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import streamlit as st
from diffusers import AutoPipelineForText2Image
import torch
from PIL import Image
import io
import os
import requests
# --- CONFIG ---
USE_GROQ = False # Set to True when Groq image API is available
GROQ_API_URL = "https://your-groq-image-api.com/generate" # Placeholder
# Force CPU
os.environ["CUDA_VISIBLE_DEVICES"] = ""
@st.cache_resource
def load_model():
if USE_GROQ:
return None # Skip local model
pipe = AutoPipelineForText2Image.from_pretrained(
"stabilityai/sd-turbo",
torch_dtype=torch.float32
)
pipe.to("cpu")
return pipe
def generate_image_local(prompt, guidance_scale):
pipe = load_model()
result = pipe(prompt, guidance_scale=guidance_scale, num_inference_steps=20)
return result.images[0]
def generate_image_from_groq(prompt):
response = requests.post(GROQ_API_URL, json={"prompt": prompt})
if response.status_code == 200:
image_bytes = io.BytesIO(response.content)
return Image.open(image_bytes)
else:
raise Exception(f"GROQ API failed: {response.text}")
# UI
st.title("🧠 AI Image Generator (Fast with API / Groq-ready)")
prompt = st.text_input("Prompt:", "A glowing alien forest with floating orbs, concept art, 8K")
guidance = st.slider("Guidance scale", 1.0, 10.0, 3.0)
if st.button("Generate"):
with st.spinner("Generating..."):
try:
if USE_GROQ:
image = generate_image_from_groq(prompt)
else:
image = generate_image_local(prompt, guidance)
st.image(image, caption="Generated Image", use_column_width=True)
buf = io.BytesIO()
image.save(buf, format="PNG")
st.download_button("Download Image", buf.getvalue(), "generated.png", "image/png")
except Exception as e:
st.error(f"Error: {e}")
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