Create README.md

README.md

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+---
+license: other
+license_name: kohaku-license-1.0
+datasets:
+- laion/conceptual-captions-12m-webdataset
+- CaptionEmporium/coyo-hd-11m-llavanext
+- KBlueLeaf/danbooru2023-metadata-database
+- graph-based-captions/GBC10M
+language:
+- en
+pipeline_tag: text-generation
+library_name: transformers
+---
+# TIPO: Text to Image with text presampling for Prompt Optimization
+
+200M LLaMA arch model trained for TIPO. <br>
+Tech Report: https://kblueleaf.net/document/TIPO-tech-report.pdf
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/630593e2fca1d8d92b81d2a1/fc9ovmARapQmgq9DZ7ApJ.png)
+
+## Introduction
+
+In this project, we introduce "TIPO" (**T**ext to **I**mage with text presampling for **P**rompt **O**ptimization), 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. <br>
+The total token seen is around 50B tokens. <br>
+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, 5epoch<br />Danbooru, GBC10M, Coyo11M, 3epoch              | Danbooru(pixtral), Coyo11M, 2epoch | Danbooru, GBC10M, Coyo11M, 5epoch                                            |
+| Real Token Seen*  | 40B token                                                                      | 50B (10B more from TIPO-200M)     | 30B token                                                                      |
+| Training Hardware | RTX 3090 x 4                                                                   | RTX 3090 x 4                       | H100 x 8                                                                       |
+| Training Time     | 420 hour`                                                                      | 120 hour`                          | 100 hour`                                                                      |
+| Huggingface       | [KBlueLeaf/TIPO-200M · Hugging Face](https://huggingface.co/KBlueLeaf/TIPO-200M) | You Are HERE                       | [KBlueLeaf/TIPO-500M · Hugging Face](https://huggingface.co/KBlueLeaf/TIPO-500M) |
+
+*: We only count "non-padding token" in the token seen, since all the training data have very large length range. <br>
+`: Since the training data is pretty short, it cost more time to reach same token seen than general LLM pretraining. <br>
+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** <br>
+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) <br>
+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. <br>
+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](https://kblueleaf.net/documents/kohaku-license/?[Your%20Organization/Name]=KohakuBlueLeaf&[Year]=2024) <br>
+You can check the above provided URL or check the LICENSE file in this repo.
+
+### Citation
+
+```bibtex
+@misc{yeh2024tipo,
+  title = {TIPO: Text to Image with text presampling for Prompt Optimization},
+  author = {Yeh, Shih-Ying},
+  year = {2024},
+  month = {10},
+  day = {6},
+  note = {Technical report available at \url{https://kblueleaf.net/document/TIPO-tech-report.pdf}. 
+          Model available at \url{https://huggingface.co/KBlueLeaf/TIPO-500M}. 
+          Source code available at \url{https://github.com/KohakuBlueleaf/KGen}},
+}
+```