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from typing import Union
import torch
from torch import Tensor
import math
from comfy.sd import VAE
from .ad_settings import AnimateDiffSettings
from .logger import logger
from .utils_model import BIGMIN, BIGMAX, get_available_motion_models
from .utils_motion import ADKeyframeGroup, InputPIA, InputPIA_Multival, extend_list_to_batch_size, extend_to_batch_size, prepare_mask_batch
from .motion_lora import MotionLoraList
from .model_injection import MotionModelGroup, MotionModelPatcher, get_mm_attachment, load_motion_module_gen2, inject_pia_conv_in_into_model
from .motion_module_ad import AnimateDiffFormat
from .nodes_gen2 import ApplyAnimateDiffModelNode, ADKeyframeNode
# Preset values ported over from PIA repository:
# https://github.com/open-mmlab/PIA/blob/main/animatediff/utils/util.py
class PIA_RANGES:
ANIMATION_SMALL = "Animation (Small Motion)"
ANIMATION_MEDIUM = "Animation (Medium Motion)"
ANIMATION_LARGE = "Animation (Large Motion)"
LOOP_SMALL = "Loop (Small Motion)"
LOOP_MEDIUM = "Loop (Medium Motion)"
LOOP_LARGE = "Loop (Large Motion)"
STYLE_TRANSFER_SMALL = "Style Transfer (Small Motion)"
STYLE_TRANSFER_MEDIUM = "Style Transfer (Medium Motion)"
STYLE_TRANSFER_LARGE = "Style Transfer (Large Motion)"
_LOOPED = [LOOP_SMALL, LOOP_MEDIUM, LOOP_LARGE]
_LIST_ALL = [ANIMATION_SMALL, ANIMATION_MEDIUM, ANIMATION_LARGE,
LOOP_SMALL, LOOP_MEDIUM, LOOP_LARGE,
STYLE_TRANSFER_SMALL, STYLE_TRANSFER_MEDIUM, STYLE_TRANSFER_LARGE]
_MAPPING = {
ANIMATION_SMALL: [1.0, 0.9, 0.85, 0.85, 0.85, 0.8],
ANIMATION_MEDIUM: [1.0, 0.8, 0.8, 0.8, 0.79, 0.78, 0.75],
ANIMATION_LARGE: [1.0, 0.8, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.6, 0.5, 0.5],
LOOP_SMALL: [1.0, 0.9, 0.85, 0.85, 0.85, 0.8],
LOOP_MEDIUM: [1.0, 0.8, 0.8, 0.8, 0.79, 0.78, 0.75],
LOOP_LARGE: [1.0, 0.8, 0.7, 0.7, 0.7, 0.7, 0.6, 0.5],
STYLE_TRANSFER_SMALL: [0.5, 0.4, 0.4, 0.4, 0.35, 0.3],
STYLE_TRANSFER_MEDIUM: [0.5, 0.4, 0.4, 0.4, 0.35, 0.35, 0.3, 0.25, 0.2],
STYLE_TRANSFER_LARGE: [0.5, 0.2],
}
@classmethod
def get_preset(cls, preset: str) -> list[float]:
if preset in cls._MAPPING:
return cls._MAPPING[preset]
raise Exception(f"PIA Preset '{preset}' is not recognized.")
@classmethod
def is_looped(cls, preset: str) -> bool:
return preset in cls._LOOPED
class InputPIA_PaperPresets(InputPIA):
def __init__(self, preset: str, index: int, mult_multival: Union[float, Tensor]=None, effect_multival: Union[float, Tensor]=None):
super().__init__(effect_multival=effect_multival)
self.preset = preset
self.index = index
self.mult_multival = mult_multival if mult_multival is not None else 1.0
def get_mask(self, x: Tensor):
b, c, h, w = x.shape
values = PIA_RANGES.get_preset(self.preset)
# if preset is looped, make values loop
if PIA_RANGES.is_looped(self.preset):
# even length
if b % 2 == 0:
# extend to half length to get half of the loop
values = extend_list_to_batch_size(values, b // 2)
# apply second half of loop (just reverse it)
values += list(reversed(values))
# odd length
else:
inter_values = extend_list_to_batch_size(values, b // 2)
middle_vals = [values[min(len(inter_values), len(values)-1)]]
# make middle vals long enough to fill in gaps (or none if not needed)
middle_vals = middle_vals * (max(0, b-2*len(inter_values)))
values = inter_values + middle_vals + list(reversed(inter_values))
# otherwise, just extend values to desired length
else:
values = extend_list_to_batch_size(values, b)
assert len(values) == b
index = self.index
# handle negative index
if index < 0:
index = b + index
# constrain index between 0 and b-1
index = max(0, min(b-1, index))
# center values around targer index
order = [abs(i - index) for i in range(b)]
real_values = [values[order[i]] for i in range(b)]
# using real values, generate masks
tensor_values = torch.tensor(real_values).unsqueeze(-1).unsqueeze(-1)
mask = torch.ones(size=(b, h, w)) * tensor_values
# apply multi_multival to mask
if type(self.mult_multival) == Tensor or not math.isclose(self.mult_multival, 1.0):
real_mult = self.mult_multival
if type(real_mult) == Tensor:
real_mult = extend_to_batch_size(prepare_mask_batch(real_mult, x.shape), b).squeeze(1)
mask = mask * real_mult
return mask
class ApplyAnimateDiffPIAModel:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"motion_model": ("MOTION_MODEL_ADE",),
"image": ("IMAGE",),
"vae": ("VAE",),
"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": {
"pia_input": ("PIA_INPUT",),
"motion_lora": ("MOTION_LORA",),
"scale_multival": ("MULTIVAL",),
"effect_multival": ("MULTIVAL",),
"ad_keyframes": ("AD_KEYFRAMES",),
"prev_m_models": ("M_MODELS",),
"per_block": ("PER_BLOCK",),
"autosize": ("ADEAUTOSIZE", {"padding": 0}),
}
}
RETURN_TYPES = ("M_MODELS",)
CATEGORY = "Animate Diff 🎭🅐🅓/② Gen2 nodes ②/PIA"
FUNCTION = "apply_motion_model"
def apply_motion_model(self, motion_model: MotionModelPatcher, image: Tensor, vae: VAE,
start_percent: float=0.0, end_percent: float=1.0, pia_input: InputPIA=None,
motion_lora: MotionLoraList=None, ad_keyframes: ADKeyframeGroup=None,
scale_multival=None, effect_multival=None, ref_multival=None, per_block=None,
prev_m_models: MotionModelGroup=None,):
new_m_models = ApplyAnimateDiffModelNode.apply_motion_model(self, motion_model, start_percent=start_percent, end_percent=end_percent,
motion_lora=motion_lora, ad_keyframes=ad_keyframes,
scale_multival=scale_multival, effect_multival=effect_multival, per_block=per_block,
prev_m_models=prev_m_models)
# most recent added model will always be first in list;
curr_model = new_m_models[0].models[0]
# confirm that model is PIA
if curr_model.model.mm_info.mm_format != AnimateDiffFormat.PIA:
raise Exception(f"Motion model '{curr_model.model.mm_info.mm_name}' is not a PIA model; cannot be used with Apply AnimateDiff-PIA Model node.")
attachment = get_mm_attachment(curr_model)
attachment.orig_pia_images = image
attachment.pia_vae = vae
if pia_input is None:
pia_input = InputPIA_Multival(1.0)
attachment.pia_input = pia_input
#curr_model.pia_multival = ref_multival
return new_m_models
class LoadAnimateDiffAndInjectPIANode:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model_name": (get_available_motion_models(),),
"motion_model": ("MOTION_MODEL_ADE",),
},
"optional": {
"ad_settings": ("AD_SETTINGS",),
"deprecation_warning": ("ADEWARN", {"text": "Experimental. Don't expect to work.", "warn_type": "experimental", "color": "#CFC"}),
}
}
RETURN_TYPES = ("MOTION_MODEL_ADE",)
RETURN_NAMES = ("MOTION_MODEL",)
CATEGORY = "Animate Diff 🎭🅐🅓/② Gen2 nodes ②/PIA/🧪experimental"
FUNCTION = "load_motion_model"
def load_motion_model(self, model_name: str, motion_model: MotionModelPatcher, ad_settings: AnimateDiffSettings=None):
# make sure model actually has PIA conv_in
if motion_model.model.conv_in is None:
raise Exception("Passed-in motion model was expected to be PIA (contain conv_in), but did not.")
# load motion module and motion settings, if included
loaded_motion_model = load_motion_module_gen2(model_name=model_name, motion_model_settings=ad_settings)
inject_pia_conv_in_into_model(motion_model=loaded_motion_model, w_pia=motion_model)
return (loaded_motion_model,)
class PIA_ADKeyframeNode:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}, ),
},
"optional": {
"prev_ad_keyframes": ("AD_KEYFRAMES", ),
"scale_multival": ("MULTIVAL",),
"effect_multival": ("MULTIVAL",),
"pia_input": ("PIA_INPUT",),
"inherit_missing": ("BOOLEAN", {"default": True}, ),
"guarantee_steps": ("INT", {"default": 1, "min": 0, "max": BIGMAX}),
"autosize": ("ADEAUTOSIZE", {"padding": 0}),
}
}
RETURN_TYPES = ("AD_KEYFRAMES", )
FUNCTION = "load_keyframe"
CATEGORY = "Animate Diff 🎭🅐🅓/② Gen2 nodes ②/PIA"
def load_keyframe(self,
start_percent: float, prev_ad_keyframes=None,
scale_multival: Union[float, torch.Tensor]=None, effect_multival: Union[float, torch.Tensor]=None,
pia_input: InputPIA=None,
inherit_missing: bool=True, guarantee_steps: int=1):
return ADKeyframeNode.load_keyframe(self,
start_percent=start_percent, prev_ad_keyframes=prev_ad_keyframes,
scale_multival=scale_multival, effect_multival=effect_multival, pia_input=pia_input,
inherit_missing=inherit_missing, guarantee_steps=guarantee_steps
)
class InputPIA_MultivalNode:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"multival": ("MULTIVAL",),
},
# "optional": {
# "effect_multival": ("MULTIVAL",),
# }
}
RETURN_TYPES = ("PIA_INPUT",)
CATEGORY = "Animate Diff 🎭🅐🅓/② Gen2 nodes ②/PIA"
FUNCTION = "create_pia_input"
def create_pia_input(self, multival: Union[float, Tensor], effect_multival: Union[float, Tensor]=None):
return (InputPIA_Multival(multival, effect_multival),)
class InputPIA_PaperPresetsNode:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"preset": (PIA_RANGES._LIST_ALL,),
"batch_index": ("INT", {"default": 0, "min": BIGMIN, "max": BIGMAX, "step": 1}),
},
"optional": {
"mult_multival": ("MULTIVAL",),
"print_values": ("BOOLEAN", {"default": False},),
"autosize": ("ADEAUTOSIZE", {"padding": 0}),
#"effect_multival": ("MULTIVAL",),
}
}
RETURN_TYPES = ("PIA_INPUT",)
CATEGORY = "Animate Diff 🎭🅐🅓/② Gen2 nodes ②/PIA"
FUNCTION = "create_pia_input"
def create_pia_input(self, preset: str, batch_index: int, mult_multival: Union[float, Tensor]=None, print_values: bool=False, effect_multival: Union[float, Tensor]=None):
# verify preset exists - function will throw error if does not
values = PIA_RANGES.get_preset(preset)
if print_values:
logger.info(f"PIA Preset '{preset}': {values}")
return (InputPIA_PaperPresets(preset=preset, index=batch_index, mult_multival=mult_multival, effect_multival=effect_multival),)
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