Instructions to use johnrobinsn/sd-model-gameNgen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use johnrobinsn/sd-model-gameNgen with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("johnrobinsn/sd-model-gameNgen", 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
- Draw Things
- DiffusionBee
GameNgen fine-tuning - johnrobinsn/sd-model-gameNgen
Full finetune of CompVis/stable-diffusion-v1-4. The weights were fine-tuned on the johnrobinsn/ViZDoom-500 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
[TODO: describe the data used to train the model]
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Model tree for johnrobinsn/sd-model-gameNgen
Base model
CompVis/stable-diffusion-v1-4