Instructions to use milaidy/r4v3n with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use milaidy/r4v3n with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("milaidy/r4v3n", 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 text_encoder/pytorch_model.bin from milaidy/r4v3n: direct link, hf CLI and curl.
- Browser
- Download file 246 MB
-
https://huggingface.co/milaidy/r4v3n/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://milaidy/r4v3n/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/milaidy/r4v3n/resolve/main/text_encoder/pytorch_model.bin
246 MB
- Xet hash:
- 13d3e8bc1a50d9184cb624cfbac6f16499f14957e9547a51062d411b2ab59fb9
- Size of remote file:
- 246 MB
- SHA256:
- 6b917328b351a7607f386348bf8f9d70f2a077f9edc084ba7664de5b4ab4603a
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