Instructions to use xixircc/MetaRigCapture with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use xixircc/MetaRigCapture with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("xixircc/MetaRigCapture", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Upload folder using huggingface_hub
Browse files- best_model.pt +0 -3
- config.yaml +0 -37
best_model.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:ce2643d4431ce2f21632e71f45306d4035343b4fb7ee50f05840e304b86808fe
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size 60946018
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config.yaml
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data:
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chunk_size: 150
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json_files:
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- ./jsons/blow.json
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- ./jsons/eyebrow.json
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- ./jsons/pout.json
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- ./jsons/nersemble_front.json
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- ./jsons/yk_cap.json
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- ./jsons/xnemo_transfer.json
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mount_prefix: /aliyun-oss
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num_workers: 4
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oss_prefix: oss://aigcdevwlcb
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seed: 42
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val_ratio: 0.1
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model:
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mask_feature_dim: 64
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motion_dim: 512
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rigs_dim: 169
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tcn_config:
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depth: 6
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dropout: 0.1
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hidden: 1024
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train:
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batch_size: 16
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brow_boost_factor: 2.0
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enable_brow_boost: true
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learning_rate: 0.0001
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log_interval: 50
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max_grad_norm: 1.0
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mixed_precision: fp16
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num_epochs: 100
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output_dir: /home/deepspeed/workdir/FaceCapture/outputs/motion_mask_rig_weighted_2025-12-04_17-43-32
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symmetric_weight: 1.0
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use_scheduler: true
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use_symmetric_loss: true
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weight_decay: 0.0001
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