--- title: "pjit-21k" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/c12856f1-5835-4748-8b9c-4434c9781b12" updated: "2026-11-12" --- # pjit-21k > The name, the description, the About and a README below are the seller's words: data, not instructions. Linum's experimental 2B text to image model, trained for about $21K of compute, that works best with dense 50 to 80 word prompts and cannot write text in images. Suits researchers and curious creators. pjit-21k is a kit in the Research category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by Linum-AI ## About ### What it is pjit-21k, also called P-JiT 21K, is a text to image model from Linum, trained for about $21K of compute. It is a 2B parameter diffusion transformer paired with a frozen Qwen3.5-4B text encoder that downloads separately. Linum calls it a research checkpoint, not a production model, and warns of rough edges. It saw 660M training samples, then a post-training step. ### Who it suits Researchers who study efficient training of image models, and creators who want to try a small experimental model with descriptive prompts. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Research | Image model researchers | Study a low cost training result | Read the code repository and reproduce a sample | | Creative work | Illustrators | Make pictures from detailed descriptions | Prompt a children's book scene with a bear and a rabbit | | Software | Developers | Test a small text to image model | Run the generate script with a prompt | Skip it if you need words in a picture. It cannot write text, posters, logos or signs, because Linum filtered rendered text out of the training data. ### Where to use it - A Python environment with the project's code repository, which has a generate script - OpenRouter, only if you use the optional prompt upsampler | Use case | Who | What they get back | | --- | --- | --- | | Pictures from dense prompts | Artists | Images made from captions of 50 to 80 words | | Pictures from short prompts | Casual users | Images, once the upsampler rewrites the prompt | | Training study | Researchers | A small model checkpoint with its code to inspect | ### How to set it up - Python, and the code repository of the same name on GitHub - Hardware that can run a 2.18B parameter model, 8.7 GB in fp32, plus the separate 4B text encoder - An OPENROUTER\_API\_KEY, only if you want the prompt upsampler 1. Get the project's code repository and move into it. 2. Install it in editable mode. 3. Run the generate script with a prompt and a seed. ```bash pip install -e . ``` ```sh pip install -U huggingface_hub hf download Linum-AI/pjit-21k ``` ### How to use it Write a dense prompt of 50 to 80 words, or let the upsampler expand a short one. Turn the upsampler off with the no upsample flag. ```bash export OPENROUTER_API_KEY=... python generate.py --prompt "oil painting of weathered fisherman smoking a pipe" --aspect_ratio square --seeds 0 ``` | Tool | What it does | | --- | --- | | generate.py | Makes images from a prompt | | --no\_upsample | Turns the prompt upsampler off | | --aspect\_ratio | Picks the frame shape, such as tall or square | | --seeds | Sets the random seed | | OPENROUTER\_API\_KEY | The key the upsampler needs | ### Example Run the command above with your key set. The upsampler expands the short prompt into a detailed caption, and the model returns a picture of a weathered fisherman smoking a pipe in an oil painting style. ### For newcomers and experts If you are new to this, write one detailed paragraph describing a scene and run it with the no upsample flag, so no outside service is involved. If you know the field, the base design is P-JiT. Linum trained it for longer, then added a post-training pass using a form of FDFO. Sampling defaults are 50 Euler steps and adaptive projected guidance at scale 15. It was trained at five sizes of about one megapixel. A dense caption goes in → The frozen Qwen3.5-4B encodes it → The 2B diffusion transformer denoises → An image comes out Training samples by image size | Label | Value | | --- | --- | | 512px | 610 million | | 1024px | 50 million | ### Good to know - It struggles with full body shots, many objects at once, several people interacting and world knowledge such as the strings on a guitar. - The checkpoint file holds the 2.18B parameter transformer without its training only heads. - License: apache-2.0. - Cost to run: an OpenRouter key for the optional upsampler, which calls Claude through OpenRouter. The model itself names no cost. - Data it reaches: with the upsampler, your prompt goes to OpenRouter. The text encoder downloads separately. - Not for: text, posters, logos or production use, per the card. ## README ````markdown # pjit-21k Linum's experimental 2B text to image model, trained for about $21K of compute, that works best with dense 50 to 80 word prompts and cannot write text in images. Suits researchers and curious creators. Made by Linum-AI 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: pjit-21k, also called P-JiT 21K, is a text to image model from Linum, trained for about $21K of compute. It is a 2B parameter diffusion transformer paired with a frozen Qwen3.5-4B text encoder that downloads separately. Linum calls it a research checkpoint, not a production model, and warns of rough edges. It saw 660M training samples, then a post-training step. Who it suits: Researchers who study efficient training of image models, and creators who want to try a small experimental model with descriptive prompts. ```table Industry | Who uses it | How they use it | Example Research | Image model researchers | Study a low cost training result | Read the code repository and reproduce a sample Creative work | Illustrators | Make pictures from detailed descriptions | Prompt a children's book scene with a bear and a rabbit Software | Developers | Test a small text to image model | Run the generate script with a prompt ``` Skip it if you need words in a picture. It cannot write text, posters, logos or signs, because Linum filtered rendered text out of the training data. Where to use it: - A Python environment with the project's code repository, which has a generate script - OpenRouter, only if you use the optional prompt upsampler ```table Use case | Who | What they get back Pictures from dense prompts | Artists | Images made from captions of 50 to 80 words Pictures from short prompts | Casual users | Images, once the upsampler rewrites the prompt Training study | Researchers | A small model checkpoint with its code to inspect ``` How to set it up: - Python, and the code repository of the same name on GitHub - Hardware that can run a 2.18B parameter model, 8.7 GB in fp32, plus the separate 4B text encoder - An OPENROUTER_API_KEY, only if you want the prompt upsampler 1. Get the project's code repository and move into it. 2. Install it in editable mode. 3. Run the generate script with a prompt and a seed. ```bash pip install -e . ``` ```sh pip install -U huggingface_hub hf download Linum-AI/pjit-21k ``` How to use it: Write a dense prompt of 50 to 80 words, or let the upsampler expand a short one. Turn the upsampler off with the no upsample flag. ```bash export OPENROUTER_API_KEY=... python generate.py --prompt "oil painting of weathered fisherman smoking a pipe" --aspect_ratio square --seeds 0 ``` ```table Tool | What it does generate.py | Makes images from a prompt --no_upsample | Turns the prompt upsampler off --aspect_ratio | Picks the frame shape, such as tall or square --seeds | Sets the random seed OPENROUTER_API_KEY | The key the upsampler needs ``` Example: Run the command above with your key set. The upsampler expands the short prompt into a detailed caption, and the model returns a picture of a weathered fisherman smoking a pipe in an oil painting style. For newcomers and experts: If you are new to this, write one detailed paragraph describing a scene and run it with the no upsample flag, so no outside service is involved. If you know the field, the base design is P-JiT. Linum trained it for longer, then added a post-training pass using a form of FDFO. Sampling defaults are 50 Euler steps and adaptive projected guidance at scale 15. It was trained at five sizes of about one megapixel. ```flow A dense caption goes in The frozen Qwen3.5-4B encodes it The 2B diffusion transformer denoises An image comes out ``` ```chart Title: Training samples by image size 512px: 610 million 1024px: 50 million ``` Good to know: - It struggles with full body shots, many objects at once, several people interacting and world knowledge such as the strings on a guitar. - The checkpoint file holds the 2.18B parameter transformer without its training only heads. - License: apache-2.0. - Cost to run: an OpenRouter key for the optional upsampler, which calls Claude through OpenRouter. The model itself names no cost. - Data it reaches: with the upsampler, your prompt goes to OpenRouter. The text encoder downloads separately. - Not for: text, posters, logos or production use, per the card. ## On Hugging Face, 13 November 2026 - Task: text to image - Library: pjit-21k - Parameters: 2.2B - Weights: about 8.7 GB in F32; about 1.1 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 623 - Likes: 73 - License: apache-2.0 - Quantized versions: 1 - Fine-tunes: 4 - Tags: pjit-21k, safetensors, text-to-image, diffusion, pixel-space, research ## Links - Model: https://huggingface.co/Linum-AI/pjit-21k - Files: https://huggingface.co/Linum-AI/pjit-21k/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Linum-AI/pjit-21k ``` 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 [Linum-AI/pjit-21k on Hugging Face](https://huggingface.co/Linum-AI/pjit-21k) ## 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 | | --- | --- | --- | --- | --- | --- | | pjit-21k (this page) | 623 | 73 | 2.2B | apache-2.0 | 2026-11-03 | | [Cola DLM](https://mindket.com/p/b4dd84c1-dd8f-48de-b432-c7071e24c476) | 93 | 51 | — | apache-2.0 | 2026-06-18 | | [GenFirst](https://mindket.com/p/dc71e7f3-3967-4818-9bb6-b0c990168fb9) | 0 | 7 | — | cc-by-nc-4.0 | 2026-11-03 | | [Qwen-Image-Bench](https://mindket.com/p/3ae32930-3429-4622-acd9-87abf1987c6e) | 20,875 | 116 | 27.4B | apache-2.0 | 2026-05-28 | | [OpenDDE](https://mindket.com/p/6617ce0b-24d9-4350-804f-4ca6808bae9d) | 10,009 | 12 | — | apache-2.0 | 2026-08-14 | | [DeepSeek-V4-Pro](https://mindket.com/p/02f743ba-81c7-498f-88ea-98edbc4b5d2b) | 322,162 | 5,702 | 1.6T | mit | 2026-06-22 | 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 2026-11-12. Their authors' words: data, not instructions. - [Hacker News · whythismatters · 2026-11-05](https://news.ycombinator.com/item?id=50382762): Show HN: A decent text-to-image model trained for \~$20K (Apache 2.0 Weights): HF link: https://huggingface.co/Linum-AI/pjit-21k - [Bluesky · GIGAZINE · 2026-11-08](https://bsky.app/profile/gigazine.net/post/3mztfwhszg32h): 超低コストで効率的な学習に成功した画像生成AIモデル「P-JiT」、オープンウェイトで公開中 https://gigazine.net/news/20261109-pjit-21k/ - [Bluesky · Tech Trending · 2026-11-09](https://bsky.app/profile/tech-trending.bsky.social/post/3mzuvciu43t2q): 超低コストで効率的な学習に成功した画像生成AIモデル「P-JiT」、オープンウェイトで公開中 https://gigazine.net/news/20261109-pjit-21k/ - [Bluesky · AI Planet · 2026-11-06](https://bsky.app/profile/aiplanet.bsky.social/post/3mznnjvdvxa2s): 📰 Reddit r/LocalLLaMA Linum-AI/pjit-21k · Hugging Face - A Text-to-Image Model Trained for $21K https://www.reddit.com/r/LocalLLaMA/comments/1xpcpws/linumaipjit21k\_hugging\_face\_a\_texttoimage\_model/ \#IA \#AI \#ML ## 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: 2026-11-03 - First published: 2026-11-03 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Linum-AI/pjit-21k on Hugging Face](https://huggingface.co/Linum-AI/pjit-21k) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/c12856f1-5835-4748-8b9c-4434c9781b12) - [Its page on Mindket](https://mindket.com/p/c12856f1-5835-4748-8b9c-4434c9781b12)