lllyasviel / lllyasviel/ControlNet
Latent vector input
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- Dominant language
- Python
- Stars
- 34.1k
- Forks
- 3k
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Description
The model performs really well, and I would like to further explore its potential benefits in other domains.
When input (latents) is not provided, ControlNet automatically fill in latents with random Gaussian.
But I want to generate samples from the input (latent) I provide to the model.
Should I encode input (latents) before providing into the network? (Maybe using VAE as in stable diffusion)
Or as an raw image?
I tried to find it in the code but it seems no encoding is performed for input. (Maybe Stable Diffusion API does it)
I'm confused since I am getting low quality images when I provide encoded vector of an image as latent.
Any thoughts ?
Contributor guide
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
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Research direction
No files, tests, or entry points are identified in the issue. Start by tracing ControlNet’s latent-input path and comparing the expected input with the VAE-encoded image described in the report. Done means establishing a clear supported latent format and confirming that supplied latents produce expected-quality samples.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- ai, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100