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import torch
def apply_controlnet_advanced(
unet,
controlnet,
image_bchw,
strength,
start_percent,
end_percent,
positive_advanced_weighting=None,
negative_advanced_weighting=None,
advanced_frame_weighting=None,
advanced_sigma_weighting=None,
advanced_mask_weighting=None,
):
"""
### positive_advanced_weighting or negative_advanced_weighting
UNet has input, middle, output blocks, and we can give different weights to each layers in all blocks.
This is helpful for some high-res fix passes.
Below is an example for stronger control in middle block:
```
positive_advanced_weighting = {
'input': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2],
'middle': [1.0],
'output': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2]
}
negative_advanced_weighting = {
'input': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2],
'middle': [1.0],
'output': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2]
}
```
### advanced_frame_weighting
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, 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.sigma_max
sigma_min = unet.model.model_sampling.sigma_min
advanced_sigma_weighting = lambda s: (s - sigma_min) / (sigma_max - sigma_min)
```
### advanced_mask_weighting
A mask can be applied to control signals.
This should be a tensor with shape [B, 1, H, W] where the H and W can be arbitrary.
This mask will be resized automatically to match the shape of all injection layers.
"""
cnet = controlnet.copy().set_cond_hint(
image_bchw,
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
if advanced_mask_weighting is not None:
assert isinstance(advanced_mask_weighting, torch.Tensor)
B, C, H, W = advanced_mask_weighting.shape
assert B > 0 and C == 1 and H > 0 and W > 0
cnet.advanced_mask_weighting = advanced_mask_weighting
m = unet.clone()
m.add_patched_controlnet(cnet)
return m
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