83 lines
3.3 KiB
Python
83 lines
3.3 KiB
Python
import folder_paths
|
|
import comfy.controlnet
|
|
import comfy.model_management
|
|
from nodes import NODE_CLASS_MAPPINGS
|
|
|
|
class easyControlnet:
|
|
def __init__(self):
|
|
pass
|
|
|
|
def load_controlnet(self, control_net_name, control_net, scale_soft_weights):
|
|
if control_net is None:
|
|
if scale_soft_weights < 1:
|
|
if "ScaledSoftControlNetWeights" in NODE_CLASS_MAPPINGS:
|
|
soft_weight_cls = NODE_CLASS_MAPPINGS['ScaledSoftControlNetWeights']
|
|
(weights, timestep_keyframe) = soft_weight_cls().load_weights(scale_soft_weights, False)
|
|
cn_adv_cls = NODE_CLASS_MAPPINGS['ControlNetLoaderAdvanced']
|
|
control_net, = cn_adv_cls().load_controlnet(control_net_name, timestep_keyframe)
|
|
else:
|
|
raise Exception(f"[Advanced-ControlNet Not Found] you need to install 'COMFYUI-Advanced-ControlNet'")
|
|
else:
|
|
controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
|
|
control_net = comfy.controlnet.load_controlnet(controlnet_path)
|
|
return control_net
|
|
|
|
def apply(self, control_net_name, image, positive, negative, strength, start_percent=0, end_percent=1, control_net=None, scale_soft_weights=1, mask=None):
|
|
if strength == 0:
|
|
return (positive, negative)
|
|
|
|
control_net = self.load_controlnet(control_net_name, control_net, scale_soft_weights)
|
|
|
|
if mask is not None:
|
|
mask = mask.to(self.device)
|
|
|
|
if mask is not None and len(mask.shape) < 3:
|
|
mask = mask.unsqueeze(0)
|
|
|
|
control_hint = image.movedim(-1, 1)
|
|
|
|
is_cond = True
|
|
if negative is None:
|
|
p = []
|
|
for t in positive:
|
|
n = [t[0], t[1].copy()]
|
|
c_net = control_net.copy().set_cond_hint(control_hint, strength, (start_percent, end_percent))
|
|
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 mask is not None:
|
|
n[1]['mask'] = mask
|
|
n[1]['set_area_to_bounds'] = False
|
|
p.append(n)
|
|
positive = p
|
|
else:
|
|
cnets = {}
|
|
out = []
|
|
for conditioning in [positive, negative]:
|
|
c = []
|
|
for t in conditioning:
|
|
d = t[1].copy()
|
|
|
|
prev_cnet = d.get('control', None)
|
|
if prev_cnet in cnets:
|
|
c_net = cnets[prev_cnet]
|
|
else:
|
|
c_net = control_net.copy().set_cond_hint(control_hint, strength, (start_percent, 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 mask is not None:
|
|
d['mask'] = mask
|
|
d['set_area_to_bounds'] = False
|
|
|
|
n = [t[0], d]
|
|
c.append(n)
|
|
out.append(c)
|
|
positive = out[0]
|
|
negative = out[1]
|
|
|
|
return (positive, negative) |