We now keep track of the original field type, derived from the python type annotation in addition to the override type provided by `ui_type`.
This makes `ui_type` work more like it sound like it should work - change the UI input component only.
Connection validation is extend to also check the original types. If there is any match between two fields' "final" or original types, we consider the connection valid.This change is backwards-compatible; there is no workflow migration needed.
When clearing the processor config, we shouldn't re-process the image. This logic wasn't handled correctly, but coincidentally the bug didn't cause a user-facing issue.
Without a config, we had a runtime error when trying to build the node for the processor graph and the listener failed.
So while we didn't re-process the image, it was because there was an error, not because the logic was correct.
Fix this by bailing if there is no image or config.
If you change the control model and the new model has the same default processor, we would still re-process the image, even if there was no need to do so.
With this change, if the image and processor config are unchanged, we bail out.
Graph, metadata and workflow all take stringified JSON only. This makes the API consistent and means we don't need to do a round-trip of pydantic parsing when handling this data.
It also prevents a failure mode where an uploaded image's metadata, workflow or graph are old and don't match the current schema.
As before, the frontend does strict validation and parsing when loading these values.
The previous super-minimal implementation had a major issue - the saved workflow didn't take into account batched field values. When generating with multiple iterations or dynamic prompts, the same workflow with the first prompt, seed, etc was stored in each image.
As a result, when the batch results in multiple queue items, only one of the images has the correct workflow - the others are mismatched.
To work around this, we can store the _graph_ in the image metadata (alongside the workflow, if generated via workflow editor). When loading a workflow from an image, we can choose to load the workflow or the graph, preferring the workflow.
Internally, we need to update images router image-saving services. The changes are minimal.
To avoid pydantic errors deserializing the graph, when we extract it from the image, we will leave it as stringified JSON and let the frontend's more sophisticated and flexible parsing handle it. The worklow is also changed to just return stringified JSON, so the API is consistent.
