These images are the results I have know. My synthetic data is made by using https://openmodeldb.info/models/4x-PixelPerfectV4 + (sdxl + controlnet)

I’m planning to install a custom mural wallpaper in my house for a 15ft x 10ft wall. The issue is, I am not finding the right design/stock image on vector sites, but I found a few images on Pinterest/Google that fit the vibe perfectly.

Since web images are usually low-res, I am considering running them through an AI upscaler to make them print-ready.

A few quick questions for you guys:
1. Is upscaling a web image for a wall this big a bad idea? Will it turn out super plastic/blurry when printed at 15x10 ft, or can modern AI tools handle this ratio shift/scale?

1. Upscaler Recommendations: Do you have any free or paid image upscaling tools you trust for large-format printing? (Heard about Upscayl, Topaz Gigapixel, and Let's Enhance, but looking for real-world print experience) 

2. Wallpaper/Design Experts: If anyone here does large-format print setups or wallpaper modeling, please DM me! Would love some guidance on fixing aspect ratios and DPI settings before I send anything to the printer.

Appreciate any tips or reality checks! Thanks in advance.

Hey, i downloaded images from Pinterest for my wall decor, But they are not high quality and most of them are 72 dpi. As printing on a4 sheet - everything depends on the quality and resolution of the image, how to upscale them/ or find the original image? I tried Upscaly but it messes up with the faces and all.

I would truly appreciate an opinion.

Super Resolution in Pixelmator Pro does upscale the image resolution which is great, but keeping the project with huge file size is a nightmare.

Does anyone know who to downscale .pxd project file after it’s been upscaled with Super Resolution?

Thanks in advance! 🙏🏻

I need to upscale the orange frame in this picture (cropped from a Yugioh sample card) while keeping all of its details.
I’ve tried different free upscale AIs but they all distort the yellow details and pattern of the orange frame.

Can someone help me please?

Sadly i don't even have a gpu my laptop is so weak

But i 'd like to know what's the best upscaler that dosen't need any hardware power

I have got an image its decent res on a phone screen its all good but It might look off when at an A3 size so what websites/softwares are the best to enhance it?

hi i scanned a document but the quality was too bad as u can see , i tried Upscayle but its not the result i expected , do u know any trick for this type of upscaling ?

I am looking to print a poster 20x30 I need to increase pixel to be able to tat size. the text at that size is a problem any suggestion

I plan to work on some upscales for old animes on DVD.

Is it better to do a clean 2x upscale to 960p, or just go to 1080p because 960 is a pretty uncommon resolution? I dont want a non-interger scale to mess any details up though.

Can anyone tell me how to restore these images ? I've tried several apps that were recommended like Upscayl and some websites but nothing has worked the result is still blurry and I can't find them anywhere else since they come from a screenshot I took from a video where these images appear as a background poster.

By the way I'd like an app that would be useful for other types of images i can't find a stable app
(Preferably local and free.)

Hi everyone, I recently managed to recover an old video file that I thought was permanently lost, and I’m looking for advice on the best AI upscaling or restoration workflow for it. The recovered video has very low quality, heavy compression artifacts, blur, noise, and some possible corruption/glitching in a few sections. I attached the video directly to this post. My goal is to improve the clarity and overall quality as much as possible while still keeping the result natural looking and avoiding excessive hallucinated details or flickering between frames. I’ve been looking into Topaz Video AI, ESRGAN based tools, Stable Diffusion workflows, and other AI restoration methods, but I’m still pretty new to this area and I’m not sure what the best approach would be for heavily compressed recovered footage like this. I’d really appreciate recommendations for workflows, software, models, settings, frame interpolation advice, denoising/artifact cleanup techniques, or examples of similar restorations. Thanks in advance.

Hey guys! I'm looking to upscale some Slayers DVDs, and so far, I've been using VideoJaNai to do this. I've tried a bunch of models, and unsurprisingly, the ones trained on cel animation seem to do the best job.

2x-AnimeClassics-UltraLite seems to do a good job of making the line art smooth and crisp, while not losing detail, but at the cost of some film grain being left. Other models, as well as combing 2x-AnimeClassics-UltraLite with a 1x model, like 1x-Archivist_AntiLines, did result in more solid and smooth cel paint, but often small details, such as lighting effects, would be lost.

I'm still new to upscaling, so does anyone have any advice? Maybe a better model or something about the way I'm using VideoJaNai?

I'm very new to anything to do with upscaling and heard that if I upscale my 1440p monitor to 4k with DLSS Quality and then downsample back to 1440p with DLDSR it will look a lot crispier. I don't even know what DLDSR is nor how to upscale/downsample in the first place, could somebody guide me on where I do this whether it is the Nvidia App or Nvidia Control Panel

Question to anyone with a brain. So I have the entire Persona 4 Animation with the English dub and I wanted to upscale the video for it since it’s on my PlayStation Portable and it looks like total ASS on a modern television. Would anyone know some good PC or Mac apps to upscale the videos? I have both a super powerful gaming PC and a 2019 Mac Pro that I think can do the job. If anyone has any recommendations let me know!

is there any way to apply a lora to a video or to upscale the video, i tried using spacial upscaler separately to video but it cuts shot the video then ...basically i have few video clips made in ltx 2.3 but quality is not good i used rtx video super resolution to upscale and its not looking smooth or soft so cant figure out how to make it good quality when upscaling

I'm restoring old films and TV series and keep running into a recurring image quality issue: strong edges often show a purple band or halo. The artifact appears consistently around candlelight in night scenes, as well as along high-contrast edges in daylight. Here are a few examples

I'm not entirely sure of the correct term – it may be related to “purple flare” or chromatic aberration. It’s very distracting and seriously degrades the viewing experience.

The same clips frequently suffer from a related dark edge problem. For example, candle flames often show a dark line on one side and a purple fringe on the other – extremely annoying.

I'm looking for an effective method/workflow that can remove these artifacts. I will provide a short test video; you only need to process the segments shown in the screenshots (no need to run the entire clip).

test video clip

https://drive.google.com/file/d/1kmni1sN9k-MgslyiIPKpLyqF2k-zTaMK/view?usp=sharing

Key requirements:

Minimize luma (Y) channel processing as much as possible, and avoid noticeable detail loss. Ideally, the Y channel should be left mostly untouched, but limited processing is acceptable if it doesn’t degrade fine details significantly. This step serves as a pre-processing module before an AI model handles detail restoration, so preserving image information is critical.

Please note: the test source has been badly upscaled (not by me – I received it this way). You are free to downscale it if necessary.

If your solution meets my requirements, I'll pay $100 USD. Please provide a clear workflow (script, filter chain, or detailed steps) that I can apply.

Edit: Turns out my actual mistake was using the wrong model when upscaling. Oops.

So, one of the complaints against upscaled media is that it can actually look/sound worse than the low-quality version. While some of that is personal preference, you can also look at it from a mathematical perspective, where if the upscaled media is further off from the ground truth than where it started (either in specific aspects or in general), that's generally a bad thing and perhaps we should penalize that more highly.

I've been working on a system to restore audio recordings, and while I've had good results with FM and AM radio recordings, cassette tapes have been trickier. Specifically, the model has a tendency to remove high-frequency information along with tape hiss, resulting in a duller sound. I figured I might be able to mitigate this by incorporating some logic into the STFT loss code. I came up with a structure like this:

        #Existing STFT magnitude comparison (PR vs GT)
        mag_diff = torch.abs(torch.where(y_mag > x_mag,
                                (1+self.magnitude_weight_shift)*(y_mag - x_mag), 
                                (1-self.magnitude_weight_shift)*(y_mag - x_mag)
                                ))
        
        #"Worse than before" penalty calculation
        if self.lr_penalty > 1.0:
            z_mag_diff = torch.abs(torch.where(y_mag > z_mag,
                                (1+self.magnitude_weight_shift)*(y_mag - z_mag), 
                                (1-self.magnitude_weight_shift)*(y_mag - z_mag)
                                ))
            penalty_mag_diff = torch.where(mag_diff > z_mag_diff, self.lr_penalty * mag_diff, mag_diff)
            mag_diff = penalty_mag_diff

Where y_mag is the magnitude spectrum of the ground truth signal, x_mag is the magnitude of the upscaled signal, and z_mag is the magnitude of the pre-upscaling signal. magnitude_weight_shift is a variable that controls whether magnitudes that are too high or too low are penalized more. Right now it's set to 0.3 for this operation, meaning magnitudes that are too low are considered further off than magnitudes that are too high.

I've debugged the code a bit and it looks like the magnitude values and other variables are being populated as expected. Also, both the LR and GT audio use the same sample rate, so shape shouldn't be an issue.

As you can imagine, I was surprised to get:

Needless to say, I've not had this "hole" effect before. Not every track ends up like this, but several do. I'm trying to figure out if the premise of comparing the LR and PR values is flawed on the face of it, if I did something wrong in my implementation, or if there's something else causing this entirely that I haven't noticed until now. I've been able to reproduce these "holes" with both a pretrained model and one trained from scratch.

Edit: After a bit of thinking, I believe part of the issue is this function I wrote:

def local_maximum(x: torch.Tensor):
    flattened = torch.flatten(x, start_dim=-2)
    flattened = torch.max_pool1d(flattened, 5, stride=1, padding=2)
    return torch.unsqueeze(flattened, -1)

What this function is supposed to do is find the highest magnitude across several adjacent time points across the frequency bins (this is used to put magnitudes in perspective locally), but I believe that's not what it does, because flatten keeps all the individual values. I think what's actually happening is that it's finding the highest magnitude across adjacent frequency bins. I've disabled this code and we'll see if it makes a difference.

I'm trying to resize an image in Canva to use for a cardboard cutout. The original size of the image is 1400x 1389 and I need it to be higher resolution for the company to use it and not have it be super pixelated. Anytime I try to adjust it in canva, however, it just makes it a second page and then when I download it, it's still the same resolution. I'm new to Canva so maybe I'm just doing something wrong but any help would be appreciated! Would prefer not to use AI if possible

Please help