Merge pull request #3197 from AUTOMATIC1111/training-help-text

Training UI Changes

6 files changed, 50 insertions, 23 deletions

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 3f96361c..3274a802 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -396,7 +396,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 Loss: {mean_loss:.7f}<br/>

 Step: {hypernetwork.step}<br/>

 Last prompt: {html.escape(entries[0].cond_text)}<br/>

-Last saved embedding: {html.escape(last_saved_file)}<br/>

+Last saved hypernetwork: {html.escape(last_saved_file)}<br/>

 Last saved image: {html.escape(last_saved_image)}<br/>

 </p>

 """

diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
index 1a5a27d8..266f04f6 100644
--- a/modules/hypernetworks/ui.py
+++ b/modules/hypernetworks/ui.py
@@ -10,9 +10,10 @@ from modules import sd_hijack, shared, devices
 from modules.hypernetworks import hypernetwork

 

 

-def create_hypernetwork(name, enable_sizes, layer_structure=None, add_layer_norm=False, activation_func=None):

+def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, add_layer_norm=False, activation_func=None):

     fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt")

-    assert not os.path.exists(fn), f"file {fn} already exists"

+    if not overwrite_old:

+        assert not os.path.exists(fn), f"file {fn} already exists"

 

     if type(layer_structure) == str:

         layer_structure = [float(x.strip()) for x in layer_structure.split(",")]

diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index 886cf0c3..6bba3852 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -11,7 +11,7 @@ if cmd_opts.deepdanbooru:
     import modules.deepbooru as deepbooru

 

 

-def preprocess(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False):

+def preprocess(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False):

     try:

         if process_caption:

             shared.interrogator.load()

@@ -21,7 +21,7 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
             db_opts[deepbooru.OPT_INCLUDE_RANKS] = False

             deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, db_opts)

 

-        preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru)

+        preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru)

 

     finally:

 

@@ -33,7 +33,7 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
 

 

 

-def preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False):

+def preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False):

     width = process_width

     height = process_height

     src = os.path.abspath(process_src)

@@ -48,7 +48,7 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
     shared.state.textinfo = "Preprocessing..."

     shared.state.job_count = len(files)

 

-    def save_pic_with_caption(image, index):

+    def save_pic_with_caption(image, index, existing_caption=None):

         caption = ""

 

         if process_caption:

@@ -66,17 +66,26 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
         basename = f"{index:05}-{subindex[0]}-{filename_part}"

         image.save(os.path.join(dst, f"{basename}.png"))

 

+        if preprocess_txt_action == 'prepend' and existing_caption:

+            caption = existing_caption + ' ' + caption

+        elif preprocess_txt_action == 'append' and existing_caption:

+            caption = caption + ' ' + existing_caption

+        elif preprocess_txt_action == 'copy' and existing_caption:

+            caption = existing_caption

+

+        caption = caption.strip()

+        

         if len(caption) > 0:

             with open(os.path.join(dst, f"{basename}.txt"), "w", encoding="utf8") as file:

                 file.write(caption)

 

         subindex[0] += 1

 

-    def save_pic(image, index):

-        save_pic_with_caption(image, index)

+    def save_pic(image, index, existing_caption=None):

+        save_pic_with_caption(image, index, existing_caption=existing_caption)

 

         if process_flip:

-            save_pic_with_caption(ImageOps.mirror(image), index)

+            save_pic_with_caption(ImageOps.mirror(image), index, existing_caption=existing_caption)

 

     for index, imagefile in enumerate(tqdm.tqdm(files)):

         subindex = [0]

@@ -86,6 +95,13 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
         except Exception:

             continue

 

+        existing_caption = None

+

+        try:

+            existing_caption = open(os.path.splitext(filename)[0] + '.txt', 'r').read()

+        except Exception as e:

+            print(e)

+

         if shared.state.interrupted:

             break

 

@@ -97,20 +113,20 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
             img = img.resize((width, height * img.height // img.width))

 

             top = img.crop((0, 0, width, height))

-            save_pic(top, index)

+            save_pic(top, index, existing_caption=existing_caption)

 

             bot = img.crop((0, img.height - height, width, img.height))

-            save_pic(bot, index)

+            save_pic(bot, index, existing_caption=existing_caption)

         elif process_split and is_wide:

             img = img.resize((width * img.width // img.height, height))

 

             left = img.crop((0, 0, width, height))

-            save_pic(left, index)

+            save_pic(left, index, existing_caption=existing_caption)

 

             right = img.crop((img.width - width, 0, img.width, height))

-            save_pic(right, index)

+            save_pic(right, index, existing_caption=existing_caption)

         else:

             img = images.resize_image(1, img, width, height)

-            save_pic(img, index)

+            save_pic(img, index, existing_caption=existing_caption)

 

         shared.state.nextjob()

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 3be69562..5776778b 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -153,7 +153,7 @@ class EmbeddingDatabase:
         return None, None

 

 

-def create_embedding(name, num_vectors_per_token, init_text='*'):

+def create_embedding(name, num_vectors_per_token, overwrite_old, init_text='*'):

     cond_model = shared.sd_model.cond_stage_model

     embedding_layer = cond_model.wrapped.transformer.text_model.embeddings

 

@@ -165,7 +165,8 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
         vec[i] = embedded[i * int(embedded.shape[0]) // num_vectors_per_token]

 

     fn = os.path.join(shared.cmd_opts.embeddings_dir, f"{name}.pt")

-    assert not os.path.exists(fn), f"file {fn} already exists"

+    if not overwrite_old:

+        assert not os.path.exists(fn), f"file {fn} already exists"

 

     embedding = Embedding(vec, name)

     embedding.step = 0

diff --git a/modules/textual_inversion/ui.py b/modules/textual_inversion/ui.py
index 36881e7a..e712284d 100644
--- a/modules/textual_inversion/ui.py
+++ b/modules/textual_inversion/ui.py
@@ -7,8 +7,8 @@ import modules.textual_inversion.preprocess
 from modules import sd_hijack, shared

 

 

-def create_embedding(name, initialization_text, nvpt):

-    filename = modules.textual_inversion.textual_inversion.create_embedding(name, nvpt, init_text=initialization_text)

+def create_embedding(name, initialization_text, nvpt, overwrite_old):

+    filename = modules.textual_inversion.textual_inversion.create_embedding(name, nvpt, overwrite_old, init_text=initialization_text)

 

     sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()

 

diff --git a/modules/ui.py b/modules/ui.py
index 716f14b8..f6a92ddc 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1211,6 +1211,7 @@ def create_ui(wrap_gradio_gpu_call):
                     new_embedding_name = gr.Textbox(label="Name")

                     initialization_text = gr.Textbox(label="Initialization text", value="*")

                     nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1)

+                    overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding")

 

                     with gr.Row():

                         with gr.Column(scale=3):

@@ -1224,6 +1225,7 @@ def create_ui(wrap_gradio_gpu_call):
                     new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])

                     new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")

                     new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")

+                    overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork")

                     new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork", choices=["linear", "relu", "leakyrelu"])

 

                     with gr.Row():

@@ -1238,6 +1240,7 @@ def create_ui(wrap_gradio_gpu_call):
                     process_dst = gr.Textbox(label='Destination directory')

                     process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)

                     process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)

+                    preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"])

 

                     with gr.Row():

                         process_flip = gr.Checkbox(label='Create flipped copies')

@@ -1253,14 +1256,17 @@ def create_ui(wrap_gradio_gpu_call):
                             run_preprocess = gr.Button(value="Preprocess", variant='primary')

 

                 with gr.Tab(label="Train"):

-                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 1:1 ratio images</p>")

+                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images <a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\" style=\"font-weight:bold;\">[wiki]</a></p>")

                     with gr.Row():

                         train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))

                         create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name")

                     with gr.Row():

                         train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=[x for x in shared.hypernetworks.keys()])

                         create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted([x for x in shared.hypernetworks.keys()])}, "refresh_train_hypernetwork_name")

-                    learn_rate = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.005")

+                    with gr.Row():

+                        embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005")

+                        hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001")

+                    

                     batch_size = gr.Number(label='Batch size', value=1, precision=0)

                     dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")

                     log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")

@@ -1294,6 +1300,7 @@ def create_ui(wrap_gradio_gpu_call):
                 new_embedding_name,

                 initialization_text,

                 nvpt,

+                overwrite_old_embedding,

             ],

             outputs=[

                 train_embedding_name,

@@ -1307,6 +1314,7 @@ def create_ui(wrap_gradio_gpu_call):
             inputs=[

                 new_hypernetwork_name,

                 new_hypernetwork_sizes,

+                overwrite_old_hypernetwork,

                 new_hypernetwork_layer_structure,

                 new_hypernetwork_add_layer_norm,

                 new_hypernetwork_activation_func,

@@ -1326,6 +1334,7 @@ def create_ui(wrap_gradio_gpu_call):
                 process_dst,

                 process_width,

                 process_height,

+                preprocess_txt_action,

                 process_flip,

                 process_split,

                 process_caption,

@@ -1342,7 +1351,7 @@ def create_ui(wrap_gradio_gpu_call):
             _js="start_training_textual_inversion",

             inputs=[

                 train_embedding_name,

-                learn_rate,

+                embedding_learn_rate,

                 batch_size,

                 dataset_directory,

                 log_directory,

@@ -1367,7 +1376,7 @@ def create_ui(wrap_gradio_gpu_call):
             _js="start_training_textual_inversion",

             inputs=[

                 train_hypernetwork_name,

-                learn_rate,

+                hypernetwork_learn_rate,

                 batch_size,

                 dataset_directory,

                 log_directory,