app.py

import gradio as gr
import numpy as np

import spaces
import torch
import random
from PIL import Image

from kontext_pipeline import FluxKontextPipeline
from diffusers import FluxTransformer2DModel
from diffusers.utils import load_image

from huggingface_hub import hf_hub_download


kontext_path = hf_hub_download(repo_id="diffusers/kontext", filename="kontext.safetensors")

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

transformer = FluxTransformer2DModel.from_single_file(kontext_path, torch_dtype=torch.bfloat16)
pipe = FluxKontextPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", transformer=transformer, torch_dtype=torch.bfloat16).to("cuda")

@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")
    # original_width, original_height = input_image.size
    
    # if original_width >= original_height:
    #     new_width = 1024
    #     new_height = int(original_height * (new_width / original_width))
    #     new_height = round(new_height / 64) * 64
    # else:
    #     new_height = 1024
    #     new_width = int(original_width * (new_height / original_height))
    #     new_width = round(new_width / 64) * 64
    
    #input_image_resized = input_image.resize((new_width, new_height), Image.LANCZOS)
    image = pipe(
        image=input_image, 
        prompt=prompt,
        guidance_scale=guidance_scale,
        # width=new_width,
        # height=new_height,
        generator=torch.Generator().manual_seed(seed),
    ).images[0]
    return image, seed, gr.update(visible=True)

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]
        """)

        with gr.Row():
            with gr.Column():
                input_image = gr.Image(label="Upload the image for editing", type="pil")
                with gr.Row():
                    prompt = gr.Text(
                        label="Prompt",
                        show_label=False,
                        max_lines=1,
                        placeholder="Enter your prompt for editing (e.g., 'Remove glasses', 'Add a hat')",
                        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)
                reuse_button = gr.Button("Reuse this image", visible=False)
        
        

    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn = infer,
        inputs = [input_image, prompt, seed, randomize_seed, guidance_scale],
        outputs = [result, seed, reuse_button]
    )
    reuse_button.click(
        fn = lambda image: image,
        inputs = [result],
        outputs = [input_image]
    )

demo.launch()