MiniMAX-H3 x Z-Image: A hybrid video model with spatial attention implantation It combines the MiniMAX H3 video engine and the Z-Image spatial detail architecture (Lumina2 6B) to create video distributed by text and images. The development solves the chronic disease of open video networks of diffusion: the loss of microtextures, the washing of the fortresses and the accumulation of objects when creating scenes. While video models usually smooth small surfaces, the static Z-image model has exceptional sharpness and the ability to draw complex textures: folded paint, rust, roughness of fabric and shine on water. Instead of additional expensive training, the researchers used the technique of grafting an attention profile (detail and graft details): the size of spatial attention Z-image is transferred in the minimax H3 blocks by scaling the normalization weight q norm without changing the weights K and feed forward layers. In the new hybrid edition, the structure is divided by block at the level of the original safety restraints: - Blocks B00-B24: FL2VA branch, responsible for stable kinematics and movement trajectories; - Blocks B25-B49: REF2VA with Z-Images for maximum density; B50+ blocks: back to FLVA final frame. In comparative tests of continuous cloaks from three scenes, the hybrid holds a stable average of high endurance parts (0.99 vs. 1.11 for stock H3, where excesses are accumulated). The model is available for open access under Apache 2.0 in BF16 (37.46 GB) and INT8 formats, and for the ComfiUI ecosystem, GGUF Assemblies (Q4 0, Q5 1, Q8 0) are ready, which works 4-8 times faster on RTX 30 / 40 graphics cards and fits in 16-24 GB VRAM. At the end: • Hybrid assembly: B00-B24 (FL2VA) + B25-B49 (REF2VA Z-Image) + B50+ (FL2VA) • Spatial details ft using Norma Q weight rating.

MiniMAX-H3 x Z-Image: A hybrid video model with spatial attention implantation It combines the MiniMAX H3 video engine and the Z-Image spatial detail architecture (Lumina2 6B) to create video distributed by text and images. The development solves the chronic disease of open video networks of diffusion: the loss of microtextures, the washing of the fortresses and the accumulation of objects when creating scenes. While video models usually smooth small surfaces, the static Z-image model has exceptional sharpness and the ability to draw complex textures: folded paint, rust, roughness of fabric and shine on water. Instead of additional expensive training, the researchers used the technique of grafting an attention profile (detail and graft details): the size of spatial attention Z-image is transferred in the minimax H3 blocks by scaling the normalization weight q norm without changing the weights K and feed forward layers. In the new hybrid edition, the structure is divided by block at the level of the original safety restraints: - Blocks B00-B24: FL2VA branch, responsible for stable kinematics and movement trajectories; - Blocks B25-B49: REF2VA with Z-Images for maximum density; B50+ blocks: back to FLVA final frame. In comparative tests of continuous cloaks from three scenes, the hybrid holds a stable average of high endurance parts (0.99 vs. 1.11 for stock H3, where excesses are accumulated). The model is available for open access under Apache 2.0 in BF16 (37.46 GB) and INT8 formats, and for the ComfiUI ecosystem, GGUF Assemblies (Q4 0, Q5 1, Q8 0) are ready, which works 4-8 times faster on RTX 30 / 40 graphics cards and fits in 16-24 GB VRAM. At the end: • Hybrid assembly: B00-B24 (FL2VA) + B25-B49 (REF2VA Z-Image) + B50+ (FL2VA) • Spatial details ft using Norma Q weight rating.