Instructions to use Aquinlabs/h3-avatar-aq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Aquinlabs/h3-avatar-aq with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fill-in-base-model", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Aquinlabs/h3-avatar-aq") prompt = "A man with short gray hair plays a red electric guitar." input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png") output = pipe(image=input_image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
H3 Avatar
Single-image talking-avatar generation powered by a locked MiniMax-H3 Ref2VA LoRA.
What It Does
H3 Avatar turns one character image plus a short line of dialogue into a talking avatar clip with model-generated speech, synchronized mouth movement, subtle head motion, and controllable scene direction.
The locked product recipe is intentionally simple:
- upload one reference image
- write the dialogue the character should say
- optionally describe the direction/background
- generate with the locked H3 Avatar LoRA
Model Details
| Item | Value |
|---|---|
| Base | MiniMax-H3 Ref2VA |
| Adapter | LoRA |
| Rank / alpha | 16 / 32 |
| Target modules | to_qkv, to_out.0, linear_1, linear_2 |
| Trainable params | 10,655,744 |
| Locked checkpoint | models/locked/h3-avatar-audio-v2-step1000.pt |
| Partner adapter | partner_bundle/adapter_model.safetensors |
Training summary:
- 481 accepted transcript-conditioned clips
- 385 train clips
- 1000 optimizer steps
- about 2.6 train-set passes
- 384x384, 56-frame cache
- locked inference default: 30 diffusion steps
Files
partner_bundle/- clean LoRA adapter export for hosted inference partners.models/locked/- locked training checkpoint used by the local/Modal reference path.modal_h3_avatar.py- Modal reference endpoint.ui/h3_avatar_ui.py- local H3 Avatar Studio UI.training/- cache prep and LoRA training code.recipes/- training recipes, including the locked recipe.evals/andbenchmarks/- prompts, scorecards, and checkpoint reports.aq/andaqit/- AQ handoff and AQ interpretability notes.
Recommended Inference
{
"reference_image": "path_or_url_to_image",
"dialogue": "Hey Chandan, wassup yaar, how are you doing these days?",
"direction": "The same character faces the camera and speaks with an audible natural voice, expressive synchronized mouth movement, subtle head motion, and a friendly expression.",
"background": "in a clean studio with soft cinematic lighting",
"steps": 30
}
For production hosting, keep the H3 base model warm and load this LoRA adapter as the product checkpoint.
Local UI
H3_WARM_ENDPOINT_URL=https://aquin--h3-avatar-warm-generate.modal.run \
python3 ui/h3_avatar_ui.py
Then open:
http://127.0.0.1:8787
Modal Reference Endpoint
modal deploy modal_h3_avatar.py
The Modal path is a reference implementation. Production hosting should use a dedicated inference partner such as Reactor or fal with the base H3 model kept hot.
License
The H3 Avatar adapter, UI code, training recipes, and accompanying project files in this repository are released under the Apache License 2.0.
The adapter is designed to run with MiniMax-H3 Ref2VA. Use of the underlying base model remains subject to the upstream MiniMax-H3 license and any applicable dataset, likeness, and deployment restrictions.
- Downloads last month
- -