code: def main():
      parser = argparse.ArgumentParser()
  
      parser.add_argument(
          "--prompt",
          type=str,
          nargs="?",
          default="a painting of a virus monster playing guitar",
          help="the prompt to render",
      )
      parser.add_argument(
          "--outdir",
          type=str,
          nargs="?",
          help="dir to write results to",
          default="outputs/txt2img-samples",
      )
      parser.add_argument(
          "--skip_grid",
          action="store_true",
          help="do not save a grid, only individual samples. Helpful when evaluating lots of samples",
      )
      parser.add_argument(
          "--skip_save",
          action="store_true",
          help="do not save individual samples. For speed measurements.",
      )
      parser.add_argument(
          "--ddim_steps",
          type=int,
          default=50,
          help="number of ddim sampling steps",
      )
      parser.add_argument(
          "--plms",
          action="store_true",
          help="use plms sampling",
      )
      parser.add_argument(
          "--dpm_solver",
          action="store_true",
          help="use dpm_solver sampling",
      )
      parser.add_argument(
          "--laion400m",
          action="store_true",
          help="uses the LAION400M model",
      )
      parser.add_argument(
          "--fixed_code",
          action="store_true",
          help="if enabled, uses the same starting code across samples ",
      )
      parser.add_argument(
          "--ddim_eta",
          type=float,
          default=0.0,
          help="ddim eta (eta=0.0 corresponds to deterministic sampling",
      )
      parser.add_argument(
          "--n_iter",
          type=int,
          default=2,
          help="sample this often",
      )
      parser.add_argument(
          "--H",
          type=int,
          default=512,
          help="image height, in pixel space",
      )
      parser.add_argument(
          "--W",
          type=int,
          default=512,
          help="image width, in pixel space",
      )
      parser.add_argument(
          "--C",
          type=int,
          default=4,
          help="latent channels",
      )
      parser.add_argument(
          "--f",
          type=int,
          default=8,
          help="downsampling factor",
      )
      parser.add_argument(
          "--n_samples",
          type=int,
          default=3,
          help="how many samples to produce for each given prompt. A.k.a. batch size",
      )
      parser.add_argument(
          "--n_rows",
          type=int,
          default=0,
          help="rows in the grid (default: n_samples)",
      )
      parser.add_argument(
          "--scale",
          type=float,
          default=7.5,
          help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))",
      )
      parser.add_argument(
          "--from-file",
          type=str,
          help="if specified, load prompts from this file",
      )
      parser.add_argument(
          "--config",
          type=str,
          default="configs/stable-diffusion/v1-inference.yaml",
          help="path to config which constructs model",
      )
      parser.add_argument(
          "--ckpt",
          type=str,
          default="models/ldm/stable-diffusion-v1/model.ckpt",
          help="path to checkpoint of model",
      )
      parser.add_argument(
          "--seed",
          type=int,
          default=42,
          help="the seed (for reproducible sampling)",
      )
      parser.add_argument(
          "--precision",
          type=str,
          help="evaluate at this precision",
          choices=["full", "autocast"],
          default="autocast",
      )
      opt = parser.parse_args()
  
      if opt.laion400m:
          print("Falling back to LAION 400M model...")
          opt.config = "configs/latent-diffusion/txt2img-1p4B-eval.yaml"
          opt.ckpt = "models/ldm/text2img-large/model.ckpt"
          opt.outdir = "outputs/txt2img-samples-laion400m"
  
      seed_everything(opt.seed)
  
      config = OmegaConf.load(f"{opt.config}")
      model = load_model_from_config(config, f"{opt.ckpt}")
  
      device = (
          torch.device("cuda")
          if torch.cuda.is_available()
          else torch.device("cpu")
      )
      model = model.to(device)
  
      if opt.dpm_solver:
          sampler = DPMSolverSampler(model)
      elif opt.plms:
          sampler = PLMSSampler(model)
      else:
          sampler = DDIMSampler(model)
  
      os.makedirs(opt.outdir, exist_ok=True)
      outpath = opt.outdir
  
      print(
          "Creating invisible watermark encoder (see https://github.com/ShieldMnt/invisible-watermark)..."
      )
      wm = "StableDiffusionV1"
      wm_encoder = WatermarkEncoder()
      wm_encoder.set_watermark("bytes", wm.encode("utf-8"))
  
      batch_size = opt.n_samples
      n_rows = opt.n_rows if opt.n_rows > 0 else batch_size
      if not opt.from_file:
          prompt = opt.prompt
          assert prompt is not None
          data = [batch_size * [prompt]]
  
      else:
          print(f"reading prompts from {opt.from_file}")
          with open(opt.from_file, "r") as f:
              data = f.read().splitlines()
              data = list(chunk(data, batch_size))
  
      sample_path = os.path.join(outpath, "samples")
      os.makedirs(sample_path, exist_ok=True)
      base_count = len(os.listdir(sample_path))
      grid_count = len(os.listdir(outpath)) - 1
  
      start_code = None
      if opt.fixed_code:
          start_code = torch.randn(
              [opt.n_samples, opt.C, opt.H // opt.f, opt.W // opt.f],
              device=device,
          )
  
      precision_scope = (
          autocast if opt.precision == "autocast" else nullcontext
      )
      with torch.no_grad():
          with precision_scope("cuda"):
              with model.ema_scope():
                  tic = time.time()
                  all_samples = list()
                  for n in trange(opt.n_iter, desc="Sampling"):
                      for prompts in tqdm(data, desc="data"):
                          uc = None
                          if opt.scale != 1.0:
                              uc = model.get_learned_conditioning(
                                  batch_size * [""]
                              )
                          if isinstance(prompts, tuple):
                              prompts = list(prompts)
                          c = model.get_learned_conditioning(prompts)
                          shape = [
                              opt.C,
                              opt.H // opt.f,
                              opt.W // opt.f,
                          ]
                          samples_ddim, _ = sampler.sample(
                              S=opt.ddim_steps,
                              conditioning=c,
                              batch_size=opt.n_samples,
                              shape=shape,
                              verbose=False,
                              unconditional_guidance_scale=opt.scale,
                              unconditional_conditioning=uc,
                              eta=opt.ddim_eta,
                              x_T=start_code,
                          )
  
                          x_samples_ddim = model.decode_first_stage(
                              samples_ddim
                          )
                          x_samples_ddim = torch.clamp(
                              (x_samples_ddim + 1.0) / 2.0,
                              min=0.0,
                              max=1.0,
                          )
                          x_samples_ddim = (
                              x_samples_ddim.cpu()
                              .permute(0, 2, 3, 1)
                              .numpy()
                          )
  
                          (
                              x_checked_image,
                              has_nsfw_concept,
                          ) = check_safety(x_samples_ddim)
  
                          x_checked_image_torch = torch.from_numpy(
                              x_checked_image
                          ).permute(0, 3, 1, 2)
  
                          if not opt.skip_save:
                              for x_sample in x_checked_image_torch:
                                  x_sample = 255.0 * rearrange(
                                      x_sample.cpu().numpy(),
                                      "c h w -> h w c",
                                  )
                                  img = Image.fromarray(
                                      x_sample.astype(np.uint8)
                                  )
                                  img = put_watermark(img, wm_encoder)
                                  img.save(
                                      os.path.join(
                                          sample_path,
                                          f"{base_count:05}.png",
                                      )
                                  )
                                  base_count += 1
  
                          if not opt.skip_grid:
                              all_samples.append(x_checked_image_torch)
  
                  if not opt.skip_grid:
                      # additionally, save as grid
                      grid = torch.stack(all_samples, 0)
                      grid = rearrange(grid, "n b c h w -> (n b) c h w")
                      grid = make_grid(grid, nrow=n_rows)
  
                      # to image
                      grid = (
                          255.0
                          * rearrange(grid, "c h w -> h w c")
                          .cpu()
                          .numpy()
                      )
                      img = Image.fromarray(grid.astype(np.uint8))
                      img = put_watermark(img, wm_encoder)
                      img.save(
                          os.path.join(
                              outpath, f"grid-{grid_count:04}.png"
                          )
                      )
                      grid_count += 1
  
                  toc = time.time()
  
      print(
          f"Your samples are ready and waiting for you here: \n{outpath} \n"
          f" \nEnjoy."
      )
  
imports: import argparse, os, sys, glob
  import cv2
  import torch
  import numpy
  from omegaconf import OmegaConf
  from PIL import Image
  from tqdm import tqdm, trange
  from imwatermark import WatermarkEncoder
  from itertools import islice
  from einops import rearrange
  from torchvision.utils import make_grid
  import time
  from pytorch_lightning import seed_everything
  from torch import autocast
  from contextlib import contextmanager, nullcontext
  from ldm.util import instantiate_from_config
  from ldm.models.diffusion.ddim import DDIMSampler
  from ldm.models.diffusion.plms import PLMSSampler
  from ldm.models.diffusion.dpm_solver import DPMSolverSampler
  from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
  from transformers import AutoFeatureExtractor
label_desc: Use image node_id 7 for calling a global function with example usage: main() without return types