Merge Diffusers models into a new repository
Search and find similar datasets
Find and view synthetic data pipelines on Hugging Face
Generate synthetic datasets for AI training
Create a domain-specific dataset project
A collection of parsers for LLM benchmark datasets
Evaluate evaluators in Grounded Question Answering
Perform OSINT analysis, fetch URL titles, fine-tune models
Manage and label datasets for your projects
Display trending datasets from Hugging Face
Clean and process datasets
Search narrators and view network connections
The SDXL/SD1.5 DARE Merger (experiment) is a dataset creation tool designed to merge Diffusers models into a new repository. It is an experimental feature aimed at enabling the combination of different model versions or configurations to create a unified dataset. This tool is particularly useful for researchers and developers working with diffusion models, allowing them to experiment with model merging and explore new possibilities in dataset creation.
• Model Merger: Enables the combination of two Diffusers models into a single repository.
• Compatibility: Works with SDXL and SD1.5 models.
• Dataset Creation: Facilitates the generation of new datasets by merging existing ones.
• Repository Support: Allows users to create a new repository from merged models.
• Experimentation: Designed as an experimental tool for exploring model merging techniques.
Is the SDXL/SD1.5 DARE Merger (experiment) suitable for production use?
No, it is currently an experimental tool and should be used for research and development purposes only.
Do I need Git to use the SDXL/SD1.5 DARE Merger (experiment)?
Yes, Git is required to clone and manage the repositories during the merging process.
Can I merge models other than SDXL and SD1.5?
The tool is specifically designed for SDXL and SD1.5 models. Support for other models is not available at this time.