CCSR: TensorRT RTX Acceleration Engine

Ultra-fast TensorRT RTX execution engine and auxiliary modules for CCSR (Creative Content Super-Resolution), designed for real-time generative image upscaling in ComfyUI.

📦 ComfyUI Loader & Upscaler Extension: All nodes supporting TensorRT engine execution are available in: 👉 https://github.com/ussoewwin/ComfyUI-NunchakuFluxLoraStacker

🌟 Overview

Creative Content Super-Resolution (CCSR) is a diffusion-based super-resolution framework leveraging a Controlled UNet and ControlNet structure to synthesize rich photorealistic textures and fine details.

This repository provides an optimized NVIDIA TensorRT RTX Engine implementation for CCSR:

Fused Denoising Engine (ccsr_apply_f16io.rtxplan):
- Fuses the ControlNet and Controlled UNet denoising computation into a single compiled TensorRT engine.
- Fixed 512px tile resolution (64×64 latent tile) executing at 24 ms/step (4.7× speedup over PyTorch FP16 at ~113 ms/step on modern RTX GPUs).
- Synchronized stream execution on current PyTorch CUDA streams to eliminate race conditions and deadlocks.
Engine-Only Deployment (ccsr_trt_aux.safetensors):
- Contains only the essential companion modules: FP16 AutoencoderKL (VAE encoder/decoder) and condition encoder.
- Automatically loaded alongside the engine, eliminating the need to download large full checkpoints (~3.2 GB saved).

📦 Available Files

Filename	Description	Architecture / Components	File Size	Recommended Location	License
`ccsr_apply_f16io.rtxplan`	TensorRT Fused Denoising Engine	ControlNet + UNet fused RTX Engine (Tile 512px / Latent 64×64)	~1.4 GB	`custom_nodes/.../nodes/CCSR/trt_engines/`	Apache-2.0
`ccsr_trt_aux.safetensors`	TRT Auxiliary Weights	FP16 VAE AutoencoderKL + Condition Encoder	~450 MB	`custom_nodes/.../nodes/CCSR/trt_engines/`	Apache-2.0

⚙️ Performance & Benchmark Comparison

Measurements conducted on an NVIDIA RTX 4090 / RTX 5090 environment:

Execution Mode	Files Required	VRAM Overhead (Denoising)	Step Latency (Tile 512)	Speedup
Stock CCSR (FP16 PyTorch)	Full Checkpoint (~3.2 GB)	~3.8 GiB	~113 ms / step	1.0× (Baseline)
CCSR TensorRT RTX	Engine + Aux (~1.85 GB total)	~2.2 GiB	~24 ms / step	~4.7× faster

🚀 Usage in ComfyUI

TensorRT engine execution for CCSR is integrated natively into the ComfyUI-NunchakuFluxLoraStacker custom-node pack.

Workflow Example

CCSR TensorRT RTX Engine Workflow in ComfyUI

Installation & Setup

Install the Custom Node Pack:

cd ComfyUI/custom_nodes
git clone https://github.com/ussoewwin/ComfyUI-NunchakuFluxLoraStacker.git

Place Engine & Aux Files: Download both ccsr_apply_f16io.rtxplan and ccsr_trt_aux.safetensors and place them directly into the engine directory:

ComfyUI/custom_nodes/ComfyUI-NunchakuFluxLoraStacker/nodes/CCSR/trt_engines/
├── ccsr_apply_f16io.rtxplan
└── ccsr_trt_aux.safetensors

In ComfyUI:
- Add Load CCSR Model (TensorRT) (LoadCCSRModelTensorRT). The node automatically discovers .rtxplan files in trt_engines/ and loads the companion ccsr_trt_aux.safetensors.
- Connect the ccsr_model output to CCSR Upscale (TRT) (CCSR_Upscale_TRT).
- Connect an input image to image.
- Configure upscale parameters:
  - tile_size: 512 (fixed to match the compiled static engine shape)
  - tile_stride: 256 (recommended for seamless blending)
  - color_fix_type: adain (or wavelet / none)
  - steps: Effective diffusion step count (densified schedule guarantees exact execution of requested step count)

📜 Credits & License

Original CCSR Implementation & Weights: csslc/CCSR (Apache-2.0 License)
Research Paper: "Creative Content Super-Resolution with Pre-trained Diffusion Model" by Liang et al.
ComfyUI CCSR Node Foundation: kijai/ComfyUI-CCSR & Kijai/ccsr-safetensors (Apache-2.0 License)
TensorRT Engine & ComfyUI Loader: ussoewwin/ComfyUI-NunchakuFluxLoraStacker (CCSR nodes located under nodes/CCSR/)
License: Apache-2.0

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