Instructions to use hiddenbox/pore_dream2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hiddenbox/pore_dream2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("hiddenbox/pore_dream2") prompt = "a photo of pore dog" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-100/optimizer.bin from hiddenbox/pore_dream2: direct link, hf CLI and curl.
- Browser
- Download file 6.53 MB
-
https://huggingface.co/hiddenbox/pore_dream2/resolve/main/checkpoint-100/optimizer.bin
- Command line
-
hf download hf://hiddenbox/pore_dream2/checkpoint-100/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/hiddenbox/pore_dream2/resolve/main/checkpoint-100/optimizer.bin
6.53 MB
- Xet hash:
- 01b6644425308e35e3ed492a79bb03fcc57f6e98146cd3636c4f9de096927353
- Size of remote file:
- 6.53 MB
- SHA256:
- 045c0f30e41104e08111a675781d59a0cf52da915340b333797f25d4ef4eb7c4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.