TIPO-v2.1-1B-A200M

Model overview

**TIPO-v2.1-1B-A200M** is a **1B-A200M** sparse model. 991M total parameters, ~200M active per token, plus a ~50M embedding table. It is a full retrain of TIPOv2 on a **corrected dataset**, for **longer**, at **4096 context**, in **pure fp16**. TIPO is a framework for improving Text-to-Image generation by *text presampling*: a small language model expands a short user prompt into a detailed one before the diffusion model ever sees it. Pre-sampling a smaller distribution by narrowing the range indicated by a brief prompt to a more specific description which matches the original prompt allows the diffusion model to have more information to work with, while preserving overall diversity and fidelity. Instead of asking the user to write 200 tokens of booru tags and natural language, TIPO samples that expansion from a distribution learned over real caption data. This is **v2.1**. Same architecture and same data sources as v2, retrained from scratch after three defects were found in how v2's training examples were built.

Key features

特点摘要待依据来源补齐，不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

Source prices & cost analysis

Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

Frequently asked questions

Sources & traceability

许可证：来源未披露，须进一步核验。模型介绍与第三方评测均以原来源的具体版本和方法为准。