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MiniMaxMusicTraining/
soad-mm3-nextlat-xm-daron-continuation128-20260824-5k-adamw2e-5-bsz2-singersplit-reginst

Name: soad-mm3-nextlat-xm-daron-continuation128-20260824-5k-adamw2e-5-bsz2-singersplit-reginst
Availability: InStock

diffusers

minimaxmusic

minimaxmusic-diffusers

text-to-audio

audio

simpletuner

not-for-all-audiences

lora

sd-lora

standard

MiniMaxAI/MiniMax-Music3

adapter

apache-2.0

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MiniMaxMusicTraining/soad-mm3-nextlat-xm-daron-continuation128-20260824-5k-adamw2e-5-bsz2-singersplit-reginst

This is a PEFT LoRA derived from MiniMaxAI/MiniMax-Music3.

Validation was disabled during training.

The text encoder was not trained. You may reuse the base model text encoder for inference.

Training settings

Training epochs: 833
Training steps: 5000
Learning rate: 2e-05
- Learning rate schedule: constant_with_warmup
- Warmup steps: 50
Max grad value: 0.5
Effective batch size: 2
- Micro-batch size: 2
- Gradient accumulation steps: 1
- Number of GPUs: 1
Gradient checkpointing: True
Prediction type: autoregressive_next_token
Optimizer: adamw_bf16
Trainable parameter precision: Pure BF16
Base model precision: no_change
Caption dropout probability: 0.0%
LoRA Rank: 64
LoRA Alpha: None
LoRA Dropout: 0.1
LoRA initialisation style: default
LoRA mode: Standard

Training modes

MiniMax Music train component: language_model (global LM / RVQ planner)
MiniMax Music LM max frames: 128
MiniMax Music LM window mode: continuation
NextLat: Enabled
- Block index: -1
- Weight: 0.1
- State loss: smooth_l1
- KL weight: 0.0
XM: Enabled
- Candidate count: 2
- Selection scope: block
- Training target: route
- Block size: 16

Datasets

soad-mm3-nextlat-xm-daron-continuation128-20260824-5k-adamw2e-5-bsz2-singersplit-reginst-vocals

Repeats: 0
Total number of audio files: 6
Duration buckets: audio (6)
Used for regularisation data: No

soad-mm3-nextlat-xm-daron-continuation128-20260824-5k-adamw2e-5-bsz2-singersplit-reginst-instrumental-regularisation

Repeats: 0
Total number of audio files: 6
Duration buckets: audio (6)
Used for regularisation data: Yes

Inference

python

import torch
from diffusers import DiffusionPipeline

model_id = 'MiniMaxAI/MiniMax-Music3'
adapter_id = 'MiniMaxMusicTraining/soad-mm3-nextlat-xm-daron-continuation128-20260824-5k-adamw2e-5-bsz2-singersplit-reginst'
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
pipeline.load_lora_weights(adapter_id)

prompt = "An astronaut is riding a horse through the jungles of Thailand."
negative_prompt = 'blurry, cropped, ugly'

## Optional: quantise the model to save on vram.
## Note: The model was not quantised during training, so it is not necessary to quantise it during inference time.
#from optimum.quanto import quantize, freeze, qint8
#quantize(pipeline.transformer, weights=qint8)
#freeze(pipeline.transformer)
    
pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') # the pipeline is already in its target precision level
model_output = pipeline(
    prompt=prompt,
    negative_prompt=negative_prompt,
    num_inference_steps=30,
    generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(42),
    width=256,
    height=256,
    guidance_scale=7.5,
).images[0]

model_output.save("output.png", format="PNG")