app.py

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

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

import warnings
from typing import Tuple

"""
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.
"""

subprocess.run("rm -rf /data-nvme/zerogpu-offload/*", env={}, shell=True)

# --------------------------------------------------
# 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

print(1)
controlnet_single = FluxControlNetModel.from_pretrained(
    CONTROLNET_MODEL, torch_dtype=dtype
)
print(2)
controlnet = FluxMultiControlNetModel([controlnet_single])

print(3)
pipe = FluxControlNetPipeline.from_pretrained(
    BASE_MODEL, controlnet=controlnet, torch_dtype=dtype
).to(device)
print(4)
pipe.set_progress_bar_config(disable=True)
print(5)

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

MAX_SEED = 100

# -----------------------------------------------------------------------------
# Preview helpers – one small, self‑contained function per mode
# -----------------------------------------------------------------------------


def _preview_canny(
    pil_img: Image.Image, canny_threshold_1: int, canny_threshold_2: int
) -> Image.Image:
    """Fast Canny‑edge preview (already implemented)."""

    arr = np.array(pil_img.convert("RGB"))
    blurred = cv2.GaussianBlur(arr, (5, 5), 1.4)
    edges = cv2.Canny(blurred, threshold1=canny_threshold_1, threshold2=canny_threshold_2)
    edges_rgb = cv2.cvtColor(edges, cv2.COLOR_GRAY2RGB)
    return Image.fromarray(edges_rgb)


# ――― tile ―――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――― #


def _preview_tile(pil_img: Image.Image, grid: Tuple[int, int] = (2, 2)) -> Image.Image:
    """Replicates *pil_img* into an *n×m* tiled grid (default 2×2).

    This offers a quick visual hint of what a *tiling* control mode will do
    (repeatable textures, etc.)."""

    cols, rows = grid
    img_rgb = pil_img.convert("RGB")
    w, h = img_rgb.size
    tiled = Image.new("RGB", (w * cols, h * rows))
    for c in range(cols):
        for r in range(rows):
            tiled.paste(img_rgb, (c * w, r * h))
    return tiled


# ――― depth ――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――― #


def _preview_depth(pil_img: Image.Image) -> Image.Image:
    """Very rough *depth* proxy using the Laplacian and a colormap.

    ▸ Convert to gray
    ▸ Run Laplacian to highlight depth‑like gradients
    ▸ Apply a TURBO colormap to mimic depth heat‑map appearance"""

    gray = cv2.cvtColor(np.array(pil_img), cv2.COLOR_RGB2GRAY)
    lap = cv2.Laplacian(gray, cv2.CV_16S, ksize=3)
    depth = cv2.convertScaleAbs(lap)
    depth_color = cv2.applyColorMap(depth, cv2.COLORMAP_TURBO)
    return Image.fromarray(depth_color)


# ――― blur ――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――― #


def _preview_blur(pil_img: Image.Image, ksize: int = 15) -> Image.Image:
    """Gaussian blur preview.
    A single, relatively large kernel is enough for UI illustration."""

    if ksize % 2 == 0:
        ksize += 1  # kernel must be odd
    blurred = cv2.GaussianBlur(np.array(pil_img), (ksize, ksize), sigmaX=0)
    return Image.fromarray(blurred)


# ――― pose ―――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――― #


def _preview_pose(pil_img: Image.Image) -> Image.Image:
    """Attempt a lightweight 2‑D pose overlay using *mediapipe* if available.

    If *mediapipe* is not installed (or CPU inference fails), we gracefully
    fallback to an edge‑map preview so the UI never crashes."""

    try:
        import mediapipe as mp  # type: ignore

        mp_pose = mp.solutions.pose
        mp_drawing = mp.solutions.drawing_utils

        img_bgr = cv2.cvtColor(np.array(pil_img), cv2.COLOR_RGB2BGR)
        with mp_pose.Pose(static_image_mode=True) as pose_estimator:
            results = pose_estimator.process(
                img_bgr[..., ::-1]
            )  # Mediapipe expects RGB

        annotated = img_bgr.copy()
        if results.pose_landmarks:
            mp_drawing.draw_landmarks(
                annotated, results.pose_landmarks, mp_pose.POSE_CONNECTIONS
            )
        annotated_rgb = cv2.cvtColor(annotated, cv2.COLOR_BGR2RGB)
        return Image.fromarray(annotated_rgb)

    except Exception as exc:  # pragma: no cover – any import / runtime error
        warnings.warn(
            f"Pose preview failed ({exc!s}); falling back to Canny.", RuntimeWarning
        )
        # Return an edge map as a sensible fallback rather than exploding the UI
        return _preview_canny(pil_img, 100, 200)


# ――― gray ――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――― #


def _preview_gray(pil_img: Image.Image) -> Image.Image:
    """Simple grayscale conversion, but keep a 3‑channel RGB image so the UI
    widget pipeline stays consistent."""

    gray = cv2.cvtColor(np.array(pil_img.convert("RGB")), cv2.COLOR_RGB2GRAY)
    gray_rgb = cv2.cvtColor(gray, cv2.COLOR_GRAY2RGB)
    return Image.fromarray(gray_rgb)


# ――― low quality ――――――――――――――――――――――――――――――――――――――――――――――――――――――――― #


def _preview_low_quality(pil_img: Image.Image, factor: int = 8) -> Image.Image:
    """Mimic a low‑quality thumbnail: aggressively downsample then upscale.

    The default *factor* (8×) is chosen to make artefacts obvious."""

    img_rgb = pil_img.convert("RGB")
    w, h = img_rgb.size
    small = img_rgb.resize((max(1, w // factor), max(1, h // factor)), Image.BILINEAR)
    low_q = small.resize(
        (w, h), Image.NEAREST
    )  # upsample w/ Nearest to exaggerate blocks
    return low_q


# -----------------------------------------------------------------------------
# Master dispatch
# -----------------------------------------------------------------------------


def _make_preview(
    control_image: Image.Image,
    mode: str,
    canny_threshold_1: int = 100,
    canny_threshold_2: int = 200,
) -> Image.Image:
    """Return a *quick‑n‑dirty* preview image for the requested *mode*.

    Parameters
    ----------
    control_image : PIL.Image
        The input image selected by the user.
    mode : str
        One of the keys of :data:`MODE_MAPPING`.
    canny_threshold_1 / 2 : int, optional
        Only used if *mode* is "canny" (passed straight to OpenCV Canny).
    """

    mode = mode.lower()
    if mode not in MODE_MAPPING:
        warnings.warn(f"Unknown preview mode '{mode}'. Returning untouched image.")
        return control_image

    if mode == "canny":
        return _preview_canny(control_image, canny_threshold_1, canny_threshold_2)
    if mode == "tile":
        return _preview_tile(control_image)
    if mode == "depth":
        return _preview_depth(control_image)
    if mode == "blur":
        return _preview_blur(control_image)
    if mode == "pose":
        return _preview_pose(control_image)
    if mode == "gray":
        return _preview_gray(control_image)
    if mode == "low quality":
        return _preview_low_quality(control_image)

    # Fallback – should never happen due to early mode check
    return control_image


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


@spaces.GPU(duration=120)
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,
    canny_threshold_1: int,
    canny_threshold_2: 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

    preprocessed = _make_preview(
        control_image, mode, canny_threshold_1, canny_threshold_2
    )

    result = pipe(
        prompt=prompt,
        control_image=[preprocessed],
        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]

    return result, seed, preprocessed


# --------------------------------------------------
# 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 by Frank")
    gr.Markdown(
        "A unified ControlNet for **FLUX.1‑dev** from the InstantX team and Shakker Labs.  "
        + "Recommended strengths: *canny 0.76*. Long prompts usually help."
    )

    # ------------ Image panel row ------------
    with gr.Row():
        control_image = gr.Image(
            label="Upload animage",
            type="pil",
            height=512 + 256,
        )
        result_image = gr.Image(label="Result", height=512 + 256)
        preview_image = gr.Image(label="Pre‑processed Cond", height=512 + 256)

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

    # ------------ ControlNet settings ------------
    with gr.Row():
        with gr.Column():
            gr.Markdown("### ControlNet")
            mode_radio = gr.Radio(
                choices=list(MODE_MAPPING.keys()), value="canny", label="Mode"
            )
            strength_slider = gr.Slider(
                0.0, 1.0, value=0.76, step=0.01, label="control strength"
            )
            gr.Markdown("### Preprocess")
            canny_threshold_1 = gr.Slider(
                0, 500, step=1, value=100, label="Canny threshold 1"
            )
            canny_threshold_2 = gr.Slider(
                0, 500, step=1, value=200, label="Canny threshold 2"
            )

        with gr.Column():
            seed_slider = gr.Slider(0, MAX_SEED, step=1, value=42, label="Seed")
            randomize_chk = gr.Checkbox(label="Randomize seed", value=False)
            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=50, 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,
            canny_threshold_1,
            canny_threshold_2,
        ],
        outputs=[result_image, seed_slider, preview_image],
    )

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