Diffusion-Model-Generalizability

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Diffusion-Model-Generalizability

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# Understanding Generalizability of Diffusion Models through Low-dimensional Distribution Learning

This is an official implementation of the paper 
  1. [The Emergence of Reproducibility and Consistency in Diffusion Models](https://arxiv.org/abs/2310.05264) **NeurIPS 2023 workshop Best Paper,  ICML 2024**
  2. [Diffusion Models Learn Low-Dimensional Distributions via Subspace Clustering](https://arxiv.org/abs/2409.02426)

The codebase mainly focuses on the implementation of three main figures from these two papers, including:
1. "Memorization" and "Generalization" regimes for unconditional diffusion models. (Figure 2 in [Paper 1](https://arxiv.org/abs/2310.05264))
2. Convergence of the optimal denoiser. (Figure 4 Left in [Paper 1](https://arxiv.org/abs/2310.05264))
3. Similarity among different unconditional diffusion model settings in generalization regime. (Figure 6 and Figure 12 in [Paper 1](https://arxiv.org/abs/2310.05264))
4. Low-rank property of the denoising autoencoder of trained diffusion models. (Figure 3 in [Paper 2](https://arxiv.org/abs/2409.02426))

For the implementation of Figure 1 (Correspondence between the singular vectors of the Jacobian of the DAE and semantic
image attributes) in [Paper 2](https://arxiv.org/abs/2409.02426), please go through our concurrent work [Exploring Low-Dimensional Subspaces in Diffusion Models for Controllable Image Editing](https://arxiv.org/abs/2409.02374), the codebase could be found [here](https://github.com/ChicyChen/LOCO-Edit).

### Requirements

```bash
conda env create -f edm/environment.yml -n generalizability
conda activate generalizability
```

## "Memorization" and "Generalization" regimes for unconditional diffusion models.

Slightly different from Figure 2 in [Paper 1](https://arxiv.org/abs/2310.05264), the code we release is under a finetuning setting: the training dataset is generated from a pre-trained diffusion model (teacher model).

### Create Dataset

Create a dataset of specific dataset size as follows:

```bash
# generate images from teacher model
python edm/generate.py --outdir=out --seeds=0-49999 --batch=64 --network=https://nvlabs-fi-cdn.nvidia.com/edm/pretrained/edm-cifar10-32x32-uncond-vp.pkl

# create dataset with different size
python edm/dataset_tool.py --source=out --max-images=128 --dest=datasets/synthetic-cifar10-32x32-n128.zip
```

### Training

```bash
torchrun --standalone --nproc_per_node=1 edm/train.py --outdir=training --data=datasets/synthetic-cifar10-32x32-n128.zip --cond=0 --arch=ddpmpp --duration 50 --batch 128 --snap 500 --dump 500 --precond vp --model_channels 64
```

### Evaluation

All checkpoints we released can be found [here](https://www.dropbox.com/scl/fo/m8tf61cengcp1qyevwiwv/AKoLuvIY5Fx0Tz1g8eRFWoI?rlkey=x7t1iqunpzofddgv533bx48q8&st=wtfeg1a9&dl=0), and all training dataset we released can be found [here](https://www.dropbox.com/scl/fo/fqwgl5pvqe4jgvuw945k6/AHEH9P8AYVYhMx_ABTclVC4?rlkey=frsgki669ny9lmxiwlpafanhg&st=kbgrn9za&dl=0)

```bash

### generate image from diffusion model, the seeds is different from the one (which is 0-49999) used to generate training images from teacher model.
python edm/generate.py --outdir=evaluation/ddpm-dim64-n64 --seeds=100000-109999 --batch=64 --network=training/ckpt/ddpm-dim64-n64.pkl

python edm/generate.py --outdir=evaluation/ddpm-dim128-n64 --seeds=100000-109999 --batch=64 --network=training/ckpt/ddpm-dim128-n64.pkl

### Calculate SSCD feature 
torchrun --standalone --nproc_per_node=1 edm/sscd.py feature --images ./evaluation/ddpm-dim64-n64 --features ./evaluation/sscd-dim64-n64.npz

torchrun --standalone --nproc_per_node=1 edm/sscd.py feature --images ./evaluation/ddpm-dim128-n64 --features ./evaluation/sscd-dim128-n64.npz

torchrun --standalone --nproc_per_node=1 edm/sscd.py feature --images datasets/synthetic-cifar10-32x32-n64.zip --features ./evaluation/sscd-training-dataset-synthetic-cifar10-32x32-n64.npz

# Compute reproducibility score
python edm/sscd.py rpscore --source ./evaluation/sscd-dim128-n64.npz --target ./evaluation/sscd-dim64-n64.npz

# Compute generalization score
python edm/sscd.py mscore --source ./evaluation/sscd-dim128-n64.npz --target ./evaluation/sscd-training-dataset-synthetic-cifar10-32x32-n64.npz

```

## Convergence of the optimal denoiser.

We implement the optimal denoiser (derived from the score function of the empirial distribution). And compare the RP score between real diffusion model and the optimal denoiser.

```bash
### generate image from optimal denoiser
python edm/generate.py --outdir=evaluation/memorization-n64 --seeds=100000-109999 --batch=64 --optimal_denoiser --dataset=datasets/synthetic-cifar10-32x32-n64.zip --network=https://nvlabs-fi-cdn.nvidia.com/edm/pretrained/edm-cifar10-32x32-uncond-vp.pkl

### Calculate SSCD feature 
torchrun --standalone --nproc_per_node=1 edm/sscd.py feature --images ./evaluation/memorization-n64 --features ./evaluation/sscd-memorization-n64.npz

### Compute reproducibility score
python edm/sscd.py rpscore --source ./evaluation/sscd-dim128-n64.npz --target ./evaluation/sscd-memorization-n64.npz

```

## Similarity among different unconditional diffusion model settings in generalization regime.

We provide generated samples from those different diffusion models [here](https://www.dropbox.com/scl/fo/xq0yvr92ohzb6ov313928/ANo8GzZ5GybCrzJRb2P1qU8?rlkey=iaf6316aezz4wznigj4ir2v29&st=psa7il3e&dl=0). To generate new samples, you need to go through their own github repo and use the same initial noise for generation.

```bash

### Calculate SSCD feature 
torchrun --standalone --nproc_per_node=1 edm/sscd.py feature --images ./samples/ddpmv4 --features ./evaluation/sscd-ddpmv4.npz

torchrun --standalone --nproc_per_node=1 edm/sscd.py feature --images ./samples/ddpmv6 --features ./evaluation/sscd-ddpmv6.npz

# Compute reproducibility score
python edm/sscd.py rpscore --source ./evaluation/sscd-ddpmv4.npz --target ./evaluation/sscd-ddpmv6.npz

```

## Low-rank property of the denoising autoencoder of trained diffusion models.

<div align="center">
     <img src="figures/jacobian-real.png" width="300" />
     <img src="figures/jacobian-MoLRG.png" width="290" />
</div>
These figures illustrate the low-dimensionality of the jacobian of the denoising autoencoder (DAE) trained on real dataset and Mixture of Low Rank Gaussian distribution (MoLRG).

To training diffusion model with MoLRG:
```bash
torchrun --standalone --nproc_per_node=1 edm/trainMoLRG.py --outdir training --path datasets  --img_res 4 --class_num 2 --per_class_dim 7 --sample_per_class 350 --embed_channels 128

## eval the loss with gt x^_0  
torchrun --standalone --nproc_per_node=1 edm/trainMoLRG.py --outdir training --path datasets  --img_res 4 --class_num 2 --per_class_dim 7 --sample_per_class 350 --embed_channels 128 --eval_gt_denoiser True
```

To Evaluate rank of the jacobian:
```bash
torchrun --standalone --nproc_per_node=1 edm/jacobian.py --network_pkl <path/to/pkl>

e.g.
torchrun --standalone --nproc_per_node=1 edm/jacobian.py --network_pkl https://nvlabs-fi-cdn.nvidia.com/edm/pretrained/edm-cifar10-32x32-uncond-vp.pkl
```

Notably, built upon [NVlabs/edm](https://github.com/NVlabs/edm), our codebase is compatible with all training ckpts released from their repo, where you could find [here](https://nvlabs-fi-cdn.nvidia.com/edm/pretrained/) and [here](https://nvlabs-fi-cdn.nvidia.com/edm/pretrained/baseline/).

## Acknowledgements
This repository is highly based on [NVlabs/edm](https://github.com/NVlabs/edm).

## BibTeX
```
@inproceedings{
  zhang2024the,
  title={The Emergence of Reproducibility and Consistency in Diffusion Models},
  author={Huijie Zhang and Jinfan Zhou and Yifu Lu and Minzhe Guo and Peng Wang and Liyue Shen and Qing Qu},
  booktitle={Forty-first International Conference on Machine Learning},
  year={2024},
  url={https://openreview.net/forum?id=HsliOqZkc0}
}

@article{wang2024diffusion,
  title={Diffusion models learn low-dimensional distributions via subspace clustering},
  author={Wang, Peng and Zhang, Huijie and Zhang, Zekai and Chen, Siyi and Ma, Yi and Qu, Qing},
  journal={arXiv preprint arXiv:2409.02426},
  year={2024}
}
```

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