app.py

#!/usr/bin/env python
import spaces
import os
import random

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
from pathlib import Path
from PIL import Image, ImageOps
import torch
import numpy as np
import cv2
import os
import random
from gradio_imageslider import ImageSlider

js_func = """
function refresh() {
    const url = new URL(window.location);

    if (url.searchParams.get('__theme') !== 'dark') {
        url.searchParams.set('__theme', 'dark');
        window.location.href = url.href;
    }
}
"""
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 = '''# ⚡️Flash⚡️ Scribble SDXL 

🖋️🌄super fast sketch to image with SDXL Flash, using [@xinsir](https://huggingface.co/xinsir) [scribble sdxl controlnet](https://huggingface.co/xinsir/controlnet-scribble-sdxl-1.0) and [sdxl flash](https://huggingface.co/sd-community/sdxl-flash)

sketch (or upload an image and have it auto-turned into a sketch) and turn your vision into art ✨
'''

if not torch.cuda.is_available():
    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"

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")


controlnet = ControlNetModel.from_pretrained(
    "xinsir/controlnet-scribble-sdxl-1.0",
    torch_dtype=torch.float16
)

controlnet_canny = ControlNetModel.from_pretrained(
    "xinsir/controlnet-canny-sdxl-1.0",
    torch_dtype=torch.float16
)



# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)

pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
    "sd-community/sdxl-flash",
    controlnet=controlnet,
    vae=vae,
    torch_dtype=torch.float16,
    # scheduler=eulera_scheduler,
)
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)

pipe.to(device)

pipe_canny = StableDiffusionXLControlNetPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    controlnet=controlnet_canny,
    vae=vae,
    safety_checker=None,
    torch_dtype=torch.float16,
    # scheduler=eulera_scheduler,
)

pipe_canny.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe_canny.scheduler.config)

pipe_canny.to(device)
# Load model.

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: PIL.Image.Image,
    prompt: str,
    negative_prompt: str,
    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:
    width, height  = image['composite'].size
    ratio = np.sqrt(1024. * 1024. / (width * height))
    new_width, new_height = int(width * ratio), int(height * ratio)
    image = image['composite'].resize((new_width, new_height))
    
    if use_canny:
        controlnet_img = np.array(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 = image
    else:
        controlnet_img = processor(image, scribble=False)
      # following is some processing to simulate human sketch draw, different threshold can generate different width of lines
        controlnet_img = np.array(controlnet_img)
        controlnet_img = nms(controlnet_img, 127, 3)
        controlnet_img = cv2.GaussianBlur(controlnet_img, (0, 0), 3)

        # higher threshold, thiner line
        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)
    if use_canny:
        out = pipe_canny(
        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", image_mode="L", crop_size=(512, 512))
                prompt = gr.Textbox(label="Prompt")
                style = gr.Dropdown(label="Style", choices=STYLE_NAMES, value=DEFAULT_STYLE_NAME)
                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=20,
                    step=1,
                    value=10,
                )
                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,
        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()