MERIC — Unified Multimodal Music Generation and Retrieval

MERIC generates music from images, text, and video — and retrieves music for the same inputs — within a single framework, built around a music semantic anchor (the MuQ embedding space) that decouples multimodal understanding from acoustic synthesis.

• Stage 1 maps any modality into the anchor space: image/text → Qwen3-VL embedding [2048] (primary) or CLIP ViT-H-14 [1024] (alternative) → a "Music Head" (RDM diffusion, ~20 steps) → MuQ [512].
• Stage 2 renders the anchor into 44.1 kHz audio: MuQ [512] → DiT + Flow Matching (~50 steps) → Oobleck VAE → audio.

Decoupling the two stages lets Stage 2 train on large unpaired music corpora, model the inherent one-to-many nature of cross-modal generation by sampling in anchor space, and serve both generation and retrieval from one model.

Code: github.com/TODO/meric · Paper: MERIC: Unified Multimodal Music Generation and Retrieval via a Music Semantic Anchor (ECCV 2026)

Files in this repository

File	meric model name	Description
rdm_sft_v3.pth	meric-sft-v3	Paper model "Meric" — Stage-1 Music Head, Qwen3-VL backbone, ARIA-finetuned (squaredcos + v-prediction + EMA + min-SNR).
rdm_sft_instrumental.pth	meric-instrumental	Stage-1 Music Head trained on vocal-filtered data — cleaner instrumental output.
mericldm.ckpt	(shared Stage-2)	The Stage-2 Flow Decoder (MuQ → audio). Both Music Heads run through it.

The Stage-2 acoustic components it builds on (Stable Audio Open VAE/DiT, CLAP) are downloaded automatically from their public upstream repos at first use.

Usage

Install the package, then generate — weights download automatically on first use:

pip install git+https://github.com/TODO/meric.git

from meric import MericPipeline

pipe = MericPipeline.from_pretrained("meric-sft-v3", device="cuda:0")
wavs = pipe.generate(image="photo.jpg", n=3, output_dir="out/")     # list of WAV paths
# also: pipe.generate(text="a calm piano melody with gentle rain")
# cleaner instrumental output: MericPipeline.from_pretrained("meric-instrumental")

Or from the command line:

meric generate --image photo.jpg -o out/            # image -> music
meric generate --text "lo-fi hip hop, 90 BPM" -o out/
meric models                                        # list available models

See the repository and docs/USAGE.md for the full guide.

Intended use & limitations

• Intended use: research and creative prototyping of music generation from visual/textual prompts, and cross-modal music retrieval.
• The Qwen3-VL backbone (used by both published heads) requires the external Qwen3-VL embedding environment for image/video inputs; see the repository setup guide.
• Generation is one-to-many and stochastic: different seeds yield different plausible scores for the same input. Use meric-instrumental when vocal-like artifacts are undesirable.
• Outputs are AI-generated audio and may reflect biases of the training data; not intended for use as production music without review.

Training data

The Stage-1 heads are fine-tuned on ARIA (Art-Referenced Instrumental Audio): source images (Unsplash + WikiArt) paired with model-generated captions and AI-generated instrumental music. Stage 2 is trained on large unpaired music corpora. ARIA's source images, captions, and audio carry their own upstream terms.

License & attribution

• The MERIC source code is licensed under Apache-2.0.
• These model weights are NOT Apache-2.0. The Stage-2 decoder derives from Stable Audio Open, so the released weights inherit the Stability AI Community License — a non-OSI license with use restrictions (including commercial-use conditions). Review it before any redistribution or commercial use.
• Built on: MuQ (music anchor encoder), Qwen3-VL (vision-language embeddings), OpenCLIP ViT-H-14, CLAP, and Stable Audio Open (VAE/DiT). Each carries its own upstream license — see the repository NOTICE.

Citation

@inproceedings{meric2026,
  title     = {MERIC: Unified Multimodal Music Generation and Retrieval via a Music Semantic Anchor},
  author    = {MERIC authors},
  booktitle = {European Conference on Computer Vision (ECCV)},
  year      = {2026},
  note      = {TODO: update with final author list, pages, and DOI on publication}
}