Instructions to use camenduru/facechain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use camenduru/facechain with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("camenduru/facechain", 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
- cv_ddsar_face-detection_iclr23-damofd
- cv_gpen_image-portrait-enhancement-hires
- cv_ir101_facerecognition_cfglint
- cv_ir_face-recognition-ood_rts
- cv_manual_face-quality-assessment_fqa
- cv_manual_facial-landmark-confidence_flcm
- cv_resnet101_image-multiple-human-parsing
- cv_resnet34_face-attribute-recognition_fairface
- cv_resnet50_face-detection_retinaface
- cv_unet-image-face-fusion_damo
- cv_unet_skin_retouching_torch