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Update altCLIP model card (#38306)
* Update altclip.md * Update altclip.md * Update altclip.md * Update altclip.md * Update altclip.md * Update altclip.md * Rename altclip.md to altclip.mdx * Rename altclip.mdx to altclip.md * Update altclip.md * Update altclip.md * Update altclip.md --------- Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
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Emile Aydar
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# AltCLIP
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<div class="flex flex-wrap space-x-1">
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<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
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<div style="float: right;">
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<div class="flex flex-wrap space-x-1">
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<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
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</div>
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## Overview
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# AltCLIP
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The AltCLIP model was proposed in [AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities](https://arxiv.org/abs/2211.06679v2) by Zhongzhi Chen, Guang Liu, Bo-Wen Zhang, Fulong Ye, Qinghong Yang, Ledell Wu. AltCLIP
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(Altering the Language Encoder in CLIP) is a neural network trained on a variety of image-text and text-text pairs. By switching CLIP's
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text encoder with a pretrained multilingual text encoder XLM-R, we could obtain very close performances with CLIP on almost all tasks, and extended original CLIP's capabilities such as multilingual understanding.
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[AltCLIP](https://huggingface.co/papers/2211.06679v2) replaces the [CLIP](./clip) text encoder with a multilingual XLM-R encoder and aligns image and text representations with teacher learning and contrastive learning.
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The abstract from the paper is the following:
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You can find all the original AltCLIP checkpoints under the [AltClip](https://huggingface.co/collections/BAAI/alt-clip-diffusion-66987a97de8525205f1221bf) collection.
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*In this work, we present a conceptually simple and effective method to train a strong bilingual multimodal representation model.
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Starting from the pretrained multimodal representation model CLIP released by OpenAI, we switched its text encoder with a pretrained
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multilingual text encoder XLM-R, and aligned both languages and image representations by a two-stage training schema consisting of
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teacher learning and contrastive learning. We validate our method through evaluations of a wide range of tasks. We set new state-of-the-art
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performances on a bunch of tasks including ImageNet-CN, Flicker30k- CN, and COCO-CN. Further, we obtain very close performances with
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CLIP on almost all tasks, suggesting that one can simply alter the text encoder in CLIP for extended capabilities such as multilingual understanding.*
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> [!TIP]
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> Click on the AltCLIP models in the right sidebar for more examples of how to apply AltCLIP to different tasks.
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This model was contributed by [jongjyh](https://huggingface.co/jongjyh).
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The examples below demonstrates how to calculate similarity scores between an image and one or more captions with the [`AutoModel`] class.
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## Usage tips and example
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The usage of AltCLIP is very similar to the CLIP. the difference between CLIP is the text encoder. Note that we use bidirectional attention instead of casual attention
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and we take the [CLS] token in XLM-R to represent text embedding.
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AltCLIP is a multi-modal vision and language model. It can be used for image-text similarity and for zero-shot image
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classification. AltCLIP uses a ViT like transformer to get visual features and a bidirectional language model to get the text
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features. Both the text and visual features are then projected to a latent space with identical dimension. The dot
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product between the projected image and text features is then used as a similar score.
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To feed images to the Transformer encoder, each image is split into a sequence of fixed-size non-overlapping patches,
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which are then linearly embedded. A [CLS] token is added to serve as representation of an entire image. The authors
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also add absolute position embeddings, and feed the resulting sequence of vectors to a standard Transformer encoder.
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The [`CLIPImageProcessor`] can be used to resize (or rescale) and normalize images for the model.
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The [`AltCLIPProcessor`] wraps a [`CLIPImageProcessor`] and a [`XLMRobertaTokenizer`] into a single instance to both
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encode the text and prepare the images. The following example shows how to get the image-text similarity scores using
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[`AltCLIPProcessor`] and [`AltCLIPModel`].
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<hfoptions id="usage">
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<hfoption id="AutoModel">
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```python
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>>> from PIL import Image
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>>> import requests
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import torch
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import requests
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from PIL import Image
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from transformers import AltCLIPModel, AltCLIPProcessor
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>>> from transformers import AltCLIPModel, AltCLIPProcessor
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model = AltCLIPModel.from_pretrained("BAAI/AltCLIP", torch_dtype=torch.bfloat16)
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processor = AltCLIPProcessor.from_pretrained("BAAI/AltCLIP")
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>>> model = AltCLIPModel.from_pretrained("BAAI/AltCLIP")
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>>> processor = AltCLIPProcessor.from_pretrained("BAAI/AltCLIP")
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url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
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image = Image.open(requests.get(url, stream=True).raw)
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>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
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>>> image = Image.open(requests.get(url, stream=True).raw)
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inputs = processor(text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True)
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>>> inputs = processor(text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True)
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outputs = model(**inputs)
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logits_per_image = outputs.logits_per_image # this is the image-text similarity score
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probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
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>>> outputs = model(**inputs)
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>>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score
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>>> probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
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labels = ["a photo of a cat", "a photo of a dog"]
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for label, prob in zip(labels, probs[0]):
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print(f"{label}: {prob.item():.4f}")
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```
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<Tip>
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</hfoption>
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</hfoptions>
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This model is based on `CLIPModel`, use it like you would use the original [CLIP](clip).
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Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the [Quantization](../quantization/overview) overview for more available quantization backends.
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</Tip>
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The example below uses [torchao](../quantization/torchao) to only quantize the weights to int4.
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```python
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# !pip install torchao
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import torch
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import requests
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from PIL import Image
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from transformers import AltCLIPModel, AltCLIPProcessor, TorchAoConfig
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model = AltCLIPModel.from_pretrained(
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"BAAI/AltCLIP",
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quantization_config=TorchAoConfig("int4_weight_only", group_size=128),
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torch_dtype=torch.bfloat16,
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)
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processor = AltCLIPProcessor.from_pretrained("BAAI/AltCLIP")
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url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
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image = Image.open(requests.get(url, stream=True).raw)
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inputs = processor(text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True)
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outputs = model(**inputs)
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logits_per_image = outputs.logits_per_image # this is the image-text similarity score
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probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
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labels = ["a photo of a cat", "a photo of a dog"]
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for label, prob in zip(labels, probs[0]):
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print(f"{label}: {prob.item():.4f}")
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```
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## Notes
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- AltCLIP uses bidirectional attention instead of causal attention and it uses the `[CLS]` token in XLM-R to represent a text embedding.
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- Use [`CLIPImageProcessor`] to resize (or rescale) and normalize images for the model.
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- [`AltCLIPProcessor`] combines [`CLIPImageProcessor`] and [`XLMRobertaTokenizer`] into a single instance to encode text and prepare images.
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## AltCLIPConfig
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[[autodoc]] AltCLIPConfig
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- from_text_vision_configs
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## AltCLIPTextConfig
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[[autodoc]] AltCLIPTextConfig
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## AltCLIPVisionConfig
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[[autodoc]] AltCLIPVisionConfig
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## AltCLIPProcessor
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[[autodoc]] AltCLIPProcessor
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## AltCLIPModel
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[[autodoc]] AltCLIPModel
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- forward
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- get_text_features
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- get_image_features
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## AltCLIPTextModel
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[[autodoc]] AltCLIPTextModel
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- forward
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## AltCLIPVisionModel
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[[autodoc]] AltCLIPVisionModel
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- forward
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## AltCLIPProcessor
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[[autodoc]] AltCLIPProcessor
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