Instructions to use ByteDance/ID-Patch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ByteDance/ID-Patch with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ByteDance/ID-Patch", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 70d24b7f6ebfdfd0119c925aabc05528076963737daac5241c74d899174bf017
- Size of remote file:
- 5 GB
- SHA256:
- 01219cd449154a04c479a45e012d40488d99d9353a661cd5cb486af42e3912ff
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