[None][doc] Add doc for TRTLLM AIGV initial release (#11489)

Signed-off-by: Chang Liu <9713593+chang-l@users.noreply.github.com>

docs/source/commands/trtllm-serve/trtllm-serve.rst

@@ -212,6 +212,23 @@ model.
 
 
 
+Visual Generation Serving
+~~~~~~~~~~~~~~~~~~~~~~~~~
+
+``trtllm-serve`` supports diffusion-based visual generation models (Wan2.1, Wan2.2) for image and video generation. When a diffusion model directory is provided (detected by the presence of ``model_index.json``), the server automatically launches in visual generation mode with dedicated endpoints.
+
+.. note::
+   This is the initial release of TensorRT-LLM VisualGen. APIs, supported models, and optimization options are actively evolving and may change in future releases.
+
+.. code-block:: bash
+
+   trtllm-serve Wan-AI/Wan2.1-T2V-1.3B-Diffusers \
+       --extra_visual_gen_options config.yml
+
+The ``--extra_visual_gen_options`` flag accepts a YAML file that configures quantization, parallelism, and TeaCache. Available visual generation endpoints include ``/v1/images/generations``, ``/v1/videos``, ``/v1/videos/generations``, and video management APIs.
+
+For full details, see the :doc:`../../features/visual-generation` feature documentation. Example client scripts are available in the `examples/visual_gen/serve/ <https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/visual_gen/serve>`_ directory.
+
 Multi-node Serving with Slurm
 -----------------------------
 

docs/source/features/visual-generation.md

@@ -0,0 +1,219 @@
+# Visual Generation (Diffusion Models) [Beta]
+
+- [Background and Motivation](#background-and-motivation)
+- [Quick Start](#quick-start)
+  - [Python API](#python-api)
+  - [Usage with `trtllm-serve`](#usage-with-trtllm-serve)
+- [Quantization](#quantization)
+- [Developer Guide](#developer-guide)
+  - [Architecture Overview](#architecture-overview)
+  - [Implementing a New Diffusion Model](#implementing-a-new-diffusion-model)
+- [Summary and Future Work](#summary-and-future-work)
+  - [Current Status](#current-status)
+  - [Future Work](#future-work)
+
+## Background and Motivation
+
+Visual generation models based on diffusion transformers (DiT) have become the standard for high-quality image and video synthesis. These models iteratively denoise latent representations through a learned transformer backbone, then decode the final latents with a VAE to produce pixels. As model sizes and output resolutions grow, efficient inference becomes critical — demanding multi-GPU parallelism, weight quantization, and runtime caching to achieve practical throughput and latency.
+
+TensorRT-LLM **VisualGen** module provides a unified inference stack for diffusion models. Key capabilities include (subject to change as the feature matures):
+
+- A shared pipeline abstraction for diffusion model families, covering the denoising loop, guidance strategies, and component loading.
+- Pluggable attention backends.
+- Quantization support (dynamic and static) using the [ModelOpt](https://github.com/NVIDIA/TensorRT-Model-Optimizer) configuration format.
+- Multi-GPU parallelism strategies.
+- **TeaCache** — a runtime caching optimization for the transformer backbone.
+- `trtllm-serve` integration with OpenAI-compatible API endpoints.
+
+> **Note:** This is the initial release of TensorRT-LLM VisualGen. APIs, supported models, and optimization options are actively evolving and may change in future releases.
+
+## Quick Start
+
+### Prerequisites
+
+```bash
+pip install -r requirements-dev.txt
+pip install git+https://github.com/huggingface/diffusers.git
+pip install av
+```
+
+### Python API
+
+The example scripts under `examples/visual_gen/` demonstrate direct Python usage. For Wan2.1 text-to-video generation:
+
+```bash
+cd examples/visual_gen
+
+python visual_gen_wan_t2v.py \
+    --model_path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \
+    --prompt "A cute cat playing piano" \
+    --height 480 --width 832 --num_frames 33 \
+    --output_path output.mp4
+```
+
+Run `python visual_gen_wan_t2v.py --help` for the full list of arguments. Key options control resolution, denoising steps, quantization mode, attention backend, parallelism, and TeaCache settings.
+
+### Usage with `trtllm-serve`
+
+The `trtllm-serve` command automatically detects diffusion models (by the presence of `model_index.json`) and launches an OpenAI-compatible visual generation server.
+
+**1. Create a YAML configuration file:**
+
+```yaml
+# wan_config.yml
+linear:
+  type: default
+teacache:
+  enable_teacache: true
+  teacache_thresh: 0.2
+parallel:
+  dit_cfg_size: 1
+  dit_ulysses_size: 1
+```
+
+**2. Launch the server:**
+
+```bash
+trtllm-serve Wan-AI/Wan2.1-T2V-1.3B-Diffusers \
+    --extra_visual_gen_options wan_config.yml
+```
+
+**3. Send requests** using curl or any OpenAI-compatible client:
+
+Synchronous video generation:
+
+```bash
+curl -X POST "http://localhost:8000/v1/videos/generations" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "prompt": "A cool cat on a motorcycle in the night",
+    "seconds": 4.0,
+    "fps": 24,
+    "size": "480x832"
+  }' -o output.mp4
+```
+
+Asynchronous video generation:
+
+```bash
+# Submit the job
+curl -X POST "http://localhost:8000/v1/videos" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "prompt": "A cool cat on a motorcycle in the night",
+    "seconds": 4.0,
+    "fps": 24,
+    "size": "480x832"
+  }'
+# Returns: {"id": "<video_id>", "status": "processing", ...}
+
+# Poll for status
+curl -X GET "http://localhost:8000/v1/videos/<video_id>"
+
+# Download when complete
+curl -X GET "http://localhost:8000/v1/videos/<video_id>/content" -o output.mp4
+```
+
+The server exposes OpenAI-compatible endpoints for image generation (`/v1/images/generations`), video generation (`/v1/videos`, `/v1/videos/generations`), video management, and standard health/model info endpoints.
+
+The `--extra_visual_gen_options` YAML file configures quantization (`linear`), TeaCache (`teacache`), and parallelism (`parallel`). See [`examples/visual_gen/serve/configs/`](https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/visual_gen/serve/configs) for reference configurations.
+
+## Quantization
+
+TensorRT-LLM VisualGen supports both **dynamic quantization** (on-the-fly at weight-loading time from BF16 checkpoints) and **static quantization** (loading pre-quantized checkpoints with embedded scales). Both modes use the same [ModelOpt](https://github.com/NVIDIA/TensorRT-Model-Optimizer) `quantization_config` format.
+
+**Quick start — dynamic quantization via `--linear_type`:**
+
+```bash
+python visual_gen_wan_t2v.py \
+    --model_path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \
+    --prompt "A cute cat playing piano" \
+    --linear_type trtllm-fp8-per-tensor \
+    --output_path output_fp8.mp4
+```
+
+Supported `--linear_type` values: `default` (BF16/FP16), `trtllm-fp8-per-tensor`, `trtllm-fp8-blockwise`, `svd-nvfp4`.
+
+**ModelOpt `quantization_config` format:**
+
+Both dynamic and static quantization use the [ModelOpt](https://github.com/NVIDIA/TensorRT-Model-Optimizer) `quantization_config` format — the same format found in a model's `config.json` under the `quantization_config` field. This config can be passed as a dict to `DiffusionArgs.quant_config` when constructing the pipeline programmatically:
+
+```python
+from tensorrt_llm._torch.visual_gen.config import DiffusionArgs
+
+args = DiffusionArgs(
+    checkpoint_path="/path/to/model",
+    quant_config={"quant_algo": "FP8", "dynamic": True},  # dynamic FP8
+)
+```
+
+The `--linear_type` CLI flag is a convenience shorthand that maps to these configs internally (e.g., `trtllm-fp8-per-tensor` → `{"quant_algo": "FP8", "dynamic": True}`).
+
+Key fields: `"dynamic"` controls load-time quantization (`true`) vs pre-quantized checkpoint (`false`); `"ignore"` excludes specific modules from quantization.
+
+## Developer Guide
+
+This section describes the TensorRT-LLM VisualGen module architecture and guides developers on how to add support for new diffusion model families.
+
+### Architecture Overview
+
+The VisualGen module lives under `tensorrt_llm._torch.visual_gen`. At a high level, the flow is:
+
+1. **Config** — User-facing `DiffusionArgs` (CLI / YAML) is merged with checkpoint metadata into `DiffusionModelConfig`.
+2. **Pipeline creation & loading** — `AutoPipeline` detects the model type from `model_index.json`, instantiates the matching `BasePipeline` subclass, and loads weights (with optional dynamic quantization) and standard components (VAE, text encoder, tokenizer, scheduler).
+3. **Execution** — `DiffusionExecutor` coordinates multi-GPU inference via worker processes.
+
+> **Note:** Internal module structure is subject to change. Refer to inline docstrings in `tensorrt_llm/_torch/visual_gen/` for the latest details.
+
+### Implementing a New Diffusion Model
+
+Adding a new model (e.g., a hypothetical "MyDiT") requires four steps. The framework handles weight loading, parallelism, quantization, and serving automatically once the pipeline is registered.
+
+#### 1. Create the Transformer Module
+
+Create the DiT backbone in `tensorrt_llm/_torch/visual_gen/models/mydit/transformer_mydit.py`. It should be an `nn.Module` that:
+
+- Uses existing modules (e.g., `Attention` with configurable attention backend, `Linear` for builtin linear ops) wherever possible.
+- Implements `load_weights(weights: Dict[str, torch.Tensor])` to map checkpoint weight names to module parameters.
+
+#### 2. Create the Pipeline Class
+
+Create a pipeline class extending `BasePipeline` in `tensorrt_llm/_torch/visual_gen/models/mydit/`. Override methods for transformer initialization, component loading, and inference. `BasePipeline` provides the denoising loop, CFG handling, and TeaCache integration — your pipeline only needs to implement model-specific logic. See `WanPipeline` for a reference implementation.
+
+#### 3. Register the Pipeline
+
+Use the `@register_pipeline("MyDiTPipeline")` decorator on your pipeline class to register it in the global `PIPELINE_REGISTRY`. Make sure to export it from `models/__init__.py`.
+
+#### 4. Update AutoPipeline Detection
+
+In `pipeline_registry.py`, add detection logic for your model's `_class_name` in `model_index.json`.
+
+After these steps, the framework automatically handles:
+
+- Weight loading with optional dynamic quantization via `PipelineLoader`
+- Multi-GPU execution via `DiffusionExecutor`
+- TeaCache integration (if you call `self._setup_teacache()` in `post_load_weights()`)
+- Serving via `trtllm-serve` with the full endpoint set
+
+## Summary and Future Work
+
+### Current Status
+
+**Supported models:** Wan2.1 and Wan2.2 families (text-to-video, image-to-video; 1.3B and 14B variants).
+
+**Supported features:**
+
+| Feature | Status |
+|---------|--------|
+| **Multi-GPU Parallelism** | CFG parallel, Ulysses sequence parallel (more strategies planned) |
+| **TeaCache** | Caches transformer outputs when timestep embeddings change slowly |
+| **Quantization** | Dynamic (on-the-fly from BF16) and static (pre-quantized checkpoints), both via ModelOpt `quantization_config` format |
+| **Attention Backends** | Vanilla (torch SDPA) and TRT-LLM optimized fused kernels |
+| **`trtllm-serve`** | OpenAI-compatible endpoints for image/video generation (sync + async) |
+
+### Future Work
+
+- **Additional model support**: Extend to more diffusion model families.
+- **More attention backends**: Support for additional attention backends.
+- **Advanced parallelism**: Additional parallelism strategies for larger models and higher resolutions.
+- **Serving enhancements**: Improved throughput and user experience for production serving workloads.

docs/source/index.rst

@@ -65,6 +65,7 @@ Welcome to TensorRT LLM's Documentation!
    features/long-sequence.md
    features/lora.md
    features/multi-modality.md
+   features/visual-generation.md
    features/overlap-scheduler.md
    features/paged-attention-ifb-scheduler.md
    features/parallel-strategy.md