Spaces:
Running
on
A100
Running
on
A100
File size: 10,523 Bytes
c01188e 2951b6b c01188e 2951b6b c01188e 2951b6b c01188e 46bd9ac c01188e 46bd9ac c01188e 46bd9ac c01188e a659304 c01188e 46bd9ac c01188e 46bd9ac c01188e a659304 c01188e 2951b6b c01188e 2951b6b c01188e a659304 cf3ff1a c01188e a659304 c01188e 592470d 2951b6b c01188e a659304 c01188e 2951b6b c01188e a659304 c01188e 2951b6b c01188e |
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 |
from diffusers import (
StableDiffusionXLControlNetImg2ImgPipeline,
ControlNetModel,
LCMScheduler,
AutoencoderKL,
AutoencoderTiny,
)
from compel import Compel, ReturnedEmbeddingsType
import torch
from pipelines.utils.canny_gpu import SobelOperator
try:
import intel_extension_for_pytorch as ipex # type: ignore
except:
pass
import psutil
from config import Args
from pydantic import BaseModel, Field
from PIL import Image
import math
controlnet_model = "diffusers/controlnet-canny-sdxl-1.0"
model_id = "stabilityai/stable-diffusion-xl-base-1.0"
lcm_lora_id = "latent-consistency/lcm-lora-sdxl"
taesd_model = "madebyollin/taesdxl"
default_prompt = "Portrait of The Terminator with , glare pose, detailed, intricate, full of colour, cinematic lighting, trending on artstation, 8k, hyperrealistic, focused, extreme details, unreal engine 5 cinematic, masterpiece"
default_negative_prompt = "blurry, low quality, render, 3D, oversaturated"
page_content = """
<h1 class="text-3xl font-bold">Real-Time Latent Consistency Model SDXL</h1>
<h3 class="text-xl font-bold">SDXL + LCM + LoRA + Controlnet</h3>
<p class="text-sm">
This demo showcases
<a
href="https://huggingface.co/blog/lcm_lora"
target="_blank"
class="text-blue-500 underline hover:no-underline">LCM LoRA</a>
+ SDXL + Controlnet + Image to Image pipeline using
<a
href="https://huggingface.co/docs/diffusers/main/en/using-diffusers/lcm#performing-inference-with-lcm"
target="_blank"
class="text-blue-500 underline hover:no-underline">Diffusers</a
> with a MJPEG stream server.
</p>
<p class="text-sm text-gray-500">
Change the prompt to generate different images, accepts <a
href="https://github.com/damian0815/compel/blob/main/doc/syntax.md"
target="_blank"
class="text-blue-500 underline hover:no-underline">Compel</a
> syntax.
</p>
"""
class Pipeline:
class Info(BaseModel):
name: str = "controlnet+loras+sdxl"
title: str = "SDXL + LCM + LoRA + Controlnet"
description: str = "Generates an image from a text prompt"
input_mode: str = "image"
page_content: str = page_content
class InputParams(BaseModel):
prompt: str = Field(
default_prompt,
title="Prompt",
field="textarea",
id="prompt",
)
negative_prompt: str = Field(
default_negative_prompt,
title="Negative Prompt",
field="textarea",
id="negative_prompt",
hide=True,
)
seed: int = Field(
2159232, min=0, title="Seed", field="seed", hide=True, id="seed"
)
steps: int = Field(
1, min=1, max=10, title="Steps", field="range", hide=True, id="steps"
)
width: int = Field(
1024, min=2, max=15, title="Width", disabled=True, hide=True, id="width"
)
height: int = Field(
1024, min=2, max=15, title="Height", disabled=True, hide=True, id="height"
)
guidance_scale: float = Field(
1.0,
min=0,
max=2.0,
step=0.001,
title="Guidance Scale",
field="range",
hide=True,
id="guidance_scale",
)
strength: float = Field(
1,
min=0.25,
max=1.0,
step=0.0001,
title="Strength",
field="range",
hide=True,
id="strength",
)
controlnet_scale: float = Field(
0.5,
min=0,
max=1.0,
step=0.001,
title="Controlnet Scale",
field="range",
hide=True,
id="controlnet_scale",
)
controlnet_start: float = Field(
0.0,
min=0,
max=1.0,
step=0.001,
title="Controlnet Start",
field="range",
hide=True,
id="controlnet_start",
)
controlnet_end: float = Field(
1.0,
min=0,
max=1.0,
step=0.001,
title="Controlnet End",
field="range",
hide=True,
id="controlnet_end",
)
canny_low_threshold: float = Field(
0.31,
min=0,
max=1.0,
step=0.001,
title="Canny Low Threshold",
field="range",
hide=True,
id="canny_low_threshold",
)
canny_high_threshold: float = Field(
0.125,
min=0,
max=1.0,
step=0.001,
title="Canny High Threshold",
field="range",
hide=True,
id="canny_high_threshold",
)
debug_canny: bool = Field(
False,
title="Debug Canny",
field="checkbox",
hide=True,
id="debug_canny",
)
def __init__(self, args: Args, device: torch.device, torch_dtype: torch.dtype):
controlnet_canny = ControlNetModel.from_pretrained(
controlnet_model, torch_dtype=torch_dtype
).to(device)
vae = AutoencoderKL.from_pretrained(
"madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch_dtype
)
if args.safety_checker:
self.pipe = StableDiffusionXLControlNetImg2ImgPipeline.from_pretrained(
model_id,
controlnet=controlnet_canny,
vae=vae,
)
else:
self.pipe = StableDiffusionXLControlNetImg2ImgPipeline.from_pretrained(
model_id,
safety_checker=None,
controlnet=controlnet_canny,
vae=vae,
)
self.canny_torch = SobelOperator(device=device)
# Load LCM LoRA
self.pipe.load_lora_weights(lcm_lora_id, adapter_name="lcm")
self.pipe.load_lora_weights(
"CiroN2022/toy-face",
weight_name="toy_face_sdxl.safetensors",
adapter_name="toy",
)
self.pipe.set_adapters(["lcm", "toy"], adapter_weights=[1.0, 0.8])
self.pipe.scheduler = LCMScheduler.from_config(self.pipe.scheduler.config)
self.pipe.set_progress_bar_config(disable=True)
self.pipe.to(device=device, dtype=torch_dtype).to(device)
if args.sfast:
from sfast.compilers.stable_diffusion_pipeline_compiler import (
compile,
CompilationConfig,
)
config = CompilationConfig.Default()
config.enable_xformers = True
config.enable_triton = True
config.enable_cuda_graph = True
self.pipe = compile(self.pipe, config=config)
if device.type != "mps":
self.pipe.unet.to(memory_format=torch.channels_last)
if args.compel:
self.pipe.compel_proc = Compel(
tokenizer=[self.pipe.tokenizer, self.pipe.tokenizer_2],
text_encoder=[self.pipe.text_encoder, self.pipe.text_encoder_2],
returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,
requires_pooled=[False, True],
)
if args.taesd:
self.pipe.vae = AutoencoderTiny.from_pretrained(
taesd_model, torch_dtype=torch_dtype, use_safetensors=True
).to(device)
if args.torch_compile:
self.pipe.unet = torch.compile(
self.pipe.unet, mode="reduce-overhead", fullgraph=True
)
self.pipe.vae = torch.compile(
self.pipe.vae, mode="reduce-overhead", fullgraph=True
)
self.pipe(
prompt="warmup",
image=[Image.new("RGB", (768, 768))],
control_image=[Image.new("RGB", (768, 768))],
)
def predict(self, params: "Pipeline.InputParams") -> Image.Image:
generator = torch.manual_seed(params.seed)
prompt = params.prompt
negative_prompt = params.negative_prompt
prompt_embeds = None
pooled_prompt_embeds = None
negative_prompt_embeds = None
negative_pooled_prompt_embeds = None
if hasattr(self.pipe, "compel_proc"):
_prompt_embeds, pooled_prompt_embeds = self.pipe.compel_proc(
[params.prompt, params.negative_prompt]
)
prompt = None
negative_prompt = None
prompt_embeds = _prompt_embeds[0:1]
pooled_prompt_embeds = pooled_prompt_embeds[0:1]
negative_prompt_embeds = _prompt_embeds[1:2]
negative_pooled_prompt_embeds = pooled_prompt_embeds[1:2]
control_image = self.canny_torch(
params.image, params.canny_low_threshold, params.canny_high_threshold
)
steps = params.steps
strength = params.strength
if int(steps * strength) < 1:
steps = math.ceil(1 / max(0.10, strength))
results = self.pipe(
image=params.image,
control_image=control_image,
prompt=prompt,
negative_prompt=negative_prompt,
prompt_embeds=prompt_embeds,
pooled_prompt_embeds=pooled_prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
generator=generator,
strength=strength,
num_inference_steps=steps,
guidance_scale=params.guidance_scale,
width=params.width,
height=params.height,
output_type="pil",
controlnet_conditioning_scale=params.controlnet_scale,
control_guidance_start=params.controlnet_start,
control_guidance_end=params.controlnet_end,
)
nsfw_content_detected = (
results.nsfw_content_detected[0]
if "nsfw_content_detected" in results
else False
)
if nsfw_content_detected:
return None
result_image = results.images[0]
if params.debug_canny:
# paste control_image on top of result_image
w0, h0 = (200, 200)
control_image = control_image.resize((w0, h0))
w1, h1 = result_image.size
result_image.paste(control_image, (w1 - w0, h1 - h0))
return result_image
|