Text-to-Image
Diffusers
TensorBoard
Safetensors
StableDiffusionPipeline
stable-diffusion
stable-diffusion-diffusers
textual_inversion
diffusers-training
Instructions to use ssdv8/output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ssdv8/output 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("ssdv8/output") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-1000/optimizer.bin from ssdv8/output: direct link, hf CLI and curl.
- Browser
- Download file 304 MB
-
https://huggingface.co/ssdv8/output/resolve/main/checkpoint-1000/optimizer.bin
- Command line
-
hf download hf://ssdv8/output/checkpoint-1000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/ssdv8/output/resolve/main/checkpoint-1000/optimizer.bin
304 MB
- Xet hash:
- 6de7666762392a3d2bd6bfd28607bf29791376a37cc94aaaf75187a64d1d0d0d
- Size of remote file:
- 304 MB
- SHA256:
- b5720127fd55766d85606150b0c43d88a2dc1ca84da8215502648051deea1121
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.