diff options
Diffstat (limited to 'modules')
| -rw-r--r-- | modules/processing.py | 36 | ||||
| -rw-r--r-- | modules/safety.py | 42 |
2 files changed, 44 insertions, 34 deletions
diff --git a/modules/processing.py b/modules/processing.py index e777a965..65ae4846 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -19,20 +19,11 @@ import modules.face_restoration import modules.images as images
import modules.styles
-from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
-from transformers import AutoFeatureExtractor
-
-# load safety model
-safety_model_id = "CompVis/stable-diffusion-safety-checker"
-safety_feature_extractor = None
-safety_checker = None
-
# some of those options should not be changed at all because they would break the model, so I removed them from options.
opt_C = 4
opt_f = 8
-
class StableDiffusionProcessing:
def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", prompt_style="None", seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None):
self.sd_model = sd_model
@@ -154,28 +145,6 @@ def fix_seed(p): p.subseed = int(random.randrange(4294967294)) if p.subseed is None or p.subseed == -1 else p.subseed
-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 process_images(p: StableDiffusionProcessing) -> Processed:
"""this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
@@ -279,9 +248,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed: x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
if opts.filter_nsfw:
- x_samples_ddim_numpy = x_samples_ddim.cpu().permute(0, 2, 3, 1).numpy()
- x_checked_image, has_nsfw_concept = check_safety(x_samples_ddim_numpy)
- x_samples_ddim = torch.from_numpy(x_checked_image).permute(0, 3, 1, 2)
+ import modules.safety as safety
+ x_samples_ddim = modules.safety.censor_batch(x_samples_ddim)
for i, x_sample in enumerate(x_samples_ddim):
x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
diff --git a/modules/safety.py b/modules/safety.py new file mode 100644 index 00000000..cff4b278 --- /dev/null +++ b/modules/safety.py @@ -0,0 +1,42 @@ +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
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