NOTE (Sept 7th 2023): At this point you may want to use Transformers.js instead since it's well-maintained and supports quantized models which are much smaller. That said, if you don't want to include the whole Transformers.js library in your app (as of writing I'm not sure if tree-shaking is supported yet), then you can still directly use ONNX Runtime Web with the quantized models produced by the Transformers.js conversion scripts.

Here are the relevant modules for Transformers.js:

Here's a full working example that uses Transformers.js:

let quantized = false; // change to `true` for a much smaller model (e.g. 87mb vs 345mb for image model), but lower accuracy
let { AutoProcessor, CLIPVisionModelWithProjection, RawImage, AutoTokenizer, CLIPTextModelWithProjection } = await import('https://cdn.jsdelivr.net/npm/@xenova/transformers@2.5.4/dist/transformers.js');
let imageProcessor = await AutoProcessor.from_pretrained('Xenova/clip-vit-base-patch16');
let visionModel = await CLIPVisionModelWithProjection.from_pretrained('Xenova/clip-vit-base-patch16', {quantized});
let tokenizer = await AutoTokenizer.from_pretrained('Xenova/clip-vit-base-patch16');
let textModel = await CLIPTextModelWithProjection.from_pretrained('Xenova/clip-vit-base-patch16', {quantized});

function cosineSimilarity(A, B) {
  if(A.length !== B.length) throw new Error("A.length !== B.length");
  let dotProduct = 0, mA = 0, mB = 0;
  for(let i = 0; i < A.length; i++){
    dotProduct += A[i] * B[i];
    mA += A[i] * A[i];
    mB += B[i] * B[i];
  }
  mA = Math.sqrt(mA);
  mB = Math.sqrt(mB);
  let similarity = dotProduct / (mA * mB);
  return similarity;
}

// get image embedding:
let image = await RawImage.read('https://i.imgur.com/RKsLoNB.png');
let imageInputs = await imageProcessor(image);
let { image_embeds } = await visionModel(imageInputs);
console.log(image_embeds.data);

// get text embedding:
let texts = ['a photo of an astronaut'];
let textInputs = tokenizer(texts, { padding: true, truncation: true });
let { text_embeds } = await textModel(textInputs);
console.log(text_embeds.data);

let similarity = cosineSimilarity(image_embeds.data, text_embeds.data);
console.log(similarity);

Note that the above code uses clip-vit-base-patch16 instead of what's used in this repo, clip-vit-base-patch32 - not sure which is best, but you can change patch16 to patch32 in the above code if you want to test it. Also note that you'll see some GET/404 errors in the console - that's expected, since Transformers.js tries to load models locally first. There's probably a way to disable this.

Transformers.js also has a ton of other models available, and it's quite easy to use. E.g. here's an example of a text embedding / retrieval model:

let { pipeline } = await import('https://cdn.jsdelivr.net/npm/@xenova/transformers@2.5.4/dist/transformers.js');
let extractor = await pipeline('feature-extraction', 'Xenova/e5-large-v2');
let dotProduct = (vec1, vec2) => vec1.reduce((sum, val, i) => sum + val * vec2[i], 0);

let passage1 = await extractor('passage: She likes carrots and celery.', { pooling: 'mean', normalize: true });
let passage2 = await extractor('passage: This is a good calculus guide.', { pooling: 'mean', normalize: true });
let query = await extractor('query: Taking care of rabbits', { pooling: 'mean', normalize: true });

let similarity1 = dotProduct(query.data, passage1.data);
let similarity2 = dotProduct(query.data, passage2.data);

OpenAI CLIP JavaScript

OpenAI's CLIP model ported to JavaScript using the ONNX web runtime. I also got the LiT models working here.

Minimal demos:

Image model: https://josephrocca.github.io/openai-clip-js/onnx-image-demo.html
Text model: https://josephrocca.github.io/openai-clip-js/onnx-text-demo.html

Example applications:

Sorting/searching a local folder of images using a text prompt: https://github.com/josephrocca/clip-image-sorter

Server side:

Notes:

The model files are about 4x larger than they actually need to be - params are float32 instead of uint8. If you're using CLIP in a "real" web app, you should probably quantize it. @minimaxir has done it (1, 2), and that model worked first try with ORT Web (which is amazing), but it outputs a 768 element vector instead of 512, which I think is because @minimaxir's model is missing the final projection head which puts image embeddings into same-sized space as text embeddings. I had a quick attempt at it in the ONNX export notebook (see cell after ONNX conversion), but it doesn't seem to be working. If you investigate this and get it working, please open an issue. Thanks to @congraIiIso on Twitter for bringing the uint8 quantization to my attention! Edit: I've managed to get quantization "working", but the embeddings that the quantized models produce don't seem to be close enough to the correct embeddings. See this comment for details.
You should use bicubic resizing of images to get the most accurate embeddings. Here's a simple copy-paste JavaScript bicubic resize + center crop function that uses wasm-vips.
- More info: In the above-linked image model demo, the image encoder demo uses the default HTML5 canvas resize algorithm when pre-processing the input image. This is apparently not bicubic (which is what OpenAI's CLIP repo uses). This leads to the embeddings being a bit different to what Pytorch gives. I'm not sure if this will end up mattering in practical usage, but in case it matters to you, you should not use canvas resizing, and instead use an actual bicubic resizer. For example, this astronaut pic has this embedding with the Pytorch model: [0.3181,0.3054,-0.1548,0.0767,-0.1699,0.1320,-0.2974,-0.1940,-0.3052,0.2299,0.1995, -0.3025,0.3108,-0.2305,0.2368, ...] and ONNX Runtime Web (wasm backend) gives: [0.3635,0.3301,-0.1093,0.0598,-0.1526,0.1127,-0.3373,-0.1544,-0.2627,0.2372,-0.2012,-0.3182,0.3022,-0.2940,0.2227, ...]. If you pre-resize the image with a bicubic algorithm (like this - the default image used in the demo), then the embeddings are basically the same.
The ONNX text model produces embeddings that seem to be close enough to the Pytorch model based on "eyeballing" some image/text matching tasks, but note that there are some non-trivial-looking differences. Again, I don't know whether these differences are enough to significantly affect real-world usage. Please feel free to open an issue if you manage to run some proper tests. Here are the embeddings for "a portrait of an astronaut with the American flag" in Pytorch and ONNX:
- Pytorch: [-0.16650, 0.05167, -0.15320, 0.44922, 0.20642, -0.29565, 0.04041, -0.41064, -0.15015, 0.31934, -0.06842, -0.25464, 0.12311, -0.09509, 0.24109, -0.04883, 0.26074, 0.00045, 0.20972, 0.36987, ...]
- ONNX: [-0.19535, 0.01808, -0.09647, 0.61671, 0.17760, -0.30735, -0.03580, -0.31977, -0.21485, 0.38863, 0.05983, -0.24685, 0.17829, -0.16579, 0.17799, -0.07826, 0.28496, -0.02429, 0.11830, 0.37698, ...]
Models are served to the browser directly from this HuggingFace 🤗 repo.
Regarding model conversion:
- I used this Colab notebook to convert the Pytorch models to ONNX/tfjs/etc.
- I used this Colab notebook to convert weights from float16 to float32 because the ONNX web runtime doesn't currently support float16. This means that the model files are twice as big as they should be (issue).
- See the comment at the top of this file for an extra conversion step that needs to be applied to the text model to avoid this error.

Todo (maybe):

Try tfjs runtime if this issue gets resolved.
Try to get tflite model exporting and working.

License

josephrocca/openai-clip-js

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