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

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: constantwithwarmup
• —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: autoregressivenexttoken

• —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

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")