🔥 Update SUMMER 2025: 🔥

• 🌑 https://huggingface.co/zer0int/CLIP-KO-LITE-TypoAttack-Attn-Dropout-ViT-L-14

A fine-tune of CLIP-L. Original model: openai/clip-vit-large-patch14

• Want to feed it yourself? All code for fine-tuning and much more is on my GitHub.

Update 23/SEP/2024:

• Huggingface Transformers / Diffusers pipeline now implemented.
• See here for an example script: Integrating my CLIP-L with Flux.1
• Otherwise, use as normal / any HF model:

from transformers import CLIPModel, CLIPProcessor, CLIPConfig
model_id = "zer0int/CLIP-GmP-ViT-L-14"
config = CLIPConfig.from_pretrained(model_id)

Update 03/SEP/2024 / edit 05/AUG:

👋 Looking for a Text Encoder for Flux.1 (or SD3, SDXL, SD, ...) to replace CLIP-L? 👀

• 👉 The "TEXT" model has superior prompt following, especially for text, but also for other details. DOWNLOAD
• 👉 The "SMOOTH" model can sometimes** have better details (when there's no text in the image). DOWNLOAD
• The "GmP" initial fine-tune is deprecated / inferior to the above models. Still, you can DOWNLOAD it.

🤓👨‍💻 In general (because we're not limited to text-to-image generative AI), I provide four versions / downloads:

• Text encoder only .safetensors.
• Full model .safetensors.
• State_dict pickle.
• Full model pickle (can be used as-is with "import clip" -> clip.load() after bypassing SHA checksum verification).

The TEXT model has a modality gap of 0.80 (OpenAI pre-trained: 0.82).

• Trained with high temperature of 0.1 + tinkering.
• ImageNet/ObjectNet accuracy ~0.91 for both "SMOOTH" and "TEXT" models (pre-trained: ~0.84).
• The models (this plot = "TEXT" model on MSCOCO) are also golden retrievers: 🥰🐕

Update 11/AUG/2024:

• Geometric Parametrization (GmP) (same as before)
• Activation Value manipulation for 'adverb neuron' (same as before)
• NEW: Custom loss function with label smoothing!
• For in-depth details, see my GitHub. 🤗

A fine-tune of OpenAI / CLIP ViT-L/14 that has an unprecedented ImageNet/ObjectNet accuracy of ~0.90 (original pre-trained model / OpenAI's CLIP: ~0.85)**.

"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)

• ** For details on training and those numbers / the eval, please see https://github.com/zer0int/CLIP-fine-tune
• -> You can use "exp-acts-ft-finetune-OpenAI-CLIP-ViT-L-14-GmP-manipulate-neurons.py" to replicate my exact model fine-tune.