Fixed issue where batched inpainting (batch size > 1) wouldn't work because of mismatched tensor sizes. The 'already_decoded' decoded case should also be handled correctly (tested indirectly).

Organized the settings and UI of soft inpainting to allow for toggling the feature, and centralizes default values to reduce the amount of copy-pasta.

"Uncrop" the original denoised image for the composite step, fixing a "ValueError: Images do not match" *shudder*

# Conflicts:
#	modules/processing.py

Rewrote latent_blend() to use in-place operations and to aggressively "del" references with the intention of minimizing allocations and easing garbage collection.

Blend masks are now produced afterward, based on an estimate of the visual difference between the original and modified latent images. This should remove ghosting and clipping artifacts from masks, while preserving the details of largely unchanged content.

Added slider for detail preservation strength, removed largely needless offset parameter, changed labels in UI and for saving to/pasting data from PNG files.

Implements "scheduling" for blending of the original latents and a latent blending formula that preserves details in blend transition areas.

Removed conflicting step that replaces the softly inpainted latents with a naive blend with the original latents.

Removed code which forces the inpainting mask to be 0 or 1. Now fractional values (e.g. 0.5) are accepted.

json.dump(ensure_ascii=False)

improve json readability

Compatibility

Update Ruff to 0.1.6

Move exception_records related methods to errors.py

support HyperTile optimization