195 lines
8.5 KiB
Python
195 lines
8.5 KiB
Python
import numpy as np
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from torch import Tensor
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import folder_paths
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from .control import load_controlnet, convert_to_advanced, ControlWeightsImport, ControlWeightTypeImport,\
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LatentKeyframeGroupImport, TimestepKeyframeImport, TimestepKeyframeGroupImport, is_advanced_controlnet
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from .control import StrengthInterpolationImport as SI
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from .weight_nodes import DefaultWeightsImport, ScaledSoftMaskedUniversalWeightsImport, ScaledSoftUniversalWeightsImport, SoftControlNetWeightsImport, CustomControlNetWeightsImport, \
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SoftT2IAdapterWeightsImport, CustomT2IAdapterWeightsImport
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from .latent_keyframe_nodes import LatentKeyframeGroupNodeImport, LatentKeyframeInterpolationNodeImport, LatentKeyframeBatchedGroupNodeImport, LatentKeyframeNodeImport
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from .logger import logger
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class TimestepKeyframeNodeImport:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}, ),
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},
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"optional": {
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"prev_timestep_kf": ("TIMESTEP_KEYFRAME", ),
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"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"cn_weights": ("CONTROL_NET_WEIGHTS", ),
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"latent_keyframe": ("LATENT_KEYFRAME", ),
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"null_latent_kf_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"inherit_missing": ("BOOLEAN", {"default": True}, ),
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"guarantee_usage": ("BOOLEAN", {"default": True}, ),
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"mask_optional": ("MASK", ),
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#"interpolation": ([SI.LINEAR, SI.EASE_IN, SI.EASE_OUT, SI.EASE_IN_OUT, SI.NONE], {"default": SI.NONE}, ),
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}
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}
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RETURN_NAMES = ("TIMESTEP_KF", )
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RETURN_TYPES = ("TIMESTEP_KEYFRAME", )
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FUNCTION = "load_keyframe"
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CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/keyframes"
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def load_keyframe(self,
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start_percent: float,
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strength: float=1.0,
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cn_weights: ControlWeightsImport=None, control_net_weights: ControlWeightsImport=None, # old name
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latent_keyframe: LatentKeyframeGroupImport=None,
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prev_timestep_kf: TimestepKeyframeGroupImport=None, prev_timestep_keyframe: TimestepKeyframeGroupImport=None, # old name
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null_latent_kf_strength: float=0.0,
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inherit_missing=True,
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guarantee_usage=True,
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mask_optional=None,
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interpolation: str=SI.NONE,):
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control_net_weights = control_net_weights if control_net_weights else cn_weights
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prev_timestep_keyframe = prev_timestep_keyframe if prev_timestep_keyframe else prev_timestep_kf
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if not prev_timestep_keyframe:
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prev_timestep_keyframe = TimestepKeyframeGroupImport()
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else:
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prev_timestep_keyframe = prev_timestep_keyframe.clone()
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keyframe = TimestepKeyframeImport(start_percent=start_percent, strength=strength, interpolation=interpolation, null_latent_kf_strength=null_latent_kf_strength,
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control_weights=control_net_weights, latent_keyframes=latent_keyframe, inherit_missing=inherit_missing, guarantee_usage=guarantee_usage,
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mask_hint_orig=mask_optional)
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prev_timestep_keyframe.add(keyframe)
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return (prev_timestep_keyframe,)
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class ControlNetLoaderAdvancedImport:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"control_net_name": (folder_paths.get_filename_list("controlnet"), ),
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},
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"optional": {
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"timestep_keyframe": ("TIMESTEP_KEYFRAME", ),
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}
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}
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RETURN_TYPES = ("CONTROL_NET", )
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FUNCTION = "load_controlnet"
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CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝"
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def load_controlnet(self, control_net_name,
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timestep_keyframe: TimestepKeyframeGroupImport=None
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):
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controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
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controlnet = load_controlnet(controlnet_path, timestep_keyframe)
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return (controlnet,)
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class DiffControlNetLoaderAdvancedImport:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"model": ("MODEL",),
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"control_net_name": (folder_paths.get_filename_list("controlnet"), )
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},
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"optional": {
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"timestep_keyframe": ("TIMESTEP_KEYFRAME", ),
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}
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}
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RETURN_TYPES = ("CONTROL_NET", )
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FUNCTION = "load_controlnet"
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CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝"
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def load_controlnet(self, control_net_name, model,
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timestep_keyframe: TimestepKeyframeGroupImport=None
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):
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controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
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controlnet = load_controlnet(controlnet_path, timestep_keyframe, model)
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if is_advanced_controlnet(controlnet):
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controlnet.verify_all_weights()
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return (controlnet,)
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class AdvancedControlNetApplyImport:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"positive": ("CONDITIONING", ),
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"negative": ("CONDITIONING", ),
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"control_net": ("CONTROL_NET", ),
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"image": ("IMAGE", ),
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"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
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"start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
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"end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001})
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},
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"optional": {
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"mask_optional": ("MASK", ),
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"timestep_kf": ("TIMESTEP_KEYFRAME", ),
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"latent_kf_override": ("LATENT_KEYFRAME", ),
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"weights_override": ("CONTROL_NET_WEIGHTS", ),
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}
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}
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RETURN_TYPES = ("CONDITIONING","CONDITIONING")
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RETURN_NAMES = ("positive", "negative")
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FUNCTION = "apply_controlnet"
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CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝"
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def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent,
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mask_optional: Tensor=None,
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timestep_kf: TimestepKeyframeGroupImport=None, latent_kf_override: LatentKeyframeGroupImport=None,
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weights_override: ControlWeightsImport=None):
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if strength == 0:
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return (positive, negative)
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control_hint = image.movedim(-1,1)
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cnets = {}
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out = []
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for conditioning in [positive, negative]:
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c = []
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for t in conditioning:
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d = t[1].copy()
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prev_cnet = d.get('control', None)
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if prev_cnet in cnets:
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c_net = cnets[prev_cnet]
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else:
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# copy, convert to advanced if needed, and set cond
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c_net = convert_to_advanced(control_net.copy()).set_cond_hint(control_hint, strength, (start_percent, end_percent))
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if is_advanced_controlnet(c_net):
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# apply optional parameters and overrides, if provided
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if timestep_kf is not None:
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c_net.set_timestep_keyframes(timestep_kf)
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if latent_kf_override is not None:
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c_net.latent_keyframe_override = latent_kf_override
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if weights_override is not None:
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c_net.weights_override = weights_override
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# verify weights are compatible
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c_net.verify_all_weights()
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# set cond hint mask
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if mask_optional is not None:
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mask_optional = mask_optional.clone()
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# if not in the form of a batch, make it so
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if len(mask_optional.shape) < 3:
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mask_optional = mask_optional.unsqueeze(0)
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c_net.set_cond_hint_mask(mask_optional)
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c_net.set_previous_controlnet(prev_cnet)
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cnets[prev_cnet] = c_net
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d['control'] = c_net
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d['control_apply_to_uncond'] = False
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n = [t[0], d]
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c.append(n)
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out.append(c)
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return (out[0], out[1])
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