File size: 11,643 Bytes
4a2500d 925c814 7ecd3fc 4a2500d 7ecd3fc 4a2500d 925c814 4a2500d 2de91e5 4a2500d 2de91e5 4a2500d 664a77c 4a2500d 2de91e5 4a2500d 2de91e5 664a77c 4a2500d 664a77c 4029c04 2de91e5 4a2500d 2de91e5 4a2500d b8dcdf0 4a2500d 2de91e5 4a2500d b8dcdf0 4a2500d 4029c04 4a2500d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 |
#!/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
import torch
import numpy as np
import cv2
import os
import random
import spaces
from gradio_imageslider import ImageSlider
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
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": "",
},
{
"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
)
# 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(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
pipe.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,
progress=gr.Progress(track_tqdm=True),
) -> PIL.Image.Image:
# image = image.convert("RGB")
# image = TF.to_tensor(image) > 0.5
# image = TF.to_pil_image(image.to(torch.float32))
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 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)
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 (out, controlnet_img)
with gr.Blocks(css="style.css") 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),brush=gr.Brush(color_mode="fixed", colors=["#00000"]))
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 instead of sketching")
run_button = gr.Button("Run")
with gr.Accordion("Advanced options", open=False):
negative_prompt = gr.Textbox(
label="Negative prompt",
value=" extra digit, fewer digits, cropped, worst quality, low quality, glitch, deformed, mutated, ugly, disfigured",
)
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)
# with gr.Row():
# result = gr.Image(label="result", height=400)
# sketch_image = gr.Image(label="sketch")
inputs = [
image,
prompt,
negative_prompt,
style,
num_steps,
guidance_scale,
controlnet_conditioning_scale,
seed,
use_hed,
]
outputs = [image_slider]
run_button.click(lambda x: None, inputs=None, outputs=image_slider).then(
fn=run, inputs=inputs, outputs=outputs
)
prompt.submit(
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,
api_name=False,
)
negative_prompt.submit(
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,
api_name=False,
)
demo.queue().launch()
|