#
AuraFlow

AuraFlow is inspired by 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 benchmark.

It was developed by the Fal team and more details about it can be found in this blog post.

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 for more details.

##
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 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.

    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 are also supported:

    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. The snippet below shows the changes needed to enable this:

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

Specifying `use_duck_shape` to be `False` instructs the compiler if it should use the same symbolic variable to represent input sizes that are the same. For more details, check out this comment.

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

Thanks to AstraliteHeart who helped us rewrite the AuraFlowTransformer2DModel class so that the above works for different resolutions (PR).

##
AuraFlowPipeline

###
class diffusers.AuraFlowPipeline

< source >

( tokenizer: T5Tokenizer text_encoder: UMT5EncoderModel vae: AutoencoderKL transformer: AuraFlowTransformer2DModel scheduler: FlowMatchEulerDiscreteScheduler )

Parameters

- tokenizer (`T5TokenizerFast`) — Tokenizer of class T5Tokenizer.
- text_encoder (`T5EncoderModel`) — Frozen text-encoder. AuraFlow uses T5, specifically the EleutherAI/pile-t5-xl variant.
- vae (AutoencoderKL) — Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
- transformer (AuraFlowTransformer2DModel) — Conditional Transformer (MMDiT and DiT) architecture to denoise the encoded image latents.
- scheduler (FlowMatchEulerDiscreteScheduler) — A scheduler to be used in combination with `transformer` to denoise the encoded image latents.

####
__call__

< source >

( prompt: typing.Union[str, typing.List[str]] = None negative_prompt: typing.Union[str, typing.List[str]] = None num_inference_steps: int = 50 sigmas: typing.List[float] = None guidance_scale: float = 3.5 num_images_per_prompt: typing.Optional[int] = 1 height: typing.Optional[int] = 1024 width: typing.Optional[int] = 1024 generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None latents: typing.Optional[torch.Tensor] = None prompt_embeds: typing.Optional[torch.Tensor] = None prompt_attention_mask: typing.Optional[torch.Tensor] = None negative_prompt_embeds: typing.Optional[torch.Tensor] = None negative_prompt_attention_mask: typing.Optional[torch.Tensor] = None max_sequence_length: int = 256 output_type: typing.Optional[str] = 'pil' return_dict: bool = True attention_kwargs: typing.Optional[typing.Dict[str, typing.Any]] = None callback_on_step_end: typing.Union[typing.Callable[[int, int, typing.Dict], NoneType], diffusers.callbacks.PipelineCallback, diffusers.callbacks.MultiPipelineCallbacks, NoneType] = None callback_on_step_end_tensor_inputs: typing.List[str] = ['latents'] )

Parameters

- prompt (`str` or `List[str]`, optional) — The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead.
- negative_prompt (`str` or `List[str]`, optional) — The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
- height (`int`, optional, defaults to self.transformer.config.sample_size * self.vae_scale_factor) — The height in pixels of the generated image. This is set to 1024 by default for best results.
- width (`int`, optional, defaults to self.transformer.config.sample_size * self.vae_scale_factor) — The width in pixels of the generated image. This is set to 1024 by default for best results.
- num_inference_steps (`int`, optional, defaults to 50) — The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference.
- sigmas (`List[float]`, optional) — Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed, `num_inference_steps` and `timesteps` must be `None`.
- guidance_scale (`float`, optional, defaults to 5.0) — Guidance scale as defined in Classifier-Free Diffusion Guidance. `guidance_scale` is defined as `w` of equation 2. of Imagen Paper. Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality.
- num_images_per_prompt (`int`, optional, defaults to 1) — The number of images to generate per prompt.
- generator (`torch.Generator` or `List[torch.Generator]`, optional) — One or a list of torch generator(s) to make generation deterministic.
- latents (`torch.FloatTensor`, optional) — Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will be generated by sampling using the supplied random `generator`.
- prompt_embeds (`torch.FloatTensor`, optional) — Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument.
- prompt_attention_mask (`torch.Tensor`, optional) — Pre-generated attention mask for text embeddings.
- negative_prompt_embeds (`torch.FloatTensor`, optional) — Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument.
- negative_prompt_attention_mask (`torch.Tensor`, optional) — Pre-generated attention mask for negative text embeddings.
- output_type (`str`, optional, defaults to `"pil"`) — The output format of the generate image. Choose between PIL: `PIL.Image.Image` or `np.array`.
- return_dict (`bool`, optional, defaults to `True`) — Whether or not to return a `~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput` instead of a plain tuple.
- attention_kwargs (`dict`, optional) — A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in diffusers.models.attention_processor.
- callback_on_step_end (`Callable`, optional) — A function that calls at the end of each denoising steps during the inference. The function is called with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by `callback_on_step_end_tensor_inputs`.
- callback_on_step_end_tensor_inputs (`List`, optional) — The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the `._callback_tensor_inputs` attribute of your pipeline class.
- max_sequence_length (`int` defaults to 256) — Maximum sequence length to use with the `prompt`.

Function invoked when calling the pipeline for generation.

Examples:

    >>> import torch
    >>> from diffusers import AuraFlowPipeline

    >>> pipe = AuraFlowPipeline.from_pretrained("fal/AuraFlow", torch_dtype=torch.float16)
    >>> pipe = pipe.to("cuda")
    >>> prompt = "A cat holding a sign that says hello world"
    >>> image = pipe(prompt).images[0]
    >>> image.save("aura_flow.png")

Returns: ImagePipelineOutput or `tuple`: If `return_dict` is `True`, ImagePipelineOutput is returned, otherwise a `tuple` is returned where the first element is a list with the generated images.

####
encode_prompt

< source >

( prompt: typing.Union[str, typing.List[str]] negative_prompt: typing.Union[str, typing.List[str]] = None do_classifier_free_guidance: bool = True num_images_per_prompt: int = 1 device: typing.Optional[torch.device] = None prompt_embeds: typing.Optional[torch.Tensor] = None negative_prompt_embeds: typing.Optional[torch.Tensor] = None prompt_attention_mask: typing.Optional[torch.Tensor] = None negative_prompt_attention_mask: typing.Optional[torch.Tensor] = None max_sequence_length: int = 256 lora_scale: typing.Optional[float] = None )

Parameters

- prompt (`str` or `List[str]`, optional) — prompt to be encoded
- negative_prompt (`str` or `List[str]`, optional) — The prompt not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
- do_classifier_free_guidance (`bool`, optional, defaults to `True`) — whether to use classifier free guidance or not
- num_images_per_prompt (`int`, optional, defaults to 1) — number of images that should be generated per prompt
- device — (`torch.device`, optional): torch device to place the resulting embeddings on
- prompt_embeds (`torch.Tensor`, optional) — Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument.
- prompt_attention_mask (`torch.Tensor`, optional) — Pre-generated attention mask for text embeddings.
- negative_prompt_embeds (`torch.Tensor`, optional) — Pre-generated negative text embeddings.
- negative_prompt_attention_mask (`torch.Tensor`, optional) — Pre-generated attention mask for negative text embeddings.
- max_sequence_length (`int`, defaults to 256) — Maximum sequence length to use for the prompt.
- lora_scale (`float`, optional) — A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.

Encodes the prompt into text encoder hidden states.

< > Update on GitHub