Depth-to-image
The Stable Diffusion model can also infer depth based on an image using MiDas. This allows you to pass a text prompt and an initial image to condition the generation of new images as well as a depth_map
to preserve the image structure.
Make sure to check out the Stable Diffusion Tips section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!
If youβre interested in using one of the official checkpoints for a task, explore the CompVis, Runway, and Stability AI Hub organizations!
StableDiffusionDepth2ImgPipeline
class diffusers.StableDiffusionDepth2ImgPipeline
< source >( vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer unet: UNet2DConditionModel scheduler: KarrasDiffusionSchedulers depth_estimator: DPTForDepthEstimation feature_extractor: DPTFeatureExtractor )
Parameters
- vae (AutoencoderKL) — Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
-
text_encoder (
CLIPTextModel
) — Frozen text-encoder (clip-vit-large-patch14). -
tokenizer (
CLIPTokenizer
) — ACLIPTokenizer
to tokenize text. -
unet (UNet2DConditionModel) —
A
UNet2DConditionModel
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.
Pipeline for text-guided depth-based image-to-image generation using Stable Diffusion.
This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular device, etc.).
The pipeline also inherits the following loading methods:
- load_textual_inversion() for loading textual inversion embeddings
- load_lora_weights() for loading LoRA weights
- save_lora_weights() for saving LoRA weights
__call__
< source >(
prompt: typing.Union[str, typing.List[str]] = None
image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.FloatTensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.FloatTensor]] = None
depth_map: typing.Optional[torch.FloatTensor] = None
strength: float = 0.8
num_inference_steps: typing.Optional[int] = 50
guidance_scale: typing.Optional[float] = 7.5
negative_prompt: typing.Union[str, typing.List[str], NoneType] = None
num_images_per_prompt: typing.Optional[int] = 1
eta: typing.Optional[float] = 0.0
generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None
prompt_embeds: typing.Optional[torch.FloatTensor] = None
negative_prompt_embeds: typing.Optional[torch.FloatTensor] = None
output_type: typing.Optional[str] = 'pil'
return_dict: bool = True
callback: typing.Union[typing.Callable[[int, int, torch.FloatTensor], NoneType], NoneType] = None
callback_steps: int = 1
cross_attention_kwargs: typing.Union[typing.Dict[str, typing.Any], NoneType] = None
)
β
StableDiffusionPipelineOutput or tuple
Parameters
-
prompt (
str
orList[str]
, optional) — The prompt or prompts to guide image generation. If not defined, you need to passprompt_embeds
. -
image (
torch.FloatTensor
,PIL.Image.Image
,np.ndarray
,List[torch.FloatTensor]
,List[PIL.Image.Image]
, orList[np.ndarray]
) —Image
or tensor representing an image batch to be used as the starting point. Can accept image latents asimage
only ifdepth_map
is notNone
. -
depth_map (
torch.FloatTensor
, optional) — Depth prediction to be used as additional conditioning for the image generation process. If not defined, it automatically predicts the depth withself.depth_estimator
. -
strength (
float
, optional, defaults to 0.8) — Indicates extent to transform the referenceimage
. Must be between 0 and 1.image
is used as a starting point and more noise is added the higher thestrength
. The number of denoising steps depends on the amount of noise initially added. Whenstrength
is 1, added noise is maximum and the denoising process runs for the full number of iterations specified innum_inference_steps
. A value of 1 essentially ignoresimage
. -
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. This parameter is modulated bystrength
. -
guidance_scale (
float
, optional, defaults to 7.5) — A higher guidance scale value encourages the model to generate images closely linked to the textprompt
at the expense of lower image quality. Guidance scale is enabled whenguidance_scale > 1
. -
negative_prompt (
str
orList[str]
, optional) — The prompt or prompts to guide what to not include in image generation. If not defined, you need to passnegative_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 paper. Only applies to the DDIMScheduler, and is ignored in other schedulers. -
generator (
torch.Generator
orList[torch.Generator]
, optional) — Atorch.Generator
to make generation deterministic. -
prompt_embeds (
torch.FloatTensor
, optional) — Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided, text embeddings are generated from theprompt
input argument. -
negative_prompt_embeds (
torch.FloatTensor
, optional) — Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided,negative_prompt_embeds
are generated from thenegative_prompt
input argument. -
output_type (
str
, optional, defaults to"pil"
) — The output format of the generated image. Choose betweenPIL.Image
ornp.array
. -
return_dict (
bool
, optional, defaults toTrue
) — Whether or not to return a StableDiffusionPipelineOutput instead of a plain tuple. -
callback (
Callable
, optional) — A function that calls everycallback_steps
steps during inference. The function is called with the following arguments:callback(step: int, timestep: int, latents: torch.FloatTensor)
. -
callback_steps (
int
, optional, defaults to 1) — The frequency at which thecallback
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 theAttentionProcessor
as defined inself.processor
.
Returns
StableDiffusionPipelineOutput or tuple
If return_dict
is True
, StableDiffusionPipelineOutput is returned,
otherwise a tuple
is returned where the first element is a list with the generated images.
The call function to the pipeline for generation.
Examples:
>>> import torch
>>> import requests
>>> from PIL import Image
>>> from diffusers import StableDiffusionDepth2ImgPipeline
>>> pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(
... "stabilityai/stable-diffusion-2-depth",
... torch_dtype=torch.float16,
... )
>>> pipe.to("cuda")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> init_image = Image.open(requests.get(url, stream=True).raw)
>>> prompt = "two tigers"
>>> n_propmt = "bad, deformed, ugly, bad anotomy"
>>> image = pipe(prompt=prompt, image=init_image, negative_prompt=n_propmt, strength=0.7).images[0]
enable_attention_slicing
< source >( slice_size: typing.Union[str, int, NoneType] = 'auto' )
Parameters
-
slice_size (
str
orint
, 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 asattention_head_dim // slice_size
. In this case,attention_head_dim
must be a multiple ofslice_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.
β οΈ Donβt enable attention slicing if youβre already using scaled_dot_product_attention
(SDPA) from PyTorch
2.0 or xFormers. These attention computations are already very memory efficient so you wonβt 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 sliced attention computation. If enable_attention_slicing
was previously called, attention is
computed in one step.
enable_xformers_memory_efficient_attention
< source >( attention_op: typing.Optional[typing.Callable] = None )
Parameters
-
attention_op (
Callable
, optional) — Override the defaultNone
operator for use asop
argument to thememory_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.
β οΈ 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 memory efficient attention from xFormers.
load_textual_inversion
< source >( pretrained_model_name_or_path: typing.Union[str, typing.List[str], typing.Dict[str, torch.Tensor], typing.List[typing.Dict[str, torch.Tensor]]] token: typing.Union[str, typing.List[str], NoneType] = None tokenizer: typing.Optional[transformers.tokenization_utils.PreTrainedTokenizer] = None text_encoder: typing.Optional[transformers.modeling_utils.PreTrainedModel] = None **kwargs )
Parameters
-
pretrained_model_name_or_path (
str
oros.PathLike
orList[str or os.PathLike]
orDict
orList[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.
- A string, the model id (for example
-
token (
str
orList[str]
, optional) — Override the token to use for the textual inversion weights. Ifpretrained_model_name_or_path
is a list, thentoken
must also be a list of equal length. -
text_encoder (
CLIPTextModel
, optional) — Frozen text-encoder (clip-vit-large-patch14). If not specified, function will take self.tokenizer. -
tokenizer (
CLIPTokenizer
, optional) — ACLIPTokenizer
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.
- The saved textual inversion file is in 🤗 Diffusers format, but was saved under a specific weight
name such as
-
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 toFalse
) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool
, optional, defaults toFalse
) — Whether or not to resume downloading the model weights and configuration files. If set toFalse
, any incompletely downloaded files are deleted. -
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 toFalse
) — Whether to only load local model weights and configuration files or not. If set toTrue
, the model won’t be downloaded from the Hub. -
use_auth_token (
str
or bool, optional) — The token to use as HTTP bearer authorization for remote files. IfTrue
, the token generated fromdiffusers-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.
Load textual inversion embeddings into the text encoder of StableDiffusionPipeline (both π€ Diffusers and Automatic1111 formats are supported).
Example:
To load a textual inversion embedding vector in π€ Diffusers format:
from diffusers import StableDiffusionPipeline
import torch
model_id = "runwayml/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")
To load a textual inversion embedding vector in Automatic1111 format, make sure to download the vector first (for example from civitAI) and then load the vector
locally:
from diffusers import StableDiffusionPipeline
import torch
model_id = "runwayml/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")
load_lora_weights
< source >( pretrained_model_name_or_path_or_dict: typing.Union[str, typing.Dict[str, torch.Tensor]] **kwargs )
Parameters
-
pretrained_model_name_or_path_or_dict (
str
oros.PathLike
ordict
) — See lora_state_dict(). -
kwargs (
dict
, optional) — See lora_state_dict().
Load LoRA weights specified in pretrained_model_name_or_path_or_dict
into self.unet
and
self.text_encoder
.
All kwargs are forwarded to self.lora_state_dict
.
See lora_state_dict() for more details on how the state dict is loaded.
See load_lora_into_unet() for more details on how the state dict is loaded into
self.unet
.
See load_lora_into_text_encoder() for more details on how the state dict is loaded
into self.text_encoder
.
save_lora_weights
< source >( save_directory: typing.Union[str, os.PathLike] unet_lora_layers: typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None text_encoder_lora_layers: typing.Dict[str, torch.nn.modules.module.Module] = None is_main_process: bool = True weight_name: str = None save_function: typing.Callable = None safe_serialization: bool = True )
Parameters
-
save_directory (
str
oros.PathLike
) — Directory to save LoRA parameters to. Will be created if it doesn’t exist. -
unet_lora_layers (
Dict[str, torch.nn.Module]
orDict[str, torch.Tensor]
) — State dict of the LoRA layers corresponding to theunet
. -
text_encoder_lora_layers (
Dict[str, torch.nn.Module]
orDict[str, torch.Tensor]
) — State dict of the LoRA layers corresponding to thetext_encoder
. Must explicitly pass the text encoder LoRA state dict because it comes from 🤗 Transformers. -
is_main_process (
bool
, optional, defaults toTrue
) — Whether the process calling this is the main process or not. Useful during distributed training and you need to call this function on all processes. In this case, setis_main_process=True
only on the main process to avoid race conditions. -
save_function (
Callable
) — The function to use to save the state dictionary. Useful during distributed training when you need to replacetorch.save
with another method. Can be configured with the environment variableDIFFUSERS_SAVE_MODE
. -
safe_serialization (
bool
, optional, defaults toTrue
) — Whether to save the model usingsafetensors
or the traditional PyTorch way withpickle
.
Save the LoRA parameters corresponding to the UNet and text encoder.
encode_prompt
< source >( prompt device num_images_per_prompt do_classifier_free_guidance negative_prompt = None prompt_embeds: typing.Optional[torch.FloatTensor] = None negative_prompt_embeds: typing.Optional[torch.FloatTensor] = None lora_scale: typing.Optional[float] = None )
Parameters
-
prompt (
str
orList[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
orList[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 ifguidance_scale
is less than1
). -
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 fromprompt
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 fromnegative_prompt
input argument. -
lora_scale (
float
, optional) — A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
Encodes the prompt into text encoder hidden states.
StableDiffusionPipelineOutput
class diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput
< source >( images: typing.Union[typing.List[PIL.Image.Image], numpy.ndarray] nsfw_content_detected: typing.Optional[typing.List[bool]] )
Parameters
-
images (
List[PIL.Image.Image]
ornp.ndarray
) — List of denoised PIL images of lengthbatch_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 orNone
if safety checking could not be performed.
Output class for Stable Diffusion pipelines.