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Fix training, validation split, revert to using upstream implemenation

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rockerBOO
2025-01-03 15:20:25 -05:00
parent 6604b36044
commit 0522070d19
5 changed files with 152 additions and 160 deletions
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6
library/custom_train_functions.py
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@@ -455,7 +455,7 @@ def get_weighted_text_embeddings(
# https://wandb.ai/johnowhitaker/multires_noise/reports/Multi-Resolution-Noise-for-Diffusion-Model-Training--VmlldzozNjYyOTU2
def pyramid_noise_like(noise, device, iterations=6, discount=0.4):
def pyramid_noise_like(noise, device, iterations=6, discount=0.4) -> torch.FloatTensor:
b, c, w, h = noise.shape # EDIT: w and h get over-written, rename for a different variant!
u = torch.nn.Upsample(size=(w, h), mode="bilinear").to(device)
for i in range(iterations):
@@ -468,7 +468,7 @@ def pyramid_noise_like(noise, device, iterations=6, discount=0.4):
# https://www.crosslabs.org//blog/diffusion-with-offset-noise
def apply_noise_offset(latents, noise, noise_offset, adaptive_noise_scale):
def apply_noise_offset(latents, noise, noise_offset, adaptive_noise_scale) -> torch.FloatTensor:
if noise_offset is None:
return noise
if adaptive_noise_scale is not None:
@@ -484,7 +484,7 @@ def apply_noise_offset(latents, noise, noise_offset, adaptive_noise_scale):
return noise
def apply_masked_loss(loss, batch):
def apply_masked_loss(loss, batch) -> torch.FloatTensor:
if "conditioning_images" in batch:
# conditioning image is -1 to 1. we need to convert it to 0 to 1
mask_image = batch["conditioning_images"].to(dtype=loss.dtype)[:, 0].unsqueeze(1) # use R channel