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RicemanT/Nanosaur-1.2B-Preview-Latest
Nanosaur-1.2B-Preview-Latest is a machine learning model from RicemanT. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
The original trainer of the model, well9472/Metal63 throughout the project's lifetime would keep deleting the model repo due to an unknown reason. As a silent observer of the project progress, I'm uploading my latest…
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Updated Jul 19, 2026
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From the Hugging Face model README
The original trainer of the model, well9472/Metal63 throughout the project's lifetime would keep deleting the model repo due to an unknown reason. As a silent observer of the project progress, I'm uploading my latest saved checkpoint of the project here to perserve a promising open source image model even if it's original creator have abandoned it. It's also still potentially useful as a toy model for training practice.
Nanosaur 1.2B is a custom pretrained from scratch arch with (will be updated once I scour back arch info- basically a custom arch, Gemma3 270m and a custom VAE called PS-VAE from what I remember), using a mixed 621/Danbooru dataset. So this is actually the first model of 1-3B param range that was trained on e621 since NoobAI XL. Money roughly spent before it was discontinued was somewhere around 1k-3k dollars, with H100 support from Lodestone Rock.
Original README.md:
WIP model for research purposes. Still in progress. 18 days of 1xH100 from scratch.
Install instructions
Copy nanosaur_support folder to custom_nodes in ComfyUI
Copy diffusion model, text encoder and VAE to model folders in ComfyUI
Start ComfyUI
Drag nanosaur_workflow.json onto ComfyUI
Tags or natural language. Model responds well to prompt emphasis like (character:2) or (artist:2)
Lora training:
uv run cache_lora.py --dataset-dir /path/to/images_and_txt_caption_files --batch-size 4
uv run train_lora.py --cache-path cache/lora_latents.pt --batch-size 1 --rank 16 --alpha 16