Comments (2)
Hi!
This is not on my roadmap. Too busy! But I agree with you, it does make things more difficult to reuse. If there are specific things you think would be useful to pull out, happy to consider how to do this.
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Closing for now. If anyone feels like helping on this please feel free to reopen!
from conditional-flow-matching.
Related Issues (20)
- How to compute inverse trajectories HOT 5
- Modelling conditional pdfs HOT 2
- [Improvement Suggestion] Device Handling - Reduce CPU-GPU Overhead HOT 4
- How long does it need to train a cifar-10 model with FID 3.5? HOT 2
- Is it normal that FID affected by the num_gen option? HOT 1
- NeuralODE trajectory API is quite limiting HOT 2
- Conditional Flow Matching == Rectified Flow Matching? HOT 1
- Generating time series
- OT-CFM performs worse on conditional generation tasks HOT 1
- Confusion about the SB-CFM HOT 2
- Why score_loss = torch.mean((lambda_t[:, None, None, None] * st + eps) ** 2)? HOT 1
- Batch OT sampling of conditional PDFs HOT 2
- Inpainting with CFM HOT 3
- Problem training 8 gaussians to moons with TargetConditionalFlowMatcher HOT 3
- How about applying this conditional flow matching to tasks without paired training data? HOT 4
- Question regarding entropic OT
- Computing Average Probabilities in ODE-based Generative Model
- omegaconf.errors.InterpolationKeyError: Interpolation key 'local.log_dir' not found HOT 2
- ExactOptimalTransportConditionalFlowMatcher with multiple conditions HOT 1
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