Model
AUDIT

InstructME
M2UGen v1
M2UGen v2

FADvgg↓ KL↓
6.267
2.855
2.442
6.018
5.654
2.223
2.191
5.118

LSD↓
0.987
0.846
0.790
0.735

Table 3 illustrates the superior performance of our
M2UGen model compared to the AUDIT and InstructME
models. This achievement can be attributed to the utiliza-
tion of the LLaMA 2 model for comprehending prompts
in the editing task. This allows the model to effectively
edit music based on natural language prompts. Further-
more, the use of the MERT Encoder enhances the model’s
capability to better understand the source music, conse-
quently improving its effectiveness during the editing pro-
cess.

Table 4: Comparison of models for image-to-music
generation. The best values of different metrics are made
bold.

Model
CoDi

M2UGen v1
M2UGen v2

FADvgg↓ KL↓
9.925
10.788
6.014
7.326
6.968
5.878

IB Rank↑

0.493
0.688
0.819

Table 5: Comparison of models for video-to-music gen-
eration. The best values of different metrics are made
bold.

Model
CoDi
CMT
M2UGen