| --- |
| library_name: spandrel |
| license: other |
| license_name: mixed |
| license_link: LICENSE |
| tags: |
| - image-super-resolution |
| - super-resolution |
| - upscaling |
| - real-esrgan |
| - hat |
| - swinir |
| - comfyui |
| - ffmpega |
| - video-processing |
| pipeline_tag: image-to-image |
| --- |
| |
| # AI Upscale Models for FFMPEGA |
|
|
| Pre-trained super-resolution models for use with [ComfyUI-FFMPEGA](https://github.com/AEmotionStudio/ComfyUI-FFMPEGA)'s AI Upscale feature. |
|
|
| Models are automatically downloaded on first use — no manual setup required. |
|
|
| ## Models |
|
|
| | File | Architecture | Scale | Size | VRAM | Best For | |
| |------|-------------|-------|------|------|----------| |
| | `RealESRGAN_x4plus.pth` | RRDBNet (GAN) | 4× | 67 MB | ~2 GB | General real-world photos | |
| | `RealESRGAN_x4plus_anime_6B.pth` | RRDBNet (compact) | 4× | 18 MB | ~1 GB | Anime, cartoon, illustration | |
| | `Real_HAT_GAN_SRx4.pth` | HAT (hybrid attention) | 4× | 170 MB | ~4 GB | SOTA quality, fine detail | |
| | `003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_GAN.pth` | SwinIR-Large | 4× | 48 MB | ~3 GB | Clean images, classical SR | |
|
|
| All models output 4× resolution. For 2× output, the upscaler runs at 4× then applies high-quality Lanczos downscaling. |
|
|
| ## Usage in FFMPEGA |
|
|
| 1. Set `llm_model` → `none` |
| 2. Set `no_llm_mode` → `ai_upscale` |
| 3. Choose `upscale_model` (e.g. `hat_x4` for best quality) |
| 4. Choose `upscale_scale` (`4` or `2`) |
| 5. Connect an image or video input and run |
|
|
| ## Model Loading |
|
|
| Models are loaded via [spandrel](https://github.com/chaiNNer-org/spandrel), which auto-detects the architecture from the checkpoint file. No additional dependencies are needed beyond what ComfyUI already provides. |
|
|
| ## Credits |
|
|
| - **Real-ESRGAN**: [xinntao/Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) — BSD-3-Clause |
| - **HAT**: [XPixelGroup/HAT](https://github.com/XPixelGroup/HAT) — MIT |
| - **SwinIR**: [JingyunLiang/SwinIR](https://github.com/JingyunLiang/SwinIR) — Apache 2.0 |
|
|
