A fine-tune of BeichenZhang/LongCLIP-L -- Long-CLIP ViT-L/14 expanded to 248 tokens.
Update 12/AUG/2024:
New BEST model, custom loss with label smoothing. Small gain for a diverse and large good quality dataset, but big relative gains for an overfit-prone fine-tune (small batch size, 1 GPU, narrow dataset of e.g. 'sneakers', etc.) are possible! Fine-tune your model with the provided code for GmP-Smooth: https://github.com/zer0int/Long-CLIP
The fine-tune has an improved ImageNet/ObjectNet accuracy of 0.89 (original Long-CLIP by the authors:~0.81)**.
Made possible with Geometric Parametrization (GmP):
"Normal" CLIP MLP (multi-layer perceptron):
(mlp): Sequential(
|-(c_fc): Linear(in_features=1024, out_features=4096, bias=True)
| (gelu): QuickGELU()
|-}-(c_proj): Linear(in_features=4096, out_features=1024, bias=True)
| |
| |-- visual.transformer.resblocks.0.mlp.c_fc.weight
| |-- visual.transformer.resblocks.0.mlp.c_fc.bias
|
|---- visual.transformer.resblocks.0.mlp.c_proj.weight
|---- visual.transformer.resblocks.0.mlp.c_proj.bias
GmP CLIP MLP:
Weight decomposition into:
- radial component 'r' as norm of pre-trained weights
- angular component 'theta' as normalized direction
-> preserves weight vectors' directionality and magnitude
(mlp): Sequential(
|-(c_fc): GeometricLinear()
| (gelu): QuickGELU()
|-}-(c_proj): GeometricLinear()
| |
| |-- visual.transformer.resblocks.0.mlp.c_fc.r
| |-- visual.transformer.resblocks.0.mlp.c_fc.theta
| |-- visual.transformer.resblocks.0.mlp.c_fc.bias
|
|---- visual.transformer.resblocks.0.mlp.c_proj.r
|---- visual.transformer.resblocks.0.mlp.c_proj.theta
|---- visual.transformer.resblocks.0.mlp.c_proj.bias
(Same thing for [text] transformer.resblocks)
✅ The model / state_dict I am sharing was converted back to .weight after fine-tuning - alas, it can be used in the same manner as any state_dict, e.g. for use with ComfyUI as the SDXL / SD3 Text Encoder using SeaArtLab/ComfyUI-Long-CLIP custom nodes! 🤗
** For details on training and those numbers / the eval, or for just fine-tuning the model yourself, see: https://github.com/zer0int/Long-CLIP
@article{zhang2024longclip,
title={Long-CLIP: Unlocking the Long-Text Capability of CLIP},
author={Beichen Zhang and Pan Zhang and Xiaoyi Dong and Yuhang Zang and Jiaqi Wang},
journal={arXiv preprint arXiv:2403.15378},
year={2024}
}
Pre-trained CLIP model by OpenAI, License: MIT License