Bringing Nunchaku 4-bit Diffusion Inference to Diffusers

Bringing Nunchaku 4-bit Diffusion Inference to Diffusers
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Bringing Nunchaku 4-bit Diffusion Inference to Diffusers
Published
July 23, 2026
Update on GitHub
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Pham Hong Vinh
rootonchair
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Sayak Paul
sayakpaul
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Table of Contents
Getting started with Nunchaku Lite
Background: SVDQuant and Nunchaku
Introducing Nunchaku Lite
Native loading in Diffusers
Hardware support
Getting more speed and lower memory
Benchmarks
End-to-end latency and memory
Image quality
Quantizing your own model
1. Inspect what will be quantized
2. Run quantization
3. Package a Diffusers pipeline
4. Load, verify, and push to the Hub
Quantizing models with structural rewrites
Ready-to-use checkpoints
Conclusion
Acknowledgements
Large diffusion transformers can create stunning images (or even videos, audio snippets, and now text), but loading a modern text-to-image model in BF16 precision often requires 20-30 GB of VRAM, which puts these models out of reach of most consumer GPUs. Quantization is a powerful solution to this problem, and Diffusers already integrates several quantization backends such as bitsandbytes, GGUF, torchao, and Quanto, which we covered in
Exploring Quantization Backends in Diffusers
.
Most of these backends are
weight-only
. This means that they store the weights in low precision and dequantize them back to high precision at compute time. This reduces memory usage significantly, but it usually does not make inference faster, and can even add a small latency overhead.
SVDQuant
, the quantization method behind the popular
Nunchaku
inference engine, takes a different approach. It runs the main transformer layers with 4-bit weights and activations (W4A4), reducing memory while also speeding up the denoising loop. The details are covered below, but until now, using these checkpoints required a separate inference library.
With current Diffusers, loading a Nunchaku checkpoint is as simple as calling
from_pretrained()
, with no local CUDA compilation required thanks to the
kernels
package. In addition, the companion
diffuse-compressor
toolkit lets you quantize new architectures yourself and publish them as regular Diffusers repositories.
Table of Contents
Getting started with Nunchaku Lite
Background: SVDQuant and Nunchaku
Introducing Nunchaku Lite
Native loading in Diffusers
Getting more speed and lower memory
Benchmarks
Quantizing your own model
Ready-to-use checkpoints
Conclusion
Acknowledgements
Getting started with Nunchaku Lite
First, install the requirements. You need a recent version of Diffusers and the Hugging Face
kernels
package:
pip install -U diffusers transformers accelerate kernels bitsandbytes
Then load a pre-quantized pipeline like any other Diffusers model:
import
torch
from
diffusers
import
ErnieImagePipeline
pipe = ErnieImagePipeline.from_pretrained(
"lite-infer/ERNIE-Image-Turbo-nunchaku-lite-nvfp4_r32-bnb4-text-encoder"
,
torch_dtype=torch.bfloat16,
).to(
"cuda"
)
image = pipe(
prompt=
"A cinematic portrait of a red fox in a misty forest at sunrise, "
"detailed fur, volumetric light"
,
height=
1024
,
width=
1024
,
num_inference_steps=
8
,
guidance_scale=
1.0
,
generator=torch.Generator(
"cuda"
).manual_seed(
42
),
).images[
0
]
image.save(
"output.png"
)
No custom pipeline class or separate inference engine is needed, and there is nothing to compile locally. The NVFP4 kernels are downloaded from the Hub through the
Nunchaku Lite kernels page
the first time they are used. This checkpoint pairs a Nunchaku NVFP4 transformer with a bitsandbytes NF4 text encoder, and generates a 1024x1024 image in about 1.7 seconds on an RTX 5090 with a peak memory usage of about 12 GB, compared with about 24 GB for the BF16 pipeline. You can find more details about the Nunchaku Lite checkpoint format in the
official Diffusers documentation
.
NVFP4 checkpoints require an NVIDIA Blackwell GPU (RTX 50 series, RTX PRO 6000, B200). For earlier generations, use the INT4 variants. See the
hardware support
table below for details.
Background: SVDQuant and Nunchaku
SVDQuant
is the quantization method behind
Nunchaku
, its reference CUDA inference engine. Standard 4-bit quantization is difficult for diffusion transformers because both weights and activations contain large outliers. SVDQuant handles this by moving activation outliers into the weights, representing the hardest part of each weight matrix with a small 16-bit low-rank branch, and quantizing the remaining residual to 4 bits. Nunchaku makes this fast with fused kernels for the 4-bit path and the low-rank branch.
Nunchaku fuses the low-rank down projection with the quantization kernel and the low-rank up projection with the 4-bit compute kernel, eliminating the memory access overhead of the 16-bit branch. Figure from the
SVDQuant paper
.
Introducing Nunchaku Lite
The original
Nunchaku engine
gets much of its speed from
model-specific fused execution paths
, such as fused QKV projections and fused GELU/MLP kernels. Those optimizations are tied to each architecture's module layout and checkpoint format, so supporting a new model family usually requires model-specific integration work.
Nunchaku Lite
is the new integration path in Diffusers. With it, Diffusers can load Nunchaku-style checkpoints without a custom pipeline or a separate inference engine. Under the hood, Nunchaku Lite patches the relevant
nn.Linear
modules of a stock Diffusers model with runtime SVDQ/AWQ linear layers before the checkpoint is loaded. The CUDA kernels come from the Hub through the
kernels
package. Two kernel families are used:
svdq_w4a4
: 4-bit weights and activations with the SVDQuant low-rank correction. This layer is used for the transformer's attention and MLP projections, where nearly all of the compute is spent, and is available in INT4 and NVFP4 variants.
awq_w4a16
: 4-bit weights with 16-bit activations, used for adaptive normalization and modulation projections such as FLUX
adanorm_single
/
adanorm_zero
or Qwen-Image modulation layers. These layers are memory-bound and precision-sensitive, making AWQ a good fit to preserve precision while still saving memory and space.
The trade-off is that, without architecture-specific fused kernels and modules, Nunchaku Lite cannot match the speedup of the original Nunchaku engine. However, the bare-bones implementation still delivers around
30% speedup
while retaining the same level of
VRAM reduction
.
Native loading in Diffusers
If you have used bitsandbytes or torchao in Diffusers, the mechanics will feel familiar. A Nunchaku Lite model repository is an ordinary Diffusers repository. The only special part is a
quantization_config
block inside the transformer's
config.json
:
"quantization_config"
:
{
"quant_method"
:
"nunchaku_lite"
,
"compute_dtype"
:
"bfloat16"
,
"svdq_w4a4"
:
{
"precision"
:
"nvfp4"
,
"group_size"
:
16
,
"rank"
:
32
,
"targets"
:
[
"layers.0.self_attention.to_q"
,
"layers.0.self_attention.to_k"
,
"..."
]
}
,
"awq_w4a16"
:
{
"precision"
:
"int4"
,
"group_size"
:
64
,
"targets"
:
[
"adaLN_modulation.1"
,
"..."
]
}
}
This config tells Diffusers which modules were quantized, which scheme they use, and which Nunchaku Lite runtime layer to instantiate (
SVDQW4A4Linear
or
AWQW4A16Linear
).
Because the quantized model keeps the exact module structure of the dense one, everything downstream (schedulers, LoRA loading hooks, offloading,
torch.compile
) sees a normal Diffusers model.
Hardware support
Nunchaku Lite uses different kernel variants depending on the GPU generation and checkpoint precision:
Scheme
Precision
Supported GPUs
svdq_w4a4
nvfp4
Blackwell (RTX 50 series, RTX PRO 6000, B200)
svdq_w4a4
int4
Turing / Ampere / Ada (RTX 30 & 40 series, A100, L40S)
awq_w4a16
int4
Turing / Ampere / Ada (RTX 30 & 40 series, A100, L40S)
Volta and Hopper GPUs are currently not supported by the 4-bit kernels. The quantizer validates the GPU's CUDA capability at load time and raises a clear error instead of producing incorrect outputs.
Getting more speed and lower memory
Nunchaku Lite can be combined with other Diffusers memory and speed optimizations.
torch.compile
.
Compiling the transformer improves the end-to-end speedup from 1.35x to 1.8x:
pipe.transformer.
compile
(fullgraph=
True
)
# or compile_repeated_blocks() for faster compilation
pipe.transformer.compile_repeated_blocks(fullgraph=
True
)
Quantized text encoders.
The transformer is not the only component with a large memory footprint. Text encoders such as T5 or Qwen3 can occupy several gigabytes on their own. Further quantizing the text encoder with bitsandbytes NF4 reduces peak VRAM by about 22% in our benchmark.
Offloading.
Diffusers offloading helpers such as
enable_model_cpu_offload()
and
enable_sequential_cpu_offload()
work as usual if you need to fit the pipeline onto a smaller GPU.
Benchmarks
All numbers below were measured on an NVIDIA RTX PRO 6000 (Blackwell) at 1024x1024 using
rootonchair/ERNIE-Image-Turbo-nunchaku-lite-int4-bnb4-text-encoder
.
End-to-end latency and memory
Configuration
Full pipeline
Denoise loop
Peak VRAM
Speedup
BF16 baseline
3.00 s
2.86 s
31.1 GB
1.0x
Nunchaku Lite NVFP4
2.27 s
2.13 s
20.6 GB
1.35x
Nunchaku Lite NVFP4 +
torch.compile
1.68 s
1.53 s
20.6 GB
1.8x
Nunchaku Lite NVFP4 + NF4 text encoder
2.29 s
2.13 s
16.0 GB
1.35x
As shown above, Nunchaku reduces peak VRAM by up to 50% while still improving latency by roughly 30%. The remaining overhead comes largely from extra kernel launches, which
torch.compile
can mitigate, bringing the full pipeline down to 1.68 s, or 1.8x faster than the BF16 baseline.
Image quality
BF16 vs 4-bit outputs with identical seeds and settings.
Quantizing your own model
Nunchaku Lite support in Diffusers is architecture-agnostic, and the
diffuse-compressor
toolkit provides an end-to-end SVDQuant workflow for Diffusers models: calibrate, quantize, package, and publish.
Below, we walk through quantizing FLUX.2 Klein 4B as an example. It covers the main steps: inspect the model, calibrate and quantize the transformer, package the result as a Diffusers pipeline, then verify and push it to the Hub. The
full tutorial
covers every flag in detail.
1. Inspect what will be quantized
The generic scanner walks the model and decides what to target: compatible linears inside the repeated transformer-block stack become SVDQ W4A4 targets, recognized modulation linears become AWQ W4A16 targets, and everything else stays dense.
python examples/text_to_image/quantize_hf.py black-forest-labs/FLUX.2-klein-4B \
--precision int4 --rank 32 --inspect-config
Always read this report before quantizing. For FLUX.2 Klein 4B, the expected result is 100 SVDQ targets, 3 AWQ targets, and 6 dense outer linears, with no missing patterns or duplicate names.
2. Run quantization
The following command runs SVDQuant on the transformer and writes the quantized checkpoint to
outputs/checkpoints/svdq-int4_r32-flux-2-klein-4b.safetensors
:
python examples/text_to_image/quantize_hf.py black-forest-labs/FLUX.2-klein-4B \
--precision int4 \
--output outputs/checkpoints/svdq-int4_r32-flux-2-klein-4b.safetensors
Replace
--precision int4
with
nvfp4
to build Blackwell-native weights.
3. Package a Diffusers pipeline
The converter combines the quantized transformer with the base pipeline's other components, writes the compact
nunchaku_lite
configuration into
transformer/config.json
, and can optionally convert text encoders to NF4:
python examples/convert_nunchaku_lite_diffusers.py \
--checkpoint outputs/checkpoints/svdq-int4_r32-flux-2-klein-4b.safetensors \
--model-id black-forest-labs/FLUX.2-klein-4B \
--bnb4-text-encoder text_encoder \
--compute-dtype bfloat16 \
--output-dir outputs/diffusers/FLUX.2-klein-4B-nunchaku-lite-int4-bnb4-text-encoder
4. Load, verify, and push to the Hub
import
torch
from
diffusers
import
DiffusionPipeline
pipe = DiffusionPipeline.from_pretrained(
"outputs/diffusers/FLUX.2-klein-4B-nunchaku-lite-int4-bnb4-text-encoder"
,
device_map=
"cuda"
,
)
image = pipe(
"A glass robot in a greenhouse, cinematic lighting"
,
num_inference_steps=
4
, guidance_scale=
1.0
,
generator=torch.Generator(
"cuda"
).manual_seed(
12345
),
).images[
0
]
Once the outputs look good, run
pipe.push_to_hub("your-name/your-model-nunchaku-lite-int4")
. Other users can then load it with the same
from_pretrained()
pattern shown above.
Quantizing models with structural rewrites
Note that the generic path assumes the architecture can be quantized without structural rewrites. For additional speedup, the original Nunchaku engine rewrites groups of Diffusers layers as fused modules. The generic path cannot infer these changes on its own, such as combining separate Q, K, and V projections into one module or splitting a fused projection across several modules.
FLUX.1-dev's QKV projection is a concrete example.
Diffusers defines three separate modules
:
self.to_q = torch.nn.Linear(query_dim, self.inner_dim, bias=bias)
self.to_k = torch.nn.Linear(query_dim, self.inner_dim, bias=bias)
self.to_v = torch.nn.Linear(query_dim, self.inner_dim, bias=bias)
The
Nunchaku FLUX module combines those layers
into one quantized
to_qkv
module:
to_qkv = fuse_linears([other.to_q, other.to_k, other.to_v])
self.to_qkv = SVDQW4A4Linear.from_linear(to_qkv, **kwargs)
This grouped module is required because Nunchaku's fused operator consumes the QKV projection, Q/K normalization, and rotary embeddings together. By comparison, the
default Diffusers path
executes them separately:
query = attn.to_q(hidden_states)
key = attn.to_k(hidden_states)
value = attn.to_v(hidden_states)
query = query.unflatten(-
1
, (attn.heads, -
1
))
key = key.unflatten(-
1
, (attn.heads, -
1
))
value = value.unflatten(-
1
, (attn.heads, -
1
))
query = attn.norm_q(query)
key = attn.norm_k(key)
if
image_rotary_emb
is
not
None
:
query = apply_rotary_emb(query, image_rotary_emb, sequence_dim=
1
)
key = apply_rotary_emb(key, image_rotary_emb, sequence_dim=
1
)
The
Nunchaku path
supplies the grouped projection, normalization modules, and rotary embeddings to one fused operator:
qkv = fused_qkv_norm_rottary(
hidden_states, attn.to_qkv, attn.norm_q, attn.norm_k, image_rotary_emb
)
This is the structural rewrite that the generic path cannot infer. Diffusers has three destination modules with
to_q
,
to_k
, and
to_v
parameter prefixes, while Nunchaku has one grouped module under
to_qkv
. A model-specific target config or adapter must state that the Q, K, and V parameters should be concatenated along the output dimension, in that order, and loaded into
to_qkv
.
Structural rewrites like these are described by a model-specific target config during quantization and handled by a small runtime adapter when the checkpoint is loaded.
The
FLUX.2 Klein 4B quantization script
provides a concrete target-config example for producing a structurally rewritten checkpoint, while
rootonchair/nunchaku-lite
provides the runtime adapters needed to load grouped QKV tensors, split fused projections, and other fused operations.
For the complete workflow, you can check the
Adding A New Model
guide.
Ready-to-use checkpoints
To get started right away, check out the following repositories:
rootonchair/ERNIE-Image-Turbo-nunchaku-lite-int4-bnb4-text-encoder
: INT4 ERNIE-Image-Turbo with a bitsandbytes NF4 text encoder
rootonchair/ERNIE-Image-Turbo-nunchaku-lite-nvfp4-bnb4-text-encoder
: NVFP4 ERNIE-Image-Turbo with a bitsandbytes NF4 text encoder
OzzyGT/Krea_2_Turbo_nunchaku_lite_nvfp4
: NVFP4 Krea 2 Turbo checkpoint
lite-infer
: more Nunchaku Lite checkpoints and collections
Conclusion
Nunchaku's SVDQuant kernels are one of the most effective ways to run diffusion transformers efficiently on consumer hardware, and they are now natively supported in Diffusers. Pre-quantized checkpoints load with
from_pretrained()
, and the diffuse-compressor toolkit makes it possible to quantize new architectures without waiting for engine support. By quantizing both weights and activations, the W4A4 path lowers memory use while improving denoising latency, keeping image quality close to the BF16 original.
If you quantize and publish a new model, we would love to hear about it. Share it on the Hub and let us know! If you have any questions about this feature, feel free to join our
Discord
.
To learn more, check out the following resources:
Diffusers Nunchaku documentation
The integration PR (huggingface/diffusers#14100)
SVDQuant paper
and the
Nunchaku engine
diffuse-compressor
Previous posts:
Exploring Quantization Backends in Diffusers
and
Memory-efficient Diffusion Transformers with Quanto and Diffusers
Acknowledgements
Thanks to the Diffusers maintainers for reviews and guidance throughout the integration, and to the MIT HAN Lab / Nunchaku team for the original SVDQuant work. Thanks to Marc Sun for providing feedback on the blog post. Thanks to Álvaro Somoza for trying out
nunchaku-lite
and for providing feedback.
rootonchair
is also grateful to SilverAI for supporting this work and providing the environment in which much of this development took place.
Models mentioned in this article
1
OzzyGT/Krea_2_Turbo_nunchaku_lite_nvfp4
Text-to-Image
•
8B
•
Updated
15 days ago
•
39
•
2
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Models mentioned in this article
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