Instructions to use arnaudstiegler/gameNgen-baseline-45ksteps with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use arnaudstiegler/gameNgen-baseline-45ksteps with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("arnaudstiegler/gameNgen-baseline-45ksteps", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
metadata
base_model: CompVis/stable-diffusion-v1-4
library_name: diffusers
license: creativeml-openrail-m
inference: true
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
GameNgen fine-tuning - arnaudstiegler/gameNgen-baseline-45ksteps
Full finetune of CompVis/stable-diffusion-v1-4. The weights were fine-tuned on the P-H-B-D-a16z/ViZDoom-Deathmatch-PPO-Lrg dataset. You can find some example images in the following.
Intended uses & limitations
How to use
# TODO: add an example code snippet for running this diffusion pipeline
Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
Training details
45k steps on P-H-B-D-a16z/ViZDoom-Deathmatch-PPO-XLrg


