Instructions to use rbln/tiny-cosmos-2.5-predict with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use rbln/tiny-cosmos-2.5-predict with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("rbln/tiny-cosmos-2.5-predict", 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 text_encoder/generation_config.json from rbln/tiny-cosmos-2.5-predict: direct link, hf CLI and curl.
- Browser
- Download file 204 Bytes
-
https://huggingface.co/rbln/tiny-cosmos-2.5-predict/resolve/main/text_encoder/generation_config.json
- Command line
-
hf download hf://rbln/tiny-cosmos-2.5-predict/text_encoder/generation_config.json
-
curl -L -o generation_config.json https://huggingface.co/rbln/tiny-cosmos-2.5-predict/resolve/main/text_encoder/generation_config.json
204 Bytes
| { | |
| "_from_model_config": true, | |
| "bos_token_id": 151643, | |
| "eos_token_id": 151645, | |
| "output_attentions": false, | |
| "output_hidden_states": false, | |
| "transformers_version": "5.8.1", | |
| "use_cache": true | |
| } | |