[WIP] Consistency models distillation examples #3992

ayushtues · 2023-07-07T10:59:47Z

Add distillation training scripts for Consistency models

Continuation of dg845#2, adding distillation examples for the recently added Consistency Models (paper, original code, diffusers pipeline)

Model/Pipeline Description

Consistency Models (paper, code) are a new family of generative models similar to continuous-time diffusion models which support fast one-step generation. Diffusion models can be distilled into a consistency model for faster sampling, and consistency models can also be trained from scratch. From the paper abstract:

Diffusion models have made significant breakthroughs in image, audio, and video generation, but they depend on an iterative generation process that causes slow sampling speed and caps their potential for real-time applications. To overcome this limitation, we propose consistency models, a new family of generative models that achieve high sample quality without adversarial training. They support fast one-step generation by design, while still allowing for few-step sampling to trade compute for sample quality. They also support zero-shot data editing, like image inpainting, colorization, and super-resolution, without requiring explicit training on these tasks. Consistency models can be trained either as a way to distill pre-trained diffusion models, or as standalone generative models. Through extensive experiments, we demonstrate that they outperform existing distillation techniques for diffusion models in one- and few-step generation. For example, we achieve the new state-of-the-art FID of 3.55 on CIFAR-10 and 6.20 on ImageNet 64x64 for one-step generation. When trained as standalone generative models, consistency models also outperform single-step, non-adversarial generative models on standard benchmarks like CIFAR-10, ImageNet 64x64 and LSUN 256x256.

In this PR, we implement the distillation procedure to distill a diffusion model into a consistency model as described in the paper

TODO

Add training scripts for distillation

CC
@dg845
@williamberman
@patrickvonplaten

…ampler (stochastic_iterative_sampler) in the original code, and make further improvements to sampling.

[WIP] Add Unet for consistency models

…IterativeScheduler and add initial version of tests.

…le_shift to 'scale_shift' to better match consistency model checkpoints.

…s to the consistency models conversion script.

…ncy models implementation.

- Get small testing checkpoints from hub - Modify tests to take into account "distillation" parameter of ConsistencyModelPipeline - Add onestep, multistep tests for distillation and distillation + class conditional - Add expected image slices for onestep tests

- Add initial support for class-conditional generation - Fix initial sigma for onestep generation - Fix some sigma shape issues

- add latents __call__ argument and prepare_latents method - add check_inputs method - add initial docstrings for ConsistencyModelPipeline.__call__

…eneration.

… and make related changes to the pipeline and tests.

- in pipeline, call self.scheduler.scale_model_input before denoising - get expected slices for Euler and Heun scheduler tests - make Euler test pass - mark Heun test as expected fail because it doesn't support prediction_type "sample" yet - remove DPM and Euler Ancestral tests because they don't support use_karras_sigmas

HuggingFaceDocBuilderDev · 2023-07-07T11:07:55Z

The docs for this PR live here. All of your documentation changes will be reflected on that endpoint.

sayakpaul · 2023-07-10T09:47:56Z

@ayushtues, thanks for this work! Let us know when you'd like a review from our end. The PR doesn't have to be super tight for an initial review just as an FYI.

ayushtues · 2023-07-10T09:57:08Z

Hey @sayakpaul yes I think it is good enough for an initial review right now, one major thing we need to figure out is how to do the scaling of the inputs for different teacher models (as mentioned in dg845#2 (comment)). The original paper used EDM as a teacher model, and that was trained using a Karras Scheduler, so it followed the same scaling and processing as the Conistency model, but if we want to use something else like Stable Diffusion or DDPM as the teacher model, need to figure out what input scaling and processing to use for them

ayushtues · 2023-07-10T11:43:56Z

@dg845, @patrickvonplaten @williamberman @sayakpaul do you think we should add examples of other applications of Consistency models mentioned in the paper like inpainting, colorization, super-resolution? They mostly seem to involve some clever processing of the inputs and the outputs of the model, for eg code reference in the original repo : https://github.com/openai/consistency_models/blob/ac278060af7175e37f4c1e79e69fa521234c04de/cm/karras_diffusion.py#L722

sayakpaul · 2023-07-10T12:27:11Z

one major thing we need to figure out is how to do the scaling of the inputs for different teacher models (as mentioned in dg845#2 (comment)).

If we decided to say, distill https://huggingface.co/valhalla/sd-pokemon-model, can't we use the same scheduler for distilling the student (with appropriate changes as needed) and use the same preprocessing steps? I like what @dg845 is proposing here: dg845#2 (comment).

@dg845, @patrickvonplaten @williamberman @sayakpaul do you think we should add examples of other applications of Consistency models mentioned in the paper like inpainting, colorization, super-resolution?

Let's gauge the community interest for that first and then we can definitely consider.

sayakpaul · 2023-07-10T12:30:15Z

examples/consistency_models/script.sh

@@ -0,0 +1,3 @@
+#!/bin/bash


We don't include shell-scripts in the examples.

sayakpaul · 2023-07-10T12:30:48Z

src/diffusers/schedulers/scheduling_consistency_models.py

@@ -268,6 +268,7 @@ def step(
        sample: torch.FloatTensor,
        generator: Optional[torch.Generator] = None,
        return_dict: bool = True,
+        use_noise: bool = True


Why do we need to introduce this argument?

Can we maybe make this a config attribute instead? use_noise=True will rarely changed to use_noise=False during inference - either it's always False or True. Could we add a force_deterministic=True/False to the config?

I think it would be better to put the section of the function that computes the unnoised prediction in its own function that can be called.

i.e.

def step(...): denoised = self.denoise(...) if len(self.timesteps) > 1: prev_sample = ... else: prev_sample = denoised return prev_sample def denoise(...): ...

this would avoid extra global config to the class and the training script could just call denoise()

That would also work for me - is probably indeed the better option (as my first proposal would force people to update the config after training, which is probs more brittle)

examples/consistency_models/train_consistency_distillation.py

sayakpaul · 2023-07-10T12:32:58Z

examples/consistency_models/train_consistency_distillation.py

+    if args.logger == "tensorboard":
+        if not is_tensorboard_available():
+            raise ImportError("Make sure to install tensorboard if you want to use it for logging during training.")
+
+    elif args.logger == "wandb":
+        if not is_wandb_available():
+            raise ImportError("Make sure to install wandb if you want to use it for logging during training.")
+        import wandb


Both of these dependencies need to go to requirements.txt.

sayakpaul · 2023-07-10T12:35:07Z

examples/consistency_models/train_consistency_distillation.py

+        model = UNet2DModel.from_config(config)
+        target_model = UNet2DModel.from_config(config)


So, this, in a sense, is step distillation i.e., we're not essentially compressing the model but reducing the number of steps.

Perhaps it is a distinction without a difference, but I think of it as reducing the number of steps for a (diffusion model, sampler) pair. In my mind, this distinction is important because my guess is that the quality of the sampler affects the quality of the distilled consistency model; if we distill using a sampler which produces bad samples for the diffusion model, the consistency model might learn to faithfully produce similar samples to sampling from the diffusion model using the sampler, but which are still poor quality because the samples it learned from are poor quality. (Conversely, using a better sampler might produce a better consistency model.)

[Another way to phrase it might be that it's compressing the sampling procedure, which depends on both the diffusion model and sampler.]

examples/consistency_models/train_consistency_distillation.py

sayakpaul · 2023-07-10T12:36:31Z

examples/consistency_models/train_consistency_distillation.py

+
+
+    # load the model to distill into a consistency model
+    teacher_model = DDPMPipeline.from_pretrained("google/ddpm-cifar10-32").unet


Let's make this path ("google/ddpm-cifar10-32") a CLI arg.

And it should also work with DiffusionPipeline.

sayakpaul · 2023-07-10T12:38:04Z

examples/consistency_models/train_consistency_distillation.py

+
+
+    # load the model to distill into a consistency model
+    teacher_model = DDPMPipeline.from_pretrained("google/ddpm-cifar10-32").unet


Not clear what model, target_model, and teacher_model mean.

My understanding is as follows (things in square brackets [] are the variable names in the current PR code / terminology follows Section 4/Algorithm 2 in the paper):

The teacher_model $s_{\phi}$ is the already-trained diffusion model we want to distill to a consistency model (for example, google/ddpm-cifar10-32).

The model is a consistency model $\boldsymbol{f_\theta}$ which is used to calculate the current output of the distilled consistency model $\boldsymbol{f_\theta}(x_{t_{n + 1}}, t_{n + 1})$ [model_output no. 1*] on the noisy sample $x_{t_{n + 1}}$ [noised_image]. The parameters $\boldsymbol{\theta}$ are updated via gradient descent. (The paper calls this the "online" model, following RL terminology.)

The target_model is a consistency model $\boldsymbol{f_{\theta^-}}$ which is used to calculate the distillation target, which we want the consistency model output $\boldsymbol{f_\theta}(x_{t_{n + 1}}, t_{n + 1})$ to match. This is obtained by first running one step of the sampler (in the paper and code, one step of the Karras Heun sampler) on the teacher_model to obtain a previous noisy sample $\hat{x}_{t_n}^\phi$ [model_output no. 2*], and then getting the target_model consistency model's output $\boldsymbol{f_{\theta^-}}(\hat{x}_{t_{n}}^\phi, t_{n})$ [model_output no. 3*] on $\hat{x}_{t_n}^\phi$. The target parameters $\boldsymbol{\theta^-}$ are updated via an EMA update (using $\boldsymbol{\theta}$). (This is analogous to target models in DQN-style RL algorithms, and I believe the EMA update is equivalent to what is sometimes called polyak averaging in RL.)

The loss function $L^{\phi}(\boldsymbol{\theta}, \boldsymbol{\theta^-})$ is then calculated using a distance metric $d(\cdot, \cdot)$ (MSE in the code)**:
$$L^{\phi}(\boldsymbol{\theta}, \boldsymbol{\theta^-}) = d(\boldsymbol{f_\theta}(x_{t_{n + 1}}, t_{n + 1}), \boldsymbol{f_{\theta^-}}(\hat{x}_{t_{n}}^\phi, t_{n}))$$
and then we take a gradient descent step with respect to model's parameters $\boldsymbol{\theta}$ and run an EMA update for target_model's parameters $\boldsymbol{\theta^-}$. (It's not clear to me which of model and target_model is outputted as the final trained checkpoint in the end; according to the paper, theoretically the parameters for the models should converge during training [e.g. $\boldsymbol{\theta} = \boldsymbol{\theta^-}$ at the end of training], so using either one should be fine.)

Some proposals:

I agree that what model, target_model, teacher_model are doing might be hard to follow from the code alone, perhaps it might make sense to add comments explaining what each one does when they are initialized?

Rename model_output no. 2 to teacher_model_output and model_output no. 3 to target_model_output. (If we want to rename model [see below], perhaps model_output no. 1 can be renamed similarly.)

Perhaps renaming model to something like online_model or current_consistency_model might be more clear? (Not sure on this one, especially the current_consistency_model suggestion, since I'm not sure if it's accurate; would feel more comfortable with this if model is what is outputted as the final trained checkpoint, see above.)

(*) Technically, these quantities correspond to distiller, denoised_image, and distiller_target in the code, respectively, but they're all preceded by a calculated raw U-Net output named model_output.
(**) The loss function in Algorithm 2 includes a timestep-dependent weight function $\lambda(t_n)$, but in practice it seems that a uniform weight schedule ($\lambda(t_n) = 1$) is usually used for consistency distillation.

Thanks so much for explaining @dg845. See my responses below:

I agree that what model, target_model, teacher_model are doing might be hard to follow from the code alone, perhaps it might make sense to add comments explaining what each one does when they are initialized?

100% agree. The math is heavy enough and we should grab the opportunity here to make it easily digestible to the end users.

Rename model_output no. 2 to teacher_model_output and model_output no. 3 to target_model_output.

Works for me.

Perhaps renaming model to something like online_model or current_consistency_model might be more clear? (Not sure on this one, especially the current_consistency_model suggestion, since I'm not sure if it's accurate; would feel more comfortable with this if model is what is outputted as the final trained checkpoint, see above.)

Let's maybe try to output EMA'd model only since that is more common in practice?

This is obtained by first running one step of the sampler (in the paper and code, one step of the Karras Heun sampler) on the teacher_model to obtain a previous noisy sample
[model_output no. 2*]

Have we checked if the teacher_model performs okay with that sampler? Does it matter?

Let's maybe try to output EMA'd model only since that is more common in practice?

Sounds good.

Have we checked if the teacher_model performs okay with that sampler? Does it matter?

I think there is a bug in the current code where we use noise_scheduler when performing one step of sampling on the teacher_model:

diffusers/examples/consistency_models/train_consistency_distillation.py

Lines 554 to 557 in 8742e4e

teacher_model_output = teacher_model(noise_scheduler.scale_model_input(samples, timestep_prev), timestep_prev, class_labels=labels).sample

teacher_denoiser = noise_scheduler.step(

teacher_model_output, timestep_prev, samples, use_noise=False

).prev_sample

because noise_scheduler is a CMStochasticIterativeScheduler:

diffusers/examples/consistency_models/train_consistency_distillation.py

Line 378 in 8742e4e

noise_scheduler = CMStochasticIterativeScheduler()

which isn't compatible with diffusion models. I think the scheduler should either be teacher_scheduler = DiffusionPipeline.from_pretrained(<model_id>).scheduler (if we want to use the scheduler the teacher_model was trained with) or HeunDiscreteScheduler (if we follow the original implementation). (We would still need noise_scheduler to get the model outputs for the consistency models model and teacher_model.)

As for whether the Heun scheduler works well with google/ddpm-cifar10-32, which is presumably trained with DDPMScheduler, I'm not sure whether it will affect the training results. I guess practically speaking we could try it out as is, and if the resulting model doesn't create reasonable samples, then we can try either switching the sampler for getting teacher samples to DDPMScheduler or switching the teacher_model to something that was trained with HeunDiscreteScheduler.

Sounds like a plan! Thanks so much for being generous with your explanations!

sayakpaul · 2023-07-10T12:39:12Z

examples/consistency_models/train_consistency_distillation.py

+    # The trackers initializes automatically on the main process.
+    if accelerator.is_main_process:
+        run = os.path.split(__file__)[-1].split(".")[0]
+        accelerator.init_trackers(run)


We usually perform this initialization like so:

diffusers/examples/dreambooth/train_dreambooth_lora.py

Line 1068 in 7a91ea6

accelerator.init_trackers("dreambooth-lora", config=vars(args))

sayakpaul · 2023-07-10T12:41:17Z

examples/consistency_models/train_consistency_distillation.py

+
+            with accelerator.accumulate(model):
+                # Predict the noise residual
+                model_output = model(noise_scheduler.scale_model_input(noised_image, timestep), timestep, class_labels=labels).sample


There are three model_output variables here. Let's maybe try to give them more appropriate names.

See #3992 (comment) for some suggestions.

sayakpaul

Looking good.

From my end, I think we need to:

Have accelerate deeply and better integrated.
Improve the readability of the code by using better variable names, adding comments wherever applicable, and refactoring some bits wherever applicable.
Use this method to upload the trained checkpoints to the Hub.
Add tests and docs.
Add utility for performing validation inference runs with Weights and Biases.
Demonstrate a full run with reasonable results.

More than happy to help with any of these :)

Also, IIUC, Consistency Models can either be trained in isolation or can be used to step-distill a pre-trained model. If that's the case, should we allow both from the training script? Or am I wrong?

ayushtues · 2023-07-11T11:28:46Z

Hey @sayakpaul can you help with the better integration with accelerator? I don't have much idea about how to use it better. Also can you link me to some relevant tests in existing examples which I can refer to

sayakpaul · 2023-07-11T11:39:15Z

Hey @sayakpaul can you help with the better integration with accelerator? I don't have much idea about how to use it better.

If you follow one of our examples from here, I think you will have a better idea this for example).

Also can you link me to some relevant tests in existing examples which I can refer to

Here you go: https://github.com/huggingface/diffusers/blob/main/examples/test_examples.py

williamberman · 2023-07-17T22:55:16Z

examples/consistency_models/train_consistency_distillation.py

+    else:
+        model = UNet2DModel(
+                sample_size= args.resolution,
+                in_channels=3,
+                out_channels=3,
+                layers_per_block=2,
+                num_class_embeds=1000,
+                block_out_channels= [32, 64],
+                attention_head_dim=8,
+                down_block_types= [
+                    "ResnetDownsampleBlock2D",
+                    "AttnDownBlock2D",
+                ],
+                up_block_types= [
+                    "AttnUpBlock2D",
+                    "ResnetUpsampleBlock2D",
+                ],
+                resnet_time_scale_shift="scale_shift",
+                upsample_type="resnet",
+                downsample_type="resnet"
+                )
+        target_model = UNet2DModel(
+                sample_size= args.resolution,
+                in_channels=3,
+                out_channels=3,
+                layers_per_block=2,
+                num_class_embeds=1000,
+                block_out_channels= [32, 64],
+                attention_head_dim=8,
+                down_block_types= [
+                    "ResnetDownsampleBlock2D",
+                    "AttnDownBlock2D",
+                ],
+                up_block_types= [
+                    "AttnUpBlock2D",
+                    "ResnetUpsampleBlock2D",
+                ],
+                resnet_time_scale_shift="scale_shift",
+                upsample_type="resnet",
+                downsample_type="resnet"
+                )    


could we remove this? would prefer to not hardcode a config in the training script

williamberman · 2023-07-17T22:56:20Z

examples/consistency_models/train_consistency_distillation.py

+    if args.testing: 
+        config = UNet2DModel.load_config('diffusers/consistency-models-test', subfolder="test_unet")


can we remove this? we should be able to pass the preferred model in from the standard cli args

github-actions · 2023-08-12T15:03:27Z

This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the contributing guidelines are likely to be ignored.

dg845 and others added 30 commits May 15, 2023 08:04

initial commit

799ab23

Improve consistency models sampling implementation.

63b7f01

Add CMStochasticIterativeScheduler, which implements the multi-step s…

f2e53da

…ampler (stochastic_iterative_sampler) in the original code, and make further improvements to sampling.

Merge branch 'main2' into consistency_unet

37e1895

Add Unet blocks for consistency models

7c1e81f

Add conversion script for Unet

3a151bd

Fix bug in new unet blocks

b6c5e15

Fix attention weight loading

4e93f09

Merge pull request huggingface#1 from ayushtues/consistency_unet

d137d11

[WIP] Add Unet for consistency models

Make design improvements to ConsistencyModelPipeline and CMStochastic…

9ae7669

…IterativeScheduler and add initial version of tests.

make style

54b287e

Add initial training script

067a9ef

Make small random test UNet class conditional and set resnet_time_sca…

5a27c2f

…le_shift to 'scale_shift' to better match consistency model checkpoints.

Add support for converting a test UNet and non-class-conditional UNet…

f2783a8

…s to the consistency models conversion script.

make style

ed53b85

Change num_class_embeds to 1000 to better match the original consiste…

a505c6c

…ncy models implementation.

Add support for distillation in pipeline_consistency_models.py.

a927a4a

make style

c37e302

Improve ConsistencyModelPipeline:

a0a164c

- Add initial support for class-conditional generation - Fix initial sigma for onestep generation - Fix some sigma shape issues

make style

0d1de08

Improve ConsistencyModelPipeline:

5f4f406

- add latents __call__ argument and prepare_latents method - add check_inputs method - add initial docstrings for ConsistencyModelPipeline.__call__

make style

213b25d

Fix bug when randomly generating class labels for class-conditional g…

fbe34c3

…eneration.

Switch CMStochasticIterativeScheduler to configuring a sigma schedule…

0e53d8b

… and make related changes to the pipeline and tests.

Remove some unused code and make style.

c3b242e

Fix small bug in CMStochasticIterativeScheduler.

43e4379

Add expected slices for multistep sampling tests and make them pass.

94c99ca

make style

6867a3a

ayushtues added 2 commits July 10, 2023 12:58

Update training script to main, fix timesteps

ea7d75f

Fix bug in timestep ordering

a32b869

sayakpaul reviewed Jul 10, 2023

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@@ -0,0 +1,3 @@

#!/bin/bash

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We don't include shell-scripts in the examples.

sayakpaul reviewed Jul 10, 2023

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ayushtues added 2 commits July 12, 2023 12:30

Integrate accelerator better, change model upload

943c88b

Fix checkpointing and add test

6b58d81

williamberman reviewed Jul 17, 2023

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Remove hardcoded configs, add DiffusionPipeline

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[WIP] Consistency models distillation examples #3992

[WIP] Consistency models distillation examples #3992

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		model = UNet2DModel.from_config(config)
		target_model = UNet2DModel.from_config(config)



		# load the model to distill into a consistency model
		teacher_model = DDPMPipeline.from_pretrained("google/ddpm-cifar10-32").unet

	teacher_model_output = teacher_model(noise_scheduler.scale_model_input(samples, timestep_prev), timestep_prev, class_labels=labels).sample
	teacher_denoiser = noise_scheduler.step(
	teacher_model_output, timestep_prev, samples, use_noise=False
	).prev_sample

		if args.testing:
		config = UNet2DModel.load_config('diffusers/consistency-models-test', subfolder="test_unet")

[WIP] Consistency models distillation examples #3992

[WIP] Consistency models distillation examples #3992

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ayushtues commented Jul 7, 2023

Add distillation training scripts for Consistency models

Model/Pipeline Description

TODO

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