Updated Aoply Advanced ControlNet nodes to remove long unnecessaru model_optional fields, shifted some of the nodes.py code around to separate files to make deprecation cleaner
This commit is contained in:
Jedrzej Kosinski
+13
-212
@@ -7,6 +7,9 @@ from comfy.model_patcher import ModelPatcher
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from .control import load_controlnet, convert_to_advanced, is_advanced_controlnet, is_sd3_advanced_controlnet
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from .utils import ControlWeights, LatentKeyframeGroup, TimestepKeyframeGroup, AbstractPreprocWrapper, BIGMAX
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from .nodes_main import (ControlNetLoaderAdvanced, DiffControlNetLoaderAdvanced,
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AdvancedControlNetApply, AdvancedControlNetApplySingle)
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from .nodes_weight import (DefaultWeights, ScaledSoftMaskedUniversalWeights, ScaledSoftUniversalWeights,
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SoftControlNetWeightsSD15, CustomControlNetWeightsSD15, CustomControlNetWeightsFlux,
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SoftT2IAdapterWeights, CustomT2IAdapterWeights)
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@@ -18,7 +21,8 @@ from .nodes_plusplus import PlusPlusLoaderAdvanced, PlusPlusLoaderSingle, PlusPl
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from .nodes_loosecontrol import ControlNetLoaderWithLoraAdvanced
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from .nodes_deprecated import (LoadImagesFromDirectory, ScaledSoftUniversalWeightsDeprecated,
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SoftControlNetWeightsDeprecated, CustomControlNetWeightsDeprecated,
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SoftT2IAdapterWeightsDeprecated, CustomT2IAdapterWeightsDeprecated)
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SoftT2IAdapterWeightsDeprecated, CustomT2IAdapterWeightsDeprecated,
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AdvancedControlNetApplyDEPR, AdvancedControlNetApplySingleDEPR)
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from .logger import logger
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from .sampling import acn_sample_factory
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@@ -27,213 +31,6 @@ comfy.sample.sample = acn_sample_factory(comfy.sample.sample)
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comfy.sample.sample_custom = acn_sample_factory(comfy.sample.sample_custom, is_custom=True)
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class ControlNetLoaderAdvanced:
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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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"tk_optional": ("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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tk_optional: TimestepKeyframeGroup=None,
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timestep_keyframe: TimestepKeyframeGroup=None,
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):
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if timestep_keyframe is not None: # backwards compatibility
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tk_optional = timestep_keyframe
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controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
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controlnet = load_controlnet(controlnet_path, tk_optional)
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return (controlnet,)
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class DiffControlNetLoaderAdvanced:
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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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"tk_optional": ("TIMESTEP_KEYFRAME", ),
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"autosize": ("ACNAUTOSIZE", {"padding": 160}),
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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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tk_optional: TimestepKeyframeGroup=None,
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timestep_keyframe: TimestepKeyframeGroup=None
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):
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if timestep_keyframe is not None: # backwards compatibility
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tk_optional = timestep_keyframe
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controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
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controlnet = load_controlnet(controlnet_path, tk_optional, 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 AdvancedControlNetApply:
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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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"model_optional": ("MODEL",),
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"vae_optional": ("VAE",),
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"autosize": ("ACNAUTOSIZE", {"padding": 0}),
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}
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}
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RETURN_TYPES = ("CONDITIONING","CONDITIONING","MODEL",)
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RETURN_NAMES = ("positive", "negative", "model_opt")
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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, model_optional: ModelPatcher=None, vae_optional=None,
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timestep_kf: TimestepKeyframeGroup=None, latent_kf_override: LatentKeyframeGroup=None,
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weights_override: ControlWeights=None, control_apply_to_uncond=False):
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if strength == 0:
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return (positive, negative, model_optional)
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if model_optional:
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model_optional = model_optional.clone()
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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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if conditioning is not None:
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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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# make sure control_net is not None to avoid confusing error messages
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if control_net is None:
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raise Exception("Passed in control_net is None; something must have went wrong when loading it from a Load ControlNet node.")
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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), vae_optional)
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if is_advanced_controlnet(c_net):
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# disarm node check
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c_net.disarm()
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# check for allow_condhint_latents where vae_optional can't handle it itself
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if c_net.allow_condhint_latents and not c_net.require_vae:
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if not isinstance(control_hint, AbstractPreprocWrapper):
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raise Exception(f"Type '{type(c_net).__name__}' requires proc_IMAGE input via a corresponding preprocessor, but received a normal Image instead.")
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else:
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if isinstance(control_hint, AbstractPreprocWrapper) and not c_net.postpone_condhint_latents_check:
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raise Exception(f"Type '{type(c_net).__name__}' requires a normal Image input, but received a proc_IMAGE input instead.")
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# if vae required, verify vae is passed in
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if c_net.require_vae:
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# if controlnet can accept preprocced condhint latents and is the case, ignore vae requirement
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if c_net.allow_condhint_latents and isinstance(control_hint, AbstractPreprocWrapper):
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pass
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elif not vae_optional:
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# make sure SD3 ControlNet will get a special message instead of generic type mention
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if is_sd3_advanced_controlnet:
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raise Exception(f"SD3 ControlNet requires vae_optional input, but got None.")
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else:
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raise Exception(f"Type '{type(c_net).__name__}' requires vae_optional input, but got None.")
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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'] = control_apply_to_uncond
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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], model_optional)
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class AdvancedControlNetApplySingle:
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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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"conditioning": ("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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"model_optional": ("MODEL",),
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"vae_optional": ("VAE",),
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"autosize": ("ACNAUTOSIZE", {"padding": 0}),
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}
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}
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RETURN_TYPES = ("CONDITIONING","MODEL",)
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RETURN_NAMES = ("CONDITIONING", "model_opt")
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FUNCTION = "apply_controlnet"
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CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝"
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def apply_controlnet(self, conditioning, control_net, image, strength, start_percent, end_percent,
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mask_optional: Tensor=None, model_optional: ModelPatcher=None, vae_optional=None,
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timestep_kf: TimestepKeyframeGroup=None, latent_kf_override: LatentKeyframeGroup=None,
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weights_override: ControlWeights=None):
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values = AdvancedControlNetApply.apply_controlnet(self, positive=conditioning, negative=None, control_net=control_net, image=image,
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strength=strength, start_percent=start_percent, end_percent=end_percent,
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mask_optional=mask_optional, model_optional=model_optional, vae_optional=vae_optional,
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timestep_kf=timestep_kf, latent_kf_override=latent_kf_override, weights_override=weights_override,
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control_apply_to_uncond=True)
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return (values[0], values[2])
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# NODE MAPPING
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NODE_CLASS_MAPPINGS = {
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# Keyframes
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@@ -245,8 +42,8 @@ NODE_CLASS_MAPPINGS = {
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"LatentKeyframeBatchedGroup": LatentKeyframeBatchedGroupNode,
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"LatentKeyframeGroup": LatentKeyframeGroupNode,
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# Conditioning
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"ACN_AdvancedControlNetApply": AdvancedControlNetApply,
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"ACN_AdvancedControlNetApplySingle": AdvancedControlNetApplySingle,
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"ACN_AdvancedControlNetApply_v2": AdvancedControlNetApply,
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"ACN_AdvancedControlNetApplySingle_v2": AdvancedControlNetApplySingle,
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# Loaders
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"ControlNetLoaderAdvanced": ControlNetLoaderAdvanced,
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"DiffControlNetLoaderAdvanced": DiffControlNetLoaderAdvanced,
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@@ -283,6 +80,8 @@ NODE_CLASS_MAPPINGS = {
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"CustomControlNetWeights": CustomControlNetWeightsDeprecated,
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"SoftT2IAdapterWeights": SoftT2IAdapterWeightsDeprecated,
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"CustomT2IAdapterWeights": CustomT2IAdapterWeightsDeprecated,
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"ACN_AdvancedControlNetApply": AdvancedControlNetApplyDEPR,
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"ACN_AdvancedControlNetApplySingle": AdvancedControlNetApplySingleDEPR,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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@@ -295,8 +94,8 @@ NODE_DISPLAY_NAME_MAPPINGS = {
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"LatentKeyframeBatchedGroup": "Latent Keyframe From List 🛂🅐🅒🅝",
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"LatentKeyframeGroup": "Latent Keyframe Group 🛂🅐🅒🅝",
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# Conditioning
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"ACN_AdvancedControlNetApply": "Apply Advanced ControlNet 🛂🅐🅒🅝",
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"ACN_AdvancedControlNetApplySingle": "Apply Advanced ControlNet(1) 🛂🅐🅒🅝",
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"ACN_AdvancedControlNetApply_v2": "Apply Advanced ControlNet 🛂🅐🅒🅝",
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"ACN_AdvancedControlNetApplySingle_v2": "Apply Advanced ControlNet(1) 🛂🅐🅒🅝",
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# Loaders
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"ControlNetLoaderAdvanced": "Load Advanced ControlNet Model 🛂🅐🅒🅝",
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"DiffControlNetLoaderAdvanced": "Load Advanced ControlNet Model (diff) 🛂🅐🅒🅝",
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@@ -333,4 +132,6 @@ NODE_DISPLAY_NAME_MAPPINGS = {
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"CustomControlNetWeights": "ControlNet Custom Weights 🛂🅐🅒🅝",
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"SoftT2IAdapterWeights": "T2IAdapter Soft Weights 🛂🅐🅒🅝",
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"CustomT2IAdapterWeights": "T2IAdapter Custom Weights 🛂🅐🅒🅝",
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"ACN_AdvancedControlNetApply": "Apply Advanced ControlNet 🛂🅐🅒🅝",
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"ACN_AdvancedControlNetApplySingle": "Apply Advanced ControlNet(1) 🛂🅐🅒🅝",
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}
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@@ -4,6 +4,7 @@ import torch
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import numpy as np
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from PIL import Image, ImageOps
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from .nodes_main import AdvancedControlNetApply
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from .utils import BIGMAX, ControlWeights, TimestepKeyframeGroup, TimestepKeyframe, get_properly_arranged_t2i_weights
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from .logger import logger
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@@ -249,3 +250,87 @@ class CustomT2IAdapterWeightsDeprecated:
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weights = get_properly_arranged_t2i_weights(weights)
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weights = ControlWeights.t2iadapter(weights_input=weights, uncond_multiplier=uncond_multiplier, extras=cn_extras)
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return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
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class AdvancedControlNetApplyDEPR:
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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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"model_optional": ("MODEL",),
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"vae_optional": ("VAE",),
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"autosize": ("ACNAUTOSIZE", {"padding": 0}),
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}
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}
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DEPRECATED = True
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RETURN_TYPES = ("CONDITIONING","CONDITIONING","MODEL",)
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RETURN_NAMES = ("positive", "negative", "model_opt")
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FUNCTION = "apply_controlnet"
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CATEGORY = ""
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def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent,
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mask_optional=None, model_optional=None, vae_optional=None,
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timestep_kf: TimestepKeyframeGroup=None, latent_kf_override=None,
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weights_override: ControlWeights=None, control_apply_to_uncond=False):
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new_positive, new_negative = AdvancedControlNetApply.apply_controlnet(self, positive=positive, negative=negative, control_net=control_net, image=image,
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strength=strength, start_percent=start_percent, end_percent=end_percent,
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mask_optional=mask_optional, vae_optional=vae_optional,
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timestep_kf=timestep_kf, latent_kf_override=latent_kf_override, weights_override=weights_override,)
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return (new_positive, new_negative, model_optional)
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class AdvancedControlNetApplySingleDEPR:
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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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"conditioning": ("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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"model_optional": ("MODEL",),
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"vae_optional": ("VAE",),
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"autosize": ("ACNAUTOSIZE", {"padding": 0}),
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}
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}
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DEPRECATED = True
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RETURN_TYPES = ("CONDITIONING","MODEL",)
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RETURN_NAMES = ("CONDITIONING", "model_opt")
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FUNCTION = "apply_controlnet"
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CATEGORY = ""
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def apply_controlnet(self, conditioning, control_net, image, strength, start_percent, end_percent,
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mask_optional=None, model_optional=None, vae_optional=None,
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timestep_kf: TimestepKeyframeGroup=None, latent_kf_override=None,
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weights_override: ControlWeights=None):
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values = AdvancedControlNetApply.apply_controlnet(self, positive=conditioning, negative=None, control_net=control_net, image=image,
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strength=strength, start_percent=start_percent, end_percent=end_percent,
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mask_optional=mask_optional, vae_optional=vae_optional,
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timestep_kf=timestep_kf, latent_kf_override=latent_kf_override, weights_override=weights_override,
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control_apply_to_uncond=True)
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return (values[0], model_optional)
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@@ -0,0 +1,213 @@
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from torch import Tensor
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import folder_paths
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from comfy.model_patcher import ModelPatcher
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from .control import load_controlnet, convert_to_advanced, is_advanced_controlnet, is_sd3_advanced_controlnet
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from .utils import ControlWeights, LatentKeyframeGroup, TimestepKeyframeGroup, AbstractPreprocWrapper, BIGMAX
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from .logger import logger
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class ControlNetLoaderAdvanced:
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@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"control_net_name": (folder_paths.get_filename_list("controlnet"), ),
|
||||
},
|
||||
"optional": {
|
||||
"tk_optional": ("TIMESTEP_KEYFRAME", ),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("CONTROL_NET", )
|
||||
FUNCTION = "load_controlnet"
|
||||
|
||||
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝"
|
||||
|
||||
def load_controlnet(self, control_net_name,
|
||||
tk_optional: TimestepKeyframeGroup=None,
|
||||
timestep_keyframe: TimestepKeyframeGroup=None,
|
||||
):
|
||||
if timestep_keyframe is not None: # backwards compatibility
|
||||
tk_optional = timestep_keyframe
|
||||
controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
|
||||
controlnet = load_controlnet(controlnet_path, tk_optional)
|
||||
return (controlnet,)
|
||||
|
||||
|
||||
class DiffControlNetLoaderAdvanced:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"model": ("MODEL",),
|
||||
"control_net_name": (folder_paths.get_filename_list("controlnet"), )
|
||||
},
|
||||
"optional": {
|
||||
"tk_optional": ("TIMESTEP_KEYFRAME", ),
|
||||
"autosize": ("ACNAUTOSIZE", {"padding": 160}),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("CONTROL_NET", )
|
||||
FUNCTION = "load_controlnet"
|
||||
|
||||
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝"
|
||||
|
||||
def load_controlnet(self, control_net_name, model,
|
||||
tk_optional: TimestepKeyframeGroup=None,
|
||||
timestep_keyframe: TimestepKeyframeGroup=None
|
||||
):
|
||||
if timestep_keyframe is not None: # backwards compatibility
|
||||
tk_optional = timestep_keyframe
|
||||
controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
|
||||
controlnet = load_controlnet(controlnet_path, tk_optional, 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",),
|
||||
"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:
|
||||
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", ),
|
||||
"model_optional": ("MODEL",),
|
||||
"vae_optional": ("VAE",),
|
||||
"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, model_optional: ModelPatcher=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, model_optional=model_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],)
|
||||
Reference in New Issue
Block a user