Stable Diffusion Image Variations Model
| Property | Value |
|---|---|
| License | Other |
| Base Model | CompVis/stable-diffusion-v1-3-original |
| Training Hardware | 4 x A6000 GPUs |
| Training Steps | 87,000 |
What is stable-diffusion-image-conditioned?
This is a specialized version of Stable Diffusion that has been fine-tuned to accept CLIP image embeddings rather than text embeddings. It enables the creation of image variations similar to DALLE-2 using the Stable Diffusion framework. The model was trained on LAION-2B dataset using ViT-L/14 image-encoder architecture.
Implementation Details
The model was trained using 4 A6000 GPUs with AdamW optimizer, maintaining a constant learning rate of 0.0001 after a 1,000-step warmup period. The training process utilized a batch size of 24 (6 x 4) and accumulated gradients over 87,000 steps.
- Replaces text encoder with ViT-L/14 image-encoder
- Includes final projection layer to CLIP shared embedding space
- Maintains original Stable Diffusion architecture for image generation
Core Capabilities
- Generate image variations without text prompts
- Create artistic interpretations of input images
- Support for research and creative applications
- Educational and design tool applications
Frequently Asked Questions
Q: What makes this model unique?
This model's uniqueness lies in its ability to generate image variations using CLIP image embeddings rather than text prompts, making it more similar to DALLE-2's variation capability while leveraging Stable Diffusion's architecture.
Q: What are the recommended use cases?
The model is recommended for research purposes, artistic processes, educational tools, and creative applications. It should not be used for generating harmful content, misrepresentation, or commercial purposes without proper safety mechanisms.
