Instructions to use JSCS/YUHUI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use JSCS/YUHUI with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("undefined", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("JSCS/YUHUI") prompt = "yuhui" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
| tags: | |
| - flux | |
| - text-to-image | |
| - lora | |
| - diffusers | |
| - fal | |
| base_model: undefined | |
| instance_prompt: yuhui | |
| license: other | |
| # YUHUI | |
| <Gallery /> | |
| ## Model description | |
| ## Trigger words | |
| You should use `yuhui` to trigger the image generation. | |
| ## Download model | |
| Weights for this model are available in Safetensors format. | |
| [Download](/JSCS/YUHUI/tree/main) them in the Files & versions tab. | |
| ## Training at fal.ai | |
| Training was done using [fal.ai/models/fal-ai/z-image-turbo-trainer-v2](https://fal.ai/models/fal-ai/z-image-turbo-trainer-v2). | |