Instructions to use alppo/vae-conditioned-diffusion-model_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use alppo/vae-conditioned-diffusion-model_v2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("alppo/vae-conditioned-diffusion-model_v2", 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 training_state.pt from alppo/vae-conditioned-diffusion-model_v2: direct link, hf CLI and curl.
- Browser
- Download file 944 MB
-
https://huggingface.co/alppo/vae-conditioned-diffusion-model_v2/resolve/main/training_state.pt
- Command line
-
hf download hf://alppo/vae-conditioned-diffusion-model_v2/training_state.pt
-
curl -L -o training_state.pt https://huggingface.co/alppo/vae-conditioned-diffusion-model_v2/resolve/main/training_state.pt
944 MB
- Xet hash:
- 646657ea38e88ed4229a3ec570800c673c9086ba21190e618bd2019dda5cb7a8
- Size of remote file:
- 944 MB
- SHA256:
- ba0b50f24ebedf2466f02541fff8bc81df0a168b601f62d86c1fd440e2563dd0
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