Seed Diffusion: Validating High-Speed Language Models for Next-Generation Architectures

Seed Diffusion is an experimental language model, launched by the ByteDance Seed team in conjunction with the Academy of Intelligent Industry Research (AIR) at Tsinghua University. This website is a technology demonstration platform for the model. The model is based on the discrete diffusion technique, and its main goal is to explore the feasibility of the next-generation language modeling infrastructure framework. It conducts experiments in the specific domain of code generation as a way to validate the potential of its architecture. Unlike traditional autoregressive (AR) models, which are generated verbatim, the diffusion model is generated in parallel, progressively refining from a fuzzy draft of the whole to a complete result. This technique allows Seed Diffusion Preview to achieve inference speeds of up to 2,146 characters (tokens) per second, which is 5.4 times faster than a traditional model of the same size, while maintaining comparable levels of performance in multiple code generation benchmarks. It is designed to address the core pain points of existing large models in terms of inference speed and global planning capabilities, and provides new solution ideas for dealing with more complex structured inference problems.

Function List

• High-speed code generationThe core feature of the : model is its extremely fast inference speed, capable of reaching 2,146 tokens/s, which is substantially ahead of autoregressive models of the same size.
• parallel decoding: The use of non-serialized parallel generation overcomes the latency bottleneck of verbatim output of traditional models and allows for simultaneous generation of content from a global perspective.
• Code Performance: Performance is comparable to autoregressive models of the same size on several publicly available code review benchmarks (e.g. LiveCodeBench, Bigcode Bench, Mbpp, Human Eval).
• Code editing skills: Demonstrate the intrinsic advantages of the diffusion modeling framework and outperform traditional models in code editing tasks that require global perspective planning.
• Structured a priori learning:: Through the technique of "constraint order diffusion", the model learns and understands the logical dependencies inherent in the code, such as the need for variables to be declared before they are used.
• Two-stage diffusion training: The training process is divided into two phases, mask-based diffusion training followed by edit-based diffusion training, to improve the model's ability to synthesize from local contextual complementation to global code rationality.

Using Help

This website is a technology demonstration and experience platform for the Seed Diffusion model. It is a tool that can be used directly online, so there is no complicated installation process. Users visiting the site can interact directly with the model and experience its powerful code generation and editing capabilities.

Core Operating Procedures

As a model centered on code generation and language understanding, its operation is mainly done through text input boxes. The following is a detailed description of the usage steps and functions:

1. Visit the experience page:
   Open the URL in your browser https://studio.seed.ai/exp/seed_diffusion/ You can access the experience screen of the model.
2. Understanding Interface Layout:
   Typically, this type of demo interface will have a main input box at its core for user input commands (Prompt), and an output box next to or below it for displaying the code or text generated by the model. There may also be some parameter setting options attached, such as selecting a model version, adjusting the length of generation, etc., but as a technical preview, the interface is likely to remain simple and focused on the core functionality.
3. Perform code generation:
   • Identify needs: In the input box, clearly describe in natural language the function of the code you want to generate. The more specific and clear the description, the more the results generated by the model will meet expectations.
     • Example 1 (generating function): "Write a function in Python that takes a list of integers and returns all the even numbers in the list."
     • Example 2 (Generating Classes): "Please create a class named 'Uploader' in JavaScript that should contain a constructor to receive a server URL and a 'upload' method that takes a file object and simulates the upload process."
   • Send command: After entering a description, click "Generate" or a similar button.
   • View Results: The model will utilize its high-speed parallel decoding capability to quickly generate complete code snippets in the output frame. With its speed of up to 2146 tokens/s, even longer code blocks can be completed in a short time.
4. Experience code editing and modification:
   One of the major strengths of the diffusion model is its powerful global planning capability, which makes it excel at code editing tasks.
   • Provide original code: Paste an existing piece of code into the input box.
   • Requests for amendments: After the code, clearly state your modification needs in natural language.
     • typical example:

       # 原始代码
       def calculate_sum(a, b):
       return a + b
       # 修改指令
       # 请将上面的函数重构，增加一个参数c，并使其能够计算三个数的和。
       

   • Get the refactored code: The model understands the intent of your changes and generates a new, conforming version of the code. Because it understands the structure of the code from a global perspective, it performs more reliably than traditional models when performing tasks such as variable renaming and logic refactoring.
5. Explore structured comprehension of models:
   Seed Diffusion Preview has been trained in "constrained sequential diffusion" to better understand causal and dependency relationships in code. You can test this with some specific instructions.
   • Test variable declarations: "Write a program that uses the variable x before declaring it." The model should either reject this logical error or automatically correct it to declare before using, thus demonstrating its understanding of a priori knowledge of code structuring.

Real-world experience with technological advantages

• Instant Feedback: When traditional models generate long code, you can see the text popping up one by one. With Seed Diffusion, you'll feel like the results are "instantaneous", which greatly improves productivity for developers who need to iterate and test code quickly.
• More reliable reconfiguration: When you ask to refactor a piece of code, the model doesn't just do a simple text replacement, it understands the logic of the entire block of code. This means that when it changes a function name or variable, it will update all calls to that function or variable at the same time, reducing the likelihood of errors due to oversights.

This demo site is a window to the next generation of language modeling technology, where users can visualize the breakthroughs in speed and global comprehension that diffusion modeling offers through hands-on experience.

application scenario

1. Software Development Assistance
   Developers can utilize its high-speed generation capabilities to quickly create functions, classes, test cases, or code snippets during the coding process, allowing them to focus more on system design and business logic.
2. Code refactoring and optimization
   For existing old code, it can be modernized and refactored with the global editing capabilities of the model. For example, old process code can be rewritten to an object-oriented structure or the code style can be optimized according to new programming specifications.
3. Education and learning
   Programming beginners can learn how to implement specific features and compare the differences between implementations by interacting with the model. The model can act as a 24/7 programming tutor, providing instant answers and code samples.
4. Technology prototyping
   When a technical idea needs to be validated quickly, Seed Diffusion Preview can be used to quickly generate prototype code to assess the feasibility of a solution before committing significant development resources.

QA

1. What is Seed Diffusion Preview?
   It is an experimental diffusion language model released by the ByteDance Seed team, focusing on verifying the feasibility of discrete diffusion techniques as the underlying framework for the next generation of language models. Its main features are extremely fast code generation and powerful global planning capabilities.
2. What is the difference between this model and a model like GPT?
   The biggest difference lies in the generation method. autoregressive models (AR) such as GPT generate content sequentially, word by word (token), like popping out one word at a time. Diffusion models such as Seed Diffusion (DD) generate in parallel, starting with a fuzzy draft of the whole, and then continuously refining it to produce a complete result at once. This gives it a huge speed advantage, and is especially suitable for generating structured data such as code.
3. Why is it going so fast?
   Because it adopts a parallel decoding scheme, it gets rid of the limitation that traditional models can only generate one word at a time. Through the optimization of "same-strategy learning" and other techniques, the model learns to complete high-quality generation in fewer steps, and its inference speed can reach 2146 tokens/s, which is 5.4 times faster than that of the same-size autoregressive model.
4. Can it only be used to generate code?
   Currently, the model has structured code generation as its main area of experimentation and demonstration, as it best represents the advantages of the diffusion model for structural understanding and global planning. Although it can technically be applied to natural language, the current version has been optimized and reviewed primarily for code tasks.