Update app.py

app.py

@@ -60,7 +60,7 @@ def load_model(custom_model):
     return gr.update(choices=sfts_available_files, value=sfts_available_files[0], visible=True), gr.update(value=instance_prompt, visible=True)
 
 
-
+@spaces.GPU
 def infer (custom_model, weight_name, prompt, inf_steps, guidance_scale, seed, lora_weight, progress=gr.Progress(track_tqdm=True)):
     
     vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
@@ -105,7 +105,7 @@ def infer (custom_model, weight_name, prompt, inf_steps, guidance_scale, seed, l
 
     #pipe.unfuse_lora()
     
-    return image
+    return image, seed
 
 css="""
 #col-container{
@@ -205,6 +205,8 @@ with gr.Blocks(css=css) as demo:
                 step=0.1,
                 value=7.5
             )
+
+        with gr.Row():
             seed = gr.Slider(
                 label="Seed",
                 info = "-1 denotes a random seed",
@@ -213,6 +215,10 @@ with gr.Blocks(css=css) as demo:
                 step=1,
                 value=-1
             )
+            last_used_seed = gr.Number(
+                label = "Last used seed",
+                info = "the seed used in the last generation",
+            )
         lora_weight = gr.Slider(
             label="LoRa weigth",
             minimum=0.0,
@@ -232,7 +238,7 @@ with gr.Blocks(css=css) as demo:
     submit_btn.click(
         fn = infer,
         inputs = [custom_model, weight_name, prompt_in, inf_steps, guidance_scale, seed, lora_weight],
-        outputs = [image_out]
+        outputs = [image_out, last_used_seed]
     )
 
 demo.queue().launch()