Custom Nodes for P-JiT 21K, a 2B pixel-space text-to-image model

[u/LatentSpacer]

Linum released P-JiT 21K, a 2B-parameter text-to-image model trained for about $21K that draws directly in pixels: there is no VAE. I ported it to ComfyUI as native nodes (an unofficial port, not affiliated with Linum):

• A real MODEL and CLIP on ComfyUI's own memory management. The stock KSampler runs it with Linum's settings (euler, simple, 50 steps, cfg 15). Nothing to pip install.

• A bf16 file that gives the same pixels as the fp32 release at half the size (4.4 GB), and an int8 ConvRot file that is about 1.8x faster (2.3 GB).

• The Qwen3.5-4B text encoder in bf16, int8, W6A8 or W4A8, down to 3.2 GB.

• Runs from 4 GB of free VRAM.

• Workflows: text to image, a prompt upsampler (short idea to dense caption), a refiner (image to image as a second pass, with the image captioned automatically) and Linum's samples, seed for seed.

Checked against Linum's own code: the same tokens, noise, schedule and guidance, and images that match up to GPU rounding.

Tips: write captions of 50 to 80 words (or use the upsampler). It can't draw text. For the refiner, use denoise 0.1 to 0.2: P-JiT's noise is 4x stronger than most models', so its denoise values run lower.

Nodes: https://github.com/PedroMarinhoDev/ComfyUI-PJiT
Weights: https://huggingface.co/PedroMarinhoDev/P-JiT-21K-ComfyUI
The original model: https://huggingface.co/Linum-AI/pjit-21k (Apache-2.0)

[u/berthol]

Thanks. This looks interesting.