lllyasviel/stable-diffusion-webui-forge
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lllyasviel
2024-01-28 07:02:58 -08:00
parent 3496403b54
commit 0b3c331189
3 changed files with 85 additions and 4 deletions
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31
README.md
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@@ -422,6 +422,7 @@ class ControlNetExampleForge(scripts.Script):
B, C, H, W = kwargs['noise'].shape # latent_shape
height = H * 8
width = W * 8
batch_size = p.batch_size
input_image = cv2.resize(input_image, (width, height))
@@ -446,10 +447,36 @@ class ControlNetExampleForge(scripts.Script):
unet = p.sd_model.forge_objects.unet
# Unet has input, middle, output blocks, and we can give different weights to each layers in all blocks.
# Below is an example for stronger control in middle block.
# This is helpful for some high-res fix passes. (p.is_hr_pass)
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': [1.2, 1.1, 1.0, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1]
}
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': [1.2, 1.1, 1.0, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1]
}
# 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]
# 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)]
# But in this simple example we do not use them
positive_advanced_weighting = None
negative_advanced_weighting = None
advanced_frame_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,
positive_advanced_weighting=None, negative_advanced_weighting=None,
advanced_frame_weighting=None)
positive_advanced_weighting=positive_advanced_weighting,
negative_advanced_weighting=negative_advanced_weighting,
advanced_frame_weighting=advanced_frame_weighting)
p.sd_model.forge_objects.unet = unet

31
extensions-builtin/sd_forge_controlnet_example/scripts/sd_forge_controlnet_example.py
View File

@@ -74,6 +74,7 @@ class ControlNetExampleForge(scripts.Script):
B, C, H, W = kwargs['noise'].shape # latent_shape
height = H * 8
width = W * 8
batch_size = p.batch_size
input_image = cv2.resize(input_image, (width, height))
@@ -98,10 +99,36 @@ class ControlNetExampleForge(scripts.Script):
unet = p.sd_model.forge_objects.unet
# Unet has input, middle, output blocks, and we can give different weights to each layers in all blocks.
# Below is an example for stronger control in middle block.
# This is helpful for some high-res fix passes. (p.is_hr_pass)
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': [1.2, 1.1, 1.0, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1]
}
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': [1.2, 1.1, 1.0, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1]
}
# 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]
# 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)]
# But in this simple example we do not use them
positive_advanced_weighting = None
negative_advanced_weighting = None
advanced_frame_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,
positive_advanced_weighting=None, negative_advanced_weighting=None,
advanced_frame_weighting=None)
positive_advanced_weighting=positive_advanced_weighting,
negative_advanced_weighting=negative_advanced_weighting,
advanced_frame_weighting=advanced_frame_weighting)
p.sd_model.forge_objects.unet = unet

27
modules_forge/controlnet.py
View File

@@ -9,6 +9,33 @@ def apply_controlnet_advanced(
negative_advanced_weighting=None,
advanced_frame_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.
Below is an example for stronger control in middle block.
This is helpful for some high-res fix passes.
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': [1.2, 1.1, 1.0, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1]
}
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': [1.2, 1.1, 1.0, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1]
}
# 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.
"""
cnet = controlnet.copy().set_cond_hint(image_bhwc.movedim(-1, 1), strength, (start_percent, end_percent))
cnet.positive_advanced_weighting = positive_advanced_weighting