Instructions to use Ling277/FaceComposer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Ling277/FaceComposer 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-Fill-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Ling277/FaceComposer") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
FaceComposer
Model description
Official research code for FaceComposer: Learning Coherent and Realistic Face Composition from References.
FaceComposer performs reference-based face composition: given a source portrait, a reference portrait, and selected facial components such as eyebrows, eyes, nose, or mouth, the model edits the source ROI toward the reference while preserving source pose, expression, illumination, and context.
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Model tree for Ling277/FaceComposer
Base model
black-forest-labs/FLUX.1-Fill-dev