Instructions to use siyich/ijd-unlearned-checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use siyich/ijd-unlearned-checkpoints with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("siyich/ijd-unlearned-checkpoints", 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
Download style_classifier/preprocessor_config.json from siyich/ijd-unlearned-checkpoints: direct link, hf CLI and curl.
- Browser
- Download file 228 Bytes
-
https://huggingface.co/siyich/ijd-unlearned-checkpoints/resolve/main/style_classifier/preprocessor_config.json
- Command line
-
hf download hf://siyich/ijd-unlearned-checkpoints/style_classifier/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/siyich/ijd-unlearned-checkpoints/resolve/main/style_classifier/preprocessor_config.json
228 Bytes
| { | |
| "do_normalize": true, | |
| "do_resize": true, | |
| "feature_extractor_type": "ViTFeatureExtractor", | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "resample": 2, | |
| "size": 224 | |
| } | |