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lllyasviel
2024-01-28 07:56:42 -08:00
parent 8158e31d80
commit 3d6d19a893
4 changed files with 29 additions and 16 deletions
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12
extensions-builtin/sd_forge_controlnet_example/scripts/sd_forge_controlnet_example.py
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@@ -112,10 +112,10 @@ class ControlNetExampleForge(scripts.Script):
# The advanced_frame_weighting is a weight applied to each image in a batch.
# The length of this list must be same with batch size
# For example, if batch size is 5, the below list is [0, 0.25, 0.5, 0.75, 1.0]
# For example, if batch size is 5, the below list is [0.2, 0.4, 0.6, 0.8, 1.0]
# If you view the 5 images as 5 frames in a video, this will lead to
# progressively stronger control over time.
advanced_frame_weighting = [float(i) / float(batch_size - 1) for i in range(batch_size)]
advanced_frame_weighting = [float(i + 1) / float(batch_size) for i in range(batch_size)]
# The advanced_sigma_weighting allows you to dynamically compute control
# weights given diffusion timestep (sigma).
@@ -125,10 +125,10 @@ class ControlNetExampleForge(scripts.Script):
advanced_sigma_weighting = lambda s: (s - sigma_min) / (sigma_max - sigma_min)
# But in this simple example we do not use them
positive_advanced_weighting = None
negative_advanced_weighting = None
advanced_frame_weighting = None
advanced_sigma_weighting = None
# positive_advanced_weighting = None
# negative_advanced_weighting = None
# advanced_frame_weighting = None
# advanced_sigma_weighting = None
unet = apply_controlnet_advanced(unet=unet, controlnet=self.model, image_bhwc=control_image,
strength=0.6, start_percent=0.0, end_percent=0.8,