torchao

torchao provides high-performance dtypes and optimizations based on quantization and sparsity for inference and training PyTorch models. It is supported for any model in any modality, as long as it supports loading with Accelerate and contains torch.nn.Linear layers.

Make sure Pytorch 2.5+ and torchao are installed with the command below.

uv pip install -U torch torchao

Each quantization dtype is available as a separate instance of a AOBaseConfig class. This provides more flexible configuration options by exposing more available arguments.

Pass the AOBaseConfig of a quantization dtype, like Int4WeightOnlyConfig to [TorchAoConfig] in [~ModelMixin.from_pretrained].

import torch
from diffusers import DiffusionPipeline, PipelineQuantizationConfig, TorchAoConfig
from torchao.quantization import Int8WeightOnlyConfig

pipeline_quant_config = PipelineQuantizationConfig(
    quant_mapping={"transformer": TorchAoConfig(Int8WeightOnlyConfig(group_size=128)))}
)
pipeline = DiffusionPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev",
    quantzation_config=pipeline_quant_config,
    torch_dtype=torch.bfloat16,
    device_map="cuda"
)

For simple use cases, you could also provide a string identifier in [TorchAo] as shown below.

import torch
from diffusers import DiffusionPipeline, PipelineQuantizationConfig, TorchAoConfig

pipeline_quant_config = PipelineQuantizationConfig(
    quant_mapping={"transformer": TorchAoConfig("int8wo")}
)
pipeline = DiffusionPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev",
    quantzation_config=pipeline_quant_config,
    torch_dtype=torch.bfloat16,
    device_map="cuda"
)

torch.compile

torchao supports torch.compile which can speed up inference with one line of code.

import torch
from diffusers import DiffusionPipeline, PipelineQuantizationConfig, TorchAoConfig
from torchao.quantization import Int4WeightOnlyConfig

pipeline_quant_config = PipelineQuantizationConfig(
    quant_mapping={"transformer": TorchAoConfig(Int4WeightOnlyConfig(group_size=128)))}
)
pipeline = DiffusionPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev",
    quantzation_config=pipeline_quant_config,
    torch_dtype=torch.bfloat16,
    device_map="cuda"
)

pipeline.transformer.compile(transformer, mode="max-autotune", fullgraph=True)

Refer to this table for inference speed and memory usage benchmarks with Flux and CogVideoX. More benchmarks on various hardware are also available in the torchao repository.

Tip

The FP8 post-training quantization schemes in torchao are effective for GPUs with compute capability of at least 8.9 (RTX-4090, Hopper, etc.). FP8 often provides the best speed, memory, and quality trade-off when generating images and videos. We recommend combining FP8 and torch.compile if your GPU is compatible.

autoquant

torchao provides autoquant an automatic quantization API. Autoquantization chooses the best quantization strategy by comparing the performance of each strategy on chosen input types and shapes. This is only supported in Diffusers for individual models at the moment.

import torch
from diffusers import DiffusionPipeline
from torchao.quantization import autoquant

# Load the pipeline
pipeline = DiffusionPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell",
    torch_dtype=torch.bfloat16,
    device_map="cuda"
)

transformer = autoquant(pipeline.transformer)

Supported quantization types

torchao supports weight-only quantization and weight and dynamic-activation quantization for int8, float3-float8, and uint1-uint7.

Weight-only quantization stores the model weights in a specific low-bit data type but performs computation with a higher-precision data type, like bfloat16. This lowers the memory requirements from model weights but retains the memory peaks for activation computation.

Dynamic activation quantization stores the model weights in a low-bit dtype, while also quantizing the activations on-the-fly to save additional memory. This lowers the memory requirements from model weights, while also lowering the memory overhead from activation computations. However, this may come at a quality tradeoff at times, so it is recommended to test different models thoroughly.

The quantization methods supported are as follows:

Category	Full Function Names	Shorthands
Integer quantization	`int4_weight_only`, `int8_dynamic_activation_int4_weight`, `int8_weight_only`, `int8_dynamic_activation_int8_weight`	`int4wo`, `int4dq`, `int8wo`, `int8dq`
Floating point 8-bit quantization	`float8_weight_only`, `float8_dynamic_activation_float8_weight`, `float8_static_activation_float8_weight`	`float8wo`, `float8wo_e5m2`, `float8wo_e4m3`, `float8dq`, `float8dq_e4m3`, `float8dq_e4m3_tensor`, `float8dq_e4m3_row`
Floating point X-bit quantization	`fpx_weight_only`	`fpX_eAwB` where `X` is the number of bits (1-7), `A` is exponent bits, and `B` is mantissa bits. Constraint: `X == A + B + 1`
Unsigned Integer quantization	`uintx_weight_only`	`uint1wo`, `uint2wo`, `uint3wo`, `uint4wo`, `uint5wo`, `uint6wo`, `uint7wo`

Some quantization methods are aliases (for example, int8wo is the commonly used shorthand for int8_weight_only). This allows using the quantization methods described in the torchao docs as-is, while also making it convenient to remember their shorthand notations.

Refer to the official torchao documentation for a better understanding of the available quantization methods and the exhaustive list of configuration options available.

Serializing and Deserializing quantized models

To serialize a quantized model in a given dtype, first load the model with the desired quantization dtype and then save it using the [~ModelMixin.save_pretrained] method.

import torch
from diffusers import AutoModel, TorchAoConfig

quantization_config = TorchAoConfig("int8wo")
transformer = AutoModel.from_pretrained(
    "black-forest-labs/Flux.1-Dev",
    subfolder="transformer",
    quantization_config=quantization_config,
    torch_dtype=torch.bfloat16,
)
transformer.save_pretrained("/path/to/flux_int8wo", safe_serialization=False)

To load a serialized quantized model, use the [~ModelMixin.from_pretrained] method.

import torch
from diffusers import FluxPipeline, AutoModel

transformer = AutoModel.from_pretrained("/path/to/flux_int8wo", torch_dtype=torch.bfloat16, use_safetensors=False)
pipe = FluxPipeline.from_pretrained("black-forest-labs/Flux.1-Dev", transformer=transformer, torch_dtype=torch.bfloat16)
pipe.to("cuda")

prompt = "A cat holding a sign that says hello world"
image = pipe(prompt, num_inference_steps=30, guidance_scale=7.0).images[0]
image.save("output.png")

If you are using torch<=2.6.0, some quantization methods, such as uint4wo, cannot be loaded directly and may result in an UnpicklingError when trying to load the models, but work as expected when saving them. In order to work around this, one can load the state dict manually into the model. Note, however, that this requires using weights_only=False in torch.load, so it should be run only if the weights were obtained from a trustable source.

import torch
from accelerate import init_empty_weights
from diffusers import FluxPipeline, AutoModel, TorchAoConfig

# Serialize the model
transformer = AutoModel.from_pretrained(
    "black-forest-labs/Flux.1-Dev",
    subfolder="transformer",
    quantization_config=TorchAoConfig("uint4wo"),
    torch_dtype=torch.bfloat16,
)
transformer.save_pretrained("/path/to/flux_uint4wo", safe_serialization=False, max_shard_size="50GB")
# ...

# Load the model
state_dict = torch.load("/path/to/flux_uint4wo/diffusion_pytorch_model.bin", weights_only=False, map_location="cpu")
with init_empty_weights():
    transformer = AutoModel.from_config("/path/to/flux_uint4wo/config.json")
transformer.load_state_dict(state_dict, strict=True, assign=True)

Tip

The [AutoModel] API is supported for PyTorch >= 2.6 as shown in the examples below.

Resources

TorchAO Quantization API
Diffusers-TorchAO examples

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