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	# ControlNet

	<div class="flex flex-wrap space-x-1">
	<img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
	</div>

	ControlNet was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang, Anyi Rao, 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 ControlNet, a neural network architecture to add spatial conditioning controls to large, pretrained text-to-image diffusion models. ControlNet locks the production-ready large diffusion models, and reuses their deep and robust encoding layers pretrained with billions of images as a strong backbone to learn a diverse set of conditional controls. The neural architecture is connected with "zero convolutions" (zero-initialized convolution layers) that progressively grow the parameters from zero and ensure that no harmful noise could affect the finetuning. We test various conditioning controls, eg, edges, depth, segmentation, human pose, etc, with Stable Diffusion, using single or multiple conditions, with or without prompts. We show that the training of ControlNets is robust with small (<50k) and large (>1m) datasets. Extensive results show that ControlNet may facilitate wider applications to control image diffusion models.

	This model was contributed by [takuma104](https://huggingface.co/takuma104). ❤️

	The original codebase can be found at [lllyasviel/ControlNet](https://github.com/lllyasviel/ControlNet), and you can find official ControlNet checkpoints on [lllyasviel's](https://huggingface.co/lllyasviel) Hub profile.

	> [!TIP]
	> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.

	## StableDiffusionControlNetPipeline[[diffusers.StableDiffusionControlNetPipeline]]
	<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


	<docstring><name>class diffusers.StableDiffusionControlNetPipeline</name><anchor>diffusers.StableDiffusionControlNetPipeline</anchor><source>https://github.com/huggingface/diffusers/blob/vr_12595/src/diffusers/pipelines/controlnet/pipeline_controlnet.py#L162</source><parameters>[{"name": "vae", "val": ": AutoencoderKL"}, {"name": "text_encoder", "val": ": CLIPTextModel"}, {"name": "tokenizer", "val": ": CLIPTokenizer"}, {"name": "unet", "val": ": UNet2DConditionModel"}, {"name": "controlnet", "val": ": typing.Union[diffusers.models.controlnets.controlnet.ControlNetModel, typing.List[diffusers.models.controlnets.controlnet.ControlNetModel], typing.Tuple[diffusers.models.controlnets.controlnet.ControlNetModel], diffusers.models.controlnets.multicontrolnet.MultiControlNetModel]"}, {"name": "scheduler", "val": ": KarrasDiffusionSchedulers"}, {"name": "safety_checker", "val": ": StableDiffusionSafetyChecker"}, {"name": "feature_extractor", "val": ": CLIPImageProcessor"}, {"name": "image_encoder", "val": ": CLIPVisionModelWithProjection = None"}, {"name": "requires_safety_checker", "val": ": bool = True"}]</parameters><paramsdesc>- vae ([AutoencoderKL](/docs/diffusers/pr_12595/en/api/models/autoencoderkl#diffusers.AutoencoderKL)) --
	Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
	- text_encoder ([CLIPTextModel](https://huggingface.co/docs/transformers/main/en/model_doc/clip#transformers.CLIPTextModel)) --
	Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
	- tokenizer ([CLIPTokenizer](https://huggingface.co/docs/transformers/main/en/model_doc/clip#transformers.CLIPTokenizer)) --
	A `CLIPTokenizer` to tokenize text.
	- unet ([UNet2DConditionModel](/docs/diffusers/pr_12595/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel)) --
	A `UNet2DConditionModel` to denoise the encoded image latents.
	- controlnet ([ControlNetModel](/docs/diffusers/pr_12595/en/api/models/controlnet#diffusers.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](/docs/diffusers/pr_12595/en/api/schedulers/overview#diffusers.SchedulerMixin)) --
	A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
	[DDIMScheduler](/docs/diffusers/pr_12595/en/api/schedulers/ddim#diffusers.DDIMScheduler), [LMSDiscreteScheduler](/docs/diffusers/pr_12595/en/api/schedulers/lms_discrete#diffusers.LMSDiscreteScheduler), or [PNDMScheduler](/docs/diffusers/pr_12595/en/api/schedulers/pndm#diffusers.PNDMScheduler).
	- safety_checker (`StableDiffusionSafetyChecker`) --
	Classification module that estimates whether generated images could be considered offensive or harmful.
	Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for
	more details about a model's potential harms.
	- feature_extractor ([CLIPImageProcessor](https://huggingface.co/docs/transformers/main/en/model_doc/clip#transformers.CLIPImageProcessor)) --
	A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.</paramsdesc><paramgroups>0</paramgroups></docstring>

	Pipeline for text-to-image generation using Stable Diffusion with ControlNet guidance.

	This model inherits from [DiffusionPipeline](/docs/diffusers/pr_12595/en/api/pipelines/overview#diffusers.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()](/docs/diffusers/pr_12595/en/api/loaders/textual_inversion#diffusers.loaders.TextualInversionLoaderMixin.load_textual_inversion) for loading textual inversion embeddings
	- [load_lora_weights()](/docs/diffusers/pr_12595/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights) for loading LoRA weights
	- [save_lora_weights()](/docs/diffusers/pr_12595/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights) for saving LoRA weights
	- [from_single_file()](/docs/diffusers/pr_12595/en/api/loaders/single_file#diffusers.loaders.FromSingleFileMixin.from_single_file) for loading `.ckpt` files
	- [load_ip_adapter()](/docs/diffusers/pr_12595/en/api/loaders/ip_adapter#diffusers.loaders.IPAdapterMixin.load_ip_adapter) for loading IP Adapters





	<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


	<docstring><name>__call__</name><anchor>diffusers.StableDiffusionControlNetPipeline.__call__</anchor><source>https://github.com/huggingface/diffusers/blob/vr_12595/src/diffusers/pipelines/controlnet/pipeline_controlnet.py#L907</source><parameters>[{"name": "prompt", "val": ": typing.Union[str, typing.List[str]] = None"}, {"name": "image", "val": ": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = None"}, {"name": "height", "val": ": typing.Optional[int] = None"}, {"name": "width", "val": ": typing.Optional[int] = None"}, {"name": "num_inference_steps", "val": ": int = 50"}, {"name": "timesteps", "val": ": typing.List[int] = None"}, {"name": "sigmas", "val": ": typing.List[float] = None"}, {"name": "guidance_scale", "val": ": float = 7.5"}, {"name": "negative_prompt", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "num_images_per_prompt", "val": ": typing.Optional[int] = 1"}, {"name": "eta", "val": ": float = 0.0"}, {"name": "generator", "val": ": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"}, {"name": "latents", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "negative_prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "ip_adapter_image", "val": ": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor], NoneType] = None"}, {"name": "ip_adapter_image_embeds", "val": ": typing.Optional[typing.List[torch.Tensor]] = None"}, {"name": "output_type", "val": ": typing.Optional[str] = 'pil'"}, {"name": "return_dict", "val": ": bool = True"}, {"name": "cross_attention_kwargs", "val": ": typing.Optional[typing.Dict[str, typing.Any]] = None"}, {"name": "controlnet_conditioning_scale", "val": ": typing.Union[float, typing.List[float]] = 1.0"}, {"name": "guess_mode", "val": ": bool = False"}, {"name": "control_guidance_start", "val": ": typing.Union[float, typing.List[float]] = 0.0"}, {"name": "control_guidance_end", "val": ": typing.Union[float, typing.List[float]] = 1.0"}, {"name": "clip_skip", "val": ": typing.Optional[int] = None"}, {"name": "callback_on_step_end", "val": ": typing.Union[typing.Callable[[int, int, typing.Dict], NoneType], diffusers.callbacks.PipelineCallback, diffusers.callbacks.MultiPipelineCallbacks, NoneType] = None"}, {"name": "callback_on_step_end_tensor_inputs", "val": ": typing.List[str] = ['latents']"}, {"name": "kwargs", "val": ""}]</parameters><paramsdesc>- prompt** (`str` or `List[str]`, optional) --
	The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
	- 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. When `prompt` is a list, and if a list of images is passed for a single
	ControlNet, each will be paired with each prompt in the `prompt` list. This also applies to multiple
	ControlNets, where a list of image lists can be passed to batch for each prompt and each ControlNet.
	- height (`int`, optional, defaults to `self.unet.config.sample_size * self.vae_scale_factor`) --
	The height in pixels of the generated image.
	- width (`int`, optional, defaults to `self.unet.config.sample_size * self.vae_scale_factor`) --
	The width in pixels of the generated 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.
	- timesteps (`List[int]`, optional) --
	Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
	in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
	passed will be used. Must be in descending order.
	- 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 7.5) --
	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`).
	- 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](https://huggingface.co/papers/2010.02502) paper. Only
	applies to the [DDIMScheduler](/docs/diffusers/pr_12595/en/api/schedulers/ddim#diffusers.DDIMScheduler), and is ignored in other schedulers.
	- generator (`torch.Generator` or `List[torch.Generator]`, optional) --
	A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
	generation deterministic.
	- latents (`torch.Tensor`, 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.Tensor`, 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.Tensor`, 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.
	- 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)`. It should
	contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. 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 generated image. Choose between `PIL.Image` or `np.array`.
	- return_dict (`bool`, optional, defaults to `True`) --
	Whether or not to return a [StableDiffusionPipelineOutput](/docs/diffusers/pr_12595/en/api/pipelines/stable_diffusion/depth2img#diffusers.pipelines.stable_diffusion.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.Tensor)`.
	- 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`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
	- 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.
	- clip_skip (`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.
	- callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, optional) --
	A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of
	each denoising step during the inference. with the following arguments: `callback_on_step_end(self:
	DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a
	list of all tensors as specified by `callback_on_step_end_tensor_inputs`.
	- callback_on_step_end_tensor_inputs (`List`, optional) --
	The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
	will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
	`._callback_tensor_inputs` attribute of your pipeline class.</paramsdesc><paramgroups>0</paramgroups><rettype>[StableDiffusionPipelineOutput](/docs/diffusers/pr_12595/en/api/pipelines/stable_diffusion/depth2img#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput) or `tuple`</rettype><retdesc>If `return_dict` is `True`, [StableDiffusionPipelineOutput](/docs/diffusers/pr_12595/en/api/pipelines/stable_diffusion/depth2img#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput) is returned,
	otherwise a `tuple` is returned where the first element is a list with the generated images and the
	second element is a list of `bool`s indicating whether the corresponding generated image contains
	"not-safe-for-work" (nsfw) content.</retdesc></docstring>

	The call function to the pipeline for generation.



	<ExampleCodeBlock anchor="diffusers.StableDiffusionControlNetPipeline.__call__.example">

	Examples:
	```py
	>>> # !pip install opencv-python transformers accelerate
	>>> from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
	>>> from diffusers.utils import load_image
	>>> import numpy as np
	>>> import torch

	>>> import cv2
	>>> from PIL import Image

	>>> # download an image
	>>> image = load_image(
	... "https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png"
	... )
	>>> image = np.array(image)

	>>> # get canny image
	>>> image = cv2.Canny(image, 100, 200)
	>>> image = image[:, :, None]
	>>> image = np.concatenate([image, image, image], axis=2)
	>>> canny_image = Image.fromarray(image)

	>>> # load control net and stable diffusion v1-5
	>>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
	>>> pipe = StableDiffusionControlNetPipeline.from_pretrained(
	... "stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
	... )

	>>> # speed up diffusion process with faster scheduler and memory optimization
	>>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
	>>> # remove following line if xformers is not installed
	>>> pipe.enable_xformers_memory_efficient_attention()

	>>> pipe.enable_model_cpu_offload()

	>>> # generate image
	>>> generator = torch.manual_seed(0)
	>>> image = pipe(
	... "futuristic-looking woman", num_inference_steps=20, generator=generator, image=canny_image
	... ).images[0]
	```

	</ExampleCodeBlock>







	</div>
	<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


	<docstring><name>enable_attention_slicing</name><anchor>diffusers.StableDiffusionControlNetPipeline.enable_attention_slicing</anchor><source>https://github.com/huggingface/diffusers/blob/vr_12595/src/diffusers/pipelines/pipeline_utils.py#L1978</source><parameters>[{"name": "slice_size", "val": ": typing.Union[int, str, NoneType] = 'auto'"}]</parameters><paramsdesc>- slice_size (`str` or `int`, optional, defaults to `"auto"`) --
	When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
	`"max"`, maximum amount of memory will be saved by running only one slice at a time. If a number is
	provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
	must be a multiple of `slice_size`.</paramsdesc><paramgroups>0</paramgroups></docstring>

	Enable sliced attention computation. When this option is enabled, the attention module splits the input tensor
	in slices to compute attention in several steps. For more than one attention head, the computation is performed
	sequentially over each head. This is useful to save some memory in exchange for a small speed decrease.

	> [!WARNING] > ⚠️ Don't enable attention slicing if you're already using `scaled_dot_product_attention` (SDPA)
	from PyTorch > 2.0 or xFormers. These attention computations are already very memory efficient so you won't
	need to enable > this function. If you enable attention slicing with SDPA or xFormers, it can lead to serious
	slow downs!



	<ExampleCodeBlock anchor="diffusers.StableDiffusionControlNetPipeline.enable_attention_slicing.example">

	Examples:

	```py
	>>> import torch
	>>> from diffusers import StableDiffusionPipeline

	>>> pipe = StableDiffusionPipeline.from_pretrained(
	... "stable-diffusion-v1-5/stable-diffusion-v1-5",
	... torch_dtype=torch.float16,
	... use_safetensors=True,
	... )

	>>> prompt = "a photo of an astronaut riding a horse on mars"
	>>> pipe.enable_attention_slicing()
	>>> image = pipe(prompt).images[0]
	```

	</ExampleCodeBlock>


	</div>
	<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


	<docstring><name>disable_attention_slicing</name><anchor>diffusers.StableDiffusionControlNetPipeline.disable_attention_slicing</anchor><source>https://github.com/huggingface/diffusers/blob/vr_12595/src/diffusers/pipelines/pipeline_utils.py#L2015</source><parameters>[]</parameters></docstring>

	Disable sliced attention computation. If `enable_attention_slicing` was previously called, attention is
	computed in one step.


	</div>
	<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


	<docstring><name>enable_vae_slicing</name><anchor>diffusers.StableDiffusionControlNetPipeline.enable_vae_slicing</anchor><source>https://github.com/huggingface/diffusers/blob/vr_12595/src/diffusers/pipelines/pipeline_utils.py#L2180</source><parameters>[]</parameters></docstring>

	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.


	</div>
	<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


	<docstring><name>disable_vae_slicing</name><anchor>diffusers.StableDiffusionControlNetPipeline.disable_vae_slicing</anchor><source>https://github.com/huggingface/diffusers/blob/vr_12595/src/diffusers/pipelines/pipeline_utils.py#L2193</source><parameters>[]</parameters></docstring>

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


	</div>
	<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


	<docstring><name>enable_xformers_memory_efficient_attention</name><anchor>diffusers.StableDiffusionControlNetPipeline.enable_xformers_memory_efficient_attention</anchor><source>https://github.com/huggingface/diffusers/blob/vr_12595/src/diffusers/pipelines/pipeline_utils.py#L1921</source><parameters>[{"name": "attention_op", "val": ": typing.Optional[typing.Callable] = None"}]</parameters><paramsdesc>- attention_op (`Callable`, optional) --
	Override the default `None` operator for use as `op` argument to the
	[`memory_efficient_attention()`](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.memory_efficient_attention)
	function of xFormers.</paramsdesc><paramgroups>0</paramgroups></docstring>

	Enable memory efficient attention from [xFormers](https://facebookresearch.github.io/xformers/). When this
	option is enabled, you should observe lower GPU memory usage and a potential speed up during inference. Speed
	up during training is not guaranteed.

	> [!WARNING] > ⚠️ When memory efficient attention and sliced attention are both enabled, memory efficient
	attention takes > precedent.



	<ExampleCodeBlock anchor="diffusers.StableDiffusionControlNetPipeline.enable_xformers_memory_efficient_attention.example">

	Examples:

	```py
	>>> import torch
	>>> from diffusers import DiffusionPipeline
	>>> from xformers.ops import MemoryEfficientAttentionFlashAttentionOp

	>>> pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16)
	>>> pipe = pipe.to("cuda")
	>>> pipe.enable_xformers_memory_efficient_attention(attention_op=MemoryEfficientAttentionFlashAttentionOp)
	>>> # Workaround for not accepting attention shape using VAE for Flash Attention
	>>> pipe.vae.enable_xformers_memory_efficient_attention(attention_op=None)
	```

	</ExampleCodeBlock>


	</div>
	<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


	<docstring><name>disable_xformers_memory_efficient_attention</name><anchor>diffusers.StableDiffusionControlNetPipeline.disable_xformers_memory_efficient_attention</anchor><source>https://github.com/huggingface/diffusers/blob/vr_12595/src/diffusers/pipelines/pipeline_utils.py#L1952</source><parameters>[]</parameters></docstring>

	Disable memory efficient attention from [xFormers](https://facebookresearch.github.io/xformers/).


	</div>
	<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


	<docstring><name>load_textual_inversion</name><anchor>diffusers.StableDiffusionControlNetPipeline.load_textual_inversion</anchor><source>https://github.com/huggingface/diffusers/blob/vr_12595/src/diffusers/loaders/textual_inversion.py#L263</source><parameters>[{"name": "pretrained_model_name_or_path", "val": ": typing.Union[str, typing.List[str], typing.Dict[str, torch.Tensor], typing.List[typing.Dict[str, torch.Tensor]]]"}, {"name": "token", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "tokenizer", "val": ": typing.Optional[ForwardRef('PreTrainedTokenizer')] = None"}, {"name": "text_encoder", "val": ": typing.Optional[ForwardRef('PreTrainedModel')] = None"}, {"name": "kwargs", "val": ""}]</parameters><paramsdesc>- pretrained_model_name_or_path** (`str` or `os.PathLike` or `List[str or os.PathLike]` or `Dict` or `List[Dict]`) --
	Can be either one of the following or a list of them:

	- A string, the model id (for example `sd-concepts-library/low-poly-hd-logos-icons`) of a
	pretrained model hosted on the Hub.
	- A path to a directory (for example `./my_text_inversion_directory/`) containing the textual
	inversion weights.
	- A path to a file (for example `./my_text_inversions.pt`) containing textual inversion weights.
	- A [torch state
	dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict).

	- token (`str` or `List[str]`, optional) --
	Override the token to use for the textual inversion weights. If `pretrained_model_name_or_path` is a
	list, then `token` must also be a list of equal length.
	- text_encoder ([CLIPTextModel](https://huggingface.co/docs/transformers/main/en/model_doc/clip#transformers.CLIPTextModel), optional) --
	Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
	If not specified, function will take self.tokenizer.
	- tokenizer ([CLIPTokenizer](https://huggingface.co/docs/transformers/main/en/model_doc/clip#transformers.CLIPTokenizer), optional) --
	A `CLIPTokenizer` to tokenize text. If not specified, function will take self.tokenizer.
	- weight_name (`str`, optional) --
	Name of a custom weight file. This should be used when:

	- The saved textual inversion file is in 🤗 Diffusers format, but was saved under a specific weight
	name such as `text_inv.bin`.
	- The saved textual inversion file is in the Automatic1111 format.
	- cache_dir (`Union[str, os.PathLike]`, optional) --
	Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
	is not used.
	- force_download (`bool`, optional, defaults to `False`) --
	Whether or not to force the (re-)download of the model weights and configuration files, overriding the
	cached versions if they exist.

	- proxies (`Dict[str, str]`, optional) --
	A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',
	'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
	- local_files_only (`bool`, optional, defaults to `False`) --
	Whether to only load local model weights and configuration files or not. If set to `True`, the model
	won't be downloaded from the Hub.
	- hf_token (`str` or bool, optional) --
	The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from
	`diffusers-cli login` (stored in `~/.huggingface`) is used.
	- revision (`str`, optional, defaults to `"main"`) --
	The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
	allowed by Git.
	- subfolder (`str`, optional, defaults to `""`) --
	The subfolder location of a model file within a larger model repository on the Hub or locally.
	- mirror (`str`, optional) --
	Mirror source to resolve accessibility issues if you're downloading a model in China. We do not
	guarantee the timeliness or safety of the source, and you should refer to the mirror site for more
	information.</paramsdesc><paramgroups>0</paramgroups></docstring>

	Load Textual Inversion embeddings into the text encoder of [StableDiffusionPipeline](/docs/diffusers/pr_12595/en/api/pipelines/stable_diffusion/text2img#diffusers.StableDiffusionPipeline) (both 🤗 Diffusers and
	Automatic1111 formats are supported).



	Example:

	<ExampleCodeBlock anchor="diffusers.StableDiffusionControlNetPipeline.load_textual_inversion.example">

	To load a Textual Inversion embedding vector in 🤗 Diffusers format:

	```py
	from diffusers import StableDiffusionPipeline
	import torch

	model_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
	pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")

	pipe.load_textual_inversion("sd-concepts-library/cat-toy")

	prompt = "A <cat-toy> backpack"

	image = pipe(prompt, num_inference_steps=50).images[0]
	image.save("cat-backpack.png")
	```

	</ExampleCodeBlock>

	To load a Textual Inversion embedding vector in Automatic1111 format, make sure to download the vector first
	(for example from [civitAI](https://civitai.com/models/3036?modelVersionId=9857)) and then load the vector
	<ExampleCodeBlock anchor="diffusers.StableDiffusionControlNetPipeline.load_textual_inversion.example-2">

	locally:

	```py
	from diffusers import StableDiffusionPipeline
	import torch

	model_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
	pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")

	pipe.load_textual_inversion("./charturnerv2.pt", token="charturnerv2")

	prompt = "charturnerv2, multiple views of the same character in the same outfit, a character turnaround of a woman wearing a black jacket and red shirt, best quality, intricate details."

	image = pipe(prompt, num_inference_steps=50).images[0]
	image.save("character.png")
	```

	</ExampleCodeBlock>



	</div>
	<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


	<docstring><name>encode_prompt</name><anchor>diffusers.StableDiffusionControlNetPipeline.encode_prompt</anchor><source>https://github.com/huggingface/diffusers/blob/vr_12595/src/diffusers/pipelines/controlnet/pipeline_controlnet.py#L298</source><parameters>[{"name": "prompt", "val": ""}, {"name": "device", "val": ""}, {"name": "num_images_per_prompt", "val": ""}, {"name": "do_classifier_free_guidance", "val": ""}, {"name": "negative_prompt", "val": " = None"}, {"name": "prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "negative_prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "lora_scale", "val": ": typing.Optional[float] = None"}, {"name": "clip_skip", "val": ": typing.Optional[int] = None"}]</parameters><paramsdesc>- prompt (`str` or `List[str]`, optional) --
	prompt to be encoded
	- 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`).
	- prompt_embeds (`torch.Tensor`, 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.Tensor`, 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.
	- lora_scale (`float`, optional) --
	A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
	- clip_skip (`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.</paramsdesc><paramgroups>0</paramgroups></docstring>

	Encodes the prompt into text encoder hidden states.




	</div>
	<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


	<docstring><name>get_guidance_scale_embedding</name><anchor>diffusers.StableDiffusionControlNetPipeline.get_guidance_scale_embedding</anchor><source>https://github.com/huggingface/diffusers/blob/vr_12595/src/diffusers/pipelines/controlnet/pipeline_controlnet.py#L850</source><parameters>[{"name": "w", "val": ": Tensor"}, {"name": "embedding_dim", "val": ": int = 512"}, {"name": "dtype", "val": ": dtype = torch.float32"}]</parameters><paramsdesc>- w (`torch.Tensor`) --
	Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.
	- embedding_dim (`int`, optional, defaults to 512) --
	Dimension of the embeddings to generate.
	- dtype (`torch.dtype`, optional, defaults to `torch.float32`) --
	Data type of the generated embeddings.</paramsdesc><paramgroups>0</paramgroups><rettype>`torch.Tensor`</rettype><retdesc>Embedding vectors with shape `(len(w), embedding_dim)`.</retdesc></docstring>

	See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298








	</div></div>

	## StableDiffusionControlNetImg2ImgPipeline[[diffusers.StableDiffusionControlNetImg2ImgPipeline]]
	<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


	<docstring><name>class diffusers.StableDiffusionControlNetImg2ImgPipeline</name><anchor>diffusers.StableDiffusionControlNetImg2ImgPipeline</anchor><source>https://github.com/huggingface/diffusers/blob/vr_12595/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py#L140</source><parameters>[{"name": "vae", "val": ": AutoencoderKL"}, {"name": "text_encoder", "val": ": CLIPTextModel"}, {"name": "tokenizer", "val": ": CLIPTokenizer"}, {"name": "unet", "val": ": UNet2DConditionModel"}, {"name": "controlnet", "val": ": typing.Union[diffusers.models.controlnets.controlnet.ControlNetModel, typing.List[diffusers.models.controlnets.controlnet.ControlNetModel], typing.Tuple[diffusers.models.controlnets.controlnet.ControlNetModel], diffusers.models.controlnets.multicontrolnet.MultiControlNetModel]"}, {"name": "scheduler", "val": ": KarrasDiffusionSchedulers"}, {"name": "safety_checker", "val": ": StableDiffusionSafetyChecker"}, {"name": "feature_extractor", "val": ": CLIPImageProcessor"}, {"name": "image_encoder", "val": ": CLIPVisionModelWithProjection = None"}, {"name": "requires_safety_checker", "val": ": bool = True"}]</parameters><paramsdesc>- vae ([AutoencoderKL](/docs/diffusers/pr_12595/en/api/models/autoencoderkl#diffusers.AutoencoderKL)) --
	Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
	- text_encoder ([CLIPTextModel](https://huggingface.co/docs/transformers/main/en/model_doc/clip#transformers.CLIPTextModel)) --
	Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
	- tokenizer ([CLIPTokenizer](https://huggingface.co/docs/transformers/main/en/model_doc/clip#transformers.CLIPTokenizer)) --
	A `CLIPTokenizer` to tokenize text.
	- unet ([UNet2DConditionModel](/docs/diffusers/pr_12595/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel)) --
	A `UNet2DConditionModel` to denoise the encoded image latents.
	- controlnet ([ControlNetModel](/docs/diffusers/pr_12595/en/api/models/controlnet#diffusers.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](/docs/diffusers/pr_12595/en/api/schedulers/overview#diffusers.SchedulerMixin)) --
	A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
	[DDIMScheduler](/docs/diffusers/pr_12595/en/api/schedulers/ddim#diffusers.DDIMScheduler), [LMSDiscreteScheduler](/docs/diffusers/pr_12595/en/api/schedulers/lms_discrete#diffusers.LMSDiscreteScheduler), or [PNDMScheduler](/docs/diffusers/pr_12595/en/api/schedulers/pndm#diffusers.PNDMScheduler).
	- safety_checker (`StableDiffusionSafetyChecker`) --
	Classification module that estimates whether generated images could be considered offensive or harmful.
	Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for
	more details about a model's potential harms.
	- feature_extractor ([CLIPImageProcessor](https://huggingface.co/docs/transformers/main/en/model_doc/clip#transformers.CLIPImageProcessor)) --
	A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.</paramsdesc><paramgroups>0</paramgroups></docstring>

	Pipeline for image-to-image generation using Stable Diffusion with ControlNet guidance.

	This model inherits from [DiffusionPipeline](/docs/diffusers/pr_12595/en/api/pipelines/overview#diffusers.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()](/docs/diffusers/pr_12595/en/api/loaders/textual_inversion#diffusers.loaders.TextualInversionLoaderMixin.load_textual_inversion) for loading textual inversion embeddings
	- [load_lora_weights()](/docs/diffusers/pr_12595/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights) for loading LoRA weights
	- [save_lora_weights()](/docs/diffusers/pr_12595/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights) for saving LoRA weights
	- [from_single_file()](/docs/diffusers/pr_12595/en/api/loaders/single_file#diffusers.loaders.FromSingleFileMixin.from_single_file) for loading `.ckpt` files
	- [load_ip_adapter()](/docs/diffusers/pr_12595/en/api/loaders/ip_adapter#diffusers.loaders.IPAdapterMixin.load_ip_adapter) for loading IP Adapters





	<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


	<docstring><name>__call__</name><anchor>diffusers.StableDiffusionControlNetImg2ImgPipeline.__call__</anchor><source>https://github.com/huggingface/diffusers/blob/vr_12595/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py#L905</source><parameters>[{"name": "prompt", "val": ": typing.Union[str, typing.List[str]] = None"}, {"name": "image", "val": ": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = None"}, {"name": "control_image", "val": ": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = None"}, {"name": "height", "val": ": typing.Optional[int] = None"}, {"name": "width", "val": ": typing.Optional[int] = None"}, {"name": "strength", "val": ": float = 0.8"}, {"name": "num_inference_steps", "val": ": int = 50"}, {"name": "guidance_scale", "val": ": float = 7.5"}, {"name": "negative_prompt", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "num_images_per_prompt", "val": ": typing.Optional[int] = 1"}, {"name": "eta", "val": ": float = 0.0"}, {"name": "generator", "val": ": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"}, {"name": "latents", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "negative_prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "ip_adapter_image", "val": ": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor], NoneType] = None"}, {"name": "ip_adapter_image_embeds", "val": ": typing.Optional[typing.List[torch.Tensor]] = None"}, {"name": "output_type", "val": ": typing.Optional[str] = 'pil'"}, {"name": "return_dict", "val": ": bool = True"}, {"name": "cross_attention_kwargs", "val": ": typing.Optional[typing.Dict[str, typing.Any]] = None"}, {"name": "controlnet_conditioning_scale", "val": ": typing.Union[float, typing.List[float]] = 0.8"}, {"name": "guess_mode", "val": ": bool = False"}, {"name": "control_guidance_start", "val": ": typing.Union[float, typing.List[float]] = 0.0"}, {"name": "control_guidance_end", "val": ": typing.Union[float, typing.List[float]] = 1.0"}, {"name": "clip_skip", "val": ": typing.Optional[int] = None"}, {"name": "callback_on_step_end", "val": ": typing.Union[typing.Callable[[int, int, typing.Dict], NoneType], diffusers.callbacks.PipelineCallback, diffusers.callbacks.MultiPipelineCallbacks, NoneType] = None"}, {"name": "callback_on_step_end_tensor_inputs", "val": ": typing.List[str] = ['latents']"}, {"name": "kwargs", "val": ""}]</parameters><paramsdesc>- prompt** (`str` or `List[str]`, optional) --
	The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
	- 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 initial image to be used as the starting point for the image generation process. Can also accept
	image latents as `image`, and if passing latents directly they are 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.
	- width (`int`, optional, defaults to `self.unet.config.sample_size * self.vae_scale_factor`) --
	The width in pixels of the generated image.
	- strength (`float`, optional, defaults to 0.8) --
	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.
	- guidance_scale (`float`, optional, defaults to 7.5) --
	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`).
	- 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](https://huggingface.co/papers/2010.02502) paper. Only
	applies to the [DDIMScheduler](/docs/diffusers/pr_12595/en/api/schedulers/ddim#diffusers.DDIMScheduler), and is ignored in other schedulers.
	- generator (`torch.Generator` or `List[torch.Generator]`, optional) --
	A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
	generation deterministic.
	- latents (`torch.Tensor`, 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.Tensor`, 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.Tensor`, 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.
	- 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)`. It should
	contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. 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 generated image. Choose between `PIL.Image` or `np.array`.
	- return_dict (`bool`, optional, defaults to `True`) --
	Whether or not to return a [StableDiffusionPipelineOutput](/docs/diffusers/pr_12595/en/api/pipelines/stable_diffusion/depth2img#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput) instead of a
	plain tuple.
	- cross_attention_kwargs (`dict`, optional) --
	A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined in
	[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
	- 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.
	- clip_skip (`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.
	- callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, optional) --
	A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of
	each denoising step during the inference. with the following arguments: `callback_on_step_end(self:
	DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a
	list of all tensors as specified by `callback_on_step_end_tensor_inputs`.
	- callback_on_step_end_tensor_inputs (`List`, optional) --
	The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
	will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
	`._callback_tensor_inputs` attribute of your pipeline class.</paramsdesc><paramgroups>0</paramgroups><rettype>[StableDiffusionPipelineOutput](/docs/diffusers/pr_12595/en/api/pipelines/stable_diffusion/depth2img#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput) or `tuple`</rettype><retdesc>If `return_dict` is `True`, [StableDiffusionPipelineOutput](/docs/diffusers/pr_12595/en/api/pipelines/stable_diffusion/depth2img#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput) is returned,
	otherwise a `tuple` is returned where the first element is a list with the generated images and the
	second element is a list of `bool`s indicating whether the corresponding generated image contains
	"not-safe-for-work" (nsfw) content.</retdesc></docstring>

	The call function to the pipeline for generation.



	<ExampleCodeBlock anchor="diffusers.StableDiffusionControlNetImg2ImgPipeline.__call__.example">

	Examples:
	```py
	>>> # !pip install opencv-python transformers accelerate
	>>> from diffusers import StableDiffusionControlNetImg2ImgPipeline, ControlNetModel, UniPCMultistepScheduler
	>>> from diffusers.utils import load_image
	>>> import numpy as np
	>>> import torch

	>>> import cv2
	>>> from PIL import Image

	>>> # download an image
	>>> image = load_image(
	... "https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png"
	... )
	>>> np_image = np.array(image)

	>>> # get canny image
	>>> np_image = cv2.Canny(np_image, 100, 200)
	>>> np_image = np_image[:, :, None]
	>>> np_image = np.concatenate([np_image, np_image, np_image], axis=2)
	>>> canny_image = Image.fromarray(np_image)

	>>> # load control net and stable diffusion v1-5
	>>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
	>>> pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
	... "stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
	... )

	>>> # speed up diffusion process with faster scheduler and memory optimization
	>>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
	>>> pipe.enable_model_cpu_offload()

	>>> # generate image
	>>> generator = torch.manual_seed(0)
	>>> image = pipe(
	... "futuristic-looking woman",
	... num_inference_steps=20,
	... generator=generator,
	... image=image,
	... control_image=canny_image,
	... ).images[0]
	```

	</ExampleCodeBlock>







	</div>
	<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


	<docstring><name>enable_attention_slicing</name><anchor>diffusers.StableDiffusionControlNetImg2ImgPipeline.enable_attention_slicing</anchor><source>https://github.com/huggingface/diffusers/blob/vr_12595/src/diffusers/pipelines/pipeline_utils.py#L1978</source><parameters>[{"name": "slice_size", "val": ": typing.Union[int, str, NoneType] = 'auto'"}]</parameters><paramsdesc>- slice_size (`str` or `int`, optional, defaults to `"auto"`) --
	When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
	`"max"`, maximum amount of memory will be saved by running only one slice at a time. If a number is
	provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
	must be a multiple of `slice_size`.</paramsdesc><paramgroups>0</paramgroups></docstring>

	Enable sliced attention computation. When this option is enabled, the attention module splits the input tensor
	in slices to compute attention in several steps. For more than one attention head, the computation is performed
	sequentially over each head. This is useful to save some memory in exchange for a small speed decrease.

	> [!WARNING] > ⚠️ Don't enable attention slicing if you're already using `scaled_dot_product_attention` (SDPA)
	from PyTorch > 2.0 or xFormers. These attention computations are already very memory efficient so you won't
	need to enable > this function. If you enable attention slicing with SDPA or xFormers, it can lead to serious
	slow downs!



	<ExampleCodeBlock anchor="diffusers.StableDiffusionControlNetImg2ImgPipeline.enable_attention_slicing.example">

	Examples:

	```py
	>>> import torch
	>>> from diffusers import StableDiffusionPipeline

	>>> pipe = StableDiffusionPipeline.from_pretrained(
	... "stable-diffusion-v1-5/stable-diffusion-v1-5",
	... torch_dtype=torch.float16,
	... use_safetensors=True,
	... )

	>>> prompt = "a photo of an astronaut riding a horse on mars"
	>>> pipe.enable_attention_slicing()
	>>> image = pipe(prompt).images[0]
	```

	</ExampleCodeBlock>


	</div>
	<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


	<docstring><name>disable_attention_slicing</name><anchor>diffusers.StableDiffusionControlNetImg2ImgPipeline.disable_attention_slicing</anchor><source>https://github.com/huggingface/diffusers/blob/vr_12595/src/diffusers/pipelines/pipeline_utils.py#L2015</source><parameters>[]</parameters></docstring>

	Disable sliced attention computation. If `enable_attention_slicing` was previously called, attention is
	computed in one step.


	</div>
	<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


	<docstring><name>enable_vae_slicing</name><anchor>diffusers.StableDiffusionControlNetImg2ImgPipeline.enable_vae_slicing</anchor><source>https://github.com/huggingface/diffusers/blob/vr_12595/src/diffusers/pipelines/pipeline_utils.py#L2180</source><parameters>[]</parameters></docstring>

	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.


	</div>
	<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


	<docstring><name>disable_vae_slicing</name><anchor>diffusers.StableDiffusionControlNetImg2ImgPipeline.disable_vae_slicing</anchor><source>https://github.com/huggingface/diffusers/blob/vr_12595/src/diffusers/pipelines/pipeline_utils.py#L2193</source><parameters>[]</parameters></docstring>

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


	</div>
	<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


	<docstring><name>enable_xformers_memory_efficient_attention</name><anchor>diffusers.StableDiffusionControlNetImg2ImgPipeline.enable_xformers_memory_efficient_attention</anchor><source>https://github.com/huggingface/diffusers/blob/vr_12595/src/diffusers/pipelines/pipeline_utils.py#L1921</source><parameters>[{"name": "attention_op", "val": ": typing.Optional[typing.Callable] = None"}]</parameters><paramsdesc>- attention_op (`Callable`, optional) --
	Override the default `None` operator for use as `op` argument to the
	[`memory_efficient_attention()`](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.memory_efficient_attention)
	function of xFormers.</paramsdesc><paramgroups>0</paramgroups></docstring>

	Enable memory efficient attention from [xFormers](https://facebookresearch.github.io/xformers/). When this
	option is enabled, you should observe lower GPU memory usage and a potential speed up during inference. Speed
	up during training is not guaranteed.

	> [!WARNING] > ⚠️ When memory efficient attention and sliced attention are both enabled, memory efficient
	attention takes > precedent.



	<ExampleCodeBlock anchor="diffusers.StableDiffusionControlNetImg2ImgPipeline.enable_xformers_memory_efficient_attention.example">

	Examples:

	```py
	>>> import torch
	>>> from diffusers import DiffusionPipeline
	>>> from xformers.ops import MemoryEfficientAttentionFlashAttentionOp

	>>> pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16)
	>>> pipe = pipe.to("cuda")
	>>> pipe.enable_xformers_memory_efficient_attention(attention_op=MemoryEfficientAttentionFlashAttentionOp)
	>>> # Workaround for not accepting attention shape using VAE for Flash Attention
	>>> pipe.vae.enable_xformers_memory_efficient_attention(attention_op=None)
	```

	</ExampleCodeBlock>


	</div>
	<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


	<docstring><name>disable_xformers_memory_efficient_attention</name><anchor>diffusers.StableDiffusionControlNetImg2ImgPipeline.disable_xformers_memory_efficient_attention</anchor><source>https://github.com/huggingface/diffusers/blob/vr_12595/src/diffusers/pipelines/pipeline_utils.py#L1952</source><parameters>[]</parameters></docstring>

	Disable memory efficient attention from [xFormers](https://facebookresearch.github.io/xformers/).


	</div>
	<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


	<docstring><name>load_textual_inversion</name><anchor>diffusers.StableDiffusionControlNetImg2ImgPipeline.load_textual_inversion</anchor><source>https://github.com/huggingface/diffusers/blob/vr_12595/src/diffusers/loaders/textual_inversion.py#L263</source><parameters>[{"name": "pretrained_model_name_or_path", "val": ": typing.Union[str, typing.List[str], typing.Dict[str, torch.Tensor], typing.List[typing.Dict[str, torch.Tensor]]]"}, {"name": "token", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "tokenizer", "val": ": typing.Optional[ForwardRef('PreTrainedTokenizer')] = None"}, {"name": "text_encoder", "val": ": typing.Optional[ForwardRef('PreTrainedModel')] = None"}, {"name": "kwargs", "val": ""}]</parameters><paramsdesc>- pretrained_model_name_or_path** (`str` or `os.PathLike` or `List[str or os.PathLike]` or `Dict` or `List[Dict]`) --
	Can be either one of the following or a list of them:

	- A string, the model id (for example `sd-concepts-library/low-poly-hd-logos-icons`) of a
	pretrained model hosted on the Hub.
	- A path to a directory (for example `./my_text_inversion_directory/`) containing the textual
	inversion weights.
	- A path to a file (for example `./my_text_inversions.pt`) containing textual inversion weights.
	- A [torch state
	dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict).

	- token (`str` or `List[str]`, optional) --
	Override the token to use for the textual inversion weights. If `pretrained_model_name_or_path` is a
	list, then `token` must also be a list of equal length.
	- text_encoder ([CLIPTextModel](https://huggingface.co/docs/transformers/main/en/model_doc/clip#transformers.CLIPTextModel), optional) --
	Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
	If not specified, function will take self.tokenizer.
	- tokenizer ([CLIPTokenizer](https://huggingface.co/docs/transformers/main/en/model_doc/clip#transformers.CLIPTokenizer), optional) --
	A `CLIPTokenizer` to tokenize text. If not specified, function will take self.tokenizer.
	- weight_name (`str`, optional) --
	Name of a custom weight file. This should be used when:

	- The saved textual inversion file is in 🤗 Diffusers format, but was saved under a specific weight
	name such as `text_inv.bin`.
	- The saved textual inversion file is in the Automatic1111 format.
	- cache_dir (`Union[str, os.PathLike]`, optional) --
	Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
	is not used.
	- force_download (`bool`, optional, defaults to `False`) --
	Whether or not to force the (re-)download of the model weights and configuration files, overriding the
	cached versions if they exist.

	- proxies (`Dict[str, str]`, optional) --
	A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',
	'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
	- local_files_only (`bool`, optional, defaults to `False`) --
	Whether to only load local model weights and configuration files or not. If set to `True`, the model
	won't be downloaded from the Hub.
	- hf_token (`str` or bool, optional) --
	The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from
	`diffusers-cli login` (stored in `~/.huggingface`) is used.
	- revision (`str`, optional, defaults to `"main"`) --
	The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
	allowed by Git.
	- subfolder (`str`, optional, defaults to `""`) --
	The subfolder location of a model file within a larger model repository on the Hub or locally.
	- mirror (`str`, optional) --
	Mirror source to resolve accessibility issues if you're downloading a model in China. We do not
	guarantee the timeliness or safety of the source, and you should refer to the mirror site for more
	information.</paramsdesc><paramgroups>0</paramgroups></docstring>

	Load Textual Inversion embeddings into the text encoder of [StableDiffusionPipeline](/docs/diffusers/pr_12595/en/api/pipelines/stable_diffusion/text2img#diffusers.StableDiffusionPipeline) (both 🤗 Diffusers and
	Automatic1111 formats are supported).



	Example:

	<ExampleCodeBlock anchor="diffusers.StableDiffusionControlNetImg2ImgPipeline.load_textual_inversion.example">

	To load a Textual Inversion embedding vector in 🤗 Diffusers format:

	```py
	from diffusers import StableDiffusionPipeline
	import torch

	model_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
	pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")

	pipe.load_textual_inversion("sd-concepts-library/cat-toy")

	prompt = "A <cat-toy> backpack"

	image = pipe(prompt, num_inference_steps=50).images[0]
	image.save("cat-backpack.png")
	```

	</ExampleCodeBlock>

	To load a Textual Inversion embedding vector in Automatic1111 format, make sure to download the vector first
	(for example from [civitAI](https://civitai.com/models/3036?modelVersionId=9857)) and then load the vector
	<ExampleCodeBlock anchor="diffusers.StableDiffusionControlNetImg2ImgPipeline.load_textual_inversion.example-2">

	locally:

	```py
	from diffusers import StableDiffusionPipeline
	import torch

	model_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
	pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")

	pipe.load_textual_inversion("./charturnerv2.pt", token="charturnerv2")

	prompt = "charturnerv2, multiple views of the same character in the same outfit, a character turnaround of a woman wearing a black jacket and red shirt, best quality, intricate details."

	image = pipe(prompt, num_inference_steps=50).images[0]
	image.save("character.png")
	```

	</ExampleCodeBlock>



	</div>
	<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


	<docstring><name>encode_prompt</name><anchor>diffusers.StableDiffusionControlNetImg2ImgPipeline.encode_prompt</anchor><source>https://github.com/huggingface/diffusers/blob/vr_12595/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py#L276</source><parameters>[{"name": "prompt", "val": ""}, {"name": "device", "val": ""}, {"name": "num_images_per_prompt", "val": ""}, {"name": "do_classifier_free_guidance", "val": ""}, {"name": "negative_prompt", "val": " = None"}, {"name": "prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "negative_prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "lora_scale", "val": ": typing.Optional[float] = None"}, {"name": "clip_skip", "val": ": typing.Optional[int] = None"}]</parameters><paramsdesc>- prompt (`str` or `List[str]`, optional) --
	prompt to be encoded
	- 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`).
	- prompt_embeds (`torch.Tensor`, 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.Tensor`, 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.
	- lora_scale (`float`, optional) --
	A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
	- clip_skip (`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.</paramsdesc><paramgroups>0</paramgroups></docstring>

	Encodes the prompt into text encoder hidden states.




	</div></div>

	## StableDiffusionControlNetInpaintPipeline[[diffusers.StableDiffusionControlNetInpaintPipeline]]
	<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


	<docstring><name>class diffusers.StableDiffusionControlNetInpaintPipeline</name><anchor>diffusers.StableDiffusionControlNetInpaintPipeline</anchor><source>https://github.com/huggingface/diffusers/blob/vr_12595/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint.py#L128</source><parameters>[{"name": "vae", "val": ": AutoencoderKL"}, {"name": "text_encoder", "val": ": CLIPTextModel"}, {"name": "tokenizer", "val": ": CLIPTokenizer"}, {"name": "unet", "val": ": UNet2DConditionModel"}, {"name": "controlnet", "val": ": typing.Union[diffusers.models.controlnets.controlnet.ControlNetModel, typing.List[diffusers.models.controlnets.controlnet.ControlNetModel], typing.Tuple[diffusers.models.controlnets.controlnet.ControlNetModel], diffusers.models.controlnets.multicontrolnet.MultiControlNetModel]"}, {"name": "scheduler", "val": ": KarrasDiffusionSchedulers"}, {"name": "safety_checker", "val": ": StableDiffusionSafetyChecker"}, {"name": "feature_extractor", "val": ": CLIPImageProcessor"}, {"name": "image_encoder", "val": ": CLIPVisionModelWithProjection = None"}, {"name": "requires_safety_checker", "val": ": bool = True"}]</parameters><paramsdesc>- vae ([AutoencoderKL](/docs/diffusers/pr_12595/en/api/models/autoencoderkl#diffusers.AutoencoderKL)) --
	Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
	- text_encoder ([CLIPTextModel](https://huggingface.co/docs/transformers/main/en/model_doc/clip#transformers.CLIPTextModel)) --
	Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
	- tokenizer ([CLIPTokenizer](https://huggingface.co/docs/transformers/main/en/model_doc/clip#transformers.CLIPTokenizer)) --
	A `CLIPTokenizer` to tokenize text.
	- unet ([UNet2DConditionModel](/docs/diffusers/pr_12595/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel)) --
	A `UNet2DConditionModel` to denoise the encoded image latents.
	- controlnet ([ControlNetModel](/docs/diffusers/pr_12595/en/api/models/controlnet#diffusers.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](/docs/diffusers/pr_12595/en/api/schedulers/overview#diffusers.SchedulerMixin)) --
	A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
	[DDIMScheduler](/docs/diffusers/pr_12595/en/api/schedulers/ddim#diffusers.DDIMScheduler), [LMSDiscreteScheduler](/docs/diffusers/pr_12595/en/api/schedulers/lms_discrete#diffusers.LMSDiscreteScheduler), or [PNDMScheduler](/docs/diffusers/pr_12595/en/api/schedulers/pndm#diffusers.PNDMScheduler).
	- safety_checker (`StableDiffusionSafetyChecker`) --
	Classification module that estimates whether generated images could be considered offensive or harmful.
	Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for
	more details about a model's potential harms.
	- feature_extractor ([CLIPImageProcessor](https://huggingface.co/docs/transformers/main/en/model_doc/clip#transformers.CLIPImageProcessor)) --
	A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.</paramsdesc><paramgroups>0</paramgroups></docstring>

	Pipeline for image inpainting using Stable Diffusion with ControlNet guidance.

	This model inherits from [DiffusionPipeline](/docs/diffusers/pr_12595/en/api/pipelines/overview#diffusers.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()](/docs/diffusers/pr_12595/en/api/loaders/textual_inversion#diffusers.loaders.TextualInversionLoaderMixin.load_textual_inversion) for loading textual inversion embeddings
	- [load_lora_weights()](/docs/diffusers/pr_12595/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights) for loading LoRA weights
	- [save_lora_weights()](/docs/diffusers/pr_12595/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights) for saving LoRA weights
	- [from_single_file()](/docs/diffusers/pr_12595/en/api/loaders/single_file#diffusers.loaders.FromSingleFileMixin.from_single_file) for loading `.ckpt` files
	- [load_ip_adapter()](/docs/diffusers/pr_12595/en/api/loaders/ip_adapter#diffusers.loaders.IPAdapterMixin.load_ip_adapter) for loading IP Adapters

	> [!TIP] > This pipeline can be used with checkpoints that have been specifically fine-tuned for inpainting >
	([stable-diffusion-v1-5/stable-diffusion-inpainting](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting))
	> as well as default text-to-image Stable Diffusion checkpoints >
	([stable-diffusion-v1-5/stable-diffusion-v1-5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5)).
	> Default text-to-image Stable Diffusion checkpoints might be preferable for ControlNets that have been fine-tuned
	on > those, such as
	[lllyasviel/control_v11p_sd15_inpaint](https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint).





	<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


	<docstring><name>__call__</name><anchor>diffusers.StableDiffusionControlNetInpaintPipeline.__call__</anchor><source>https://github.com/huggingface/diffusers/blob/vr_12595/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint.py#L994</source><parameters>[{"name": "prompt", "val": ": typing.Union[str, typing.List[str]] = None"}, {"name": "image", "val": ": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = None"}, {"name": "mask_image", "val": ": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = None"}, {"name": "control_image", "val": ": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = None"}, {"name": "height", "val": ": typing.Optional[int] = None"}, {"name": "width", "val": ": typing.Optional[int] = None"}, {"name": "padding_mask_crop", "val": ": typing.Optional[int] = None"}, {"name": "strength", "val": ": float = 1.0"}, {"name": "num_inference_steps", "val": ": int = 50"}, {"name": "guidance_scale", "val": ": float = 7.5"}, {"name": "negative_prompt", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "num_images_per_prompt", "val": ": typing.Optional[int] = 1"}, {"name": "eta", "val": ": float = 0.0"}, {"name": "generator", "val": ": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"}, {"name": "latents", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "negative_prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "ip_adapter_image", "val": ": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor], NoneType] = None"}, {"name": "ip_adapter_image_embeds", "val": ": typing.Optional[typing.List[torch.Tensor]] = None"}, {"name": "output_type", "val": ": typing.Optional[str] = 'pil'"}, {"name": "return_dict", "val": ": bool = True"}, {"name": "cross_attention_kwargs", "val": ": typing.Optional[typing.Dict[str, typing.Any]] = None"}, {"name": "controlnet_conditioning_scale", "val": ": typing.Union[float, typing.List[float]] = 0.5"}, {"name": "guess_mode", "val": ": bool = False"}, {"name": "control_guidance_start", "val": ": typing.Union[float, typing.List[float]] = 0.0"}, {"name": "control_guidance_end", "val": ": typing.Union[float, typing.List[float]] = 1.0"}, {"name": "clip_skip", "val": ": typing.Optional[int] = None"}, {"name": "callback_on_step_end", "val": ": typing.Union[typing.Callable[[int, int, typing.Dict], NoneType], diffusers.callbacks.PipelineCallback, diffusers.callbacks.MultiPipelineCallbacks, NoneType] = None"}, {"name": "callback_on_step_end_tensor_inputs", "val": ": typing.List[str] = ['latents']"}, {"name": "kwargs", "val": ""}]</parameters><paramsdesc>- prompt** (`str` or `List[str]`, optional) --
	The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
	- 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, it would be for `(B, H, W, 1)`, `(B, H, W)`, `(H,
	W, 1)`, or `(H, W)`.
	- control_image (`torch.Tensor`, `PIL.Image.Image`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, --
	`List[List[torch.Tensor]]`, 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.
	- width (`int`, optional, defaults to `self.unet.config.sample_size * self.vae_scale_factor`) --
	The width in pixels of the generated image.
	- 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.
	- guidance_scale (`float`, optional, defaults to 7.5) --
	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`).
	- 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](https://huggingface.co/papers/2010.02502) paper. Only
	applies to the [DDIMScheduler](/docs/diffusers/pr_12595/en/api/schedulers/ddim#diffusers.DDIMScheduler), and is ignored in other schedulers.
	- generator (`torch.Generator` or `List[torch.Generator]`, optional) --
	A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
	generation deterministic.
	- latents (`torch.Tensor`, 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.Tensor`, 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.Tensor`, 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.
	- 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)`. It should
	contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. 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 generated image. Choose between `PIL.Image` or `np.array`.
	- return_dict (`bool`, optional, defaults to `True`) --
	Whether or not to return a [StableDiffusionPipelineOutput](/docs/diffusers/pr_12595/en/api/pipelines/stable_diffusion/depth2img#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput) instead of a
	plain tuple.
	- cross_attention_kwargs (`dict`, optional) --
	A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined in
	[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
	- controlnet_conditioning_scale (`float` or `List[float]`, optional, defaults to 0.5) --
	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.
	- clip_skip (`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.
	- callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, optional) --
	A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of
	each denoising step during the inference. with the following arguments: `callback_on_step_end(self:
	DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a
	list of all tensors as specified by `callback_on_step_end_tensor_inputs`.
	- callback_on_step_end_tensor_inputs (`List`, optional) --
	The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
	will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
	`._callback_tensor_inputs` attribute of your pipeline class.</paramsdesc><paramgroups>0</paramgroups><rettype>[StableDiffusionPipelineOutput](/docs/diffusers/pr_12595/en/api/pipelines/stable_diffusion/depth2img#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput) or `tuple`</rettype><retdesc>If `return_dict` is `True`, [StableDiffusionPipelineOutput](/docs/diffusers/pr_12595/en/api/pipelines/stable_diffusion/depth2img#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput) is returned,
	otherwise a `tuple` is returned where the first element is a list with the generated images and the
	second element is a list of `bool`s indicating whether the corresponding generated image contains
	"not-safe-for-work" (nsfw) content.</retdesc></docstring>

	The call function to the pipeline for generation.



	<ExampleCodeBlock anchor="diffusers.StableDiffusionControlNetInpaintPipeline.__call__.example">

	Examples:
	```py
	>>> # !pip install transformers accelerate
	>>> from diffusers import StableDiffusionControlNetInpaintPipeline, ControlNetModel, DDIMScheduler
	>>> from diffusers.utils import load_image
	>>> import numpy as np
	>>> import torch

	>>> init_image = load_image(
	... "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png"
	... )
	>>> init_image = init_image.resize((512, 512))

	>>> generator = torch.Generator(device="cpu").manual_seed(1)

	>>> mask_image = load_image(
	... "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png"
	... )
	>>> mask_image = mask_image.resize((512, 512))


	>>> def make_canny_condition(image):
	... image = np.array(image)
	... image = cv2.Canny(image, 100, 200)
	... image = image[:, :, None]
	... image = np.concatenate([image, image, image], axis=2)
	... image = Image.fromarray(image)
	... return image


	>>> control_image = make_canny_condition(init_image)

	>>> controlnet = ControlNetModel.from_pretrained(
	... "lllyasviel/control_v11p_sd15_inpaint", torch_dtype=torch.float16
	... )
	>>> pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
	... "stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
	... )

	>>> pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
	>>> pipe.enable_model_cpu_offload()

	>>> # generate image
	>>> image = pipe(
	... "a handsome man with ray-ban sunglasses",
	... num_inference_steps=20,
	... generator=generator,
	... eta=1.0,
	... image=init_image,
	... mask_image=mask_image,
	... control_image=control_image,
	... ).images[0]
	```

	</ExampleCodeBlock>







	</div>
	<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


	<docstring><name>enable_attention_slicing</name><anchor>diffusers.StableDiffusionControlNetInpaintPipeline.enable_attention_slicing</anchor><source>https://github.com/huggingface/diffusers/blob/vr_12595/src/diffusers/pipelines/pipeline_utils.py#L1978</source><parameters>[{"name": "slice_size", "val": ": typing.Union[int, str, NoneType] = 'auto'"}]</parameters><paramsdesc>- slice_size (`str` or `int`, optional, defaults to `"auto"`) --
	When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
	`"max"`, maximum amount of memory will be saved by running only one slice at a time. If a number is
	provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
	must be a multiple of `slice_size`.</paramsdesc><paramgroups>0</paramgroups></docstring>

	Enable sliced attention computation. When this option is enabled, the attention module splits the input tensor
	in slices to compute attention in several steps. For more than one attention head, the computation is performed
	sequentially over each head. This is useful to save some memory in exchange for a small speed decrease.

	> [!WARNING] > ⚠️ Don't enable attention slicing if you're already using `scaled_dot_product_attention` (SDPA)
	from PyTorch > 2.0 or xFormers. These attention computations are already very memory efficient so you won't
	need to enable > this function. If you enable attention slicing with SDPA or xFormers, it can lead to serious
	slow downs!



	<ExampleCodeBlock anchor="diffusers.StableDiffusionControlNetInpaintPipeline.enable_attention_slicing.example">

	Examples:

	```py
	>>> import torch
	>>> from diffusers import StableDiffusionPipeline

	>>> pipe = StableDiffusionPipeline.from_pretrained(
	... "stable-diffusion-v1-5/stable-diffusion-v1-5",
	... torch_dtype=torch.float16,
	... use_safetensors=True,
	... )

	>>> prompt = "a photo of an astronaut riding a horse on mars"
	>>> pipe.enable_attention_slicing()
	>>> image = pipe(prompt).images[0]
	```

	</ExampleCodeBlock>


	</div>
	<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


	<docstring><name>disable_attention_slicing</name><anchor>diffusers.StableDiffusionControlNetInpaintPipeline.disable_attention_slicing</anchor><source>https://github.com/huggingface/diffusers/blob/vr_12595/src/diffusers/pipelines/pipeline_utils.py#L2015</source><parameters>[]</parameters></docstring>

	Disable sliced attention computation. If `enable_attention_slicing` was previously called, attention is
	computed in one step.


	</div>
	<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


	<docstring><name>enable_vae_slicing</name><anchor>diffusers.StableDiffusionControlNetInpaintPipeline.enable_vae_slicing</anchor><source>https://github.com/huggingface/diffusers/blob/vr_12595/src/diffusers/pipelines/pipeline_utils.py#L2180</source><parameters>[]</parameters></docstring>

	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.


	</div>
	<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


	<docstring><name>disable_vae_slicing</name><anchor>diffusers.StableDiffusionControlNetInpaintPipeline.disable_vae_slicing</anchor><source>https://github.com/huggingface/diffusers/blob/vr_12595/src/diffusers/pipelines/pipeline_utils.py#L2193</source><parameters>[]</parameters></docstring>

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


	</div>
	<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


	<docstring><name>enable_xformers_memory_efficient_attention</name><anchor>diffusers.StableDiffusionControlNetInpaintPipeline.enable_xformers_memory_efficient_attention</anchor><source>https://github.com/huggingface/diffusers/blob/vr_12595/src/diffusers/pipelines/pipeline_utils.py#L1921</source><parameters>[{"name": "attention_op", "val": ": typing.Optional[typing.Callable] = None"}]</parameters><paramsdesc>- attention_op (`Callable`, optional) --
	Override the default `None` operator for use as `op` argument to the
	[`memory_efficient_attention()`](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.memory_efficient_attention)
	function of xFormers.</paramsdesc><paramgroups>0</paramgroups></docstring>

	Enable memory efficient attention from [xFormers](https://facebookresearch.github.io/xformers/). When this
	option is enabled, you should observe lower GPU memory usage and a potential speed up during inference. Speed
	up during training is not guaranteed.

	> [!WARNING] > ⚠️ When memory efficient attention and sliced attention are both enabled, memory efficient
	attention takes > precedent.



	<ExampleCodeBlock anchor="diffusers.StableDiffusionControlNetInpaintPipeline.enable_xformers_memory_efficient_attention.example">

	Examples:

	```py
	>>> import torch
	>>> from diffusers import DiffusionPipeline
	>>> from xformers.ops import MemoryEfficientAttentionFlashAttentionOp

	>>> pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16)
	>>> pipe = pipe.to("cuda")
	>>> pipe.enable_xformers_memory_efficient_attention(attention_op=MemoryEfficientAttentionFlashAttentionOp)
	>>> # Workaround for not accepting attention shape using VAE for Flash Attention
	>>> pipe.vae.enable_xformers_memory_efficient_attention(attention_op=None)
	```

	</ExampleCodeBlock>


	</div>
	<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


	<docstring><name>disable_xformers_memory_efficient_attention</name><anchor>diffusers.StableDiffusionControlNetInpaintPipeline.disable_xformers_memory_efficient_attention</anchor><source>https://github.com/huggingface/diffusers/blob/vr_12595/src/diffusers/pipelines/pipeline_utils.py#L1952</source><parameters>[]</parameters></docstring>

	Disable memory efficient attention from [xFormers](https://facebookresearch.github.io/xformers/).


	</div>
	<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


	<docstring><name>load_textual_inversion</name><anchor>diffusers.StableDiffusionControlNetInpaintPipeline.load_textual_inversion</anchor><source>https://github.com/huggingface/diffusers/blob/vr_12595/src/diffusers/loaders/textual_inversion.py#L263</source><parameters>[{"name": "pretrained_model_name_or_path", "val": ": typing.Union[str, typing.List[str], typing.Dict[str, torch.Tensor], typing.List[typing.Dict[str, torch.Tensor]]]"}, {"name": "token", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "tokenizer", "val": ": typing.Optional[ForwardRef('PreTrainedTokenizer')] = None"}, {"name": "text_encoder", "val": ": typing.Optional[ForwardRef('PreTrainedModel')] = None"}, {"name": "kwargs", "val": ""}]</parameters><paramsdesc>- pretrained_model_name_or_path** (`str` or `os.PathLike` or `List[str or os.PathLike]` or `Dict` or `List[Dict]`) --
	Can be either one of the following or a list of them:

	- A string, the model id (for example `sd-concepts-library/low-poly-hd-logos-icons`) of a
	pretrained model hosted on the Hub.
	- A path to a directory (for example `./my_text_inversion_directory/`) containing the textual
	inversion weights.
	- A path to a file (for example `./my_text_inversions.pt`) containing textual inversion weights.
	- A [torch state
	dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict).

	- token (`str` or `List[str]`, optional) --
	Override the token to use for the textual inversion weights. If `pretrained_model_name_or_path` is a
	list, then `token` must also be a list of equal length.
	- text_encoder ([CLIPTextModel](https://huggingface.co/docs/transformers/main/en/model_doc/clip#transformers.CLIPTextModel), optional) --
	Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
	If not specified, function will take self.tokenizer.
	- tokenizer ([CLIPTokenizer](https://huggingface.co/docs/transformers/main/en/model_doc/clip#transformers.CLIPTokenizer), optional) --
	A `CLIPTokenizer` to tokenize text. If not specified, function will take self.tokenizer.
	- weight_name (`str`, optional) --
	Name of a custom weight file. This should be used when:

	- The saved textual inversion file is in 🤗 Diffusers format, but was saved under a specific weight
	name such as `text_inv.bin`.
	- The saved textual inversion file is in the Automatic1111 format.
	- cache_dir (`Union[str, os.PathLike]`, optional) --
	Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
	is not used.
	- force_download (`bool`, optional, defaults to `False`) --
	Whether or not to force the (re-)download of the model weights and configuration files, overriding the
	cached versions if they exist.

	- proxies (`Dict[str, str]`, optional) --
	A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',
	'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
	- local_files_only (`bool`, optional, defaults to `False`) --
	Whether to only load local model weights and configuration files or not. If set to `True`, the model
	won't be downloaded from the Hub.
	- hf_token (`str` or bool, optional) --
	The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from
	`diffusers-cli login` (stored in `~/.huggingface`) is used.
	- revision (`str`, optional, defaults to `"main"`) --
	The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
	allowed by Git.
	- subfolder (`str`, optional, defaults to `""`) --
	The subfolder location of a model file within a larger model repository on the Hub or locally.
	- mirror (`str`, optional) --
	Mirror source to resolve accessibility issues if you're downloading a model in China. We do not
	guarantee the timeliness or safety of the source, and you should refer to the mirror site for more
	information.</paramsdesc><paramgroups>0</paramgroups></docstring>

	Load Textual Inversion embeddings into the text encoder of [StableDiffusionPipeline](/docs/diffusers/pr_12595/en/api/pipelines/stable_diffusion/text2img#diffusers.StableDiffusionPipeline) (both 🤗 Diffusers and
	Automatic1111 formats are supported).



	Example:

	<ExampleCodeBlock anchor="diffusers.StableDiffusionControlNetInpaintPipeline.load_textual_inversion.example">

	To load a Textual Inversion embedding vector in 🤗 Diffusers format:

	```py
	from diffusers import StableDiffusionPipeline
	import torch

	model_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
	pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")

	pipe.load_textual_inversion("sd-concepts-library/cat-toy")

	prompt = "A <cat-toy> backpack"

	image = pipe(prompt, num_inference_steps=50).images[0]
	image.save("cat-backpack.png")
	```

	</ExampleCodeBlock>

	To load a Textual Inversion embedding vector in Automatic1111 format, make sure to download the vector first
	(for example from [civitAI](https://civitai.com/models/3036?modelVersionId=9857)) and then load the vector
	<ExampleCodeBlock anchor="diffusers.StableDiffusionControlNetInpaintPipeline.load_textual_inversion.example-2">

	locally:

	```py
	from diffusers import StableDiffusionPipeline
	import torch

	model_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
	pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")

	pipe.load_textual_inversion("./charturnerv2.pt", token="charturnerv2")

	prompt = "charturnerv2, multiple views of the same character in the same outfit, a character turnaround of a woman wearing a black jacket and red shirt, best quality, intricate details."

	image = pipe(prompt, num_inference_steps=50).images[0]
	image.save("character.png")
	```

	</ExampleCodeBlock>



	</div>
	<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


	<docstring><name>encode_prompt</name><anchor>diffusers.StableDiffusionControlNetInpaintPipeline.encode_prompt</anchor><source>https://github.com/huggingface/diffusers/blob/vr_12595/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint.py#L282</source><parameters>[{"name": "prompt", "val": ""}, {"name": "device", "val": ""}, {"name": "num_images_per_prompt", "val": ""}, {"name": "do_classifier_free_guidance", "val": ""}, {"name": "negative_prompt", "val": " = None"}, {"name": "prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "negative_prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "lora_scale", "val": ": typing.Optional[float] = None"}, {"name": "clip_skip", "val": ": typing.Optional[int] = None"}]</parameters><paramsdesc>- prompt (`str` or `List[str]`, optional) --
	prompt to be encoded
	- 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`).
	- prompt_embeds (`torch.Tensor`, 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.Tensor`, 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.
	- lora_scale (`float`, optional) --
	A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
	- clip_skip (`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.</paramsdesc><paramgroups>0</paramgroups></docstring>

	Encodes the prompt into text encoder hidden states.




	</div></div>

	## StableDiffusionPipelineOutput[[diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput]]
	<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


	<docstring><name>class diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput</name><anchor>diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput</anchor><source>https://github.com/huggingface/diffusers/blob/vr_12595/src/diffusers/pipelines/stable_diffusion/pipeline_output.py#L11</source><parameters>[{"name": "images", "val": ": typing.Union[typing.List[PIL.Image.Image], numpy.ndarray]"}, {"name": "nsfw_content_detected", "val": ": typing.Optional[typing.List[bool]]"}]</parameters><paramsdesc>- 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.</paramsdesc><paramgroups>0</paramgroups></docstring>

	Output class for Stable Diffusion pipelines.




	</div>

	<EditOnGithub source="https://github.com/huggingface/diffusers/blob/main/docs/source/en/api/pipelines/controlnet.md" />

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