have a question regarding the training resources and time cost of different stages:
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Description
Hi, thanks for your great work on UniPic2.0! I have a question regarding the training resources and time cost of different stages:
For UniPic2-MetaQuery (Stage 1), when training the connector (with frozen MLLM and DiT), how many computational resources (e.g., GPUs, type, total GPU hours) were used, and approximately how many days did it take?
For UniPic2-MetaQuery (Stage 2), when jointly fine-tuning the connector and the SD3.5M-Kontext (DiT), how many resources were used and how long did this stage take?
It would be very helpful to know the approximate training setup (e.g., number/type of GPUs, training days) for both stages.
Thanks!
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Research direction
Start by checking the repository's UniPic2-MetaQuery training materials and configuration references for Stage 1 and Stage 2. Done means documenting the GPU type and count, total GPU hours or training days, and approximate setup for both stages; no specific file or test is mentioned in the issue.
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Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Documentation
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Clearly specified
- Newbie friendliness
- 25/100