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# Schedulers

Diffusion pipelines are inherently a collection of diffusion models and schedulers that are partly independent from each other. This means that one is able to switch out parts of the pipeline to better customize 
a pipeline to one's use case. The best example of this are the [Schedulers](../api/schedulers.mdx).

Whereas diffusion models usually simply define the forward pass from noise to a less noisy sample, 
schedulers define the whole denoising process, *i.e.*:
- How many denoising steps?
- Stochastic or deterministic?
- What algorithm to use to find the denoised sample

They can be quite complex and often define a trade-off between **denoising speed** and **denoising quality**.
It is extremely difficult to measure quantitatively which scheduler works best for a given diffusion pipeline, so it is often recommended to simply try out which works best.

The following paragraphs shows how to do so with the 🧨 Diffusers library.

## Load pipeline

Let's start by loading the stable diffusion pipeline.
Remember that you have to be a registered user on the 🤗 Hugging Face Hub, and have "click-accepted" the [license](https://huggingface.co/runwayml/stable-diffusion-v1-5) in order to use stable diffusion.

```python
from huggingface_hub import login
from diffusers import DiffusionPipeline
import torch

# first we need to login with our access token
login()

# Now we can download the pipeline
pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
```

Next, we move it to GPU:

```python
pipeline.to("cuda")
```

## Access the scheduler

The scheduler is always one of the components of the pipeline and is usually called `"scheduler"`.
So it can be accessed via the `"scheduler"` property.

```python
pipeline.scheduler
```

**Output**:
```
PNDMScheduler {
  "_class_name": "PNDMScheduler",
  "_diffusers_version": "0.8.0.dev0",
  "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,
  "trained_betas": null
}
```

We can see that the scheduler is of type [`PNDMScheduler`]. 
Cool, now let's compare the scheduler in its performance to other schedulers.
First we define a prompt on which we will test all the different schedulers:

```python
prompt = "A photograph of an astronaut riding a horse on Mars, high resolution, high definition."
```

Next, we create a generator from a random seed that will ensure that we can generate similar images as well as run the pipeline:

```python
generator = torch.Generator(device="cuda").manual_seed(8)
image = pipeline(prompt, generator=generator).images[0]
image
```

<p align="center">
    <br>
    <img src="https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/diffusers_docs/astronaut_pndm.png" width="400"/>
    <br>
</p>


## Changing the scheduler

Now we show how easy it is to change the scheduler of a pipeline. Every scheduler has a property [`SchedulerMixin.compatibles`] 
which defines all compatible schedulers. You can take a look at all available, compatible schedulers for the Stable Diffusion pipeline as follows.

```python
pipeline.scheduler.compatibles
```

**Output**:
```
[diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteScheduler,
 diffusers.schedulers.scheduling_ddim.DDIMScheduler,
 diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler,
 diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler,
 diffusers.schedulers.scheduling_pndm.PNDMScheduler,
 diffusers.schedulers.scheduling_ddpm.DDPMScheduler,
 diffusers.schedulers.scheduling_euler_ancestral_discrete.EulerAncestralDiscreteScheduler]
```

Cool, lots of schedulers to look at. Feel free to have a look at their respective class definitions: 

- [`LMSDiscreteScheduler`], 
- [`DDIMScheduler`], 
- [`DPMSolverMultistepScheduler`], 
- [`EulerDiscreteScheduler`], 
- [`PNDMScheduler`], 
- [`DDPMScheduler`], 
- [`EulerAncestralDiscreteScheduler`].

We will now compare the input prompt with all other schedulers. To change the scheduler of the pipeline you can make use of the 
convenient [`ConfigMixin.config`] property in combination with the [`ConfigMixin.from_config`] function.

```python
pipeline.scheduler.config
```

returns a dictionary of the configuration of the scheduler:

**Output**:
```
FrozenDict([('num_train_timesteps', 1000),
            ('beta_start', 0.00085),
            ('beta_end', 0.012),
            ('beta_schedule', 'scaled_linear'),
            ('trained_betas', None),
            ('skip_prk_steps', True),
            ('set_alpha_to_one', False),
            ('steps_offset', 1),
            ('_class_name', 'PNDMScheduler'),
            ('_diffusers_version', '0.8.0.dev0'),
            ('clip_sample', False)])
```

This configuration can then be used to instantiate a scheduler
of a different class that is compatible with the pipeline. Here, 
we change the scheduler to the [`DDIMScheduler`].

```python
from diffusers import DDIMScheduler

pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
```

Cool, now we can run the pipeline again to compare the generation quality.

```python
generator = torch.Generator(device="cuda").manual_seed(8)
image = pipeline(prompt, generator=generator).images[0]
image
```

<p align="center">
    <br>
    <img src="https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/diffusers_docs/astronaut_ddim.png" width="400"/>
    <br>
</p>


## Compare schedulers

So far we have tried running the stable diffusion pipeline with two schedulers: [`PNDMScheduler`] and [`DDIMScheduler`]. 
A number of better schedulers have been released that can be run with much fewer steps, let's compare them here:

[`LMSDiscreteScheduler`] usually leads to better results:

```python
from diffusers import LMSDiscreteScheduler

pipeline.scheduler = LMSDiscreteScheduler.from_config(pipeline.scheduler.config)

generator = torch.Generator(device="cuda").manual_seed(8)
image = pipeline(prompt, generator=generator).images[0]
image
```

<p align="center">
    <br>
    <img src="https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/diffusers_docs/astronaut_lms.png" width="400"/>
    <br>
</p>


[`EulerDiscreteScheduler`] and [`EulerAncestralDiscreteScheduler`] can generate high quality results with as little as 30 steps.

```python
from diffusers import EulerDiscreteScheduler

pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)

generator = torch.Generator(device="cuda").manual_seed(8)
image = pipeline(prompt, generator=generator, num_inference_steps=30).images[0]
image
```

<p align="center">
    <br>
    <img src="https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/diffusers_docs/astronaut_euler_discrete.png" width="400"/>
    <br>
</p>


and:

```python
from diffusers import EulerAncestralDiscreteScheduler

pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(pipeline.scheduler.config)

generator = torch.Generator(device="cuda").manual_seed(8)
image = pipeline(prompt, generator=generator, num_inference_steps=30).images[0]
image
```

<p align="center">
    <br>
    <img src="https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/diffusers_docs/astronaut_euler_ancestral.png" width="400"/>
    <br>
</p>


At the time of writing this doc [`DPMSolverMultistepScheduler`] gives arguably the best speed/quality trade-off and can be run with as little 
as 20 steps.

```python
from diffusers import DPMSolverMultistepScheduler

pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)

generator = torch.Generator(device="cuda").manual_seed(8)
image = pipeline(prompt, generator=generator, num_inference_steps=20).images[0]
image
```

<p align="center">
    <br>
    <img src="https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/diffusers_docs/astronaut_dpm.png" width="400"/>
    <br>
</p>

As you can see most images look very similar and are arguably of very similar quality. It often really depends on the specific use case which scheduler to choose. A good approach is always to run multiple different
schedulers to compare results.
