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.gitattributes

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+generated_image.png filter=lfs diff=lfs merge=lfs -text

README.md

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+# 🖼️ AI Image Generator with Stable Diffusion
+
+Generate stunning images from text prompts using Stable Diffusion and Streamlit.
+
+## 🚀 Live Demo
+👉 [Link to Hugging Face Space](https://huggingface.co/spaces/your-username/image-generator-app)
+
+## 📦 Installation
+
+```bash
+pip install -r requirements.txt
+streamlit run app.py

app.py

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+import streamlit as st
+from PIL import Image
+from generator import generate_image
+
+st.set_page_config(page_title="AI Image Generator", layout="centered")
+st.title("🎨 AI Image Generator with Stable Diffusion")
+
+st.markdown("Generate high-quality images from text prompts using 🤗 **Stable Diffusion**.")
+
+prompt = st.text_input("📝 Enter a prompt", value="a scenic view of the Milky Way over mountains")
+
+if st.button("Generate Image"):
+    with st.spinner("Generating image..."):
+        image = generate_image(prompt)
+        st.image(image, caption="Generated Image", use_column_width=True)
+
+        # Download option
+        img_bytes = image.convert("RGB")
+        img_bytes.save("generated_image.png")
+        with open("generated_image.png", "rb") as file:
+            st.download_button(
+                label="📥 Download Image",
+                data=file,
+                file_name="generated_image.png",
+                mime="image/png"
+            )
+
+st.markdown("---")
+st.markdown("Made with ❤️ using [Hugging Face 🤗](https://huggingface.co/) and [Streamlit](https://streamlit.io/)")

generator.py

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+from diffusers import StableDiffusionPipeline
+import torch
+import streamlit as st
+
+@st.cache_resource
+def load_pipeline():
+    model_id = "runwayml/stable-diffusion-v1-5"
+    pipe = StableDiffusionPipeline.from_pretrained(
+        model_id, torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
+    )
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    pipe = pipe.to(device)
+    return pipe
+
+def generate_image(prompt):
+    pipe = load_pipeline()
+    image = pipe(prompt).images[0]
+    return image

requirements.txt

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+torch
+diffusers
+transformers
+streamlit
+accelerate
+safetensors
+Pillow