fix #2201: lumina 2 timesteps handling

library/lumina_train_util.py

@@ -475,11 +475,7 @@ def sample_image_inference(
 
 
 def time_shift(mu: float, sigma: float, t: torch.Tensor):
-    # the following implementation was original for t=0: clean / t=1: noise
-    # Since we adopt the reverse, the 1-t operations are needed
-    t = 1 - t
     t = math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)
-    t = 1 - t
     return t
 
 
@@ -802,61 +798,42 @@ def compute_loss_weighting_for_sd3(weighting_scheme: str, sigmas=None) -> Tensor
         weighting = torch.ones_like(sigmas)
     return weighting
 
-
+# mainly copied from flux_train_utils.get_noisy_model_input_and_timesteps
 def get_noisy_model_input_and_timesteps(
-    args, noise_scheduler, latents, noise, device, dtype
-) -> Tuple[Tensor, Tensor, Tensor]:
-    """
-    Get noisy model input and timesteps.
-
-    Args:
-        args (argparse.Namespace): Arguments.
-        noise_scheduler (noise_scheduler): Noise scheduler.
-        latents (Tensor): Latents.
-        noise (Tensor): Latent noise.
-        device (torch.device): Device.
-        dtype (torch.dtype): Data type
-
-    Return:
-        Tuple[Tensor, Tensor, Tensor]:
-            noisy model input
-            timesteps
-            sigmas
-    """
+    args, noise_scheduler, latents: torch.Tensor, noise: torch.Tensor, device, dtype
+) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
     bsz, _, h, w = latents.shape
-    sigmas = None
-
+    assert bsz > 0, "Batch size not large enough"
+    num_timesteps = noise_scheduler.config.num_train_timesteps
     if args.timestep_sampling == "uniform" or args.timestep_sampling == "sigmoid":
-        # Simple random t-based noise sampling
+        # Simple random sigma-based noise sampling
         if args.timestep_sampling == "sigmoid":
             # https://github.com/XLabs-AI/x-flux/tree/main
-            t = torch.sigmoid(args.sigmoid_scale * torch.randn((bsz,), device=device))
+            sigmas = torch.sigmoid(args.sigmoid_scale * torch.randn((bsz,), device=device))
         else:
-            t = torch.rand((bsz,), device=device)
+            sigmas = torch.rand((bsz,), device=device)
 
-        timesteps = t * 1000.0
-        t = t.view(-1, 1, 1, 1)
-        noisy_model_input = (1 - t) * noise + t * latents
+        timesteps = sigmas * num_timesteps
     elif args.timestep_sampling == "shift":
         shift = args.discrete_flow_shift
-        logits_norm = torch.randn(bsz, device=device)
-        logits_norm = (
-            logits_norm * args.sigmoid_scale
-        )  # larger scale for more uniform sampling
-        timesteps = logits_norm.sigmoid()
-        timesteps = (timesteps * shift) / (1 + (shift - 1) * timesteps)
-
-        t = timesteps.view(-1, 1, 1, 1)
-        timesteps = timesteps * 1000.0
-        noisy_model_input = (1 - t) * noise + t * latents
+        sigmas = torch.randn(bsz, device=device)
+        sigmas = sigmas * args.sigmoid_scale  # larger scale for more uniform sampling
+        sigmas = sigmas.sigmoid()
+        sigmas = (sigmas * shift) / (1 + (shift - 1) * sigmas)
+        timesteps = sigmas * num_timesteps
     elif args.timestep_sampling == "nextdit_shift":
-        t = torch.rand((bsz,), device=device)
+        sigmas = torch.rand((bsz,), device=device)
         mu = get_lin_function(y1=0.5, y2=1.15)((h // 2) * (w // 2))
-        t = time_shift(mu, 1.0, t)
+        sigmas = time_shift(mu, 1.0, sigmas)
 
-        timesteps = t * 1000.0
-        t = t.view(-1, 1, 1, 1)
-        noisy_model_input = (1 - t) * noise + t * latents
+        timesteps = sigmas * num_timesteps
+    elif args.timestep_sampling == "flux_shift":
+        sigmas = torch.randn(bsz, device=device)
+        sigmas = sigmas * args.sigmoid_scale  # larger scale for more uniform sampling
+        sigmas = sigmas.sigmoid()
+        mu = get_lin_function(y1=0.5, y2=1.15)((h // 2) * (w // 2))  # we are pre-packed so must adjust for packed size
+        sigmas = time_shift(mu, 1.0, sigmas)
+        timesteps = sigmas * num_timesteps
     else:
         # Sample a random timestep for each image
         # for weighting schemes where we sample timesteps non-uniformly
@@ -867,14 +844,24 @@ def get_noisy_model_input_and_timesteps(
             logit_std=args.logit_std,
             mode_scale=args.mode_scale,
         )
-        indices = (u * noise_scheduler.config.num_train_timesteps).long()
+        indices = (u * num_timesteps).long()
         timesteps = noise_scheduler.timesteps[indices].to(device=device)
+        sigmas = get_sigmas(noise_scheduler, timesteps, device, n_dim=latents.ndim, dtype=dtype)
 
-        # Add noise according to flow matching.
-        sigmas = get_sigmas(
-            noise_scheduler, timesteps, device, n_dim=latents.ndim, dtype=dtype
-        )
-        noisy_model_input = sigmas * latents + (1.0 - sigmas) * noise
+    # Broadcast sigmas to latent shape
+    sigmas = sigmas.view(-1, 1, 1, 1)
+
+    # 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:
+            ip_noise_gamma = args.ip_noise_gamma
+        noisy_model_input = (1.0 - sigmas) * latents + sigmas * (noise + ip_noise_gamma * xi)
+    else:
+        noisy_model_input = (1.0 - sigmas) * latents + sigmas * noise
 
     return noisy_model_input.to(dtype), timesteps.to(dtype), sigmas
 
@@ -1049,10 +1036,10 @@ def add_lumina_train_arguments(parser: argparse.ArgumentParser):
 
     parser.add_argument(
         "--timestep_sampling",
-        choices=["sigma", "uniform", "sigmoid", "shift", "nextdit_shift"],
+        choices=["sigma", "uniform", "sigmoid", "shift", "nextdit_shift", "flux_shift"],
         default="shift",
-        help="Method to sample timesteps: sigma-based, uniform random, sigmoid of random normal, shift of sigmoid and NextDIT.1 shifting. Default is 'shift'."
-        " / タイムステップをサンプリングする方法：sigma、random uniform、random normalのsigmoid、sigmoidのシフト、NextDIT.1のシフト。デフォルトは'shift'です。",
+        help="Method to sample timesteps: sigma-based, uniform random, sigmoid of random normal, shift of sigmoid, Flux.1 and NextDIT.1 shifting. Default is 'shift'."
+        " / タイムステップをサンプリングする方法：sigma、random uniform、random normalのsigmoid、sigmoidのシフト、Flux.1、NextDIT.1のシフト。デフォルトは'shift'です。",
     )
     parser.add_argument(
         "--sigmoid_scale",

lumina_train.py

@@ -743,7 +743,7 @@ def train(args):
                     # YiYi notes: divide it by 1000 for now because we scale it by 1000 in the transformer model (we should not keep it but I want to keep the inputs same for the model for testing)
                     model_pred = nextdit(
                         x=noisy_model_input,  # image latents (B, C, H, W)
-                        t=timesteps / 1000,  # timesteps需要除以1000来匹配模型预期
+                        t=1 - timesteps / 1000,  # timesteps需要除以1000来匹配模型预期
                         cap_feats=gemma2_hidden_states,  # Gemma2的hidden states作为caption features
                         cap_mask=gemma2_attn_mask.to(
                             dtype=torch.int32

lumina_train_network.py

@@ -268,7 +268,7 @@ class LuminaNetworkTrainer(train_network.NetworkTrainer):
                 # NextDiT forward expects (x, t, cap_feats, cap_mask)
                 model_pred = dit(
                     x=img,  # image latents (B, C, H, W)
-                    t=timesteps / 1000,  # timesteps需要除以1000来匹配模型预期
+                    t=1 - timesteps / 1000,  # timesteps需要除以1000来匹配模型预期
                     cap_feats=gemma2_hidden_states,  # Gemma2的hidden states作为caption features
                     cap_mask=gemma2_attn_mask.to(dtype=torch.int32),  # Gemma2的attention mask
                 )