# Loading
A core premise of the diffusers library is to make diffusion models **as accessible as possible**.
Accessibility is therefore achieved by providing an API to load complete diffusion pipelines as well as individual components with a single line of code.
In the following we explain in-detail how to easily load:
- *Complete Diffusion Pipelines* via the [`DiffusionPipeline.from_pretrained`]
- *Diffusion Models* via [`ModelMixin.from_pretrained`]
- *Schedulers* via [`SchedulerMixin.from_pretrained`]
## Loading pipelines
The [`DiffusionPipeline`] class is the easiest way to access any diffusion model that is [available on the Hub](https://huggingface.co/models?library=diffusers). Let's look at an example on how to download [Runway's Stable Diffusion model](https://huggingface.co/runwayml/stable-diffusion-v1-5).
```python
from diffusers import DiffusionPipeline
repo_id = "runwayml/stable-diffusion-v1-5"
pipe = DiffusionPipeline.from_pretrained(repo_id)
```
Here [`DiffusionPipeline`] automatically detects the correct pipeline (*i.e.* [`StableDiffusionPipeline`]), downloads and caches all required configuration and weight files (if not already done so), and finally returns a pipeline instance, called `pipe`.
The pipeline instance can then be called using [`StableDiffusionPipeline.__call__`] (i.e., `pipe("image of a astronaut riding a horse")`) for text-to-image generation.
Instead of using the generic [`DiffusionPipeline`] class for loading, you can also load the appropriate pipeline class directly. The code snippet above yields the same instance as when doing:
```python
from diffusers import StableDiffusionPipeline
repo_id = "runwayml/stable-diffusion-v1-5"
pipe = StableDiffusionPipeline.from_pretrained(repo_id)
```
Many checkpoints, such as [CompVis/stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4) and [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) can be used for multiple tasks, *e.g.* *text-to-image* or *image-to-image*.
If you want to use those checkpoints for a task that is different from the default one, you have to load it directly from the corresponding task-specific pipeline class:
```python
from diffusers import StableDiffusionImg2ImgPipeline
repo_id = "runwayml/stable-diffusion-v1-5"
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(repo_id)
```
Diffusion pipelines like `StableDiffusionPipeline` or `StableDiffusionImg2ImgPipeline` consist of multiple components. These components can be both parameterized models, such as `"unet"`, `"vae"` and `"text_encoder"`, tokenizers or schedulers.
These components often interact in complex ways with each other when using the pipeline in inference, *e.g.* for [`StableDiffusionPipeline`] the inference call is explained [here](https://huggingface.co/blog/stable_diffusion#how-does-stable-diffusion-work).
The purpose of the [pipeline classes](./api/overview#diffusers-summary) is to wrap the complexity of these diffusion systems and give the user an easy-to-use API while staying flexible for customization, as will be shown later.
### Loading pipelines locally
If you prefer to have complete control over the pipeline and its corresponding files or, as said before, if you want to use pipelines that require an access request without having to be connected to the Hugging Face Hub,
we recommend loading pipelines locally.
To load a diffusion pipeline locally, you first need to manually download the whole folder structure on your local disk and then pass a local path to the [`DiffusionPipeline.from_pretrained`]. Let's again look at an example for
[Runway's Stable Diffusion Diffusion model](https://huggingface.co/runwayml/stable-diffusion-v1-5).
First, you should make use of [`git-lfs`](https://git-lfs.github.com/) to download the whole folder structure that has been uploaded to the [model repository](https://huggingface.co/runwayml/stable-diffusion-v1-5/tree/main):
```
git lfs install
git clone https://huggingface.co/runwayml/stable-diffusion-v1-5
```
The command above will create a local folder called `./stable-diffusion-v1-5` on your disk.
Now, all you have to do is to simply pass the local folder path to `from_pretrained`:
```python
from diffusers import DiffusionPipeline
repo_id = "./stable-diffusion-v1-5"
stable_diffusion = DiffusionPipeline.from_pretrained(repo_id)
```
If `repo_id` is a local path, as it is the case here, [`DiffusionPipeline.from_pretrained`] will automatically detect it and therefore not try to download any files from the Hub.
While we usually recommend to load weights directly from the Hub to be certain to stay up to date with the newest changes, loading pipelines locally should be preferred if one
wants to stay anonymous, self-contained applications, etc...
### Loading customized pipelines
Advanced users that want to load customized versions of diffusion pipelines can do so by swapping any of the default components, *e.g.* the scheduler, with other scheduler classes.
A classical use case of this functionality is to swap the scheduler. [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) uses the [`PNDMScheduler`] by default which is generally not the most performant scheduler. Since the release
of stable diffusion, multiple improved schedulers have been published. To use those, the user has to manually load their preferred scheduler and pass it into [`DiffusionPipeline.from_pretrained`].
*E.g.* to use [`EulerDiscreteScheduler`] or [`DPMSolverMultistepScheduler`] to have a better quality vs. generation speed trade-off for inference, one could load them as follows:
```python
from diffusers import DiffusionPipeline, EulerDiscreteScheduler, DPMSolverMultistepScheduler
repo_id = "runwayml/stable-diffusion-v1-5"
scheduler = EulerDiscreteScheduler.from_pretrained(repo_id, subfolder="scheduler")
# or
# scheduler = DPMSolverMultistepScheduler.from_pretrained(repo_id, subfolder="scheduler")
stable_diffusion = DiffusionPipeline.from_pretrained(repo_id, scheduler=scheduler)
```
Three things are worth paying attention to here.
- First, the scheduler is loaded with [`SchedulerMixin.from_pretrained`]
- Second, the scheduler is loaded with a function argument, called `subfolder="scheduler"` as the configuration of stable diffusion's scheduling is defined in a [subfolder of the official pipeline repository](https://huggingface.co/runwayml/stable-diffusion-v1-5/tree/main/scheduler)
- Third, the scheduler instance can simply be passed with the `scheduler` keyword argument to [`DiffusionPipeline.from_pretrained`]. This works because the [`StableDiffusionPipeline`] defines its scheduler with the `scheduler` attribute. It's not possible to use a different name, such as `sampler=scheduler` since `sampler` is not a defined keyword for [`StableDiffusionPipeline.__init__`]
Not only the scheduler components can be customized for diffusion pipelines; in theory, all components of a pipeline can be customized. In practice, however, it often only makes sense to switch out a component that has **compatible** alternatives to what the pipeline expects.
Many scheduler classes are compatible with each other as can be seen [here](https://github.com/huggingface/diffusers/blob/0dd8c6b4dbab4069de9ed1cafb53cbd495873879/src/diffusers/schedulers/scheduling_ddim.py#L112). This is not always the case for other components, such as the `"unet"`.
One special case that can also be customized is the `"safety_checker"` of stable diffusion. If you believe the safety checker doesn't serve you any good, you can simply disable it by passing `None`:
```python
from diffusers import DiffusionPipeline, EulerDiscreteScheduler, DPMSolverMultistepScheduler
stable_diffusion = DiffusionPipeline.from_pretrained(repo_id, safety_checker=None)
```
Another common use case is to reuse the same components in multiple pipelines, *e.g.* the weights and configurations of [`"runwayml/stable-diffusion-v1-5"`](https://huggingface.co/runwayml/stable-diffusion-v1-5) can be used for both [`StableDiffusionPipeline`] and [`StableDiffusionImg2ImgPipeline`] and we might not want to
use the exact same weights into RAM twice. In this case, customizing all the input instances would help us
to only load the weights into RAM once:
```python
from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline
model_id = "runwayml/stable-diffusion-v1-5"
stable_diffusion_txt2img = StableDiffusionPipeline.from_pretrained(model_id)
components = stable_diffusion_txt2img.components
# weights are not reloaded into RAM
stable_diffusion_img2img = StableDiffusionImg2ImgPipeline(**components)
```
Note how the above code snippet makes use of [`DiffusionPipeline.components`].
### Loading variants
Diffusion Pipeline checkpoints can offer variants of the "main" diffusion pipeline checkpoint.
Such checkpoint variants are usually variations of the checkpoint that have advantages for specific use-cases and that are so similar to the "main" checkpoint that they **should not** be put in a new checkpoint.
A variation of a checkpoint has to have **exactly** the same serialization format and **exactly** the same model structure, including all weights having the same tensor shapes.
Examples of variations are different floating point types and non-ema weights. I.e. "fp16", "bf16", and "no_ema" are common variations.
#### Let's first talk about whats **not** checkpoint variant,
Checkpoint variants do **not** include different serialization formats (such as [safetensors](https://huggingface.co/docs/diffusers/main/en/using-diffusers/using_safetensors)) as weights in different serialization formats are
identical to the weights of the "main" checkpoint, just loaded in a different framework.
Also variants do not correspond to different model structures, *e.g.* [stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) is not a variant of [stable-diffusion-2-0](https://huggingface.co/stabilityai/stable-diffusion-2) since the model structure is different (Stable Diffusion 1-5 uses a different `CLIPTextModel` compared to Stable Diffusion 2.0).
Pipeline checkpoints that are identical in model structure, but have been trained on different datasets, trained with vastly different training setups and thus correspond to different official releases (such as [Stable Diffusion v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4) and [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5)) should probably be stored in individual repositories instead of as variations of each other.
#### So what are checkpoint variants then?
Checkpoint variants usually consist of the checkpoint stored in "*low-precision, low-storage*" dtype so that less bandwith is required to download them, or of *non-exponential-averaged* weights that shall be used when continuing fine-tuning from the checkpoint.
Both use cases have clear advantages when their weights are considered variants: they share the same serialization format as the reference weights, and they correspond to a specialization of the "main" checkpoint which does not warrant a new model repository.
A checkpoint stored in [torch's half-precision / float16 format](https://pytorch.org/blog/accelerating-training-on-nvidia-gpus-with-pytorch-automatic-mixed-precision/) requires only half the bandwith and storage when downloading the checkpoint,
**but** cannot be used when continuing training or when running the checkpoint on CPU.
Similarly the *non-exponential-averaged* (or non-EMA) version of the checkpoint should be used when continuing fine-tuning of the model checkpoint, **but** should not be used when using the checkpoint for inference.
#### How to save and load variants
Saving a diffusion pipeline as a variant can be done by providing [`DiffusionPipeline.save_pretrained`] with the `variant` argument.
The `variant` extends the weight name by the provided variation, by changing the default weight name from `diffusion_pytorch_model.bin` to `diffusion_pytorch_model.{variant}.bin` or from `diffusion_pytorch_model.safetensors` to `diffusion_pytorch_model.{variant}.safetensors`. By doing so, one creates a variant of the pipeline checkpoint that can be loaded **instead** of the "main" pipeline checkpoint.
Let's have a look at how we could create a float16 variant of a pipeline. First, we load
the "main" variant of a checkpoint (stored in `float32` precision) into mixed precision format, using `torch_dtype=torch.float16`.
```py
from diffusers import DiffusionPipeline
import torch
pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
```
Now all model components of the pipeline are stored in half-precision dtype. We can now save the
pipeline under a `"fp16"` variant as follows:
```py
pipe.save_pretrained("./stable-diffusion-v1-5", variant="fp16")
```
If we don't save into an existing `stable-diffusion-v1-5` folder the new folder would look as follows:
```
stable-diffusion-v1-5
├── feature_extractor
│ └── preprocessor_config.json
├── model_index.json
├── safety_checker
│ ├── config.json
│ └── pytorch_model.fp16.bin
├── scheduler
│ └── scheduler_config.json
├── text_encoder
│ ├── config.json
│ └── pytorch_model.fp16.bin
├── tokenizer
│ ├── merges.txt
│ ├── special_tokens_map.json
│ ├── tokenizer_config.json
│ └── vocab.json
├── unet
│ ├── config.json
│ └── diffusion_pytorch_model.fp16.bin
└── vae
├── config.json
└── diffusion_pytorch_model.fp16.bin
```
As one can see, all model files now have a `.fp16.bin` extension instead of just `.bin`.
The variant now has to be loaded by also passing a `variant="fp16"` to [`DiffusionPipeline.from_pretrained`], e.g.:
```py
DiffusionPipeline.from_pretrained("./stable-diffusion-v1-5", variant="fp16", torch_dtype=torch.float16)
```
works just fine, while:
```py
DiffusionPipeline.from_pretrained("./stable-diffusion-v1-5", torch_dtype=torch.float16)
```
throws an Exception:
```
OSError: Error no file named diffusion_pytorch_model.bin found in directory ./stable-diffusion-v1-45/vae since we **only** stored the model
```
This is expected as we don't have any "non-variant" checkpoint files saved locally.
However, the whole idea of pipeline variants is that they can co-exist with the "main" variant,
so one would typically also save the "main" variant in the same folder. Let's do this:
```py
pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
pipe.save_pretrained("./stable-diffusion-v1-5")
```
and upload the pipeline to the Hub under [diffusers/stable-diffusion-variants](https://huggingface.co/diffusers/stable-diffusion-variants).
The file structure [on the Hub](https://huggingface.co/diffusers/stable-diffusion-variants/tree/main) now looks as follows:
```
├── feature_extractor
│ └── preprocessor_config.json
├── model_index.json
├── safety_checker
│ ├── config.json
│ ├── pytorch_model.bin
│ └── pytorch_model.fp16.bin
├── scheduler
│ └── scheduler_config.json
├── text_encoder
│ ├── config.json
│ ├── pytorch_model.bin
│ └── pytorch_model.fp16.bin
├── tokenizer
│ ├── merges.txt
│ ├── special_tokens_map.json
│ ├── tokenizer_config.json
│ └── vocab.json
├── unet
│ ├── config.json
│ ├── diffusion_pytorch_model.bin
│ ├── diffusion_pytorch_model.fp16.bin
└── vae
├── config.json
├── diffusion_pytorch_model.bin
└── diffusion_pytorch_model.fp16.bin
```
We can now both download the "main" and the "fp16" variant from the Hub. Both:
```py
pipe = DiffusionPipeline.from_pretrained("diffusers/stable-diffusion-variants")
```
and
```py
pipe = DiffusionPipeline.from_pretrained("diffusers/stable-diffusion-variants", variant="fp16")
```
work.
Note that Diffusers never downloads more checkpoints than needed. E.g. when downloading
the "main" variant, none of the "fp16.bin" files are downloaded and cached.
Only when the user specifies `variant="fp16"` are those files downloaded and cached.
Finally, there are cases where only some of the checkpoint files of the pipeline are of a certain
variation. E.g. it's usually only the UNet checkpoint that has both a *exponential-mean-averaged* (EMA) and a *non-exponential-mean-averaged* (non-EMA) version. All other model components, e.g. the text encoder, safety checker or variational auto-encoder usually don't have such a variation.
In such a case, one would upload just the UNet's checkpoint file with a `non_ema` version format (as done [here](https://huggingface.co/diffusers/stable-diffusion-variants/blob/main/unet/diffusion_pytorch_model.non_ema.bin)) and upon calling:
```python
pipe = DiffusionPipeline.from_pretrained("diffusers/stable-diffusion-variants", variant="non_ema")
```
the model will use only the "non_ema" checkpoint variant if it is available - otherwise it'll load the
"main" variation. In the above example, `variant="non_ema"` would therefore download the following file structure:
```
├── feature_extractor
│ └── preprocessor_config.json
├── model_index.json
├── safety_checker
│ ├── config.json
│ ├── pytorch_model.bin
├── scheduler
│ └── scheduler_config.json
├── text_encoder
│ ├── config.json
│ ├── pytorch_model.bin
├── tokenizer
│ ├── merges.txt
│ ├── special_tokens_map.json
│ ├── tokenizer_config.json
│ └── vocab.json
├── unet
│ ├── config.json
│ └── diffusion_pytorch_model.non_ema.bin
└── vae
├── config.json
├── diffusion_pytorch_model.bin
```
In a nutshell, using `variant="{variant}"` will download all files that match the `{variant}` and if for a model component such a file variant is not present it will download the "main" variant. If neither a "main" or `{variant}` variant is available, an error will the thrown.
### How does loading work?
As a class method, [`DiffusionPipeline.from_pretrained`] is responsible for two things:
- Download the latest version of the folder structure required to run the `repo_id` with `diffusers` and cache them. If the latest folder structure is available in the local cache, [`DiffusionPipeline.from_pretrained`] will simply reuse the cache and **not** re-download the files.
- Load the cached weights into the _correct_ pipeline class – one of the [officially supported pipeline classes](./api/overview#diffusers-summary) - and return an instance of the class. The _correct_ pipeline class is thereby retrieved from the `model_index.json` file.
The underlying folder structure of diffusion pipelines corresponds 1-to-1 to their corresponding class instances, *e.g.* [`StableDiffusionPipeline`] for [`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5).
This can be better understood by looking at an example. Let's load a pipeline class instance `pipe` and print it:
```python
from diffusers import DiffusionPipeline
repo_id = "runwayml/stable-diffusion-v1-5"
pipe = DiffusionPipeline.from_pretrained(repo_id)
print(pipe)
```
*Output*:
```
StableDiffusionPipeline {
"feature_extractor": [
"transformers",
"CLIPImageProcessor"
],
"safety_checker": [
"stable_diffusion",
"StableDiffusionSafetyChecker"
],
"scheduler": [
"diffusers",
"PNDMScheduler"
],
"text_encoder": [
"transformers",
"CLIPTextModel"
],
"tokenizer": [
"transformers",
"CLIPTokenizer"
],
"unet": [
"diffusers",
"UNet2DConditionModel"
],
"vae": [
"diffusers",
"AutoencoderKL"
]
}
```
First, we see that the official pipeline is the [`StableDiffusionPipeline`], and second we see that the `StableDiffusionPipeline` consists of 7 components:
- `"feature_extractor"` of class `CLIPImageProcessor` as defined [in `transformers`](https://huggingface.co/docs/transformers/main/en/model_doc/clip#transformers.CLIPImageProcessor).
- `"safety_checker"` as defined [here](https://github.com/huggingface/diffusers/blob/e55687e1e15407f60f32242027b7bb8170e58266/src/diffusers/pipelines/stable_diffusion/safety_checker.py#L32).
- `"scheduler"` of class [`PNDMScheduler`].
- `"text_encoder"` of class `CLIPTextModel` as defined [in `transformers`](https://huggingface.co/docs/transformers/main/en/model_doc/clip#transformers.CLIPTextModel).
- `"tokenizer"` of class `CLIPTokenizer` as defined [in `transformers`](https://huggingface.co/docs/transformers/main/en/model_doc/clip#transformers.CLIPTokenizer).
- `"unet"` of class [`UNet2DConditionModel`].
- `"vae"` of class [`AutoencoderKL`].
Let's now compare the pipeline instance to the folder structure of the model repository `runwayml/stable-diffusion-v1-5`. Looking at the folder structure of [`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5/tree/main) on the Hub and excluding model and saving format variants, we can see it matches 1-to-1 the printed out instance of `StableDiffusionPipeline` above:
```
.
├── feature_extractor
│ └── preprocessor_config.json
├── model_index.json
├── safety_checker
│ ├── config.json
│ └── pytorch_model.bin
├── scheduler
│ └── scheduler_config.json
├── text_encoder
│ ├── config.json
│ └── pytorch_model.bin
├── tokenizer
│ ├── merges.txt
│ ├── special_tokens_map.json
│ ├── tokenizer_config.json
│ └── vocab.json
├── unet
│ ├── config.json
│ ├── diffusion_pytorch_model.bin
└── vae
├── config.json
├── diffusion_pytorch_model.bin
```
Each attribute of the instance of `StableDiffusionPipeline` has its configuration and possibly weights defined in a subfolder that is called **exactly** like the class attribute (`"feature_extractor"`, `"safety_checker"`, `"scheduler"`, `"text_encoder"`, `"tokenizer"`, `"unet"`, `"vae"`). Importantly, every pipeline expects a `model_index.json` file that tells the `DiffusionPipeline` both:
- which pipeline class should be loaded, and
- what sub-classes from which library are stored in which subfolders
In the case of `runwayml/stable-diffusion-v1-5` the `model_index.json` is therefore defined as follows:
```
{
"_class_name": "StableDiffusionPipeline",
"_diffusers_version": "0.6.0",
"feature_extractor": [
"transformers",
"CLIPImageProcessor"
],
"safety_checker": [
"stable_diffusion",
"StableDiffusionSafetyChecker"
],
"scheduler": [
"diffusers",
"PNDMScheduler"
],
"text_encoder": [
"transformers",
"CLIPTextModel"
],
"tokenizer": [
"transformers",
"CLIPTokenizer"
],
"unet": [
"diffusers",
"UNet2DConditionModel"
],
"vae": [
"diffusers",
"AutoencoderKL"
]
}
```
- `_class_name` tells `DiffusionPipeline` which pipeline class should be loaded.
- `_diffusers_version` can be useful to know under which `diffusers` version this model was created.
- Every component of the pipeline is then defined under the form:
```
"name" : [
"library",
"class"
]
```
- The `"name"` field corresponds both to the name of the subfolder in which the configuration and weights are stored as well as the attribute name of the pipeline class (as can be seen [here](https://huggingface.co/runwayml/stable-diffusion-v1-5/tree/main/bert) and [here](https://github.com/huggingface/diffusers/blob/cd502b25cf0debac6f98d27a6638ef95208d1ea2/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py#L42))
- The `"library"` field corresponds to the name of the library, *e.g.* `diffusers` or `transformers` from which the `"class"` should be loaded
- The `"class"` field corresponds to the name of the class, *e.g.* [`CLIPTokenizer`](https://huggingface.co/docs/transformers/main/en/model_doc/clip#transformers.CLIPTokenizer) or [`UNet2DConditionModel`]
## Loading models
Models as defined under [src/diffusers/models](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models) can be loaded via the [`ModelMixin.from_pretrained`] function. The API is very similar the [`DiffusionPipeline.from_pretrained`] and works in the same way:
- Download the latest version of the model weights and configuration with `diffusers` and cache them. If the latest files are available in the local cache, [`ModelMixin.from_pretrained`] will simply reuse the cache and **not** re-download the files.
- Load the cached weights into the _defined_ model class - one of [the existing model classes](./api/models) - and return an instance of the class.
In constrast to [`DiffusionPipeline.from_pretrained`], models rely on fewer files that usually don't require a folder structure, but just a `diffusion_pytorch_model.bin` and `config.json` file.
Let's look at an example:
```python
from diffusers import UNet2DConditionModel
repo_id = "runwayml/stable-diffusion-v1-5"
model = UNet2DConditionModel.from_pretrained(repo_id, subfolder="unet")
```
Note how we have to define the `subfolder="unet"` argument to tell [`ModelMixin.from_pretrained`] that the model weights are located in a [subfolder of the repository](https://huggingface.co/runwayml/stable-diffusion-v1-5/tree/main/unet).
As explained in [Loading customized pipelines]("./using-diffusers/loading#loading-customized-pipelines"), one can pass a loaded model to a diffusion pipeline, via [`DiffusionPipeline.from_pretrained`]:
```python
from diffusers import DiffusionPipeline
repo_id = "runwayml/stable-diffusion-v1-5"
pipe = DiffusionPipeline.from_pretrained(repo_id, unet=model)
```
If the model files can be found directly at the root level, which is usually only the case for some very simple diffusion models, such as [`google/ddpm-cifar10-32`](https://huggingface.co/google/ddpm-cifar10-32), we don't
need to pass a `subfolder` argument:
```python
from diffusers import UNet2DModel
repo_id = "google/ddpm-cifar10-32"
model = UNet2DModel.from_pretrained(repo_id)
```
As motivated in [How to save and load variants?](#how-to-save-and-load-variants), models can load and
save variants. To load a model variant, one should pass the `variant` function argument to [`ModelMixin.from_pretrained`]. Analogous, to save a model variant, one should pass the `variant` function argument to [`ModelMixin.save_pretrained`]:
```python
from diffusers import UNet2DConditionModel
model = UNet2DConditionModel.from_pretrained(
"diffusers/stable-diffusion-variants", subfolder="unet", variant="non_ema"
)
model.save_pretrained("./local-unet", variant="non_ema")
```
## Loading schedulers
Schedulers rely on [`SchedulerMixin.from_pretrained`]. Schedulers are **not parameterized** or **trained**, but instead purely defined by a configuration file.
For consistency, we use the same method name as we do for models or pipelines, but no weights are loaded in this case.
In constrast to pipelines or models, loading schedulers does not consume any significant amount of memory and the same configuration file can often be used for a variety of different schedulers.
For example, all of:
- [`DDPMScheduler`]
- [`DDIMScheduler`]
- [`PNDMScheduler`]
- [`LMSDiscreteScheduler`]
- [`EulerDiscreteScheduler`]
- [`EulerAncestralDiscreteScheduler`]
- [`DPMSolverMultistepScheduler`]
are compatible with [`StableDiffusionPipeline`] and therefore the same scheduler configuration file can be loaded in any of those classes:
```python
from diffusers import StableDiffusionPipeline
from diffusers import (
DDPMScheduler,
DDIMScheduler,
PNDMScheduler,
LMSDiscreteScheduler,
EulerDiscreteScheduler,
EulerAncestralDiscreteScheduler,
DPMSolverMultistepScheduler,
)
repo_id = "runwayml/stable-diffusion-v1-5"
ddpm = DDPMScheduler.from_pretrained(repo_id, subfolder="scheduler")
ddim = DDIMScheduler.from_pretrained(repo_id, subfolder="scheduler")
pndm = PNDMScheduler.from_pretrained(repo_id, subfolder="scheduler")
lms = LMSDiscreteScheduler.from_pretrained(repo_id, subfolder="scheduler")
euler_anc = EulerAncestralDiscreteScheduler.from_pretrained(repo_id, subfolder="scheduler")
euler = EulerDiscreteScheduler.from_pretrained(repo_id, subfolder="scheduler")
dpm = DPMSolverMultistepScheduler.from_pretrained(repo_id, subfolder="scheduler")
# replace `dpm` with any of `ddpm`, `ddim`, `pndm`, `lms`, `euler_anc`, `euler`
pipeline = StableDiffusionPipeline.from_pretrained(repo_id, scheduler=dpm)
```