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Cleaning up prior to submitting ControlNet PR. Mostly turning off diagnostic printing. Also fixed error when there is no controlnet input.

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user1 2023-05-12 10:43:10 -07:00 committed by Kent Keirsey
parent f613c073c1
commit 297931f5d9
3 changed files with 13 additions and 12 deletions
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4
invokeai/app/invocations/controlnet_image_processors.py
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@ -34,7 +34,7 @@ from .image import ImageOutput, build_image_output, PILInvocationConfig
class ControlField(BaseModel):
image: ImageField = Field(default=None, description="processed image")
control_model: Optional[str] = Field(default=None, description="control model used")
control_weight: Optional[float] = Field(default=None, description="weight given to controlnet")
control_weight: Optional[float] = Field(default=1, description="weight given to controlnet")
begin_step_percent: float = Field(default=0, ge=0, le=1,
description="% of total steps at which controlnet is first applied")
end_step_percent: float = Field(default=1, ge=0, le=1,
@ -61,7 +61,7 @@ class ControlNetInvocation(BaseInvocation):
# Inputs
image: ImageField = Field(default=None, description="image to process")
control_model: str = Field(default=None, description="control model to use")
control_weight: float = Field(default=0.5, ge=0, le=1, description="weight given to controlnet")
control_weight: float = Field(default=1.0, ge=0, le=1, description="weight given to controlnet")
# TODO: add support in backend core for begin_step_percent, end_step_percent, guess_mode
begin_step_percent: float = Field(default=0, ge=0, le=1,
description="% of total steps at which controlnet is first applied")

11
invokeai/app/invocations/latent.py
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@ -265,24 +265,25 @@ class TextToLatentsInvocation(BaseInvocation):
conditioning_data = self.get_conditioning_data(context, model)
# print("type of control input: ", type(self.control))
if self.control is None:
print("control input is None")
# print("control input is None")
control_list = None
elif isinstance(self.control, list) and len(self.control) == 0:
print("control input is empty list")
# print("control input is empty list")
control_list = None
elif isinstance(self.control, ControlField):
print("control input is ControlField")
# print("control input is ControlField")
control_list = [self.control]
elif isinstance(self.control, list) and len(self.control) > 0 and isinstance(self.control[0], ControlField):
print("control input is list[ControlField]")
# print("control input is list[ControlField]")
control_list = self.control
else:
print("input control is unrecognized:", type(self.control))
#print("input control is unrecognized:", type(self.control))
control_list = None
#if (self.control is None or (isinstance(self.control, list) and len(self.control)==0)):
if (control_list is None):
control_models = None
control_data = None
# from above handling, any control that is not None should now be of type list[ControlField]
else:
# FIXME: add checks to skip entry if model or image is None

10
invokeai/backend/stable_diffusion/diffusers_pipeline.py
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@ -670,15 +670,15 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
else:
latent_control_input = latent_model_input
# control_data should be type List[ControlNetData]
# this loop covers both ControlNet (1 ControlNetData in list)
# this loop covers both ControlNet (one ControlNetData in list)
# and MultiControlNet (multiple ControlNetData in list)
for i, control_datum in enumerate(control_data):
# print("controlnet", i, "==>", type(control_datum))
first_control_step = math.floor(control_datum.begin_step_percent * total_step_count)
last_control_step = math.ceil(control_datum.end_step_percent * total_step_count)
# apply_control_this_step = step_index >= first_control_step and step_index <= last_control_step
# only apply controlnet if current step is within the controlnet's begin/end step range
if step_index >= first_control_step and step_index <= last_control_step:
print("running controlnet", i, "for step", step_index)
# print("running controlnet", i, "for step", step_index)
down_samples, mid_sample = control_datum.model(
sample=latent_control_input,
timestep=timestep,
@ -709,8 +709,8 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
conditioning_data.guidance_scale,
step_index=step_index,
total_step_count=total_step_count,
down_block_additional_residuals=down_block_res_samples,
mid_block_additional_residual=mid_block_res_sample,
down_block_additional_residuals=down_block_res_samples, # from controlnet(s)
mid_block_additional_residual=mid_block_res_sample, # from controlnet(s)
)
# compute the previous noisy sample x_t -> x_t-1