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| concepts = ['sexual', 'nude', 'sex', '18+', 'naked', 'nsfw', 'porn', 'dick', 'vagina', 'naked person (approximation)', | |
| 'explicit content', 'uncensored', 'fuck', 'nipples', 'nipples (approximation)', 'naked breasts', 'areola'] | |
| special_concepts = ["small girl (approximation)", "young child", "young girl"] | |
| import dbimutils | |
| import torch | |
| def init_nsfw_pipe(): | |
| from diffusers import StableDiffusionPipeline | |
| from torch import nn | |
| # make sure you're logged in with `huggingface-cli login` | |
| if torch.cuda.is_available(): | |
| pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", revision="fp16", | |
| torch_dtype=torch.float16) | |
| pipe = pipe.to('cuda') | |
| else: | |
| pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", | |
| torch_dtype=torch.float32) | |
| def cosine_distance(image_embeds, text_embeds): | |
| normalized_image_embeds = nn.functional.normalize(image_embeds) | |
| normalized_text_embeds = nn.functional.normalize(text_embeds) | |
| return torch.mm(normalized_image_embeds, normalized_text_embeds.t()) | |
| def forward_ours(self, clip_input, images): | |
| pooled_output = self.vision_model(clip_input)[1] # pooled_output | |
| image_embeds = self.visual_projection(pooled_output) | |
| special_cos_dist = cosine_distance(image_embeds, self.special_care_embeds).cpu().numpy() | |
| cos_dist = cosine_distance(image_embeds, self.concept_embeds).cpu().numpy() | |
| result = [] | |
| batch_size = image_embeds.shape[0] | |
| for i in range(batch_size): | |
| result_img = {"special_scores": {}, "special_care": [], "concept_scores": {}, "bad_concepts": []} | |
| # increase this value to create a stronger `nfsw` filter | |
| # at the cost of increasing the possibility of filtering benign images | |
| adjustment = 0.0 | |
| for concet_idx in range(len(special_cos_dist[0])): | |
| concept_cos = special_cos_dist[i][concet_idx] | |
| concept_threshold = self.special_care_embeds_weights[concet_idx].item() | |
| result_img["special_scores"][concet_idx] = round(concept_cos - concept_threshold + adjustment, 3) | |
| if result_img["special_scores"][concet_idx] > 0: | |
| result_img["special_care"].append({"tag": special_concepts[concet_idx], | |
| "confidence": result_img["special_scores"][concet_idx]}) | |
| adjustment = 0.01 | |
| print("Special concept matched:", special_concepts[concet_idx]) | |
| for concet_idx in range(len(cos_dist[0])): | |
| concept_cos = cos_dist[i][concet_idx] | |
| concept_threshold = self.concept_embeds_weights[concet_idx].item() | |
| result_img["concept_scores"][concet_idx] = round(concept_cos - concept_threshold + adjustment, 3) | |
| # print("no-special", concet_idx, concepts[concet_idx], concept_threshold, round(concept_cos - concept_threshold + adjustment, 3)) | |
| if result_img["concept_scores"][concet_idx] > 0: | |
| result_img["bad_concepts"].append({"tag": concepts[concet_idx], | |
| "confidence": result_img["concept_scores"][concet_idx]}) | |
| print("NSFW concept found:", concepts[concet_idx]) | |
| special_tags = list(filter(lambda x: x['confidence'] > 0.4, result_img['special_care'])) | |
| bad_tags = list(filter(lambda x: x['confidence'] > 0.4, result_img['bad_concepts'])) | |
| result.append({"special_tags": special_tags, | |
| "bad_tags": bad_tags, }) | |
| return images, result | |
| from functools import partial | |
| pipe.safety_checker.forward = partial(forward_ours, self=pipe.safety_checker) | |
| return pipe | |
| def check_nsfw(img, pipe): | |
| if isinstance(img, str): | |
| img = dbimutils.read_img_from_url(img) | |
| safety_checker_input = pipe.feature_extractor(images=img, return_tensors="pt") | |
| if torch.cuda.is_available(): | |
| safety_checker_input = safety_checker_input.to('cuda') | |
| else: | |
| safety_checker_input = safety_checker_input.to('cpu') | |
| from torch.cuda.amp import autocast | |
| with autocast(): | |
| _, nsfw_tags = pipe.safety_checker.forward(clip_input=safety_checker_input.pixel_values, images=img) | |
| return nsfw_tags | |