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name: convolutional_spatial_sampling description: Compute and validate LDM spatial latent-grid sampling plans for high-resolution and dense conditional tasks.

Convolutional Spatial Sampling

Use this skill when an LDM task needs image-to-latent spatial sizing, dense-conditioning alignment, or bounded sampler metadata. It is useful for text-to-image, inpainting, super-resolution, and recovery planning. Do not use it to compute denoising loss or claim image quality.

Inputs

• Requested image height and width.
• Autoencoder downsampling factor.
• Optional dense conditioning shape such as mask or low-resolution image dimensions.
• Sampler step count and stochasticity setting.

Outputs

• Latent grid dimensions.
• Validity status and errors for non-divisible dimensions.
• Conditioning alignment status.
• Bounded sampler plan metadata and warnings.

Workflow

1. Validate positive image dimensions and downsampling factor.
2. Compute the latent grid only when dimensions divide cleanly by the factor.
3. Check dense conditioning shapes against image-space or latent-space alignment.
4. Bound sampler step counts for recovery experiments.
5. Return a plan that downstream recovery can cite without reading the original repo.

Validation

Run python scripts/spatial_plan.py --height 256 --width 512 --factor 8 --steps 20 --output /tmp/spatial.json. Tests cover valid grids, divisibility errors, mask alignment, and bounded-step warnings.

Limitations

This skill validates spatial contracts only. It does not run DDIM/PLMS sampling, load UNet weights, or evaluate generated images.