Update app.py

app.py

@@ -10,13 +10,13 @@ pipe = accelerator.prepare(StableDiffusionXLPipeline.from_pretrained("ptx0/termi
 pipe.scheduler = accelerator.prepare(EulerDiscreteScheduler.from_config(pipe.scheduler.config))
 pipe.unet.to(memory_format=torch.channels_last)
 pipe.to("cpu")
-
+apol=[]
 def plex(prompt,neg_prompt,stips,nut):
     apol=[]
     if nut == 0:
-        nm = random.randint(1, 4836928)
+        nm = random.randint(1, 2147483616)
         while nm % 32 != 0:
-            nm = random.randint(1, 4836928)
+            nm = random.randint(1, 2147483616)
     else:
         nm=nut
     generator = torch.Generator(device="cpu").manual_seed(nm)
@@ -25,6 +25,6 @@ def plex(prompt,neg_prompt,stips,nut):
         apol.append(imge)
     return apol
 
-iface = gr.Interface(fn=plex, inputs=[gr.Textbox(label="prompt"),gr.Textbox(label="negative prompt",value="ugly, blurry, poor quality"), gr.Slider(label="num inference steps", minimum=1, step=1, maximum=5, value=5),gr.Slider(label="manual seed (leave 0 for random)", minimum=0,step=32,maximum=4836928,value=0)], outputs=gr.Gallery(label="out", columns=1),description="Running on cpu, very slow! by JoPmt.")
+iface = gr.Interface(fn=plex, inputs=[gr.Textbox(label="prompt"),gr.Textbox(label="negative prompt",value="ugly, blurry, poor quality"), gr.Slider(label="num inference steps", minimum=1, step=1, maximum=5, value=5),gr.Slider(label="manual seed (leave 0 for random)", minimum=0,step=32,maximum=2147483616,value=0)], outputs=gr.Gallery(label="out", columns=1),description="Running on cpu, very slow! by JoPmt.")
 iface.queue(max_size=1,api_open=False)
 iface.launch(max_threads=1)