Text-to-Image
Diffusers
Trained with AutoTrain
stable-diffusion
stable-diffusion-diffusers
lora
template:sd-lora
Instructions to use philipp-zettl/borderlands with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use philipp-zettl/borderlands with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("philipp-zettl/borderlands") prompt = "A cat playing with a ball" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
AutoTrain LoRA DreamBooth - philipp-zettl/borderlands
These are LoRA adaption weights for stable-diffusion-v1-5/stable-diffusion-v1-5. The weights were trained on in the style of DSYL using DreamBooth. LoRA for the text encoder was enabled: True.
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Model tree for philipp-zettl/borderlands
Base model
stable-diffusion-v1-5/stable-diffusion-v1-5