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nodes: remove duplicate LatentsToLatentsInvocation

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Eugene 2023-04-14 12:29:52 -04:00 committed by Eugene Brodsky
parent 570c3fe690
commit 3daaddf15b
1 changed files with 3 additions and 53 deletions
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56
invokeai/app/invocations/latent.py
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@ -171,7 +171,7 @@ class TextToLatentsInvocation(BaseInvocation):
# TODO: pass this an emitter method or something? or a session for dispatching?
def dispatch_progress(
self, context: InvocationContext, intermediate_state: PipelineIntermediateState
) -> None:
) -> None:
if (context.services.queue.is_canceled(context.graph_execution_state_id)):
raise CanceledException
@ -185,7 +185,7 @@ class TextToLatentsInvocation(BaseInvocation):
diffusers_step_callback_adapter(sample, step, steps=self.steps, id=self.id, context=context)
def get_model(self, model_manager: ModelManager) -> StableDiffusionGeneratorPipeline:
model_info = choose_model(model_manager, self.model)
model_name = model_info['model_name']
@ -195,7 +195,7 @@ class TextToLatentsInvocation(BaseInvocation):
model=model,
scheduler_name=self.scheduler
)
if isinstance(model, DiffusionPipeline):
for component in [model.unet, model.vae]:
configure_model_padding(component,
@ -292,57 +292,7 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
initial_latents = latent if self.strength < 1.0 else torch.zeros_like(
latent, device=model.device, dtype=latent.dtype
)
timesteps, _ = model.get_img2img_timesteps(
self.steps,
self.strength,
device=model.device,
)
result_latents, result_attention_map_saver = model.latents_from_embeddings(
latents=initial_latents,
timesteps=timesteps,
noise=noise,
num_inference_steps=self.steps,
conditioning_data=conditioning_data,
callback=step_callback
)
# https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
torch.cuda.empty_cache()
name = f'{context.graph_execution_state_id}__{self.id}'
context.services.latents.set(name, result_latents)
return LatentsOutput(
latents=LatentsField(latents_name=name)
)
class LatentsToLatentsInvocation(TextToLatentsInvocation):
"""Generates latents using latents as base image."""
type: Literal["l2l"] = "l2l"
# Inputs
latents: Optional[LatentsField] = Field(description="The latents to use as a base image")
strength: float = Field(default=0.5, description="The strength of the latents to use")
def invoke(self, context: InvocationContext) -> LatentsOutput:
noise = context.services.latents.get(self.noise.latents_name)
latent = context.services.latents.get(self.latents.latents_name)
def step_callback(state: PipelineIntermediateState):
self.dispatch_progress(context, state)
model = self.get_model(context.services.model_manager)
conditioning_data = self.get_conditioning_data(model)
# TODO: Verify the noise is the right size
initial_latents = latent if self.strength < 1.0 else torch.zeros_like(
latent, device=model.device, dtype=latent.dtype
)
timesteps, _ = model.get_img2img_timesteps(
self.steps,
self.strength,