Update app.py

app.py

@@ -5,27 +5,28 @@ import PIL.Image
 from diffusers.utils import load_image
 import gradio as gr
 from PIL import Image
-import cv2
 import os, random, gc
-import numpy as np
 from accelerate import Accelerator
 accelerator = Accelerator(cpu=True)
 pipe = accelerator.prepare(AmusedPipeline.from_pretrained("amused/amused-512", variant=None, torch_dtype=torch.float32, use_safetensors=True))
 pipe.vqvae.to(torch.float32)
 pipe.to("cpu")
 apol=[]
-def plex(prompt, guod, fifth, twice):
+def plex(prompt, guod, fifth, twice, nut):
     gc.collect()
     apol=[]
-    nm = random.randint(1, 4836928)
-    while nm % 32 != 0:
-        nm = random.randint(1, 4836928)
+    if nut == 0:
+        nm = random.randint(1, 2147483616)
+        while nm % 32 != 0:
+            nm = random.randint(1, 2147483616)
+    else:
+        nm=nut
     generator = torch.Generator(device="cpu").manual_seed(nm)
     image = pipe(prompt=prompt,guidance_scale=guod,num_inference_steps=twice,num_images_per_prompt=fifth,generator=generator)
     for a, imze in enumerate(image["images"]):
         apol.append(imze)
     return apol
 
-iface = gr.Interface(fn=plex, inputs=[gr.Textbox(label="prompt",),gr.Slider(label="guidance scale",minimum=1,step=1,maximum=10,value=4),gr.Slider(label="num images", minimum=1, step=1, maximum=4, value=1), gr.Slider(label="num inference steps", minimum=1, step=1, maximum=20, value=12)], outputs=gr.Gallery(label="out", columns=2),description="Running on cpu, very slow! by JoPmt.")
+iface = gr.Interface(fn=plex, inputs=[gr.Textbox(label="prompt",),gr.Slider(label="guidance scale",minimum=1,step=1,maximum=10,value=4),gr.Slider(label="num images", minimum=1, step=1, maximum=4, value=1), gr.Slider(label="num inference steps", minimum=1, step=1, maximum=20, value=12), gr.Slider(label="manual seed (leave 0 for random)",minimum=0,step=32,maximum=2147483616,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)