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[docs] Model formats (#12256) 

* init

* config

* lora metadata

* feedback

* fix

* cache allocator warmup for from_single_file

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- local: using-diffusers/schedulers - local: using-diffusers/schedulers
title: Schedulers title: Schedulers
- local: using-diffusers/other-formats - local: using-diffusers/other-formats
title: Model files and layouts title: Model formats
- local: using-diffusers/push_to_hub - local: using-diffusers/push_to_hub
title: Sharing pipelines and models title: Sharing pipelines and models
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docs/source/en/using-diffusers/loading.md
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) )
``` ```
> [!TIP]
> Refer to the [Single file format](./other-formats#single-file-format) docs to learn how to load single file models.
### Local pipelines ### Local pipelines
Pipelines can also be run locally. Use [`~huggingface_hub.snapshot_download`] to download a model repository. Pipelines can also be run locally. Use [`~huggingface_hub.snapshot_download`] to download a model repository.
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docs/source/en/using-diffusers/other-formats.md
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specific language governing permissions and limitations under the License. specific language governing permissions and limitations under the License.
--> -->
# Model files and layouts
[[open-in-colab]] [[open-in-colab]]
Diffusion models are saved in various file types and organized in different layouts. Diffusers stores model weights as safetensors files in *Diffusers-multifolder* layout and it also supports loading files (like safetensors and ckpt files) from a *single-file* layout which is commonly used in the diffusion ecosystem. # Model formats
Each layout has its own benefits and use cases, and this guide will show you how to load the different files and layouts, and how to convert them.
## Files
PyTorch model weights are typically saved with Python's [pickle](https://docs.python.org/3/library/pickle.html) utility as ckpt or bin files. However, pickle is not secure and pickled files may contain malicious code that can be executed. This vulnerability is a serious concern given the popularity of model sharing. To address this security issue, the [Safetensors](https://hf.co/docs/safetensors) library was developed as a secure alternative to pickle, which saves models as safetensors files.
### safetensors Diffusion models are typically stored in the Diffusers format or single-file format. Model files can be stored in various file types such as safetensors, dduf, or ckpt.
> [!TIP] > [!TIP]
> Learn more about the design decisions and why safetensor files are preferred for saving and loading model weights in the [Safetensors audited as really safe and becoming the default](https://blog.eleuther.ai/safetensors-security-audit/) blog post. > Format refers to whether the weights are stored in a directory structure and file refers to the file type.
[Safetensors](https://hf.co/docs/safetensors) is a safe and fast file format for securely storing and loading tensors. Safetensors restricts the header size to limit certain types of attacks, supports lazy loading (useful for distributed setups), and has generally faster loading speeds.
Make sure you have the [Safetensors](https://hf.co/docs/safetensors) library installed. This guide will show you how to load pipelines and models from these formats and files.
```py ## Diffusers format
!pip install safetensors
```
Safetensors stores weights in a safetensors file. Diffusers loads safetensors files by default if they're available and the Safetensors library is installed. There are two ways safetensors files can be organized: The Diffusers format stores each model (UNet, transformer, text encoder) in a separate subfolder. There are several benefits to storing models separately.
1. Diffusers-multifolder layout: there may be several separate safetensors files, one for each pipeline component (text encoder, UNet, VAE), organized in subfolders (check out the [stable-diffusion-v1-5/stable-diffusion-v1-5](https://hf.co/stable-diffusion-v1-5/stable-diffusion-v1-5/tree/main) repository as an example) - Faster overall pipeline initialization because you can load the individual model you need or load them all in parallel.
2. single-file layout: all the model weights may be saved in a single file (check out the [WarriorMama777/OrangeMixs](https://hf.co/WarriorMama777/OrangeMixs/tree/main/Models/AbyssOrangeMix) repository as an example) - Reduced memory usage because you don't need to load all the pipeline components if you only need one model. [Reuse](./loading#reusing-models-in-multiple-pipelines) a model that is shared between multiple pipelines.
- Lower storage requirements because common models shared between multiple pipelines are only downloaded once.
- Flexibility to use new or improved models in a pipeline.
<hfoptions id="safetensors"> ## Single file format
<hfoption id="multifolder">
Use the [`~DiffusionPipeline.from_pretrained`] method to load a model with safetensors files stored in multiple folders. A single-file format stores *all* the model (UNet, transformer, text encoder) weights in a single file. Benefits of single-file formats include the following.
```py
from diffusers import DiffusionPipeline
pipeline = DiffusionPipeline.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5",
use_safetensors=True
)
```
</hfoption> - Greater compatibility with [ComfyUI](https://github.com/comfyanonymous/ComfyUI) or [Automatic1111](https://github.com/AUTOMATIC1111/stable-diffusion-webui).
<hfoption id="single file"> - Easier to download and share a single file.
Use the [`~loaders.FromSingleFileMixin.from_single_file`] method to load a model with all the weights stored in a single safetensors file. Use [`~loaders.FromSingleFileMixin.from_single_file`] to load a single file.
```py ```py
from diffusers import StableDiffusionPipeline import torch
from diffusers import StableDiffusionXLPipeline
pipeline = StableDiffusionPipeline.from_single_file( pipeline = StableDiffusionXLPipeline.from_single_file(
"https://huggingface.co/WarriorMama777/OrangeMixs/blob/main/Models/AbyssOrangeMix/AbyssOrangeMix.safetensors" "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0.safetensors",
torch_dtype=torch.float16,
device_map="cuda"
) )
``` ```
</hfoption> The [`~loaders.FromSingleFileMixin.from_single_file`] method also supports passing new models or schedulers.
</hfoptions>
#### LoRAs
[LoRAs](../tutorials/using_peft_for_inference) are lightweight checkpoints fine-tuned to generate images or video in a specific style. If you are using a checkpoint trained with a Diffusers training script, the LoRA configuration is automatically saved as metadata in a safetensors file. When the safetensors file is loaded, the metadata is parsed to correctly configure the LoRA and avoids missing or incorrect LoRA configurations.
The easiest way to inspect the metadata, if available, is by clicking on the Safetensors logo next to the weights.
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/safetensors_lora.png"/>
</div>
For LoRAs that aren't trained with Diffusers, you can still save metadata with the `transformer_lora_adapter_metadata` and `text_encoder_lora_adapter_metadata` arguments in [`~loaders.FluxLoraLoaderMixin.save_lora_weights`] as long as it is a safetensors file.
```py ```py
import torch import torch
from diffusers import FluxPipeline from diffusers import FluxPipeline, FluxTransformer2DModel
transformer = FluxTransformer2DModel.from_single_file(
"https://huggingface.co/Kijai/flux-fp8/blob/main/flux1-dev-fp8.safetensors", torch_dtype=torch.bfloat16
)
pipeline = FluxPipeline.from_pretrained( pipeline = FluxPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16 "black-forest-labs/FLUX.1-dev",
).to("cuda") transformer=transformer,
pipeline.load_lora_weights("linoyts/yarn_art_Flux_LoRA") torch_dtype=torch.bfloat16,
pipeline.save_lora_weights( device_map="cuda"
transformer_lora_adapter_metadata={"r": 16, "lora_alpha": 16},
text_encoder_lora_adapter_metadata={"r": 8, "lora_alpha": 8}
) )
``` ```
### ckpt ### Configuration options
> [!WARNING]
> Pickled files may be unsafe because they can be exploited to execute malicious code. It is recommended to use safetensors files instead where possible, or convert the weights to safetensors files.
PyTorch's [torch.save](https://pytorch.org/docs/stable/generated/torch.save.html) function uses Python's [pickle](https://docs.python.org/3/library/pickle.html) utility to serialize and save models. These files are saved as a ckpt file and they contain the entire model's weights. Diffusers format models have a `config.json` file in their repositories with important attributes such as the number of layers and attention heads. The [`~loaders.FromSingleFileMixin.from_single_file`] method automatically determines the appropriate config to use from `config.json`. This may fail in a few rare instances though, in which case, you should use the `config` argument.
Use the [`~loaders.FromSingleFileMixin.from_single_file`] method to directly load a ckpt file. You should also use the `config` argument if the models in a pipeline are different from the original implementation or if it doesn't have the necessary metadata to determine the correct config.
```py ```py
from diffusers import StableDiffusionPipeline from diffusers import StableDiffusionXLPipeline
pipeline = StableDiffusionPipeline.from_single_file(
"https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/blob/main/v1-5-pruned.ckpt"
)
```
## Storage layout
There are two ways model files are organized, either in a Diffusers-multifolder layout or in a single-file layout. The Diffusers-multifolder layout is the default, and each component file (text encoder, UNet, VAE) is stored in a separate subfolder. Diffusers also supports loading models from a single-file layout where all the components are bundled together.
### Diffusers-multifolder ckpt_path = "https://huggingface.co/segmind/SSD-1B/blob/main/SSD-1B.safetensors"
The Diffusers-multifolder layout is the default storage layout for Diffusers. Each component's (text encoder, UNet, VAE) weights are stored in a separate subfolder. The weights can be stored as safetensors or ckpt files. pipeline = StableDiffusionXLPipeline.from_single_file(ckpt_path, config="segmind/SSD-1B")
```
<div class="flex flex-row gap-4"> Diffusers attempts to infer the pipeline components based on the signature types of the pipeline class when using `original_config` with `local_files_only=True`. It won't download the config files from a Hub repository to avoid backward breaking changes when you can't connect to the internet. This method isn't as reliable as providing a path to a local model with the `config` argument and may lead to errors. You should run the pipeline with `local_files_only=False` to download the config files to the local cache to avoid errors.
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/multifolder-layout.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">multifolder layout</figcaption>
</div>
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/multifolder-unet.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">UNet subfolder</figcaption>
</div>
</div>
To load from Diffusers-multifolder layout, use the [`~DiffusionPipeline.from_pretrained`] method. Override default configs by passing the arguments directly to [`~loaders.FromSingleFileMixin.from_single_file`]. The examples below demonstrate how to override the configs in a pipeline or model.
```py ```py
from diffusers import DiffusionPipeline from diffusers import StableDiffusionXLInstructPix2PixPipeline
pipeline = DiffusionPipeline.from_pretrained( ckpt_path = "https://huggingface.co/stabilityai/cosxl/blob/main/cosxl_edit.safetensors"
"stabilityai/stable-diffusion-xl-base-1.0", pipeline = StableDiffusionXLInstructPix2PixPipeline.from_single_file(
torch_dtype=torch.float16, ckpt_path, config="diffusers/sdxl-instructpix2pix-768", is_cosxl_edit=True
variant="fp16", )
use_safetensors=True,
).to("cuda")
``` ```
Benefits of using the Diffusers-multifolder layout include:
1. Faster to load each component file individually or in parallel.
2. Reduced memory usage because you only load the components you need. For example, models like [SDXL Turbo](https://hf.co/stabilityai/sdxl-turbo), [SDXL Lightning](https://hf.co/ByteDance/SDXL-Lightning), and [Hyper-SD](https://hf.co/ByteDance/Hyper-SD) have the same components except for the UNet. You can reuse their shared components with the [`~DiffusionPipeline.from_pipe`] method without consuming any additional memory (take a look at the [Reuse a pipeline](./loading#reuse-a-pipeline) guide) and only load the UNet. This way, you don't need to download redundant components and unnecessarily use more memory.
```py
import torch
from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, EulerDiscreteScheduler
# download one model
sdxl_pipeline = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
torch_dtype=torch.float16,
variant="fp16",
use_safetensors=True,
).to("cuda")
# switch UNet for another model
unet = UNet2DConditionModel.from_pretrained(
"stabilityai/sdxl-turbo",
subfolder="unet",
torch_dtype=torch.float16,
variant="fp16",
use_safetensors=True
)
# reuse all the same components in new model except for the UNet
turbo_pipeline = StableDiffusionXLPipeline.from_pipe(
sdxl_pipeline, unet=unet,
).to("cuda")
turbo_pipeline.scheduler = EulerDiscreteScheduler.from_config(
turbo_pipeline.scheduler.config,
timestep_spacing="trailing"
)
image = turbo_pipeline(
"an astronaut riding a unicorn on mars",
num_inference_steps=1,
guidance_scale=0.0,
).images[0]
image
```
3. Reduced storage requirements because if a component, such as the SDXL [VAE](https://hf.co/madebyollin/sdxl-vae-fp16-fix), is shared across multiple models, you only need to download and store a single copy of it instead of downloading and storing it multiple times. For 10 SDXL models, this can save ~3.5GB of storage. The storage savings is even greater for newer models like PixArt Sigma, where the [text encoder](https://hf.co/PixArt-alpha/PixArt-Sigma-XL-2-1024-MS/tree/main/text_encoder) alone is ~19GB!
4. Flexibility to replace a component in the model with a newer or better version.
```py
from diffusers import DiffusionPipeline, AutoencoderKL
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16, use_safetensors=True)
pipeline = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
vae=vae,
torch_dtype=torch.float16,
variant="fp16",
use_safetensors=True,
).to("cuda")
```
5. More visibility and information about a model's components, which are stored in a [config.json](https://hf.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/unet/config.json) file in each component subfolder.
### Single-file
The single-file layout stores all the model weights in a single file. All the model components (text encoder, UNet, VAE) weights are kept together instead of separately in subfolders. This can be a safetensors or ckpt file.
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/single-file-layout.png"/>
</div>
To load from a single-file layout, use the [`~loaders.FromSingleFileMixin.from_single_file`] method.
```py ```py
import torch from diffusers import UNet2DConditionModel
from diffusers import StableDiffusionXLPipeline
pipeline = StableDiffusionXLPipeline.from_single_file( ckpt_path = "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0_0.9vae.safetensors"
"https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0.safetensors", model = UNet2DConditionModel.from_single_file(ckpt_path, upcast_attention=True)
torch_dtype=torch.float16,
variant="fp16",
use_safetensors=True,
).to("cuda")
``` ```
Benefits of using a single-file layout include: ### Local files
1. Easy compatibility with diffusion interfaces such as [ComfyUI](https://github.com/comfyanonymous/ComfyUI) or [Automatic1111](https://github.com/AUTOMATIC1111/stable-diffusion-webui) which commonly use a single-file layout.
2. Easier to manage (download and share) a single file.
### DDUF
> [!WARNING]
> DDUF is an experimental file format and APIs related to it can change in the future.
DDUF (**D**DUF **D**iffusion **U**nified **F**ormat) is a file format designed to make storing, distributing, and using diffusion models much easier. Built on the ZIP file format, DDUF offers a standardized, efficient, and flexible way to package all parts of a diffusion model into a single, easy-to-manage file. It provides a balance between Diffusers multi-folder format and the widely popular single-file format.
Learn more details about DDUF on the Hugging Face Hub [documentation](https://huggingface.co/docs/hub/dduf). The [`~loaders.FromSingleFileMixin.from_single_file`] method attempts to configure a pipeline or model by inferring the model type from the keys in the checkpoint file. For example, any single file checkpoint based on the Stable Diffusion XL base model is configured from [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0).
Pass a checkpoint to the `dduf_file` parameter to load it in [`DiffusionPipeline`]. If you're working with local files, download the config files with the [`~huggingface_hub.snapshot_download`] method and the model checkpoint with [`~huggingface_hub.hf_hub_download`]. These files are downloaded to your [cache directory](https://huggingface.co/docs/huggingface_hub/en/guides/manage-cache), but you can download them to a specific directory with the `local_dir` argument.
```py ```py
from diffusers import DiffusionPipeline from huggingface_hub import hf_hub_download, snapshot_download
import torch from diffusers import StableDiffusionXLPipeline
pipe = DiffusionPipeline.from_pretrained(
"DDUF/FLUX.1-dev-DDUF", dduf_file="FLUX.1-dev.dduf", torch_dtype=torch.bfloat16
).to("cuda")
image = pipe(
"photo a cat holding a sign that says Diffusers", num_inference_steps=50, guidance_scale=3.5
).images[0]
image.save("cat.png")
```
To save a pipeline as a `.dduf` checkpoint, use the [`~huggingface_hub.export_folder_as_dduf`] utility, which takes care of all the necessary file-level validations.
```py my_local_checkpoint_path = hf_hub_download(
from huggingface_hub import export_folder_as_dduf repo_id="segmind/SSD-1B",
from diffusers import DiffusionPipeline filename="SSD-1B.safetensors"
import torch )
pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16) my_local_config_path = snapshot_download(
repo_id="segmind/SSD-1B",
allow_patterns=["*.json", "**/*.json", "*.txt", "**/*.txt"]
)
save_folder = "flux-dev" pipeline = StableDiffusionXLPipeline.from_single_file(
pipe.save_pretrained("flux-dev") my_local_checkpoint_path, config=my_local_config_path, local_files_only=True
export_folder_as_dduf("flux-dev.dduf", folder_path=save_folder) )
``` ```
> [!TIP] ### Symlink
> Packaging and loading quantized checkpoints in the DDUF format is supported as long as they respect the multi-folder structure.
## Convert layout and files
Diffusers provides many scripts and methods to convert storage layouts and file formats to enable broader support across the diffusion ecosystem.
Take a look at the [diffusers/scripts](https://github.com/huggingface/diffusers/tree/main/scripts) collection to find a script that fits your conversion needs. If you're working with a file system that does not support symlinking, download the checkpoint file to a local directory first with the `local_dir` parameter. Using the `local_dir` parameter automatically disables symlinks.
> [!TIP] ```py
> Scripts that have "`to_diffusers`" appended at the end mean they convert a model to the Diffusers-multifolder layout. Each script has their own specific set of arguments for configuring the conversion, so make sure you check what arguments are available! from huggingface_hub import hf_hub_download, snapshot_download
from diffusers import StableDiffusionXLPipeline
For example, to convert a Stable Diffusion XL model stored in Diffusers-multifolder layout to a single-file layout, run the [convert_diffusers_to_original_sdxl.py](https://github.com/huggingface/diffusers/blob/main/scripts/convert_diffusers_to_original_sdxl.py) script. Provide the path to the model to convert, and the path to save the converted model to. You can optionally specify whether you want to save the model as a safetensors file and whether to save the model in half-precision. my_local_checkpoint_path = hf_hub_download(
repo_id="segmind/SSD-1B",
filename="SSD-1B.safetensors"
local_dir="my_local_checkpoints",
)
print("My local checkpoint: ", my_local_checkpoint_path)
```bash my_local_config_path = snapshot_download(
python convert_diffusers_to_original_sdxl.py --model_path path/to/model/to/convert --checkpoint_path path/to/save/model/to --use_safetensors repo_id="segmind/SSD-1B",
allow_patterns=["*.json", "**/*.json", "*.txt", "**/*.txt"]
)
print("My local config: ", my_local_config_path)
``` ```
You can also save a model to Diffusers-multifolder layout with the [`~DiffusionPipeline.save_pretrained`] method. This creates a directory for you if it doesn't already exist, and it also saves the files as a safetensors file by default. Pass these paths to [`~loaders.FromSingleFileMixin.from_single_file`].
```py ```py
from diffusers import StableDiffusionXLPipeline
pipeline = StableDiffusionXLPipeline.from_single_file( pipeline = StableDiffusionXLPipeline.from_single_file(
"https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0.safetensors", my_local_checkpoint_path, config=my_local_config_path, local_files_only=True
) )
pipeline.save_pretrained()
``` ```
Lastly, there are also Spaces, such as [SD To Diffusers](https://hf.co/spaces/diffusers/sd-to-diffusers) and [SD-XL To Diffusers](https://hf.co/spaces/diffusers/sdxl-to-diffusers), that provide a more user-friendly interface for converting models to Diffusers-multifolder layout. This is the easiest and most convenient option for converting layouts, and it'll open a PR on your model repository with the converted files. However, this option is not as reliable as running a script, and the Space may fail for more complicated models. ## File types
## Single-file layout usage Models can be stored in several file types. Safetensors is the most common file type but you may encounter other file types on the Hub or diffusion community.
Now that you're familiar with the differences between the Diffusers-multifolder and single-file layout, this section shows you how to load models and pipeline components, customize configuration options for loading, and load local files with the [`~loaders.FromSingleFileMixin.from_single_file`] method. ### safetensors
### Load a pipeline or model [Safetensors](https://hf.co/docs/safetensors) is a safe and fast file type for securely storing and loading tensors. It restricts the header size to limit certain types of attacks, supports lazy loading (useful for distributed setups), and generally loads faster.
Pass the file path of the pipeline or model to the [`~loaders.FromSingleFileMixin.from_single_file`] method to load it. Diffusers loads safetensors file by default (a required dependency) if they are available and the Safetensors library is installed.
<hfoptions id="pipeline-model"> Use [`~DiffusionPipeline.from_pretrained`] or [`~loaders.FromSingleFileMixin.from_single_file`] to load safetensor files.
<hfoption id="pipeline">
```py ```py
from diffusers import StableDiffusionXLPipeline import torch
from diffusers import DiffusionPipeline
ckpt_path = "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0_0.9vae.safetensors"
pipeline = StableDiffusionXLPipeline.from_single_file(ckpt_path)
```
</hfoption>
<hfoption id="model">
```py pipeline = DiffusionPipeline.from_pretrained(
from diffusers import StableCascadeUNet "stabilityai/stable-diffusion-xl-base-1.0",
torch.dtype=torch.float16,
device_map="cuda"
)
ckpt_path = "https://huggingface.co/stabilityai/stable-cascade/blob/main/stage_b_lite.safetensors" pipeline = DiffusionPipeline.from_single_file(
model = StableCascadeUNet.from_single_file(ckpt_path) "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0.safetensors",
torch_dtype=torch.float16,
)
``` ```
</hfoption> If you're using a checkpoint trained with a Diffusers training script, metadata such as the LoRA configuration, is automatically saved. When the file is loaded, the metadata is parsed to correctly configure the LoRA and avoid missing or incorrect LoRA configs. Inspect the metadata of a safetensors file by clicking on the <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/safetensors/logo.png" alt="safetensors logo" style="vertical-align: middle; display: inline-block; max-height: 0.8em; max-width: 0.8em; margin: 0; padding: 0; line-height: 1;"> logo next to the file on the Hub.
</hfoptions>
Customize components in the pipeline by passing them directly to the [`~loaders.FromSingleFileMixin.from_single_file`] method. For example, you can use a different scheduler in a pipeline. Save the metadata for LoRAs that aren't trained with Diffusers with either `transformer_lora_adapter_metadata` or `unet_lora_adapter_metadata` depending on your model. For the text encoder, use the `text_encoder_lora_adapter_metadata` and `text_encoder_2_lora_adapter_metadata` arguments in [`~loaders.FluxLoraLoaderMixin.save_lora_weights`]. This is only supported for safetensors files.
```py ```py
from diffusers import StableDiffusionXLPipeline, DDIMScheduler import torch
from diffusers import FluxPipeline
ckpt_path = "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0_0.9vae.safetensors"
scheduler = DDIMScheduler()
pipeline = StableDiffusionXLPipeline.from_single_file(ckpt_path, scheduler=scheduler)
```
Or you could use a ControlNet model in the pipeline.
```py
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
ckpt_path = "https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.safetensors" pipeline = FluxPipeline.from_pretrained(
controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny") "black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16
pipeline = StableDiffusionControlNetPipeline.from_single_file(ckpt_path, controlnet=controlnet) ).to("cuda")
pipeline.load_lora_weights("linoyts/yarn_art_Flux_LoRA")
pipeline.save_lora_weights(
text_encoder_lora_adapter_metadata={"r": 8, "lora_alpha": 8},
text_encoder_2_lora_adapter_metadata={"r": 8, "lora_alpha": 8}
)
``` ```
### Customize configuration options ### ckpt
Models have a configuration file that define their attributes like the number of inputs in a UNet. Pipelines configuration options are available in the pipeline's class. For example, if you look at the [`StableDiffusionXLInstructPix2PixPipeline`] class, there is an option to scale the image latents with the `is_cosxl_edit` parameter.
These configuration files can be found in the models Hub repository or another location from which the configuration file originated (for example, a GitHub repository or locally on your device).
<hfoptions id="config-file"> Older model weights are commonly saved with Python's [pickle](https://docs.python.org/3/library/pickle.html) utility in a ckpt file.
<hfoption id="Hub configuration file">
> [!TIP] Pickled files may be unsafe because they can be exploited to execute malicious code. It is recommended to use safetensors files or convert the weights to safetensors files.
> The [`~loaders.FromSingleFileMixin.from_single_file`] method automatically maps the checkpoint to the appropriate model repository, but there are cases where it is useful to use the `config` parameter. For example, if the model components in the checkpoint are different from the original checkpoint or if a checkpoint doesn't have the necessary metadata to correctly determine the configuration to use for the pipeline.
The [`~loaders.FromSingleFileMixin.from_single_file`] method automatically determines the configuration to use from the configuration file in the model repository. You could also explicitly specify the configuration to use by providing the repository id to the `config` parameter. Use [`~loaders.FromSingleFileMixin.from_single_file`] to load a ckpt file.
```py ```py
from diffusers import StableDiffusionXLPipeline from diffusers import DiffusionPipeline
ckpt_path = "https://huggingface.co/segmind/SSD-1B/blob/main/SSD-1B.safetensors"
repo_id = "segmind/SSD-1B"
pipeline = StableDiffusionXLPipeline.from_single_file(ckpt_path, config=repo_id) pipeline = DiffusionPipeline.from_single_file(
"https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/blob/main/v1-5-pruned.ckpt"
)
``` ```
The model loads the configuration file for the [UNet](https://huggingface.co/segmind/SSD-1B/blob/main/unet/config.json), [VAE](https://huggingface.co/segmind/SSD-1B/blob/main/vae/config.json), and [text encoder](https://huggingface.co/segmind/SSD-1B/blob/main/text_encoder/config.json) from their respective subfolders in the repository. ### dduf
</hfoption>
<hfoption id="original configuration file">
The [`~loaders.FromSingleFileMixin.from_single_file`] method can also load the original configuration file of a pipeline that is stored elsewhere. Pass a local path or URL of the original configuration file to the `original_config` parameter.
```py
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"
original_config = "https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_base.yaml"
pipeline = StableDiffusionXLPipeline.from_single_file(ckpt_path, original_config=original_config)
```
> [!TIP] > [!TIP]
> Diffusers attempts to infer the pipeline components based on the type signatures of the pipeline class when you use `original_config` with `local_files_only=True`, instead of fetching the configuration files from the model repository on the Hub. This prevents backward breaking changes in code that can't connect to the internet to fetch the necessary configuration files. > DDUF is an experimental file type and the API may change. Refer to the DDUF [docs](https://huggingface.co/docs/hub/dduf) to learn more.
>
> This is not as reliable as providing a path to a local model repository with the `config` parameter, and might lead to errors during pipeline configuration. To avoid errors, run the pipeline with `local_files_only=False` once to download the appropriate pipeline configuration files to the local cache.
</hfoption>
</hfoptions>
While the configuration files specify the pipeline or models default parameters, you can override them by providing the parameters directly to the [`~loaders.FromSingleFileMixin.from_single_file`] method. Any parameter supported by the model or pipeline class can be configured in this way. DDUF is a file type designed to unify different diffusion model distribution methods and weight-saving formats. It is a standardized and flexible method to package all components of a diffusion model into a single file, providing a balance between the Diffusers and single-file formats.
<hfoptions id="override"> Use the `dduf_file` argument in [`~DiffusionPipeline.from_pretrained`] to load a DDUF file. You can also load quantized dduf files as long as they are stored in the Diffusers format.
<hfoption id="pipeline">
For example, to scale the image latents in [`StableDiffusionXLInstructPix2PixPipeline`] pass the `is_cosxl_edit` parameter. ```py
import torch
```python from diffusers import DiffusionPipeline
from diffusers import StableDiffusionXLInstructPix2PixPipeline
ckpt_path = "https://huggingface.co/stabilityai/cosxl/blob/main/cosxl_edit.safetensors" pipeline = DiffusionPipeline.from_pretrained(
pipeline = StableDiffusionXLInstructPix2PixPipeline.from_single_file(ckpt_path, config="diffusers/sdxl-instructpix2pix-768", is_cosxl_edit=True) "DDUF/FLUX.1-dev-DDUF",
dduf_file="FLUX.1-dev.dduf",
torch_dtype=torch.bfloat16,
device_map="cuda"
)
``` ```
</hfoption> To save a pipeline as a dduf file, use the [`~huggingface_hub.export_folder_as_dduf`] utility.
<hfoption id="model">
For example, to upcast the attention dimensions in a [`UNet2DConditionModel`] pass the `upcast_attention` parameter. ```py
import torch
from diffusers import DiffusionPipeline
from huggingface_hub import export_folder_as_dduf
```python pipeline = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
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" save_folder = "flux-dev"
model = UNet2DConditionModel.from_single_file(ckpt_path, upcast_attention=True) pipeline.save_pretrained("flux-dev")
export_folder_as_dduf("flux-dev.dduf", folder_path=save_folder)
``` ```
</hfoption> ## Converting formats and files
</hfoptions>
### Local files
In Diffusers>=v0.28.0, the [`~loaders.FromSingleFileMixin.from_single_file`] method attempts to configure a pipeline or model by inferring the model type from the keys in the checkpoint file. The inferred model type is used to determine the appropriate model repository on the Hugging Face Hub to configure the model or pipeline.
For example, any single file checkpoint based on the Stable Diffusion XL base model will use the [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) model repository to configure the pipeline. Diffusers provides scripts and methods to convert format and files to enable broader support across the diffusion ecosystem.
But if you're working in an environment with restricted internet access, you should download the configuration files with the [`~huggingface_hub.snapshot_download`] function, and the model checkpoint with the [`~huggingface_hub.hf_hub_download`] function. By default, these files are downloaded to the Hugging Face Hub [cache directory](https://huggingface.co/docs/huggingface_hub/en/guides/manage-cache), but you can specify a preferred directory to download the files to with the `local_dir` parameter. Take a look at the [diffusers/scripts](https://github.com/huggingface/diffusers/tree/main/scripts) folder to find a conversion script. Scripts with `"to_diffusers` appended at the end converts a model to the Diffusers format. Each script has a specific set of arguments for configuring the conversion. Make sure you check what arguments are available.
Pass the configuration and checkpoint paths to the [`~loaders.FromSingleFileMixin.from_single_file`] method to load locally. The example below converts a model stored in Diffusers format to a single-file format. Provide the path to the model to convert and where to save the converted model. You can optionally specify what file type and data type to save the model as.
<hfoptions id="local"> ```bash
<hfoption id="Hub cache directory"> python convert_diffusers_to_original_sdxl.py --model_path path/to/model/to/convert --checkpoint_path path/to/save/model/to --use_safetensors
```python
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",
allow_patterns=["*.json", "**/*.json", "*.txt", "**/*.txt"]
)
pipeline = StableDiffusionXLPipeline.from_single_file(my_local_checkpoint_path, config=my_local_config_path, local_files_only=True)
``` ```
</hfoption> The [`~DiffusionPipeline.save_pretrained`] method also saves a model in Diffusers format and takes care of creating subfolders for each model. It saves the files as safetensor files by default.
<hfoption id="specific local directory">
```python
from huggingface_hub import hf_hub_download, snapshot_download
my_local_checkpoint_path = hf_hub_download( ```py
repo_id="segmind/SSD-1B", from diffusers import DiffusionPipeline
filename="SSD-1B.safetensors"
local_dir="my_local_checkpoints"
)
my_local_config_path = snapshot_download( pipeline = DiffusionPipeline.from_single_file(
repo_id="segmind/SSD-1B", "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0.safetensors",
allow_patterns=["*.json", "**/*.json", "*.txt", "**/*.txt"]
local_dir="my_local_config"
) )
pipeline.save_pretrained()
pipeline = StableDiffusionXLPipeline.from_single_file(my_local_checkpoint_path, config=my_local_config_path, local_files_only=True)
``` ```
</hfoption> Finally, you can use a Space like [SD To Diffusers](https://hf.co/spaces/diffusers/sd-to-diffusers) or [SD-XL To Diffusers](https://hf.co/spaces/diffusers/sdxl-to-diffusers) to convert models to the Diffusers format. It'll open a PR on your model repository with the converted files. This is the easiest way to convert a model, but it may fail for more complicated models. Using a conversion script is more reliable.
</hfoptions>
#### Local files without symlink
> [!TIP]
> In huggingface_hub>=v0.23.0, the `local_dir_use_symlinks` argument isn't necessary for the [`~huggingface_hub.hf_hub_download`] and [`~huggingface_hub.snapshot_download`] functions.
The [`~loaders.FromSingleFileMixin.from_single_file`] method relies on the [huggingface_hub](https://hf.co/docs/huggingface_hub/index) caching mechanism to fetch and store checkpoints and configuration files for models and pipelines. If you're working with a file system that does not support symlinking, you should download the checkpoint file to a local directory first, and disable symlinking with the `local_dir_use_symlink=False` parameter in the [`~huggingface_hub.hf_hub_download`] function and [`~huggingface_hub.snapshot_download`] functions.
```python
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( ## Resources
repo_id="segmind/SSD-1B",
allow_patterns=["*.json", "**/*.json", "*.txt", "**/*.txt"]
local_dir_use_symlinks=False,
)
print("My local config: ", my_local_config_path)
```
Then you can pass the local paths to the `pretrained_model_link_or_path` and `config` parameters. - Learn more about the design decisions and why safetensor files are preferred for saving and loading model weights in the [Safetensors audited as really safe and becoming the default](https://blog.eleuther.ai/safetensors-security-audit/) blog post.
```python
pipeline = StableDiffusionXLPipeline.from_single_file(my_local_checkpoint_path, config=my_local_config_path, local_files_only=True)
```
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