format

stable_diffusion/stable_diffusion/__init__.py

@@ -73,8 +73,7 @@ class StableDiffusion:
 
         # Create the latent variables
         x_T = self.sampler.sample_prior(
-            (n_images, *latent_size, self.autoencoder.latent_channels),
-            dtype=self.dtype
+            (n_images, *latent_size, self.autoencoder.latent_channels), dtype=self.dtype
         )
 
         # Perform the denoising loop

stable_diffusion/stable_diffusion/sampler.py

@@ -51,7 +51,9 @@ class SimpleEulerSampler:
 
     def sample_prior(self, shape, dtype=mx.float32, key=None):
         noise = mx.random.normal(shape, key=key)
-        return (noise * self._sigmas[-1] * (self._sigmas[-1].square() + 1).rsqrt()).astype(dtype)
+        return (
+            noise * self._sigmas[-1] * (self._sigmas[-1].square() + 1).rsqrt()
+        ).astype(dtype)
 
     def sigmas(self, t):
         return _interp(self._sigmas, t)

stable_diffusion/stable_diffusion/tokenizer.py

@@ -5,6 +5,7 @@ import regex
 
 class Tokenizer:
     """A simple port of CLIPTokenizer from https://github.com/huggingface/transformers/ ."""
+
     def __init__(self, bpe_ranks, vocab):
         self.bpe_ranks = bpe_ranks
         self.vocab = vocab
@@ -46,7 +47,9 @@ class Tokenizer:
         #
         # Ported from https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/tokenization_clip.py
         while unique_bigrams:
-            bigram = min(unique_bigrams, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
+            bigram = min(
+                unique_bigrams, key=lambda pair: self.bpe_ranks.get(pair, float("inf"))
+            )
             if bigram not in self.bpe_ranks:
                 break