Pytorch人体姿态骨架生成图像

ControlNet是一个稳定扩散模型，可以复制构图和人体姿势。ControlNet解决了生成想要的确切姿势困难的问题。 Human Pose使用OpenPose检测关键点，如头部、肩膀、手的位置等。它适用于复制人类姿势，但不适用于其他细节，如服装、发型和背景。

使用方法： 输入一个图像，并提示模型生成一个图像。Openpose将为你检测姿势。

🔹 本案例需使用Pytorch-1.8 GPU-P100及以上规格运行

🔹 点击Run in ModelArts，将会进入到ModelArts CodeLab中，这时需要你登录华为云账号，如果没有账号，则需要注册一个，且要进行实名认证，登录之后，等待片刻，即可进入到CodeLab的运行环境

1. 环境准备

为了方便用户下载使用及快速体验，本案例已将代码及control_sd15_openpose预训练模型转存至华为云OBS中。注意：为了使用该模型与权重，你必须接受该模型所要求的License，请访问huggingface的lllyasviel/ControlNet, 仔细阅读里面的License。模型下载与加载需要几分钟时间。

import os
import moxing as mox

parent = os.path.join(os.getcwd(),'ControlNet')
if not os.path.exists(parent):
    mox.file.copy_parallel('obs://modelarts-labs-bj4-v2/case_zoo/ControlNet/ControlNet',parent)
    if os.path.exists(parent):
        print('Code Copy Completed.')
    else:
        raise Exception('Failed to Copy the Code.')
else:
    print("Code already exists!")

pose_model_path = os.path.join(os.getcwd(),"ControlNet/models/control_sd15_openpose.pth")
body_model_path = os.path.join(os.getcwd(),"ControlNet/annotator/ckpts/body_pose_model.pth")
hand_model_path = os.path.join(os.getcwd(),"ControlNet/annotator/ckpts/hand_pose_model.pth")
if not os.path.exists(pose_model_path):
    mox.file.copy_parallel('obs://modelarts-labs-bj4-v2/case_zoo/ControlNet/ControlNet_models/control_sd15_openpose.pth',pose_model_path)
    mox.file.copy_parallel('obs://modelarts-labs-bj4-v2/case_zoo/ControlNet/ControlNet_models/body_pose_model.pth',body_model_path)
    mox.file.copy_parallel('obs://modelarts-labs-bj4-v2/case_zoo/ControlNet/ControlNet_models/hand_pose_model.pth',hand_model_path)
    if os.path.exists(pose_model_path):
        print('Models Download Completed')
    else:
        raise Exception('Failed to Copy the Models.')
else:
    print("Model Packages already exists!")

check GPU & 安装依赖

大约耗时1min

!nvidia-smi

%cd ControlNet

!pip uninstall torch torchtext -y
!pip install torch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 
!pip install omegaconf==2.1.1 einops==0.3.0
!pip install pytorch-lightning==1.5.0
!pip install transformers==4.19.2 open_clip_torch==2.0.2
!pip install gradio==3.24.1
!pip install translate==3.6.1
!pip install scikit-image==0.19.3
!pip install basicsr==1.4.2

导包

import config
import cv2
import einops
import gradio as gr
import numpy as np
import torch
import random

from pytorch_lightning import seed_everything
from annotator.util import resize_image, HWC3
from annotator.openpose import OpenposeDetector
from cldm.model import create_model, load_state_dict
from cldm.ddim_hacked import DDIMSampler

from translate import Translator
from PIL import Image
import matplotlib.pyplot as plt

2. 加载模型

apply_openpose = OpenposeDetector()

model = create_model('./models/cldm_v15.yaml').cpu()
model.load_state_dict(load_state_dict('./models/control_sd15_openpose.pth', location='cuda'))
model = model.cuda()
ddim_sampler = DDIMSampler(model)

3. 人体姿态生成图像

def infer(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, detect_resolution, ddim_steps, guess_mode, strength, scale, seed, eta):
    trans = Translator(from_lang="ZH",to_lang="EN-US")
    prompt = trans.translate(prompt)
    a_prompt = trans.translate(a_prompt)
    n_prompt = trans.translate(n_prompt)
    
    # 图像预处理
    with torch.no_grad():
        if type(input_image) is str:
            input_image = np.array(Image.open(input_image))
        input_image = HWC3(input_image)
        detected_map, _ = apply_openpose(resize_image(input_image, detect_resolution))
        detected_map = HWC3(detected_map)
        img = resize_image(input_image, image_resolution)
        H, W, C = img.shape
        
        # 初始化检测映射
        detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_NEAREST)

        control = torch.from_numpy(detected_map.copy()).float().cuda() / 255.0
        control = torch.stack([control for _ in range(num_samples)], dim=0)
        control = einops.rearrange(control, 'b h w c -> b c h w').clone()

        # 设置随机种子
        if seed == -1:
            seed = random.randint(0, 65535)
        seed_everything(seed)

        if config.save_memory:
            model.low_vram_shift(is_diffusing=False)

        cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning([prompt + ', ' + a_prompt] * num_samples)]}
        un_cond = {"c_concat": None if guess_mode else [control], "c_crossattn": [model.get_learned_conditioning([n_prompt] * num_samples)]}
        shape = (4, H // 8, W // 8)

        if config.save_memory:
            model.low_vram_shift(is_diffusing=True)

        # 采样
        model.control_scales = [strength * (0.825 ** float(12 - i)) for i in range(13)] if guess_mode else ([strength] * 13)  # Magic number. IDK why. Perhaps because 0.825**12<0.01 but 0.826**12>0.01
        samples, intermediates = ddim_sampler.sample(ddim_steps, num_samples,
                                                     shape, cond, verbose=False, eta=eta,
                                                     unconditional_guidance_scale=scale,
                                                     unconditional_conditioning=un_cond)

        if config.save_memory:
            model.low_vram_shift(is_diffusing=False)

        # 后处理
        x_samples = model.decode_first_stage(samples)
        x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c') * 127.5 + 127.5).cpu().numpy().clip(0, 255).astype(np.uint8)

        results = [x_samples[i] for i in range(num_samples)]
    return [detected_map] + results

设置参数，生成图像

上传您的图像至./ControlNet/test_imgs/ 路径下,然后更改图像路径及其他参数后，点击运行。

➡参数说明：

🔸 img_path：输入图像路径，黑白稿

🔸 prompt：提示词

🔸 a_prompt：次要的提示

🔸 n_prompt: 负面提示，不想要的内容

🔸 image_resolution: 对输入的图片进行最长边等比resize

🔸 detect_resolution: 中间生成条件图像的分辨率

🔸 scale：文本提示的控制强度，越大越强

🔸 guess_mode: 盲猜模式，默认关闭，开启后生成图像将不受prompt影响，使用更多样性的结果，生成后得到不那么遵守图像条件的结果

🔸 seed: 随机种子

🔸 ddim_steps: 采样步数，一般15-30，值越大越精细,耗时越长

🔸 DDIM eta: 生成过程中的随机噪声系数，一般选0或1，1表示有噪声更多样，0表示无噪声，更遵守描述条件

🔸 strength: 这是应用 ControlNet 的步骤数。它类似于图像到图像中的去噪强度。如果指导强度为 1，则 ControlNet 应用于 100% 的采样步骤。如果引导强度为 0.7 并且您正在执行 50 个步骤，则 ControlNet 将应用于前 70% 的采样步骤，即前 35 个步骤。

#@title ControlNet-OpenPose 

img_path = "test_imgs/pose1.png" #@param {type:"string"}
prompt = "优雅的女士" #@param {type:"string"}
seed = 1685862398 #@param {type:"slider", min:-1, max:2147483647, step:1}
guess_mode = False #@param {type:"raw", dropdown}

a_prompt = '质量最好，非常详细'
n_prompt = '长体，下肢，解剖不好，手不好，手指缺失，手指多，手指少，裁剪，质量最差，质量低'
num_samples = 1
image_resolution = 512
detect_resolution = 512
ddim_steps = 20
strength = 1.0
scale = 9.0
eta = 0.0

np_imgs = infer(img_path, prompt, a_prompt, n_prompt, num_samples, image_resolution, detect_resolution, ddim_steps, guess_mode, strength, scale, seed, eta)

ori = Image.open(img_path)
src = Image.fromarray(np_imgs[0])
dst = Image.fromarray(np_imgs[1])
fig = plt.figure(figsize=(25, 10))

ax1 = fig.add_subplot(1, 3, 1)
plt.title('Orginal image', fontsize=16)
ax1.axis('off')
ax1.imshow(ori)

ax2 = fig.add_subplot(1, 3, 2)
plt.title('Pose image', fontsize=16)
ax2.axis('off')
ax2.imshow(src)

ax3 = fig.add_subplot(1, 3, 3)
plt.title('Generate image', fontsize=16)
ax3.axis('off')
ax3.imshow(dst)

plt.show()

上传自己的照片，输入你的prompt提示词

运行结果：

4. Gradio可视化部署

Gradio应用启动后可在下方页面上传图片根据提示生成图像，您也可以分享public url在手机端，PC端进行访问生成图像。

请注意：在图像生成需要消耗显存，您可以在左侧操作栏查看您的实时资源使用情况，点击GPU显存使用率即可查看，当显存不足时，您生成图像可能会报错，此时，您可以通过重启kernel的方式重置，然后重头运行即可规避。

block = gr.Blocks().queue()
with block:
    with gr.Row():
        gr.Markdown("## 💃人体姿态生成图像")
    with gr.Row():
        with gr.Column():
            gr.Markdown("请上传一张人像图，设置好参数后，点击Run")
            input_image = gr.Image(source='upload', type="numpy")
            prompt = gr.Textbox(label="描述")
            run_button = gr.Button(label="Run")
            with gr.Accordion("高级选项", open=False):
                num_samples = gr.Slider(label="Images", minimum=1, maximum=3, value=1, step=1)
                image_resolution = gr.Slider(label="Image Resolution", minimum=256, maximum=768, value=512, step=64)
                strength = gr.Slider(label="Control Strength", minimum=0.0, maximum=2.0, value=1.0, step=0.01)
                guess_mode = gr.Checkbox(label='Guess Mode', value=False)
                detect_resolution = gr.Slider(label="OpenPose Resolution", minimum=128, maximum=1024, value=512, step=1)
                ddim_steps = gr.Slider(label="Steps", minimum=1, maximum=30, value=20, step=1)
                scale = gr.Slider(label="Guidance Scale", minimum=0.1, maximum=30.0, value=9.0, step=0.1)
                seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, randomize=True)
                eta = gr.Number(label="eta (DDIM)", value=0.0)
                a_prompt = gr.Textbox(label="Added Prompt", value='best quality, extremely detailed')
                n_prompt = gr.Textbox(label="Negative Prompt",
                                      value='longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality')
        with gr.Column():
            result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery").style(grid=2, height='auto')
    ips = [input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, detect_resolution, ddim_steps, guess_mode, strength, scale, seed, eta]
    run_button.click(fn=infer, inputs=ips, outputs=[result_gallery])
block.launch(share=True)

INFO:botocore.vendored.requests.packages.urllib3.connectionpool:Starting new HTTP connection (1): proxy.modelarts.com

INFO:botocore.vendored.requests.packages.urllib3.connectionpool:Starting new HTTPS connection (1): www.huaweicloud.com
Running on local URL:  http://127.0.0.1:7860

Running on public URL: https://96b421e81ebf0fe302.gradio.live
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