MakeAnything - A consistent image sequence generation framework open-sourced by the National University of Singapore.
MakeAnything is a multi-domain procedural sequence generation framework based on the Diffusion Transformer, developed by the Show Lab team at the National University of Singapore. It generates high-quality sequences from text descriptions or image inputs...
What is MakeAnything?
MakeAnything, developed by the Show Lab team at the National University of Singapore, is a multi-domain procedural sequence generation framework based on the Diffusion Transformer. It generates high-quality step-by-step tutorials from text descriptions or image inputs. MakeAnything balances generalization ability and task-specific performance using asymmetric low-rank adaptation (LoRA) and introduces the ReCraft model to achieve reverse generation from image to process. MakeAnything has built a large dataset covering 21 domains (such as painting, crafts, and cooking), containing over 24,000 labeled sequences. The MakeAnything framework can generate logically coherent and visually consistent tutorials, supporting both text-to-process and image-to-process generation paradigms, demonstrating excellent performance and generalization ability across various tasks.
MakeAnything's main functions
- From Text Generation TutorialBased on the text description provided by the user (such as "how to paint an oil painting" or "how to make a LEGO model"), it automatically generates detailed step-by-step tutorials to help users complete complex creative tasks step by step.
- From Image Generation TutorialsUsers upload an image of a finished product (such as a painting or a handmade item), and MakeAnything can reverse-engineer the creation process of that work, showing the steps from nothing to something.
- Cross-domain generation capabilitiesIt supports multiple fields (such as painting, crafts, cooking, 3D modeling, etc.) and generates logically coherent and visually consistent tutorials based on different tasks.
- High-quality tutorial outputThe generated tutorials are logically clear and maintain a high degree of consistency with the input text or images.
The technical principles of MakeAnything
- diffusion converterIt utilizes the core idea of a diffusion model to generate image sequences by progressively removing noise. Combined with the Transformer architecture, it processes complex visual and textual information to generate high-quality procedural sequences.
- Asymmetric low-rank adaptationThis approach freezes the pre-trained encoder parameters and fine-tunes only the decoder, balancing the model's generalization ability with task-specific performance. It is suitable for tasks with scarce or imbalanced data, effectively avoiding overfitting.
- ReCraft ModelThis approach decomposes static images into a step-by-step creation process, enabling reverse generation from image to process. A multimodal attention mechanism is used to incorporate image conditional information into the generation process, ensuring that the generated sequence is highly consistent with the input image.
- Multi-domain datasetsA large dataset containing over 24,000 labeled sequences across 21 domains was constructed, providing rich data support for model training.
- Conditional flow matching loss: Optimize the conditional vector field in the noise removal process to ensure that the generated sequence is logically and visually consistent with the input conditions (text or image).
MakeAnything's project address
- GitHub repository:https://github.com/showlab/MakeAnything
- HuggingFace model library:
- arXiv technical paper:https://arxiv.org/pdf/2502.01572
Application scenarios of MakeAnything
- EducationIt provides students with step-by-step tutorials in painting, crafts, cooking, etc., to aid their learning.
- Artistic CreationIt helps artists throughout the creative process, from initial idea to finished product, and inspires them.
- Craftsmanship inheritanceThe process of reversing production steps from finished product images helps to record and pass on traditional crafts.
- Product Development: Quickly generate production processes based on design concepts to optimize development efficiency.
- Content creationGenerate fun tutorials or videos for use on social media and in entertainment.