Create app.py

app.py

@@ -0,0 +1,37 @@
+from diffusers import StableDiffusionPipeline
+from PIL import Image
+import streamlit as st
+import torch
+from transformers import CLIPTextModel, CLIPFeatureExtractor
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Load the model and tokenizer
+@st.cache(allow_output_mutation=True)
+def load_model():
+    model_id = "CompVis/stable-diffusion-2-1"
+    pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
+    pipe.to("cuda" if torch.cuda.is_available() else "cpu")
+    return pipe
+
+# Main function
+def main():
+    st.title("Stable Diffusion 2.1 Image Generator")
+    st.header("Generate images from your prompts!")
+    
+    prompt = st.text_input("Enter your prompt:", "")
+    steps = st.slider("Number of steps:", min_value=20, max_value=200, value=50)
+    guidance = st.slider("Guidance scale:", min_value=1.0, max_value=20.0, value=7.5)
+    
+    if st.button("Generate"):
+        if not prompt:
+            st.error("Please enter a prompt")
+            return
+        
+        with st.spinner("Generating image..."):
+            pipe = load_model()
+            image = pipe(prompt, guidance_scale=guidance, num_inference_steps=steps).images[0]
+            st.image(image, caption="Generated Image")
+
+if __name__ == "__main__":
+    main()