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
Download checkpoint-2500/optimizer.bin from dxli/can: direct link, hf CLI and curl.
- Browser
- Download file 304 MB
-
https://huggingface.co/dxli/can/resolve/main/checkpoint-2500/optimizer.bin
- Command line
-
hf download hf://dxli/can/checkpoint-2500/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/dxli/can/resolve/main/checkpoint-2500/optimizer.bin
304 MB
- Xet hash:
- 81a08b4a66185866023377b71a34da2dd9b1689acc049df41115805e06456911
- Size of remote file:
- 304 MB
- SHA256:
- 29c273aaa909207a6f35fb0e24ff5be0e8f4c2a22731381bd03a81624295ed6b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.