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I trained a 210M text-to-image diffusion transformer from scratch on one GPU in 3.5 days, on 4.2M curated images at 256². Same six prompts and seeds at every checkpoint of the run 👇
🎨 Try it: ivanmikhnenkov/tinydit
What mattered most: captions that fit the images, a timestep shift for the 32-channel FLUX.2 latent, aspect-ratio buckets from step one, register tokens with learned null attention slots, and torch.compile for training.
🤗 Weights: ivanmikhnenkov/tinydit-256
💻 Code, every decision with its sources: https://github.com/ivanmikhnenkov/tinydit
🎨 Try it: ivanmikhnenkov/tinydit
What mattered most: captions that fit the images, a timestep shift for the 32-channel FLUX.2 latent, aspect-ratio buckets from step one, register tokens with learned null attention slots, and torch.compile for training.
🤗 Weights: ivanmikhnenkov/tinydit-256
💻 Code, every decision with its sources: https://github.com/ivanmikhnenkov/tinydit