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AutoModel

The AutoModel is designed to make it easy to load a checkpoint without needing to know the specific model class. AutoModel automatically retrieves the correct model class from the checkpoint config.json file.

from diffusers import AutoModel, AutoPipelineForText2Image

unet = AutoModel.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="unet")
pipe = AutoPipelineForText2Image.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", unet=unet)

AutoModel

class diffusers.AutoModel

< source >

( *args **kwargs )

from_pretrained

< source >

( pretrained_model_or_path: typing.Union[str, os.PathLike, NoneType] = None **kwargs )

Parameters

pretrained_model_name_or_path (str or os.PathLike, optional) — Can be either:
- A string, the model id (for example google/ddpm-celebahq-256) of a pretrained model hosted on the Hub.
- A path to a directory (for example ./my_model_directory) containing the model weights saved with save_pretrained().
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.
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.
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.
output_loading_info (bool, optional, defaults to False) — Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
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.
from_flax (bool, optional, defaults to False) — Load the model weights from a Flax checkpoint save file.
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.
device_map (str or Dict[str, Union[int, str, torch.device]], optional) — A map that specifies where each submodule should go. It doesn’t need to be defined for each parameter/buffer name; once a given module name is inside, every submodule of it will be sent to the same device. Defaults to None, meaning that the model will be loaded on CPU.

Set device_map="auto" to have 🤗 Accelerate automatically compute the most optimized device_map. For more information about each option see designing a device map.
max_memory (Dict, optional) — A dictionary device identifier for the maximum memory. Will default to the maximum memory available for each GPU and the available CPU RAM if unset.
offload_folder (str or os.PathLike, optional) — The path to offload weights if device_map contains the value "disk".
offload_state_dict (bool, optional) — If True, temporarily offloads the CPU state dict to the hard drive to avoid running out of CPU RAM if the weight of the CPU state dict + the biggest shard of the checkpoint does not fit. Defaults to True when there is some disk offload.
low_cpu_mem_usage (bool, optional, defaults to True if torch version >= 1.9.0 else False) — Speed up model loading only loading the pretrained weights and not initializing the weights. This also tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model. Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this argument to True will raise an error.
variant (str, optional) — Load weights from a specified variant filename such as "fp16" or "ema". This is ignored when loading from_flax.
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.
disable_mmap (‘bool’, optional, defaults to ‘False’) — Whether to disable mmap when loading a Safetensors model. This option can perform better when the model is on a network mount or hard drive, which may not handle the seeky-ness of mmap very well.

Instantiate a pretrained PyTorch model from a pretrained model configuration.

The model is set in evaluation mode - model.eval() - by default, and dropout modules are deactivated. To train the model, set it back in training mode with model.train().

To use private or gated models, log-in with huggingface-cli login. You can also activate the special “offline-mode” to use this method in a firewalled environment.

Example:

from diffusers import AutoModel

unet = AutoModel.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="unet")

If you get the error message below, you need to finetune the weights for your downstream task:

Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:
- conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

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