distilgpt2-stable-diffusion-v2

Maintained By
FredZhang7

distilgpt2-stable-diffusion-v2

PropertyValue
Parameter Count88.2M
LicenseCreativeML OpenRAIL-M
Paperarxiv:2210.14140
AuthorFredZhang7

What is distilgpt2-stable-diffusion-v2?

This is an optimized prompt generation model specifically designed for Stable Diffusion image generation. Built on DistilGPT2, it has been trained on 2.47M carefully curated prompts and fine-tuned for an additional 4.27M steps. The model stands out for its efficiency, delivering 50% faster forward propagation while consuming 40% less disk space and RAM compared to standard GPT2-based prompt generators.

Implementation Details

The model utilizes PyTorch and the Transformers library, implementing a contrastive search approach for generating diverse and coherent prompts. It features cleaned training data that excludes NSFW content, removes duplicates, and ensures high-quality prompt generation.

  • Optimized architecture with 88.2M parameters
  • Supports both comma and no-comma generation styles
  • Implements temperature and repetition penalty controls
  • Includes top-k sampling for diverse outputs

Core Capabilities

  • 25% more prompt variations compared to v1
  • Generates descriptive, coherent image generation prompts
  • Supports batch generation of multiple prompts
  • Customizable generation parameters for different use cases
  • Efficient resource utilization with faster inference

Frequently Asked Questions

Q: What makes this model unique?

The model's primary strengths lie in its optimization for speed and resource efficiency, offering 50% faster processing while maintaining high-quality prompt generation. It's been trained on carefully cleaned data, removing NSFW content and ensuring consistent output quality.

Q: What are the recommended use cases?

This model is ideal for applications requiring fast, high-quality prompt generation for Stable Diffusion image generation, particularly in scenarios where resource efficiency is important. It's suitable for both interactive applications and batch processing needs.

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