Compute embedding distances with torch.cdist (#1459)

small but mighty

src/diffusers/models/vae.py

@@ -290,15 +290,10 @@ class VectorQuantizer(nn.Module):
         # reshape z -> (batch, height, width, channel) and flatten
         z = z.permute(0, 2, 3, 1).contiguous()
         z_flattened = z.view(-1, self.vq_embed_dim)
+
         # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
+        min_encoding_indices = torch.argmin(torch.cdist(z_flattened, self.embedding.weight), dim=1)
 
-        d = (
-            torch.sum(z_flattened**2, dim=1, keepdim=True)
-            + torch.sum(self.embedding.weight**2, dim=1)
-            - 2 * torch.einsum("bd,dn->bn", z_flattened, self.embedding.weight.t())
-        )
-
-        min_encoding_indices = torch.argmin(d, dim=1)
         z_q = self.embedding(min_encoding_indices).view(z.shape)
         perplexity = None
         min_encodings = None