README.md

metadata

license: mit
language:
  - en
pipeline_tag: image-to-image
library_name: pytorch
tags:
  - e3diff
  - diffusion
  - sar-to-optical
  - image-translation
  - checkpoint

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
• Community implementation used here: Bili-Sakura/pytorch-image-translation-models

Variants

Variant directory	Notes
SEN12	Flat diffusion checkpoint export (config.json + diffusion_pytorch_model.safetensors)

Repository layout

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)

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

@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}
}