Visual Generation (Beta)#

Note

This feature is in beta stage. APIs, supported models, and optimization options are actively evolving and may change in future releases.

Background#

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.

TensorRT-LLM VisualGen provides a unified inference stack for diffusion models, with a pipeline architecture separate from the LLM inference path. Key capabilities include:

A shared pipeline abstraction covering the denoising loop, guidance strategies, and component loading.
Pluggable attention backends (PyTorch SDPA and TRT-LLM optimized kernels).
Quantization support (dynamic and static) using the ModelOpt configuration format.
Multi-GPU parallelism (CFG parallel, Ulysses sequence parallel).
TeaCache — a runtime caching optimization that skips transformer steps when timestep embeddings change slowly.
trtllm-serve integration with OpenAI-compatible API endpoints for image and video generation.

Supported Models#

HuggingFace Model ID	Tasks
`black-forest-labs/FLUX.1-dev`	Text-to-Image
`black-forest-labs/FLUX.2-dev`	Text-to-Image
`Wan-AI/Wan2.1-T2V-1.3B-Diffusers`	Text-to-Video
`Wan-AI/Wan2.1-T2V-14B-Diffusers`	Text-to-Video
`Wan-AI/Wan2.1-I2V-14B-480P-Diffusers`	Image-to-Video
`Wan-AI/Wan2.1-I2V-14B-720P-Diffusers`	Image-to-Video
`Wan-AI/Wan2.2-T2V-A14B-Diffusers`	Text-to-Video
`Wan-AI/Wan2.2-I2V-A14B-Diffusers`	Image-to-Video
`Wan-AI/Wan2.2-TI2V-5B-Diffusers`	Text-to-Video, Image-to-Video
`Lightricks/LTX-2`	Text-to-Video (with Audio), Image-to-Video (with Audio)

Models are auto-detected from the checkpoint directory. Diffusers-format models are detected via model_index.json; LTX-2 monolithic safetensors checkpoints are detected via embedded metadata. The AutoPipeline registry selects the appropriate pipeline class automatically.

Feature Matrix#

Model	FP8 blockwise	NVFP4	TeaCache	CFG Parallelism	Ulysses Parallelism	Parallel VAE	CUDA Graph	torch.compile	trtllm-serve	Attention2D	Ring Attention
FLUX.1	Yes	Yes	Yes	No [1]	Yes	No	Yes	Yes	Yes	Yes	Yes
FLUX.2	Yes	Yes	Yes	No [1]	Yes	No	Yes	Yes	Yes	Yes	Yes
Wan 2.1	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Wan 2.2	Yes	Yes	No	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
LTX-2	Yes	Yes	No	Yes	Yes	No	No	Yes	Yes	Yes	Yes

Quick Start#

Here is a simple example to generate a video with Wan 2.1:

python examples/visual_gen/quickstart_example.py

To learn more about VisualGen, see examples/visual_gen/ for more examples including text-to-image, image-to-video, and batch generation.

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 with image and video generation endpoints.

See examples/visual_gen/serve/ for server launch instructions, example configurations, and API usage.

Serving Endpoints#

When served via trtllm-serve, the following OpenAI-compatible endpoints are available:

Endpoint	Method	Purpose
`/v1/images/generations`	POST	Synchronous image generation
`/v1/images/edits`	POST	Image editing
`/v1/videos`	POST	Asynchronous video generation
`/v1/videos/generations`	POST	Synchronous video generation
`/v1/videos/{id}`	GET	Video status / metadata
`/v1/videos/{id}/content`	GET	Download generated video
`/v1/videos/{id}`	DELETE	Delete generated video
`/v1/videos`	GET	List all videos

Optimizations#

Quantization#

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 ModelOpt quantization_config format.

Dynamic quantization via --linear_type:

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, trtllm-nvfp4.

Programmatic usage via VisualGenArgs.quant_config:

from tensorrt_llm import VisualGenArgs

args = VisualGenArgs(
    model="/path/to/model",
    quant_config={"quant_algo": "FP8", "dynamic": True},
)

TeaCache#

TeaCache caches transformer outputs when timestep embeddings change slowly between denoising steps, skipping redundant computation. Enable via VisualGenArgs.cache_config (YAML or programmatic):

cache_config:
  cache_backend: teacache
  teacache_thresh: 0.2

The teacache_thresh parameter controls the similarity threshold. Cache-DiT is also supported via cache_backend: cache_dit with its own set of knobs (see CacheDiTConfig).

Multi-GPU Parallelism#

5 parallelism modes can be combined:

CFG Parallelism (--cfg_size 2): Splits positive/negative guidance prompts across GPUs.
Ulysses Parallelism (--ulysses_size N): Splits the sequence dimension across GPUs for longer sequences.
Parallel VAE (--parallel_vae_size N): Shards the final VAE decode along a spatial axis across GPUs, useful to reduce VAE latency and improve GPU utilization (Constraint: parallel_vae_size ≤ world_size). Currently only supported for WAN models.
Attention Parallel: There are 2 methods supported to run attention parallel. Both of these methods require the attention backend to support LSE (only FA4 currently) -
- Attention2D Parallelism (--attn2d_row_size N, --attn2d_col_size M): Shards the sequence axis across a 2D N x M device mesh, all-gathering Q along rows and K/V along columns so each rank computes a sub-block of the attention matrix (total CP degree = N * M; not currently combinable with Ulysses).
- Ring Attention Parallelism (--ring_size N): Shards the sequence axis across a 1D ring of N ranks and streams K/V blocks around the ring so each rank computes its attention output without materializing the full K/V (mutually exclusive with Attention2D).

Developer Guide#

Architecture Overview#

The VisualGen module lives under tensorrt_llm._torch.visual_gen. At a high level, the inference flow is:

Config — User-facing VisualGenArgs (CLI / YAML) is merged with checkpoint metadata into DiffusionModelConfig.
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).
Execution — DiffusionExecutor coordinates multi-GPU inference via worker processes communicating over ZeroMQ IPC.

Key components:

Component	Location	Role
`VisualGen`	`tensorrt_llm/visual_gen/__init__.py`	High-level API: manages workers, `generate()` / `generate_async()`
`DiffusionExecutor`	`visual_gen/executor.py`	Worker process: loads pipeline, processes requests via ZeroMQ
`BasePipeline`	`visual_gen/pipeline.py`	Base class: denoising loop, CFG handling, TeaCache, CUDA graph
`AutoPipeline`	`visual_gen/pipeline_registry.py`	Factory: auto-detects model type, selects pipeline class
`PipelineLoader`	`visual_gen/pipeline_loader.py`	Resolves checkpoint, loads config/weights, creates pipeline
`TeaCacheAccelerator` / `CacheDiTAccelerator`	`visual_gen/cache/`	Runtime caching backends (TeaCache, Cache-DiT) wrapping the transformer forward
`WeightLoader`	`visual_gen/checkpoints/`	Loads transformer weights from safetensors/bin

VisualGen is a parallel inference subsystem within TensorRT-LLM. It shares low-level primitives (Mapping, QuantConfig, Linear, RMSNorm, ZeroMqQueue, TrtllmAttention) but has its own executor, scheduler (diffusers-based), request types, and pipeline architecture separate from the LLM autoregressive decode path.

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
Cache acceleration (if you call self._setup_cache_acceleration(self.transformer, coefficients=...) in post_load_weights(); supports both TeaCache and Cache-DiT via VisualGenArgs.cache_config)
Serving via trtllm-serve with the full endpoint set

Visual Generation (Beta)#

Background#

Supported Models#

Feature Matrix#

Quick Start#

Usage with trtllm-serve#

Serving Endpoints#

Optimizations#

Quantization#

TeaCache#

Multi-GPU Parallelism#

Developer Guide#

Architecture Overview#

Implementing a New Diffusion Model#

1. Create the Transformer Module#

2. Create the Pipeline Class#

3. Register the Pipeline#

4. Update AutoPipeline Detection#

Usage with `trtllm-serve`#