trying with base 64

app.py

@@ -2,6 +2,8 @@ import gradio as gr
 import json
 import logging
 import torch
+import base64
+from io import BytesIO
 from PIL import Image
 from diffusers import (
     DiffusionPipeline, 
@@ -27,6 +29,14 @@ base_model = "stabilityai/stable-diffusion-xl-base-1.0"
 pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.float16)
 pipe.to("cuda")
 
+def image_to_base64(image: Image) -> str:
+    buffered = BytesIO()
+    image.save(buffered, format="PNG")  # You can change the format as needed (e.g., "JPEG")
+    img_base64 = base64.b64encode(buffered.getvalue()).decode('utf-8')
+    return img_base64
+
+
+
 def update_selection(evt: gr.SelectData):
     selected_lora = loras[evt.index]
     new_placeholder = f"Type a prompt for {selected_lora['title']}"
@@ -106,7 +116,7 @@ def run_lora(prompt, negative_prompt, cfg_scale, steps, scheduler, seed, width,
     # Unload LoRA weights
     pipe.unload_lora_weights()
 
-    return image
+    return image_to_base64(image)
 
 with gr.Blocks(theme=gr.themes.Soft()) as app:
 
@@ -114,7 +124,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as app:
 
     with gr.Row():
         with gr.Column(scale=2):
-            result = gr.Image(label="Generated Image", height=768)
+            result = gr.Text(label="Generated Image")
             generate_button = gr.Button("Generate", variant="primary")
 
         # with gr.Column(scale=1):