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Update nodes.py

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Added the soft injection mechanism, which will modulate how the weights are injected. It is best to use this when performing high-res fix with controlnet, as well as controlnet inpaint. To use this simply pass to the controlnet apply node a dict like this {'image': image, 'soft_injection': True} instead of a image tensor .
This will work with the advanced apply controlnet(it will assign 2 scaled weights, one for each controlnet).
It is SDXL safe. it will guess the model based on the controlnet architecture.
This will not break anything since if you pass only an image to the node, the standard beheviour will not change
This commit is contained in:
Marco 2023-09-29 01:37:46 +02:00
parent 26b7372805
commit a05005ab9d
1 changed files with 28 additions and 4 deletions
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32
nodes.py
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@ -636,15 +636,26 @@ class ControlNetApply:
if strength == 0:
return (conditioning, )
c = []
control_hint = image.movedim(-1,1)
if isinstance(image, dict):
control_hint = image['image'].movedim(-1, 1)
is_soft_injection = image['soft_injection']
else: #check if image tells to do a soft injection method
control_hint = image.movedim(-1,1)
is_soft_injection = False
for t in conditioning:
n = [t[0], t[1].copy()]
c_net = control_net.copy().set_cond_hint(control_hint, strength)
c_net = control_net.copy().set_cond_hint(control_hint, strength, is_soft_injection)
if 'control' in t[1]:
c_net.set_previous_controlnet(t[1]['control'])
n[1]['control'] = c_net
n[1]['control_apply_to_uncond'] = True
if is_soft_injection:
scaled_weights = [strength * (0.825 ** float(12 - i)) for i in range(13)]
if len(c_net.control_model.input_blocks) == 9: #is a sdxl controlnet
scaled_weights = scaled_weights[:10]
c_net.set_cond_scaled_weight(scaled_weights)
c.append(n)
return (c, )
@ -671,7 +682,12 @@ class ControlNetApplyAdvanced:
if strength == 0:
return (positive, negative)
control_hint = image.movedim(-1,1)
if isinstance(image, dict):
control_hint = image['image'].movedim(-1, 1)
is_soft_injection = image['soft_injection']
else: # check if image tells to do a soft injection method
control_hint = image.movedim(-1, 1)
is_soft_injection = False
cnets = {}
out = []
@ -684,12 +700,17 @@ class ControlNetApplyAdvanced:
if prev_cnet in cnets:
c_net = cnets[prev_cnet]
else:
c_net = control_net.copy().set_cond_hint(control_hint, strength, (1.0 - start_percent, 1.0 - end_percent))
c_net = control_net.copy().set_cond_hint(control_hint, strength, is_soft_injection, (1.0 - start_percent, 1.0 - end_percent))
c_net.set_previous_controlnet(prev_cnet)
cnets[prev_cnet] = c_net
d['control'] = c_net
d['control_apply_to_uncond'] = False
if is_soft_injection:
scaled_weights = [strength * (0.825 ** float(12 - i)) for i in range(13)]
if len(c_net.control_model.input_blocks) == 9: # is a sdxl controlnet
scaled_weights = scaled_weights[:10]
c_net.set_cond_scaled_weight(scaled_weights)
n = [t[0], d]
c.append(n)
out.append(c)
@ -1189,8 +1210,11 @@ class SetLatentNoiseMask:
s["noise_mask"] = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1]))
return (s,)
def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False):
device = comfy.model_management.get_torch_device()
latent_image = latent["samples"]
if disable_noise:
noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
else: