#
Flux

Flux is a series of text-to-image generation models based on diffusion transformers. To know more about Flux, check out the original blog post by the creators of Flux, Black Forest Labs.

Original model checkpoints for Flux can be found here. Original inference code can be found here.

Flux 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. Additionally, Flux can benefit from quantization for memory efficiency with a trade-off in inference latency. Refer to this blog post to learn more. For an exhaustive list of resources, check out this gist.

Flux comes in two variants:

- Timestep-distilled (`black-forest-labs/FLUX.1-schnell`)
- Guidance-distilled (`black-forest-labs/FLUX.1-dev`)

Both checkpoints have slightly difference usage which we detail below.

###
Timestep-distilled

- `max_sequence_length` cannot be more than 256.
- `guidance_scale` needs to be 0.
- As this is a timestep-distilled model, it benefits from fewer sampling steps.

    import torch
    from diffusers import FluxPipeline

    pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16)
    pipe.enable_model_cpu_offload()

    prompt = "A cat holding a sign that says hello world"
    out = pipe(
        prompt=prompt,
        guidance_scale=0.,
        height=768,
        width=1360,
        num_inference_steps=4,
        max_sequence_length=256,
    ).images[0]
    out.save("image.png")

###
Guidance-distilled

- The guidance-distilled variant takes about 50 sampling steps for good-quality generation.
- It doesn’t have any limitations around the `max_sequence_length`.

    import torch
    from diffusers import FluxPipeline

    pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
    pipe.enable_model_cpu_offload()

    prompt = "a tiny astronaut hatching from an egg on the moon"
    out = pipe(
        prompt=prompt,
        guidance_scale=3.5,
        height=768,
        width=1360,
        num_inference_steps=50,
    ).images[0]
    out.save("image.png")

##
Running FP16 inference

Flux can generate high-quality images with FP16 (i.e. to accelerate inference on Turing/Volta GPUs) but produces different outputs compared to FP32/BF16. The issue is that some activations in the text encoders have to be clipped when running in FP16, which affects the overall image. Forcing text encoders to run with FP32 inference thus removes this output difference. See here for details.

FP16 inference code:

    import torch
    from diffusers import FluxPipeline

    pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16) # can replace schnell with dev
    # to run on low vram GPUs (i.e. between 4 and 32 GB VRAM)
    pipe.enable_sequential_cpu_offload()
    pipe.vae.enable_slicing()
    pipe.vae.enable_tiling()

    pipe.to(torch.float16) # casting here instead of in the pipeline constructor because doing so in the constructor loads all models into CPU memory at once

    prompt = "A cat holding a sign that says hello world"
    out = pipe(
        prompt=prompt,
        guidance_scale=0.,
        height=768,
        width=1360,
        num_inference_steps=4,
        max_sequence_length=256,
    ).images[0]
    out.save("image.png")

##
Single File Loading for the FluxTransformer2DModel

The `FluxTransformer2DModel` supports loading checkpoints in the original format shipped by Black Forest Labs. This is also useful when trying to load finetunes or quantized versions of the models that have been published by the community.

`FP8` inference can be brittle depending on the GPU type, CUDA version, and `torch` version that you are using. It is recommended that you use the `optimum-quanto` library in order to run FP8 inference on your machine.

The following example demonstrates how to run Flux with less than 16GB of VRAM.

First install `optimum-quanto`

    pip install optimum-quanto

Then run the following example

    import torch
    from diffusers import FluxTransformer2DModel, FluxPipeline
    from transformers import T5EncoderModel, CLIPTextModel
    from optimum.quanto import freeze, qfloat8, quantize

    bfl_repo = "black-forest-labs/FLUX.1-dev"
    dtype = torch.bfloat16

    transformer = FluxTransformer2DModel.from_single_file("https://huggingface.co/Kijai/flux-fp8/blob/main/flux1-dev-fp8.safetensors", torch_dtype=dtype)
    quantize(transformer, weights=qfloat8)
    freeze(transformer)

    text_encoder_2 = T5EncoderModel.from_pretrained(bfl_repo, subfolder="text_encoder_2", torch_dtype=dtype)
    quantize(text_encoder_2, weights=qfloat8)
    freeze(text_encoder_2)

    pipe = FluxPipeline.from_pretrained(bfl_repo, transformer=None, text_encoder_2=None, torch_dtype=dtype)
    pipe.transformer = transformer
    pipe.text_encoder_2 = text_encoder_2

    pipe.enable_model_cpu_offload()

    prompt = "A cat holding a sign that says hello world"
    image = pipe(
        prompt,
        guidance_scale=3.5,
        output_type="pil",
        num_inference_steps=20,
        generator=torch.Generator("cpu").manual_seed(0)
    ).images[0]

    image.save("flux-fp8-dev.png")

##
FluxPipeline

###
class diffusers.FluxPipeline

< source >

( scheduler: FlowMatchEulerDiscreteScheduler vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer text_encoder_2: T5EncoderModel tokenizer_2: T5TokenizerFast transformer: FluxTransformer2DModel )

Parameters

- transformer (FluxTransformer2DModel) — Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
- scheduler (FlowMatchEulerDiscreteScheduler) — A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
- vae (AutoencoderKL) — Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
- text_encoder (`CLIPTextModel`) — CLIP, specifically the clip-vit-large-patch14 variant.
- text_encoder_2 (`T5EncoderModel`) — T5, specifically the google/t5-v1_1-xxl variant.
- tokenizer (`CLIPTokenizer`) — Tokenizer of class CLIPTokenizer.
- tokenizer_2 (`T5TokenizerFast`) — Second Tokenizer of class T5TokenizerFast.

The Flux pipeline for text-to-image generation.

Reference: https://blackforestlabs.ai/announcing-black-forest-labs/

####
__call__

< source >

( prompt: Union = None prompt_2: Union = None height: Optional = None width: Optional = None num_inference_steps: int = 28 timesteps: List = None guidance_scale: float = 3.5 num_images_per_prompt: Optional = 1 generator: Union = None latents: Optional = None prompt_embeds: Optional = None pooled_prompt_embeds: Optional = None output_type: Optional = 'pil' return_dict: bool = True joint_attention_kwargs: Optional = None callback_on_step_end: Optional = None callback_on_step_end_tensor_inputs: List = ['latents'] max_sequence_length: int = 512 ) →
`~pipelines.flux.FluxPipelineOutput` or `tuple`

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.
- prompt_2 (`str` or `List[str]`, optional) — The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is will be used instead
- height (`int`, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) — The height in pixels of the generated image. This is set to 1024 by default for the best results.
- width (`int`, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) — The width in pixels of the generated image. This is set to 1024 by default for the 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.
- timesteps (`List[int]`, optional) — Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed will be used. Must be in descending order.
- guidance_scale (`float`, optional, defaults to 7.0) — 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 ge 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.
- pooled_prompt_embeds (`torch.FloatTensor`, optional) — Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, pooled text embeddings will be generated from `prompt` input argument.
- 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.flux.FluxPipelineOutput` instead of a plain tuple.
- joint_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 512) — Maximum sequence length to use with the `prompt`.

Returns

`~pipelines.flux.FluxPipelineOutput` or `tuple`

`~pipelines.flux.FluxPipelineOutput` if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated images.

Function invoked when calling the pipeline for generation.

Examples:

    >>> import torch
    >>> from diffusers import FluxPipeline

    >>> pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16)
    >>> pipe.to("cuda")
    >>> prompt = "A cat holding a sign that says hello world"
    >>> # Depending on the variant being used, the pipeline call will slightly vary.
    >>> # Refer to the pipeline documentation for more details.
    >>> image = pipe(prompt, num_inference_steps=4, guidance_scale=0.0).images[0]
    >>> image.save("flux.png")

####
disable_vae_slicing

< source >

( )

Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to computing decoding in one step.

####
disable_vae_tiling

< source >

( )

Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to computing decoding in one step.

####
enable_vae_slicing

< source >

( )

Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.

####
enable_vae_tiling

< source >

( )

Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow processing larger images.

####
encode_prompt

< source >

( prompt: Union prompt_2: Union device: Optional = None num_images_per_prompt: int = 1 prompt_embeds: Optional = None pooled_prompt_embeds: Optional = None max_sequence_length: int = 512 lora_scale: Optional = None )

Parameters

- prompt (`str` or `List[str]`, optional) — prompt to be encoded
- prompt_2 (`str` or `List[str]`, optional) — The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is used in all text-encoders device — (`torch.device`): torch device
- num_images_per_prompt (`int`) — number of images that should be generated per prompt
- 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.
- pooled_prompt_embeds (`torch.FloatTensor`, optional) — Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, pooled text embeddings will be generated from `prompt` input argument.
- lora_scale (`float`, optional) — A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.

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