Instructions to use c-eshih/human_stable_diffusion_concat_weighted with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use c-eshih/human_stable_diffusion_concat_weighted with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("c-eshih/human_stable_diffusion_concat_weighted", 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 checkpoint-1600/optimizer.bin from c-eshih/human_stable_diffusion_concat_weighted: direct link, hf CLI and curl.
- Browser
- Download file 6.88 GB
-
https://huggingface.co/c-eshih/human_stable_diffusion_concat_weighted/resolve/main/checkpoint-1600/optimizer.bin
- Command line
-
hf download hf://c-eshih/human_stable_diffusion_concat_weighted/checkpoint-1600/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/c-eshih/human_stable_diffusion_concat_weighted/resolve/main/checkpoint-1600/optimizer.bin
6.88 GB
- Xet hash:
- fc2a01fa971c4495243e7c326b3d122ce345067e9191c16f270a1d219c99ad0e
- Size of remote file:
- 6.88 GB
- SHA256:
- 221cba2c5b00f540ab9c6100b40a242dcc5d29d54bce0efd1f78a8c93e16ffcd
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