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Safety Checker jitting #11

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Safety Checker jitting #11

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43 changes: 28 additions & 15 deletions stable_diffusion_jax/safety_checker.py
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Original file line number Diff line number Diff line change
Expand Up @@ -43,32 +43,36 @@ def __call__(self, clip_input, images=None):
if images is None:
return special_cos_dist, cos_dist

# I think this should not be invoked here
return self.filtered_with_scores(special_cos_dist, cos_dist, images)

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 = []
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 image inputs
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]})
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 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)
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)

Expand Down Expand Up @@ -129,11 +133,20 @@ def __call__(
params: dict = None,
images=None,
):
pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1))
# pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1))

return self.module.apply(
{"params": params or self.params},
jnp.array(pixel_values, dtype=jnp.float32),
pixel_values,
images,
rngs={},
)

def filtered_with_scores(self, special_cos_dist, cos_dist, images, params: dict=None):
return self.module.apply(
{"params": params} or self.params,
special_cos_dist,
cos_dist,
images,
method=self.module.filtered_with_scores,
)