diff --git a/helpers/publishing/metadata.py b/helpers/publishing/metadata.py
index b3cd0568..4aeccbc5 100644
--- a/helpers/publishing/metadata.py
+++ b/helpers/publishing/metadata.py
@@ -362,12 +362,39 @@ def sd3_schedule_info(args):
 
     return output_str
 
+def ddpm_schedule_info(args):
+    """Information about DDPM schedules, eg. rescaled betas or offset noise"""
+    output_args = []
+    if args.snr_gamma:
+        output_args.append(f"snr_gamma={args.snr_gamma}")
+    if args.use_soft_min_snr:
+        output_args.append(f"use_soft_min_snr")
+        if args.soft_min_snr_sigma_data:
+            output_args.append(f"soft_min_snr_sigma_data={args.soft_min_snr_sigma_data}")
+    if args.rescale_betas_zero_snr:
+        output_args.append(f"rescale_betas_zero_snr")
+    if args.offset_noise:
+        output_args.append(f"offset_noise")
+        output_args.append(f"noise_offset={args.noise_offset}")
+        output_args.append(f"noise_offset_probability={args.noise_offset_probability}")
+    output_args.append(f"training_scheduler_timestep_spacing={args.training_scheduler_timestep_spacing}")
+    output_args.append(f"validation_scheduler_timestep_spacing={args.validation_scheduler_timestep_spacing}")
+    output_str = (
+        f" (extra parameters={output_args})"
+        if output_args
+        else " (no special parameters set)"
+    )
+
+    return output_str
 
 def model_schedule_info(args):
     if args.model_family == "flux":
         return flux_schedule_info(args)
     if args.model_family == "sd3":
         return sd3_schedule_info(args)
+    else:
+        return ddpm_schedule_info(args)
+
 
 
 def save_model_card(
@@ -495,10 +522,9 @@ def save_model_card(
   - Gradient accumulation steps: {StateTracker.get_args().gradient_accumulation_steps}
   - Number of GPUs: {StateTracker.get_accelerator().num_processes}
 - Prediction type: {'flow-matching' if (StateTracker.get_args().model_family in ["sd3", "flux"]) else StateTracker.get_args().prediction_type}{model_schedule_info(args=StateTracker.get_args())}
-- Rescaled betas zero SNR: {StateTracker.get_args().rescale_betas_zero_snr}
 - Optimizer: {StateTracker.get_args().optimizer}{optimizer_config if optimizer_config is not None else ''}
-- Precision: {'Pure BF16' if torch.backends.mps.is_available() or StateTracker.get_args().mixed_precision == "bf16" else 'FP32'}
-- Quantised: {f'Yes: {StateTracker.get_args().base_model_precision}' if StateTracker.get_args().base_model_precision != "no_change" else 'No'}
+- Trainable parameter precision: {'Pure BF16' if torch.backends.mps.is_available() or StateTracker.get_args().mixed_precision == "bf16" else 'FP32'}
+- Quantised base model: {f'Yes ({StateTracker.get_args().base_model_precision})' if StateTracker.get_args().base_model_precision != "no_change" else 'No'}
 - Xformers: {'Enabled' if StateTracker.get_args().enable_xformers_memory_efficient_attention else 'Not used'}
 {lora_info(args=StateTracker.get_args())}