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remove old comfyUI

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workflows/comfy/ComfyUI/custom_nodes/ComfyUI-Advanced-ControlNet/README.md
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# ComfyUI-Advanced-ControlNet
Nodes for scheduling ControlNet strength across timesteps and batched latents, as well as applying custom weights and attention masks. The ControlNet nodes here fully support sliding context sampling, like the one used in the [ComfyUI-AnimateDiff-Evolved](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved) nodes. Currently supports ControlNets, T2IAdapters, ControlLoRAs, ControlLLLite, SparseCtrls, and SVD-ControlNets.
Custom weights allow replication of the "My prompt is more important" feature of Auto1111's sd-webui ControlNet extension.
ControlNet preprocessors are available through [comfyui_controlnet_aux](https://github.com/Fannovel16/comfyui_controlnet_aux) nodes
## Features
- Timestep and latent strength scheduling
- Attention masks
- Soft weights to replicate "My prompt is more important" feature from sd-webui ControlNet extension, and also change the scaling.
- ControlNet, T2IAdapter, and ControlLoRA support for sliding context windows.
- ControlLLLite support (requires model_optional to be passed into and out of Apply Advanced ControlNet node)
- SparseCtrl support
- SVD-ControlNet support
- Stable Video Diffusion ControlNets trained by **CiaraRowles**: [Depth](https://huggingface.co/CiaraRowles/temporal-controlnet-depth-svd-v1/tree/main/controlnet), [Lineart](https://huggingface.co/CiaraRowles/temporal-controlnet-lineart-svd-v1/tree/main/controlnet)
## Table of Contents:
- [Scheduling Explanation](#scheduling-explanation)
- [Nodes](#nodes)
- [Usage](#usage) (will fill this out soon)
# Scheduling Explanation
The two core concepts for scheduling are ***Timestep Keyframes*** and ***Latent Keyframes***.
***Timestep Keyframes*** hold the values that guide the settings for a controlnet, and begin to take effect based on their start_percent, which corresponds to the percentage of the sampling process. They can contain masks for the strengths of each latent, control_net_weights, and latent_keyframes (specific strengths for each latent), all optional.
***Latent Keyframes*** determine the strength of the controlnet for specific latents - all they contain is the batch_index of the latent, and the strength the controlnet should apply for that latent. As a concept, latent keyframes achieve the same affect as a uniform mask with the chosen strength value.
![advcn_image](https://github.com/Kosinkadink/ComfyUI-Advanced-ControlNet/assets/7365912/e6275264-6c3f-4246-a319-111ee48f4cd9)
# Nodes
The ControlNet nodes provided here are the ***Apply Advanced ControlNet*** and ***Load Advanced ControlNet Model*** (or diff) nodes. The vanilla ControlNet nodes are also compatible, and can be used almost interchangeably - the only difference is that **at least one of these nodes must be used** for Advanced versions of ControlNets to be used (important for sliding context sampling, like with AnimateDiff-Evolved).
Key:
- 🟩 - required inputs
- 🟨 - optional inputs
- 🟦 - start as widgets, can be converted to inputs
- 🟥 - optional input/output, but not recommended to use unless needed
- 🟪 - output
## Apply Advanced ControlNet
![image](https://github.com/Kosinkadink/ComfyUI-Advanced-ControlNet/assets/7365912/dc541d41-70df-4a71-b832-efa65af98f06)
Same functionality as the vanilla Apply Advanced ControlNet (Advanced) node, except with Advanced ControlNet features added to it. Automatically converts any ControlNet from ControlNet loaders into Advanced versions.
### Inputs
- 🟩***positive***: conditioning (positive).
- 🟩***negative***: conditioning (negative).
- 🟩***control_net***: loaded controlnet; will be converted to Advanced version automatically by this node, if it's a supported type.
- 🟩***image***: images to guide controlnets - if the loaded controlnet requires it, they must preprocessed images. If one image provided, will be used for all latents. If more images provided, will use each image separately for each latent. If not enough images to meet latent count, will repeat the images from the beginning to match vanilla ControlNet functionality.
- 🟨***mask_optional***: attention masks to apply to controlnets; basically, decides what part of the image the controlnet to apply to (and the relative strength, if the mask is not binary). Same as image input, if you provide more than one mask, each can apply to a different latent.
- 🟨***timestep_kf***: timestep keyframes to guide controlnet effect throughout sampling steps.
- 🟨***latent_kf_override***: override for latent keyframes, useful if no other features from timestep keyframes is needed. *NOTE: this latent keyframe will be applied to ALL timesteps, regardless if there are other latent keyframes attached to connected timestep keyframes.*
- 🟨***weights_override***: override for weights, useful if no other features from timestep keyframes is needed. *NOTE: this weight will be applied to ALL timesteps, regardless if there are other weights attached to connected timestep keyframes.*
- 🟦***strength***: strength of controlnet; 1.0 is full strength, 0.0 is no effect at all.
- 🟦***start_percent***: sampling step percentage at which controlnet should start to be applied - no matter what start_percent is set on timestep keyframes, they won't take effect until this start_percent is reached.
- 🟦***stop_percent***: sampling step percentage at which controlnet should stop being applied - no matter what start_percent is set on timestep keyframes, they won't take effect once this end_percent is reached.
### Outputs
- 🟪***positive***: conditioning (positive) with applied controlnets
- 🟪***negative***: conditioning (negative) with applied controlnets
## Load Advanced ControlNet Model
![image](https://github.com/Kosinkadink/ComfyUI-Advanced-ControlNet/assets/7365912/4a7f58a9-783d-4da4-bf82-bc9c167e4722)
Loads a ControlNet model and converts it into an Advanced version that supports all the features in this repo. When used with **Apply Advanced ControlNet** node, there is no reason to use the timestep_keyframe input on this node - use timestep_kf on the Apply node instead.
### Inputs
- 🟥***timestep_keyframe***: optional and likely unnecessary input to have ControlNet use selected timestep_keyframes - should not be used unless you need to. Useful if this node is not attached to **Apply Advanced ControlNet** node, but still want to use Timestep Keyframe, or to use TK_SHORTCUT outputs from ControlWeights in the same scenario. Will be overriden by the timestep_kf input on **Apply Advanced ControlNet** node, if one is provided there.
- 🟨***model***: model to plug into the diff version of the node. Some controlnets are designed for receive the model; if you don't know what this does, you probably don't want tot use the diff version of the node.
### Outputs
- 🟪***CONTROL_NET***: loaded Advanced ControlNet
## Timestep Keyframe
![image](https://github.com/Kosinkadink/ComfyUI-Advanced-ControlNet/assets/7365912/c6f2a86e-fc96-4f8b-b976-7c2062a6eba2)
Scheduling node across timesteps (sampling steps) based on the set start_percent. Chaining Timestep Keyframes allows ControlNet scheduling across sampling steps (percentage-wise), through a timestep keyframe schedule.
### Inputs
- 🟨***prev_timestep_kf***: used to chain Timestep Keyframes together to create a schedule. The order does not matter - the Timestep Keyframes sort themselves automatically by their start_percent. *Any Timestep Keyframe contained in the prev_timestep_keyframe that contains the same start_percent as the Timestep Keyframe will be overwritten.*
- 🟨***cn_weights***: weights to apply to controlnet while this Timestep Keyframe is in effect. Must be compatible with the loaded controlnet, or will throw an error explaining what weight types are compatible. If inherit_missing is True, if no control_net_weight is passed in, will attempt to reuse the last-used weights in the timestep keyframe schedule. *If Apply Advanced ControlNet node has a weight_override, the weight_override will be used during sampling instead of control_net_weight.*
- 🟨***latent_keyframe***: latent keyframes to apply to controlnet while this Timestep Keyframe is in effect. If inherit_missing is True, if no latent_keyframe is passed in, will attempt to reuse the last-used weights in the timestep keyframe schedule. *If Apply Advanced ControlNet node has a latent_kf_override, the latent_lf_override will be used during sampling instead of latent_keyframe.*
- 🟨***mask_optional***: attention masks to apply to controlnets; basically, decides what part of the image the controlnet to apply to (and the relative strength, if the mask is not binary). Same as mask_optional on the Apply Advanced ControlNet node, can apply either one maks to all latents, or individual masks for each latent. If inherit_missing is True, if no mask_optional is passed in, will attempt to reuse the last-used mask_optional in the timestep keyframe schedule. It is NOT overriden by mask_optional on the Apply Advanced ControlNet node; will be used together.
- 🟦***start_percent***: sampling step percentage at which this Timestep Keyframe qualifies to be used. Acts as the 'key' for the Timestep Keyframe in the timestep keyframe schedule.
- 🟦***strength***: strength of the controlnet; multiplies the controlnet by this value, basically, applied alongside the strength on the Apply ControlNet node. If set to 0.0 will not have any effect during the duration of this Timestep Keyframe's effect, and will increase sampling speed by not doing any work.
- 🟦***null_latent_kf_strength***: strength to assign to latents that are unaccounted for in the passed in latent_keyframes. Has no effect if no latent_keyframes are passed in, or no batch_indeces are unaccounted in the latent_keyframes for during sampling.
- 🟦***inherit_missing***: determines if should reuse values from previous Timestep Keyframes for optional values (control_net_weights, latent_keyframe, and mask_option) that are not included on this TimestepKeyframe. To inherit only specific inputs, use default inputs.
- 🟦***guarantee_usage***: when true, even if a Timestep Keyframe's start_percent ahead of this one in the schedule is closer to current sampling percentage, this Timestep Keyframe will still be used for one step before moving on to the next selected Timestep Keyframe in the following step. Whether the Timestep Keyframe is used or not, its inputs will still be accounted for inherit_missing purposes.
### Outputs
- 🟪***TIMESTEP_KF***: the created Timestep Keyframe, that can either be linked to another or into a Timestep Keyframe input.
## Latent Keyframe
![image](https://github.com/Kosinkadink/ComfyUI-Advanced-ControlNet/assets/7365912/7eb2cc4c-255c-4f32-b09b-699f713fada3)
A singular Latent Keyframe, selects the strength for a specific batch_index. If batch_index is not present during sampling, will simply have no effect. Can be chained with any other Latent Keyframe-type node to create a latent keyframe schedule.
### Inputs
- 🟨***prev_latent_kf***: used to chain Latent Keyframes together to create a schedule. *If a Latent Keyframe contained in prev_latent_keyframes have the same batch_index as this Latent Keyframe, they will take priority over this node's value.*
- 🟦***batch_index***: index of latent in batch to apply controlnet strength to. Acts as the 'key' for the Latent Keyframe in the latent keyframe schedule.
- 🟦***strength***: strength of controlnet to apply to the corresponding latent.
### Outputs
- 🟪***LATENT_KF***: the created Latent Keyframe, that can either be linked to another or into a Latent Keyframe input.
## Latent Keyframe Group
![image](https://github.com/Kosinkadink/ComfyUI-Advanced-ControlNet/assets/7365912/5ce3b795-f5fc-4dc3-ae30-a4c7f87e278c)
Allows to create Latent Keyframes via individual indeces or python-style ranges.
### Inputs
- 🟨***prev_latent_kf***: used to chain Latent Keyframes together to create a schedule. *If any Latent Keyframes contained in prev_latent_keyframes have the same batch_index as a this Latent Keyframe, they will take priority over this node's version.*
- 🟨***latent_optional***: the latents expected to be passed in for sampling; only required if you wish to use negative indeces (will be automatically converted to real values).
- 🟦***index_strengths***: string list of indeces or python-style ranges of indeces to assign strengths to. If latent_optional is passed in, can contain negative indeces or ranges that contain negative numbers, python-style. The different indeces must be comma separated. Individual latents can be specified by ```batch_index=strength```, like ```0=0.9```. Ranges can be specified by ```start_index_inclusive:end_index_exclusive=strength```, like ```0:8=strength```. Negative indeces are possible when latents_optional has an input, with a string such as ```0,-4=0.25```.
- 🟦***print_keyframes***: if True, will print the Latent Keyframes generated by this node for debugging purposes.
### Outputs
- 🟪***LATENT_KF***: the created Latent Keyframe, that can either be linked to another or into a Latent Keyframe input.
## Latent Keyframe Interpolation
![image](https://github.com/Kosinkadink/ComfyUI-Advanced-ControlNet/assets/7365912/7986c737-83b9-46bc-aab0-ae4c368df446)
Allows to create Latent Keyframes with interpolated values in a range.
### Inputs
- 🟨***prev_latent_kf***: used to chain Latent Keyframes together to create a schedule. *If any Latent Keyframes contained in prev_latent_keyframes have the same batch_index as a this Latent Keyframe, they will take priority over this node's version.*
- 🟦***batch_index_from***: starting batch_index of range, included.
- 🟦***batch_index_to***: end batch_index of range, excluded (python-style range).
- 🟦***strength_from***: starting strength of interpolation.
- 🟦***strength_to***: end strength of interpolation.
- 🟦***interpolation***: the method of interpolation.
- 🟦***print_keyframes***: if True, will print the Latent Keyframes generated by this node for debugging purposes.
### Outputs
- 🟪***LATENT_KF***: the created Latent Keyframe, that can either be linked to another or into a Latent Keyframe input.
## Latent Keyframe Batched Group
![image](https://github.com/Kosinkadink/ComfyUI-Advanced-ControlNet/assets/7365912/6cec701f-6183-4aeb-af5c-cac76f5591b7)
Allows to create Latent Keyframes via a list of floats, such as with Batch Value Schedule from [ComfyUI_FizzNodes](https://github.com/FizzleDorf/ComfyUI_FizzNodes) nodes.
### Inputs
- 🟨***prev_latent_kf***: used to chain Latent Keyframes together to create a schedule. *If any Latent Keyframes contained in prev_latent_keyframes have the same batch_index as a this Latent Keyframe, they will take priority over this node's version.*
- 🟦***float_strengths***: a list of floats, that will correspond to the strength of each Latent Keyframe; the batch_index is the index of each float value in the list.
- 🟦***print_keyframes***: if True, will print the Latent Keyframes generated by this node for debugging purposes.
### Outputs
- 🟪***LATENT_KF***: the created Latent Keyframe, that can either be linked to another or into a Latent Keyframe input.
# There are more nodes to document and show usage - will add this soon! TODO
workflows/comfy/ComfyUI/custom_nodes/ComfyUI-Advanced-ControlNet/__init__.py
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from .adv_control.nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
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from typing import Callable, Union
from torch import Tensor
import torch
import os
import comfy.utils
import comfy.model_management
import comfy.model_detection
import comfy.controlnet as comfy_cn
from comfy.controlnet import ControlBase, ControlNet, ControlLora, T2IAdapter, broadcast_image_to
from model_patcher import ModelPatcher
from .control_sparsectrl import SparseControlNet, SparseCtrlMotionWrapper, SparseMethod, SparseSettings, SparseSpreadMethod, PreprocSparseRGBWrapper
from .control_lllite import LLLiteModule, LLLitePatch
from .control_svd import svd_unet_config_from_diffusers_unet, SVDControlNet, svd_unet_to_diffusers
from .utils import (AdvancedControlBase, TimestepKeyframeGroup, LatentKeyframeGroup, ControlWeightType, ControlWeights, WeightTypeException,
manual_cast_clean_groupnorm, disable_weight_init_clean_groupnorm, prepare_mask_batch, get_properly_arranged_t2i_weights, load_torch_file_with_dict_factory)
from .logger import logger
class ControlNetAdvanced(ControlNet, AdvancedControlBase):
def __init__(self, control_model, timestep_keyframes: TimestepKeyframeGroup, global_average_pooling=False, device=None, load_device=None, manual_cast_dtype=None):
super().__init__(control_model=control_model, global_average_pooling=global_average_pooling, device=device, load_device=load_device, manual_cast_dtype=manual_cast_dtype)
AdvancedControlBase.__init__(self, super(), timestep_keyframes=timestep_keyframes, weights_default=ControlWeights.controlnet())
def get_universal_weights(self) -> ControlWeights:
raw_weights = [(self.weights.base_multiplier ** float(12 - i)) for i in range(13)]
return ControlWeights.controlnet(raw_weights, self.weights.flip_weights)
def get_control_advanced(self, x_noisy, t, cond, batched_number):
# perform special version of get_control that supports sliding context and masks
return self.sliding_get_control(x_noisy, t, cond, batched_number)
def sliding_get_control(self, x_noisy: Tensor, t, cond, batched_number):
control_prev = None
if self.previous_controlnet is not None:
control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number)
if self.timestep_range is not None:
if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]:
if control_prev is not None:
return control_prev
else:
return None
dtype = self.control_model.dtype
if self.manual_cast_dtype is not None:
dtype = self.manual_cast_dtype
output_dtype = x_noisy.dtype
# make cond_hint appropriate dimensions
# TODO: change this to not require cond_hint upscaling every step when self.sub_idxs are present
if self.sub_idxs is not None or self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]:
if self.cond_hint is not None:
del self.cond_hint
self.cond_hint = None
# if self.cond_hint_original length greater or equal to real latent count, subdivide it before scaling
if self.sub_idxs is not None and self.cond_hint_original.size(0) >= self.full_latent_length:
self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original[self.sub_idxs], x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', "center").to(dtype).to(self.device)
else:
self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', "center").to(dtype).to(self.device)
if x_noisy.shape[0] != self.cond_hint.shape[0]:
self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number)
# prepare mask_cond_hint
self.prepare_mask_cond_hint(x_noisy=x_noisy, t=t, cond=cond, batched_number=batched_number, dtype=dtype)
context = cond.get('crossattn_controlnet', cond['c_crossattn'])
# uses 'y' in new ComfyUI update
y = cond.get('y', None)
if y is None: # TODO: remove this in the future since no longer used by newest ComfyUI
y = cond.get('c_adm', None)
if y is not None:
y = y.to(dtype)
timestep = self.model_sampling_current.timestep(t)
x_noisy = self.model_sampling_current.calculate_input(t, x_noisy)
control = self.control_model(x=x_noisy.to(dtype), hint=self.cond_hint, timesteps=timestep.float(), context=context.to(dtype), y=y)
return self.control_merge(None, control, control_prev, output_dtype)
def copy(self):
c = ControlNetAdvanced(self.control_model, self.timestep_keyframes, global_average_pooling=self.global_average_pooling, load_device=self.load_device, manual_cast_dtype=self.manual_cast_dtype)
self.copy_to(c)
self.copy_to_advanced(c)
return c
@staticmethod
def from_vanilla(v: ControlNet, timestep_keyframe: TimestepKeyframeGroup=None) -> 'ControlNetAdvanced':
return ControlNetAdvanced(control_model=v.control_model, timestep_keyframes=timestep_keyframe,
global_average_pooling=v.global_average_pooling, device=v.device, load_device=v.load_device, manual_cast_dtype=v.manual_cast_dtype)
class T2IAdapterAdvanced(T2IAdapter, AdvancedControlBase):
def __init__(self, t2i_model, timestep_keyframes: TimestepKeyframeGroup, channels_in, device=None):
super().__init__(t2i_model=t2i_model, channels_in=channels_in, device=device)
AdvancedControlBase.__init__(self, super(), timestep_keyframes=timestep_keyframes, weights_default=ControlWeights.t2iadapter())
def get_universal_weights(self) -> ControlWeights:
raw_weights = [(self.weights.base_multiplier ** float(7 - i)) for i in range(8)]
raw_weights = [raw_weights[-8], raw_weights[-3], raw_weights[-2], raw_weights[-1]]
raw_weights = get_properly_arranged_t2i_weights(raw_weights)
return ControlWeights.t2iadapter(raw_weights, self.weights.flip_weights)
def get_calc_pow(self, idx: int, layers: int) -> int:
# match how T2IAdapterAdvanced deals with universal weights
indeces = [7 - i for i in range(8)]
indeces = [indeces[-8], indeces[-3], indeces[-2], indeces[-1]]
indeces = get_properly_arranged_t2i_weights(indeces)
return indeces[idx]
def get_control_advanced(self, x_noisy, t, cond, batched_number):
try:
# if sub indexes present, replace original hint with subsection
if self.sub_idxs is not None:
# cond hints
full_cond_hint_original = self.cond_hint_original
del self.cond_hint
self.cond_hint = None
self.cond_hint_original = full_cond_hint_original[self.sub_idxs]
# mask hints
self.prepare_mask_cond_hint(x_noisy=x_noisy, t=t, cond=cond, batched_number=batched_number)
return super().get_control(x_noisy, t, cond, batched_number)
finally:
if self.sub_idxs is not None:
# replace original cond hint
self.cond_hint_original = full_cond_hint_original
del full_cond_hint_original
def copy(self):
c = T2IAdapterAdvanced(self.t2i_model, self.timestep_keyframes, self.channels_in)
self.copy_to(c)
self.copy_to_advanced(c)
return c
def cleanup(self):
super().cleanup()
self.cleanup_advanced()
@staticmethod
def from_vanilla(v: T2IAdapter, timestep_keyframe: TimestepKeyframeGroup=None) -> 'T2IAdapterAdvanced':
return T2IAdapterAdvanced(t2i_model=v.t2i_model, timestep_keyframes=timestep_keyframe, channels_in=v.channels_in, device=v.device)
class ControlLoraAdvanced(ControlLora, AdvancedControlBase):
def __init__(self, control_weights, timestep_keyframes: TimestepKeyframeGroup, global_average_pooling=False, device=None):
super().__init__(control_weights=control_weights, global_average_pooling=global_average_pooling, device=device)
AdvancedControlBase.__init__(self, super(), timestep_keyframes=timestep_keyframes, weights_default=ControlWeights.controllora())
# use some functions from ControlNetAdvanced
self.get_control_advanced = ControlNetAdvanced.get_control_advanced.__get__(self, type(self))
self.sliding_get_control = ControlNetAdvanced.sliding_get_control.__get__(self, type(self))
def get_universal_weights(self) -> ControlWeights:
raw_weights = [(self.weights.base_multiplier ** float(9 - i)) for i in range(10)]
return ControlWeights.controllora(raw_weights, self.weights.flip_weights)
def copy(self):
c = ControlLoraAdvanced(self.control_weights, self.timestep_keyframes, global_average_pooling=self.global_average_pooling)
self.copy_to(c)
self.copy_to_advanced(c)
return c
def cleanup(self):
super().cleanup()
self.cleanup_advanced()
@staticmethod
def from_vanilla(v: ControlLora, timestep_keyframe: TimestepKeyframeGroup=None) -> 'ControlLoraAdvanced':
return ControlLoraAdvanced(control_weights=v.control_weights, timestep_keyframes=timestep_keyframe,
global_average_pooling=v.global_average_pooling, device=v.device)
class SVDControlNetAdvanced(ControlNetAdvanced):
def __init__(self, control_model: SVDControlNet, timestep_keyframes: TimestepKeyframeGroup, global_average_pooling=False, device=None, load_device=None, manual_cast_dtype=None):
super().__init__(control_model=control_model, timestep_keyframes=timestep_keyframes, global_average_pooling=global_average_pooling, device=device, load_device=load_device, manual_cast_dtype=manual_cast_dtype)
def set_cond_hint(self, *args, **kwargs):
to_return = super().set_cond_hint(*args, **kwargs)
# cond hint for SVD-ControlNet needs to be scaled between (-1, 1) instead of (0, 1)
self.cond_hint_original = self.cond_hint_original * 2.0 - 1.0
return to_return
def get_control_advanced(self, x_noisy, t, cond, batched_number):
control_prev = None
if self.previous_controlnet is not None:
control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number)
if self.timestep_range is not None:
if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]:
if control_prev is not None:
return control_prev
else:
return None
dtype = self.control_model.dtype
if self.manual_cast_dtype is not None:
dtype = self.manual_cast_dtype
output_dtype = x_noisy.dtype
# make cond_hint appropriate dimensions
# TODO: change this to not require cond_hint upscaling every step when self.sub_idxs are present
if self.sub_idxs is not None or self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]:
if self.cond_hint is not None:
del self.cond_hint
self.cond_hint = None
# if self.cond_hint_original length greater or equal to real latent count, subdivide it before scaling
if self.sub_idxs is not None and self.cond_hint_original.size(0) >= self.full_latent_length:
self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original[self.sub_idxs], x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', "center").to(dtype).to(self.device)
else:
self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', "center").to(dtype).to(self.device)
if x_noisy.shape[0] != self.cond_hint.shape[0]:
self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number)
# prepare mask_cond_hint
self.prepare_mask_cond_hint(x_noisy=x_noisy, t=t, cond=cond, batched_number=batched_number, dtype=dtype)
context = cond.get('crossattn_controlnet', cond['c_crossattn'])
# uses 'y' in new ComfyUI update
y = cond.get('y', None)
if y is not None:
y = y.to(dtype)
timestep = self.model_sampling_current.timestep(t)
x_noisy = self.model_sampling_current.calculate_input(t, x_noisy)
# concat c_concat if exists (should exist for SVD), doubling channels to 8
if cond.get('c_concat', None) is not None:
x_noisy = torch.cat([x_noisy] + [cond['c_concat']], dim=1)
control = self.control_model(x=x_noisy.to(dtype), hint=self.cond_hint, timesteps=timestep.float(), context=context.to(dtype), y=y, cond=cond)
return self.control_merge(None, control, control_prev, output_dtype)
def copy(self):
c = SVDControlNetAdvanced(self.control_model, self.timestep_keyframes, global_average_pooling=self.global_average_pooling, load_device=self.load_device, manual_cast_dtype=self.manual_cast_dtype)
self.copy_to(c)
self.copy_to_advanced(c)
return c
class SparseCtrlAdvanced(ControlNetAdvanced):
def __init__(self, control_model, timestep_keyframes: TimestepKeyframeGroup, sparse_settings: SparseSettings=None, global_average_pooling=False, device=None, load_device=None, manual_cast_dtype=None):
super().__init__(control_model=control_model, timestep_keyframes=timestep_keyframes, global_average_pooling=global_average_pooling, device=device, load_device=load_device, manual_cast_dtype=manual_cast_dtype)
self.add_compatible_weight(ControlWeightType.SPARSECTRL)
self.control_model: SparseControlNet = self.control_model # does nothing except help with IDE hints
self.sparse_settings = sparse_settings if sparse_settings is not None else SparseSettings.default()
self.latent_format = None
self.preprocessed = False
def get_control_advanced(self, x_noisy: Tensor, t, cond, batched_number: int):
# normal ControlNet stuff
control_prev = None
if self.previous_controlnet is not None:
control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number)
if self.timestep_range is not None:
if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]:
if control_prev is not None:
return control_prev
else:
return None
dtype = self.control_model.dtype
if self.manual_cast_dtype is not None:
dtype = self.manual_cast_dtype
output_dtype = x_noisy.dtype
# set actual input length on motion model
actual_length = x_noisy.size(0)//batched_number
full_length = actual_length if self.sub_idxs is None else self.full_latent_length
self.control_model.set_actual_length(actual_length=actual_length, full_length=full_length)
# prepare cond_hint, if needed
dim_mult = 1 if self.control_model.use_simplified_conditioning_embedding else 8
if self.sub_idxs is not None or self.cond_hint is None or x_noisy.shape[2]*dim_mult != self.cond_hint.shape[2] or x_noisy.shape[3]*dim_mult != self.cond_hint.shape[3]:
# clear out cond_hint and conditioning_mask
if self.cond_hint is not None:
del self.cond_hint
self.cond_hint = None
# first, figure out which cond idxs are relevant, and where they fit in
cond_idxs = self.sparse_settings.sparse_method.get_indexes(hint_length=self.cond_hint_original.size(0), full_length=full_length)
range_idxs = list(range(full_length)) if self.sub_idxs is None else self.sub_idxs
hint_idxs = [] # idxs in cond_idxs
local_idxs = [] # idx to pun in final cond_hint
for i,cond_idx in enumerate(cond_idxs):
if cond_idx in range_idxs:
hint_idxs.append(i)
local_idxs.append(range_idxs.index(cond_idx))
# sub_cond_hint now contains the hints relevant to current x_noisy
sub_cond_hint = self.cond_hint_original[hint_idxs].to(dtype).to(self.device)
# scale cond_hints to match noisy input
if self.control_model.use_simplified_conditioning_embedding:
# RGB SparseCtrl; the inputs are latents - use bilinear to avoid blocky artifacts
sub_cond_hint = self.latent_format.process_in(sub_cond_hint) # multiplies by model scale factor
sub_cond_hint = comfy.utils.common_upscale(sub_cond_hint, x_noisy.shape[3], x_noisy.shape[2], "nearest-exact", "center").to(dtype).to(self.device)
else:
# other SparseCtrl; inputs are typical images
sub_cond_hint = comfy.utils.common_upscale(sub_cond_hint, x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', "center").to(dtype).to(self.device)
# prepare cond_hint (b, c, h ,w)
cond_shape = list(sub_cond_hint.shape)
cond_shape[0] = len(range_idxs)
self.cond_hint = torch.zeros(cond_shape).to(dtype).to(self.device)
self.cond_hint[local_idxs] = sub_cond_hint[:]
# prepare cond_mask (b, 1, h, w)
cond_shape[1] = 1
cond_mask = torch.zeros(cond_shape).to(dtype).to(self.device)
cond_mask[local_idxs] = 1.0
# combine cond_hint and cond_mask into (b, c+1, h, w)
if not self.sparse_settings.merged:
self.cond_hint = torch.cat([self.cond_hint, cond_mask], dim=1)
del sub_cond_hint
del cond_mask
# make cond_hint match x_noisy batch
if x_noisy.shape[0] != self.cond_hint.shape[0]:
self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number)
# prepare mask_cond_hint
self.prepare_mask_cond_hint(x_noisy=x_noisy, t=t, cond=cond, batched_number=batched_number, dtype=dtype)
context = cond['c_crossattn']
y = cond.get('y', None)
if y is not None:
y = y.to(dtype)
timestep = self.model_sampling_current.timestep(t)
x_noisy = self.model_sampling_current.calculate_input(t, x_noisy)
control = self.control_model(x=x_noisy.to(dtype), hint=self.cond_hint, timesteps=timestep.float(), context=context.to(dtype), y=y)
return self.control_merge(None, control, control_prev, output_dtype)
def pre_run_advanced(self, model, percent_to_timestep_function):
super().pre_run_advanced(model, percent_to_timestep_function)
if type(self.cond_hint_original) == PreprocSparseRGBWrapper:
if not self.control_model.use_simplified_conditioning_embedding:
raise ValueError("Any model besides RGB SparseCtrl should NOT have its images go through the RGB SparseCtrl preprocessor.")
self.cond_hint_original = self.cond_hint_original.condhint
self.latent_format = model.latent_format # LatentFormat object, used to process_in latent cond hint
if self.control_model.motion_holder is not None:
self.control_model.motion_holder.motion_wrapper.reset()
self.control_model.motion_holder.motion_wrapper.set_strength(self.sparse_settings.motion_strength)
self.control_model.motion_holder.motion_wrapper.set_scale_multiplier(self.sparse_settings.motion_scale)
def cleanup_advanced(self):
super().cleanup_advanced()
if self.latent_format is not None:
del self.latent_format
self.latent_format = None
def copy(self):
c = SparseCtrlAdvanced(self.control_model, self.timestep_keyframes, self.sparse_settings, self.global_average_pooling, self.device, self.load_device, self.manual_cast_dtype)
self.copy_to(c)
self.copy_to_advanced(c)
return c
class ReferenceAdvanced(ControlBase, AdvancedControlBase):
def __init__(self, timestep_keyframes: TimestepKeyframeGroup, device=None):
super().__init__(device)
AdvancedControlBase.__init__(self, super(), timestep_keyframes=timestep_keyframes, weights_default=ControlWeights.controllllite(), require_model=True)
# TODO: save attn patches here
def patch_model(self, model: ModelPatcher):
# TODO: do model patching here
pass
def pre_run_advanced(self, *args, **kwargs):
AdvancedControlBase.pre_run_advanced(self, *args, **kwargs)
# TODO: set control on patches
def get_control_advanced(self, x_noisy: Tensor, t, cond, batched_number: int):
# normal ControlNet stuff
control_prev = None
if self.previous_controlnet is not None:
control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number)
if self.timestep_range is not None:
if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]:
return control_prev
dtype = x_noisy.dtype
# prepare cond_hint
if self.sub_idxs is not None or self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]:
if self.cond_hint is not None:
del self.cond_hint
self.cond_hint = None
# if self.cond_hint_original length greater or equal to real latent count, subdivide it before scaling
if self.sub_idxs is not None and self.cond_hint_original.size(0) >= self.full_latent_length:
self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original[self.sub_idxs], x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', "center").to(dtype).to(self.device)
else:
self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', "center").to(dtype).to(self.device)
if x_noisy.shape[0] != self.cond_hint.shape[0]:
self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number)
# prepare mask
self.prepare_mask_cond_hint(x_noisy=x_noisy, t=t, cond=cond, batched_number=batched_number)
# done preparing; model patches will take care of everything now.
# return normal controlnet stuff
return control_prev
def cleanup_advanced(self):
super().cleanup_advanced()
# TODO: cleanup patches here
def copy(self):
c = ReferenceAdvanced(self.timestep_keyframes)
self.copy_to(c)
self.copy_to_advanced(c)
return c
class ControlLLLiteAdvanced(ControlBase, AdvancedControlBase):
# This ControlNet is more of an attention patch than a traditional controlnet
def __init__(self, patch_attn1: LLLitePatch, patch_attn2: LLLitePatch, timestep_keyframes: TimestepKeyframeGroup, device=None):
super().__init__(device)
AdvancedControlBase.__init__(self, super(), timestep_keyframes=timestep_keyframes, weights_default=ControlWeights.controllllite(), require_model=True)
self.patch_attn1 = patch_attn1.set_control(self)
self.patch_attn2 = patch_attn2.set_control(self)
self.latent_dims_div2 = None
self.latent_dims_div4 = None
def patch_model(self, model: ModelPatcher):
model.set_model_attn1_patch(self.patch_attn1)
model.set_model_attn2_patch(self.patch_attn2)
def set_cond_hint(self, *args, **kwargs):
to_return = super().set_cond_hint(*args, **kwargs)
# cond hint for LLLite needs to be scaled between (-1, 1) instead of (0, 1)
self.cond_hint_original = self.cond_hint_original * 2.0 - 1.0
return to_return
def pre_run_advanced(self, *args, **kwargs):
AdvancedControlBase.pre_run_advanced(self, *args, **kwargs)
#logger.error(f"in cn: {id(self.patch_attn1)},{id(self.patch_attn2)}")
self.patch_attn1.set_control(self)
self.patch_attn2.set_control(self)
#logger.warn(f"in pre_run_advanced: {id(self)}")
def get_control_advanced(self, x_noisy: Tensor, t, cond, batched_number: int):
# normal ControlNet stuff
control_prev = None
if self.previous_controlnet is not None:
control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number)
if self.timestep_range is not None:
if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]:
return control_prev
dtype = x_noisy.dtype
# prepare cond_hint
if self.sub_idxs is not None or self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]:
if self.cond_hint is not None:
del self.cond_hint
self.cond_hint = None
# if self.cond_hint_original length greater or equal to real latent count, subdivide it before scaling
if self.sub_idxs is not None and self.cond_hint_original.size(0) >= self.full_latent_length:
self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original[self.sub_idxs], x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', "center").to(dtype).to(self.device)
else:
self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', "center").to(dtype).to(self.device)
if x_noisy.shape[0] != self.cond_hint.shape[0]:
self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number)
# some special logic here compared to other controlnets:
# * The cond_emb in attn patches will divide latent dims by 2 or 4, integer
# * Due to this loss, the cond_emb will become smaller than x input if latent dims are not divisble by 2 or 4
divisible_by_2_h = x_noisy.shape[2]%2==0
divisible_by_2_w = x_noisy.shape[3]%2==0
if not (divisible_by_2_h and divisible_by_2_w):
#logger.warn(f"{x_noisy.shape} not divisible by 2!")
new_h = (x_noisy.shape[2]//2)*2
new_w = (x_noisy.shape[3]//2)*2
if not divisible_by_2_h:
new_h += 2
if not divisible_by_2_w:
new_w += 2
self.latent_dims_div2 = (new_h, new_w)
divisible_by_4_h = x_noisy.shape[2]%4==0
divisible_by_4_w = x_noisy.shape[3]%4==0
if not (divisible_by_4_h and divisible_by_4_w):
#logger.warn(f"{x_noisy.shape} not divisible by 4!")
new_h = (x_noisy.shape[2]//4)*4
new_w = (x_noisy.shape[3]//4)*4
if not divisible_by_4_h:
new_h += 4
if not divisible_by_4_w:
new_w += 4
self.latent_dims_div4 = (new_h, new_w)
# prepare mask
self.prepare_mask_cond_hint(x_noisy=x_noisy, t=t, cond=cond, batched_number=batched_number)
# done preparing; model patches will take care of everything now.
# return normal controlnet stuff
return control_prev
def cleanup_advanced(self):
super().cleanup_advanced()
self.patch_attn1.cleanup()
self.patch_attn2.cleanup()
self.latent_dims_div2 = None
self.latent_dims_div4 = None
def copy(self):
c = ControlLLLiteAdvanced(self.patch_attn1, self.patch_attn2, self.timestep_keyframes)
self.copy_to(c)
self.copy_to_advanced(c)
return c
# deepcopy needs to properly keep track of objects to work between model.clone calls!
# def __deepcopy__(self, *args, **kwargs):
# self.cleanup_advanced()
# return self
# def get_models(self):
# # get_models is called once at the start of every KSampler run - use to reset already_patched status
# out = super().get_models()
# logger.error(f"in get_models! {id(self)}")
# return out
def load_controlnet(ckpt_path, timestep_keyframe: TimestepKeyframeGroup=None, model=None):
controlnet_data = comfy.utils.load_torch_file(ckpt_path, safe_load=True)
control = None
# check if a non-vanilla ControlNet
controlnet_type = ControlWeightType.DEFAULT
has_controlnet_key = False
has_motion_modules_key = False
has_temporal_res_block_key = False
for key in controlnet_data:
# LLLite check
if "lllite" in key:
controlnet_type = ControlWeightType.CONTROLLLLITE
break
# SparseCtrl check
elif "motion_modules" in key:
has_motion_modules_key = True
elif "controlnet" in key:
has_controlnet_key = True
# SVD-ControlNet check
elif "temporal_res_block" in key:
has_temporal_res_block_key = True
if has_controlnet_key and has_motion_modules_key:
controlnet_type = ControlWeightType.SPARSECTRL
elif has_controlnet_key and has_temporal_res_block_key:
controlnet_type = ControlWeightType.SVD_CONTROLNET
if controlnet_type != ControlWeightType.DEFAULT:
if controlnet_type == ControlWeightType.CONTROLLLLITE:
control = load_controllllite(ckpt_path, controlnet_data=controlnet_data, timestep_keyframe=timestep_keyframe)
elif controlnet_type == ControlWeightType.SPARSECTRL:
control = load_sparsectrl(ckpt_path, controlnet_data=controlnet_data, timestep_keyframe=timestep_keyframe, model=model)
elif controlnet_type == ControlWeightType.SVD_CONTROLNET:
control = load_svdcontrolnet(ckpt_path, controlnet_data=controlnet_data, timestep_keyframe=timestep_keyframe)
#raise Exception(f"SVD-ControlNet is not supported yet!")
#control = comfy_cn.load_controlnet(ckpt_path, model=model)
# otherwise, load vanilla ControlNet
else:
try:
# hacky way of getting load_torch_file in load_controlnet to use already-present controlnet_data and not redo loading
orig_load_torch_file = comfy.utils.load_torch_file
comfy.utils.load_torch_file = load_torch_file_with_dict_factory(controlnet_data, orig_load_torch_file)
control = comfy_cn.load_controlnet(ckpt_path, model=model)
finally:
comfy.utils.load_torch_file = orig_load_torch_file
return convert_to_advanced(control, timestep_keyframe=timestep_keyframe)
def convert_to_advanced(control, timestep_keyframe: TimestepKeyframeGroup=None):
# if already advanced, leave it be
if is_advanced_controlnet(control):
return control
# if exactly ControlNet returned, transform it into ControlNetAdvanced
if type(control) == ControlNet:
return ControlNetAdvanced.from_vanilla(v=control, timestep_keyframe=timestep_keyframe)
# if exactly ControlLora returned, transform it into ControlLoraAdvanced
elif type(control) == ControlLora:
return ControlLoraAdvanced.from_vanilla(v=control, timestep_keyframe=timestep_keyframe)
# if T2IAdapter returned, transform it into T2IAdapterAdvanced
elif isinstance(control, T2IAdapter):
return T2IAdapterAdvanced.from_vanilla(v=control, timestep_keyframe=timestep_keyframe)
# otherwise, leave it be - might be something I am not supporting yet
return control
def is_advanced_controlnet(input_object):
return hasattr(input_object, "sub_idxs")
def load_sparsectrl(ckpt_path: str, controlnet_data: dict[str, Tensor]=None, timestep_keyframe: TimestepKeyframeGroup=None, sparse_settings=SparseSettings.default(), model=None) -> SparseCtrlAdvanced:
if controlnet_data is None:
controlnet_data = comfy.utils.load_torch_file(ckpt_path, safe_load=True)
# first, separate out motion part from normal controlnet part and attempt to load that portion
motion_data = {}
for key in list(controlnet_data.keys()):
if "temporal" in key:
motion_data[key] = controlnet_data.pop(key)
if len(motion_data) == 0:
raise ValueError(f"No motion-related keys in '{ckpt_path}'; not a valid SparseCtrl model!")
motion_wrapper: SparseCtrlMotionWrapper = SparseCtrlMotionWrapper(motion_data).to(comfy.model_management.unet_dtype())
missing, unexpected = motion_wrapper.load_state_dict(motion_data)
if len(missing) > 0 or len(unexpected) > 0:
logger.info(f"SparseCtrlMotionWrapper: {missing}, {unexpected}")
# now, load as if it was a normal controlnet - mostly copied from comfy load_controlnet function
controlnet_config = None
is_diffusers = False
use_simplified_conditioning_embedding = False
if "controlnet_cond_embedding.conv_in.weight" in controlnet_data:
is_diffusers = True
if "controlnet_cond_embedding.weight" in controlnet_data:
is_diffusers = True
use_simplified_conditioning_embedding = True
if is_diffusers: #diffusers format
unet_dtype = comfy.model_management.unet_dtype()
controlnet_config = comfy.model_detection.unet_config_from_diffusers_unet(controlnet_data, unet_dtype)
diffusers_keys = comfy.utils.unet_to_diffusers(controlnet_config)
diffusers_keys["controlnet_mid_block.weight"] = "middle_block_out.0.weight"
diffusers_keys["controlnet_mid_block.bias"] = "middle_block_out.0.bias"
count = 0
loop = True
while loop:
suffix = [".weight", ".bias"]
for s in suffix:
k_in = "controlnet_down_blocks.{}{}".format(count, s)
k_out = "zero_convs.{}.0{}".format(count, s)
if k_in not in controlnet_data:
loop = False
break
diffusers_keys[k_in] = k_out
count += 1
# normal conditioning embedding
if not use_simplified_conditioning_embedding:
count = 0
loop = True
while loop:
suffix = [".weight", ".bias"]
for s in suffix:
if count == 0:
k_in = "controlnet_cond_embedding.conv_in{}".format(s)
else:
k_in = "controlnet_cond_embedding.blocks.{}{}".format(count - 1, s)
k_out = "input_hint_block.{}{}".format(count * 2, s)
if k_in not in controlnet_data:
k_in = "controlnet_cond_embedding.conv_out{}".format(s)
loop = False
diffusers_keys[k_in] = k_out
count += 1
# simplified conditioning embedding
else:
count = 0
suffix = [".weight", ".bias"]
for s in suffix:
k_in = "controlnet_cond_embedding{}".format(s)
k_out = "input_hint_block.{}{}".format(count, s)
diffusers_keys[k_in] = k_out
new_sd = {}
for k in diffusers_keys:
if k in controlnet_data:
new_sd[diffusers_keys[k]] = controlnet_data.pop(k)
leftover_keys = controlnet_data.keys()
if len(leftover_keys) > 0:
logger.info("leftover keys:", leftover_keys)
controlnet_data = new_sd
pth_key = 'control_model.zero_convs.0.0.weight'
pth = False
key = 'zero_convs.0.0.weight'
if pth_key in controlnet_data:
pth = True
key = pth_key
prefix = "control_model."
elif key in controlnet_data:
prefix = ""
else:
raise ValueError("The provided model is not a valid SparseCtrl model! [ErrorCode: HORSERADISH]")
if controlnet_config is None:
unet_dtype = comfy.model_management.unet_dtype()
controlnet_config = comfy.model_detection.model_config_from_unet(controlnet_data, prefix, unet_dtype, True).unet_config
load_device = comfy.model_management.get_torch_device()
manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device)
if manual_cast_dtype is not None:
controlnet_config["operations"] = manual_cast_clean_groupnorm
else:
controlnet_config["operations"] = disable_weight_init_clean_groupnorm
controlnet_config.pop("out_channels")
# get proper hint channels
if use_simplified_conditioning_embedding:
controlnet_config["hint_channels"] = controlnet_data["{}input_hint_block.0.weight".format(prefix)].shape[1]
controlnet_config["use_simplified_conditioning_embedding"] = use_simplified_conditioning_embedding
else:
controlnet_config["hint_channels"] = controlnet_data["{}input_hint_block.0.weight".format(prefix)].shape[1]
controlnet_config["use_simplified_conditioning_embedding"] = use_simplified_conditioning_embedding
control_model = SparseControlNet(**controlnet_config)
if pth:
if 'difference' in controlnet_data:
if model is not None:
comfy.model_management.load_models_gpu([model])
model_sd = model.model_state_dict()
for x in controlnet_data:
c_m = "control_model."
if x.startswith(c_m):
sd_key = "diffusion_model.{}".format(x[len(c_m):])
if sd_key in model_sd:
cd = controlnet_data[x]
cd += model_sd[sd_key].type(cd.dtype).to(cd.device)
else:
logger.warning("WARNING: Loaded a diff SparseCtrl without a model. It will very likely not work.")
class WeightsLoader(torch.nn.Module):
pass
w = WeightsLoader()
w.control_model = control_model
missing, unexpected = w.load_state_dict(controlnet_data, strict=False)
else:
missing, unexpected = control_model.load_state_dict(controlnet_data, strict=False)
if len(missing) > 0 or len(unexpected) > 0:
logger.info(f"SparseCtrl ControlNet: {missing}, {unexpected}")
global_average_pooling = False
filename = os.path.splitext(ckpt_path)[0]
if filename.endswith("_shuffle") or filename.endswith("_shuffle_fp16"): #TODO: smarter way of enabling global_average_pooling
global_average_pooling = True
# both motion portion and controlnet portions are loaded; bring them together if using motion model
if sparse_settings.use_motion:
motion_wrapper.inject(control_model)
control = SparseCtrlAdvanced(control_model, timestep_keyframes=timestep_keyframe, sparse_settings=sparse_settings, global_average_pooling=global_average_pooling, load_device=load_device, manual_cast_dtype=manual_cast_dtype)
return control
def load_controllllite(ckpt_path: str, controlnet_data: dict[str, Tensor]=None, timestep_keyframe: TimestepKeyframeGroup=None):
if controlnet_data is None:
controlnet_data = comfy.utils.load_torch_file(ckpt_path, safe_load=True)
# adapted from https://github.com/kohya-ss/ControlNet-LLLite-ComfyUI
# first, split weights for each module
module_weights = {}
for key, value in controlnet_data.items():
fragments = key.split(".")
module_name = fragments[0]
weight_name = ".".join(fragments[1:])
if module_name not in module_weights:
module_weights[module_name] = {}
module_weights[module_name][weight_name] = value
# next, load each module
modules = {}
for module_name, weights in module_weights.items():
# kohya planned to do something about how these should be chosen, so I'm not touching this
# since I am not familiar with the logic for this
if "conditioning1.4.weight" in weights:
depth = 3
elif weights["conditioning1.2.weight"].shape[-1] == 4:
depth = 2
else:
depth = 1
module = LLLiteModule(
name=module_name,
is_conv2d=weights["down.0.weight"].ndim == 4,
in_dim=weights["down.0.weight"].shape[1],
depth=depth,
cond_emb_dim=weights["conditioning1.0.weight"].shape[0] * 2,
mlp_dim=weights["down.0.weight"].shape[0],
)
# load weights into module
module.load_state_dict(weights)
modules[module_name] = module
if len(modules) == 1:
module.is_first = True
#logger.info(f"loaded {ckpt_path} successfully, {len(modules)} modules")
patch_attn1 = LLLitePatch(modules=modules, patch_type=LLLitePatch.ATTN1)
patch_attn2 = LLLitePatch(modules=modules, patch_type=LLLitePatch.ATTN2)
control = ControlLLLiteAdvanced(patch_attn1=patch_attn1, patch_attn2=patch_attn2, timestep_keyframes=timestep_keyframe)
return control
def load_svdcontrolnet(ckpt_path: str, controlnet_data: dict[str, Tensor]=None, timestep_keyframe: TimestepKeyframeGroup=None, model=None):
if controlnet_data is None:
controlnet_data = comfy.utils.load_torch_file(ckpt_path, safe_load=True)
controlnet_config = None
if "controlnet_cond_embedding.conv_in.weight" in controlnet_data: #diffusers format
unet_dtype = comfy.model_management.unet_dtype()
controlnet_config = svd_unet_config_from_diffusers_unet(controlnet_data, unet_dtype)
diffusers_keys = svd_unet_to_diffusers(controlnet_config)
diffusers_keys["controlnet_mid_block.weight"] = "middle_block_out.0.weight"
diffusers_keys["controlnet_mid_block.bias"] = "middle_block_out.0.bias"
count = 0
loop = True
while loop:
suffix = [".weight", ".bias"]
for s in suffix:
k_in = "controlnet_down_blocks.{}{}".format(count, s)
k_out = "zero_convs.{}.0{}".format(count, s)
if k_in not in controlnet_data:
loop = False
break
diffusers_keys[k_in] = k_out
count += 1
count = 0
loop = True
while loop:
suffix = [".weight", ".bias"]
for s in suffix:
if count == 0:
k_in = "controlnet_cond_embedding.conv_in{}".format(s)
else:
k_in = "controlnet_cond_embedding.blocks.{}{}".format(count - 1, s)
k_out = "input_hint_block.{}{}".format(count * 2, s)
if k_in not in controlnet_data:
k_in = "controlnet_cond_embedding.conv_out{}".format(s)
loop = False
diffusers_keys[k_in] = k_out
count += 1
new_sd = {}
for k in diffusers_keys:
if k in controlnet_data:
new_sd[diffusers_keys[k]] = controlnet_data.pop(k)
leftover_keys = controlnet_data.keys()
if len(leftover_keys) > 0:
spatial_leftover_keys = []
temporal_leftover_keys = []
other_leftover_keys = []
for key in leftover_keys:
if "spatial" in key:
spatial_leftover_keys.append(key)
elif "temporal" in key:
temporal_leftover_keys.append(key)
else:
other_leftover_keys.append(key)
logger.warn(f"spatial_leftover_keys ({len(spatial_leftover_keys)}): {spatial_leftover_keys}")
logger.warn(f"temporal_leftover_keys ({len(temporal_leftover_keys)}): {temporal_leftover_keys}")
logger.warn(f"other_leftover_keys ({len(other_leftover_keys)}): {other_leftover_keys}")
#print("leftover keys:", leftover_keys)
controlnet_data = new_sd
pth_key = 'control_model.zero_convs.0.0.weight'
pth = False
key = 'zero_convs.0.0.weight'
if pth_key in controlnet_data:
pth = True
key = pth_key
prefix = "control_model."
elif key in controlnet_data:
prefix = ""
else:
raise ValueError("The provided model is not a valid SVD-ControlNet model! [ErrorCode: MUSTARD]")
if controlnet_config is None:
unet_dtype = comfy.model_management.unet_dtype()
controlnet_config = comfy.model_detection.model_config_from_unet(controlnet_data, prefix, unet_dtype, True).unet_config
load_device = comfy.model_management.get_torch_device()
manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device)
if manual_cast_dtype is not None:
controlnet_config["operations"] = comfy.ops.manual_cast
controlnet_config.pop("out_channels")
controlnet_config["hint_channels"] = controlnet_data["{}input_hint_block.0.weight".format(prefix)].shape[1]
control_model = SVDControlNet(**controlnet_config)
if pth:
if 'difference' in controlnet_data:
if model is not None:
comfy.model_management.load_models_gpu([model])
model_sd = model.model_state_dict()
for x in controlnet_data:
c_m = "control_model."
if x.startswith(c_m):
sd_key = "diffusion_model.{}".format(x[len(c_m):])
if sd_key in model_sd:
cd = controlnet_data[x]
cd += model_sd[sd_key].type(cd.dtype).to(cd.device)
else:
print("WARNING: Loaded a diff controlnet without a model. It will very likely not work.")
class WeightsLoader(torch.nn.Module):
pass
w = WeightsLoader()
w.control_model = control_model
missing, unexpected = w.load_state_dict(controlnet_data, strict=False)
else:
missing, unexpected = control_model.load_state_dict(controlnet_data, strict=False)
if len(missing) > 0 or len(unexpected) > 0:
logger.info(f"SVD-ControlNet: {missing}, {unexpected}")
global_average_pooling = False
filename = os.path.splitext(ckpt_path)[0]
if filename.endswith("_shuffle") or filename.endswith("_shuffle_fp16"): #TODO: smarter way of enabling global_average_pooling
global_average_pooling = True
control = SVDControlNetAdvanced(control_model, timestep_keyframes=timestep_keyframe, global_average_pooling=global_average_pooling, load_device=load_device, manual_cast_dtype=manual_cast_dtype)
return control
workflows/comfy/ComfyUI/custom_nodes/ComfyUI-Advanced-ControlNet/adv_control/control_lllite.py
-247
View File
@@ -1,247 +0,0 @@
# adapted from https://github.com/kohya-ss/ControlNet-LLLite-ComfyUI
# basically, all the LLLite core code is from there, which I then combined with
# Advanced-ControlNet features and QoL
import math
from typing import Union
from torch import Tensor
import torch
import os
import comfy.utils
from comfy.controlnet import ControlBase
from .logger import logger
from .utils import AdvancedControlBase, deepcopy_with_sharing, prepare_mask_batch
def extra_options_to_module_prefix(extra_options):
# extra_options = {'transformer_index': 2, 'block_index': 8, 'original_shape': [2, 4, 128, 128], 'block': ('input', 7), 'n_heads': 20, 'dim_head': 64}
# block is: [('input', 4), ('input', 5), ('input', 7), ('input', 8), ('middle', 0),
# ('output', 0), ('output', 1), ('output', 2), ('output', 3), ('output', 4), ('output', 5)]
# transformer_index is: [0, 1, 2, 3, 4, 5, 6, 7, 8], for each block
# block_index is: 0-1 or 0-9, depends on the block
# input 7 and 8, middle has 10 blocks
# make module name from extra_options
block = extra_options["block"]
block_index = extra_options["block_index"]
if block[0] == "input":
module_pfx = f"lllite_unet_input_blocks_{block[1]}_1_transformer_blocks_{block_index}"
elif block[0] == "middle":
module_pfx = f"lllite_unet_middle_block_1_transformer_blocks_{block_index}"
elif block[0] == "output":
module_pfx = f"lllite_unet_output_blocks_{block[1]}_1_transformer_blocks_{block_index}"
else:
raise Exception(f"ControlLLLite: invalid block name '{block[0]}'. Expected 'input', 'middle', or 'output'.")
return module_pfx
class LLLitePatch:
ATTN1 = "attn1"
ATTN2 = "attn2"
def __init__(self, modules: dict[str, 'LLLiteModule'], patch_type: str, control: Union[AdvancedControlBase, ControlBase]=None):
self.modules = modules
self.control = control
self.patch_type = patch_type
#logger.error(f"create LLLitePatch: {id(self)},{control}")
def __call__(self, q, k, v, extra_options):
#logger.error(f"in __call__: {id(self)}")
# determine if have anything to run
if self.control.timestep_range is not None:
# it turns out comparing single-value tensors to floats is extremely slow
# a: Tensor = extra_options["sigmas"][0]
if self.control.t > self.control.timestep_range[0] or self.control.t < self.control.timestep_range[1]:
return q, k, v
module_pfx = extra_options_to_module_prefix(extra_options)
is_attn1 = q.shape[-1] == k.shape[-1] # self attention
if is_attn1:
module_pfx = module_pfx + "_attn1"
else:
module_pfx = module_pfx + "_attn2"
module_pfx_to_q = module_pfx + "_to_q"
module_pfx_to_k = module_pfx + "_to_k"
module_pfx_to_v = module_pfx + "_to_v"
if module_pfx_to_q in self.modules:
q = q + self.modules[module_pfx_to_q](q, self.control)
if module_pfx_to_k in self.modules:
k = k + self.modules[module_pfx_to_k](k, self.control)
if module_pfx_to_v in self.modules:
v = v + self.modules[module_pfx_to_v](v, self.control)
return q, k, v
def to(self, device):
#logger.info(f"to... has control? {self.control}")
for d in self.modules.keys():
self.modules[d] = self.modules[d].to(device)
return self
def set_control(self, control: Union[AdvancedControlBase, ControlBase]) -> 'LLLitePatch':
self.control = control
return self
#logger.error(f"set control for LLLitePatch: {id(self)}, cn: {id(control)}")
def clone_with_control(self, control: AdvancedControlBase):
#logger.error(f"clone-set control for LLLitePatch: {id(self)},{id(control)}")
return LLLitePatch(self.modules, self.patch_type, control)
def cleanup(self):
#total_cleaned = 0
for module in self.modules.values():
module.cleanup()
# total_cleaned += 1
#logger.info(f"cleaned modules: {total_cleaned}, {id(self)}")
#logger.error(f"cleanup LLLitePatch: {id(self)}")
# make sure deepcopy does not copy control, and deepcopied LLLitePatch should be assigned to control
def __deepcopy__(self, memo):
self.cleanup()
to_return: LLLitePatch = deepcopy_with_sharing(self, shared_attribute_names = ['control'], memo=memo)
#logger.warn(f"patch {id(self)} turned into {id(to_return)}")
try:
if self.patch_type == self.ATTN1:
to_return.control.patch_attn1 = to_return
elif self.patch_type == self.ATTN2:
to_return.control.patch_attn2 = to_return
except Exception:
pass
return to_return
# TODO: use comfy.ops to support fp8 properly
class LLLiteModule(torch.nn.Module):
def __init__(
self,
name: str,
is_conv2d: bool,
in_dim: int,
depth: int,
cond_emb_dim: int,
mlp_dim: int,
):
super().__init__()
self.name = name
self.is_conv2d = is_conv2d
self.is_first = False
modules = []
modules.append(torch.nn.Conv2d(3, cond_emb_dim // 2, kernel_size=4, stride=4, padding=0)) # to latent (from VAE) size*2
if depth == 1:
modules.append(torch.nn.ReLU(inplace=True))
modules.append(torch.nn.Conv2d(cond_emb_dim // 2, cond_emb_dim, kernel_size=2, stride=2, padding=0))
elif depth == 2:
modules.append(torch.nn.ReLU(inplace=True))
modules.append(torch.nn.Conv2d(cond_emb_dim // 2, cond_emb_dim, kernel_size=4, stride=4, padding=0))
elif depth == 3:
# kernel size 8 is too large, so set it to 4
modules.append(torch.nn.ReLU(inplace=True))
modules.append(torch.nn.Conv2d(cond_emb_dim // 2, cond_emb_dim // 2, kernel_size=4, stride=4, padding=0))
modules.append(torch.nn.ReLU(inplace=True))
modules.append(torch.nn.Conv2d(cond_emb_dim // 2, cond_emb_dim, kernel_size=2, stride=2, padding=0))
self.conditioning1 = torch.nn.Sequential(*modules)
if self.is_conv2d:
self.down = torch.nn.Sequential(
torch.nn.Conv2d(in_dim, mlp_dim, kernel_size=1, stride=1, padding=0),
torch.nn.ReLU(inplace=True),
)
self.mid = torch.nn.Sequential(
torch.nn.Conv2d(mlp_dim + cond_emb_dim, mlp_dim, kernel_size=1, stride=1, padding=0),
torch.nn.ReLU(inplace=True),
)
self.up = torch.nn.Sequential(
torch.nn.Conv2d(mlp_dim, in_dim, kernel_size=1, stride=1, padding=0),
)
else:
self.down = torch.nn.Sequential(
torch.nn.Linear(in_dim, mlp_dim),
torch.nn.ReLU(inplace=True),
)
self.mid = torch.nn.Sequential(
torch.nn.Linear(mlp_dim + cond_emb_dim, mlp_dim),
torch.nn.ReLU(inplace=True),
)
self.up = torch.nn.Sequential(
torch.nn.Linear(mlp_dim, in_dim),
)
self.depth = depth
self.cond_emb = None
self.cx_shape = None
self.prev_batch = 0
self.prev_sub_idxs = None
def cleanup(self):
del self.cond_emb
self.cond_emb = None
self.cx_shape = None
self.prev_batch = 0
self.prev_sub_idxs = None
def forward(self, x: Tensor, control: Union[AdvancedControlBase, ControlBase]):
mask = None
mask_tk = None
#logger.info(x.shape)
if self.cond_emb is None or control.sub_idxs != self.prev_sub_idxs or x.shape[0] != self.prev_batch:
# print(f"cond_emb is None, {self.name}")
cond_hint = control.cond_hint.to(x.device, dtype=x.dtype)
if control.latent_dims_div2 is not None and x.shape[-1] != 1280:
cond_hint = comfy.utils.common_upscale(cond_hint, control.latent_dims_div2[0] * 8, control.latent_dims_div2[1] * 8, 'nearest-exact', "center").to(x.device, dtype=x.dtype)
elif control.latent_dims_div4 is not None and x.shape[-1] == 1280:
cond_hint = comfy.utils.common_upscale(cond_hint, control.latent_dims_div4[0] * 8, control.latent_dims_div4[1] * 8, 'nearest-exact', "center").to(x.device, dtype=x.dtype)
cx = self.conditioning1(cond_hint)
self.cx_shape = cx.shape
if not self.is_conv2d:
# reshape / b,c,h,w -> b,h*w,c
n, c, h, w = cx.shape
cx = cx.view(n, c, h * w).permute(0, 2, 1)
self.cond_emb = cx
# save prev values
self.prev_batch = x.shape[0]
self.prev_sub_idxs = control.sub_idxs
cx: torch.Tensor = self.cond_emb
# print(f"forward {self.name}, {cx.shape}, {x.shape}")
# TODO: make masks work for conv2d (could not find any ControlLLLites at this time that use them)
# create masks
if not self.is_conv2d:
n, c, h, w = self.cx_shape
if control.mask_cond_hint is not None:
mask = prepare_mask_batch(control.mask_cond_hint, (1, 1, h, w)).to(cx.dtype)
mask = mask.view(mask.shape[0], 1, h * w).permute(0, 2, 1)
if control.tk_mask_cond_hint is not None:
mask_tk = prepare_mask_batch(control.mask_cond_hint, (1, 1, h, w)).to(cx.dtype)
mask_tk = mask_tk.view(mask_tk.shape[0], 1, h * w).permute(0, 2, 1)
# x in uncond/cond doubles batch size
if x.shape[0] != cx.shape[0]:
if self.is_conv2d:
cx = cx.repeat(x.shape[0] // cx.shape[0], 1, 1, 1)
else:
# print("x.shape[0] != cx.shape[0]", x.shape[0], cx.shape[0])
cx = cx.repeat(x.shape[0] // cx.shape[0], 1, 1)
if mask is not None:
mask = mask.repeat(x.shape[0] // mask.shape[0], 1, 1)
if mask_tk is not None:
mask_tk = mask_tk.repeat(x.shape[0] // mask_tk.shape[0], 1, 1)
if mask is None:
mask = 1.0
elif mask_tk is not None:
mask = mask * mask_tk
#logger.info(f"cs: {cx.shape}, x: {x.shape}, is_conv2d: {self.is_conv2d}")
cx = torch.cat([cx, self.down(x)], dim=1 if self.is_conv2d else 2)
cx = self.mid(cx)
cx = self.up(cx)
if control.latent_keyframes is not None:
cx = cx * control.calc_latent_keyframe_mults(x=cx, batched_number=control.batched_number)
return cx * mask * control.strength * control.current_timestep_keyframe.strength
workflows/comfy/ComfyUI/custom_nodes/ComfyUI-Advanced-ControlNet/adv_control/control_reference.py
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workflows/comfy/ComfyUI/custom_nodes/ComfyUI-Advanced-ControlNet/adv_control/control_sparsectrl.py
-907
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@@ -1,907 +0,0 @@
#taken from: https://github.com/lllyasviel/ControlNet
#and modified
#and then taken from comfy/cldm/cldm.py and modified again
from abc import ABC, abstractmethod
import math
import numpy as np
from typing import Iterable, Union
import torch
import torch as th
import torch.nn as nn
from torch import Tensor
from einops import rearrange, repeat
from comfy.ldm.modules.diffusionmodules.util import (
zero_module,
timestep_embedding,
)
from comfy.cli_args import args
from comfy.cldm.cldm import ControlNet as ControlNetCLDM
from comfy.ldm.modules.attention import SpatialTransformer
from comfy.ldm.modules.attention import attention_basic, attention_pytorch, attention_split, attention_sub_quad, default
from comfy.ldm.modules.attention import FeedForward, SpatialTransformer
from comfy.ldm.modules.diffusionmodules.openaimodel import TimestepEmbedSequential, ResBlock, Downsample
from comfy.controlnet import broadcast_image_to
from comfy.utils import repeat_to_batch_size
import comfy.ops
import comfy.model_management
from .utils import TimestepKeyframeGroup, disable_weight_init_clean_groupnorm, prepare_mask_batch
# until xformers bug is fixed, do not use xformers for VersatileAttention! TODO: change this when fix is out
# logic for choosing optimized_attention method taken from comfy/ldm/modules/attention.py
optimized_attention_mm = attention_basic
if comfy.model_management.xformers_enabled():
pass
#optimized_attention_mm = attention_xformers
if comfy.model_management.pytorch_attention_enabled():
optimized_attention_mm = attention_pytorch
else:
if args.use_split_cross_attention:
optimized_attention_mm = attention_split
else:
optimized_attention_mm = attention_sub_quad
class SparseControlNet(ControlNetCLDM):
def __init__(self, *args,**kwargs):
super().__init__(*args, **kwargs)
hint_channels = kwargs.get("hint_channels")
operations: disable_weight_init_clean_groupnorm = kwargs.get("operations", disable_weight_init_clean_groupnorm)
device = kwargs.get("device", None)
self.use_simplified_conditioning_embedding = kwargs.get("use_simplified_conditioning_embedding", False)
if self.use_simplified_conditioning_embedding:
self.input_hint_block = TimestepEmbedSequential(
zero_module(operations.conv_nd(self.dims, hint_channels, self.model_channels, 3, padding=1, dtype=self.dtype, device=device)),
)
self.motion_holder: MotionWrapperHolder = None
def set_actual_length(self, actual_length: int, full_length: int):
if self.motion_holder is not None:
self.motion_holder.motion_wrapper.set_video_length(video_length=actual_length, full_length=full_length)
def forward(self, x: Tensor, hint: Tensor, timesteps, context, y=None, **kwargs):
t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype)
emb = self.time_embed(t_emb)
# SparseCtrl sets noisy input to zeros
x = torch.zeros_like(x)
guided_hint = self.input_hint_block(hint, emb, context)
outs = []
hs = []
if self.num_classes is not None:
assert y.shape[0] == x.shape[0]
emb = emb + self.label_emb(y)
h = x
for module, zero_conv in zip(self.input_blocks, self.zero_convs):
if guided_hint is not None:
h = module(h, emb, context)
h += guided_hint
guided_hint = None
else:
h = module(h, emb, context)
outs.append(zero_conv(h, emb, context))
h = self.middle_block(h, emb, context)
outs.append(self.middle_block_out(h, emb, context))
return outs
class PreprocSparseRGBWrapper:
error_msg = "Invalid use of RGB SparseCtrl output. The output of RGB SparseCtrl preprocessor is NOT a usual image, but a latent pretending to be an image - you must connect the output directly to an Apply ControlNet node (advanced or otherwise). It cannot be used for anything else that accepts IMAGE input."
def __init__(self, condhint: Tensor):
self.condhint = condhint
def movedim(self, *args, **kwargs):
return self
def __getattr__(self, *args, **kwargs):
raise AttributeError(self.error_msg)
def __setattr__(self, name, value):
if name != "condhint":
raise AttributeError(self.error_msg)
super().__setattr__(name, value)
def __iter__(self, *args, **kwargs):
raise AttributeError(self.error_msg)
def __next__(self, *args, **kwargs):
raise AttributeError(self.error_msg)
def __len__(self, *args, **kwargs):
raise AttributeError(self.error_msg)
def __getitem__(self, *args, **kwargs):
raise AttributeError(self.error_msg)
def __setitem__(self, *args, **kwargs):
raise AttributeError(self.error_msg)
class SparseSettings:
def __init__(self, sparse_method: 'SparseMethod', use_motion: bool=True, motion_strength=1.0, motion_scale=1.0, merged=False):
self.sparse_method = sparse_method
self.use_motion = use_motion
self.motion_strength = motion_strength
self.motion_scale = motion_scale
self.merged = merged
@classmethod
def default(cls):
return SparseSettings(sparse_method=SparseSpreadMethod(), use_motion=True)
class SparseMethod(ABC):
SPREAD = "spread"
INDEX = "index"
def __init__(self, method: str):
self.method = method
@abstractmethod
def get_indexes(self, hint_length: int, full_length: int) -> list[int]:
pass
class SparseSpreadMethod(SparseMethod):
UNIFORM = "uniform"
STARTING = "starting"
ENDING = "ending"
CENTER = "center"
LIST = [UNIFORM, STARTING, ENDING, CENTER]
def __init__(self, spread=UNIFORM):
super().__init__(self.SPREAD)
self.spread = spread
def get_indexes(self, hint_length: int, full_length: int) -> list[int]:
# if hint_length >= full_length, limit hints to full_length
if hint_length >= full_length:
return list(range(full_length))
# handle special case of 1 hint image
if hint_length == 1:
if self.spread in [self.UNIFORM, self.STARTING]:
return [0]
elif self.spread == self.ENDING:
return [full_length-1]
elif self.spread == self.CENTER:
# return second (of three) values as the center
return [np.linspace(0, full_length-1, 3, endpoint=True, dtype=int)[1]]
else:
raise ValueError(f"Unrecognized spread: {self.spread}")
# otherwise, handle other cases
if self.spread == self.UNIFORM:
return list(np.linspace(0, full_length-1, hint_length, endpoint=True, dtype=int))
elif self.spread == self.STARTING:
# make split 1 larger, remove last element
return list(np.linspace(0, full_length-1, hint_length+1, endpoint=True, dtype=int))[:-1]
elif self.spread == self.ENDING:
# make split 1 larger, remove first element
return list(np.linspace(0, full_length-1, hint_length+1, endpoint=True, dtype=int))[1:]
elif self.spread == self.CENTER:
# if hint length is not 3 greater than full length, do STARTING behavior
if full_length-hint_length < 3:
return list(np.linspace(0, full_length-1, hint_length+1, endpoint=True, dtype=int))[:-1]
# otherwise, get linspace of 2 greater than needed, then cut off first and last
return list(np.linspace(0, full_length-1, hint_length+2, endpoint=True, dtype=int))[1:-1]
return ValueError(f"Unrecognized spread: {self.spread}")
class SparseIndexMethod(SparseMethod):
def __init__(self, idxs: list[int]):
super().__init__(self.INDEX)
self.idxs = idxs
def get_indexes(self, hint_length: int, full_length: int) -> list[int]:
orig_hint_length = hint_length
if hint_length > full_length:
hint_length = full_length
# if idxs is less than hint_length, throw error
if len(self.idxs) < hint_length:
err_msg = f"There are not enough indexes ({len(self.idxs)}) provided to fit the usable {hint_length} input images."
if orig_hint_length != hint_length:
err_msg = f"{err_msg} (original input images: {orig_hint_length})"
raise ValueError(err_msg)
# cap idxs to hint_length
idxs = self.idxs[:hint_length]
new_idxs = []
real_idxs = set()
for idx in idxs:
if idx < 0:
real_idx = full_length+idx
if real_idx in real_idxs:
raise ValueError(f"Index '{idx}' maps to '{real_idx}' and is duplicate - indexes in Sparse Index Method must be unique.")
else:
real_idx = idx
if real_idx in real_idxs:
raise ValueError(f"Index '{idx}' is duplicate (or a negative index is equivalent) - indexes in Sparse Index Method must be unique.")
real_idxs.add(real_idx)
new_idxs.append(real_idx)
return new_idxs
#########################################
# motion-related portion of controlnet
class BlockType:
UP = "up"
DOWN = "down"
MID = "mid"
def get_down_block_max(mm_state_dict: dict[str, Tensor]) -> int:
return get_block_max(mm_state_dict, "down_blocks")
def get_up_block_max(mm_state_dict: dict[str, Tensor]) -> int:
return get_block_max(mm_state_dict, "up_blocks")
def get_block_max(mm_state_dict: dict[str, Tensor], block_name: str) -> int:
# keep track of biggest down_block count in module
biggest_block = -1
for key in mm_state_dict.keys():
if block_name in key:
try:
block_int = key.split(".")[1]
block_num = int(block_int)
if block_num > biggest_block:
biggest_block = block_num
except ValueError:
pass
return biggest_block
def has_mid_block(mm_state_dict: dict[str, Tensor]):
# check if keys contain mid_block
for key in mm_state_dict.keys():
if key.startswith("mid_block."):
return True
return False
def get_position_encoding_max_len(mm_state_dict: dict[str, Tensor], mm_name: str=None) -> int:
# use pos_encoder.pe entries to determine max length - [1, {max_length}, {320|640|1280}]
for key in mm_state_dict.keys():
if key.endswith("pos_encoder.pe"):
return mm_state_dict[key].size(1) # get middle dim
raise ValueError(f"No pos_encoder.pe found in SparseCtrl state_dict - {mm_name} is not a valid SparseCtrl model!")
class MotionWrapperHolder:
def __init__(self, motion_wrapper: 'SparseCtrlMotionWrapper'):
self.motion_wrapper = motion_wrapper
class SparseCtrlMotionWrapper(nn.Module):
def __init__(self, mm_state_dict: dict[str, Tensor]):
super().__init__()
self.down_blocks: Iterable[MotionModule] = None
self.up_blocks: Iterable[MotionModule] = None
self.mid_block: MotionModule = None
self.encoding_max_len = get_position_encoding_max_len(mm_state_dict, "")
layer_channels = (320, 640, 1280, 1280)
if get_down_block_max(mm_state_dict) > -1:
self.down_blocks = nn.ModuleList([])
for c in layer_channels:
self.down_blocks.append(MotionModule(c, temporal_position_encoding_max_len=self.encoding_max_len, block_type=BlockType.DOWN))
if get_up_block_max(mm_state_dict) > -1:
self.up_blocks = nn.ModuleList([])
for c in reversed(layer_channels):
self.up_blocks.append(MotionModule(c, temporal_position_encoding_max_len=self.encoding_max_len, block_type=BlockType.UP))
if has_mid_block(mm_state_dict):
self.mid_block = MotionModule(1280, temporal_position_encoding_max_len=self.encoding_max_len, block_type=BlockType.MID)
def inject(self, unet: SparseControlNet):
# inject input (down) blocks
self._inject(unet.input_blocks, self.down_blocks)
# inject mid block, if present
if self.mid_block is not None:
self._inject([unet.middle_block], [self.mid_block])
unet.motion_holder = MotionWrapperHolder(self)
def _inject(self, unet_blocks: nn.ModuleList, mm_blocks: nn.ModuleList):
# Rules for injection:
# For each component list in a unet block:
# if SpatialTransformer exists in list, place next block after last occurrence
# elif ResBlock exists in list, place next block after first occurrence
# else don't place block
injection_count = 0
unet_idx = 0
# details about blocks passed in
per_block = len(mm_blocks[0].motion_modules)
injection_goal = len(mm_blocks) * per_block
# only stop injecting when modules exhausted
while injection_count < injection_goal:
# figure out which VanillaTemporalModule from mm to inject
mm_blk_idx, mm_vtm_idx = injection_count // per_block, injection_count % per_block
# figure out layout of unet block components
st_idx = -1 # SpatialTransformer index
res_idx = -1 # first ResBlock index
# first, figure out indeces of relevant blocks
for idx, component in enumerate(unet_blocks[unet_idx]):
if type(component) == SpatialTransformer:
st_idx = idx
elif type(component).__name__ == "ResBlock" and res_idx < 0:
res_idx = idx
# if SpatialTransformer exists, inject right after
if st_idx >= 0:
unet_blocks[unet_idx].insert(st_idx+1, mm_blocks[mm_blk_idx].motion_modules[mm_vtm_idx])
injection_count += 1
# otherwise, if only ResBlock exists, inject right after
elif res_idx >= 0:
unet_blocks[unet_idx].insert(res_idx+1, mm_blocks[mm_blk_idx].motion_modules[mm_vtm_idx])
injection_count += 1
# increment unet_idx
unet_idx += 1
def eject(self, unet: SparseControlNet):
# remove from input blocks (downblocks)
self._eject(unet.input_blocks)
# remove from middle block (encapsulate in list to make compatible)
self._eject([unet.middle_block])
del unet.motion_holder
unet.motion_holder = None
def _eject(self, unet_blocks: nn.ModuleList):
# eject all VanillaTemporalModule objects from all blocks
for block in unet_blocks:
idx_to_pop = []
for idx, component in enumerate(block):
if type(component) == VanillaTemporalModule:
idx_to_pop.append(idx)
# pop in backwards order, as to not disturb what the indeces refer to
for idx in sorted(idx_to_pop, reverse=True):
block.pop(idx)
def set_video_length(self, video_length: int, full_length: int):
self.AD_video_length = video_length
if self.down_blocks is not None:
for block in self.down_blocks:
block.set_video_length(video_length, full_length)
if self.up_blocks is not None:
for block in self.up_blocks:
block.set_video_length(video_length, full_length)
if self.mid_block is not None:
self.mid_block.set_video_length(video_length, full_length)
def set_scale_multiplier(self, multiplier: Union[float, None]):
if self.down_blocks is not None:
for block in self.down_blocks:
block.set_scale_multiplier(multiplier)
if self.up_blocks is not None:
for block in self.up_blocks:
block.set_scale_multiplier(multiplier)
if self.mid_block is not None:
self.mid_block.set_scale_multiplier(multiplier)
def set_strength(self, strength: float):
if self.down_blocks is not None:
for block in self.down_blocks:
block.set_strength(strength)
if self.up_blocks is not None:
for block in self.up_blocks:
block.set_strength(strength)
if self.mid_block is not None:
self.mid_block.set_strength(strength)
def reset_temp_vars(self):
if self.down_blocks is not None:
for block in self.down_blocks:
block.reset_temp_vars()
if self.up_blocks is not None:
for block in self.up_blocks:
block.reset_temp_vars()
if self.mid_block is not None:
self.mid_block.reset_temp_vars()
def reset_scale_multiplier(self):
self.set_scale_multiplier(None)
def reset(self):
self.reset_scale_multiplier()
self.reset_temp_vars()
class MotionModule(nn.Module):
def __init__(self, in_channels, temporal_position_encoding_max_len=24, block_type: str=BlockType.DOWN):
super().__init__()
if block_type == BlockType.MID:
# mid blocks contain only a single VanillaTemporalModule
self.motion_modules: Iterable[VanillaTemporalModule] = nn.ModuleList([get_motion_module(in_channels, temporal_position_encoding_max_len)])
else:
# down blocks contain two VanillaTemporalModules
self.motion_modules: Iterable[VanillaTemporalModule] = nn.ModuleList(
[
get_motion_module(in_channels, temporal_position_encoding_max_len),
get_motion_module(in_channels, temporal_position_encoding_max_len)
]
)
# up blocks contain one additional VanillaTemporalModule
if block_type == BlockType.UP:
self.motion_modules.append(get_motion_module(in_channels, temporal_position_encoding_max_len))
def set_video_length(self, video_length: int, full_length: int):
for motion_module in self.motion_modules:
motion_module.set_video_length(video_length, full_length)
def set_scale_multiplier(self, multiplier: Union[float, None]):
for motion_module in self.motion_modules:
motion_module.set_scale_multiplier(multiplier)
def set_masks(self, masks: Tensor, min_val: float, max_val: float):
for motion_module in self.motion_modules:
motion_module.set_masks(masks, min_val, max_val)
def set_sub_idxs(self, sub_idxs: list[int]):
for motion_module in self.motion_modules:
motion_module.set_sub_idxs(sub_idxs)
def set_strength(self, strength: float):
for motion_module in self.motion_modules:
motion_module.set_strength(strength)
def reset_temp_vars(self):
for motion_module in self.motion_modules:
motion_module.reset_temp_vars()
def get_motion_module(in_channels, temporal_position_encoding_max_len):
# unlike normal AD, there is only one attention block expected in SparseCtrl models
return VanillaTemporalModule(in_channels=in_channels, attention_block_types=("Temporal_Self",), temporal_position_encoding_max_len=temporal_position_encoding_max_len)
class VanillaTemporalModule(nn.Module):
def __init__(
self,
in_channels,
num_attention_heads=8,
num_transformer_block=1,
attention_block_types=("Temporal_Self", "Temporal_Self"),
cross_frame_attention_mode=None,
temporal_position_encoding=True,
temporal_position_encoding_max_len=24,
temporal_attention_dim_div=1,
zero_initialize=True,
):
super().__init__()
self.strength = 1.0
self.temporal_transformer = TemporalTransformer3DModel(
in_channels=in_channels,
num_attention_heads=num_attention_heads,
attention_head_dim=in_channels
// num_attention_heads
// temporal_attention_dim_div,
num_layers=num_transformer_block,
attention_block_types=attention_block_types,
cross_frame_attention_mode=cross_frame_attention_mode,
temporal_position_encoding=temporal_position_encoding,
temporal_position_encoding_max_len=temporal_position_encoding_max_len,
)
if zero_initialize:
self.temporal_transformer.proj_out = zero_module(
self.temporal_transformer.proj_out
)
def set_video_length(self, video_length: int, full_length: int):
self.temporal_transformer.set_video_length(video_length, full_length)
def set_scale_multiplier(self, multiplier: Union[float, None]):
self.temporal_transformer.set_scale_multiplier(multiplier)
def set_masks(self, masks: Tensor, min_val: float, max_val: float):
self.temporal_transformer.set_masks(masks, min_val, max_val)
def set_sub_idxs(self, sub_idxs: list[int]):
self.temporal_transformer.set_sub_idxs(sub_idxs)
def set_strength(self, strength: float):
self.strength = strength
def reset_temp_vars(self):
self.set_strength(1.0)
self.temporal_transformer.reset_temp_vars()
def forward(self, input_tensor, encoder_hidden_states=None, attention_mask=None):
if math.isclose(self.strength, 1.0):
return self.temporal_transformer(input_tensor, encoder_hidden_states, attention_mask)
elif math.isclose(self.strength, 0.0):
return input_tensor
elif self.strength > 1.0:
return self.temporal_transformer(input_tensor, encoder_hidden_states, attention_mask)*self.strength
else:
return self.temporal_transformer(input_tensor, encoder_hidden_states, attention_mask)*self.strength + input_tensor*(1.0-self.strength)
class TemporalTransformer3DModel(nn.Module):
def __init__(
self,
in_channels,
num_attention_heads,
attention_head_dim,
num_layers,
attention_block_types=(
"Temporal_Self",
"Temporal_Self",
),
dropout=0.0,
norm_num_groups=32,
cross_attention_dim=768,
activation_fn="geglu",
attention_bias=False,
upcast_attention=False,
cross_frame_attention_mode=None,
temporal_position_encoding=False,
temporal_position_encoding_max_len=24,
):
super().__init__()
self.video_length = 16
self.full_length = 16
self.scale_min = 1.0
self.scale_max = 1.0
self.raw_scale_mask: Union[Tensor, None] = None
self.temp_scale_mask: Union[Tensor, None] = None
self.sub_idxs: Union[list[int], None] = None
self.prev_hidden_states_batch = 0
inner_dim = num_attention_heads * attention_head_dim
self.norm = disable_weight_init_clean_groupnorm.GroupNorm(
num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True
)
self.proj_in = nn.Linear(in_channels, inner_dim)
self.transformer_blocks: Iterable[TemporalTransformerBlock] = nn.ModuleList(
[
TemporalTransformerBlock(
dim=inner_dim,
num_attention_heads=num_attention_heads,
attention_head_dim=attention_head_dim,
attention_block_types=attention_block_types,
dropout=dropout,
norm_num_groups=norm_num_groups,
cross_attention_dim=cross_attention_dim,
activation_fn=activation_fn,
attention_bias=attention_bias,
upcast_attention=upcast_attention,
cross_frame_attention_mode=cross_frame_attention_mode,
temporal_position_encoding=temporal_position_encoding,
temporal_position_encoding_max_len=temporal_position_encoding_max_len,
)
for d in range(num_layers)
]
)
self.proj_out = nn.Linear(inner_dim, in_channels)
def set_video_length(self, video_length: int, full_length: int):
self.video_length = video_length
self.full_length = full_length
def set_scale_multiplier(self, multiplier: Union[float, None]):
for block in self.transformer_blocks:
block.set_scale_multiplier(multiplier)
def set_masks(self, masks: Tensor, min_val: float, max_val: float):
self.scale_min = min_val
self.scale_max = max_val
self.raw_scale_mask = masks
def set_sub_idxs(self, sub_idxs: list[int]):
self.sub_idxs = sub_idxs
for block in self.transformer_blocks:
block.set_sub_idxs(sub_idxs)
def reset_temp_vars(self):
del self.temp_scale_mask
self.temp_scale_mask = None
self.prev_hidden_states_batch = 0
def get_scale_mask(self, hidden_states: Tensor) -> Union[Tensor, None]:
# if no raw mask, return None
if self.raw_scale_mask is None:
return None
shape = hidden_states.shape
batch, channel, height, width = shape
# if temp mask already calculated, return it
if self.temp_scale_mask != None:
# check if hidden_states batch matches
if batch == self.prev_hidden_states_batch:
if self.sub_idxs is not None:
return self.temp_scale_mask[:, self.sub_idxs, :]
return self.temp_scale_mask
# if does not match, reset cached temp_scale_mask and recalculate it
del self.temp_scale_mask
self.temp_scale_mask = None
# otherwise, calculate temp mask
self.prev_hidden_states_batch = batch
mask = prepare_mask_batch(self.raw_scale_mask, shape=(self.full_length, 1, height, width))
mask = repeat_to_batch_size(mask, self.full_length)
# if mask not the same amount length as full length, make it match
if self.full_length != mask.shape[0]:
mask = broadcast_image_to(mask, self.full_length, 1)
# reshape mask to attention K shape (h*w, latent_count, 1)
batch, channel, height, width = mask.shape
# first, perform same operations as on hidden_states,
# turning (b, c, h, w) -> (b, h*w, c)
mask = mask.permute(0, 2, 3, 1).reshape(batch, height*width, channel)
# then, make it the same shape as attention's k, (h*w, b, c)
mask = mask.permute(1, 0, 2)
# make masks match the expected length of h*w
batched_number = shape[0] // self.video_length
if batched_number > 1:
mask = torch.cat([mask] * batched_number, dim=0)
# cache mask and set to proper device
self.temp_scale_mask = mask
# move temp_scale_mask to proper dtype + device
self.temp_scale_mask = self.temp_scale_mask.to(dtype=hidden_states.dtype, device=hidden_states.device)
# return subset of masks, if needed
if self.sub_idxs is not None:
return self.temp_scale_mask[:, self.sub_idxs, :]
return self.temp_scale_mask
def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None):
batch, channel, height, width = hidden_states.shape
residual = hidden_states
scale_mask = self.get_scale_mask(hidden_states)
# add some casts for fp8 purposes - does not affect speed otherwise
hidden_states = self.norm(hidden_states).to(hidden_states.dtype)
inner_dim = hidden_states.shape[1]
hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(
batch, height * width, inner_dim
)
hidden_states = self.proj_in(hidden_states).to(hidden_states.dtype)
# Transformer Blocks
for block in self.transformer_blocks:
hidden_states = block(
hidden_states,
encoder_hidden_states=encoder_hidden_states,
attention_mask=attention_mask,
video_length=self.video_length,
scale_mask=scale_mask
)
# output
hidden_states = self.proj_out(hidden_states)
hidden_states = (
hidden_states.reshape(batch, height, width, inner_dim)
.permute(0, 3, 1, 2)
.contiguous()
)
output = hidden_states + residual
return output
class TemporalTransformerBlock(nn.Module):
def __init__(
self,
dim,
num_attention_heads,
attention_head_dim,
attention_block_types=(
"Temporal_Self",
"Temporal_Self",
),
dropout=0.0,
norm_num_groups=32,
cross_attention_dim=768,
activation_fn="geglu",
attention_bias=False,
upcast_attention=False,
cross_frame_attention_mode=None,
temporal_position_encoding=False,
temporal_position_encoding_max_len=24,
):
super().__init__()
attention_blocks = []
norms = []
for block_name in attention_block_types:
attention_blocks.append(
VersatileAttention(
attention_mode=block_name.split("_")[0],
context_dim=cross_attention_dim # called context_dim for ComfyUI impl
if block_name.endswith("_Cross")
else None,
query_dim=dim,
heads=num_attention_heads,
dim_head=attention_head_dim,
dropout=dropout,
#bias=attention_bias, # remove for Comfy CrossAttention
#upcast_attention=upcast_attention, # remove for Comfy CrossAttention
cross_frame_attention_mode=cross_frame_attention_mode,
temporal_position_encoding=temporal_position_encoding,
temporal_position_encoding_max_len=temporal_position_encoding_max_len,
)
)
norms.append(nn.LayerNorm(dim))
self.attention_blocks: Iterable[VersatileAttention] = nn.ModuleList(attention_blocks)
self.norms = nn.ModuleList(norms)
self.ff = FeedForward(dim, dropout=dropout, glu=(activation_fn == "geglu"))
self.ff_norm = nn.LayerNorm(dim)
def set_scale_multiplier(self, multiplier: Union[float, None]):
for block in self.attention_blocks:
block.set_scale_multiplier(multiplier)
def set_sub_idxs(self, sub_idxs: list[int]):
for block in self.attention_blocks:
block.set_sub_idxs(sub_idxs)
def forward(
self,
hidden_states,
encoder_hidden_states=None,
attention_mask=None,
video_length=None,
scale_mask=None
):
for attention_block, norm in zip(self.attention_blocks, self.norms):
norm_hidden_states = norm(hidden_states).to(hidden_states.dtype)
hidden_states = (
attention_block(
norm_hidden_states,
encoder_hidden_states=encoder_hidden_states
if attention_block.is_cross_attention
else None,
attention_mask=attention_mask,
video_length=video_length,
scale_mask=scale_mask
)
+ hidden_states
)
hidden_states = self.ff(self.ff_norm(hidden_states)) + hidden_states
output = hidden_states
return output
class PositionalEncoding(nn.Module):
def __init__(self, d_model, dropout=0.0, max_len=24):
super().__init__()
self.dropout = nn.Dropout(p=dropout)
position = torch.arange(max_len).unsqueeze(1)
div_term = torch.exp(
torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model)
)
pe = torch.zeros(1, max_len, d_model)
pe[0, :, 0::2] = torch.sin(position * div_term)
pe[0, :, 1::2] = torch.cos(position * div_term)
self.register_buffer("pe", pe)
self.sub_idxs = None
def set_sub_idxs(self, sub_idxs: list[int]):
self.sub_idxs = sub_idxs
def forward(self, x):
#if self.sub_idxs is not None:
# x = x + self.pe[:, self.sub_idxs]
#else:
x = x + self.pe[:, : x.size(1)]
return self.dropout(x)
class CrossAttentionMM(nn.Module):
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None,
operations=comfy.ops.disable_weight_init):
super().__init__()
inner_dim = dim_head * heads
context_dim = default(context_dim, query_dim)
self.heads = heads
self.dim_head = dim_head
self.scale = None
self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
def forward(self, x, context=None, value=None, mask=None, scale_mask=None):
q = self.to_q(x)
context = default(context, x)
k: Tensor = self.to_k(context)
if value is not None:
v = self.to_v(value)
del value
else:
v = self.to_v(context)
# apply custom scale by multiplying k by scale factor
if self.scale is not None:
k *= self.scale
# apply scale mask, if present
if scale_mask is not None:
k *= scale_mask
out = optimized_attention_mm(q, k, v, self.heads, mask)
return self.to_out(out)
class VersatileAttention(CrossAttentionMM):
def __init__(
self,
attention_mode=None,
cross_frame_attention_mode=None,
temporal_position_encoding=False,
temporal_position_encoding_max_len=24,
*args,
**kwargs,
):
super().__init__(*args, **kwargs)
assert attention_mode == "Temporal"
self.attention_mode = attention_mode
self.is_cross_attention = kwargs["context_dim"] is not None
self.pos_encoder = (
PositionalEncoding(
kwargs["query_dim"],
dropout=0.0,
max_len=temporal_position_encoding_max_len,
)
if (temporal_position_encoding and attention_mode == "Temporal")
else None
)
def extra_repr(self):
return f"(Module Info) Attention_Mode: {self.attention_mode}, Is_Cross_Attention: {self.is_cross_attention}"
def set_scale_multiplier(self, multiplier: Union[float, None]):
if multiplier is None or math.isclose(multiplier, 1.0):
self.scale = None
else:
self.scale = multiplier
def set_sub_idxs(self, sub_idxs: list[int]):
if self.pos_encoder != None:
self.pos_encoder.set_sub_idxs(sub_idxs)
def forward(
self,
hidden_states: Tensor,
encoder_hidden_states=None,
attention_mask=None,
video_length=None,
scale_mask=None,
):
if self.attention_mode != "Temporal":
raise NotImplementedError
d = hidden_states.shape[1]
hidden_states = rearrange(
hidden_states, "(b f) d c -> (b d) f c", f=video_length
)
if self.pos_encoder is not None:
hidden_states = self.pos_encoder(hidden_states).to(hidden_states.dtype)
encoder_hidden_states = (
repeat(encoder_hidden_states, "b n c -> (b d) n c", d=d)
if encoder_hidden_states is not None
else encoder_hidden_states
)
hidden_states = super().forward(
hidden_states,
encoder_hidden_states,
value=None,
mask=attention_mask,
scale_mask=scale_mask,
)
hidden_states = rearrange(hidden_states, "(b d) f c -> (b f) d c", d=d)
return hidden_states
workflows/comfy/ComfyUI/custom_nodes/ComfyUI-Advanced-ControlNet/adv_control/control_svd.py
-517
View File
@@ -1,517 +0,0 @@
import torch
import torch.nn as nn
from torch import Tensor
import comfy.model_detection
from comfy.utils import UNET_MAP_BASIC, UNET_MAP_RESNET, UNET_MAP_ATTENTIONS, TRANSFORMER_BLOCKS
import torch
from comfy.ldm.modules.diffusionmodules.util import (
zero_module,
timestep_embedding,
)
from comfy.ldm.modules.attention import SpatialVideoTransformer
from comfy.ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, VideoResBlock, Downsample
from comfy.ldm.util import exists
import comfy.ops
class SVDControlNet(nn.Module):
def __init__(
self,
image_size,
in_channels,
model_channels,
hint_channels,
num_res_blocks,
dropout=0,
channel_mult=(1, 2, 4, 8),
conv_resample=True,
dims=2,
num_classes=None,
use_checkpoint=False,
dtype=torch.float32,
num_heads=-1,
num_head_channels=-1,
num_heads_upsample=-1,
use_scale_shift_norm=False,
resblock_updown=False,
use_new_attention_order=False,
use_spatial_transformer=False, # custom transformer support
transformer_depth=1, # custom transformer support
context_dim=None, # custom transformer support
n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model
legacy=True,
disable_self_attentions=None,
num_attention_blocks=None,
disable_middle_self_attn=False,
use_linear_in_transformer=False,
adm_in_channels=None,
transformer_depth_middle=None,
transformer_depth_output=None,
use_spatial_context=False,
extra_ff_mix_layer=False,
merge_strategy="fixed",
merge_factor=0.5,
video_kernel_size=3,
device=None,
operations=comfy.ops.disable_weight_init,
**kwargs,
):
super().__init__()
assert use_spatial_transformer == True, "use_spatial_transformer has to be true"
if use_spatial_transformer:
assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
if context_dim is not None:
assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
# from omegaconf.listconfig import ListConfig
# if type(context_dim) == ListConfig:
# context_dim = list(context_dim)
if num_heads_upsample == -1:
num_heads_upsample = num_heads
if num_heads == -1:
assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
if num_head_channels == -1:
assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
self.dims = dims
self.image_size = image_size
self.in_channels = in_channels
self.model_channels = model_channels
if isinstance(num_res_blocks, int):
self.num_res_blocks = len(channel_mult) * [num_res_blocks]
else:
if len(num_res_blocks) != len(channel_mult):
raise ValueError("provide num_res_blocks either as an int (globally constant) or "
"as a list/tuple (per-level) with the same length as channel_mult")
self.num_res_blocks = num_res_blocks
if disable_self_attentions is not None:
# should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not
assert len(disable_self_attentions) == len(channel_mult)
if num_attention_blocks is not None:
assert len(num_attention_blocks) == len(self.num_res_blocks)
assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks))))
transformer_depth = transformer_depth[:]
self.dropout = dropout
self.channel_mult = channel_mult
self.conv_resample = conv_resample
self.num_classes = num_classes
self.use_checkpoint = use_checkpoint
self.dtype = dtype
self.num_heads = num_heads
self.num_head_channels = num_head_channels
self.num_heads_upsample = num_heads_upsample
self.predict_codebook_ids = n_embed is not None
time_embed_dim = model_channels * 4
self.time_embed = nn.Sequential(
operations.Linear(model_channels, time_embed_dim, dtype=self.dtype, device=device),
nn.SiLU(),
operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
)
if self.num_classes is not None:
if isinstance(self.num_classes, int):
self.label_emb = nn.Embedding(num_classes, time_embed_dim)
elif self.num_classes == "continuous":
print("setting up linear c_adm embedding layer")
self.label_emb = nn.Linear(1, time_embed_dim)
elif self.num_classes == "sequential":
assert adm_in_channels is not None
self.label_emb = nn.Sequential(
nn.Sequential(
operations.Linear(adm_in_channels, time_embed_dim, dtype=self.dtype, device=device),
nn.SiLU(),
operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
)
)
else:
raise ValueError()
self.input_blocks = nn.ModuleList(
[
TimestepEmbedSequential(
operations.conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype, device=device)
)
]
)
self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels, operations=operations, dtype=self.dtype, device=device)])
self.input_hint_block = TimestepEmbedSequential(
operations.conv_nd(dims, hint_channels, 16, 3, padding=1, dtype=self.dtype, device=device),
nn.SiLU(),
operations.conv_nd(dims, 16, 16, 3, padding=1, dtype=self.dtype, device=device),
nn.SiLU(),
operations.conv_nd(dims, 16, 32, 3, padding=1, stride=2, dtype=self.dtype, device=device),
nn.SiLU(),
operations.conv_nd(dims, 32, 32, 3, padding=1, dtype=self.dtype, device=device),
nn.SiLU(),
operations.conv_nd(dims, 32, 96, 3, padding=1, stride=2, dtype=self.dtype, device=device),
nn.SiLU(),
operations.conv_nd(dims, 96, 96, 3, padding=1, dtype=self.dtype, device=device),
nn.SiLU(),
operations.conv_nd(dims, 96, 256, 3, padding=1, stride=2, dtype=self.dtype, device=device),
nn.SiLU(),
operations.conv_nd(dims, 256, model_channels, 3, padding=1, dtype=self.dtype, device=device)
)
self._feature_size = model_channels
input_block_chans = [model_channels]
ch = model_channels
ds = 1
for level, mult in enumerate(channel_mult):
for nr in range(self.num_res_blocks[level]):
layers = [
VideoResBlock(
ch,
time_embed_dim,
dropout,
out_channels=mult * model_channels,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
dtype=self.dtype,
device=device,
operations=operations,
video_kernel_size=video_kernel_size,
merge_strategy=merge_strategy, merge_factor=merge_factor,
)
]
ch = mult * model_channels
num_transformers = transformer_depth.pop(0)
if num_transformers > 0:
if num_head_channels == -1:
dim_head = ch // num_heads
else:
num_heads = ch // num_head_channels
dim_head = num_head_channels
if legacy:
#num_heads = 1
dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
if exists(disable_self_attentions):
disabled_sa = disable_self_attentions[level]
else:
disabled_sa = False
if not exists(num_attention_blocks) or nr < num_attention_blocks[level]:
layers.append(
SpatialVideoTransformer(
ch, num_heads, dim_head, depth=num_transformers, context_dim=context_dim,
disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations,
use_spatial_context=use_spatial_context, ff_in=extra_ff_mix_layer,
merge_strategy=merge_strategy, merge_factor=merge_factor,
)
)
self.input_blocks.append(TimestepEmbedSequential(*layers))
self.zero_convs.append(self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device))
self._feature_size += ch
input_block_chans.append(ch)
if level != len(channel_mult) - 1:
out_ch = ch
self.input_blocks.append(
TimestepEmbedSequential(
VideoResBlock(
ch,
time_embed_dim,
dropout,
out_channels=out_ch,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
down=True,
dtype=self.dtype,
device=device,
operations=operations,
video_kernel_size=video_kernel_size,
merge_strategy=merge_strategy, merge_factor=merge_factor,
)
if resblock_updown
else Downsample(
ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations
)
)
)
ch = out_ch
input_block_chans.append(ch)
self.zero_convs.append(self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device))
ds *= 2
self._feature_size += ch
if num_head_channels == -1:
dim_head = ch // num_heads
else:
num_heads = ch // num_head_channels
dim_head = num_head_channels
if legacy:
#num_heads = 1
dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
mid_block = [
VideoResBlock(
ch,
time_embed_dim,
dropout,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
dtype=self.dtype,
device=device,
operations=operations,
video_kernel_size=video_kernel_size,
merge_strategy=merge_strategy, merge_factor=merge_factor,
)]
if transformer_depth_middle >= 0:
mid_block += [SpatialVideoTransformer( # always uses a self-attn
ch, num_heads, dim_head, depth=transformer_depth_middle, context_dim=context_dim,
disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer,
checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations,
use_spatial_context=use_spatial_context, ff_in=extra_ff_mix_layer,
merge_strategy=merge_strategy, merge_factor=merge_factor,
),
VideoResBlock(
ch,
time_embed_dim,
dropout,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
dtype=self.dtype,
device=device,
operations=operations,
video_kernel_size=video_kernel_size,
merge_strategy=merge_strategy, merge_factor=merge_factor,
)]
self.middle_block = TimestepEmbedSequential(*mid_block)
self.middle_block_out = self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device)
self._feature_size += ch
def make_zero_conv(self, channels, operations=None, dtype=None, device=None):
return TimestepEmbedSequential(operations.conv_nd(self.dims, channels, channels, 1, padding=0, dtype=dtype, device=device))
def forward(self, x, hint, timesteps, context, y=None, **kwargs):
t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype)
emb = self.time_embed(t_emb)
cond = kwargs["cond"]
num_video_frames = cond["num_video_frames"]
image_only_indicator = cond["image_only_indicator"]
time_context = cond.get("time_context", None)
del cond
guided_hint = self.input_hint_block(hint, emb, context, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator)
outs = []
hs = []
if self.num_classes is not None:
assert y.shape[0] == x.shape[0]
emb = emb + self.label_emb(y)
h = x
for module, zero_conv in zip(self.input_blocks, self.zero_convs):
if guided_hint is not None:
h = module(h, emb, context, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator)
h += guided_hint
guided_hint = None
else:
h = module(h, emb, context, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator)
outs.append(zero_conv(h, emb, context, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator))
h = self.middle_block(h, emb, context, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator)
outs.append(self.middle_block_out(h, emb, context, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator))
return outs
TEMPORAL_TRANSFORMER_BLOCKS = {
"norm_in.weight",
"norm_in.bias",
"ff_in.net.0.proj.weight",
"ff_in.net.0.proj.bias",
"ff_in.net.2.weight",
"ff_in.net.2.bias",
}
TEMPORAL_TRANSFORMER_BLOCKS.update(TRANSFORMER_BLOCKS)
TEMPORAL_UNET_MAP_ATTENTIONS = {
"time_mixer.mix_factor",
}
TEMPORAL_UNET_MAP_ATTENTIONS.update(UNET_MAP_ATTENTIONS)
TEMPORAL_TRANSFORMER_MAP = {
"time_pos_embed.0.weight": "time_pos_embed.linear_1.weight",
"time_pos_embed.0.bias": "time_pos_embed.linear_1.bias",
"time_pos_embed.2.weight": "time_pos_embed.linear_2.weight",
"time_pos_embed.2.bias": "time_pos_embed.linear_2.bias",
}
TEMPORAL_RESNET = {
"time_mixer.mix_factor",
}
def svd_unet_config_from_diffusers_unet(state_dict: dict[str, Tensor], dtype):
match = {}
transformer_depth = []
attn_res = 1
down_blocks = comfy.model_detection.count_blocks(state_dict, "down_blocks.{}")
for i in range(down_blocks):
attn_blocks = comfy.model_detection.count_blocks(state_dict, "down_blocks.{}.attentions.".format(i) + '{}')
for ab in range(attn_blocks):
transformer_count = comfy.model_detection.count_blocks(state_dict, "down_blocks.{}.attentions.{}.transformer_blocks.".format(i, ab) + '{}')
transformer_depth.append(transformer_count)
if transformer_count > 0:
match["context_dim"] = state_dict["down_blocks.{}.attentions.{}.transformer_blocks.0.attn2.to_k.weight".format(i, ab)].shape[1]
attn_res *= 2
if attn_blocks == 0:
transformer_depth.append(0)
transformer_depth.append(0)
match["transformer_depth"] = transformer_depth
match["model_channels"] = state_dict["conv_in.weight"].shape[0]
match["in_channels"] = state_dict["conv_in.weight"].shape[1]
match["adm_in_channels"] = None
if "class_embedding.linear_1.weight" in state_dict:
match["adm_in_channels"] = state_dict["class_embedding.linear_1.weight"].shape[1]
elif "add_embedding.linear_1.weight" in state_dict:
match["adm_in_channels"] = state_dict["add_embedding.linear_1.weight"].shape[1]
# based on unet_config of SVD
SVD = {
'use_checkpoint': False,
'image_size': 32,
'use_spatial_transformer': True,
'legacy': False,
'num_classes': 'sequential',
'adm_in_channels': 768,
'dtype': dtype,
'in_channels': 8,
'out_channels': 4,
'model_channels': 320,
'num_res_blocks': [2, 2, 2, 2],
'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0],
'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
'channel_mult': [1, 2, 4, 4],
'transformer_depth_middle': 1,
'use_linear_in_transformer': True,
'context_dim': 1024,
'extra_ff_mix_layer': True,
'use_spatial_context': True,
'merge_strategy': 'learned_with_images',
'merge_factor': 0.0,
'video_kernel_size': [3, 1, 1],
'use_temporal_attention': True,
'use_temporal_resblock': True,
'num_heads': -1,
'num_head_channels': 64,
}
supported_models = [SVD]
for unet_config in supported_models:
matches = True
for k in match:
if match[k] != unet_config[k]:
matches = False
break
if matches:
return comfy.model_detection.convert_config(unet_config)
return None
def svd_unet_to_diffusers(unet_config):
num_res_blocks = unet_config["num_res_blocks"]
channel_mult = unet_config["channel_mult"]
transformer_depth = unet_config["transformer_depth"][:]
transformer_depth_output = unet_config["transformer_depth_output"][:]
num_blocks = len(channel_mult)
transformers_mid = unet_config.get("transformer_depth_middle", None)
diffusers_unet_map = {}
for x in range(num_blocks):
n = 1 + (num_res_blocks[x] + 1) * x
for i in range(num_res_blocks[x]):
for b in TEMPORAL_RESNET:
diffusers_unet_map["down_blocks.{}.resnets.{}.{}".format(x, i, b)] = "input_blocks.{}.0.{}".format(n, b)
for b in UNET_MAP_RESNET:
diffusers_unet_map["down_blocks.{}.resnets.{}.spatial_res_block.{}".format(x, i, UNET_MAP_RESNET[b])] = "input_blocks.{}.0.{}".format(n, b)
diffusers_unet_map["down_blocks.{}.resnets.{}.temporal_res_block.{}".format(x, i, UNET_MAP_RESNET[b])] = "input_blocks.{}.0.time_stack.{}".format(n, b)
#diffusers_unet_map["down_blocks.{}.resnets.{}.{}".format(x, i, UNET_MAP_RESNET[b])] = "input_blocks.{}.0.{}".format(n, b)
num_transformers = transformer_depth.pop(0)
if num_transformers > 0:
for b in TEMPORAL_UNET_MAP_ATTENTIONS:
diffusers_unet_map["down_blocks.{}.attentions.{}.{}".format(x, i, b)] = "input_blocks.{}.1.{}".format(n, b)
for b in TEMPORAL_TRANSFORMER_MAP:
diffusers_unet_map["down_blocks.{}.attentions.{}.{}".format(x, i, TEMPORAL_TRANSFORMER_MAP[b])] = "input_blocks.{}.1.{}".format(n, b)
for t in range(num_transformers):
for b in TRANSFORMER_BLOCKS:
diffusers_unet_map["down_blocks.{}.attentions.{}.transformer_blocks.{}.{}".format(x, i, t, b)] = "input_blocks.{}.1.transformer_blocks.{}.{}".format(n, t, b)
for b in TEMPORAL_TRANSFORMER_BLOCKS:
diffusers_unet_map["down_blocks.{}.attentions.{}.temporal_transformer_blocks.{}.{}".format(x, i, t, b)] = "input_blocks.{}.1.time_stack.{}.{}".format(n, t, b)
n += 1
for k in ["weight", "bias"]:
diffusers_unet_map["down_blocks.{}.downsamplers.0.conv.{}".format(x, k)] = "input_blocks.{}.0.op.{}".format(n, k)
i = 0
for b in TEMPORAL_UNET_MAP_ATTENTIONS:
diffusers_unet_map["mid_block.attentions.{}.{}".format(i, b)] = "middle_block.1.{}".format(b)
for b in TEMPORAL_TRANSFORMER_MAP:
diffusers_unet_map["mid_block.attentions.{}.{}".format(i, TEMPORAL_TRANSFORMER_MAP[b])] = "middle_block.1.{}".format(b)
for t in range(transformers_mid):
for b in TRANSFORMER_BLOCKS:
diffusers_unet_map["mid_block.attentions.{}.transformer_blocks.{}.{}".format(i, t, b)] = "middle_block.1.transformer_blocks.{}.{}".format(t, b)
for b in TEMPORAL_TRANSFORMER_BLOCKS:
diffusers_unet_map["mid_block.attentions.{}.temporal_transformer_blocks.{}.{}".format(i, t, b)] = "middle_block.1.time_stack.{}.{}".format(t, b)
for i, n in enumerate([0, 2]):
for b in TEMPORAL_RESNET:
diffusers_unet_map["mid_block.resnets.{}.{}".format(i, b)] = "middle_block.{}.{}".format(n, b)
for b in UNET_MAP_RESNET:
diffusers_unet_map["mid_block.resnets.{}.spatial_res_block.{}".format(i, UNET_MAP_RESNET[b])] = "middle_block.{}.{}".format(n, b)
diffusers_unet_map["mid_block.resnets.{}.temporal_res_block.{}".format(i, UNET_MAP_RESNET[b])] = "middle_block.{}.time_stack.{}".format(n, b)
#diffusers_unet_map["mid_block.resnets.{}.{}".format(i, UNET_MAP_RESNET[b])] = "middle_block.{}.{}".format(n, b)
num_res_blocks = list(reversed(num_res_blocks))
for x in range(num_blocks):
n = (num_res_blocks[x] + 1) * x
l = num_res_blocks[x] + 1
for i in range(l):
c = 0
for b in UNET_MAP_RESNET:
diffusers_unet_map["up_blocks.{}.resnets.{}.{}".format(x, i, UNET_MAP_RESNET[b])] = "output_blocks.{}.0.{}".format(n, b)
c += 1
num_transformers = transformer_depth_output.pop()
if num_transformers > 0:
c += 1
for b in UNET_MAP_ATTENTIONS:
diffusers_unet_map["up_blocks.{}.attentions.{}.{}".format(x, i, b)] = "output_blocks.{}.1.{}".format(n, b)
for t in range(num_transformers):
for b in TRANSFORMER_BLOCKS:
diffusers_unet_map["up_blocks.{}.attentions.{}.transformer_blocks.{}.{}".format(x, i, t, b)] = "output_blocks.{}.1.transformer_blocks.{}.{}".format(n, t, b)
if i == l - 1:
for k in ["weight", "bias"]:
diffusers_unet_map["up_blocks.{}.upsamplers.0.conv.{}".format(x, k)] = "output_blocks.{}.{}.conv.{}".format(n, c, k)
n += 1
for k in UNET_MAP_BASIC:
diffusers_unet_map[k[1]] = k[0]
return diffusers_unet_map
workflows/comfy/ComfyUI/custom_nodes/ComfyUI-Advanced-ControlNet/adv_control/logger.py
-36
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@@ -1,36 +0,0 @@
import sys
import copy
import logging
class ColoredFormatter(logging.Formatter):
COLORS = {
"DEBUG": "\033[0;36m", # CYAN
"INFO": "\033[0;32m", # GREEN
"WARNING": "\033[0;33m", # YELLOW
"ERROR": "\033[0;31m", # RED
"CRITICAL": "\033[0;37;41m", # WHITE ON RED
"RESET": "\033[0m", # RESET COLOR
}
def format(self, record):
colored_record = copy.copy(record)
levelname = colored_record.levelname
seq = self.COLORS.get(levelname, self.COLORS["RESET"])
colored_record.levelname = f"{seq}{levelname}{self.COLORS['RESET']}"
return super().format(colored_record)
# Create a new logger
logger = logging.getLogger("Advanced-ControlNet")
logger.propagate = False
# Add handler if we don't have one.
if not logger.handlers:
handler = logging.StreamHandler(sys.stdout)
handler.setFormatter(ColoredFormatter("[%(name)s] - %(levelname)s - %(message)s"))
logger.addHandler(handler)
# Configure logger
loglevel = logging.INFO
logger.setLevel(loglevel)
workflows/comfy/ComfyUI/custom_nodes/ComfyUI-Advanced-ControlNet/adv_control/nodes.py
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@@ -1,272 +0,0 @@
import numpy as np
from torch import Tensor
import folder_paths
from comfy.model_patcher import ModelPatcher
from .control import load_controlnet, convert_to_advanced, is_advanced_controlnet
from .utils import ControlWeights, ControlWeightType, LatentKeyframeGroup, TimestepKeyframe, TimestepKeyframeGroup
from .utils import StrengthInterpolation as SI
from .nodes_weight import (DefaultWeights, ScaledSoftMaskedUniversalWeights, ScaledSoftUniversalWeights, SoftControlNetWeights, CustomControlNetWeights,
SoftT2IAdapterWeights, CustomT2IAdapterWeights)
from .nodes_latent_keyframe import LatentKeyframeGroupNode, LatentKeyframeInterpolationNode, LatentKeyframeBatchedGroupNode, LatentKeyframeNode
from .nodes_sparsectrl import SparseCtrlMergedLoaderAdvanced, SparseCtrlLoaderAdvanced, SparseIndexMethodNode, SparseSpreadMethodNode, RgbSparseCtrlPreprocessor
from .nodes_loosecontrol import ControlNetLoaderWithLoraAdvanced
from .nodes_deprecated import LoadImagesFromDirectory
from .logger import logger
class TimestepKeyframeNode:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}, ),
},
"optional": {
"prev_timestep_kf": ("TIMESTEP_KEYFRAME", ),
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
"cn_weights": ("CONTROL_NET_WEIGHTS", ),
"latent_keyframe": ("LATENT_KEYFRAME", ),
"null_latent_kf_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
"inherit_missing": ("BOOLEAN", {"default": True}, ),
"guarantee_usage": ("BOOLEAN", {"default": True}, ),
"mask_optional": ("MASK", ),
#"interpolation": ([SI.LINEAR, SI.EASE_IN, SI.EASE_OUT, SI.EASE_IN_OUT, SI.NONE], {"default": SI.NONE}, ),
}
}
RETURN_NAMES = ("TIMESTEP_KF", )
RETURN_TYPES = ("TIMESTEP_KEYFRAME", )
FUNCTION = "load_keyframe"
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/keyframes"
def load_keyframe(self,
start_percent: float,
strength: float=1.0,
cn_weights: ControlWeights=None, control_net_weights: ControlWeights=None, # old name
latent_keyframe: LatentKeyframeGroup=None,
prev_timestep_kf: TimestepKeyframeGroup=None, prev_timestep_keyframe: TimestepKeyframeGroup=None, # old name
null_latent_kf_strength: float=0.0,
inherit_missing=True,
guarantee_usage=True,
mask_optional=None,
interpolation: str=SI.NONE,):
control_net_weights = control_net_weights if control_net_weights else cn_weights
prev_timestep_keyframe = prev_timestep_keyframe if prev_timestep_keyframe else prev_timestep_kf
if not prev_timestep_keyframe:
prev_timestep_keyframe = TimestepKeyframeGroup()
else:
prev_timestep_keyframe = prev_timestep_keyframe.clone()
keyframe = TimestepKeyframe(start_percent=start_percent, strength=strength, interpolation=interpolation, null_latent_kf_strength=null_latent_kf_strength,
control_weights=control_net_weights, latent_keyframes=latent_keyframe, inherit_missing=inherit_missing, guarantee_usage=guarantee_usage,
mask_hint_orig=mask_optional)
prev_timestep_keyframe.add(keyframe)
return (prev_timestep_keyframe,)
class ControlNetLoaderAdvanced:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"control_net_name": (folder_paths.get_filename_list("controlnet"), ),
},
"optional": {
"timestep_keyframe": ("TIMESTEP_KEYFRAME", ),
}
}
RETURN_TYPES = ("CONTROL_NET", )
FUNCTION = "load_controlnet"
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝"
def load_controlnet(self, control_net_name,
timestep_keyframe: TimestepKeyframeGroup=None
):
controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
controlnet = load_controlnet(controlnet_path, timestep_keyframe)
return (controlnet,)
class DiffControlNetLoaderAdvanced:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model": ("MODEL",),
"control_net_name": (folder_paths.get_filename_list("controlnet"), )
},
"optional": {
"timestep_keyframe": ("TIMESTEP_KEYFRAME", ),
}
}
RETURN_TYPES = ("CONTROL_NET", )
FUNCTION = "load_controlnet"
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝"
def load_controlnet(self, control_net_name, model,
timestep_keyframe: TimestepKeyframeGroup=None
):
controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
controlnet = load_controlnet(controlnet_path, timestep_keyframe, 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", ),
"model_optional": ("MODEL",),
}
}
RETURN_TYPES = ("CONDITIONING","CONDITIONING","MODEL",)
RETURN_NAMES = ("positive", "negative", "model_opt")
FUNCTION = "apply_controlnet"
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝"
def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent,
mask_optional: Tensor=None, model_optional: ModelPatcher=None,
timestep_kf: TimestepKeyframeGroup=None, latent_kf_override: LatentKeyframeGroup=None,
weights_override: ControlWeights=None):
if strength == 0:
return (positive, negative, model_optional)
if model_optional:
model_optional = model_optional.clone()
control_hint = image.movedim(-1,1)
cnets = {}
out = []
for conditioning in [positive, negative]:
c = []
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:
# 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))
if is_advanced_controlnet(c_net):
# disarm node check
c_net.disarm()
# if model required, verify model is passed in, and if so patch it
if c_net.require_model:
if not model_optional:
raise Exception(f"Type '{type(c_net).__name__}' requires model_optional input, but got None.")
c_net.patch_model(model=model_optional)
# 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'] = False
n = [t[0], d]
c.append(n)
out.append(c)
return (out[0], out[1], model_optional)
# NODE MAPPING
NODE_CLASS_MAPPINGS = {
# Keyframes
"TimestepKeyframe": TimestepKeyframeNode,
"LatentKeyframe": LatentKeyframeNode,
"LatentKeyframeGroup": LatentKeyframeGroupNode,
"LatentKeyframeBatchedGroup": LatentKeyframeBatchedGroupNode,
"LatentKeyframeTiming": LatentKeyframeInterpolationNode,
# Conditioning
"ACN_AdvancedControlNetApply": AdvancedControlNetApply,
# Loaders
"ControlNetLoaderAdvanced": ControlNetLoaderAdvanced,
"DiffControlNetLoaderAdvanced": DiffControlNetLoaderAdvanced,
# Weights
"ScaledSoftControlNetWeights": ScaledSoftUniversalWeights,
"ScaledSoftMaskedUniversalWeights": ScaledSoftMaskedUniversalWeights,
"SoftControlNetWeights": SoftControlNetWeights,
"CustomControlNetWeights": CustomControlNetWeights,
"SoftT2IAdapterWeights": SoftT2IAdapterWeights,
"CustomT2IAdapterWeights": CustomT2IAdapterWeights,
"ACN_DefaultUniversalWeights": DefaultWeights,
# SparseCtrl
"ACN_SparseCtrlRGBPreprocessor": RgbSparseCtrlPreprocessor,
"ACN_SparseCtrlLoaderAdvanced": SparseCtrlLoaderAdvanced,
"ACN_SparseCtrlMergedLoaderAdvanced": SparseCtrlMergedLoaderAdvanced,
"ACN_SparseCtrlIndexMethodNode": SparseIndexMethodNode,
"ACN_SparseCtrlSpreadMethodNode": SparseSpreadMethodNode,
# LOOSEControl
#"ACN_ControlNetLoaderWithLoraAdvanced": ControlNetLoaderWithLoraAdvanced,
# Deprecated
"LoadImagesFromDirectory": LoadImagesFromDirectory,
}
NODE_DISPLAY_NAME_MAPPINGS = {
# Keyframes
"TimestepKeyframe": "Timestep Keyframe 🛂🅐🅒🅝",
"LatentKeyframe": "Latent Keyframe 🛂🅐🅒🅝",
"LatentKeyframeGroup": "Latent Keyframe Group 🛂🅐🅒🅝",
"LatentKeyframeBatchedGroup": "Latent Keyframe Batched Group 🛂🅐🅒🅝",
"LatentKeyframeTiming": "Latent Keyframe Interpolation 🛂🅐🅒🅝",
# Conditioning
"ACN_AdvancedControlNetApply": "Apply Advanced ControlNet 🛂🅐🅒🅝",
# Loaders
"ControlNetLoaderAdvanced": "Load Advanced ControlNet Model 🛂🅐🅒🅝",
"DiffControlNetLoaderAdvanced": "Load Advanced ControlNet Model (diff) 🛂🅐🅒🅝",
# Weights
"ScaledSoftControlNetWeights": "Scaled Soft Weights 🛂🅐🅒🅝",
"ScaledSoftMaskedUniversalWeights": "Scaled Soft Masked Weights 🛂🅐🅒🅝",
"SoftControlNetWeights": "ControlNet Soft Weights 🛂🅐🅒🅝",
"CustomControlNetWeights": "ControlNet Custom Weights 🛂🅐🅒🅝",
"SoftT2IAdapterWeights": "T2IAdapter Soft Weights 🛂🅐🅒🅝",
"CustomT2IAdapterWeights": "T2IAdapter Custom Weights 🛂🅐🅒🅝",
"ACN_DefaultUniversalWeights": "Force Default Weights 🛂🅐🅒🅝",
# SparseCtrl
"ACN_SparseCtrlRGBPreprocessor": "RGB SparseCtrl 🛂🅐🅒🅝",
"ACN_SparseCtrlLoaderAdvanced": "Load SparseCtrl Model 🛂🅐🅒🅝",
"ACN_SparseCtrlMergedLoaderAdvanced": "Load Merged SparseCtrl Model 🛂🅐🅒🅝",
"ACN_SparseCtrlIndexMethodNode": "SparseCtrl Index Method 🛂🅐🅒🅝",
"ACN_SparseCtrlSpreadMethodNode": "SparseCtrl Spread Method 🛂🅐🅒🅝",
# LOOSEControl
#"ACN_ControlNetLoaderWithLoraAdvanced": "Load Adv. ControlNet Model w/ LoRA 🛂🅐🅒🅝",
# Deprecated
"LoadImagesFromDirectory": "Load Images [DEPRECATED] 🛂🅐🅒🅝",
}
workflows/comfy/ComfyUI/custom_nodes/ComfyUI-Advanced-ControlNet/adv_control/nodes_deprecated.py
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import os
import torch
import numpy as np
from PIL import Image, ImageOps
from .utils import ControlWeights, LatentKeyframeGroup, TimestepKeyframeGroup, TimestepKeyframe, BIGMAX
from .logger import logger
class LoadImagesFromDirectory:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"directory": ("STRING", {"default": ""}),
},
"optional": {
"image_load_cap": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1}),
"start_index": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1}),
}
}
RETURN_TYPES = ("IMAGE", "MASK", "INT")
FUNCTION = "load_images"
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/deprecated"
def load_images(self, directory: str, image_load_cap: int = 0, start_index: int = 0):
if not os.path.isdir(directory):
raise FileNotFoundError(f"Directory '{directory} cannot be found.'")
dir_files = os.listdir(directory)
if len(dir_files) == 0:
raise FileNotFoundError(f"No files in directory '{directory}'.")
dir_files = sorted(dir_files)
dir_files = [os.path.join(directory, x) for x in dir_files]
# start at start_index
dir_files = dir_files[start_index:]
images = []
masks = []
limit_images = False
if image_load_cap > 0:
limit_images = True
image_count = 0
for image_path in dir_files:
if os.path.isdir(image_path):
continue
if limit_images and image_count >= image_load_cap:
break
i = Image.open(image_path)
i = ImageOps.exif_transpose(i)
image = i.convert("RGB")
image = np.array(image).astype(np.float32) / 255.0
image = torch.from_numpy(image)[None,]
if 'A' in i.getbands():
mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
mask = 1. - torch.from_numpy(mask)
else:
mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
images.append(image)
masks.append(mask)
image_count += 1
if len(images) == 0:
raise FileNotFoundError(f"No images could be loaded from directory '{directory}'.")
return (torch.cat(images, dim=0), torch.stack(masks, dim=0), image_count)
class TimestepKeyframeNodeDeprecated:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}, ),
},
"optional": {
"control_net_weights": ("CONTROL_NET_WEIGHTS", ),
"t2i_adapter_weights": ("T2I_ADAPTER_WEIGHTS", ),
"latent_keyframe": ("LATENT_KEYFRAME", ),
"prev_timestep_keyframe": ("TIMESTEP_KEYFRAME", ),
}
}
RETURN_TYPES = ("TIMESTEP_KEYFRAME", )
FUNCTION = "load_keyframe"
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/keyframes"
def load_keyframe(self,
start_percent: float,
control_net_weights: ControlWeights=None,
latent_keyframe: LatentKeyframeGroup=None,
prev_timestep_keyframe: TimestepKeyframeGroup=None):
if not prev_timestep_keyframe:
prev_timestep_keyframe = TimestepKeyframeGroup()
keyframe = TimestepKeyframe(start_percent, control_net_weights, latent_keyframe)
prev_timestep_keyframe.add(keyframe)
return (prev_timestep_keyframe,)
workflows/comfy/ComfyUI/custom_nodes/ComfyUI-Advanced-ControlNet/adv_control/nodes_latent_keyframe.py
-283
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@@ -1,283 +0,0 @@
from typing import Union
import numpy as np
from collections.abc import Iterable
from .utils import LatentKeyframe, LatentKeyframeGroup, BIGMIN, BIGMAX
from .utils import StrengthInterpolation as SI
from .logger import logger
class LatentKeyframeNode:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"batch_index": ("INT", {"default": 0, "min": BIGMIN, "max": BIGMAX, "step": 1}),
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
},
"optional": {
"prev_latent_kf": ("LATENT_KEYFRAME", ),
}
}
RETURN_NAMES = ("LATENT_KF", )
RETURN_TYPES = ("LATENT_KEYFRAME", )
FUNCTION = "load_keyframe"
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/keyframes"
def load_keyframe(self,
batch_index: int,
strength: float,
prev_latent_kf: LatentKeyframeGroup=None,
prev_latent_keyframe: LatentKeyframeGroup=None, # old name
):
prev_latent_keyframe = prev_latent_keyframe if prev_latent_keyframe else prev_latent_kf
if not prev_latent_keyframe:
prev_latent_keyframe = LatentKeyframeGroup()
else:
prev_latent_keyframe = prev_latent_keyframe.clone()
keyframe = LatentKeyframe(batch_index, strength)
prev_latent_keyframe.add(keyframe)
return (prev_latent_keyframe,)
class LatentKeyframeGroupNode:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"index_strengths": ("STRING", {"multiline": True, "default": ""}),
},
"optional": {
"prev_latent_kf": ("LATENT_KEYFRAME", ),
"latent_optional": ("LATENT", ),
"print_keyframes": ("BOOLEAN", {"default": False})
}
}
RETURN_NAMES = ("LATENT_KF", )
RETURN_TYPES = ("LATENT_KEYFRAME", )
FUNCTION = "load_keyframes"
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/keyframes"
def validate_index(self, index: int, latent_count: int = 0, is_range: bool = False, allow_negative = False) -> int:
# if part of range, do nothing
if is_range:
return index
# otherwise, validate index
# validate not out of range - only when latent_count is passed in
if latent_count > 0 and index > latent_count-1:
raise IndexError(f"Index '{index}' out of range for the total {latent_count} latents.")
# if negative, validate not out of range
if index < 0:
if not allow_negative:
raise IndexError(f"Negative indeces not allowed, but was {index}.")
conv_index = latent_count+index
if conv_index < 0:
raise IndexError(f"Index '{index}', converted to '{conv_index}' out of range for the total {latent_count} latents.")
index = conv_index
return index
def convert_to_index_int(self, raw_index: str, latent_count: int = 0, is_range: bool = False, allow_negative = False) -> int:
try:
return self.validate_index(int(raw_index), latent_count=latent_count, is_range=is_range, allow_negative=allow_negative)
except ValueError as e:
raise ValueError(f"index '{raw_index}' must be an integer.", e)
def convert_to_latent_keyframes(self, latent_indeces: str, latent_count: int) -> set[LatentKeyframe]:
if not latent_indeces:
return set()
int_latent_indeces = [i for i in range(0, latent_count)]
allow_negative = latent_count > 0
chosen_indeces = set()
# parse string - allow positive ints, negative ints, and ranges separated by ':'
groups = latent_indeces.split(",")
groups = [g.strip() for g in groups]
for g in groups:
# parse strengths - default to 1.0 if no strength given
strength = 1.0
if '=' in g:
g, strength_str = g.split("=", 1)
g = g.strip()
try:
strength = float(strength_str.strip())
except ValueError as e:
raise ValueError(f"strength '{strength_str}' must be a float.", e)
if strength < 0:
raise ValueError(f"Strength '{strength}' cannot be negative.")
# parse range of indeces (e.g. 2:16)
if ':' in g:
index_range = g.split(":", 1)
index_range = [r.strip() for r in index_range]
start_index = self.convert_to_index_int(index_range[0], latent_count=latent_count, is_range=True, allow_negative=allow_negative)
end_index = self.convert_to_index_int(index_range[1], latent_count=latent_count, is_range=True, allow_negative=allow_negative)
# if latents were passed in, base indeces on known latent count
if len(int_latent_indeces) > 0:
for i in int_latent_indeces[start_index:end_index]:
chosen_indeces.add(LatentKeyframe(i, strength))
# otherwise, assume indeces are valid
else:
for i in range(start_index, end_index):
chosen_indeces.add(LatentKeyframe(i, strength))
# parse individual indeces
else:
chosen_indeces.add(LatentKeyframe(self.convert_to_index_int(g, latent_count=latent_count, allow_negative=allow_negative), strength))
return chosen_indeces
def load_keyframes(self,
index_strengths: str,
prev_latent_kf: LatentKeyframeGroup=None,
prev_latent_keyframe: LatentKeyframeGroup=None, # old name
latent_image_opt=None,
print_keyframes=False):
prev_latent_keyframe = prev_latent_keyframe if prev_latent_keyframe else prev_latent_kf
if not prev_latent_keyframe:
prev_latent_keyframe = LatentKeyframeGroup()
else:
prev_latent_keyframe = prev_latent_keyframe.clone()
curr_latent_keyframe = LatentKeyframeGroup()
latent_count = -1
if latent_image_opt:
latent_count = latent_image_opt['samples'].size()[0]
latent_keyframes = self.convert_to_latent_keyframes(index_strengths, latent_count=latent_count)
for latent_keyframe in latent_keyframes:
curr_latent_keyframe.add(latent_keyframe)
if print_keyframes:
for keyframe in curr_latent_keyframe.keyframes:
logger.info(f"keyframe {keyframe.batch_index}:{keyframe.strength}")
# replace values with prev_latent_keyframes
for latent_keyframe in prev_latent_keyframe.keyframes:
curr_latent_keyframe.add(latent_keyframe)
return (curr_latent_keyframe,)
class LatentKeyframeInterpolationNode:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"batch_index_from": ("INT", {"default": 0, "min": BIGMIN, "max": BIGMAX, "step": 1}),
"batch_index_to_excl": ("INT", {"default": 0, "min": BIGMIN, "max": BIGMAX, "step": 1}),
"strength_from": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
"strength_to": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
"interpolation": ([SI.LINEAR, SI.EASE_IN, SI.EASE_OUT, SI.EASE_IN_OUT], ),
},
"optional": {
"prev_latent_kf": ("LATENT_KEYFRAME", ),
"print_keyframes": ("BOOLEAN", {"default": False})
}
}
RETURN_NAMES = ("LATENT_KF", )
RETURN_TYPES = ("LATENT_KEYFRAME", )
FUNCTION = "load_keyframe"
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/keyframes"
def load_keyframe(self,
batch_index_from: int,
strength_from: float,
batch_index_to_excl: int,
strength_to: float,
interpolation: str,
prev_latent_kf: LatentKeyframeGroup=None,
prev_latent_keyframe: LatentKeyframeGroup=None, # old name
print_keyframes=False):
if (batch_index_from > batch_index_to_excl):
raise ValueError("batch_index_from must be less than or equal to batch_index_to.")
if (batch_index_from < 0 and batch_index_to_excl >= 0):
raise ValueError("batch_index_from and batch_index_to must be either both positive or both negative.")
prev_latent_keyframe = prev_latent_keyframe if prev_latent_keyframe else prev_latent_kf
if not prev_latent_keyframe:
prev_latent_keyframe = LatentKeyframeGroup()
else:
prev_latent_keyframe = prev_latent_keyframe.clone()
curr_latent_keyframe = LatentKeyframeGroup()
steps = batch_index_to_excl - batch_index_from
diff = strength_to - strength_from
if interpolation == SI.LINEAR:
weights = np.linspace(strength_from, strength_to, steps)
elif interpolation == SI.EASE_IN:
index = np.linspace(0, 1, steps)
weights = diff * np.power(index, 2) + strength_from
elif interpolation == SI.EASE_OUT:
index = np.linspace(0, 1, steps)
weights = diff * (1 - np.power(1 - index, 2)) + strength_from
elif interpolation == SI.EASE_IN_OUT:
index = np.linspace(0, 1, steps)
weights = diff * ((1 - np.cos(index * np.pi)) / 2) + strength_from
for i in range(steps):
keyframe = LatentKeyframe(batch_index_from + i, float(weights[i]))
curr_latent_keyframe.add(keyframe)
if print_keyframes:
for keyframe in curr_latent_keyframe.keyframes:
logger.info(f"keyframe {keyframe.batch_index}:{keyframe.strength}")
# replace values with prev_latent_keyframes
for latent_keyframe in prev_latent_keyframe.keyframes:
curr_latent_keyframe.add(latent_keyframe)
return (curr_latent_keyframe,)
class LatentKeyframeBatchedGroupNode:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"float_strengths": ("FLOAT", {"default": -1, "min": -1, "step": 0.001, "forceInput": True}),
},
"optional": {
"prev_latent_kf": ("LATENT_KEYFRAME", ),
"print_keyframes": ("BOOLEAN", {"default": False})
}
}
RETURN_NAMES = ("LATENT_KF", )
RETURN_TYPES = ("LATENT_KEYFRAME", )
FUNCTION = "load_keyframe"
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/keyframes"
def load_keyframe(self, float_strengths: Union[float, list[float]],
prev_latent_kf: LatentKeyframeGroup=None,
prev_latent_keyframe: LatentKeyframeGroup=None, # old name
print_keyframes=False):
prev_latent_keyframe = prev_latent_keyframe if prev_latent_keyframe else prev_latent_kf
if not prev_latent_keyframe:
prev_latent_keyframe = LatentKeyframeGroup()
else:
prev_latent_keyframe = prev_latent_keyframe.clone()
curr_latent_keyframe = LatentKeyframeGroup()
# if received a normal float input, do nothing
if type(float_strengths) in (float, int):
logger.info("No batched float_strengths passed into Latent Keyframe Batch Group node; will not create any new keyframes.")
# if iterable, attempt to create LatentKeyframes with chosen strengths
elif isinstance(float_strengths, Iterable):
for idx, strength in enumerate(float_strengths):
keyframe = LatentKeyframe(idx, strength)
curr_latent_keyframe.add(keyframe)
else:
raise ValueError(f"Expected strengths to be an iterable input, but was {type(float_strengths).__repr__}.")
if print_keyframes:
for keyframe in curr_latent_keyframe.keyframes:
logger.info(f"keyframe {keyframe.batch_index}:{keyframe.strength}")
# replace values with prev_latent_keyframes
for latent_keyframe in prev_latent_keyframe.keyframes:
curr_latent_keyframe.add(latent_keyframe)
return (curr_latent_keyframe,)
workflows/comfy/ComfyUI/custom_nodes/ComfyUI-Advanced-ControlNet/adv_control/nodes_loosecontrol.py
-67
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@@ -1,67 +0,0 @@
import folder_paths
import comfy.utils
import comfy.model_detection
import comfy.model_management
import comfy.lora
from comfy.model_patcher import ModelPatcher
from .utils import TimestepKeyframeGroup
from .control import ControlNetAdvanced, load_controlnet
def convert_cn_lora_from_diffusers(cn_model: ModelPatcher, lora_path: str):
lora_data = comfy.utils.load_torch_file(lora_path, safe_load=True)
unet_dtype = comfy.model_management.unet_dtype()
for key, value in lora_data.items():
lora_data[key] = value.to(unet_dtype)
diffusers_keys = comfy.utils.unet_to_diffusers(cn_model.model.state_dict())
#lora_data = comfy.model_detection.unet_config_from_diffusers_unet(lora_data, dtype=unet_dtype)
#key_map = comfy.lora.model_lora_keys_unet(cn_model.model, key_map)
lora_data = comfy.lora.load_lora(lora_data, to_load=diffusers_keys)
# TODO: detect if diffusers for sure? not sure if needed at this time, since cn loras are
# only used currently for LOOSEControl, and those are all in diffusers format
#unet_dtype = comfy.model_management.unet_dtype()
#lora_data = comfy.model_detection.unet_config_from_diffusers_unet(lora_data, unet_dtype)
return lora_data
class ControlNetLoaderWithLoraAdvanced:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"control_net_name": (folder_paths.get_filename_list("controlnet"), ),
"cn_lora_name": (folder_paths.get_filename_list("controlnet"), ),
"cn_lora_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}),
},
"optional": {
"timestep_keyframe": ("TIMESTEP_KEYFRAME", ),
}
}
RETURN_TYPES = ("CONTROL_NET", )
FUNCTION = "load_controlnet"
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/LOOSEControl"
def load_controlnet(self, control_net_name, cn_lora_name, cn_lora_strength: float,
timestep_keyframe: TimestepKeyframeGroup=None
):
controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
controlnet: ControlNetAdvanced = load_controlnet(controlnet_path, timestep_keyframe)
if not isinstance(controlnet, ControlNetAdvanced):
raise ValueError("Type {} is not compatible with CN LoRA features at this time.")
# now, try to load CN LoRA
lora_path = folder_paths.get_full_path("controlnet", cn_lora_name)
lora_data = convert_cn_lora_from_diffusers(cn_model=controlnet.control_model_wrapped, lora_path=lora_path)
# apply patches to wrapped control_model
controlnet.control_model_wrapped.add_patches(lora_data, strength_patch=cn_lora_strength)
# all done
return (controlnet,)
workflows/comfy/ComfyUI/custom_nodes/ComfyUI-Advanced-ControlNet/adv_control/nodes_reference.py
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workflows/comfy/ComfyUI/custom_nodes/ComfyUI-Advanced-ControlNet/adv_control/nodes_sparsectrl.py
-163
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from torch import Tensor
import folder_paths
from nodes import VAEEncode
import comfy.utils
from comfy.sd import VAE
from .utils import TimestepKeyframeGroup
from .control_sparsectrl import SparseMethod, SparseIndexMethod, SparseSettings, SparseSpreadMethod, PreprocSparseRGBWrapper
from .control import load_sparsectrl, load_controlnet, ControlNetAdvanced, SparseCtrlAdvanced
# node for SparseCtrl loading
class SparseCtrlLoaderAdvanced:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"sparsectrl_name": (folder_paths.get_filename_list("controlnet"), ),
"use_motion": ("BOOLEAN", {"default": True}, ),
"motion_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
"motion_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
},
"optional": {
"sparse_method": ("SPARSE_METHOD", ),
"tk_optional": ("TIMESTEP_KEYFRAME", ),
}
}
RETURN_TYPES = ("CONTROL_NET", )
FUNCTION = "load_controlnet"
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/SparseCtrl"
def load_controlnet(self, sparsectrl_name: str, use_motion: bool, motion_strength: float, motion_scale: float, sparse_method: SparseMethod=SparseSpreadMethod(), tk_optional: TimestepKeyframeGroup=None):
sparsectrl_path = folder_paths.get_full_path("controlnet", sparsectrl_name)
sparse_settings = SparseSettings(sparse_method=sparse_method, use_motion=use_motion, motion_strength=motion_strength, motion_scale=motion_scale)
sparsectrl = load_sparsectrl(sparsectrl_path, timestep_keyframe=tk_optional, sparse_settings=sparse_settings)
return (sparsectrl,)
class SparseCtrlMergedLoaderAdvanced:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"sparsectrl_name": (folder_paths.get_filename_list("controlnet"), ),
"control_net_name": (folder_paths.get_filename_list("controlnet"), ),
"use_motion": ("BOOLEAN", {"default": True}, ),
"motion_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
"motion_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
},
"optional": {
"sparse_method": ("SPARSE_METHOD", ),
"tk_optional": ("TIMESTEP_KEYFRAME", ),
}
}
RETURN_TYPES = ("CONTROL_NET", )
FUNCTION = "load_controlnet"
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/SparseCtrl/experimental"
def load_controlnet(self, sparsectrl_name: str, control_net_name: str, use_motion: bool, motion_strength: float, motion_scale: float, sparse_method: SparseMethod=SparseSpreadMethod(), tk_optional: TimestepKeyframeGroup=None):
sparsectrl_path = folder_paths.get_full_path("controlnet", sparsectrl_name)
controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
sparse_settings = SparseSettings(sparse_method=sparse_method, use_motion=use_motion, motion_strength=motion_strength, motion_scale=motion_scale, merged=True)
# first, load normal controlnet
controlnet = load_controlnet(controlnet_path, timestep_keyframe=tk_optional)
# confirm that controlnet is ControlNetAdvanced
if controlnet is None or type(controlnet) != ControlNetAdvanced:
raise ValueError(f"controlnet_path must point to a normal ControlNet, but instead: {type(controlnet).__name__}")
# next, load sparsectrl, making sure to load motion portion
sparsectrl = load_sparsectrl(sparsectrl_path, timestep_keyframe=tk_optional, sparse_settings=SparseSettings.default())
# now, combine state dicts
new_state_dict = controlnet.control_model.state_dict()
for key, value in sparsectrl.control_model.motion_holder.motion_wrapper.state_dict().items():
new_state_dict[key] = value
# now, reload sparsectrl with real settings
sparsectrl = load_sparsectrl(sparsectrl_path, controlnet_data=new_state_dict, timestep_keyframe=tk_optional, sparse_settings=sparse_settings)
return (sparsectrl,)
class SparseIndexMethodNode:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"indexes": ("STRING", {"default": "0"}),
}
}
RETURN_TYPES = ("SPARSE_METHOD",)
FUNCTION = "get_method"
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/SparseCtrl"
def get_method(self, indexes: str):
idxs = []
unique_idxs = set()
# get indeces from string
str_idxs = [x.strip() for x in indexes.strip().split(",")]
for str_idx in str_idxs:
try:
idx = int(str_idx)
if idx in unique_idxs:
raise ValueError(f"'{idx}' is duplicated; indexes must be unique.")
idxs.append(idx)
unique_idxs.add(idx)
except ValueError:
raise ValueError(f"'{str_idx}' is not a valid integer index.")
if len(idxs) == 0:
raise ValueError(f"No indexes were listed in Sparse Index Method.")
return (SparseIndexMethod(idxs),)
class SparseSpreadMethodNode:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"spread": (SparseSpreadMethod.LIST,),
}
}
RETURN_TYPES = ("SPARSE_METHOD",)
FUNCTION = "get_method"
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/SparseCtrl"
def get_method(self, spread: str):
return (SparseSpreadMethod(spread=spread),)
class RgbSparseCtrlPreprocessor:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": ("IMAGE", ),
"vae": ("VAE", ),
"latent_size": ("LATENT", ),
}
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("proc_IMAGE",)
FUNCTION = "preprocess_images"
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/SparseCtrl/preprocess"
def preprocess_images(self, vae: VAE, image: Tensor, latent_size: Tensor):
# first, resize image to match latents
image = image.movedim(-1,1)
image = comfy.utils.common_upscale(image, latent_size["samples"].shape[3] * 8, latent_size["samples"].shape[2] * 8, 'nearest-exact', "center")
image = image.movedim(1,-1)
# then, vae encode
try:
image = vae.vae_encode_crop_pixels(image)
except Exception:
image = VAEEncode.vae_encode_crop_pixels(image)
encoded = vae.encode(image[:,:,:,:3])
return (PreprocSparseRGBWrapper(condhint=encoded),)
workflows/comfy/ComfyUI/custom_nodes/ComfyUI-Advanced-ControlNet/adv_control/nodes_weight.py
-201
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@@ -1,201 +0,0 @@
from torch import Tensor
import torch
from .utils import TimestepKeyframe, TimestepKeyframeGroup, ControlWeights, get_properly_arranged_t2i_weights, linear_conversion
from .logger import logger
WEIGHTS_RETURN_NAMES = ("CN_WEIGHTS", "TK_SHORTCUT")
class DefaultWeights:
@classmethod
def INPUT_TYPES(s):
return {
}
RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
RETURN_NAMES = WEIGHTS_RETURN_NAMES
FUNCTION = "load_weights"
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights"
def load_weights(self):
weights = ControlWeights.default()
return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
class ScaledSoftMaskedUniversalWeights:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"mask": ("MASK", ),
"min_base_multiplier": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}, ),
"max_base_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}, ),
#"lock_min": ("BOOLEAN", {"default": False}, ),
#"lock_max": ("BOOLEAN", {"default": False}, ),
},
}
RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
RETURN_NAMES = WEIGHTS_RETURN_NAMES
FUNCTION = "load_weights"
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights"
def load_weights(self, mask: Tensor, min_base_multiplier: float, max_base_multiplier: float, lock_min=False, lock_max=False):
# normalize mask
mask = mask.clone()
x_min = 0.0 if lock_min else mask.min()
x_max = 1.0 if lock_max else mask.max()
if x_min == x_max:
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)
return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
class ScaledSoftUniversalWeights:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"base_multiplier": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 1.0, "step": 0.001}, ),
"flip_weights": ("BOOLEAN", {"default": False}),
},
}
RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
RETURN_NAMES = WEIGHTS_RETURN_NAMES
FUNCTION = "load_weights"
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights"
def load_weights(self, base_multiplier, flip_weights):
weights = ControlWeights.universal(base_multiplier=base_multiplier, flip_weights=flip_weights)
return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
class SoftControlNetWeights:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"weight_00": ("FLOAT", {"default": 0.09941396206337118, "min": 0.0, "max": 10.0, "step": 0.001}, ),
"weight_01": ("FLOAT", {"default": 0.12050177219802567, "min": 0.0, "max": 10.0, "step": 0.001}, ),
"weight_02": ("FLOAT", {"default": 0.14606275417942507, "min": 0.0, "max": 10.0, "step": 0.001}, ),
"weight_03": ("FLOAT", {"default": 0.17704576264172736, "min": 0.0, "max": 10.0, "step": 0.001}, ),
"weight_04": ("FLOAT", {"default": 0.214600924414215, "min": 0.0, "max": 10.0, "step": 0.001}, ),
"weight_05": ("FLOAT", {"default": 0.26012233262329093, "min": 0.0, "max": 10.0, "step": 0.001}, ),
"weight_06": ("FLOAT", {"default": 0.3152997971191405, "min": 0.0, "max": 10.0, "step": 0.001}, ),
"weight_07": ("FLOAT", {"default": 0.3821815722656249, "min": 0.0, "max": 10.0, "step": 0.001}, ),
"weight_08": ("FLOAT", {"default": 0.4632503906249999, "min": 0.0, "max": 10.0, "step": 0.001}, ),
"weight_09": ("FLOAT", {"default": 0.561515625, "min": 0.0, "max": 10.0, "step": 0.001}, ),
"weight_10": ("FLOAT", {"default": 0.6806249999999999, "min": 0.0, "max": 10.0, "step": 0.001}, ),
"weight_11": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 10.0, "step": 0.001}, ),
"weight_12": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
"flip_weights": ("BOOLEAN", {"default": False}),
},
}
RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
RETURN_NAMES = WEIGHTS_RETURN_NAMES
FUNCTION = "load_weights"
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/ControlNet"
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):
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)
return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
class CustomControlNetWeights:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"weight_00": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
"weight_01": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
"weight_02": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
"weight_03": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
"weight_04": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
"weight_05": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
"weight_06": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
"weight_07": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
"weight_08": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
"weight_09": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
"weight_10": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
"weight_11": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
"weight_12": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
"flip_weights": ("BOOLEAN", {"default": False}),
}
}
RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
RETURN_NAMES = WEIGHTS_RETURN_NAMES
FUNCTION = "load_weights"
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/ControlNet"
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):
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)
return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
class SoftT2IAdapterWeights:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"weight_00": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 10.0, "step": 0.001}, ),
"weight_01": ("FLOAT", {"default": 0.62, "min": 0.0, "max": 10.0, "step": 0.001}, ),
"weight_02": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 10.0, "step": 0.001}, ),
"weight_03": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
"flip_weights": ("BOOLEAN", {"default": False}),
},
}
RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
RETURN_NAMES = WEIGHTS_RETURN_NAMES
FUNCTION = "load_weights"
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/T2IAdapter"
def load_weights(self, weight_00, weight_01, weight_02, weight_03, flip_weights):
weights = [weight_00, weight_01, weight_02, weight_03]
weights = get_properly_arranged_t2i_weights(weights)
weights = ControlWeights.t2iadapter(weights, flip_weights=flip_weights)
return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
class CustomT2IAdapterWeights:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"weight_00": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
"weight_01": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
"weight_02": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
"weight_03": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
"flip_weights": ("BOOLEAN", {"default": False}),
},
}
RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
RETURN_NAMES = WEIGHTS_RETURN_NAMES
FUNCTION = "load_weights"
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/T2IAdapter"
def load_weights(self, weight_00, weight_01, weight_02, weight_03, flip_weights):
weights = [weight_00, weight_01, weight_02, weight_03]
weights = get_properly_arranged_t2i_weights(weights)
weights = ControlWeights.t2iadapter(weights, flip_weights=flip_weights)
return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
workflows/comfy/ComfyUI/custom_nodes/ComfyUI-Advanced-ControlNet/adv_control/utils.py
-677
View File
@@ -1,677 +0,0 @@
from copy import deepcopy
from typing import Callable, Union
import torch
from torch import Tensor
import torch.nn.functional as F
import comfy.ops
import comfy.utils
from comfy.controlnet import ControlBase, broadcast_image_to
from comfy.model_patcher import ModelPatcher
from .logger import logger
BIGMIN = -(2**63-1)
BIGMAX = (2**63-1)
def load_torch_file_with_dict_factory(controlnet_data: dict[str, Tensor], orig_load_torch_file: Callable):
def load_torch_file_with_dict(*args, **kwargs):
# immediately restore load_torch_file to original version
comfy.utils.load_torch_file = orig_load_torch_file
return controlnet_data
return load_torch_file_with_dict
def get_properly_arranged_t2i_weights(initial_weights: list[float]):
new_weights = []
new_weights.extend([initial_weights[0]]*3)
new_weights.extend([initial_weights[1]]*3)
new_weights.extend([initial_weights[2]]*3)
new_weights.extend([initial_weights[3]]*3)
return new_weights
class ControlWeightType:
DEFAULT = "default"
UNIVERSAL = "universal"
T2IADAPTER = "t2iadapter"
CONTROLNET = "controlnet"
CONTROLLORA = "controllora"
CONTROLLLLITE = "controllllite"
SVD_CONTROLNET = "svd_controlnet"
SPARSECTRL = "sparsectrl"
class ControlWeights:
def __init__(self, weight_type: str, base_multiplier: float=1.0, flip_weights: bool=False, weights: list[float]=None, weight_mask: Tensor=None):
self.weight_type = weight_type
self.base_multiplier = base_multiplier
self.flip_weights = flip_weights
self.weights = weights
if self.weights is not None and self.flip_weights:
self.weights.reverse()
self.weight_mask = weight_mask
def get(self, idx: int) -> Union[float, Tensor]:
# if weights is not none, return index
if self.weights is not None:
return self.weights[idx]
return 1.0
@classmethod
def default(cls):
return cls(ControlWeightType.DEFAULT)
@classmethod
def universal(cls, base_multiplier: float, flip_weights: bool=False):
return cls(ControlWeightType.UNIVERSAL, base_multiplier=base_multiplier, flip_weights=flip_weights)
@classmethod
def universal_mask(cls, weight_mask: Tensor):
return cls(ControlWeightType.UNIVERSAL, weight_mask=weight_mask)
@classmethod
def t2iadapter(cls, weights: list[float]=None, flip_weights: bool=False):
if weights is None:
weights = [1.0]*12
return cls(ControlWeightType.T2IADAPTER, weights=weights,flip_weights=flip_weights)
@classmethod
def controlnet(cls, weights: list[float]=None, flip_weights: bool=False):
if weights is None:
weights = [1.0]*13
return cls(ControlWeightType.CONTROLNET, weights=weights, flip_weights=flip_weights)
@classmethod
def controllora(cls, weights: list[float]=None, flip_weights: bool=False):
if weights is None:
weights = [1.0]*10
return cls(ControlWeightType.CONTROLLORA, weights=weights, flip_weights=flip_weights)
@classmethod
def controllllite(cls, weights: list[float]=None, flip_weights: bool=False):
if weights is None:
# TODO: make this have a real value
weights = [1.0]*200
return cls(ControlWeightType.CONTROLLLLITE, weights=weights, flip_weights=flip_weights)
class StrengthInterpolation:
LINEAR = "linear"
EASE_IN = "ease-in"
EASE_OUT = "ease-out"
EASE_IN_OUT = "ease-in-out"
NONE = "none"
class LatentKeyframe:
def __init__(self, batch_index: int, strength: float) -> None:
self.batch_index = batch_index
self.strength = strength
# always maintain sorted state (by batch_index of LatentKeyframe)
class LatentKeyframeGroup:
def __init__(self) -> None:
self.keyframes: list[LatentKeyframe] = []
def add(self, keyframe: LatentKeyframe) -> None:
added = False
# replace existing keyframe if same batch_index
for i in range(len(self.keyframes)):
if self.keyframes[i].batch_index == keyframe.batch_index:
self.keyframes[i] = keyframe
added = True
break
if not added:
self.keyframes.append(keyframe)
self.keyframes.sort(key=lambda k: k.batch_index)
def get_index(self, index: int) -> Union[LatentKeyframe, None]:
try:
return self.keyframes[index]
except IndexError:
return None
def __getitem__(self, index) -> LatentKeyframe:
return self.keyframes[index]
def is_empty(self) -> bool:
return len(self.keyframes) == 0
def clone(self) -> 'LatentKeyframeGroup':
cloned = LatentKeyframeGroup()
for tk in self.keyframes:
cloned.add(tk)
return cloned
class TimestepKeyframe:
def __init__(self,
start_percent: float = 0.0,
strength: float = 1.0,
interpolation: str = StrengthInterpolation.NONE,
control_weights: ControlWeights = None,
latent_keyframes: LatentKeyframeGroup = None,
null_latent_kf_strength: float = 0.0,
inherit_missing: bool = True,
guarantee_usage: bool = True,
mask_hint_orig: Tensor = None) -> None:
self.start_percent = start_percent
self.start_t = 999999999.9
self.strength = strength
self.interpolation = interpolation
self.control_weights = control_weights
self.latent_keyframes = latent_keyframes
self.null_latent_kf_strength = null_latent_kf_strength
self.inherit_missing = inherit_missing
self.guarantee_usage = guarantee_usage
self.mask_hint_orig = mask_hint_orig
def has_control_weights(self):
return self.control_weights is not None
def has_latent_keyframes(self):
return self.latent_keyframes is not None
def has_mask_hint(self):
return self.mask_hint_orig is not None
@classmethod
def default(cls) -> 'TimestepKeyframe':
return cls(0.0)
# always maintain sorted state (by start_percent of TimestepKeyFrame)
class TimestepKeyframeGroup:
def __init__(self) -> None:
self.keyframes: list[TimestepKeyframe] = []
self.keyframes.append(TimestepKeyframe.default())
def add(self, keyframe: TimestepKeyframe) -> None:
added = False
# replace existing keyframe if same start_percent
for i in range(len(self.keyframes)):
if self.keyframes[i].start_percent == keyframe.start_percent:
self.keyframes[i] = keyframe
added = True
break
if not added:
self.keyframes.append(keyframe)
self.keyframes.sort(key=lambda k: k.start_percent)
def get_index(self, index: int) -> Union[TimestepKeyframe, None]:
try:
return self.keyframes[index]
except IndexError:
return None
def has_index(self, index: int) -> int:
return index >=0 and index < len(self.keyframes)
def __getitem__(self, index) -> TimestepKeyframe:
return self.keyframes[index]
def __len__(self) -> int:
return len(self.keyframes)
def is_empty(self) -> bool:
return len(self.keyframes) == 0
def clone(self) -> 'TimestepKeyframeGroup':
cloned = TimestepKeyframeGroup()
for tk in self.keyframes:
cloned.add(tk)
return cloned
@classmethod
def default(cls, keyframe: TimestepKeyframe) -> 'TimestepKeyframeGroup':
group = cls()
group.keyframes[0] = keyframe
return group
# depending on model, AnimateDiff may inject into GroupNorm, so make sure GroupNorm will be clean
class disable_weight_init_clean_groupnorm(comfy.ops.disable_weight_init):
class GroupNorm(comfy.ops.disable_weight_init.GroupNorm):
def forward(self, input: Tensor) -> Tensor:
return F.group_norm(
input, self.num_groups, self.weight, self.bias, self.eps)
class manual_cast_clean_groupnorm(comfy.ops.manual_cast):
class GroupNorm(comfy.ops.manual_cast.GroupNorm):
def forward(self, input: Tensor) -> Tensor:
return F.group_norm(
input, self.num_groups, self.weight, self.bias, self.eps)
# adapted from comfy/sample.py
def prepare_mask_batch(mask: Tensor, shape: Tensor, multiplier: int=1, match_dim1=False):
mask = mask.clone()
mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(shape[2]*multiplier, shape[3]*multiplier), mode="bilinear")
if match_dim1:
mask = torch.cat([mask] * shape[1], dim=1)
return mask
# applies min-max normalization, from:
# https://stackoverflow.com/questions/68791508/min-max-normalization-of-a-tensor-in-pytorch
def normalize_min_max(x: Tensor, new_min = 0.0, new_max = 1.0):
x_min, x_max = x.min(), x.max()
return (((x - x_min)/(x_max - x_min)) * (new_max - new_min)) + new_min
def linear_conversion(x, x_min=0.0, x_max=1.0, new_min=0.0, new_max=1.0):
return (((x - x_min)/(x_max - x_min)) * (new_max - new_min)) + new_min
# from https://stackoverflow.com/a/24621200
def deepcopy_with_sharing(obj, shared_attribute_names, memo=None):
'''
Deepcopy an object, except for a given list of attributes, which should
be shared between the original object and its copy.
obj is some object
shared_attribute_names: A list of strings identifying the attributes that
should be shared between the original and its copy.
memo is the dictionary passed into __deepcopy__. Ignore this argument if
not calling from within __deepcopy__.
'''
assert isinstance(shared_attribute_names, (list, tuple))
shared_attributes = {k: getattr(obj, k) for k in shared_attribute_names}
if hasattr(obj, '__deepcopy__'):
# Do hack to prevent infinite recursion in call to deepcopy
deepcopy_method = obj.__deepcopy__
obj.__deepcopy__ = None
for attr in shared_attribute_names:
del obj.__dict__[attr]
clone = deepcopy(obj)
for attr, val in shared_attributes.items():
setattr(obj, attr, val)
setattr(clone, attr, val)
if hasattr(obj, '__deepcopy__'):
# Undo hack
obj.__deepcopy__ = deepcopy_method
del clone.__deepcopy__
return clone
class WeightTypeException(TypeError):
"Raised when weight not compatible with AdvancedControlBase object"
pass
class AdvancedControlBase:
def __init__(self, base: ControlBase, timestep_keyframes: TimestepKeyframeGroup, weights_default: ControlWeights, require_model=False):
self.base = base
self.compatible_weights = [ControlWeightType.UNIVERSAL]
self.add_compatible_weight(weights_default.weight_type)
# mask for which parts of controlnet output to keep
self.mask_cond_hint_original = None
self.mask_cond_hint = None
self.tk_mask_cond_hint_original = None
self.tk_mask_cond_hint = None
self.weight_mask_cond_hint = None
# actual index values
self.sub_idxs = None
self.full_latent_length = 0
self.context_length = 0
# timesteps
self.t: Tensor = None
self.batched_number: int = None
# weights + override
self.weights: ControlWeights = None
self.weights_default: ControlWeights = weights_default
self.weights_override: ControlWeights = None
# latent keyframe + override
self.latent_keyframes: LatentKeyframeGroup = None
self.latent_keyframe_override: LatentKeyframeGroup = None
# initialize timestep_keyframes
self.set_timestep_keyframes(timestep_keyframes)
# override some functions
self.get_control = self.get_control_inject
self.control_merge = self.control_merge_inject#.__get__(self, type(self))
self.pre_run = self.pre_run_inject
self.cleanup = self.cleanup_inject
self.set_previous_controlnet = self.set_previous_controlnet_inject
# require model to be passed into Apply Advanced ControlNet 🛂🅐🅒🅝 node
self.require_model = require_model
# disarm - when set to False, used to force usage of Apply Advanced ControlNet 🛂🅐🅒🅝 node (which will set it to True)
self.disarmed = not require_model
def patch_model(self, model: ModelPatcher):
pass
def add_compatible_weight(self, control_weight_type: str):
self.compatible_weights.append(control_weight_type)
def verify_all_weights(self, throw_error=True):
# first, check if override exists - if so, only need to check the override
if self.weights_override is not None:
if self.weights_override.weight_type not in self.compatible_weights:
msg = f"Weight override is type {self.weights_override.weight_type}, but loaded {type(self).__name__}" + \
f"only supports {self.compatible_weights} weights."
raise WeightTypeException(msg)
# otherwise, check all timestep keyframe weights
else:
for tk in self.timestep_keyframes.keyframes:
if tk.has_control_weights() and tk.control_weights.weight_type not in self.compatible_weights:
msg = f"Weight on Timestep Keyframe with start_percent={tk.start_percent} is type" + \
f"{tk.control_weights.weight_type}, but loaded {type(self).__name__} only supports {self.compatible_weights} weights."
raise WeightTypeException(msg)
def set_timestep_keyframes(self, timestep_keyframes: TimestepKeyframeGroup):
self.timestep_keyframes = timestep_keyframes if timestep_keyframes else TimestepKeyframeGroup()
# prepare first timestep_keyframe related stuff
self.current_timestep_keyframe = None
self.current_timestep_index = -1
self.next_timestep_keyframe = None
self.weights = None
self.latent_keyframes = None
def prepare_current_timestep(self, t: Tensor, batched_number: int):
self.t = float(t[0])
self.batched_number = batched_number
# get current step percent
curr_t: float = self.t
prev_index = self.current_timestep_index
# if has next index, loop through and see if need to switch
if self.timestep_keyframes.has_index(self.current_timestep_index+1):
for i in range(self.current_timestep_index+1, len(self.timestep_keyframes)):
eval_tk = self.timestep_keyframes[i]
# check if start percent is less or equal to curr_t
if eval_tk.start_t >= curr_t:
self.current_timestep_index = i
self.current_timestep_keyframe = eval_tk
# keep track of control weights, latent keyframes, and masks,
# accounting for inherit_missing
if self.current_timestep_keyframe.has_control_weights():
self.weights = self.current_timestep_keyframe.control_weights
elif not self.current_timestep_keyframe.inherit_missing:
self.weights = self.weights_default
if self.current_timestep_keyframe.has_latent_keyframes():
self.latent_keyframes = self.current_timestep_keyframe.latent_keyframes
elif not self.current_timestep_keyframe.inherit_missing:
self.latent_keyframes = None
if self.current_timestep_keyframe.has_mask_hint():
self.tk_mask_cond_hint_original = self.current_timestep_keyframe.mask_hint_orig
elif not self.current_timestep_keyframe.inherit_missing:
del self.tk_mask_cond_hint_original
self.tk_mask_cond_hint_original = None
# if guarantee_usage, stop searching for other TKs
if self.current_timestep_keyframe.guarantee_usage:
break
# if eval_tk is outside of percent range, stop looking further
else:
break
# if index changed, apply overrides
if prev_index != self.current_timestep_index:
if self.weights_override is not None:
self.weights = self.weights_override
if self.latent_keyframe_override is not None:
self.latent_keyframes = self.latent_keyframe_override
# make sure weights and latent_keyframes are in a workable state
# Note: each AdvancedControlBase should create their own get_universal_weights class
self.prepare_weights()
def prepare_weights(self):
if self.weights is None or self.weights.weight_type == ControlWeightType.DEFAULT:
self.weights = self.weights_default
elif self.weights.weight_type == ControlWeightType.UNIVERSAL:
# if universal and weight_mask present, no need to convert
if self.weights.weight_mask is not None:
return
self.weights = self.get_universal_weights()
def get_universal_weights(self) -> ControlWeights:
return self.weights
def set_cond_hint_mask(self, mask_hint):
self.mask_cond_hint_original = mask_hint
return self
def pre_run_inject(self, model, percent_to_timestep_function):
self.base.pre_run(model, percent_to_timestep_function)
self.pre_run_advanced(model, percent_to_timestep_function)
def pre_run_advanced(self, model, percent_to_timestep_function):
# for each timestep keyframe, calculate the start_t
for tk in self.timestep_keyframes.keyframes:
tk.start_t = percent_to_timestep_function(tk.start_percent)
# clear variables
self.cleanup_advanced()
def set_previous_controlnet_inject(self, *args, **kwargs):
to_return = self.base.set_previous_controlnet(*args, **kwargs)
if not self.disarmed:
raise Exception(f"Type '{type(self).__name__}' must be used with Apply Advanced ControlNet 🛂🅐🅒🅝 node (with model_optional passed in); otherwise, it will not work.")
return to_return
def disarm(self):
self.disarmed = True
def get_control_inject(self, x_noisy, t, cond, batched_number):
# prepare timestep and everything related
self.prepare_current_timestep(t=t, batched_number=batched_number)
# if should not perform any actions for the controlnet, exit without doing any work
if self.strength == 0.0 or self.current_timestep_keyframe.strength == 0.0:
return self.default_control_actions(x_noisy, t, cond, batched_number)
# otherwise, perform normal function
return self.get_control_advanced(x_noisy, t, cond, batched_number)
def get_control_advanced(self, x_noisy, t, cond, batched_number):
return self.default_control_actions(x_noisy, t, cond, batched_number)
def default_control_actions(self, x_noisy, t, cond, batched_number):
control_prev = None
if self.previous_controlnet is not None:
control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number)
return control_prev
def calc_weight(self, idx: int, x: Tensor, layers: int) -> Union[float, Tensor]:
if self.weights.weight_mask is not None:
# prepare weight mask
self.prepare_weight_mask_cond_hint(x, self.batched_number)
# adjust mask for current layer and return
return torch.pow(self.weight_mask_cond_hint, self.get_calc_pow(idx=idx, layers=layers))
return self.weights.get(idx=idx)
def get_calc_pow(self, idx: int, layers: int) -> int:
return (layers-1)-idx
def calc_latent_keyframe_mults(self, x: Tensor, batched_number: int) -> Tensor:
# apply strengths, and get batch indeces to null out
# AKA latents that should not be influenced by ControlNet
final_mults = [1.0] * x.shape[0]
if self.latent_keyframes:
latent_count = x.shape[0] // batched_number
indeces_to_null = set(range(latent_count))
mapped_indeces = None
# if expecting subdivision, will need to translate between subset and actual idx values
if self.sub_idxs:
mapped_indeces = {}
for i, actual in enumerate(self.sub_idxs):
mapped_indeces[actual] = i
for keyframe in self.latent_keyframes:
real_index = keyframe.batch_index
# if negative, count from end
if real_index < 0:
real_index += latent_count if self.sub_idxs is None else self.full_latent_length
# if not mapping indeces, what you see is what you get
if mapped_indeces is None:
if real_index in indeces_to_null:
indeces_to_null.remove(real_index)
# otherwise, see if batch_index is even included in this set of latents
else:
real_index = mapped_indeces.get(real_index, None)
if real_index is None:
continue
indeces_to_null.remove(real_index)
# if real_index is outside the bounds of latents, don't apply
if real_index >= latent_count or real_index < 0:
continue
# apply strength for each batched cond/uncond
for b in range(batched_number):
final_mults[(latent_count*b)+real_index] = keyframe.strength
# null them out by multiplying by null_latent_kf_strength
for batch_index in indeces_to_null:
# apply null for each batched cond/uncond
for b in range(batched_number):
final_mults[(latent_count*b)+batch_index] = self.current_timestep_keyframe.null_latent_kf_strength
# convert final_mults into tensor and match expected dimension count
final_tensor = torch.tensor(final_mults, dtype=x.dtype, device=x.device)
while len(final_tensor.shape) < len(x.shape):
final_tensor = final_tensor.unsqueeze(-1)
return final_tensor
def apply_advanced_strengths_and_masks(self, x: Tensor, batched_number: int):
if self.latent_keyframes is not None:
x[:] = x[:] * self.calc_latent_keyframe_mults(x=x, batched_number=batched_number)
# apply masks, resizing mask to required dims
if self.mask_cond_hint is not None:
masks = prepare_mask_batch(self.mask_cond_hint, x.shape)
x[:] = x[:] * masks
if self.tk_mask_cond_hint is not None:
masks = prepare_mask_batch(self.tk_mask_cond_hint, x.shape)
x[:] = x[:] * masks
# apply timestep keyframe strengths
if self.current_timestep_keyframe.strength != 1.0:
x[:] *= self.current_timestep_keyframe.strength
def control_merge_inject(self: 'AdvancedControlBase', control_input, control_output, control_prev, output_dtype):
out = {'input':[], 'middle':[], 'output': []}
if control_input is not None:
for i in range(len(control_input)):
key = 'input'
x = control_input[i]
if x is not None:
self.apply_advanced_strengths_and_masks(x, self.batched_number)
x *= self.strength * self.calc_weight(i, x, len(control_input))
if x.dtype != output_dtype:
x = x.to(output_dtype)
out[key].insert(0, x)
if control_output is not None:
for i in range(len(control_output)):
if i == (len(control_output) - 1):
key = 'middle'
index = 0
else:
key = 'output'
index = i
x = control_output[i]
if x is not None:
self.apply_advanced_strengths_and_masks(x, self.batched_number)
if self.global_average_pooling:
x = torch.mean(x, dim=(2, 3), keepdim=True).repeat(1, 1, x.shape[2], x.shape[3])
x *= self.strength * self.calc_weight(i, x, len(control_output))
if x.dtype != output_dtype:
x = x.to(output_dtype)
out[key].append(x)
if control_prev is not None:
for x in ['input', 'middle', 'output']:
o = out[x]
for i in range(len(control_prev[x])):
prev_val = control_prev[x][i]
if i >= len(o):
o.append(prev_val)
elif prev_val is not None:
if o[i] is None:
o[i] = prev_val
else:
if o[i].shape[0] < prev_val.shape[0]:
o[i] = prev_val + o[i]
else:
o[i] += prev_val
return out
def prepare_mask_cond_hint(self, x_noisy: Tensor, t, cond, batched_number, dtype=None):
self._prepare_mask("mask_cond_hint", self.mask_cond_hint_original, x_noisy, t, cond, batched_number, dtype)
self.prepare_tk_mask_cond_hint(x_noisy, t, cond, batched_number, dtype)
def prepare_tk_mask_cond_hint(self, x_noisy: Tensor, t, cond, batched_number, dtype=None):
return self._prepare_mask("tk_mask_cond_hint", self.current_timestep_keyframe.mask_hint_orig, x_noisy, t, cond, batched_number, dtype)
def prepare_weight_mask_cond_hint(self, x_noisy: Tensor, batched_number, dtype=None):
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)
def _prepare_mask(self, attr_name, orig_mask: Tensor, x_noisy: Tensor, t, cond, batched_number, dtype=None, direct_attn=False):
# make mask appropriate dimensions, if present
if orig_mask is not None:
out_mask = getattr(self, attr_name)
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]:
self._reset_attr(attr_name)
del out_mask
# TODO: perform upscale on only the sub_idxs masks at a time instead of all to conserve RAM
# resize mask and match batch count
multiplier = 1 if direct_attn else 8
out_mask = prepare_mask_batch(orig_mask, x_noisy.shape, multiplier=multiplier)
actual_latent_length = x_noisy.shape[0] // batched_number
out_mask = comfy.utils.repeat_to_batch_size(out_mask, actual_latent_length if self.sub_idxs is None else self.full_latent_length)
if self.sub_idxs is not None:
out_mask = out_mask[self.sub_idxs]
# make cond_hint_mask length match x_noise
if x_noisy.shape[0] != out_mask.shape[0]:
out_mask = broadcast_image_to(out_mask, x_noisy.shape[0], batched_number)
# default dtype to be same as x_noisy
if dtype is None:
dtype = x_noisy.dtype
setattr(self, attr_name, out_mask.to(dtype=dtype).to(self.device))
del out_mask
def _reset_attr(self, attr_name, new_value=None):
if hasattr(self, attr_name):
delattr(self, attr_name)
setattr(self, attr_name, new_value)
def cleanup_inject(self):
self.base.cleanup()
self.cleanup_advanced()
def cleanup_advanced(self):
self.sub_idxs = None
self.full_latent_length = 0
self.context_length = 0
self.t = None
self.batched_number = None
self.weights = None
self.latent_keyframes = None
# timestep stuff
self.current_timestep_keyframe = None
self.next_timestep_keyframe = None
self.current_timestep_index = -1
# clear mask hints
if self.mask_cond_hint is not None:
del self.mask_cond_hint
self.mask_cond_hint = None
if self.tk_mask_cond_hint_original is not None:
del self.tk_mask_cond_hint_original
self.tk_mask_cond_hint_original = None
if self.tk_mask_cond_hint is not None:
del self.tk_mask_cond_hint
self.tk_mask_cond_hint = None
if self.weight_mask_cond_hint is not None:
del self.weight_mask_cond_hint
self.weight_mask_cond_hint = None
def copy_to_advanced(self, copied: 'AdvancedControlBase'):
copied.mask_cond_hint_original = self.mask_cond_hint_original
copied.weights_override = self.weights_override
copied.latent_keyframe_override = self.latent_keyframe_override
copied.disarmed = self.disarmed
workflows/comfy/ComfyUI/custom_nodes/ComfyUI-Advanced-ControlNet/requirements.txt
View File