Add ability to choose using weighted loss or not

4 files changed, 32 insertions, 13 deletions

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 9c79b7d0..f4fb69e0 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -496,7 +496,7 @@ def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None,
     shared.reload_hypernetworks()

 

 

-def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_hypernetwork_every, template_filename, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):

+def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_hypernetwork_every, template_filename, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):

     # images allows training previews to have infotext. Importing it at the top causes a circular import problem.

     from modules import images

 

@@ -554,7 +554,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
 

     pin_memory = shared.opts.pin_memory

 

-    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=hypernetwork_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method, varsize=varsize)

+    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=hypernetwork_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method, varsize=varsize, use_weight=use_weight)

 

     if shared.opts.save_training_settings_to_txt:

         saved_params = dict(

@@ -640,14 +640,19 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
                 

                 with devices.autocast():

                     x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)

-                    w = batch.weight.to(devices.device, non_blocking=pin_memory)

+                    if use_weight:

+                        w = batch.weight.to(devices.device, non_blocking=pin_memory)

                     if tag_drop_out != 0 or shuffle_tags:

                         shared.sd_model.cond_stage_model.to(devices.device)

                         c = shared.sd_model.cond_stage_model(batch.cond_text).to(devices.device, non_blocking=pin_memory)

                         shared.sd_model.cond_stage_model.to(devices.cpu)

                     else:

                         c = stack_conds(batch.cond).to(devices.device, non_blocking=pin_memory)

-                    loss = shared.sd_model.weighted_forward(x, c, w)[0] / gradient_step

+                    if use_weight:

+                        loss = shared.sd_model.weighted_forward(x, c, w)[0] / gradient_step

+                        del w

+                    else:

+                        loss = shared.sd_model.forward(x, c)[0] / gradient_step

                     del x

                     del c

 

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index f4ce4552..1568b2b8 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -31,7 +31,7 @@ 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', varsize=False):

+    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', varsize=False, use_weight=False):

         re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex) > 0 else None

 

         self.placeholder_token = placeholder_token

@@ -64,7 +64,7 @@ class PersonalizedBase(Dataset):
                 image = Image.open(path)

                 #Currently does not work for single color transparency

                 #We would need to read image.info['transparency'] for that

-                if 'A' in image.getbands():

+                if use_weight and 'A' in image.getbands():

                     alpha_channel = image.getchannel('A')

                 image = image.convert('RGB')

                 if not varsize:

@@ -104,7 +104,7 @@ class PersonalizedBase(Dataset):
                     latent_sampling_method = "once"

             latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu)

 

-            if alpha_channel is not None:

+            if use_weight and alpha_channel is not None:

                 channels, *latent_size = latent_sample.shape

                 weight_img = alpha_channel.resize(latent_size)

                 npweight = np.array(weight_img).astype(np.float32)

@@ -113,9 +113,11 @@ class PersonalizedBase(Dataset):
                 #Normalize the weight to a minimum of 0 and a mean of 1, that way the loss will be comparable to default.

                 weight -= weight.min()

                 weight /= weight.mean()

-            else:

+            elif use_weight:

                 #If an image does not have a alpha channel, add a ones weight map anyway so we can stack it later

                 weight = torch.ones([channels] + latent_size)

+            else:

+                weight = None

             

             if latent_sampling_method == "random":

                 entry = DatasetEntry(filename=path, filename_text=filename_text, latent_dist=latent_dist, weight=weight)

@@ -219,7 +221,10 @@ class BatchLoader:
         self.cond_text = [entry.cond_text for entry in data]

         self.cond = [entry.cond for entry in data]

         self.latent_sample = torch.stack([entry.latent_sample for entry in data]).squeeze(1)

-        self.weight = torch.stack([entry.weight for entry in data]).squeeze(1)

+        if all(entry.weight is not None for entry in data):

+            self.weight = torch.stack([entry.weight for entry in data]).squeeze(1)

+        else:

+            self.weight = None

         #self.emb_index = [entry.emb_index for entry in data]

         #print(self.latent_sample.device)

 

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 8853c868..c63c7d1d 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -351,7 +351,7 @@ def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, dat
         assert log_directory, "Log directory is empty"

 

 

-def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_embedding_every, template_filename, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):

+def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_embedding_every, template_filename, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):

     save_embedding_every = save_embedding_every or 0

     create_image_every = create_image_every or 0

     template_file = textual_inversion_templates.get(template_filename, None)

@@ -410,7 +410,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
 

     pin_memory = shared.opts.pin_memory

 

-    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, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method, varsize=varsize)

+    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, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method, varsize=varsize, use_weight=use_weight)

 

     if shared.opts.save_training_settings_to_txt:

         save_settings_to_file(log_directory, {**dict(model_name=checkpoint.model_name, model_hash=checkpoint.shorthash, num_of_dataset_images=len(ds), num_vectors_per_token=len(embedding.vec)), **locals()})

@@ -480,7 +480,8 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
             

                 with devices.autocast():

                     x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)

-                    w = batch.weight.to(devices.device, non_blocking=pin_memory)

+                    if use_weight:

+                        w = batch.weight.to(devices.device, non_blocking=pin_memory)

                     c = shared.sd_model.cond_stage_model(batch.cond_text)

 

                     if is_training_inpainting_model:

@@ -491,7 +492,11 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
                     else:

                         cond = c

 

-                    loss = shared.sd_model.weighted_forward(x, cond, w)[0] / gradient_step

+                    if use_weight:

+                        loss = shared.sd_model.weighted_forward(x, cond, w)[0] / gradient_step

+                        del w

+                    else:

+                        loss = shared.sd_model.forward(x, cond)[0] / gradient_step

                     del x

 

                     _loss_step += loss.item()

diff --git a/modules/ui.py b/modules/ui.py
index f5df1ffe..efb87c23 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1191,6 +1191,8 @@ def create_ui():
                         create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_create_image_every")

                         save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_save_embedding_every")

 

+                    use_weight = gr.Checkbox(label="Use PNG alpha channel as loss weight", value=False, elem_id="use_weight")

+

                     save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True, elem_id="train_save_image_with_stored_embedding")

                     preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False, elem_id="train_preview_from_txt2img")

 

@@ -1304,6 +1306,7 @@ def create_ui():
                 shuffle_tags,

                 tag_drop_out,

                 latent_sampling_method,

+                use_weight,

                 create_image_every,

                 save_embedding_every,

                 template_file,

@@ -1337,6 +1340,7 @@ def create_ui():
                 shuffle_tags,

                 tag_drop_out,

                 latent_sampling_method,

+                use_weight,

                 create_image_every,

                 save_embedding_every,

                 template_file,