Move safety detection to model call in Flax safety checker (#1023)

* Move safety detection to model call in Flax safety checker

* Update src/diffusers/pipelines/stable_diffusion/safety_checker_flax.py

Author: jonatanklosko

src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion.py

@@ -1,3 +1,4 @@
+import warnings
 from functools import partial
 from typing import Dict, List, Optional, Union
 
@@ -97,9 +98,9 @@ def prepare_inputs(self, prompt: Union[str, List[str]]):
         )
         return text_input.input_ids
 
-    def _get_safety_scores(self, features, params):
-        special_cos_dist, cos_dist = self.safety_checker(features, params)
-        return (special_cos_dist, cos_dist)
+    def _get_has_nsfw_concepts(self, features, params):
+        has_nsfw_concepts = self.safety_checker(features, params)
+        return has_nsfw_concepts
 
     def _run_safety_checker(self, images, safety_model_params, jit=False):
         # safety_model_params should already be replicated when jit is True
@@ -108,20 +109,28 @@ def _run_safety_checker(self, images, safety_model_params, jit=False):
 
         if jit:
             features = shard(features)
-            special_cos_dist, cos_dist = _p_get_safety_scores(self, features, safety_model_params)
-            special_cos_dist = unshard(special_cos_dist)
-            cos_dist = unshard(cos_dist)
+            has_nsfw_concepts = _p_get_has_nsfw_concepts(self, features, safety_model_params)
+            has_nsfw_concepts = unshard(has_nsfw_concepts)
             safety_model_params = unreplicate(safety_model_params)
         else:
-            special_cos_dist, cos_dist = self._get_safety_scores(features, safety_model_params)
+            has_nsfw_concepts = self._get_has_nsfw_concepts(features, safety_model_params)
 
-        images, has_nsfw = self.safety_checker.filtered_with_scores(
-            special_cos_dist,
-            cos_dist,
-            images,
-            safety_model_params,
-        )
-        return images, has_nsfw
+        images_was_copied = False
+        for idx, has_nsfw_concept in enumerate(has_nsfw_concepts):
+            if has_nsfw_concept:
+                if not images_was_copied:
+                    images_was_copied = True
+                    images = images.copy()
+
+                images[idx] = np.zeros(images[idx].shape, dtype=np.uint8)  # black image
+
+            if any(has_nsfw_concepts):
+                warnings.warn(
+                    "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
 
     def _generate(
         self,
@@ -310,8 +319,8 @@ def _p_generate(
 
 
 @partial(jax.pmap, static_broadcasted_argnums=(0,))
-def _p_get_safety_scores(pipe, features, params):
-    return pipe._get_safety_scores(features, params)
+def _p_get_has_nsfw_concepts(pipe, features, params):
+    return pipe._get_has_nsfw_concepts(features, params)
 
 
 def unshard(x: jnp.ndarray):

src/diffusers/pipelines/stable_diffusion/safety_checker_flax.py

@@ -1,8 +1,5 @@
-import warnings
 from typing import Optional, Tuple
 
-import numpy as np
-
 import jax
 import jax.numpy as jnp
 from flax import linen as nn
@@ -39,56 +36,22 @@ def __call__(self, clip_input):
 
         special_cos_dist = jax_cosine_distance(image_embeds, self.special_care_embeds)
         cos_dist = jax_cosine_distance(image_embeds, self.concept_embeds)
-        return special_cos_dist, cos_dist
-
-    def filtered_with_scores(self, special_cos_dist, cos_dist, images):
-        batch_size = special_cos_dist.shape[0]
-        special_cos_dist = np.asarray(special_cos_dist)
-        cos_dist = np.asarray(cos_dist)
-
-        result = []
-        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 image inputs
-            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]
+        # increase this value to create a stronger `nfsw` filter
+        # at the cost of increasing the possibility of filtering benign image inputs
+        adjustment = 0.0
 
-        images_was_copied = False
-        for idx, has_nsfw_concept in enumerate(has_nsfw_concepts):
-            if has_nsfw_concept:
-                if not images_was_copied:
-                    images_was_copied = True
-                    images = images.copy()
+        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
 
-                images[idx] = np.zeros(images[idx].shape)  # black image
+        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)
 
-            if any(has_nsfw_concepts):
-                warnings.warn(
-                    "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
+        return has_nsfw_concepts
 
 
 class FlaxStableDiffusionSafetyChecker(FlaxPreTrainedModel):
@@ -133,15 +96,3 @@ def __call__(
             jnp.array(clip_input, dtype=jnp.float32),
             rngs={},
         )
-
-    def filtered_with_scores(self, special_cos_dist, cos_dist, images, params: dict = None):
-        def _filtered_with_scores(module, special_cos_dist, cos_dist, images):
-            return module.filtered_with_scores(special_cos_dist, cos_dist, images)
-
-        return self.module.apply(
-            {"params": params or self.params},
-            special_cos_dist,
-            cos_dist,
-            images,
-            method=_filtered_with_scores,
-        )