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…hDataset ControlNetDataset.__init__ constructs an internal DreamBoothDataset via positional arguments. PR #2309 inserted a new train_inpainting parameter into DreamBoothDataset.__init__ between prior_loss_weight and debug_dataset, but this delegate call was not updated, so every subsequent positional argument was shifted by one slot: - train_inpainting <- received debug_dataset (bool, type-correct but logically wrong: enables inpainting whenever debug mode is on) - debug_dataset <- received validation_split (float) - validation_split <- received validation_seed (int) - validation_seed <- received resize_interpolation (str) - resize_interpolation <- received skip_image_resolution (tuple) - skip_image_resolution <- defaulted to None (missing) This breaks ControlNet training entirely. Forward the dataset's own train_inpainting attribute to keep the delegate consistent.
Commit ff7945d (PR #2309) fixed the masks/masked_images batch shapes from [B,1,1,H,W]/[B,1,C,H,W] to [B,1,H,W]/[B,C,H,W] and replaced the per-item F.interpolate loop with a single batched call in fine_tune.py, train_db.py, train_network.py, and sdxl_train.py — but train_textual_inversion.py was missed. After ff7945d batch["masks"] is [B,1,H,W]; iterating over it yields [1,H,W] slices. F.interpolate on a 3D tensor with a 2-element size argument performs 1D interpolation and errors on the dimension mismatch, so textual-inversion inpainting training crashes on the first step. Replace the loop+stack with the same single-call form used by the other training scripts, and drop the now-redundant .reshape() workaround.
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Follow up for #2309.