* feat(openapi): add optional description field to workspace API key schemas
Add an optional `description` property (type: string) to three
workspace API key schemas in openapi.yaml:
- Inline request body of createWorkspaceApiKey (POST /api/workspace/api-keys)
- WorkspaceApiKey (list/info schema)
- WorkspaceApiKeyCreated (creation response schema)
The field is not added to any `required` array, making it fully
backward-compatible with existing clients.
Refs: BE-1005, BE-1004
Co-authored-by: Matt Miller <mattmillerai@users.noreply.github.com>
* fix(openapi): mark description nullable in workspace API key response schemas
Per CodeRabbit review on PR #13993: the underlying DB column is nullable
varchar (default ''), so the response schemas should permit null to match
stored data reality. Without nullable: true the OpenAPI contract would
require coercion on the handler side or risk a contract violation.
Request schema unchanged — clients shouldn't be sending null on create.
Per CodeRabbit follow-up on #13991: bool is a subclass of int in Python,
so isinstance(True, int) is True. The previous strict-int gate would
have accepted width=True (truthy + > 0) as a valid dimension.
Realistic occurrence is low (extract_image_dimensions returns proper
ints, JSON doesn't serialize bools as numbers), but the validation gate
exists for defense-in-depth so it should be actually strict.
Per CodeRabbit review on #13991: the previous loop accepted any sibling
with `kind == "image"` and copied whichever dimension keys happened to
be present, then returned. A partial sibling (kind set but missing or
invalid width/height) could persist incomplete metadata onto the new
reference even when a later sibling had valid dimensions.
Now we validate that the sibling has both width and height as positive
integers before adopting its dimensions, and continue scanning to the
next sibling otherwise.
Image assets now carry width/height under the existing `metadata` field on
asset responses, shaped as `{"kind": "image", "width": W, "height": H}`.
This lets consumers get original dimensions (e.g. for clients that render
server-side thumbnails and can't recover them from naturalWidth/Height)
without an extra round-trip.
Dimensions are written to AssetReference.system_metadata across three
ingest paths:
- Direct file ingest (upload, in-place registration): Pillow reads the
image header right after hashing, while the file is still in OS page
cache. Non-image MIME types are skipped without touching the file.
- From-hash registration: this path never reads the file bytes, so
dimensions are best-effort copied from any prior sibling reference of
the same asset that already carries kind=image metadata. Missing
siblings, non-image siblings, or absent dimension keys leave the new
reference's metadata unchanged.
- Scanner enrichment: extends the existing system_metadata write in
enrich_asset so scanner-registered images get the same treatment as
uploaded ones.
Existing system_metadata keys (e.g. safetensors fields written by the
enricher, download provenance) are preserved through merge. Existing
assets ingested before this change retain their current metadata — no
automatic backfill in this PR.
Tests cover image emission, non-image no-op, merge preservation, and the
from-hash sibling back-fill (including the no-sibling and non-image-sibling
cases).
These two fields were added recently to the Asset schema as nullable
integers, with the intent of exposing original image dimensions for FE
consumers (cloud-side thumbnailing makes naturalWidth/Height return
the wrong size for an image card's dimension label).
The implementation effort that consumes them subsequently converged on
a different shape — dimensions nested under the existing free-form
`metadata` JSON field as `{kind: "image", width, height}` — to avoid
introducing type-specific flat fields on the canonical Asset shape,
and to leave room for forward-compatible additions (video duration,
fps, etc.) without further schema churn.
This removes the now-unused top-level fields so the spec reflects the
agreed direction. No other schema definitions reference these fields
directly: AssetCreated, AssetUpdated, etc. inherit Asset via allOf and
do not redefine them.
The runtime ingest implementation that would have populated these
fields was not yet shipped, so no clients are relying on the
top-level shape.
Co-authored-by: Alexis Rolland <alexisrolland@hotmail.com>
Mark the uploadMask operation as deprecated and point clients at
/api/upload/image. The mask-compositing behavior the endpoint provides
(alpha-compositing the supplied mask onto an original_ref image) is now
expected to happen client-side, with the composited result uploaded
through the unified /api/upload/image path.
The endpoint continues to function for older clients; no runtime
behavior changes ship with this commit. Only the OpenAPI annotation
and the human-facing description are updated.
* Move detection category under image category
* Add missing categories
* Move detection nodes to detection category
* Move save nodes to image root catefory
* Rename postprocessors
* Move mask category under image
* Move guiders category to parent level at root of sampling category
* Move custom_sampling category to parent level at the root of sampling category
* Modify description of LoRA loaders
* Fix node id SolidMask
* Move VOID Quadmask under image/mask
* Group compositing nodes under image/compositing
* Move load image as mask to image category for consistency with other load image nodes
* Align display name with Load Checkpoint
* Move dataset category under training category
* Rename Number Convert to Conver Number (verb first)
* Rename Canny node
* Revert wanBlockSwap + description
* Add description to RemoveBackground node
* Revert category update of dataset
Split GLB save logic out of nodes_hunyuan3d.py into a new nodes_save_3d.py, and extend the writer to support UVs, per-vertex colors, and embedded baseColor textures.
Extend the MESH type with optional uvs, vertex_colors, and texture fields so meshes can carry texture data through the graph.
Add pack_variable_mesh_batch / get_mesh_batch_item helpers and switch VoxelToMesh / VoxelToMeshBasic to use them so batches with differing vertex/face counts no longer fail at torch.stack.
