前言：本篇博客记录使用Stable Diffusion模型进行推断时借鉴的相关资料和操作流程。

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1. 环境搭建和预训练模型准备

环境搭建

pip install diffusers transformers accelerate scipy safetensors

预训练模型下载

关于 huggingface 网站总是崩溃的情况，找到一个解决办法，就是可以通过脚本来下载

第一步：安装 huggingface_hub，使用命令 pip install huggingface_hub
第二步：下载具体模型，使用命令 python model_download.py --repo_id model_id，其中，model_id 为要下载的模型，比如SD v2.1 版本的model_id可以是 stabilityai/stable-diffusion-2-1；SD v1.5 版本的model_id可以是 runwayml/stable-diffusion-v1-5. model_id 的查找方式是在huggingface 网站直接搜索需要的模型（如下图），得到的「模型来源/版本」的组合即为所需。

model_download.py文件来自这个链接。

# usage     : python model_download.py --repo_id repo_id
# example   : python model_download.py --repo_id facebook/opt-350m
import argparse
import time
import requests
import json
import os
from huggingface_hub import snapshot_download
import platform
from tqdm import tqdm
from urllib.request import urlretrieve


def _log(_repo_id, _type, _msg):
    date1 = time.strftime('%Y-%m-%d %H:%M:%S')
    print(date1 + " " + _repo_id + " " + _type + " :" + _msg)


def _download_model(_repo_id, _repo_type):
    if _repo_type == "model":
        _local_dir = 'dataroot/models/' + _repo_id
    else:
        _local_dir = 'dataroot/datasets/' + _repo_id
    try:
        if _check_Completed(_repo_id, _local_dir):
            return True, "check_Completed ok"
    except Exception as e:
        return False, "check_Complete exception," + str(e)
    _cache_dir = 'caches/' + _repo_id

    _local_dir_use_symlinks = True
    if platform.system().lower() == 'windows':
        _local_dir_use_symlinks = False
    try:
        if _repo_type == "model":
            snapshot_download(repo_id=_repo_id, cache_dir=_cache_dir, local_dir=_local_dir, local_dir_use_symlinks=_local_dir_use_symlinks,
                              resume_download=True, max_workers=4)
        else:
            snapshot_download(repo_id=_repo_id, cache_dir=_cache_dir, local_dir=_local_dir, local_dir_use_symlinks=_local_dir_use_symlinks,
                              resume_download=True, max_workers=4, repo_type="dataset")
    except Exception as e:
        error_msg = str(e)
        if ("401 Client Error" in error_msg):
            return True, error_msg
        else:
            return False, error_msg
    _removeHintFile(_local_dir)
    return True, ""


def _writeHintFile(_local_dir):
    file_path = _local_dir + '/~incomplete.txt'
    if not os.path.exists(file_path):
        if not os.path.exists(_local_dir):
            os.makedirs(_local_dir)
        open(file_path, 'w').close()


def _removeHintFile(_local_dir):
    file_path = _local_dir + '/~incomplete.txt'
    if os.path.exists(file_path):
        os.remove(file_path)


def _check_Completed(_repo_id, _local_dir):
    _writeHintFile(_local_dir)
    url = 'https://huggingface.co/api/models/' + _repo_id
    response = requests.get(url)
    if response.status_code == 200:
        data = json.loads(response.text)
    else:
        return False
    for sibling in data["siblings"]:
        if not os.path.exists(_local_dir + "/" + sibling["rfilename"]):
            return False
    _removeHintFile(_local_dir)
    return True


def download_model_retry(_repo_id, _repo_type):
    i = 0
    flag = False
    msg = ""
    while True:
        flag, msg = _download_model(_repo_id, _repo_type)
        if flag:
            _log(_repo_id, "success", msg)
            break
        else:
            _log(_repo_id, "fail", msg)
            if i > 1440:
                msg = "retry over one day"
                _log(_repo_id, "fail", msg)
                break
            timeout = 60
            time.sleep(timeout)
            i = i + 1
            _log(_repo_id, "retry", str(i))
    return flag, msg


def _fetchFileList(files):
    _files = []
    for file in files:
        if file['type'] == 'dir':
            filesUrl = 'https://e.aliendao.cn/' + file['path'] + '?json=true'
            response = requests.get(filesUrl)
            if response.status_code == 200:
                data = json.loads(response.text)
                for file1 in data['data']['files']:
                    if file1['type'] == 'dir':
                        filesUrl = 'https://e.aliendao.cn/' + \
                            file1['path'] + '?json=true'
                        response = requests.get(filesUrl)
                        if response.status_code == 200:
                            data = json.loads(response.text)
                            for file2 in data['data']['files']:
                                _files.append(file2)
                    else:
                        _files.append(file1)
        else:
            if file['name'] != '.gitattributes':
                _files.append(file)
    return _files


def _download_file_resumable(url, save_path, i, j, chunk_size=1024*1024):
    headers = {}
    r = requests.get(url, headers=headers, stream=True, timeout=(20, 60))
    if r.status_code == 403:
        _log(url, "download", '下载资源发生了错误，请使用正确的token')
        return False
    bar_format = '{desc}{percentage:3.0f}%|{bar}|{n_fmt}M/{total_fmt}M [{elapsed}<{remaining}, {rate_fmt}]'
    _desc = str(i) + ' of ' + str(j) + '(' + save_path.split('/')[-1] + ')'
    total_length = int(r.headers.get('content-length'))
    if os.path.exists(save_path):
        temp_size = os.path.getsize(save_path)
    else:
        temp_size = 0
    retries = 0
    if temp_size >= total_length:
        return True
    # 小文件显示
    if total_length < chunk_size:
        with open(save_path, 'wb') as f:
            for chunk in r.iter_content(chunk_size=chunk_size):
                if chunk:
                    f.write(chunk)
        with tqdm(total=1, desc=_desc, unit='MB', bar_format=bar_format) as pbar:
            pbar.update(1)
    else:
        headers['Range'] = f'bytes={temp_size}-{total_length}'
        r = requests.get(url, headers=headers, stream=True,
                         verify=False, timeout=(20, 60))
        data_size = round(total_length / 1024 / 1024)
        with open(save_path, 'ab') as fd:
            fd.seek(temp_size)
            initial = temp_size//chunk_size
            for chunk in tqdm(iterable=r.iter_content(chunk_size=chunk_size), initial=initial, total=data_size, desc=_desc, unit='MB', bar_format=bar_format):
                if chunk:
                    temp_size += len(chunk)
                    fd.write(chunk)
                    fd.flush()
    return True


def _download_model_from_mirror(_repo_id, _repo_type, _token, _e):
    if _repo_type == "model":
        filesUrl = 'https://e.aliendao.cn/models/' + _repo_id + '?json=true'
    else:
        filesUrl = 'https://e.aliendao.cn/datasets/' + _repo_id + '?json=true'
    response = requests.get(filesUrl)
    if response.status_code != 200:
        _log(_repo_id, "mirror", str(response.status_code))
        return False
    data = json.loads(response.text)
    files = data['data']['files']
    for file in files:
        if file['name'] == '~incomplete.txt':
            _log(_repo_id, "mirror", 'downloading')
            return False
    files = _fetchFileList(files)
    i = 1
    for file in files:
        url = 'http://61.133.217.142:20800/download' + file['path']
        if _e:
            url = 'http://61.133.217.139:20800/download' + \
                file['path'] + "?token=" + _token
        file_name = 'dataroot/' + file['path']
        if not os.path.exists(os.path.dirname(file_name)):
            os.makedirs(os.path.dirname(file_name))
        i = i + 1
        if not _download_file_resumable(url, file_name, i, len(files)):
            return False
    return True


def download_model_from_mirror(_repo_id, _repo_type, _token, _e):
    if _download_model_from_mirror(_repo_id, _repo_type, _token, _e):
        return
    else:
        #return download_model_retry(_repo_id, _repo_type)
        _log(_repo_id, "download", '下载资源发生了错误，请使用正确的token')


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--repo_id', default=None, type=str, required=True)
    parser.add_argument('--repo_type', default="model",
                        type=str, required=False)  # models,dataset
    # --mirror为从aliendao.cn镜像下载，如果aliendao.cn没有镜像，则会转到hf
    # 默认为True
    parser.add_argument('--mirror', action='store_true',
                        default=True, required=False)
    parser.add_argument('--token', default="", type=str, required=False)
    # --e为企业付费版
    parser.add_argument('--e', action='store_true',
                        default=False, required=False)
    args = parser.parse_args()
    if args.mirror:
        download_model_from_mirror(
            args.repo_id, args.repo_type, args.token, args.e)
    else:
        download_model_retry(args.repo_id, args.repo_type)

2. 代码

Stable Diffusion 完整推断流程如下（from https://huggingface.co/stabilityai/stable-diffusion-2-1）：

import torch
from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler

model_id = "/dataroot/models/stabilityai/stable-diffusion-2-1"  # 预训练模型的下载路径

# Use the DPMSolverMultistepScheduler (DPM-Solver++) scheduler here instead
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to("cuda")

prompt = "a photo of an astronaut riding a horse on mars"
image = pipe(prompt).images[0]
    
image.save("astronaut_rides_horse.png")

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参考文献

1. https://aliendao.cn/model_download.py
2. https://github.com/Stability-AI/stablediffusion