| --- |
| library_name: pytorch |
| tags: |
| - super-resolution |
| - diffusion |
| - pixel-diffusion-decoder |
| - vae-decoder |
| pipeline_tag: image-to-image |
| base_model: |
| - nvidia/PixelDiT-1300M-1024px |
| - Tongyi-MAI/Z-Image |
| - black-forest-labs/FLUX.1-dev |
| - black-forest-labs/FLUX.2-dev |
| - nyu-visionx/Scale-RAE-Qwen7B_DiT9.8B |
| --- |
| |
| # PiD — Pixel Diffusion Decoder |
|
|
| <p align="center"> |
| <img src="figures/teaser.jpg" alt="PiD teaser" width="100%"> |
| </p> |
|
|
|
|
| **[Paper](https://arxiv.org/abs/2605.23902), [Project Page](https://research.nvidia.com/labs/sil/projects/pid/)** |
|
|
| [Yifan Lu](https://yifanlu0227.github.io/), |
| [Qi Wu](https://wilsoncernwq.github.io/), |
| [Jay Zhangjie Wu](https://zhangjiewu.github.io/), |
| [Zian Wang](https://www.cs.toronto.edu/~zianwang/), |
| [Huan Ling](https://www.cs.toronto.edu/~linghuan/), |
| [Sanja Fidler](https://www.cs.utoronto.ca/~fidler/), |
| [Xuanchi Ren](https://xuanchiren.com/) <br> |
|
|
|
|
| PiD reformulates the latent-to-pixel decoder as a conditional pixel-space |
| diffusion model, unifying decoding and upsampling into a single generative |
| module. It denoises directly in high-resolution pixel space and produces a |
| super-resolved image in one pass. This repository hosts the released decoder |
| checkpoints, plus the encoder/decoder ("VAE") weights they depend on. |
|
|
| All `PiD_*` checkpoints in this repo are **4-step distilled**. The non-`PiD_*` |
| entries (`ae.safetensors`, `flux2_ae.safetensors`, `sd3_vae/`, `rae/`, |
| `scale_rae/`) are **the corresponding encoder/decoder VAE weights** that PiD |
| plugs into — they're not PiD checkpoints themselves. |
|
|
| ### License/Terms of Use |
|
|
| This model is released under the NVIDIA Open Model License. |
|
|
| ### Deployment Geography: |
| Global |
|
|
| ## PiD checkpoints |
|
|
| Two variants are released for each diffusers-style backbone: |
|
|
| - **`2k`** — trained at 2048px, used as a 4× decoder (512 LDM → 2048 px), or as |
| an 8× decoder for the Scale-RAE backbone (256 → 2048). |
| - **`2kto4k`** — trained with multi-resolution data bucketing 2048→3840 and an |
| SD3-style dynamic shift; designed for 1024 LDM → 4K (4096 px) decoding. |
|
|
| Both checkpoint variants support multiple aspect ratios. |
|
|
| | Path | Backbone (encoder side) | SR factor | Variant | |
| |---------------------------------------------------------------|--------------------------------------------|-----------|-----------| |
| | `checkpoints/PiD_res2k_sr4x_official_flux_distill_4step` | Flux1-dev (16-ch VAE) | 4× | 2k | |
| | `checkpoints/PiD_res2k_sr4x_official_flux2_distill_4step` | Flux2-dev (128-ch BN VAE) | 4× | 2k | |
| | `checkpoints/PiD_res2k_sr4x_official_sd3_distill_4step` | SD3 medium (16-ch VAE) | 4× | 2k | |
| | `checkpoints/PiD_res2k_sr4x_official_dinov2_distill_4step` | DINOv2-B + RAE ViT-XL (768-ch) | 4× | 2k | |
| | `checkpoints/PiD_res2k_sr8x_official_siglip_distill_4step` | SigLIP-2 So400M + Scale-RAE ViT-XL (1152) | 8× | 2k | |
| | `checkpoints/PiD_res2kto4k_sr4x_official_flux_distill_4step` | Flux1-dev (16-ch VAE) | 4× | 2kto4k | |
| | `checkpoints/PiD_res2kto4k_sr4x_official_flux2_distill_4step` | Flux2-dev (128-ch BN VAE) | 4× | 2kto4k | |
| | `checkpoints/PiD_res2kto4k_sr4x_official_sd3_distill_4step` | SD3 medium (16-ch VAE) | 4× | 2kto4k | |
|
|
| Z-Image shares Flux1's VAE, so its inference path reuses the `flux` checkpoints |
| (both `2k` and `2kto4k`) — no separate `zimage` checkpoint is shipped. |
|
|
| Each directory contains a single file, `model_ema_bf16.pth`, which is the EMA |
| weights cast to bfloat16 — the format the inference scripts load by default. |
|
|
| ## VAE / encoder weights |
|
|
| These are the per-backbone encoder (and, where applicable, original decoder) |
| weights that PiD pairs with. They're hosted here so a single download brings |
| everything needed end-to-end. |
|
|
| | Path | Description | |
| |---------------------------------|--------------------------------------------------------------------------------------| |
| | `checkpoints/ae.safetensors` | Flux1-dev / Z-Image 16-ch VAE (encoder + original Flux decoder). | |
| | `checkpoints/flux2_ae.safetensors` | Flux2-dev 128-ch BN VAE. | |
| | `checkpoints/sd3_vae/` | SD3 medium 16-ch VAE in diffusers format. | |
| | `checkpoints/rae/` | DINOv2-B image encoder + RAE ViT-XL decoder + ImageNet-512 normalization statistics. | |
| | `checkpoints/scale_rae/` | SigLIP-2 So400M encoder + Scale-RAE ViT-XL decoder + decoder config. | |
|
|
| ## Usage |
|
|
| The decoder checkpoints are loaded by the inference scripts in the [PiD |
| codebase](https://github.com/nv-tlabs/pid). The exact `(backbone, ckpt_type) → path` mapping is the single source |
| of truth in |
| [`pid/_src/inference/checkpoint_registry.py`](https://github.com/nv-tlabs/PiD/blob/main/pid/_src/inference/checkpoint_registry.py) — clone the |
| repo, point it at this snapshot, and the demos pick the right file |
| automatically: |
|
|
| ```bash |
| # Pull just the checkpoints/ tree into the repo root (skips this README and |
| # the teaser figure so they don't clobber the files in the source repo). |
| hf download nvidia/PiD --local-dir . --include "checkpoints/*" |
| |
| # Then run any of the demos, e.g.: |
| PYTHONPATH=. python -m pid._src.inference.from_ldm_flux \ |
| --prompt "A photorealistic cat" \ |
| --ldm_inference_steps 28 --save_xt_steps 22 24 26 \ |
| --output_dir ./results/demo \ |
| --cfg_scale 1 --pid_inference_steps 4 --scale 4 |
| ``` |
|
|
| Pick the `2kto4k` variant via `--pid_ckpt_type 2kto4k` when decoding at 4K. |
|
|
| ## Citation |
| ``` |
| @article{lu2026pid, |
| title={PiD: Fast and High-Resolution Latent Decoding with Pixel Diffusion}, |
| author={Lu, Yifan and Wu, Qi and Wu, Jay Zhangjie and Wang, Zian and Ling, Huan and Fidler, Sanja and Ren, Xuanchi}, |
| journal={arXiv preprint arXiv:2605.23902}, |
| year={2026} |
| } |
| ``` |
