Add diffusers usage section to the README

Usage snippet + low-VRAM offloading for the diffusers weights PR (#2); install pin until huggingface/diffusers#14456 is merged.

README.md

@@ -143,6 +143,70 @@ The following end-to-end example contains the complete lyrics, music description
 |---|---|---|
 | Text-to-music | [View script](https://huggingface.co/MiniMaxAI/MiniMax-Music3/blob/main/scripts/end_to_end/minimax_ttm_test.py) | [minimax_ttm.wav](https://huggingface.co/MiniMaxAI/MiniMax-Music3/blob/main/assets/minimax_ttm.wav) |
 
+## 🧨 Diffusers
+
+MiniMax Music 3 is available as a [diffusers](https://github.com/huggingface/diffusers) modular pipeline. Until [huggingface/diffusers#14456](https://github.com/huggingface/diffusers/pull/14456) is merged, install diffusers from the PR commit:
+
+The snippet below fits 24GB+ VRAM GPUs
+
+```bash
+pip install git+https://github.com/huggingface/diffusers@dafe3733fcfdbf3c48915fe77be3aef65b5d6a2d transformers accelerate soundfile
+```
+
+```python
+import soundfile as sf
+import torch
+from diffusers import ModularPipeline
+
+pipe = ModularPipeline.from_pretrained("MiniMaxAI/MiniMax-Music3")
+pipe.load_components(dtype=torch.bfloat16)
+pipe.to("cuda")
+
+lyrics = """[verse]
+Morning light filtering through the pine
+Every quiet street is yours and mine
+[chorus]
+Softly the world begins to breathe"""
+
+prompt = (
+    "Genre: acoustic pop. BPM: 96. Key: C major. Warm and intimate, building gently into the chorus. "
+    "Vocals: soft female lead, close and breathy, light stacked harmonies in the chorus. "
+    "Arrangement: fingerpicked guitar and soft piano; brushed drums and upright bass enter in the chorus."
+)
+
+audio = pipe(
+    prompt=prompt,
+    lyrics=lyrics,
+    audio_duration=60.0,
+    generator=torch.Generator("cuda").manual_seed(7),
+    output="audios",
+)[0]
+
+sf.write("song.wav", audio.T.float().cpu().numpy(), pipe.sampling_rate)
+```
+
+### Low VRAM
+
+The full precision fits under 24GB of VRAM. With automatic CPU offloading, generation takes in ~22 GB; additionally streaming the language model layer by layer makes it fit even 8 GB video cards:
+
+```python
+import torch
+from diffusers import ComponentsManager, ModularPipeline
+from diffusers.hooks import apply_group_offloading
+
+manager = ComponentsManager()
+manager.enable_auto_cpu_offload(device="cuda")
+pipe = ModularPipeline.from_pretrained("MiniMaxAI/MiniMax-Music3", components_manager=manager)
+pipe.load_components(dtype=torch.bfloat16)
+
+# Only needed below ~22 GB of VRAM — slower, but fits in 8 GB.
+apply_group_offloading(
+    pipe.language_model, onload_device=torch.device("cuda"), offload_type="leaf_level", use_stream=True
+)
+
+
+```
+
 ## Prompt Enhancement
 
 A concise natural-language description can be used directly. For richer prompts and more precise control, use the provided [`music-caption-rewriter`](skills/music-caption-rewriter/SKILL.md) skill to expand it into a Structured Caption containing `Global Metadata`, `Vocal Details`, and `Arrangement`. The skill preserves musical instructions attached to lyric section tags in the arrangement description while keeping the lyric text in the lyrics input.