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Jiahao-Wang
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lingbot-va-robotwin-2task-to-8task-full-step10000

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How to use from the
Use from the
Diffusers library
pip install -U diffusers transformers accelerate
import torch
from diffusers import DiffusionPipeline

# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("Jiahao-Wang/lingbot-va-robotwin-2task-to-8task-full-step10000", dtype=torch.bfloat16, device_map="cuda")

prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

LingBot-VA RoboTwin 2-Task-to-8-Task Full Fine-Tune Step 10000

This is a LingBot-VA inference bundle trained in two stages:

  1. Full post-training on RoboTwin 2.0 blocks_ranking_rgb and stamp_seal for 5000 steps.
  2. Continued full post-training on eight RoboTwin 2.0 tasks for 10000 steps.

The eight-task continuation used the default full post-training method, not LoRA.

Training tasks for the continuation stage:

  • click_alarmclock
  • stack_blocks_two
  • handover_block
  • place_object_basket
  • pick_dual_bottles
  • shake_bottle
  • turn_switch
  • beat_block_hammer

The bundle contains tokenizer/, text_encoder/, vae/, and the final merged transformer/. It can be used directly with wan_va_server.py --pretrained-model-path.

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