diff --git a/library/flux_train_utils.py b/library/flux_train_utils.py
index f7f06c5c..f866fd4a 100644
--- a/library/flux_train_utils.py
+++ b/library/flux_train_utils.py
@@ -415,6 +415,16 @@ def get_noisy_model_input_and_timesteps(
     bsz, _, h, w = latents.shape
     sigmas = None
 
+    ip_noise_gamma = 0.0
+
+    # Add noise to the latents according to the noise magnitude at each timestep
+    # (this is the forward diffusion process)
+    if args.ip_noise_gamma:
+        if args.ip_noise_gamma_random_strength:
+            ip_noise_gamma = torch.rand(1, device=latents.device) * args.ip_noise_gamma
+        else:
+            ip_noise_gamma = args.ip_noise_gamma
+
     if args.timestep_sampling == "uniform" or args.timestep_sampling == "sigmoid":
         # Simple random t-based noise sampling
         if args.timestep_sampling == "sigmoid":
@@ -425,7 +435,7 @@ def get_noisy_model_input_and_timesteps(
 
         timesteps = t * 1000.0
         t = t.view(-1, 1, 1, 1)
-        noisy_model_input = (1 - t) * latents + t * noise
+        noisy_model_input = (1 - t) * latents + t * noise + ip_noise_gamma
     elif args.timestep_sampling == "shift":
         shift = args.discrete_flow_shift
         logits_norm = torch.randn(bsz, device=device)
@@ -435,7 +445,7 @@ def get_noisy_model_input_and_timesteps(
 
         t = timesteps.view(-1, 1, 1, 1)
         timesteps = timesteps * 1000.0
-        noisy_model_input = (1 - t) * latents + t * noise
+        noisy_model_input = (1 - t) * latents + t * noise + ip_noise_gamma
     elif args.timestep_sampling == "flux_shift":
         logits_norm = torch.randn(bsz, device=device)
         logits_norm = logits_norm * args.sigmoid_scale  # larger scale for more uniform sampling
@@ -445,7 +455,7 @@ def get_noisy_model_input_and_timesteps(
 
         t = timesteps.view(-1, 1, 1, 1)
         timesteps = timesteps * 1000.0
-        noisy_model_input = (1 - t) * latents + t * noise
+        noisy_model_input = (1 - t) * latents + t * noise + ip_noise_gamma
     else:
         # Sample a random timestep for each image
         # for weighting schemes where we sample timesteps non-uniformly
@@ -461,7 +471,8 @@ def get_noisy_model_input_and_timesteps(
 
         # Add noise according to flow matching.
         sigmas = get_sigmas(noise_scheduler, timesteps, device, n_dim=latents.ndim, dtype=dtype)
-        noisy_model_input = sigmas * noise + (1.0 - sigmas) * latents
+        noisy_model_input = sigmas * noise + (1.0 - sigmas) * latents + ip_noise_gamma
+
 
     return noisy_model_input.to(dtype), timesteps.to(dtype), sigmas