[docs] Add NeMo Automodel training guide#13306
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pthombre wants to merge 5 commits intohuggingface:mainfrom
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[docs] Add NeMo Automodel training guide#13306pthombre wants to merge 5 commits intohuggingface:mainfrom
pthombre wants to merge 5 commits intohuggingface:mainfrom
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Signed-off-by: Pranav Prashant Thombre <pthombre@nvidia.com>
stevhliu reviewed Mar 23, 2026
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super nice, thanks for the docs!
Comment on lines +19 to +25
| ### Why NeMo Automodel? | ||
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| - **Hugging Face native**: Train any Diffusers-format model from the Hub with no checkpoint conversion — day-0 support for new model releases. | ||
| - **Any scale**: The same YAML recipe and training script runs on 1 GPU or across hundreds of nodes. Parallelism is configuration, not code. | ||
| - **High performance**: FSDP2 distributed training with multiresolution bucketed dataloading and pre-encoded latent space training for maximum GPU utilization. | ||
| - **Hackable**: Linear training scripts with YAML configuration files. No hidden trainer abstractions — you can read and modify the entire training loop. | ||
| - **Open source**: Apache 2.0 licensed, NVIDIA-supported, and actively maintained. |
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i would integrate this info in the opening intro paragraph to simplify the structure a bit
| - **Hackable**: Linear training scripts with YAML configuration files. No hidden trainer abstractions — you can read and modify the entire training loop. | ||
| - **Open source**: Apache 2.0 licensed, NVIDIA-supported, and actively maintained. | ||
| | ||
| ### Workflow overview |
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hmm i don't know if this workflow adds that much value that words could just convey?
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| | Model | Hugging Face ID | Task | Parameters | | ||
| |-------|----------------|------|------------| | ||
| | Wan 2.1 T2V 1.3B | [`Wan-AI/Wan2.1-T2V-1.3B-Diffusers`](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B-Diffusers) | Text-to-Video | 1.3B | |
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lets remove the backticks around the model name as its not a code element
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| > [!TIP] | ||
| > Full example configs for all models are available in the [NeMo Automodel examples](https://github.com/NVIDIA-NeMo/Automodel/tree/main/examples/diffusion/finetune). |
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| > [!TIP] | |
| > Full example configs for all models are available in the [NeMo Automodel examples](https://github.com/NVIDIA-NeMo/Automodel/tree/main/examples/diffusion/finetune). |
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| > [!NOTE] | ||
| > NeMo Automodel also supports **pretraining** diffusion models from randomly initialized weights. Set `mode: pretrain` in the model config. Pretraining example configs are available in the [NeMo Automodel examples](https://github.com/NVIDIA-NeMo/Automodel/tree/main/examples/diffusion/pretrain). |
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| > [!NOTE] | |
| > NeMo Automodel also supports **pretraining** diffusion models from randomly initialized weights. Set `mode: pretrain` in the model config. Pretraining example configs are available in the [NeMo Automodel examples](https://github.com/NVIDIA-NeMo/Automodel/tree/main/examples/diffusion/pretrain). |
| | ||
| ## Launch training | ||
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| **Single-node training:** |
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let's also use the <hfoptions> tags for single-node training and multi-node training
| | ||
| After training, generate videos or images from text prompts using the fine-tuned checkpoint. | ||
| | ||
| **Wan 2.1 (single-GPU):** |
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also use <hfoptions> tags here
| - **Scalable training for Diffusers models**: NeMo Automodel adds distributed training capabilities (FSDP2, multi-node, multiresolution bucketing) that go beyond what the built-in Diffusers training scripts provide, while keeping the same model and pipeline interfaces. | ||
| - **Shared ecosystem**: any model, LoRA adapter, or pipeline component from the Diffusers ecosystem remains compatible throughout the training and inference workflow. | ||
| | ||
| ## Hardware requirements |
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lets add the hardware requirements to the ## Installation section. better for users to know what the requirements are up front :)
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
adding contacts into the readme
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What does this PR do?
Adds a new documentation page for NeMo Automodel, NVIDIA's PyTorch DTensor-native training library for fine-tuning and pretraining
diffusion models at scale. NeMo Automodel integrates directly with Diffusers — it loads pretrained models from the Hugging Face Hub using
Diffusers model classes and generates outputs via Diffusers pipelines with no checkpoint conversion needed.
The new guide covers:
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Who can review?
@stevhliu @sayakpaul