https://github.com/py-img-gen/diffusers-ncsn
🤗 diffusers implementation of Noise Conditional Score Network (NCSN)
https://github.com/py-img-gen/diffusers-ncsn
annealed-langevin-dynamics diffusers generative-models noise-conditional-score-network score-based-generative-modeling score-matching
Last synced: 4 months ago
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🤗 diffusers implementation of Noise Conditional Score Network (NCSN)
- Host: GitHub
- URL: https://github.com/py-img-gen/diffusers-ncsn
- Owner: py-img-gen
- License: apache-2.0
- Created: 2024-12-26T08:12:15.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-02-21T09:30:03.000Z (4 months ago)
- Last Synced: 2025-02-21T09:36:48.285Z (4 months ago)
- Topics: annealed-langevin-dynamics, diffusers, generative-models, noise-conditional-score-network, score-based-generative-modeling, score-matching
- Language: Python
- Homepage: https://py-img-gen.github.io/diffusers-ncsn/
- Size: 224 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 🤗 Noise Conditional Score Networks
[](https://github.com/py-img-gen/diffusers-ncsn/actions/workflows/ci.yaml)
[](https://github.com/py-img-gen/diffusers-ncsn/actions/workflows/gh-pages.yaml)
[](https://github.com/ermongroup/ncsn)
[](https://huggingface.co/py-img-gen/ncsn-mnist)
[](https://pypi.org/project/diffusers-ncsn/)
[`🤗 diffusers`](https://github.com/huggingface/diffusers) implementation of the paper ["Generative Modeling by Estimating Gradients of the Data Distribution" [Yang+ NeurIPS'19]](https://arxiv.org/abs/1907.05600).
## How to use
### Use without installation
You can load the pretrained pipeline directly from the HF Hub as follows:
```python
import torch
from diffusers import DiffusionPipeline
from diffusers.utils import make_image_grid
# Specify the device to use
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
#
# Load the pipeline from the Hugging Face Hub
#
pipe = DiffusionPipeline.from_pretrained(
"py-img-gen/ncsn-mnist", trust_remote_code=True
)
pipe = pipe.to(device)
# Generate samples; here, we specify the seed and generate 16 images
output = pipe(
batch_size=16,
generator=torch.manual_seed(42),
)
# Create a grid image from the generated samples
image = make_image_grid(images=output.images, rows=4, cols=4)
image.save("output.png")
```
### Use with installation
First, install the package from this repository:
```shell
pip install diffusers-ncsn
```
Then, you can use the package as follows:
```python
import torch
from ncsn.pipeline_ncsn import NCSNPipeline
# Specify the device to use
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
#
# Load the pipeline from the HF Hub through the NCSNPipeline of this library
#
pipe = NCSNPipeline.from_pretrained("py-img-gen/ncsn-mnist", trust_remote_code=True)
pipe = pipe.to(device)
# Generate samples; here, we specify the seed and generate 16 images
output = pipe(
batch_size=16,
generator=torch.manual_seed(42),
)
# Create a grid image from the generated samples
image = make_image_grid(images=output.images, rows=4, cols=4)
image.save("output.png")
```
## Pretrained models and pipeline
[](https://huggingface.co/py-img-gen/ncsn-mnist)
## Showcase
### MNIST
Example of generating MNIST character images using the model trained with [`train_mnist.py`](https://github.com/py-img-gen/diffusers-ncsn/blob/main/train_mnist.py).
## Notes on uploading pipelines to the HF Hub with custom components
While referring to 📝 [Load community pipelines and components - huggingface diffusers](https://huggingface.co/docs/diffusers/using-diffusers/custom_pipeline_overview#community-components), pay attention to the following points.
- Change [the `_class_name` attribute](https://huggingface.co/py-img-gen/ncsn-mnist/blob/main/model_index.json#L2) in [`model_index.json`](https://huggingface.co/py-img-gen/ncsn-mnist/blob/main/model_index.json) to `["pipeline_ncsn", "NCSNPipeline"]`.
- Upload [`pipeline_ncsn.py`](https://github.com/py-img-gen/diffusers-ncsn/blob/main/src/ncsn/pipeline_ncsn.py) to [the root of the pipeline repository](https://huggingface.co/py-img-gen/ncsn-mnist/blob/main/pipeline_ncsn.py).
- Upload custom components to each subfolder:
- Upload [`scheduling_ncsn.py`](https://github.com/py-img-gen/diffusers-ncsn/blob/main/src/ncsn/scheduler/scheduling_ncsn.py) to [the `scheduler` subfolder](https://huggingface.co/py-img-gen/ncsn-mnist/tree/main/scheduler).
- Upload [`unet_2d_ncsn.py`](https://github.com/py-img-gen/diffusers-ncsn/blob/main/src/ncsn/unet/unet_2d_ncsn.py) to [the `unet` subfolder](https://huggingface.co/py-img-gen/ncsn-mnist/tree/main/unet).
- Ensure that the custom components are placed in each subfolder because they are referenced by relative paths from `pipeline_ncsn.py`.
- Based on this, the code in this library is also placed in the same directory structure as the HF Hub.
- For example, `pipeline_ncsn.py` imports `unet_2d_ncsn.py` as `from .unet.unet_2d_ncsn import UNet2DModelForNCSN` because it is placed in the `unet` subfolder.
## License
**diffusers-ncsn** is licensed under [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0).
A full copy of the license can be found [on GitHub](https://github.com/py-img-gen/diffusers-ncsn/blob/main/LICENSE).
## Acknowledgements
- JeongJiHeon/ScoreDiffusionModel: The Pytorch Tutorial of Score-based and Diffusion Model https://github.com/JeongJiHeon/ScoreDiffusionModel/tree/main