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https://github.com/invoke-ai/InvokeAI
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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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@ -659,19 +659,21 @@ class DenoiseLatentsInvocation(BaseInvocation):
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return 1 - mask, masked_latents, self.denoise_mask.gradient
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@torch.no_grad()
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@SilenceWarnings() # This quenches the NSFW nag from diffusers.
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def invoke(self, context: InvocationContext) -> LatentsOutput:
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@staticmethod
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def prepare_noise_and_latents(
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context: InvocationContext, noise_field: LatentsField | None, latents_field: LatentsField | None
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) -> Tuple[float, torch.Tensor | None, torch.Tensor]:
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seed = None
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noise = None
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if self.noise is not None:
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noise = context.tensors.load(self.noise.latents_name)
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seed = self.noise.seed
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if self.latents is not None:
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latents = context.tensors.load(self.latents.latents_name)
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if noise_field is not None:
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noise = context.tensors.load(noise_field.latents_name)
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seed = noise_field.seed
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if latents_field is not None:
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latents = context.tensors.load(latents_field.latents_name)
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if seed is None:
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seed = self.latents.seed
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seed = latents_field.seed
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if noise is not None and noise.shape[1:] != latents.shape[1:]:
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raise Exception(f"Incompatable 'noise' and 'latents' shapes: {latents.shape=} {noise.shape=}")
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@ -684,6 +686,13 @@ class DenoiseLatentsInvocation(BaseInvocation):
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if seed is None:
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seed = 0
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return seed, noise, latents
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@torch.no_grad()
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@SilenceWarnings() # This quenches the NSFW nag from diffusers.
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def invoke(self, context: InvocationContext) -> LatentsOutput:
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seed, noise, latents = self.prepare_noise_and_latents(context, self.noise, self.latents)
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mask, masked_latents, gradient_mask = self.prep_inpaint_mask(context, latents)
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# TODO(ryand): I have hard-coded `do_classifier_free_guidance=True` to mirror the behaviour of ControlNets,
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