Important
NeMo 2.0 is an experimental feature and currently released in the dev container only: nvcr.io/nvidia/nemo:dev. Please refer to NeMo 2.0 overview for information on getting started.
ControlNet
[ControlNet](https://github.com/lllyasviel/ControlNet) is a neural network structure to control diffusion models by adding extra conditions. It copys the weights of neural network blocks into a “locked” copy and a “trainable” copy. The “trainable” one learns your condition. The “locked” one preserves your model. In this way, the ControlNet can reuse the SD encoder as a deep, strong, robust, and powerful backbone to learn diverse controls.
NeMo Multimodal provides a training pipeline and example implementation for generating images based on segmentation maps. Users have the flexibility to explore other implementations using their own control input dataset and recipe.
Feature |
Training |
Inference |
|---|---|---|
Data parallelism |
Yes |
N/A |
Tensor parallelism |
No |
No |
Pipeline parallelism |
No |
No |
Sequence parallelism |
No |
No |
Activation checkpointing |
No |
No |
FP32/TF32 |
Yes |
Yes (FP16 enabled by default) |
AMP/FP16 |
Yes |
Yes |
AMP/BF16 |
Yes |
No |
BF16 O2 |
No |
No |
TransformerEngine/FP8 |
No |
No |
Multi-GPU |
Yes |
Yes |
Multi-Node |
Yes |
Yes |
Inference deployment |
N/A |
|
SW stack support |
Slurm DeepOps/Base Command Manager/Base Command Platform |
Slurm DeepOps/Base Command Manager/Base Command Platform |
NVfuser |
No |
N/A |
Distributed Optimizer |
No |
N/A |
TorchInductor |
Yes |
N/A |
Flash Attention |
Yes |
N/A |