#
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 the following variants:

model type | model id
--- | ---
Timestep-distilled | `black-forest-labs/FLUX.1-schnell`
Guidance-distilled | `black-forest-labs/FLUX.1-dev`
Fill Inpainting/Outpainting (Guidance-distilled) | `black-forest-labs/FLUX.1-Fill-dev`
Canny Control (Guidance-distilled) | `black-forest-labs/FLUX.1-Canny-dev`
Depth Control (Guidance-distilled) | `black-forest-labs/FLUX.1-Depth-dev`
Canny Control (LoRA) | `black-forest-labs/FLUX.1-Canny-dev-lora`
Depth Control (LoRA) | `black-forest-labs/FLUX.1-Depth-dev-lora`
Redux (Adapter) | `black-forest-labs/FLUX.1-Redux-dev`

All checkpoints have different 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")

###
Fill Inpainting/Outpainting

- Flux Fill pipeline does not require `strength` as an input like regular inpainting pipelines.
- It supports both inpainting and outpainting.

    import torch
    from diffusers import FluxFillPipeline
    from diffusers.utils import load_image

    image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/cup.png")
    mask = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/cup_mask.png")

    repo_id = "black-forest-labs/FLUX.1-Fill-dev"
    pipe = FluxFillPipeline.from_pretrained(repo_id, torch_dtype=torch.bfloat16).to("cuda")

    image = pipe(
        prompt="a white paper cup",
        image=image,
        mask_image=mask,
        height=1632,
        width=1232,
        max_sequence_length=512,
        generator=torch.Generator("cpu").manual_seed(0)
    ).images[0]
    image.save(f"output.png")

###
Canny Control

Note: `black-forest-labs/Flux.1-Canny-dev` is not a ControlNetModel model. ControlNet models are a separate component from the UNet/Transformer whose residuals are added to the actual underlying model. Canny Control is an alternate architecture that achieves effectively the same results as a ControlNet model would, by using channel-wise concatenation with input control condition and ensuring the transformer learns structure control by following the condition as closely as possible.

    # !pip install -U controlnet-aux
    import torch
    from controlnet_aux import CannyDetector
    from diffusers import FluxControlPipeline
    from diffusers.utils import load_image

    pipe = FluxControlPipeline.from_pretrained("black-forest-labs/FLUX.1-Canny-dev", torch_dtype=torch.bfloat16).to("cuda")

    prompt = "A robot made of exotic candies and chocolates of different kinds. The background is filled with confetti and celebratory gifts."
    control_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/robot.png")

    processor = CannyDetector()
    control_image = processor(control_image, low_threshold=50, high_threshold=200, detect_resolution=1024, image_resolution=1024)

    image = pipe(
        prompt=prompt,
        control_image=control_image,
        height=1024,
        width=1024,
        num_inference_steps=50,
        guidance_scale=30.0,
    ).images[0]
    image.save("output.png")

Canny Control is also possible with a LoRA variant of this condition. The usage is as follows:

    # !pip install -U controlnet-aux
    import torch
    from controlnet_aux import CannyDetector
    from diffusers import FluxControlPipeline
    from diffusers.utils import load_image

    pipe = FluxControlPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to("cuda")
    pipe.load_lora_weights("black-forest-labs/FLUX.1-Canny-dev-lora")

    prompt = "A robot made of exotic candies and chocolates of different kinds. The background is filled with confetti and celebratory gifts."
    control_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/robot.png")

    processor = CannyDetector()
    control_image = processor(control_image, low_threshold=50, high_threshold=200, detect_resolution=1024, image_resolution=1024)

    image = pipe(
        prompt=prompt,
        control_image=control_image,
        height=1024,
        width=1024,
        num_inference_steps=50,
        guidance_scale=30.0,
    ).images[0]
    image.save("output.png")

###
Depth Control

Note: `black-forest-labs/Flux.1-Depth-dev` is not a ControlNet model. ControlNetModel models are a separate component from the UNet/Transformer whose residuals are added to the actual underlying model. Depth Control is an alternate architecture that achieves effectively the same results as a ControlNet model would, by using channel-wise concatenation with input control condition and ensuring the transformer learns structure control by following the condition as closely as possible.

    # !pip install git+https://github.com/huggingface/image_gen_aux
    import torch
    from diffusers import FluxControlPipeline, FluxTransformer2DModel
    from diffusers.utils import load_image
    from image_gen_aux import DepthPreprocessor

    pipe = FluxControlPipeline.from_pretrained("black-forest-labs/FLUX.1-Depth-dev", torch_dtype=torch.bfloat16).to("cuda")

    prompt = "A robot made of exotic candies and chocolates of different kinds. The background is filled with confetti and celebratory gifts."
    control_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/robot.png")

    processor = DepthPreprocessor.from_pretrained("LiheYoung/depth-anything-large-hf")
    control_image = processor(control_image)[0].convert("RGB")

    image = pipe(
        prompt=prompt,
        control_image=control_image,
        height=1024,
        width=1024,
        num_inference_steps=30,
        guidance_scale=10.0,
        generator=torch.Generator().manual_seed(42),
    ).images[0]
    image.save("output.png")

Depth Control is also possible with a LoRA variant of this condition. The usage is as follows:

    # !pip install git+https://github.com/huggingface/image_gen_aux
    import torch
    from diffusers import FluxControlPipeline, FluxTransformer2DModel
    from diffusers.utils import load_image
    from image_gen_aux import DepthPreprocessor

    pipe = FluxControlPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to("cuda")
    pipe.load_lora_weights("black-forest-labs/FLUX.1-Depth-dev-lora")

    prompt = "A robot made of exotic candies and chocolates of different kinds. The background is filled with confetti and celebratory gifts."
    control_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/robot.png")

    processor = DepthPreprocessor.from_pretrained("LiheYoung/depth-anything-large-hf")
    control_image = processor(control_image)[0].convert("RGB")

    image = pipe(
        prompt=prompt,
        control_image=control_image,
        height=1024,
        width=1024,
        num_inference_steps=30,
        guidance_scale=10.0,
        generator=torch.Generator().manual_seed(42),
    ).images[0]
    image.save("output.png")

###
Redux

- Flux Redux pipeline is an adapter for FLUX.1 base models. It can be used with both flux-dev and flux-schnell, for image-to-image generation.
- You can first use the `FluxPriorReduxPipeline` to get the `prompt_embeds` and `pooled_prompt_embeds`, and then feed them into the `FluxPipeline` for image-to-image generation.
- When use `FluxPriorReduxPipeline` with a base pipeline, you can set `text_encoder=None` and `text_encoder_2=None` in the base pipeline, in order to save VRAM.

    import torch
    from diffusers import FluxPriorReduxPipeline, FluxPipeline
    from diffusers.utils import load_image
    device = "cuda"
    dtype = torch.bfloat16

    repo_redux = "black-forest-labs/FLUX.1-Redux-dev"
    repo_base = "black-forest-labs/FLUX.1-dev"
    pipe_prior_redux = FluxPriorReduxPipeline.from_pretrained(repo_redux, torch_dtype=dtype).to(device)
    pipe = FluxPipeline.from_pretrained(
        repo_base,
        text_encoder=None,
        text_encoder_2=None,
        torch_dtype=torch.bfloat16
    ).to(device)

    image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/style_ziggy/img5.png")
    pipe_prior_output = pipe_prior_redux(image)
    images = pipe(
        guidance_scale=2.5,
        num_inference_steps=50,
        generator=torch.Generator("cpu").manual_seed(0),
        **pipe_prior_output,
    ).images
    images[0].save("flux-redux.png")

##
Combining Flux Turbo LoRAs with Flux Control, Fill, and Redux

We can combine Flux Turbo LoRAs with Flux Control and other pipelines like Fill and Redux to enable few-steps’ inference. The example below shows how to do that for Flux Control LoRA for depth and turbo LoRA from `ByteDance/Hyper-SD`.

    from diffusers import FluxControlPipeline
    from image_gen_aux import DepthPreprocessor
    from diffusers.utils import load_image
    from huggingface_hub import hf_hub_download
    import torch

    control_pipe = FluxControlPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
    control_pipe.load_lora_weights("black-forest-labs/FLUX.1-Depth-dev-lora", adapter_name="depth")
    control_pipe.load_lora_weights(
        hf_hub_download("ByteDance/Hyper-SD", "Hyper-FLUX.1-dev-8steps-lora.safetensors"), adapter_name="hyper-sd"
    )
    control_pipe.set_adapters(["depth", "hyper-sd"], adapter_weights=[0.85, 0.125])
    control_pipe.enable_model_cpu_offload()

    prompt = "A robot made of exotic candies and chocolates of different kinds. The background is filled with confetti and celebratory gifts."
    control_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/robot.png")

    processor = DepthPreprocessor.from_pretrained("LiheYoung/depth-anything-large-hf")
    control_image = processor(control_image)[0].convert("RGB")

    image = control_pipe(
        prompt=prompt,
        control_image=control_image,
        height=1024,
        width=1024,
        num_inference_steps=8,
        guidance_scale=10.0,
        generator=torch.Generator().manual_seed(42),
    ).images[0]
    image.save("output.png")

##
Note about unload_lora_weights() when using Flux LoRAs

When unloading the Control LoRA weights, call `pipe.unload_lora_weights(reset_to_overwritten_params=True)` to reset the `pipe.transformer` completely back to its original form. The resultant pipeline can then be used with methods like DiffusionPipeline.from_pipe(). More details about this argument are available in this PR.

##
IP-Adapter

Check out IP-Adapter to learn more about how IP-Adapters work.

An IP-Adapter lets you prompt Flux with images, in addition to the text prompt. This is especially useful when describing complex concepts that are difficult to articulate through text alone and you have reference images.

    import torch
    from diffusers import FluxPipeline
    from diffusers.utils import load_image

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

    image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/flux_ip_adapter_input.jpg").resize((1024, 1024))

    pipe.load_ip_adapter(
        "XLabs-AI/flux-ip-adapter",
        weight_name="ip_adapter.safetensors",
        image_encoder_pretrained_model_name_or_path="openai/clip-vit-large-patch14"
    )
    pipe.set_ip_adapter_scale(1.0)

    image = pipe(
        width=1024,
        height=1024,
        prompt="wearing sunglasses",
        negative_prompt="",
        true_cfg_scale=4.0,
        generator=torch.Generator().manual_seed(4444),
        ip_adapter_image=image,
    ).images[0]

    image.save('flux_ip_adapter_output.jpg')

IP-Adapter examples with prompt "wearing sunglasses"

##
Optimize

Flux is a very large model and requires ~50GB of RAM/VRAM to load all the modeling components. Enable some of the optimizations below to lower the memory requirements.

###
Group offloading

Group offloading lowers VRAM usage by offloading groups of internal layers rather than the whole model or weights. You need to use apply_group_offloading() on all the model components of a pipeline. The `offload_type` parameter allows you to toggle between block and leaf-level offloading. Setting it to `leaf_level` offloads the lowest leaf-level parameters to the CPU instead of offloading at the module-level.

On CUDA devices that support asynchronous data streaming, set `use_stream=True` to overlap data transfer and computation to accelerate inference.

It is possible to mix block and leaf-level offloading for different components in a pipeline.

    import torch
    from diffusers import FluxPipeline
    from diffusers.hooks import apply_group_offloading

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

    apply_group_offloading(
        pipe.transformer,
        offload_type="leaf_level",
        offload_device=torch.device("cpu"),
        onload_device=torch.device("cuda"),
        use_stream=True,
    )
    apply_group_offloading(
        pipe.text_encoder,
        offload_device=torch.device("cpu"),
        onload_device=torch.device("cuda"),
        offload_type="leaf_level",
        use_stream=True,
    )
    apply_group_offloading(
        pipe.text_encoder_2,
        offload_device=torch.device("cpu"),
        onload_device=torch.device("cuda"),
        offload_type="leaf_level",
        use_stream=True,
    )
    apply_group_offloading(
        pipe.vae,
        offload_device=torch.device("cpu"),
        onload_device=torch.device("cuda"),
        offload_type="leaf_level",
        use_stream=True,
    )

    prompt="A cat wearing sunglasses and working as a lifeguard at pool."

    generator = torch.Generator().manual_seed(181201)
    image = pipe(
        prompt,
        width=576,
        height=1024,
        num_inference_steps=30,
        generator=generator
    ).images[0]
    image

###
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")

###
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 FluxPipeline for inference with bitsandbytes.

    import torch
    from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig, FluxTransformer2DModel, FluxPipeline
    from transformers import BitsAndBytesConfig as BitsAndBytesConfig, T5EncoderModel

    quant_config = BitsAndBytesConfig(load_in_8bit=True)
    text_encoder_8bit = T5EncoderModel.from_pretrained(
        "black-forest-labs/FLUX.1-dev",
        subfolder="text_encoder_2",
        quantization_config=quant_config,
        torch_dtype=torch.float16,
    )

    quant_config = DiffusersBitsAndBytesConfig(load_in_8bit=True)
    transformer_8bit = FluxTransformer2DModel.from_pretrained(
        "black-forest-labs/FLUX.1-dev",
        subfolder="transformer",
        quantization_config=quant_config,
        torch_dtype=torch.float16,
    )

    pipeline = FluxPipeline.from_pretrained(
        "black-forest-labs/FLUX.1-dev",
        text_encoder_2=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, guidance_scale=3.5, height=768, width=1360, num_inference_steps=50).images[0]
    image.save("flux.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 image_encoder: CLIPVisionModelWithProjection = None feature_extractor: CLIPImageProcessor = None )

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: typing.Union[str, typing.List[str]] = None prompt_2: typing.Union[str, typing.List[str], NoneType] = None negative_prompt: typing.Union[str, typing.List[str]] = None negative_prompt_2: typing.Union[str, typing.List[str], NoneType] = None true_cfg_scale: float = 1.0 height: typing.Optional[int] = None width: typing.Optional[int] = None num_inference_steps: int = 28 sigmas: typing.Optional[typing.List[float]] = None guidance_scale: float = 3.5 num_images_per_prompt: typing.Optional[int] = 1 generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None latents: typing.Optional[torch.FloatTensor] = None prompt_embeds: typing.Optional[torch.FloatTensor] = None pooled_prompt_embeds: typing.Optional[torch.FloatTensor] = None ip_adapter_image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor], NoneType] = None ip_adapter_image_embeds: typing.Optional[typing.List[torch.Tensor]] = None negative_ip_adapter_image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor], NoneType] = None negative_ip_adapter_image_embeds: typing.Optional[typing.List[torch.Tensor]] = None negative_prompt_embeds: typing.Optional[torch.FloatTensor] = None negative_pooled_prompt_embeds: typing.Optional[torch.FloatTensor] = None output_type: typing.Optional[str] = 'pil' return_dict: bool = True joint_attention_kwargs: typing.Optional[typing.Dict[str, typing.Any]] = None callback_on_step_end: typing.Optional[typing.Callable[[int, int, typing.Dict], NoneType]] = None callback_on_step_end_tensor_inputs: typing.List[str] = ['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.
- 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 `true_cfg_scale` is not greater than `1`).
- negative_prompt_2 (`str` or `List[str]`, optional) — The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `negative_prompt` is used in all the text-encoders.
- true_cfg_scale (`float`, optional, defaults to 1.0) — When > 1.0 and a provided `negative_prompt`, enables true classifier-free guidance.
- 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.
- sigmas (`List[float]`, optional) — Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed will be used.
- guidance_scale (`float`, optional, defaults to 3.5) — 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.
- 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.
- ip_adapter_image — (`PipelineImageInput`, optional): Optional image input to work with IP Adapters.
- ip_adapter_image_embeds (`List[torch.Tensor]`, optional) — Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. If not provided, embeddings are computed from the `ip_adapter_image` input argument.
- negative_ip_adapter_image — (`PipelineImageInput`, optional): Optional image input to work with IP Adapters.
- negative_ip_adapter_image_embeds (`List[torch.Tensor]`, optional) — Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. If not provided, embeddings are computed from the `ip_adapter_image` input argument.
- 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_pooled_prompt_embeds (`torch.FloatTensor`, optional) — Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_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: typing.Union[str, typing.List[str]] prompt_2: typing.Union[str, typing.List[str]] device: typing.Optional[torch.device] = None num_images_per_prompt: int = 1 prompt_embeds: typing.Optional[torch.FloatTensor] = None pooled_prompt_embeds: typing.Optional[torch.FloatTensor] = None max_sequence_length: int = 512 lora_scale: typing.Optional[float] = 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.

##
FluxImg2ImgPipeline

###
class diffusers.FluxImg2ImgPipeline

< source >

( scheduler: FlowMatchEulerDiscreteScheduler vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer text_encoder_2: T5EncoderModel tokenizer_2: T5TokenizerFast transformer: FluxTransformer2DModel image_encoder: CLIPVisionModelWithProjection = None feature_extractor: CLIPImageProcessor = None )

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 image inpainting.

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

####
__call__

< source >

( prompt: typing.Union[str, typing.List[str]] = None prompt_2: typing.Union[str, typing.List[str], NoneType] = None negative_prompt: typing.Union[str, typing.List[str]] = None negative_prompt_2: typing.Union[str, typing.List[str], NoneType] = None true_cfg_scale: float = 1.0 image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = None height: typing.Optional[int] = None width: typing.Optional[int] = None strength: float = 0.6 num_inference_steps: int = 28 sigmas: typing.Optional[typing.List[float]] = None guidance_scale: float = 7.0 num_images_per_prompt: typing.Optional[int] = 1 generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None latents: typing.Optional[torch.FloatTensor] = None prompt_embeds: typing.Optional[torch.FloatTensor] = None pooled_prompt_embeds: typing.Optional[torch.FloatTensor] = None ip_adapter_image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor], NoneType] = None ip_adapter_image_embeds: typing.Optional[typing.List[torch.Tensor]] = None negative_ip_adapter_image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor], NoneType] = None negative_ip_adapter_image_embeds: typing.Optional[typing.List[torch.Tensor]] = None negative_prompt_embeds: typing.Optional[torch.FloatTensor] = None negative_pooled_prompt_embeds: typing.Optional[torch.FloatTensor] = None output_type: typing.Optional[str] = 'pil' return_dict: bool = True joint_attention_kwargs: typing.Optional[typing.Dict[str, typing.Any]] = None callback_on_step_end: typing.Optional[typing.Callable[[int, int, typing.Dict], NoneType]] = None callback_on_step_end_tensor_inputs: typing.List[str] = ['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
- image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`) — `Image`, numpy array or tensor representing an image batch to be used as the starting point. For both numpy array and pytorch tensor, the expected value range is between `[0, 1]` If it’s a tensor or a list or tensors, the expected shape should be `(B, C, H, W)` or `(C, H, W)`. If it is a numpy array or a list of arrays, the expected shape should be `(B, H, W, C)` or `(H, W, C)` It can also accept image latents as `image`, but if passing latents directly it is not encoded again.
- 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.
- strength (`float`, optional, defaults to 1.0) — Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a starting point and more noise is added the higher the `strength`. The number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising process runs for the full number of iterations specified in `num_inference_steps`. A value of 1 essentially ignores `image`.
- 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 to use for the denoising process with schedulers which support a `sigmas` argument in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed will be used.
- 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.
- ip_adapter_image — (`PipelineImageInput`, optional): Optional image input to work with IP Adapters.
- ip_adapter_image_embeds (`List[torch.Tensor]`, optional) — Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. If not provided, embeddings are computed from the `ip_adapter_image` input argument.
- negative_ip_adapter_image — (`PipelineImageInput`, optional): Optional image input to work with IP Adapters.
- negative_ip_adapter_image_embeds (`List[torch.Tensor]`, optional) — Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. If not provided, embeddings are computed from the `ip_adapter_image` 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 FluxImg2ImgPipeline
    >>> from diffusers.utils import load_image

    >>> device = "cuda"
    >>> pipe = FluxImg2ImgPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16)
    >>> pipe = pipe.to(device)

    >>> url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
    >>> init_image = load_image(url).resize((1024, 1024))

    >>> prompt = "cat wizard, gandalf, lord of the rings, detailed, fantasy, cute, adorable, Pixar, Disney, 8k"

    >>> images = pipe(
    ...     prompt=prompt, image=init_image, num_inference_steps=4, strength=0.95, guidance_scale=0.0
    ... ).images[0]

####
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: typing.Union[str, typing.List[str]] prompt_2: typing.Union[str, typing.List[str]] device: typing.Optional[torch.device] = None num_images_per_prompt: int = 1 prompt_embeds: typing.Optional[torch.FloatTensor] = None pooled_prompt_embeds: typing.Optional[torch.FloatTensor] = None max_sequence_length: int = 512 lora_scale: typing.Optional[float] = 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.

##
FluxInpaintPipeline

###
class diffusers.FluxInpaintPipeline

< source >

( scheduler: FlowMatchEulerDiscreteScheduler vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer text_encoder_2: T5EncoderModel tokenizer_2: T5TokenizerFast transformer: FluxTransformer2DModel image_encoder: CLIPVisionModelWithProjection = None feature_extractor: CLIPImageProcessor = None )

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 image inpainting.

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

####
__call__

< source >

( prompt: typing.Union[str, typing.List[str]] = None prompt_2: typing.Union[str, typing.List[str], NoneType] = None negative_prompt: typing.Union[str, typing.List[str]] = None negative_prompt_2: typing.Union[str, typing.List[str], NoneType] = None true_cfg_scale: float = 1.0 image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = None mask_image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = None masked_image_latents: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = None height: typing.Optional[int] = None width: typing.Optional[int] = None padding_mask_crop: typing.Optional[int] = None strength: float = 0.6 num_inference_steps: int = 28 sigmas: typing.Optional[typing.List[float]] = None guidance_scale: float = 7.0 num_images_per_prompt: typing.Optional[int] = 1 generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None latents: typing.Optional[torch.FloatTensor] = None prompt_embeds: typing.Optional[torch.FloatTensor] = None pooled_prompt_embeds: typing.Optional[torch.FloatTensor] = None ip_adapter_image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor], NoneType] = None ip_adapter_image_embeds: typing.Optional[typing.List[torch.Tensor]] = None negative_ip_adapter_image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor], NoneType] = None negative_ip_adapter_image_embeds: typing.Optional[typing.List[torch.Tensor]] = None negative_prompt_embeds: typing.Optional[torch.FloatTensor] = None negative_pooled_prompt_embeds: typing.Optional[torch.FloatTensor] = None output_type: typing.Optional[str] = 'pil' return_dict: bool = True joint_attention_kwargs: typing.Optional[typing.Dict[str, typing.Any]] = None callback_on_step_end: typing.Optional[typing.Callable[[int, int, typing.Dict], NoneType]] = None callback_on_step_end_tensor_inputs: typing.List[str] = ['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
- image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`) — `Image`, numpy array or tensor representing an image batch to be used as the starting point. For both numpy array and pytorch tensor, the expected value range is between `[0, 1]` If it’s a tensor or a list or tensors, the expected shape should be `(B, C, H, W)` or `(C, H, W)`. If it is a numpy array or a list of arrays, the expected shape should be `(B, H, W, C)` or `(H, W, C)` It can also accept image latents as `image`, but if passing latents directly it is not encoded again.
- mask_image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`) — `Image`, numpy array or tensor representing an image batch to mask `image`. White pixels in the mask are repainted while black pixels are preserved. If `mask_image` is a PIL image, it is converted to a single channel (luminance) before use. If it’s a numpy array or pytorch tensor, it should contain one color channel (L) instead of 3, so the expected shape for pytorch tensor would be `(B, 1, H, W)`, `(B, H, W)`, `(1, H, W)`, `(H, W)`. And for numpy array would be for `(B, H, W, 1)`, `(B, H, W)`, `(H, W, 1)`, or `(H, W)`.
- mask_image_latent (`torch.Tensor`, `List[torch.Tensor]`) — `Tensor` representing an image batch to mask `image` generated by VAE. If not provided, the mask latents tensor will ge generated by `mask_image`.
- 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.
- padding_mask_crop (`int`, optional, defaults to `None`) — The size of margin in the crop to be applied to the image and masking. If `None`, no crop is applied to image and mask_image. If `padding_mask_crop` is not `None`, it will first find a rectangular region with the same aspect ration of the image and contains all masked area, and then expand that area based on `padding_mask_crop`. The image and mask_image will then be cropped based on the expanded area before resizing to the original image size for inpainting. This is useful when the masked area is small while the image is large and contain information irrelevant for inpainting, such as background.
- strength (`float`, optional, defaults to 1.0) — Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a starting point and more noise is added the higher the `strength`. The number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising process runs for the full number of iterations specified in `num_inference_steps`. A value of 1 essentially ignores `image`.
- 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 to use for the denoising process with schedulers which support a `sigmas` argument in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed will be used.
- 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.
- ip_adapter_image — (`PipelineImageInput`, optional): Optional image input to work with IP Adapters.
- ip_adapter_image_embeds (`List[torch.Tensor]`, optional) — Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. If not provided, embeddings are computed from the `ip_adapter_image` input argument.
- negative_ip_adapter_image — (`PipelineImageInput`, optional): Optional image input to work with IP Adapters.
- negative_ip_adapter_image_embeds (`List[torch.Tensor]`, optional) — Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. If not provided, embeddings are computed from the `ip_adapter_image` 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 FluxInpaintPipeline
    >>> from diffusers.utils import load_image

    >>> pipe = FluxInpaintPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16)
    >>> pipe.to("cuda")
    >>> prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
    >>> img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
    >>> mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
    >>> source = load_image(img_url)
    >>> mask = load_image(mask_url)
    >>> image = pipe(prompt=prompt, image=source, mask_image=mask).images[0]
    >>> image.save("flux_inpainting.png")

####
encode_prompt

< source >

( prompt: typing.Union[str, typing.List[str]] prompt_2: typing.Union[str, typing.List[str]] device: typing.Optional[torch.device] = None num_images_per_prompt: int = 1 prompt_embeds: typing.Optional[torch.FloatTensor] = None pooled_prompt_embeds: typing.Optional[torch.FloatTensor] = None max_sequence_length: int = 512 lora_scale: typing.Optional[float] = 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.

##
FluxControlNetInpaintPipeline

###
class diffusers.FluxControlNetInpaintPipeline

< source >

( scheduler: FlowMatchEulerDiscreteScheduler vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer text_encoder_2: T5EncoderModel tokenizer_2: T5TokenizerFast transformer: FluxTransformer2DModel controlnet: typing.Union[diffusers.models.controlnets.controlnet_flux.FluxControlNetModel, typing.List[diffusers.models.controlnets.controlnet_flux.FluxControlNetModel], typing.Tuple[diffusers.models.controlnets.controlnet_flux.FluxControlNetModel], diffusers.models.controlnets.controlnet_flux.FluxMultiControlNetModel] )

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 controlnet pipeline for inpainting.

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

####
__call__

< source >

( prompt: typing.Union[str, typing.List[str]] = None prompt_2: typing.Union[str, typing.List[str], NoneType] = None image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = None mask_image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = None masked_image_latents: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = None control_image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = None height: typing.Optional[int] = None width: typing.Optional[int] = None strength: float = 0.6 padding_mask_crop: typing.Optional[int] = None sigmas: typing.Optional[typing.List[float]] = None num_inference_steps: int = 28 guidance_scale: float = 7.0 control_guidance_start: typing.Union[float, typing.List[float]] = 0.0 control_guidance_end: typing.Union[float, typing.List[float]] = 1.0 control_mode: typing.Union[int, typing.List[int], NoneType] = None controlnet_conditioning_scale: typing.Union[float, typing.List[float]] = 1.0 num_images_per_prompt: typing.Optional[int] = 1 generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None latents: typing.Optional[torch.FloatTensor] = None prompt_embeds: typing.Optional[torch.FloatTensor] = None pooled_prompt_embeds: typing.Optional[torch.FloatTensor] = None output_type: typing.Optional[str] = 'pil' return_dict: bool = True joint_attention_kwargs: typing.Optional[typing.Dict[str, typing.Any]] = None callback_on_step_end: typing.Optional[typing.Callable[[int, int, typing.Dict], NoneType]] = None callback_on_step_end_tensor_inputs: typing.List[str] = ['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.
- prompt_2 (`str` or `List[str]`, optional) — The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`.
- image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.FloatTensor`) — The image(s) to inpaint.
- mask_image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.FloatTensor`) — The mask image(s) to use for inpainting. White pixels in the mask will be repainted, while black pixels will be preserved.
- masked_image_latents (`torch.FloatTensor`, optional) — Pre-generated masked image latents.
- control_image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.FloatTensor`) — The ControlNet input condition. Image to control the generation.
- height (`int`, optional, defaults to self.default_sample_size * self.vae_scale_factor) — The height in pixels of the generated image.
- width (`int`, optional, defaults to self.default_sample_size * self.vae_scale_factor) — The width in pixels of the generated image.
- strength (`float`, optional, defaults to 0.6) — Conceptually, indicates how much to inpaint the masked area. Must be between 0 and 1.
- padding_mask_crop (`int`, optional) — The size of the padding to use when cropping the mask.
- num_inference_steps (`int`, optional, defaults to 28) — 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 to use for the denoising process with schedulers which support a `sigmas` argument in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed will be used.
- guidance_scale (`float`, optional, defaults to 7.0) — Guidance scale as defined in Classifier-Free Diffusion Guidance.
- control_guidance_start (`float` or `List[float]`, optional, defaults to 0.0) — The percentage of total steps at which the ControlNet starts applying.
- control_guidance_end (`float` or `List[float]`, optional, defaults to 1.0) — The percentage of total steps at which the ControlNet stops applying.
- control_mode (`int` or `List[int]`, optional) — The mode for the ControlNet. If multiple ControlNets are used, this should be a list.
- controlnet_conditioning_scale (`float` or `List[float]`, optional, defaults to 1.0) — The outputs of the ControlNet are multiplied by `controlnet_conditioning_scale` before they are added to the residual in the original transformer.
- 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 more 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.
- prompt_embeds (`torch.FloatTensor`, optional) — Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting.
- pooled_prompt_embeds (`torch.FloatTensor`, optional) — Pre-generated pooled text embeddings.
- output_type (`str`, optional, defaults to `"pil"`) — The output format of the generate image. Choose between `PIL.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) — Additional keyword arguments to be passed to the joint attention mechanism.
- callback_on_step_end (`Callable`, optional) — A function that calls at the end of each denoising step during the inference.
- callback_on_step_end_tensor_inputs (`List[str]`, optional) — The list of tensor inputs for the `callback_on_step_end` function.
- max_sequence_length (`int`, optional, defaults to 512) — The maximum length of the sequence to be generated.

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 FluxControlNetInpaintPipeline
    >>> from diffusers.models import FluxControlNetModel
    >>> from diffusers.utils import load_image

    >>> controlnet = FluxControlNetModel.from_pretrained(
    ...     "InstantX/FLUX.1-dev-controlnet-canny", torch_dtype=torch.float16
    ... )
    >>> pipe = FluxControlNetInpaintPipeline.from_pretrained(
    ...     "black-forest-labs/FLUX.1-schnell", controlnet=controlnet, torch_dtype=torch.float16
    ... )
    >>> pipe.to("cuda")

    >>> control_image = load_image(
    ...     "https://huggingface.co/InstantX/FLUX.1-dev-Controlnet-Canny-alpha/resolve/main/canny.jpg"
    ... )
    >>> init_image = load_image(
    ...     "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
    ... )
    >>> mask_image = load_image(
    ...     "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
    ... )

    >>> prompt = "A girl holding a sign that says InstantX"
    >>> image = pipe(
    ...     prompt,
    ...     image=init_image,
    ...     mask_image=mask_image,
    ...     control_image=control_image,
    ...     control_guidance_start=0.2,
    ...     control_guidance_end=0.8,
    ...     controlnet_conditioning_scale=0.7,
    ...     strength=0.7,
    ...     num_inference_steps=28,
    ...     guidance_scale=3.5,
    ... ).images[0]
    >>> image.save("flux_controlnet_inpaint.png")

####
encode_prompt

< source >

( prompt: typing.Union[str, typing.List[str]] prompt_2: typing.Union[str, typing.List[str]] device: typing.Optional[torch.device] = None num_images_per_prompt: int = 1 prompt_embeds: typing.Optional[torch.FloatTensor] = None pooled_prompt_embeds: typing.Optional[torch.FloatTensor] = None max_sequence_length: int = 512 lora_scale: typing.Optional[float] = 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.

##
FluxControlNetImg2ImgPipeline

###
class diffusers.FluxControlNetImg2ImgPipeline

< source >

( scheduler: FlowMatchEulerDiscreteScheduler vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer text_encoder_2: T5EncoderModel tokenizer_2: T5TokenizerFast transformer: FluxTransformer2DModel controlnet: typing.Union[diffusers.models.controlnets.controlnet_flux.FluxControlNetModel, typing.List[diffusers.models.controlnets.controlnet_flux.FluxControlNetModel], typing.Tuple[diffusers.models.controlnets.controlnet_flux.FluxControlNetModel], diffusers.models.controlnets.controlnet_flux.FluxMultiControlNetModel] )

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 controlnet pipeline for image-to-image generation.

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

####
__call__

< source >

( prompt: typing.Union[str, typing.List[str]] = None prompt_2: typing.Union[str, typing.List[str], NoneType] = None image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = None control_image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = None height: typing.Optional[int] = None width: typing.Optional[int] = None strength: float = 0.6 num_inference_steps: int = 28 sigmas: typing.Optional[typing.List[float]] = None guidance_scale: float = 7.0 control_guidance_start: typing.Union[float, typing.List[float]] = 0.0 control_guidance_end: typing.Union[float, typing.List[float]] = 1.0 control_mode: typing.Union[int, typing.List[int], NoneType] = None controlnet_conditioning_scale: typing.Union[float, typing.List[float]] = 1.0 num_images_per_prompt: typing.Optional[int] = 1 generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None latents: typing.Optional[torch.FloatTensor] = None prompt_embeds: typing.Optional[torch.FloatTensor] = None pooled_prompt_embeds: typing.Optional[torch.FloatTensor] = None output_type: typing.Optional[str] = 'pil' return_dict: bool = True joint_attention_kwargs: typing.Optional[typing.Dict[str, typing.Any]] = None callback_on_step_end: typing.Optional[typing.Callable[[int, int, typing.Dict], NoneType]] = None callback_on_step_end_tensor_inputs: typing.List[str] = ['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.
- prompt_2 (`str` or `List[str]`, optional) — The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`.
- image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.FloatTensor`) — The image(s) to modify with the pipeline.
- control_image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.FloatTensor`) — The ControlNet input condition. Image to control the generation.
- height (`int`, optional, defaults to self.default_sample_size * self.vae_scale_factor) — The height in pixels of the generated image.
- width (`int`, optional, defaults to self.default_sample_size * self.vae_scale_factor) — The width in pixels of the generated image.
- strength (`float`, optional, defaults to 0.6) — Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1.
- num_inference_steps (`int`, optional, defaults to 28) — 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 to use for the denoising process with schedulers which support a `sigmas` argument in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed will be used.
- guidance_scale (`float`, optional, defaults to 7.0) — Guidance scale as defined in Classifier-Free Diffusion Guidance.
- control_mode (`int` or `List[int]`, optional) — The mode for the ControlNet. If multiple ControlNets are used, this should be a list.
- controlnet_conditioning_scale (`float` or `List[float]`, optional, defaults to 1.0) — The outputs of the ControlNet are multiplied by `controlnet_conditioning_scale` before they are added to the residual in the original transformer.
- 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 more 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.
- prompt_embeds (`torch.FloatTensor`, optional) — Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting.
- pooled_prompt_embeds (`torch.FloatTensor`, optional) — Pre-generated pooled text embeddings.
- output_type (`str`, optional, defaults to `"pil"`) — The output format of the generate image. Choose between `PIL.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) — Additional keyword arguments to be passed to the joint attention mechanism.
- callback_on_step_end (`Callable`, optional) — A function that calls at the end of each denoising step during the inference.
- callback_on_step_end_tensor_inputs (`List[str]`, optional) — The list of tensor inputs for the `callback_on_step_end` function.
- max_sequence_length (`int`, optional, defaults to 512) — The maximum length of the sequence to be generated.

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 FluxControlNetImg2ImgPipeline, FluxControlNetModel
    >>> from diffusers.utils import load_image

    >>> device = "cuda" if torch.cuda.is_available() else "cpu"

    >>> controlnet = FluxControlNetModel.from_pretrained(
    ...     "InstantX/FLUX.1-dev-Controlnet-Canny-alpha", torch_dtype=torch.bfloat16
    ... )

    >>> pipe = FluxControlNetImg2ImgPipeline.from_pretrained(
    ...     "black-forest-labs/FLUX.1-schnell", controlnet=controlnet, torch_dtype=torch.float16
    ... )

    >>> pipe.text_encoder.to(torch.float16)
    >>> pipe.controlnet.to(torch.float16)
    >>> pipe.to("cuda")

    >>> control_image = load_image("https://huggingface.co/InstantX/SD3-Controlnet-Canny/resolve/main/canny.jpg")
    >>> init_image = load_image(
    ...     "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
    ... )

    >>> prompt = "A girl in city, 25 years old, cool, futuristic"
    >>> image = pipe(
    ...     prompt,
    ...     image=init_image,
    ...     control_image=control_image,
    ...     control_guidance_start=0.2,
    ...     control_guidance_end=0.8,
    ...     controlnet_conditioning_scale=1.0,
    ...     strength=0.7,
    ...     num_inference_steps=2,
    ...     guidance_scale=3.5,
    ... ).images[0]
    >>> image.save("flux_controlnet_img2img.png")

####
encode_prompt

< source >

( prompt: typing.Union[str, typing.List[str]] prompt_2: typing.Union[str, typing.List[str]] device: typing.Optional[torch.device] = None num_images_per_prompt: int = 1 prompt_embeds: typing.Optional[torch.FloatTensor] = None pooled_prompt_embeds: typing.Optional[torch.FloatTensor] = None max_sequence_length: int = 512 lora_scale: typing.Optional[float] = 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.

##
FluxControlPipeline

###
class diffusers.FluxControlPipeline

< 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 controllable text-to-image generation.

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

####
__call__

< source >

( prompt: typing.Union[str, typing.List[str]] = None prompt_2: typing.Union[str, typing.List[str], NoneType] = None control_image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = None height: typing.Optional[int] = None width: typing.Optional[int] = None num_inference_steps: int = 28 sigmas: typing.Optional[typing.List[float]] = None guidance_scale: float = 3.5 num_images_per_prompt: typing.Optional[int] = 1 generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None latents: typing.Optional[torch.FloatTensor] = None prompt_embeds: typing.Optional[torch.FloatTensor] = None pooled_prompt_embeds: typing.Optional[torch.FloatTensor] = None output_type: typing.Optional[str] = 'pil' return_dict: bool = True joint_attention_kwargs: typing.Optional[typing.Dict[str, typing.Any]] = None callback_on_step_end: typing.Optional[typing.Callable[[int, int, typing.Dict], NoneType]] = None callback_on_step_end_tensor_inputs: typing.List[str] = ['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
- control_image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`, — `List[List[torch.Tensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`): The ControlNet input condition to provide guidance to the `unet` for generation. If the type is specified as `torch.Tensor`, it is passed to ControlNet as is. `PIL.Image.Image` can also be accepted as an image. The dimensions of the output image defaults to `image`’s dimensions. If height and/or width are passed, `image` is resized accordingly. If multiple ControlNets are specified in `init`, images must be passed as a list such that each element of the list can be correctly batched for input to a single ControlNet.
- 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.
- sigmas (`List[float]`, optional) — Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed will be used.
- guidance_scale (`float`, optional, defaults to 3.5) — 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 controlnet_aux import CannyDetector
    >>> from diffusers import FluxControlPipeline
    >>> from diffusers.utils import load_image

    >>> pipe = FluxControlPipeline.from_pretrained(
    ...     "black-forest-labs/FLUX.1-Canny-dev", torch_dtype=torch.bfloat16
    ... ).to("cuda")

    >>> prompt = "A robot made of exotic candies and chocolates of different kinds. The background is filled with confetti and celebratory gifts."
    >>> control_image = load_image(
    ...     "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/robot.png"
    ... )

    >>> processor = CannyDetector()
    >>> control_image = processor(
    ...     control_image, low_threshold=50, high_threshold=200, detect_resolution=1024, image_resolution=1024
    ... )

    >>> image = pipe(
    ...     prompt=prompt,
    ...     control_image=control_image,
    ...     height=1024,
    ...     width=1024,
    ...     num_inference_steps=50,
    ...     guidance_scale=30.0,
    ... ).images[0]
    >>> image.save("output.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: typing.Union[str, typing.List[str]] prompt_2: typing.Union[str, typing.List[str]] device: typing.Optional[torch.device] = None num_images_per_prompt: int = 1 prompt_embeds: typing.Optional[torch.FloatTensor] = None pooled_prompt_embeds: typing.Optional[torch.FloatTensor] = None max_sequence_length: int = 512 lora_scale: typing.Optional[float] = 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.

##
FluxControlImg2ImgPipeline

###
class diffusers.FluxControlImg2ImgPipeline

< 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 image inpainting.

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

####
__call__

< source >

( prompt: typing.Union[str, typing.List[str]] = None prompt_2: typing.Union[str, typing.List[str], NoneType] = None image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = None control_image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = None height: typing.Optional[int] = None width: typing.Optional[int] = None strength: float = 0.6 num_inference_steps: int = 28 sigmas: typing.Optional[typing.List[float]] = None guidance_scale: float = 7.0 num_images_per_prompt: typing.Optional[int] = 1 generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None latents: typing.Optional[torch.FloatTensor] = None prompt_embeds: typing.Optional[torch.FloatTensor] = None pooled_prompt_embeds: typing.Optional[torch.FloatTensor] = None output_type: typing.Optional[str] = 'pil' return_dict: bool = True joint_attention_kwargs: typing.Optional[typing.Dict[str, typing.Any]] = None callback_on_step_end: typing.Optional[typing.Callable[[int, int, typing.Dict], NoneType]] = None callback_on_step_end_tensor_inputs: typing.List[str] = ['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
- image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`) — `Image`, numpy array or tensor representing an image batch to be used as the starting point. For both numpy array and pytorch tensor, the expected value range is between `[0, 1]` If it’s a tensor or a list or tensors, the expected shape should be `(B, C, H, W)` or `(C, H, W)`. If it is a numpy array or a list of arrays, the expected shape should be `(B, H, W, C)` or `(H, W, C)` It can also accept image latents as `image`, but if passing latents directly it is not encoded again.
- control_image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`, — `List[List[torch.Tensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`): The ControlNet input condition to provide guidance to the `unet` for generation. If the type is specified as `torch.Tensor`, it is passed to ControlNet as is. `PIL.Image.Image` can also be accepted as an image. The dimensions of the output image defaults to `image`’s dimensions. If height and/or width are passed, `image` is resized accordingly. If multiple ControlNets are specified in `init`, images must be passed as a list such that each element of the list can be correctly batched for input to a single ControlNet.
- 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.
- strength (`float`, optional, defaults to 1.0) — Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a starting point and more noise is added the higher the `strength`. The number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising process runs for the full number of iterations specified in `num_inference_steps`. A value of 1 essentially ignores `image`.
- 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 to use for the denoising process with schedulers which support a `sigmas` argument in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed will be used.
- 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 controlnet_aux import CannyDetector
    >>> from diffusers import FluxControlImg2ImgPipeline
    >>> from diffusers.utils import load_image

    >>> pipe = FluxControlImg2ImgPipeline.from_pretrained(
    ...     "black-forest-labs/FLUX.1-Canny-dev", torch_dtype=torch.bfloat16
    ... ).to("cuda")

    >>> prompt = "A robot made of exotic candies and chocolates of different kinds. Abstract background"
    >>> image = load_image(
    ...     "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/watercolor-painting.jpg"
    ... )
    >>> control_image = load_image(
    ...     "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/robot.png"
    ... )

    >>> processor = CannyDetector()
    >>> control_image = processor(
    ...     control_image, low_threshold=50, high_threshold=200, detect_resolution=1024, image_resolution=1024
    ... )

    >>> image = pipe(
    ...     prompt=prompt,
    ...     image=image,
    ...     control_image=control_image,
    ...     strength=0.8,
    ...     height=1024,
    ...     width=1024,
    ...     num_inference_steps=50,
    ...     guidance_scale=30.0,
    ... ).images[0]
    >>> image.save("output.png")

####
encode_prompt

< source >

( prompt: typing.Union[str, typing.List[str]] prompt_2: typing.Union[str, typing.List[str]] device: typing.Optional[torch.device] = None num_images_per_prompt: int = 1 prompt_embeds: typing.Optional[torch.FloatTensor] = None pooled_prompt_embeds: typing.Optional[torch.FloatTensor] = None max_sequence_length: int = 512 lora_scale: typing.Optional[float] = 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.

##
FluxPriorReduxPipeline

###
class diffusers.FluxPriorReduxPipeline

< source >

( image_encoder: SiglipVisionModel feature_extractor: SiglipImageProcessor image_embedder: ReduxImageEncoder text_encoder: CLIPTextModel = None tokenizer: CLIPTokenizer = None text_encoder_2: T5EncoderModel = None tokenizer_2: T5TokenizerFast = None )

Parameters

- image_encoder (`SiglipVisionModel`) — SIGLIP vision model to encode the input image.
- feature_extractor (`SiglipImageProcessor`) — Image processor for preprocessing images for the SIGLIP model.
- image_embedder (`ReduxImageEncoder`) — Redux image encoder to process the SIGLIP embeddings.
- text_encoder (`CLIPTextModel`, optional) — CLIP, specifically the clip-vit-large-patch14 variant.
- text_encoder_2 (`T5EncoderModel`, optional) — T5, specifically the google/t5-v1_1-xxl variant.
- tokenizer (`CLIPTokenizer`, optional) — Tokenizer of class CLIPTokenizer.
- tokenizer_2 (`T5TokenizerFast`, optional) — Second Tokenizer of class T5TokenizerFast.

The Flux Redux pipeline for image-to-image generation.

Reference: https://blackforestlabs.ai/flux-1-tools/

####
__call__

< source >

( image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] prompt: typing.Union[str, typing.List[str]] = None prompt_2: typing.Union[str, typing.List[str], NoneType] = None prompt_embeds: typing.Optional[torch.FloatTensor] = None pooled_prompt_embeds: typing.Optional[torch.FloatTensor] = None prompt_embeds_scale: typing.Union[float, typing.List[float], NoneType] = 1.0 pooled_prompt_embeds_scale: typing.Union[float, typing.List[float], NoneType] = 1.0 return_dict: bool = True ) →
`~pipelines.flux.FluxPriorReduxPipelineOutput` or `tuple`

Parameters

- image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`) — `Image`, numpy array or tensor representing an image batch to be used as the starting point. For both numpy array and pytorch tensor, the expected value range is between `[0, 1]` If it’s a tensor or a list or tensors, the expected shape should be `(B, C, H, W)` or `(C, H, W)`. If it is a numpy array or a list of arrays, the expected shape should be `(B, H, W, C)` or `(H, W, C)`
- prompt (`str` or `List[str]`, optional) — The prompt or prompts to guide the image generation. experimental feature: to use this feature, make sure to explicitly load text encoders to the pipeline. Prompts will be ignored if text encoders are not loaded.
- prompt_2 (`str` or `List[str]`, optional) — The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`.
- prompt_embeds (`torch.FloatTensor`, optional) — Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting.
- pooled_prompt_embeds (`torch.FloatTensor`, optional) — Pre-generated pooled text embeddings.
- return_dict (`bool`, optional, defaults to `True`) — Whether or not to return a `~pipelines.flux.FluxPriorReduxPipelineOutput` instead of a plain tuple.

Returns

`~pipelines.flux.FluxPriorReduxPipelineOutput` or `tuple`

`~pipelines.flux.FluxPriorReduxPipelineOutput` 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 FluxPriorReduxPipeline, FluxPipeline
    >>> from diffusers.utils import load_image

    >>> device = "cuda"
    >>> dtype = torch.bfloat16

    >>> repo_redux = "black-forest-labs/FLUX.1-Redux-dev"
    >>> repo_base = "black-forest-labs/FLUX.1-dev"
    >>> pipe_prior_redux = FluxPriorReduxPipeline.from_pretrained(repo_redux, torch_dtype=dtype).to(device)
    >>> pipe = FluxPipeline.from_pretrained(
    ...     repo_base, text_encoder=None, text_encoder_2=None, torch_dtype=torch.bfloat16
    ... ).to(device)

    >>> image = load_image(
    ...     "https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/style_ziggy/img5.png"
    ... )
    >>> pipe_prior_output = pipe_prior_redux(image)
    >>> images = pipe(
    ...     guidance_scale=2.5,
    ...     num_inference_steps=50,
    ...     generator=torch.Generator("cpu").manual_seed(0),
    ...     **pipe_prior_output,
    ... ).images
    >>> images[0].save("flux-redux.png")

####
encode_prompt

< source >

( prompt: typing.Union[str, typing.List[str]] prompt_2: typing.Union[str, typing.List[str]] device: typing.Optional[torch.device] = None num_images_per_prompt: int = 1 prompt_embeds: typing.Optional[torch.FloatTensor] = None pooled_prompt_embeds: typing.Optional[torch.FloatTensor] = None max_sequence_length: int = 512 lora_scale: typing.Optional[float] = 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.

##
FluxFillPipeline

###
class diffusers.FluxFillPipeline

< 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 Fill pipeline for image inpainting/outpainting.

Reference: https://blackforestlabs.ai/flux-1-tools/

####
__call__

< source >

( prompt: typing.Union[str, typing.List[str]] = None prompt_2: typing.Union[str, typing.List[str], NoneType] = None image: typing.Optional[torch.FloatTensor] = None mask_image: typing.Optional[torch.FloatTensor] = None masked_image_latents: typing.Optional[torch.FloatTensor] = None height: typing.Optional[int] = None width: typing.Optional[int] = None strength: float = 1.0 num_inference_steps: int = 50 sigmas: typing.Optional[typing.List[float]] = None guidance_scale: float = 30.0 num_images_per_prompt: typing.Optional[int] = 1 generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None latents: typing.Optional[torch.FloatTensor] = None prompt_embeds: typing.Optional[torch.FloatTensor] = None pooled_prompt_embeds: typing.Optional[torch.FloatTensor] = None output_type: typing.Optional[str] = 'pil' return_dict: bool = True joint_attention_kwargs: typing.Optional[typing.Dict[str, typing.Any]] = None callback_on_step_end: typing.Optional[typing.Callable[[int, int, typing.Dict], NoneType]] = None callback_on_step_end_tensor_inputs: typing.List[str] = ['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
- image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`) — `Image`, numpy array or tensor representing an image batch to be used as the starting point. For both numpy array and pytorch tensor, the expected value range is between `[0, 1]` If it’s a tensor or a list or tensors, the expected shape should be `(B, C, H, W)` or `(C, H, W)`. If it is a numpy array or a list of arrays, the expected shape should be `(B, H, W, C)` or `(H, W, C)`.
- mask_image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`) — `Image`, numpy array or tensor representing an image batch to mask `image`. White pixels in the mask are repainted while black pixels are preserved. If `mask_image` is a PIL image, it is converted to a single channel (luminance) before use. If it’s a numpy array or pytorch tensor, it should contain one color channel (L) instead of 3, so the expected shape for pytorch tensor would be `(B, 1, H, W)`, `(B, H, W)`, `(1, H, W)`, `(H, W)`. And for numpy array would be for `(B, H, W, 1)`, `(B, H, W)`, `(H, W, 1)`, or `(H, W)`.
- mask_image_latent (`torch.Tensor`, `List[torch.Tensor]`) — `Tensor` representing an image batch to mask `image` generated by VAE. If not provided, the mask latents tensor will ge generated by `mask_image`.
- 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.
- strength (`float`, optional, defaults to 1.0) — Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a starting point and more noise is added the higher the `strength`. The number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising process runs for the full number of iterations specified in `num_inference_steps`. A value of 1 essentially ignores `image`.
- 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 to use for the denoising process with schedulers which support a `sigmas` argument in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed will be used.
- guidance_scale (`float`, optional, defaults to 30.0) — 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 FluxFillPipeline
    >>> from diffusers.utils import load_image

    >>> image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/cup.png")
    >>> mask = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/cup_mask.png")

    >>> pipe = FluxFillPipeline.from_pretrained("black-forest-labs/FLUX.1-Fill-dev", torch_dtype=torch.bfloat16)
    >>> pipe.enable_model_cpu_offload()  # save some VRAM by offloading the model to CPU

    >>> image = pipe(
    ...     prompt="a white paper cup",
    ...     image=image,
    ...     mask_image=mask,
    ...     height=1632,
    ...     width=1232,
    ...     guidance_scale=30,
    ...     num_inference_steps=50,
    ...     max_sequence_length=512,
    ...     generator=torch.Generator("cpu").manual_seed(0),
    ... ).images[0]
    >>> image.save("flux_fill.png")

####
disable_vae_slicing

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( )

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

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( )

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

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enable_vae_slicing

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( )

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

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( )

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

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( prompt: typing.Union[str, typing.List[str]] prompt_2: typing.Union[str, typing.List[str]] device: typing.Optional[torch.device] = None num_images_per_prompt: int = 1 prompt_embeds: typing.Optional[torch.FloatTensor] = None pooled_prompt_embeds: typing.Optional[torch.FloatTensor] = None max_sequence_length: int = 512 lora_scale: typing.Optional[float] = 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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