Upload safety_checker.py

safety_checker.py

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+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