PiD — Pixel Diffusion Decoder

Paper, Project Page

Yifan Lu, Qi Wu, Jay Zhangjie Wu, Zian Wang, Huan Ling, Sanja Fidler, Xuanchi Ren

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 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 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, Z-Image, Z-Image-Turbo
Flux2-dev	checkpoints/flux2_ae.safetensors	flux2, flux2-klein-4b, flux2-klein-9b	FLUX.2-dev, FLUX.2-klein-4B, FLUX.2-klein-9B
SD3 medium	checkpoints/sd3_vae/	sd3	SD3-medium
SDXL	checkpoints/sdxl_vae.safetensors	sdxl	SDXL-base-1.0
Qwen-Image	checkpoints/QwenImage_VAE_2d.pth	qwenimage, qwenimage-2512	Qwen-Image, Qwen-Image-2512
DINOv2-B	checkpoints/rae/	dinov2	RAE (class-conditional; DINOv2-B)
SigLIP-2	checkpoints/scale_rae/	siglip	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. The exact (backbone, ckpt_type) → path mapping is the single source of truth in pid/_src/inference/checkpoint_registry.py — clone the repo, point it at this snapshot, and the demos pick the right file automatically:

# 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}
}