Instructions to use armanakbari4/g1_fdmv2_broccoli_500step with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use armanakbari4/g1_fdmv2_broccoli_500step with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("armanakbari4/g1_fdmv2_broccoli_500step", 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
g1 cup_broccoli FDM-v2 transformer @ step 500
Browse files- README.md +40 -0
- transformer/config.json +23 -0
- transformer/diffusion_pytorch_model.safetensors +3 -0
README.md
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---
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license: apache-2.0
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tags:
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- robotics
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- lingbot-va
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- unitree-g1
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- world-model
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---
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# g1_fdmv2_broccoli_500step — LingBot-VA G1 post-trained transformer
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Fine-tuned `transformer` for LingBot-VA on Unitree G1 (Dex1), task
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`yigao7117/put_cup_n_broccoli`:
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*"Pick the pink object and put it in the orange basket, then pick up the
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broccoli and put it inside the pink object."*
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- Base: `robbyant/lingbot-va-base`
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- Post-training: 50 demos, single task, lr 1e-5, **FDM v2 recipe** — the
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mutually-exclusive per-microstep regime (rank-synced coin `fdm_prob=0.5`:
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EITHER FDM video-only L_fdm Eq.13 `lambda_fdm=1.0` OR standard IDM
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L_dyn+L_inv; one forward, one backward). Optimizer **step 500** of a 2000-step
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run.
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- This repo contains **only `transformer/`** — `vae/`, `text_encoder/`,
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`tokenizer/` are unchanged from `robbyant/lingbot-va-base`.
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## Assemble an eval-ready checkpoint
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```bash
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hf download robbyant/lingbot-va-base --local-dir lingbot-va-base
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hf download armanakbari4/g1_fdmv2_broccoli_500step --local-dir g1_broc_500_dl
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mkdir -p g1_broc_500
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ln -sf $(realpath g1_broc_500_dl/transformer) g1_broc_500/transformer
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ln -sf $(realpath lingbot-va-base/vae) g1_broc_500/vae
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ln -sf $(realpath lingbot-va-base/text_encoder) g1_broc_500/text_encoder
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ln -sf $(realpath lingbot-va-base/tokenizer) g1_broc_500/tokenizer
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```
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Serve with `CONFIG_NAME=g1_cupbroc MODEL_PATH=g1_broc_500`.
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`transformer/config.json` has `attn_mode: torch` (inference-ready).
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transformer/config.json
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{
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"patch_size": [
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1,
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2,
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2
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],
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"num_attention_heads": 24,
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"attention_head_dim": 128,
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"in_channels": 48,
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"out_channels": 48,
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"action_dim": 30,
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"text_dim": 4096,
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"freq_dim": 256,
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"ffn_dim": 14336,
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"num_layers": 30,
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"cross_attn_norm": true,
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"eps": 1e-06,
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"rope_max_seq_len": 1024,
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"pos_embed_seq_len": null,
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"attn_mode": "torch",
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"_class_name": "WanTransformer3DModel",
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"_diffusers_version": "0.35.0.dev0"
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}
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transformer/diffusion_pytorch_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:55b9131338da8b831334f35f9289c91b52c089ef2c2996e1a59efd136032eeb6
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size 10177831668
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