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
File size: 2,202 Bytes
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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.
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