Instructions to use zz001/llll with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use zz001/llll with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("InstantX/InstantID", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("zz001/llll") prompt = "wewe" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
| tags: | |
| - text-to-image | |
| - stable-diffusion | |
| - lora | |
| - diffusers | |
| - template:sd-lora | |
| widget: | |
| - text: wewe | |
| parameters: | |
| negative_prompt: eretr | |
| output: | |
| url: images/test.png | |
| base_model: InstantX/InstantID | |
| instance_prompt: dsfds | |
| license: bigscience-openrail-m | |
| # fcdsf | |
| <Gallery /> | |
| ## Model description | |
| fgsdg | |
|  | |
| ## Trigger words | |
| You should use `dsfds` to trigger the image generation. | |
| ## Download model | |
| [Download](/zz001/llll/tree/main) them in the Files & versions tab. | |