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],)