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from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel, AutoencoderKL
import torch
import pickle as pkl
device = "cuda"
def get_cn_pipeline():
controlnets = [
ControlNetModel.from_pretrained("./controlnet/lineart", torch_dtype=torch.float16, use_safetensors=True),
ControlNetModel.from_pretrained("mattyamonaca/controlnet_line2line_xl", torch_dtype=torch.float16)
]
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"cagliostrolab/animagine-xl-3.1", controlnet=controlnets, vae=vae, torch_dtype=torch.float16
)
return pipe
def get_ip_pipeline():
controlnets = [
ControlNetModel.from_pretrained("./controlnet/lineart", torch_dtype=torch.float16, use_safetensors=True),
ControlNetModel.from_pretrained("mattyamonaca/controlnet_line2line_xl", torch_dtype=torch.float16)
]
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"cagliostrolab/animagine-xl-3.1", controlnet=controlnets, vae=vae, torch_dtype=torch.float16
)
pipe.load_ip_adapter(
"ozzygt/sdxl-ip-adapter",
"",
weight_name="ip-adapter_sdxl_vit-h.safetensors"
)
return pipe
def invert_image(img):
# 画像を読み込む
# 画像をグレースケールに変換（もしもともと白黒でない場合）
img = img.convert('L')
# 画像の各ピクセルを反転
inverted_img = img.point(lambda p: 255 - p)
# 反転した画像を保存
return inverted_img
def get_cn_detector(image):
re_image = invert_image(image)
detectors = [re_image, image]
return detectors
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