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 Settings
- Draw Things
- DiffusionBee
poor quality when used with picxReal
Hi, @CHNtentes
Could you please tell us what sampler you are using right now?
Are the results getting better when using our 8-Step LoRA?
Hi, @CHNtentes
These results should be expected.
We are currently working on CFG-Preserved Hyper-SD15/SDXL that facilitate negative prompts and larger guidance scales.
You may try removing these artifacts by the functionality of negative prompt after releasing.
Hi, @CHNtentes
Could you try different lora weights? Our acceleration lora will inevitably change the base model.
Thanks for your attention.
This is picxReal without lora, 8 steps cfg 4.0. IMO it is cleaner than HyperSD.
@CHNtentes , we have uploaded the CFG-preserved hyper-SD15 LoRA and hyper-SDXL LoRA just now, higher cfg and negative prompts may be helpful in your usecase, looking forward to your use and feedback!


