| ---
|
| license: mit
|
| tags:
|
| - diffusers
|
| - ddpm-cd
|
| - change-detection
|
| - remote-sensing
|
| ---
|
|
|
| > [!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/ddpm-cd
|
|
|
| **Consolidated DDPM-CD change detection** — Single repo with shared UNet backbone and multiple cd_head variants (trained on different datasets and timestep configs).
|
|
|
| ## Model Structure
|
|
|
| - **Backbone**: Shared SR3-style UNet (same across all variants)
|
| - **cd_head**: Dataset-specific change detection heads in `cd_head/{variant}/`
|
|
|
| ### Available cd_head Variants
|
|
|
| | Variant | Dataset | Timesteps | Path |
|
| |---------|---------|-----------|------|
|
| | cdd-50-100 | CDD | [50, 100] | `cd_head/cdd-50-100/` |
|
| | cdd-50-100-400 | CDD | [50, 100, 400] | `cd_head/cdd-50-100-400/` |
|
| | cdd-50-100-400-650 | CDD | [50, 100, 400, 650] | `cd_head/cdd-50-100-400-650/` |
|
| | dsifn-50-100 | DSIFN | [50, 100] | `cd_head/dsifn-50-100/` |
|
| | dsifn-50-100-400 | DSIFN | [50, 100, 400] | `cd_head/dsifn-50-100-400/` |
|
| | dsifn-50-100-400-650 | DSIFN | [50, 100, 400, 650] | `cd_head/dsifn-50-100-400-650/` |
|
| | levir-50-100 | LEVIR | [50, 100] | `cd_head/levir-50-100/` |
|
| | levir-50-100-400 | LEVIR | [50, 100, 400] | `cd_head/levir-50-100-400/` |
|
| | levir-50-100-400-650 | LEVIR | [50, 100, 400, 650] | `cd_head/levir-50-100-400-650/` |
|
| | whu-50-100 | WHU | [50, 100] | `cd_head/whu-50-100/` |
|
| | whu-50-100-400 | WHU | [50, 100, 400] | `cd_head/whu-50-100-400/` |
|
| | whu-50-100-400-650 | WHU | [50, 100, 400, 650] | `cd_head/whu-50-100-400-650/` |
|
|
|
| ## Usage
|
|
|
| Load with explicit `custom_pipeline` (pipeline.py is in the repo, use relative path) and `cd_head_subfolder`:
|
|
|
| ```python
|
| from diffusers import DiffusionPipeline
|
|
|
| pipe = DiffusionPipeline.from_pretrained(
|
| "BiliSakura/ddpm-cd",
|
| custom_pipeline="pipeline",
|
| trust_remote_code=True,
|
| cd_head_subfolder="levir-50-100",
|
| ).to("cuda")
|
|
|
| # Images in [-1, 1], shape (B, 3, H, W)
|
| change_map = pipe(image_A, image_B, timesteps=[50, 100])
|
| pred = change_map.argmax(1) # (B, H, W), 0=no-change, 1=change
|
| ```
|
|
|
| **Important**: Pass the same `timesteps` used during training for each variant (see table above).
|
|
|
| ### Switching cd_head at Runtime
|
|
|
| ```python
|
| pipe = DiffusionPipeline.from_pretrained(
|
| "BiliSakura/ddpm-cd",
|
| custom_pipeline="pipeline",
|
| trust_remote_code=True,
|
| cd_head_subfolder="levir-50-100",
|
| ).to("cuda")
|
| # Load different cd_head
|
| pipe.load_cd_head(subfolder="whu-50-100-400")
|
| change_map = pipe(image_A, image_B, timesteps=[50, 100, 400])
|
| ```
|
|
|
| ## Citation
|
|
|
| ```bibtex
|
| @inproceedings{bandaraDDPMCDDenoisingDiffusion2025,
|
| title = {{{DDPM-CD}}: {{Denoising Diffusion Probabilistic Models}} as {{Feature Extractors}} for {{Remote Sensing Change Detection}}},
|
| shorttitle = {{{DDPM-CD}}},
|
| booktitle = {Proceedings of the {{Winter Conference}} on {{Applications}} of {{Computer Vision}}},
|
| author = {Bandara, Wele Gedara Chaminda and Nair, Nithin Gopalakrishnan and Patel, Vishal},
|
| year = 2025,
|
| pages = {5250--5262},
|
| urldate = {2025-12-28}
|
| }
|
| ```
|
| |
