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from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler | |
from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel, AutoencoderKL | |
import torch | |
import pickle as pkl | |
import spaces | |
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 | |
) | |
#pipe.enable_model_cpu_offload() | |
#if pipe.safety_checker is not None: | |
# pipe.safety_checker = lambda images, **kwargs: (images, [False]) | |
#pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) | |
#pipe.to(device) | |
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): | |
#lineart_anime = LineartAnimeDetector.from_pretrained("lllyasviel/Annotators") | |
#canny = CannyDetector() | |
#lineart_anime_img = lineart_anime(image) | |
#canny_img = canny(image) | |
#canny_img = canny_img.resize((lineart_anime(image).width, lineart_anime(image).height)) | |
re_image = invert_image(image) | |
detectors = [re_image, image] | |
print(detectors) | |
return detectors | |
def generate(pipe, detectors, prompt, negative_prompt): | |
pipe.to("cuda") | |
default_pos = "" | |
default_neg = "" | |
prompt = default_pos + prompt | |
negative_prompt = default_neg + negative_prompt | |
print(type(pipe)) | |
image = pipe( | |
prompt=prompt, | |
negative_prompt = negative_prompt, | |
image=detectors, | |
num_inference_steps=50, | |
controlnet_conditioning_scale=[1.0, 0.2], | |
).images[0] | |
return image |