lite-infer/flux.1-krea-dev-nunchaku-lite-int4_r32-bnb4-text-encoder

FLUX.1 Krea Dev Nunchaku Lite INT4 r32

Diffusers-loadable conversion of:

• —Base model: black-forest-labs/FLUX.1-Krea-dev
• —Source repo: nunchaku-ai/nunchaku-flux.1-krea-dev
• —Source checkpoint: svdq-int4_r32-flux.1-krea-dev.safetensors

The transformer uses quant_method: nunchaku_lite, INT4 SVDQ with group size 64, runtime rank 64, 418 SVDQ targets, and 76 AWQ W4A16 targets. The CLIP encoder is copied from the base model and T5 text_encoder_2 is BitsAndBytes 4-bit NF4. Fused QKV modules are split in logical tensor layout; single-block proj_out is merged from attention and MLP projections; low-rank tensors are logically padded to rank 64. INT4 shifted down-projection biases are compensated for signed-unfused Diffusers execution.

Benchmark

RTX 5090, 1024×1024, 28 steps, guidance scale 3.5, one warmup and three measured runs, full GPU placement. VRAM is peak total device usage sampled with nvidia-smi, including allocations outside PyTorch's caching allocator.

Output Comparison

[image]

Both images use the same prompt, seed 0, scheduler, resolution, and step count. Native Nunchaku 1.x refuses INT4 checkpoints on Blackwell GPUs, so a same-precision native benchmark was unavailable on the RTX 5090.

Run

Requires the Hugging Face kernels package and a Turing, Ampere, Ada, or Blackwell NVIDIA GPU; Hopper is unsupported for INT4 kernels.

import torch
from diffusers import FluxPipeline

pipe = FluxPipeline.from_pretrained(
    "lite-infer/flux.1-krea-dev-nunchaku-lite-int4_r32-bnb4-text-encoder",
    torch_dtype=torch.bfloat16,
).to("cuda")

image = pipe(
    prompt='A cinematic photograph of a red fox standing in a misty forest at sunrise, detailed fur, volumetric light',
    generator=torch.Generator("cuda").manual_seed(0),
    width=1024,
    height=1024,
    num_inference_steps=28,
    guidance_scale=3.5,
).images[0]
image.save("output.png")