#
Single files

Diffusers supports loading pretrained pipeline (or model) weights stored in a single file, such as a `ckpt` or `safetensors` file. These single file types are typically produced from community trained models. There are three classes for loading single file weights:

- `FromSingleFileMixin` supports loading pretrained pipeline weights stored in a single file, which can either be a `ckpt` or `safetensors` file.
- `FromOriginalVAEMixin` supports loading a pretrained AutoencoderKL from pretrained ControlNet weights stored in a single file, which can either be a `ckpt` or `safetensors` file.
- `FromOriginalControlnetMixin` supports loading pretrained ControlNet weights stored in a single file, which can either be a `ckpt` or `safetensors` file.

To learn more about how to load single file weights, see the Load different Stable Diffusion formats loading guide.

##
FromSingleFileMixin

###
class diffusers.loaders.FromSingleFileMixin

< source >

( )

Load model weights saved in the `.ckpt` format into a DiffusionPipeline.

####
from_single_file

< 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 (for example `"https://huggingface.co/<repo_id>/blob/main/<path_to_file>.ckpt"`) on the Hub.
  - 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 with another dtype. If `"auto"` is passed, the dtype is 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 where a downloaded pretrained model configuration is cached if the standard cache is not used.
- 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.
- use_safetensors (`bool`, optional, defaults to `None`) — If set to `None`, the safetensors weights are downloaded if they’re available and if the safetensors library is installed. If set to `True`, the model is forcibly loaded from safetensors weights. If set to `False`, safetensors weights are not loaded.
- extract_ema (`bool`, optional, defaults to `False`) — Whether to extract the EMA weights or not. Pass `True` to extract the EMA weights which usually yield higher quality images for inference. Non-EMA weights are usually better for continuing finetuning.
- upcast_attention (`bool`, optional, defaults to `None`) — Whether the attention computation should always be upcasted.
- image_size (`int`, optional, defaults to 512) — The image size the model was trained on. Use 512 for all Stable Diffusion v1 models and the Stable Diffusion v2 base model. Use 768 for Stable Diffusion v2.
- prediction_type (`str`, optional) — The prediction type the model was trained on. Use `'epsilon'` for all Stable Diffusion v1 models and the Stable Diffusion v2 base model. Use `'v_prediction'` for Stable Diffusion v2.
- num_in_channels (`int`, optional, defaults to `None`) — The number of input channels. If `None`, it is 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.
- text_encoder (CLIPTextModel, optional, defaults to `None`) — An instance of `CLIPTextModel` to use, specifically the clip-vit-large-patch14 variant. If this parameter is `None`, the function loads a new instance of `CLIPTextModel` by itself if needed.
- vae (`AutoencoderKL`, optional, defaults to `None`) — Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. If this parameter is `None`, the function will load a new instance of [CLIP] by itself, if needed.
- tokenizer (CLIPTokenizer, optional, defaults to `None`) — An instance of `CLIPTokenizer` to use. If this parameter is `None`, the function loads a new instance of `CLIPTokenizer` by itself if needed.
- original_config_file (`str`) — Path to `.yaml` config file corresponding to the original architecture. If `None`, will be automatically inferred by looking for a key that only exists in SD2.0 models.
- kwargs (remaining dictionary of keyword arguments, optional) — Can be used to overwrite load and saveable variables (for example the pipeline components of the specific pipeline class). The overwritten components are directly passed to the pipelines `__init__` method. See example below for more information.

Instantiate a DiffusionPipeline from pretrained pipeline weights saved in the `.ckpt` or `.safetensors` format. The pipeline is set in evaluation mode (`model.eval()`) by default.

Examples:

    >>> from diffusers import StableDiffusionPipeline

    >>> # Download pipeline from huggingface.co and cache.
    >>> pipeline = StableDiffusionPipeline.from_single_file(
    ...     "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_single_file("./v1-5-pruned-emaonly")

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

##
FromOriginalVAEMixin

###
class diffusers.loaders.FromOriginalVAEMixin

< source >

( )

Load pretrained ControlNet weights saved in the `.ckpt` or `.safetensors` format into an AutoencoderKL.

####
from_single_file

< 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 (for example `"https://huggingface.co/<repo_id>/blob/main/<path_to_file>.ckpt"`) on the Hub.
  - 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 with another dtype. If `"auto"` is passed, the dtype is 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 where a downloaded pretrained model configuration is cached if the standard cache is not used.
- 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.
- image_size (`int`, optional, defaults to 512) — The image size the model was trained on. Use 512 for all Stable Diffusion v1 models and the Stable Diffusion v2 base model. Use 768 for Stable Diffusion v2.
- use_safetensors (`bool`, optional, defaults to `None`) — If set to `None`, the safetensors weights are downloaded if they’re available and if the safetensors library is installed. If set to `True`, the model is forcibly loaded from safetensors weights. If set to `False`, safetensors weights are not loaded.
- upcast_attention (`bool`, optional, defaults to `None`) — Whether the attention computation should always be upcasted.
- scaling_factor (`float`, optional, defaults to 0.18215) — The component-wise standard deviation of the trained latent space computed using the first batch of the training set. This is used to scale the latent space to have unit variance when training the diffusion model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z = 1 / scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the High-Resolution Image Synthesis with Latent Diffusion Models paper.
- kwargs (remaining dictionary of keyword arguments, optional) — Can be used to overwrite load and saveable variables (for example the pipeline components of the specific pipeline class). The overwritten components are directly passed to the pipelines `__init__` method. See example below for more information.

Instantiate a AutoencoderKL from pretrained ControlNet weights saved in the original `.ckpt` or `.safetensors` format. The pipeline is set in evaluation mode (`model.eval()`) by default.

Make sure to pass both `image_size` and `scaling_factor` to `from_single_file()` if you’re loading a VAE from SDXL or a Stable Diffusion v2 model or higher.

Examples:

    from diffusers import AutoencoderKL

    url = "https://huggingface.co/stabilityai/sd-vae-ft-mse-original/blob/main/vae-ft-mse-840000-ema-pruned.safetensors"  # can also be local file
    model = AutoencoderKL.from_single_file(url)

##
FromOriginalControlnetMixin

###
class diffusers.loaders.FromOriginalControlnetMixin

< source >

( )

Load pretrained ControlNet weights saved in the `.ckpt` or `.safetensors` format into a ControlNetModel.

####
from_single_file

< 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 (for example `"https://huggingface.co/<repo_id>/blob/main/<path_to_file>.ckpt"`) on the Hub.
  - 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 with another dtype. If `"auto"` is passed, the dtype is 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 where a downloaded pretrained model configuration is cached if the standard cache is not used.
- 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.
- use_safetensors (`bool`, optional, defaults to `None`) — If set to `None`, the safetensors weights are downloaded if they’re available and if the safetensors library is installed. If set to `True`, the model is forcibly loaded from safetensors weights. If set to `False`, safetensors weights are not loaded.
- image_size (`int`, optional, defaults to 512) — The image size the model was trained on. Use 512 for all Stable Diffusion v1 models and the Stable Diffusion v2 base model. Use 768 for Stable Diffusion v2.
- upcast_attention (`bool`, optional, defaults to `None`) — Whether the attention computation should always be upcasted.
- kwargs (remaining dictionary of keyword arguments, optional) — Can be used to overwrite load and saveable variables (for example the pipeline components of the specific pipeline class). The overwritten components are directly passed to the pipelines `__init__` method. See example below for more information.

Instantiate a ControlNetModel from pretrained ControlNet weights saved in the original `.ckpt` or `.safetensors` format. The pipeline is set in evaluation mode (`model.eval()`) by default.

Examples:

    from diffusers import StableDiffusionControlNetPipeline, ControlNetModel

    url = "https://huggingface.co/lllyasviel/ControlNet-v1-1/blob/main/control_v11p_sd15_canny.pth"  # can also be a local path
    model = ControlNetModel.from_single_file(url)

    url = "https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned.safetensors"  # can also be a local path
    pipe = StableDiffusionControlNetPipeline.from_single_file(url, controlnet=controlnet)