Instructions to use Outer-Spatial/antslora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Outer-Spatial/antslora with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("davidberenstein1957/p-image-classic-painting-lora", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Outer-Spatial/antslora") prompt = "<symbolic_oil_painting>" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
File size: 586 Bytes
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tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- output:
url: images/replicate-prediction-w0h9jp19p9rmw0cwr4sa7xj80w.webp
text: <symbolic_oil_painting>
base_model: davidberenstein1957/p-image-classic-painting-lora
instance_prompt: <symbolic_oil_painting>
---
# antslora
<Gallery />
## Model description
<symbolic_oil_painting>
## Trigger words
You should use `<symbolic_oil_painting>` to trigger the image generation.
## Download model
[Download](/Outer-Spatial/antslora/tree/main) them in the Files & versions tab.
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