Update app.py

app.py

@@ -14,20 +14,18 @@ pipe = accelerator.prepare(AmusedPipeline.from_pretrained("amused/amused-512", v
 pipe.vqvae.to(torch.float32)
 pipe.to("cpu")
 apol=[]
-def plex(prompt, neg_prompt, fifth, twice):
+def plex(prompt, guod, fifth, twice):
     gc.collect()
     apol=[]
-    prompt = prompt
-    negative_prompt = neg_prompt
     nm = random.randint(1, 4836928)
     while nm % 32 != 0:
         nm = random.randint(1, 4836928)
     generator = torch.Generator(device="cpu").manual_seed(nm)
-    image = pipe([prompt]*fifth,guidance_scale=4,num_inference_steps=twice,generator=generator)
+    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.Textbox(label="negative prompt", value="low quality,blurry, low resolution, image artifacts"),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)], 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)