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Facts
| Fact | guanjq/targetdiff The official implementation of 3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction (ICLR 2023) | Tencent-Hunyuan/Hunyuan3D-2 High-Resolution 3D Assets Generation with Large Scale Hunyuan3D Diffusion Models. |
|---|---|---|
| Stars | 351 | 15,330 |
| Net stars, last 30 days | +4 | +326 |
| Largest star day | +2 on Jul 12, 2026 | +101 on Jul 11, 2026 |
| Milestone reached in the record | None | None |
| Forks | 53 | 1,588 |
| Language | Python | Python |
| License | No license | Other license |
| Created | Mar 2, 2023 | Jan 21, 2025 |
| Last push | Jan 10, 2024 | Oct 28, 2025 |
| Archived | No | No |
| Health facts: Maintenance | 1 of 4 checks (2 without data) | 1 of 4 checks |
| Health facts: Releases | 0 of 3 checks (2 without data) | 0 of 3 checks (1 without data) |
| Health facts: Security practice | 0 of 1 check (1 without data) | 0 of 1 check |
| Health facts: Community files | 0 of 3 checks (2 without data) | 1 of 3 checks |
| Commits, last 13 weeks | No data | 0 |
| Weeks with commits, last 52 | No data | 0 of 52 |
| In the Repository Index since | Oct 22, 2026 | Oct 22, 2026 |
Topics that all of them have on the Repository Host: diffusion-models. The facts come from the Repository Host and from the Star History that AwesomeRepos records. A shared topic does not mean that one repository can replace another. Each Health Facts row is one dimension alone. AwesomeRepos does not add them into one score. How each check is defined