[Safety Checker] Add Safety Checker Module

scripts/txt2img.py

@@ -16,12 +16,29 @@
 from ldm.models.diffusion.ddim import DDIMSampler
 from ldm.models.diffusion.plms import PLMSSampler
 
+from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
+from transformers import AutoFeatureExtractor
+
+feature_extractor = AutoFeatureExtractor.from_pretrained("CompVis/stable-diffusion-v-1-3", use_auth_token=True)
+safety_checker = StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-v-1-3", use_auth_token=True)
 
 def chunk(it, size):
  it = iter(it)
  return iter(lambda: tuple(islice(it, size)), ())
 
 
+def numpy_to_pil(images):
+ """
+ Convert a numpy image or a batch of images to a PIL image.
+ """
+ if images.ndim == 3:
+ images = images[None, ...]
+ images = (images * 255).round().astype("uint8")
+ pil_images = [Image.fromarray(image) for image in images]
+
+ return pil_images
+
+
 def load_model_from_config(config, ckpt, verbose=False):
  print(f"Loading model from {ckpt}")
  pl_sd = torch.load(ckpt, map_location="cpu")
@@ -220,7 +237,9 @@ def main():
  if opt.fixed_code:
  start_code = torch.randn([opt.n_samples, opt.C, opt.H // opt.f, opt.W // opt.f], device=device)
 
+ print("start code", start_code.abs().sum())
  precision_scope = autocast if opt.precision=="autocast" else nullcontext
+ precision_scope = nullcontext
  with torch.no_grad():
  with precision_scope("cuda"):
  with model.ema_scope():
@@ -269,7 +288,11 @@ def main():
  Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
  grid_count += 1
 
- toc = time.time()
+ image = x_samples_ddim.cpu().permute(0, 2, 3, 1).numpy()
+
+ # run safety checker
+ safety_checker_input = pipe.feature_extractor(numpy_to_pil(image), return_tensors="pt")
+ image, has_nsfw_concept = pipe.safety_checker(images=image, clip_input=safety_checker_input.pixel_values)
 
  print(f"Your samples are ready and waiting for you here: \n{outpath} \n"
  f" \nEnjoy.")