#
Loaders

There are many ways to train adapter neural networks for diffusion models, such as

- Textual Inversion
- LoRA
- Hypernetworks

Such adapter neural networks often only consist of a fraction of the number of weights compared to the pretrained model and as such are very portable. The Diffusers library offers an easy-to-use API to load such adapter neural networks via the `loaders.py` module.

Note: This module is still highly experimental and prone to future changes.

##
LoaderMixins

###
UNet2DConditionLoadersMixin

###
class diffusers.loaders.UNet2DConditionLoadersMixin

< source >

( )

####
load_attn_procs

< 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` or `os.PathLike` or `dict`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co. Valid model ids should have an organization name, like `google/ddpm-celebahq-256`.
  - A path to a directory containing model weights saved using `~ModelMixin.save_config`, e.g., `./my_model_directory/`.
  - A torch state dict.
- cache_dir (`Union[str, os.PathLike]`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be 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 delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'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 or not to only look at local files (i.e., do not try to download the model).
- use_auth_token (`str` or bool, optional) — The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated when running `diffusers-cli login` (stored in `~/.huggingface`).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- subfolder (`str`, optional, defaults to `""`) — In case the relevant files are located inside a subfolder of the model repo (either remote in huggingface.co or downloaded locally), you can specify the folder name here.
- mirror (`str`, optional) — Mirror source to accelerate downloads in China. If you are from China and have an accessibility problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety. Please refer to the mirror site for more information.

Load pretrained attention processor layers into `UNet2DConditionModel`. Attention processor layers have to be defined in `cross_attention.py` and be a `torch.nn.Module` class.

This function is experimental and might change in the future.

It is required to be logged in (`huggingface-cli login`) when you want to use private or gated models.

####
save_attn_procs

< source >

( save_directory: typing.Union[str, os.PathLike] is_main_process: bool = True weight_name: str = None save_function: typing.Callable = None safe_serialization: bool = False **kwargs )

Parameters

- save_directory (`str` or `os.PathLike`) — Directory to which to save. Will be created if it doesn’t exist.
- is_main_process (`bool`, optional, defaults to `True`) — Whether the process calling this is the main process or not. Useful when in distributed training like TPUs and need to call this function on all processes. In this case, set `is_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 on distributed training like TPUs when one need to replace `torch.save` by another method. Can be configured with the environment variable `DIFFUSERS_SAVE_MODE`.

Save an attention processor to a directory, so that it can be re-loaded using the load_attn_procs() method.

###
TextualInversionLoaderMixin

###
class diffusers.loaders.TextualInversionLoaderMixin

< source >

( )

Mixin class for loading textual inversion tokens and embeddings to the tokenizer and text encoder.

####
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 **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike` or `List[str or os.PathLike]` or `Dict` or `List[Dict]`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co. Valid model ids should have an organization name, like `"sd-concepts-library/low-poly-hd-logos-icons"`.
  - A path to a directory containing textual inversion weights, e.g. `./my_text_inversion_directory/`.
  - A path to a file containing textual inversion weights, e.g. `./my_text_inversions.pt`.
  - A torch state dict.

  Or a list of those elements.

- 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.
- weight_name (`str`, optional) — Name of a custom weight file. This should be used in two cases:
  - 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” form.
- cache_dir (`Union[str, os.PathLike]`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be 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 delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'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 or not to only look at local files (i.e., do not try to download the model).
- use_auth_token (`str` or bool, optional) — The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated when running `diffusers-cli login` (stored in `~/.huggingface`).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- subfolder (`str`, optional, defaults to `""`) — In case the relevant files are located inside a subfolder of the model repo (either remote in huggingface.co or downloaded locally), you can specify the folder name here.
- mirror (`str`, optional) — Mirror source to accelerate downloads in China. If you are from China and have an accessibility problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety. Please refer to the mirror site for more information.

Load textual inversion embeddings into the text encoder of stable diffusion pipelines. Both `diffusers` and `Automatic1111` formats are supported (see example below).

This function is experimental and might change in the future.

It is required to be logged in (`huggingface-cli login`) when you want to use private or gated models.

Example:

To load a textual inversion embedding vector in `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 first download the vector,

e.g. 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")

####
maybe_convert_prompt

< source >

( prompt: typing.Union[str, typing.List[str]] tokenizer: PreTrainedTokenizer ) → `str` or list of `str`

Parameters

- prompt (`str` or list of `str`) — The prompt or prompts to guide the image generation.
- tokenizer (`PreTrainedTokenizer`) — The tokenizer responsible for encoding the prompt into input tokens.

Returns

`str` or list of `str`

The converted prompt

Maybe convert a prompt into a “multi vector”-compatible prompt. If the prompt includes a token that corresponds to a multi-vector textual inversion embedding, this function will process the prompt so that the special token is replaced with multiple special tokens each corresponding to one of the vectors. If the prompt has no textual inversion token or a textual inversion token that is a single vector, the input prompt is simply returned.

###
LoraLoaderMixin

###
class diffusers.loaders.LoraLoaderMixin

< source >

( )

Utility class for handling the loading LoRA layers into UNet (of class UNet2DConditionModel) and Text Encoder (of class `CLIPTextModel`).

This function is experimental and might change in the future.

####
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` or `os.PathLike` or `dict`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co. Valid model ids should have an organization name, like `google/ddpm-celebahq-256`.
  - A path to a directory containing model weights saved using `~ModelMixin.save_config`, e.g., `./my_model_directory/`.
  - A torch state dict.
- cache_dir (`Union[str, os.PathLike]`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be 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 delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'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 or not to only look at local files (i.e., do not try to download the model).
- use_auth_token (`str` or bool, optional) — The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated when running `diffusers-cli login` (stored in `~/.huggingface`).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- subfolder (`str`, optional, defaults to `""`) — In case the relevant files are located inside a subfolder of the model repo (either remote in huggingface.co or downloaded locally), you can specify the folder name here.
- mirror (`str`, optional) — Mirror source to accelerate downloads in China. If you are from China and have an accessibility problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety. Please refer to the mirror site for more information.

Load pretrained attention processor layers (such as LoRA) into UNet2DConditionModel and `CLIPTextModel`).

We support loading A1111 formatted LoRA checkpoints in a limited capacity.

This function is experimental and might change in the future.

It is required to be logged in (`huggingface-cli login`) when you want to use private or gated models.

####
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 = False )

Parameters

- save_directory (`str` or `os.PathLike`) — Directory to which to save. Will be created if it doesn’t exist.
- unet_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`) — State dict of the LoRA layers corresponding to the UNet. Specifying this helps to make the serialization process easier and cleaner. Values can be both LoRA torch.nn.Modules layers or torch weights.
- text_encoder_lora_layers (`Dict[str, torch.nn.Module] or` Dict[str, torch.Tensor]`) -- State dict of the LoRA layers corresponding to the` text_encoder`. Since the` text_encoder`comes from`transformers`, we cannot rejig it. That is why we have to explicitly pass the text encoder LoRA state dict. Values can be both LoRA torch.nn.Modules layers or torch weights.
- is_main_process (`bool`, optional, defaults to `True`) — Whether the process calling this is the main process or not. Useful when in distributed training like TPUs and need to call this function on all processes. In this case, set `is_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 on distributed training like TPUs when one need to replace `torch.save` by another method. Can be configured with the environment variable `DIFFUSERS_SAVE_MODE`.

Save the LoRA parameters corresponding to the UNet and the text encoder.

###
FromCkptMixin

###
class diffusers.loaders.FromCkptMixin

< source >

( )

This helper class allows to directly load .ckpt stable diffusion file_extension into the respective classes.

####
from_ckpt

< source >

( pretrained_model_link_or_path **kwargs )

Parameters

- pretrained_model_link_or_path (`str` or `os.PathLike`, optional) — Can be either:
  - A link to the .ckpt file on the Hub. Should be in the format `"https://huggingface.co/<repo_id>/blob/main/<path_to_file>"`
  - A path to a file containing all pipeline weights.
- torch_dtype (`str` or `torch.dtype`, optional) — Override the default `torch.dtype` and load the model under this dtype. If `"auto"` is passed the dtype will be automatically derived from the model’s weights.
- 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.
- cache_dir (`Union[str, os.PathLike]`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'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 or not to only look at local files (i.e., do not try to download the model).
- use_auth_token (`str` or bool, optional) — The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated when running `huggingface-cli login` (stored in `~/.huggingface`).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- use_safetensors (`bool`, optional, defaults to `None`) — If set to `None`, the pipeline will load the `safetensors` weights if they’re available and if the `safetensors` library is installed. If set to `True`, the pipeline will forcibly load the models from `safetensors` weights. If set to `False` the pipeline will not use `safetensors`.
- extract_ema (`bool`, optional, defaults to `False`) — Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights or not. Defaults to `False`. Pass `True` to extract the EMA weights. EMA weights usually yield higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.
- upcast_attention (`bool`, optional, defaults to `None`) — Whether the attention computation should always be upcasted. This is necessary when running stable
- image_size (`int`, optional, defaults to 512) — The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Diffusion v2 Base. Use 768 for Stable Diffusion v2.
- prediction_type (`str`, optional) — The prediction type that the model was trained on. Use `'epsilon'` for Stable Diffusion v1.X and Stable Diffusion v2 Base. Use `'v_prediction'` for Stable Diffusion v2.
- num_in_channels (`int`, optional, defaults to None) — The number of input channels. If `None`, it will be automatically inferred.
- scheduler_type (`str`, optional, defaults to ‘pndm’) — Type of scheduler to use. Should be one of `["pndm", "lms", "heun", "euler", "euler-ancestral", "dpm", "ddim"]`.
- load_safety_checker (`bool`, optional, defaults to `True`) — Whether to load the safety checker or not. Defaults to `True`.
- kwargs (remaining dictionary of keyword arguments, optional) — Can be used to overwrite load - and saveable variables - i.e. the pipeline components - of the specific pipeline class. The overwritten components are then directly passed to the pipelines `__init__` method. See example below for more information.

Instantiate a PyTorch diffusion pipeline from pre-trained pipeline weights saved in the original .ckpt format.

The pipeline is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated).

Examples:

    >>> from diffusers import StableDiffusionPipeline

    >>> # Download pipeline from huggingface.co and cache.
    >>> pipeline = StableDiffusionPipeline.from_ckpt(
    ...     "https://huggingface.co/WarriorMama777/OrangeMixs/blob/main/Models/AbyssOrangeMix/AbyssOrangeMix.safetensors"
    ... )

    >>> # Download pipeline from local file
    >>> # file is downloaded under ./v1-5-pruned-emaonly.ckpt
    >>> pipeline = StableDiffusionPipeline.from_ckpt("./v1-5-pruned-emaonly")

    >>> # Enable float16 and move to GPU
    >>> pipeline = StableDiffusionPipeline.from_ckpt(
    ...     "https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.ckpt",
    ...     torch_dtype=torch.float16,
    ... )
    >>> pipeline.to("cuda")