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Working POC for IP-Adapters. Not fully nodified yet, lots of caveats, hardwired model paths, etc.

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user1 2023-08-29 06:32:48 -07:00
parent 9ed4d487d2
commit 35b7ae90ae
1 changed files with 12 additions and 2 deletions
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14
invokeai/app/invocations/latent.py
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@ -52,9 +52,10 @@ from .compel import ConditioningField
from .controlnet_image_processors import ControlField
from .model import ModelInfo, UNetField, VaeField
DEFAULT_PRECISION = choose_precision(choose_torch_device())
DEFAULT_PRECISION = choose_precision(choose_torch_device())
SAMPLER_NAME_VALUES = Literal[tuple(list(SCHEDULER_MAP.keys()))]
@ -191,6 +192,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
default=None,
description=FieldDescriptions.mask,
)
ip_adapter_image: Optional[ImageField] = InputField(input=Input.Connection)
@validator("cfg_scale")
def ge_one(cls, v):
@ -476,6 +478,13 @@ class DenoiseLatentsInvocation(BaseInvocation):
pipeline = self.create_pipeline(unet, scheduler)
conditioning_data = self.get_conditioning_data(context, scheduler, unet, seed)
if self.ip_adapter_image is not None:
print("ip_adapter_image:", self.ip_adapter_image)
unwrapped_ip_adapter_image = context.services.images.get_pil_image(self.ip_adapter_image.image_name)
print("unwrapped ip_adapter_image:", unwrapped_ip_adapter_image)
else:
unwrapped_ip_adapter_image = None
control_data = self.prep_control_data(
model=pipeline,
context=context,
@ -504,7 +513,8 @@ class DenoiseLatentsInvocation(BaseInvocation):
masked_latents=masked_latents,
num_inference_steps=num_inference_steps,
conditioning_data=conditioning_data,
control_data=control_data, # list[ControlNetData]
control_data=control_data, # list[ControlNetData],
ip_adapter_image=unwrapped_ip_adapter_image,
callback=step_callback,
)