jaidaken
f09734b0ee
Some checks failed
Python Linting / Run Ruff (push) Has been cancelled
Python Linting / Run Pylint (push) Has been cancelled
Full Comfy CI Workflow Runs / test-stable (12.1, , linux, 3.10, [self-hosted Linux], stable) (push) Has been cancelled
Full Comfy CI Workflow Runs / test-stable (12.1, , linux, 3.11, [self-hosted Linux], stable) (push) Has been cancelled
Full Comfy CI Workflow Runs / test-stable (12.1, , linux, 3.12, [self-hosted Linux], stable) (push) Has been cancelled
Full Comfy CI Workflow Runs / test-unix-nightly (12.1, , linux, 3.11, [self-hosted Linux], nightly) (push) Has been cancelled
Execution Tests / test (macos-latest) (push) Has been cancelled
Execution Tests / test (ubuntu-latest) (push) Has been cancelled
Execution Tests / test (windows-latest) (push) Has been cancelled
Test server launches without errors / test (push) Has been cancelled
Unit Tests / test (macos-latest) (push) Has been cancelled
Unit Tests / test (ubuntu-latest) (push) Has been cancelled
Unit Tests / test (windows-2022) (push) Has been cancelled
Includes 30 custom nodes committed directly, 7 Civitai-exclusive loras stored via Git LFS, and a setup script that installs all dependencies and downloads HuggingFace-hosted models on vast.ai. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
93 lines
3.7 KiB
Python
93 lines
3.7 KiB
Python
import folder_paths
|
|
import comfy.controlnet
|
|
import comfy.model_management
|
|
from nodes import NODE_CLASS_MAPPINGS
|
|
|
|
union_controlnet_types = {"auto": -1, "openpose": 0, "depth": 1, "hed/pidi/scribble/ted": 2, "canny/lineart/anime_lineart/mlsd": 3, "normal": 4, "segment": 5, "tile": 6, "repaint": 7}
|
|
|
|
class easyControlnet:
|
|
def __init__(self):
|
|
pass
|
|
|
|
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, union_type=None, easyCache=None, use_cache=True, model=None, vae=None):
|
|
if strength == 0:
|
|
return (positive, negative)
|
|
|
|
# kolors controlnet patch
|
|
from ..modules.kolors.loader import is_kolors_model, applyKolorsUnet
|
|
if is_kolors_model(model):
|
|
from ..modules.kolors.model_patch import patch_controlnet
|
|
if control_net is None:
|
|
with applyKolorsUnet():
|
|
control_net = easyCache.load_controlnet(control_net_name, scale_soft_weights, use_cache)
|
|
control_net = patch_controlnet(model, control_net)
|
|
else:
|
|
if control_net is None:
|
|
if easyCache is not None:
|
|
control_net = easyCache.load_controlnet(control_net_name, scale_soft_weights, use_cache)
|
|
else:
|
|
controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
|
|
control_net = comfy.controlnet.load_controlnet(controlnet_path)
|
|
|
|
# union controlnet
|
|
if union_type is not None:
|
|
control_net = control_net.copy()
|
|
type_number = union_controlnet_types[union_type]
|
|
if type_number >= 0:
|
|
control_net.set_extra_arg("control_type", [type_number])
|
|
else:
|
|
control_net.set_extra_arg("control_type", [])
|
|
|
|
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), vae)
|
|
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) |