HiDream Skeleton Mode: Prompt Beats OpenPose Ref — 8 Patterns Benchmarked
The article discusses the benchmarking of the HiDream-O1-Image model in skeleton mode across various patterns. It highlights three key findings regarding the use of reference images and their impact on pose generation. The author also shares insights on the model's performance and the technical aspects of its implementation.
- ▪HiDream-O1-Image was benchmarked across 8 skeleton mode patterns and 3 layout patterns.
- ▪Using an openpose reference locks the pose to its composition, limiting dynamic movement.
- ▪Maintaining 3-4 reference images yields better quality compared to using 6 references, which degrades fine details.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3945785) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } shinji shimizu Posted on May 22 • Originally published at kotonia.ai HiDream Skeleton Mode: Prompt Beats OpenPose Ref — 8 Patterns Benchmarked #ai #machinelearning #python #gpu TL;DR After benchmarking HiDream-O1-Image (released 2026-05, OpenWeight 8B, ranked #8 on Artificial Analysis Text-to-Image Arena) across 8 skeleton (try-on) mode patterns plus 3 layout patterns, three counterintuitive findings emerged. Passing an openpose ref actually locks the pose to the ref's composition.
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