Instructions to use JoPmt/kandinsky-2-2-decoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use JoPmt/kandinsky-2-2-decoder with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("JoPmt/kandinsky-2-2-decoder", 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 movq/diffusion_pytorch_model.safetensors from JoPmt/kandinsky-2-2-decoder: direct link, hf CLI and curl.
- Browser
- Download file 271 MB
-
https://huggingface.co/JoPmt/kandinsky-2-2-decoder/resolve/main/movq/diffusion_pytorch_model.safetensors
- Command line
-
hf download hf://JoPmt/kandinsky-2-2-decoder/movq/diffusion_pytorch_model.safetensors
-
curl -L -o diffusion_pytorch_model.safetensors https://huggingface.co/JoPmt/kandinsky-2-2-decoder/resolve/main/movq/diffusion_pytorch_model.safetensors
271 MB
- Xet hash:
- 45dce1b374a50442879bc49558d144e9e364489f2eed2be732c8b978db74c979
- Size of remote file:
- 271 MB
- SHA256:
- 43a5860fea195a7116f2471396c5cc9535fade9b63c4857d8a192ffd924b7002
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