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Split out the prepare_noise_and_latents(...) logic in DenoiseLatentsInvocation so that it can be called from other invocations.

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Ryan Dick 2024-06-06 15:10:04 -04:00 committed by Kent Keirsey
parent 43108eec13
commit 610a1fd611
1 changed files with 18 additions and 9 deletions
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27
invokeai/app/invocations/denoise_latents.py
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@ -659,19 +659,21 @@ class DenoiseLatentsInvocation(BaseInvocation):
return 1 - mask, masked_latents, self.denoise_mask.gradient return 1 - mask, masked_latents, self.denoise_mask.gradient
@torch.no_grad() @staticmethod
@SilenceWarnings() # This quenches the NSFW nag from diffusers. def prepare_noise_and_latents(
def invoke(self, context: InvocationContext) -> LatentsOutput: context: InvocationContext, noise_field: LatentsField | None, latents_field: LatentsField | None
) -> Tuple[float, torch.Tensor | None, torch.Tensor]:
seed = None seed = None
noise = None noise = None
if self.noise is not None:
noise = context.tensors.load(self.noise.latents_name)
seed = self.noise.seed
if self.latents is not None: if noise_field is not None:
latents = context.tensors.load(self.latents.latents_name) noise = context.tensors.load(noise_field.latents_name)
seed = noise_field.seed
if latents_field is not None:
latents = context.tensors.load(latents_field.latents_name)
if seed is None: if seed is None:
seed = self.latents.seed seed = latents_field.seed
if noise is not None and noise.shape[1:] != latents.shape[1:]: if noise is not None and noise.shape[1:] != latents.shape[1:]:
raise Exception(f"Incompatable 'noise' and 'latents' shapes: {latents.shape=} {noise.shape=}") raise Exception(f"Incompatable 'noise' and 'latents' shapes: {latents.shape=} {noise.shape=}")
@ -684,6 +686,13 @@ class DenoiseLatentsInvocation(BaseInvocation):
if seed is None: if seed is None:
seed = 0 seed = 0
return seed, noise, latents
@torch.no_grad()
@SilenceWarnings() # This quenches the NSFW nag from diffusers.
def invoke(self, context: InvocationContext) -> LatentsOutput:
seed, noise, latents = self.prepare_noise_and_latents(context, self.noise, self.latents)
mask, masked_latents, gradient_mask = self.prep_inpaint_mask(context, latents) mask, masked_latents, gradient_mask = self.prep_inpaint_mask(context, latents)
# TODO(ryand): I have hard-coded `do_classifier_free_guidance=True` to mirror the behaviour of ControlNets, # TODO(ryand): I have hard-coded `do_classifier_free_guidance=True` to mirror the behaviour of ControlNets,