README.md

Reverse Prompt - Hard Fork

From a given image, we first optimize a hard prompt using the algorithms and CLIP encoders. Then, we take the optimized prompts and feed them into Stable Diffusion to generate new images.

Colab Notebook for early testing More Jupyter notebook examples can be found in the examples/ folder.

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

Usage

import open_clip
from optim_utils import *
import argparse
from PIL import Image

# load the target image
image = Image.open(image_path)

# load args
args = argparse.Namespace()
args.__dict__.update(read_json("sample_config.json"))

# load CLIP model
device = "cuda" if torch.cuda.is_available() else "cpu"
model, _, preprocess = open_clip.create_model_and_transforms(args.clip_model, pretrained=args.clip_pretrain, device=device)

# You may modify the hyperparamters
args.prompt_len = 8 # number of tokens for the learned prompt

# optimize prompt
learned_prompt = optimize_prompt(model, preprocess, args, device, target_images=[image])
print(learned_prompt)

Note:
prompt_len: number of tokens in the optimized prompt
batch_size: number of target images/prompts been used for each iteraion
prompt_bs: number of intializations

Langugae Model Prompt Experiments

Check out prompt_lm/ folder.

TODOs

• Figure out how we can load multiple base images at the same time to comapre cosims
• Get 1.5 base working 512x512