marty1885/fast-style-transfer-ttbuda — explained in plain English
Analysis updated 2026-07-18 · repo last pushed 2024-06-09
Test how well an artistic style transfer model performs on Tenstorrent BUDA hardware.
Explore running ONNX model zoo models on specialized AI chips instead of GPUs.
Use as a starting point for benchmarking creative AI workloads on Tenstorrent devices.
| marty1885/fast-style-transfer-ttbuda | 0xallam/my-recipe | 0xhassaan/nn-from-scratch | |
|---|---|---|---|
| Stars | — | — | 0 |
| Language | Python | Python | Python |
| Last pushed | 2024-06-09 | 2022-11-22 | — |
| Maintenance | Dormant | Dormant | — |
| Setup difficulty | hard | moderate | moderate |
| Complexity | 4/5 | 2/5 | 4/5 |
| Audience | researcher | general | developer |
Figures from each repo's GitHub metadata at analysis time.
Requires PyBUDA and Tenstorrent hardware, README states the project is not yet working.
An early-stage experiment that applies artistic styles like pointillism to photos using neural style transfer running on Tenstorrent AI chips.
Mainly Python. The stack also includes Python, PyBUDA, ONNX.
Dormant — no commits in 2+ years (last push 2024-06-09).
Setup difficulty is rated hard, with roughly 1day+ to a first successful run.
Mainly researcher.
This repo across BitVibe Labs
Verify against the repo before relying on details.