class

DDPMForImageGeneration

extendsPretrainedModelDiffusionMixin

DDPMForImageGeneration(config: DDPMConfig)

source edit

DDPM with the noise-prediction training loss and Markov-chain sampler.

Wraps a DDPMUNet denoiser, supplies the Ho 2020 §3.2 "simplified" training objective when target is provided, and inherits DiffusionMixin.generate for ancestral sampling over the configured noise schedule. This is the standard entry point for training and inference on the DDPM family.

Training * forward(sample, timestep) returns the raw network output. * forward(sample, timestep, target=...) (target = ground-truth noise for epsilon parameterisation) additionally fills the loss field with the MSE loss of the paper's simple objective (Ho 2020 Eq. 14).

Sampling * Use generate from DiffusionMixin — pass any Scheduler (DDPMScheduler for ancestral sampling).

Parameters

configDDPMConfig

Hyperparameters. config.learn_sigma=True is rejected at construction time — Improved-DDPM's hybrid

L_{\text{simple}} + \lambda L_{\text{vlb}}

loss is not yet implemented in Lucid.

Attributes

unetDDPMUNet

Underlying U-Net denoiser (see DDPMUNet).

Notes

Reference: Ho, Jain, and Abbeel, "Denoising Diffusion Probabilistic Models", NeurIPS, 2020 (arXiv:2006.11239); Improved-DDPM additions from Nichol and Dhariwal, ICML 2021 (arXiv:2102.09672).

Training loss (when target is supplied):

\mathcal{L}_{\text{simple}}(\theta) = \mathbb{E}_{t, x_0, \epsilon}\!\left[ \big\lVert \epsilon - \epsilon_\theta(x_t, t) \big\rVert^2 \right],

where $x_t = \sqrt{\bar{\alpha}_t} x_0 + \sqrt{1 - \bar{\alpha}_t}\, \epsilon$ .

Examples

>>> import lucid
>>> from lucid.models.generative.ddpm import (
...     DDPMConfig, DDPMForImageGeneration,
... )
>>> cfg = DDPMConfig(sample_size=32, base_channels=32,
...                  channel_mult=(1, 2), num_res_blocks=1,
...                  resnet_groups=16, num_train_timesteps=100)
>>> model = DDPMForImageGeneration(cfg).eval()
>>> x_t = lucid.randn((1, 3, 32, 32))
>>> t = lucid.tensor([42]).long()
>>> out = model(x_t, t)
>>> out.sample.shape   # (1, 3, 32, 32)
(1, 3, 32, 32)

Used by 2

Constructors

dunder

init

→None

__init__(config: DDPMConfig)

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Instance methods

forward

→DiffusionModelOutput

forward(sample: Tensor, timestep: Tensor, target: Tensor | None = None)

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Predict noise (or [noise, σ²]) at the given timestep.

Parameters

sample

(B, in_channels, H, W) noisy image x_t.

timestep

(B,) or () int timestep tensor. Scalar tensors are broadcast.

Returns

DiffusionModelOutput

(B, out_channels_effective, H, W) raw network output.

>>> import lucid >>> from lucid.models.generative.ddpm import ( ... DDPMConfig, DDPMForImageGeneration, ... ) >>> cfg = DDPMConfig(sample_size=32, base_channels=32, ... channel_mult=(1, 2), num_res_blocks=1, ... resnet_groups=16, num_train_timesteps=100) >>> model = DDPMForImageGeneration(cfg).eval() >>> x_t = lucid.randn((1, 3, 32, 32)) >>> t = lucid.tensor([42]).long() >>> out = model(x_t, t) >>> out.sample.shape # (1, 3, 32, 32) (1, 3, 32, 32)