96575cebf5
Nine review findings on the SDXL training dialog: - Forward the saved Hub token so a gated/private SDXL base can be trained (the image load flow already sends it). - Re-seed the base-model field from the current default each time the dialog opens; the keep-alive dialog otherwise kept its mount-time default after a model loaded. - Prefill from base_repo (the diffusers pipeline) rather than repo_id, which for a GGUF/single-file SDXL load is the checkpoint path from_pretrained can't open. - Add client-side validation of steps/rank/resolution/batch/learning-rate before the request. - Expose a precision selector (bf16/fp16/fp32) so non-bf16 GPUs can train from the UI, not only the API. - Gate the dialog on the active Images route (active && trainOpen) so switching tabs closes it and stops its polling. - Rescan the LoRA picker when a run completes, so a freshly-trained adapter appears without a model reload. - Cap the dialog height and scroll the body so the Start/Stop footer stays reachable on short viewports. - Correct the copy to not over-promise picker auto-discovery. Freeing the resident Images pipeline before training is handled backend-side in the diffusion training start route. |
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