Hugging Face's logo

Diffusers documentation

Models

You are viewing v0.19.2 version. A newer version v0.31.0 is available.
Hugging Face's logo
Join the Hugging Face community

and get access to the augmented documentation experience

to get started

Models

🤗 Diffusers provides pretrained models for popular algorithms and modules to create custom diffusion systems. The primary function of models is to denoise an input sample as modeled by the distribution pθ(xt−1∣xt)p_{\theta}(x_{t-1}|x_{t}).

All models are built from the base ModelMixin class which is a torch.nn.module providing basic functionality for saving and loading models, locally and from the Hugging Face Hub.

ModelMixin

class diffusers.ModelMixin

< >

( )

Base class for all models.

ModelMixin takes care of storing the model configuration and provides methods for loading, downloading and saving models.

disable_gradient_checkpointing

< >

( )

Deactivates gradient checkpointing for the current model (may be referred to as activation checkpointing or checkpoint activations in other frameworks).

disable_xformers_memory_efficient_attention

< >

( )

Disable memory efficient attention from xFormers.

enable_gradient_checkpointing

< >

( )

Activates gradient checkpointing for the current model (may be referred to as activation checkpointing or checkpoint activations in other frameworks).

enable_xformers_memory_efficient_attention

< >

( attention_op: typing.Optional[typing.Callable] = 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:

>>> import torch
>>> from diffusers import UNet2DConditionModel
>>> from xformers.ops import MemoryEfficientAttentionFlashAttentionOp

>>> model = UNet2DConditionModel.from_pretrained(
...     "stabilityai/stable-diffusion-2-1", subfolder="unet", torch_dtype=torch.float16
... )
>>> model = model.to("cuda")
>>> model.enable_xformers_memory_efficient_attention(attention_op=MemoryEfficientAttentionFlashAttentionOp)

from_pretrained

< >

( pretrained_model_name_or_path: typing.Union[str, os.PathLike, NoneType] **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.
  • 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.
  • 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.
  • use_auth_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.

    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.

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 UNet2DConditionModel

unet = UNet2DConditionModel.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.

num_parameters

< >

( only_trainable: bool = False exclude_embeddings: bool = False ) → int

Parameters

  • only_trainable (bool, optional, defaults to False) — Whether or not to return only the number of trainable parameters.
  • exclude_embeddings (bool, optional, defaults to False) — Whether or not to return only the number of non-embedding parameters.

Returns

int

The number of parameters.

Get number of (trainable or non-embedding) parameters in the module.

Example:

from diffusers import UNet2DConditionModel

model_id = "runwayml/stable-diffusion-v1-5"
unet = UNet2DConditionModel.from_pretrained(model_id, subfolder="unet")
unet.num_parameters(only_trainable=True)
859520964

save_pretrained

< >

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

Parameters

  • save_directory (str or os.PathLike) — Directory to save a model and its configuration file to. 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 during distributed training and you 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 during distributed training when you need to replace torch.save with another method. Can be configured with the environment variable DIFFUSERS_SAVE_MODE.
  • safe_serialization (bool, optional, defaults to False) — Whether to save the model using safetensors or the traditional PyTorch way with pickle.
  • variant (str, optional) — If specified, weights are saved in the format pytorch_model.<variant>.bin.

Save a model and its configuration file to a directory so that it can be reloaded using the from_pretrained() class method.

FlaxModelMixin

class diffusers.FlaxModelMixin

< >

( )

Base class for all Flax models.

FlaxModelMixin takes care of storing the model configuration and provides methods for loading, downloading and saving models.

from_pretrained

< >

( pretrained_model_name_or_path: typing.Union[str, os.PathLike] dtype: dtype = <class 'jax.numpy.float32'> *model_args **kwargs )

Parameters

  • pretrained_model_name_or_path (str or os.PathLike) — Can be either:

    • A string, the model id (for example runwayml/stable-diffusion-v1-5) of a pretrained model hosted on the Hub.
    • A path to a directory (for example ./my_model_directory) containing the model weights saved using save_pretrained().
  • dtype (jax.numpy.dtype, optional, defaults to jax.numpy.float32) — The data type of the computation. Can be one of jax.numpy.float32, jax.numpy.float16 (on GPUs) and jax.numpy.bfloat16 (on TPUs).

    This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If specified, all the computation will be performed with the given dtype.

    This only specifies the dtype of the computation and does not influence the dtype of model parameters.

    If you wish to change the dtype of the model parameters, see to_fp16() and to_bf16().
  • model_args (sequence of positional arguments, optional) — All remaining positional arguments are passed to the underlying model’s __init__ method.
  • 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.
  • 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_pt (bool, optional, defaults to False) — Load the model weights from a PyTorch checkpoint save file.
  • kwargs (remaining dictionary of keyword arguments, optional) — Can be used to update the configuration object (after it is loaded) and initiate the model (for example, output_attentions=True). Behaves differently depending on whether a config is provided or automatically loaded:

    • If a configuration is provided with config, kwargs are directly passed to the underlying model’s __init__ method (we assume all relevant updates to the configuration have already been done).
    • If a configuration is not provided, kwargs are first passed to the configuration class initialization function from_config(). Each key of the kwargs that corresponds to a configuration attribute is used to override said attribute with the supplied kwargs value. Remaining keys that do not correspond to any configuration attribute are passed to the underlying model’s __init__ function.

Instantiate a pretrained Flax model from a pretrained model configuration.

Examples:

>>> from diffusers import FlaxUNet2DConditionModel

>>> # Download model and configuration from huggingface.co and cache.
>>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5")
>>> # Model was saved using *save_pretrained('./test/saved_model/')* (for example purposes, not runnable).
>>> model, params = FlaxUNet2DConditionModel.from_pretrained("./test/saved_model/")

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.

save_pretrained

< >

( save_directory: typing.Union[str, os.PathLike] params: typing.Union[typing.Dict, flax.core.frozen_dict.FrozenDict] is_main_process: bool = True )

Parameters

  • save_directory (str or os.PathLike) — Directory to save a model and its configuration file to. Will be created if it doesn’t exist.
  • params (Union[Dict, FrozenDict]) — A PyTree of model parameters.
  • is_main_process (bool, optional, defaults to True) — 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, set is_main_process=True only on the main process to avoid race conditions.

Save a model and its configuration file to a directory so that it can be reloaded using the from_pretrained() class method.

to_bf16

< >

( params: typing.Union[typing.Dict, flax.core.frozen_dict.FrozenDict] mask: typing.Any = None )

Parameters

  • params (Union[Dict, FrozenDict]) — A PyTree of model parameters.
  • mask (Union[Dict, FrozenDict]) — A PyTree with same structure as the params tree. The leaves should be booleans. It should be True for params you want to cast, and False for those you want to skip.

Cast the floating-point params to jax.numpy.bfloat16. This returns a new params tree and does not cast the params in place.

This method can be used on a TPU to explicitly convert the model parameters to bfloat16 precision to do full half-precision training or to save weights in bfloat16 for inference in order to save memory and improve speed.

Examples:

>>> from diffusers import FlaxUNet2DConditionModel

>>> # load model
>>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5")
>>> # By default, the model parameters will be in fp32 precision, to cast these to bfloat16 precision
>>> params = model.to_bf16(params)
>>> # If you don't want to cast certain parameters (for example layer norm bias and scale)
>>> # then pass the mask as follows
>>> from flax import traverse_util

>>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5")
>>> flat_params = traverse_util.flatten_dict(params)
>>> mask = {
...     path: (path[-2] != ("LayerNorm", "bias") and path[-2:] != ("LayerNorm", "scale"))
...     for path in flat_params
... }
>>> mask = traverse_util.unflatten_dict(mask)
>>> params = model.to_bf16(params, mask)

to_fp16

< >

( params: typing.Union[typing.Dict, flax.core.frozen_dict.FrozenDict] mask: typing.Any = None )

Parameters

  • params (Union[Dict, FrozenDict]) — A PyTree of model parameters.
  • mask (Union[Dict, FrozenDict]) — A PyTree with same structure as the params tree. The leaves should be booleans. It should be True for params you want to cast, and False for those you want to skip.

Cast the floating-point params to jax.numpy.float16. This returns a new params tree and does not cast the params in place.

This method can be used on a GPU to explicitly convert the model parameters to float16 precision to do full half-precision training or to save weights in float16 for inference in order to save memory and improve speed.

Examples:

>>> from diffusers import FlaxUNet2DConditionModel

>>> # load model
>>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5")
>>> # By default, the model params will be in fp32, to cast these to float16
>>> params = model.to_fp16(params)
>>> # If you want don't want to cast certain parameters (for example layer norm bias and scale)
>>> # then pass the mask as follows
>>> from flax import traverse_util

>>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5")
>>> flat_params = traverse_util.flatten_dict(params)
>>> mask = {
...     path: (path[-2] != ("LayerNorm", "bias") and path[-2:] != ("LayerNorm", "scale"))
...     for path in flat_params
... }
>>> mask = traverse_util.unflatten_dict(mask)
>>> params = model.to_fp16(params, mask)

to_fp32

< >

( params: typing.Union[typing.Dict, flax.core.frozen_dict.FrozenDict] mask: typing.Any = None )

Parameters

  • params (Union[Dict, FrozenDict]) — A PyTree of model parameters.
  • mask (Union[Dict, FrozenDict]) — A PyTree with same structure as the params tree. The leaves should be booleans. It should be True for params you want to cast, and False for those you want to skip.

Cast the floating-point params to jax.numpy.float32. This method can be used to explicitly convert the model parameters to fp32 precision. This returns a new params tree and does not cast the params in place.

Examples:

>>> from diffusers import FlaxUNet2DConditionModel

>>> # Download model and configuration from huggingface.co
>>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5")
>>> # By default, the model params will be in fp32, to illustrate the use of this method,
>>> # we'll first cast to fp16 and back to fp32
>>> params = model.to_f16(params)
>>> # now cast back to fp32
>>> params = model.to_fp32(params)
←VAE Image Processor UNet1DModel→