DiffSynth-Studio/EntroPackPreQuants

EntroPack Pre-Quantized Models

This repository releases pre-quantized weight packages produced by EntroPack for representative SOTA diffusion models, to be used with the quantization features of DiffSynth-Studio:

• Z-Image-Turbo (DiT + text encoder)
• Qwen-Image-2.1 (DiT + text encoder)
• MiniMax-H3 (two DiTs, FL2VA / Ref2VA, sharing the text encoder and video VAE)

For each model, both the DiT and the text encoder are provided at five fine-grained compression rates (4 / 5 / 6 / 7 / 8 bpp), plus two kinds of specialized packages:

• fp8@5 (dit_fp8_5bpp): the weights are first FP8 W8A8-quantized (8-bit fp8 codes), then entropy-coded by EntroPack down to 5-bit storage (actual bpp = 5); the forward pass uses FP8 GEMM for faster inference;
• Extreme mixed-bit packages: bit-rate is allocated per layer by sensitivity (the most sensitive layers keep a high bpp while the rest use a low bpp), pushing the whole model to a much lower bpp (Z-Image-Turbo 2.3bpp / Qwen-Image-2.1 3.0bpp / MiniMax-H3 3.0bpp).

All packages are built by entropy-coding the original weights directly with EntroPack; the metrics in the tables are weighted measurements on the package artifacts:

• bpp (bits per parameter): average bits per weight, bpp = 8 × Σ storage bytes / Σ numel.
• Linear weight compression ratio: derived from bpp, ratio = bpp / 16.
• Weight reconstruction error (DiT only): relative deviation between the dequantized and the original weights, sqrt(Σ‖Wq−W‖²) / sqrt(Σ‖W‖²).
• Registration entry: each *.safetensors ships with a same-named *.json; see the end of this document for loading.

Validation outputs (images / videos) for each model can be found under assets/.

Update History

2026-10-15 — checkpoints repacked under new key names. All packages in this repository were repacked into the single-entry container layout: a compressed layer's whole encoding now lives under one <layer>.weight.entropack_quant_state tensor, and the W8A8 row scale under <layer>.weight_quant_scale. The updated weights require entropack 0.1.3; the packages published before this update pair with entropack ≤ 0.1.2.

Z-Image-Turbo

DiT

name	file	actual bpp	weight reconstruction error	size
dit_4bpp	models/Z-Image-Turbo/dit_4bpp.safetensors	4.024	7.013%	2.88 GB
dit_5bpp	models/Z-Image-Turbo/dit_5bpp.safetensors	5.046	3.630%	3.62 GB
dit_6bpp	models/Z-Image-Turbo/dit_6bpp.safetensors	6.055	1.854%	4.34 GB
dit_7bpp	models/Z-Image-Turbo/dit_7bpp.safetensors	7.070	0.971%	5.07 GB
dit_8bpp	models/Z-Image-Turbo/dit_8bpp.safetensors	8.060	0.532%	5.78 GB
dit_fp8_5bpp	models/Z-Image-Turbo/dit_fp8_5bpp.safetensors	5.041	4.648%	3.62 GB
dit_extreme_2.3bpp	models/Z-Image-Turbo/dit_extreme_2.3bpp.safetensors	2.319	24.007%	1.66 GB
nf4 (bitsandbytes) (for comparison)	-	4.127	9.415%	-
torchao int8 w8a16 (for comparison)	-	8.012	1.078%	-
torchao fp8 w8a16 (for comparison)	-	8.010	2.650%	-

Text Encoder

name	file	actual bpp	size
text_encoder_4bpp	models/Z-Image-Turbo/text_encoder_4bpp.safetensors	4.028	2.43 GB
text_encoder_5bpp	models/Z-Image-Turbo/text_encoder_5bpp.safetensors	5.051	2.86 GB
text_encoder_6bpp	models/Z-Image-Turbo/text_encoder_6bpp.safetensors	6.058	3.29 GB
text_encoder_7bpp	models/Z-Image-Turbo/text_encoder_7bpp.safetensors	7.061	3.71 GB
text_encoder_8bpp	models/Z-Image-Turbo/text_encoder_8bpp.safetensors	8.041	4.13 GB

Extreme Mixed-Bit Package

The extreme package dit_extreme_2.3bpp uses mixed quantization (per-layer-group bit allocation, weighted target = 2.3bpp): modulation / embedding / adaLN / final layers (38 layers) @ 4.0bpp, attention output projections *.attention.to_out.0 (34 layers) @ 3.0bpp, and the remaining 204 layers @ 2.1939bpp. Measured 2.319bpp / weight reconstruction error 24.0%.

Qwen-Image-2.1

DiT

name	file	actual bpp	weight reconstruction error	size
dit_4bpp	models/Qwen-Image-2.1/dit_4bpp.safetensors	4.027	7.064%	3.34 GB
dit_5bpp	models/Qwen-Image-2.1/dit_5bpp.safetensors	5.052	3.658%	4.19 GB
dit_6bpp	models/Qwen-Image-2.1/dit_6bpp.safetensors	6.066	1.868%	5.02 GB
dit_7bpp	models/Qwen-Image-2.1/dit_7bpp.safetensors	7.085	0.978%	5.87 GB
dit_8bpp	models/Qwen-Image-2.1/dit_8bpp.safetensors	8.068	0.536%	6.68 GB
dit_fp8_5bpp	models/Qwen-Image-2.1/dit_fp8_5bpp.safetensors	5.048	4.662%	4.19 GB
dit_extreme_3.0bpp	models/Qwen-Image-2.1/dit_extreme_3.0bpp.safetensors	3.023	14.247%	2.50 GB
nf4 (bitsandbytes) (for comparison)	-	4.127	9.428%	-
torchao int8 w8a16 (for comparison)	-	8.008	1.095%	-
torchao fp8 w8a16 (for comparison)	-	8.007	2.648%	-

Text Encoder

name	file	actual bpp	size
text_encoder_4bpp	models/Qwen-Image-2.1/text_encoder_4bpp.safetensors	4.034	4.99 GB
text_encoder_5bpp	models/Qwen-Image-2.1/text_encoder_5bpp.safetensors	5.062	5.97 GB
text_encoder_6bpp	models/Qwen-Image-2.1/text_encoder_6bpp.safetensors	6.082	6.93 GB
text_encoder_7bpp	models/Qwen-Image-2.1/text_encoder_7bpp.safetensors	7.092	7.89 GB
text_encoder_8bpp	models/Qwen-Image-2.1/text_encoder_8bpp.safetensors	8.072	8.82 GB

Extreme Mixed-Bit Package

The extreme package dit_extreme_3.0bpp uses mixed quantization (weighted target = 3.0bpp): modulation-related layers (modulation.1, norm_out.linear, time_text_embed.*, 4 layers) @ 4.0bpp, and the remaining 228 layers @ 2.9855bpp. Measured 3.023bpp / weight reconstruction error 14.2%.

MiniMax-H3

DiT

name	file	actual bpp	weight reconstruction error	size
fl2va_dit_4bpp	models/MiniMax-H3/FL2VA/dit_4bpp.safetensors	4.038	6.567%	15.58 GB
ref2va_dit_4bpp	models/MiniMax-H3/REF2VA/dit_4bpp.safetensors	4.038	6.567%	15.58 GB
fl2va_dit_5bpp	models/MiniMax-H3/FL2VA/dit_5bpp.safetensors	5.065	3.392%	19.54 GB
ref2va_dit_5bpp	models/MiniMax-H3/REF2VA/dit_5bpp.safetensors	5.065	3.392%	19.54 GB
fl2va_dit_6bpp	models/MiniMax-H3/FL2VA/dit_6bpp.safetensors	6.081	1.734%	23.45 GB
ref2va_dit_6bpp	models/MiniMax-H3/REF2VA/dit_6bpp.safetensors	6.082	1.732%	23.46 GB
fl2va_dit_7bpp	models/MiniMax-H3/FL2VA/dit_7bpp.safetensors	7.093	0.911%	27.36 GB
ref2va_dit_7bpp	models/MiniMax-H3/REF2VA/dit_7bpp.safetensors	7.095	0.908%	27.37 GB
fl2va_dit_8bpp	models/MiniMax-H3/FL2VA/dit_8bpp.safetensors	8.081	0.500%	31.16 GB
ref2va_dit_8bpp	models/MiniMax-H3/REF2VA/dit_8bpp.safetensors	8.079	0.500%	31.16 GB
fl2va_dit_fp8_5bpp	models/MiniMax-H3/FL2VA/dit_fp8_5bpp.safetensors	5.058	4.510%	19.54 GB
ref2va_dit_fp8_5bpp	models/MiniMax-H3/REF2VA/dit_fp8_5bpp.safetensors	5.058	4.510%	19.54 GB
fl2va_dit_extreme_3.0bpp	models/MiniMax-H3/FL2VA/dit_extreme_3.0bpp.safetensors	3.026	13.225%	11.68 GB
ref2va_dit_extreme_3.0bpp	models/MiniMax-H3/REF2VA/dit_extreme_3.0bpp.safetensors	3.026	13.225%	11.68 GB
nf4 (bitsandbytes) (for comparison)	-	4.127	9.151%	-
torchao int8 w8a16 (for comparison)	-	8.010	0.984%	-
torchao fp8 w8a16 (for comparison)	-	8.008	2.642%	-

Text Encoder

name	file	actual bpp	size
text_encoder_4bpp	models/MiniMax-H3/text_encoder_4bpp.safetensors	4.039	13.20 GB
text_encoder_5bpp	models/MiniMax-H3/text_encoder_5bpp.safetensors	5.073	16.21 GB
text_encoder_6bpp	models/MiniMax-H3/text_encoder_6bpp.safetensors	6.102	19.20 GB
text_encoder_7bpp	models/MiniMax-H3/text_encoder_7bpp.safetensors	7.127	22.18 GB
text_encoder_8bpp	models/MiniMax-H3/text_encoder_8bpp.safetensors	8.095	24.99 GB

Video VAE

name	file	actual bpp	size
video_vae_4bpp	models/MiniMax-H3/video_vae_4bpp.safetensors	4.020	1.47 GB
video_vae_5bpp	models/MiniMax-H3/video_vae_5bpp.safetensors	5.040	1.76 GB
video_vae_6bpp	models/MiniMax-H3/video_vae_6bpp.safetensors	6.045	2.04 GB
video_vae_7bpp	models/MiniMax-H3/video_vae_7bpp.safetensors	7.055	2.33 GB
video_vae_8bpp	models/MiniMax-H3/video_vae_8bpp.safetensors	8.056	2.61 GB

Extreme Mixed-Bit Package

The extreme package dit_extreme_3.0bpp (identical configuration for FL2VA / Ref2VA) uses mixed quantization (weighted target = 3.0bpp): low-dimensional patch/final modules (time_embedder.proj_in/proj_out, video/audio_patch_proj, condition_proj, final_layer.video_out/audio_out, 7 layers) @ 8.0bpp, modulation (*.adaln_proj.linear, 51 layers) and attn.out_proj (52 layers) @ 3.0bpp, and the remaining 156 layers @ 2.9876bpp. Measured 3.026bpp / weight reconstruction error 13.2%.

Usage

Install from Source

# DiffSynth-Studio
git clone https://github.com/modelscope/DiffSynth-Studio.git
cd DiffSynth-Studio && pip install -e .

# EntroPack (pick the extra matching your CUDA version: cuda13 / cuda12)
git clone https://github.com/modelscope/entropack.git
cd entropack && pip install -e '.[cuda13]'

Example Script (Z-Image-Turbo)

"""
Z-Image-Turbo — EntroPack pre-quantized inference example.

Downloads the pre-quantized packages and their json sidecars from the
`DiffSynth-Studio/EntroPackPreQuants` model repo (each json is a ready-to-use
`MODEL_CONFIGS` entry), then runs inference.

Available packages (paths inside the prequant model repo):

    dit:
        models/Z-Image-Turbo/dit_4bpp.safetensors             4bpp
        models/Z-Image-Turbo/dit_5bpp.safetensors             5bpp
        models/Z-Image-Turbo/dit_6bpp.safetensors             6bpp
        models/Z-Image-Turbo/dit_7bpp.safetensors             7bpp
        models/Z-Image-Turbo/dit_8bpp.safetensors             8bpp
        models/Z-Image-Turbo/dit_extreme_2.3bpp.safetensors   mixed allocation, 2.3bpp
        models/Z-Image-Turbo/dit_fp8_5bpp.safetensors         fp8 weights, 5bpp
    text_encoder:
        models/Z-Image-Turbo/text_encoder_4bpp.safetensors    4bpp
        models/Z-Image-Turbo/text_encoder_5bpp.safetensors    5bpp
        models/Z-Image-Turbo/text_encoder_6bpp.safetensors    6bpp
        models/Z-Image-Turbo/text_encoder_7bpp.safetensors    7bpp
        models/Z-Image-Turbo/text_encoder_8bpp.safetensors    8bpp

`dit_package` and `text_encoder_package` are chosen independently: any combination works
(e.g. `dit_8bpp` with `text_encoder_4bpp`).
"""
import json

import torch

from diffsynth.configs import MODEL_CONFIGS
from diffsynth.pipelines.z_image import ZImagePipeline, ModelConfig

prequant_model_id = "DiffSynth-Studio/EntroPackPreQuants"
dit_package = "models/Z-Image-Turbo/dit_4bpp.safetensors"
text_encoder_package = "models/Z-Image-Turbo/text_encoder_4bpp.safetensors"


def register_prequantized(package_pattern):
    """Download the pre-quantized package and its json sidecar (a ready-to-use MODEL_CONFIGS entry), return a loadable ModelConfig."""
    package = ModelConfig(model_id=prequant_model_id, origin_file_pattern=package_pattern)
    package.download_if_necessary()
    sidecar = ModelConfig(model_id=prequant_model_id, origin_file_pattern=package_pattern.replace(".safetensors", ".json"))
    sidecar.download_if_necessary()
    with open(sidecar.path) as handle:
        MODEL_CONFIGS.append(json.load(handle))
    return package


pipe = ZImagePipeline.from_pretrained(
    torch_dtype=torch.bfloat16,
    device="cuda",
    model_configs=[
        register_prequantized(dit_package),
        register_prequantized(text_encoder_package),
        ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
    ],
    tokenizer_config=ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="tokenizer/"),
)
prompt = "Young Chinese woman in red Hanfu, intricate embroidery. Impeccable makeup, red floral forehead pattern. Elaborate high bun, golden phoenix headdress, red flowers, beads. Holds round folding fan with lady, trees, bird. Neon lightning-bolt lamp (⚡️), bright yellow glow, above extended left palm. Soft-lit outdoor night background, silhouetted tiered pagoda (西安大雁塔), blurred colorful distant lights."
image = pipe(prompt=prompt, seed=42, rand_device="cuda")
image.save("image_Z-Image-Turbo-EntroPack.jpg")

dit_package and text_encoder_package can be chosen independently in any combination; the extreme and fp8 packages work in exactly the same way — just point to the corresponding .safetensors path (mixed-quantization configs and other details live in the same-named .json, no script changes needed). For Qwen-Image-2.1 and MiniMax-H3, swap in the corresponding pipeline and package paths.