Update app.py

app.py

@@ -11,17 +11,20 @@ pipe.scheduler = accelerator.prepare(EulerDiscreteScheduler.from_config(pipe.sch
 pipe.unet.to(memory_format=torch.channels_last)
 pipe.to("cpu")
 
-def plex(prompt,neg_prompt,stips):
+def plex(prompt,neg_prompt,stips,nut):
     apol=[]
-    nm = random.randint(1, 4836928)
-    while nm % 32 != 0:
+    if nut == 0:
         nm = random.randint(1, 4836928)
+        while nm % 32 != 0:
+            nm = random.randint(1, 4836928)
+    else:
+        nm=nut
     generator = torch.Generator(device="cpu").manual_seed(nm)
-    image = pipe(prompt=[prompt]*2, negative_prompt=[neg_prompt]*2, num_inference_steps=stips, output_type="pil",generator=generator)
+    image = pipe(prompt=prompt, negative_prompt=neg_prompt, num_inference_steps=stips, output_type="pil",generator=generator)
     for i, imge in enumerate(image["images"]):
         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=4)], outputs=gr.Gallery(label="out", columns=2))
+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=4),gr.Slider(label="manual seed", minimum=0,step=32,maximum=4836928,value=0)], outputs=gr.Gallery(label="out", columns=2),description="Running on cpu, very slow! by JoPmt.")
 iface.queue(max_size=1,api_open=False)
 iface.launch(max_threads=1)