Model overview
**Veda** is a learned sparse-attention method for video diffusion models. This checkpoint brings Veda to MiniMax-H3 reference-to-audio-video generation. It selects visual reference and generated video tiles independently; text, VLM conditioning, and audio attention remain dense. The predictor is not a LoRA and leaves the backbone weights unchanged. This preview was fine-tuned for 600 updates from the T2VA predictor on 5.17-second R2VA clips. It uses the MiniMax-H3 Turbo LoRA for eight-step generation, with up to 32 visual-reference tiles and 32 current-video tiles per query tile and head. The predictor is stored in FP8 and dequantized to BF16 when loaded. - **Learned sparse attention.** Visual reference and generated-video attention are selected independently; text, VLM conditioning, and audio stay dense. - **Fixed visual budgets.** The released preview uses 32 reference tiles and 32 current-video tiles per query tile and head. - **Plug-and-play predictor.** The FP8 predictor is separate from the H3 backbone and Turbo LoRA weights.
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
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Source prices & cost analysis
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Estimate monthly base token costs
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Public benchmarks & run configuration
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Sources & traceability
许可证：other。模型介绍与第三方评测均以原来源的具体版本和方法为准。