r/upscaling

What came out of it:

- Upscale first, interpolate second - seems to be better

- 24->48 looks better than 60fps - at 48 every original frame survives, at 60 only half of them do, because the grids don't line up

- FlashVSR ends up with more edge detail than the source, so it's adding texture, not recovering it. RealESRGAN ends up with less.

Side by side with a draggable wipe, pick any two variants: https://dawidope.github.io/minimax-h3-upscale/

Hello, are you still use this Xbox 360 game a picture and really miss it. I want to use it again and was able to find it online but it's very blurry. Could someone upscale it?

Use these links to view the full size images

Cyberpunk Cityscape Original
Cyberpunk Cityscape 4k
Cyberpunk Cityscape 8k

Orbital Shipyard Hangar Original
Orbital Shipyard Hangar 4k
Orbital Shipyard Hangar 8k

https://github.com/Blakeem/ComfyUI-ContextAnchoredTileRefine

These were upscaled using high denoise (0.5), captions, live canvas anchoring, and a stochastic (sde) sampler. If you have used other tile upscalers you will know that coherent creative upscaling is the hardest thing to do, since it requires maintaining coherence over a massive canvas. These images have 37,748,736 pixels being generated by a model that is only processing 2,013,696 pixels at any one time.

If you want to preserve the original image. Use low denoise (0.35), vision tokens, anchor to the source image, and use a deterministic sampler. Here is what that looks like:

Cyberpunk Cityscape Original
Cyberpunk Cityscape Conservative 4k
Cyberpunk Cityscape Conservative 8k

Compared to the Tiled Diffusion node (ComfyUI-TiledDiffusion):

>Theirs is a model patch below the sampler.
Mine wraps above the sampler and guider.

>Theirs has one sampler.
With mine each tile has it's own full sampler.

>Mine uses region of interest (RoI) token slicing in a tile upscaler (see my previous post on this subject).

>Both refine an upscaled image inside one latent canvas one step at a time so tiles don't drift apart.

>Theirs uses an average (uniform MultiDiffusion and Gaussian Mixture of Diffusers) that causes the image to be soft.
Mine does a directional blend in raster order, the later tiles blend into the earlier ones whose context they reach out to, so it maintains the models sharp raw output.

>Mine also supports masks, something the other method does not. Mine doesn't support ControlNet (at least not the VL node, the standard node does). But Krea 2 has no good ControlNet model because it isn't built for it and requires a LoRA.

I've been testing out different methods to upscale and hide seams in Krea 2 and I had a massive breakthrough last night during A/B testing. Everything happens in the latent canvas, so there is no color drift across tiles because everything happens with a single decode.

The 8k images were done in two stages, first to 4k with 6 tiles and then to 8k with 30 tiles on my 3090ti. You could go to 8k in a single pass and probably up to 16k. Creating a larger image does not increase memory by much, it just takes more time.

These are my first two test images I made, so don't judge based on that. The quality is staggering compared to what I've been able to do before. In the Cyberpunk Cityscape you can make out a McDonald's on the street as well as people, desks, and computers inside the office windows. The cables and wires in the Orbital Shipyard Hangar do not cut off across tiles. These are things that I only dreamed of with previous methods and there is still room to improve.

Please view the full size images on github so you can zoom in, reddit doesn't do them justice. This is where you will find the technical details for how I'm doing this as well as finding the workflow that I used to make the images.

Follow-up to my last post: 4× AI upscale from 1536×1024 to 6144×4096 — detail comparison

In my previous post, someone asked whether HAN was actually an upscaler or mainly a filter/enhancer.

That was a fair question, so I made a clearer comparison.

For this test:

Original: 1536 × 1024
Scale: 4×
Output: 6144 × 4096

The image shows the original and processed result side by side, along with a magnified crop of the same area.

One of my main goals with HAN is to improve the existing structure and texture of the image without aggressively inventing new visual details.

I’m especially interested in feedback from people who regularly use AI upscalers:

Does the 4× result look natural to you, or do you see any areas that still look overprocessed?

Taiwan, 2015. Dash cam.

An update that further improves the upscaling tool.

https://perchance.org/jscad310fl

I’ve been experimenting with MCP and wanted to see how far I could push the “just ask Claude to do it” workflow.

In this case, I asked Claude to increase the resolution of a video. Behind the scenes, it uses an MCP server connected to Qencode to actually process the video.

Recorded a demo because I thought the interaction was pretty interesting.

I’m mostly curious about the UX here: does asking an LLM to handle video processing like this feel useful, or does it just add an unnecessary layer?

Would love feedback, especially from people upscaling video

Hey r/computervision!

This is my first post here...

I’ve been working on PixelUp, a zero-shot feature upsampler for Vision Foundation Models (VFMs), and wanted to share it here!

Most VFMs produce semantically rich features, but they’re usually on a pretty coarse patch-level grid (often ~16× lower resolution than the input). This can be limiting for dense vision tasks where fine spatial details really matter.

PixelUp upsamples these coarse VFM features to pixel-level representations, while preserving their semantic information.

I’ve also put together an interactive demo on the project page where you can drag a lens across an image and compare the original coarse VFM features with PixelUp’s upsampled features. It’s pretty fun to play around with :)

📄 Preprint: https://arxiv.org/abs/2608.02792
🔬 Project + interactive demo: https://pixelup-project.vercel.app/
💻 Code: https://github.com/deepankkumar/PixelUp

Would love to hear your thoughts or feedback!

Here’s a quick example using Qencode’s AI upscaling.

I started with a low-resolution video, selected the AI upscaling option, and ran the transcode.

The video shows the full setup in about 30 seconds, plus the before/after result.

Hope this is useful for anyone curious about how AI video upscaling works.

I created this video with minimax h3 in 480p, upscaled to 4k, but it did not work properly.

https://youtu.be/mAWQFiCyMX0?si=IdjbTvYaNSAuJUzz

Suggest some best upscaler for me.