modified: src/flux/modules/conditioner.py

src/flux/modules/conditioner.py

@@ -19,6 +19,29 @@ class HFEmbedder(nn.Module):
 
         self.hf_module = self.hf_module.eval().requires_grad_(False)
 
+        # In your HFEmbedder class's __init__ method, or after loading the clip model
+        # Assuming 'self.hf_module' is the CLIPTextModel instance for your CLIP embedder
+        if self.is_clip: # Add this check if HFEmbedder is used for both T5 and CLIP
+            clip_model_config = self.hf_module.config
+            print(f"CLIP Model Version: {self.hf_module.name_or_path}") # or 'version' if that's the attribute
+            print(f"CLIP Config max_position_embeddings: {clip_model_config.max_position_embeddings}")
+
+            # Accessing the CLIPTextEmbeddings module
+            # Path is typically model.embeddings for CLIPTextModel
+            text_embeddings_module = self.hf_module.embeddings
+
+            print(f"CLIP Position Embedding Layer num_embeddings: {text_embeddings_module.position_embedding.num_embeddings}")
+            print(f"CLIP Position IDs buffer 'position_ids' (from CLIPTextEmbeddings) shape: {text_embeddings_module.position_ids.shape}")
+
+            if text_embeddings_module.position_ids.shape[1] != text_embeddings_module.position_embedding.num_embeddings:
+                print("CRITICAL WARNING: Mismatch between position_ids buffer length and actual embedding layer size!")
+            if clip_model_config.max_position_embeddings != text_embeddings_module.position_embedding.num_embeddings:
+                print("CRITICAL WARNING: Mismatch between config.max_position_embeddings and actual embedding layer size!")
+            if self.max_length != text_embeddings_module.position_embedding.num_embeddings:
+                print(f"WARNING: Tokenizer max_length ({self.max_length}) "
+                    f"does not match position embedding size ({text_embeddings_module.position_embedding.num_embeddings})")
+
+
     def forward(self, text: list[str]) -> Tensor:
         batch_encoding = self.tokenizer(
             text,