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Helios-Base speed comparison and the impact of `Multi-Term Memory Patchification` on T2V tasks.

Technical Positioning
A 'Real Real-Time Long Video Generation Model' emphasizing speed.
SaaS Insight & Market Implications
This issue critically questions Helios-Base's reported speed advantage over Wan 2.1 in T2V tasks, despite using similar sampling steps and a compression mechanism (`Multi-Term Memory Patchification`) that should be irrelevant for T2V. The user's detailed questions about generation methodology (autoregressive vs. single-pass) and the actual impact of token compression highlight a demand for transparent and precise benchmarking. For B2B SaaS in the video generation space, performance claims are a primary differentiator. Ambiguity in how benchmarks are achieved, especially when core architectural features seem inapplicable, erodes trust. Clear explanations of experimental setups and the specific contributions of each component are essential for developers to validate claims and integrate models effectively. This issue underscores the need for rigorous, well-documented performance metrics.
Proprietary Technical Taxonomy
speed comparison Table 3 Helios-Base (14B) Wan 2.1 14B FPS 50 sampling steps UniPC scheduler Multi-Term Memory Patchification

Raw Developer Origin & Technical Request

Source Icon GitHub Issue Apr 2, 2026
Repo: PKU-YuanGroup/Helios
Question about Helios-Base speed in Table 3

Hi, thanks for the great work! I have a question about the speed comparison in Table 3.
In Table 3, Helios-Base (14B) achieves **0.54 FPS** while Wan 2.1 14B achieves **0.33 FPS**. However, I'm confused about it since:
1. **Helios-Base uses 50 sampling steps** (as stated in Section 5.1: "For Stages 1–2, we adopt UniPC scheduler with 50 sampling steps"), which is the same as the original Wan 14B.
2. **Multi-Term Memory Patchification** is designed to compress the historical context XHist. But for pure T2V tasks (where XHist = all zeros, as mentioned in Section 3.1.1: "if XHist is all zeros, the model performs T2V"), there's no history to compress.
**My questions:**
1. Was the 81-frame benchmark in Table 3 evaluated using **autoregressive chunk-by-chunk generation** (like 9 frames per chunk) or **single-pass bidirectional generation**?
2. If it was autoregressive generation, how many frames were generated per chunk? And what's the actual token count reduction from Multi-Term Memory Patchification?
3. If it was single-pass generation, then what caused Helios-Base to be faster than Wan 14B? The token compression should only work when there's actual history context.
Thanks for your attention !

Developer Debate & Comments

No active discussions extracted for this entry yet.

Adjacent Repository Pain Points

Other highly discussed features and pain points extracted from PKU-YuanGroup/Helios.

Extracted Positioning
Helios's training process, specifically the noise application to reference image `x0` during Stage 1.
A real-time long video generation model.
Extracted Positioning
Helios's training data availability.
A 'Real Real-Time Long Video Generation Model.'
Extracted Positioning
Helios model training strategies: `is_amplify_history` and `restrict_self_attn`.
A real-time long video generation model.
Top Replies
SHYuanBest • Mar 24, 2026
@Iriya99 感谢关注!请使用`merge_lora_for_helios.py`进行代码合并。 https://github.com/PKU-YuanGroup/Helios/blob/main/tools/merge_lora_for_helios.py
Iriya99 • Mar 24, 2026
> [@Iriya99](https://github.com/Iriya99) 感谢关注!请使用`merge_lora_for_helios.py`进行代码合并。 https://github.com/PKU-YuanGroup/Helios/blob/main/tools/merge_lora_for_helios.py transformer和pipe...
SHYuanBest • Mar 24, 2026
pipe填wan或者helios的路径都行。transformer得看你训练的时候用了哪个transformer,比如stage-1-init用的是wan的transformer,此时填wan的路径,其他阶段以此类推。
Top Replies
SHYuanBest • Mar 31, 2026
排除代码/权重加载问题前提下,训练一开始是这样的,往后训会逐渐连贯。你这个大概训了多久?batchsize和lr分别是多少?
hotfinda • Mar 31, 2026
您好,我训练了8000step, batchsize1,lr固定5e-5
SHYuanBest • Mar 31, 2026
bs有点小,可以试着把`random_drop_t2v_ratio`关小,不然模型没学多少v2v任务

Frequently Asked Questions

Market intelligence mapped to Helios-Base speed comparison and the impact of `Multi-Term Memory Patchification` on T2V tasks..

What problem does Helios-Base speed comparison and the impact of `Multi-Term Memory Patchification` on T2V tasks. solve?
Based on our AI analysis of the original developer request, its primary technical positioning is: A 'Real Real-Time Long Video Generation Model' emphasizing speed.
What is the general sentiment around Helios-Base speed comparison and the impact of `Multi-Term Memory Patchification` on T2V tasks.?
Yes, we have tracked 1 direct responses and active debates regarding this specific topic originating from GitHub Issue.
What architecture is tied to Helios-Base speed comparison and the impact of `Multi-Term Memory Patchification` on T2V tasks.?
Our proprietary extraction maps Helios-Base speed comparison and the impact of `Multi-Term Memory Patchification` on T2V tasks. to adjacent architectural concepts including speed comparison, Table 3, Helios-Base (14B), Wan 2.1 14B.

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Cross-Market Term Frequency

Quantifies the cross-market adoption of foundational terms like speed comparison and Table 3 by tracking occurrence frequency across active SaaS architectures and enterprise developer debates.