Delete safety_checker.py

safety_checker.py

@@ -1,137 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import numpy as np
-import torch
-import torch.nn as nn
-from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
-
-
-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
-
-    _no_split_modules = ["CLIPEncoderLayer"]
-
-    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.concept_embeds_weights = nn.Parameter(torch.ones(17), requires_grad=False)
-        self.special_care_embeds_weights = nn.Parameter(
-            torch.ones(3), requires_grad=False
-        )
-
-    @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)
-
-        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
-        special_cos_dist = (
-            cosine_distance(image_embeds, self.special_care_embeds)
-            .cpu()
-            .float()
-            .numpy()
-        )
-        cos_dist = (
-            cosine_distance(image_embeds, self.concept_embeds).cpu().float().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 concept_idx in range(len(special_cos_dist[0])):
-                concept_cos = special_cos_dist[i][concept_idx]
-                concept_threshold = self.special_care_embeds_weights[concept_idx].item()
-                result_img["special_scores"][concept_idx] = round(
-                    concept_cos - concept_threshold + adjustment, 3
-                )
-                if result_img["special_scores"][concept_idx] > 0:
-                    result_img["special_care"].append(
-                        {concept_idx, result_img["special_scores"][concept_idx]}
-                    )
-                    adjustment = 0.01
-
-            for concept_idx in range(len(cos_dist[0])):
-                concept_cos = cos_dist[i][concept_idx]
-                concept_threshold = self.concept_embeds_weights[concept_idx].item()
-                result_img["concept_scores"][concept_idx] = round(
-                    concept_cos - concept_threshold + adjustment, 3
-                )
-                if result_img["concept_scores"][concept_idx] > 0:
-                    result_img["bad_concepts"].append(concept_idx)
-
-            result.append(result_img)
-
-        has_nsfw_concepts = [len(res["bad_concepts"]) > 0 for res in result]
-
-        return has_nsfw_concepts
-
-    @torch.no_grad()
-    def forward_onnx(self, clip_input: torch.FloatTensor, images: torch.FloatTensor):
-        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)
-        cos_dist = cosine_distance(image_embeds, self.concept_embeds)
-
-        # increase this value to create a stronger `nsfw` filter
-        # at the cost of increasing the possibility of filtering benign images
-        adjustment = 0.0
-
-        special_scores = (
-            special_cos_dist - self.special_care_embeds_weights + adjustment
-        )
-        # special_scores = special_scores.round(decimals=3)
-        special_care = torch.any(special_scores > 0, dim=1)
-        special_adjustment = special_care * 0.01
-        special_adjustment = special_adjustment.unsqueeze(1).expand(
-            -1, cos_dist.shape[1]
-        )
-
-        concept_scores = (cos_dist - self.concept_embeds_weights) + special_adjustment
-        # concept_scores = concept_scores.round(decimals=3)
-        has_nsfw_concepts = torch.any(concept_scores > 0, dim=1)
-
-        images[has_nsfw_concepts] = 0.0  # black image
-
-        return images, has_nsfw_concepts