Real-ESRGAN: Practical Image and Video Upscaling
Real-ESRGAN is a practical blind super-resolution model from the paper "Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data" (ICCVW 2021), developed by Xintao Wang and collaborators at Tencent ARC Lab. It upscales real-world photos and video frames by 4x while removing the blur, noise, ringing, and compression artifacts that accumulate in images found in the wild. Years after release it remains one of the most widely used open upscaling models, and its checkpoints power the upscaling features of many image-generation frontends.
Overview
Classic super-resolution models are trained on clean images downscaled with a single known operation, so they fall apart on real photographs whose degradations are unknown and compounded. Real-ESRGAN instead trains on pure synthetic data produced by a high-order degradation pipeline: multiple randomized rounds of blur, resizing, noise, and JPEG compression are applied in sequence, so the model learns to invert the messy, layered degradations of real-world images rather than a single idealized one. The result is an upscaler that behaves well on ordinary photos, screenshots, scans, and compressed video frames, not just laboratory benchmarks.
Key Features
- 4x blind super-resolution - Upscales without needing to know how the input was degraded, restoring texture and edges while suppressing noise and compression blocking
- Trained on synthetic high-order degradations - Randomized chains of blur, resize, noise, and JPEG compression during training generalize to the compound degradations of real images
- Compact single-file checkpoint - The RealESRGAN_x4plus generator is a small model that loads in seconds and leaves nearly all GPU memory free for large inputs
- Video upscaling - Works frame-by-frame on video; the bundled workflow decodes a clip, upscales every frame, and re-encodes the result with its audio
- Model family - The same codebase ships sibling checkpoints for anime imagery, anime video, and a 2x general model, all loadable through the same ComfyUI nodes
Use Cases
- Upscaling AI-generated images from 1024px to 4K for print or display
- Restoring old, low-resolution, or heavily compressed photos
- Upscaling video clips and animation for delivery at higher resolutions
- Cleaning up screenshots, scans, and web images before reuse
- Batch upscaling via the ComfyUI API as a step in automated media pipelines
- Preparing low-resolution source material for editing or compositing
Architecture
The generator is an RRDBNet, the deep residual-in-residual dense block network introduced by ESRGAN, trained here against a U-Net discriminator with spectral normalization for stable adversarial training on synthetic degradations. Inference in ComfyUI is tiled, so inputs far larger than the training resolution upscale within a fixed memory budget.
On This Template
The template runs Real-ESRGAN inside ComfyUI using only native nodes: Load Upscale Model reads the checkpoint and Upscale Image (using Model) applies it with automatic tiling. A ready-made video upscaling workflow and sample input are preloaded, and the same two nodes upscale still images by swapping the video loader for Load Image. The checkpoint served is the RealESRGAN_x4plus safetensors conversion published by the ComfyUI team at Comfy-Org/Real-ESRGAN_repackaged.
