Text-to-Image
Diffusers
TensorBoard
Safetensors
StableDiffusionPipeline
stable-diffusion
stable-diffusion-diffusers
textual_inversion
diffusers-training
Instructions to use JJSLL/Cat_AdvDM_eps4_object with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use JJSLL/Cat_AdvDM_eps4_object 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("JJSLL/Cat_AdvDM_eps4_object") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- ab739be8b6a4fa5c4a6032084b50034821fcb714e1b7300c1de020e19c6e387b
- Size of remote file:
- 304 MB
- SHA256:
- a311b2f2289f32c6e1112d0df1e8fc59b020c4a4dcb1019a6320e0011711d07f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.