VictorMylle/Kohya-ss-sd-scripts
1
0
Fork 1
mirror of https://github.com/kohya-ss/sd-scripts.git synced 2026-04-18 01:30:02 +00:00
Code Issues Packages Projects Releases Wiki Activity

Fix: Replace CDC integer index lookup with image_key strings

Browse Source 
Fixes shape mismatch bug in multi-subset training where CDC preprocessing
and training used different index calculations, causing wrong CDC data to
be loaded for samples.

Changes:
- CDC cache now stores/loads data using image_key strings instead of integer indices
- Training passes image_key list instead of computed integer indices
- All CDC lookups use stable image_key identifiers
- Improved device compatibility check (handles "cuda" vs "cuda:0")
- Updated all 30 CDC tests to use image_key-based access

Root cause: Preprocessing used cumulative dataset indices while training
used sorted keys, resulting in mismatched lookups during shuffled multi-subset
training.
This commit is contained in:
rockerBOO
2025-10-09 17:15:07 -04:00
parent 4bea582601
commit 1d4c4d4cb2
9 changed files with 129 additions and 115 deletions
Download Patch File Download Diff File Expand all files Collapse all files

24
tests/library/test_cdc_standalone.py
View File

@@ -28,7 +28,8 @@ class TestCDCPreprocessor:
# Add 10 small latents
for i in range(10):
latent = torch.randn(16, 4, 4, dtype=torch.float32) # C, H, W
preprocessor.add_latent(latent=latent, global_idx=i, shape=latent.shape)
metadata = {'image_key': f'test_image_{i}'}
preprocessor.add_latent(latent=latent, global_idx=i, shape=latent.shape, metadata=metadata)
# Compute and save
output_path = tmp_path / "test_gamma_b.safetensors"
@@ -46,8 +47,8 @@ class TestCDCPreprocessor:
assert f.get_tensor("metadata/d_cdc").item() == 4
# Check first sample
eigvecs = f.get_tensor("eigenvectors/0")
eigvals = f.get_tensor("eigenvalues/0")
eigvecs = f.get_tensor("eigenvectors/test_image_0")
eigvals = f.get_tensor("eigenvalues/test_image_0")
assert eigvecs.shape[0] == 4 # d_cdc
assert eigvals.shape[0] == 4 # d_cdc
@@ -61,12 +62,14 @@ class TestCDCPreprocessor:
# Add 5 latents of shape (16, 4, 4)
for i in range(5):
latent = torch.randn(16, 4, 4, dtype=torch.float32)
preprocessor.add_latent(latent=latent, global_idx=i, shape=latent.shape)
metadata = {'image_key': f'test_image_{i}'}
preprocessor.add_latent(latent=latent, global_idx=i, shape=latent.shape, metadata=metadata)
# Add 5 latents of different shape (16, 8, 8)
for i in range(5, 10):
latent = torch.randn(16, 8, 8, dtype=torch.float32)
preprocessor.add_latent(latent=latent, global_idx=i, shape=latent.shape)
metadata = {'image_key': f'test_image_{i}'}
preprocessor.add_latent(latent=latent, global_idx=i, shape=latent.shape, metadata=metadata)
# Compute and save
output_path = tmp_path / "test_gamma_b_multi.safetensors"
@@ -77,8 +80,8 @@ class TestCDCPreprocessor:
with safe_open(str(result_path), framework="pt", device="cpu") as f:
# Check shapes are stored
shape_0 = f.get_tensor("shapes/0")
shape_5 = f.get_tensor("shapes/5")
shape_0 = f.get_tensor("shapes/test_image_0")
shape_5 = f.get_tensor("shapes/test_image_5")
assert tuple(shape_0.tolist()) == (16, 4, 4)
assert tuple(shape_5.tolist()) == (16, 8, 8)
@@ -192,7 +195,8 @@ class TestCDCEndToEnd:
num_samples = 10
for i in range(num_samples):
latent = torch.randn(16, 4, 4, dtype=torch.float32)
preprocessor.add_latent(latent=latent, global_idx=i, shape=latent.shape)
metadata = {'image_key': f'test_image_{i}'}
preprocessor.add_latent(latent=latent, global_idx=i, shape=latent.shape, metadata=metadata)
output_path = tmp_path / "cdc_gamma_b.safetensors"
cdc_path = preprocessor.compute_all(save_path=output_path)
@@ -206,10 +210,10 @@ class TestCDCEndToEnd:
batch_size = 3
batch_latents_flat = torch.randn(batch_size, 256) # B, d (flattened 16*4*4=256)
batch_t = torch.rand(batch_size)
batch_indices = [0, 5, 9]
image_keys = ['test_image_0', 'test_image_5', 'test_image_9']
# Get Γ_b components
eigvecs, eigvals = gamma_b_dataset.get_gamma_b_sqrt(batch_indices, device="cpu")
eigvecs, eigvals = gamma_b_dataset.get_gamma_b_sqrt(image_keys, device="cpu")
# Compute geometry-aware noise
sigma_t_x = gamma_b_dataset.compute_sigma_t_x(eigvecs, eigvals, batch_latents_flat, batch_t)