diff --git a/README.md b/README.md
index 282f3b3bd..562dcdb2a 100644
--- a/README.md
+++ b/README.md
@@ -9,6 +9,10 @@ __Please update PyTorch to 2.4.0. We have tested with `torch==2.4.0` and `torchv
 The command to install PyTorch is as follows:
 `pip3 install torch==2.4.0 torchvision==0.19.0 --index-url https://download.pytorch.org/whl/cu124`
 
+Aug 25, 2024:
+Added `shift` option to `--timestep_sampling` in FLUX.1 fine-tuning and LoRA training. Shifts timesteps according to the value of `--discrete_flow_shift` (shifts the value of sigmoid of normal distribution random number). It may be good to start with a value of 3.1582 (=e^1.15) for `--discrete_flow_shift`.
+Sample command: `--timestep_sampling shift --discrete_flow_shift 3.1582 --model_prediction_type raw --guidance_scale 1.0`
+
 Aug 24, 2024 (update 2):
 
 __Experimental__ Added an option to split the projection layers of q/k/v/txt in the attention and apply LoRA to each of them in FLUX.1 LoRA training. Specify `"split_qkv=True"` in network_args like `--network_args "split_qkv=True"` (`train_blocks` is also available). 
diff --git a/library/flux_train_utils.py b/library/flux_train_utils.py
index 1d3f80d72..75f70a54f 100644
--- a/library/flux_train_utils.py
+++ b/library/flux_train_utils.py
@@ -380,9 +380,19 @@ def get_noisy_model_input_and_timesteps(
             t = torch.sigmoid(args.sigmoid_scale * torch.randn((bsz,), device=device))
         else:
             t = torch.rand((bsz,), device=device)
+
         timesteps = t * 1000.0
         t = t.view(-1, 1, 1, 1)
         noisy_model_input = (1 - t) * latents + t * noise
+    elif args.timestep_sampling == "shift":
+        shift = args.discrete_flow_shift
+        logits_norm = torch.randn(bsz, device=device)
+        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) * latents + t * noise
     else:
         # Sample a random timestep for each image
         # for weighting schemes where we sample timesteps non-uniformly
@@ -559,9 +569,10 @@ def add_flux_train_arguments(parser: argparse.ArgumentParser):
 
     parser.add_argument(
         "--timestep_sampling",
-        choices=["sigma", "uniform", "sigmoid"],
+        choices=["sigma", "uniform", "sigmoid", "shift"],
         default="sigma",
-        help="Method to sample timesteps: sigma-based, uniform random, or sigmoid of random normal. / タイムステップをサンプリングする方法：sigma、random uniform、またはrandom normalのsigmoid。",
+        help="Method to sample timesteps: sigma-based, uniform random, sigmoid of random normal and shift of sigmoid."
+        " / 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。",
     )
     parser.add_argument(
         "--sigmoid_scale",