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import torch
import comfy.samplers
from .utils_model import BetaSchedules, SigmaSchedule, ModelSamplingType, ModelSamplingConfig, InterpolationMethod
def validate_sigma_schedule_compatibility(schedule_A: SigmaSchedule, schedule_B: SigmaSchedule,
name_a: str="sigma_schedule_A", name_b: str="sigma_schedule_B"):
if schedule_A.total_sigmas() != schedule_B.total_sigmas():
raise Exception(f"Weighted Average cannot be taken of Sigma Schedules that do not have the same amount of sigmas; " +
f"{name_a} has {schedule_A.total_sigmas()} sigmas (lcm={schedule_A.is_lcm()}), " +
f"{name_b} has {schedule_B.total_sigmas()} sigmas (lcm={schedule_B.is_lcm()}).")
class SigmaScheduleNode:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"beta_schedule": (BetaSchedules.ALIAS_ACTIVE_LIST,),
}
}
RETURN_TYPES = ("SIGMA_SCHEDULE",)
CATEGORY = "Animate Diff 🎭🅐🅓/sample settings/sigma schedule"
FUNCTION = "get_sigma_schedule"
def get_sigma_schedule(self, beta_schedule: str):
model_type = ModelSamplingType.from_alias(ModelSamplingType.EPS)
new_model_sampling = BetaSchedules._to_model_sampling(alias=beta_schedule,
model_type=model_type)
return (SigmaSchedule(model_sampling=new_model_sampling, model_type=model_type),)
class RawSigmaScheduleNode:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"raw_beta_schedule": (BetaSchedules.RAW_BETA_SCHEDULE_LIST,),
"linear_start": ("FLOAT", {"default": 0.00085, "min": 0.0, "max": 1.0, "step": 0.000001}),
"linear_end": ("FLOAT", {"default": 0.012, "min": 0.0, "max": 1.0, "step": 0.000001}),
#"cosine_s": ("FLOAT", {"default": 8e-3, "min": 0.0, "max": 1.0, "step": 0.000001}),
"sampling": (ModelSamplingType._FULL_LIST,),
"lcm_original_timesteps": ("INT", {"default": 50, "min": 1, "max": 1000}),
"zsnr": ("BOOLEAN", {"default": False}),
},
"hidden": {
"autosize": ("ADEAUTOSIZE", {"padding": 0}),
}
}
RETURN_TYPES = ("SIGMA_SCHEDULE",)
CATEGORY = "Animate Diff 🎭🅐🅓/sample settings/sigma schedule"
FUNCTION = "get_sigma_schedule"
def get_sigma_schedule(self, raw_beta_schedule: str, linear_start: float, linear_end: float,# cosine_s: float,
sampling: str, lcm_original_timesteps: int, zsnr: bool, lcm_zsnr: bool=None):
if lcm_zsnr is not None:
zsnr = lcm_zsnr
# from pathlib import Path
# log_name = 'enforce_zero_terminal_snr_betas'
# betas_file = Path(__file__).parent.parent / rf"{log_name}.pt"
# given_betas = torch.load(betas_file, weights_only=True)
# given_betas[-1] = 0.0
new_config = ModelSamplingConfig(beta_schedule=raw_beta_schedule, linear_start=linear_start, linear_end=linear_end)#, given_betas=given_betas)
if sampling != ModelSamplingType.LCM:
lcm_original_timesteps=None
model_type = ModelSamplingType.from_alias(sampling)
new_model_sampling = BetaSchedules._to_model_sampling(alias=BetaSchedules.AUTOSELECT, model_type=model_type, config_override=new_config, original_timesteps=lcm_original_timesteps)
if zsnr:
SigmaSchedule.apply_zsnr(new_model_sampling=new_model_sampling)
return (SigmaSchedule(model_sampling=new_model_sampling, model_type=model_type),)
class WeightedAverageSigmaScheduleNode:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"schedule_A": ("SIGMA_SCHEDULE",),
"schedule_B": ("SIGMA_SCHEDULE",),
"weight_A": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.001}),
},
"hidden": {
"autosize": ("ADEAUTOSIZE", {"padding": 0}),
}
}
RETURN_TYPES = ("SIGMA_SCHEDULE",)
CATEGORY = "Animate Diff 🎭🅐🅓/sample settings/sigma schedule"
FUNCTION = "get_sigma_schedule"
def get_sigma_schedule(self, schedule_A: SigmaSchedule, schedule_B: SigmaSchedule, weight_A: float):
validate_sigma_schedule_compatibility(schedule_A, schedule_B)
new_sigmas = schedule_A.model_sampling.sigmas * weight_A + schedule_B.model_sampling.sigmas * (1-weight_A)
combo_schedule = schedule_A.clone()
combo_schedule.model_sampling.set_sigmas(new_sigmas)
return (combo_schedule,)
class InterpolatedWeightedAverageSigmaScheduleNode:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"schedule_A": ("SIGMA_SCHEDULE",),
"schedule_B": ("SIGMA_SCHEDULE",),
"weight_A_Start": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.001}),
"weight_A_End": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.001}),
"interpolation": (InterpolationMethod._LIST,),
},
"hidden": {
"autosize": ("ADEAUTOSIZE", {"padding": 0}),
}
}
RETURN_TYPES = ("SIGMA_SCHEDULE",)
CATEGORY = "Animate Diff 🎭🅐🅓/sample settings/sigma schedule"
FUNCTION = "get_sigma_schedule"
def get_sigma_schedule(self, schedule_A: SigmaSchedule, schedule_B: SigmaSchedule,
weight_A_Start: float, weight_A_End: float, interpolation: str):
validate_sigma_schedule_compatibility(schedule_A, schedule_B)
# get reverse weights, since sigmas are currently reversed
weights = InterpolationMethod.get_weights(num_from=weight_A_Start, num_to=weight_A_End,
length=schedule_A.total_sigmas(), method=interpolation, reverse=True)
weights = weights.to(schedule_A.model_sampling.sigmas.dtype).to(schedule_A.model_sampling.sigmas.device)
new_sigmas = schedule_A.model_sampling.sigmas * weights + schedule_B.model_sampling.sigmas * (1.0-weights)
combo_schedule = schedule_A.clone()
combo_schedule.model_sampling.set_sigmas(new_sigmas)
return (combo_schedule,)
class SplitAndCombineSigmaScheduleNode:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"schedule_Start": ("SIGMA_SCHEDULE",),
"schedule_End": ("SIGMA_SCHEDULE",),
"idx_split_percent": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.001})
},
"hidden": {
"autosize": ("ADEAUTOSIZE", {"padding": 0}),
}
}
RETURN_TYPES = ("SIGMA_SCHEDULE",)
CATEGORY = "Animate Diff 🎭🅐🅓/sample settings/sigma schedule"
FUNCTION = "get_sigma_schedule"
def get_sigma_schedule(self, schedule_Start: SigmaSchedule, schedule_End: SigmaSchedule, idx_split_percent: float):
validate_sigma_schedule_compatibility(schedule_Start, schedule_End)
# first, calculate index to act as split; get diff from 1.0 since sigmas are flipped at this stage
idx = int((1.0-idx_split_percent) * schedule_Start.total_sigmas())
new_sigmas = torch.cat([schedule_End.model_sampling.sigmas[:idx], schedule_Start.model_sampling.sigmas[idx:]], dim=0)
new_schedule = schedule_Start.clone()
new_schedule.model_sampling.set_sigmas(new_sigmas)
return (new_schedule,)
class SigmaScheduleToSigmasNode:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"sigma_schedule": ("SIGMA_SCHEDULE",),
"scheduler": (comfy.samplers.SCHEDULER_NAMES, ),
"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
},
"hidden": {
"autosize": ("ADEAUTOSIZE", {"padding": 0}),
}
}
RETURN_TYPES = ("SIGMAS",)
CATEGORY = "Animate Diff 🎭🅐🅓/sample settings/sigma schedule"
FUNCTION = "get_sigmas"
def get_sigmas(self, sigma_schedule: SigmaSchedule, scheduler: str, steps: int, denoise: float):
total_steps = steps
if denoise < 1.0:
if denoise <= 0.0:
return (torch.FloatTensor([]),)
total_steps = int(steps/denoise)
sigmas = comfy.samplers.calculate_sigmas(sigma_schedule, scheduler, total_steps).cpu()
sigmas = sigmas[-(steps + 1):]
return (sigmas, )
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