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# AuraFlow

AuraFlow is inspired by [Stable Diffusion 3](../pipelines/stable_diffusion/stable_diffusion_3) and is by far the largest text-to-image generation model that comes with an Apache 2.0 license. This model achieves state-of-the-art results on the [GenEval](https://github.com/djghosh13/geneval) benchmark.

It was developed by the Fal team and more details about it can be found in [this blog post](https://blog.fal.ai/auraflow/).

<Tip>

AuraFlow can be quite expensive to run on consumer hardware devices. However, you can perform a suite of optimizations to run it faster and in a more memory-friendly manner. Check out [this section](https://huggingface.co/blog/sd3#memory-optimizations-for-sd3) for more details.

</Tip>

## Quantization

Quantization helps reduce the memory requirements of very large models by storing model weights in a lower precision data type. However, quantization may have varying impact on video quality depending on the video model.

Refer to the [Quantization](../../quantization/overview) overview to learn more about supported quantization backends and selecting a quantization backend that supports your use case. The example below demonstrates how to load a quantized [`AuraFlowPipeline`] for inference with bitsandbytes.

```py
import torch
from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig, AuraFlowTransformer2DModel, AuraFlowPipeline
from transformers import BitsAndBytesConfig as BitsAndBytesConfig, T5EncoderModel

quant_config = BitsAndBytesConfig(load_in_8bit=True)
text_encoder_8bit = T5EncoderModel.from_pretrained(
    "fal/AuraFlow",
    subfolder="text_encoder",
    quantization_config=quant_config,
    torch_dtype=torch.float16,
)

quant_config = DiffusersBitsAndBytesConfig(load_in_8bit=True)
transformer_8bit = AuraFlowTransformer2DModel.from_pretrained(
    "fal/AuraFlow",
    subfolder="transformer",
    quantization_config=quant_config,
    torch_dtype=torch.float16,
)

pipeline = AuraFlowPipeline.from_pretrained(
    "fal/AuraFlow",
    text_encoder=text_encoder_8bit,
    transformer=transformer_8bit,
    torch_dtype=torch.float16,
    device_map="balanced",
)

prompt = "a tiny astronaut hatching from an egg on the moon"
image = pipeline(prompt).images[0]
image.save("auraflow.png")
```

Loading [GGUF checkpoints](https://huggingface.co/docs/diffusers/quantization/gguf) are also supported:

```py
import torch
from diffusers import (
    AuraFlowPipeline,
    GGUFQuantizationConfig,
    AuraFlowTransformer2DModel,
)

transformer = AuraFlowTransformer2DModel.from_single_file(
    "https://huggingface.co/city96/AuraFlow-v0.3-gguf/blob/main/aura_flow_0.3-Q2_K.gguf",
    quantization_config=GGUFQuantizationConfig(compute_dtype=torch.bfloat16),
    torch_dtype=torch.bfloat16,
)

pipeline = AuraFlowPipeline.from_pretrained(
    "fal/AuraFlow-v0.3",
    transformer=transformer,
    torch_dtype=torch.bfloat16,
)

prompt = "a cute pony in a field of flowers"
image = pipeline(prompt).images[0]
image.save("auraflow.png")
```

## Support for `torch.compile()`

AuraFlow can be compiled with `torch.compile()` to speed up inference latency even for different resolutions. First, install PyTorch nightly following the instructions from [here](https://pytorch.org/). The snippet below shows the changes needed to enable this:

```diff
+ torch.fx.experimental._config.use_duck_shape = False
+ pipeline.transformer = torch.compile(
    pipeline.transformer, fullgraph=True, dynamic=True
)
```

This enables from 100% (on low resolutions) to a 30% (on 1536x1536 resolution) speed improvements.

Thanks to [AstraliteHeart](https://github.com/huggingface/diffusers/pull/11297/) who helped us rewrite the [`AuraFlowTransformer2DModel`] class so that the above works for different resolutions ([PR](https://github.com/huggingface/diffusers/pull/11297/)).

## AuraFlowPipeline

[[autodoc]] AuraFlowPipeline
	- all
	- __call__