Instructions to use MaxReynolds/SouderRocketLauncherNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use MaxReynolds/SouderRocketLauncherNet with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-2", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("MaxReynolds/SouderRocketLauncherNet") 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
| license: creativeml-openrail-m | |
| base_model: CompVis/stable-diffusion-v1-2 | |
| tags: | |
| - stable-diffusion | |
| - stable-diffusion-diffusers | |
| - text-to-image | |
| - diffusers | |
| - lora | |
| inference: true | |
| # LoRA text2image fine-tuning - MaxReynolds/SouderRocketLauncherNet | |
| These are LoRA adaption weights for CompVis/stable-diffusion-v1-2. The weights were fine-tuned on the MaxReynolds/Lee_Souder_RocketLauncher dataset. You can find some example images in the following. | |
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