Instructions to use paths1551/surface-fall with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use paths1551/surface-fall with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("paths1551/ds6-paths1223-30", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("paths1551/surface-fall") prompt = "sks artstyle" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
LoRA DreamBooth - paths1551/surface-fall
These are LoRA adaption weights for paths1551/ds6-paths1223-30. The weights were trained on sks artstyle using DreamBooth. You can find some example images in the following.
LoRA for the text encoder was enabled: True.
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Model tree for paths1551/surface-fall
Base model
paths1551/ds6-paths1223-30


