README.md

---
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`, `sdxl_vae.safetensors`, `QwenImage_VAE_2d.pth`, `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 [NSCLv1](https://huggingface.co/nvidia/PixelDiT-1300M-1024px/blob/main/LICENSE) License. The work and any derivative works may only be used for non-commercial (research or evaluation) purposes.

### 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→4096 and an
  SD3-style dynamic shift; designed for 1024 LDM → 4K (4096 px) decoding.

Both checkpoint variants support multiple aspect ratios.

| Path                                                            | Latent space | SR factor | Variant |
|-----------------------------------------------------------------|--------------|-----------|---------|
| `checkpoints/PiD_res2k_sr4x_official_flux_distill_4step`        | Flux1-dev    | 4×        | 2k      |
| `checkpoints/PiD_res2k_sr4x_official_flux2_distill_4step`       | Flux2-dev    | 4×        | 2k      |
| `checkpoints/PiD_res2k_sr4x_official_sd3_distill_4step`         | SD3 medium   | 4×        | 2k      |
| `checkpoints/PiD_res2k_sr4x_official_dinov2_distill_4step`      | DINOv2-B     | 4×        | 2k      |
| `checkpoints/PiD_res2k_sr8x_official_siglip_distill_4step`      | SigLIP-2     | 8×        | 2k      |
| `checkpoints/PiD_res2kto4k_sr4x_official_flux_distill_4step`    | Flux1-dev    | 4×        | 2kto4k  |
| `checkpoints/PiD_res2kto4k_sr4x_official_flux2_distill_4step_2606` | Flux2-dev  | 4×        | 2kto4k  |
| `checkpoints/PiD_res2kto4k_sr4x_official_sd3_distill_4step`     | SD3 medium   | 4×        | 2kto4k  |
| `checkpoints/PiD_res2kto4k_sr4x_official_sdxl_distill_4step`    | SDXL         | 4×        | 2kto4k  |
| `checkpoints/PiD_res2kto4k_sr4x_official_qwenimage_distill_4step` | Qwen-Image  | 4×        | 2kto4k  |

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.

> **⚠️ Flux2-dev `2kto4k` — use the new `_2606` checkpoint.** The previous
> `PiD_res2kto4k_sr4x_official_flux2_distill_4step` (without the `_2606` suffix)
> suffered from a color-drifting issue. The new
> `PiD_res2kto4k_sr4x_official_flux2_distill_4step_2606` fixes it — please use it
> and do **not** use the old one. See the
> [comparison](https://github.com/nv-tlabs/pid/blob/main/docs/FLUX2_2kto4k_new_ckpt_compare.md)
> for details.

### Latent space → compatible LDMs

A PiD decoder is tied to a *latent space*, not to a single generative model. Any
LDM that produces latents in that space can reuse the same checkpoint. The
`--backbone` aliases below pick the right LDM pipeline; they all decode through
the latent space's checkpoint above.

| Latent space | VAE / vision encoder weights       | compatible `--backbone`                   | Corresponding LDM Links |
|--------------|------------------------------------|-------------------------------------------|-----------------|
| Flux1-dev    | `checkpoints/ae.safetensors`       | `flux`, `zimage`, `zimage-turbo`          | [FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev), [Z-Image](https://huggingface.co/Tongyi-MAI/Z-Image), [Z-Image-Turbo](https://huggingface.co/Tongyi-MAI/Z-Image-Turbo) |
| Flux2-dev    | `checkpoints/flux2_ae.safetensors` | `flux2`, `flux2-klein-4b`, `flux2-klein-9b` | [FLUX.2-dev](https://huggingface.co/black-forest-labs/FLUX.2-dev), [FLUX.2-klein-4B](https://huggingface.co/black-forest-labs/FLUX.2-klein-4B), [FLUX.2-klein-9B](https://huggingface.co/black-forest-labs/FLUX.2-klein-9B) |
| SD3 medium   | `checkpoints/sd3_vae/`             | `sd3`                                     | [SD3-medium](https://huggingface.co/stabilityai/stable-diffusion-3-medium-diffusers) |
| SDXL         | `checkpoints/sdxl_vae.safetensors` | `sdxl`                                    | [SDXL-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) |
| Qwen-Image   | `checkpoints/QwenImage_VAE_2d.pth` | `qwenimage`, `qwenimage-2512`             | [Qwen-Image](https://huggingface.co/Qwen/Qwen-Image), [Qwen-Image-2512](https://huggingface.co/Qwen/Qwen-Image-2512) |
| DINOv2-B     | `checkpoints/rae/`                 | `dinov2`                                  | [RAE](https://github.com/bytetriper/RAE) (class-conditional; DINOv2-B) |
| SigLIP-2     | `checkpoints/scale_rae/`           | `siglip`                                  | [Scale-RAE](https://github.com/ZitengWangNYU/Scale-RAE) (text-conditional; nyu-visionx/Scale-RAE-Qwen1.5B_DiT2.4B) |

For example, Z-Image and Z-Image-Turbo share Flux1-dev's VAE, so they reuse the
`flux` checkpoints (both `2k` and `2kto4k`) — no separate `zimage` checkpoint is
shipped. Likewise `qwenimage-2512` reuses the `qwenimage` decoder (same VAE,
different transformer). 

## 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 --backbone flux \
    --prompt "A photorealistic half-body portrait of a brown tabby cat with bold stripes sitting attentively on a rustic wooden kitchen table, soft morning light streaming sideways through a large window, fine fur detail and stripe patterns sharply visible, intense amber-green eyes in razor-sharp focus, warm farmhouse kitchen softly out of focus, cinematic shallow depth of field, ultra-detailed fur texture, photorealistic" \
    --ldm_inference_steps 28 --save_xt_steps 24 \
    --output_dir ./results/official_demo/flux \
    --pid_inference_steps 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}
}
```