Instructions to use LegoClipStars/Spike with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use LegoClipStars/Spike with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("dataautogpt3/OpenDalleV1.1", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("LegoClipStars/Spike") prompt = "NEFT" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Download README.md from LegoClipStars/Spike: direct link, hf CLI and curl.
- Browser
- Download file 610 Bytes
-
https://huggingface.co/LegoClipStars/Spike/resolve/main/README.md
- Command line
-
hf download hf://LegoClipStars/Spike/README.md
-
curl -L -o README.md https://huggingface.co/LegoClipStars/Spike/resolve/main/README.md
610 Bytes
metadata
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: NEFT
parameters:
negative_prompt: flying dragon
output:
url: images/Spike_%28My_Little_Pony%29.png
base_model: dataautogpt3/OpenDalleV1.1
instance_prompt: Please spare me
license: cc-by-4.0
Spike

- Prompt
- NEFT
- Negative Prompt
- flying dragon
Model description
Here's the RVC (700 epoch) voice model of Spike the dragon from MLP:FIM
Trigger words
You should use Please spare me to trigger the image generation.
Download model
Download them in the Files & versions tab.