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Add Ultimate SD Upscale pipeline for high-quality tiled image upscaling #9740

@BasimBashir

Description

@BasimBashir

Is your feature request related to a problem? Please describe.
Currently, diffusers library lacks advanced tiled upscaling capabilities that are available in other Stable Diffusion implementations. While the library supports basic img2img and upscaling, there's no built-in solution for handling large images through intelligent tiling and seam fixing. This makes it difficult to process high-resolution images while maintaining quality and managing memory efficiently.

Describe the solution you'd like
Implement Ultimate SD Upscale functionality (similar to Automatic1111's WebUI extension) as a pipeline in diffusers. Key features should include:

  1. Progressive upscaling with intelligent scale factor determination
  2. Multiple tiling modes:
    • Linear processing
    • Chess pattern processing
  3. Advanced seam fixing options:
    • Band pass mode
    • Half tile offset
    • Half tile with intersections
  4. Configurable parameters:
    • Tile sizes
    • Padding
    • Mask blur
    • Denoise strength for seam fixing

The implementation should integrate smoothly with existing diffusers pipelines and maintain the library's user-friendly API style.

Describe alternatives you've considered

  • Using basic tiling without seam fixing (leads to visible artifacts)
  • Running multiple separate upscale passes (inefficient and lower quality)
  • Implementing as a separate package (loses benefits of diffusers' optimization and integration)
  • Using other libraries like PIL or cv2 for tiling (lacks SD-specific optimizations)

Additional context

  • Reference implementation: https://github.com/Coyote-A/ultimate-upscale-for-automatic1111
  • This feature would be particularly valuable for:
    • Professional image upscaling
    • Batch processing of large images
    • Creating high-resolution outputs while managing VRAM
    • Maintaining image quality in tiled processing
  • Could potentially be implemented as either a standalone pipeline or an enhancement to existing img2img pipelines
  • Would complement existing super-resolution models in the library

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