ControlNet with Stable Diffusion 3 (original) (raw)

StableDiffusion3ControlNetPipeline is an implementation of ControlNet for Stable Diffusion 3.

ControlNet was introduced in Adding Conditional Control to Text-to-Image Diffusion Models 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 controlnet code is mainly implemented by The InstantX Team. The inpainting-related code was developed by The Alimama Creative Team. You can find pre-trained checkpoints for SD3-ControlNet in the table below:

Make sure to check out the Schedulers guide to learn how to explore the tradeoff between scheduler speed and quality, and see the reuse components across pipelines section to learn how to efficiently load the same components into multiple pipelines.

StableDiffusion3ControlNetPipeline

class diffusers.StableDiffusion3ControlNetPipeline

< source >

( transformer: SD3Transformer2DModel scheduler: FlowMatchEulerDiscreteScheduler vae: AutoencoderKL text_encoder: CLIPTextModelWithProjection tokenizer: CLIPTokenizer text_encoder_2: CLIPTextModelWithProjection tokenizer_2: CLIPTokenizer text_encoder_3: T5EncoderModel tokenizer_3: T5TokenizerFast controlnet: typing.Union[diffusers.models.controlnets.controlnet_sd3.SD3ControlNetModel, typing.List[diffusers.models.controlnets.controlnet_sd3.SD3ControlNetModel], typing.Tuple[diffusers.models.controlnets.controlnet_sd3.SD3ControlNetModel], diffusers.models.controlnets.controlnet_sd3.SD3MultiControlNetModel] image_encoder: typing.Optional[transformers.models.siglip.modeling_siglip.SiglipVisionModel] = None feature_extractor: typing.Optional[transformers.models.siglip.image_processing_siglip.SiglipImageProcessor] = None )

Parameters

• transformer (SD3Transformer2DModel) — Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
• scheduler (FlowMatchEulerDiscreteScheduler) — A scheduler to be used in combination with transformer to denoise the encoded image latents.
• vae (AutoencoderKL) — Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
• text_encoder (CLIPTextModelWithProjection) —CLIP, specifically the clip-vit-large-patch14 variant, with an additional added projection layer that is initialized with a diagonal matrix with the hidden_sizeas its dimension.
• text_encoder_2 (CLIPTextModelWithProjection) —CLIP, specifically thelaion/CLIP-ViT-bigG-14-laion2B-39B-b160kvariant.
• text_encoder_3 (T5EncoderModel) — Frozen text-encoder. Stable Diffusion 3 usesT5, specifically thet5-v1_1-xxl variant.
• tokenizer (CLIPTokenizer) — Tokenizer of classCLIPTokenizer.
• tokenizer_2 (CLIPTokenizer) — Second Tokenizer of classCLIPTokenizer.
• tokenizer_3 (T5TokenizerFast) — Tokenizer of classT5Tokenizer.
• controlnet (SD3ControlNetModel or List[SD3ControlNetModel] or SD3MultiControlNetModel) — 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.
• image_encoder (SiglipVisionModel, optional) — Pre-trained Vision Model for IP Adapter.
• feature_extractor (SiglipImageProcessor, optional) — Image processor for IP Adapter.

__call__

< source >

( prompt: typing.Union[str, typing.List[str]] = None prompt_2: typing.Union[str, typing.List[str], NoneType] = None prompt_3: typing.Union[str, typing.List[str], NoneType] = None height: typing.Optional[int] = None width: typing.Optional[int] = None num_inference_steps: int = 28 sigmas: typing.Optional[typing.List[float]] = None guidance_scale: float = 7.0 control_guidance_start: typing.Union[float, typing.List[float]] = 0.0 control_guidance_end: typing.Union[float, typing.List[float]] = 1.0 control_image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = None controlnet_conditioning_scale: typing.Union[float, typing.List[float]] = 1.0 controlnet_pooled_projections: typing.Optional[torch.FloatTensor] = None negative_prompt: typing.Union[str, typing.List[str], NoneType] = None negative_prompt_2: typing.Union[str, typing.List[str], NoneType] = None negative_prompt_3: typing.Union[str, typing.List[str], NoneType] = None num_images_per_prompt: typing.Optional[int] = 1 generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None latents: typing.Optional[torch.FloatTensor] = None prompt_embeds: typing.Optional[torch.FloatTensor] = None negative_prompt_embeds: typing.Optional[torch.FloatTensor] = None pooled_prompt_embeds: typing.Optional[torch.FloatTensor] = None negative_pooled_prompt_embeds: typing.Optional[torch.FloatTensor] = None ip_adapter_image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor], NoneType] = None ip_adapter_image_embeds: typing.Optional[torch.Tensor] = None output_type: typing.Optional[str] = 'pil' return_dict: bool = True joint_attention_kwargs: typing.Optional[typing.Dict[str, typing.Any]] = None clip_skip: typing.Optional[int] = None callback_on_step_end: typing.Optional[typing.Callable[[int, int, typing.Dict], NoneType]] = None callback_on_step_end_tensor_inputs: typing.List[str] = ['latents'] max_sequence_length: int = 256 ) → ~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput or tuple

Parameters

• prompt (str or List[str], optional) — The prompt or prompts to guide the image generation. If not defined, one has to pass prompt_embeds. instead.
• prompt_2 (str or List[str], optional) — The prompt or prompts to be sent to tokenizer_2 and text_encoder_2. If not defined, prompt is will be used instead
• prompt_3 (str or List[str], optional) — The prompt or prompts to be sent to tokenizer_3 and text_encoder_3. If not defined, prompt is will be used instead
• height (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) — The height in pixels of the generated image. This is set to 1024 by default for the best results.
• width (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) — The width in pixels of the generated image. This is set to 1024 by default for the best results.
• num_inference_steps (int, optional, defaults to 50) — The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference.
• sigmas (List[float], optional) — Custom sigmas to use for the denoising process with schedulers which support a sigmas argument in their set_timesteps method. If not defined, the default behavior when num_inference_steps is passed will be used.
• guidance_scale (float, optional, defaults to 5.0) — Guidance scale as defined in Classifier-Free Diffusion Guidance.guidance_scale is defined as w of equation 2. of Imagen Paper. Guidance scale is enabled by setting guidance_scale > 1. Higher guidance scale encourages to generate images that are closely linked to the text prompt, usually at the expense of lower image quality.
• control_guidance_start (float or List[float], optional, defaults to 0.0) — The percentage of total steps at which the ControlNet starts applying.
• control_guidance_end (float or List[float], optional, defaults to 1.0) — The percentage of total steps at which the ControlNet stops applying.
• control_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.
• 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.
• controlnet_pooled_projections (torch.FloatTensor of shape (batch_size, projection_dim)) — Embeddings projected from the embeddings of controlnet input conditions.
• negative_prompt (str or List[str], optional) — The prompt or prompts not to guide the image generation. If not defined, one has to passnegative_prompt_embeds instead. Ignored when not using guidance (i.e., ignored if guidance_scale is less than 1).
• negative_prompt_2 (str or List[str], optional) — The prompt or prompts not to guide the image generation to be sent to tokenizer_2 andtext_encoder_2. If not defined, negative_prompt is used instead
• negative_prompt_3 (str or List[str], optional) — The prompt or prompts not to guide the image generation to be sent to tokenizer_3 andtext_encoder_3. If not defined, negative_prompt is used instead
• num_images_per_prompt (int, optional, defaults to 1) — The number of images to generate per prompt.
• generator (torch.Generator or List[torch.Generator], optional) — One or a list of torch generator(s)to make generation deterministic.
• latents (torch.FloatTensor, optional) — Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random generator.
• prompt_embeds (torch.FloatTensor, optional) — Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, text embeddings will be generated from prompt input argument.
• negative_prompt_embeds (torch.FloatTensor, optional) — Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, negative_prompt_embeds will be generated from negative_prompt input argument.
• pooled_prompt_embeds (torch.FloatTensor, optional) — Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, pooled text embeddings will be generated from prompt input argument.
• negative_pooled_prompt_embeds (torch.FloatTensor, optional) — Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, pooled negative_prompt_embeds will be generated from negative_promptinput argument.
• ip_adapter_image (PipelineImageInput, optional) — Optional image input to work with IP Adapters.
• ip_adapter_image_embeds (torch.Tensor, optional) — Pre-generated image embeddings for IP-Adapter. 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 toTrue. If not provided, embeddings are computed from the ip_adapter_image input argument.
• output_type (str, optional, defaults to "pil") — The output format of the generate image. Choose betweenPIL: PIL.Image.Image or np.array.
• return_dict (bool, optional, defaults to True) — Whether or not to return a ~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput instead of a plain tuple.
• joint_attention_kwargs (dict, optional) — A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined underself.processor indiffusers.models.attention_processor.
• callback_on_step_end (Callable, optional) — A function that calls at the end of each denoising steps during the inference. The function is called with the following arguments: callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict). callback_kwargs will include a list of all tensors as specified bycallback_on_step_end_tensor_inputs.
• callback_on_step_end_tensor_inputs (List, optional) — The list of tensor inputs for the callback_on_step_end function. The tensors specified in the list will be passed as callback_kwargs argument. You will only be able to include variables listed in the._callback_tensor_inputs attribute of your pipeline class.
• max_sequence_length (int defaults to 256) — Maximum sequence length to use with the prompt.

Returns

~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput or tuple

~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput if return_dict is True, otherwise atuple. When returning a tuple, the first element is a list with the generated images.

Function invoked when calling the pipeline for generation.

Examples:

import torch from diffusers import StableDiffusion3ControlNetPipeline from diffusers.models import SD3ControlNetModel, SD3MultiControlNetModel from diffusers.utils import load_image

controlnet = SD3ControlNetModel.from_pretrained("InstantX/SD3-Controlnet-Canny", torch_dtype=torch.float16)

pipe = StableDiffusion3ControlNetPipeline.from_pretrained( ... "stabilityai/stable-diffusion-3-medium-diffusers", controlnet=controlnet, torch_dtype=torch.float16 ... ) pipe.to("cuda") control_image = load_image( ... "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png" ... ) prompt = "A bird in space" image = pipe( ... prompt, control_image=control_image, height=1024, width=768, controlnet_conditioning_scale=0.7 ... ).images[0] image.save("sd3.png")

encode_image

< source >

( image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] device: device ) → torch.Tensor

Parameters

• image (PipelineImageInput) — Input image to be encoded.
• device — (torch.device): Torch device.

The encoded image feature representation.

Encodes the given image into a feature representation using a pre-trained image encoder.

encode_prompt

< source >

( prompt: typing.Union[str, typing.List[str]] prompt_2: typing.Union[str, typing.List[str]] prompt_3: typing.Union[str, typing.List[str]] device: typing.Optional[torch.device] = None num_images_per_prompt: int = 1 do_classifier_free_guidance: bool = True negative_prompt: typing.Union[str, typing.List[str], NoneType] = None negative_prompt_2: typing.Union[str, typing.List[str], NoneType] = None negative_prompt_3: typing.Union[str, typing.List[str], NoneType] = None prompt_embeds: typing.Optional[torch.FloatTensor] = None negative_prompt_embeds: typing.Optional[torch.FloatTensor] = None pooled_prompt_embeds: typing.Optional[torch.FloatTensor] = None negative_pooled_prompt_embeds: typing.Optional[torch.FloatTensor] = None clip_skip: typing.Optional[int] = None max_sequence_length: int = 256 lora_scale: typing.Optional[float] = None )

Parameters

• prompt (str or List[str], optional) — prompt to be encoded
• prompt_2 (str or List[str], optional) — The prompt or prompts to be sent to the tokenizer_2 and text_encoder_2. If not defined, prompt is used in all text-encoders
• prompt_3 (str or List[str], optional) — The prompt or prompts to be sent to the tokenizer_3 and text_encoder_3. If not defined, prompt is used in all text-encoders
• device — (torch.device): torch device
• num_images_per_prompt (int) — number of images that should be generated per prompt
• 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 passnegative_prompt_embeds instead. Ignored when not using guidance (i.e., ignored if guidance_scale is less than 1).
• negative_prompt_2 (str or List[str], optional) — The prompt or prompts not to guide the image generation to be sent to tokenizer_2 andtext_encoder_2. If not defined, negative_prompt is used in all the text-encoders.
• negative_prompt_3 (str or List[str], optional) — The prompt or prompts not to guide the image generation to be sent to tokenizer_3 andtext_encoder_3. If not defined, negative_prompt is used in all the text-encoders.
• prompt_embeds (torch.FloatTensor, optional) — Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, text embeddings will be generated from prompt input argument.
• negative_prompt_embeds (torch.FloatTensor, optional) — Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, negative_prompt_embeds will be generated from negative_prompt input argument.
• pooled_prompt_embeds (torch.FloatTensor, optional) — Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, pooled text embeddings will be generated from prompt input argument.
• negative_pooled_prompt_embeds (torch.FloatTensor, optional) — Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, pooled negative_prompt_embeds will be generated from negative_promptinput argument.
• 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.
• lora_scale (float, optional) — A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.

prepare_ip_adapter_image_embeds

< source >

( ip_adapter_image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor], NoneType] = None ip_adapter_image_embeds: typing.Optional[torch.Tensor] = None device: typing.Optional[torch.device] = None num_images_per_prompt: int = 1 do_classifier_free_guidance: bool = True )

Parameters

• ip_adapter_image (PipelineImageInput, optional) — The input image to extract features from for IP-Adapter.
• ip_adapter_image_embeds (torch.Tensor, optional) — Precomputed image embeddings.
• device — (torch.device, optional): Torch device.
• num_images_per_prompt (int, defaults to 1) — Number of images that should be generated per prompt.
• do_classifier_free_guidance (bool, defaults to True) — Whether to use classifier free guidance or not.

Prepares image embeddings for use in the IP-Adapter.

Either ip_adapter_image or ip_adapter_image_embeds must be passed.

StableDiffusion3ControlNetInpaintingPipeline

class diffusers.StableDiffusion3ControlNetInpaintingPipeline

< source >

( transformer: SD3Transformer2DModel scheduler: FlowMatchEulerDiscreteScheduler vae: AutoencoderKL text_encoder: CLIPTextModelWithProjection tokenizer: CLIPTokenizer text_encoder_2: CLIPTextModelWithProjection tokenizer_2: CLIPTokenizer text_encoder_3: T5EncoderModel tokenizer_3: T5TokenizerFast controlnet: typing.Union[diffusers.models.controlnets.controlnet_sd3.SD3ControlNetModel, typing.List[diffusers.models.controlnets.controlnet_sd3.SD3ControlNetModel], typing.Tuple[diffusers.models.controlnets.controlnet_sd3.SD3ControlNetModel], diffusers.models.controlnets.controlnet_sd3.SD3MultiControlNetModel] image_encoder: SiglipModel = None feature_extractor: typing.Optional[transformers.models.siglip.image_processing_siglip.SiglipImageProcessor] = None )

Parameters

• transformer (SD3Transformer2DModel) — Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
• scheduler (FlowMatchEulerDiscreteScheduler) — A scheduler to be used in combination with transformer to denoise the encoded image latents.
• vae (AutoencoderKL) — Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
• text_encoder (CLIPTextModelWithProjection) —CLIP, specifically the clip-vit-large-patch14 variant, with an additional added projection layer that is initialized with a diagonal matrix with the hidden_sizeas its dimension.
• text_encoder_2 (CLIPTextModelWithProjection) —CLIP, specifically thelaion/CLIP-ViT-bigG-14-laion2B-39B-b160kvariant.
• text_encoder_3 (T5EncoderModel) — Frozen text-encoder. Stable Diffusion 3 usesT5, specifically thet5-v1_1-xxl variant.
• tokenizer (CLIPTokenizer) — Tokenizer of classCLIPTokenizer.
• tokenizer_2 (CLIPTokenizer) — Second Tokenizer of classCLIPTokenizer.
• tokenizer_3 (T5TokenizerFast) — Tokenizer of classT5Tokenizer.
• controlnet (SD3ControlNetModel or List[SD3ControlNetModel] or SD3MultiControlNetModel) — Provides additional conditioning to the transformer 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.
• image_encoder (PreTrainedModel, optional) — Pre-trained Vision Model for IP Adapter.
• feature_extractor (BaseImageProcessor, optional) — Image processor for IP Adapter.

__call__

< source >

( prompt: typing.Union[str, typing.List[str]] = None prompt_2: typing.Union[str, typing.List[str], NoneType] = None prompt_3: typing.Union[str, typing.List[str], NoneType] = None height: typing.Optional[int] = None width: typing.Optional[int] = None num_inference_steps: int = 28 sigmas: typing.Optional[typing.List[float]] = None guidance_scale: float = 7.0 control_guidance_start: typing.Union[float, typing.List[float]] = 0.0 control_guidance_end: typing.Union[float, typing.List[float]] = 1.0 control_image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = None control_mask: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = None controlnet_conditioning_scale: typing.Union[float, typing.List[float]] = 1.0 controlnet_pooled_projections: typing.Optional[torch.FloatTensor] = None negative_prompt: typing.Union[str, typing.List[str], NoneType] = None negative_prompt_2: typing.Union[str, typing.List[str], NoneType] = None negative_prompt_3: typing.Union[str, typing.List[str], NoneType] = None num_images_per_prompt: typing.Optional[int] = 1 generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None latents: typing.Optional[torch.FloatTensor] = None prompt_embeds: typing.Optional[torch.FloatTensor] = None negative_prompt_embeds: typing.Optional[torch.FloatTensor] = None pooled_prompt_embeds: typing.Optional[torch.FloatTensor] = None negative_pooled_prompt_embeds: typing.Optional[torch.FloatTensor] = None ip_adapter_image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor], NoneType] = None ip_adapter_image_embeds: typing.Optional[torch.Tensor] = None output_type: typing.Optional[str] = 'pil' return_dict: bool = True joint_attention_kwargs: typing.Optional[typing.Dict[str, typing.Any]] = None clip_skip: typing.Optional[int] = None callback_on_step_end: typing.Optional[typing.Callable[[int, int, typing.Dict], NoneType]] = None callback_on_step_end_tensor_inputs: typing.List[str] = ['latents'] max_sequence_length: int = 256 ) → ~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput or tuple

Parameters

• prompt (str or List[str], optional) — The prompt or prompts to guide the image generation. If not defined, one has to pass prompt_embeds. instead.
• prompt_2 (str or List[str], optional) — The prompt or prompts to be sent to tokenizer_2 and text_encoder_2. If not defined, prompt is will be used instead
• prompt_3 (str or List[str], optional) — The prompt or prompts to be sent to tokenizer_3 and text_encoder_3. If not defined, prompt is will be used instead
• height (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) — The height in pixels of the generated image. This is set to 1024 by default for the best results.
• width (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) — The width in pixels of the generated image. This is set to 1024 by default for the best results.
• num_inference_steps (int, optional, defaults to 50) — The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference.
• sigmas (List[float], optional) — Custom sigmas to use for the denoising process with schedulers which support a sigmas argument in their set_timesteps method. If not defined, the default behavior when num_inference_steps is passed will be used.
• guidance_scale (float, optional, defaults to 5.0) — Guidance scale as defined in Classifier-Free Diffusion Guidance.guidance_scale is defined as w of equation 2. of Imagen Paper. Guidance scale is enabled by setting guidance_scale > 1. Higher guidance scale encourages to generate images that are closely linked to the text prompt, usually at the expense of lower image quality.
• control_guidance_start (float or List[float], optional, defaults to 0.0) — The percentage of total steps at which the ControlNet starts applying.
• control_guidance_end (float or List[float], optional, defaults to 1.0) — The percentage of total steps at which the ControlNet stops applying.
• control_image (torch.Tensor, PIL.Image.Image, np.ndarray, List[torch.Tensor], List[PIL.Image.Image], List[np.ndarray]) —Image, numpy array or tensor representing an image batch to be inpainted (which parts of the image to be masked out with control_mask and repainted according to prompt). 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). If it is a numpy array or a list of arrays, the expected shape should be (B, H, W, C) or (H, W, C).
• control_mask (torch.Tensor, PIL.Image.Image, np.ndarray, List[torch.Tensor], List[PIL.Image.Image], 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). And for numpy array would be for (B, H, W, 1), (B, H, W), (H, W, 1), or (H, W).
• 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.
• controlnet_pooled_projections (torch.FloatTensor of shape (batch_size, projection_dim)) — Embeddings projected from the embeddings of controlnet input conditions.
• negative_prompt (str or List[str], optional) — The prompt or prompts not to guide the image generation. If not defined, one has to passnegative_prompt_embeds instead. Ignored when not using guidance (i.e., ignored if guidance_scale is less than 1).
• negative_prompt_2 (str or List[str], optional) — The prompt or prompts not to guide the image generation to be sent to tokenizer_2 andtext_encoder_2. If not defined, negative_prompt is used instead
• negative_prompt_3 (str or List[str], optional) — The prompt or prompts not to guide the image generation to be sent to tokenizer_3 andtext_encoder_3. If not defined, negative_prompt is used instead
• num_images_per_prompt (int, optional, defaults to 1) — The number of images to generate per prompt.
• generator (torch.Generator or List[torch.Generator], optional) — One or a list of torch generator(s)to make generation deterministic.
• latents (torch.FloatTensor, optional) — Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random generator.
• prompt_embeds (torch.FloatTensor, optional) — Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, text embeddings will be generated from prompt input argument.
• negative_prompt_embeds (torch.FloatTensor, optional) — Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, negative_prompt_embeds will be generated from negative_prompt input argument.
• pooled_prompt_embeds (torch.FloatTensor, optional) — Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, pooled text embeddings will be generated from prompt input argument.
• negative_pooled_prompt_embeds (torch.FloatTensor, optional) — Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, pooled negative_prompt_embeds will be generated from negative_promptinput argument.
• ip_adapter_image (PipelineImageInput, optional) — Optional image input to work with IP Adapters.
• ip_adapter_image_embeds (torch.Tensor, optional) — Pre-generated image embeddings for IP-Adapter. 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 toTrue. If not provided, embeddings are computed from the ip_adapter_image input argument.
• output_type (str, optional, defaults to "pil") — The output format of the generate image. Choose betweenPIL: PIL.Image.Image or np.array.
• return_dict (bool, optional, defaults to True) — Whether or not to return a ~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput instead of a plain tuple.
• joint_attention_kwargs (dict, optional) — A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined underself.processor indiffusers.models.attention_processor.
• callback_on_step_end (Callable, optional) — A function that calls at the end of each denoising steps during the inference. The function is called with the following arguments: callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict). callback_kwargs will include a list of all tensors as specified bycallback_on_step_end_tensor_inputs.
• callback_on_step_end_tensor_inputs (List, optional) — The list of tensor inputs for the callback_on_step_end function. The tensors specified in the list will be passed as callback_kwargs argument. You will only be able to include variables listed in the._callback_tensor_inputs attribute of your pipeline class.
• max_sequence_length (int defaults to 256) — Maximum sequence length to use with the prompt.

Returns

~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput or tuple

~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput if return_dict is True, otherwise atuple. When returning a tuple, the first element is a list with the generated images.

Function invoked when calling the pipeline for generation.

Examples:

import torch from diffusers.utils import load_image, check_min_version from diffusers.pipelines import StableDiffusion3ControlNetInpaintingPipeline from diffusers.models.controlnet_sd3 import SD3ControlNetModel

controlnet = SD3ControlNetModel.from_pretrained( ... "alimama-creative/SD3-Controlnet-Inpainting", use_safetensors=True, extra_conditioning_channels=1 ... ) pipe = StableDiffusion3ControlNetInpaintingPipeline.from_pretrained( ... "stabilityai/stable-diffusion-3-medium-diffusers", ... controlnet=controlnet, ... torch_dtype=torch.float16, ... ) pipe.text_encoder.to(torch.float16) pipe.controlnet.to(torch.float16) pipe.to("cuda")

image = load_image( ... "https://huggingface.co/alimama-creative/SD3-Controlnet-Inpainting/resolve/main/images/dog.png" ... ) mask = load_image( ... "https://huggingface.co/alimama-creative/SD3-Controlnet-Inpainting/resolve/main/images/dog_mask.png" ... ) width = 1024 height = 1024 prompt = "A cat is sitting next to a puppy." generator = torch.Generator(device="cuda").manual_seed(24) res_image = pipe( ... negative_prompt="deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, mutated hands and fingers, disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, NSFW", ... prompt=prompt, ... height=height, ... width=width, ... control_image=image, ... control_mask=mask, ... num_inference_steps=28, ... generator=generator, ... controlnet_conditioning_scale=0.95, ... guidance_scale=7, ... ).images[0] res_image.save(f"sd3.png")

encode_image

< source >

( image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] device: device ) → torch.Tensor

Parameters

• image (PipelineImageInput) — Input image to be encoded.
• device — (torch.device): Torch device.

The encoded image feature representation.

Encodes the given image into a feature representation using a pre-trained image encoder.

encode_prompt

< source >

( prompt: typing.Union[str, typing.List[str]] prompt_2: typing.Union[str, typing.List[str]] prompt_3: typing.Union[str, typing.List[str]] device: typing.Optional[torch.device] = None num_images_per_prompt: int = 1 do_classifier_free_guidance: bool = True negative_prompt: typing.Union[str, typing.List[str], NoneType] = None negative_prompt_2: typing.Union[str, typing.List[str], NoneType] = None negative_prompt_3: typing.Union[str, typing.List[str], NoneType] = None prompt_embeds: typing.Optional[torch.FloatTensor] = None negative_prompt_embeds: typing.Optional[torch.FloatTensor] = None pooled_prompt_embeds: typing.Optional[torch.FloatTensor] = None negative_pooled_prompt_embeds: typing.Optional[torch.FloatTensor] = None clip_skip: typing.Optional[int] = None max_sequence_length: int = 256 lora_scale: typing.Optional[float] = None )

Parameters

• prompt (str or List[str], optional) — prompt to be encoded
• prompt_2 (str or List[str], optional) — The prompt or prompts to be sent to the tokenizer_2 and text_encoder_2. If not defined, prompt is used in all text-encoders
• prompt_3 (str or List[str], optional) — The prompt or prompts to be sent to the tokenizer_3 and text_encoder_3. If not defined, prompt is used in all text-encoders
• device — (torch.device): torch device
• num_images_per_prompt (int) — number of images that should be generated per prompt
• 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 passnegative_prompt_embeds instead. Ignored when not using guidance (i.e., ignored if guidance_scale is less than 1).
• negative_prompt_2 (str or List[str], optional) — The prompt or prompts not to guide the image generation to be sent to tokenizer_2 andtext_encoder_2. If not defined, negative_prompt is used in all the text-encoders.
• negative_prompt_3 (str or List[str], optional) — The prompt or prompts not to guide the image generation to be sent to tokenizer_3 andtext_encoder_3. If not defined, negative_prompt is used in all the text-encoders.
• prompt_embeds (torch.FloatTensor, optional) — Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, text embeddings will be generated from prompt input argument.
• negative_prompt_embeds (torch.FloatTensor, optional) — Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, negative_prompt_embeds will be generated from negative_prompt input argument.
• pooled_prompt_embeds (torch.FloatTensor, optional) — Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, pooled text embeddings will be generated from prompt input argument.
• negative_pooled_prompt_embeds (torch.FloatTensor, optional) — Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, pooled negative_prompt_embeds will be generated from negative_promptinput argument.
• 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.
• lora_scale (float, optional) — A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.

prepare_ip_adapter_image_embeds

< source >

( ip_adapter_image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor], NoneType] = None ip_adapter_image_embeds: typing.Optional[torch.Tensor] = None device: typing.Optional[torch.device] = None num_images_per_prompt: int = 1 do_classifier_free_guidance: bool = True )

Parameters

• ip_adapter_image (PipelineImageInput, optional) — The input image to extract features from for IP-Adapter.
• ip_adapter_image_embeds (torch.Tensor, optional) — Precomputed image embeddings.
• device — (torch.device, optional): Torch device.
• num_images_per_prompt (int, defaults to 1) — Number of images that should be generated per prompt.
• do_classifier_free_guidance (bool, defaults to True) — Whether to use classifier free guidance or not.

Prepares image embeddings for use in the IP-Adapter.

Either ip_adapter_image or ip_adapter_image_embeds must be passed.

StableDiffusion3PipelineOutput

class diffusers.pipelines.stable_diffusion_3.pipeline_output.StableDiffusion3PipelineOutput

< source >

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

Parameters

• images (List[PIL.Image.Image] or np.ndarray) — List of denoised PIL images of length batch_size or numpy array of shape (batch_size, height, width, num_channels). PIL images or numpy array present the denoised images of the diffusion pipeline.

Output class for Stable Diffusion pipelines.

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