Scaled Soft Mask Weights added, fixed latent keyframe error when batch_index out of range, removed duplicate weight nodes, renamed TK outputs on weight nodes
This commit is contained in:
Jedrzej Kosinski
+41
-34
@@ -35,11 +35,7 @@ class ControlWeights:
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self.weights.reverse()
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self.weight_mask = weight_mask
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def get(self, idx: int, shape: Tensor) -> Union[float, Tensor]:
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# if weight_mask present, return normalized mask
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if self.base_multiplier != 1.0 and self.weight_mask is not None:
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# TODO: fill out
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pass
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def get(self, idx: int) -> Union[float, Tensor]:
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# if weights is not none, return index
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if self.weights is not None:
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return self.weights[idx]
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@@ -53,6 +49,10 @@ class ControlWeights:
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def universal(cls, base_multiplier: float, flip_weights: bool=False):
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return cls(ControlWeightType.UNIVERSAL, base_multiplier=base_multiplier, flip_weights=flip_weights)
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@classmethod
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def universal_mask(cls, weight_mask: Tensor):
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return cls(ControlWeightType.UNIVERSAL, weight_mask=weight_mask)
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@classmethod
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def t2iadapter(cls, weights: list[float]=None, flip_weights: bool=False):
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if weights is None:
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@@ -268,7 +268,6 @@ class AdvancedControlBase:
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self.batched_number = batched_number
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# get current step percent
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curr_t: float = t[0]
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print(f"$$$$ {curr_t}")
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prev_index = self.current_timestep_index
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# if has next index, loop through and see if need to switch
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if self.timestep_keyframes.has_index(self.current_timestep_index+1):
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@@ -278,7 +277,8 @@ class AdvancedControlBase:
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if eval_tk.start_t >= curr_t:
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self.current_timestep_index = i
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self.current_timestep_keyframe = eval_tk
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# keep track of weights and control weights, accounting for inherit_missing
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# keep track of control weights, latent keyframes, and masks,
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# accounting for inherit_missing
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if self.current_timestep_keyframe.has_control_weights():
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self.weights = self.current_timestep_keyframe.control_weights
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elif not self.current_timestep_keyframe.inherit_missing:
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@@ -314,6 +314,9 @@ class AdvancedControlBase:
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if self.weights is None or self.weights.weight_type == ControlWeightType.DEFAULT:
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self.weights = self.weights_default
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elif self.weights.weight_type == ControlWeightType.UNIVERSAL:
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# if universal and weight_mask present, no need to convert
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if self.weights.weight_mask is not None:
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return
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self.weights = self.get_universal_weights()
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def get_universal_weights(self) -> ControlWeights:
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@@ -352,6 +355,14 @@ class AdvancedControlBase:
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def get_control_advanced(self, x_noisy, t, cond, batched_number):
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pass
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def calc_weight(self, idx: int, x: Tensor, layers: int) -> Union[float, Tensor]:
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if self.weights.weight_mask is not None:
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# prepare weight mask
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self.prepare_weight_mask_cond_hint(x, self.batched_number)
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# adjust mask for current layer and return
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return torch.pow(self.weight_mask_cond_hint, (layers-1)-idx)
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return self.weights.get(idx=idx)
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def apply_advanced_strengths_and_masks(self, x: Tensor, batched_number: int):
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# apply strengths, and get batch indeces to null out
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# AKA latents that should not be influenced by ControlNet
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@@ -381,6 +392,10 @@ class AdvancedControlBase:
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continue
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indeces_to_null.remove(real_index)
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# if real_index is outside the bounds of latents, don't apply
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if real_index >= latent_count or real_index < 0:
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continue
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# apply strength for each batched cond/uncond
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for b in range(batched_number):
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x[(latent_count*b)+real_index] = x[(latent_count*b)+real_index] * keyframe.strength
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@@ -411,7 +426,7 @@ class AdvancedControlBase:
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if x is not None:
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self.apply_advanced_strengths_and_masks(x, self.batched_number)
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x *= self.strength * self.weights.get(i, x.shape)
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x *= self.strength * self.calc_weight(i, x, len(control_input))
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if x.dtype != output_dtype:
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x = x.to(output_dtype)
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out[key].insert(0, x)
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@@ -431,7 +446,7 @@ class AdvancedControlBase:
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if self.global_average_pooling:
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x = torch.mean(x, dim=(2, 3), keepdim=True).repeat(1, 1, x.shape[2], x.shape[3])
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x *= self.strength * self.weights.get(i, x.shape)
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x *= self.strength * self.calc_weight(i, x, len(control_output))
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if x.dtype != output_dtype:
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x = x.to(output_dtype)
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@@ -451,36 +466,17 @@ class AdvancedControlBase:
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return out
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def prepare_mask_cond_hint(self, x_noisy: Tensor, t, cond, batched_number, dtype=None):
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# make mask appropriate dimensions, if present
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if self.mask_cond_hint_original is not None:
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if self.sub_idxs is not None or self.mask_cond_hint is None or x_noisy.shape[2] * 8 != self.mask_cond_hint.shape[1] or x_noisy.shape[3] * 8 != self.mask_cond_hint.shape[2]:
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if self.mask_cond_hint is not None:
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del self.mask_cond_hint
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self.mask_cond_hint = None
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# TODO: perform upscale on only the sub_idxs masks at a time instead of all to conserve RAM
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# resize mask and match batch count
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self.mask_cond_hint = prepare_mask_batch(self.mask_cond_hint_original, x_noisy.shape, multiplier=8)
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actual_latent_length = x_noisy.shape[0] // batched_number
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self.mask_cond_hint = comfy.utils.repeat_to_batch_size(self.mask_cond_hint, actual_latent_length if self.sub_idxs is None else self.full_latent_length)
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if self.sub_idxs is not None:
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self.mask_cond_hint = self.mask_cond_hint[self.sub_idxs]
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# make cond_hint_mask length match x_noise
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if x_noisy.shape[0] != self.mask_cond_hint.shape[0]:
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self.mask_cond_hint = broadcast_image_to(self.mask_cond_hint, x_noisy.shape[0], batched_number)
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# default dtype to be same as x_noisy
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if dtype is None:
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dtype = x_noisy.dtype
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self.mask_cond_hint = self.mask_cond_hint.to(dtype=dtype).to(self.device)
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# prepare other masks
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self._prepare_mask("mask_cond_hint", self.mask_cond_hint_original, x_noisy, t, cond, batched_number, dtype)
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self.prepare_tk_mask_cond_hint(x_noisy, t, cond, batched_number, dtype)
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def prepare_tk_mask_cond_hint(self, x_noisy: Tensor, t, cond, batched_number, dtype=None):
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return self._prepare_mask("tk_mask_cond_hint", self.current_timestep_keyframe.mask_hint_orig, x_noisy, t, cond, batched_number, dtype)
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def prepare_weight_mask_cond_hint(self, x_noisy: Tensor, t, cond, batched_number, dtype=None):
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return self._prepare_mask("weight_mask_cond_hint", self.current_timestep_keyframe.mask_hint_orig, x_noisy, t, cond, batched_number, dtype)
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def prepare_weight_mask_cond_hint(self, x_noisy: Tensor, batched_number, dtype=None):
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return self._prepare_mask("weight_mask_cond_hint", self.weights.weight_mask, x_noisy, t=None, cond=None, batched_number=batched_number, dtype=dtype, direct_attn=True)
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def _prepare_mask(self, attr_name, orig_mask: Tensor, x_noisy: Tensor, t, cond, batched_number, dtype=None):
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def _prepare_mask(self, attr_name, orig_mask: Tensor, x_noisy: Tensor, t, cond, batched_number, dtype=None, direct_attn=False):
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# make mask appropriate dimensions, if present
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if orig_mask is not None:
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out_mask = getattr(self, attr_name)
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if self.sub_idxs is not None or out_mask is None or x_noisy.shape[2] * 8 != out_mask.shape[1] or x_noisy.shape[3] * 8 != out_mask.shape[2]:
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@@ -488,7 +484,8 @@ class AdvancedControlBase:
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del out_mask
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# TODO: perform upscale on only the sub_idxs masks at a time instead of all to conserve RAM
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# resize mask and match batch count
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out_mask = prepare_mask_batch(orig_mask, x_noisy.shape, multiplier=8)
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multiplier = 1 if direct_attn else 8
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out_mask = prepare_mask_batch(orig_mask, x_noisy.shape, multiplier=multiplier)
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actual_latent_length = x_noisy.shape[0] // batched_number
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out_mask = comfy.utils.repeat_to_batch_size(out_mask, actual_latent_length if self.sub_idxs is None else self.full_latent_length)
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if self.sub_idxs is not None:
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@@ -734,3 +731,13 @@ def prepare_mask_batch(mask: Tensor, shape: Tensor, multiplier: int=1, match_dim
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if match_dim1:
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mask = torch.cat([mask] * shape[1], dim=1)
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return mask
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# applies min-max normalization, from:
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# https://stackoverflow.com/questions/68791508/min-max-normalization-of-a-tensor-in-pytorch
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def normalize_min_max(x: Tensor, new_min = 0.0, new_max = 1.0):
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x_min, x_max = x.min(), x.max()
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return (((x - x_min)/(x_max - x_min)) * (new_max - new_min)) + new_min
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def linear_conversion(x, x_min=0.0, x_max=1.0, new_min=0.0, new_max=1.0):
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return (((x - x_min)/(x_max - x_min)) * (new_max - new_min)) + new_min
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+11
-11
@@ -1,11 +1,12 @@
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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, ControlWeights, ControlWeightType,\
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LatentKeyframeGroup, TimestepKeyframe, TimestepKeyframeGroup, is_advanced_controlnet
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from .control import StrengthInterpolation as SI
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from .weight_nodes import DefaultWeights, ScaledSoftControlLoraWeights, ScaledSoftControlNetWeights, ScaledSoftUniversalWeights, SoftControlNetWeights, CustomControlNetWeights, \
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from .weight_nodes import DefaultWeights, ScaledSoftMaskedUniversalWeights, ScaledSoftUniversalWeights, SoftControlNetWeights, CustomControlNetWeights, \
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SoftT2IAdapterWeights, CustomT2IAdapterWeights
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from .latent_keyframe_nodes import LatentKeyframeGroupNode, LatentKeyframeInterpolationNode, LatentKeyframeBatchedGroupNode, LatentKeyframeNode
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from .deprecated_nodes import LoadImagesFromDirectory
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@@ -124,7 +125,7 @@ class AdvancedControlNetApply:
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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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"cn_weights_override": ("CONTROL_NET_WEIGHTS", ),
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"weights_override": ("CONTROL_NET_WEIGHTS", ),
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}
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}
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@@ -135,7 +136,7 @@ class AdvancedControlNetApply:
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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=None,
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mask_optional: Tensor=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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if strength == 0:
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@@ -166,6 +167,7 @@ class AdvancedControlNetApply:
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c_net.weights_override = weights_override
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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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@@ -195,13 +197,12 @@ NODE_CLASS_MAPPINGS = {
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"ControlNetLoaderAdvanced": ControlNetLoaderAdvanced,
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"DiffControlNetLoaderAdvanced": DiffControlNetLoaderAdvanced,
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# Weights
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"ScaledSoftUniversalWeights": ScaledSoftUniversalWeights,
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"ScaledSoftControlNetWeights": ScaledSoftControlNetWeights,
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"ScaledSoftControlNetWeights": ScaledSoftUniversalWeights,
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"ScaledSoftMaskedUniversalWeights": ScaledSoftMaskedUniversalWeights,
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"SoftControlNetWeights": SoftControlNetWeights,
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"CustomControlNetWeights": CustomControlNetWeights,
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"SoftT2IAdapterWeights": SoftT2IAdapterWeights,
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"CustomT2IAdapterWeights": CustomT2IAdapterWeights,
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"ScaledSoftControlLoraWeights": ScaledSoftControlLoraWeights,
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"ACN_DefaultUniversalWeights": DefaultWeights,
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# Image
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"LoadImagesFromDirectory": LoadImagesFromDirectory
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@@ -217,16 +218,15 @@ NODE_DISPLAY_NAME_MAPPINGS = {
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# Conditioning
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"ACN_AdvancedControlNetApply": "Apply Advanced ControlNet 🛂🅐🅒🅝",
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# Loaders
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"ControlNetLoaderAdvanced": "Load ControlNet Model (Advanced) 🛂🅐🅒🅝",
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"DiffControlNetLoaderAdvanced": "Load ControlNet Model (diff Advanced) 🛂🅐🅒🅝",
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"ControlNetLoaderAdvanced": "Load Advanced ControlNet Model 🛂🅐🅒🅝",
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"DiffControlNetLoaderAdvanced": "Load Advanced ControlNet Model (diff) 🛂🅐🅒🅝",
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# Weights
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"ScaledSoftUniversalWeights": "Scaled Soft Weights 🛂🅐🅒🅝",
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"ScaledSoftControlNetWeights": "ControlNet Scaled Soft Weights 🛂🅐🅒🅝",
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"ScaledSoftControlNetWeights": "Scaled Soft Weights 🛂🅐🅒🅝",
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"ScaledSoftMaskedUniversalWeights": "Scaled Soft Masked Weights 🛂🅐🅒🅝",
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"SoftControlNetWeights": "ControlNet Soft Weights 🛂🅐🅒🅝",
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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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"ScaledSoftControlLoraWeights": "ControlLora Scaled Soft Weights 🛂🅐🅒🅝",
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"ACN_DefaultUniversalWeights": "Force Default Weights 🛂🅐🅒🅝",
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# Image
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"LoadImagesFromDirectory": "Load Images [DEPRECATED] 🛂🅐🅒🅝"
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+41
-44
@@ -1,7 +1,11 @@
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from .control import TimestepKeyframe, TimestepKeyframeGroup, ControlWeights, get_properly_arranged_t2i_weights
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from torch import Tensor
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from .control import TimestepKeyframe, TimestepKeyframeGroup, ControlWeights, get_properly_arranged_t2i_weights, linear_conversion
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from .logger import logger
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WEIGHTS_RETURN_NAMES = ("CONTROL_NET_WEIGHTS", "TK_SHORTCUT")
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class DefaultWeights:
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@classmethod
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def INPUT_TYPES(s):
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@@ -9,6 +13,7 @@ class DefaultWeights:
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}
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RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
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RETURN_NAMES = WEIGHTS_RETURN_NAMES
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FUNCTION = "load_weights"
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CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights"
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@@ -18,17 +23,47 @@ class DefaultWeights:
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return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
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class ScaledSoftMaskedUniversalWeights:
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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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"mask": ("MASK", ),
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"min_base_multiplier": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}, ),
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"max_base_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}, ),
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#"lock_min": ("BOOLEAN", {"default": False}, ),
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#"lock_max": ("BOOLEAN", {"default": False}, ),
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},
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}
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RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
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RETURN_NAMES = WEIGHTS_RETURN_NAMES
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FUNCTION = "load_weights"
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CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights"
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def load_weights(self, mask: Tensor, min_base_multiplier: float, max_base_multiplier: float, lock_min=False, lock_max=False):
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# normalize mask
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mask = mask.clone()
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x_min = 0.0 if lock_min else mask.min()
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x_max = 1.0 if lock_max else mask.max()
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mask = linear_conversion(mask, x_min, x_max, min_base_multiplier, max_base_multiplier)
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weights = ControlWeights.universal_mask(weight_mask=mask)
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return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
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class ScaledSoftUniversalWeights:
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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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"base_multiplier": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"base_multiplier": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 1.0, "step": 0.001}, ),
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"flip_weights": ("BOOLEAN", {"default": False}),
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},
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}
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RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
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RETURN_NAMES = WEIGHTS_RETURN_NAMES
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FUNCTION = "load_weights"
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CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights"
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@@ -38,48 +73,6 @@ class ScaledSoftUniversalWeights:
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return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
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class ScaledSoftControlLoraWeights:
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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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"base_multiplier": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"flip_weights": ("BOOLEAN", {"default": False}),
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},
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}
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RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
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FUNCTION = "load_weights"
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CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/ControlLoRA"
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def load_weights(self, base_multiplier, flip_weights):
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weights = [(base_multiplier ** float(9 - i)) for i in range(10)]
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weights = ControlWeights.controllora(weights, flip_weights=flip_weights)
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return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
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class ScaledSoftControlNetWeights:
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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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"base_multiplier": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"flip_weights": ("BOOLEAN", {"default": False}),
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},
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}
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RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
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FUNCTION = "load_weights"
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CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/ControlNet"
|
||||
|
||||
def load_weights(self, base_multiplier, flip_weights):
|
||||
weights = [(base_multiplier ** float(12 - i)) for i in range(13)]
|
||||
weights = ControlWeights.controlnet(weights, flip_weights=flip_weights)
|
||||
return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
|
||||
|
||||
|
||||
class SoftControlNetWeights:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
@@ -103,6 +96,7 @@ class SoftControlNetWeights:
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
|
||||
RETURN_NAMES = WEIGHTS_RETURN_NAMES
|
||||
FUNCTION = "load_weights"
|
||||
|
||||
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/ControlNet"
|
||||
@@ -138,6 +132,7 @@ class CustomControlNetWeights:
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
|
||||
RETURN_NAMES = WEIGHTS_RETURN_NAMES
|
||||
FUNCTION = "load_weights"
|
||||
|
||||
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/ControlNet"
|
||||
@@ -164,6 +159,7 @@ class SoftT2IAdapterWeights:
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
|
||||
RETURN_NAMES = WEIGHTS_RETURN_NAMES
|
||||
FUNCTION = "load_weights"
|
||||
|
||||
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/T2IAdapter"
|
||||
@@ -189,6 +185,7 @@ class CustomT2IAdapterWeights:
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
|
||||
RETURN_NAMES = WEIGHTS_RETURN_NAMES
|
||||
FUNCTION = "load_weights"
|
||||
|
||||
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/T2IAdapter"
|
||||
|
||||
Reference in New Issue
Block a user