AIsplain available AI models / easy download links

README.md

@@ -3,13 +3,33 @@ license: mit
 datasets:
 - SPRIGHT-T2I/spright_coco
 ---
-## Update 03/SEP/2024:
+## Update 03/SEP/2024 / edit 05/AUG:
 
-Improved CLIP-L for use with Flux.1 (improved TEXT and prompt detail adherence). Trained with high temperature of 0.1 + tinkering. Otherwise, same GmP-smooth-labels code as previous model fine-tune; same dataset (see below for link to GitHub).
+## 👋 Looking for a Text Encoder for Flux.1 (or SD3, SDXL, SD, ...) to replace CLIP-L? 👀
+You'll generally want the "TE-only" .safetensors:
 
+- 👉 The "TEXT" model has superior prompt following, especially for text, but also for other details. [DOWNLOAD](https://huggingface.co/zer0int/CLIP-GmP-ViT-L-14/blob/main/ViT-L-14-TEXT-detail-improved-hiT-GmP-TE-only-HF.safetensors)
+- 👉 The "SMOOTH" model can sometimes** have better details (when there's no text in the image). [DOWNLOAD](https://huggingface.co/zer0int/CLIP-GmP-ViT-L-14/blob/main/ViT-L-14-BEST-smooth-GmP-TE-only-HF-format.safetensors)
+- The "GmP" initial fine-tune is deprecated / inferior to the above models. Still, you can [DOWNLOAD](https://huggingface.co/zer0int/CLIP-GmP-ViT-L-14/blob/main/ViT-L-14-GmP-ft-TE-only-HF-format.safetensors) it.
+
+**: The "TEXT" model is the best for text. Full stop. But whether the "SMOOTH" model is better for your (text-free) scenario than the "TEXT" model really depends on the specific prompt. It might also be the case that the "TEXT" model leads to images that you prefer over "SMOOTH"; the only way to know is to experiment with both.
 
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6490359a877fc29cb1b09451/y-B-FimzahYqskNr2MV1C.png)
 
+## 🤓👨‍💻 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) are also golden retrievers: 🥰🐕
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6490359a877fc29cb1b09451/WiyuZLZVyjBTdPwHaVG_6.png)
+
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 ## Update 11/AUG/2024: