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reformulate the conditioning and patchification

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This commit is contained in:
CrossProduct
2025-01-18 03:11:09 +00:00
parent 59afd55775
commit 44df9e3bce
2 changed files with 46 additions and 22 deletions
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finetrainers/conditioning/condition_latents_prepare.py
+11 -5
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@@ -21,8 +21,9 @@ def _pack_latents(latents: torch.Tensor, patch_size: int = 1, patch_size_t: int
post_patch_height = height // patch_size
post_patch_width = width // patch_size
dim1 = num_frames // patch_size_t * height // patch_size * width // patch_size
dim2 = num_channels * patch_size_t * patch_size * patch_size
# to check math lol
# dim1 = num_frames // patch_size_t * height // patch_size * width // patch_size
# dim2 = num_channels * patch_size_t * patch_size * patch_size
latents = latents.reshape(
batch_size,
@@ -75,6 +76,11 @@ def prepare_latents_for_conditioning(
latents = latents.to(dtype=dtype)
_, _, num_frames, height, width = latents.shape
latents = _normalize_latents(latents, vae.latents_mean, vae.latents_std)
latents = _pack_latents(latents, patch_size, patch_size_t)
return {"latents": latents, "num_frames": num_frames, "height": height, "width": width}
# latents = _normalize_latents(latents, vae.latents_mean, vae.latents_std)
# latents = _pack_latents(latents, patch_size, patch_size_t)
return {"latents": latents,
"num_frames": num_frames,
"height": height,
"width": width,
"mean":vae.latents_mean,
"std":vae.latents_std}