File size: 10,501 Bytes
7948dbf a776847 7948dbf a776847 7948dbf a776847 7948dbf |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 |
#!/usr/bin/env python
from __future__ import annotations
import os
import random
import gradio as gr
import numpy as np
import PIL.Image
import torch
from diffusers import DiffusionPipeline
DESCRIPTION = '# SD-XL'
if not torch.cuda.is_available():
DESCRIPTION += '\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>'
MAX_SEED = np.iinfo(np.int32).max
CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv(
'CACHE_EXAMPLES') == '1'
MAX_IMAGE_SIZE = int(os.getenv('MAX_IMAGE_SIZE', '1024'))
USE_TORCH_COMPILE = os.getenv('USE_TORCH_COMPILE') == '1'
ENABLE_CPU_OFFLOAD = os.getenv('ENABLE_CPU_OFFLOAD') == '1'
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
if torch.cuda.is_available():
model = "stabilityai/stable-diffusion-xl-base-1.0"
pipe = DiffusionPipeline.from_pretrained(
model,
torch_dtype=torch.float16,
use_safetensors=True,
variant='fp16')
pipe.load_lora_weights("model", weight_name="tk_pytorch_lora_weights.safetensors")
refiner = DiffusionPipeline.from_pretrained(
'stabilityai/stable-diffusion-xl-refiner-1.0',
torch_dtype=torch.float16,
use_safetensors=True,
variant='fp16')
if ENABLE_CPU_OFFLOAD:
pipe.enable_model_cpu_offload()
refiner.enable_model_cpu_offload()
else:
pipe.to(device)
refiner.to(device)
if USE_TORCH_COMPILE:
pipe.unet = torch.compile(pipe.unet,
mode='reduce-overhead',
fullgraph=True)
else:
pipe = None
refiner = None
def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
if randomize_seed:
seed = random.randint(0, MAX_SEED)
return seed
def generate(prompt: str,
negative_prompt: str = '',
prompt_2: str = '',
negative_prompt_2: str = '',
use_negative_prompt: bool = False,
use_prompt_2: bool = False,
use_negative_prompt_2: bool = False,
seed: int = 0,
width: int = 1024,
height: int = 1024,
guidance_scale_base: float = 5.0,
guidance_scale_refiner: float = 5.0,
num_inference_steps_base: int = 50,
num_inference_steps_refiner: int = 50,
apply_refiner: bool = False) -> PIL.Image.Image:
generator = torch.Generator().manual_seed(seed)
if not use_negative_prompt:
negative_prompt = None # type: ignore
if not use_prompt_2:
prompt_2 = None # type: ignore
if not use_negative_prompt_2:
negative_prompt_2 = None # type: ignore
if not apply_refiner:
return pipe(prompt=prompt,
negative_prompt=negative_prompt,
prompt_2=prompt_2,
negative_prompt_2=negative_prompt_2,
width=width,
height=height,
guidance_scale=guidance_scale_base,
num_inference_steps=num_inference_steps_base,
generator=generator,
output_type='pil').images[0]
else:
latents = pipe(prompt=prompt,
negative_prompt=negative_prompt,
prompt_2=prompt_2,
negative_prompt_2=negative_prompt_2,
width=width,
height=height,
guidance_scale=guidance_scale_base,
num_inference_steps=num_inference_steps_base,
generator=generator,
output_type='latent').images
image = refiner(prompt=prompt,
negative_prompt=negative_prompt,
prompt_2=prompt_2,
negative_prompt_2=negative_prompt_2,
guidance_scale=guidance_scale_refiner,
num_inference_steps=num_inference_steps_refiner,
image=latents,
generator=generator).images[0]
return image
examples = [
'Astronaut in a jungle, cold color palette, muted colors, detailed, 8k',
'An astronaut riding a green horse',
]
with gr.Blocks(css='style.css') as demo:
gr.Markdown(DESCRIPTION)
gr.DuplicateButton(value='Duplicate Space for private use',
elem_id='duplicate-button',
visible=os.getenv('SHOW_DUPLICATE_BUTTON') == '1')
with gr.Box():
with gr.Row():
prompt = gr.Text(
label='Prompt',
show_label=False,
max_lines=1,
placeholder='Enter your prompt',
container=False,
)
run_button = gr.Button('Run', scale=0)
result = gr.Image(label='Result', show_label=False)
with gr.Accordion('Advanced options', open=False):
with gr.Row():
use_negative_prompt = gr.Checkbox(label='Use negative prompt',
value=False)
use_prompt_2 = gr.Checkbox(label='Use prompt 2', value=False)
use_negative_prompt_2 = gr.Checkbox(
label='Use negative prompt 2', value=False)
negative_prompt = gr.Text(
label='Negative prompt',
max_lines=1,
placeholder='Enter a negative prompt',
visible=False,
)
prompt_2 = gr.Text(
label='Prompt 2',
max_lines=1,
placeholder='Enter your prompt',
visible=False,
)
negative_prompt_2 = gr.Text(
label='Negative prompt 2',
max_lines=1,
placeholder='Enter a negative prompt',
visible=False,
)
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.Row():
width = gr.Slider(
label='Width',
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
height = gr.Slider(
label='Height',
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
apply_refiner = gr.Checkbox(label='Apply refiner', value=False)
with gr.Row():
guidance_scale_base = gr.Slider(
label='Guidance scale for base',
minimum=1,
maximum=20,
step=0.1,
value=5.0)
num_inference_steps_base = gr.Slider(
label='Number of inference steps for base',
minimum=10,
maximum=100,
step=1,
value=50)
with gr.Row(visible=False) as refiner_params:
guidance_scale_refiner = gr.Slider(
label='Guidance scale for refiner',
minimum=1,
maximum=20,
step=0.1,
value=5.0)
num_inference_steps_refiner = gr.Slider(
label='Number of inference steps for refiner',
minimum=10,
maximum=100,
step=1,
value=50)
gr.Examples(examples=examples,
inputs=prompt,
outputs=result,
fn=generate,
cache_examples=CACHE_EXAMPLES)
use_negative_prompt.change(
fn=lambda x: gr.update(visible=x),
inputs=use_negative_prompt,
outputs=negative_prompt,
queue=False,
api_name=False,
)
use_prompt_2.change(
fn=lambda x: gr.update(visible=x),
inputs=use_prompt_2,
outputs=prompt_2,
queue=False,
api_name=False,
)
use_negative_prompt_2.change(
fn=lambda x: gr.update(visible=x),
inputs=use_negative_prompt_2,
outputs=negative_prompt_2,
queue=False,
api_name=False,
)
apply_refiner.change(
fn=lambda x: gr.update(visible=x),
inputs=apply_refiner,
outputs=refiner_params,
queue=False,
api_name=False,
)
inputs = [
prompt,
negative_prompt,
prompt_2,
negative_prompt_2,
use_negative_prompt,
use_prompt_2,
use_negative_prompt_2,
seed,
width,
height,
guidance_scale_base,
guidance_scale_refiner,
num_inference_steps_base,
num_inference_steps_refiner,
apply_refiner,
]
prompt.submit(
fn=randomize_seed_fn,
inputs=[seed, randomize_seed],
outputs=seed,
queue=False,
api_name=False,
).then(
fn=generate,
inputs=inputs,
outputs=result,
api_name='run',
)
negative_prompt.submit(
fn=randomize_seed_fn,
inputs=[seed, randomize_seed],
outputs=seed,
queue=False,
api_name=False,
).then(
fn=generate,
inputs=inputs,
outputs=result,
api_name=False,
)
prompt_2.submit(
fn=randomize_seed_fn,
inputs=[seed, randomize_seed],
outputs=seed,
queue=False,
api_name=False,
).then(
fn=generate,
inputs=inputs,
outputs=result,
api_name=False,
)
negative_prompt_2.submit(
fn=randomize_seed_fn,
inputs=[seed, randomize_seed],
outputs=seed,
queue=False,
api_name=False,
).then(
fn=generate,
inputs=inputs,
outputs=result,
api_name=False,
)
run_button.click(
fn=randomize_seed_fn,
inputs=[seed, randomize_seed],
outputs=seed,
queue=False,
api_name=False,
).then(
fn=generate,
inputs=inputs,
outputs=result,
api_name=False,
)
demo.queue(max_size=20).launch()
|