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license: mit
language:
- en
pipeline_tag: image-to-image
library_name: pytorch
tags:
- e3diff
- diffusion
- sar-to-optical
- image-translation
- checkpoint
---
> [!WARNING] we do not have a full checkpoint conversion validation, if you encounter pipeline loading failure and unsidered output, please contact me via bili_sakura@zju.edu.cn
# BiliSakura/E3Diff-ckpt
Packaged E3Diff checkpoint for use with `examples/community/e3diff` in `pytorch-image-translation-models`.
## Source repository
- E3Diff (official): [DeepSARRS/E3Diff](https://github.com/DeepSARRS/E3Diff)
- Community implementation used here: [Bili-Sakura/pytorch-image-translation-models](https://github.com/Bili-Sakura/pytorch-image-translation-models)
## Variants
| Variant directory | Notes |
| --- | --- |
| `SEN12 ` | Flat diffusion checkpoint export (`config.json` + `diffusion_pytorch_model.safetensors`) |
## Repository layout
```text
E3Diff-ckpt/
SEN12 /
config.json
diffusion_pytorch_model.safetensors
```
## Usage
Load config and weights from the variant directory directly:
- `config`: `SEN12 /config.json`
- `weights`: `SEN12 /diffusion_pytorch_model.safetensors`
### Inference demo (pipeline)
```python
from PIL import Image
from examples.community.e3diff import E3DiffPipeline
pipe = E3DiffPipeline.from_pretrained(
"/path/to/E3Diff-ckpt/SEN12 ",
device="cuda",
)
sar = Image.open("/path/to/sar_input.png").convert("RGB")
out = pipe(source_image=sar, num_inference_steps=50, eta=0.8, output_type="pil")
out.images[0].save("e3diff_output.png")
```
## Citation
```bibtex
@ARTICLE{10767752,
author={Qin, Jiang and Zou, Bin and Li, Haolin and Zhang, Lamei},
journal={IEEE Geoscience and Remote Sensing Letters},
title={Efficient End-to-End Diffusion Model for One-step SAR-to-Optical Translation},
year={2024},
pages={1-1},
doi={10.1109/LGRS.2024.3506566}
}
```
|