Create app.py

app.py

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+from diffusers import LDMPipeline
+import torch
+import PIL.Image
+import gradio as gr
+import random
+import numpy as np
+
+pipeline = LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256")
+
+def predict(steps, seed):
+    generator = torch.manual_seed(seed)
+    for i in range(1,steps):
+        yield pipeline(generator=generator, num_inference_steps=i)["sample"][0]
+
+random_seed = random.randint(0, 2147483647)
+gr.Interface(
+    predict,
+    inputs=[
+        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=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>.",
+).queue().launch()