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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_sm80.so"
model_path = "./models/rev-animated"
canny_controlnet_path = "./models/canny"
torch.classes.load_library(lib_path)
# 构建 Txt2Img 的 Pipeline
pipe = LyraSdControlnetTxt2ImgPipeline()
pipe.reload_pipe(model_path)
# load Controlnet 模型，最多load 3个
start = time.perf_counter()
pipe.load_controlnet_model_v2("canny", canny_controlnet_path)
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")
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