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Turn any text, image, or video into anime, realistic, or artistic videos. Over 30 unique styles available.
Yes—MiniMax H3 is publicly available as an open-weight model. However, the full H3 system is not completely open or unrestricted. MiniMax released the H3-Base FL2VA and Ref2VA checkpoints under a custom Community License. Context-IR remains hosted, while Regenerate-2K is absent from the current public release.
MiniMax calls H3 an open model. For technical and commercial decisions, open weight is the more precise description.
The distinction matters. Public weights let developers inspect, run, modify, and integrate released model components under the license. They do not automatically provide the complete training process, every production module, or unrestricted usage rights.
MiniMax launched H3 on July 31, 2026. It released H3-Base weights on August 2. The files appear in the official H3 model repository and GitHub repository.
Here is what the release includes and what remains outside the downloadable package:
This is a meaningful release. It is also narrower than the phrase “fully open source” suggests.
MiniMax released two task-specific H3-Base checkpoints. Both generate video and native stereo audio, but they accept different forms of control.
FL2VA stands for first-and-last-frame-to-audio-video. With no image, it handles text-to-audio-video. One image can define the first or last frame. Two images can define both endpoints.
Ref2VA stands for reference-to-audio-video. It lets images, video clips, and optional audio guide different parts of one target clip. Reference audio must accompany at least one image or video. The text explains what each reference should contribute.
The official repository distributes the original checkpoint families and Diffusers-compatible files. MiniMax documents BF16 precision for the original checkpoints. It also describes the released Omni Transformer weights as CFG-distilled.
Each checkpoint is more than one transformer file. The official H3 model card lists a specialized Omni Transformer with its supporting components:
MiniMax describes H3-Omni-Transformer as a dense, single-stream model. It predicts video and audio latents together. The corresponding VAEs then decode those latents into the final audiovisual result.
The FL2VA and Ref2VA weights are separate because their jobs differ. Downloading one task family does not silently add the other task family.
The complete official system contains three modules. Only the middle generation module has public weights today.
Free-form text, image, video, and audio context
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Context-IR — hosted; implementation not public
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H3-Base FL2VA or Ref2VA — public weights; local 768p path
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Regenerate-2K — absent from the current public release
Context-IR handles preprocessing and orchestration for free-form multimodal context before H3-Base generation.
MiniMax describes Context-IR as a multi-stage hosted system. Its implementation is not part of the open-weight release. Developers can call the official service or build their own preprocessing logic from MiniMax's prompting guidance.
An independent preprocessing pipeline may serve the same broad purpose. It does not become the official Context-IR implementation merely because it formats references for H3-Base.
H3-Base receives the prepared context and generates 768p video with stereo audio. This is the part that MiniMax released as FL2VA and Ref2VA checkpoints.
The initial public inference path uses full attention. MiniMax says it plans to publish its sparse-attention implementation later. It has not given a release date.
Regenerate-2K takes the 768p result and the original context back through H3. That design differs from a conventional standalone upscaler.
The module is not in the current public release. MiniMax documents an official hybrid workflow that combines local H3-Base with hosted Context-IR and regeneration endpoints.
That hybrid workflow can produce the official 2K path. It is not a fully local, open-weight 2K pipeline.
Running H3-Base locally gives you direct control over the two released generation checkpoints, but it does not reproduce MiniMax's complete hosted H3 system.
Choose FL2VA when you want to generate from text or guide a clip with a first frame, last frame, or both. Choose Ref2VA when you want images, video, and optional audio to guide identity, style, motion, camera behavior, voice, or sound. Both checkpoints generate audiovisual output at a documented 768p; the complete 2K path still depends on hosted components outside the open-weight package.
MiniMax lists SGLang, vLLM, Diffusers, and ComfyUI as inference options. These provide code-based and node-based ways to run or experiment with the released checkpoints.
Whichever route you choose, self-hosting transfers the operational work to you. You must manage model storage, dependencies, compute, performance, updates, access controls, safeguards, and output review. Local deployment is therefore neither automatically free nor automatically private: costs include hardware or hosting, electricity, engineering time, storage, monitoring, and failed runs, while privacy depends on your logs, asset storage, access policies, third-party nodes, and surrounding application.
H3 customization has three layers. The first two are available without changing model weights. The third depends on engineering evidence and license review.
Start by changing the input plan, not the weights. Separate identity, motion, camera, and audio references. Give each source one named job. Keep the target action narrow enough to review.
For FL2VA, decide whether the opening composition, ending composition, or both matter. For Ref2VA, decide which source controls each creative variable.
These choices can change the result substantially. They also remain ordinary inference choices, not model fine-tuning.
A team can build asset validation, prompt assembly, queues, storage, moderation, evaluation, and retry logic around the released checkpoints. Node-based tools also let teams expose selected inference settings to creators.
This layer often delivers more practical control than training. It can standardize inputs, collect comparable outputs, and prevent invalid requests before expensive inference begins.
However, a custom preprocessor is not Context-IR. A community workflow is not an official performance promise. Document those boundaries in any internal or customer-facing system.
The repository says MiniMax released the complete H3-Omni-Transformer weights to support further development, including fine-tuning. The license also defines and permits Model Derivatives within its conditions.
That does not create a ready training workflow. MiniMax has not published an official H3 fine-tuning recipe, one-click LoRA trainer, training-data release, or verified minimum hardware path.
Treat community recipes as experiments. Record their source, version, data rights, output quality, and hardware assumptions before adopting them.
This comparison concerns operational responsibility and module availability, not a general guide to choosing an H3 access route.
The MiniMax H3 Community License controls the released materials. It is a custom license, not a standard permissive license such as MIT or Apache.
This summary is for general information and is not legal advice. Review the current license and obtain qualified advice for your situation.
The license defines its Applicable Territory as worldwide except four excluded areas: the European Union, United Kingdom, Republic of Korea, and United States.
Its permissions for the released H3 materials apply only within that territory. Organizations in excluded regions may apply to MiniMax for a formal license.
The territorial restriction also reaches H3 Works and their Outputs or results. The agreement does not authorize their use, reproduction, modification, distribution, or display outside the Applicable Territory.
MiniMax's official license Q&A describes the hosted API differently. It says the API is globally available with provider-operated safeguards.
Do not transfer the raw-weight territory rule to the hosted API without that distinction. API users must still follow the platform terms and applicable law.
The license says prior written authorization is required when commercial products and services generate more than US$20 million in yearly revenue. That wording does not simply mean total company revenue.
Commercial products or services using H3 or H3 Works must prominently display MiniMax H3 in their user interface. The separate phrase Powered by MiniMax H3 appears as encouraged wording, not the same mandatory display rule.
The threshold is not the only condition. Territory, acceptable use, safeguards, downstream terms, notices, and third-party rights also apply.
Distribution requires passing the agreement to recipients. Modified files need prominent modification notices. Non-hosted distributions also need the license's specified NOTICE file.
Hosted products must bind users to protective terms and operate reasonable safeguards. The acceptable-use policy also requires clear machine-generated disclosure for content placed in public environments.
MiniMax claims no rights over generated outputs under the license. Users remain responsible for outputs, later use, and third-party rights.
MiniMax does not publish a universal minimum GPU requirement for H3-Base in the model card. Its SGLang commands show a four-GPU deployment example, but an example is not a minimum.
The official repository also lists several runtimes and deployment approaches. Different frameworks, precision choices, quantizations, resolutions, and clip lengths change memory and speed.
The ComfyUI tutorial uses optimized model files and native nodes. It still does not establish one consumer GPU as an official baseline for every H3 workflow.
Before buying hardware, test the exact checkpoint and workflow you plan to run. Record software versions, precision, quantization, peak VRAM, generation time, and output quality.
Avoid turning one community benchmark into a universal requirement. Hardware claims age quickly as runtimes and quantizations change.
Running a checkpoint is only one part of adopting an open-weight model. Before you make H3-Base a production dependency, confirm the model scope, license conditions, and operational requirements for your specific use case:
MiniMax H3 interface display and distribution notices.The model files and license terms can change. Keep a copy of the exact versions used for each production decision.
No. H3-Base has public FL2VA and Ref2VA weights, but the complete system is not public. A custom Community License also limits how and where the released materials may be used.
You can download H3-Base FL2VA and H3-Base Ref2VA. FL2VA covers text and first/last-frame tasks. Ref2VA covers reference-led audiovisual generation.
Not through the current open-weight release alone. H3-Base produces 768p output. The official 2K path uses the hosted Regenerate-2K stage and the original generation context.
The released weights support permitted modification and derivative work. However, MiniMax has not published an official H3 fine-tuning recipe or one-click LoRA training workflow.
MiniMax does not state one universal minimum GPU. Its documented examples and supported runtimes are starting points, not guarantees for every checkpoint, precision, or video setting.
The license grants rights only within its Applicable Territory and subject to its conditions, including additional commercial obligations. Review the territory, revenue threshold, interface display, notices, safeguards, and acceptable-use duties before relying on that permission.
The default Community License for the weights excludes those territories, although organizations can apply for a formal license. MiniMax's official Q&A separately describes the hosted API as globally available with safeguards.
MiniMax H3's public weights create genuine room for local inference and permitted customization. They do not make every module, territory, or use unrestricted, so compare the operational and license burden before committing infrastructure.
MiniMax H3 has been officially released, but it is not yet available in DomoAI. It is coming soon, and we’ll share an update once it becomes available in DomoAI.
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