[Advanced DreamBooth LoRA SDXL] Support EDM-style training (follow up of #7126) #7182
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The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update. |
sayakpaul
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Mar 8, 2024
examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py
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sayakpaul
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| def get_sigmas(timesteps, n_dim=4, dtype=torch.float32): | ||
| sigmas = noise_scheduler.sigmas.to(device=accelerator.device, dtype=dtype) | ||
| schedule_timesteps = noise_scheduler.timesteps.to(accelerator.device) | ||
| timesteps = timesteps.to(accelerator.device) | ||
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| step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps] | ||
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| sigma = sigmas[step_indices].flatten() | ||
| while len(sigma.shape) < n_dim: | ||
| sigma = sigma.unsqueeze(-1) | ||
| return sigma |
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Add a todo here to revisit different sampling algorithms for obtaining the sigmas. Ideally, Tero Karras should just drop a PR here just sayin' 🤷♂️
sayakpaul
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Mar 8, 2024
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Did you notice anything particularly notable @linoytsaban? |
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@sayakpaul wanna take one more look? |
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This PR adds support for EDM-style training to the advanced script, based off PR #7126