From d0c714ae4afa1c011269a956d6f260f84f77025e Mon Sep 17 00:00:00 2001
From: Patrick von Platen <patrick.v.platen@gmail.com>
Date: Fri, 19 Aug 2022 16:01:56 +0000
Subject: [PATCH] [Safety Checker] Add Safety Checker Module
---
scripts/txt2img.py | 25 ++++++++++++++++++++++++-
1 file changed, 24 insertions(+), 1 deletion(-)
diff --git a/scripts/txt2img.py b/scripts/txt2img.py
index da77e1a..0af430c 100644
--- a/scripts/txt2img.py
+++ b/scripts/txt2img.py
@@ -16,12 +16,29 @@ from ldm.util import instantiate_from_config
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.")
--
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