HE-Diffusion

[HE-Diffusion]is a privacy-preserving latent text-to-image diffusion model based on Stable diffusion. It works for protecting users' input prompts and output images during inference.

Requirements

A suitable conda environment named ldm can be created similar with stable diffusion and activated with:

conda env create -f environment.yaml
conda activate ldm

You can also update an existing latent diffusion environment by running

conda install pytorch torchvision -c pytorch
pip install transformers==4.19.2 diffusers invisible-watermark
pip install -e .

Besides requirements by stable diffusion, TenSeal is also required in He-Diffusion for homomorphic encryption. It can be simply installed by:

pip install tenseal

Reference Sampling Script

User can simply sample with

python scripts/enc_txt2img.py --prompt "a photograph of an astronaut riding a horse" --plms 

By default, this uses a guidance scale of --scale 7.5, Katherine Crowson's implementation of the PLMS sampler, and renders images of size 512x512 (which it was trained on) in 50 steps. All supported arguments are listed below (type python scripts/enc_txt2img.py --help).

usage: enc_txt2img.py [-h] [--prompt [PROMPT]] [--outdir [OUTDIR]] [--skip_grid] [--skip_save] [--ddim_steps DDIM_STEPS] [--plms] [--laion400m] [--fixed_code] [--ddim_eta DDIM_ETA]
                  [--n_iter N_ITER] [--H H] [--W W] [--C C] [--f F] [--n_samples N_SAMPLES] [--n_rows N_ROWS] [--scale SCALE] [--from-file FROM_FILE] [--config CONFIG] [--ckpt CKPT]
                  [--seed SEED] [--precision {full,autocast}]

optional arguments:
  -h, --help            show this help message and exit
  --prompt [PROMPT]     the prompt to render
  --outdir [OUTDIR]     dir to write results to
  --skip_grid           do not save a grid, only individual samples. Helpful when evaluating lots of samples
  --skip_save           do not save individual samples. For speed measurements.
  --ddim_steps DDIM_STEPS
                        number of ddim sampling steps
  --plms                use plms sampling
  --laion400m           uses the LAION400M model
  --fixed_code          if enabled, uses the same starting code across samples
  --ddim_eta DDIM_ETA   ddim eta (eta=0.0 corresponds to deterministic sampling
  --n_iter N_ITER       sample this often
  --H H                 image height, in pixel space
  --W W                 image width, in pixel space
  --C C                 latent channels
  --f F                 downsampling factor
  --n_samples N_SAMPLES
                        how many samples to produce for each given prompt. A.k.a. batch size
  --n_rows N_ROWS       rows in the grid (default: n_samples)
  --scale SCALE         unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))
  --from-file FROM_FILE
                        if specified, load prompts from this file
  --config CONFIG       path to config which constructs model
  --ckpt CKPT           path to checkpoint of model
  --seed SEED           the seed (for reproducible sampling)
  --precision {full,autocast}
                        evaluate at this precision

Note: The inference config for all v1 versions is designed to be used with EMA-only checkpoints. For this reason use_ema=False is set in the configuration, otherwise the code will try to switch from non-EMA to EMA weights. If you want to examine the effect of EMA vs no EMA, we provide "full" checkpoints which contain both types of weights. For these, use_ema=False will load and use the non-EMA weights.

Content under development and future work

Here we only support text to image task, which is most relevantly used. In the future we will try to propose more complete tasks.

问题

部署问题1

创建conda环境报错

ERROR: Command errored out with exit status 128: git clone -q https://github.com/CompVis/taming-transformers.git /mnt/HE-diffusion/src/taming-transformers Check the logs for full command output.

解决：手动下载两个包，并进入仓库目录执行安装

修改environment.yaml以下代码

• -e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers
• -e git+https://github.com/openai/CLIP.git@main#egg=clip

改为（path改为实际路径）

• -e /path/HE-diffusion/src/taming-transformers
• -e /path/HE-diffusion/src/CLIP

pip install -e .

部署问题2

Hugging Face无法连接

OSError: We couldn't connect to 'https://huggingface.co' to load this model, couldn't find it in the cached files and it looks like CompVis/stable-diffusion-safety-checker is not the path to a directory containing a preprocessor_config.json file.

解决：

临时设置：

• export HF_ENDPOINT=https://hf-mirror.com

永久设置：

• vim ~/.bashrc
• 最后一行添加：export HF_ENDPOINT=https://hf-mirror.com
• source ~/.bashrc

部署问题3

FileNotFoundError: [Errno 2] No such file or directory: 'models/ldm/stable-diffusion-v1/model.ckpt'

解决：

本地下载至[/HE-Diffusion/models/ldm/stable-diffusion-v1/]目录下：https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/blob/main/sd-v1-4-full-ema.ckpt

并修改文件名：mv sd-v1-4-full-ema.ckpt model.ckpt

执行问题1

RuntimeError: expected scalar type BFloat16 but found Float

解决（使用GPU）： 修改[scripts/enc_txt2img.py]的250-251、301-302行代码

• device = torch.device("cpu") # 250
• #device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") # 251
• #with precision_scope("cuda"): # 301
• with precision_scope("cpu"): # 302

改为

• #device = torch.device("cpu") # 250
• device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") # 251
• #with precision_scope("cuda"): # 301
• with precision_scope("cpu"): # 302