Text-to-Video
Diffusers
minimaxh3
minimaxh3-diffusers
image-to-video
simpletuner
Not-For-All-Audiences
lora
template:sd-lora
standard
Instructions to use SimpleTuner/minimaxh3-suno-reggae-rank128 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SimpleTuner/minimaxh3-suno-reggae-rank128 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("MiniMaxAI/MiniMax-H3", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("SimpleTuner/minimaxh3-suno-reggae-rank128") prompt = "<STYLE> roots reggae, warm analog dub production, laid-back one-drop drum groove, deep round bassline, skanking guitar upstrokes, organ bubble, spacious mix with tape echo and spring reverb, soulful male lead vocal, mid-tempo\n<LYRICS> [verse] Morning sun a rise pon di mountain top River run easy and di worries stop We carry good vibes from di country road Every likkle burden turn a lighter load\n[chorus] Lift up yuh heart now, sing it loud and clear One love a di message and di roots right here Drum and di bass dem a guide di way Sweet reggae music till di break of day" output = pipe(prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
Trained for 12 epochs and 1000 steps.
Browse filesTrained with datasets ['text-embeds', 'suno-reggae-audio']
Learning rate 5e-05 and batch size 1.
Training objective: flow matching.
Distillation method: h3_drift.
Distillation config: {"h3_drift": {"audio_weight": 1.0, "balance": "token", "loss_weight": 0.5, "sft_loss_weight": 1.0, "video_weight": 1.0}}
Base model: MiniMaxAI/MiniMax-H3
VAE: MiniMaxAI/MiniMax-H3
pytorch_lora_weights.safetensors
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