the training details of minimax h3 model
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Assessment
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Newbie friendliness
- 42/100
- Issue type
- Documentation
- Clarity
- Mostly clear
- Activity status
- Active
- Tech stack
- python
- Domain
- machine-learning
Research direction
Start with the linked DiffSynth-Studio/MiniMax-H3-LoRA-LineartAnime checkpoint and the original MiniMax prompt templates referenced in the issue. Determine the prompt design, training steps, batch size, timestep shift, learning rate, and LoRA rank used, then document those details so the requester can reproduce or compare the training setup.
Written by the indexing model from the issue text.
Description
I am currently training a LoRA for the MiniMax-H3 FL2VA model. However, after training on 10k videos, both the video clarity and temporal consistency have noticeably degraded. I am unsure if this issue stems from the prompts, the dataset, or certain training parameters.
I noticed that DiffSynth open-sourced a LoRA checkpoint (https://modelscope.ai/models/DiffSynth-Studio/MiniMax-H3-LoRA-LineartAnime). Could you share the specific training details for this model? I am particularly interested in the prompt design (how to align with the original MiniMax prompt templates), training steps, batch size, timestep shift, learning rate, and LoRA rank. I would love to reference these details to improve my training quality and consistency. Thank you!
- Dominant language
- Python
- Stars
- 13.1k
- Forks
- 1.3k
- Avg merge
- 13h 15m
- Merged PRs (30d)
- 46
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