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 (VANILLA), TRT-LLM kernels (TRTLLM), TRT-LLM CuTe DSL kernels (CUTEDSL, Blackwell-class GPUs), and Flash Attention 4 (FA4).

• Quantization support (dynamic and static) using the ModelOpt configuration format.

• Quantized attention support: QK16PV8 to quantize Bmm2 on CUTEDSL, SAGE to run SageAttention on TRTLLM (requires Blackwell SM100).

• Multi-GPU parallelism (CFG parallel, Ulysses sequence parallel, Tensor parallelism).

• 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)
Qwen/Qwen-Image	Text-to-Image
Qwen/Qwen-Image-2512	Text-to-Image
nvidia/Cosmos3-Nano	Text-to-Image, Text-to-Video, Image-to-Video
nvidia/Cosmos3-Super	Text-to-Image, Text-to-Video, Image-to-Video

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	Tensor Parallelism
FLUX.1	Yes	Yes	Yes	No [1]	Yes	No	Yes	Yes	Yes	Yes	Yes	Yes
FLUX.2	Yes	Yes	Yes	No [1]	Yes	No	Yes	Yes	Yes	Yes	Yes	Yes
Wan 2.1	Yes	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	Yes
LTX-2	Yes	Yes	No	Yes	Yes	No	No	Yes	Yes	Yes	Yes	No
Qwen-Image [2]	Yes	Yes	No	No	Yes	No	Yes	Yes	Yes	Yes	Yes	No
Cosmos3	Yes	Yes	No	Yes	Yes	Yes	Yes	Yes	Yes	No	No	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.

Configure via VisualGenArgs.quant_config (YAML or programmatic):

quant_config:
  quant_algo: FP8        # or FP8_BLOCK_SCALES, NVFP4
  dynamic: true

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

Omit quant_config for BF16/FP16 baseline.

Quantized Attention

In addition to linear-layer quantization, VisualGen exposes two attention-level quantization presets that operate inside the attention kernel. They are configured through AttentionConfig.quant_attention_config and are mutually exclusive with each other.

• QK16PV8 (CUTEDSL backend): Keeps Q & K in BF16 and quantizes only V to FP8 (E4M3, per-tensor), thus Bmm1 will be carried out in BF16 with Bmm2 in FP8. Targets Blackwell-class GPUs (sm_100a / sm_103a) with head_dim = 128.

• SAGE (TRTLLM backend): Quantizes Q, K, and V with per-block scaling factors. Q/K are stored as INT8 or FP8 (e4m3) and V as FP8 (e4m3); block sizes are tunable per axis (typically (q, k, v) = (1, 4, 1) for Wan-1.3B and (1, 16, 1) for larger Wan / FLUX checkpoints). Supported recipes are validated at runtime.

Python API for SageAttention:

from tensorrt_llm import VisualGenArgs

args = VisualGenArgs(
    model="Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
    attention_config={
        "backend": "TRTLLM",
        "quant_attention_config": {
            "qk_dtype": "int8",
            "q_block_size": 1,
            "k_block_size": 16,
            "v_block_size": 1,
        },
    },
)

Python API for QK16PV8:

from tensorrt_llm import VisualGenArgs

args = VisualGenArgs(
    model="Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
    attention_config={
        "backend": "CUTEDSL",
        "quant_attention_config": {
            "qk_dtype": "bf16",
            "q_block_size": 0,
            "k_block_size": 0,
            "v_block_size": 0,
        },
    },
)

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

Configured under VisualGenArgs.parallel_config. 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 (constraint: parallel_vae_size ≤ world_size; WAN/Cosmos3 only).

• Context Parallel (CP) — Partitions the sequence into shards so that each rank computes partial attention. Requires an LSE-capable attention backend (FA4 or CUTEDSL). CP can be composed with Ulysses, giving a total sequence-parallel (SP) degree = cp_size · ulysses_size. The CP degree depends on the implementation below:

  • Attention2D (attn2d_size: [N, M]): Shards the sequence axis across an N × M device mesh (CP degree = N · M; total SP degree = N · M · ulysses_size).

  • Ring Attention (ring_size: N): Shards the sequence axis across a 1D ring of N ranks, streaming K/V blocks (CP degree = N; total SP degree = N · ulysses_size; mutually exclusive with Attention2D).

• Tensor Parallelism (tp_size: N): Splits attention heads and transformer MLPs across GPUs for faster compute and reduced memory usage.

Developer Guide

Architecture Overview

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

1. Config — User-facing VisualGenArgs (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 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

---

[1] (1,2)

FLUX models use embedded guidance and do not have a separate negative prompt path, so CFG parallelism is not applicable.

[2]

Qwen-Image ships a native BF16 implementation with per-module numerical parity vs diffusers.QwenImagePipeline (cosine >= 0.999 on the full 20B transformer) and trtllm-serve / /v1/images/generations support. FP8 blockwise and NVFP4 use VisualGen dynamic quantization from BF16 checkpoints; no pre-quantized checkpoint is required.