Fix crashing if image is too tall or wide.

library/sd3_models.py

@@ -868,7 +868,7 @@ def enable_scaled_pos_embed(self, use_scaled_pos_embed: bool, latent_sizes: Opti
         self.use_scaled_pos_embed = use_scaled_pos_embed
 
         if self.use_scaled_pos_embed:
-            # # remove pos_embed to free up memory up to 0.4 GB
+            # remove pos_embed to free up memory up to 0.4 GB
             self.pos_embed = None
 
             # sort latent sizes in ascending order
@@ -977,7 +977,7 @@ def cropped_pos_embed(self, h, w, device=None, random_crop: bool = False):
         # patched size
         h = (h + 1) // p
         w = (w + 1) // p
-        if self.pos_embed is None:
+        if self.pos_embed is None:  # should not happen
             return get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, device=device)
         assert self.pos_embed_max_size is not None
         assert h <= self.pos_embed_max_size, (h, self.pos_embed_max_size)
@@ -1016,13 +1016,20 @@ def cropped_scaled_pos_embed(self, h, w, device=None, dtype=None, random_crop: b
         if patched_size is None:
             raise ValueError(f"Area {area} is too large for the given latent sizes {self.resolution_area_to_latent_size}.")
 
-        pos_embed_size = int(patched_size * MMDiT.POS_EMBED_MAX_RATIO)
+        pos_embed = self.resolution_pos_embeds[patched_size]
+        pos_embed_size = round(math.sqrt(pos_embed.shape[1]))
         if h > pos_embed_size or w > pos_embed_size:
-            # fallback to normal pos_embed
+            # # fallback to normal pos_embed
+            # return self.cropped_pos_embed(h * p, w * p, device=device, random_crop=random_crop)
+            # extend pos_embed size
             logger.warning(
                 f"Using normal pos_embed for size {h}x{w} as it exceeds the scaled pos_embed size {pos_embed_size}. Image is too tall or wide."
             )
-            return self.cropped_pos_embed(h, w, device=device, random_crop=random_crop)
+            pos_embed_size = max(h, w)
+            pos_embed = get_scaled_2d_sincos_pos_embed(self.hidden_size, pos_embed_size, sample_size=patched_size)
+            pos_embed = torch.from_numpy(pos_embed).float().unsqueeze(0)
+            self.resolution_pos_embeds[patched_size] = pos_embed
+            logger.info(f"Updated pos_embed for size {pos_embed_size}x{pos_embed_size}")
 
         if not random_crop:
             top = (pos_embed_size - h) // 2
@@ -1031,7 +1038,6 @@ def cropped_scaled_pos_embed(self, h, w, device=None, dtype=None, random_crop: b
             top = torch.randint(0, pos_embed_size - h + 1, (1,)).item()
             left = torch.randint(0, pos_embed_size - w + 1, (1,)).item()
 
-        pos_embed = self.resolution_pos_embeds[patched_size]
         if pos_embed.device != device:
             pos_embed = pos_embed.to(device)
             # which is better to update device, or transfer every time to device? -> 64x64 emb is 96*96*1536*4=56MB. It's okay to update device.