Added TI training optimizations

option to use xattention optimizations when training
option to unload vae when training

3 files changed, 16 insertions, 3 deletions

diff --git a/modules/shared.py b/modules/shared.py
index fb84afd8..4c3d0ce7 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -256,11 +256,12 @@ options_templates.update(options_section(('system', "System"), {
 }))

 

 options_templates.update(options_section(('training', "Training"), {

-    "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM."),

+    "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),

     "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),

     "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),

     "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),

     "training_write_csv_every": OptionInfo(500, "Save an csv containing the loss to log directory every N steps, 0 to disable"),

+    "training_xattention_optimizations": OptionInfo(False, "Use cross attention optimizations while training"),

 }))

 

 options_templates.update(options_section(('sd', "Stable Diffusion"), {

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 17dfb223..b0a1d26b 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -214,6 +214,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
     filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')

 

     log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), embedding_name)

+    unload = shared.opts.unload_models_when_training

 

     if save_embedding_every > 0:

         embedding_dir = os.path.join(log_directory, "embeddings")

@@ -238,6 +239,8 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."

     with torch.autocast("cuda"):

         ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file, batch_size=batch_size)

+    if unload:

+        shared.sd_model.first_stage_model.to(devices.cpu)

 

     hijack = sd_hijack.model_hijack

 

@@ -303,6 +306,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
         if images_dir is not None and steps_done % create_image_every == 0:

             forced_filename = f'{embedding_name}-{steps_done}'

             last_saved_image = os.path.join(images_dir, forced_filename)

+

+            shared.sd_model.first_stage_model.to(devices.device)

+

             p = processing.StableDiffusionProcessingTxt2Img(

                 sd_model=shared.sd_model,

                 do_not_save_grid=True,

@@ -330,6 +336,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
             processed = processing.process_images(p)

             image = processed.images[0]

 

+            if unload:

+                shared.sd_model.first_stage_model.to(devices.cpu)

+

             shared.state.current_image = image

 

             if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded:

diff --git a/modules/textual_inversion/ui.py b/modules/textual_inversion/ui.py
index e712284d..d679e6f4 100644
--- a/modules/textual_inversion/ui.py
+++ b/modules/textual_inversion/ui.py
@@ -25,8 +25,10 @@ def train_embedding(*args):
 

     assert not shared.cmd_opts.lowvram, 'Training models with lowvram not possible'

 

+    apply_optimizations = shared.opts.training_xattention_optimizations

     try:

-        sd_hijack.undo_optimizations()

+        if not apply_optimizations:

+            sd_hijack.undo_optimizations()

 

         embedding, filename = modules.textual_inversion.textual_inversion.train_embedding(*args)

 

@@ -38,5 +40,6 @@ Embedding saved to {html.escape(filename)}
     except Exception:

         raise

     finally:

-        sd_hijack.apply_optimizations()

+        if not apply_optimizations:

+            sd_hijack.apply_optimizations()