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Upload upscaler models

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  1. ESRGAN/16xPSNR.pth +3 -0
  2. ESRGAN/1xDeJPG_realplksr_otf.pth +3 -0
  3. ESRGAN/2x_Text2HD_v.1-RealPLKSR.pth +3 -0
  4. ESRGAN/4x-UltraSharp.pth +3 -0
  5. ESRGAN/4xArtFaces_realplksr_dysample.pth +3 -0
  6. ESRGAN/4xHFA2k_ludvae_realplksr_dysample.pth +3 -0
  7. ESRGAN/4xNomos2_hq_dat2.pth +3 -0
  8. ESRGAN/4xNomos8kDAT.pth +3 -0
  9. ESRGAN/4x_CountryRoads_377000_G.pth +3 -0
  10. ESRGAN/4x_Fatality_Comix_260000_G.pth +3 -0
  11. ESRGAN/4x_NMKD-Superscale-Artisoftject_210000_G.pth +3 -0
  12. ESRGAN/4x_NMKD-Superscale-SP_178000_G.pth +3 -0
  13. ESRGAN/4x_NMKD-UltraYandere_300k.pth +3 -0
  14. ESRGAN/4x_NMKD-YandereNeoXL_200k.pth +3 -0
  15. ESRGAN/4x_NMKDSuperscale_Artisoft_120000_G.pth +3 -0
  16. ESRGAN/4x_NickelbackFS_72000_G.pth +3 -0
  17. ESRGAN/4x_PBRify_RPLKSRd_V3.pth +3 -0
  18. ESRGAN/4x_RealisticRescaler_100000_G.pth +3 -0
  19. ESRGAN/4x_SmoothRealism_100000_G.pth +3 -0
  20. ESRGAN/4x_Valar_v1.pth +3 -0
  21. ESRGAN/4x_foolhardy_Remacri.pth +3 -0
  22. ESRGAN/8xPSNR.pth +3 -0
  23. ESRGAN/8x_NMKD-Superscale_150000_G.pth +3 -0
  24. ESRGAN/8x_NMKD-Typescale_175k.pth +3 -0
  25. ESRGAN/A_ESRGAN_Single.pth +3 -0
  26. ESRGAN/BSRGAN.pth +3 -0
  27. ESRGAN/BSRNet.pth +3 -0
  28. ESRGAN/WaifuGAN_v3_30000.pth +3 -0
  29. ESRGAN/lollypop.pth +3 -0
  30. LDSR/LDSR_model.ckpt +3 -0
  31. README.md +70 -0
  32. SwinIR/001_classicalSR_DF2K_s64w8_SwinIR-M_x2.pth +3 -0
  33. SwinIR/001_classicalSR_DF2K_s64w8_SwinIR-M_x3.pth +3 -0
  34. SwinIR/001_classicalSR_DF2K_s64w8_SwinIR-M_x4.pth +3 -0
  35. SwinIR/001_classicalSR_DF2K_s64w8_SwinIR-M_x8.pth +3 -0
  36. SwinIR/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_GAN-with-dict-keys-params-and-params_ema.pth +3 -0
  37. SwinIR/003_realSR_BSRGAN_DFO_s64w8_SwinIR-M_x2_GAN-with-dict-keys-params-and-params_ema.pth +3 -0
  38. SwinIR/Swin2SR_ClassicalSR_X2_64.pth +3 -0
  39. SwinIR/Swin2SR_ClassicalSR_X4_64.pth +3 -0
  40. SwinIR/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth +3 -0
  41. assets/white_tiger.jpg +3 -0
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+ # Ultimate Upscaler Models Collection
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+
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+ <div align="center">
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+ <img src="assets/white_tiger.jpg" alt="White Tiger - Upscaled with 4xNomos2_hq_dat2.pth" width="500px">
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+ <p><i>White Tiger - Upscaled with 4xNomos2_hq_dat2.pth</i></p>
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+ </div>
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+
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+ This repository contains a comprehensive collection of upscaler models organized by their primary purpose. The models are compatible with ComfyUI's Ultimate Upscaler node and other popular frameworks.
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+
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+ ## Directory Structure
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+
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+ | Directory | Description |
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+ |-----------|-------------|
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+ | ESRGAN/ | ESRGAN architecture models |
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+ | SwinIR/ | SwinIR architecture models |
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+ | LDSR/ | LDSR architecture models |
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+ | photo-realistic/ | Models optimized for photorealistic content |
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+ | anime-cartoon/ | Models for anime, manga, and cartoon content |
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+ | text-documents/ | Models for text and document upscaling |
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+ | special-purpose/ | Models for specific artistic styles |
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+ | general-purpose/ | Versatile models for various content types |
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+
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+ ## Using with ComfyUI Ultimate Upscaler
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+
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+ These models are compatible with ComfyUI's Ultimate Upscaler node. When using them:
26
+
27
+ 1. Select the appropriate model based on your content type
28
+ 2. Adjust the denoise strength based on the content:
29
+ - Lower (0.2-0.4) for preserving details, line art, and textures
30
+ - Moderate (0.4-0.6) for general content
31
+ - Higher (0.6-0.8) for smoother results and noise removal
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+ 3. Enable tile processing for large images with appropriate overlap (64-128 pixels)
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+
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+ ## Using with Hugging Face
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+
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+ ```python
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+ from huggingface_hub import hf_hub_download
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+ import torch
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+ from basicsr.archs.rrdbnet_arch import RRDBNet
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+ from PIL import Image
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+ import numpy as np
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+ import torchvision.transforms as transforms
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+
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+ # Download a model from this collection
45
+ model_path = hf_hub_download(
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+ repo_id="ABDALLALSWAITI/Upscalers",
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+ filename="ESRGAN/4xNomos2_hq_dat2.pth"
48
+ )
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+
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+ # Load the model (example for ESRGAN architecture)
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+ device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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+ model = RRDBNet(3, 3, 64, 23, gc=32)
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+ model.load_state_dict(torch.load(model_path), strict=True)
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+ model.eval()
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+ model = model.to(device)
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+
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+ # Load and preprocess image
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+ img = Image.open('input.jpg').convert('RGB')
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+ img = transforms.ToTensor()(img).unsqueeze(0).to(device)
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+
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+ # Upscale
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+ with torch.no_grad():
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+ output = model(img)
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+
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+ # Convert to image
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+ output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
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+ output = np.transpose(output[[2, 1, 0], :, :], (1, 2, 0)) * 255.0
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+ output = Image.fromarray(output.astype(np.uint8))
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+ output.save('output.jpg')
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+ ```
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