Update all files for BitDance-ImageNet-diffusers

BitDance_H_1x/transformer/utils.py

@@ -0,0 +1,113 @@
+import logging
+
+import torch
+import torch.distributed as dist
+from torch.nn import functional as F
+
+
+def create_logger(logging_dir):
+    """
+    Create a logger that writes to a log file and stdout.
+    """
+    if dist.get_rank() == 0:  # real logger
+        logging.basicConfig(
+            level=logging.INFO,
+            format="[\033[34m%(asctime)s\033[0m] %(message)s",
+            datefmt="%Y-%m-%d %H:%M:%S",
+            handlers=[
+                logging.StreamHandler(),
+                logging.FileHandler(f"{logging_dir}/log.txt"),
+            ],
+        )
+        logger = logging.getLogger(__name__)
+    else:  # dummy logger (does nothing)
+        logger = logging.getLogger(__name__)
+        logger.addHandler(logging.NullHandler())
+    return logger
+
+
+@torch.no_grad()
+def update_ema(ema_model, model, decay=0.9999):
+    """
+    Step the EMA model towards the current model.
+    """
+    ema_ps = []
+    ps = []
+
+    for e, m in zip(ema_model.parameters(), model.parameters()):
+        if m.requires_grad:
+            ema_ps.append(e)
+            ps.append(m)
+    torch._foreach_lerp_(ema_ps, ps, 1.0 - decay)
+
+
+@torch.no_grad()
+def sync_frozen_params_once(ema_model, model):
+    for e, m in zip(ema_model.parameters(), model.parameters()):
+        if not m.requires_grad:
+            e.copy_(m)
+
+
+def requires_grad(model, flag=True):
+    """
+    Set requires_grad flag for all parameters in a model.
+    """
+    for p in model.parameters():
+        p.requires_grad = flag
+
+def patchify_raster(x, p):
+    B, C, H, W = x.shape
+    
+    assert H % p == 0 and W % p == 0, f"Image dimensions ({H},{W}) must be divisible by patch size {p}"
+    
+    h_patches = H // p
+    w_patches = W // p
+    
+
+    x = x.view(B, C, h_patches, p, w_patches, p)
+    
+
+    x = x.permute(0, 2, 4, 3, 5, 1).contiguous()
+
+    x = x.reshape(B, -1, C)
+    
+    return x
+
+def unpatchify_raster(x, p, target_shape):
+    B, N, C = x.shape
+    H, W = target_shape
+    
+    h_patches = H // p
+    w_patches = W // p
+    
+    x = x.view(B, h_patches, w_patches, p, p, C)
+    
+    x = x.permute(0, 5, 1, 3, 2, 4).contiguous()
+    
+    x = x.reshape(B, C, H, W)
+    
+    return x
+
+def patchify_raster_2d(x: torch.Tensor, p: int, H: int, W: int) -> torch.Tensor:
+    N, C1, C2 = x.shape
+    
+    assert N == H * W, f"N ({N}) must equal H*W ({H*W})"
+    assert H % p == 0 and W % p == 0, f"H/W ({H}/{W}) must be divisible by patch size {p}"
+    
+    C_prime = C1 * C2
+    x_flat = x.view(N, C_prime)
+    
+    x_2d = x_flat.view(H, W, C_prime)
+    
+    h_patches = H // p
+    w_patches = W // p
+    
+    x_split = x_2d.view(h_patches, p, w_patches, p, C_prime)
+    
+    x_permuted = x_split.permute(0, 2, 1, 3, 4)
+    
+    x_reordered = x_permuted.reshape(N, C_prime)
+    
+    out = x_reordered.view(N, C1, C2)
+    
+    return out