Instructions to use adammoss/test-cond with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use adammoss/test-cond with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("adammoss/test-cond", 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-5000/optimizer.bin from adammoss/test-cond: direct link, hf CLI and curl.
- Browser
- Download file 1.12 GB
-
https://huggingface.co/adammoss/test-cond/resolve/main/checkpoint-5000/optimizer.bin
- Command line
-
hf download hf://adammoss/test-cond/checkpoint-5000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/adammoss/test-cond/resolve/main/checkpoint-5000/optimizer.bin
1.12 GB
- Xet hash:
- d0e5924111deeff9da19a63d16dcf7e0dfd99d9f7f06c5c2a08a7cf0376913e0
- Size of remote file:
- 1.12 GB
- SHA256:
- 6e09593d8fa5c9796dcb2086cae0b24798bc42777bf26bc778b14e475ca1207b
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