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Renamed ControlNet control_mode option "even_more_control" to "unbalanced"

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user1 2023-06-13 22:30:17 -07:00
parent cfd49e3921
commit 5cd0e90816
2 changed files with 3 additions and 3 deletions
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2
invokeai/app/invocations/controlnet_image_processors.py
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@ -94,7 +94,7 @@ CONTROLNET_DEFAULT_MODELS = [
] ]
CONTROLNET_NAME_VALUES = Literal[tuple(CONTROLNET_DEFAULT_MODELS)] CONTROLNET_NAME_VALUES = Literal[tuple(CONTROLNET_DEFAULT_MODELS)]
CONTROLNET_MODE_VALUES = Literal[tuple(["balanced", "more_prompt", "more_control", "even_more_control"])] CONTROLNET_MODE_VALUES = Literal[tuple(["balanced", "more_prompt", "more_control", "unbalanced"])]
class ControlField(BaseModel): class ControlField(BaseModel):
image: ImageField = Field(default=None, description="The control image") image: ImageField = Field(default=None, description="The control image")

4
invokeai/backend/stable_diffusion/diffusers_pipeline.py
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@ -676,7 +676,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
soft_injection = (control_mode == "more_prompt" or control_mode == "more_control") soft_injection = (control_mode == "more_prompt" or control_mode == "more_control")
# cfg_injection = determines whether to apply ControlNet to only the conditional (if True) # cfg_injection = determines whether to apply ControlNet to only the conditional (if True)
# or the default both conditional and unconditional (if False) # or the default both conditional and unconditional (if False)
cfg_injection = (control_mode == "more_control" or control_mode == "even_more_control") cfg_injection = (control_mode == "more_control" or control_mode == "unbalanced")
first_control_step = math.floor(control_datum.begin_step_percent * total_step_count) 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) last_control_step = math.ceil(control_datum.end_step_percent * total_step_count)
@ -1091,7 +1091,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
repeat_by = num_images_per_prompt repeat_by = num_images_per_prompt
image = image.repeat_interleave(repeat_by, dim=0) image = image.repeat_interleave(repeat_by, dim=0)
image = image.to(device=device, dtype=dtype) image = image.to(device=device, dtype=dtype)
cfg_injection = (control_mode == "more_control" or control_mode == "even_more_control") cfg_injection = (control_mode == "more_control" or control_mode == "unbalanced")
if do_classifier_free_guidance and not cfg_injection: if do_classifier_free_guidance and not cfg_injection:
image = torch.cat([image] * 2) image = torch.cat([image] * 2)
return image return image