#
ControlNet with Stable Diffusion XL

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.

##
StableDiffusionXLControlNetPipeline

###
class diffusers.StableDiffusionXLControlNetPipeline

< source >

( 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

- vae (AutoencoderKL) — Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
- text_encoder (CLIPTextModel) — Frozen text-encoder (clip-vit-large-patch14).
- text_encoder_2 (CLIPTextModelWithProjection) — Second frozen text-encoder (laion/CLIP-ViT-bigG-14-laion2B-39B-b160k).
- tokenizer (CLIPTokenizer) — A `CLIPTokenizer` to tokenize text.
- tokenizer_2 (CLIPTokenizer) — A `CLIPTokenizer` to tokenize text.
- unet (UNet2DConditionModel) — A `UNet2DConditionModel` to denoise the encoded image latents.
- controlnet (ControlNetModel or `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.
- scheduler (SchedulerMixin) — A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of DDIMScheduler, LMSDiscreteScheduler, or PNDMScheduler.
- force_zeros_for_empty_prompt (`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`.
- add_watermarker (`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:

- load_textual_inversion() for loading textual inversion embeddings
- loaders.LoraLoaderMixin.load_lora_weights() for loading LoRA weights
- loaders.FromSingleFileMixin.from_single_file() for loading `.ckpt` files

####
__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.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 ) → StableDiffusionPipelineOutput or `tuple`

Parameters

- prompt (`str` or `List[str]`, optional) — The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
- 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 used in both text-encoders.
- image (`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.
- height (`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.
- width (`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.
- 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.
- guidance_scale (`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`.
- negative_prompt (`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`).
- negative_prompt_2 (`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.
- num_images_per_prompt (`int`, optional, defaults to 1) — The number of images to generate per prompt.
- eta (`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.
- generator (`torch.Generator` or `List[torch.Generator]`, optional) — A `torch.Generator` 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 is 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 (prompt weighting). If not provided, text embeddings are generated from the `prompt` input argument.
- negative_prompt_embeds (`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.
- pooled_prompt_embeds (`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.
- negative_pooled_prompt_embeds (`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.
- output_type (`str`, optional, defaults to `"pil"`) — The output format of the generated image. Choose between `PIL.Image` or `np.array`.
- return_dict (`bool`, optional, defaults to `True`) — Whether or not to return a StableDiffusionPipelineOutput instead of a plain tuple.
- callback (`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)`.
- callback_steps (`int`, optional, defaults to 1) — The frequency at which the `callback` function is called. If not specified, the callback is called at every step.
- cross_attention_kwargs (`dict`, optional) — A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined in `self.processor`.
- 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 `unet`. If multiple ControlNets are specified in `init`, you can set the corresponding scale as a list.
- guess_mode (`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.
- 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.
- original_size (`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 `(width, height)` if not specified. Part of SDXL’s micro-conditioning as explained in section 2.2 of https://huggingface.co/papers/2307.01952.
- crops_coords_top_left (`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.
- target_size (`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 `(width, height)`. Part of SDXL’s micro-conditioning as explained in section 2.2 of https://huggingface.co/papers/2307.01952.
- negative_original_size (`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.
- negative_crops_coords_top_left (`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.
- negative_target_size (`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.

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]

####
disable_vae_slicing

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( )

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

####
disable_vae_tiling

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( )

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

####
enable_model_cpu_offload

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( gpu_id = 0 )

Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.

####
enable_vae_slicing

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( )

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

####
enable_vae_tiling

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( )

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

####
encode_prompt

< source >

( 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 )

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 both text-encoders device — (`torch.device`): torch device
- num_images_per_prompt (`int`) — number of images that should be generated per prompt
- do_classifier_free_guidance (`bool`) — whether to use classifier free guidance or not
- negative_prompt (`str` or `List[str]`, optional) — The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
- negative_prompt_2 (`str` or `List[str]`, optional) — The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
- 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.
- 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.
- 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.
- negative_pooled_prompt_embeds (`torch.FloatTensor`, optional) — Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt` input argument.
- lora_scale (`float`, optional) — A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.

Encodes the prompt into text encoder hidden states.

##
StableDiffusionPipelineOutput

###
class diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput

< source >

( images: typing.Union[typing.List[PIL.Image.Image], numpy.ndarray] nsfw_content_detected: typing.Optional[typing.List[bool]] )

Parameters

- images (`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)`.
- nsfw_content_detected (`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.