Instructions to use RockSky1/Bleza-Video with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use RockSky1/Bleza-Video with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("RockSky1/Bleza-Video", 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 text_encoder/pytorch_model.bin from RockSky1/Bleza-Video: direct link, hf CLI and curl.
- Browser
- Download file 681 MB
-
https://huggingface.co/RockSky1/Bleza-Video/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://RockSky1/Bleza-Video/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/RockSky1/Bleza-Video/resolve/main/text_encoder/pytorch_model.bin
681 MB
- Xet hash:
- 1b3d56410794513f7061505784b60055ce1f0dd814def5027ddc6005b98cc3f6
- Size of remote file:
- 681 MB
- SHA256:
- 76877281ed10a4a71f6c2aa0edd286a9e5e23a852a05d13fb05965b464a305bb
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