Veda-Sparse/Minimax-H3-R2VA-Veda-Preview
text-to-video model by Veda-Sparse
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Model card & documentation
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Demo · Project Page · Code · Paper · Deployment Guide 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. are selected independently; text, VLM conditioning, and audio stay dense. 32 current-video tiles per query tile and head. backbone and Turbo LoRA weights. The examples below use the same seed, prompt, geometry, and Turbo LoRA for Dense and Veda. Each generated…
Read the full model card ↗ · Preview checked 2026-10-29T23:00:26.902Z
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Model overview
Veda-Sparse/Minimax-H3-R2VA-Veda-Preview is a text-to-video model repository published by Veda-Sparse on Hugging Face. The source reports the minimax-h3 library.
This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.
Model facts
- Task
- text-to-video
- Library
- minimax-h3
- Recent downloads (30 days)
- 24,126
- Cumulative likes
- 48
- Hugging Face trending score
- 2
- Reported safetensors parameters
- Not reported
- Architecture
- Not reported
- License
- other
- Access
- Not gated by Hugging Face
- Created
- 2026-10-07T04:49:55.000Z
- Last modified
- 2026-10-08T10:18:08.000Z
Compatibility and lineage
Language coverage was not reported.
Reported base models: MiniMaxAI/MiniMax-H3, larryvrh/MiniMax-H3-Turbo-Lora
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
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Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-11-01T06:51:32.159Z. Daily imports are snapshots, not real-time monitoring.
Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.
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