| --- |
| license: apache-2.0 |
| library_name: mlx |
| pipeline_tag: text-to-image |
| base_model: Linum-AI/pjit-21k |
| tags: [mlx, apple-silicon, text-to-image, diffusion, pixel-space, pjit-21k] |
| --- |
| |
| # P-JiT 21K, MLX (8bit) |
|
|
| This is an MLX export of [Linum-AI/pjit-21k](https://huggingface.co/Linum-AI/pjit-21k), Linum's 2.18B pixel-space |
| text-to-image DiT. It comes with its Qwen3.5-4B caption encoder, trimmed to the 28 blocks the model reads. The runner |
| is [pjit-mlx](https://github.com/felix-ab/pjit-mlx). |
|
|
| **Tier:** 8-bit (compact: 3.6 GB peak for the whole process with `--low-memory`, 5.0 GB without). It is as clean as bf16 at 100% and usually gives the same picture for a seed |
| (14 of 15 gallery tests, with more fine-detail drift), but it can fork on sensitive prompts. |
|
|
|  |
|
|
| *Rendered on a Mac mini from Linum's gallery prompts and seeds; Linum's H100 originals are at [linum.ai/field-notes/pjit-21k](https://www.linum.ai/field-notes/pjit-21k).* |
|
|
| | M4 Pro, one 1152×864 image, 50 steps | This tier | PyTorch on MPS | |
| |---|---|---| |
| | Total time | 79 s | 139 s | |
| | Peak memory, whole process | 5.0 GB; 3.6 GB with `--low-memory` | 14.9 GB | |
| | Download | 6.9 GB | 18.0 GB | |
|
|
| Peak memory is the process's peak physical footprint as macOS reports it, the same meter in both columns. |
| `--low-memory` evaluates the DiT block by block and caps MLX's buffer cache; the pixels are identical. |
|
|
| ```bash |
| pip install git+https://github.com/felix-ab/pjit-mlx |
| pjit-mlx --tier 8bit --low-memory --seeds 4 --aspect 4:3 \ |
| --prompt "Bright midday sun lights a white man in his thirties in a head-and-shoulders portrait, squinting slightly with a relaxed half smile. His faded blue chambray shirt is open at the collar, and a field of tall grass blurs behind him." |
| ``` |
|
|
| `--tier 8bit` downloads this repo on first use. To keep a local copy, run |
| `hf download VisualInference/P-JiT-21K-MLX-8bit --local-dir P-JiT-21K-MLX-8bit` and pass |
| `--dit P-JiT-21K-MLX-8bit/dit --text-encoder P-JiT-21K-MLX-8bit/text_encoder` instead. |
|
|
| ## Files |
|
|
| - `dit/`: the DiT (`model.safetensors`) with Linum's `config.json`, plus a `pjit_mlx` block that gives the format, |
| dtype, quantization and source revision. |
| - `text_encoder/`: Qwen3.5-4B blocks 0-27 (`model.safetensors`), the bf16 token embedding |
| (`embed_tokens.safetensors`, read row by row from a memory map), and the tokenizer. |
|
|
| ## How exact it is |
|
|
| - In fp32 the MLX DiT and text encoder match the PyTorch reference to a relative error of about 3e-6. |
| - A full 50-step fp32 run matches the reference sampler on 99.5% of pixels, with the rest within 1/255. |
| - In bf16 they sit at the reference's own bf16 rounding noise. |
| - The bf16 tier reproduces Linum's gallery images from their published prompt and seed: 15 tested, all the same |
| picture, median PSNR 35 dB against the AVIF originals. It does this by reproducing the H100 SXM noise draw exactly. |
| - The 8-bit tier shifts the first prediction a little more than bf16 does. Most seeds still give the gallery picture, |
| with more fine-detail drift (median 29.6 dB), but sensitive prompts can fork to a different image of the same |
| quality. |
|
|
| Full numbers and the scripts behind them are in the [runner's README](https://github.com/felix-ab/pjit-mlx#exactness). |
|
|
| ## Limits |
|
|
| These are the model's own limits (see Linum's card): |
|
|
| - It was trained on dense 50-80 word captions. |
| - It cannot render text. |
| - It struggles with full-body figures and crowds. |
|
|
| ## License |
|
|
| - **P-JiT 21K weights:** Apache-2.0, © Linum Inc. |
| - **Qwen3.5-4B:** Apache-2.0, © Alibaba Cloud. |
| - **This conversion and the runner:** Apache-2.0. |
|
|
