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 samples/0039_process.png from alppo/vae-conditioned-diffusion-model_v2: direct link, hf CLI and curl.
- Browser
- Download file 1.54 MB
-
https://huggingface.co/alppo/vae-conditioned-diffusion-model_v2/resolve/main/samples/0039_process.png
- Command line
-
hf download hf://alppo/vae-conditioned-diffusion-model_v2/samples/0039_process.png
-
curl -L -o 0039_process.png https://huggingface.co/alppo/vae-conditioned-diffusion-model_v2/resolve/main/samples/0039_process.png
1.54 MB

- Xet hash:
- 68d3310d2ea03d76d2863ff36d2f78430ac609e77a720e8fe189d002826e243a
- Size of remote file:
- 1.54 MB
- SHA256:
- bfe65d1245303466b1be557c275df69879c0a86b2d37cfe1e0841bb59af3236d
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