lllyasviel / lllyasviel/FramePack
Code understand. How the code implement the FramePack?
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- Dominant language
- Python
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Description
Firstly thanks for amazing work! The paper seems not so hard to understand. I have a few questions about the code.
Does the indices means the frame_latent axis? The default code is
latent_paddings = [3] + [2] * (total_latent_sections - 3) + [1, 0]
and use the latent_padding to calculate indices = torch.arange(0, sum([1, latent_padding_size, latent_window_size, 1, 2, 16])).unsqueeze(0)
if generated frames is longer, like 30s or 60s, there will be much more [2, 2, 2, 2 ...], so that indice will repeat the same sequence. how this influence the generation process?
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Research direction
No file or test is named. Start by locating the implementation that defines latent_paddings and constructs indices, then compare it with the FramePack paper's description of frame latents and generation sections. Done means documenting how repeated padding values affect the generation process, with enough context for the questioner to verify the explanation.
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Assessment
- Tech stack
- python
- Domain
- documentation, machine-learning
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100