app.py

import numpy as np
import gradio as gr
import spaces
import os
import random

import torch
from PIL import Image
import cv2
from huggingface_hub import login
from diffusers import FluxControlNetPipeline, FluxControlNetModel
from diffusers.models import FluxMultiControlNetModel

"""
FLUX‑1 ControlNet demo
----------------------
This script rebuilds the Gradio interface shown in your screenshot with **one** control‑image upload
slot and integrates the FLUX.1‑dev‑ControlNet‑Union‑Pro model.  

Key points
~~~~~~~~~~
* Single *control image* input (left).  
* *Result* and *Pre‑processed Cond* previews side‑by‑side (center & right).  
* *Prompt* textbox plus a dedicated **ControlNet** panel for choosing the mode and strength.  
* Seed handling with optional randomisation.  
* Advanced sliders for *Guidance scale* and *Inference steps*.  
* Works on CUDA (bfloat16) or CPU (float32).  
* Minimal Canny preview implementation when the *canny* mode is selected (extend as you like for the
  other modes).

Before running, set the `HUGGINGFACE_TOKEN` environment variable **or** call
`login("<YOUR_HF_TOKEN>")` explicitly.
"""

# --------------------------------------------------
# Model & pipeline setup
# --------------------------------------------------
HF_TOKEN = os.getenv("HF_TOKEN_NEW")
login(HF_TOKEN)
# If you prefer to hard‑code the token, uncomment:
# login("hf_your_token_here")

BASE_MODEL = "black-forest-labs/FLUX.1-dev"
CONTROLNET_MODEL = "Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro"

device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.bfloat16 if torch.cuda.is_available() else torch.float32

controlnet_single = FluxControlNetModel.from_pretrained(
    CONTROLNET_MODEL, torch_dtype=dtype
)
controlnet = FluxMultiControlNetModel([controlnet_single])

pipe = FluxControlNetPipeline.from_pretrained(
    BASE_MODEL, controlnet=controlnet, torch_dtype=dtype
).to(device)
pipe.set_progress_bar_config(disable=True)

# --------------------------------------------------
# UI ‑> model value mapping
# --------------------------------------------------
MODE_MAPPING = {
    "canny": 0,
    "depth": 1,
    "openpose": 2,
    "gray": 3,
    "blur": 4,
    "tile": 5,
    "low quality": 6,
}

MAX_SEED = 100

# --------------------------------------------------
# Helper: quick‑n‑dirty Canny preview (only for UI display)
# --------------------------------------------------


def _preview_canny(pil_img: Image.Image) -> Image.Image:
    arr = np.array(pil_img.convert("RGB"))
    edges = cv2.Canny(arr, 100, 200)
    edges_rgb = cv2.cvtColor(edges, cv2.COLOR_GRAY2RGB)
    return Image.fromarray(edges_rgb)


def _make_preview(control_image: Image.Image, mode: str) -> Image.Image:
    if mode == "canny":
        return _preview_canny(control_image)
    # For other modes you can plug in your own visualiser later
    return control_image


# --------------------------------------------------
# Inference function
# --------------------------------------------------


@spaces.GPU
def infer(
    control_image: Image.Image,
    prompt: str,
    mode: str,
    control_strength: float,
    seed: int,
    randomize_seed: bool,
    guidance_scale: float,
    num_inference_steps: int,
):
    if control_image is None:
        raise gr.Error("Please upload a control image first.")

    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

    gen = torch.Generator(device).manual_seed(seed)
    w, h = control_image.size

    result = pipe(
        prompt=prompt,
        control_image=[control_image],
        control_mode=[MODE_MAPPING[mode]],
        width=w,
        height=h,
        controlnet_conditioning_scale=[control_strength],
        num_inference_steps=num_inference_steps,
        guidance_scale=guidance_scale,
        generator=gen,
    ).images[0]

    preview = _make_preview(control_image, mode)
    return result, seed, preview


# --------------------------------------------------
# Gradio UI
# --------------------------------------------------
css = """#wrapper {max-width: 960px; margin: 0 auto;}"""
with gr.Blocks(css=css, elem_id="wrapper") as demo:
    gr.Markdown("## FLUX.1‑dev‑ControlNet‑Union‑Pro")
    gr.Markdown(
        "A unified ControlNet for **FLUX.1‑dev** from the InstantX team and Shakker Labs.  "
        + "Recommended strengths: *canny 0.65*, *tile 0.45*, *depth 0.55*, *blur 0.45*, "
        + "*openpose 0.55*, *gray 0.45*, *low quality 0.40*.  Long prompts usually help."
    )

    # ------------ Image panel row ------------
    with gr.Row():
        control_image = gr.Image(
            label="Upload a processed control image",
            type="pil",
            height=512,
        )
        result_image = gr.Image(label="Result", height=512)
        preview_image = gr.Image(label="Pre‑processed Cond", height=512)

    # ------------ Prompt ------------
    prompt_txt = gr.Textbox(label="Prompt", value="best quality", lines=1)

    # ------------ ControlNet settings ------------
    with gr.Row():
        with gr.Column():
            gr.Markdown("### ControlNet")
            mode_radio = gr.Radio(
                choices=list(MODE_MAPPING.keys()), value="gray", label="Mode"
            )
            strength_slider = gr.Slider(
                0.0, 1.0, value=0.5, step=0.01, label="control strength"
            )
        with gr.Column():
            seed_slider = gr.Slider(0, MAX_SEED, step=1, value=42, label="Seed")
            randomize_chk = gr.Checkbox(label="Randomize seed", value=True)
            guidance_slider = gr.Slider(
                0.0, 10.0, step=0.1, value=3.5, label="Guidance scale"
            )
            steps_slider = gr.Slider(1, 50, step=1, value=24, label="Inference steps")

    submit_btn = gr.Button("Submit")

    submit_btn.click(
        fn=infer,
        inputs=[
            control_image,
            prompt_txt,
            mode_radio,
            strength_slider,
            seed_slider,
            randomize_chk,
            guidance_slider,
            steps_slider,
        ],
        outputs=[result_image, seed_slider, preview_image],
    )

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