Update app.py

app.py

@@ -5,7 +5,9 @@ import time
 from optimum.intel import OVStableDiffusionXLPipeline
 import torch
 from diffusers import EulerDiscreteScheduler
-from diffusers import LCMScheduler
+from io import BytesIO
+from PIL import Image
+import base64
 
 model_id = "None1145/noobai-XL-Vpred-0.65s-openvino"
 
@@ -21,13 +23,9 @@ def reload_model(new_model_id):
     try:
         print(f"{model_id}...")
         pipe = OVStableDiffusionXLPipeline.from_pretrained(model_id, compile=False)
-        # pipe.to("gpu")
         if model_id == "None1145/noobai-XL-Vpred-0.65s-openvino":
             scheduler_args = {"prediction_type": "v_prediction", "rescale_betas_zero_snr": True}
             pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, **scheduler_args)
-            # pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config, **scheduler_args)
-            # pipe.load_lora_weights("latent-consistency/lcm-lora-sdxl")
-            # pipe.fuse_lora()
         pipe.reshape(batch_size=1, height=prev_height, width=prev_width, num_images_per_prompt=1)
         pipe.compile()
         print(f"{model_id}!!!")
@@ -35,7 +33,7 @@ def reload_model(new_model_id):
     except Exception as e:
         return f"Failed to load model: {str(e)}"
 reload_model(model_id)
-        
+
 def infer(
     prompt,
     negative_prompt,
@@ -70,7 +68,11 @@ def infer(
         generator=generator,
     ).images[0]
 
-    return image, seed
+    # Save image as Base64
+    buffered = BytesIO()
+    image.save(buffered, format="PNG")
+    base64_image = base64.b64encode(buffered.getvalue()).decode("utf-8")
+    return image, seed, f"data:image/png;base64,{base64_image}"
 
 
 examples = ["murasame \(senren\), senren banka",]
@@ -99,6 +101,8 @@ with gr.Blocks() as img:
 
         result = gr.Image(label="Result", show_label=False)
 
+        base64_view = gr.HTML(label="Base64 Image Preview", interactive=True)
+
         with gr.Accordion("Advanced Settings", open=False):
             negative_prompt = gr.Text(
                 label="Negative prompt",
@@ -144,7 +148,7 @@ with gr.Blocks() as img:
                 )
 
         gr.Examples(examples=examples, inputs=[prompt])
-    
+
     gr.Markdown("### Model Reload")
     with gr.Row():
         new_model_id = gr.Text(label="New Model ID", placeholder="Enter model ID", value=model_id)
@@ -157,8 +161,7 @@ with gr.Blocks() as img:
         outputs=reload_status,
     )
 
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
+    run_button.click(
         fn=infer,
         inputs=[
             prompt,
@@ -170,8 +173,18 @@ with gr.Blocks() as img:
             guidance_scale,
             num_inference_steps,
         ],
-        outputs=[result, seed],
+        outputs=[result, seed, base64_view],
     )
 
+    # JavaScript logic to dynamically update HTML with Base64
+    js_script = """
+    <script>
+        function updateBase64(html_id, base64_src) {
+            document.getElementById(html_id).innerHTML = `<img src="${base64_src}" alt="Generated Image"/>`;
+        }
+    </script>
+    """
+    gr.HTML(js_script)
+
 if __name__ == "__main__":
-    img.queue(max_size=10, timeout=None).launch()
+    img.launch()