update

Signed-off-by: linnan wang <wangnan318@gmail.com>

Author: linnanwang

docs/automodel_training_doc.md

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+# Diffusion Model Fine-tuning with Automodel Backend
+
+Train diffusion models with distributed training support using NeMo Automodel and flow matching.
+
+**Currently Supported:** Wan 2.1 Text-to-Video (1.3B and 14B models)
+
+---
+
+## Quick Start
+
+### 1. Docker Setup
+
+```bash
+# Build image
+docker build -f docker/Dockerfile.ci -t dfm-training .
+
+# Run container
+docker run --gpus all -it \
+  -v $(pwd):/workspace \
+  -v /path/to/data:/data \
+  --ipc=host \
+  --ulimit memlock=-1 \
+  --ulimit stack=67108864 \
+  dfm-training bash
+
+# Inside container: Initialize submodules
+export UV_PROJECT_ENVIRONMENT=
+git submodule update --init --recursive 3rdparty/
+```
+
+### 2. Prepare Data
+
+**Create video dataset:**
+```
+<your_video_folder>/
+├── video1.mp4
+├── video2.mp4
+└── meta.json
+```
+
+**meta.json format:**
+```json
+[
+  {
+    "file_name": "video1.mp4",
+    "width": 1280,
+    "height": 720,
+    "start_frame": 0,
+    "end_frame": 121,
+    "vila_caption": "A detailed description of the video content..."
+  }
+]
+```
+
+**Preprocess videos to .meta files:**
+
+There are two preprocessing modes:
+
+**Mode 1: Full video (recommended for training)**
+```bash
+python dfm/src/automodel/utils/data/preprocess_resize.py \
+  --mode video \
+  --video_folder <your_video_folder> \
+  --output_folder ./processed_meta \
+  --model Wan-AI/Wan2.1-T2V-1.3B-Diffusers \
+  --height 480 \
+  --width 720 \
+  --center-crop
+```
+
+**Mode 2: Extract frames (for frame-based training)**
+```bash
+python dfm/src/automodel/utils/data/preprocess_resize.py \
+  --mode frames \
+  --num-frames 40 \
+  --video_folder <your_video_folder> \
+  --output_folder ./processed_frames \
+  --model Wan-AI/Wan2.1-T2V-1.3B-Diffusers \
+  --height 240 \
+  --width 416 \
+  --center-crop
+```
+
+**Key arguments:**
+- `--mode`: `video` (full video) or `frames` (extract evenly-spaced frames)
+- `--num-frames`: Number of frames to extract (only for `frames` mode)
+- `--height/--width`: Target resolution
+- `--center-crop`: Crop to exact size after aspect-preserving resize
+
+**Preprocessing modes:**
+- **`video` mode**: Processes entire video sequence, creates one `.meta` file per video
+- **`frames` mode**: Extracts N evenly-spaced frames, creates one `.meta` file per frame (treated as 1-frame videos)
+
+**Output:** Creates `.meta` files containing:
+- Encoded video latents (normalized)
+- Text embeddings (from UMT5)
+- First frame as JPEG (video mode only)
+- Metadata
+
+### 3. Train
+
+**Single-node (8 GPUs):**
+```bash
+export UV_PROJECT_ENVIRONMENT=
+
+uv run --group automodel --with . \
+  torchrun --nproc-per-node=8 \
+  examples/automodel/finetune/finetune.py \
+  -c examples/automodel/finetune/wan2_1_t2v_flow.yaml
+```
+
+**Multi-node with SLURM:**
+```bash
+#!/bin/bash
+#SBATCH -N 2
+#SBATCH --ntasks-per-node 1
+#SBATCH --gpus-per-node=8
+#SBATCH --exclusive
+
+export MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
+export MASTER_PORT=29500
+export NUM_GPUS=8
+
+# Per-rank UV cache to avoid conflicts
+unset UV_PROJECT_ENVIRONMENT
+mkdir -p /opt/uv_cache/${SLURM_JOB_ID}_${SLURM_PROCID}
+export UV_CACHE_DIR=/opt/uv_cache/${SLURM_JOB_ID}_${SLURM_PROCID}
+
+uv run --group automodel --with . \
+  torchrun \
+  --nnodes=$SLURM_NNODES \
+  --nproc-per-node=$NUM_GPUS \
+  --rdzv_backend=c10d \
+  --rdzv_endpoint=$MASTER_ADDR:$MASTER_PORT \
+  examples/automodel/finetune/finetune.py \
+  -c examples/automodel/finetune/wan2_1_t2v_flow_multinode.yaml
+```
+
+### 4. Validate
+
+```bash
+uv run --group automodel --with . \
+  python examples/automodel/generate/wan_validate.py \
+  --meta_folder <your_meta_folder> \
+  --guidance_scale 5 \
+  --checkpoint ./checkpoints/step_1000 \
+  --num_samples 10
+```
+
+**Note:** You can use `--checkpoint ./checkpoints/LATEST` to automatically use the most recent checkpoint.
+
+---
+
+## Configuration
+
+### Fine-tuning Config (`wan2_1_t2v_flow.yaml`)
+
+```yaml
+model:
+  pretrained_model_name_or_path: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+
+step_scheduler:
+  global_batch_size: 8
+  local_batch_size: 1
+  num_epochs: 10
+  ckpt_every_steps: 100
+
+data:
+  dataloader:
+    meta_folder: "<your_processed_meta_folder>"
+    num_workers: 2
+
+optim:
+  learning_rate: 5e-6
+
+flow_matching:
+  timestep_sampling: "uniform"
+  flow_shift: 3.0
+
+fsdp:
+  dp_size: 8  # Single node: 8 GPUs
+
+checkpoint:
+  enabled: true
+  checkpoint_dir: "./checkpoints"
+```
+
+### Multi-node Config Differences
+
+```yaml
+fsdp:
+  dp_size: 16           # 2 nodes × 8 GPUs
+  dp_replicate_size: 2  # Replicate across 2 nodes
+```
+
+### Pretraining vs Fine-tuning
+
+| Setting | Fine-tuning | Pretraining |
+|---------|-------------|-------------|
+| `learning_rate` | 5e-6 | 5e-5 |
+| `weight_decay` | 0.01 | 0.1 |
+| `flow_shift` | 3.0 | 2.5 |
+| `logit_std` | 1.0 | 1.5 |
+| Dataset size | 100s-1000s | 10K+ |
+
+---
+
+## Hardware Requirements
+
+| Component | Minimum | Recommended |
+|-----------|---------|-------------|
+| GPU | A100 40GB | A100 80GB / H100 |
+| GPUs | 4 | 8+ |
+| RAM | 128 GB | 256 GB+ |
+| Storage | 500 GB SSD | 2 TB NVMe |
+
+---
+
+## Features
+
+- ✅ **Flow Matching**: Pure flow matching training
+- ✅ **Distributed**: FSDP2 + Tensor Parallelism
+- ✅ **Mixed Precision**: BF16 by default
+- ✅ **WandB**: Automatic logging
+- ✅ **Checkpointing**: consolidated, and sharded formats
+- ✅ **Multi-node**: SLURM and torchrun support
+
+---
+
+## Supported Models
+
+| Model | Parameters | Parallelization | Status |
+|-------|------------|-----------------|--------|
+| Wan 2.1 T2V 1.3B | 1.3B | FSDP2 via Automodel + DDP | ✅ |
+| Wan 2.1 T2V 14B | 14B | FSDP2 via Automodel + DDP | ✅ |
+| FLUX | TBD | TBD | 🔄 In Progress |
+
+---
+
+## Advanced
+
+**Custom parallelization:**
+```yaml
+fsdp:
+  tp_size: 2  # Tensor parallel
+  dp_size: 4  # Data parallel
+```
+
+**Checkpoint cleanup:**
+```python
+from pathlib import Path
+import shutil
+
+def cleanup_old_checkpoints(checkpoint_dir, keep_last_n=3):
+    checkpoints = sorted(Path(checkpoint_dir).glob("step_*"))
+    for old_ckpt in checkpoints[:-keep_last_n]:
+        shutil.rmtree(old_ckpt)
+```