dalle-2-preview VS CLIP

Compare dalle-2-preview vs CLIP and see what are their differences.

CLIP

CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image (by openai)
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dalle-2-preview CLIP
61 105
1,044 27,161
0.0% 1.7%
1.8 2.4
over 2 years ago 7 months ago
Jupyter Notebook
- MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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dalle-2-preview

Posts with mentions or reviews of dalle-2-preview. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-08-16.

CLIP

Posts with mentions or reviews of CLIP. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2025-01-14.
  • We used GPT-4o for image detection with 350 similar illustrations
    2 projects | news.ycombinator.com | 14 Jan 2025
    Yes, you could implement image similarity search using embeddings: Create embeddings for the entire image set, save the embeddings in a database, and add embeddings incrementally as new images come in. To search for a similar image, create the embedding for the image that you are looking for and compute the cosine similarity between that embedding and the embeddings in your database. The closer the cosine similarity is to 1.0 the more similar the images.

    For choosing a model, the article mentions the AWS Titan multimodal model, but you’d have to pay for API access to create the embeddings. Alternatively, self-hosting the CLIP model [0] to create embeddings would avoid API costs.

    Follow-up question: Would the embeddings from the llama3.2-vision models be of higher quality (contain more information) than the original CLIP model?

    The llama vision models use CLIP under the hood, but they add a projection head to align with the text model and the CLIP weights are mutated during alignment training, so I assume the llama vision embeddings would be of higher quality, but I don’t know for sure. Does anybody know?

    (I would love to test this quality myself but Ollama does not yet support creating image embeddings from the llama vision models - a feature request with several upvotes has been opened [1].)

    [0] https://github.com/openai/CLIP

  • Anomaly Detection with FiftyOne and Anomalib
    4 projects | dev.to | 6 May 2024
    pip install -U huggingface_hub umap-learn git+https://github.com/openai/CLIP.git
  • How to Cluster Images
    5 projects | dev.to | 9 Apr 2024
    We will also need two more libraries: OpenAI’s CLIP GitHub repo, enabling us to generate image features with the CLIP model, and the umap-learn library, which will let us apply a dimensionality reduction technique called Uniform Manifold Approximation and Projection (UMAP) to those features to visualize them in 2D:
  • Show HN: Memories, FOSS Google Photos alternative built for high performance
    11 projects | news.ycombinator.com | 21 Mar 2024
    Biggest missing feature for all these self hosted photo hosting is the lack of a real search. Being able to search for things like "beach at night" is a time saver instead of browsing through hundreds or thousands of photos. There are trained neural networks out there like https://github.com/openai/CLIP which are quite good.
  • Zero-Shot Prediction Plugin for FiftyOne
    6 projects | dev.to | 13 Mar 2024
    In computer vision, this is known as zero-shot learning, or zero-shot prediction, because the goal is to generate predictions without explicitly being given any example predictions to learn from. With the advent of high quality multimodal models like CLIP and foundation models like Segment Anything, it is now possible to generate remarkably good zero-shot predictions for a variety of computer vision tasks, including:
  • A History of CLIP Model Training Data Advances
    8 projects | dev.to | 13 Mar 2024
    (Github Repo | Most Popular Model | Paper | Project Page)
  • NLP Algorithms for Clustering AI Content Search Keywords
    1 project | news.ycombinator.com | 20 Feb 2024
    the first thing that comes to mind is CLIP: https://github.com/openai/CLIP
  • How to Build a Semantic Search Engine for Emojis
    6 projects | dev.to | 10 Jan 2024
    Whenever I’m working on semantic search applications that connect images and text, I start with a family of models known as contrastive language image pre-training (CLIP). These models are trained on image-text pairs to generate similar vector representations or embeddings for images and their captions, and dissimilar vectors when images are paired with other text strings. There are multiple CLIP-style models, including OpenCLIP and MetaCLIP, but for simplicity we’ll focus on the original CLIP model from OpenAI. No model is perfect, and at a fundamental level there is no right way to compare images and text, but CLIP certainly provides a good starting point.
  • COMFYUI SDXL WORKFLOW INBOUND! Q&A NOW OPEN! (WIP EARLY ACCESS WORKFLOW INCLUDED!)
    8 projects | /r/StableDiffusion | 10 Jul 2023
    in the modal card it says: pretrained text encoders (OpenCLIP-ViT/G and CLIP-ViT/L).
  • Stability Matrix v1.1.0 - Portable mode, Automatic updates, Revamped console, and more
    3 projects | /r/StableDiffusion | 3 Jul 2023
    Command: "C:\StabilityMatrix\Packages\stable-diffusion-webui\venv\Scripts\python.exe" -m pip install https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip --prefer-binary

What are some alternatives?

When comparing dalle-2-preview and CLIP you can also consider the following projects:

dalle-mini - DALL·E Mini - Generate images from a text prompt

open_clip - An open source implementation of CLIP.

glide-text2im - GLIDE: a diffusion-based text-conditional image synthesis model

BLIP - PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation

DALL-E - PyTorch package for the discrete VAE used for DALL·E.

sentence-transformers - State-of-the-Art Text Embeddings

latent-diffusion - High-Resolution Image Synthesis with Latent Diffusion Models

segment-anything - The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

clip-interrogator - Image to prompt with BLIP and CLIP

txtai - 💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows

disco-diffusion

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