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import streamlit as st
import torch
from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, EulerDiscreteScheduler
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
# Define a function to generate the image
def generate_image(prompt, num_inference_steps):
base = "stable-diffusion"
repo = "Geek7/testing"
ckpt = "dreamshaper_8.safetensors" # Use the correct ckpt for your step setting!
# Load model.
unet = UNet2DConditionModel.from_config(base, subfolder="unet").to("cuda", torch.float16)
unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device="cuda"))
pipe = StableDiffusionXLPipeline.from_pretrained(base, unet=unet, torch_dtype=torch.float16, variant="fp16").to("cuda")
# Ensure sampler uses "trailing" timesteps.
pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
# Generate image
image = pipe(prompt, num_inference_steps=num_inference_steps, guidance_scale=0).images[0]
return image
# Main function for Streamlit app
def main():
st.title("AI Image Generator")
# Input fields
prompt = st.text_input("Enter prompt")
num_inference_steps = st.slider("Number of Inference Steps", min_value=1, max_value=10, value=2)
if st.button("Generate Image"):
# Check if prompt is provided
if prompt:
# Generate image
generated_image = generate_image(prompt, num_inference_steps)
# Save image
generated_image.save("output.png")
# Display image
st.image(generated_image, caption='Generated Image', use_column_width=True)
else:
st.error("Please enter a prompt.")
if __name__ == "__main__":
main() |