rotary embedding refactor 2: update comments, fix dtype for use_real=…

…False (huggingface#9312)

fix notes and dtype

src/diffusers/models/embeddings.py

@@ -514,7 +514,7 @@ def get_1d_rotary_pos_embed(
     linear_factor=1.0,
     ntk_factor=1.0,
     repeat_interleave_real=True,
-    freqs_dtype=torch.float32,  # torch.float32 (hunyuan, stable audio), torch.float64 (flux)
+    freqs_dtype=torch.float32,  #  torch.float32, torch.float64 (flux)
 ):
     """
     Precompute the frequency tensor for complex exponentials (cis) with given dimensions.
@@ -551,15 +551,18 @@ def get_1d_rotary_pos_embed(
     t = torch.from_numpy(pos).to(freqs.device)  # type: ignore  # [S]
     freqs = torch.outer(t, freqs)  # type: ignore   # [S, D/2]
     if use_real and repeat_interleave_real:
+        # flux, hunyuan-dit, cogvideox
         freqs_cos = freqs.cos().repeat_interleave(2, dim=1).float()  # [S, D]
         freqs_sin = freqs.sin().repeat_interleave(2, dim=1).float()  # [S, D]
         return freqs_cos, freqs_sin
     elif use_real:
+        # stable audio
         freqs_cos = torch.cat([freqs.cos(), freqs.cos()], dim=-1).float()  # [S, D]
         freqs_sin = torch.cat([freqs.sin(), freqs.sin()], dim=-1).float()  # [S, D]
         return freqs_cos, freqs_sin
     else:
-        freqs_cis = torch.polar(torch.ones_like(freqs), freqs).float()  # complex64     # [S, D/2]
+        # lumina
+        freqs_cis = torch.polar(torch.ones_like(freqs), freqs)  # complex64     # [S, D/2]
         return freqs_cis
 
 
@@ -590,11 +593,11 @@ def apply_rotary_emb(
         cos, sin = cos.to(x.device), sin.to(x.device)
 
         if use_real_unbind_dim == -1:
-            # Use for example in Lumina
+            # Used for flux, cogvideox, hunyuan-dit
             x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1)  # [B, S, H, D//2]
             x_rotated = torch.stack([-x_imag, x_real], dim=-1).flatten(3)
         elif use_real_unbind_dim == -2:
-            # Use for example in Stable Audio
+            # Used for Stable Audio
             x_real, x_imag = x.reshape(*x.shape[:-1], 2, -1).unbind(-2)  # [B, S, H, D//2]
             x_rotated = torch.cat([-x_imag, x_real], dim=-1)
         else:
@@ -604,6 +607,7 @@ def apply_rotary_emb(
 
         return out
     else:
+        # used for lumina
         x_rotated = torch.view_as_complex(x.float().reshape(*x.shape[:-1], -1, 2))
         freqs_cis = freqs_cis.unsqueeze(2)
         x_out = torch.view_as_real(x_rotated * freqs_cis).flatten(3)