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revert(nodes): revert making tensors/conditioning use item storage

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Turns out they are just different enough in purpose that the implementations would be rather unintuitive. I've made a separate ObjectSerializer service to handle tensors and conditioning.

Refined the class a bit too.
This commit is contained in:
psychedelicious
2024-02-07 23:30:46 +11:00
parent 73d871116c
commit 9f382419dc
14 changed files with 243 additions and 205 deletions
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24
invokeai/app/invocations/latent.py
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@ -304,11 +304,11 @@ class DenoiseLatentsInvocation(BaseInvocation):
unet,
seed,
) -> ConditioningData:
positive_cond_data = context.conditioning.get(self.positive_conditioning.conditioning_name)
positive_cond_data = context.conditioning.load(self.positive_conditioning.conditioning_name)
c = positive_cond_data.conditionings[0].to(device=unet.device, dtype=unet.dtype)
extra_conditioning_info = c.extra_conditioning
negative_cond_data = context.conditioning.get(self.negative_conditioning.conditioning_name)
negative_cond_data = context.conditioning.load(self.negative_conditioning.conditioning_name)
uc = negative_cond_data.conditionings[0].to(device=unet.device, dtype=unet.dtype)
conditioning_data = ConditioningData(
@ -621,10 +621,10 @@ class DenoiseLatentsInvocation(BaseInvocation):
if self.denoise_mask is None:
return None, None
mask = context.tensors.get(self.denoise_mask.mask_name)
mask = context.tensors.load(self.denoise_mask.mask_name)
mask = tv_resize(mask, latents.shape[-2:], T.InterpolationMode.BILINEAR, antialias=False)
if self.denoise_mask.masked_latents_name is not None:
masked_latents = context.tensors.get(self.denoise_mask.masked_latents_name)
masked_latents = context.tensors.load(self.denoise_mask.masked_latents_name)
else:
masked_latents = None
@ -636,11 +636,11 @@ class DenoiseLatentsInvocation(BaseInvocation):
seed = None
noise = None
if self.noise is not None:
noise = context.tensors.get(self.noise.latents_name)
noise = context.tensors.load(self.noise.latents_name)
seed = self.noise.seed
if self.latents is not None:
latents = context.tensors.get(self.latents.latents_name)
latents = context.tensors.load(self.latents.latents_name)
if seed is None:
seed = self.latents.seed
@ -779,7 +779,7 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
@torch.no_grad()
def invoke(self, context: InvocationContext) -> ImageOutput:
latents = context.tensors.get(self.latents.latents_name)
latents = context.tensors.load(self.latents.latents_name)
vae_info = context.models.load(**self.vae.vae.model_dump())
@ -870,7 +870,7 @@ class ResizeLatentsInvocation(BaseInvocation):
antialias: bool = InputField(default=False, description=FieldDescriptions.torch_antialias)
def invoke(self, context: InvocationContext) -> LatentsOutput:
latents = context.tensors.get(self.latents.latents_name)
latents = context.tensors.load(self.latents.latents_name)
# TODO:
device = choose_torch_device()
@ -911,7 +911,7 @@ class ScaleLatentsInvocation(BaseInvocation):
antialias: bool = InputField(default=False, description=FieldDescriptions.torch_antialias)
def invoke(self, context: InvocationContext) -> LatentsOutput:
latents = context.tensors.get(self.latents.latents_name)
latents = context.tensors.load(self.latents.latents_name)
# TODO:
device = choose_torch_device()
@ -1048,8 +1048,8 @@ class BlendLatentsInvocation(BaseInvocation):
alpha: float = InputField(default=0.5, description=FieldDescriptions.blend_alpha)
def invoke(self, context: InvocationContext) -> LatentsOutput:
latents_a = context.tensors.get(self.latents_a.latents_name)
latents_b = context.tensors.get(self.latents_b.latents_name)
latents_a = context.tensors.load(self.latents_a.latents_name)
latents_b = context.tensors.load(self.latents_b.latents_name)
if latents_a.shape != latents_b.shape:
raise Exception("Latents to blend must be the same size.")
@ -1149,7 +1149,7 @@ class CropLatentsCoreInvocation(BaseInvocation):
)
def invoke(self, context: InvocationContext) -> LatentsOutput:
latents = context.tensors.get(self.latents.latents_name)
latents = context.tensors.load(self.latents.latents_name)
x1 = self.x // LATENT_SCALE_FACTOR
y1 = self.y // LATENT_SCALE_FACTOR