import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("Jiahao-Wang/lingbot-va-robotwin-8task-step10000", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]LingBot-VA RoboTwin 8-Task Post-Trained Checkpoint
This repository contains a complete LingBot-VA inference bundle for RoboTwin evaluation.
Checkpoint
- Training run: RoboTwin 8-task post-training
- Checkpoint step:
10000 - Base checkpoint source: LingBot-VA base checkpoint
- Post-training update: transformer weights only
- Bundle layout: full inference bundle with
tokenizer/,text_encoder/,vae/, andtransformer/
Training Tasks
click_alarmclockstack_blocks_twohandover_blockplace_object_basketpick_dual_bottlesshake_bottleturn_switchbeat_block_hammer
Usage
Download the model:
huggingface-cli download Jiahao-Wang/lingbot-va-robotwin-8task-step10000 \
--repo-type model \
--local-dir /path/to/lingbot-va-robotwin-8task-step10000
Start the LingBot-VA server:
python -m torch.distributed.run --nproc_per_node 1 \
wan_va/wan_va_server.py \
--config-name robotwin \
--pretrained-model-path /path/to/lingbot-va-robotwin-8task-step10000 \
--port 29056
Run RoboTwin rollout evaluation on an x86_64 NVIDIA/Vulkan-capable machine with RoboTwin 2.0 and SAPIEN installed.
Notes
The DeltaAI GH200/aarch64 environment used for training can load and serve this checkpoint, but RoboTwin 2.0 rollout evaluation is blocked there by the lack of an upstream sapien==3.0.0b1 linux-aarch64 wheel.
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