File size: 5,572 Bytes
a3d6c18 42ef134 a3d6c18 b71808f a3d6c18 1377831 a3d6c18 42ef134 a3d6c18 b71808f 42ef134 1377831 42ef134 1bc457e a3d6c18 42ef134 a3d6c18 1377831 42ef134 a3d6c18 42ef134 a3d6c18 42ef134 a3d6c18 1377831 a3d6c18 1bc457e a3d6c18 1377831 a3d6c18 1bc457e a3d6c18 1377831 a3d6c18 |
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 |
from io import BytesIO
from typing import List, Optional, Union
import torch
from diffusers import (
ControlNetModel,
StableDiffusionControlNetInpaintPipeline,
StableDiffusionInpaintPipeline,
UniPCMultistepScheduler,
)
from PIL import Image, ImageFilter, ImageOps
import internals.util.image as ImageUtil
from internals.data.result import Result
from internals.pipelines.commons import AbstractPipeline
from internals.pipelines.controlnets import ControlNet
from internals.pipelines.high_res import HighRes
from internals.pipelines.remove_background import RemoveBackgroundV2
from internals.pipelines.upscaler import Upscaler
from internals.util.commons import download_image
from internals.util.config import get_hf_cache_dir, get_model_dir
class ReplaceBackground(AbstractPipeline):
__loaded = False
def load(
self,
upscaler: Optional[Upscaler] = None,
remove_background: Optional[RemoveBackgroundV2] = None,
controlnet: Optional[ControlNet] = None,
high_res: Optional[HighRes] = None,
):
if self.__loaded:
return
controlnet_model = ControlNetModel.from_pretrained(
"lllyasviel/control_v11p_sd15_lineart",
torch_dtype=torch.float16,
cache_dir=get_hf_cache_dir(),
).to("cuda")
if controlnet:
controlnet.load_linearart()
pipe = StableDiffusionControlNetInpaintPipeline(
**controlnet.pipe.components
)
pipe.controlnet = controlnet_model
else:
pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
get_model_dir(),
controlnet=controlnet_model,
torch_dtype=torch.float16,
cache_dir=get_hf_cache_dir(),
)
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
pipe.to("cuda")
self.pipe = pipe
if not high_res:
high_res = HighRes()
high_res.load()
self.high_res = high_res
if not upscaler:
upscaler = Upscaler()
upscaler.load()
self.upscaler = upscaler
if not remove_background:
remove_background = RemoveBackgroundV2()
self.remove_background = remove_background
self.__loaded = True
@torch.inference_mode()
def replace(
self,
image: Union[str, Image.Image],
width: int,
height: int,
product_scale_width: float,
prompt: List[str],
negative_prompt: List[str],
resize_dimension: int,
conditioning_scale: float,
seed: int,
steps: int,
apply_high_res: bool = False,
):
if type(image) is str:
image = download_image(image)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
image = image.convert("RGB")
if max(image.size) > 1536:
image = ImageUtil.resize_image(image, dimension=1536)
image = self.remove_background.remove(image)
width = int(width)
height = int(height)
n_width = int(width * product_scale_width)
n_height = int(n_width * height // width)
print(width, height, n_width, n_height)
image = ImageUtil.padd_image(image, n_width, n_height)
f_image = Image.new("RGBA", (width, height), (0, 0, 0, 0))
f_image.paste(image, ((width - n_width) // 2, (height - n_height) // 2))
image = f_image
mask = image.copy()
pixdata = mask.load()
w, h = mask.size
for y in range(h):
for x in range(w):
item = pixdata[x, y]
if item[3] == 0:
pixdata[x, y] = (255, 255, 255, 255)
else:
pixdata[x, y] = (0, 0, 0, 255)
mask = mask.convert("RGB")
condition_image = ControlNet.linearart_condition_image(image)
if apply_high_res and hasattr(self, "high_res"):
(w, h) = self.high_res.get_intermediate_dimension(width, height)
images = self.pipe.__call__(
prompt=prompt,
negative_prompt=negative_prompt,
image=image,
mask_image=mask,
control_image=condition_image,
controlnet_conditioning_scale=conditioning_scale,
guidance_scale=9,
strength=1,
num_inference_steps=steps,
height=w,
width=h,
).images
result = self.high_res.apply(
prompt=prompt,
negative_prompt=negative_prompt,
images=images,
width=width,
height=width,
steps=steps,
)
else:
result = self.pipe.__call__(
prompt=prompt,
negative_prompt=negative_prompt,
image=image,
mask_image=mask,
control_image=condition_image,
controlnet_conditioning_scale=conditioning_scale,
guidance_scale=9,
strength=1,
height=height,
num_inference_steps=steps,
width=width,
)
result = Result.from_result(result)
images, has_nsfw = result
if not has_nsfw:
for i in range(len(images)):
images[i].paste(image, (0, 0), image)
return (images, has_nsfw)
|