Flux (original) (raw)

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:

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.

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:

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.

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:

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)

pipe.enable_sequential_cpu_offload() pipe.vae.enable_slicing() pipe.vae.enable_tiling()

pipe.to(torch.float16)

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 classCLIPTokenizer.
• tokenizer_2 (T5TokenizerFast) — Second Tokenizer of classT5TokenizerFast.

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 passnegative_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 andtext_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 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.
• 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_promptinput argument.
• output_type (str, optional, defaults to "pil") — The output format of the generate image. Choose betweenPIL: 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 underself.processor indiffusers.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 bycallback_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_dictis 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"

image = pipe(prompt, num_inference_steps=4, guidance_scale=0.0).images[0] image.save("flux.png")

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

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

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 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 classCLIPTokenizer.
• tokenizer_2 (T5TokenizerFast) — Second Tokenizer of classT5TokenizerFast.

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 betweenPIL: 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 underself.processor indiffusers.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 bycallback_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_dictis 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]

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 classCLIPTokenizer.
• tokenizer_2 (T5TokenizerFast) — Second Tokenizer of classT5TokenizerFast.

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 betweenPIL: 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 underself.processor indiffusers.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 bycallback_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_dictis 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 classCLIPTokenizer.
• tokenizer_2 (T5TokenizerFast) — Second Tokenizer of classT5TokenizerFast.

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_dictis 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 classCLIPTokenizer.
• tokenizer_2 (T5TokenizerFast) — Second Tokenizer of classT5TokenizerFast.

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_dictis 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 classCLIPTokenizer.
• tokenizer_2 (T5TokenizerFast) — Second Tokenizer of classT5TokenizerFast.

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 betweenPIL: 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 underself.processor indiffusers.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 bycallback_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_dictis 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 sliced VAE decoding. If enable_vae_slicing was previously enabled, this method will go back to computing decoding in one step.

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

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 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 classCLIPTokenizer.
• tokenizer_2 (T5TokenizerFast) — Second Tokenizer of classT5TokenizerFast.

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 betweenPIL: 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 underself.processor indiffusers.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 bycallback_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_dictis 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 classCLIPTokenizer.
• tokenizer_2 (T5TokenizerFast, optional) — Second Tokenizer of classT5TokenizerFast.

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 classCLIPTokenizer.
• tokenizer_2 (T5TokenizerFast) — Second Tokenizer of classT5TokenizerFast.

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 betweenPIL: 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 underself.processor indiffusers.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 bycallback_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_dictis 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()

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

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

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 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.

< > Update on GitHub