The from_single_file
method allows you to load supported pipelines using a single checkpoint file as opposed to the folder format used by Diffusers. This is useful if you are working with many of the Stable Diffusion Web UI’s (such as A1111) that extensively rely on a single file to distribute all the components of a diffusion model.
The from_single_file
method also supports loading models in their originally distributed format. This means that supported models that have been finetuned with other services can be loaded directly into supported Diffusers model objects and pipelines.
StableCascadeUNet
from diffusers import StableDiffusionXLPipeline
ckpt_path = "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0_0.9vae.safetensors"
pipe = StableDiffusionXLPipeline.from_single_file(ckpt_path)
Swap components of the pipeline by passing them directly to the from_single_file
method. e.g If you would like use a different scheduler than the pipeline default.
from diffusers import StableDiffusionXLPipeline, DDIMScheduler
ckpt_path = "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0_0.9vae.safetensors"
scheduler = DDIMScheduler()
pipe = StableDiffusionXLPipeline.from_single_file(ckpt_path, scheduler=scheduler)
from diffusers import StableDiffusionPipeline, ControlNetModel
ckpt_path = "https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.safetensors"
controlnet = ControlNetModel.from_pretrained("https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.safetensors")
pipe = StableDiffusionPipeline.from_single_file(ckpt_path, controlnet=controlnet)
from diffusers import StableCascadeUNet
ckpt_path = "https://huggingface.co/stabilityai/stable-cascade/blob/main/stage_b_lite.safetensors"
model = StableCascadeUNet.from_single_file(ckpt_path)
Under the hood, from_single_file
will try to determine a model repository to use to configure the components of the pipeline. You can also pass in a repository id to the config
argument of the from_single_file
method to explicitly set the repository to use.
from diffusers import StableDiffusionXLPipeline
ckpt_path = "https://huggingface.co/segmind/SSD-1B/blob/main/SSD-1B.safetensors"
repo_id = "segmind/SSD-1B"
pipe = StableDiffusionXLPipeline.from_single_file(ckpt_path, config=repo_id)
Override the default model or pipeline configuration options when using from_single_file
by passing in the relevant arguments directly to the from_single_file
method. Any argument that is supported by the model or pipeline class can be configured in this way:
from diffusers import StableDiffusionXLInstructPix2PixPipeline
ckpt_path = "https://huggingface.co/stabilityai/cosxl/blob/main/cosxl_edit.safetensors"
pipe = StableDiffusionXLInstructPix2PixPipeline.from_single_file(ckpt_path, config="diffusers/sdxl-instructpix2pix-768", is_cosxl_edit=True)
from diffusers import UNet2DConditionModel
ckpt_path = "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0_0.9vae.safetensors"
model = UNet2DConditionModel.from_single_file(ckpt_path, upcast_attention=True)
In the example above, since we explicitly passed repo_id="segmind/SSD-1B"
, it will use this configuration file from the “unet” subfolder in "segmind/SSD-1B"
to configure the unet component included in the checkpoint; Similarly, it will use the config.json
file from "vae"
subfolder to configure the vae model, config.json
file from text_encoder folder to configure text_encoder and so on.
Note that most of the time you do not need to explicitly a config
argument, from_single_file
will automatically map the checkpoint to a repo id (we will discuss this in more details in next section). However, this can be useful in cases where model components might have been changed from what was originally distributed or in cases where a checkpoint file might not have the necessary metadata to correctly determine the configuration to use for the pipeline.
To learn more about how to load single file weights, see the Load different Stable Diffusion formats loading guide.
As of diffusers>=0.28.0
the from_single_file
method will attempt to configure a pipeline or model by first inferring the model type from the checkpoint file and then using the model type to determine the appropriate model repo configuration to use from the Hugging Face Hub. For example, any single file checkpoint based on the Stable Diffusion XL base model will use the stabilityai/stable-diffusion-xl-base-1.0
model repo to configure the pipeline.
If you are working in an environment with restricted internet access, it is recommended to download the config files and checkpoints for the model to your preferred directory and pass the local paths to the pretrained_model_link_or_path
and config
arguments of the from_single_file
method.
from huggingface_hub import hf_hub_download, snapshot_download
my_local_checkpoint_path = hf_hub_download(
repo_id="segmind/SSD-1B",
filename="SSD-1B.safetensors"
)
my_local_config_path = snapshot_download(
repo_id="segmind/SSD-1B",
allowed_patterns=["*.json", "**/*.json", "*.txt", "**/*.txt"]
)
pipe = StableDiffusionXLPipeline.from_single_file(my_local_checkpoint_path, config=my_local_config_path, local_files_only=True)
By default this will download the checkpoints and config files to the Hugging Face Hub cache directory. You can also specify a local directory to download the files to by passing the local_dir
argument to the hf_hub_download
and snapshot_download
functions.
from huggingface_hub import hf_hub_download, snapshot_download
my_local_checkpoint_path = hf_hub_download(
repo_id="segmind/SSD-1B",
filename="SSD-1B.safetensors"
local_dir="my_local_checkpoints"
)
my_local_config_path = snapshot_download(
repo_id="segmind/SSD-1B",
allowed_patterns=["*.json", "**/*.json", "*.txt", "**/*.txt"]
local_dir="my_local_config"
)
pipe = StableDiffusionXLPipeline.from_single_file(my_local_checkpoint_path, config=my_local_config_path, local_files_only=True)
By default the from_single_file
method relies on the huggingface_hub
caching mechanism to fetch and store checkpoints and config files for models and pipelines. If you are working with a file system that does not support symlinking, it is recommended that you first download the checkpoint file to a local directory and disable symlinking by passing the local_dir_use_symlink=False
argument to the hf_hub_download
and snapshot_download
functions.
from huggingface_hub import hf_hub_download, snapshot_download
my_local_checkpoint_path = hf_hub_download(
repo_id="segmind/SSD-1B",
filename="SSD-1B.safetensors"
local_dir="my_local_checkpoints",
local_dir_use_symlinks=False
)
print("My local checkpoint: ", my_local_checkpoint_path)
my_local_config_path = snapshot_download(
repo_id="segmind/SSD-1B",
allowed_patterns=["*.json", "**/*.json", "*.txt", "**/*.txt"]
local_dir_use_symlinks=False,
)
print("My local config: ", my_local_config_path)
Then pass the local paths to the pretrained_model_link_or_path
and config
arguments of the from_single_file
method.
pipe = StableDiffusionXLPipeline.from_single_file(my_local_checkpoint_path, config=my_local_config_path, local_files_only=True)
If you would like to configure the parameters of the model components in the pipeline using the orignal YAML configuration file, you can pass a local path or url to the original configuration file to the original_config
argument of the from_single_file
method.
from diffusers import StableDiffusionXLPipeline
ckpt_path = "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0_0.9vae.safetensors"
repo_id = "stabilityai/stable-diffusion-xl-base-1.0"
original_config = "https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_base.yaml"
pipe = StableDiffusionXLPipeline.from_single_file(ckpt_path, original_config=original_config)
In the example above, the original_config
file is only used to configure the parameters of the individual model components of the pipeline. For example it will be used to configure parameters such as the in_channels
of the vae
model and unet
model. It is not used to determine the type of component objects in the pipeline.
This is not as reliable as providing a path to a local config repo and might lead to errors when configuring the pipeline. To avoid this, please run the pipeline with local_files_only=False
once to download the appropriate pipeline config files to the local cache.
Load model weights saved in the .ckpt
format into a DiffusionPipeline.
( pretrained_model_link_or_path **kwargs )
Parameters
str
or os.PathLike
, optional) —
Can be either:.ckpt
file (for example
"https://huggingface.co/<repo_id>/blob/main/<path_to_file>.ckpt"
) on the Hub.str
or torch.dtype
, optional) —
Override the default torch.dtype
and load the model with another dtype. 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. 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 —
Deprecated and ignored. All downloads are now resumed by default when possible. Will be removed in v1
of Diffusers. 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
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. str
, optional) —
The path to the original config file that was used to train the model. If not provided, the config file
will be inferred from the checkpoint file. str
, optional) —
Can be either:CompVis/ldm-text2im-large-256
) of a pretrained pipeline
hosted on the Hub../my_pipeline_directory/
) containing the pipeline
component configs in Diffusers format.__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.ckpt")
>>> # 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")
Load pretrained weights saved in the .ckpt
or .safetensors
format into a model.
( pretrained_model_link_or_path_or_dict: Optional = None **kwargs )
Parameters
str
, optional) —
Can be either:.safetensors
or .ckpt
file (for example
"https://huggingface.co/<repo_id>/blob/main/<path_to_file>.safetensors"
) on the Hub.str
, optional) —CompVis/ldm-text2im-large-256
) of a pretrained pipeline hosted
on the Hub../my_pipeline_directory/
) containing the pipeline component
configs in Diffusers format.str
, optional, defaults to ""
) —
The subfolder location of a model file within a larger model repository on the Hub or locally. str
, optional) —
Dict or path to a yaml file containing the configuration for the model in its original format.
If a dict is provided, it will be used to initialize the model configuration. 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. 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 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
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. __init__
method. See example below for more information. Instantiate a model from pretrained weights saved in the original .ckpt
or .safetensors
format. The model
is set in evaluation mode (model.eval()
) by default.