Spaces:
Sleeping
Sleeping
File size: 27,829 Bytes
d8fcee4 8ec67ee d8fcee4 2f00ac5 d8fcee4 2f00ac5 d8fcee4 b9c2637 d8fcee4 c73951b d8fcee4 b9c2637 d8fcee4 51d2448 d8fcee4 2f00ac5 d8fcee4 d8c3206 d8fcee4 2f00ac5 d8fcee4 b9c2637 d8fcee4 8024d12 e923469 d8c3206 8024d12 d8c3206 bb9a542 91ec6fc 4f26878 8024d12 d8c3206 a1c1647 8024d12 2f00ac5 8024d12 d8c3206 8024d12 d8c3206 a1c1647 8024d12 2f00ac5 8024d12 157cba0 b9fa23f 69149d4 6ad5a35 b77f3c0 d8fcee4 157cba0 d8fcee4 eedafde d8fcee4 a039a6a d8fcee4 e923469 d8fcee4 e7ae5d7 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 |
import gradio as gr
import torch
import numpy as np
import modin.pandas as pd
from PIL import Image
from diffusers import DiffusionPipeline, StableDiffusionLatentUpscalePipeline
from huggingface_hub import hf_hub_download
device = 'cuda' if torch.cuda.is_available() else 'cpu'
torch.cuda.max_memory_allocated(device=device)
torch.cuda.empty_cache()
def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed, refine, high_noise_frac, upscale):
generator = np.random.seed(0) if seed == 0 else torch.manual_seed(seed)
if Model == "PhotoReal":
pipe = DiffusionPipeline.from_pretrained("circulus/canvers-real-v3.8.1", torch_dtype=torch.float16, safety_checker=None) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("circulus/canvers-real-v3.8.1")
pipe.enable_xformers_memory_efficient_attention()
pipe = pipe.to(device)
torch.cuda.empty_cache()
if refine == "Yes":
refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", use_safetensors=True, torch_dtype=torch.float16, variant="fp16") if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0")
refiner.enable_xformers_memory_efficient_attention()
refiner = refiner.to(device)
torch.cuda.empty_cache()
int_image = pipe(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images
image = refiner(Prompt, negative_prompt=negative_prompt, image=int_image, denoising_start=high_noise_frac).images[0]
torch.cuda.empty_cache()
if upscale == "Yes":
refiner = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
refiner.enable_xformers_memory_efficient_attention()
refiner = refiner.to(device)
torch.cuda.empty_cache()
upscaled = refiner(prompt=Prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=15, guidance_scale=0).images[0]
torch.cuda.empty_cache()
return upscaled
else:
return image
else:
if upscale == "Yes":
image = pipe(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
upscaler = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
upscaler.enable_xformers_memory_efficient_attention()
upscaler = upscaler.to(device)
torch.cuda.empty_cache()
upscaled = upscaler(prompt=Prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=15, guidance_scale=0).images[0]
torch.cuda.empty_cache()
return upscaled
else:
image = pipe(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
torch.cuda.empty_cache()
return image
if Model == "Anime":
anime = DiffusionPipeline.from_pretrained("circulus/canvers-anime-v3.8.1", torch_dtype=torch.float16, safety_checker=None) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("circulus/canvers-anime-v3.8.1")
anime.enable_xformers_memory_efficient_attention()
anime = anime.to(device)
torch.cuda.empty_cache()
if refine == "Yes":
refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", use_safetensors=True, torch_dtype=torch.float16, variant="fp16") if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0")
refiner.enable_xformers_memory_efficient_attention()
refiner = refiner.to(device)
torch.cuda.empty_cache()
int_image = anime(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images
image = refiner(Prompt, negative_prompt=negative_prompt, image=int_image, denoising_start=high_noise_frac).images[0]
torch.cuda.empty_cache()
if upscale == "Yes":
refiner = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
refiner.enable_xformers_memory_efficient_attention()
refiner = refiner.to(device)
torch.cuda.empty_cache()
upscaled = refiner(prompt=Prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=15, guidance_scale=0).images[0]
torch.cuda.empty_cache()
return upscaled
else:
return image
else:
if upscale == "Yes":
image = anime(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
upscaler = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
upscaler.enable_xformers_memory_efficient_attention()
upscaler = upscaler.to(device)
torch.cuda.empty_cache()
upscaled = upscaler(prompt=Prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=15, guidance_scale=0).images[0]
torch.cuda.empty_cache()
return upscaled
else:
image = anime(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
torch.cuda.empty_cache()
return image
if Model == "Disney":
disney = DiffusionPipeline.from_pretrained("circulus/canvers-disney-v3.8.1", torch_dtype=torch.float16, safety_checker=None) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("circulus/canvers-disney-v3.8.1")
disney.enable_xformers_memory_efficient_attention()
disney = disney.to(device)
torch.cuda.empty_cache()
if refine == "Yes":
refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", use_safetensors=True, torch_dtype=torch.float16, variant="fp16") if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0")
refiner.enable_xformers_memory_efficient_attention()
refiner = refiner.to(device)
torch.cuda.empty_cache()
int_image = disney(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images
image = refiner(Prompt, negative_prompt=negative_prompt, image=int_image, denoising_start=high_noise_frac).images[0]
torch.cuda.empty_cache()
if upscale == "Yes":
refiner = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
refiner.enable_xformers_memory_efficient_attention()
refiner = refiner.to(device)
torch.cuda.empty_cache()
upscaled = refiner(prompt=Prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=15, guidance_scale=0).images[0]
torch.cuda.empty_cache()
return upscaled
else:
return image
else:
if upscale == "Yes":
image = disney(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
upscaler = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
upscaler.enable_xformers_memory_efficient_attention()
upscaler = upscaler.to(device)
torch.cuda.empty_cache()
upscaled = upscaler(prompt=Prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=15, guidance_scale=0).images[0]
torch.cuda.empty_cache()
return upscaled
else:
image = disney(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
torch.cuda.empty_cache()
return image
if Model == "StoryBook":
story = DiffusionPipeline.from_pretrained("circulus/canvers-story-v3.8.1", torch_dtype=torch.float16, safety_checker=None) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("circulus/canvers-story-v3.8.1")
story.enable_xformers_memory_efficient_attention()
story = story.to(device)
torch.cuda.empty_cache()
if refine == "Yes":
refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", use_safetensors=True, torch_dtype=torch.float16, variant="fp16") if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0")
refiner.enable_xformers_memory_efficient_attention()
refiner = refiner.to(device)
torch.cuda.empty_cache()
int_image = story(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images
image = refiner(Prompt, negative_prompt=negative_prompt, image=int_image, denoising_start=high_noise_frac).images[0]
torch.cuda.empty_cache()
if upscale == "Yes":
refiner = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
refiner.enable_xformers_memory_efficient_attention()
refiner = refiner.to(device)
torch.cuda.empty_cache()
upscaled = refiner(prompt=Prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=15, guidance_scale=0).images[0]
torch.cuda.empty_cache()
return upscaled
else:
return image
else:
if upscale == "Yes":
image = story(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
upscaler = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
upscaler.enable_xformers_memory_efficient_attention()
upscaler = upscaler.to(device)
torch.cuda.empty_cache()
upscaled = upscaler(prompt=Prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=15, guidance_scale=0).images[0]
torch.cuda.empty_cache()
return upscaled
else:
image = story(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
torch.cuda.empty_cache()
return image
if Model == "SemiReal":
semi = DiffusionPipeline.from_pretrained("circulus/canvers-semi-v3.8.1", torch_dtype=torch.float16, safety_checker=None) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("circulus/canvers-semi-v3.8.1")
semi.enable_xformers_memory_efficient_attention()
semi = semi.to(device)
torch.cuda.empty_cache()
if refine == "Yes":
refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", use_safetensors=True, torch_dtype=torch.float16, variant="fp16") if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0")
refiner.enable_xformers_memory_efficient_attention()
refiner = refiner.to(device)
torch.cuda.empty_cache()
image = semi(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images
image = refiner(Prompt, negative_prompt=negative_prompt, image=image, denoising_start=high_noise_frac).images[0]
torch.cuda.empty_cache()
if upscale == "Yes":
refiner = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
refiner.enable_xformers_memory_efficient_attention()
refiner = refiner.to(device)
torch.cuda.empty_cache()
upscaled = refiner(prompt=Prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=15, guidance_scale=0).images[0]
torch.cuda.empty_cache()
return upscaled
else:
return image
else:
if upscale == "Yes":
image = semi(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
upscaler = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
upscaler.enable_xformers_memory_efficient_attention()
upscaler = upscaler.to(device)
torch.cuda.empty_cache()
upscaled = upscaler(prompt=Prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=15, guidance_scale=0).images[0]
torch.cuda.empty_cache()
return upscaled
else:
image = semi(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
torch.cuda.empty_cache()
return image
if Model == "Animagine XL 3.0":
animagine = DiffusionPipeline.from_pretrained("cagliostrolab/animagine-xl-3.0", torch_dtype=torch.float16, safety_checker=None) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("cagliostrolab/animagine-xl-3.0")
animagine.enable_xformers_memory_efficient_attention()
animagine = animagine.to(device)
torch.cuda.empty_cache()
if refine == "Yes":
torch.cuda.empty_cache()
torch.cuda.max_memory_allocated(device=device)
int_image = animagine(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale, output_type="latent").images
torch.cuda.empty_cache()
animagine = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", use_safetensors=True, torch_dtype=torch.float16, variant="fp16") if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0")
animagine.enable_xformers_memory_efficient_attention()
animagine = animagine.to(device)
torch.cuda.empty_cache()
image = animagine(Prompt, negative_prompt=negative_prompt, image=int_image, denoising_start=high_noise_frac).images[0]
torch.cuda.empty_cache()
if upscale == "Yes":
animagine = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
animagine.enable_xformers_memory_efficient_attention()
animagine = animagine.to(device)
torch.cuda.empty_cache()
upscaled = animagine(prompt=Prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=5, guidance_scale=0).images[0]
torch.cuda.empty_cache()
return upscaled
else:
return image
else:
if upscale == "Yes":
image = animagine(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
upscaler = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
upscaler.enable_xformers_memory_efficient_attention()
upscaler = upscaler.to(device)
torch.cuda.empty_cache()
upscaled = upscaler(prompt=Prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=5, guidance_scale=0).images[0]
torch.cuda.empty_cache()
return upscaled
else:
image = animagine(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
torch.cuda.empty_cache()
return image
if Model == "SDXL 1.0":
torch.cuda.empty_cache()
torch.cuda.max_memory_allocated(device=device)
sdxl = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
sdxl.enable_xformers_memory_efficient_attention()
sdxl = sdxl.to(device)
torch.cuda.empty_cache()
if refine == "Yes":
torch.cuda.max_memory_allocated(device=device)
torch.cuda.empty_cache()
image = sdxl(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale, output_type="latent").images
torch.cuda.empty_cache()
sdxl = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", use_safetensors=True, torch_dtype=torch.float16, variant="fp16") if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0")
sdxl.enable_xformers_memory_efficient_attention()
sdxl = sdxl.to(device)
torch.cuda.empty_cache()
refined = sdxl(Prompt, negative_prompt=negative_prompt, image=image, denoising_start=high_noise_frac).images[0]
torch.cuda.empty_cache()
if upscale == "Yes":
sdxl = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
sdxl.enable_xformers_memory_efficient_attention()
sdxl = sdxl.to(device)
torch.cuda.empty_cache()
upscaled = sdxl(prompt=Prompt, negative_prompt=negative_prompt, image=refined, num_inference_steps=5, guidance_scale=0).images[0]
torch.cuda.empty_cache()
return upscaled
else:
return refined
else:
if upscale == "Yes":
image = sdxl(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
torch.cuda.empty_cache()
torch.cuda.max_memory_allocated(device=device)
upscaler = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
upscaler.enable_xformers_memory_efficient_attention()
upscaler = upscaler.to(device)
torch.cuda.empty_cache()
upscaled = upscaler(prompt=Prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=5, guidance_scale=0).images[0]
torch.cuda.empty_cache()
return upscaled
else:
image = sdxl(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
torch.cuda.empty_cache()
if Model == 'FusionXL':
torch.cuda.empty_cache()
torch.cuda.max_memory_allocated(device=device)
pipe = DiffusionPipeline.from_pretrained("circulus/canvers-fusionXL-v1", torch_dtype=torch.float16, safety_checker=None) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("circulus/canvers-real-v3.8.1")
pipe.enable_xformers_memory_efficient_attention()
pipe = pipe.to(device)
torch.cuda.empty_cache()
if refine == "Yes":
torch.cuda.empty_cache()
torch.cuda.max_memory_allocated(device=device)
int_image = pipe(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale, output_type="latent").images
pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", use_safetensors=True, torch_dtype=torch.float16, variant="fp16") if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0")
pipe.enable_xformers_memory_efficient_attention()
pipe = pipe.to(device)
torch.cuda.empty_cache()
image = pipe(Prompt, negative_prompt=negative_prompt, image=int_image, denoising_start=high_noise_frac).images[0]
torch.cuda.empty_cache()
if upscale == "Yes":
torch.cuda.empty_cache()
torch.cuda.max_memory_allocated(device=device)
upscaler = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
upscaler.enable_xformers_memory_efficient_attention()
upscaler = upscaler.to(device)
torch.cuda.empty_cache()
upscaled = upscaler(prompt=Prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=5, guidance_scale=0).images[0]
torch.cuda.empty_cache()
return upscaled
else:
return image
else:
if upscale == "Yes":
image = pipe(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
torch.cuda.empty_cache()
torch.cuda.max_memory_allocated(device=device)
upscaler = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
upscaler.enable_xformers_memory_efficient_attention()
upscaler = upscaler.to(device)
torch.cuda.empty_cache()
upscaled = upscaler(prompt=Prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=5, guidance_scale=0).images[0]
torch.cuda.empty_cache()
return upscaled
else:
image = pipe(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
torch.cuda.empty_cache()
return image
if Model == 'SDXL-Turbo':
torch.cuda.empty_cache()
pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16")
pipe.enable_xformers_memory_efficient_attention()
pipe = pipe.to(device)
image = pipe(prompt=Prompt, num_inference_steps=1, guidance_scale=0.0).images[0]
if refine == "Yes":
refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", use_safetensors=True, torch_dtype=torch.float16, variant="fp16") if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0")
refiner.enable_xformers_memory_efficient_attention()
refiner = refiner.to(device)
torch.cuda.empty_cache()
refined = refiner(Prompt, negative_prompt=negative_prompt, image=image, denoising_start=high_noise_frac).images[0]
torch.cuda.empty_cache()
if upscale == 'Yes':
upscaler = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
upscaler.enable_xformers_memory_efficient_attention()
upscaler = upscaler.to(device)
torch.cuda.empty_cache()
upscaled = upscaler(prompt=Prompt, negative_prompt=negative_prompt, image=refined, num_inference_steps=5, guidance_scale=0).images[0]
torch.cuda.empty_cache()
return upscaled
else:
return refined
if upscale == "Yes":
upscaler = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
upscaler.enable_xformers_memory_efficient_attention()
upscaler = upscaler.to(device)
torch.cuda.empty_cache()
upscaled = upscaler(prompt=Prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=5, guidance_scale=0).images[0]
torch.cuda.empty_cache()
return upscaled
else:
image = pipe(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
torch.cuda.empty_cache()
return image
gr.Interface(fn=genie, inputs=[gr.Radio(['PhotoReal', 'Anime', 'Disney', 'StoryBook', 'SemiReal', 'Animagine XL 3.0', 'SDXL 1.0', 'FusionXL', 'SDXL-Turbo'], value='PhotoReal', label='Choose Model'),
gr.Textbox(label='What you want the AI to generate. 77 Token Limit.'),
gr.Textbox(label='What you Do Not want the AI to generate. 77 Token Limit'),
gr.Slider(512, 1024, 768, step=128, label='Height'),
gr.Slider(512, 1024, 768, step=128, label='Width'),
gr.Slider(1, maximum=15, value=5, step=.25, label='Guidance Scale'),
gr.Slider(5, maximum=100, value=50, step=5, label='Number of Iterations'),
gr.Slider(minimum=0, step=1, maximum=9999999999999999, randomize=True, label='Seed: 0 is Random'),
gr.Radio(["Yes", "No"], label='SDXL 1.0 Refiner: Use if the Image has too much Noise', value='No'),
gr.Slider(minimum=.9, maximum=.99, value=.95, step=.01, label='Refiner Denoise Start %'),
gr.Radio(["Yes", "No"], label = 'SD X2 Latent Upscaler?', value="No")],
outputs=gr.Image(label='Generated Image'),
title="Manju Dream Booth V1.8 with SDXL 1.0 Refiner and SD X2 Latent Upscaler - GPU",
description="<br><br><b/>Warning: This Demo is capable of producing NSFW content.",
article = "If You Enjoyed this Demo and would like to Donate, you can send any amount to any of these Wallets. <br><br>SHIB (BEP20): 0xbE8f2f3B71DFEB84E5F7E3aae1909d60658aB891 <br>PayPal: https://www.paypal.me/ManjushriBodhisattva <br>ETH: 0xbE8f2f3B71DFEB84E5F7E3aae1909d60658aB891 <br>DOGE: D9QdVPtcU1EFH8jDC8jhU9uBcSTqUiA8h6<br><br>Code Monkey: <a href=\"https://huggingface.co/Manjushri\">Manjushri</a>").launch(debug=True, max_threads=80) |