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pdmd_2NFE_lora

pdmd2026

This repository holds the **LoRA adapter** of a 2-step (2 NFE) student distilled from MiniMax-H3 with **Projected Distribution Matching Distillation (PDMD)**. PDMD projects the DMD update onto the subspace orthogonal to the student–critic endpoint residual; it is a one-line change to DMD with no auxiliary loss, extra network, extra model pass, or extra training stage. The adapter is applied to the transformer (`MiniMaxH3Transformer3DModel`) of the base model. Every other component (VAE, audio VAE, schedulers, text encoder, processor) is unchanged and comes from the base model. Tensor keys have the form `transformer. .lora_A.weight` / `transformer. .lora_B.weight`, where ` .weight` is the corresponding parameter of `MiniMaxH3Transformer3DModel`. Fusing rule (also stored in the safetensors metadata):

Video generation

Input Text→ Output Video

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pdmd_4NFE_full

pdmd2026

Full transformer weights (`MiniMaxH3Transformer3DModel`, bf16) of a 4-step (4 NFE) student distilled from MiniMax-H3 with **Projected Distribution Matching Distillation (PDMD)**. It is a drop-in replacement for the `transformer/` of the base model; every other component comes from the base model. The same model is also available as a LoRA in pdmd2026/pdmd_4NFE_lora. Sample with **4 denoising steps** using the base model's released scheduler configuration (shift 12 / 3).

Category pending

Input Not verified→ Output Not verified

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pdmd_4NFE_lora

pdmd2026

This repository holds the **LoRA adapter** of a 4-step (4 NFE) student distilled from MiniMax-H3 with **Projected Distribution Matching Distillation (PDMD)**. The same model is also available as full transformer weights in pdmd2026/pdmd_4NFE_full. The adapter is applied to the transformer (`MiniMaxH3Transformer3DModel`) of the base model. Every other component (VAE, audio VAE, schedulers, text encoder, processor) is unchanged and comes from the base model. Tensor keys have the form `transformer. .lora_A.weight` / `transformer. .lora_B.weight`, where ` .weight` is the corresponding parameter of `MiniMaxH3Transformer3DModel`. Fusing rule (also stored in the safetensors metadata):

Video generation

Input Text→ Output Video

Price not verifiedCheck multimodal and channel pricing separately

View details ↗

CompareSource / docs ↗

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Cost analysis

Adjust monthly token usage to compare estimated API costs.

Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
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Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.

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Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.

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Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.

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Explore AI models 3

pdmd_2NFE_lora

pdmd_4NFE_full

pdmd_4NFE_lora

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