Instructions to use echarlaix/tiny-random-latent-consistency with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use echarlaix/tiny-random-latent-consistency with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("echarlaix/tiny-random-latent-consistency", 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 vae_decoder/openvino_model.bin from echarlaix/tiny-random-latent-consistency: direct link, hf CLI and curl.
- Browser
- Download file 27.1 kB
-
https://huggingface.co/echarlaix/tiny-random-latent-consistency/resolve/refs%2Fpr%2F1/vae_decoder/openvino_model.bin
- Command line
-
hf download hf://echarlaix/tiny-random-latent-consistency@refs/pr/1/vae_decoder/openvino_model.bin
-
curl -L -o openvino_model.bin https://huggingface.co/echarlaix/tiny-random-latent-consistency/resolve/refs%2Fpr%2F1/vae_decoder/openvino_model.bin
27.1 kB
- Xet hash:
- 0abd859a1343b541a4cace3cefbea615dc5adacccd2e569efa3c3ddbd9dae2b9
- Size of remote file:
- 27.1 kB
- SHA256:
- 368be5c512eb8d4850e6ba1be46ce708105bccec7eba3b2986e5705abce4ffd4
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