Instructions to use BennyDaBall/PiD-1.5-flux1-nvfp4-comfy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusion Single File
How to use BennyDaBall/PiD-1.5-flux1-nvfp4-comfy with Diffusion Single File:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
π PiD 1.5 FLUX.1 / Z-Image β NVFP4 (ComfyUI-ready)
The FLUX.1 leg of the quantized PiD decoder trio on HF. 2.61 GB β 1.84 GB, drops
straight into a stock UNETLoader, decodes faster than the bf16 original. No custom loader.
No core patches. No Blackwell requirement either β this runs anywhere the bf16 file runs.
π§ What is this?
NVIDIA's PiD v1.5 decoder for the FLUX.1 latent family (FLUX.1 dev/schnell + Z-Image-Turbo, 16-channel flux latents) β the 4-step pixel-diffusion decode that replaces your VAE decode and hands back 4Γ the resolution. Same decoder, 70% of the size, a little faster.
This family holds more of its quality in a handful of layers than its siblings do, so the converter keeps more of them in bf16 β that's why this file is bigger than the qwen/flux2 builds. What you get for it: pixel-identical character to bf16 at 100% zoom.
Running other stage-1 families? The rest of the trio: PiD-1.5-qwenimage-nvfp4-comfy Β· PiD-1.5-flux2-nvfp4-comfy
π Same seed, same latent, both builds
100% crops β faces, lettering, bokeh all hold:
π Specs
| Attribute | Details |
|---|---|
| Base | nvidia/PiD v1.5 flux1 (4-step distill), via the Comfy-Org/PixelDiT repackage |
| Quantization | NVFP4 (group 16), quality-critical layers kept bf16 |
| File size | 1.84 GB (bf16: 2.61 GB) |
| Requirements | ComfyUI β₯ 0.32 (tested on 0.33.0) Β· any GPU that runs PiD bf16 |
| Text encoder | gemma_2_2b_it_elm_bf16.safetensors from Comfy-Org/PixelDiT (CLIPLoader type pixeldit) |
π» Usage
Drop the model in ComfyUI/models/diffusion_models/, select it in UNETLoader, done. Both
included workflows run Z-Image-Turbo as the stage-1 (qwen_3_4b.safetensors text encoder
on CLIPLoader type lumina2, ModelSamplingAuraFlow shift 3.0) β swap in any FLUX.1-family
stage-1 and the decode side doesn't change.
Simple (core nodes only) β PiD-1.5-flux1-nvfp4_simple_zimage_1mp_to_16mp.json.
Z-Image-Turbo at 1024Γ1024 β single-shot PiD 4Γ β 4096Γ4096, built from nothing but core
nodes (PiDConditioning + a 4-step sampler on the exact distill sigmas). Drop the included
pixel_space_vae.safetensors into ComfyUI/models/vae/ for the final decode step.
β flux1's single-shot envelope is tighter than its siblings: keep the output at 4096 on the
long edge and feed it a ~1MP stage-1 β square is the sweet spot. For 16:9 or anything bigger,
use the tiled workflow.
Tiled (the big guns) β PiD-1.5-flux1-nvfp4_tiled_zimage.json. Z-Image stage-1 at
1920Γ1088 β seam-free 7680Γ4352 in one queue via
ComfyUI-Latent-Tiled-PiD:
install from ComfyUI Manager (search "Latent-Tiled-PiD") or the
Comfy Registry. Set latent_format to
flux and every 16-channel flux latent decodes clean to the big rungs.
Every included workflow was executed through the actual ComfyUI frontend against this exact file before upload.
βοΈ License & credits
NVIDIA created PiD (paper); Comfy-Org published the bf16 ComfyUI repackage this converts. NSCLv1 β non-commercial research/evaluation use only, derivatives included. Read the nvidia/PiD card before you build anything on it. Not affiliated with NVIDIA or Comfy-Org. Conversion and validation by BennyDaBall_OG.
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