fix transformers issue for text_model.embeddings.position_ids

library/model_util.py

@@ -9,6 +9,7 @@
 init_ipex()
 
 import diffusers
+import importlib.metadata
 from transformers import CLIPTextModel, CLIPTokenizer, CLIPTextConfig, logging
 from diffusers import AutoencoderKL, DDIMScheduler, StableDiffusionPipeline  # , UNet2DConditionModel
 from safetensors.torch import load_file, save_file
@@ -572,6 +573,17 @@ def convert_ldm_clip_checkpoint_v1(checkpoint):
         if key.startswith("cond_stage_model.transformer"):
             text_model_dict[key[len("cond_stage_model.transformer.") :]] = checkpoint[key]
 
+    try:
+        vers = importlib.metadata.version("transformers").split(".")
+    except Exception:
+        vers = None
+
+    if vers is not None and tuple(vers) <= ('4', '30', '2'):
+        # support checkpoint without position_ids (invalid checkpoint)
+        if "text_model.embeddings.position_ids" not in text_model_dict:
+            text_model_dict["text_model.embeddings.position_ids"] = torch.arange(77).unsqueeze(0)  # 77 is the max length of the text
+        return text_model_dict
+
     # remove position_ids for newer transformer, which causes error :(
     if "text_model.embeddings.position_ids" in text_model_dict:
         text_model_dict.pop("text_model.embeddings.position_ids")