import numpy as np from torch import Tensor import folder_paths from .control import load_controlnet, convert_to_advanced, ControlWeightsImport, ControlWeightTypeImport,\ LatentKeyframeGroupImport, TimestepKeyframeImport, TimestepKeyframeGroupImport, is_advanced_controlnet from .control import StrengthInterpolationImport as SI from .weight_nodes import DefaultWeightsImport, ScaledSoftMaskedUniversalWeightsImport, ScaledSoftUniversalWeightsImport, SoftControlNetWeightsImport, CustomControlNetWeightsImport, \ SoftT2IAdapterWeightsImport, CustomT2IAdapterWeightsImport from .latent_keyframe_nodes import LatentKeyframeGroupNodeImport, LatentKeyframeInterpolationNodeImport, LatentKeyframeBatchedGroupNodeImport, LatentKeyframeNodeImport from .logger import logger class TimestepKeyframeNodeImport: @classmethod def INPUT_TYPES(s): return { "required": { "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}, ), }, "optional": { "prev_timestep_kf": ("TIMESTEP_KEYFRAME", ), "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), "cn_weights": ("CONTROL_NET_WEIGHTS", ), "latent_keyframe": ("LATENT_KEYFRAME", ), "null_latent_kf_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), "inherit_missing": ("BOOLEAN", {"default": True}, ), "guarantee_usage": ("BOOLEAN", {"default": True}, ), "mask_optional": ("MASK", ), #"interpolation": ([SI.LINEAR, SI.EASE_IN, SI.EASE_OUT, SI.EASE_IN_OUT, SI.NONE], {"default": SI.NONE}, ), } } RETURN_NAMES = ("TIMESTEP_KF", ) RETURN_TYPES = ("TIMESTEP_KEYFRAME", ) FUNCTION = "load_keyframe" CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/keyframes" def load_keyframe(self, start_percent: float, strength: float=1.0, cn_weights: ControlWeightsImport=None, control_net_weights: ControlWeightsImport=None, # old name latent_keyframe: LatentKeyframeGroupImport=None, prev_timestep_kf: TimestepKeyframeGroupImport=None, prev_timestep_keyframe: TimestepKeyframeGroupImport=None, # old name null_latent_kf_strength: float=0.0, inherit_missing=True, guarantee_usage=True, mask_optional=None, interpolation: str=SI.NONE,): control_net_weights = control_net_weights if control_net_weights else cn_weights prev_timestep_keyframe = prev_timestep_keyframe if prev_timestep_keyframe else prev_timestep_kf if not prev_timestep_keyframe: prev_timestep_keyframe = TimestepKeyframeGroupImport() else: prev_timestep_keyframe = prev_timestep_keyframe.clone() keyframe = TimestepKeyframeImport(start_percent=start_percent, strength=strength, interpolation=interpolation, null_latent_kf_strength=null_latent_kf_strength, control_weights=control_net_weights, latent_keyframes=latent_keyframe, inherit_missing=inherit_missing, guarantee_usage=guarantee_usage, mask_hint_orig=mask_optional) prev_timestep_keyframe.add(keyframe) return (prev_timestep_keyframe,) class ControlNetLoaderAdvancedImport: @classmethod def INPUT_TYPES(s): return { "required": { "control_net_name": (folder_paths.get_filename_list("controlnet"), ), }, "optional": { "timestep_keyframe": ("TIMESTEP_KEYFRAME", ), } } RETURN_TYPES = ("CONTROL_NET", ) FUNCTION = "load_controlnet" CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝" def load_controlnet(self, control_net_name, timestep_keyframe: TimestepKeyframeGroupImport=None ): controlnet_path = folder_paths.get_full_path("controlnet", control_net_name) controlnet = load_controlnet(controlnet_path, timestep_keyframe) return (controlnet,) class DiffControlNetLoaderAdvancedImport: @classmethod def INPUT_TYPES(s): return { "required": { "model": ("MODEL",), "control_net_name": (folder_paths.get_filename_list("controlnet"), ) }, "optional": { "timestep_keyframe": ("TIMESTEP_KEYFRAME", ), } } RETURN_TYPES = ("CONTROL_NET", ) FUNCTION = "load_controlnet" CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝" def load_controlnet(self, control_net_name, model, timestep_keyframe: TimestepKeyframeGroupImport=None ): controlnet_path = folder_paths.get_full_path("controlnet", control_net_name) controlnet = load_controlnet(controlnet_path, timestep_keyframe, model) if is_advanced_controlnet(controlnet): controlnet.verify_all_weights() return (controlnet,) class AdvancedControlNetApplyImport: @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", ), } } 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, timestep_kf: TimestepKeyframeGroupImport=None, latent_kf_override: LatentKeyframeGroupImport=None, weights_override: ControlWeightsImport=None): if strength == 0: return (positive, negative) control_hint = image.movedim(-1,1) 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: # 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)) if is_advanced_controlnet(c_net): # 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'] = False n = [t[0], d] c.append(n) out.append(c) return (out[0], out[1])