app.py

import os
import random
import spaces
import gradio as gr
import numpy as np
import PIL.Image
import torch
import torchvision.transforms.functional as TF

from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from controlnet_aux import PidiNetDetector, HEDdetector
from diffusers.utils import load_image
from huggingface_hub import HfApi, snapshot_download
from pathlib import Path
from PIL import Image, ImageOps
import cv2
from gradio_imageslider import ImageSlider

js_func = """
function refresh() {
    const url = new URL(window.location);
}
"""
def nms(x, t, s):
    x = cv2.GaussianBlur(x.astype(np.float32), (0, 0), s)

    f1 = np.array([[0, 0, 0], [1, 1, 1], [0, 0, 0]], dtype=np.uint8)
    f2 = np.array([[0, 1, 0], [0, 1, 0], [0, 1, 0]], dtype=np.uint8)
    f3 = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=np.uint8)
    f4 = np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]], dtype=np.uint8)

    y = np.zeros_like(x)

    for f in [f1, f2, f3, f4]:
        np.putmask(y, cv2.dilate(x, kernel=f) == x, x)

    z = np.zeros_like(y, dtype=np.uint8)
    z[y > t] = 255
    return z

def HWC3(x):
    assert x.dtype == np.uint8
    if x.ndim == 2:
        x = x[:, :, None]
    assert x.ndim == 3
    H, W, C = x.shape
    assert C == 1 or C == 3 or C == 4
    if C == 3:
        return x
    if C == 1:
        return np.concatenate([x, x, x], axis=2)
    if C == 4:
        color = x[:, :, 0:3].astype(np.float32)
        alpha = x[:, :, 3:4].astype(np.float32) / 255.0
        y = color * alpha + 255.0 * (1.0 - alpha)
        y = y.clip(0, 255).astype(np.uint8)
        return y

DESCRIPTION = ''''''

if not torch.cuda.is_available():
    DESCRIPTION += ""

style_list = [
    {
        "name": "(No style)",
        "prompt": "{prompt}",
        "negative_prompt": "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
    },
    {
        "name": "Cinematic",
        "prompt": "cinematic still {prompt} . emotional, harmonious, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy",
        "negative_prompt": "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured",
    },
    {
        "name": "3D Model",
        "prompt": "professional 3d model {prompt} . octane render, highly detailed, volumetric, dramatic lighting",
        "negative_prompt": "ugly, deformed, noisy, low poly, blurry, painting",
    },
    {
        "name": "Anime",
        "prompt": "anime artwork {prompt} . anime style, key visual, vibrant, studio anime,  highly detailed",
        "negative_prompt": "photo, deformed, black and white, realism, disfigured, low contrast",
    },
    {
        "name": "Digital Art",
        "prompt": "concept art {prompt} . digital artwork, illustrative, painterly, matte painting, highly detailed",
        "negative_prompt": "photo, photorealistic, realism, ugly",
    },
    {
        "name": "Photographic",
        "prompt": "cinematic photo {prompt} . 35mm photograph, film, bokeh, professional, 4k, highly detailed",
        "negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly",
    },
    {
        "name": "Pixel art",
        "prompt": "pixel-art {prompt} . low-res, blocky, pixel art style, 8-bit graphics",
        "negative_prompt": "sloppy, messy, blurry, noisy, highly detailed, ultra textured, photo, realistic",
    },
    {
        "name": "Fantasy art",
        "prompt": "ethereal fantasy concept art of  {prompt} . magnificent, celestial, ethereal, painterly, epic, majestic, magical, fantasy art, cover art, dreamy",
        "negative_prompt": "photographic, realistic, realism, 35mm film, dslr, cropped, frame, text, deformed, glitch, noise, noisy, off-center, deformed, cross-eyed, closed eyes, bad anatomy, ugly, disfigured, sloppy, duplicate, mutated, black and white",
    },
    {
        "name": "Neonpunk",
        "prompt": "neonpunk style {prompt} . cyberpunk, vaporwave, neon, vibes, vibrant, stunningly beautiful, crisp, detailed, sleek, ultramodern, magenta highlights, dark purple shadows, high contrast, cinematic, ultra detailed, intricate, professional",
        "negative_prompt": "painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured",
    },
    {
        "name": "Manga",
        "prompt": "manga style {prompt} . vibrant, high-energy, detailed, iconic, Japanese comic style",
        "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, Western comic style",
    },
]

styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list}
STYLE_NAMES = list(styles.keys())
DEFAULT_STYLE_NAME = "(No style)"


def apply_style(style_name: str, positive: str, negative: str = "") -> tuple[str, str]:
    p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME])
    return p.replace("{prompt}", positive), n + negative


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

eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")

# Download the model files
ckpt_dir_pony = snapshot_download(repo_id="John6666/pony-realism-v21main-sdxl")
ckpt_dir_cyber = snapshot_download(repo_id="John6666/cyberrealistic-pony-v61-sdxl")
ckpt_dir_stable = snapshot_download(repo_id="stabilityai/stable-diffusion-xl-base-1.0")


# Load the models
vae_pony = AutoencoderKL.from_pretrained(os.path.join(ckpt_dir_pony, "vae"), torch_dtype=torch.float16)
vae_cyber = AutoencoderKL.from_pretrained(os.path.join(ckpt_dir_cyber, "vae"), torch_dtype=torch.float16)
vae_stable = AutoencoderKL.from_pretrained(os.path.join(ckpt_dir_stable, "vae"), torch_dtype=torch.float16)


controlnet_pony = ControlNetModel.from_pretrained("xinsir/controlnet-union-sdxl-1.0", torch_dtype=torch.float16)
controlnet_cyber = ControlNetModel.from_pretrained("xinsir/controlnet-union-sdxl-1.0", torch_dtype=torch.float16)
controlnet_stable = ControlNetModel.from_pretrained("xinsir/controlnet-union-sdxl-1.0", torch_dtype=torch.float16)

pipe_pony = StableDiffusionXLControlNetPipeline.from_pretrained(
    ckpt_dir_pony, controlnet=controlnet_pony, vae=vae_pony, torch_dtype=torch.float16, scheduler=eulera_scheduler
)
pipe_cyber = StableDiffusionXLControlNetPipeline.from_pretrained(
    ckpt_dir_cyber, controlnet=controlnet_cyber, vae=vae_cyber, torch_dtype=torch.float16, scheduler=eulera_scheduler
)
pipe_stable = StableDiffusionXLControlNetPipeline.from_pretrained(
    ckpt_dir_stable, controlnet=controlnet_stable, vae=vae_stable, torch_dtype=torch.float16, scheduler=eulera_scheduler
)


pipe_pony.to(device)
pipe_cyber.to(device)
pipe_stable.to(device)

MAX_SEED = np.iinfo(np.int32).max
processor = HEDdetector.from_pretrained('lllyasviel/Annotators')
def nms(x, t, s):
    x = cv2.GaussianBlur(x.astype(np.float32), (0, 0), s)

    f1 = np.array([[0, 0, 0], [1, 1, 1], [0, 0, 0]], dtype=np.uint8)
    f2 = np.array([[0, 1, 0], [0, 1, 0], [0, 1, 0]], dtype=np.uint8)
    f3 = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=np.uint8)
    f4 = np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]], dtype=np.uint8)

    y = np.zeros_like(x)

    for f in [f1, f2, f3, f4]:
        np.putmask(y, cv2.dilate(x, kernel=f) == x, x)

    z = np.zeros_like(y, dtype=np.uint8)
    z[y > t] = 255
    return z

def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    return seed

@spaces.GPU
def run(
    image: dict,
    prompt: str,
    negative_prompt: str,
    model_choice: str,  # Add this new input
    style_name: str = DEFAULT_STYLE_NAME,
    num_steps: int = 25,
    guidance_scale: float = 5,
    controlnet_conditioning_scale: float = 1.0,
    seed: int = 0,
    use_hed: bool = False,
    use_canny: bool = False,
    progress=gr.Progress(track_tqdm=True),
) -> PIL.Image.Image:
    # Get the composite image from the EditorValue dict
    composite_image = image['composite']
    width, height = composite_image.size
    
    # Calculate new dimensions to fit within 1024x1024 while maintaining aspect ratio
    max_size = 1024
    ratio = min(max_size / width, max_size / height)
    new_width = int(width * ratio)
    new_height = int(height * ratio)
    
    # Resize the image
    resized_image = composite_image.resize((new_width, new_height), Image.LANCZOS)
    
    if use_canny:
        controlnet_img = np.array(resized_image)
        controlnet_img = cv2.Canny(controlnet_img, 100, 200)
        controlnet_img = HWC3(controlnet_img)
        image = Image.fromarray(controlnet_img)
    elif not use_hed:
        controlnet_img = resized_image
        image = resized_image
    else:
        controlnet_img = processor(resized_image, scribble=False)
        controlnet_img = np.array(controlnet_img)
        controlnet_img = nms(controlnet_img, 127, 3)
        controlnet_img = cv2.GaussianBlur(controlnet_img, (0, 0), 3)
        random_val = int(round(random.uniform(0.01, 0.10), 2) * 255)
        controlnet_img[controlnet_img > random_val] = 255
        controlnet_img[controlnet_img < 255] = 0
        image = Image.fromarray(controlnet_img)
    
    prompt, negative_prompt = apply_style(style_name, prompt, negative_prompt)

    generator = torch.Generator(device=device).manual_seed(seed)
    
    # Select the appropriate pipe based on the model choice
    if model_choice == "Stable Diffusion XL":
        pipe = pipe_stable
    elif model_choice == "Pony Realism v21":
        pipe = pipe_pony
    elif model_choice == "Cyber Realistic Pony v61":
        pipe = pipe_cyber
    else:
        raise ValueError("Invalid model choice")
    
    if use_canny:
        out = pipe(
            prompt=prompt,
            negative_prompt=negative_prompt,
            image=image,
            num_inference_steps=num_steps,
            generator=generator,
            controlnet_conditioning_scale=controlnet_conditioning_scale,
            guidance_scale=guidance_scale,
            width=new_width,
            height=new_height,
        ).images[0]
    else:
        out = pipe(
            prompt=prompt,
            negative_prompt=negative_prompt,
            image=image,
            num_inference_steps=num_steps,
            generator=generator,
            controlnet_conditioning_scale=controlnet_conditioning_scale,
            guidance_scale=guidance_scale,
            width=new_width,
            height=new_height,
        ).images[0]

    return (controlnet_img, out)

with gr.Blocks(css="style.css", js=js_func) as demo:
    gr.Markdown(DESCRIPTION, elem_id="description")
    gr.DuplicateButton(
        value="Duplicate Space for private use",
        elem_id="duplicate-button",
        visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
    )

    with gr.Row():
        with gr.Column():
            with gr.Group():
                image = gr.ImageEditor(type="pil", label="Sketch your image or upload one", width=512, height=512)
                prompt = gr.Textbox(label="Prompt")
                style = gr.Dropdown(label="Style", choices=STYLE_NAMES, value=DEFAULT_STYLE_NAME)
                model_choice = gr.Dropdown(
                    ["Pony Realism v21", "Cyber Realistic Pony v61", "Stable Diffusion XL"],
                    label="Model Choice",
                    value="Pony Realism v21"
                )
                use_hed = gr.Checkbox(label="use HED detector", value=False, info="check this box if you upload an image and want to turn it to a sketch")
                use_canny = gr.Checkbox(label="use Canny", value=False, info="check this to use ControlNet canny instead of scribble")
                run_button = gr.Button("Run")
            with gr.Accordion("Advanced options", open=False):
                negative_prompt = gr.Textbox(
                    label="Negative prompt",
                    value="longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
                )
                num_steps = gr.Slider(
                    label="Number of steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=25,
                )
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.1,
                    maximum=10.0,
                    step=0.1,
                    value=5,
                )
                controlnet_conditioning_scale = gr.Slider(
                    label="controlnet conditioning scale",
                    minimum=0.5,
                    maximum=5.0,
                    step=0.1,
                    value=0.9,
                )
                seed = gr.Slider(
                    label="Seed",
                    minimum=0,
                    maximum=MAX_SEED,
                    step=1,
                    value=0,
                )
                randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
               
        with gr.Column():
            with gr.Group():
                image_slider = ImageSlider(position=0.5)


    inputs = [
        image,
        prompt,
        negative_prompt,
        model_choice,  # Add this new input
        style,
        num_steps,
        guidance_scale,
        controlnet_conditioning_scale,
        seed,
        use_hed,
        use_canny
    ]
    outputs = [image_slider]
    run_button.click(
        fn=randomize_seed_fn,
        inputs=[seed, randomize_seed],
        outputs=seed,
        queue=False,
        api_name=False,
    ).then(lambda x: None, inputs=None, outputs=image_slider).then(
        fn=run, inputs=inputs, outputs=outputs
    )
    
    

demo.queue().launch(show_error=True, ssl_verify=False)