Spaces:
Running
on
Zero
Running
on
Zero
File size: 13,490 Bytes
d25b2e6 e0b1273 ab4e488 e0b1273 9395e21 e0b1273 ab4e488 89165e2 ab4e488 e0b1273 76b0dfc e0b1273 df8ede9 e0b1273 914bc5d e0b1273 8c4042b e0b1273 ab4e488 e0b1273 0cfdbbe e0b1273 0cfdbbe e0b1273 0cfdbbe ff2f35f 0cfdbbe e0b1273 433fc87 e0b1273 433fc87 e0b1273 433fc87 e0b1273 433fc87 ab4e488 0cfdbbe ab4e488 0cfdbbe e0b1273 ab4e488 e0b1273 c2cfe45 ab4e488 c2cfe45 e0b1273 0cfdbbe e0b1273 0cfdbbe e0b1273 ab4e488 e0b1273 ab4e488 0cfdbbe ab4e488 e0b1273 ab4e488 89165e2 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 |
import spaces
import os
import gc
import gradio as gr
import numpy as np
import torch
import json
import config
import utils
import logging
from PIL import Image, PngImagePlugin
from datetime import datetime
from diffusers.models import AutoencoderKL
from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
DESCRIPTION = "Animagine XL 3.1"
if not torch.cuda.is_available():
DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU. </p>"
IS_COLAB = utils.is_google_colab() or os.getenv("IS_COLAB") == "1"
HF_TOKEN = os.getenv("HF_TOKEN")
CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES") == "0"
MIN_IMAGE_SIZE = int(os.getenv("MIN_IMAGE_SIZE", "512"))
MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "2048"))
USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE") == "1"
ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD") == "1"
OUTPUT_DIR = os.getenv("OUTPUT_DIR", "./outputs")
MODEL = os.getenv(
"MODEL",
"cagliostrolab/animagine-xl-3.1",
)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
torch.backends.cuda.matmul.allow_tf32 = True
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
def load_pipeline(model_name):
vae = AutoencoderKL.from_pretrained(
"madebyollin/sdxl-vae-fp16-fix",
torch_dtype=torch.float16,
)
pipeline = (
StableDiffusionXLPipeline.from_single_file
if MODEL.endswith(".safetensors")
else StableDiffusionXLPipeline.from_pretrained
)
pipe = pipeline(
model_name,
vae=vae,
torch_dtype=torch.float16,
custom_pipeline="lpw_stable_diffusion_xl",
use_safetensors=True,
add_watermarker=False,
use_auth_token=HF_TOKEN,
)
pipe.to(device)
return pipe
@spaces.GPU
def generate(
prompt: str,
negative_prompt: str = "",
seed: int = 0,
custom_width: int = 1024,
custom_height: int = 1024,
guidance_scale: float = 7.0,
num_inference_steps: int = 28,
sampler: str = "Euler a",
aspect_ratio_selector: str = "896 x 1152",
style_selector: str = "(None)",
quality_selector: str = "Standard v3.1",
use_upscaler: bool = False,
upscaler_strength: float = 0.55,
upscale_by: float = 1.5,
add_quality_tags: bool = True,
progress=gr.Progress(track_tqdm=True),
):
generator = utils.seed_everything(seed)
width, height = utils.aspect_ratio_handler(
aspect_ratio_selector,
custom_width,
custom_height,
)
prompt = utils.add_wildcard(prompt, wildcard_files)
prompt, negative_prompt = utils.preprocess_prompt(
quality_prompt, quality_selector, prompt, negative_prompt, add_quality_tags
)
prompt, negative_prompt = utils.preprocess_prompt(
styles, style_selector, prompt, negative_prompt
)
width, height = utils.preprocess_image_dimensions(width, height)
backup_scheduler = pipe.scheduler
pipe.scheduler = utils.get_scheduler(pipe.scheduler.config, sampler)
if use_upscaler:
upscaler_pipe = StableDiffusionXLImg2ImgPipeline(**pipe.components)
metadata = {
"prompt": prompt,
"negative_prompt": negative_prompt,
"resolution": f"{width} x {height}",
"guidance_scale": guidance_scale,
"num_inference_steps": num_inference_steps,
"seed": seed,
"sampler": sampler,
"sdxl_style": style_selector,
"add_quality_tags": add_quality_tags,
"quality_tags": quality_selector,
}
if use_upscaler:
new_width = int(width * upscale_by)
new_height = int(height * upscale_by)
metadata["use_upscaler"] = {
"upscale_method": "nearest-exact",
"upscaler_strength": upscaler_strength,
"upscale_by": upscale_by,
"new_resolution": f"{new_width} x {new_height}",
}
else:
metadata["use_upscaler"] = None
metadata["Model"] = {
"Model": DESCRIPTION,
"Model hash": "e3c47aedb0",
}
logger.info(json.dumps(metadata, indent=4))
try:
if use_upscaler:
latents = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
width=width,
height=height,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
generator=generator,
output_type="latent",
).images
upscaled_latents = utils.upscale(latents, "nearest-exact", upscale_by)
images = upscaler_pipe(
prompt=prompt,
negative_prompt=negative_prompt,
image=upscaled_latents,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
strength=upscaler_strength,
generator=generator,
output_type="pil",
).images
else:
images = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
width=width,
height=height,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
generator=generator,
output_type="pil",
).images
if images:
image_paths = [
utils.save_image(image, metadata, OUTPUT_DIR, IS_COLAB)
for image in images
]
for image_path in image_paths:
logger.info(f"Image saved as {image_path} with metadata")
return image_paths, metadata
except Exception as e:
logger.exception(f"An error occurred: {e}")
raise
finally:
if use_upscaler:
del upscaler_pipe
pipe.scheduler = backup_scheduler
utils.free_memory()
if torch.cuda.is_available():
pipe = load_pipeline(MODEL)
logger.info("Loaded on Device!")
else:
pipe = None
styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in config.style_list}
quality_prompt = {
k["name"]: (k["prompt"], k["negative_prompt"]) for k in config.quality_prompt_list
}
wildcard_files = utils.load_wildcard_files("wildcard")
with gr.Blocks(css="style.css", theme="NoCrypt/[email protected]") as demo:
title = gr.HTML(
f"""<h1><span>{DESCRIPTION}</span></h1>""",
elem_id="title",
)
gr.Markdown(
f"""Gradio demo for [cagliostrolab/animagine-xl-3.1](https://huggingface.co/cagliostrolab/animagine-xl-3.1)""",
elem_id="subtitle",
)
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(scale=2):
with gr.Tab("Txt2img"):
with gr.Group():
prompt = gr.Text(
label="Prompt",
max_lines=5,
placeholder="Enter your prompt",
)
negative_prompt = gr.Text(
label="Negative Prompt",
max_lines=5,
placeholder="Enter a negative prompt",
)
with gr.Accordion(label="Quality Tags", open=True):
add_quality_tags = gr.Checkbox(
label="Add Quality Tags", value=True
)
quality_selector = gr.Dropdown(
label="Quality Tags Presets",
interactive=True,
choices=list(quality_prompt.keys()),
value="Standard v3.1",
)
with gr.Tab("Advanced Settings"):
with gr.Group():
style_selector = gr.Radio(
label="Style Preset",
container=True,
interactive=True,
choices=list(styles.keys()),
value="(None)",
)
with gr.Group():
aspect_ratio_selector = gr.Radio(
label="Aspect Ratio",
choices=config.aspect_ratios,
value="896 x 1152",
container=True,
)
with gr.Group(visible=False) as custom_resolution:
with gr.Row():
custom_width = gr.Slider(
label="Width",
minimum=MIN_IMAGE_SIZE,
maximum=MAX_IMAGE_SIZE,
step=8,
value=1024,
)
custom_height = gr.Slider(
label="Height",
minimum=MIN_IMAGE_SIZE,
maximum=MAX_IMAGE_SIZE,
step=8,
value=1024,
)
with gr.Group():
use_upscaler = gr.Checkbox(label="Use Upscaler", value=False)
with gr.Row() as upscaler_row:
upscaler_strength = gr.Slider(
label="Strength",
minimum=0,
maximum=1,
step=0.05,
value=0.55,
visible=False,
)
upscale_by = gr.Slider(
label="Upscale by",
minimum=1,
maximum=1.5,
step=0.1,
value=1.5,
visible=False,
)
with gr.Group():
sampler = gr.Dropdown(
label="Sampler",
choices=config.sampler_list,
interactive=True,
value="Euler a",
)
with gr.Group():
seed = gr.Slider(
label="Seed", minimum=0, maximum=utils.MAX_SEED, step=1, value=0
)
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
with gr.Group():
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=1,
maximum=12,
step=0.1,
value=7.0,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=50,
step=1,
value=28,
)
with gr.Column(scale=3):
with gr.Blocks():
run_button = gr.Button("Generate", variant="primary")
result = gr.Gallery(
label="Result",
columns=1,
height='100%',
preview=True,
show_label=False
)
with gr.Accordion(label="Generation Parameters", open=False):
gr_metadata = gr.JSON(label="metadata", show_label=False)
gr.Examples(
examples=config.examples,
inputs=prompt,
outputs=[result, gr_metadata],
fn=lambda *args, **kwargs: generate(*args, use_upscaler=True, **kwargs),
cache_examples=CACHE_EXAMPLES,
)
use_upscaler.change(
fn=lambda x: [gr.update(visible=x), gr.update(visible=x)],
inputs=use_upscaler,
outputs=[upscaler_strength, upscale_by],
queue=False,
api_name=False,
)
aspect_ratio_selector.change(
fn=lambda x: gr.update(visible=x == "Custom"),
inputs=aspect_ratio_selector,
outputs=custom_resolution,
queue=False,
api_name=False,
)
gr.on(
triggers=[
prompt.submit,
negative_prompt.submit,
run_button.click,
],
fn=utils.randomize_seed_fn,
inputs=[seed, randomize_seed],
outputs=seed,
queue=False,
api_name=False,
).then(
fn=generate,
inputs=[
prompt,
negative_prompt,
seed,
custom_width,
custom_height,
guidance_scale,
num_inference_steps,
sampler,
aspect_ratio_selector,
style_selector,
quality_selector,
use_upscaler,
upscaler_strength,
upscale_by,
add_quality_tags,
],
outputs=[result, gr_metadata],
api_name="run",
)
if __name__ == "__main__":
demo.queue(max_size=20).launch(debug=IS_COLAB, share=IS_COLAB) |