Merge branch 'main' into feat_rolling_save

doc/ADVANCED_TWEAKING.md

@@ -230,10 +230,10 @@ In this mode, the EMA model will be saved alongside the regular checkpoint from
 For more information, consult the [research paper](https://arxiv.org/abs/2101.08482) or continue reading the tuning notes below. 
 **Parameters:**  
 - `--ema_decay_rate`: Determines the EMA decay rate. It defines how much the EMA model is updated from training at each update. Values should be close to 1 but not exceed it. Activating this parameter triggers the EMA decay feature.
-- `--ema_decay_target`: Set the EMA decay target value within the (0,1) range. The `ema_decay_rate` is computed based on the relation: decay_rate to the power of (total_steps/decay_interval) equals decay_target. Enabling this parameter will override `ema_decay_rate` and will enable EMA feature.
+- `--ema_strength_target`: Set the EMA strength target value within the (0,1) range. The `ema_decay_rate` is computed based on the relation: decay_rate to the power of (total_steps/decay_interval) equals decay_target. Enabling this parameter will override `ema_decay_rate` and will enable EMA feature. See [ema_strength_target](#ema_strength_target) for more information.
 - `--ema_update_interval`: Set the interval in steps between EMA updates. The update occurs at each optimizer step.  If you use grad_accum, actual update interval will be multipled by your grad_accum value.
 - `--ema_device`: Choose between `cpu` and `cuda` for EMA. Opting for 'cpu' takes around 4 seconds per update and uses approximately 3.2GB RAM, while 'cuda' is much faster but requires a similar amount of VRAM.
-- `--ema_sample_raw_training`: Activate to display samples from the trained model, mirroring conventional training. They will not be presented by default with EMA decay enabled.
+- `--ema_sample_nonema_model`: Activate to display samples from the non-ema trained model, mirroring conventional training. They will not be presented by default with EMA decay enabled.
 - `--ema_sample_ema_model`: Turn on to exhibit samples from the EMA model. EMA models will be used for samples generations by default with EMA decay enabled, unless disabled.
 - `--ema_resume_model`: Indicate the EMA decay checkpoint to continue from, working like `--resume_ckpt` but will load EMA model. Using `findlast` will only load EMA version and not regular training.
 

train.json

@@ -4,7 +4,7 @@
   "clip_grad_norm": null,
   "clip_skip": 0,
   "cond_dropout": 0.04,
-  "data_root": "X:\\my_project_data\\project_abc",
+  "data_root": "/mnt/q/training_samples/ff7r/man",
   "disable_amp": false,
   "disable_textenc_training": false,
   "disable_xformers": false,
@@ -19,7 +19,7 @@
   "lr_decay_steps": 0,
   "lr_scheduler": "constant",
   "lr_warmup_steps": null,
-  "max_epochs": 30,
+  "max_epochs": 1,
   "notebook": false,
   "optimizer_config": "optimizer.json",
   "project_name": "project_abc",
@@ -45,10 +45,10 @@
   "load_settings_every_epoch": false,
   "min_snr_gamma": null,
   "ema_decay_rate": null,
-  "ema_decay_target": null,
+  "ema_strength_target": null,
   "ema_update_interval": null,
   "ema_device": null,
-  "ema_sample_raw_training": false,
+  "ema_sample_nonema_model": false,
   "ema_sample_ema_model": false,
   "ema_resume_model" : null
 }

train.py

@@ -473,7 +473,6 @@ def log_args(log_writer, args):
     log_writer.add_text("config", arglog)
 
 def update_ema(model, ema_model, decay, default_device, ema_device):
-
     with torch.no_grad():
         original_model_on_proper_device = model
         need_to_delete_original = False
@@ -984,7 +983,7 @@ def main(args):
 
             models_info = []
 
-            if (args.ema_decay_rate is None) or args.ema_sample_raw_training:
+            if (args.ema_decay_rate is None) or args.ema_sample_nonema_model:
                 models_info.append({"is_ema": False, "swap_required": False})
 
             if (args.ema_decay_rate is not None) and args.ema_sample_ema_model:
@@ -1054,8 +1053,6 @@ def main(args):
         return os.path.join(log_folder, "ckpts", basename)
 
 
-
-
     # Pre-train validation to establish a starting point on the loss graph
     if validator:
         validator.do_validation(global_step=0,
@@ -1108,6 +1105,7 @@ def main(args):
                 data_root=args.data_root
             )
 
+
             loss_epoch = []
             epoch_start_time = time.time()
             images_per_sec_log_step = []
@@ -1123,6 +1121,7 @@ def main(args):
             for step, batch in enumerate(train_dataloader):
 
                 step_start_time = time.time()
+
                 plugin_runner.run_on_step_start(epoch=epoch,
                         local_step=step,
                         global_step=global_step,

trainSD21.json

@@ -44,10 +44,10 @@
   "load_settings_every_epoch": false,
   "min_snr_gamma": null,
   "ema_decay_rate": null,
-  "ema_decay_target": null,
+  "ema_strength_target": null,
-  "ema_decay_interval": null,
+  "ema_update_interval": null,
-  "ema_decay_device": null,
+  "ema_device": null,
-  "ema_decay_sample_raw_training": false,
+  "ema_sample_nonema_model": false,
-  "ema_decay_sample_ema_model": false,
+  "ema_sample_ema_model": false,
-  "ema_decay_resume_model" : null
+  "ema_resume_model" : null
 }