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fix: Make TE5 Optional

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blessedcoolant 2024-06-20 08:45:36 +05:30
parent cd99ef2f46
commit c403efa83f
3 changed files with 344 additions and 166 deletions
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4
invokeai/app/invocations/model.py
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@ -65,8 +65,8 @@ class SD3CLIPField(BaseModel):
text_encoder_1: ModelIdentifierField = Field(description="Info to load text_encoder 1 submodel") text_encoder_1: ModelIdentifierField = Field(description="Info to load text_encoder 1 submodel")
tokenizer_2: ModelIdentifierField = Field(description="Info to load tokenizer 2 submodel") tokenizer_2: ModelIdentifierField = Field(description="Info to load tokenizer 2 submodel")
text_encoder_2: ModelIdentifierField = Field(description="Info to load text_encoder 2 submodel") text_encoder_2: ModelIdentifierField = Field(description="Info to load text_encoder 2 submodel")
tokenizer_3: ModelIdentifierField = Field(description="Info to load tokenizer 3 submodel") tokenizer_3: Optional[ModelIdentifierField] = Field(description="Info to load tokenizer 3 submodel")
text_encoder_3: ModelIdentifierField = Field(description="Info to load text_encoder 3 submodel") text_encoder_3: Optional[ModelIdentifierField] = Field(description="Info to load text_encoder 3 submodel")
class VAEField(BaseModel): class VAEField(BaseModel):

33
invokeai/app/invocations/sd3.py
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@ -23,16 +23,8 @@ from invokeai.app.invocations.primitives import LatentsOutput
from invokeai.app.services.shared.invocation_context import InvocationContext from invokeai.app.services.shared.invocation_context import InvocationContext
from invokeai.app.util.misc import SEED_MAX from invokeai.app.util.misc import SEED_MAX
from invokeai.backend.model_manager.config import SubModelType from invokeai.backend.model_manager.config import SubModelType
from invokeai.backend.model_manager.load.load_base import LoadedModel
sd3_pipeline: Optional[StableDiffusion3Pipeline] = None sd3_pipeline: Optional[StableDiffusion3Pipeline] = None
transformer_info: Optional[LoadedModel] = None
tokenizer_1_info: Optional[LoadedModel] = None
tokenizer_2_info: Optional[LoadedModel] = None
tokenizer_3_info: Optional[LoadedModel] = None
text_encoder_1_info: Optional[LoadedModel] = None
text_encoder_2_info: Optional[LoadedModel] = None
text_encoder_3_info: Optional[LoadedModel] = None
class FakeVae: class FakeVae:
@ -71,8 +63,12 @@ class SD3ModelLoaderInvocation(BaseInvocation):
text_encoder_1 = self.model.model_copy(update={"submodel_type": SubModelType.TextEncoder}) text_encoder_1 = self.model.model_copy(update={"submodel_type": SubModelType.TextEncoder})
tokenizer_2 = self.model.model_copy(update={"submodel_type": SubModelType.Tokenizer2}) tokenizer_2 = self.model.model_copy(update={"submodel_type": SubModelType.Tokenizer2})
text_encoder_2 = self.model.model_copy(update={"submodel_type": SubModelType.TextEncoder2}) text_encoder_2 = self.model.model_copy(update={"submodel_type": SubModelType.TextEncoder2})
try:
tokenizer_3 = self.model.model_copy(update={"submodel_type": SubModelType.Tokenizer3}) tokenizer_3 = self.model.model_copy(update={"submodel_type": SubModelType.Tokenizer3})
text_encoder_3 = self.model.model_copy(update={"submodel_type": SubModelType.TextEncoder3}) text_encoder_3 = self.model.model_copy(update={"submodel_type": SubModelType.TextEncoder3})
except Exception:
tokenizer_3 = None
text_encoder_3 = None
vae = self.model.model_copy(update={"submodel_type": SubModelType.VAE}) vae = self.model.model_copy(update={"submodel_type": SubModelType.VAE})
return SD3ModelLoaderOutput( return SD3ModelLoaderOutput(
@ -122,6 +118,7 @@ class StableDiffusion3Invocation(BaseInvocation):
negative_prompt: str = InputField(default="", title="Negative Prompt") negative_prompt: str = InputField(default="", title="Negative Prompt")
steps: int = InputField(default=20, gt=0, description=FieldDescriptions.steps) steps: int = InputField(default=20, gt=0, description=FieldDescriptions.steps)
guidance_scale: float = InputField(default=7.0, description=FieldDescriptions.cfg_scale, title="CFG Scale") guidance_scale: float = InputField(default=7.0, description=FieldDescriptions.cfg_scale, title="CFG Scale")
use_clip_3: bool = InputField(default=True, description="Use TE5 Encoder of SD3")
seed: int = InputField( seed: int = InputField(
default=0, default=0,
@ -149,27 +146,21 @@ class StableDiffusion3Invocation(BaseInvocation):
def invoke(self, context: InvocationContext) -> LatentsOutput: def invoke(self, context: InvocationContext) -> LatentsOutput:
app_config = context.config.get() app_config = context.config.get()
load_te3 = app_config.load_sd3_encoder_3
transformer_info = context.models.load(self.transformer.transformer)
tokenizer_1_info = context.models.load(self.clip.tokenizer_1)
tokenizer_2_info = context.models.load(self.clip.tokenizer_2)
text_encoder_1_info = context.models.load(self.clip.text_encoder_1)
text_encoder_2_info = context.models.load(self.clip.text_encoder_2)
with ExitStack() as stack: with ExitStack() as stack:
tokenizer_1 = stack.enter_context(tokenizer_1_info) tokenizer_1 = stack.enter_context(context.models.load(self.clip.tokenizer_1))
tokenizer_2 = stack.enter_context(tokenizer_2_info) tokenizer_2 = stack.enter_context(context.models.load(self.clip.tokenizer_2))
text_encoder_1 = stack.enter_context(text_encoder_1_info) text_encoder_1 = stack.enter_context(context.models.load(self.clip.text_encoder_1))
text_encoder_2 = stack.enter_context(text_encoder_2_info) text_encoder_2 = stack.enter_context(context.models.load(self.clip.text_encoder_2))
transformer = stack.enter_context(transformer_info) transformer = stack.enter_context(context.models.load(self.transformer.transformer))
assert isinstance(transformer, SD3Transformer2DModel) assert isinstance(transformer, SD3Transformer2DModel)
assert isinstance(text_encoder_1, CLIPTextModelWithProjection) assert isinstance(text_encoder_1, CLIPTextModelWithProjection)
assert isinstance(text_encoder_2, CLIPTextModelWithProjection) assert isinstance(text_encoder_2, CLIPTextModelWithProjection)
assert isinstance(tokenizer_1, CLIPTokenizer) assert isinstance(tokenizer_1, CLIPTokenizer)
assert isinstance(tokenizer_2, CLIPTokenizer) assert isinstance(tokenizer_2, CLIPTokenizer)
if load_te3: if self.use_clip_3 and self.clip.tokenizer_3 and self.clip.text_encoder_3:
tokenizer_3 = stack.enter_context(context.models.load(self.clip.tokenizer_3)) tokenizer_3 = stack.enter_context(context.models.load(self.clip.tokenizer_3))
text_encoder_3 = stack.enter_context(context.models.load(self.clip.text_encoder_3)) text_encoder_3 = stack.enter_context(context.models.load(self.clip.text_encoder_3))
assert isinstance(text_encoder_3, T5EncoderModel) assert isinstance(text_encoder_3, T5EncoderModel)

467
invokeai/frontend/web/src/services/api/schema.ts
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