Spaces:
Running
on
Zero
Running
on
Zero
File size: 9,098 Bytes
3aa0ca8 eebec00 3aa0ca8 f734c44 3aa0ca8 f734c44 3aa0ca8 eebec00 f734c44 eebec00 3aa0ca8 f734c44 3aa0ca8 eebec00 3aa0ca8 802b807 3aa0ca8 eebec00 b6a0d59 eebec00 f734c44 802b807 eebec00 f734c44 b6a0d59 eebec00 802b807 eebec00 3aa0ca8 15417c3 3aa0ca8 eebec00 15417c3 eebec00 3aa0ca8 15417c3 802b807 15417c3 eebec00 3aa0ca8 e5bd30a 802b807 e5bd30a 1a904df f734c44 3aa0ca8 eebec00 3aa0ca8 17c74fe eebec00 17c74fe b6a0d59 17c74fe 3aa0ca8 f734c44 3aa0ca8 f734c44 3aa0ca8 f734c44 3aa0ca8 17c74fe 3aa0ca8 eebec00 3aa0ca8 802b807 3aa0ca8 1a904df eebec00 1a904df eebec00 1a904df 802b807 eebec00 1a904df f734c44 eebec00 b564c46 17c74fe 802b807 eebec00 a8a414c 17c74fe 802b807 eebec00 3aa0ca8 eebec00 3aa0ca8 802b807 3aa0ca8 b6a0d59 3aa0ca8 |
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 |
import torch
import gradio as gr
from PIL import Image
import qrcode
from pathlib import Path
from diffusers import (
StableDiffusionPipeline,
StableDiffusionControlNetImg2ImgPipeline,
ControlNetModel,
DDIMScheduler,
DPMSolverMultistepScheduler,
)
from PIL import Image
qrcode_generator = qrcode.QRCode(
version=1,
error_correction=qrcode.constants.ERROR_CORRECT_H,
box_size=10,
border=0,
)
controlnet = ControlNetModel.from_pretrained(
"DionTimmer/controlnet_qrcode-control_v1p_sd15", torch_dtype=torch.float16
)
pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
controlnet=controlnet,
safety_checker=None,
torch_dtype=torch.float16,
)
pipe.enable_xformers_memory_efficient_attention()
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
sd_pipe = StableDiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16
)
sd_pipe.scheduler = DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe = sd_pipe.to("cuda")
sd_pipe.enable_xformers_memory_efficient_attention()
sd_pipe.enable_model_cpu_offload()
def resize_for_condition_image(input_image: Image.Image, resolution: int):
input_image = input_image.convert("RGB")
W, H = input_image.size
k = float(resolution) / min(H, W)
H *= k
W *= k
H = int(round(H / 64.0)) * 64
W = int(round(W / 64.0)) * 64
img = input_image.resize((W, H), resample=Image.LANCZOS)
return img
def inference(
qr_code_content: str,
prompt: str,
negative_prompt: str,
guidance_scale: float = 10.0,
controlnet_conditioning_scale: float = 2.0,
strength: float = 0.8,
seed: int = -1,
init_image: Image.Image | None = None,
qrcode_image: Image.Image | None = None,
):
if prompt is None or prompt == "":
raise gr.Error("Prompt is required")
if qrcode_image is None and qr_code_content == "":
raise gr.Error("QR Code Image or QR Code Content is required")
generator = torch.manual_seed(seed) if seed != -1 else torch.Generator()
# hack due to gradio examples
if init_image is None or init_image.size == (1, 1):
print("Generating random image from prompt using Stable Diffusion")
# generate image from prompt
out = sd_pipe(
prompt=prompt,
negative_prompt=negative_prompt,
generator=generator,
num_inference_steps=25,
num_images_per_prompt=1,
) # type: ignore
init_image = out.images[0]
else:
print("Using provided init image")
init_image = resize_for_condition_image(init_image, 768)
if qr_code_content != "" or qrcode_image.size == (1, 1):
print("Generating QR Code from content")
qr = qrcode.QRCode(
version=1,
error_correction=qrcode.constants.ERROR_CORRECT_H,
box_size=10,
border=4,
)
qr.add_data(qr_code_content)
qr.make(fit=True)
qrcode_image = qr.make_image(fill_color="black", back_color="white")
qrcode_image = resize_for_condition_image(qrcode_image, 768)
else:
print("Using QR Code Image")
qrcode_image = resize_for_condition_image(qrcode_image, 768)
out = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
image=init_image,
control_image=qrcode_image, # type: ignore
width=768, # type: ignore
height=768, # type: ignore
guidance_scale=float(guidance_scale),
controlnet_conditioning_scale=float(controlnet_conditioning_scale), # type: ignore
generator=generator,
strength=float(strength),
num_inference_steps=40,
)
return out.images[0] # type: ignore
with gr.Blocks() as blocks:
gr.Markdown(
"""
# QR Code AI Art Generator
model: https://huggingface.co/DionTimmer/controlnet_qrcode-control_v1p_sd15
<a href="https://huggingface.co/spaces/huggingface-projects/QR-code-AI-art-generator?duplicate=true" style="display: inline-block;margin-top: .5em;margin-right: .25em;" target="_blank">
<img style="margin-bottom: 0em;display: inline;margin-top: -.25em;" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a> for no queue on your own hardware.</p>
"""
)
with gr.Row():
with gr.Column():
qr_code_content = gr.Textbox(
label="QR Code Content",
info="QR Code Content or URL",
value="",
)
prompt = gr.Textbox(
label="Prompt",
info="Prompt is required. If init image is not provided, then it will be generated from prompt using Stable Diffusion 2.1",
)
negative_prompt = gr.Textbox(
label="Negative Prompt",
value="ugly, disfigured, low quality, blurry, nsfw",
)
with gr.Accordion(label="Init Images (Optional)", open=False):
init_image = gr.Image(label="Init Image (Optional)", type="pil")
qr_code_image = gr.Image(
label="QR Code Image (Optional)",
type="pil",
)
with gr.Accordion(
label="Params: The generated QR Code functionality is largely influenced by the parameters detailed below",
open=False,
):
guidance_scale = gr.Slider(
minimum=0.0,
maximum=50.0,
step=0.01,
value=10.0,
label="Guidance Scale",
)
controlnet_conditioning_scale = gr.Slider(
minimum=0.0,
maximum=5.0,
step=0.01,
value=2.0,
label="Controlnet Conditioning Scale",
)
strength = gr.Slider(
minimum=0.0, maximum=1.0, step=0.01, value=0.8, label="Strength"
)
seed = gr.Slider(
minimum=-1,
maximum=9999999999,
step=1,
value=2313123,
label="Seed",
randomize=True,
)
with gr.Row():
run_btn = gr.Button("Run")
with gr.Column():
result_image = gr.Image(label="Result Image")
run_btn.click(
inference,
inputs=[
qr_code_content,
prompt,
negative_prompt,
guidance_scale,
controlnet_conditioning_scale,
strength,
seed,
init_image,
qr_code_image,
],
outputs=[result_image],
)
gr.Examples(
examples=[
[
"https://huggingface.co/spaces/huggingface-projects/QR-code-AI-art-generator",
"billboard amidst the bustling skyline of New York City, with iconic landmarks subtly featured in the background.",
"ugly, disfigured, low quality, blurry, nsfw",
13.37,
2.81,
0.68,
2313123,
"./examples/hack.png",
"./examples/hack.png",
],
[
"https://huggingface.co/spaces/huggingface-projects/QR-code-AI-art-generator",
"beautiful sunset in San Francisco with Golden Gate bridge in the background",
"ugly, disfigured, low quality, blurry, nsfw",
11.01,
2.61,
0.66,
1423585430,
"./examples/hack.png",
"./examples/hack.png",
],
[
"https://huggingface.co",
"A flying cat over a jungle",
"ugly, disfigured, low quality, blurry, nsfw",
13,
2.81,
0.66,
2702246671,
"./examples/hack.png",
"./examples/hack.png",
],
[
"",
"crisp QR code prominently displayed on a billboard amidst the bustling skyline of New York City, with iconic landmarks subtly featured in the background.",
"ugly, disfigured, low quality, blurry, nsfw",
10.0,
2.0,
0.8,
2313123,
"./examples/init.jpeg",
"./examples/qrcode.png",
],
],
fn=inference,
inputs=[
qr_code_content,
prompt,
negative_prompt,
guidance_scale,
controlnet_conditioning_scale,
strength,
seed,
init_image,
qr_code_image,
],
outputs=[result_image],
cache_examples=True,
)
blocks.queue()
blocks.launch()
|