Text2Image.py

import gradio as gr
import torch
from diffusers import StableDiffusionPipeline as sdp
from huggingface_hub import HfApi
import os

def cuda_availability():
    x=torch.cuda.is_available()
    device='cpu'
    if x:
        device='cuda'
    return device

print("Creating a Pipeline")
pipeline=sdp.from_pretrained("stabilityai/stable-diffusion-2-1",torch_dtype=torch.float32)
print("Pipeline has been created")
print("Now checking if we can use CUDA or not")
device=cuda_availability()
pipeline.to(device)

os.makedirs("outputs", exist_ok=True)  # Create 'outputs/' if it doesn't exist

def create_image(prompt):

    print("Create an Image")
    image=pipeline(prompt).images[0]
    
    fn=os.path.join("image.jpg")
    print("The Image was created succefully")
    image.save("outputs/my_image.png")
    
    api=HfApi()
    api.upload_file(
        path_or_fileobj="outputs/my_image.png",
        path_in_repo="outputs/my_image.png",
        repo_id="username/space_name",
        repo_type="space"
    )
    print("Image saved successfully")
    
    return image

demo=gr.Interface(fn=create_image,inputs="text",outputs="image")
demo.launch(share=True)