Experimental features and bug fixes
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@@ -250,13 +250,14 @@ class DiffusionFeatureExtractor3(nn.Module):
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bs = noise_pred.shape[0]
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noise_pred_chunks = torch.chunk(noise_pred, bs)
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timestep_chunks = torch.chunk(timesteps, bs)
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noisy_latent_chunks = torch.chunk(noisy_latents, bs)
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stepped_chunks = []
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for idx in range(bs):
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model_output = noise_pred_chunks[idx]
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timestep = timestep_chunks[idx]
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scheduler._step_index = None
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scheduler._init_step_index(timestep)
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sample = noisy_latents.to(torch.float32)
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sample = noisy_latent_chunks[idx].to(torch.float32)
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sigma = scheduler.sigmas[scheduler.step_index]
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sigma_next = scheduler.sigmas[-1] # use last sigma for final step
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