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Make a Multimodal AI

Generate Stylized Art and Captions from a Single Seed

Overview

This project blends generative creativity with semantic understanding. Your goal is to build a multimodal model that outputs both:

  • An image in a selected artistic style (e.g., oil painting, sculpture, engraving).
  • A caption that accurately describes the generated image.

Both outputs must originate from a shared latent input — a single seed. The image and caption should reflect a unified concept, not two disconnected processes.

Dataset for reference:
Art Images Dataset — Drawing, Painting, Sculpture, Engraving

Objective

Design a system that can:

  1. Generate an image in a specific art style.
  2. Simultaneously generate a caption that reflects the content and style of the image.
  3. Ensure both outputs are generated from a shared latent representation or seed — not a sequential pipeline.

This is about building a joint generative system, where semantics are shared and outputs are inherently aligned.

What You Will Build

1. Style Selection and Dataset Preparation

  • Choose one artistic style from the dataset (e.g., engravings, sculptures).
  • Clean, resize, and normalize the dataset.
  • Use a captioning model (e.g., BLIP2) to generate accurate textual descriptions for each image.
  • Align each image with its corresponding caption for training.

2. Unified Multimodal Generator

Develop a single model or tightly integrated architecture that, given a random seed, produces:

  • A stylized image.
  • A grounded caption that describes that exact image.

This should not be a two-stage pipeline (e.g., image → caption). Both outputs must emerge from the same latent representation.

Suggested Architectures

  • Latent Diffusion Model + Text Decoder

    • Use a latent diffusion model to generate images.
    • Attach a transformer decoder to generate text from the same latent.

  • GAN-based Generator + Caption Head

    • Extend models like StyleGAN or VQGAN to output a text embedding.
    • Train both image and caption outputs from the same noise vector.

  • Multimodal Token Stream Model

    • Inspired by DALL·E or Flamingo. Decode both image and text from a single token stream using a shared transformer architecture.

  • Contrastive Latent Mapping

    • Generate both outputs from the same seed and align them in shared latent space using a contrastive loss.

Bonus: Ensure that captions capture not just objects, but also artistic elements (e.g., "an oil painting of a stormy sea" vs. "a wave").

Sample Interface

# Unified generation from a single seed
seed = get_random_seed()
image, caption = multimodal_model(seed)

return {
  "image": image,
  "caption": caption
}

Deliverables

  1. Working codebase for:

    • Dataset preparation and caption alignment.
    • Model training and inference.

  2. A script or demo (e.g., Colab or Gradio) that:

    • Accepts a seed.
    • Generates and displays both image and caption.

  3. Sample output images and captions.

  4. A README.md including:

    • Chosen style and preprocessing methodology.
    • Model architecture and reasoning.
    • Training approach and trade-offs.
    • Scalability plan (e.g., supporting 100,000+ generations).

Evaluation Criteria

Submissions will be evaluated based on the following:

  • Scalability: Can your system handle large datasets or long training runs efficiently?
  • Multimodal Coherence: Do the image and caption clearly correspond?
  • Style Consistency: Is the selected art style reflected reliably in the outputs?
  • Code Quality: Is the implementation clean, modular, and reproducible?
  • Creative Architecture: Innovative design decisions are encouraged.

Technical Recommendations

  • Use BLIP2, OFA, or GIT for dataset captioning.
  • Experiment with Stable Diffusion + LoRA for lightweight image generation.
  • Use JSONL or Parquet to store structured data at scale.
  • Track progress and visualizations with Weights & Biases, TensorBoard, or custom dashboards.

Bonus Considerations

  • Allow conditioning on both prompt and seed.
  • Make captions style-aware (e.g., "baroque portrait of...").
  • Implement latent space interpolation for smooth transitions.
  • Package a shareable demo (Gradio or HuggingFace Space).

Contact

If you want to brainstorm approaches or share early prototypes, feel free to reach out. soumyadeep [at] dashtoon.com

Summary

  • Select a visual art style.
  • Build a unified model that generates both image and caption from a shared seed.
  • Focus on semantic alignment, consistent style, and scalable design.
  • Submit a demo, clean code, and sample results.

We look forward to seeing what you create. — Dashverse Research Team