MiniMax-H3 4-Step LoRA (FlashGen)
17
3 commits
1 linked in READMEs
updated Sep 1, 2026
README
MiniMax-H3 4-Step LoRA (FlashGen)
4-step distilled LoRA for MiniMax-H3 text-to-video+audio (T2VA). Trained on Ascend NPU with data-free distillation; merge into the base model and inject base_schedule before vllm-omni / MindIE-SD deployment.
Files
| File | Description |
|---|---|
minimax_h3_4step_lora_flashgen_v1.0_768p_bf16.safetensors | Student LoRA, rank 64, bf16 |
merge_lora_ckpt.py | Merge LoRA into base model and inject base_schedule |
Training config
| Item | Value |
|---|---|
| Method | Distribution matching distillation (VSD, no GAN), trained on Ascend NPU |
| Task | T2VA |
| Inference steps | 4 |
| Resolution | 1344×768, 5.2s |
| base_schedule | [1.0, 0.7, 0.4, 0.15, 0.0] |
| LoRA targets | qkv_proj, out_proj, fc1, fc2, adaln_proj.linear |
| LoRA rank | 64 |
Usage
Requires base model MiniMaxAI/MiniMax-H3.
Merge with merge_lora_ckpt.py before vllm-omni / MindIE deployment:
python merge_lora_ckpt.py \
${MODEL_ROOT}/FL2VA \
minimax_h3_4step_lora_flashgen_v1.0_768p_bf16.safetensors \
./minimax-h3-4step-flashgen
export MODEL=./minimax-h3-4step-flashgen/FL2VA
Deployment (MindIE-SD / vllm-omni)
For Ascend NPU inference and serving, see the MindIE-SD deployment guide:
- MiniMax-H3 推理部署文档（含 4 步蒸馏权重合并与启动）
- MindIE-SD 仓库：Ascend/MindIE-SD
Contributors
3 commits
Similar repos
Linked in READMEs
MiniMax-H3 4-Step LoRA (FlashGen)
17
3 commits
1 linked in READMEs
updated Sep 1, 2026
README
MiniMax-H3 4-Step LoRA (FlashGen)
4-step distilled LoRA for MiniMax-H3 text-to-video+audio (T2VA). Trained on Ascend NPU with data-free distillation; merge into the base model and inject base_schedule before vllm-omni / MindIE-SD deployment.
Files
| File | Description |
|---|---|
minimax_h3_4step_lora_flashgen_v1.0_768p_bf16.safetensors | Student LoRA, rank 64, bf16 |
merge_lora_ckpt.py | Merge LoRA into base model and inject base_schedule |
Training config
| Item | Value |
|---|---|
| Method | Distribution matching distillation (VSD, no GAN), trained on Ascend NPU |
| Task | T2VA |
| Inference steps | 4 |
| Resolution | 1344×768, 5.2s |
| base_schedule | [1.0, 0.7, 0.4, 0.15, 0.0] |
| LoRA targets | qkv_proj, out_proj, fc1, fc2, adaln_proj.linear |
| LoRA rank | 64 |
Usage
Requires base model MiniMaxAI/MiniMax-H3.
Merge with merge_lora_ckpt.py before vllm-omni / MindIE deployment:
python merge_lora_ckpt.py \
${MODEL_ROOT}/FL2VA \
minimax_h3_4step_lora_flashgen_v1.0_768p_bf16.safetensors \
./minimax-h3-4step-flashgen
export MODEL=./minimax-h3-4step-flashgen/FL2VA
Deployment (MindIE-SD / vllm-omni)
For Ascend NPU inference and serving, see the MindIE-SD deployment guide:
- MiniMax-H3 推理部署文档（含 4 步蒸馏权重合并与启动）
- MindIE-SD 仓库：Ascend/MindIE-SD
Similar repos
Linked in READMEs
Contributors
3 commits