Spaces:
Sleeping
Sleeping
#!/usr/bin/env python | |
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 | |
import spaces | |
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 = '''# Scribble SDXL 🖋️🌄 | |
sketch to image with SDXL, using [@xinsir](https://huggingface.co/xinsir) [scribble sdxl controlnet](https://huggingface.co/xinsir/controlnet-scribble-sdxl-1.0) | |
''' | |
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 | |
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=50, | |
step=1, | |
value=1, | |
) | |
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() | |