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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16
README.md
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@@ -447,8 +447,8 @@ 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.
# 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 = {
@@ -465,10 +465,17 @@ 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]
# If you view the 5 images as 5 frames in a video, this will lead to
# 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)]
# 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)
# But in this simple example we do not use them
positive_advanced_weighting = None
negative_advanced_weighting = None
@@ -478,7 +485,8 @@ class ControlNetExampleForge(scripts.Script):
strength=0.6, start_percent=0.0, end_percent=0.8,
positive_advanced_weighting=positive_advanced_weighting,
negative_advanced_weighting=negative_advanced_weighting,
advanced_frame_weighting=advanced_frame_weighting)
advanced_frame_weighting=advanced_frame_weighting,
advanced_sigma_weighting=advanced_sigma_weighting)
p.sd_model.forge_objects.unet = unet

16
extensions-builtin/sd_forge_controlnet_example/scripts/sd_forge_controlnet_example.py
View File

@@ -99,7 +99,8 @@ 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.
# 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 = {
@@ -116,9 +117,17 @@ 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]
# If you view the 5 images as 5 frames in a video, this will lead to progressively stronger control over time.
# 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)]
# 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)
# But in this simple example we do not use them
positive_advanced_weighting = None
negative_advanced_weighting = None
@@ -128,7 +137,8 @@ class ControlNetExampleForge(scripts.Script):
strength=0.6, start_percent=0.0, end_percent=0.8,
positive_advanced_weighting=positive_advanced_weighting,
negative_advanced_weighting=negative_advanced_weighting,
advanced_frame_weighting=advanced_frame_weighting)
advanced_frame_weighting=advanced_frame_weighting,
advanced_sigma_weighting=advanced_sigma_weighting)
p.sd_model.forge_objects.unet = unet

12
modules_forge/controlnet.py
View File

@@ -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)