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>>> prompt = "a photo of an astronaut riding a horse on mars" |
>>> pipe.enable_attention_slicing() |
>>> image = pipe(prompt).images[0] disable_attention_slicing < source > ( ) Disable sliced attention computation. If enable_attention_slicing was previously called, attention is |
computed in one step. enable_vae_slicing < source > ( ) 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 < source > ( ) 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 < source > ( 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: Copied >>> 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 < source > ( ) Disable memory efficient attention from xFormers. load_textual_inversion < source > ( pretrained_model_name_or_path: Union token: Union = None tokenizer: Optional = None text_enco... |
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. |
token (str or List[str], optional) — |
Override the token to use for the textual inversion weights. If pretrained_model_name_or_path is a |
list, then token must also be a list of equal length. text_encoder (CLIPTextModel, optional) — |
Frozen text-encoder (clip-vit-large-patch14). |
If not specified, function will take self.tokenizer. tokenizer (CLIPTokenizer, optional) — |
A CLIPTokenizer to tokenize text. If not specified, function will take self.tokenizer. weight_name (str, optional) — |
Name of a custom weight file. This should be used when: |
The saved textual inversion file is in 🤗 Diffusers format, but was saved under a specific weight |
name such as text_inv.bin. |
The saved textual inversion file is in the Automatic1111 format. |
cache_dir (Union[str, os.PathLike], optional) — |
Path to a directory where a downloaded pretrained model configuration is cached if the standard cache |
is not used. force_download (bool, optional, defaults to False) — |
Whether or not to force the (re-)download of the model weights and configuration files, overriding the |
cached versions if they exist. resume_download (bool, optional, defaults to False) — |
Whether or not to resume downloading the model weights and configuration files. If set to False, 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 to False) — |
Whether to only load local model weights and configuration files or not. If set to True, the model |
won’t be downloaded from the Hub. token (str or bool, optional) — |
The token to use as HTTP bearer authorization for remote files. If True, the token generated from |
diffusers-cli login (stored in ~/.huggingface) is used. revision (str, optional, defaults to "main") — |
The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier |
allowed by Git. subfolder (str, optional, defaults to "") — |
The subfolder location of a model file within a larger model repository on the Hub or locally. mirror (str, optional) — |
Mirror source to resolve accessibility issues if you’re downloading a model in China. We do not |
guarantee the timeliness or safety of the source, and you should refer to the mirror site for more |
information. Load Textual Inversion embeddings into the text encoder of StableDiffusionPipeline (both 🤗 Diffusers and |
Automatic1111 formats are supported). Example: To load a Textual Inversion embedding vector in 🤗 Diffusers format: Copied 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: Copied 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") encode_prompt < source > ( 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], option... |
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.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. 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. StableDiffusionControlNetInpaintPipeline class diffusers.StableDiffusionControlNetInpaintPipeline < source > ( vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPToken... |
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) — |
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