update for error of early stopping

app.py

@@ -1,12 +1,26 @@
 import gradio as gr
-from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
+from transformers import pipeline, AutoModelForSeq2SeqLM, AutoTokenizer, GenerationConfig
 from diffusers import StableDiffusionPipeline
 import torch
 import io
 from PIL import Image
 
-# Load the summarization model (lacos03/bart-base-finetuned-xsum)
-summarizer = pipeline("summarization", model="lacos03/bart-base-finetuned-xsum")
+# Load the summarization model (lacos03/bart-base-finetuned-xsum) manually
+model_name = "lacos03/bart-base-finetuned-xsum"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+# Create a custom GenerationConfig to fix early_stopping issue
+generation_config = GenerationConfig.from_pretrained(model_name)
+generation_config.early_stopping = True  # Set to True to fix the error
+
+# Initialize the summarization pipeline
+summarizer = pipeline(
+    "summarization",
+    model=model,
+    tokenizer=tokenizer,
+    generation_config=generation_config
+)
 
 # Load the prompt generation model (microsoft/Promptist)
 promptist_model = AutoModelForCausalLM.from_pretrained("microsoft/Promptist")
@@ -20,7 +34,7 @@ device = "cuda" if torch.cuda.is_available() else "cpu"
 image_generator = StableDiffusionPipeline.from_pretrained(
     model_id,
     torch_dtype=torch.float16 if device == "cuda" else torch.float32,
-    use_auth_token=False
+    use_safetensors=True
 ).to(device)
 
 # Load LoRA weights