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
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license:
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license: apache-2.0
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Pre-trained models and output samples of ControlNet-LLLite form bdsqlsz
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Inference with ComfyUI: https://github.com/kohya-ss/ControlNet-LLLite-ComfyUI
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For 1111's Web UI, [sd-webui-controlnet](https://github.com/Mikubill/sd-webui-controlnet) extension supports ControlNet-LLLite.
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Training: https://github.com/kohya-ss/sd-scripts/blob/sdxl/docs/train_lllite_README.md
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The recommended preprocessing for the animeface model is [Anime-Face-Segmentation](https://github.com/siyeong0/Anime-Face-Segmentation)
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# Models
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## Trained on anime model
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Anime Base Model use[Kohaku-XL](https://civitai.com/models/136389?modelVersionId=150441)
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