Instructions to use armanakbari4/g1_fdmv2_broccoli_1500step with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use armanakbari4/g1_fdmv2_broccoli_1500step 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_1500step", 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_fdmv2_broccoli_1500step โ LingBot-VA G1 post-trained transformer
Fine-tuned transformer for LingBot-VA on Unitree G1 (Dex1), task
yigao7117/put_cup_n_broccoli:
"Pick the pink object and put it in the orange basket, then pick up the
broccoli and put it inside the pink object."
- Base:
robbyant/lingbot-va-base - Post-training: 50 demos, single task, lr 1e-5, FDM v2 recipe โ the
mutually-exclusive per-microstep regime (rank-synced coin
fdm_prob=0.5: EITHER FDM video-only L_fdm Eq.13lambda_fdm=1.0OR standard IDM L_dyn+L_inv; one forward, one backward). Optimizer step 1500 of a 2000-step run. - This repo contains only
transformer/โvae/,text_encoder/,tokenizer/are unchanged fromrobbyant/lingbot-va-base.
Assemble an eval-ready checkpoint
hf download robbyant/lingbot-va-base --local-dir lingbot-va-base
hf download armanakbari4/g1_fdmv2_broccoli_1500step --local-dir g1_broc_1500_dl
mkdir -p g1_broc_1500
ln -sf $(realpath g1_broc_1500_dl/transformer) g1_broc_1500/transformer
ln -sf $(realpath lingbot-va-base/vae) g1_broc_1500/vae
ln -sf $(realpath lingbot-va-base/text_encoder) g1_broc_1500/text_encoder
ln -sf $(realpath lingbot-va-base/tokenizer) g1_broc_1500/tokenizer
Serve with CONFIG_NAME=g1_cupbroc MODEL_PATH=g1_broc_1500.
transformer/config.json has attn_mode: torch (inference-ready).
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