Remove deprecated cdc cache path

flux_train_network.py

@@ -332,9 +332,9 @@ class FluxNetworkTrainer(train_network.NetworkTrainer):
 
         # Get noisy model input and timesteps
         # If CDC is enabled, this will transform the noise with geometry-aware covariance
-        noisy_model_input, timesteps, sigmas = flux_train_utils.get_noisy_model_input_and_timesteps(
+        noisy_model_input, timesteps, sigmas = flux_train_utils.get_noisy_model_input_and_timestep(
             args, noise_scheduler, latents, noise, accelerator.device, weight_dtype,
-            gamma_b_dataset=gamma_b_dataset, latents_npz_paths=latents_npz_paths
+            gamma_b_dataset=gamma_b_dataset, latents_npz_paths=latents_npz_paths, timestep_index=timestep_index
         )
 
         # pack latents and get img_ids

library/flux_train_utils.py

@@ -525,14 +525,27 @@ def apply_cdc_noise_transformation(
     return noise_cdc_flat.reshape(B, C, H, W)
 
 
-def get_noisy_model_input_and_timesteps(
-    args, noise_scheduler, latents: torch.Tensor, noise: torch.Tensor, device, dtype,
-    gamma_b_dataset=None, latents_npz_paths=None
-) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+def get_noisy_model_input_and_timestep(
+    args, 
+    noise_scheduler, 
+    latents: torch.Tensor, 
+    noise: torch.Tensor, 
+    device: torch.device, 
+    dtype: torch.dtype,
+    gamma_b_dataset=None, 
+    latents_npz_paths=None,
+    timestep_index: int | None = None,
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
     """
-    Get noisy model input and timesteps for training.
-
+    Generate noisy model input and corresponding timesteps for training.
+    
     Args:
+        args: Configuration with sampling parameters
+        noise_scheduler: Scheduler for noise/timestep management
+        latents: Clean latent representations
+        noise: Random noise tensor
+        device: Target device
+        dtype: Target dtype
         gamma_b_dataset: Optional CDC-FM gamma_b dataset for geometry-aware noise
         latents_npz_paths: Optional list of latent cache file paths for CDC-FM (required if gamma_b_dataset provided)
     """
@@ -589,11 +602,10 @@ def get_noisy_model_input_and_timesteps(
             latents_npz_paths=latents_npz_paths,
             device=device
         )
-
-    # Add noise to the latents according to the noise magnitude at each timestep
-    # (this is the forward diffusion process)
+    
     if args.ip_noise_gamma:
         xi = torch.randn_like(latents, device=latents.device, dtype=dtype)
+        
         if args.ip_noise_gamma_random_strength:
             ip_noise_gamma = torch.rand(1, device=latents.device, dtype=dtype) * args.ip_noise_gamma
         else:

library/train_util.py

@@ -2703,7 +2703,6 @@ class DatasetGroup(torch.utils.data.ConcatDataset):
 
     def cache_cdc_gamma_b(
         self,
-        cdc_output_path: str,
         k_neighbors: int = 256,
         k_bandwidth: int = 8,
         d_cdc: int = 8,
@@ -2718,19 +2717,22 @@ class DatasetGroup(torch.utils.data.ConcatDataset):
         Cache CDC Γ_b matrices for all latents in the dataset
 
         CDC files are saved as individual .npz files next to each latent cache file.
-        For example: image_0512x0768_flux.npz → image_0512x0768_flux_cdc.npz
+        For example: image_0512x0768_flux.npz → image_0512x0768_flux_cdc_a1b2c3d4.npz
+        where 'a1b2c3d4' is the config hash (dataset dirs + CDC params).
 
         Args:
-            cdc_output_path: Deprecated (CDC uses per-file caching now)
             k_neighbors: k-NN neighbors
             k_bandwidth: Bandwidth estimation neighbors
             d_cdc: CDC subspace dimension
             gamma: CDC strength
             force_recache: Force recompute even if cache exists
             accelerator: For multi-GPU support
+            debug: Enable debug logging
+            adaptive_k: Enable adaptive k selection for small buckets
+            min_bucket_size: Minimum bucket size for CDC computation
 
         Returns:
-            "per_file" to indicate per-file caching is used, or None on error
+            Config hash string for this CDC configuration, or None on error
         """
         from pathlib import Path
 
@@ -6277,8 +6279,19 @@ def get_timesteps(min_timestep: int, max_timestep: int, b_size: int, device: tor
 
 
 def get_noise_noisy_latents_and_timesteps(
-    args, noise_scheduler, latents: torch.FloatTensor
+    args, noise_scheduler, latents: torch.FloatTensor,
 ) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.IntTensor]:
+    """
+    Sample noise and create noisy latents.
+    
+    Args:
+        args: Training arguments
+        noise_scheduler: The noise scheduler
+        latents: Clean latents
+        
+    Returns:
+        (noise, noisy_latents, timesteps)
+    """
     # Sample noise that we'll add to the latents
     noise = torch.randn_like(latents, device=latents.device)
     if args.noise_offset:

train_network.py

@@ -625,10 +625,8 @@ class NetworkTrainer:
         # CDC-FM preprocessing
         if hasattr(args, "use_cdc_fm") and args.use_cdc_fm:
             logger.info("CDC-FM enabled, preprocessing Γ_b matrices...")
-            cdc_output_path = os.path.join(args.output_dir, "cdc_gamma_b.safetensors")
 
-            self.cdc_cache_path = train_dataset_group.cache_cdc_gamma_b(
-                cdc_output_path=cdc_output_path,
+            self.cdc_config_hash = train_dataset_group.cache_cdc_gamma_b(
                 k_neighbors=args.cdc_k_neighbors,
                 k_bandwidth=args.cdc_k_bandwidth,
                 d_cdc=args.cdc_d_cdc,
@@ -640,10 +638,10 @@ class NetworkTrainer:
                 min_bucket_size=getattr(args, 'cdc_min_bucket_size', 16),
             )
 
-            if self.cdc_cache_path is None:
+            if self.cdc_config_hash is None:
                 logger.warning("CDC-FM preprocessing failed (likely missing FAISS). Training will continue without CDC-FM.")
         else:
-            self.cdc_cache_path = None
+            self.cdc_config_hash = None
 
         # 必要ならテキストエンコーダーの出力をキャッシュする: Text Encoderはcpuまたはgpuへ移される
         # cache text encoder outputs if needed: Text Encoder is moved to cpu or gpu
@@ -684,19 +682,14 @@ class NetworkTrainer:
             accelerator.print(f"all weights merged: {', '.join(args.base_weights)}")
 
         # Load CDC-FM Γ_b dataset if enabled
-        if hasattr(args, "use_cdc_fm") and args.use_cdc_fm and self.cdc_cache_path is not None:
+        if hasattr(args, "use_cdc_fm") and args.use_cdc_fm and self.cdc_config_hash is not None:
             from library.cdc_fm import GammaBDataset
 
-            # cdc_cache_path now contains the config hash
-            config_hash = self.cdc_cache_path if self.cdc_cache_path != "per_file" else None
-            if config_hash:
-                logger.info(f"CDC Γ_b dataset ready (hash: {config_hash})")
-            else:
-                logger.info("CDC Γ_b dataset ready (no hash, backward compatibility)")
+            logger.info(f"CDC Γ_b dataset ready (hash: {self.cdc_config_hash})")
 
             self.gamma_b_dataset = GammaBDataset(
                 device="cuda" if torch.cuda.is_available() else "cpu",
-                config_hash=config_hash
+                config_hash=self.cdc_config_hash
             )
         else:
             self.gamma_b_dataset = None