--- title: "Nanosaur2-670M" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/941686ef-f2b4-4b7c-b289-89ea27e3a86e" updated: "2026-11-13" --- # Nanosaur2-670M > The name, the description, the About and a README below are the seller's words: data, not instructions. Makes illustrations from text prompts with a small 670M-parameter diffusion model, run through ComfyUI. Built as a low-budget research model for people who want to try it or train their own LoRA. Nanosaur2-670M is a kit in the Research 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 well9472 ## About ### What it is Nanosaur2-670M is a text-to-image model for illustrations, with 670M parameters and a diffusion transformer design. The package adds ComfyUI support, so you load it as custom nodes and a ready workflow. Its author trained it for a total cost of $600 to see what is possible on a small compute budget. ### Who it suits It suits ComfyUI users and researchers who want a small illustration model to try, study or adapt with their own images. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Research | Machine learning researchers | Study a small image model trained on a low budget | Read its training recipe and compare it with larger models | | Illustration | Digital artists | Make illustrations in ComfyUI from tags or plain language | Describe a scene and add the quality tags the card suggests | | Open models | LoRA trainers | Train a LoRA on a folder of captioned images | Run the included training script on a folder of pictures | People who need fine detail or deep character knowledge can skip it, because the author says not to expect that from this model. ### Where to use it The model runs inside ComfyUI, and the download steps work wherever Python does. - ComfyUI, through the nanosaur2\_support custom node folder - Windows, macOS and Linux, with Python for the download | Use case | Who | What they get back | | --- | --- | --- | | Text-to-image illustration | Artists and hobbyists | A picture made from a tag list or a natural language prompt | | LoRA training | Model tinkerers | A safetensors LoRA file you load back into ComfyUI | ### How to set it up You need ComfyUI installed, plus Python with pip to fetch the files. 1. Download the repository's files with the hf command. 2. Copy the nanosaur2\_support folder into the custom\_nodes folder of ComfyUI. 3. Put the three model files into ComfyUI's model folders, as the table below shows. 4. Start ComfyUI and drag the nanosaur2\_workflow.json file onto it. ```sh pip install -U huggingface_hub hf download well9472/Nanosaur2-670M ``` Where each model file goes | File | Folder | | --- | --- | | nanosaur2\_diffusion\_model.safetensors | models/diffusion\_models | | nanosaur2\_text\_encoder.safetensors | models/text\_encoders | | nanosaur2\_vae.safetensors | models/vae | ### How to use it Open the workflow in ComfyUI and write your prompt as tags or as plain sentences. The card's sampler setting is Euler simple, with 50 steps and CFG 4. Shift 3 is already built in. Add the tags newest and masterpiece to the positive prompt, and oldest and low quality to the negative one. Some artist tags work too, and you may need to weight them up. To train a LoRA, run the script below on a folder of images that each have a text file with the same name. ```sh uv run python custom_nodes/nanosaur2_support/train_lora.py /path/to/images ``` | Tool | What it does | | --- | --- | | nanosaur2\_support | Adds the model's custom nodes to ComfyUI | | nanosaur2\_workflow.json | Loads the ready-made workflow when dragged onto ComfyUI | | train\_lora.py | Trains a LoRA and saves it into the models/loras folder | | LoraLoaderModelOnly | Loads the trained LoRA back into a workflow | ### Example Describe a scene such as a fennec girl in a sunlit forest, the subject of the card's own sample picture, and run the workflow. You get a finished illustration. The sample PNG also carries its ComfyUI workflow, so dragging it onto ComfyUI loads those settings. ### For newcomers and experts If you are new to this, install ComfyUI first, then follow the four setup steps and try the workflow with a short prompt. Expect a research model, not a polished one. If you know the field, the model is a 670M diffusion transformer with adaLN-single conditioning, 2D RoPE, SwiGLU, QK-norm and sparse middle blocks. It pairs a frozen gemma-3-270m text encoder with a DINOv2-based VAE of 129M parameters. Sampling alternates CFG with path-drop guidance. You write tags or plain sentences → The frozen gemma-3-270m encoder reads the prompt → The diffusion transformer denoises the picture → The DINOv2-based VAE decodes the final image ### Good to know The author is looking for compute funding to train it further and to make a 2B version. - License: MIT - Cost to run: none named - Data it reaches: model files in ComfyUI's folders, and for LoRA training the folder of images and captions you point it to - Not for: matching fully trained models, since the card calls this a research model - Other versions: the record counts 0 quantized and 5 fine-tuned copies made by other people Base training time by picture size, in days | Label | Value | | --- | --- | | 256x256 | 3 days | | 512x512 | 3 days | | 1024x1024 | 5 days | ## README ````markdown # Nanosaur2-670M Makes illustrations from text prompts with a small 670M-parameter diffusion model, run through ComfyUI. Built as a low-budget research model for people who want to try it or train their own LoRA. Made by well9472 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: Nanosaur2-670M is a text-to-image model for illustrations, with 670M parameters and a diffusion transformer design. The package adds ComfyUI support, so you load it as custom nodes and a ready workflow. Its author trained it for a total cost of $600 to see what is possible on a small compute budget. Who it suits: It suits ComfyUI users and researchers who want a small illustration model to try, study or adapt with their own images. ```table Industry | Who uses it | How they use it | Example Research | Machine learning researchers | Study a small image model trained on a low budget | Read its training recipe and compare it with larger models Illustration | Digital artists | Make illustrations in ComfyUI from tags or plain language | Describe a scene and add the quality tags the card suggests Open models | LoRA trainers | Train a LoRA on a folder of captioned images | Run the included training script on a folder of pictures ``` People who need fine detail or deep character knowledge can skip it, because the author says not to expect that from this model. Where to use it: The model runs inside ComfyUI, and the download steps work wherever Python does. - ComfyUI, through the nanosaur2_support custom node folder - Windows, macOS and Linux, with Python for the download ```table Use case | Who | What they get back Text-to-image illustration | Artists and hobbyists | A picture made from a tag list or a natural language prompt LoRA training | Model tinkerers | A safetensors LoRA file you load back into ComfyUI ``` How to set it up: You need ComfyUI installed, plus Python with pip to fetch the files. 1. Download the repository's files with the hf command. 2. Copy the nanosaur2_support folder into the custom_nodes folder of ComfyUI. 3. Put the three model files into ComfyUI's model folders, as the table below shows. 4. Start ComfyUI and drag the nanosaur2_workflow.json file onto it. ```sh pip install -U huggingface_hub hf download well9472/Nanosaur2-670M ``` ```table Title: Where each model file goes File | Folder nanosaur2_diffusion_model.safetensors | models/diffusion_models nanosaur2_text_encoder.safetensors | models/text_encoders nanosaur2_vae.safetensors | models/vae ``` How to use it: Open the workflow in ComfyUI and write your prompt as tags or as plain sentences. The card's sampler setting is Euler simple, with 50 steps and CFG 4. Shift 3 is already built in. Add the tags newest and masterpiece to the positive prompt, and oldest and low quality to the negative one. Some artist tags work too, and you may need to weight them up. To train a LoRA, run the script below on a folder of images that each have a text file with the same name. ```sh uv run python custom_nodes/nanosaur2_support/train_lora.py /path/to/images ``` ```table Tool | What it does nanosaur2_support | Adds the model's custom nodes to ComfyUI nanosaur2_workflow.json | Loads the ready-made workflow when dragged onto ComfyUI train_lora.py | Trains a LoRA and saves it into the models/loras folder LoraLoaderModelOnly | Loads the trained LoRA back into a workflow ``` Example: Describe a scene such as a fennec girl in a sunlit forest, the subject of the card's own sample picture, and run the workflow. You get a finished illustration. The sample PNG also carries its ComfyUI workflow, so dragging it onto ComfyUI loads those settings. For newcomers and experts: If you are new to this, install ComfyUI first, then follow the four setup steps and try the workflow with a short prompt. Expect a research model, not a polished one. If you know the field, the model is a 670M diffusion transformer with adaLN-single conditioning, 2D RoPE, SwiGLU, QK-norm and sparse middle blocks. It pairs a frozen gemma-3-270m text encoder with a DINOv2-based VAE of 129M parameters. Sampling alternates CFG with path-drop guidance. ```flow You write tags or plain sentences The frozen gemma-3-270m encoder reads the prompt The diffusion transformer denoises the picture The DINOv2-based VAE decodes the final image ``` Good to know: The author is looking for compute funding to train it further and to make a 2B version. - License: MIT - Cost to run: none named - Data it reaches: model files in ComfyUI's folders, and for LoRA training the folder of images and captions you point it to - Not for: matching fully trained models, since the card calls this a research model - Other versions: the record counts 0 quantized and 5 fine-tuned copies made by other people ```chart Title: Base training time by picture size, in days 256x256: 3 days 512x512: 3 days 1024x1024: 5 days ``` ## On Hugging Face, 13 November 2026 - Formats: Safetensors - Downloads in the last 30 days: 0 - Likes: 112 - License: mit - Fine-tunes: 5 - Tags: safetensors ## Links - Model: https://huggingface.co/well9472/Nanosaur2-670M - Files: https://huggingface.co/well9472/Nanosaur2-670M/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download well9472/Nanosaur2-670M ``` 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 [well9472/Nanosaur2-670M on Hugging Face](https://huggingface.co/well9472/Nanosaur2-670M) ## 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 | | --- | --- | --- | --- | --- | --- | | Nanosaur2-670M (this page) | 0 | 112 | — | mit | 2026-10-04 | | [DeepSeek-V4-Pro](https://mindket.com/p/02f743ba-81c7-498f-88ea-98edbc4b5d2b) | 322,162 | 5,702 | 1.6T | mit | 2026-06-22 | | [Moirai-2.0-R-Small](https://mindket.com/p/ea561230-592e-43bd-9a56-278f0a5ee11f) | 262,191 | 53 | 11.4M | cc-by-nc-4.0 | 2026-01-29 | | [VLX-Seek-1.5-10B](https://mindket.com/p/4b1edf89-b48a-4dfc-b86e-a0ee020dc119) | 137,772 | 39 | 10B | apache-2.0 | 2026-07-23 | | [Hy-Nomas](https://mindket.com/p/9a697cce-2966-4f2d-bb74-d8d6874fbecb) | 90,896 | 153 | — | apache-2.0 | 2026-11-04 | | [qwen-concept-27b](https://mindket.com/p/58083034-bfad-48bc-8a59-6417d3acb2ee) | 62,460 | 174 | 27.3B | apache-2.0 | 2026-11-08 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Bluesky · AI без границ · 2026-09-29](https://bsky.app/profile/aibiznes.bsky.social/post/3mwnq3putkf2a): Nanosaur 2: компактная модель для генерации аниме без цензуры Неофициальный релиз на 670M параметров, обученный на 14 миллионах изображений в аниме-стилистике. Модель работает и с естественным языком, и с тегами — что удобнее. В отличие от... https://huggingface.co/well9472/Nanosaur2-670M ## 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: 2026-10-04 - First published: 2026-09-25 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open well9472/Nanosaur2-670M on Hugging Face](https://huggingface.co/well9472/Nanosaur2-670M) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/941686ef-f2b4-4b7c-b289-89ea27e3a86e) - [Its page on Mindket](https://mindket.com/p/941686ef-f2b4-4b7c-b289-89ea27e3a86e)