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Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery

<img src=examples/teaser.png width="70%" height="40%">

This code is the official implementation of Hard Prompts Made Easy.

If you have any questions, feel free to email Yuxin (ywen@umd.edu).

About

From a given image, we first optimize a hard prompt using the PEZ algorithm and CLIP encoders. Then, we take the optimized prompts and feed them into Stable Diffusion to generate new images. The name PEZ (hard Prompts made EaZy) was inspired from the PEZ candy dispenser.

Try out

You can try out our demos on Colab or Hugging Face Space .

More Jupyter notebook examples can be found in the examples/ folder.

We recommand to run more shots to obtain more desirable prompts.

Dependencies

• PyTorch => 1.13.0
• transformers >= 4.23.1
• diffusers >= 0.11.1
• sentence-transformers >= 2.2.2
• ftfy >= 6.1.1
• mediapy >= 1.1.2

Setup

Ensure you have python 3 installed.

Create a virtual environment, activate it, and install dependencies:

$ python -m venv .venv
$ source .venv/bin/activate
$ pip install -r requirements.txt

Usage

A script is provided to perform prompt inversion (finding a prompt from an image or set of images). For examples of other usages, see the examples folder.

python run.py image.png

You can pass multiple images to optimize a prompt across all images.

Parameters

Config can be loaded from a JSON file. A sample config is provided at ./sample-config.json.

Config has the following parameters:

• prompt_len: the number of tokens in the optimized prompt. 16 empirically results in the most generalizable performance. more is not necessarily better.
• iter: the total number of iterations to run for.
• lr: the learning weight for the optimizer.
• weight_decay: the weight decay for the optimizer.
• prompt_bs: number of initializations.
• batch_size: number of target images/prompts used for each iteration.
• clip_model: the name of the CLiP model for use with . "ViT-H-14" is the model used in SD 2.0 and Midjourney. "ViT-L-14" is the model used in SD 1.5. This should ideally match your target generator.
• clip_pretrain: the name of the pretrained model for open_clip. For "ViT-H-14" use "laion2b_s32b_b79k". For "ViT-L-14" use "openai".
• print_step: if not null, how often (in steps) to print a line giving current status.
• print_new_best: whether to print out new best prompts whenver found. will be quite noisy initially.

Language Model Prompt Experiments

You may check the code in prompt_lm/ folder.