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feat(nodes): extract denoise function

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psychedelicious
2023-10-20 16:30:36 +11:00
parent c8481d29eb
commit 527c806f7b
1 changed files with 14 additions and 9 deletions
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23
invokeai/app/invocations/latent.py
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@ -2,7 +2,7 @@
from contextlib import ExitStack
from functools import singledispatchmethod
from typing import List, Literal, Optional, Union
from typing import Callable, List, Literal, Optional, Union
import einops
import numpy as np
@ -651,8 +651,20 @@ class DenoiseLatentsInvocation(BaseInvocation):
return 1 - mask, masked_latents
@torch.no_grad()
def invoke(self, context: InvocationContext) -> LatentsOutput:
# Get the source node id (we are invoking the prepared node)
graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
source_node_id = graph_execution_state.prepared_source_mapping[self.id]
def step_callback(state: PipelineIntermediateState):
self.dispatch_progress(context, source_node_id, state, self.unet.unet.base_model)
return self.denoise(context, step_callback)
@torch.no_grad()
def denoise(
self, context: InvocationContext, step_callback: Callable[[PipelineIntermediateState], None]
) -> LatentsOutput:
with SilenceWarnings(): # this quenches NSFW nag from diffusers
seed = None
noise = None
@ -687,13 +699,6 @@ class DenoiseLatentsInvocation(BaseInvocation):
do_classifier_free_guidance=True,
)
# Get the source node id (we are invoking the prepared node)
graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
source_node_id = graph_execution_state.prepared_source_mapping[self.id]
def step_callback(state: PipelineIntermediateState):
self.dispatch_progress(context, source_node_id, state, self.unet.unet.base_model)
def _lora_loader():
for lora in self.unet.loras:
lora_info = context.services.model_manager.get_model(