4xRealWebPhoto v3 Atd
Name: 4xRealWebPhotov3atd License: CC BY 4.0 Author: Philip Hofmann Network: ATD Scale: 4 Release Date: 22.03.2024 Purpose: 4x upscaler for photos downloaded from the web Iterations: 250'000 epoch: 10 batchsize: 6, 3...
Model source
- Kind
- Other
- File size
- 77.9–78.2 MB
- Version
- vmain
- Updated
- Sep 29, 2026
- Downloads
- 0
- Tasks
- Image to image
- Source
- Hugging Face
Source description
4xRealWebPhoto_v3_atd
Name: 4xRealWebPhoto_v3_atd
License: CC BY 4.0
Author: Philip Hofmann
Network: ATD
Scale: 4
Release Date: 22.03.2024
Purpose: 4x upscaler for photos downloaded from the web
Iterations: 250'000
epoch: 10
batch_size: 6, 3
HR_size: 128, 192
Dataset: 4xRealWebPhoto_v3
Number of train images: 101'904
OTF Training: No
Pretrained_Model_G: 003_ATD_SRx4_finetune
Description:
4x real web photo upscaler, meant for upscaling photos downloaded from the web. Trained on my v3 of my 4xRealWebPhoto dataset, it should be able to handle noise, jpg and webp (re)compression, (re)scaling, and just a little bit of lens blur, while also be able to handle good quality input. Trained on the very recently released (~2 weeks ago) Adaptive-Token-Dictionary network.
My 4xRealWebPhoto dataset tried to simulate the use-case of a photo being uploaded to the web and being processed by the service provides (like on a social media platform) so compression/downscaling, then maybe being downloaded and re-uploaded by another used where it, again, were processed by the service provider. I included different variants in the dataset. The pdf with info to the v2 dataset can be found here, while i simply included whats different in the v3 png:
4xRealWebPhoto_v3
Training details:
AdamW optimizer with U-Net SN discriminator and BFloat16.
Degraded with otf jpg compression down to 40, re-compression down to 40, together with resizes and the blur kernels.
Losses: PixelLoss using CHC (Clipped Huber with Cosine Similarity Loss), PerceptualLoss using Huber, GANLoss, LDL using Huber, Focal Frequency, Gradient Variance with Huber, YCbCr Color Loss (bt601) and Luma Loss (CIE XYZ) on neosr with norm: true.
11 Examples:
Slowpics
4xRealWebPhoto_v3_atd_example1 4xRealWebPhoto_v3_atd_example2 4xRealWebPhoto_v3_atd_example3 4xRealWebPhoto_v3_atd_example4 4xRealWebPhoto_v3_atd_example5 4xRealWebPhoto_v3_atd_example6 4xRealWebPhoto_v3_atd_example7
Sources
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Model artifacts
2 artifactsSource excerpts
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Source excerpts
2 excerpts--- license: cc-by-4.0 pipeline_tag: image-to-image tags: - pytorch - super-resolution --- # 4xRealWebPhoto_v3_atd Name: 4xRealWebPhoto_v3_atd License: CC BY 4.0 Author: Philip Hofmann Network: [ATD](https://github.com/LabShuHangGU/Adaptive-Token-Dictionary) Scale: 4 Release Date: 22.03.2024 Purpose: 4x upscaler for photos downloaded from the web Iterations: 250'000 epoch: 10 batch_size: 6, 3 HR_size: 128, 192 Dataset: 4xRealWebPhoto_v3 Number of train images: 101'904 OTF Training: No Pretrained_Model_G: 003_ATD_SRx4_finetune Description: 4x real web photo upscaler, meant for upscaling photos downloaded from the web. Trained on my v3 of my 4xRealWebPhoto dataset, it should be able to handle noise, jpg and webp (re)compression, (re)scaling, and just a little bit of lens blur, while also be able to handle good quality input. Trained on the very recently released (~2 weeks ago) Adaptive-Token-Dictionary network. My 4xRealWebPhoto dataset tried to simulate the use-case of a photo being uploaded to the web and being processed by the service provides (like on a social media platform) so compression/downscaling, then maybe being downloaded and re-uploaded by another used where it, again, were processed by the service provider. I included different variants in the dataset. The pdf with info to the v2 dataset can be found [here](https://github.com/Phhofm/models/releases/download/4xRealWebPhoto_v2_rgt_s/4xRealWebPhoto_v2.pdf), while i simply included whats different in the v3 png:  Training details: AdamW optimizer with U-Net SN discriminator and BFloat16. Degraded with otf jpg compression down to 40, re-compression down to 40, together with resizes and the blur kernels. Losses: PixelLoss using CHC (Clipped Huber with Cosine Similarity Loss), PerceptualLoss using Huber, GANLoss, [LDL](https://github.com/csjliang/LDL) using Huber, [Focal Frequency](https://github.com/EndlessSora/focal-frequency-loss), [Gradient Variance](https://github.com/lusinlu/gradient-variance-loss) with Huber, YCbCr Color Loss (bt601) and Luma Loss (CIE XYZ) on [neosr](https://github.com/muslll/neosr) with norm: true. 11 Examples: [Slowpics](https://slow.pics/s/plgWVh4j) 