code: def train_step(
      self,
      text_embeddings,
      pred_rgb,
      guidance_scale=100,
      as_latent=False,
  ):
      if as_latent:
          latents = (
              F.interpolate(
                  pred_rgb,
                  (64, 64),
                  mode="bilinear",
                  align_corners=False,
              )
              * 2
              - 1
          )
      else:
          # interp to 512x512 to be fed into vae.
          pred_rgb_512 = F.interpolate(
              pred_rgb, (512, 512), mode="bilinear", align_corners=False
          )
          # encode image into latents with vae, requires grad!
          latents = self.encode_imgs(pred_rgb_512)
  
      # timestep ~ U(0.02, 0.98) to avoid very high/low noise level
      t = torch.randint(
          self.min_step,
          self.max_step + 1,
          (latents.shape[0],),
          dtype=torch.long,
          device=self.device,
      )
  
      with torch.no_grad():
          noise = torch.randn_like(latents)
          latents_noisy = self.model.q_sample(
              x_start=latents, t=t, noise=noise
          )
          latent_model_input = torch.cat([latents_noisy] * 2)
          td = torch.cat([t] * 2)
          noise_pred = self.model.apply_model(
              latent_model_input, td, text_embeddings
          )
  
          noise_pred_uncond, noise_pred_pos = noise_pred.chunk(2)
          noise_pred = noise_pred_uncond + guidance_scale * (
              noise_pred_pos - noise_pred_uncond
          )
  
      # w(t), sigma_t^2
      w = 1 - self.alphas[t]
      grad = w[:, None, None, None] * (noise_pred - noise)
      grad = torch.nan_to_num(grad)
  
      targets = (latents - grad).detach()
      loss = (
          0.5
          * F.mse_loss(latents.float(), targets, reduction="sum")
          / latents.shape[0]
      )
      return loss
  
file_path: NeMo/nemo/collections/multimodal/modules/nerf/guidance/stablediffusion_nemo_pipeline.py
imports: import os
  import tempfile
  import torch
  import torch.nn.functional
  from omegaconf import OmegaConf
  from nemo.collections.multimodal.models.text_to_image.stable_diffusion.ldm.ddpm import LatentDiffusion
  from nemo.collections.multimodal.modules.nerf.guidance.txt2img_guidance_base import Txt2ImgGuidanceBase
  from nemo.collections.multimodal.modules.stable_diffusion.distributions.distributions import DiagonalGaussianDistribution
  from nemo.collections.nlp.parts.nlp_overrides import NLPSaveRestoreConnector
label_desc: Use image node_id 3 for calling the StableDiffusion obj's underlying member method code with example usage: obj.train_step(text_embeddings, pred_rgb, guidance_scale, as_latent) and returns: loss