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Store vae.config.scaling_factor to prevent missing attr reference (sdxl advanced dreambooth training script) #12346

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Store vae.config.scaling_factor to prevent missing attr reference (sdxl advanced dreambooth training script) #12346

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11c5978
Store vae.config.scaling_factor to prevent missing attr reference
Teriks Sep 17, 2025
44077da
Merge branch 'main' into dreambooth_lora_sdxl_vae_attr_fix
linoytsaban Sep 18, 2025
bbbdd8a
Merge branch 'main' into dreambooth_lora_sdxl_vae_attr_fix
linoytsaban Sep 22, 2025
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8 changes: 6 additions & 2 deletions examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1929,6 +1929,8 @@ def compute_text_embeddings(prompt, text_encoders, tokenizers, clip_skip):

if args.cache_latents:
latents_cache = []
# Store vae config before potential deletion
vae_scaling_factor = vae.config.scaling_factor
for batch in tqdm(train_dataloader, desc="Caching latents"):
with torch.no_grad():
batch["pixel_values"] = batch["pixel_values"].to(
Expand All @@ -1940,6 +1942,8 @@ def compute_text_embeddings(prompt, text_encoders, tokenizers, clip_skip):
del vae
if torch.cuda.is_available():
torch.cuda.empty_cache()
else:
vae_scaling_factor = vae.config.scaling_factor

# Scheduler and math around the number of training steps.
# Check the PR https://github.com/huggingface/diffusers/pull/8312 for detailed explanation.
Expand Down Expand Up @@ -2109,13 +2113,13 @@ def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
model_input = vae.encode(pixel_values).latent_dist.sample()

if latents_mean is None and latents_std is None:
model_input = model_input * vae.config.scaling_factor
model_input = model_input * vae_scaling_factor
if args.pretrained_vae_model_name_or_path is None:
model_input = model_input.to(weight_dtype)
else:
latents_mean = latents_mean.to(device=model_input.device, dtype=model_input.dtype)
latents_std = latents_std.to(device=model_input.device, dtype=model_input.dtype)
model_input = (model_input - latents_mean) * vae.config.scaling_factor / latents_std
model_input = (model_input - latents_mean) * vae_scaling_factor / latents_std
model_input = model_input.to(dtype=weight_dtype)

# Sample noise that we'll add to the latents
Expand Down