import numpy as np import torch import torch.nn as nn from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel from diffusers.utils import logging logger = logging.get_logger(__name__) 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) class StableDiffusionSafetyChecker(PreTrainedModel): config_class = CLIPConfig def __init__(self, config: CLIPConfig): super().__init__(config) self.vision_model = CLIPVisionModel(config.vision_config) self.visual_projection = nn.Linear(config.vision_config.hidden_size, config.projection_dim, bias=False) self.concept_embeds = nn.Parameter(torch.ones(17, config.projection_dim), requires_grad=False) self.special_care_embeds = nn.Parameter(torch.ones(3, config.projection_dim), requires_grad=False) self.register_buffer("concept_embeds_weights", torch.ones(17)) self.register_buffer("special_care_embeds_weights", torch.ones(3)) @torch.no_grad() def forward(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({concet_idx, result_img["special_scores"][concet_idx]}) adjustment = 0.01 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) if result_img["concept_scores"][concet_idx] > 0: result_img["bad_concepts"].append(concet_idx) result.append(result_img) has_nsfw_concepts = [len(res["bad_concepts"]) > 0 for res in result] for idx, has_nsfw_concept in enumerate(has_nsfw_concepts): if has_nsfw_concept: images[idx] = np.zeros(images[idx].shape) # black image if any(has_nsfw_concepts): logger.warning( "Potential NSFW content was detected in one or more images. A black image will be returned instead." " Try again with a different prompt and/or seed." ) return images, has_nsfw_concepts