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Fix typing to reflect that the callback arg to latents_from_embeddings is never None.

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Ryan Dick 2024-06-12 14:41:01 -04:00
parent 803a24bc0a
commit c2cf1137e9
1 changed files with 19 additions and 21 deletions
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40
invokeai/backend/stable_diffusion/diffusers_pipeline.py
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@ -289,7 +289,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
seed: int,
timesteps: torch.Tensor,
init_timestep: torch.Tensor,
callback: Callable[[PipelineIntermediateState], None] = None,
callback: Callable[[PipelineIntermediateState], None],
control_data: List[ControlNetData] = None,
ip_adapter_data: Optional[list[IPAdapterData]] = None,
t2i_adapter_data: Optional[list[T2IAdapterData]] = None,
@ -363,11 +363,11 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
timesteps,
conditioning_data: TextConditioningData,
scheduler_step_kwargs: dict[str, Any],
callback: Callable[[PipelineIntermediateState], None],
mask_guidance: AddsMaskGuidance | None = None,
control_data: List[ControlNetData] = None,
ip_adapter_data: Optional[list[IPAdapterData]] = None,
t2i_adapter_data: Optional[list[T2IAdapterData]] = None,
callback: Callable[[PipelineIntermediateState], None] = None,
) -> torch.Tensor:
self._adjust_memory_efficient_attention(latents)
@ -394,16 +394,15 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
attn_ctx = unet_attention_patcher.apply_ip_adapter_attention(self.invokeai_diffuser.model)
with attn_ctx:
if callback is not None:
callback(
PipelineIntermediateState(
step=-1,
order=self.scheduler.order,
total_steps=len(timesteps),
timestep=self.scheduler.config.num_train_timesteps,
latents=latents,
)
callback(
PipelineIntermediateState(
step=-1,
order=self.scheduler.order,
total_steps=len(timesteps),
timestep=self.scheduler.config.num_train_timesteps,
latents=latents,
)
)
for i, t in enumerate(self.progress_bar(timesteps)):
batched_t = t.expand(batch_size)
@ -422,17 +421,16 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
latents = step_output.prev_sample
predicted_original = getattr(step_output, "pred_original_sample", None)
if callback is not None:
callback(
PipelineIntermediateState(
step=i,
order=self.scheduler.order,
total_steps=len(timesteps),
timestep=int(t),
latents=latents,
predicted_original=predicted_original,
)
callback(
PipelineIntermediateState(
step=i,
order=self.scheduler.order,
total_steps=len(timesteps),
timestep=int(t),
latents=latents,
predicted_original=predicted_original,
)
)
return latents