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@@ -108,14 +108,6 @@ def load_gemma2(
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logger.info(f"Loaded Gemma2: {info}")
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return gemma2
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def prepare_img_ids(batch_size: int, packed_latent_height: int, packed_latent_width: int):
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img_ids = torch.zeros(packed_latent_height, packed_latent_width, 3)
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img_ids[..., 1] = img_ids[..., 1] + torch.arange(packed_latent_height)[:, None]
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img_ids[..., 2] = img_ids[..., 2] + torch.arange(packed_latent_width)[None, :]
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img_ids = einops.repeat(img_ids, "h w c -> b (h w) c", b=batch_size)
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return img_ids
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def unpack_latents(x: torch.Tensor, packed_latent_height: int, packed_latent_width: int) -> torch.Tensor:
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"""
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x: [b (h w) (c ph pw)] -> [b c (h ph) (w pw)], ph=2, pw=2
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