import torch
import time
from PIL import Image

from lyrasd_model import LyraSdControlnetTxt2ImgPipeline

# 存放模型文件的路径，应该包含一下结构：
#   1. clip 模型
#   2. 转换好的优化后的 unet 模型
#   3. 转换好的优化后的 controlnet 模型
#   4. vae 模型
#   5. scheduler 配置

# LyraSD 的 C++ 编译动态链接库，其中包含 C++ CUDA 计算的细节
lib_path = "./lyrasd_model/lyrasd_lib/libth_lyrasd_cu12_sm86.so"
model_path = "./models/lyrasd_rev_animated"
canny_controlnet_path = "./models/lyrasd_canny"
# 构建 Txt2Img 的 Pipeline
pipe = LyraSdControlnetTxt2ImgPipeline(model_path, lib_path)

# load Controlnet 模型，最多load 3个
start = time.perf_counter()
pipe.load_controlnet_model("canny", canny_controlnet_path, "fp32")
print(f"controlnet load cost: {time.perf_counter() - start}")
# 可以通过 get_loaded_controlnet 方法获取目前已经load 好的Controlnet list
print(pipe.get_loaded_controlnet())

# 可以通过unload_controlnet_model 方法unload Controlnet
# pipe.unload_controlnet_model("canny")

control_img = Image.open("control_bird_canny.png")

# 准备应用的输入和超参数
prompt = "a blue bird"
negative_prompt = "NSFW"
height, width = 512, 512
steps = 20
guidance_scale = 7.5
generator = torch.Generator().manual_seed(123)
num_images = 1
guess_mode = False

# 可以一次性load 3 个 Controlnets，达到multi Controlnet的效果，这里的参数的长度需要对其
# Controlnet 所输入的img list 长度应该和 controlnet scale 与 Controlnet name 一致，而内部的list长度需要和batch size一致
# 对应的index 可以对其
controlnet_images = [[control_img]]
controlnet_scale = [0.5]
controlnet_names = ['canny']

# 推理生成，返回结果都是生成好的 PIL.Image

start = time.perf_counter()
images = pipe(prompt, height, width, steps,
                guidance_scale, negative_prompt, num_images,
                generator=generator, controlnet_images=controlnet_images,
                controlnet_scale=controlnet_scale, controlnet_names=controlnet_names,
                guess_mode=guess_mode
                )
print("cur cost: ",time.perf_counter() - start)

# 存储生成的图片
for i, image in enumerate(images):
    image.save(f"./outputs/res_controlnet_txt2img_{i}.png")