Views
No views yet
embedding checkpoint (one of three TPIPS models, each in its own repo — see
the table at the bottom). Code and full docs: https://github.com/adobe-research/TPIPS.(text, image) is encoded independently into an L2-normalised embedding. The pairwise score is cos(e_a, e_b) (higher = more similar).
Odd-one-out probabilities are a softmax over the three "other-pair" scores
divided by the temperature; 2AFC compares the two reference-candidate scores.
The pairwise score is the model's raw output (temperature is applied at the
probability step).| Property | Value |
|---|---|
| Base model | Qwen/Qwen3-VL-Embedding-8B |
| Pairwise score | cos(e_a, e_b) |
| Fine-tuning | LoRA (r=16, α=32) on the LLM layers |
| Pooling | last-token |
| Temperature | 0.05 (applied at the probability step) |
Prompt X | Represent the similarity of the image based on X. |
pip install tpipsembedding model:1import tpips
2from PIL import Image
3
4model = tpips.load_model("embedding", device="cuda")
5a = Image.open("a.jpg").convert("RGB")
6b = Image.open("b.jpg").convert("RGB")
7
8similarity = model.similarity(a, b, factor="lighting") # higher is more similar
9distance = model.distance(a, b, factor="lighting") # lower is more similar| Model | Repo |
|---|---|
| Embedding (late fusion) | sywang/TPIPS-Embed-Qwen3VL-8B |
| Early Fusion | sywang/TPIPS-EarlyFusion-Qwen3VL-8B |
| Activation Distance | sywang/TPIPS-ActDiff-Qwen3VL-8B |