class_docstr: Utilities for training and sampling diffusion models.
  
  Ported directly from here, and then adapted over time to further experimentation.
  https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/diffusion_utils_2.py#L42
  
  :param betas: a 1-D numpy array of betas for each diffusion timestep,
                starting at T and going to 1.
  :param model_mean_type: a ModelMeanType determining what the model outputs.
  :param model_var_type: a ModelVarType determining how variance is output.
  :param loss_type: a LossType determining the loss function to use.
  :param rescale_timesteps: if True, pass floating point timesteps into the
                            model so that they are always scaled like in the
                            original paper (0 to 1000).
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docstr: Generate samples from the model and yield intermediate samples from
  each timestep of diffusion.
  
  Arguments are the same as p_sample_loop().
  Returns a generator over dicts, where each dict is the return value of
  p_sample().
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code: def p_sample_loop_progressive(
      self,
      model,
      shape,
      noise=None,
      clip_denoised=True,
      denoised_fn=None,
      cond_fn=None,
      model_kwargs=None,
      device=None,
      progress=False,
  ):
      """
      Generate samples from the model and yield intermediate samples from
      each timestep of diffusion.
  
      Arguments are the same as p_sample_loop().
      Returns a generator over dicts, where each dict is the return value of
      p_sample().
      """
      if device is None:
          device = next(model.parameters()).device
      assert isinstance(shape, (tuple, list))
      if noise is not None:
          img = noise
      else:
          img = th.randn(*shape, device=device)
      indices = list(range(self.num_timesteps))[::-1]
  
      for i in tqdm(indices, disable=not progress):
          t = th.tensor([i] * shape[0], device=device)
          with th.no_grad():
              out = self.p_sample(
                  model,
                  img,
                  t,
                  clip_denoised=clip_denoised,
                  denoised_fn=denoised_fn,
                  cond_fn=cond_fn,
                  model_kwargs=model_kwargs,
              )
              yield out
              img = out["sample"]
  
imports: import enum
  import math
  import numpy
  import torch
  import torch
  from tqdm import tqdm
  from TTS.tts.layers.tortoise.dpm_solver import DPM_Solver, NoiseScheduleVP, model_wrapper
label_desc: Use image node_id 16 for calling the GaussianDiffusion obj's underlying member method code with example usage: obj.p_sample_loop_progressive(model, shape, noise, clip_denoised, denoised_fn, cond_fn, model_kwargs, device, progress) without return types