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PositivePassion/openfwi-diffusion-priors

sourceHugging Faceupdated 12d agoView on Hugging Face

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OpenFWI Diffusion Priors

Pretrained unconditional DDPM priors for six OpenFWI velocity-model families. Each model contains a single-channel UNet2DModel and a DDPMScheduler in a Diffusers-compatible directory layout.

Included models

Directory	OpenFWI family
`FlatFault-A`	FlatFault A
`FlatFault-B`	FlatFault B
`CurveFault-A`	CurveFault A
`CurveFault-B`	CurveFault B
`CurveVel-A`	CurveVel A
`CurveVel-B`	CurveVel B

The UNets operate on normalized, single-channel 72 x 72 inputs. The inversion workflow crops the generated result to the 70 x 70 OpenFWI model domain.

Download

bash

python3 -m pip install -U huggingface_hub
hf download PositivePassion/openfwi-diffusion-priors --local-dir models

To download only one family:

bash

hf download PositivePassion/openfwi-diffusion-priors \
  --include "CurveFault-A/*" \
  --local-dir models

Loading

python

from diffusers import DDPMScheduler, UNet2DModel

model_dir = "models/CurveFault-A"
unet = UNet2DModel.from_pretrained(model_dir, subfolder="unet")
scheduler = DDPMScheduler.from_pretrained(model_dir, subfolder="scheduler")

See SHA256SUMS for weight-file checksums.