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modeling_diffusionvl_qwen2_5.py

@@ -1057,7 +1057,9 @@ class DiffusionVL_Qwen2_5_ForConditionalGeneration(DiffusionVL_Qwen2_5_PreTraine
             cur_pos_ids = position_ids[:, block_start:block_end]
 
             for step in range(steps + 1):
-                is_mask = torch.all(torch.abs(cur_block_embeds - mask_embed) < 1e-5, dim=-1)
+                # Ensure mask_embed is on same device as cur_block_embeds (for device_map="auto")
+                mask_embed_local = mask_embed.to(cur_block_embeds.device)
+                is_mask = torch.all(torch.abs(cur_block_embeds - mask_embed_local) < 1e-5, dim=-1)
                 if not is_mask.any():
                     if use_kv_cache:
                         _ = self.model(
@@ -1113,8 +1115,9 @@ class DiffusionVL_Qwen2_5_ForConditionalGeneration(DiffusionVL_Qwen2_5_PreTraine
                 x0_embeds = self.get_input_embeddings()(x0).to(output_device)
                 cur_block_embeds = torch.where(transfer_mask.unsqueeze(-1), x0_embeds, cur_block_embeds)
 
-            x_embeds[:, block_start:block_end] = cur_block_embeds
-            x_ids[:, block_start:block_end] = cur_block_ids
+            # Move back to original device before assignment (for device_map="auto")
+            x_embeds[:, block_start:block_end] = cur_block_embeds.to(x_embeds.device)
+            x_ids[:, block_start:block_end] = cur_block_ids.to(x_ids.device)
 
             # EOS check: stop generation if EOS token is generated
             if block_end > prompt_len: