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license: apache-2.0
tags:
- ccsr
- super-resolution
- image-to-image
- upscaling
- controlnet
- tensorrt
- rtx
- comfyui
pipeline_tag: image-to-image
---
# 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](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](https://github.com/ussoewwin/ComfyUI-NunchakuFluxLoraStacker)** custom-node pack.
### Workflow Example
<p align="center">
<img src="https://huggingface.co/ussoewwin/CCSR-TensorRT-Engine/resolve/main/png/ccsrtensor.png" alt="CCSR TensorRT RTX Engine Workflow in ComfyUI" width="800">
</p>
### Installation & Setup
1. **Install the Custom Node Pack**:
```bash
cd ComfyUI/custom_nodes
git clone https://github.com/ussoewwin/ComfyUI-NunchakuFluxLoraStacker.git
```
2. **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
```
3. **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](https://github.com/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](https://github.com/kijai/ComfyUI-CCSR) & [Kijai/ccsr-safetensors](https://huggingface.co/Kijai/ccsr-safetensors) (Apache-2.0 License)
- **TensorRT Engine & ComfyUI Loader**: [ussoewwin/ComfyUI-NunchakuFluxLoraStacker](https://github.com/ussoewwin/ComfyUI-NunchakuFluxLoraStacker) (CCSR nodes located under `nodes/CCSR/`)
- **License**: **Apache-2.0**
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