Name: 4x UltraSharpV2
Author: Kim2091

## Ani4K v2  

 Ani4K v2, as the successor to the original Ani4K, retains its predecessor's fantastic detail retention, depth of field preservation and faithfulness to the original source. As the name suggests, the model is targeted at modern anime, ranging from high-quality Bluray to crappy WEB releases, for upscaling either to 2K or 4K.

An **UltraCompact** version of the model is available on the [Github release](https://github.com/Sirosky/Upscale-Hub/releases/tag/Ani4K-v2). The UltraCompact version is faster, without a perceptual hit to quality in most cases.

📌 [More comparisons](https://slow.pics/c/1HykdyT5)

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**FAQ**

- *How does v2 differ from v1?* I'm so glad you asked! A shortcoming of the v1 models is that the model really struggled on sources which were poorly mastered. This is unfortunately still very common even with modern anime. v2 is far more capable of dealing with such sources. 
- *How does Ani4K v2 differ from JaNai v3? Which one should I pick?* JaNai v3 is a fantastic model, and shares many of the fundamental training objectives behind Ani4K (DOF preservation, faithfulness, etc.). I'd say that the primary difference is one of training philosophy-- JaNai seeks to render the source as if it was originally mastered in 4K, whereas Ani4K seeks to produce an upscale that is as close as possible to the source (while cleaning up any issues). Long story short, test both models and see which you prefer.
- *What model versions are there?* Ani4K comes in Compact and UltraCompact flavors. Compact is of course a standard option. UltraCompact provides a noticeable performance uplift, without too much impact on quality. I ultimately did not train a SuperUltraCompact variant as I felt the hit to the model quality was far too significant.

Ani4VK v2 Compact

SPAN model for live action film and digital video. The main goal is to fix/reduce common video quality problems while maintaining fidelity. I tried the existing video-focused models and they all denoise or cause colour shifts so I decided to train my own.

The model is trained with compression (JPEG, MPEG-4 ASP, H264, VP9, H265), chroma subsampling, blurriness from multiple scaling, uneven horizontal and vertical resolution, oversharpening halos, bad deinterlacing jaggies, and onscreen text. It is not trained to remove noise at all so it preserves details in the source well. To prevent colour/brightness shifts, I used consistency loss in neosr. I had to modify consistency loss to use a stronger blur so it doesn't interfere with the halo removal.

Limitations:
1. The model has limited ability to see details through heavy grain, but light to moderate grain is fine.
2. The model still does not handle bad deinterlacing perfectly, especially if the source is vertically resized. Fixing bad deinterlacing is not the main goal so it is what it is. Sources that are line-doubled throughout should be descaled back to half height first for best results.
3. The model sometimes oversharpens a little. This is probably because the training data has some oversharpened images.
4. This model generally cannot handle VHS degradation.

More comparisons: https://slow.pics/c/DtDN7gaq

The training config and image degradation scripts used to create training data can be found in https://github.com/jcj83429/upscaling/tree/9332e7d5b07747ff347e5abdc43f8144364de9f7/2xLiveActionV1_SPAN

2xLiveActionV1_SPAN

Purpose: Upscale text in very low quality to normal quality.

The upscale model is specifically designed to enhance lower-quality text images, improving their clarity and readability by upscaling them by 2x. It excels at processing moderately sized text, effectively transforming it into high-quality, legible scans. However, the model may encounter challenges when dealing with very small text, as its performance is optimized for text of a certain minimum size. For best results, input images should contain text that is not excessively small.

Text2HD v.1

Skilled in working with cats, hair, parties, and creating clear images. Also proficient in resizing photos and enlarging large, sharp images. Can effectively improve images from small sizes as well (300px at smallest on one side, depending on the subject). Experienced in experimenting with techniques like upscaling with this model twice and then reducing it by 50% to enhance details, especially in features like hair or animals.

4xPurePhoto-RealPLSKR

Please consider supporting my work on [Ko-Fi](https://ko-fi.com/kim20913944)!

This model has been in the works for a very very very long time. I spent years creating my own private dataset just to train this model. It can handle realistic images, anime, cartoons, and quite a bit more without much hassle. It also works amazingly on illustrations and artwork. This is easily the best model I've ever trained.

I hope you all like it.

The Lite model is based on the RealPLKSR architecture, rather than DAT2.

And please consider donating! I'm going through a rough time right now & any support helps a LOT

**Showcase:**

Flux comparisons: <https://slow.pics/s/bsfbNpMA>

Image Upscales: <https://slow.pics/s/RORQ4R0U?image-fit=contain>

V1 vs V2 Upscales: <https://slow.pics/c/2HIBlzAh>

UltraSharpV2

Good for

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