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