Seed Diffusion - A diffusion language model launched by ByteDance

What is Seed Diffusion?

Seed Diffusion, an experimental diffusion language model developed by ByteDance's Seed team, focuses on code generation tasks. The model achieves significant inference acceleration through key technologies such as two-stage diffusion training, constrained sequential learning, and reinforced efficient parallel decoding. Its inference speed reaches 2146 tokens/s, 5.4 times faster than autoregressive models of similar scale. It performs comparably to autoregressive models in multiple code benchmarks and surpasses them in code editing tasks. Seed Diffusion demonstrates the immense potential of discrete diffusion models as a foundational framework for next-generation generative models.

The main functions of Seed Diffusion

• High-efficiency code generationThrough a parallel decoding mechanism, the code generation speed is significantly improved, with an inference speed of up to 2146 tokens/s, which is 5.4 times faster than an autoregressive model of the same size.
• High-quality code generationIn multiple code benchmark tests, its performance is comparable to that of the autoregressive model, and it outperforms the autoregressive model on code editing tasks.
• Code logic understanding and repairBased on two-stage diffusion training (masking stage and editing stage), the model's ability to understand and repair code logic is enhanced.
• Flexible generation orderIntroducing structured priors into the code allows the model to better handle causal dependencies within the code.

The technical principle of Seed Diffusion

• Two-stage diffusion trainingThe masking phase uses dynamic noise scheduling to replace some code tokens with [MASK] tags, allowing the model to learn local context and pattern completion capabilities. The editing phase introduces insertion/deletion operations based on edit distance constraints, constructing noise that enables the model to re-examine and correct all tokens, avoiding "spurious dependencies" on uncontaminated contexts.
• Constraint order diffusionBy distilling high-quality generated trajectories, the model is guided to master the correct dependencies, thus solving the problem of randomness in the generation order of traditional diffusion models.
• Same strategy learningThis approach optimizes the number of generation steps while maintaining output quality, achieving efficient parallel decoding. A surrogate loss function that minimizes the number of generation steps encourages the model to converge in a more efficient manner.
• Block-level parallel diffusion samplingBased on a block-level parallel diffusion sampling scheme, causal order is maintained between blocks, and KV-caching is used to reuse information from previously generated blocks, thus accelerating the generation process.

Seed Diffusion project address

• Project official websitehttps://seed.bytedance.com/zh/seed_diffusion
• Technical Papers: https://lf3-static.bytednsdoc.com/obj/eden-cn/hyvsmeh7uhobf/sdiff_updated.pdf
• Experience the demo online:https://studio.seed.ai/exp/seed_diffusion/

Application scenarios of Seed Diffusion

• Automatic code generationThe model can quickly generate code prototypes, helping developers start projects efficiently.
• Code editing and optimizationThe model can automatically detect and fix code errors, optimize code performance, and improve code quality.
• Education and TrainingThe model can generate sample code to assist in programming teaching and help students quickly understand programming concepts.
• Software development collaborationAs a team development aid, it provides code completion and suggestions to improve development efficiency.
• Intelligent Programming AssistantIntegrated into the IDE, Seed Diffusion provides intelligent code generation and optimization features, enhancing the development experience.