app.py

import gradio as gr
import numpy as np

import spaces
import torch
import random
from PIL import Image
from diffusers import FluxKontextPipeline
from diffusers import FluxTransformer2DModel
from diffusers.utils import load_image

from huggingface_hub import hf_hub_download

pipe = FluxKontextPipeline.from_pretrained("black-forest-labs/FLUX.1-Kontext-dev", torch_dtype=torch.bfloat16).to("cuda")
pipe.load_lora_weights("kontext-community/relighting-kontext-dev-lora-v3", weight_name="relighting-kontext-dev-lora-v3.safetensors", adapter_name="lora")
pipe.set_adapters(["lora"], adapter_weights=[1.0])

MAX_SEED = np.iinfo(np.int32).max

@spaces.GPU
def infer(input_image, prompt, seed=42, randomize_seed=False, guidance_scale=2.5, progress=gr.Progress(track_tqdm=True)):
    
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
        
    input_image = input_image.convert("RGB")
    prompt_with_template = f"Change the lighting conditions in this image and add {prompt}. change the background details but maintain the forground. Lighting determines how bright or dark different parts of the image appear, where shadows fall, and how colors look. When you relight an image, you're simulating what the photo would look like if it were taken under different lighting conditions."
    
    image = pipe(
        image=input_image, 
        prompt=prompt_with_template,
        guidance_scale=guidance_scale,
        width=input_image.size[0],
        height=input_image.size[1],
        generator=torch.Generator().manual_seed(seed),
    ).images[0]
    return image, seed

css="""
#col-container {
    margin: 0 auto;
    max-width: 960px;
}
"""

with gr.Blocks(css=css) as demo:
    
    with gr.Column(elem_id="col-container"):
        gr.Markdown(f"""# FLUX.1 Kontext [dev] Relight 💡
        """)
        gr.Markdown(f"""Kontext[dev] used for object relighting ✨
         """)

        with gr.Row():
            with gr.Column():
                input_image = gr.Image(label="Upload the image for relighting", type="pil")
                with gr.Row():
                    prompt = gr.Text(
                        label="Prompt",
                        show_label=False,
                        max_lines=1,
                        placeholder="scifi RGB flowing, studio lighting",
                        container=False,
                    )
                    run_button = gr.Button("Run", scale=0)
                with gr.Accordion("Advanced Settings", open=False):
            
                    seed = gr.Slider(
                        label="Seed",
                        minimum=0,
                        maximum=MAX_SEED,
                        step=1,
                        value=0,
                    )
                    
                    randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
                    
                    guidance_scale = gr.Slider(
                        label="Guidance Scale",
                        minimum=1,
                        maximum=10,
                        step=0.1,
                        value=2.5,
                    )       
                    
            with gr.Column():
                result = gr.Image(label="Result", show_label=False, interactive=False)
        
        gr.Examples(
        examples=[
            ["./assets/5_before.png", "sunset over sea lighting coming from the top right part of the photo", 0, True, 2.5],
            ["./assets/3_before.png", "sci-fi RGB glowing, studio lighting",0, True,2.5],
            ["./assets/2_before.png", "neon light, city",0, True, 2.5],
            ["./assets/before_6.png", "bright sunlight, warm, luminous", 0, True, 2.5]
        ],
        inputs=[input_image, prompt, seed, randomize_seed, guidance_scale],
        outputs=[result, seed],
        fn=infer,
        cache_examples="lazy"
    )
    
    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn = infer,
        inputs = [input_image, prompt, seed, randomize_seed, guidance_scale],
        outputs = [result, seed]
    )
    

demo.launch()