Instructions to use ByteDance/Hyper-SD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ByteDance/Hyper-SD with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("ByteDance/Hyper-SD") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
cfg-sdxl lora seems working poorly in animatediff
#30
by superag - opened
this workflow will show clearly the compare results. changing animatediff beta scheduler to linear SDXL will help a bit with the stableness, but you can still see the output being highly unstable compare to normal 8 step lora
https://drive.google.com/file/d/1Rzz7lZEQrQ3fOvQfBhUie1pjkeK-O2nL/view?usp=sharing

