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 tokenizer/tokenizer.json from rbln/tiny-cosmos-2.5-predict: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/rbln/tiny-cosmos-2.5-predict/resolve/main/tokenizer/tokenizer.json
- Command line
-
hf download hf://rbln/tiny-cosmos-2.5-predict/tokenizer/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/rbln/tiny-cosmos-2.5-predict/resolve/main/tokenizer/tokenizer.json
11.4 MB
- Xet hash:
- d3f835122bddb470f53048ff36f1a5116791b8f3a003f9d17b3b03b0b81cc5fe
- Size of remote file:
- 11.4 MB
- SHA256:
- 3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
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