ControlNet was introduced in Adding Conditional Control to Text-to-Image Diffusion Models by Lvmin Zhang and Maneesh Agrawala.
With a ControlNet model, you can provide an additional control image to condition and control Stable Diffusion generation. For example, if you provide a depth map, the ControlNet model generates an image that’ll preserve the spatial information from the depth map. It is a more flexible and accurate way to control the image generation process.
The abstract from the paper is:
We present a neural network structure, ControlNet, to control pretrained large diffusion models to support additional input conditions. The ControlNet learns task-specific conditions in an end-to-end way, and the learning is robust even when the training dataset is small (< 50k). Moreover, training a ControlNet is as fast as fine-tuning a diffusion model, and the model can be trained on a personal devices. Alternatively, if powerful computation clusters are available, the model can scale to large amounts (millions to billions) of data. We report that large diffusion models like Stable Diffusion can be augmented with ControlNets to enable conditional inputs like edge maps, segmentation maps, keypoints, etc. This may enrich the methods to control large diffusion models and further facilitate related applications.
You can find additional smaller Stable Diffusion XL (SDXL) ControlNet checkpoints from the 🤗 Diffusers Hub organization, and browse community-trained checkpoints on the Hub.
🧪 Many of the SDXL ControlNet checkpoints are experimental, and there is a lot of room for improvement. Feel free to open an Issue and leave us feedback on how we can improve!
If you don’t see a checkpoint you’re interested in, you can train your own SDXL ControlNet with our training script.
Make sure to check out the Schedulers guide to learn how to explore the tradeoff between scheduler speed and quality, and see the reuse components across pipelines section to learn how to efficiently load the same components into multiple pipelines.
( vae: AutoencoderKL text_encoder: CLIPTextModel text_encoder_2: CLIPTextModelWithProjection tokenizer: CLIPTokenizer tokenizer_2: CLIPTokenizer unet: UNet2DConditionModel controlnet: typing.Union[diffusers.models.controlnet.ControlNetModel, typing.List[diffusers.models.controlnet.ControlNetModel], typing.Tuple[diffusers.models.controlnet.ControlNetModel], diffusers.pipelines.controlnet.multicontrolnet.MultiControlNetModel] scheduler: KarrasDiffusionSchedulers force_zeros_for_empty_prompt: bool = True add_watermarker: typing.Optional[bool] = None )
Parameters
CLIPTokenizer
to tokenize text. CLIPTokenizer
to tokenize text. UNet2DConditionModel
to denoise the encoded image latents. List[ControlNetModel]
) —
Provides additional conditioning to the unet
during the denoising process. If you set multiple
ControlNets as a list, the outputs from each ControlNet are added together to create one combined
additional conditioning. unet
to denoise the encoded image latents. Can be one of
DDIMScheduler, LMSDiscreteScheduler, or PNDMScheduler. bool
, optional, defaults to "True"
) —
Whether the negative prompt embeddings should always be set to 0. Also see the config of
stabilityai/stable-diffusion-xl-base-1-0
. bool
, optional) —
Whether to use the invisible_watermark library to
watermark output images. If not defined, it defaults to True
if the package is installed; otherwise no
watermarker is used. Pipeline for text-to-image generation using Stable Diffusion XL with ControlNet guidance.
This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular device, etc.).
The pipeline also inherits the following loading methods:
.ckpt
files( 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.FloatTensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.FloatTensor]] = None height: typing.Optional[int] = None width: typing.Optional[int] = None num_inference_steps: int = 50 guidance_scale: float = 5.0 negative_prompt: typing.Union[str, typing.List[str], NoneType] = None negative_prompt_2: typing.Union[str, typing.List[str], NoneType] = None num_images_per_prompt: typing.Optional[int] = 1 eta: float = 0.0 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 negative_prompt_embeds: typing.Optional[torch.FloatTensor] = None pooled_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 callback: typing.Union[typing.Callable[[int, int, torch.FloatTensor], NoneType], NoneType] = None callback_steps: int = 1 cross_attention_kwargs: typing.Union[typing.Dict[str, typing.Any], NoneType] = None controlnet_conditioning_scale: typing.Union[float, typing.List[float]] = 1.0 guess_mode: bool = False control_guidance_start: typing.Union[float, typing.List[float]] = 0.0 control_guidance_end: typing.Union[float, typing.List[float]] = 1.0 original_size: typing.Tuple[int, int] = None crops_coords_top_left: typing.Tuple[int, int] = (0, 0) target_size: typing.Tuple[int, int] = None negative_original_size: typing.Union[typing.Tuple[int, int], NoneType] = None negative_crops_coords_top_left: typing.Tuple[int, int] = (0, 0) negative_target_size: typing.Union[typing.Tuple[int, int], NoneType] = None clip_skip: typing.Optional[int] = None ) → StableDiffusionPipelineOutput or tuple
Parameters
str
or List[str]
, optional) —
The prompt or prompts to guide image generation. If not defined, you need to pass prompt_embeds
. str
or List[str]
, optional) —
The prompt or prompts to be sent to tokenizer_2
and text_encoder_2
. If not defined, prompt
is
used in both text-encoders. torch.FloatTensor
, PIL.Image.Image
, np.ndarray
, List[torch.FloatTensor]
, List[PIL.Image.Image]
, List[np.ndarray]
, —
List[List[torch.FloatTensor]]
, 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.FloatTensor
, 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. int
, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor
) —
The height in pixels of the generated image. Anything below 512 pixels won’t work well for
stabilityai/stable-diffusion-xl-base-1.0
and checkpoints that are not specifically fine-tuned on low resolutions. int
, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor
) —
The width in pixels of the generated image. Anything below 512 pixels won’t work well for
stabilityai/stable-diffusion-xl-base-1.0
and checkpoints that are not specifically fine-tuned on low resolutions. 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. float
, optional, defaults to 5.0) —
A higher guidance scale value encourages the model to generate images closely linked to the text
prompt
at the expense of lower image quality. Guidance scale is enabled when guidance_scale > 1
. str
or List[str]
, optional) —
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass negative_prompt_embeds
instead. Ignored when not using guidance (guidance_scale < 1
). str
or List[str]
, optional) —
The prompt or prompts to guide what to not include in image generation. This is sent to tokenizer_2
and text_encoder_2
. If not defined, negative_prompt
is used in both text-encoders. int
, optional, defaults to 1) —
The number of images to generate per prompt. float
, optional, defaults to 0.0) —
Corresponds to parameter eta (η) from the DDIM paper. Only applies
to the DDIMScheduler, and is ignored in other schedulers. torch.Generator
or List[torch.Generator]
, optional) —
A torch.Generator
to make
generation deterministic. 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 is generated by sampling using the supplied random generator
. torch.FloatTensor
, optional) —
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings are generated from the prompt
input argument. torch.FloatTensor
, optional) —
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, negative_prompt_embeds
are generated from the negative_prompt
input argument. torch.FloatTensor
, optional) —
Pre-generated pooled text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, pooled text embeddings are generated from prompt
input argument. torch.FloatTensor
, optional) —
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs (prompt
weighting). If not provided, pooled negative_prompt_embeds
are generated from negative_prompt
input
argument. str
, optional, defaults to "pil"
) —
The output format of the generated image. Choose between PIL.Image
or np.array
. bool
, optional, defaults to True
) —
Whether or not to return a StableDiffusionPipelineOutput instead of a
plain tuple. Callable
, optional) —
A function that calls every callback_steps
steps during inference. The function is called with the
following arguments: callback(step: int, timestep: int, latents: torch.FloatTensor)
. int
, optional, defaults to 1) —
The frequency at which the callback
function is called. If not specified, the callback is called at
every step. dict
, optional) —
A kwargs dictionary that if specified is passed along to the AttentionProcessor
as defined in
self.processor
. 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 unet
. If multiple ControlNets are specified in init
, you can set
the corresponding scale as a list. bool
, optional, defaults to False
) —
The ControlNet encoder tries to recognize the content of the input image even if you remove all
prompts. A guidance_scale
value between 3.0 and 5.0 is recommended. float
or List[float]
, optional, defaults to 0.0) —
The percentage of total steps at which the ControlNet starts applying. float
or List[float]
, optional, defaults to 1.0) —
The percentage of total steps at which the ControlNet stops applying. Tuple[int]
, optional, defaults to (1024, 1024)) —
If original_size
is not the same as target_size
the image will appear to be down- or upsampled.
original_size
defaults to (height, width)
if not specified. Part of SDXL’s micro-conditioning as
explained in section 2.2 of
https://huggingface.co/papers/2307.01952. Tuple[int]
, optional, defaults to (0, 0)) —
crops_coords_top_left
can be used to generate an image that appears to be “cropped” from the position
crops_coords_top_left
downwards. Favorable, well-centered images are usually achieved by setting
crops_coords_top_left
to (0, 0). Part of SDXL’s micro-conditioning as explained in section 2.2 of
https://huggingface.co/papers/2307.01952. Tuple[int]
, optional, defaults to (1024, 1024)) —
For most cases, target_size
should be set to the desired height and width of the generated image. If
not specified it will default to (height, width)
. Part of SDXL’s micro-conditioning as explained in
section 2.2 of https://huggingface.co/papers/2307.01952. Tuple[int]
, optional, defaults to (1024, 1024)) —
To negatively condition the generation process based on a specific image resolution. Part of SDXL’s
micro-conditioning as explained in section 2.2 of
https://huggingface.co/papers/2307.01952. For more
information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. Tuple[int]
, optional, defaults to (0, 0)) —
To negatively condition the generation process based on a specific crop coordinates. Part of SDXL’s
micro-conditioning as explained in section 2.2 of
https://huggingface.co/papers/2307.01952. For more
information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. Tuple[int]
, optional, defaults to (1024, 1024)) —
To negatively condition the generation process based on a target image resolution. It should be as same
as the target_size
for most cases. Part of SDXL’s micro-conditioning as explained in section 2.2 of
https://huggingface.co/papers/2307.01952. For more
information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. int
, optional) —
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for computing the prompt embeddings. Returns
StableDiffusionPipelineOutput or tuple
If return_dict
is True
, StableDiffusionPipelineOutput is returned,
otherwise a tuple
is returned containing the output images.
The call function to the pipeline for generation.
Examples:
>>> # !pip install opencv-python transformers accelerate
>>> from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel, AutoencoderKL
>>> from diffusers.utils import load_image
>>> import numpy as np
>>> import torch
>>> import cv2
>>> from PIL import Image
>>> prompt = "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting"
>>> negative_prompt = "low quality, bad quality, sketches"
>>> # download an image
>>> image = load_image(
... "https://hf.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png"
... )
>>> # initialize the models and pipeline
>>> controlnet_conditioning_scale = 0.5 # recommended for good generalization
>>> controlnet = ControlNetModel.from_pretrained(
... "diffusers/controlnet-canny-sdxl-1.0", torch_dtype=torch.float16
... )
>>> vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
>>> pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
... "stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet, vae=vae, torch_dtype=torch.float16
... )
>>> pipe.enable_model_cpu_offload()
>>> # get canny image
>>> image = np.array(image)
>>> image = cv2.Canny(image, 100, 200)
>>> image = image[:, :, None]
>>> image = np.concatenate([image, image, image], axis=2)
>>> canny_image = Image.fromarray(image)
>>> # generate image
>>> image = pipe(
... prompt, controlnet_conditioning_scale=controlnet_conditioning_scale, image=canny_image
... ).images[0]
Disables the FreeU mechanism if enabled.
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.
( s1: float s2: float b1: float b2: float )
Parameters
float
) —
Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to
mitigate “oversmoothing effect” in the enhanced denoising process. float
) —
Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to
mitigate “oversmoothing effect” in the enhanced denoising process. float
) — Scaling factor for stage 1 to amplify the contributions of backbone features. float
) — Scaling factor for stage 2 to amplify the contributions of backbone features. Enables the FreeU mechanism as in https://arxiv.org/abs/2309.11497.
The suffixes after the scaling factors represent the stages where they are being applied.
Please refer to the official repository for combinations of the values that are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL.
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.
( prompt: str prompt_2: typing.Optional[str] = None device: typing.Optional[torch.device] = None num_images_per_prompt: int = 1 do_classifier_free_guidance: bool = True negative_prompt: typing.Optional[str] = None negative_prompt_2: typing.Optional[str] = None prompt_embeds: typing.Optional[torch.FloatTensor] = None negative_prompt_embeds: typing.Optional[torch.FloatTensor] = None pooled_prompt_embeds: typing.Optional[torch.FloatTensor] = None negative_pooled_prompt_embeds: typing.Optional[torch.FloatTensor] = None lora_scale: typing.Optional[float] = None clip_skip: typing.Optional[int] = None )
Parameters
str
or List[str]
, optional) —
prompt to be encoded 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 both text-encoders
device — (torch.device
):
torch device int
) —
number of images that should be generated per prompt bool
) —
whether to use classifier free guidance or not str
or List[str]
, optional) —
The prompt or prompts not to guide the image generation. If not defined, one has to pass
negative_prompt_embeds
instead. Ignored when not using guidance (i.e., ignored if guidance_scale
is
less than 1
). str
or List[str]
, optional) —
The prompt or prompts not to guide the image generation to be sent to tokenizer_2
and
text_encoder_2
. If not defined, negative_prompt
is used in both text-encoders 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. 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. 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. torch.FloatTensor
, optional) —
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt
weighting. If not provided, pooled negative_prompt_embeds will be generated from negative_prompt
input argument. float
, optional) —
A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded. int
, optional) —
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for computing the prompt embeddings. Encodes the prompt into text encoder hidden states.
( images: typing.Union[typing.List[PIL.Image.Image], numpy.ndarray] nsfw_content_detected: typing.Optional[typing.List[bool]] )
Parameters
List[PIL.Image.Image]
or np.ndarray
) —
List of denoised PIL images of length batch_size
or NumPy array of shape (batch_size, height, width, num_channels)
. List[bool]
) —
List indicating whether the corresponding generated image contains “not-safe-for-work” (nsfw) content or
None
if safety checking could not be performed. Output class for Stable Diffusion pipelines.