cjwbw / hard-prompts-made-easy

Gradient-Based Discrete Optimization for Prompt Tuning and Discovery

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Examples

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Run time and cost

This model costs approximately $0.48 to run on Replicate, or 2 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia A100 (80GB) GPU hardware. Predictions typically complete within 6 minutes. The predict time for this model varies significantly based on the inputs.

Readme

Hard Prompts Made Easy: Discrete Prompt Tuning for Language Models

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.