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realrebelai/MiniMax-Music-3_GGUFs

🤗 Hugging Face sourcetext-to-audioapache-2.09.0 GBGGUF✓ 6 checksumsupdated today
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MiniMax-Music-3 — GGUF

GGUF quantizations of MiniMax-Music3, converted from Comfy-Org's fp32 repack for ComfyUI with ComfyUI-GGUF.

MiniMax-Music-3 generates full music from a text prompt. The diffusion transformer is only 2.46B parameters — small enough that even the top tier fits comfortably on modest hardware, and Q4 runs on almost anything.

Apache-2.0, inherited from the base model.

Files

Quant Size Notes
Q6_K ~2.0 GB Excellent
Q5_K_M ~1.8 GB Very good
Q4_K_M ~1.5 GB Good balance
Q3_K_M ~1.2 GB Audible softening on transients
Q2_K ~0.9 GB Smallest; noticeable artifacts 
hf download realrebelai/MiniMax-Music-3-GGUF MiniMax-Music-3-Q8_0.gguf --local-dir ComfyUI/models/diffusion_models

Take a higher tier than you would on an image model. The fp32 source is 9.15 GB and Q8_0 is 2.6 GB — on a model this small there's little reason to drop low, and audio gives quantization error nowhere to hide. There's no visual channel to mask it: what would read as slightly soft detail in an image reads as smeared transients, ringing, or a thinner stereo field here. Q8_0 or Q6_K is the sensible default.

Companion files

The GGUF replaces only the diffusion transformer. Both of these come from Comfy-Org/MiniMax-Music-3:

File Folder
minimax_music3_text_encoder_bf16.safetensors models/text_encoders
minimax_music3_dav.safetensors models/vae

A pruned text-encoder variant is also available in that repo if the bf16 encoder is too large for your setup.

Usage

  1. Put the .gguf in ComfyUI/models/diffusion_models
  2. Load it with Unet Loader (GGUF)
  3. Text encoder, VAE and sampler settings are unchanged from the official MiniMax-Music-3 workflow

Requires ComfyUI with MiniMax-Music-3 support, plus the ComfyUI-GGUF custom node.

Conversion notes

Converted from the official fp32 transformer with a streaming converter, quantized with llama-quantize, then repaired.

This is a 1-D convolutional audio DiT, not a standard image-model architecture — preprocess_conv [2304, 2304, 1], postprocess_conv [128, 128, 1], latent_conditioners [2048, 4096, 3] — with a separate timestep-feature path and a small cond_layer_logits gate.

Kept at high precision rather than quantized:

preprocess_conv, postprocess_conv, latent_conditioners,
timestep_features, to_timestep_embed, cond_layer, proj_out, rope

The conv layers and the conditioning path are where a music model has the least tolerance for error — the timestep embedding steers every denoising step, and the latent conditioners carry the structure the audio is built on. llama-quantize does not preserve these automatically; they are restored afterward and verified per file.

Every file was checked after conversion to confirm those layers are stored at F16/F32 and that the tensor count and architecture metadata are intact.

Credits

Quantized by RealRebelAI · GitHub · X