code: def decode_sdxl_t2i_latents(
      pipeline: Any, latents: "torch_float_tensor"
  ) -> List:
      """Decode latents generated by [`diffusers.StableDiffusionXLPipeline`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/stable_diffusion_xl#stable-diffusion-xl).
  
      Arguments:
          pipeline: (diffusers.DiffusionPipeline) The Diffusion Pipeline from
              [`diffusers`](https://huggingface.co/docs/diffusers).
          latents (torch.FloatTensor): The generated latents.
  
      Returns:
          List of `PIL` images corresponding to the generated latents.
      """
      torch = get_module(
          "torch",
          required="Please ensure PyTorch is installed. You can check out https://pytorch.org/get-started/locally/#start-locally for installation instructions.",
      )
      with torch.no_grad():
          needs_upcasting = (
              pipeline.vae.dtype == torch.float16
              and pipeline.vae.config.force_upcast
          )
          if needs_upcasting:
              pipeline.upcast_vae()
              latents = latents.to(
                  next(
                      iter(pipeline.vae.post_quant_conv.parameters())
                  ).dtype
              )
          images = pipeline.vae.decode(
              latents / pipeline.vae.config.scaling_factor,
              return_dict=False,
          )[0]
          if needs_upcasting:
              pipeline.vae.to(dtype=torch.float16)
          if pipeline.watermark is not None:
              images = pipeline.watermark.apply_watermark(images)
          images = pipeline.image_processor.postprocess(
              images, output_type="pil"
          )
          pipeline.maybe_free_model_hooks()
          return images
  
file_path: wandb/wandb/integration/diffusers/resolvers/sdxl.py
imports: import logging
  from typing import TYPE_CHECKING, Any, Dict, List, Sequence
  import wandb
  from wandb.sdk.integration_utils.auto_logging import Response
  from wandb.util import get_module
  from .utils import chunkify, get_updated_kwargs
label_desc: Use image node_id 1 for calling a global function with example usage: decode_sdxl_t2i_latents(pipeline, latents) and returns: images