diff --git a/invokeai/app/invocations/latent.py b/invokeai/app/invocations/latent.py index 2cc84f80a7..5e36e73ec8 100644 --- a/invokeai/app/invocations/latent.py +++ b/invokeai/app/invocations/latent.py @@ -106,7 +106,7 @@ class SchedulerInvocation(BaseInvocation): ui_type=UIType.Scheduler, ) - def invoke(self, context) -> SchedulerOutput: + def invoke(self, context: InvocationContext) -> SchedulerOutput: return SchedulerOutput(scheduler=self.scheduler) @@ -141,7 +141,7 @@ class CreateDenoiseMaskInvocation(BaseInvocation): return mask_tensor @torch.no_grad() - def invoke(self, context) -> DenoiseMaskOutput: + def invoke(self, context: InvocationContext) -> DenoiseMaskOutput: if self.image is not None: image = context.images.get_pil(self.image.image_name) image = image_resized_to_grid_as_tensor(image.convert("RGB")) @@ -630,7 +630,7 @@ class DenoiseLatentsInvocation(BaseInvocation): return 1 - mask, masked_latents @torch.no_grad() - def invoke(self, context) -> LatentsOutput: + def invoke(self, context: InvocationContext) -> LatentsOutput: with SilenceWarnings(): # this quenches NSFW nag from diffusers seed = None noise = None @@ -777,7 +777,7 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata): fp32: bool = InputField(default=DEFAULT_PRECISION == "float32", description=FieldDescriptions.fp32) @torch.no_grad() - def invoke(self, context) -> ImageOutput: + def invoke(self, context: InvocationContext) -> ImageOutput: latents = context.latents.get(self.latents.latents_name) vae_info = context.models.load(**self.vae.vae.model_dump()) @@ -868,7 +868,7 @@ class ResizeLatentsInvocation(BaseInvocation): mode: LATENTS_INTERPOLATION_MODE = InputField(default="bilinear", description=FieldDescriptions.interp_mode) antialias: bool = InputField(default=False, description=FieldDescriptions.torch_antialias) - def invoke(self, context) -> LatentsOutput: + def invoke(self, context: InvocationContext) -> LatentsOutput: latents = context.latents.get(self.latents.latents_name) # TODO: @@ -909,7 +909,7 @@ class ScaleLatentsInvocation(BaseInvocation): mode: LATENTS_INTERPOLATION_MODE = InputField(default="bilinear", description=FieldDescriptions.interp_mode) antialias: bool = InputField(default=False, description=FieldDescriptions.torch_antialias) - def invoke(self, context) -> LatentsOutput: + def invoke(self, context: InvocationContext) -> LatentsOutput: latents = context.latents.get(self.latents.latents_name) # TODO: @@ -998,7 +998,7 @@ class ImageToLatentsInvocation(BaseInvocation): return latents @torch.no_grad() - def invoke(self, context) -> LatentsOutput: + def invoke(self, context: InvocationContext) -> LatentsOutput: image = context.images.get_pil(self.image.image_name) vae_info = context.models.load(**self.vae.vae.model_dump()) @@ -1046,7 +1046,7 @@ class BlendLatentsInvocation(BaseInvocation): ) alpha: float = InputField(default=0.5, description=FieldDescriptions.blend_alpha) - def invoke(self, context) -> LatentsOutput: + def invoke(self, context: InvocationContext) -> LatentsOutput: latents_a = context.latents.get(self.latents_a.latents_name) latents_b = context.latents.get(self.latents_b.latents_name) @@ -1147,7 +1147,7 @@ class CropLatentsCoreInvocation(BaseInvocation): description="The height (in px) of the crop rectangle in image space. This value will be converted to a dimension in latent space.", ) - def invoke(self, context) -> LatentsOutput: + def invoke(self, context: InvocationContext) -> LatentsOutput: latents = context.latents.get(self.latents.latents_name) x1 = self.x // LATENT_SCALE_FACTOR