🤗 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 .
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.
Base class for all models.
ModelMixin takes care of storing the model configuration and provides methods for loading, downloading and saving models.
str
) — Filename to save a model to when calling save_pretrained().Gets the current list of active adapters of the model.
If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: https://huggingface.co/docs/peft
Adds a new adapter to the current model for training. If no adapter name is passed, a default name is assigned to the adapter to follow the convention of the PEFT library.
If you are not familiar with adapters and PEFT methods, we invite you to read more about them in the PEFT documentation.
Disable all adapters attached to the model and fallback to inference with the base model only.
If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: https://huggingface.co/docs/peft
Deactivates gradient checkpointing for the current model (may be referred to as activation checkpointing or checkpoint activations in other frameworks).
Disable memory efficient attention from xFormers.
Enable adapters that are attached to the model. The model will use self.active_adapters()
to retrieve the
list of adapters to enable.
If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: https://huggingface.co/docs/peft
Activates gradient checkpointing for the current model (may be referred to as activation checkpointing or checkpoint activations in other frameworks).
( attention_op: typing.Optional[typing.Callable] = None )
Parameters
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)
( pretrained_model_name_or_path: typing.Union[str, os.PathLike, NoneType] **kwargs )
Parameters
str
or os.PathLike
, optional) —
Can be either:
google/ddpm-celebahq-256
) of a pretrained model hosted on
the Hub../my_model_directory
) containing the model weights saved
with save_pretrained().Union[str, os.PathLike]
, optional) —
Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
is not used. 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. 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. 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. 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. bool
, optional, defaults to False
) —
Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages. 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. 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. 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. bool
, optional, defaults to False
) —
Load the model weights from a Flax checkpoint save file. str
, optional, defaults to ""
) —
The subfolder location of a model file within a larger model repository on the Hub or locally. 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. 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.
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. str
or os.PathLike
, optional) —
The path to offload weights if device_map
contains the value "disk"
. 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. 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. str
, optional) —
Load weights from a specified variant
filename such as "fp16"
or "ema"
. This is ignored when
loading from_flax
. 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.
( only_trainable: bool = False exclude_embeddings: bool = False ) → int
Get number of (trainable or non-embedding) parameters in the module.
( save_directory: typing.Union[str, os.PathLike] is_main_process: bool = True save_function: typing.Callable = None safe_serialization: bool = True variant: typing.Optional[str] = None push_to_hub: bool = False **kwargs )
Parameters
str
or os.PathLike
) —
Directory to save a model and its configuration file to. Will be created if it doesn’t exist. 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. 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
. bool
, optional, defaults to True
) —
Whether to save the model using safetensors
or the traditional PyTorch way with pickle
. str
, optional) —
If specified, weights are saved in the format pytorch_model.<variant>.bin
. bool
, optional, defaults to False
) —
Whether or not to push your model to the Hugging Face Hub after saving it. You can specify the
repository you want to push to with repo_id
(will default to the name of save_directory
in your
namespace). Dict[str, Any]
, optional) —
Additional keyword arguments passed along to the push_to_hub() method. Save a model and its configuration file to a directory so that it can be reloaded using the from_pretrained() class method.
( adapter_name: typing.Union[str, typing.List[str]] )
Sets a specific adapter by forcing the model to only use that adapter and disables the other adapters.
If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: https://huggingface.co/docs/peft
Base class for all Flax models.
FlaxModelMixin takes care of storing the model configuration and provides methods for loading, downloading and saving models.
str
) — Filename to save a model to when calling save_pretrained().( pretrained_model_name_or_path: typing.Union[str, os.PathLike] dtype: dtype = <class 'jax.numpy.float32'> *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
runwayml/stable-diffusion-v1-5
) of a pretrained model
hosted on the Hub../my_model_directory
) containing the model weights saved
using save_pretrained().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
.
__init__
method. Union[str, os.PathLike]
, optional) —
Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
is not used. 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. 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. 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. 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. 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. bool
, optional, defaults to False
) —
Load the model weights from a PyTorch checkpoint save file. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, kwargs
are directly passed to the underlying
model’s __init__
method (we assume all relevant updates to the configuration have already been
done).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_directory: typing.Union[str, os.PathLike] params: typing.Union[typing.Dict, flax.core.frozen_dict.FrozenDict] is_main_process: bool = True push_to_hub: bool = False **kwargs )
Parameters
str
or os.PathLike
) —
Directory to save a model and its configuration file to. Will be created if it doesn’t exist. Union[Dict, FrozenDict]
) —
A PyTree
of model parameters. 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. bool
, optional, defaults to False
) —
Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the
repository you want to push to with repo_id
(will default to the name of save_directory
in your
namespace). Dict[str, Any]
, optional) —
Additional key word arguments passed along to the push_to_hub() method. Save a model and its configuration file to a directory so that it can be reloaded using the from_pretrained() class method.
( params: typing.Union[typing.Dict, flax.core.frozen_dict.FrozenDict] mask: typing.Any = None )
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)
( params: typing.Union[typing.Dict, flax.core.frozen_dict.FrozenDict] mask: typing.Any = None )
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)
( params: typing.Union[typing.Dict, flax.core.frozen_dict.FrozenDict] mask: typing.Any = None )
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)
A Mixin to push a model, scheduler, or pipeline to the Hugging Face Hub.
( repo_id: str commit_message: typing.Optional[str] = None private: typing.Optional[bool] = None token: typing.Optional[str] = None create_pr: bool = False safe_serialization: bool = True variant: typing.Optional[str] = None )
Parameters
str
) —
The name of the repository you want to push your model, scheduler, or pipeline files to. It should
contain your organization name when pushing to an organization. repo_id
can also be a path to a local
directory. str
, optional) —
Message to commit while pushing. Default to "Upload {object}"
. bool
, optional) —
Whether or not the repository created should be private. str
, optional) —
The token to use as HTTP bearer authorization for remote files. The token generated when running
huggingface-cli login
(stored in ~/.huggingface
). bool
, optional, defaults to False
) —
Whether or not to create a PR with the uploaded files or directly commit. bool
, optional, defaults to True
) —
Whether or not to convert the model weights to the safetensors
format. str
, optional) —
If specified, weights are saved in the format pytorch_model.<variant>.bin
. Upload model, scheduler, or pipeline files to the 🤗 Hugging Face Hub.
Examples:
from diffusers import UNet2DConditionModel
unet = UNet2DConditionModel.from_pretrained("stabilityai/stable-diffusion-2", subfolder="unet")
# Push the `unet` to your namespace with the name "my-finetuned-unet".
unet.push_to_hub("my-finetuned-unet")
# Push the `unet` to an organization with the name "my-finetuned-unet".
unet.push_to_hub("your-org/my-finetuned-unet")