Instructions to use Autodraft/CM2000112 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Autodraft/CM2000112 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Autodraft/CM2000112", 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 Settings
- Draw Things
- DiffusionBee
| checkpoint_config = dict(interval=10) | |
| log_config = dict( | |
| interval=50, | |
| hooks=[ | |
| dict(type='TextLoggerHook'), | |
| # dict(type='TensorboardLoggerHook') | |
| # dict(type='PaviLoggerHook') # for internal services | |
| ]) | |
| log_level = 'INFO' | |
| load_from = None | |
| resume_from = None | |
| dist_params = dict(backend='nccl') | |
| workflow = [('train', 1)] | |
| # disable opencv multithreading to avoid system being overloaded | |
| opencv_num_threads = 0 | |
| # set multi-process start method as `fork` to speed up the training | |
| mp_start_method = 'fork' | |