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Load schedulers and models

[[open-in-colab]]

Diffusion pipelines are a collection of interchangeable schedulers and models that can be mixed and matched to tailor a pipeline to a specific use case. The scheduler encapsulates the entire denoising process such as the number of denoising steps and the algorithm for finding the denoised sample. A scheduler is not parameterized or trained so they don't take very much memory. The model is usually only concerned with the forward pass of going from a noisy input to a less noisy sample.

This guide will show you how to load schedulers and models to customize a pipeline. You'll use the runwayml/stable-diffusion-v1-5 checkpoint throughout this guide, so let's load it first.

import torch
from diffusers import DiffusionPipeline

pipeline = DiffusionPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True
).to("cuda")

You can see what scheduler this pipeline uses with the pipeline.scheduler attribute.

pipeline.scheduler
PNDMScheduler {
  "_class_name": "PNDMScheduler",
  "_diffusers_version": "0.21.4",
  "beta_end": 0.012,
  "beta_schedule": "scaled_linear",
  "beta_start": 0.00085,
  "clip_sample": false,
  "num_train_timesteps": 1000,
  "set_alpha_to_one": false,
  "skip_prk_steps": true,
  "steps_offset": 1,
  "timestep_spacing": "leading",
  "trained_betas": null
}

Load a scheduler

Schedulers are defined by a configuration file that can be used by a variety of schedulers. Load a scheduler with the [SchedulerMixin.from_pretrained] method, and specify the subfolder parameter to load the configuration file into the correct subfolder of the pipeline repository.

For example, to load the [DDIMScheduler]:

from diffusers import DDIMScheduler, DiffusionPipeline

ddim = DDIMScheduler.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="scheduler")

Then you can pass the newly loaded scheduler to the pipeline.

pipeline = DiffusionPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5", scheduler=ddim, torch_dtype=torch.float16, use_safetensors=True
).to("cuda")

Compare schedulers

Schedulers have their own unique strengths and weaknesses, making it difficult to quantitatively compare which scheduler works best for a pipeline. You typically have to make a trade-off between denoising speed and denoising quality. We recommend trying out different schedulers to find one that works best for your use case. Call the pipeline.scheduler.compatibles attribute to see what schedulers are compatible with a pipeline.

Let's compare the [LMSDiscreteScheduler], [EulerDiscreteScheduler], [EulerAncestralDiscreteScheduler], and the [DPMSolverMultistepScheduler] on the following prompt and seed.

import torch
from diffusers import DiffusionPipeline

pipeline = DiffusionPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True
).to("cuda")

prompt = "A photograph of an astronaut riding a horse on Mars, high resolution, high definition."
generator = torch.Generator(device="cuda").manual_seed(8)

To change the pipelines scheduler, use the [~ConfigMixin.from_config] method to load a different scheduler's pipeline.scheduler.config into the pipeline.

[LMSDiscreteScheduler] typically generates higher quality images than the default scheduler.

from diffusers import LMSDiscreteScheduler

pipeline.scheduler = LMSDiscreteScheduler.from_config(pipeline.scheduler.config)
image = pipeline(prompt, generator=generator).images[0]
image

[EulerDiscreteScheduler] can generate higher quality images in just 30 steps.

from diffusers import EulerDiscreteScheduler

pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)
image = pipeline(prompt, generator=generator).images[0]
image

[EulerAncestralDiscreteScheduler] can generate higher quality images in just 30 steps.

from diffusers import EulerAncestralDiscreteScheduler

pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(pipeline.scheduler.config)
image = pipeline(prompt, generator=generator).images[0]
image

[DPMSolverMultistepScheduler] provides a balance between speed and quality and can generate higher quality images in just 20 steps.

from diffusers import DPMSolverMultistepScheduler

pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
image = pipeline(prompt, generator=generator).images[0]
image
LMSDiscreteScheduler
EulerDiscreteScheduler
EulerAncestralDiscreteScheduler
DPMSolverMultistepScheduler

Most images look very similar and are comparable in quality. Again, it often comes down to your specific use case so a good approach is to run multiple different schedulers and compare the results.

Flax schedulers

To compare Flax schedulers, you need to additionally load the scheduler state into the model parameters. For example, let's change the default scheduler in [FlaxStableDiffusionPipeline] to use the super fast [FlaxDPMSolverMultistepScheduler].

Warning

The [FlaxLMSDiscreteScheduler] and [FlaxDDPMScheduler] are not compatible with the [FlaxStableDiffusionPipeline] yet.

import jax
import numpy as np
from flax.jax_utils import replicate
from flax.training.common_utils import shard
from diffusers import FlaxStableDiffusionPipeline, FlaxDPMSolverMultistepScheduler

scheduler, scheduler_state = FlaxDPMSolverMultistepScheduler.from_pretrained(
    "runwayml/stable-diffusion-v1-5",
    subfolder="scheduler"
)
pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5",
    scheduler=scheduler,
    variant="bf16",
    dtype=jax.numpy.bfloat16,
)
params["scheduler"] = scheduler_state

Then you can take advantage of Flax's compatibility with TPUs to generate a number of images in parallel. You'll need to make a copy of the model parameters for each available device and then split the inputs across them to generate your desired number of images.

# Generate 1 image per parallel device (8 on TPUv2-8 or TPUv3-8)
prompt = "A photograph of an astronaut riding a horse on Mars, high resolution, high definition."
num_samples = jax.device_count()
prompt_ids = pipeline.prepare_inputs([prompt] * num_samples)

prng_seed = jax.random.PRNGKey(0)
num_inference_steps = 25

# shard inputs and rng
params = replicate(params)
prng_seed = jax.random.split(prng_seed, jax.device_count())
prompt_ids = shard(prompt_ids)

images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images
images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))

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, [~ModelMixin.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

unet = UNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="unet", use_safetensors=True)

They can also be directly loaded from a repository.

from diffusers import UNet2DModel

unet = UNet2DModel.from_pretrained("google/ddpm-cifar10-32", use_safetensors=True)

To load and save model variants, specify the variant argument in [ModelMixin.from_pretrained] and [ModelMixin.save_pretrained].

from diffusers import UNet2DConditionModel

unet = UNet2DConditionModel.from_pretrained(
    "runwayml/stable-diffusion-v1-5", subfolder="unet", variant="non_ema", use_safetensors=True
)
unet.save_pretrained("./local-unet", variant="non_ema")