6 AI Upscale Models for Anime Illustrations Compared: UltraSharpV2 vs. IllustrationJaNai_V3
- AI upscale models correct contours
- ESRGAN makes bold modifications; DAT2 stays faithful to the original
- Use models in safetensors format
Introduction
Hello, this is Easygoing. In this article, I’d like to compare AI upscale models that can easily enhance image quality.
I’ve also converted AI upscale models—which are usually released in pickle format—into the safer and potentially higher-performance safetensors format and published them, so I’ll introduce those as well.
AI Upscale Models Correct Details
In a previous article, I explained that there are two ways to increase image resolution: pixel interpolation using mathematical formulas and methods that use AI upscale models.
About AI Upscale Models
AI upscale models can perform high-quality upscaling by correcting elements like contours—which tend to break down with mathematical methods—while increasing resolution.
Comparison Between Mathematical Methods and AI Upscaling
Strong Magnification
The illustration processed with the AI upscale model on the right has reduced noise in the hair and eyes and clearer contours compared to the Lanczos method on the left.
There are many types of AI upscale models, but this time I’ll compare six models that are particularly suitable for anime illustrations.
The Comparison Baseline Is the Lanczos Method
The comparison will be conducted using the following procedure.
Baseline Image
First, create a baseline image by enlarging the original image to 2× resolution using the Lanczos method.
flowchart LR
A1(Original Image<br>1024 x 1024)
B1(Lanczos Image<br>2048 x 2048)
A1--Lanczos x2-->B1AI Upscaled Images
Next, generate comparison images using AI upscale models.
flowchart LR
A1(Original Image<br>1024 x 1024)
B1(4096 x 4096)
C1(AI Upscaled Image<br>2048 x 2048)
A1--AI Upscale x4-->B1
B1--Lanczos x0.5-->C1All AI upscale models used this time have a fixed 4× scale factor, so they are first enlarged 4× and then reduced by 0.5× with the Lanczos method to match the baseline image resolution.
AI Upscale Models Being Compared
The six AI upscale models compared this time are as follows.
gantt
title AI Upscale Models
dateFormat YYYY-MM-DD
axisFormat %Y
section Nmkd
YandereNeoXL_ESRGAN : 2021-01-26, 2026-08-29
section Tencent ARC Lab
RealESRGAN_plus_anime_6B : 2021-08-31, 2026-08-29
section Kim2091
UltraSharp_ESRGAN : 2021-10-27, 2026-08-29
UltraSharpV2_DAT2 : 2025-05-23, 2026-08-29
section the-database
IllustrationJaNai_V1_ESRGAN : 2024-02-23, 2026-08-29
IllustrationJaNai_V3_denoise_DAT2 : 2025-11-26, 2026-08-29| Model Name | Release Month | Architecture | License |
|---|---|---|---|
| YandereNeoXL_ESRGAN | Jan 2021 | ESRGAN | WTFPL |
| RealESRGAN_plus_anime_6B | Aug 2021 | ESRGAN | BSD 3-Clause |
| UltraSharp_ESRGAN | Oct 2021 | ESRGAN | CC-BY-NC-SA-4.0 |
| UltraSharpV2_DAT2 | May 2025 | DAT2 | |
| IllustrationJaNai_V1_ESRGAN | Feb 2024 | ESRGAN | |
| IllustrationJaNai_V3_denoise_DAT2 | Nov 2025 | DAT2 |
- ESRGAN (2018–): Fast, aggressively modifies the original illustration
- DAT2 (2023–): Faithful to the original illustration, but heavier processing
The most widespread format among AI upscale models is ESRGAN, which aims to improve quality by aggressively modifying the original illustration. In contrast, the DAT2 format that appeared in 2023 performs upscaling that is faithful to the original, but takes longer to process than ESRGAN models.
Actual Comparisons
Let’s compare actual illustrations. In the comparison images, the left side shows the illustration generated by the AI upscale model, and the right side shows a color difference map compared to the baseline image, with differences emphasized 4× for clarity.
1. High-Quality Anime Illustration
First, let’s upscale a high-quality anime illustration.
Baseline Image
YandereNeoXL_ESRGAN
RealESRGAN_plus_anime_6B
UltraSharp_ESRGAN
UltraSharpV2_DAT2
IllustrationJaNai_V1_ESRGAN
IllustrationJaNai_V3_denoise_DAT2
Image1 result
| image1 | Time (sec) | MAE_similarity | SSIM_similarity | Red | Green | Blue | Brightness |
|---|---|---|---|---|---|---|---|
| YandereNeoXL_ESRGAN | 13.3 | 99.0 % | 99.6 % | 0.0 % | 0.0 % | 0.1 % | 0.0 % |
| RealESRGAN_plus_anime_6B | 7.8 | 98.4 % | 99.5 % | -0.3 % | -0.4 % | -0.5 % | -0.4 % |
| UltraSharp_ESRGAN | 13.3 | 98.8 % | 99.6 % | -0.2 % | 0.1 % | -0.4 % | -0.1 % |
| UltraSharpV2_DAT2 | 35.5 | 99.1 % | 99.8 % | -0.2 % | -0.1 % | -0.2 % | -0.2 % |
| IllustrationJaNai_V1_ESRGAN | 12.9 | 99.1 % | 99.7 % | -0.2 % | -0.2 % | -0.2 % | -0.2 % |
| IllustrationJaNai_V3_denoise_DAT2 | 34.7 | 99.3 % | 99.8 % | -0.2 % | -0.2 % | -0.2 % | -0.2 % |
First, a common characteristic of AI upscale models is that they tend to lower overall brightness and emphasize blacks compared to mathematical methods. Looking at the difference maps and the MAE_similarity and SSIM_similarity values (similarity to the baseline image) in the table, we can see that DAT2-based models make fewer changes from the baseline image than ESRGAN-based models.
Among ESRGAN models, YandereNeoXL_ESRGAN is a relatively conservative model that stays close to the original, while RealESRGAN_plus_anime_6B strongly redraws local areas, especially around character contours.
The IllustrationJaNai series is distinctive in that it adjusts noise and colors across the entire illustration in addition to contours, while the UltraSharp series sits in a well-balanced middle ground among the models compared this time.
2. Raw SDXL Output Illustrations
Next, let’s compare with raw SDXL outputs—the type of illustration where AI upscale models are used most frequently.
Baseline Image
YandereNeoXL_ESRGAN
RealESRGAN_plus_anime_6B
UltraSharp_ESRGAN
UltraSharpV2_DAT2
IllustrationJaNai_V1_ESRGAN
IllustrationJaNai_V3_denoise_DAT2
Image2 result
| image2 | Time (sec) | MAE_similarity | SSIM_similarity | Red | Green | Blue | Brightness |
|---|---|---|---|---|---|---|---|
| YandereNeoXL_ESRGAN | 12.6 | 99.2 % | 99.7 % | 0.0 % | 0.0 % | 0.2 % | 0.0 % |
| RealESRGAN_plus_anime_6B | 7.9 | 98.5 % | 99.5 % | -0.1 % | -0.2 % | 0.0 % | -0.2 % |
| UltraSharp_ESRGAN | 13.1 | 98.7 % | 99.5 % | -0.3 % | 0.2 % | 0.0 % | -0.1 % |
| UltraSharpV2_DAT2 | 34.2 | 99.2 % | 99.7 % | -0.1 % | 0.0 % | -0.2 % | -0.1 % |
| IllustrationJaNai_V1_ESRGAN | 12.9 | 99.2 % | 99.7 % | -0.1 % | -0.1 % | -0.1 % | -0.2 % |
| IllustrationJaNai_V3_denoise_DAT2 | 34.3 | 99.4 % | 99.8 % | -0.2 % | -0.1 % | -0.1 % | -0.2 % |
In this illustration, the areas each model corrects are clearly different. RealESRGAN_plus_anime_6B and UltraSharp_ESRGAN models react strongly to edge areas such as the lines of the waterfall.
On the other hand, UltraSharpV2_DAT2 and the IllustrationJaNai series do not react to the waterfall lines and instead focus on overall illustration correction and noise removal.
Because AI upscale models respond differently depending on the environment they were trained in, the optimal model varies from illustration to illustration.
3. Photorealistic Illustrations
Finally, let’s compare with photorealistic illustrations.
Baseline Image
YandereNeoXL_ESRGAN
RealESRGAN_plus_anime_6B
UltraSharp_ESRGAN
UltraSharpV2_DAT2
IllustrationJaNai_V1_ESRGAN
IllustrationJaNai_V3_denoise_DAT2
Image3 result
| image3 | Time (sec) | MAE_similarity | SSIM_similarity | Red | Green | Blue | Brightness |
|---|---|---|---|---|---|---|---|
| YandereNeoXL_ESRGAN | 12.7 | 99.6 % | 100.0 % | 0.1 % | 0.0 % | 0.0 % | 0.0 % |
| RealESRGAN_plus_anime_6B | 8.1 | 98.8 % | 99.6 % | -0.3 % | -0.3 % | 0.0 % | -0.3 % |
| UltraSharp_ESRGAN | 13.2 | 99.1 % | 99.7 % | 0.0 % | 0.0 % | -0.1 % | 0.0 % |
| UltraSharpV2_DAT2 | 34.5 | 99.4 % | 99.8 % | -0.1 % | -0.2 % | -0.1 % | -0.2 % |
| IllustrationJaNai_V1_ESRGAN | 12.8 | 99.5 % | 99.9 % | -0.2 % | -0.3 % | -0.2 % | -0.3 % |
| IllustrationJaNai_V3_denoise_DAT2 | 34.0 | 99.7 % | 100.0 % | -0.2 % | -0.2 % | -0.2 % | -0.2 % |
Since all the models used this time are designed for anime illustrations, YandereNeoXL_ESRGAN and the IllustrationJaNai series were barely able to correct the photorealistic illustration.
RealESRGAN_plus_anime_6B and the UltraSharp series were able to make some corrections to the photorealistic illustration, but the results still differ from the precise corrections they apply to anime illustrations.
When correcting photorealistic illustrations, it’s better to use AI upscale models specialized for photorealistic images.
Overall Evaluation
Now for the overall evaluation.
Upscaling Processing Time
Looking at processing times, the newer DAT2-based models take more than twice as long as the ESRGAN-based models.
Among ESRGAN models, RealESRGAN_plus_anime_6B is especially fast compared to other ESRGAN models. This is because, as the “6B” name indicates, it is a compact model with only 6 layers (typical ESRGAN models have 23 layers).
Characteristics of Each Model
Next, I had Claude analyze the measurement data and all difference maps from this test and create an infographic summarizing the characteristics of each model.
According to Claude’s analysis, RealESRGAN_plus_anime_6B performs bold local corrections, while YandereNeoXL_ESRGAN makes corrections that are faithful to the original.
The IllustrationJaNai series applies processing that improves the overall quality of the illustration beyond just contours, while the UltraSharp series delivers well-balanced upscaling overall.
Based on this comparison, IllustrationJaNai series is a good choice when you want to raise the overall quality of an illustration, UltraSharp series when balance is the priority, and RealESRGAN_plus_anime_6B when local corrections are most important.
UltraSharp and IllustrationJaNai Have Non-Commercial Licenses!
When choosing an AI upscale model, checking the license is also important. Among the models compared this time, the UltraSharp series and IllustrationJaNai series are under the CC-BY-NC-SA-4.0 license, which does not permit commercial use.
| Model Name | Release Month | Architecture | License |
|---|---|---|---|
| YandereNeoXL_ESRGAN | Jan 2021 | ESRGAN | WTFPL |
| RealESRGAN_plus_anime_6B | Aug 2021 | ESRGAN | BSD 3-Clause |
| UltraSharp_ESRGAN | Oct 2021 | ESRGAN | CC-BY-NC-SA-4.0 |
| UltraSharpV2_DAT2 | May 2025 | DAT2 | |
| IllustrationJaNai_V1_ESRGAN | Feb 2024 | ESRGAN | |
| IllustrationJaNai_V3_denoise_DAT2 | Nov 2025 | DAT2 |
About Licenses for Image Generation AI Models
Because the UltraSharp series and IllustrationJaNai series cannot be used commercially, please use different models when generating commercial illustrations.
So What Do I Recommend?
My conclusions are as follows:
- To raise quality through upscaling → IllustrationJaNai_V3_denoise_DAT2
- Balance-focused → UltraSharp series
- Fast generation of commercial illustrations → RealESRGAN_plus_anime_6B
Use Models in Safetensors Format!
ComfyUI’s Load Upscale Model node can load both pickle format and safetensors format models.
- Pickle format (.pt, .pth): Executable code
- Safetensors format (.safetensors): Model weights only, efficient loading
Because pickle format is distributed as executable code, the risk of malicious code such as malware being included cannot be completely ruled out. In contrast, safetensors format extracts only the model weights, making it safer than pickle format.
AI upscale models are small, so there is almost no difference in load time between pickle and safetensors formats. From a security perspective, however, it is better to use safetensors format whenever possible.
Repository of models converted from pickle to safetensors
I’ve published a repository containing many older pickle-format models converted to safetensors format, so feel free to try them out.
Summary: Use AI Upscale Models
- AI upscale models correct contours
- ESRGAN makes bold modifications; DAT2 stays faithful to the original
- Use models in safetensors format
In this article, I compared AI upscale models. AI upscale models are convenient tools that make it easy to upscale images, but the way they apply corrections reflects the philosophy of their creators. Trying various models and experiencing the creators’ thinking is a lot of fun.
The Hugging Face repository mentioned above publishes many models beyond the six introduced here.
The latest IllustrationJaNai_V3 series also includes various variants such as a detail series that enhances details and a lighter FDAT format.
Finding the AI upscale model that best matches your own illustrations through trial and error is very enjoyable, so I encourage everyone to try different ones and discover your favorites.
Thank you for reading to the end!