Update app.py

app.py

@@ -36,14 +36,14 @@ def prompt_enhance(client, image_caption, text_caption):
 
 pipe = load_models()
 
-def truncate_caption_by_tokens(caption, max_tokens=256):
-    """Truncate the caption to fit within the max token limit"""
-    tokens = tokenizer.encode(caption)
-    if len(tokens) > max_tokens:
-        truncated_tokens = tokens[:max_tokens]
-        caption = tokenizer.decode(truncated_tokens, skip_special_tokens=True)
-        print(f"Caption was truncated from {len(tokens)} tokens to {max_tokens} tokens")
-    return caption
+# def truncate_caption_by_tokens(caption, max_tokens=256):
+#     """Truncate the caption to fit within the max token limit"""
+#     tokens = tokenizer.encode(caption)
+#     if len(tokens) > max_tokens:
+#         truncated_tokens = tokens[:max_tokens]
+#         caption = tokenizer.decode(truncated_tokens, skip_special_tokens=True)
+#         print(f"Caption was truncated from {len(tokens)} tokens to {max_tokens} tokens")
+#     return caption
 
 
 @spaces.GPU(duration=60)
@@ -52,7 +52,7 @@ def generate_image(enhanced_caption, seed, num_inference_steps, guidance_scale):
     pipe.enable_model_cpu_offload()
     """Generate image using LeX-Lumina"""
     # Truncate the caption if it's too long
-    enhanced_caption = truncate_caption_by_tokens(enhanced_caption, max_tokens=256)
+    # enhanced_caption = truncate_caption_by_tokens(enhanced_caption, max_tokens=256)
 
     print(f"enhanced caption:\n{enhanced_caption}")