Instructions to use dde/js2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dde/js2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("dde/js2", 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 vae/diffusion_pytorch_model.safetensors from dde/js2: direct link, hf CLI and curl.
- Browser
- Download file 335 MB
-
https://huggingface.co/dde/js2/resolve/main/vae/diffusion_pytorch_model.safetensors
- Command line
-
hf download hf://dde/js2/vae/diffusion_pytorch_model.safetensors
-
curl -L -o diffusion_pytorch_model.safetensors https://huggingface.co/dde/js2/resolve/main/vae/diffusion_pytorch_model.safetensors
335 MB
- Xet hash:
- f76c367831b1b2c4293f1ec35907b215a921b36572853492675dcb68e31f5168
- Size of remote file:
- 335 MB
- SHA256:
- f062a686d37761b99d20b04032fdadd41b9be4b1c168049434eb49e7a58c25ab
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