Fix training with 5D latents in bucket and multi-res modes

TrainLoraNode crashes for any model whose VAE produces 5D latents
(Qwen Image / Krea 2 and other video-style [B, C, T, H, W] VAEs) when
bucket_mode is enabled or the dataset has mixed resolutions:

    RuntimeError: Number of dimensions of repeat dims can not be
    smaller than number of dimensions of tensor

Both dummy-latent constructions in _run_training_loop hardcode a
4D .repeat(num_images, 1, 1, 1). Repeat along the batch dim only,
keeping every remaining dim, so the guider dummy works for latents
of any rank. The train steps themselves (standard/bucket/multi-res)
already index only the batch dim, so no other change is needed.

Verified on Krea 2 RAW fp8 (Qwen Image VAE, 5D latents) with a
14-image mixed-resolution dataset: both ResolutionBucket+bucket_mode
and the multi-res fallback path now train to completion; 4D SD/SDXL
behavior is unchanged (repeat expansion is identical for rank 4).

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_011ruwJZq4Tv7rVZa7pz7cqS

comfy_extras/nodes_train.py

@@ -923,7 +923,7 @@ def _run_training_loop(
 
     if bucket_mode:
         # Use first bucket's first latent as dummy for guider
-        dummy_latent = latents[0][:1].repeat(num_images, 1, 1, 1)
+        dummy_latent = latents[0][:1].repeat(num_images, *([1] * (latents[0].dim() - 1)))
         guider.sample(
             noise.generate_noise({"samples": dummy_latent}),
             dummy_latent,
@@ -933,7 +933,7 @@ def _run_training_loop(
         )
     elif multi_res:
         # use first latent as dummy latent if multi_res
-        latents = latents[0].repeat(num_images, 1, 1, 1)
+        latents = latents[0].repeat(num_images, *([1] * (latents[0].dim() - 1)))
         guider.sample(
             noise.generate_noise({"samples": latents}),
             latents,