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(reference_flg):
    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
    )

    if reference_flg == True:
        pipe.load_ip_adapter(
            "h94/IP-Adapter",
            subfolder="sdxl_models",
            weight_name="ip-adapter-plus_sdxl_vit-h.bin"
        )

    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

@spaces.GPU
def generate(pipe, detectors, prompt, negative_prompt, reference_flg=False, reference_img=None):
    pipe.to("cuda")
    default_pos = ""
    default_neg = ""
    prompt = default_pos + prompt 
    negative_prompt = default_neg + negative_prompt 
    

    if reference_flg==False:
        image = pipe(
                    prompt=prompt,
                    negative_prompt = negative_prompt,
                    image=detectors,
                    num_inference_steps=50,
                    controlnet_conditioning_scale=[1.0, 0.2],
                ).images[0]
    else:
        image = pipe(
                    prompt=prompt,
                    negative_prompt = negative_prompt,
                    image=detectors,
                    num_inference_steps=50,
                    controlnet_conditioning_scale=[1.0, 0.2],
                    ip_adapter_image=reference_img,
                ).images[0]

    return image