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Makeanything
Overview :
MakeAnything is a diffusion transformer-based model focused on multi-domain procedural sequence generation. By combining advanced diffusion models and transformer architecture, it can generate high-quality, step-by-step creative sequences, such as paintings, sculptures, icon designs, and more. Its main advantage lies in its ability to handle generative tasks across multiple domains and quickly adapt to new domains with a small number of samples. Developed by the Show Lab team at the National University of Singapore, this model is currently available as open-source, aiming to promote the development of multi-domain generation technology.
Target Users :
This model is ideal for designers, artists, researchers, and developers interested in generative AI who require multi-domain procedural creation. It helps users quickly generate high-quality creative sequences, improve creative efficiency, and explore new art forms.
Use Cases
Use MakeAnything to generate a 9-step painting sequence from sketch to complete painting.
Utilize the Asymmetric LoRA model to generate creative sequences in specific domains based on text prompts, such as the LEGO assembly process.
Transform an image into a step-by-step sculpture creation sequence using the Recraft Model, showcasing the creative process from scratch.
Features
Supports multi-domain procedural sequence generation, covering various fields such as painting, sculpture, and icon design.
Provides two model architectures: Asymmetric LoRA and Recraft Model, for text-to-sequence and image-to-sequence generation, respectively.
Can quickly adapt to new domains with a small number of samples, demonstrating good generalization ability.
Offers pre-trained model weights and training scripts, allowing users to perform local training and fine-tuning.
Supports high-resolution (e.g., 1024x1024 and 1056x1056) sequence generation, suitable for high-quality creations.
Provides a Gradio application interface for users to have online experiences on Hugging Face Space.
Supports custom dataset training, allowing users to prepare data and train models according to their needs.
How to Use
1. Clone the MakeAnything repository and navigate to the project directory.
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2. Create and activate a Python environment, installing the necessary dependencies.
3. Choose either Asymmetric LoRA or Recraft Model based on your needs and download the corresponding pre-trained weights.
4. Prepare your dataset, organizing text prompts and image files as required, and write a configuration file.
5. Use the provided training scripts to train the model or use the inference scripts for generation tasks.
6. For online experience, interactively generate content through the Gradio application on Hugging Face Space.
7. Adjust model parameters or datasets based on the generated results to optimize the generation effect.
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