File size: 2,474 Bytes
e610900 3300319 07f6f14 3300319 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 |
---
datasets:
- SPRIGHT-T2I/spright_coco
---
## A fine-tune of [BeichenZhang/LongCLIP-L](https://huggingface.co/BeichenZhang/LongCLIP-L) -- Long-CLIP ViT-L/14 expanded to 248 tokens.
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](https://github.com/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](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](https://github.com/openai/CLIP/blob/main/LICENSE) |