rootonchair/ERNIE-Image-Turbo-nunchaku-lite-nvfp4

ERNIE-Image-Turbo Nunchaku Lite NVFP4

[!IMPORTANT] Experimental: This Nunchaku Lite quantized ERNIE-Image-Turbo checkpoint is experimental. APIs, loading behavior may change.

This repository contains a full Diffusers pipeline for baidu/ERNIE-Image-Turbo with the transformer replaced by a Nunchaku Lite quantized transformer.

The tokenizer, text encoder, prompt enhancer, scheduler, and VAE are inherited from the original ERNIE-Image-Turbo release. Only the Diffusion Transformer component is replaced.

Quantization

The transformer uses a compact Nunchaku Lite quantization config stored in transformer/config.json.

Requirements

This checkpoint requires a Diffusers version that includes NunchakuLiteQuantizationConfig and the Nunchaku Lite loader path.

pip install git+https://github.com/huggingface/diffusers
pip install kernels

Use a CUDA-capable PyTorch environment. The quantized transformer uses kernels from the Hugging Face kernels package.

Usage

import torch
from diffusers import ErnieImagePipeline

pipe = ErnieImagePipeline.from_pretrained(
    "rootonchair/ERNIE-Image-Turbo-nunchaku-lite-nvfp4",
    torch_dtype=torch.bfloat16,
).to("cuda")

image = pipe(
    prompt="A modern red armchair in a quiet studio, soft window light, realistic product photography",
    height=1024,
    width=1024,
    num_inference_steps=8,
    guidance_scale=1.0,
    use_pe=False,
).images[0]

image.save("ernie-image-turbo-nunchaku-lite-nvfp4.png")

Recommended ERNIE-Image-Turbo settings:

• —num_inference_steps=8
• —guidance_scale=1.0
• —use_pe=False for direct prompt usage, or use_pe=True to enable the inherited prompt enhancer

Provenance

• —Base pipeline: baidu/ERNIE-Image-Turbo
• —Source quantized checkpoint: rootonchair/ERNIE-Image-Turbo-nunchaku-lite

Limitations

• —This is an experimental quantized checkpoint.
• —Only the transformer is quantized; the text encoder, prompt enhancer, scheduler, and VAE are inherited from the base model.
• —save_pretrained() serialization for Nunchaku Lite models is not supported yet.
• —Output quality and performance may differ from the original BF16 transformer.

License

This repository follows the Apache-2.0 license from the base ERNIE-Image-Turbo release.