Create app.py

app.py

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+import gradio as gr
+import diffusers
+import streamlit as st
+device = "cpu"
+from diffusers import StableDiffusionPipeline
+pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", revision = "fp16", use_auth_token = st.secrets["USER_TOKEN"])
+pipe = pipe.to("cpu")
+from PIL import Image
+import torch
+def StableDiffusionPipeline (prompt, Guide, iSteps, seed):
+    generator = torch.Generator("cpu").manual_seed(seed)
+    image = pipe(prompt, num_inference_steps = iSteps, guidence_scale = Guide).images[0]
+    return image
+iface = gr.Interface(fn = StableDiffusionPipeline, inputs = [
+    gr.Textbox(label = 'Prompt Input Text'),
+    gr.Slider(2, 15, value = 7, label = 'Guidence Scale'),
+    gr.Slider(10, 100, value = 25, step = 1, label = 'Number of Iterations'),
+    gr.Slider(
+        label = "Seed",
+        minimum = 0,
+        maximum = 2147483647,
+        step = 1,
+        randomize = True)
+    ],
+    outputs = 'image')
+iface.launch()