HiPredict Diffusion

Bidirectional conditional flow-matching checkpoints for reconstructing HiRISE RED4 from IR10, or IR10 from RED4. Use these checkpoints with the HiPredict Python package.

Checkpoints

File	Model	Training steps	Gradient-loss weight
`FM_Condition/latest.pt`	BidirectionalFM, Condition mode	100,000	0.0
`FM_Condition_gradient/latest.pt`	BidirectionalFM, Condition mode	100,000	0.1
`brightness/model.pt`	Auxiliary RED3 boundary-residual brightness head	—	—

Choose either FM checkpoint as the main model. Both support ir2red and red2ir. The brightness head is optional: the default boundary-based correction does not need it.

Both main checkpoints include model weights, EMA weights, optimizer state, learning-rate scheduler state, and model/training settings. Each file is approximately 2.05 GB. The brightness head is approximately 32 KB.

Installation

Use a HiPredict checkout containing hipredict.diffusion and install its diffusion dependencies from the checkout root:

pip install ".[diffusion]"

For evaluation with HiPredict's shared metrics, install .[diffusion,postprocess] instead.

Download and load

from huggingface_hub import hf_hub_download
from hipredict.diffusion import load_checkpoint

checkpoint = hf_hub_download(
    repo_id="syding9/HiPredict_Diffusion",
    filename="FM_Condition/latest.pt",
)
model, model_config, training_config, step = load_checkpoint(
    checkpoint, device="cuda"
)

Select FM_Condition_gradient/latest.pt for the gradient-loss model.

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