Instructions to use EndeavourDD/gnn_wm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use EndeavourDD/gnn_wm with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("EndeavourDD/gnn_wm", 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
| """Helpers for importing original Ctrl-World modules without modifying that repo.""" | |
| from __future__ import annotations | |
| import sys | |
| from pathlib import Path | |
| def ensure_ctrl_world_on_path(ctrl_world_root: str) -> Path: | |
| root = Path(ctrl_world_root).resolve() | |
| if not root.exists(): | |
| raise FileNotFoundError(f"Ctrl-World root not found: {root}") | |
| root_str = str(root) | |
| if root_str not in sys.path: | |
| sys.path.insert(0, root_str) | |
| return root | |
| def import_original_modules(ctrl_world_root: str): | |
| ensure_ctrl_world_on_path(ctrl_world_root) | |
| from models.pipeline_stable_video_diffusion import StableVideoDiffusionPipeline | |
| from models.pipeline_ctrl_world import CtrlWorldDiffusionPipeline | |
| from models.unet_spatio_temporal_condition import UNetSpatioTemporalConditionModel | |
| return { | |
| "StableVideoDiffusionPipeline": StableVideoDiffusionPipeline, | |
| "CtrlWorldDiffusionPipeline": CtrlWorldDiffusionPipeline, | |
| "UNetSpatioTemporalConditionModel": UNetSpatioTemporalConditionModel, | |
| } | |