diff --git a/modules/omnigen2/pipeline_utils.py b/modules/omnigen2/pipeline_utils.py
deleted file mode 100644
index 4efebc260..000000000
--- a/modules/omnigen2/pipeline_utils.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import torch
-
-
-def get_pipeline_embeds(pipeline, prompt, negative_prompt, device):
-    """ Get pipeline embeds for prompts bigger than the maxlength of the pipe
-    :param pipeline:
-    :param prompt:
-    :param negative_prompt:
-    :param device:
-    :return:
-    """
-    max_length = pipeline.tokenizer.model_max_length
-
-    # simple way to determine length of tokens
-    # count_prompt = len(prompt.split(" "))
-    # count_negative_prompt = len(negative_prompt.split(" "))
-
-    # create the tensor based on which prompt is longer
-    # if count_prompt >= count_negative_prompt:
-    input_ids = pipeline.tokenizer(prompt, return_tensors="pt", truncation=False, padding='longest').input_ids.to(device)
-    # input_ids = pipeline.tokenizer(prompt, padding="max_length",
-    #             max_length=pipeline.tokenizer.model_max_length,
-    #             truncation=True,
-    #             return_tensors="pt",).input_ids.to(device)
-    shape_max_length = input_ids.shape[-1]
-
-    if negative_prompt is not None:
-        negative_ids = pipeline.tokenizer(negative_prompt, truncation=True, padding="max_length",
-                                        max_length=shape_max_length, return_tensors="pt").input_ids.to(device)
-
-    # else:
-    #     negative_ids = pipeline.tokenizer(negative_prompt, return_tensors="pt", truncation=False).input_ids.to(device)
-    #     shape_max_length = negative_ids.shape[-1]
-    #     input_ids = pipeline.tokenizer(prompt, return_tensors="pt", truncation=False, padding="max_length",
-    #                                    max_length=shape_max_length).input_ids.to(device)
-
-    concat_embeds = []
-    neg_embeds = []
-    for i in range(0, shape_max_length, max_length):
-        if hasattr(pipeline.text_encoder.config, "use_attention_mask") and pipeline.text_encoder.config.use_attention_mask:
-            attention_mask = input_ids[:, i: i + max_length].attention_mask.to(device)
-        else:
-            attention_mask = None
-        concat_embeds.append(pipeline.text_encoder(input_ids[:, i: i + max_length],
-                                                   attention_mask=attention_mask)[0])
-
-        if negative_prompt is not None:
-            if hasattr(pipeline.text_encoder.config, "use_attention_mask") and pipeline.text_encoder.config.use_attention_mask:
-                attention_mask = negative_ids[:, i: i + max_length].attention_mask.to(device)
-            else:
-                attention_mask = None
-            neg_embeds.append(pipeline.text_encoder(negative_ids[:, i: i + max_length],
-                                                    attention_mask=attention_mask)[0])
-
-    concat_embeds = torch.cat(concat_embeds, dim=1)
-
-    if negative_prompt is not None:
-        neg_embeds = torch.cat(neg_embeds, dim=1)
-    else:
-        neg_embeds = None
-
-    return concat_embeds, neg_embeds