Ddpm Cifar10

Usage

import lucid.models as models
from lucid.models.weights import DdpmCifarWeights

# default tag
model = models.ddpm_cifar(pretrained=True)

# explicit tag (enum or string)
model = models.ddpm_cifar(weights=DdpmCifarWeights.CIFAR10)
model = models.ddpm_cifar(pretrained="CIFAR10")

# preprocessing travels with the weights
weights = DdpmCifarWeights.CIFAR10
preprocess = weights.transforms()
out = model(preprocess(image)[None])
logits = out.logits  # (B, num_classes)

Conversion

Converted from diffusers/google/ddpm-cifar10-32 via python -m tools.convert_weights ddpm_cifar --tag CIFAR10. Key mapping + numerical parity verified against the source.

License

apache-2.0 — inherited from the original weights.

Citation

Ho, Jonathan, Ajay Jain, and Pieter Abbeel. "Denoising Diffusion Probabilistic Models." Advances in Neural Information Processing Systems, 2020, pp. 6840–6851.

--- library_name: lucid license: apache-2.0 tags: - unconditional-image-generation - ddpm - lucid datasets: - cifar10 pipeline_tag: unconditional-image-generation --- # DDPM (CIFAR-10 32×32) > https://arxiv.org/abs/2006.11239 [Lucid](https://github.com/ChanLumerico/lucid) port of `diffusers/google/ddpm-cifar10-32`, converted to Lucid-native safetensors. ## Available weights | Tag | Params | GFLOPs | Size | Source | |---|---|---|---|---| | `CIFAR10` *(default)* | 35.7M | — | 136.39 MB | diffusers | ## Usage ```python import lucid.models as models from lucid.models.weights import DdpmCifarWeights # default tag model = models.ddpm_cifar(pretrained=True) # explicit tag (enum or string) model = models.ddpm_cifar(weights=DdpmCifarWeights.CIFAR10) model = models.ddpm_cifar(pretrained="CIFAR10") # preprocessing travels with the weights weights = DdpmCifarWeights.CIFAR10 preprocess = weights.transforms() out = model(preprocess(image)[None]) logits = out.logits # (B, num_classes) ``` ## Conversion Converted from `diffusers/google/ddpm-cifar10-32` via `python -m tools.convert_weights ddpm_cifar --tag CIFAR10`. Key mapping + numerical parity verified against the source. ## License `apache-2.0` — inherited from the original weights. ## Citation ``` Ho, Jonathan, Ajay Jain, and Pieter Abbeel. "Denoising Diffusion Probabilistic Models." Advances in Neural Information Processing Systems, 2020, pp. 6840–6851. ```