class_docstr: Solver for compression task.
  
  The diffusion task allows for MultiBand diffusion model training.
  
  Args:
      cfg (DictConfig): Configuration.
class_docstr_tok: ["Solver","for","compression","task",".","The","diffusion","task","allows","for","MultiBand","diffusion","model","training",".","Args",":","cfg","(","DictConfig",")",":","Configuration","."]
docstr: Generate stage.
docstr_tok: ["Generate","stage","."]
code: def generate(self):
      """Generate stage."""
      sample_manager = SampleManager(self.xp)
      self.model.eval()
      generate_stage_name = f"{self.current_stage}"
  
      loader = self.dataloaders["generate"]
      updates = len(loader)
      lp = self.log_progress(
          generate_stage_name,
          loader,
          total=updates,
          updates=self.log_updates,
      )
  
      for batch in lp:
          reference, _ = batch
          reference = reference.to(self.device)
          estimate = self.regenerate(reference)
          reference = reference.cpu()
          estimate = estimate.cpu()
          sample_manager.add_samples(
              estimate, self.epoch, ground_truth_wavs=reference
          )
      flashy.distrib.barrier()
  
imports: import typing
  import flashy
  import julius
  import omegaconf
  import torch
  import torch.nn.functional
  from .None import builders
  from .None import base
  from ..None import models
  from ..modules.diffusion_schedule import NoiseSchedule
  from ..metrics import RelativeVolumeMel
  from ..models.builders import get_processor
  from ..utils.samples.manager import SampleManager
  from ..solvers.compression import CompressionSolver
label_desc: Use image node_id 11 for calling the DiffusionSolver obj's underlying member method code with example usage: obj.generate() without return types