Instructions to use JosephusCheung/ACertainModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use JosephusCheung/ACertainModel with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("JosephusCheung/ACertainModel", dtype=torch.bfloat16, device_map="cuda") prompt = "masterpiece, best quality, 1girl, brown hair, green eyes, colorful, autumn, cumulonimbus clouds, lighting, blue sky, falling leaves, garden" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
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README.md
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Parameters are not allowed to be modified, as it seems that it is generated with `Clip skip: 1`, for better performance, it is strongly recommended to use `Clip skip: 2` instead.
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Here is an example of inference settings, if it is applicable with you on your own server: `Steps: 28, Sampler: Euler a, CFG scale: 11,
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## 🧨 Diffusers
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Parameters are not allowed to be modified, as it seems that it is generated with `Clip skip: 1`, for better performance, it is strongly recommended to use `Clip skip: 2` instead.
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Here is an example of inference settings, if it is applicable with you on your own server: `Steps: 28, Sampler: Euler a, CFG scale: 11, Clip skip: 2`.
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## 🧨 Diffusers
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