Diffusers documentation

T2I-Adapter

Diffusers

You are viewing v0.30.1 version. A newer version v0.31.0 is available.

Join the Hugging Face community

and get access to the augmented documentation experience

Collaborate on models, datasets and Spaces

Faster examples with accelerated inference

Switch between documentation themes

to get started

T2I-Adapter

T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models by Chong Mou, Xintao Wang, Liangbin Xie, Jian Zhang, Zhongang Qi, Ying Shan, Xiaohu Qie.

Using the pretrained models we can provide control images (for example, a depth map) to control Stable Diffusion text-to-image generation so that it follows the structure of the depth image and fills in the details.

The abstract of the paper is the following:

The incredible generative ability of large-scale text-to-image (T2I) models has demonstrated strong power of learning complex structures and meaningful semantics. However, relying solely on text prompts cannot fully take advantage of the knowledge learned by the model, especially when flexible and accurate controlling (e.g., color and structure) is needed. In this paper, we aim to “dig out” the capabilities that T2I models have implicitly learned, and then explicitly use them to control the generation more granularly. Specifically, we propose to learn simple and lightweight T2I-Adapters to align internal knowledge in T2I models with external control signals, while freezing the original large T2I models. In this way, we can train various adapters according to different conditions, achieving rich control and editing effects in the color and structure of the generation results. Further, the proposed T2I-Adapters have attractive properties of practical value, such as composability and generalization ability. Extensive experiments demonstrate that our T2I-Adapter has promising generation quality and a wide range of applications.

This model was contributed by the community contributor HimariO ❤️ .

StableDiffusionAdapterPipeline

class diffusers.StableDiffusionAdapterPipeline

( vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer unet: UNet2DConditionModel adapter: Union scheduler: KarrasDiffusionSchedulers safety_checker: StableDiffusionSafetyChecker feature_extractor: CLIPImageProcessor requires_safety_checker: bool = True )

Parameters

adapter (T2IAdapter or MultiAdapter or List[T2IAdapter]) — Provides additional conditioning to the unet during the denoising process. If you set multiple Adapter as a list, the outputs from each Adapter are added together to create one combined additional conditioning.
adapter_weights (List[float], optional, defaults to None) — List of floats representing the weight which will be multiply to each adapter’s output before adding them together.
vae (AutoencoderKL) — Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder (CLIPTextModel) — Frozen text-encoder. Stable Diffusion uses the text portion of CLIP, specifically the clip-vit-large-patch14 variant.
tokenizer (CLIPTokenizer) — Tokenizer of class CLIPTokenizer.
unet (UNet2DConditionModel) — Conditional U-Net architecture to denoise the encoded image latents.
scheduler (SchedulerMixin) — A scheduler to be used in combination with unet to denoise the encoded image latents. Can be one of DDIMScheduler, LMSDiscreteScheduler, or PNDMScheduler.
safety_checker (StableDiffusionSafetyChecker) — Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the model card for details.
feature_extractor (CLIPImageProcessor) — Model that extracts features from generated images to be used as inputs for the safety_checker.

Pipeline for text-to-image generation using Stable Diffusion augmented with T2I-Adapter https://arxiv.org/abs/2302.08453

This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)

call

( prompt: Union = None image: Union = None height: Optional = None width: Optional = None num_inference_steps: int = 50 timesteps: List = None sigmas: List = None guidance_scale: float = 7.5 negative_prompt: Union = None num_images_per_prompt: Optional = 1 eta: float = 0.0 generator: Union = None latents: Optional = None prompt_embeds: Optional = None negative_prompt_embeds: Optional = None output_type: Optional = 'pil' return_dict: bool = True callback: Optional = None callback_steps: int = 1 cross_attention_kwargs: Optional = None adapter_conditioning_scale: Union = 1.0 clip_skip: Optional = None ) → ~pipelines.stable_diffusion.StableDiffusionAdapterPipelineOutput 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.
image (torch.Tensor, PIL.Image.Image, List[torch.Tensor] or List[PIL.Image.Image] or List[List[PIL.Image.Image]]) — The Adapter input condition. Adapter uses this input condition to generate guidance to Unet. If the type is specified as torch.Tensor, it is passed to Adapter as is. PIL.Image.Image` can also be accepted as an image. The control image is automatically resized to fit the output image.
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) — 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.
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. 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).
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) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to schedulers.DDIMScheduler, will be ignored for others.
generator (torch.Generator or List[torch.Generator], optional) — One or a list of torch generator(s) 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 will ge 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, 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.
output_type (str, optional, defaults to "pil") — The output format of the generate image. Choose between PIL: PIL.Image.Image or np.array.
return_dict (bool, optional, defaults to True) — Whether or not to return a ~pipelines.stable_diffusion.StableDiffusionAdapterPipelineOutput instead of a plain tuple.
callback (Callable, optional) — A function that will be called every callback_steps steps during inference. The function will be 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 will be called. If not specified, the callback will be called at every step.
cross_attention_kwargs (dict, optional) — A kwargs dictionary that if specified is passed along to the AttnProcessor as defined under self.processor in diffusers.models.attention_processor.
adapter_conditioning_scale (float or List[float], optional, defaults to 1.0) — The outputs of the adapter are multiplied by adapter_conditioning_scale before they are added to the residual in the original unet. If multiple adapters are specified in init, you can set the corresponding scale as a list.
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.

Returns

~pipelines.stable_diffusion.StableDiffusionAdapterPipelineOutput or tuple

~pipelines.stable_diffusion.StableDiffusionAdapterPipelineOutput if return_dict is True, otherwise a tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of bools denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the safety_checker`.

Function invoked when calling the pipeline for generation.

Examples:

>>> from PIL import Image
>>> from diffusers.utils import load_image
>>> import torch
>>> from diffusers import StableDiffusionAdapterPipeline, T2IAdapter

>>> image = load_image(
...     "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/color_ref.png"
... )

>>> color_palette = image.resize((8, 8))
>>> color_palette = color_palette.resize((512, 512), resample=Image.Resampling.NEAREST)

>>> adapter = T2IAdapter.from_pretrained("TencentARC/t2iadapter_color_sd14v1", torch_dtype=torch.float16)
>>> pipe = StableDiffusionAdapterPipeline.from_pretrained(
...     "CompVis/stable-diffusion-v1-4",
...     adapter=adapter,
...     torch_dtype=torch.float16,
... )

>>> pipe.to("cuda")

>>> out_image = pipe(
...     "At night, glowing cubes in front of the beach",
...     image=color_palette,
... ).images[0]

enable_attention_slicing

( slice_size: Union = 'auto' )

Parameters

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.

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.

⚠️ 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!

Examples:

>>> import torch
>>> from diffusers import StableDiffusionPipeline

>>> pipe = StableDiffusionPipeline.from_pretrained(
...     "runwayml/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]

disable_attention_slicing

( )

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

enable_vae_slicing

( )

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.

disable_vae_slicing

( )

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

enable_xformers_memory_efficient_attention

( attention_op: Optional = None )

Parameters

attention_op (Callable, optional) — Override the default None operator for use as op argument to the memory_efficient_attention() function of xFormers.

Enable memory efficient attention from 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.

⚠️ When memory efficient attention and sliced attention are both enabled, memory efficient attention takes precedent.

Examples:

>>> 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)

disable_xformers_memory_efficient_attention

( )

Disable memory efficient attention from xFormers.

encode_prompt

( prompt device num_images_per_prompt do_classifier_free_guidance negative_prompt = None prompt_embeds: Optional = None negative_prompt_embeds: Optional = None lora_scale: Optional = None clip_skip: Optional = None )

Parameters

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.

Encodes the prompt into text encoder hidden states.

get_guidance_scale_embedding

( w: Tensor embedding_dim: int = 512 dtype: dtype = torch.float32 ) → torch.Tensor

Parameters

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.

Returns

torch.Tensor

Embedding vectors with shape (len(w), embedding_dim).

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

StableDiffusionXLAdapterPipeline

class diffusers.StableDiffusionXLAdapterPipeline

( vae: AutoencoderKL text_encoder: CLIPTextModel text_encoder_2: CLIPTextModelWithProjection tokenizer: CLIPTokenizer tokenizer_2: CLIPTokenizer unet: UNet2DConditionModel adapter: Union scheduler: KarrasDiffusionSchedulers force_zeros_for_empty_prompt: bool = True feature_extractor: CLIPImageProcessor = None image_encoder: CLIPVisionModelWithProjection = None )

Parameters

adapter (T2IAdapter or MultiAdapter or List[T2IAdapter]) — Provides additional conditioning to the unet during the denoising process. If you set multiple Adapter as a list, the outputs from each Adapter are added together to create one combined additional conditioning.
adapter_weights (List[float], optional, defaults to None) — List of floats representing the weight which will be multiply to each adapter’s output before adding them together.
vae (AutoencoderKL) — Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder (CLIPTextModel) — Frozen text-encoder. Stable Diffusion uses the text portion of CLIP, specifically the clip-vit-large-patch14 variant.
tokenizer (CLIPTokenizer) — Tokenizer of class CLIPTokenizer.
unet (UNet2DConditionModel) — Conditional U-Net architecture to denoise the encoded image latents.
scheduler (SchedulerMixin) — A scheduler to be used in combination with unet to denoise the encoded image latents. Can be one of DDIMScheduler, LMSDiscreteScheduler, or PNDMScheduler.
safety_checker (StableDiffusionSafetyChecker) — Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the model card for details.
feature_extractor (CLIPImageProcessor) — Model that extracts features from generated images to be used as inputs for the safety_checker.

Pipeline for text-to-image generation using Stable Diffusion augmented with T2I-Adapter https://arxiv.org/abs/2302.08453

This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)

The pipeline also inherits the following loading methods:

load_textual_inversion() for loading textual inversion embeddings
from_single_file() for loading .ckpt files
load_lora_weights() for loading LoRA weights
save_lora_weights() for saving LoRA weights
load_ip_adapter() for loading IP Adapters

call

( prompt: Union = None prompt_2: Union = None image: Union = None height: Optional = None width: Optional = None num_inference_steps: int = 50 timesteps: List = None sigmas: List = None denoising_end: Optional = None guidance_scale: float = 5.0 negative_prompt: Union = None negative_prompt_2: Union = None num_images_per_prompt: Optional = 1 eta: float = 0.0 generator: Union = None latents: Optional = None prompt_embeds: Optional = None negative_prompt_embeds: Optional = None pooled_prompt_embeds: Optional = None negative_pooled_prompt_embeds: Optional = None ip_adapter_image: Union = None ip_adapter_image_embeds: Optional = None output_type: Optional = 'pil' return_dict: bool = True callback: Optional = None callback_steps: int = 1 cross_attention_kwargs: Optional = None guidance_rescale: float = 0.0 original_size: Optional = None crops_coords_top_left: Tuple = (0, 0) target_size: Optional = None negative_original_size: Optional = None negative_crops_coords_top_left: Tuple = (0, 0) negative_target_size: Optional = None adapter_conditioning_scale: Union = 1.0 adapter_conditioning_factor: float = 1.0 clip_skip: Optional = None ) → ~pipelines.stable_diffusion.StableDiffusionAdapterPipelineOutput 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 the tokenizer_2 and text_encoder_2. If not defined, prompt is used in both text-encoders
image (torch.Tensor, PIL.Image.Image, List[torch.Tensor] or List[PIL.Image.Image] or List[List[PIL.Image.Image]]) — The Adapter input condition. Adapter uses this input condition to generate guidance to Unet. If the type is specified as torch.Tensor, it is passed to Adapter as is. PIL.Image.Image` can also be accepted as an image. The control image is automatically resized to fit the output image.
height (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) — The height in pixels of the generated image. Anything below 512 pixels won’t work well for stabilityai/stable-diffusion-xl-base-1.0 and checkpoints that are not specifically fine-tuned on low resolutions.
width (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) — The width in pixels of the generated image. Anything below 512 pixels won’t work well for stabilityai/stable-diffusion-xl-base-1.0 and checkpoints that are not specifically fine-tuned on low resolutions.
num_inference_steps (int, optional, defaults to 50) — The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference.
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.
denoising_end (float, optional) — When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be completed before it is intentionally prematurely terminated. As a result, the returned sample will still retain a substantial amount of noise as determined by the discrete timesteps selected by the scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a “Mixture of Denoisers” multi-pipeline setup, as elaborated in Refining the Image Output
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.
negative_prompt (str or List[str], optional) — The prompt or prompts not to guide the image generation. If not defined, one has to pass negative_prompt_embeds instead. Ignored when not using guidance (i.e., ignored if guidance_scale is less than 1).
negative_prompt_2 (str or List[str], optional) — The prompt or prompts not to guide the image generation to be sent to tokenizer_2 and text_encoder_2. If not defined, negative_prompt is used in both text-encoders
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) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to schedulers.DDIMScheduler, will be ignored for others.
generator (torch.Generator or List[torch.Generator], optional) — One or a list of torch generator(s) 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 will ge 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, 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.
pooled_prompt_embeds (torch.Tensor, 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.Tensor, optional) — Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, pooled negative_prompt_embeds will be generated from negative_prompt input argument. 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 generate image. Choose between PIL: PIL.Image.Image or np.array.
return_dict (bool, optional, defaults to True) — Whether or not to return a ~pipelines.stable_diffusion_xl.StableDiffusionAdapterPipelineOutput instead of a plain tuple.
callback (Callable, optional) — A function that will be called every callback_steps steps during inference. The function will be 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 will be called. If not specified, the callback will be called at every step.
cross_attention_kwargs (dict, optional) — A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined under self.processor in diffusers.models.attention_processor.
guidance_rescale (float, optional, defaults to 0.0) — Guidance rescale factor proposed by Common Diffusion Noise Schedules and Sample Steps are Flawed guidance_scale is defined as φ in equation 16. of Common Diffusion Noise Schedules and Sample Steps are Flawed. Guidance rescale factor should fix overexposure when using zero terminal SNR.
original_size (Tuple[int], optional, defaults to (1024, 1024)) — If original_size is not the same as target_size the image will appear to be down- or upsampled. original_size defaults to (height, width) if not specified. Part of SDXL’s micro-conditioning as explained in section 2.2 of https://huggingface.co/papers/2307.01952.
crops_coords_top_left (Tuple[int], optional, defaults to (0, 0)) — crops_coords_top_left can be used to generate an image that appears to be “cropped” from the position crops_coords_top_left downwards. Favorable, well-centered images are usually achieved by setting crops_coords_top_left to (0, 0). Part of SDXL’s micro-conditioning as explained in section 2.2 of https://huggingface.co/papers/2307.01952.
target_size (Tuple[int], optional, defaults to (1024, 1024)) — For most cases, target_size should be set to the desired height and width of the generated image. If not specified it will default to (height, width). Part of SDXL’s micro-conditioning as explained in section 2.2 of https://huggingface.co/papers/2307.01952. section 2.2 of https://huggingface.co/papers/2307.01952.
negative_original_size (Tuple[int], optional, defaults to (1024, 1024)) — To negatively condition the generation process based on a specific image resolution. Part of SDXL’s micro-conditioning as explained in section 2.2 of https://huggingface.co/papers/2307.01952. For more information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
negative_crops_coords_top_left (Tuple[int], optional, defaults to (0, 0)) — To negatively condition the generation process based on a specific crop coordinates. Part of SDXL’s micro-conditioning as explained in section 2.2 of https://huggingface.co/papers/2307.01952. For more information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
negative_target_size (Tuple[int], optional, defaults to (1024, 1024)) — To negatively condition the generation process based on a target image resolution. It should be as same as the target_size for most cases. Part of SDXL’s micro-conditioning as explained in section 2.2 of https://huggingface.co/papers/2307.01952. For more information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
adapter_conditioning_scale (float or List[float], optional, defaults to 1.0) — The outputs of the adapter are multiplied by adapter_conditioning_scale before they are added to the residual in the original unet. If multiple adapters are specified in init, you can set the corresponding scale as a list.
adapter_conditioning_factor (float, optional, defaults to 1.0) — The fraction of timesteps for which adapter should be applied. If adapter_conditioning_factor is 0.0, adapter is not applied at all. If adapter_conditioning_factor is 1.0, adapter is applied for all timesteps. If adapter_conditioning_factor is 0.5, adapter is applied for half of the timesteps.
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.

Returns

~pipelines.stable_diffusion.StableDiffusionAdapterPipelineOutput or tuple

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

Function invoked when calling the pipeline for generation.

Examples:

>>> import torch
>>> from diffusers import T2IAdapter, StableDiffusionXLAdapterPipeline, DDPMScheduler
>>> from diffusers.utils import load_image

>>> sketch_image = load_image("https://huggingface.co/Adapter/t2iadapter/resolve/main/sketch.png").convert("L")

>>> model_id = "stabilityai/stable-diffusion-xl-base-1.0"

>>> adapter = T2IAdapter.from_pretrained(
...     "Adapter/t2iadapter",
...     subfolder="sketch_sdxl_1.0",
...     torch_dtype=torch.float16,
...     adapter_type="full_adapter_xl",
... )
>>> scheduler = DDPMScheduler.from_pretrained(model_id, subfolder="scheduler")

>>> pipe = StableDiffusionXLAdapterPipeline.from_pretrained(
...     model_id, adapter=adapter, torch_dtype=torch.float16, variant="fp16", scheduler=scheduler
... ).to("cuda")

>>> generator = torch.manual_seed(42)
>>> sketch_image_out = pipe(
...     prompt="a photo of a dog in real world, high quality",
...     negative_prompt="extra digit, fewer digits, cropped, worst quality, low quality",
...     image=sketch_image,
...     generator=generator,
...     guidance_scale=7.5,
... ).images[0]

enable_attention_slicing

( slice_size: Union = 'auto' )

Parameters

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.

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.

⚠️ 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!

Examples:

>>> import torch
>>> from diffusers import StableDiffusionPipeline

>>> pipe = StableDiffusionPipeline.from_pretrained(
...     "runwayml/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]

disable_attention_slicing

( )

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

enable_vae_slicing

( )

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.

disable_vae_slicing

( )

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

enable_xformers_memory_efficient_attention

( attention_op: Optional = None )

Parameters

attention_op (Callable, optional) — Override the default None operator for use as op argument to the memory_efficient_attention() function of xFormers.

Enable memory efficient attention from 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.

⚠️ When memory efficient attention and sliced attention are both enabled, memory efficient attention takes precedent.

Examples:

>>> 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)

disable_xformers_memory_efficient_attention

( )

Disable memory efficient attention from xFormers.

encode_prompt

( prompt: str prompt_2: Optional = None device: Optional = None num_images_per_prompt: int = 1 do_classifier_free_guidance: bool = True negative_prompt: Optional = None negative_prompt_2: Optional = None prompt_embeds: Optional = None negative_prompt_embeds: Optional = None pooled_prompt_embeds: Optional = None negative_pooled_prompt_embeds: Optional = None lora_scale: Optional = None clip_skip: Optional = None )

Parameters

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

Encodes the prompt into text encoder hidden states.

get_guidance_scale_embedding

( w: Tensor embedding_dim: int = 512 dtype: dtype = torch.float32 ) → torch.Tensor

Parameters

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.

Returns

torch.Tensor

Embedding vectors with shape (len(w), embedding_dim).

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

< > Update on GitHub

←LDM3D Text-to-(RGB, Depth), Text-to-(RGB-pano, Depth-pano), LDM3D Upscaler GLIGEN (Grounded Language-to-Image Generation)→