Load pipelines

Diffusion systems consist of multiple components like parameterized models 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. At the same time, the DiffusionPipeline is entirely customizable so you can modify each component to build a diffusion system for your use case.

This guide will show you how to load:

• pipelines from the Hub and locally
• different components into a pipeline
• multiple pipelines without increasing memory usage
• checkpoint variants such as different floating point types or non-exponential mean averaged (EMA) weights

Load a pipeline

Tip

Skip to the DiffusionPipeline explained section if you're interested in an explanation about how the DiffusionPipeline class works.

There are two ways to load a pipeline for a task:

1. Load the generic DiffusionPipeline class and allow it to automatically detect the correct pipeline class from the checkpoint.
2. Load a specific pipeline class for a specific task.

Generic pipelineSpecific pipeline

The DiffusionPipeline class is a simple and generic way to load the latest trending diffusion model from the Hub. It uses the from_pretrained method to automatically detect the correct pipeline class for a task from the checkpoint, downloads and caches all the required configuration and weight files, and returns a pipeline ready for inference.

from mindone.diffusers import DiffusionPipeline

pipeline = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", use_safetensors=True)

This same checkpoint can also be used for an image-to-image task. The DiffusionPipeline class can handle any task as long as you provide the appropriate inputs. For example, for an image-to-image task, you need to pass an initial image to the pipeline.

from mindone.diffusers import DiffusionPipeline
from mindone.diffusers.utils import load_image

pipeline = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", use_safetensors=True)

init_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/img2img-init.png")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipeline("Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", image=init_image)[0][0]

Checkpoints can be loaded by their specific pipeline class if you already know it. For example, to load a Stable Diffusion model, use the StableDiffusionPipeline class.

from mindone.diffusers import StableDiffusionPipeline

pipeline = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", use_safetensors=True)

This same checkpoint may also be used for another task like image-to-image. To differentiate what task you want to use the checkpoint for, you have to use the corresponding task-specific pipeline class. For example, to use the same checkpoint for image-to-image, use the StableDiffusionImg2ImgPipeline class.

from mindone.diffusers import StableDiffusionImg2ImgPipeline

pipeline = StableDiffusionImg2ImgPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", use_safetensors=True)

Local pipeline

To load a pipeline locally, use git-lfs to manually download a checkpoint to your local disk.

git-lfs install
git clone https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5

This creates a local folder, ./stable-diffusion-v1-5, on your disk and you should pass its path to from_pretrained.

from mindone.diffusers import DiffusionPipeline

stable_diffusion = DiffusionPipeline.from_pretrained("./stable-diffusion-v1-5", use_safetensors=True)

The from_pretrained method won't download files from the Hub when it detects a local path, but this also means it won't download and cache the latest changes to a checkpoint.

Customize a pipeline

You can customize a pipeline by loading different components into it. This is important because you can:

• change to a scheduler with faster generation speed or higher generation quality depending on your needs (call the scheduler.compatibles method on your pipeline to see compatible schedulers)
• change a default pipeline component to a newer and better performing one

For example, let's customize the default stabilityai/stable-diffusion-xl-base-1.0 checkpoint with:

• The HeunDiscreteScheduler to generate higher quality images at the expense of slower generation speed. You must pass the subfolder="scheduler" parameter in from_pretrained to load the scheduler configuration into the correct subfolder of the pipeline repository.
• A more stable VAE that runs in fp16.

from mindone.diffusers import StableDiffusionXLPipeline, HeunDiscreteScheduler, AutoencoderKL
import mindspore as ms

scheduler = HeunDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", mindspore_dtype=ms.float16, use_safetensors=True)

Now pass the new scheduler and VAE to the StableDiffusionXLPipeline.

pipeline = StableDiffusionXLPipeline.from_pretrained(
  "stabilityai/stable-diffusion-xl-base-1.0",
  scheduler=scheduler,
  vae=vae,
  mindspore_dtype=ms.float16,
  use_safetensors=True
)

Safety checker

Diffusers implements a safety checker for Stable Diffusion models which can generate harmful content. The safety checker screens the generated output against known hardcoded not-safe-for-work (NSFW) content. If for whatever reason you'd like to disable the safety checker, pass safety_checker=None to the from_pretrained method.

from mindone.diffusers import DiffusionPipeline

pipeline = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", 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 .
"""

Checkpoint variants

A checkpoint variant is usually a checkpoint whose weights are:

• Stored in a different floating point type, such as mindspore.float16, because it only requires half the bandwidth and storage to download. You can't use this variant if you're continuing training or using a CPU.
• Non-exponential mean averaged (EMA) weights which shouldn't be used for inference. You should use this variant to continue finetuning a model.

Tip

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. For example, stabilityai/stable-diffusion-2 and stabilityai/stable-diffusion-2-1 are stored in separate repositories.

Otherwise, a variant is identical to the original checkpoint. They have exactly the same serialization format (like safetensors), model structure, and their weights have identical tensor shapes.

checkpoint type	weight name	argument for loading weights
original	diffusion_model.safetensors
floating point	diffusion_model.fp16.safetensors	variant, mindspore_dtype
non-EMA	diffusion_model.non_ema.safetensors	variant

There are two important arguments for loading variants:

• mindspore_dtype specifies the floating point precision of the loaded checkpoint. For example, if you want to save bandwidth by loading a fp16 variant, you should set variant="fp16" and mindspore_dtype=mindspore.float16 to convert the weights to fp16. Otherwise, the fp16 weights are converted to the default fp32 precision.

If you only set mindspore_dtype=mindspore.float16, the default fp32 weights are downloaded first and then converted to fp16.

• variant specifies which files should be loaded from the repository. For example, if you want to load a non-EMA variant of a UNet from stable-diffusion-v1-5/stable-diffusion-v1-5, set variant="non_ema" to download the non_ema file.

fp16non-EMA

from mindone.diffusers import DiffusionPipeline
import mindspore as ms

pipeline = DiffusionPipeline.from_pretrained(
    "stable-diffusion-v1-5/stable-diffusion-v1-5", variant="fp16", mindspore_dtype=ms.float16, use_safetensors=True
)

pipeline = DiffusionPipeline.from_pretrained(
    "stable-diffusion-v1-5/stable-diffusion-v1-5", variant="non_ema", use_safetensors=True
)

Use the variant parameter in the save_pretrained method to save a checkpoint as a different floating point type or as a non-EMA variant. You should try save a variant to the same folder as the original checkpoint, so you have the option of loading both from the same folder.

fp16non_ema

from mindone.diffusers import DiffusionPipeline

pipeline.save_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", variant="fp16")

pipeline.save_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", variant="non_ema")

If you don't save the variant to an existing folder, you must specify the variant argument otherwise it'll throw an Exception because it can't find the original checkpoint.

# 👎 this won't work
pipeline = DiffusionPipeline.from_pretrained(
    "./stable-diffusion-v1-5", mindspore_dtype=mindspore.float16, use_safetensors=True
)
# 👍 this works
pipeline = DiffusionPipeline.from_pretrained(
    "./stable-diffusion-v1-5", variant="fp16", mindspore_dtype=mindspore.float16, use_safetensors=True
)

DiffusionPipeline explained

As a class method, 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, from_pretrained reuses the cache and won't 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 pipelines' underlying folder structure corresponds directly with their class instances. For example, the StableDiffusionPipeline corresponds to the folder structure in stable-diffusion-v1-5/stable-diffusion-v1-5.

from mindone.diffusers import DiffusionPipeline

repo_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
pipeline = DiffusionPipeline.from_pretrained(repo_id, use_safetensors=True)
print(pipeline)

You'll see pipeline is an instance of StableDiffusionPipeline, which consists of seven components:

• "feature_extractor": a [~transformers.CLIPImageProcessor] from 🤗 Transformers.
• "safety_checker": a component for screening against harmful content.
• "scheduler": an instance of PNDMScheduler.
• "text_encoder": a [~transformers.CLIPTextModel] from 🤗 Transformers.
• "tokenizer": a [~transformers.CLIPTokenizer] from 🤗 Transformers.
• "unet": an instance of UNet2DConditionModel.
• "vae": an instance of AutoencoderKL.

StableDiffusionPipeline {
  "_class_name": "StableDiffusionPipeline",
  "_diffusers_version": "0.29.2",
  "_name_or_path": "stable-diffusion-v1-5/stable-diffusion-v1-5",
  "feature_extractor": [
    "transformers",
    "CLIPImageProcessor"
  ],
  "image_encoder": [
    null,
    null
  ],
  "requires_safety_checker": true,
  "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 stable-diffusion-v1-5/stable-diffusion-v1-5 folder structure, and you'll see there is a separate folder for each of the components in the repository:

.
├── feature_extractor
│   └── preprocessor_config.json
├── model_index.json
├── safety_checker
│   ├── config.json
|   ├── model.fp16.safetensors
│   ├── model.safetensors
│   ├── pytorch_model.bin
|   └── pytorch_model.fp16.bin
├── scheduler
│   └── scheduler_config.json
├── text_encoder
│   ├── config.json
|   ├── model.fp16.safetensors
│   ├── model.safetensors
│   |── 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
│   |── diffusion_pytorch_model.f16.safetensors
│   |── diffusion_pytorch_model.non_ema.bin
│   |── diffusion_pytorch_model.non_ema.safetensors
│   └── diffusion_pytorch_model.safetensors
|── vae
.   ├── config.json
.   ├── diffusion_pytorch_model.bin
    ├── diffusion_pytorch_model.fp16.bin
    ├── diffusion_pytorch_model.fp16.safetensors
    └── 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--stable-diffusion-v1-5--stable-diffusion-v1-5/snapshots/f03de327dd89b501a01da37fc5240cf4fdba85a1/tokenizer',
    vocab_size=49408,
    model_max_length=77,
    is_fast=False,
    padding_side='right',
    truncation_side='right',
    special_tokens={
        'bos_token': '<|startoftext|>',
        'eos_token': '<|endoftext|>',
        'unk_token': '<|endoftext|>',
        'pad_token': '<|endoftext|>'},
    clean_up_tokenization_spaces=True
),
added_tokens_decoder={
    49406: AddedToken("<|startoftext|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=True),
    49407: AddedToken("<|endoftext|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=True),
}

Every pipeline expects a model_index.json file that tells the DiffusionPipeline:

• which pipeline class to load from _class_name
• which version of 🧨 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, and class 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"
  ]
}