undo

Signed-off-by: Pablo Garay <pagaray@nvidia.com>

Author: pablo-garay

nemo_vfm/diffusion/readme.rst

@@ -23,13 +23,13 @@ We support image diffusion training. Video training incoming.
 +---------------------------+------------------+
 | Parallelism               | Status           |
 +===========================+==================+
-| FSDP                      | ✅ Supported     |
+| FSDP                      | ✅ Supported     |
 +---------------------------+------------------+
-| CP+TP+SP+distopt          | ✅ Supported     |
+| CP+TP+SP+distopt          | ✅ Supported     |
 +---------------------------+------------------+
-| CP+TP+SP+PP+distopt       | ✅ Supported     |
+| CP+TP+SP+PP+distopt       | ✅ Supported     |
 +---------------------------+------------------+
-| CP+TP+SP+FSDP             | 🕒 Coming Soon   |
+| CP+TP+SP+FSDP             | 🕐 Coming Soon   |
 +---------------------------+------------------+
 
 
@@ -44,11 +44,11 @@ We support image diffusion training. Video training incoming.
 +--------------+-------------------+-----------------+
 | Model Size   | Modality          | Status          |
 +==============+===================+=================+
-| DiT 30B+     | 256px image       | ✅ Supported    |
+| DiT 30B+     | 256px image       | ✅ Supported    |
 +--------------+-------------------+-----------------+
-| DiT 30B+     | 256px image+video | 🕒 Coming Soon  |
+| DiT 30B+     | 256px image+video | 🕐 Coming Soon  |
 +--------------+-------------------+-----------------+
-| DiT 30B+     | 768px image+video | 🕒 Coming Soon  |
+| DiT 30B+     | 768px image+video | 🕐 Coming Soon  |
 +--------------+-------------------+-----------------+
 
 
@@ -116,13 +116,13 @@ this will generate a folder a tar files. .pth contains image/video latent repres
 .. code-block:: bash
 
    shard_000.tar
-   ├── samples/sample_0000.pth
-   ├── samples/sample_0000.pickle
-   ├── samples/sample_0000.json
-   ├── samples/sample_0001.pth
-   ├── samples/sample_0001.pickle
-   ├── samples/sample_0001.json
-   └── ...
+   ├── samples/sample_0000.pth
+   ├── samples/sample_0000.pickle
+   ├── samples/sample_0000.json
+   ├── samples/sample_0001.pth
+   ├── samples/sample_0001.pickle
+   ├── samples/sample_0001.json
+   └── ...
    shard_001.tar
 
 The following is a sample command to prepare prepare webdataset into energon dataset:

nemo_vfm/diffusion/vae/readme.rst

@@ -49,11 +49,11 @@ Data Preparation
     .. code-block:: bash
 
         000000.tar
-        ├── 1.jpg
-        ├── 2.jpg
+        ├── 1.jpg
+        ├── 2.jpg
         000001.tar
-        ├── 3.jpg
-        ├── 4.jpg
+        ├── 3.jpg
+        ├── 4.jpg
 
 2. next we need to index the webdataset with `energon <https://nvidia.github.io/Megatron-Energon/>`_. navigate to the dataset directory and run the following command:
 

nemo_vfm/physicalai/Cosmos/cosmos1/models/autoregressive/nemo/post_training/tokenizer/README.md

@@ -49,20 +49,20 @@ The NeMo Framework supports the following Cosmos Autoregressive (AR) models for
 
 ## Why Change Tokenizers?
 
-Cosmos Autoregressive models are typically trained on a specific tokenizer configuration (e.g., 8×16×16). If you’d like to reduce patch size or change compression (e.g., to 4×8×8), you can post-train the existing weights so that the model effectively aligns its internal representations with the new token embeddings—without re-training the tokenizer.
+Cosmos Autoregressive models are typically trained on a specific tokenizer configuration (e.g., 8×16×16). If you'd like to reduce patch size or change compression (e.g., to 4×8×8), you can post-train the existing weights so that the model effectively aligns its internal representations with the new token embeddings—without re-training the tokenizer.
 
 ## Tutorial: Finetuning Cosmos-4B on 10k Videos with a New Tokenizer
 
 In this tutorial, we will:
-- Take a model originally trained on an 8×16×16 tokenizer.
-- Post-train it on a 4×8×8 tokenizer.
+- Take a model originally trained on an 8×16×16 tokenizer.
+- Post-train it on a 4×8×8 tokenizer.
 - Demonstrate using a sample dataset of 10 (in production, we recommend using 10k videos from a distribution similar to that of pretraining).
 
 ### 1. Calculate Sequence Lengths
 
-- **Original Sequence Length:** 12,800 tokens (33 frames * 640 px width * 1024 px height, when tokenized with an 8×16×16 tokenizer becomes ⌈33/8⌉ * (640/16) * (1024/16) = 5 * 64 * 80 = 12,800 tokens)
-- **New Sequence Length:** 12,800 tokens (17 frames * 320 px width * 512 px height, when tokenized with a 4×8×8 tokenizer becomes ⌈17/8⌉ * (320/4) * (512/8) = 5 * 80 * 64 = 12,800 tokens)
-- For other resolutions or frame counts, recalculate your maximum tokens to ensure you do not exceed the model’s capacity.
+- **Original Sequence Length:** 12,800 tokens (33 frames * 640 px width * 1024 px height, when tokenized with an 8×16×16 tokenizer becomes ⌈33/8⌉ * (640/16) * (1024/16) = 5 * 64 * 80 = 12,800 tokens)
+- **New Sequence Length:** 12,800 tokens (17 frames * 320 px width * 512 px height, when tokenized with a 4×8×8 tokenizer becomes ⌈17/8⌉ * (320/4) * (512/8) = 5 * 80 * 64 = 12,800 tokens)
+- For other resolutions or frame counts, recalculate your maximum tokens to ensure you do not exceed the model's capacity.
 
 ```bash
 export WIDTH=512
@@ -137,7 +137,7 @@ torchrun --nproc-per-node=$NUM_GPUS cosmos1/models/autoregressive/nemo/post_trai
 ```
 
 - Adjust parameters such as `--max_steps`, `--global_batch_size`, and `--lr` to suit your needs.
-- Ensure that `--model_path` matches the checkpoint originally trained on an 8×16×16 tokenizer.
+- Ensure that `--model_path` matches the checkpoint originally trained on an 8×16×16 tokenizer.
 - For an explanation on other configuration options, please see [the general post-training tutorial](https://github.com/NVIDIA/Cosmos/blob/main/cosmos1/models/autoregressive/nemo/post_training/README.md#configuration-options).
 
 ### 5. Monitor Quality
@@ -245,7 +245,7 @@ python cosmos1/models/autoregressive/nemo/post_training/video2world_prepare_data
 
 ### 3. Training/Finetuning for Video2World
 
-Fine-tune a Video2World–specific checkpoint (or any compatible Cosmos AR model) on your new data. For example, using the 5B Video2World model:
+Fine-tune a Video2World—specific checkpoint (or any compatible Cosmos AR model) on your new data. For example, using the 5B Video2World model:
 
 ```bash
 torchrun --nproc-per-node=$NUM_GPUS cosmos1/models/autoregressive/nemo/post_training/video2world_finetuning.py \