ginigen/FLUX.1-Kontext-Dev

1import gradio as gr2import numpy as np3import spaces4import torch5import random6from PIL import Image7 8from diffusers import FluxKontextPipeline9from diffusers.utils import load_image10 11MAX_SEED = np.iinfo(np.int32).max12 13pipe = FluxKontextPipeline.from_pretrained("black-forest-labs/FLUX.1-Kontext-dev", torch_dtype=torch.bfloat16).to("cuda")14 15@spaces.GPU16def infer(input_image, prompt, seed=42, randomize_seed=False, guidance_scale=2.5, steps=28, progress=gr.Progress(track_tqdm=True)):17    """18    Perform image editing using the FLUX.1 Kontext pipeline.19    20    This function takes an input image and a text prompt to generate a modified version21    of the image based on the provided instructions. It uses the FLUX.1 Kontext model22    for contextual image editing tasks.23    24    Args:25        input_image (PIL.Image.Image): The input image to be edited. Will be converted26            to RGB format if not already in that format.27        prompt (str): Text description of the desired edit to apply to the image.28            Examples: "Remove glasses", "Add a hat", "Change background to beach".29        seed (int, optional): Random seed for reproducible generation. Defaults to 42.30            Must be between 0 and MAX_SEED (2^31 - 1).31        randomize_seed (bool, optional): If True, generates a random seed instead of32            using the provided seed value. Defaults to False.33        guidance_scale (float, optional): Controls how closely the model follows the34            prompt. Higher values mean stronger adherence to the prompt but may reduce35            image quality. Range: 1.0-10.0. Defaults to 2.5.36        steps (int, optional): Controls how many steps to run the diffusion model for.37            Range: 1-30. Defaults to 28.38        progress (gr.Progress, optional): Gradio progress tracker for monitoring39            generation progress. Defaults to gr.Progress(track_tqdm=True).40    41    Returns:42        tuple: A 3-tuple containing:43            - PIL.Image.Image: The generated/edited image44            - int: The seed value used for generation (useful when randomize_seed=True)45            - gr.update: Gradio update object to make the reuse button visible46    47    Example:48        >>> edited_image, used_seed, button_update = infer(49        ...     input_image=my_image,50        ...     prompt="Add sunglasses",51        ...     seed=123,52        ...     randomize_seed=False,53        ...     guidance_scale=2.554        ... )55    """56    if randomize_seed:57        seed = random.randint(0, MAX_SEED)58    59    if input_image:60        input_image = input_image.convert("RGB")61        image = pipe(62            image=input_image, 63            prompt=prompt,64            guidance_scale=guidance_scale,65            num_inference_steps=steps,66            generator=torch.Generator().manual_seed(seed),67        ).images[0]68    else:69        image = pipe(70            prompt=prompt,71            guidance_scale=guidance_scale,72            num_inference_steps=steps,73            generator=torch.Generator().manual_seed(seed),74        ).images[0]75    return image, seed, gr.update(visible=True)76 77css="""78#col-container {79    margin: 0 auto;80    max-width: 960px;81}82"""83 84with gr.Blocks(css=css) as demo:85    86    with gr.Column(elem_id="col-container"):87        gr.Markdown(f"""# FLUX.1 Kontext [dev]88Image editing and manipulation model guidance-distilled from FLUX.1 Kontext [pro], [[blog]](https://bfl.ai/announcements/flux-1-kontext-dev) [[model]](https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev)89        """)90        with gr.Row():91            with gr.Column():92                input_image = gr.Image(label="Upload the image for editing", type="pil")93                with gr.Row():94                    prompt = gr.Text(95                        label="Prompt",96                        show_label=False,97                        max_lines=1,98                        placeholder="Enter your prompt for editing (e.g., 'Remove glasses', 'Add a hat')",99                        container=False,100                    )101                    run_button = gr.Button("Run", scale=0)102                with gr.Accordion("Advanced Settings", open=False):103                    104                    seed = gr.Slider(105                        label="Seed",106                        minimum=0,107                        maximum=MAX_SEED,108                        step=1,109                        value=0,110                    )111                    112                    randomize_seed = gr.Checkbox(label="Randomize seed", value=True)113                    114                    guidance_scale = gr.Slider(115                        label="Guidance Scale",116                        minimum=1,117                        maximum=10,118                        step=0.1,119                        value=2.5,120                    )       121                    122                    steps = gr.Slider(123                        label="Steps",124                        minimum=1,125                        maximum=30,126                        value=28,127                        step=1128                    )129                    130            with gr.Column():131                result = gr.Image(label="Result", show_label=False, interactive=False)132                reuse_button = gr.Button("Reuse this image", visible=False)133        134        135    gr.on(136        triggers=[run_button.click, prompt.submit],137        fn = infer,138        inputs = [input_image, prompt, seed, randomize_seed, guidance_scale, steps],139        outputs = [result, seed, reuse_button]140    )141    reuse_button.click(142        fn = lambda image: image,143        inputs = [result],144        outputs = [input_image]145    )146 147demo.launch(mcp_server=True)