DanTagGen(Danbooru Tag Generator) is inspired from p1atdev's dart project. But with different arch, dataset, format and different training strategy. This version of DTG is trained from scratch with 400M param LLaMA arch.(In my personal preference I will call it NanoLLaMA) Since it is llama arch. Theoritically it should be able to be used in any LLaMA inference interface. This repo also provided converted FP16 gguf model and quantized 8bit/6bit gguf models. Basically it is recommended to use llama.cpp or llama-cpp-python to run this model. Which will be very fast.
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DanTagGen(Danbooru Tag Generator) is inspired from p1atdev's dart project. But with different arch, dataset, format and different training strategy. alpha: pretrain on 2M dataset, smaller batch size. Limited ability beta: pretrain on 5.3M dataset, larger batch size. More stable, better ability with only a few information provided. This version of DTG is trained from scratch with 400M param LLaMA arch.(In my personal preference I will call it NanoLLaMA) Since it is llama arch. Theoritically it should be able to be used in any LLaMA inference interface.
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DanTagGen(Danbooru Tag Generator) is inspired from p1atdev's dart project. But with different arch, dataset, format and different training strategy. alpha: pretrain on 2M dataset, smaller batch size. Limited ability beta: pretrain on 5.3M dataset, larger batch size. More stable, better ability with only a few information provided. delta: pretrain on 7.2M dataset, larger batch size. Slightly underfit but better diversity. quality tag introduced. This version of DTG is trained from scratch with 400M param LLaMA arch.(In my personal preference I will call it NanoLLaMA) Since it is llama arch. Theoritically it should be able to be used in any LLaMA inference interface.
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DanTagGen(Danbooru Tag Generator) is inspired from p1atdev's dart project. But with different arch, dataset, format and different training strategy. - alpha: pretrain on 2M dataset, smaller batch size. Limited ability - beta: pretrain on 5.3M dataset, larger batch size. More stable, better ability with only a few information provided. - delta: pretrain on 7.2M dataset, larger batch size. Slightly underfit but better diversity. quality tag introduced. - rev2: resumed from delta, same dataset, 2 more epoch. This version of DTG is trained from scratch with 400M param LLaMA arch.(In my personal preference I will call it NanoLLaMA) Since it is llama arch. Theoritically it should be able to be used in any LLaMA inference interface.
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.image-viewer {position: relative;width: 100%;margin: 0 auto;display: flex;flex-flow: wrap;align-items: center;justify-content: center;} .image-viewer input[type="radio"] {display: none;} .image-viewer label {border-radius: 10%;padding: 20px;background-color: #B398F5;background-size: cover;background-position: center;cursor: pointer;color: black;margin: 8px;} .image-viewer label:hover {background-color: #4C88F5;padding: 24px;margin: 4px;} .image-viewer input[type="radio"]:checked + label {background-color: #6296F5;padding: 28px;margin: 0px;} .image-container {position: relative;width: 100%;height: 50vh;margin: 1rem 1rem 0 0;} .inner-container {position: absolute;width:100%;height: 100%;display: flex;align-items: center;justify-content: center;} .inner-container img {border-radius: 10px;max-height: 100%;max-width: 100%;height: 0;width: 0;opacity: 0;transition: opacity 0.5s ease, height 0.25s ease, width 0.25s ease;} As same as Kohaku XL Epsilon or Delta, but you can replace "general tags" with "natural language caption". You can also put both together. For better ability on some certain concepts, I use full danbooru dataset instead of filterd one. Than use crawled Pixiv dataset (from 3~5 tag with popularity sort) as addon dataset. Since Pixiv's search system only allow 5000 page per tag so there is not much meaningful image, and some of them are duplicated with danbooru set(but since I want to reinforce these concept I directly ignore the duplication)
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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. 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 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.
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500M LLaMA arch model trained for TIPO. Tech Report: https://arxiv.org/abs/2411.08127 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. 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
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500M LLaMA arch model trained for TIPO. Tech Report: https://arxiv.org/abs/2411.08127 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. 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
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**TIPO-v2.1-1B-A200M** is a **1B-A200M** sparse model. 991M total parameters, ~200M active per token, plus a ~50M embedding table. It is a full retrain of TIPOv2 on a **corrected dataset**, for **longer**, at **4096 context**, in **pure fp16**. TIPO is a framework for improving Text-to-Image generation by *text presampling*: a small language model expands a short user prompt into a detailed one before the diffusion model ever sees it. Pre-sampling a smaller distribution by narrowing the range indicated by a brief prompt to a more specific description which matches the original prompt allows the diffusion model to have more information to work with, while preserving overall diversity and fidelity. Instead of asking the user to write 200 tokens of booru tags and natural language, TIPO samples that expansion from a distribution learned over real caption data. This is **v2.1**. Same architecture and same data sources as v2, retrained from scratch after three defects were found in how v2's training examples were built.
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