Instructions to use easylearning/FutureWorlds with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use easylearning/FutureWorlds with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("easylearning/FutureWorlds", 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
File size: 1,724 Bytes
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"schema_version": 1,
"dataset": "rt1",
"status": "staged_research_preview",
"codec": "rt1/codec",
"action_ranges": "rt1/preprocessing/frac_action_ranges.pth",
"action_tokens_per_step": 13,
"text_conditioning": {
"enabled": true,
"repo_id": "google-t5/t5-base",
"revision": "a9723ea7f1b39c1eae772870f3b547bf6ef7e6c1",
"layers": [
3,
7,
11
],
"inner": 256,
"width": 768,
"max_tokens": 64,
"bundled": false
},
"initial_observations": 2,
"memory": {
"anchor_states": 1,
"recent_states": 6,
"ground_truth_refresh": false
},
"evaluation": {
"strategy": "per_frame_beam",
"beam_width": 4,
"horizon": 32,
"visual_tokens_per_frame": 80,
"valid_visual_vocab": 4375
},
"variants": {
"main": {
"path": "rt1/models/main",
"method": "MemSPO",
"updates": 500,
"reward": "R0",
"purpose": "paper_main_table",
"text": true
},
"sft": {
"path": "rt1/models/sft",
"method": "SFT",
"updates": 50000,
"reward": null,
"purpose": "matched_ablation",
"text": true
},
"memspo200": {
"path": "rt1/models/memspo200",
"method": "MemSPO",
"updates": 200,
"reward": "R0",
"purpose": "matched_ablation",
"text": true
},
"grpo200": {
"path": "rt1/models/grpo200",
"method": "GRPO",
"updates": 200,
"reward": "R0",
"purpose": "matched_ablation",
"text": true
},
"ordinary200": {
"path": "rt1/models/ordinary200",
"method": "OrdinaryBeam",
"updates": 200,
"reward": "R0",
"purpose": "matched_ablation",
"text": true
}
}
}
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