Load pipelines, models, and schedulers
Having an easy way to use a diffusion system for inference is essential to 𧨠Diffusers. Diffusion systems often consist of multiple components like parameterized models, tokenizers, and schedulers that interact in complex ways. That is why we designed the DiffusionPipeline to wrap the complexity of the entire diffusion system into an easy-to-use API, while remaining flexible enough to be adapted for other use cases, such as loading each component individually as building blocks to assemble your own diffusion system.
Everything you need for inference or training is accessible with the from_pretrained()
method.
This guide will show you how to load:
- pipelines from the Hub and locally
- different components into a pipeline
- checkpoint variants such as different floating point types or non-exponential mean averaged (EMA) weights
- models and schedulers
Diffusion Pipeline
π‘ Skip to the DiffusionPipeline explained section if you are interested in learning in more detail about how the DiffusionPipeline class works.
The DiffusionPipeline class is the simplest and most generic way to load the latest trending diffusion model from the Hub. The DiffusionPipeline.from_pretrained() method automatically detects the correct pipeline class from the checkpoint, downloads, and caches all the required configuration and weight files, and returns a pipeline instance ready for inference.
from diffusers import DiffusionPipeline
repo_id = "runwayml/stable-diffusion-v1-5"
pipe = DiffusionPipeline.from_pretrained(repo_id, use_safetensors=True)
You can also load a checkpoint with its specific pipeline class. The example above loaded a Stable Diffusion model; to get the same result, use the StableDiffusionPipeline class:
from diffusers import StableDiffusionPipeline
repo_id = "runwayml/stable-diffusion-v1-5"
pipe = StableDiffusionPipeline.from_pretrained(repo_id, use_safetensors=True)
A checkpoint (such as CompVis/stable-diffusion-v1-4
or runwayml/stable-diffusion-v1-5
) may also be used for more than one task, like text-to-image or image-to-image. To differentiate what task you want to use the checkpoint for, you have to load it directly with its corresponding task-specific pipeline class:
from diffusers import StableDiffusionImg2ImgPipeline
repo_id = "runwayml/stable-diffusion-v1-5"
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(repo_id)
Local pipeline
To load a diffusion pipeline locally, use git-lfs
to manually download the checkpoint (in this case, runwayml/stable-diffusion-v1-5
) to your local disk. This creates a local folder, ./stable-diffusion-v1-5
, on your disk:
git-lfs install
git clone https://huggingface.co/runwayml/stable-diffusion-v1-5
Then pass the local path to from_pretrained():
from diffusers import DiffusionPipeline
repo_id = "./stable-diffusion-v1-5"
stable_diffusion = DiffusionPipeline.from_pretrained(repo_id, use_safetensors=True)
The from_pretrained() method wonβt download any files from the Hub when it detects a local path, but this also means it wonβt download and cache the latest changes to a checkpoint.
Swap components in a pipeline
You can customize the default components of any pipeline with another compatible component. Customization is important because:
- Changing the scheduler is important for exploring the trade-off between generation speed and quality.
- Different components of a model are typically trained independently and you can swap out a component with a better-performing one.
- During finetuning, usually only some components - like the UNet or text encoder - are trained.
To find out which schedulers are compatible for customization, you can use the compatibles
method:
from diffusers import DiffusionPipeline
repo_id = "runwayml/stable-diffusion-v1-5"
stable_diffusion = DiffusionPipeline.from_pretrained(repo_id, use_safetensors=True)
stable_diffusion.scheduler.compatibles
Letβs use the SchedulerMixin.from_pretrained() method to replace the default PNDMScheduler with a more performant scheduler, EulerDiscreteScheduler. The subfolder="scheduler"
argument is required to load the scheduler configuration from the correct subfolder of the pipeline repository.
Then you can pass the new EulerDiscreteScheduler instance to the scheduler
argument in DiffusionPipeline:
from diffusers import DiffusionPipeline, EulerDiscreteScheduler
repo_id = "runwayml/stable-diffusion-v1-5"
scheduler = EulerDiscreteScheduler.from_pretrained(repo_id, subfolder="scheduler")
stable_diffusion = DiffusionPipeline.from_pretrained(repo_id, scheduler=scheduler, use_safetensors=True)
Safety checker
Diffusion models like Stable Diffusion can generate harmful content, which is why 𧨠Diffusers has a safety checker to check generated outputs against known hardcoded NSFW content. If youβd like to disable the safety checker for whatever reason, pass None
to the safety_checker
argument:
from diffusers import DiffusionPipeline
repo_id = "runwayml/stable-diffusion-v1-5"
stable_diffusion = DiffusionPipeline.from_pretrained(repo_id, safety_checker=None, use_safetensors=True)
"""
You have disabled the safety checker for <class 'diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline'> by passing `safety_checker=None`. Ensure that you abide by the conditions of the Stable Diffusion license and do not expose unfiltered results in services or applications open to the public. Both the diffusers team and Hugging Face strongly recommend keeping the safety filter enabled in all public-facing circumstances, disabling it only for use cases that involve analyzing network behavior or auditing its results. For more information, please have a look at https://github.com/huggingface/diffusers/pull/254 .
"""
Reuse components across pipelines
You can also reuse the same components in multiple pipelines to avoid loading the weights into RAM twice. Use the components method to save the components:
from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline
model_id = "runwayml/stable-diffusion-v1-5"
stable_diffusion_txt2img = StableDiffusionPipeline.from_pretrained(model_id, use_safetensors=True)
components = stable_diffusion_txt2img.components
Then you can pass the components
to another pipeline without reloading the weights into RAM:
stable_diffusion_img2img = StableDiffusionImg2ImgPipeline(**components)
You can also pass the components individually to the pipeline if you want more flexibility over which components to reuse or disable. For example, to reuse the same components in the text-to-image pipeline, except for the safety checker and feature extractor, in the image-to-image pipeline:
from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline
model_id = "runwayml/stable-diffusion-v1-5"
stable_diffusion_txt2img = StableDiffusionPipeline.from_pretrained(model_id, use_safetensors=True)
stable_diffusion_img2img = StableDiffusionImg2ImgPipeline(
vae=stable_diffusion_txt2img.vae,
text_encoder=stable_diffusion_txt2img.text_encoder,
tokenizer=stable_diffusion_txt2img.tokenizer,
unet=stable_diffusion_txt2img.unet,
scheduler=stable_diffusion_txt2img.scheduler,
safety_checker=None,
feature_extractor=None,
requires_safety_checker=False,
)
Checkpoint variants
A checkpoint variant is usually a checkpoint whose weights are:
- Stored in a different floating point type for lower precision and lower storage, such as
torch.float16
, because it only requires half the bandwidth and storage to download. You canβt use this variant if youβre continuing training or using a CPU. - Non-exponential mean averaged (EMA) weights, which shouldnβt be used for inference. You should use these to continue fine-tuning a model.
π‘ When the checkpoints have identical model structures, but they were trained on different datasets and with a different training setup, they should be stored in separate repositories instead of variations (for example, stable-diffusion-v1-4
and stable-diffusion-v1-5
).
Otherwise, a variant is identical to the original checkpoint. They have exactly the same serialization format (like Safetensors), model structure, and weights that have identical tensor shapes.
checkpoint type | weight name | argument for loading weights |
---|---|---|
original | diffusion_pytorch_model.bin | |
floating point | diffusion_pytorch_model.fp16.bin | variant , torch_dtype |
non-EMA | diffusion_pytorch_model.non_ema.bin | variant |
There are two important arguments to know for loading variants:
torch_dtype
defines the floating point precision of the loaded checkpoints. For example, if you want to save bandwidth by loading afp16
variant, you should specifytorch_dtype=torch.float16
to convert the weights tofp16
. Otherwise, thefp16
weights are converted to the defaultfp32
precision. You can also load the original checkpoint without defining thevariant
argument, and convert it tofp16
withtorch_dtype=torch.float16
. In this case, the defaultfp32
weights are downloaded first, and then theyβre converted tofp16
after loading.variant
defines which files should be loaded from the repository. For example, if you want to load anon_ema
variant from thediffusers/stable-diffusion-variants
repository, you should specifyvariant="non_ema"
to download thenon_ema
files.
from diffusers import DiffusionPipeline
import torch
# load fp16 variant
stable_diffusion = DiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", variant="fp16", torch_dtype=torch.float16, use_safetensors=True
)
# load non_ema variant
stable_diffusion = DiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", variant="non_ema", use_safetensors=True
)
To save a checkpoint stored in a different floating-point type or as a non-EMA variant, use the DiffusionPipeline.save_pretrained() method and specify the variant
argument. You should try and save a variant to the same folder as the original checkpoint, so you can load both from the same folder:
from diffusers import DiffusionPipeline
# save as fp16 variant
stable_diffusion.save_pretrained("runwayml/stable-diffusion-v1-5", variant="fp16")
# save as non-ema variant
stable_diffusion.save_pretrained("runwayml/stable-diffusion-v1-5", variant="non_ema")
If you donβt save the variant to an existing folder, you must specify the variant
argument otherwise itβll throw an Exception
because it canβt find the original checkpoint:
# π this won't work
stable_diffusion = DiffusionPipeline.from_pretrained(
"./stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True
)
# π this works
stable_diffusion = DiffusionPipeline.from_pretrained(
"./stable-diffusion-v1-5", variant="fp16", torch_dtype=torch.float16, use_safetensors=True
)
Models
Models are loaded from the ModelMixin.from_pretrained() method, which downloads and caches the latest version of the model weights and configurations. If the latest files are available in the local cache, from_pretrained() reuses files in the cache instead of re-downloading them.
Models can be loaded from a subfolder with the subfolder
argument. For example, the model weights for runwayml/stable-diffusion-v1-5
are stored in the unet
subfolder:
from diffusers import UNet2DConditionModel
repo_id = "runwayml/stable-diffusion-v1-5"
model = UNet2DConditionModel.from_pretrained(repo_id, subfolder="unet", use_safetensors=True)
Or directly from a repositoryβs directory:
from diffusers import UNet2DModel
repo_id = "google/ddpm-cifar10-32"
model = UNet2DModel.from_pretrained(repo_id, use_safetensors=True)
You can also load and save model variants by specifying the variant
argument in ModelMixin.from_pretrained() and ModelMixin.save_pretrained():
from diffusers import UNet2DConditionModel
model = UNet2DConditionModel.from_pretrained(
"runwayml/stable-diffusion-v1-5", subfolder="unet", variant="non_ema", use_safetensors=True
)
model.save_pretrained("./local-unet", variant="non_ema")
Schedulers
Schedulers are loaded from the SchedulerMixin.from_pretrained() method, and unlike models, schedulers are not parameterized or trained; they are defined by a configuration file.
Loading schedulers does not consume any significant amount of memory and the same configuration file can be used for a variety of different schedulers. For example, the following schedulers are compatible with StableDiffusionPipeline, which means you can load the same scheduler configuration file in any of these classes:
from diffusers import StableDiffusionPipeline
from diffusers import (
DDPMScheduler,
DDIMScheduler,
PNDMScheduler,
LMSDiscreteScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
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, use_safetensors=True)
DiffusionPipeline explained
As a class method, DiffusionPipeline.from_pretrained() is responsible for two things:
- Download the latest version of the folder structure required for inference and cache it. If the latest folder structure is available in the local cache, DiffusionPipeline.from_pretrained() reuses the cache and wonβt redownload the files.
- Load the cached weights into the correct pipeline class - retrieved from the
model_index.json
file - and return an instance of it.
The pipelinesβ underlying folder structure corresponds directly with their class instances. For example, the StableDiffusionPipeline corresponds to the folder structure in runwayml/stable-diffusion-v1-5
.
from diffusers import DiffusionPipeline
repo_id = "runwayml/stable-diffusion-v1-5"
pipeline = DiffusionPipeline.from_pretrained(repo_id, use_safetensors=True)
print(pipeline)
Youβll see pipeline is an instance of StableDiffusionPipeline, which consists of seven components:
"feature_extractor"
: a CLIPImageProcessor from π€ Transformers."safety_checker"
: a component for screening against harmful content."scheduler"
: an instance of PNDMScheduler."text_encoder"
: a CLIPTextModel from π€ Transformers."tokenizer"
: a CLIPTokenizer from π€ Transformers."unet"
: an instance of UNet2DConditionModel."vae"
: an instance of AutoencoderKL.
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"
]
}
Compare the components of the pipeline instance to the runwayml/stable-diffusion-v1-5
folder structure, and youβll see there is a separate folder for each of the components in the repository:
.
βββ feature_extractor
βΒ Β βββ preprocessor_config.json
βββ model_index.json
βββ safety_checker
βΒ Β βββ config.json
| βββ model.fp16.safetensors
β βββ model.safetensors
β βββ pytorch_model.bin
| βββ pytorch_model.fp16.bin
βββ scheduler
βΒ Β βββ scheduler_config.json
βββ text_encoder
βΒ Β βββ config.json
| βββ model.fp16.safetensors
β βββ model.safetensors
β |ββ pytorch_model.bin
| βββ pytorch_model.fp16.bin
βββ tokenizer
βΒ Β βββ merges.txt
βΒ Β βββ special_tokens_map.json
βΒ Β βββ tokenizer_config.json
βΒ Β βββ vocab.json
βββ unet
βΒ Β βββ config.json
βΒ Β βββ diffusion_pytorch_model.bin
| |ββ diffusion_pytorch_model.fp16.bin
β |ββ diffusion_pytorch_model.f16.safetensors
β |ββ diffusion_pytorch_model.non_ema.bin
β |ββ diffusion_pytorch_model.non_ema.safetensors
β βββ diffusion_pytorch_model.safetensors
|ββ vae
. βββ config.json
. βββ diffusion_pytorch_model.bin
βββ diffusion_pytorch_model.fp16.bin
βββ diffusion_pytorch_model.fp16.safetensors
βββ diffusion_pytorch_model.safetensors
You can access each of the components of the pipeline as an attribute to view its configuration:
pipeline.tokenizer
CLIPTokenizer(
name_or_path="/root/.cache/huggingface/hub/models--runwayml--stable-diffusion-v1-5/snapshots/39593d5650112b4cc580433f6b0435385882d819/tokenizer",
vocab_size=49408,
model_max_length=77,
is_fast=False,
padding_side="right",
truncation_side="right",
special_tokens={
"bos_token": AddedToken("<|startoftext|>", rstrip=False, lstrip=False, single_word=False, normalized=True),
"eos_token": AddedToken("<|endoftext|>", rstrip=False, lstrip=False, single_word=False, normalized=True),
"unk_token": AddedToken("<|endoftext|>", rstrip=False, lstrip=False, single_word=False, normalized=True),
"pad_token": "<|endoftext|>",
},
clean_up_tokenization_spaces=True
)
Every pipeline expects a model_index.json
file that tells the DiffusionPipeline:
- which pipeline class to load from
_class_name
- which version of 𧨠Diffusers was used to create the model in
_diffusers_version
- what components from which library are stored in the subfolders (
name
corresponds to the component and subfolder name,library
corresponds to the name of the library to load the class from, andclass
corresponds to the class name)
{
"_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"
]
}