Instructions to use jacklishufan/diffusion-kto with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jacklishufan/diffusion-kto with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("jacklishufan/diffusion-kto", 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
Download data/train_processed_v3.csv from jacklishufan/diffusion-kto: direct link, hf CLI and curl.
- Browser
- Download file 250 MB
-
https://huggingface.co/jacklishufan/diffusion-kto/resolve/refs%2Fpr%2F1/data/train_processed_v3.csv
- Command line
-
hf download hf://jacklishufan/diffusion-kto@refs/pr/1/data/train_processed_v3.csv
-
curl -L -o train_processed_v3.csv https://huggingface.co/jacklishufan/diffusion-kto/resolve/refs%2Fpr%2F1/data/train_processed_v3.csv
250 MB
- Xet hash:
- 2b0d483bca2a011e8f8a50c91c9311d2480d623e90ff7b98d7de910293fc4a4e
- Size of remote file:
- 250 MB
- SHA256:
- cfc08c7d25bbf860be207e2ce48a59c83668b02e1efacdc559014834b289c2ef
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