code: def betas_for_alpha_bar(
      num_diffusion_timesteps, alpha_bar, max_beta=0.999
  ):
      # from: https://github.com/openai/guided-diffusion/blob/22e0df8183507e13a7813f8d38d51b072ca1e67c/guided_diffusion/gaussian_diffusion.py#L45
      betas = []
      for i in range(num_diffusion_timesteps):
          t1 = i / num_diffusion_timesteps
          t2 = (i + 1) / num_diffusion_timesteps
          betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
      return torch.tensor(betas)
  
imports: import math
  import os
  import urllib
  import warnings
  from argparse import ArgumentParser
  import torch
  import torch.nn
  import torch.nn.functional
  from huggingface_hub.utils import insecure_hashlib
  from safetensors.torch import load_file
  from tqdm import tqdm
  from diffusers import AutoencoderKL, ConsistencyDecoderVAE, DiffusionPipeline, StableDiffusionPipeline, UNet2DModel
  from diffusers.models.autoencoders.vae import Encoder
  from diffusers.models.embeddings import TimestepEmbedding
  from diffusers.models.unet_2d_blocks import ResnetDownsampleBlock2D, ResnetUpsampleBlock2D, UNetMidBlock2D
label_desc: Use image node_id 2 for calling a global function with example usage: betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta) and returns: torch