MiniMax H3 团队公布开源路线图：计划推出 Regenerate-2K 与统一图像模型

## Model Architecture ### H3-Context-IR H3-Context-IR is a hosted preprocessing and orchestration system designed for free-form multimodal inputs. It interprets the relationships among text, images, audio, and reference videos, as well as how these materials relate to the intended generation output. Its internal workflow includes instruction parsing, cross-modal association, temporal understanding, and complex logical reasoning. H3-Context-IR serializes its understanding of the context into a structured representation accepted by H3-Base. Without deviating from the user’s original intent, it may also supplement missing or underspecified semantic details where appropriate. [...] ## System Overview MiniMax H3 is a general-purpose, omni-modal generative system. It supports unified understanding of multimodal contexts composed of text, images, video, and audio, and can generate video with native stereo audio at resolutions up to 2K and durations of up to 15 seconds. Thanks to its task

# MiniMax H3 ## System Overview MiniMax H3 is a general-purpose, omni-modal generative system. It supports unified understanding of multimodal contexts composed of text, images, video, and audio, and can generate video with native stereo audio at resolutions up to 2K and durations of up to 15 seconds. Thanks to its task-generalization-oriented system design, H3 already possesses broad multimodal context understanding and generation capabilities at the pre-training stage, enabling outstanding performance in following complex multimodal instructions. H3 supports the following input and output specifications: [...] ## Online API Use MiniMax-H3 directly via API. Global: platform.minimax.io | CN: platform.minimaxi.com ## Online App Use MiniMax-H3 directly via App. WebApp Global: hailuoai.video | CN: hailuoai.com Desktop Global: hub.minimax.io | CN: hub.minimaxi.com ## Model Architecture ### H3-Context-IR H3-Context-IR is a hosted preprocessing and orchestration system designed

Skip to main contentAMA: MiniMax H3 Team — Ask us anything about our open video generation model, training, and future plans : r/StableDiffusion Open menu Open navigation u/Affectionate-War8374 -> Luigi (H3 Researcher) u/MM_Nero_H3 -> Nero (H3 Researcher) u/Kiro_Song -> Kiro (H3 Researcher) u/New_Estimate9277 -> Reynor (H3 system engineer) u/ryan85127704 - >Ryanlee (Head of Devrel) We are the MiniMax team behind MiniMax-H3. We’re here to answer your questions, including: Model architecture and training Video generation capabilities Image-to-video and reference-based generation Inference and optimization Future plans Ask us anything — we’d love to hear your feedback and discuss with the community! Coming Up Live AMA Finished·9 hr. ago Share [...] This is really terrifying - Minimax H3Image 32 r/StableDiffusion•5d ago ### This is really terrifying - Minimax H3 Image 33: r/StableDiffusion - This is really terrifying - Minimax H30:07 62

What did not ship: H3-Context-IR and H3-Regenerate-2K. MiniMax's full system has three layers. H3-Context-IR is the hosted preprocessing and orchestration layer that parses free-form multimodal inputs — images, video clips, audio tracks, text — into a structured intermediate representation the generator can follow. H3-Regenerate-2K is the in-context regeneration pass that takes the 768p base output and reprocesses it at 2K resolution, using the original multimodal context to recover fine details that standard upscaling would lose. Both modules remain hosted API services. For the flagship advertised use case — native 2K generation — the local weights are the middle step of a workflow that still requires two MiniMax API calls. Those API calls route through infrastructure subject to China's

NEXT VIDEO — ARCHITECTURE DEEP DIVE: MiniMax scheduled the H3 model release for August 3, 2026 at 00:00 China Standard Time. After the configuration, weight index, model code, and tensor files become publicly accessible and can be verified, the next video will reverse-engineer the real H3 architecture: layer structure, parameter distribution, attention design, VAE latent geometry, and inference requirements. Publication will follow verification of the released files. updated: [...] instead asks its conditional base model to regenerate the high-resolution output from both Y low and the original multimodal context. Formally, compare S phi of Y low with P theta of Y high given Y low in context. The second expression keeps prompt references and other conditioning evidence load-bearing while reusing generation capability already learned by the base model. Minimax says contextual regeneration can recover small text and fine detail that context-free super resolution must guess, but i

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