Text-to-Image
Diffusers
TensorBoard
StableDiffusionPipeline
stable-diffusion
stable-diffusion-diffusers
textual_inversion
Instructions to use dxli/can with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dxli/can with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_textual_inversion("dxli/can") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Download README.md from dxli/can: direct link, hf CLI and curl.
- Browser
- Download file 395 Bytes
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https://huggingface.co/dxli/can/resolve/main/README.md
- Command line
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hf download hf://dxli/can/README.md
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curl -L -o README.md https://huggingface.co/dxli/can/resolve/main/README.md
395 Bytes
| license: creativeml-openrail-m | |
| base_model: runwayml/stable-diffusion-v1-5 | |
| tags: | |
| - stable-diffusion | |
| - stable-diffusion-diffusers | |
| - text-to-image | |
| - diffusers | |
| - textual_inversion | |
| inference: true | |
| # Textual inversion text2image fine-tuning - dxli/can | |
| These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following. | |