Tiled Inference for High-Resolution Processing¶

Techniques for processing images larger than model input size by splitting into overlapping tiles, processing individually, and stitching back. Critical for production photography where originals are 5000-15000px.

Core Problem¶

Most models accept 512-1024px max. Production photography needs pixel-perfect results at 5000-15000px. Naive splitting → visible seams at tile boundaries, especially on: - Smooth gradients (skin, sky, metal surfaces) - Repeating patterns (textures, fabrics) - Fine details crossing tile boundaries (jewelry edges, hair)

SAHI (Slicing Aided Hyper Inference)¶

Standard tiled inference library for detection models (YOLO, etc.):

from sahi.predict import get_sliced_prediction
result = get_sliced_prediction(image, model, slice_height=640, slice_width=640, overlap_height_ratio=0.2, overlap_width_ratio=0.2)

Docs: docs.ultralytics.com/guides/sahi-tiled-inference/

Works for detection (merges bounding boxes across tiles). For generation/editing tasks, stitching is more complex.

Overlap-Based Stitching for Generation¶

From chat discussions (Vladimir's approach):

1. Take tiles with overlap (e.g., input 1000×1000, keep center 800×800)
2. For next tile, include edges from already-processed tiles as overlap
3. Model sees context from neighboring processed tiles
4. Crop to center, avoiding boundary artifacts

Gradient-Aware Blending¶

For smooth gradients (jewelry metal surfaces): - Linear blend in overlap zone: output = alpha * tile_A + (1-alpha) * tile_B - Poisson blending at seams (laplacian-based, preserves gradients) - Latent-space stitching (as in X Dub): average latents at boundaries, then decode

FLUX Kontext Diff-Merge Approach¶

flux-kontext-diff-merge detects changed regions in LAB color space and selectively merges only the diff back into original using Poisson blending. Applicable to tiled processing — process tile, merge only changed pixels.

Known Issues (from team experience)¶

Gradient discontinuity — smooth backgrounds show tile boundaries after processing. Mitigation: larger overlap, gradient-aware blending
Detail loss at boundaries — fine elements (gemstone edges, chain links) crossing tiles get corrupted. Mitigation: tile grid aligned to segmentation masks, process objects wholly
Color drift — cumulative color shift across many tiles. Mitigation: per-tile color correction against reference (as in X Dub's sliding window)
Segmentation across tiles — models like SAM/ZIM eat max ~1200px per side; ideal edges need tiled segmentation with post-merge. Preliminary full-image seg at lower res → refine edges with high-res tiles

Latent-Space Tiling (for diffusion models)¶

Alternative: tile in latent space (after VAE encode, before denoising):

Full image → VAE encode → latent (H/8 × W/8 × C)
Tile latents → denoise each tile with overlap
Stitch latents → VAE decode full

Advantage: VAE decode of full stitched latent produces globally coherent output. This is how X Dub handles long video.

Solar Curves (Edge Visualization)¶

Non-standard technique from retouching: apply solar curve (tone inversion at midtones) to reveal subtle edges invisible at normal viewing. Can be used as auxiliary input for segmentation models to improve edge detection on white-on-white or low-contrast boundaries.

# Solar curve: invert midtones to reveal hidden edges
# See solar-curves-saver.py, solar-1-effect.py, solar-2-effect.py (team scripts)

Discussed for: white jewelry on white background segmentation, metal edge detection, gradient quality verification after tiled processing.