full codespell coverage

Signed-off-by: Vladimir Mandic <mandic00@live.com>

scripts/pulid/eva_clip/eva_vit_model.py

@@ -71,7 +71,7 @@ class Mlp(nn.Module):
         x = self.fc1(x)
         x = self.act(x)
         # x = self.drop(x)
-        # commit this for the orignal BERT implement
+        # commit this for the original BERT implement
         x = self.ffn_ln(x)
 
         x = self.fc2(x)

scripts/pulid/eva_clip/rope.py

@@ -4,7 +4,7 @@ from torch import nn
 from einops import rearrange, repeat
 import logging
 
-def broadcat(tensors, dim = -1):
+def broadcast(tensors, dim = -1):
     num_tensors = len(tensors)
     shape_lens = set(map(lambda t: len(t.shape), tensors))
     assert len(shape_lens) == 1, 'tensors must all have the same number of dimensions'
@@ -60,7 +60,7 @@ class VisionRotaryEmbedding(nn.Module):
         freqs_w = torch.einsum('..., f -> ... f', t, freqs)
         freqs_w = repeat(freqs_w, '... n -> ... (n r)', r = 2)
 
-        freqs = broadcat((freqs_h[:, None, :], freqs_w[None, :, :]), dim = -1)
+        freqs = broadcast((freqs_h[:, None, :], freqs_w[None, :, :]), dim = -1)
 
         self.register_buffer("freqs_cos", freqs.cos())
         self.register_buffer("freqs_sin", freqs.sin())
@@ -106,7 +106,7 @@ class VisionRotaryEmbeddingFast(nn.Module):
 
         freqs = torch.einsum('..., f -> ... f', t, freqs)
         freqs = repeat(freqs, '... n -> ... (n r)', r = 2)
-        freqs = broadcat((freqs[:, None, :], freqs[None, :, :]), dim = -1)
+        freqs = broadcast((freqs[:, None, :], freqs[None, :, :]), dim = -1)
 
         freqs_cos = freqs.cos().view(-1, freqs.shape[-1])
         freqs_sin = freqs.sin().view(-1, freqs.shape[-1])

scripts/pulid/eva_clip/tokenizer.py

@@ -27,7 +27,7 @@ def bytes_to_unicode():
     The reversible bpe codes work on unicode strings.
     This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
     When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
-    This is a signficant percentage of your normal, say, 32K bpe vocab.
+    This is a significant percentage of your normal, say, 32K bpe vocab.
     To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
     And avoids mapping to whitespace/control characters the bpe code barfs on.
     """

scripts/pulid/pulid_sdxl.py

@@ -229,7 +229,7 @@ class StableDiffusionXLPuLIDPipeline:
             if len(self.face_helper.cropped_faces) == 0:
                 raise RuntimeError('facexlib align face fail')
             align_face = self.face_helper.cropped_faces[0]
-            # incase insightface didn't detect face
+            # in case insightface didn't detect face
             if id_ante_embedding is None:
                 id_ante_embedding = self.handler_ante.get_feat(align_face)