Instructions to use SyntheticMDProductions/Roblox_Oasis_V4_AI_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SyntheticMDProductions/Roblox_Oasis_V4_AI_Model with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("SyntheticMDProductions/Roblox_Oasis_V4_AI_Model", 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
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Download README.md from SyntheticMDProductions/Roblox_Oasis_V4_AI_Model: direct link, hf CLI and curl.
- Browser
- Download file 2.2 kB
-
https://huggingface.co/SyntheticMDProductions/Roblox_Oasis_V4_AI_Model/resolve/main/README.md
- Command line
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hf download hf://SyntheticMDProductions/Roblox_Oasis_V4_AI_Model/README.md
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curl -L -o README.md https://huggingface.co/SyntheticMDProductions/Roblox_Oasis_V4_AI_Model/resolve/main/README.md
2.2 kB
| license: apache-2.0 | |
| tags: | |
| - roblox | |
| - world-model | |
| - video-generation | |
| - action-conditioned | |
| - diffusers | |
| # Roblox Oasis V 2.3.7 V1 | |
| An experimental action-conditioned, rectified-flow video world model trained on recorded Roblox gameplay. Given a current visual state and supported input actions, it predicts the next simulated game frame. | |
| This is a research/demo model, not a playable Roblox client. It does not connect to Roblox, its servers, or any Roblox accounts. | |
| ## Game Previews | |
|  | |
| ## Model details | |
| - Resolution: 256 x 144 (16:9) | |
| - Architecture: action-conditioned rectified-flow video model | |
| - Inference weights: `unet/diffusion_pytorch_model.safetensors` | |
| - Supported demonstrated inputs: movement (W/A/S/D), jump, shift, `1`, left/right mouse, and relative mouse movement | |
| - Training result: 29 completed epochs; best recorded counterfactual accuracy: 85.4% on 48 validation samples | |
| ## Files | |
| - `unet/`: model architecture configuration and inference weights | |
| - `action_flow_model_info.json`: input encoding, actions, and training metadata | |
| - `gui_previews/`: training preview images | |
| ## Use | |
| Use this model with the companion trainer/player application: [Oasis-Game-Trainer](https://github.com/dreamsartificial648-sketch/Oasis-Game-Trainer). Copy the downloaded folder into that application's model-output directory, then select it from the player interface. | |
| The project uses Python, PyTorch, Diffusers, OpenCV, Pillow, MSS, and Pynput. See the companion repository for installation and runtime instructions. | |
| ## Limitations | |
| This model is a learned visual simulation. It may drift, become inconsistent over longer rollouts, or react imperfectly to inputs. It was trained on a limited action distribution and should be evaluated as an experimental project rather than a game replacement. | |
| ## Attribution and rights | |
| Unofficial research project. “Roblox” is used descriptively; this project is not affiliated with, endorsed by, or sponsored by Roblox Corporation. Only use or redistribute data and footage for which you have the necessary rights. | |