Instructions to use jamesmcbennett/MVMesh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jamesmcbennett/MVMesh with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("jamesmcbennett/MVMesh", 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/merges.txt from jamesmcbennett/MVMesh: direct link, hf CLI and curl.
- Browser
- Download file 525 kB
-
https://huggingface.co/jamesmcbennett/MVMesh/resolve/main/tokenizer/merges.txt
- Command line
-
hf download hf://jamesmcbennett/MVMesh/tokenizer/merges.txt
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curl -L -o merges.txt https://huggingface.co/jamesmcbennett/MVMesh/resolve/main/tokenizer/merges.txt
525 kB
File too large to display, you can check the raw version instead.