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Merge branch 'main' into release/invokeai-3-0-alpha

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This commit is contained in:
Eugene Brodsky
2023-07-07 14:18:12 -04:00
committed by GitHub
parent 8d88ad3b8d 3ddbb70bd7
commit 97b2ec58e2
55 changed files with 1084 additions and 589 deletions
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19
invokeai/app/invocations/compel.py
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@ -92,6 +92,7 @@ class CompelInvocation(BaseInvocation):
with ModelPatcher.apply_lora_text_encoder(text_encoder_info.context.model, _lora_loader()),\
ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (tokenizer, ti_manager),\
ModelPatcher.apply_clip_skip(text_encoder_info.context.model, self.clip.skipped_layers),\
text_encoder_info as text_encoder:
compel = Compel(
@ -131,6 +132,24 @@ class CompelInvocation(BaseInvocation):
),
)
class ClipSkipInvocationOutput(BaseInvocationOutput):
"""Clip skip node output"""
type: Literal["clip_skip_output"] = "clip_skip_output"
clip: ClipField = Field(None, description="Clip with skipped layers")
class ClipSkipInvocation(BaseInvocation):
"""Skip layers in clip text_encoder model."""
type: Literal["clip_skip"] = "clip_skip"
clip: ClipField = Field(None, description="Clip to use")
skipped_layers: int = Field(0, description="Number of layers to skip in text_encoder")
def invoke(self, context: InvocationContext) -> ClipSkipInvocationOutput:
self.clip.skipped_layers += self.skipped_layers
return ClipSkipInvocationOutput(
clip=self.clip,
)
def get_max_token_count(
tokenizer, prompt: Union[FlattenedPrompt, Blend, Conjunction],

2
invokeai/app/invocations/model.py
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@ -30,6 +30,7 @@ class UNetField(BaseModel):
class ClipField(BaseModel):
tokenizer: ModelInfo = Field(description="Info to load tokenizer submodel")
text_encoder: ModelInfo = Field(description="Info to load text_encoder submodel")
skipped_layers: int = Field(description="Number of skipped layers in text_encoder")
loras: List[LoraInfo] = Field(description="Loras to apply on model loading")
@ -154,6 +155,7 @@ class MainModelLoaderInvocation(BaseInvocation):
submodel=SubModelType.TextEncoder,
),
loras=[],
skipped_layers=0,
),
vae=VaeField(
vae=ModelInfo(