Added new implementation of the textual inversion page. (#1342)

configs/webui/webui_streamlit.yaml

@@ -299,7 +299,9 @@ gfpgan:
   strength: 100
 
 textual_inversion:
-    value: 0
+    pretrained_model_name_or_path: "models/ldm/stable-diffusion-v1-4"
+    tokenizer_name: ""
+    
     
 daisi_app:
     running_on_daisi_io: False

environment.yaml

@@ -60,8 +60,10 @@ dependencies:
     - streamlit-option-menu==0.3.2
     - streamlit_nested_layout
     - streamlit-server-state==0.14.2
+    - streamlit-tensorboard==0.0.2
     - test-tube>=0.7.5
-    - tensorboard
+    - tensorboard==2.10.1
     - torch-fidelity==0.3.0
     - torchmetrics==0.6.0
     - transformers==4.19.2
+    - wxPython==4.2.0

scripts/sd_utils.py

@@ -18,7 +18,7 @@ from webui_streamlit import st
 
 
 # streamlit imports
-from streamlit import StopException
+from streamlit import StopException, StreamlitAPIException
 
 #streamlit components section
 from streamlit_server_state import server_state, server_state_lock
@@ -267,8 +267,11 @@ def load_models(continue_prev_run = False, use_GFPGAN=False, use_RealESRGAN=Fals
             if "pipe" in server_state:
                 del server_state["pipe"]    
         
+        if "textual_inversion" in st.session_state:
+            del st.session_state['textual_inversion']
+        
         # At this point the model is either
-        # is not loaded yet or have been evicted:
+        # not loaded yet or have been evicted:
         # load new model into memory
         server_state["custom_model"] = custom_model
     
@@ -612,6 +615,52 @@ def find_noise_for_image(model, device, init_image, prompt, steps=200, cond_scal
 
     return x / sigmas[-1]
 
+#
+def folder_picker(label="Select:", value="", help="", folder_button_label="Select", folder_button_help="", folder_button_key=""):
+    """A folder picker that has a text_input field next to it and a button to select the folder. 
+    Returns the text_input field with the folder path."""
+    import tkinter as tk
+    from tkinter import filedialog
+    import string    
+
+    # Set up tkinter
+    root = tk.Tk()
+    root.withdraw()
+
+    # Make folder picker dialog appear on top of other windows
+    root.wm_attributes('-topmost', 1)
+
+    col1, col2 = st.columns([2,1], gap="small")
+
+    with col1:
+        dirname = st.empty()
+    with col2:
+        st.write("")
+        st.write("")
+        folder_picker = st.empty()
+
+    # Folder picker button
+    #st.title('Folder Picker')
+    #st.write('Please select a folder:')
+    
+    # Create a label and add a random number of invisible characters
+    # to it so no two buttons inside a form are the same.
+    #folder_button_label = ''.join(random.choice(f"{folder_button_label}") for _ in range(5))
+    folder_button_label = f"{str(folder_button_label)}{'‎' * random.randint(1, 500)}"
+    clicked = folder_button_key + '‎' * random.randint(5, 500)
+    
+    #try:
+    #clicked = folder_picker.button(folder_button_label, help=folder_button_help, key=folder_button_key)
+    #except StreamlitAPIException:
+    clicked = folder_picker.form_submit_button(folder_button_label, help=folder_button_help)
+
+    if clicked:
+        dirname = dirname.text_input(label, filedialog.askdirectory(master=root), help=help)	
+    else:
+        dirname = dirname.text_input(label, value, help=help)	
+
+    return dirname
+
 
 def get_sigmas_exponential(n, sigma_min, sigma_max, device='cpu'):
     """Constructs an exponential noise schedule."""

scripts/txt2vid.py

@@ -225,6 +225,9 @@ def load_diffusers_model(weights_path,torch_device):
 	with server_state_lock["model"]:
 		if "model" in server_state:
 			del server_state["model"]
+			
+	if "textual_inversion" in st.session_state:
+		del st.session_state['textual_inversion']	
 	
 	try:
 		with server_state_lock["pipe"]:

scripts/webui_streamlit.py

@@ -162,7 +162,6 @@ def layout():
 	elif tabs == 'Textual Inversion':
 		# set the page url and title
 		st.experimental_set_query_params(page='textual-inversion')
-		set_page_title("Textual Inversion - Stable Diffusion Playground")
 		
 		from textual_inversion import layout
 		layout()