app.py

import os
import base64
import io
from typing import TypedDict
import requests
import gradio as gr
from PIL import Image

# Read Baseten configuration from environment variables.
BTEN_API_KEY = os.getenv("API_KEY")
URL = os.getenv("URL")

def image_to_base64(image: Image.Image) -> str:
    """Convert a PIL image to a base64-encoded PNG string."""
    with io.BytesIO() as buffer:
        image.save(buffer, format="PNG")
        return base64.b64encode(buffer.getvalue()).decode("utf-8")


def ensure_image(img) -> Image.Image:
    """
    Ensure the input is a PIL Image.
    If it's already a PIL Image, return it.
    If it's a string (file path), open it.
    If it's a dict with a "name" key, open the file at that path.
    """
    if isinstance(img, Image.Image):
        return img
    elif isinstance(img, str):
        return Image.open(img)
    elif isinstance(img, dict) and "name" in img:
        return Image.open(img["name"])
    else:
        raise ValueError("Cannot convert input to a PIL Image.")


def call_baseten_generate(
    image: Image.Image,
    prompt: str,
    steps: int,
    strength: float,
    height: int,
    width: int,
    lora_name: str,
    remove_bg: bool,
) -> Image.Image | None:
    """
    Call the Baseten /predict endpoint with provided parameters and return the generated image.
    """
    image = ensure_image(image)
    b64_image = image_to_base64(image)
    payload = {
        "image": b64_image,
        "prompt": prompt,
        "steps": steps,
        "strength": strength,
        "height": height,
        "width": width,
        "lora_name": lora_name,
        "bgrm": remove_bg,
    }
    if not BTEN_API_KEY:
        headers = {"Authorization": f"Api-Key {os.getenv('API_KEY')}"}
    else:
        headers = {"Authorization": f"Api-Key {BTEN_API_KEY}"}
    try:
        if not URL:
            raise ValueError("The URL environment variable is not set.")

        response = requests.post(URL, headers=headers, json=payload)
        if response.status_code == 200:
            data = response.json()
            gen_b64 = data.get("generated_image", None)
            if gen_b64:
                return Image.open(io.BytesIO(base64.b64decode(gen_b64)))
            else:
                return None
        else:
            print(f"Error: HTTP {response.status_code}\n{response.text}")
            return None
    except Exception as e:
        print(f"Error: {e}")
        return None


# Mode defaults for each tab.

Mode = TypedDict(
    "Mode",
    {
        "model": str,
        "prompt": str,
        "default_strength": float,
        "default_height": int,
        "default_width": int,
        "models": list[str],
        "remove_bg": bool,
    },
)

MODE_DEFAULTS: dict[str, Mode] = {
    "Subject Generation": {
        "model": "subject_99000_512",
        "prompt": "A detailed portrait with soft lighting",
        "default_strength": 1.2,
        "default_height": 512,
        "default_width": 512,
        "models": [
            "zendsd_512_146000",
            "subject_99000_512",
            # "zen_pers_11000",
            "zen_26000_512",
        ],
        "remove_bg": True,
    },
    "Background Generation": {
        "model": "bg_canny_58000_1024",
        "prompt": "A vibrant background with dynamic lighting and textures",
        "default_strength": 1.2,
        "default_height": 1024,
        "default_width": 1024,
        "models": [
            "bgwlight_15000_1024",
            # "rmgb_12000_1024",
            "bg_canny_58000_1024",
            # "gen_back_3000_1024",
            "gen_back_7000_1024",
            # "gen_bckgnd_18000_512",
            # "gen_bckgnd_18000_512",
            # "loose_25000_512",
            # "looser_23000_1024",
            # "looser_bg_gen_21000_1280",
            # "old_looser_46000_1024",
            # "relight_bg_gen_31000_1024",
        ],
        "remove_bg": True,
    },
    "Canny": {
        "model": "canny_21000_1024",
        "prompt": "A futuristic cityscape with neon lights",
        "default_strength": 1.2,
        "default_height": 1024,
        "default_width": 1024,
        "models": ["canny_21000_1024"],
        "remove_bg": True,
    },
    "Depth": {
        "model": "depth_9800_1024",
        "prompt": "A scene with pronounced depth and perspective",
        "default_strength": 1.2,
        "default_height": 1024,
        "default_width": 1024,
        "models": [
            "depth_9800_1024",
        ],
        "remove_bg": True,
    },
    "Deblurring": {
        "model": "deblurr_1024_10000",
        "prompt": "A scene with pronounced depth and perspective",
        "default_strength": 1.2,
        "default_height": 1024,
        "default_width": 1024,
        "models": ["deblurr_1024_10000"],  # "slight_deblurr_18000",
        "remove_bg": False,
    },
}


header = """
<h1>🌍 ZenCtrl / FLUX</h1>
<div align="center" style="line-height: 1;">
    <a href="https://github.com/FotographerAI/ZenCtrl/tree/main" target="_blank" style="margin: 2px;" name="github_repo_link"><img src="https://img.shields.io/badge/GitHub-Repo-181717.svg" alt="GitHub Repo" style="display: inline-block; vertical-align: middle;"></a>
    <a href="https://huggingface.co/spaces/fotographerai/ZenCtrl" target="_blank" name="huggingface_space_link"><img src="https://img.shields.io/badge/🤗_HuggingFace-Space-ffbd45.svg" alt="HuggingFace Space" style="display: inline-block; vertical-align: middle;"></a>
    <a href="https://discord.com/invite/b9RuYQ3F8k" target="_blank" style="margin: 2px;" name="discord_link"><img src="https://img.shields.io/badge/Discord-Join-7289da.svg?logo=discord" alt="Discord" style="display: inline-block; vertical-align: middle;"></a>
    <a href="https://fotographer.ai/" target="_blank" style="margin: 2px;" name="lp_link"><img src="https://img.shields.io/badge/Website-Landing_Page-blue" alt="LP" style="display: inline-block; vertical-align: middle;"></a>
    <a href="https://x.com/FotographerAI" target="_blank" style="margin: 2px;" name="twitter_link"><img src="https://img.shields.io/twitter/follow/FotographerAI?style=social" alt="X" style="display: inline-block; vertical-align: middle;"></a>
</div>
"""

defaults = MODE_DEFAULTS["Subject Generation"]


with gr.Blocks(title="🌍 ZenCtrl") as demo:
    gr.HTML(header)
    gr.Markdown(
        """
        # ZenCtrl Demo
        [WIP] One Agent to Generate multi-view, diverse-scene, and task-specific high-resolution images from a single subject image—without fine-tuning.
        We are first releasing some of the task specific weights and will release the codes soon.
        The goal is to unify all of the visual content generation tasks with a single LLM...
        
        **Modes:**
        - **Subject Generation:** Focuses on generating detailed subject portraits.
        - **Background Generation:** Creates dynamic, vibrant backgrounds:
            You can generate part of the image from sketch while keeping part of it as it is.
        - **Canny:** Emphasizes strong edge detection.
        - **Depth:** Produces images with realistic depth and perspective.
        
        For more details, shoot us a message on discord.
        """
    )
    with gr.Tabs():
        for mode in MODE_DEFAULTS:
            with gr.Tab(mode):
                defaults = MODE_DEFAULTS[mode]
                gr.Markdown(f"### {mode} Mode")
                gr.Markdown(f"**Default Model:** {defaults['model']}")

                with gr.Row():
                    with gr.Column(scale=2, min_width=370):
                        input_image = gr.Image(
                            label="Upload Image",
                            type="pil",
                            scale=3,
                            height=370,
                            min_width=100,
                        )
                        generate_button = gr.Button("Generate")
                        with gr.Blocks(title="Options"):
                            model_dropdown = gr.Dropdown(
                                label="Model",
                                choices=defaults["models"],
                                value=defaults["model"],
                                interactive=True,
                            )
                            remove_bg_checkbox = gr.Checkbox(
                                label="Remove Background", value=defaults["remove_bg"]
                            )

                    with gr.Column(scale=2):
                        output_image = gr.Image(
                            label="Generated Image",
                            type="pil",
                            height=573,
                            scale=4,
                            min_width=100,
                        )

                gr.Markdown("#### Prompt")
                prompt_box = gr.Textbox(
                    label="Prompt", value=defaults["prompt"], lines=2
                )

                # Wrap generation parameters in an Accordion for collapsible view.
                with gr.Accordion("Generation Parameters", open=False):
                    with gr.Row():
                        step_slider = gr.Slider(
                            minimum=2, maximum=28, value=2, step=2, label="Steps"
                        )
                        strength_slider = gr.Slider(
                            minimum=0.5,
                            maximum=2.0,
                            value=defaults["default_strength"],
                            step=0.1,
                            label="Strength",
                        )
                    with gr.Row():
                        height_slider = gr.Slider(
                            minimum=512,
                            maximum=1360,
                            value=defaults["default_height"],
                            step=1,
                            label="Height",
                        )
                        width_slider = gr.Slider(
                            minimum=512,
                            maximum=1360,
                            value=defaults["default_width"],
                            step=1,
                            label="Width",
                        )

                def on_generate_click(
                    model_name,
                    prompt,
                    steps,
                    strength,
                    height,
                    width,
                    remove_bg,
                    image,
                ):
                    return call_baseten_generate(
                        image,
                        prompt,
                        steps,
                        strength,
                        height,
                        width,
                        model_name,
                        remove_bg,
                    )

                generate_button.click(
                    fn=on_generate_click,
                    inputs=[
                        model_dropdown,
                        prompt_box,
                        step_slider,
                        strength_slider,
                        height_slider,
                        width_slider,
                        remove_bg_checkbox,
                        input_image,
                    ],
                    outputs=[output_image],
                    concurrency_limit=None
                )


if __name__ == "__main__":
    demo.launch()