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Minimax-H3-R2VA-Veda-Preview

Veda-Sparse

**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.

Video generation

Input Text→ Output Video

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Minimax-H3-T2VA-Veda-8NFE-600Step-Preview

Veda-Sparse

Don't just take our samples for it. Try your own prompts in the online demo. Available as a custom node on the Comfy Registry; see ComfyUI below. This checkpoint applies to **all generation modalities** supported by MiniMax-H3, including **T2VA, FL2VA, and R2VA**, as well as to **arbitrary numbers of diffusion steps**. The T2VA and 8NFE tags in the repository name only denote the training configuration and do not restrict where the predictor can be used. A dedicated R2VA fine-tuned checkpoint with further improved quality will be released in the near future.

Video generation

Input Text→ Output Video

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Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
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