Instructions to use bdsqlsz/qinglong_controlnet-lllite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use bdsqlsz/qinglong_controlnet-lllite with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("bdsqlsz/qinglong_controlnet-lllite", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
metadata
license: apache-2.0
Pre-trained models and output samples of ControlNet-LLLite form bdsqlsz
Inference with ComfyUI: https://github.com/kohya-ss/ControlNet-LLLite-ComfyUI
For 1111's Web UI, sd-webui-controlnet extension supports ControlNet-LLLite.
Training: https://github.com/kohya-ss/sd-scripts/blob/sdxl/docs/train_lllite_README.md
The recommended preprocessing for the animeface model is Anime-Face-Segmentation
Models
Trained on anime model
AnimeFaceSegment、Normal、T2i-Color/Shuffle
Base Model useKohaku-XL
MLSD
Base Model useProtoVision XL - High Fidelity 3D
Samples
AnimeFaceSegmentV1
AnimeFaceSegmentV2
MLSDV2
.png)











