So far, this release is an experiment on MiniMax H3 base model that combines learned patterns from two other video diffusion models — LTX 2.3 and Wan 2.2, and in test3 and later, one image diffusion model — Krea 2 — into H3's transformer architecture.
H3's transformer is organized into 50 blocks, each containing self-attention and feed-forward layers that process video, audio, and text together in a single unified stream. This work modified those attention and feed-forward layers in specific blocks, blending in the corresponding weights from LTX 2.3, Wan 2.2, and Krea 2. Nothing outside those layers was touched — H3's input processing, output projections, timestep modulation, and all the machinery that decides how to route different modalities remained fully intact and original.
The LTX pass was applied first to H3's front blocks, followed by a Wan pass targeting H3's middle-to-back blocks. Each pass modified attention weights directly; the second pass also modified the feed-forward layers in a broader block range. Together, these two video-donor passes demonstrated that completely different video model architectures could each contribute their own recognizable character into H3.
A third pass then brought in Krea 2 — an image diffusion model rather than a video model. This was a more speculative direction, since image models learn spatial relationships within a single frame while video models learn spatiotemporal relationships across many frames. Krea's contribution was scoped narrowly to the parts of its attention structure that carry that spatial learning: the query projection (which encodes what visual features to attend to) and, in a smaller number of positions, the paired key and value projections (which encode how those features relate). The feed-forward layers were partially transferred as well, but only the input side of Krea's gated MLP — the output projection was skipped because Krea's combining logic was trained for an image-model output pathway that doesn't match H3's video processing. This narrower scope let Krea contribute finer visual character and detail work without disrupting H3's temporal coherence.
MiniMax's own architecture documentation notes that H3's attention and feed-forward layers do not contain modality-specific structure — the parts of the model that handle "this is audio vs video vs text" live elsewhere in the model. That means the attention and feed-forward weights are the architecturally-appropriate location to transfer character from other trained models without disrupting how H3 handles each modality. This applies equally to same-domain video donors and to cross-domain image donors, though cross-domain transfer requires more careful scoping since not every part of an image model's learned patterns has a coherent home in a video model.
The modifications use orthogonal projection math — new weight patterns are added into H3 primarily in directions the base model wasn't already using. This lets the transferred character supplement H3 rather than override it. For the cross-domain Krea pass, this was especially important: Krea's patterns and H3's patterns turned out to be nearly perpendicular in weight space, giving orthogonal projection maximum room to work while preserving H3's own directions almost entirely.
Combined with careful control over which blocks are affected — and, for Krea specifically, careful control over which attention head positions receive the injection to avoid disturbing the head slots most involved in audio processing — this preserves H3's core capability to produce coherent video with audio while shifting its aesthetic and motion character toward the donor models. The model's actual block weights, safety features, and guardrails remain unmodified in the architectural sense — every modification is a controlled additive perturbation of existing weights, not a replacement of the model's own structure.
The standard H3 community license applies. Because this release now carries transferred character from LTX 2.3, Wan 2.2, and Krea 2, the community licenses for those source models apply as well to the portions of character that came from each.