Update app.py

app.py

@@ -9,12 +9,34 @@ from diffusers import DiffusionPipeline, AutoencoderTiny, AutoencoderKL, AutoPip
 from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images
 from diffusers.utils import load_image
 from huggingface_hub import hf_hub_download, HfFileSystem, ModelCard, snapshot_download
+from transformers import AutoModelForCausalLM, CLIPTokenizer, CLIPProcessor, CLIPModel, LongformerTokenizer, LongformerModel
 import copy
 import random
 import time
 import requests
 import pandas as pd
 
+# Disable tokenizer parallelism
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+# Initialize the CLIP tokenizer and model
+clip_tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch16")
+clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch16")
+clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch16")
+
+# Initialize the Longformer tokenizer and model
+longformer_tokenizer = LongformerTokenizer.from_pretrained("allenai/longformer-base-4096")
+longformer_model = LongformerModel.from_pretrained("allenai/longformer-base-4096")
+
+def process_input(input_text):
+    # Tokenize and truncate input
+    inputs = clip_processor(text=input_text, return_tensors="pt", padding=True, truncation=True, max_length=77)
+    return inputs
+
+# Example usage
+input_text = "Your long prompt goes here..."
+inputs = process_input(input_text)
+
 #Load prompts for randomization
 df = pd.read_csv('prompts.csv', header=None)
 prompt_values = df.values.flatten()