lllyasviel/stable-diffusion-webui-forge
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lllyasviel
2024-01-28 07:27:51 -08:00
parent 5b8ca63b61
commit 878c7ecfad
3 changed files with 37 additions and 7 deletions
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12
modules_forge/controlnet.py
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@@ -8,6 +8,7 @@ def apply_controlnet_advanced(
positive_advanced_weighting=None,
negative_advanced_weighting=None,
advanced_frame_weighting=None,
advanced_sigma_weighting=None
):
"""
@@ -35,12 +36,23 @@ def apply_controlnet_advanced(
For example, if batch size is 5, you can use advanced_frame_weighting = [0, 0.25, 0.5, 0.75, 1.0]
If you view the 5 images as 5 frames in a video, this will lead to progressively stronger control over time.
# advanced_sigma_weighting
The advanced_sigma_weighting allows you to dynamically compute control
weights given diffusion timestep (sigma).
For example below code can softly make beginning steps stronger than ending steps.
sigma_max = unet.model.model_sampling.percent_to_sigma(0.0)
sigma_min = unet.model.model_sampling.percent_to_sigma(1.0)
advanced_sigma_weighting = lambda s: (s - sigma_min) / (sigma_max - sigma_min)
"""
cnet = controlnet.copy().set_cond_hint(image_bhwc.movedim(-1, 1), strength, (start_percent, end_percent))
cnet.positive_advanced_weighting = positive_advanced_weighting
cnet.negative_advanced_weighting = negative_advanced_weighting
cnet.advanced_frame_weighting = advanced_frame_weighting
cnet.advanced_sigma_weighting = advanced_sigma_weighting
m = unet.clone()
m.add_patched_controlnet(cnet)