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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. |
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: Copied 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: Copied 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 < source > ( 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: Copied 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 < source > ( save_directory: Union is_main_process: bool = True save_function: Optional = None safe_serialization: bool = True variant: Optional = None push_to_hub: bool = False **kwargs ) 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 True) — |
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. push_to_hub (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). kwargs (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. FlaxModelMixin class diffusers.FlaxModelMixin < source > ( ) Base class for all Flax models. FlaxModelMixin takes care of storing the model configuration and provides methods for loading, downloading and |
saving models. config_name (str) — Filename to save a model to when calling save_pretrained(). from_pretrained < source > ( pretrained_model_name_or_path: Union 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: |
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