Instructions to use swsqy/yili with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use swsqy/yili with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stablediffusionapi/mklan-xxx-nsfw-pony", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("swsqy/yili") prompt = "ce" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("stablediffusionapi/mklan-xxx-nsfw-pony", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("swsqy/yili")
prompt = "ce"
image = pipe(prompt).images[0]erwver
.webp)
- Prompt
- ce
Trigger words
You should use ve to trigger the image generation.
Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
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Model tree for swsqy/yili
Base model
stablediffusionapi/mklan-xxx-nsfw-pony