Merge branch 'master' into gradient-clipping

1 files changed, 0 insertions, 42 deletions

diff --git a/modules/safety.py b/modules/safety.py
deleted file mode 100644
index cff4b278..00000000
--- a/modules/safety.py
+++ /dev/null
@@ -1,42 +0,0 @@
-import torch

-from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker

-from transformers import AutoFeatureExtractor

-from PIL import Image

-

-import modules.shared as shared

-

-safety_model_id = "CompVis/stable-diffusion-safety-checker"

-safety_feature_extractor = None

-safety_checker = None

-

-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

-

-# check and replace nsfw content

-def check_safety(x_image):

-    global safety_feature_extractor, safety_checker

-

-    if safety_feature_extractor is None:

-        safety_feature_extractor = AutoFeatureExtractor.from_pretrained(safety_model_id)

-        safety_checker = StableDiffusionSafetyChecker.from_pretrained(safety_model_id)

-

-    safety_checker_input = safety_feature_extractor(numpy_to_pil(x_image), return_tensors="pt")

-    x_checked_image, has_nsfw_concept = safety_checker(images=x_image, clip_input=safety_checker_input.pixel_values)

-

-    return x_checked_image, has_nsfw_concept

-

-

-def censor_batch(x):

-    x_samples_ddim_numpy = x.cpu().permute(0, 2, 3, 1).numpy()

-    x_checked_image, has_nsfw_concept = check_safety(x_samples_ddim_numpy)

-    x = torch.from_numpy(x_checked_image).permute(0, 3, 1, 2)

-

-    return x