Add Learned PE selection for Auraflow

src/diffusers/models/transformers/auraflow_transformer_2d.py

@@ -68,6 +68,21 @@ def __init__(
         self.height, self.width = height // patch_size, width // patch_size
         self.base_size = height // patch_size
 
+    def pe_selection_index_based_on_dim(self, h, w):
+        # select subset of positional embedding based on H, W, where H, W is size of latent
+        # PE will be viewed as 2d-grid, and H/p x W/p of the PE will be selected
+        # because original input are in flattened format, we have to flatten this 2d grid as well.
+        h_p, w_p = h // self.patch_size, w // self.patch_size
+        original_pe_indexes = torch.arange(self.pos_embed.shape[1])
+        h_max, w_max = int(self.pos_embed_max_size ** 0.5), int(self.pos_embed_max_size ** 0.5)
+        original_pe_indexes = original_pe_indexes.view(h_max, w_max)
+        starth =  h_max // 2 - h_p // 2
+        endh = starth + h_p
+        startw = w_max // 2 - w_p // 2
+        endw = startw + w_p
+        original_pe_indexes = original_pe_indexes[ starth:endh, startw:endw]
+        return original_pe_indexes.flatten()
+
     def forward(self, latent):
         batch_size, num_channels, height, width = latent.size()
         latent = latent.view(
@@ -80,7 +95,8 @@ def forward(self, latent):
         )
         latent = latent.permute(0, 2, 4, 1, 3, 5).flatten(-3).flatten(1, 2)
         latent = self.proj(latent)
-        return latent + self.pos_embed
+        pe_index = self.pe_selection_index_based_on_dim(height, width)
+        return latent + self.pos_embed[:, pe_index]
 
 
 # Taken from the original Aura flow inference code.