Clarification on SLAT/PVC conditioner implementation vs. paper Figure 2

Hi, thanks for releasing ReconViaGen. I’m studying the training code and had a question about the local per-view condition used for SLAT Flow.

In the paper, Section 3.2 and Figure 2 seem to describe the SLAT/PVC path as using a Condition Net similar to the SS/global path: random/learnable per-view tokens are updated by cross-attention blocks over VGGT features, producing per-view token lists `T_k`. Figure 2 also shows per-view cross-attention in SLAT Flow followed by a learned/weighted fusion.

In the released code, the SLAT conditioner appears to be simpler:

- `ModulatedSLATMultiViewCond` in `trellis/models/structured_latent_flow.py` starts from DINO `image_cond`, not learned/random per-view query tokens.
- It applies four `Linear(ctx_channels -> channels) + ReLU` blocks over concatenated VGGT features and the current condition.
- I do not see self-attention or cross-attention blocks inside the SLAT conditioner, unlike the SS conditioner.
- In `trellis/modules/transformer/modulated.py`, when the SLAT DiT receives a list of per-view conditions, it seems to average per-view cross-attention outputs with `/ len(context)` rather than using a learned weighted fusion MLP as described in the paper.
- In the `v0.5` branch I noticed `fuse_blocks` are declared in `ModulatedSLATMultiViewCond`, but they do not appear to be used in `forward`.

Could you clarify whether this released implementation is the one used for the paper’s SLAT/PVC results? If so, was the simpler MLP-based SLAT conditioner chosen for stability, memory, or ease of training compared with the cross-attention Condition Net shown in the paper? Or is the paper diagram describing an earlier/internal variant?

I’m asking because the SS/global conditioner in code closely matches the paper, while the SLAT/PVC path looks architecturally different. Any explanation of the intended design tradeoff would be very helpful.

Read trellis/models/structured_latent_flow.py and trellis/modules/transformer/modulated.py, focusing on ModulatedSLATMultiViewCond, fuse_blocks, and per-view context fusion. Compare those paths with Section 3.2 and Figure 2 of the paper. Done means documenting whether the released SLAT/PVC implementation produced the reported results and explaining any intended architectural difference.

Written by the indexing model from the issue text.