Update in line with the other spaces

app.py

@@ -2,10 +2,10 @@ from diffusers import LatentDiffusionUncondPipeline
 import torch
 import PIL.Image
 import gradio as gr
+import random
 import numpy as np
 
-pipeline = LatentDiffusionUncondPipeline.from_pretrained("CompVis/latent-diffusion-celeba-256")
-
+pipeline = LatentDiffusionUncondPipeline.from_pretrained("CompVis/ldm-celebahq-256")
 
 def predict(steps=1, seed=42):
     generator = torch.manual_seed(seed)
@@ -14,12 +14,16 @@ def predict(steps=1, seed=42):
     image_processed = (image_processed + 1.0) * 127.5
     image_processed = image_processed.clamp(0, 255).numpy().astype(np.uint8)
     return PIL.Image.fromarray(image_processed[0])
-    
+
+random_seed = random.randint(0, 2147483647)
 gr.Interface(
     predict,
     inputs=[
-        gr.inputs.Slider(1, 10, label='Inference Steps', default=1, step=1),
-        gr.inputs.Slider(0, 1000, label='Seed', default=42),
+        gr.inputs.Slider(1, 100, label='Inference Steps', default=5, step=1),
+        gr.inputs.Slider(0, 2147483647, label='Seed', default=random_seed, step=1),
     ],
-    outputs="image",
+    outputs=gr.Image(shape=[256,256], type="pil", elem_id="output_image"),
+    css="#output_image{width: 256px}",
+    title="ldm-celebahq-256 - 🧨 diffusers library",
+    description="This Spaces contains an unconditional Latent Diffusion process for the <a href=\"https://huggingface.co/CompVis/ldm-celebahq-256\">ldm-celebahq-256</a> face generator model by <a href=\"https://huggingface.co/CompVis\">CompVis</a> using the <a href=\"https://github.com/huggingface/diffusers\">diffusers library</a>. The goal of this demo is to showcase the diffusers library capabilities. If you want the state-of-the-art experience with Latent Diffusion text-to-image check out the <a href=\"https://huggingface.co/spaces/multimodalart/latentdiffusion\">main Spaces</a>.",
 ).launch()