app.py

import gradio as gr
import torch
from diffusers import AutoPipelineForImage2Image
from diffusers.utils import make_image_grid, load_image

# gr.load("models/NSTiwari/SDXL_LoRA_model").launch()

pipeline = AutoPipelineForImage2Image.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
)
pipeline.load_lora_weights('pytorch_lora_weights_00.safetensors')
# _ = pipeline.to("cuda")

pipeline.enable_model_cpu_offload()

url = "https://img.onmanorama.com/content/dam/mm/en/lifestyle/decor/images/2020/12/1/25-lakh-living-hall.jpg.transform/576x300/image.jpg"
# init_image = load_image(url)
# image = init_image.resize((1024, 576))

prompt = "A cozy Indian living room glows with morning sunshine on Republic Day, its walls decked in saffron, white, and green tapestries and art, while colorful cushions and festive garlands add a vibrant, celebratory air."

# pass prompt and image to pipeline
image_out = pipeline(prompt, image=image, strength=0.5).images[0]
# make_image_grid([image, image_out], rows=1, cols=2)


# Define the image generation function
def generate_image(prompt, image_url):
    init_image = load_image(image_url)
    image = init_image.resize((1024, 576))
    image_out = pipeline(prompt, image=image, strength=0.5).images[0]
    return image_out

    
# Set up Gradio interface
iface = gr.Interface(
    fn=generate_image, 
    inputs=[gr.Textbox(label="Prompt"), gr.Textbox(label="Image URL")], 
    outputs="image"
)

# Launch the Gradio app
iface.launch()