---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-Krea-dev/blob/main/LICENSE.md
base_model: black-forest-labs/FLUX.1-Krea-dev
pipeline_tag: text-to-image
library_name: diffusers
tags:
  - flux
  - nunchaku
  - svdquant
  - int4
  - quantization
---

# 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

| Checkpoint | Latency | Max VRAM |
| --- | ---: | ---: |
| Converted Diffusers Nunchaku Lite INT4 r32 + BNB4 T5 | 26.99 s (stdev 0.03 s) | 16.42 GiB |

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

![Native reference (left) and converted output (right)](output_comparison.png)

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.

```python
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")
```