizer_model, text_encoder_model, ti_list) as (
tokenizer,
ti_manager,
),
# apply all patches while the model is on the target device
text_encoder_info as text_encoder,
# Apply the LoRA after text_encoder has been moved to its target device for faster patching.
tokenizer_info as tokenizer,
ModelPatcher.apply_lora(text_encoder, _lora_loader(), lora_prefix),
# Apply CLIP Skip after LoRA to prevent LoRA application from failing on skipped layers.
ModelPatcher.apply_clip_skip(text_encoder_model, clip_field.skipped_layers),
ModelPatcher.apply_clip_skip(text_encoder, clip_field.skipped_layers),
ModelPatcher.apply_ti(tokenizer, text_encoder, ti_list) as (
patched_tokenizer,
ti_manager,
),
):
assert isinstance(text_encoder, (CLIPTextModel, CLIPTextModelWithProjection))
assert isinstance(tokenizer, CLIPTokenizer)
text_encoder = cast(CLIPTextModel, text_encoder)
compel = Compel(
tokenizer=tokenizer,
tokenizer=patched_tokenizer,
text_encoder=text_encoder,
textual_inversion_manager=ti_manager,
dtype_for_device_getter=TorchDevice.choose_torch_dtype,
@ -203,7 +200,7 @@ class SDXLPromptInvocationBase:
if context.config.get().log_tokenization:
# TODO: better logging for and syntax
log_tokenization_for_conjunction(conjunction, tokenizer)
log_tokenization_for_conjunction(conjunction, patched_tokenizer)
# TODO: ask for optimizations? to not run text_encoder twice
c, _options = compel.build_conditioning_tensor_for_conjunction(conjunction)

5
invokeai/app/invocations/controlnet_image_processors.py
View File

@ -25,7 +25,6 @@ from pydantic import BaseModel, Field, field_validator, model_validator
from invokeai.app.invocations.fields import (
FieldDescriptions,
ImageField,
Input,
InputField,
OutputField,
UIType,
@ -82,13 +81,13 @@ class ControlOutput(BaseInvocationOutput):
control: ControlField = OutputField(description=FieldDescriptions.control)
@invocation("controlnet", title="ControlNet", tags=["controlnet"], category="controlnet", version="1.1.1")
@invocation("controlnet", title="ControlNet", tags=["controlnet"], category="controlnet", version="1.1.2")
class ControlNetInvocation(BaseInvocation):
"""Collects ControlNet info to pass to other nodes"""
image: ImageField = InputField(description="The control image")
control_model: ModelIdentifierField = InputField(
description=FieldDescriptions.controlnet_model, input=Input.Direct, ui_type=UIType.ControlNetModel
description=FieldDescriptions.controlnet_model, ui_type=UIType.ControlNetModel
)
control_weight: Union[float, List[float]] = InputField(
default=1.0, ge=-1, le=2, description="The weight given to the ControlNet"

5
invokeai/app/invocations/ip_adapter.py
View File

@ -5,7 +5,7 @@ from pydantic import BaseModel, Field, field_validator, model_validator
from typing_extensions import Self
from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, TensorField, UIType
from invokeai.app.invocations.fields import FieldDescriptions, InputField, OutputField, TensorField, UIType
from invokeai.app.invocations.model import ModelIdentifierField
from invokeai.app.invocations.primitives import ImageField
from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
@ -58,7 +58,7 @@ class IPAdapterOutput(BaseInvocationOutput):
CLIP_VISION_MODEL_MAP = {"ViT-H": "ip_adapter_sd_image_encoder", "ViT-G": "ip_adapter_sdxl_image_encoder"}
@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.4.0")
@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.4.1")
class IPAdapterInvocation(BaseInvocation):
"""Collects IP-Adapter info to pass to other nodes."""
@ -67,7 +67,6 @@ class IPAdapterInvocation(BaseInvocation):
ip_adapter_model: ModelIdentifierField = InputField(
description="The IP-Adapter model.",
title="IP-Adapter Model",
input=Input.Direct,
ui_order=-1,
ui_type=UIType.IPAdapterModel,
)

4
invokeai/app/invocations/latent.py
View File

@ -930,9 +930,9 @@ class DenoiseLatentsInvocation(BaseInvocation):
assert isinstance(unet_info.model, UNet2DConditionModel)
with (
ExitStack() as exit_stack,
ModelPatcher.apply_freeu(unet_info.model, self.unet.freeu_config),
set_seamless(unet_info.model, self.unet.seamless_axes), # FIXME
unet_info as unet,
ModelPatcher.apply_freeu(unet, self.unet.freeu_config),
set_seamless(unet, self.unet.seamless_axes), # FIXME
# Apply the LoRA after unet has been moved to its target device for faster patching.
ModelPatcher.apply_lora_unet(unet, _lora_loader()),
):

52
invokeai/app/invocations/model.py
View File

@ -11,6 +11,7 @@ from invokeai.backend.model_manager.config import AnyModelConfig, BaseModelType,
from .baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
Classification,
invocation,
invocation_output,
)
@ -93,19 +94,46 @@ class ModelLoaderOutput(UNetOutput, CLIPOutput, VAEOutput):
pass
@invocation_output("model_identifier_output")
class ModelIdentifierOutput(BaseInvocationOutput):
"""Model identifier output"""
model: ModelIdentifierField = OutputField(description="Model identifier", title="Model")
@invocation(
"model_identifier",
title="Model identifier",
tags=["model"],
category="model",
version="1.0.0",
classification=Classification.Prototype,
)
class ModelIdentifierInvocation(BaseInvocation):
"""Selects any model, outputting it its identifier. Be careful with this one! The identifier will be accepted as
input for any model, even if the model types don't match. If you connect this to a mismatched input, you'll get an
error."""
model: ModelIdentifierField = InputField(description="The model to select", title="Model")
def invoke(self, context: InvocationContext) -> ModelIdentifierOutput:
if not context.models.exists(self.model.key):
raise Exception(f"Unknown model {self.model.key}")
return ModelIdentifierOutput(model=self.model)
@invocation(
"main_model_loader",
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 +162,12 @@ 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 +225,12 @@ 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 +301,13 @@ 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 +442,12 @@ 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:

10
invokeai/app/invocations/sdxl.py
View File

@ -1,4 +1,4 @@
from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType
from invokeai.app.invocations.fields import FieldDescriptions, InputField, OutputField, UIType
from invokeai.app.services.shared.invocation_context import InvocationContext
from invokeai.backend.model_manager import SubModelType
@ -30,12 +30,12 @@ class SDXLRefinerModelLoaderOutput(BaseInvocationOutput):
vae: VAEField = OutputField(description=FieldDescriptions.vae, title="VAE")
@invocation("sdxl_model_loader", title="SDXL Main Model", tags=["model", "sdxl"], category="model", version="1.0.2")
@invocation("sdxl_model_loader", title="SDXL Main Model", tags=["model", "sdxl"], category="model", version="1.0.3")
class SDXLModelLoaderInvocation(BaseInvocation):
"""Loads an sdxl base model, outputting its submodels."""
model: ModelIdentifierField = InputField(
description=FieldDescriptions.sdxl_main_model, input=Input.Direct, ui_type=UIType.SDXLMainModel
description=FieldDescriptions.sdxl_main_model, ui_type=UIType.SDXLMainModel
)
# TODO: precision?
@ -67,13 +67,13 @@ class SDXLModelLoaderInvocation(BaseInvocation):
title="SDXL Refiner Model",
tags=["model", "sdxl", "refiner"],
category="model",
version="1.0.2",
version="1.0.3",
)
class SDXLRefinerModelLoaderInvocation(BaseInvocation):
"""Loads an sdxl refiner model, outputting its submodels."""
model: ModelIdentifierField = InputField(
description=FieldDescriptions.sdxl_refiner_model, input=Input.Direct, ui_type=UIType.SDXLRefinerModel
description=FieldDescriptions.sdxl_refiner_model, ui_type=UIType.SDXLRefinerModel
)
# TODO: precision?

5
invokeai/app/invocations/t2i_adapter.py
View File

@ -8,7 +8,7 @@ from invokeai.app.invocations.baseinvocation import (
invocation,
invocation_output,
)
from invokeai.app.invocations.fields import FieldDescriptions, ImageField, Input, InputField, OutputField, UIType
from invokeai.app.invocations.fields import FieldDescriptions, ImageField, InputField, OutputField, UIType
from invokeai.app.invocations.model import ModelIdentifierField
from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
from invokeai.app.services.shared.invocation_context import InvocationContext
@ -45,7 +45,7 @@ class T2IAdapterOutput(BaseInvocationOutput):
@invocation(
"t2i_adapter", title="T2I-Adapter", tags=["t2i_adapter", "control"], category="t2i_adapter", version="1.0.2"
"t2i_adapter", title="T2I-Adapter", tags=["t2i_adapter", "control"], category="t2i_adapter", version="1.0.3"
)
class T2IAdapterInvocation(BaseInvocation):
"""Collects T2I-Adapter info to pass to other nodes."""
@ -55,7 +55,6 @@ class T2IAdapterInvocation(BaseInvocation):
t2i_adapter_model: ModelIdentifierField = InputField(
description="The T2I-Adapter model.",
title="T2I-Adapter Model",
input=Input.Direct,
ui_order=-1,
ui_type=UIType.T2IAdapterModel,
)