Instructions to use tranmc/model-diffuser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use tranmc/model-diffuser with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("tranmc/model-diffuser", 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 sample_data/mnist_train_small.csv from tranmc/model-diffuser: direct link, hf CLI and curl.
- Browser
- Download file 36.5 MB
-
https://huggingface.co/tranmc/model-diffuser/resolve/86b768323f43eaa1b4ffdfe03ca85f596e52db30/sample_data/mnist_train_small.csv
- Command line
-
hf download hf://tranmc/model-diffuser@86b768323f43eaa1b4ffdfe03ca85f596e52db30/sample_data/mnist_train_small.csv
-
curl -L -o mnist_train_small.csv https://huggingface.co/tranmc/model-diffuser/resolve/86b768323f43eaa1b4ffdfe03ca85f596e52db30/sample_data/mnist_train_small.csv
36.5 MB
- Xet hash:
- 7abf407e9f8c5c70bc35de579582b5ba02a7ede091389676b21fe7305042e068
- Size of remote file:
- 36.5 MB
- SHA256:
- 1ef64781aa03180f4f5ce504314f058f5d0227277df86060473d973cf43b033e
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