Fix a typo: bfloa16 -> bfloat16 (#2243)

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_k_diffusion.py

@@ -316,7 +316,7 @@ class StableDiffusionKDiffusionPipeline(DiffusionPipeline):
         latents = 1 / self.vae.config.scaling_factor * latents
         image = self.vae.decode(latents).sample
         image = (image / 2 + 0.5).clamp(0, 1)
-        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
         image = image.cpu().permute(0, 2, 3, 1).float().numpy()
         return image
 

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py

@@ -313,7 +313,7 @@ class StableDiffusionUpscalePipeline(DiffusionPipeline):
         latents = 1 / self.vae.config.scaling_factor * latents
         image = self.vae.decode(latents).sample
         image = (image / 2 + 0.5).clamp(0, 1)
-        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
         image = image.cpu().permute(0, 2, 3, 1).float().numpy()
         return image
 

src/diffusers/pipelines/stable_diffusion/safety_checker.py

@@ -52,7 +52,7 @@ class StableDiffusionSafetyChecker(PreTrainedModel):
         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 bfloa16
+        # 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()
 

src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py

@@ -367,7 +367,7 @@ class StableDiffusionPipelineSafe(DiffusionPipeline):
         latents = 1 / self.vae.config.scaling_factor * latents
         image = self.vae.decode(latents).sample
         image = (image / 2 + 0.5).clamp(0, 1)
-        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
         image = image.cpu().permute(0, 2, 3, 1).float().numpy()
         return image
 

src/diffusers/pipelines/stable_diffusion_safe/safety_checker.py

@@ -51,7 +51,7 @@ class SafeStableDiffusionSafetyChecker(PreTrainedModel):
         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 bfloa16
+        # 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()
 

src/diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py

@@ -333,7 +333,7 @@ class VersatileDiffusionDualGuidedPipeline(DiffusionPipeline):
         latents = 1 / self.vae.config.scaling_factor * latents
         image = self.vae.decode(latents).sample
         image = (image / 2 + 0.5).clamp(0, 1)
-        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
         image = image.cpu().permute(0, 2, 3, 1).float().numpy()
         return image
 

src/diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion_image_variation.py

@@ -193,7 +193,7 @@ class VersatileDiffusionImageVariationPipeline(DiffusionPipeline):
         latents = 1 / self.vae.config.scaling_factor * latents
         image = self.vae.decode(latents).sample
         image = (image / 2 + 0.5).clamp(0, 1)
-        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
         image = image.cpu().permute(0, 2, 3, 1).float().numpy()
         return image
 

src/diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion_text_to_image.py

@@ -250,7 +250,7 @@ class VersatileDiffusionTextToImagePipeline(DiffusionPipeline):
         latents = 1 / self.vae.config.scaling_factor * latents
         image = self.vae.decode(latents).sample
         image = (image / 2 + 0.5).clamp(0, 1)
-        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
         image = image.cpu().permute(0, 2, 3, 1).float().numpy()
         return image