This repository contains the released online weights for MGFlow's ImageNet-256 and text-to-image experiments. Each checkpoint is a PyTorch dictionary with exactly two top-level entries: - model: the online model state dict used for sampling; - step: the checkpoint's training step. EMA weights, optimizer state, random-number-generator state, and training metadata are not included. Load with torch.load(path, maplocation="cpu", weightsonly=True) and use the model state dict with the corresponding architecture. ImageNet/ contains 12 final checkpoints: one W2 and one KL checkpoint for each of JiT-B/L/H and pMF-B/L/H. - W2-k1+4.pth: MGFlow-W2 with K=1+4. - KL-k1+4+16.pth: MGFlow-KL with K=1+4+16…
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MGFlow
MGFlow
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