diff --git a/scripts/Clip_IO.py b/scripts/Clip_IO.py
index 36aec7b..e1af555 100644
--- a/scripts/Clip_IO.py
+++ b/scripts/Clip_IO.py
@@ -7,7 +7,8 @@ import open_clip
 
 from modules import scripts, script_callbacks, shared, devices, processing, prompt_parser 
 from modules.shared import opts
-from modules.sd_hijack_clip import PromptChunkFix, PromptChunk, FrozenCLIPEmbedderWithCustomWordsBase
+from modules.sd_hijack_clip import PromptChunkFix, PromptChunk, FrozenCLIPEmbedderWithCustomWords
+from modules.sd_hijack_open_clip import FrozenOpenCLIPEmbedderWithCustomWords
 
 mode_types = ["replace", "concatenate", "command"]
 
@@ -421,7 +422,7 @@ class Clip_IO(scripts.Script):
         Clip_IO.mode_negative = "Disabled"
         pass
 
-    def tokenize_line_manual_chunk(prompt: str, clip: FrozenCLIPEmbedderWithCustomWordsBase) -> list[PromptChunk]:
+    def tokenize_line_manual_chunk(prompt: str, clip: FrozenCLIPEmbedderWithCustomWords | FrozenOpenCLIPEmbedderWithCustomWords) -> list[PromptChunk]:
         if opts.enable_emphasis:
             parsed = prompt_parser.parse_prompt_attention(prompt)
             pass
@@ -508,9 +509,9 @@ class Clip_IO(scripts.Script):
 
         return chunks
 
-    def process_texts_manual_chunk(prompts: list[str], clip: FrozenCLIPEmbedderWithCustomWordsBase) -> list[list[PromptChunk]]:
+    def process_texts_manual_chunk(prompts: list[str], clip: FrozenCLIPEmbedderWithCustomWords | FrozenOpenCLIPEmbedderWithCustomWords) -> list[list[PromptChunk]]:
         cache = {}
-        batch_chunks = []
+        batch_chunks: list[list[PromptChunk]] = []
         for prompt in prompts:
             if prompt in cache:
                 chunks = cache[prompt]
@@ -520,9 +521,30 @@ class Clip_IO(scripts.Script):
 
             batch_chunks.append(chunks)
 
+        # We have to ensure all chunk in batch_chunks have same length.
+        # If not, fill with padding token and raise warning.
+        max_length = -1
+        warned = False
+        for chunks in batch_chunks:
+            for chunk in chunks:
+                if max_length != -1 and max_length != len(chunk.tokens) and not warned:
+                    warnings.warn("All chunk doesn't have same length. For processing, we'll fill with padding token to match same length.")
+                    warned = True
+                    pass
+                max_length = max(max_length, len(chunk.tokens))
+                pass
+            pass
+
+        for chunks in batch_chunks:
+            for chunk in chunks:
+                chunk.tokens += [clip.id_pad] * max(max_length - len(chunk.tokens), 0)
+                chunk.multipliers += [1.0] * max(max_length - len(chunk.multipliers), 0)
+                pass
+            pass
+
         return batch_chunks
 
-    def get_chunks(prompt: str, clip: FrozenCLIPEmbedderWithCustomWordsBase, manual_chunk: bool) -> list[list[PromptChunk]]:
+    def get_chunks(prompt: str, clip: FrozenCLIPEmbedderWithCustomWords | FrozenOpenCLIPEmbedderWithCustomWords, manual_chunk: bool) -> list[list[PromptChunk]]:
         if opts.use_old_emphasis_implementation:
             raise NotImplementedError
             pass
@@ -535,7 +557,7 @@ class Clip_IO(scripts.Script):
             pass
         pass
 
-    def get_flat_embeddings(batch_chunks: list[list[PromptChunk]], clip: FrozenCLIPEmbedderWithCustomWordsBase, manual_chunk: bool) -> tuple[torch.Tensor, list[str]]:
+    def get_flat_embeddings(batch_chunks: list[list[PromptChunk]], clip: FrozenCLIPEmbedderWithCustomWords | FrozenOpenCLIPEmbedderWithCustomWords, manual_chunk: bool) -> tuple[torch.Tensor, list[str]]:
         input_ids = []
         fixes = []
         offset = 0
@@ -584,7 +606,7 @@ class Clip_IO(scripts.Script):
 
     def on_save_embeddings_as_pt(prompt: str, filename: str, overwrite: bool, manual_chunk: bool):
         try:
-            clip: FrozenCLIPEmbedderWithCustomWordsBase = shared.sd_model.cond_stage_model
+            clip: FrozenCLIPEmbedderWithCustomWords | FrozenOpenCLIPEmbedderWithCustomWords = shared.sd_model.cond_stage_model
             batch_chunks = Clip_IO.get_chunks(prompt, clip, manual_chunk)
             embeddings: torch.Tensor = Clip_IO.get_flat_embeddings(batch_chunks, clip, manual_chunk)
 
@@ -611,7 +633,7 @@ class Clip_IO(scripts.Script):
 
     def on_save_embeddings_as_csv(prompt: str, filename: str, transpose: bool, add_token: bool, overwrite: bool, manual_chunk: bool):
         try:
-            clip: FrozenCLIPEmbedderWithCustomWordsBase = shared.sd_model.cond_stage_model
+            clip: FrozenCLIPEmbedderWithCustomWords | FrozenOpenCLIPEmbedderWithCustomWords = shared.sd_model.cond_stage_model
             batch_chunks = Clip_IO.get_chunks(prompt, clip, manual_chunk)
             embeddings, tokens = Clip_IO.get_flat_embeddings(batch_chunks, clip, manual_chunk)
 
@@ -658,7 +680,7 @@ class Clip_IO(scripts.Script):
     def on_save_conditioning_as_pt(prompt: str, filename: str, no_emphasis: bool, no_norm: bool, overwrite: bool, manual_chunk: bool):
         try:
             with devices.autocast():
-                clip: FrozenCLIPEmbedderWithCustomWordsBase = shared.sd_model.cond_stage_model
+                clip: FrozenCLIPEmbedderWithCustomWords | FrozenOpenCLIPEmbedderWithCustomWords = shared.sd_model.cond_stage_model
                 batch_chunks = Clip_IO.get_chunks(prompt, clip, manual_chunk)
                 chunk_count = max([len(x) for x in batch_chunks])
                 zs = []
@@ -707,7 +729,7 @@ class Clip_IO(scripts.Script):
     def on_save_conditioning_as_csv(prompt: str, filename: str, transpose: bool, no_emphasis: bool, no_norm: bool, add_token: bool, overwrite: bool, manual_chunk: bool):
         try:
             with devices.autocast():
-                clip: FrozenCLIPEmbedderWithCustomWordsBase = shared.sd_model.cond_stage_model
+                clip: FrozenCLIPEmbedderWithCustomWords | FrozenOpenCLIPEmbedderWithCustomWords = shared.sd_model.cond_stage_model
                 batch_chunks = Clip_IO.get_chunks(prompt, clip, manual_chunk)
                 _, token_list = Clip_IO.get_flat_embeddings(batch_chunks, clip, manual_chunk)
                 chunk_count = max([len(x) for x in batch_chunks])