TIPO: Text to Image with text presampling for Prompt Optimization
200M LLaMA arch model trained for TIPO. Tech Report: https://arxiv.org/abs/2411.08127
Introduction
In this project, we introduce "TIPO" (Text to Image with text presampling for Prompt Optimization), an innovative framework designed to significantly enhance the quality and usability of Text-to-Image (T2I) generative models. TIPO utilizes the Large Language Models (LLMs) to perform "Text Presampling" within the inference pipeline of text-to-image generative modeling. By refining and extending user input prompts, TIPO enables generative models to produce superior results with minimal user effort, making T2I systems more accessible and effective for a wider range of users.
Usage
Use updated version of DTG extension (renamed to z-tipo-extension), current version of z-tipo-extension support stable-diffusion-webui, stable-diffusion-webui-forge and ComfyUI. SD-Next haven't been tested. https://github.com/KohakuBlueleaf/z-tipo-extension
Model arch and Training
This model is LLaMA arch with 200M parameters, the training data is combined version of Danbooru2023, Coyo-HD-11M. The total token seen is around 50B tokens. For more information please refer to the tech report and following table.
| TIPO-200M | TIPO-200M-ft- | TIPO-500M | |
|---|---|---|---|
| Arch | LLaMA | LLaMA | LLaMA |
| Max ctx length | 1024 | 1024 | 1024 |
| Batch Size | 2048 | 2048 | 3584 |
| Training dataset | Danbooru, GBC10M, 5epochDanbooru, GBC10M, Coyo11M, 3epoch | Danbooru(pixtral), GBC10M, Coyo11M, 2epoch | Danbooru, GBC10M, Coyo11M, 5epoch |
| Real Token Seen* | 40B token | 62B (12B more from TIPO-200M-ft) | 30B token |
| Training Hardware | RTX 3090 x 4 | RTX 3090 x 4 | H100 x 8 |
| Training Time | 420 hour` | 130 hour` (670hour total) | 100 hour` |
| Huggingface | KBlueLeaf/TIPO-200M · Hugging Face | You Are HERE | KBlueLeaf/TIPO-500M · Hugging Face |
*: We only count "non-padding token" in the token seen, since all the training data have very large length range. `: Since the training data is pretty short, it cost more time to reach same token seen than general LLM pretraining. As reference, with 4096 as max ctx length and almost all the data have reach that length, you may only need 2days to reach 10B token seen on RTX 3090 x 4 with 200M model.
Evaluation
Evaluation are done on TIPO-200M model We have tested TIPO compared to other Model in several test and metrics:
Scenery tag test
In this test we use single "scenery" tag as input. (With some certain meta) To test each prompt gen method to see if they can obtain the desired distribution of outputs while maintain the quality of images.
| Scenery Tag Test | Original | GPT4o-mini | Prompt DB | Promptis | TIPO(ours) |
|---|---|---|---|---|---|
| FDD ↓ | 0.3558 | 0.5414 | 0.3247 | 0.2350 | 0.2282 |
| Aesthetic ↑ | 5.0569 | 6.3676 | 6.1609 | 5.9468 | 6.2571 |
| AI Corrupt ↑ | 0.4257 | 0.7490 | 0.5024 | 0.5669 | 0.9195 |
Short/Truncated Long test
In this test we use short caption or manually truncated caption from GBC10M and CoyoHD11M. This test examine the ability of prompt gen method on handling almostly completed prompts.
| Short | Original | GPT4o-mini | Prompt DB | Promptis | TIPO(ours) |
|---|---|---|---|---|---|
| FDD ↓ | 0.0957 | 0.1668 | 0.0980 | 0.1783 | 0.1168 |
| Aesthetic ↑ | 5.8370 | 6.0589 | 5.8213 | 5.7963 | 5.8531 |
| AI Corrupt ↑ | 0.7113 | 0.6985 | 0.7064 | 0.6314 | 0.7131 |
| Truncated Long | Original | GPT4o-mini | Prompt DB | Promptis | TIPO(ours) |
|---|---|---|---|---|---|
| FDD ↓ | 0.0955 | 0.1683 | 0.1247 | 0.2096 | 0.1210 |
| Aesthetic ↑ | 5.7497 | 6.0168 | 5.8191 | 5.7759 | 5.8364 |
| AI Corrupt ↑ | 0.6868 | 0.6712 | 0.6741 | 0.5925 | 0.7130 |
LICENSE
This model is released under Kohaku License 1.0 You can check the above provided URL or check the LICENSE file in this repo.
Citation
@misc{yeh2024tipotextimagetext,
title={TIPO: Text to Image with Text Presampling for Prompt Optimization},
author={Shih-Ying Yeh and Sang-Hyun Park and Giyeong Oh and Min Song and Youngjae Yu},
year={2024},
eprint={2411.08127},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2411.08127},
}