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| # Copyright 2022 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. | |
| from typing import Optional, Tuple | |
| import jax | |
| import jax.numpy as jnp | |
| from flax import linen as nn | |
| from flax.core.frozen_dict import FrozenDict | |
| from transformers import CLIPConfig, FlaxPreTrainedModel | |
| from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule | |
| def jax_cosine_distance(emb_1, emb_2, eps=1e-12): | |
| norm_emb_1 = jnp.divide(emb_1.T, jnp.clip(jnp.linalg.norm(emb_1, axis=1), a_min=eps)).T | |
| norm_emb_2 = jnp.divide(emb_2.T, jnp.clip(jnp.linalg.norm(emb_2, axis=1), a_min=eps)).T | |
| return jnp.matmul(norm_emb_1, norm_emb_2.T) | |
| class FlaxStableDiffusionSafetyCheckerModule(nn.Module): | |
| config: CLIPConfig | |
| dtype: jnp.dtype = jnp.float32 | |
| def setup(self): | |
| self.vision_model = FlaxCLIPVisionModule(self.config.vision_config) | |
| self.visual_projection = nn.Dense(self.config.projection_dim, use_bias=False, dtype=self.dtype) | |
| self.concept_embeds = self.param("concept_embeds", jax.nn.initializers.ones, (17, self.config.projection_dim)) | |
| self.special_care_embeds = self.param( | |
| "special_care_embeds", jax.nn.initializers.ones, (3, self.config.projection_dim) | |
| ) | |
| self.concept_embeds_weights = self.param("concept_embeds_weights", jax.nn.initializers.ones, (17,)) | |
| self.special_care_embeds_weights = self.param("special_care_embeds_weights", jax.nn.initializers.ones, (3,)) | |
| def __call__(self, clip_input): | |
| pooled_output = self.vision_model(clip_input)[1] | |
| image_embeds = self.visual_projection(pooled_output) | |
| special_cos_dist = jax_cosine_distance(image_embeds, self.special_care_embeds) | |
| cos_dist = jax_cosine_distance(image_embeds, self.concept_embeds) | |
| # increase this value to create a stronger `nfsw` filter | |
| # at the cost of increasing the possibility of filtering benign image inputs | |
| adjustment = 0.0 | |
| special_scores = special_cos_dist - self.special_care_embeds_weights[None, :] + adjustment | |
| special_scores = jnp.round(special_scores, 3) | |
| is_special_care = jnp.any(special_scores > 0, axis=1, keepdims=True) | |
| # Use a lower threshold if an image has any special care concept | |
| special_adjustment = is_special_care * 0.01 | |
| concept_scores = cos_dist - self.concept_embeds_weights[None, :] + special_adjustment | |
| concept_scores = jnp.round(concept_scores, 3) | |
| has_nsfw_concepts = jnp.any(concept_scores > 0, axis=1) | |
| return has_nsfw_concepts | |
| class FlaxStableDiffusionSafetyChecker(FlaxPreTrainedModel): | |
| config_class = CLIPConfig | |
| main_input_name = "clip_input" | |
| module_class = FlaxStableDiffusionSafetyCheckerModule | |
| def __init__( | |
| self, | |
| config: CLIPConfig, | |
| input_shape: Optional[Tuple] = None, | |
| seed: int = 0, | |
| dtype: jnp.dtype = jnp.float32, | |
| _do_init: bool = True, | |
| **kwargs, | |
| ): | |
| if input_shape is None: | |
| input_shape = (1, 224, 224, 3) | |
| module = self.module_class(config=config, dtype=dtype, **kwargs) | |
| super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init) | |
| def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict: | |
| # init input tensor | |
| clip_input = jax.random.normal(rng, input_shape) | |
| params_rng, dropout_rng = jax.random.split(rng) | |
| rngs = {"params": params_rng, "dropout": dropout_rng} | |
| random_params = self.module.init(rngs, clip_input)["params"] | |
| return random_params | |
| def __call__( | |
| self, | |
| clip_input, | |
| params: dict = None, | |
| ): | |
| clip_input = jnp.transpose(clip_input, (0, 2, 3, 1)) | |
| return self.module.apply( | |
| {"params": params or self.params}, | |
| jnp.array(clip_input, dtype=jnp.float32), | |
| rngs={}, | |
| ) | |