Handle unconditional conditioning for ideogram 4 more in line with example code.

extensions_built_in/diffusion_models/ideogram4/src/pipeline.py

@@ -297,7 +297,11 @@ class Ideogram4Pipeline:
         gw = width // (ae_scale * patch)
         latent_channels = transformer.config.in_channels
 
-        do_cfg = unconditional_embeds is not None and guidance_scale != 1.0
+        # Ideogram uses asymmetric CFG: the unconditional branch is image-only
+        # (no text tokens) with zeroed text features -- it does NOT run a negative
+        # prompt through the text encoder. So we ignore unconditional_embeds and
+        # build an empty (0-length) text sequence for the uncond pass below.
+        do_cfg = guidance_scale > 1.0
 
         if latents is None:
             shape = (1, latent_channels, gh, gw)
@@ -311,9 +315,15 @@ class Ideogram4Pipeline:
             conditional_embeds.text_embeds, device, dtype
         )
         if do_cfg:
-            uncond_feats, uncond_mask = pad_text_features(
-                unconditional_embeds.text_embeds, device, dtype
+            # Image-only unconditional: zero-length text sequence. predict_velocity
+            # then produces an image-token-only forward pass with zeroed llm
+            # features, matching the reference's asymmetric CFG.
+            batch_size = latents.shape[0]
+            text_dim = cond_feats.shape[-1]
+            uncond_feats = torch.zeros(
+                batch_size, 0, text_dim, device=device, dtype=dtype
             )
+            uncond_mask = torch.zeros(batch_size, 0, dtype=torch.long, device=device)
 
         for t in timesteps:
             t01 = (t / 1000.0).to(device).expand(latents.shape[0])