Instructions to use cicada-ai/Chanjing-Avatar-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use cicada-ai/Chanjing-Avatar-14B 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("cicada-ai/Chanjing-Avatar-14B", dtype=torch.bfloat16, device_map="cuda") pipe.to("cuda") prompt = "A man with short gray hair plays a red electric guitar." image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" ) output = pipe(image=image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
Chanjing-Avatar 14B
Chanjing-Avatar 14B is an audio-driven 720p avatar video generation model based on Wan2.1-T2V-14B. It adds audio conditioning and LoRA adapters to the Wan video diffusion model.
Source code and complete inference instructions: chanjing-ai/Chanjing-Avatar
Chanjing-Avatar Model Family
- Chanjing-Avatar 14B: 720p image-to-video generation from a reference image and driving audio.
- Chanjing-Avatar V2V 5B: video-to-video generation that preserves source motion and regenerates the speaking face.
- Chanjing-Avatar V2V 1.3B: a lighter video-to-video model for audio-driven face animation.
The checkpoint contains audio modules, input projection, and LoRA adapters in BF16. The Wan2.1 base model and Wav2Vec audio encoder are required separately.
Chanjing-Avatar-14B/
|-- config.json
`-- diffusion_pytorch_model.safetensors
hf download cicada-ai/Chanjing-Avatar-14B \
--local-dir models/Chanjing-Avatar-14B
Users are responsible for obtaining consent for source images and voices and for clearly disclosing synthetic media.
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Model tree for cicada-ai/Chanjing-Avatar-14B
Base model
Wan-AI/Wan2.1-T2V-14B