189 lines
8.1 KiB
Python
189 lines
8.1 KiB
Python
from torch import Tensor
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import folder_paths
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from nodes import VAEEncode
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import comfy.utils
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from comfy.sd import VAE
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from .utils import TimestepKeyframeGroup
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from .control_sparsectrl import SparseMethod, SparseIndexMethod, SparseSettings, SparseSpreadMethod, PreprocSparseRGBWrapper, SparseConst, SparseContextAware, get_idx_list_from_str
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from .control import load_sparsectrl, load_controlnet, ControlNetAdvanced, SparseCtrlAdvanced
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# node for SparseCtrl loading
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class SparseCtrlLoaderAdvanced:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"sparsectrl_name": (folder_paths.get_filename_list("controlnet"), ),
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"use_motion": ("BOOLEAN", {"default": True}, ),
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"motion_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"motion_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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},
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"optional": {
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"sparse_method": ("SPARSE_METHOD", ),
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"tk_optional": ("TIMESTEP_KEYFRAME", ),
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"context_aware": (SparseContextAware.LIST, ),
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"sparse_hint_mult": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"sparse_nonhint_mult": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"sparse_mask_mult": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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}
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}
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RETURN_TYPES = ("CONTROL_NET", )
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FUNCTION = "load_controlnet"
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CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/SparseCtrl"
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def load_controlnet(self, sparsectrl_name: str, use_motion: bool, motion_strength: float, motion_scale: float, sparse_method: SparseMethod=SparseSpreadMethod(), tk_optional: TimestepKeyframeGroup=None,
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context_aware=SparseContextAware.NEAREST_HINT, sparse_hint_mult=1.0, sparse_nonhint_mult=1.0, sparse_mask_mult=1.0):
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sparsectrl_path = folder_paths.get_full_path("controlnet", sparsectrl_name)
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sparse_settings = SparseSettings(sparse_method=sparse_method, use_motion=use_motion, motion_strength=motion_strength, motion_scale=motion_scale,
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context_aware=context_aware,
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sparse_mask_mult=sparse_mask_mult, sparse_hint_mult=sparse_hint_mult, sparse_nonhint_mult=sparse_nonhint_mult)
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sparsectrl = load_sparsectrl(sparsectrl_path, timestep_keyframe=tk_optional, sparse_settings=sparse_settings)
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return (sparsectrl,)
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class SparseCtrlMergedLoaderAdvanced:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"sparsectrl_name": (folder_paths.get_filename_list("controlnet"), ),
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"control_net_name": (folder_paths.get_filename_list("controlnet"), ),
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"use_motion": ("BOOLEAN", {"default": True}, ),
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"motion_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"motion_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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},
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"optional": {
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"sparse_method": ("SPARSE_METHOD", ),
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"tk_optional": ("TIMESTEP_KEYFRAME", ),
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}
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}
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RETURN_TYPES = ("CONTROL_NET", )
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FUNCTION = "load_controlnet"
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CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/SparseCtrl/experimental"
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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):
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sparsectrl_path = folder_paths.get_full_path("controlnet", sparsectrl_name)
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controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
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sparse_settings = SparseSettings(sparse_method=sparse_method, use_motion=use_motion, motion_strength=motion_strength, motion_scale=motion_scale, merged=True)
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# first, load normal controlnet
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controlnet = load_controlnet(controlnet_path, timestep_keyframe=tk_optional)
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# confirm that controlnet is ControlNetAdvanced
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if controlnet is None or type(controlnet) != ControlNetAdvanced:
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raise ValueError(f"controlnet_path must point to a normal ControlNet, but instead: {type(controlnet).__name__}")
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# next, load sparsectrl, making sure to load motion portion
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sparsectrl = load_sparsectrl(sparsectrl_path, timestep_keyframe=tk_optional, sparse_settings=SparseSettings.default())
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# now, combine state dicts
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new_state_dict = controlnet.control_model.state_dict()
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for key, value in sparsectrl.control_model.motion_holder.motion_wrapper.state_dict().items():
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new_state_dict[key] = value
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# now, reload sparsectrl with real settings
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sparsectrl = load_sparsectrl(sparsectrl_path, controlnet_data=new_state_dict, timestep_keyframe=tk_optional, sparse_settings=sparse_settings)
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return (sparsectrl,)
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class SparseIndexMethodNode:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"indexes": ("STRING", {"default": "0"}),
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}
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}
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RETURN_TYPES = ("SPARSE_METHOD",)
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FUNCTION = "get_method"
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CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/SparseCtrl"
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def get_method(self, indexes: str):
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idxs = get_idx_list_from_str(indexes)
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return (SparseIndexMethod(idxs),)
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class SparseSpreadMethodNode:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"spread": (SparseSpreadMethod.LIST,),
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}
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}
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RETURN_TYPES = ("SPARSE_METHOD",)
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FUNCTION = "get_method"
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CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/SparseCtrl"
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def get_method(self, spread: str):
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return (SparseSpreadMethod(spread=spread),)
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class RgbSparseCtrlPreprocessor:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image": ("IMAGE", ),
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"vae": ("VAE", ),
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"latent_size": ("LATENT", ),
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},
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"hidden": {
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"autosize": ("ACNAUTOSIZE", {"padding": 0}),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("proc_IMAGE",)
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FUNCTION = "preprocess_images"
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CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/SparseCtrl/preprocess"
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def preprocess_images(self, vae: VAE, image: Tensor, latent_size: Tensor):
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# first, resize image to match latents
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image = image.movedim(-1,1)
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image = comfy.utils.common_upscale(image, latent_size["samples"].shape[3] * 8, latent_size["samples"].shape[2] * 8, 'nearest-exact', "center")
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image = image.movedim(1,-1)
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# then, vae encode
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try:
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image = vae.vae_encode_crop_pixels(image)
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except Exception:
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image = VAEEncode.vae_encode_crop_pixels(image)
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encoded = vae.encode(image[:,:,:,:3])
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return (PreprocSparseRGBWrapper(condhint=encoded),)
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class SparseWeightExtras:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"optional": {
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"cn_extras": ("CN_WEIGHTS_EXTRAS",),
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"sparse_hint_mult": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"sparse_nonhint_mult": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"sparse_mask_mult": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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},
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"hidden": {
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"autosize": ("ACNAUTOSIZE", {"padding": 0}),
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}
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}
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RETURN_TYPES = ("CN_WEIGHTS_EXTRAS", )
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RETURN_NAMES = ("cn_extras", )
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FUNCTION = "create_weight_extras"
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CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/SparseCtrl/extras"
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def create_weight_extras(self, cn_extras: dict[str]={}, sparse_hint_mult=1.0, sparse_nonhint_mult=1.0, sparse_mask_mult=1.0):
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cn_extras = cn_extras.copy()
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cn_extras[SparseConst.HINT_MULT] = sparse_hint_mult
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cn_extras[SparseConst.NONHINT_MULT] = sparse_nonhint_mult
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cn_extras[SparseConst.MASK_MULT] = sparse_mask_mult
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return (cn_extras, )
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