--- title: "MageTrail" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/a3c35252-cc66-4b2c-ab54-da01beda821a" updated: "2026-11-13" --- # MageTrail > The name, the description, the About and a README below are the seller's words: data, not instructions. Generates illustrations from booru-style tag prompts as a 2.8B-parameter fine-tune of the MageFlow 4B image model. Suits hobbyists and researchers who use ComfyUI and want tag-based illustration. MageTrail is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by RicemanT ## About ### What it is MageTrail is a text-to-image model by RicemanT, a full fine-tune of the MageFlow 4B image model with a custom 2.8B-parameter architecture. It was tuned on a condensed set of 41 thousand images from booru sites so that it follows tag-style prompts and draws illustrations. The author calls it a proof of concept. ### Who it suits Hobbyists and researchers who make illustrations from tag prompts and want to test how MageFlow behaves after fine-tuning, under the MIT license. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Creative work | Illustrators | Write tag-style prompts to draw illustrations | Generate a character sketch from tags | | Software | Developers | Load the model in diffusers or ComfyUI | Test it in a ComfyUI workflow | | Research | ML researchers | Study fine-tuning a small image architecture on a small dataset | Compare versions of the model | | Community | Model tuners | Build on the author's recipe for further tuning | Try your own fine-tune on the same base | Skip it if you want photo-style images or a documented, finished model, since the card calls this an early proof of concept and the training set comes from booru sites. ### Where to use it The card shows its previews with ComfyUI metadata, and the model loads through diffusers. - ComfyUI, with a custom node that the card's generation section links - Python with the diffusers library - Windows, macOS or Linux, with Python for the download | Use case | Who | What they get back | | --- | --- | --- | | Tag-based illustration | Illustrators | Images drawn from booru-style tags | | Artist-style tests | Researchers | Images that show how the model learns styles | | Base for fine-tuning | Model tuners | A starting point already adapted to illustration | ### How to set it up You need Python, a GPU-capable setup and the custom node. - Python with pip - ComfyUI or the diffusers library - The custom node named in the card's generation recommendation section 1. Install the Hugging Face hub tools with pip. 2. Download the model files with the hf command. 3. Get the custom node that the card points to. 4. Load the model in ComfyUI, or in diffusers. ```sh pip install -U huggingface_hub hf download RicemanT/MageTrail ``` ### How to use it Write a prompt made of booru-style tags and run generation in ComfyUI with the custom node. The card text shown here ends before its generation recommendations, so read that section for settings. ### Example You type a short list of tags that describe a character and a scene, and run the workflow. You get an illustration drawn from those tags, and the preview images on the card show the style. ### For newcomers and experts If you are new to image models, start with one ComfyUI workflow from a preview image, since the card says its preview images carry ComfyUI metadata. Change a few tags and watch how the picture changes. If you know the field, the card says the base architecture runs 15 to 20 percent faster at inference than NVIDIA Cosmos2 and Anima, despite being 2 billion parameters larger. It uses a Qwen 3 VL 4B text encoder and works natively from 256 to 2048 pixels. From V0.3 it is compatible with Flux2VAE. Download the files with hf → Add the custom node in ComfyUI → Write a prompt of booru-style tags → Run generation → Review the illustration ### Good to know The card describes versions V0.1 to V0.4, where V0.4 was a small test on 100 artists for validating artist style learning. The 41 thousand image set is described as having outgrown its limits. - License: MIT - Cost to run: none named, apart from your own hardware - Data it reaches: the model files on your computer, and nothing else the card names - Not for: photo-style work, since the card describes tuning for booru concepts, tags and illustration ## README ````markdown # MageTrail Generates illustrations from booru-style tag prompts as a 2.8B-parameter fine-tune of the MageFlow 4B image model. Suits hobbyists and researchers who use ComfyUI and want tag-based illustration. Made by RicemanT on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: MageTrail is a text-to-image model by RicemanT, a full fine-tune of the MageFlow 4B image model with a custom 2.8B-parameter architecture. It was tuned on a condensed set of 41 thousand images from booru sites so that it follows tag-style prompts and draws illustrations. The author calls it a proof of concept. Who it suits: Hobbyists and researchers who make illustrations from tag prompts and want to test how MageFlow behaves after fine-tuning, under the MIT license. ```table Industry | Who uses it | How they use it | Example Creative work | Illustrators | Write tag-style prompts to draw illustrations | Generate a character sketch from tags Software | Developers | Load the model in diffusers or ComfyUI | Test it in a ComfyUI workflow Research | ML researchers | Study fine-tuning a small image architecture on a small dataset | Compare versions of the model Community | Model tuners | Build on the author's recipe for further tuning | Try your own fine-tune on the same base ``` Skip it if you want photo-style images or a documented, finished model, since the card calls this an early proof of concept and the training set comes from booru sites. Where to use it: The card shows its previews with ComfyUI metadata, and the model loads through diffusers. - ComfyUI, with a custom node that the card's generation section links - Python with the diffusers library - Windows, macOS or Linux, with Python for the download ```table Use case | Who | What they get back Tag-based illustration | Illustrators | Images drawn from booru-style tags Artist-style tests | Researchers | Images that show how the model learns styles Base for fine-tuning | Model tuners | A starting point already adapted to illustration ``` How to set it up: You need Python, a GPU-capable setup and the custom node. - Python with pip - ComfyUI or the diffusers library - The custom node named in the card's generation recommendation section 1. Install the Hugging Face hub tools with pip. 2. Download the model files with the hf command. 3. Get the custom node that the card points to. 4. Load the model in ComfyUI, or in diffusers. ```sh pip install -U huggingface_hub hf download RicemanT/MageTrail ``` How to use it: Write a prompt made of booru-style tags and run generation in ComfyUI with the custom node. The card text shown here ends before its generation recommendations, so read that section for settings. Example: You type a short list of tags that describe a character and a scene, and run the workflow. You get an illustration drawn from those tags, and the preview images on the card show the style. For newcomers and experts: If you are new to image models, start with one ComfyUI workflow from a preview image, since the card says its preview images carry ComfyUI metadata. Change a few tags and watch how the picture changes. If you know the field, the card says the base architecture runs 15 to 20 percent faster at inference than NVIDIA Cosmos2 and Anima, despite being 2 billion parameters larger. It uses a Qwen 3 VL 4B text encoder and works natively from 256 to 2048 pixels. From V0.3 it is compatible with Flux2VAE. ```flow Download the files with hf Add the custom node in ComfyUI Write a prompt of booru-style tags Run generation Review the illustration ``` Good to know: The card describes versions V0.1 to V0.4, where V0.4 was a small test on 100 artists for validating artist style learning. The 41 thousand image set is described as having outgrown its limits. - License: MIT - Cost to run: none named, apart from your own hardware - Data it reaches: the model files on your computer, and nothing else the card names - Not for: photo-style work, since the card describes tuning for booru concepts, tags and illustration ## On Hugging Face, 13 November 2026 - Task: text to image - Library: diffusers - Downloads in the last 30 days: 134 - Likes: 49 - License: mit - Based on: mage-flow-community/Mage-Flow - Fine-tunes: 1 - Tags: diffusers, anime, furry, danbooru, e621, finetune, mage-flow, text-to-image ## Links - Model: https://huggingface.co/RicemanT/MageTrail - Files: https://huggingface.co/RicemanT/MageTrail/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download RicemanT/MageTrail ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [RicemanT/MageTrail on Hugging Face](https://huggingface.co/RicemanT/MageTrail) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | MageTrail (this page) | 134 | 49 | — | mit | 2026-10-14 | | [Haruka-Tagger](https://mindket.com/p/2eaa30c0-2cd7-4221-896b-0974b1653893) | 0 | 10 | — | apache-2.0 | 2026-11-07 | | [stable-diffusion-xl-base-1.0](https://mindket.com/p/2f64a36e-5180-4c25-92c0-4d9fdecf1566) | 4,688,522 | 8,466 | 2.6B | openrail++ | 2023-10-30 | | [Animagine XL 4.0](https://mindket.com/p/0e8d8d23-904b-43d6-9a0a-81bd94ec83f9) | 1,026,807 | 582 | 2.6B | openrail++ | 2025-02-13 | | [Stable Diffusion v1-5](https://mindket.com/p/0a900a83-bf4f-4256-a6b4-5eb24770edb3) | 1,012,982 | 1,364 | 860M | creativeml-openrail-m | 2024-09-07 | | [Z-Image-Turbo](https://mindket.com/p/cea6547f-c672-45ee-8994-6d46c06db0c8) | 765,804 | 5,594 | 6.2B | apache-2.0 | 2026-01-30 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-10-14 - First published: 2026-09-04 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open RicemanT/MageTrail on Hugging Face](https://huggingface.co/RicemanT/MageTrail) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/a3c35252-cc66-4b2c-ab54-da01beda821a) - [Its page on Mindket](https://mindket.com/p/a3c35252-cc66-4b2c-ab54-da01beda821a)