Important
You are viewing the NeMo 2.0 documentation. This release introduces significant changes to the API and a new library, NeMo Run. We are currently porting all features from NeMo 1.0 to 2.0. For documentation on previous versions or features not yet available in 2.0, please refer to the NeMo 24.07 documentation.
ControlNet#
[ControlNet](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 |