Instructions to use aditya11997/kandi2-decoder-3.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use aditya11997/kandi2-decoder-3.2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("aditya11997/kandi2-decoder-3.2", 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 checkpoint-14000/scaler.pt from aditya11997/kandi2-decoder-3.2: direct link, hf CLI and curl.
- Browser
- Download file 988 Bytes
-
https://huggingface.co/aditya11997/kandi2-decoder-3.2/resolve/main/checkpoint-14000/scaler.pt
- Command line
-
hf download hf://aditya11997/kandi2-decoder-3.2/checkpoint-14000/scaler.pt
-
curl -L -o scaler.pt https://huggingface.co/aditya11997/kandi2-decoder-3.2/resolve/main/checkpoint-14000/scaler.pt
988 Bytes
- Xet hash:
- b5744624c4ff734ed96103fe3609af363e372e4c0994e892e84f7dacbcf6c86f
- Size of remote file:
- 988 Bytes
- SHA256:
- 00bb09f9c014e2d09a23c386a42d4dda2a729d254605589f0306cf0dd52f45c6
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