add flux_shift

library/flux_train_utils.py

@@ -362,7 +362,7 @@ def compute_loss_weighting_for_sd3(weighting_scheme: str, sigmas=None):
 def get_noisy_model_input_and_timesteps(
     args, noise_scheduler, latents, noise, device, dtype
 ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-    bsz = latents.shape[0]
+    bsz, _, H, W = latents.shape
     sigmas = None
 
     if args.timestep_sampling == "uniform" or args.timestep_sampling == "sigmoid":
@@ -385,6 +385,12 @@ 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
+    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
+        timesteps = logits_norm.sigmoid()
+        mu=get_lin_function(y1=0.5, y2=1.15)((H//2) * (W//2))
+        timesteps = time_shift(mu, 1.0, timesteps)
     else:
         # Sample a random timestep for each image
         # for weighting schemes where we sample timesteps non-uniformly

nodes.py

@@ -297,7 +297,7 @@ class InitFluxLoRATraining:
             "logit_mean": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "mean to use when using the logit_normal weighting scheme"}),
             "logit_std": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01,"tooltip": "std to use when using the logit_normal weighting scheme"}),
             "mode_scale": ("FLOAT", {"default": 1.29, "min": 0.0, "max": 10.0, "step": 0.01, "tooltip": "Scale of mode weighting scheme. Only effective when using the mode as the weighting_scheme"}),
-            "timestep_sampling": (["sigmoid", "uniform", "sigma", "shift"], {"tooltip": "Method to sample timesteps: sigma-based, uniform random, sigmoid of random normal and shift of sigmoid (recommend value of 3.1582 for discrete_flow_shift)"}),
+            "timestep_sampling": (["sigmoid", "uniform", "sigma", "shift", "flux_shift"], {"tooltip": "Method to sample timesteps: sigma-based, uniform random, sigmoid of random normal and shift of sigmoid (recommend value of 3.1582 for discrete_flow_shift)"}),
             "sigmoid_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.1, "tooltip": "Scale factor for sigmoid timestep sampling (only used when timestep-sampling is sigmoid"}),
             "model_prediction_type": (["raw", "additive", "sigma_scaled"], {"tooltip": "How to interpret and process the model prediction: raw (use as is), additive (add to noisy input), sigma_scaled (apply sigma scaling)."}),
             "guidance_scale": ("FLOAT", {"default": 1.0, "min": 1.0, "max": 32.0, "step": 0.01, "tooltip": "guidance scale, for Flux training should be 1.0"}),
@@ -461,7 +461,7 @@ class InitFluxTraining:
             "logit_std": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01,"tooltip": "std to use when using the logit_normal weighting scheme"}),
             "mode_scale": ("FLOAT", {"default": 1.29, "min": 0.0, "max": 10.0, "step": 0.01, "tooltip": "Scale of mode weighting scheme. Only effective when using the mode as the weighting_scheme"}),
             "loss_type": (["l1", "l2", "huber", "smooth_l1"], {"default": "l2", "tooltip": "loss type"}),
-            "timestep_sampling": (["sigmoid", "uniform", "sigma", "shift"], {"tooltip": "Method to sample timesteps: sigma-based, uniform random, sigmoid of random normal and shift of sigmoid (recommend value of 3.1582 for discrete_flow_shift)"}),
+            "timestep_sampling": (["sigmoid", "uniform", "sigma", "shift", "flux_shift"], {"tooltip": "Method to sample timesteps: sigma-based, uniform random, sigmoid of random normal and shift of sigmoid (recommend value of 3.1582 for discrete_flow_shift)"}),
             "sigmoid_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.1, "tooltip": "Scale factor for sigmoid timestep sampling (only used when timestep-sampling is sigmoid"}),
             "model_prediction_type": (["raw", "additive", "sigma_scaled"], {"tooltip": "How to interpret and process the model prediction: raw (use as is), additive (add to noisy input), sigma_scaled (apply sigma scaling)"}),
             "cpu_offload_checkpointing": ("BOOLEAN", {"default": True, "tooltip": "offload the gradient checkpointing to CPU. This reduces VRAM usage for about 2GB"}),