Instructions to use kellempxt/checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kellempxt/checkpoints with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("kellempxt/checkpoints", 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
- Local Apps
- Draw Things
- DiffusionBee
Upload sdxl/fp16/base/vae/copyGodiva/README.md with huggingface_hub
Browse files
sdxl/fp16/base/vae/copyGodiva/README.md
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---
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base_model:
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- stabilityai/sdxl-vae
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---
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Standard checkpoint
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Sampler: DPM++ 2M Karras
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Steps: 12 - 40
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CFG: 5 - 9
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