Instructions to use c-eshih/human_stable_diffusion_concat_loss 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_loss 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_loss", 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-6300/optimizer.bin from c-eshih/human_stable_diffusion_concat_loss: direct link, hf CLI and curl.
- Browser
- Download file 6.88 GB
-
https://huggingface.co/c-eshih/human_stable_diffusion_concat_loss/resolve/main/checkpoint-6300/optimizer.bin
- Command line
-
hf download hf://c-eshih/human_stable_diffusion_concat_loss/checkpoint-6300/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/c-eshih/human_stable_diffusion_concat_loss/resolve/main/checkpoint-6300/optimizer.bin
6.88 GB
- Xet hash:
- 371c13b5db3e6d332a2cd506a451193ca4574c675ea4914789387518c8a84ac2
- Size of remote file:
- 6.88 GB
- SHA256:
- fce705cc4d6e4cdc27f4dfc087a9e4c60bf78ae536b70d5b0aac0fdc81493ced
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