Instructions to use July66/julijxtm2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use July66/julijxtm2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("July66/julijxtm2", 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 unet/diffusion_pytorch_model.bin from July66/julijxtm2: direct link, hf CLI and curl.
- Browser
- Download file 3.46 GB
-
https://huggingface.co/July66/julijxtm2/resolve/main/unet/diffusion_pytorch_model.bin
- Command line
-
hf download hf://July66/julijxtm2/unet/diffusion_pytorch_model.bin
-
curl -L -o diffusion_pytorch_model.bin https://huggingface.co/July66/julijxtm2/resolve/main/unet/diffusion_pytorch_model.bin
3.46 GB
- Xet hash:
- 6cf32785cdea74ab6a594ba19fb64a72978b8fcabdffeb6fe7c671ac1d929458
- Size of remote file:
- 3.46 GB
- SHA256:
- 76323032ba661e0629e1d5c5eaa5b0a92d1d783d414d98bb04729a6f0e8a3e6a
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