add manual dataset seed option

patches/external_pr/dataset.py

@@ -47,12 +47,16 @@ class DatasetEntry:
 class PersonalizedBase(Dataset):
     def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None,
                  cond_model=None, device=None, template_file=None, include_cond=False, batch_size=1, gradient_step=1,
-                 shuffle_tags=False, tag_drop_out=0, latent_sampling_method='once', latent_sampling_std=-1):
+                 shuffle_tags=False, tag_drop_out=0, latent_sampling_method='once', latent_sampling_std=-1, manual_seed=-1):
         re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(
             shared.opts.dataset_filename_word_regex) > 0 else None
-        seed = randrange(sys.maxsize)
-        set_rng(seed) # reset forked RNG state when we create dataset.
-        print(f"Dataset seed was set to f{seed}")
+        if manual_seed == -1:
+            seed = randrange(sys.maxsize)
+            set_rng(seed) # reset forked RNG state when we create dataset.
+            print(f"Dataset seed was set to f{seed}")
+        else:
+            set_rng(manual_seed)
+            print(f"Dataset seed was set to f{manual_seed}")
         self.placeholder_token = placeholder_token
 
         self.width = width

patches/external_pr/hypernetwork.py

@@ -485,10 +485,11 @@ def internal_clean_training(hypernetwork_name, data_root, log_directory,
                             preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps,
                             preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height,
                             move_optimizer=True,
-                            load_hypernetworks_option='', load_training_options=''):
+                            load_hypernetworks_option='', load_training_options='', manual_dataset_seed=-1):
     # images allows training previews to have infotext. Importing it at the top causes a circular import problem.
     from modules import images
     base_hypernetwork_name = hypernetwork_name
+    manual_seed = int(manual_dataset_seed)
     if load_hypernetworks_option != '':
         timeStr = time.strftime('%Y%m%d%H%M%S')
         dump_hyper: dict = get_training_option(load_hypernetworks_option)
@@ -687,7 +688,8 @@ def internal_clean_training(hypernetwork_name, data_root, log_directory,
                           gradient_step=gradient_step, shuffle_tags=shuffle_tags,
                           tag_drop_out=tag_drop_out,
                           latent_sampling_method=latent_sampling_method,
-                          latent_sampling_std=latent_sampling_std)
+                          latent_sampling_std=latent_sampling_std,
+                          manual_seed=manual_seed)
 
     latent_sampling_method = ds.latent_sampling_method
 
@@ -977,7 +979,7 @@ def train_hypernetwork_tuning(id_task, hypernetwork_name, data_root, log_directo
                               preview_width, preview_height,
                               move_optimizer=True,
                               optional_new_hypernetwork_name='', load_hypernetworks_options='',
-                              load_training_options=''):
+                              load_training_options='', manual_dataset_seed=-1):
     load_hypernetworks_options = load_hypernetworks_options.split(',')
     load_training_options = load_training_options.split(',')
     # images allows training previews to have infotext. Importing it at the top causes a circular import problem.
@@ -998,6 +1000,6 @@ def train_hypernetwork_tuning(id_task, hypernetwork_name, data_root, log_directo
                 preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index,
                 preview_cfg_scale, preview_seed, preview_width, preview_height,
                 move_optimizer,
-                load_hypernetworks_option, load_training_option)
+                load_hypernetworks_option, load_training_option, manual_dataset_seed)
             if shared.state.interrupted:
                 return None, None

patches/external_pr/ui.py

@@ -384,6 +384,9 @@ def on_train_tuning(params=None):
             label='Save a copy of model to log directory every N steps, 0 to disable', value=500, precision=0)
         preview_from_txt2img = gr.Checkbox(
             label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False)
+        manual_dataset_seed = gr.Number(
+            label="Manual dataset seed", value=-1, precision=0
+        )
         with gr.Row():
             interrupt_training = gr.Button(value="Interrupt")
             train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary')
@@ -404,7 +407,8 @@ def on_train_tuning(params=None):
             move_optim_when_generate,
             optional_new_hypernetwork_name,
             load_hypernetworks_option,
-            load_training_options
+            load_training_options,
+            manual_dataset_seed
         ],
         outputs=[
             ti_output,