Add single cond version of Apply Advanced ControlNet node
This commit is contained in:
Jedrzej Kosinski
+95
-51
@@ -104,7 +104,7 @@ class AdvancedControlNetApply:
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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": 40}),
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"autosize": ("ACNAUTOSIZE", {"padding": 0}),
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}
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}
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@@ -117,7 +117,7 @@ class AdvancedControlNetApply:
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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):
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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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@@ -129,59 +129,101 @@ class AdvancedControlNetApply:
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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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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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# 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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# if model required, verify model is passed in, and if so patch it
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if c_net.require_model:
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if not model_optional:
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raise Exception(f"Type '{type(c_net).__name__}' requires model_optional input, but got None.")
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c_net.patch_model(model=model_optional)
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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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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), 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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# if model required, verify model is passed in, and if so patch it
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if c_net.require_model:
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if not model_optional:
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raise Exception(f"Type '{type(c_net).__name__}' requires model_optional input, but got None.")
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c_net.patch_model(model=model_optional)
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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'] = False
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n = [t[0], d]
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c.append(n)
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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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@@ -196,6 +238,7 @@ NODE_CLASS_MAPPINGS = {
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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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# Loaders
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"ControlNetLoaderAdvanced": ControlNetLoaderAdvanced,
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"DiffControlNetLoaderAdvanced": DiffControlNetLoaderAdvanced,
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@@ -239,6 +282,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
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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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# Loaders
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"ControlNetLoaderAdvanced": "Load Advanced ControlNet Model 🛂🅐🅒🅝",
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"DiffControlNetLoaderAdvanced": "Load Advanced ControlNet Model (diff) 🛂🅐🅒🅝",
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