ComfyUI Upscale Guide: Best Models, Settings & VRAM Fixes

What is AI Upscaling?

AI upscaling uses trained neural networks to increase image resolution while intelligently reconstructing details. Unlike traditional interpolation (which just blurs pixels larger), AI upscalers can generate realistic textures, sharpen edges, and add detail that wasn't in the original.

This is especially useful after generating images in ComfyUI — models like SD1.5 produce 512×512 images by default, and upscaling lets you bring them to print-quality resolution.

Quick Setup

1. Download an Upscale Model

Visit OpenModelDB to browse upscaling models. For a solid general-purpose choice, download 4x-RealESRGAN.

2. Install the Model

Place the downloaded model file in:

ComfyUI/models/upscale_models/

3. Build the Workflow

The core upscaling chain uses two specialized nodes, plus standard I/O nodes:

1. Load Image — Your input (or connect from a generation workflow)
2. Load Upscale Model — Select your downloaded model
3. Upscale Image (Using Model) — Connect the model and image inputs
4. Save Image — View and save the result

The full chain: Load Image → Upscale Image (Using Model) ← Load Upscale Model, then → Save Image.

Recommended Models

Different upscalers excel at different tasks:

Most models offer 2x or 4x upscaling. A 4x model turns a 512×512 image into 2048×2048.

Combining with Generation Workflows

The most practical use is chaining upscaling after text-to-image or image-to-image generation:

Text Prompt → KSampler → VAE Decode → Upscale Image → Save Image

This lets you generate at the model's native resolution (fast) and then upscale for final output (high quality).

Tips

• Upscale after generation, not before — generating at higher resolution is much slower and can produce diminishing returns compared to generating small + upscaling
• 4x models are usually sufficient — going beyond 4x rarely adds meaningful detail
• Multiple passes — for extreme upscaling, you can chain two 2x models instead of one 4x

Handling VRAM Limitations

Upscaling large images can use significant GPU memory. If you hit out-of-memory errors:

• Use --lowvram flag when launching ComfyUI to reduce memory usage
• Scale down first — use a 2x model instead of 4x, or resize the input image before upscaling
• Tiled upscaling — some community custom nodes (like ComfyUI-TiledKSampler) split the image into tiles, upscale each tile separately, and stitch them back together. This dramatically reduces peak VRAM usage
• Multi-pass approach — chain two 2x upscale passes instead of one 4x pass to reduce per-step memory requirements

Next Steps

• Text to Image — Generate images to upscale
• Image to Image — Refine before upscaling
• LoRA Guide — Add style variety to your generations

Source References

• ComfyUI image upscaling guide
• ComfyUI Image Upscale workflow tutorial
• ComfyUI models concept