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Make latent generation nodes use conditions instead of prompt

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StAlKeR7779 2023-04-25 04:21:03 +03:00
parent d99a08a441
commit 8f460b92f1
2 changed files with 20 additions and 14 deletions
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14
invokeai/app/invocations/latent.py
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@ -13,13 +13,13 @@ from ...backend.model_management.model_manager import ModelManager
from ...backend.util.devices import choose_torch_device, torch_dtype
from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import PostprocessingSettings
from ...backend.image_util.seamless import configure_model_padding
from ...backend.prompting.conditioning import get_uc_and_c_and_ec
from ...backend.stable_diffusion.diffusers_pipeline import ConditioningData, StableDiffusionGeneratorPipeline
from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext, InvocationConfig
import numpy as np
from ..services.image_storage import ImageType
from .baseinvocation import BaseInvocation, InvocationContext
from .image import ImageField, ImageOutput, build_image_output
from .compel import ConditioningField
from ...backend.stable_diffusion import PipelineIntermediateState
from diffusers.schedulers import SchedulerMixin as Scheduler
import diffusers
@ -143,9 +143,9 @@ class TextToLatentsInvocation(BaseInvocation):
type: Literal["t2l"] = "t2l"
# Inputs
# TODO: consider making prompt optional to enable providing prompt through a link
# fmt: off
prompt: Optional[str] = Field(description="The prompt to generate an image from")
positive: Optional[ConditioningField] = Field(description="Positive conditioning for generation")
negative: Optional[ConditioningField] = Field(description="Negative conditioning for generation")
seed: int = Field(default=-1,ge=-1, le=np.iinfo(np.uint32).max, description="The seed to use (-1 for a random seed)", )
noise: Optional[LatentsField] = Field(description="The noise to use")
steps: int = Field(default=10, gt=0, description="The number of steps to use to generate the image")
@ -206,8 +206,10 @@ class TextToLatentsInvocation(BaseInvocation):
return model
def get_conditioning_data(self, model: StableDiffusionGeneratorPipeline) -> ConditioningData:
uc, c, extra_conditioning_info = get_uc_and_c_and_ec(self.prompt, model=model)
def get_conditioning_data(self, context: InvocationContext, model: StableDiffusionGeneratorPipeline) -> ConditioningData:
c, extra_conditioning_info = context.services.latents.get(self.positive.conditioning_name)
uc, _ = context.services.latents.get(self.negative.conditioning_name)
conditioning_data = ConditioningData(
uc,
c,
@ -234,7 +236,7 @@ class TextToLatentsInvocation(BaseInvocation):
self.dispatch_progress(context, source_node_id, state)
model = self.get_model(context.services.model_manager)
conditioning_data = self.get_conditioning_data(model)
conditioning_data = self.get_conditioning_data(context, model)
# TODO: Verify the noise is the right size

20
invokeai/app/services/default_graphs.py
View File

@ -1,4 +1,5 @@
from ..invocations.latent import LatentsToImageInvocation, NoiseInvocation, TextToLatentsInvocation
from ..invocations.compel import CompelInvocation
from ..invocations.params import ParamIntInvocation
from .graph import Edge, EdgeConnection, ExposedNodeInput, ExposedNodeOutput, Graph, LibraryGraph
from .item_storage import ItemStorageABC
@ -17,25 +18,28 @@ def create_text_to_image() -> LibraryGraph:
'width': ParamIntInvocation(id='width', a=512),
'height': ParamIntInvocation(id='height', a=512),
'3': NoiseInvocation(id='3'),
'4': TextToLatentsInvocation(id='4'),
'5': LatentsToImageInvocation(id='5')
'4': CompelInvocation(id='4'),
'5': TextToLatentsInvocation(id='5'),
'6': LatentsToImageInvocation(id='6'),
},
edges=[
Edge(source=EdgeConnection(node_id='width', field='a'), destination=EdgeConnection(node_id='3', field='width')),
Edge(source=EdgeConnection(node_id='height', field='a'), destination=EdgeConnection(node_id='3', field='height')),
Edge(source=EdgeConnection(node_id='width', field='a'), destination=EdgeConnection(node_id='4', field='width')),
Edge(source=EdgeConnection(node_id='height', field='a'), destination=EdgeConnection(node_id='4', field='height')),
Edge(source=EdgeConnection(node_id='3', field='noise'), destination=EdgeConnection(node_id='4', field='noise')),
Edge(source=EdgeConnection(node_id='4', field='latents'), destination=EdgeConnection(node_id='5', field='latents')),
Edge(source=EdgeConnection(node_id='width', field='a'), destination=EdgeConnection(node_id='5', field='width')),
Edge(source=EdgeConnection(node_id='height', field='a'), destination=EdgeConnection(node_id='5', field='height')),
Edge(source=EdgeConnection(node_id='3', field='noise'), destination=EdgeConnection(node_id='5', field='noise')),
Edge(source=EdgeConnection(node_id='5', field='latents'), destination=EdgeConnection(node_id='6', field='latents')),
Edge(source=EdgeConnection(node_id='4', field='positive'), destination=EdgeConnection(node_id='5', field='positive')),
Edge(source=EdgeConnection(node_id='4', field='negative'), destination=EdgeConnection(node_id='5', field='negative')),
]
),
exposed_inputs=[
ExposedNodeInput(node_path='4', field='prompt', alias='prompt'),
ExposedNodeInput(node_path='4', field='positive_prompt', alias='prompt'), # TODO: cli uses concatenated prompt
ExposedNodeInput(node_path='width', field='a', alias='width'),
ExposedNodeInput(node_path='height', field='a', alias='height')
],
exposed_outputs=[
ExposedNodeOutput(node_path='5', field='image', alias='image')
ExposedNodeOutput(node_path='6', field='image', alias='image')
])