SaaS Metrics
Gemini Executive Synthesis
Helios's training process, specifically the noise application to reference image `x0` during Stage 1.
Technical Positioning
A real-time long video generation model.
SaaS Insight & Market Implications
This issue questions the rationale behind adding noise to the reference image `x0` during Stage 1 training in Helios, alongside noise application to history. This indicates a developer seeking deeper understanding of the model's training methodology and its impact on video generation quality. For B2B SaaS offering AI models, transparency in training procedures is vital for users to effectively fine-tune, debug, and optimize model performance for their specific applications. Unexplained design choices can create friction and reduce confidence. Providing clear explanations for such technical decisions enhances the perceived robustness and reliability of the model, fostering greater adoption and trust within the developer community.
Proprietary Technical Taxonomy
Raw Developer Origin & Technical Request
GitHub Issue
Apr 3, 2026
Repo: PKU-YuanGroup/Helios
参考图加噪问题
hi大佬，stage 1 训练过程中在对history加噪的同时也会对参考图x0加噪，想请教下这么做的考量是？
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 data availability.
A 'Real Real-Time Long Video Generation Model.'
Extracted Positioning
Helios-Base speed comparison and the impact of `Multi-Term Memory Patchification` on T2V tasks.
A 'Real Real-Time Long Video Generation Model' emphasizing speed.
Extracted Positioning
Helios model training strategies: `is_amplify_history` and `restrict_self_attn`.
A real-time long video generation model.
Top Replies
@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](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...
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
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？
您好，我训练了8000step, batchsize1，lr固定5e-5
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's training process, specifically the noise application to reference image `x0` during Stage 1..
How is Helios's training process, specifically the noise application to reference image `x0` during Stage 1. positioned in the market?
Based on our AI analysis of the original developer request, its primary technical positioning is: A real-time long video generation model.
Are engineers actively discussing Helios's training process, specifically the noise application to reference image `x0` during Stage 1.?
Yes, we have tracked 2 direct responses and active debates regarding this specific topic originating from GitHub Issue.
Which technical concepts are associated with Helios's training process, specifically the noise application to reference image `x0` during Stage 1.?
Our proprietary extraction maps Helios's training process, specifically the noise application to reference image `x0` during Stage 1. to adjacent architectural concepts including stage 1 训练过程, history加噪, 参考图x0加噪.
How does the GitHub community build with Helios's training process, specifically the noise application to reference image `x0` during Stage 1.?
Yes, open-source adoption is correlated. An active project titled 'PKU-YuanGroup/Helios' explores similar frameworks: Helios: Real Real-Time Long Video Generation Model
Engagement Signals
Cross-Market Term Frequency
Quantifies the cross-market adoption of foundational terms like stage 1 训练过程 and history加噪 by tracking occurrence frequency across active SaaS architectures and enterprise developer debates.