nodes/Kosinkadink-ComfyUI-Advanced-ControlNet
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Renamed extras to cn_extras to differentiate from potential extras on AnimateDiff in the future (which would be called ad_extras there)

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Jedrzej Kosinski
2024-05-29 01:37:56 -05:00
parent 6940e3fd4b
commit 576426a734
2 changed files with 29 additions and 29 deletions
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adv_control/nodes_sparsectrl.py
+8 -8
View File
@@ -175,7 +175,7 @@ class SparseWeightExtras:
def INPUT_TYPES(s):
return {
"optional": {
"extras": ("CN_WEIGHTS_EXTRAS",),
"cn_extras": ("CN_WEIGHTS_EXTRAS",),
"sparse_hint_mult": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
"sparse_nonhint_mult": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
"sparse_mask_mult": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
@@ -183,14 +183,14 @@ class SparseWeightExtras:
}
RETURN_TYPES = ("CN_WEIGHTS_EXTRAS", )
RETURN_NAMES = ("extras", )
RETURN_NAMES = ("cn_extras", )
FUNCTION = "create_weight_extras"
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/SparseCtrl/extras"
def create_weight_extras(self, extras: dict[str]={}, sparse_hint_mult=1.0, sparse_nonhint_mult=1.0, sparse_mask_mult=1.0):
extras = extras.copy()
extras[SparseConst.HINT_MULT] = sparse_hint_mult
extras[SparseConst.NONHINT_MULT] = sparse_nonhint_mult
extras[SparseConst.MASK_MULT] = sparse_mask_mult
return (extras, )
def create_weight_extras(self, cn_extras: dict[str]={}, sparse_hint_mult=1.0, sparse_nonhint_mult=1.0, sparse_mask_mult=1.0):
cn_extras = cn_extras.copy()
cn_extras[SparseConst.HINT_MULT] = sparse_hint_mult
cn_extras[SparseConst.NONHINT_MULT] = sparse_nonhint_mult
cn_extras[SparseConst.MASK_MULT] = sparse_mask_mult
return (cn_extras, )
adv_control/nodes_weight.py
+21 -21
View File
@@ -12,7 +12,7 @@ class DefaultWeights:
def INPUT_TYPES(s):
return {
"optional": {
"extras": ("CN_WEIGHTS_EXTRAS",),
"cn_extras": ("CN_WEIGHTS_EXTRAS",),
}
}
@@ -22,8 +22,8 @@ class DefaultWeights:
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights"
def load_weights(self, extras: dict[str]={}):
weights = ControlWeights.default(extras=extras)
def load_weights(self, cn_extras: dict[str]={}):
weights = ControlWeights.default(extras=cn_extras)
return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
@@ -40,7 +40,7 @@ class ScaledSoftMaskedUniversalWeights:
},
"optional": {
"uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
"extras": ("CN_WEIGHTS_EXTRAS",),
"cn_extras": ("CN_WEIGHTS_EXTRAS",),
}
}
@@ -51,7 +51,7 @@ class ScaledSoftMaskedUniversalWeights:
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights"
def load_weights(self, mask: Tensor, min_base_multiplier: float, max_base_multiplier: float, lock_min=False, lock_max=False,
uncond_multiplier: float=1.0, extras: dict[str]={}):
uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
# normalize mask
mask = mask.clone()
x_min = 0.0 if lock_min else mask.min()
@@ -60,7 +60,7 @@ class ScaledSoftMaskedUniversalWeights:
mask = torch.ones_like(mask) * max_base_multiplier
else:
mask = linear_conversion(mask, x_min, x_max, min_base_multiplier, max_base_multiplier)
weights = ControlWeights.universal_mask(weight_mask=mask, uncond_multiplier=uncond_multiplier, extras=extras)
weights = ControlWeights.universal_mask(weight_mask=mask, uncond_multiplier=uncond_multiplier, extras=cn_extras)
return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
@@ -74,7 +74,7 @@ class ScaledSoftUniversalWeights:
},
"optional": {
"uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
"extras": ("CN_WEIGHTS_EXTRAS",),
"cn_extras": ("CN_WEIGHTS_EXTRAS",),
}
}
@@ -84,8 +84,8 @@ class ScaledSoftUniversalWeights:
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights"
def load_weights(self, base_multiplier, flip_weights, uncond_multiplier: float=1.0, extras: dict[str]={}):
weights = ControlWeights.universal(base_multiplier=base_multiplier, flip_weights=flip_weights, uncond_multiplier=uncond_multiplier, extras=extras)
def load_weights(self, base_multiplier, flip_weights, uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
weights = ControlWeights.universal(base_multiplier=base_multiplier, flip_weights=flip_weights, uncond_multiplier=uncond_multiplier, extras=cn_extras)
return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
@@ -111,7 +111,7 @@ class SoftControlNetWeights:
},
"optional": {
"uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
"extras": ("CN_WEIGHTS_EXTRAS",),
"cn_extras": ("CN_WEIGHTS_EXTRAS",),
}
}
@@ -123,10 +123,10 @@ class SoftControlNetWeights:
def load_weights(self, weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06,
weight_07, weight_08, weight_09, weight_10, weight_11, weight_12, flip_weights,
uncond_multiplier: float=1.0, extras: dict[str]={}):
uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
weights = [weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06,
weight_07, weight_08, weight_09, weight_10, weight_11, weight_12]
weights = ControlWeights.controlnet(weights, flip_weights=flip_weights, uncond_multiplier=uncond_multiplier, extras=extras)
weights = ControlWeights.controlnet(weights, flip_weights=flip_weights, uncond_multiplier=uncond_multiplier, extras=cn_extras)
return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
@@ -152,7 +152,7 @@ class CustomControlNetWeights:
},
"optional": {
"uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
"extras": ("CN_WEIGHTS_EXTRAS",),
"cn_extras": ("CN_WEIGHTS_EXTRAS",),
}
}
@@ -164,10 +164,10 @@ class CustomControlNetWeights:
def load_weights(self, weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06,
weight_07, weight_08, weight_09, weight_10, weight_11, weight_12, flip_weights,
uncond_multiplier: float=1.0, extras: dict[str]={}):
uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
weights = [weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06,
weight_07, weight_08, weight_09, weight_10, weight_11, weight_12]
weights = ControlWeights.controlnet(weights, flip_weights=flip_weights, uncond_multiplier=uncond_multiplier, extras=extras)
weights = ControlWeights.controlnet(weights, flip_weights=flip_weights, uncond_multiplier=uncond_multiplier, extras=cn_extras)
return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
@@ -184,7 +184,7 @@ class SoftT2IAdapterWeights:
},
"optional": {
"uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
"extras": ("CN_WEIGHTS_EXTRAS",),
"cn_extras": ("CN_WEIGHTS_EXTRAS",),
}
}
@@ -195,10 +195,10 @@ class SoftT2IAdapterWeights:
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/T2IAdapter"
def load_weights(self, weight_00, weight_01, weight_02, weight_03, flip_weights,
uncond_multiplier: float=1.0, extras: dict[str]={}):
uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
weights = [weight_00, weight_01, weight_02, weight_03]
weights = get_properly_arranged_t2i_weights(weights)
weights = ControlWeights.t2iadapter(weights, flip_weights=flip_weights, uncond_multiplier=uncond_multiplier, extras=extras)
weights = ControlWeights.t2iadapter(weights, flip_weights=flip_weights, uncond_multiplier=uncond_multiplier, extras=cn_extras)
return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
@@ -215,7 +215,7 @@ class CustomT2IAdapterWeights:
},
"optional": {
"uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
"extras": ("CN_WEIGHTS_EXTRAS",),
"cn_extras": ("CN_WEIGHTS_EXTRAS",),
}
}
@@ -226,8 +226,8 @@ class CustomT2IAdapterWeights:
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/T2IAdapter"
def load_weights(self, weight_00, weight_01, weight_02, weight_03, flip_weights,
uncond_multiplier: float=1.0, extras: dict[str]={}):
uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
weights = [weight_00, weight_01, weight_02, weight_03]
weights = get_properly_arranged_t2i_weights(weights)
weights = ControlWeights.t2iadapter(weights, flip_weights=flip_weights, uncond_multiplier=uncond_multiplier, extras=extras)
weights = ControlWeights.t2iadapter(weights, flip_weights=flip_weights, uncond_multiplier=uncond_multiplier, extras=cn_extras)
return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))