Instructions to use zz001/001 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use zz001/001 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("h94/IP-Adapter-FaceID", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("zz001/001") prompt = "1" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("h94/IP-Adapter-FaceID", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("zz001/001")
prompt = "1"
image = pipe(prompt).images[0]111

- Prompt
- 1
- Negative Prompt
- 1
Model description
111
Trigger words
You should use 11 to trigger the image generation.
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h94/IP-Adapter-FaceID