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from torch import Tensor
import folder_paths
from comfy.model_patcher import ModelPatcher
from .control import load_controlnet, convert_to_advanced, is_advanced_controlnet, is_sd3_advanced_controlnet
from .utils import ControlWeights, LatentKeyframeGroup, TimestepKeyframeGroup, AbstractPreprocWrapper, BIGMAX
from .logger import logger
class ControlNetLoaderAdvanced:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"cnet": (folder_paths.get_filename_list("controlnet"), ),
},
"optional": {
"_tk_opt": ("TIMESTEP_KEYFRAME", ),
}
}
RETURN_TYPES = ("CONTROL_NET", )
FUNCTION = "load_controlnet"
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝"
def load_controlnet(self, cnet,
_tk_opt: TimestepKeyframeGroup=None,
):
controlnet_path = folder_paths.get_full_path("controlnet", cnet)
controlnet = load_controlnet(controlnet_path, _tk_opt)
return (controlnet,)
class DiffControlNetLoaderAdvanced:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model": ("MODEL",),
"cnet": (folder_paths.get_filename_list("controlnet"), )
},
"optional": {
"_tk_opt": ("TIMESTEP_KEYFRAME", ),
},
"hidden": {
"autosize": ("ACNAUTOSIZE", {"padding": 0}),
}
}
RETURN_TYPES = ("CONTROL_NET", )
FUNCTION = "load_controlnet"
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝"
def load_controlnet(self, cnet, model,
_tk_opt: TimestepKeyframeGroup=None,
):
controlnet_path = folder_paths.get_full_path("controlnet", cnet)
controlnet = load_controlnet(controlnet_path, _tk_opt, model)
if is_advanced_controlnet(controlnet):
controlnet.verify_all_weights()
return (controlnet,)
class AdvancedControlNetApply:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"positive": ("CONDITIONING", ),
"negative": ("CONDITIONING", ),
"control_net": ("CONTROL_NET", ),
"image": ("IMAGE", ),
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
"end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001})
},
"optional": {
"mask_optional": ("MASK", ),
"timestep_kf": ("TIMESTEP_KEYFRAME", ),
"latent_kf_override": ("LATENT_KEYFRAME", ),
"weights_override": ("CONTROL_NET_WEIGHTS", ),
"vae_optional": ("VAE",),
},
"hidden": {
"autosize": ("ACNAUTOSIZE", {"padding": 0}),
}
}
RETURN_TYPES = ("CONDITIONING","CONDITIONING",)
RETURN_NAMES = ("positive", "negative")
FUNCTION = "apply_controlnet"
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝"
def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent,
mask_optional: Tensor=None, vae_optional=None,
timestep_kf: TimestepKeyframeGroup=None, latent_kf_override: LatentKeyframeGroup=None,
weights_override: ControlWeights=None, control_apply_to_uncond=False):
if strength == 0:
return (positive, negative)
control_hint = image.movedim(-1,1)
cnets = {}
out = []
for conditioning in [positive, negative]:
c = []
if conditioning is not None:
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:
# make sure control_net is not None to avoid confusing error messages
if control_net is None:
raise Exception("Passed in control_net is None; something must have went wrong when loading it from a Load ControlNet node.")
# copy, convert to advanced if needed, and set cond
c_net = convert_to_advanced(control_net.copy()).set_cond_hint(control_hint, strength, (start_percent, end_percent), vae_optional)
if is_advanced_controlnet(c_net):
# disarm node check
c_net.disarm()
# check for allow_condhint_latents where vae_optional can't handle it itself
if c_net.allow_condhint_latents and not c_net.require_vae:
if not isinstance(control_hint, AbstractPreprocWrapper):
raise Exception(f"Type '{type(c_net).__name__}' requires proc_IMAGE input via a corresponding preprocessor, but received a normal Image instead.")
else:
if isinstance(control_hint, AbstractPreprocWrapper) and not c_net.postpone_condhint_latents_check:
raise Exception(f"Type '{type(c_net).__name__}' requires a normal Image input, but received a proc_IMAGE input instead.")
# if vae required, verify vae is passed in
if c_net.require_vae:
# if controlnet can accept preprocced condhint latents and is the case, ignore vae requirement
if c_net.allow_condhint_latents and isinstance(control_hint, AbstractPreprocWrapper):
pass
elif not vae_optional:
# make sure SD3 ControlNet will get a special message instead of generic type mention
if is_sd3_advanced_controlnet(c_net):
raise Exception(f"SD3 ControlNet requires vae_optional input, but got None.")
else:
raise Exception(f"Type '{type(c_net).__name__}' requires vae_optional input, but got None.")
# apply optional parameters and overrides, if provided
if timestep_kf is not None:
c_net.set_timestep_keyframes(timestep_kf)
if latent_kf_override is not None:
c_net.latent_keyframe_override = latent_kf_override
if weights_override is not None:
c_net.weights_override = weights_override
# verify weights are compatible
c_net.verify_all_weights()
# set cond hint mask
if mask_optional is not None:
mask_optional = mask_optional.clone()
# if not in the form of a batch, make it so
if len(mask_optional.shape) < 3:
mask_optional = mask_optional.unsqueeze(0)
c_net.set_cond_hint_mask(mask_optional)
c_net.set_previous_controlnet(prev_cnet)
cnets[prev_cnet] = c_net
d['control'] = c_net
d['control_apply_to_uncond'] = control_apply_to_uncond
n = [t[0], d]
c.append(n)
out.append(c)
return (out[0], out[1])
class AdvancedControlNetApplySingle:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"conditioning": ("CONDITIONING", ),
"control_net": ("CONTROL_NET", ),
"image": ("IMAGE", ),
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
"end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001})
},
"optional": {
"mask_optional": ("MASK", ),
"timestep_kf": ("TIMESTEP_KEYFRAME", ),
"latent_kf_override": ("LATENT_KEYFRAME", ),
"weights_override": ("CONTROL_NET_WEIGHTS", ),
"vae_optional": ("VAE",),
},
"hidden": {
"autosize": ("ACNAUTOSIZE", {"padding": 0}),
}
}
RETURN_TYPES = ("CONDITIONING","MODEL",)
RETURN_NAMES = ("CONDITIONING", "model_opt")
FUNCTION = "apply_controlnet"
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝"
def apply_controlnet(self, conditioning, control_net, image, strength, start_percent, end_percent,
mask_optional: Tensor=None, vae_optional=None,
timestep_kf: TimestepKeyframeGroup=None, latent_kf_override: LatentKeyframeGroup=None,
weights_override: ControlWeights=None):
values = AdvancedControlNetApply.apply_controlnet(self, positive=conditioning, negative=None, control_net=control_net, image=image,
strength=strength, start_percent=start_percent, end_percent=end_percent,
mask_optional=mask_optional, vae_optional=vae_optional,
timestep_kf=timestep_kf, latent_kf_override=latent_kf_override, weights_override=weights_override,
control_apply_to_uncond=True)
return (values[0],)
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