update with mirage branding

app.py

@@ -54,7 +54,7 @@ def swap_text(option, base):
        show_prior_preservation=False
        if(show_prior_preservation):
            prior_preservation_box_update = gr.update(visible=show_prior_preservation)
-       else: 
+       else:
            prior_preservation_box_update = gr.update(visible=show_prior_preservation, value=False)
        return [f"You are going to train a `person`(s), upload 10-20 images of each person you are planning on training on from different angles/perspectives. You can use services like <a style='text-decoration: underline' target='_blank' href='https://www.birme.net/?target_width={resize_width}&target_height={resize_width}'>birme</a> for smart cropping. {mandatory_liability}:", '''<img src="file/person.png" />''', f"You should name your concept with a unique made up word that has low chance of the model already knowing it (e.g.: `{instance_prompt_example}` here). Images will be automatically cropped to {resize_width}x{resize_width}.", freeze_for, prior_preservation_box_update]
     elif(option == "style"):
@@ -81,16 +81,13 @@ def count_files(*inputs):
             if(files):
                 concept_counter+=1
                 file_counter+=len(files)
-    uses_custom = inputs[-1] 
-    type_of_thing = inputs[-4]
-    selected_model = inputs[-5]
-    experimental_faces = inputs[-6]
+    uses_custom = inputs[-1]
+    selected_model = inputs[-4]
+    experimental_faces = inputs[-5]
     if(uses_custom):
         Training_Steps = int(inputs[-3])
     else:
         Training_Steps = file_counter*150
-        if(type_of_thing == "person" and Training_Steps > 2400):
-            Training_Steps = 2400 #Avoid overfitting on person faces
     if(is_spaces):
         if(selected_model == "v1-5"):
             its = 1.1
@@ -102,12 +99,12 @@ def count_files(*inputs):
                 its = 0.7
         elif(selected_model == "v2-768"):
             its = 0.5
-        summary_sentence = f'''You are going to train {concept_counter} {type_of_thing}(s), with {file_counter} images for {Training_Steps} steps. The training should take around {round(Training_Steps/its, 2)} seconds, or {round((Training_Steps/its)/60, 2)} minutes.
+        summary_sentence = f'''You are going to train {concept_counter}, with {file_counter} images for {Training_Steps} steps. The training should take around {round(Training_Steps/its, 2)} seconds, or {round((Training_Steps/its)/60, 2)} minutes.
             The setup, compression and uploading the model can take up to 20 minutes.<br>As the T4-Small GPU costs US$0.60 for 1h, <span style="font-size: 120%"><b>the estimated cost for this training is below US${round((((Training_Steps/its)/3600)+0.3+0.1)*0.60, 2)}.</b></span><br><br>
             If you check the box below the GPU attribution will automatically removed after training is done and the model is uploaded. If not, don't forget to come back here and swap the hardware back to CPU.<br><br>'''
     else:
-        summary_sentence = f'''You are going to train {concept_counter} {type_of_thing}(s), with {file_counter} images for {Training_Steps} steps.<br><br>'''
-        
+        summary_sentence = f'''You are going to train {concept_counter}, with {file_counter} images for {Training_Steps} steps.<br><br>'''
+
     return([gr.update(visible=True), gr.update(visible=True, value=summary_sentence)])
 
 def update_steps(*files_list):
@@ -141,7 +138,7 @@ def train(*inputs):
         del pipe
         pipe_is_set = False
         gc.collect()
-        
+
     if os.path.exists("output_model"): shutil.rmtree('output_model')
     if os.path.exists("instance_images"): shutil.rmtree('instance_images')
     if os.path.exists("diffusers_model.tar"): os.remove("diffusers_model.tar")
@@ -166,91 +163,51 @@ def train(*inputs):
                     image = image.convert('RGB')
                     image.save(f'instance_images/{prompt}_({j+1}).jpg', format="JPEG", quality = 100)
                     file_counter += 1
-    
+
     os.makedirs('output_model',exist_ok=True)
-    uses_custom = inputs[-1] 
-    type_of_thing = inputs[-4]
-    remove_attribution_after = inputs[-6]
-    experimental_face_improvement = inputs[-9]
-    
+    uses_custom = inputs[-1]
+    remove_attribution_after = inputs[-5]
+    experimental_face_improvement = inputs[-8]
+
     if(uses_custom):
         Training_Steps = int(inputs[-3])
         Train_text_encoder_for = int(inputs[-2])
     else:
-        if(type_of_thing == "object"):
-            Train_text_encoder_for=30
-            
-        elif(type_of_thing == "style"):
-            Train_text_encoder_for=15
-            
-        elif(type_of_thing == "person"):
-            Train_text_encoder_for=70
-        
+        Train_text_encoder_for=30
         Training_Steps = file_counter*150
-        if(type_of_thing == "person" and Training_Steps > 2600):
-            Training_Steps = 2600 #Avoid overfitting on people's faces
     stptxt = int((Training_Steps*Train_text_encoder_for)/100)
-    gradient_checkpointing = True if (experimental_face_improvement or which_model != "v1-5") else False 
+    gradient_checkpointing = True if (experimental_face_improvement or which_model != "v1-5") else False
     cache_latents = True if which_model != "v1-5" else False
-    if (type_of_thing == "object" or type_of_thing == "style" or (type_of_thing == "person" and not experimental_face_improvement)):
-        args_general = argparse.Namespace(
-            image_captions_filename = True,
-            train_text_encoder = True if stptxt > 0 else False,
-            stop_text_encoder_training = stptxt,
-            save_n_steps = 0,
-            pretrained_model_name_or_path = model_to_load,
-            instance_data_dir="instance_images",
-            class_data_dir=None,
-            output_dir="output_model",
-            instance_prompt="",
-            seed=42,
-            resolution=resolution,
-            mixed_precision="fp16",
-            train_batch_size=1,
-            gradient_accumulation_steps=1,
-            use_8bit_adam=True,
-            learning_rate=2e-6,
-            lr_scheduler="polynomial",
-            lr_warmup_steps = 0,
-            max_train_steps=Training_Steps,     
-            gradient_checkpointing=gradient_checkpointing,
-            cache_latents=cache_latents,
-        )
-        print("Starting single training...")
-        lock_file = open("intraining.lock", "w")
-        lock_file.close()
-        run_training(args_general)
-    else:
-        args_general = argparse.Namespace(
-            image_captions_filename = True,
-            train_text_encoder = True if stptxt > 0 else False,
-            stop_text_encoder_training = stptxt,
-            save_n_steps = 0,
-            pretrained_model_name_or_path = model_to_load,
-            instance_data_dir="instance_images",
-            class_data_dir="Mix",
-            output_dir="output_model",
-            with_prior_preservation=True,
-            prior_loss_weight=1.0,
-            instance_prompt="",
-            seed=42,
-            resolution=resolution,
-            mixed_precision="fp16",
-            train_batch_size=1,
-            gradient_accumulation_steps=1,
-            use_8bit_adam=True,
-            learning_rate=2e-6,
-            lr_scheduler="polynomial",
-            lr_warmup_steps = 0,
-            max_train_steps=Training_Steps,
-            num_class_images=200,     
-            gradient_checkpointing=gradient_checkpointing,
-            cache_latents=cache_latents,
-        )
-        print("Starting multi-training...")
-        lock_file = open("intraining.lock", "w")
-        lock_file.close()
-        run_training(args_general)
+    args_general = argparse.Namespace(
+        image_captions_filename = True,
+        train_text_encoder = True if stptxt > 0 else False,
+        stop_text_encoder_training = stptxt,
+        save_n_steps = 0,
+        pretrained_model_name_or_path = model_to_load,
+        instance_data_dir="instance_images",
+        class_data_dir="Mix",
+        output_dir="output_model",
+        with_prior_preservation=True,
+        prior_loss_weight=1.0,
+        instance_prompt="",
+        seed=42,
+        resolution=resolution,
+        mixed_precision="fp16",
+        train_batch_size=1,
+        gradient_accumulation_steps=1,
+        use_8bit_adam=True,
+        learning_rate=2e-6,
+        lr_scheduler="polynomial",
+        lr_warmup_steps = 0,
+        max_train_steps=Training_Steps,
+        num_class_images=200,
+        gradient_checkpointing=gradient_checkpointing,
+        cache_latents=cache_latents,
+    )
+    print("Starting multi-training...")
+    lock_file = open("intraining.lock", "w")
+    lock_file.close()
+    run_training(args_general)
     gc.collect()
     torch.cuda.empty_cache()
     if(which_model == "v1-5"):
@@ -258,7 +215,7 @@ def train(*inputs):
         shutil.copytree(f"{safety_checker}/feature_extractor", "output_model/feature_extractor")
         shutil.copytree(f"{safety_checker}/safety_checker", "output_model/safety_checker")
         shutil.copy(f"model_index.json", "output_model/model_index.json")
-    
+
     if(not remove_attribution_after):
         print("Archiving model file...")
         with tarfile.open("diffusers_model.tar", "w") as tar:
@@ -295,10 +252,10 @@ def generate(prompt, steps):
         pipe = StableDiffusionPipeline.from_pretrained("./output_model", torch_dtype=torch.float16)
         pipe = pipe.to("cuda")
         pipe_is_set = True
-        
-    image = pipe(prompt, num_inference_steps=steps).images[0]  
+
+    image = pipe(prompt, num_inference_steps=steps).images[0]
     return(image)
-    
+
 def push(model_name, where_to_upload, hf_token, which_model, comes_from_automated=False):
     if(not os.path.exists("model.ckpt")):
         convert("output_model", "model.ckpt")
@@ -307,13 +264,13 @@ def push(model_name, where_to_upload, hf_token, which_model, comes_from_automate
     model_name_slug = slugify(model_name)
     api = HfApi()
     your_username = api.whoami(token=hf_token)["name"]
-    if(where_to_upload == "My personal profile"):    
+    if(where_to_upload == "My personal profile"):
         model_id = f"{your_username}/{model_name_slug}"
     else:
         model_id = f"sd-dreambooth-library/{model_name_slug}"
         headers = {"Authorization" : f"Bearer: {hf_token}", "Content-Type": "application/json"}
         response = requests.post("https://huggingface.co/organizations/sd-dreambooth-library/share/SSeOwppVCscfTEzFGQaqpfcjukVeNrKNHX", headers=headers)
-    
+
     images_upload = os.listdir("instance_images")
     image_string = ""
     instance_prompt_list = []
@@ -326,7 +283,7 @@ def push(model_name, where_to_upload, hf_token, which_model, comes_from_automate
         else:
             title_instance_prompt_string = ''
         previous_instance_prompt = instance_prompt
-        image_string = f'''{title_instance_prompt_string} {"(use that on your prompt)" if title_instance_prompt_string != "" else ""} 
+        image_string = f'''{title_instance_prompt_string} {"(use that on your prompt)" if title_instance_prompt_string != "" else ""}
 {image_string}![{instance_prompt} {i}](https://huggingface.co/{model_id}/resolve/main/concept_images/{urllib.parse.quote(image)})'''
     readme_text = f'''---
 license: creativeml-openrail-m
@@ -337,7 +294,7 @@ widget:
 ---
 ### {model_name} Dreambooth model trained by {api.whoami(token=hf_token)["name"]} with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the {which_model} base model
 
-You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts! 
+You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts!
 
 Sample pictures of:
 {image_string}
@@ -402,14 +359,14 @@ def check_status(top_description):
             update_top_tag = gr.update(value=f'''
             <div class="gr-prose" style="max-width: 80%">
                 <h2>Your model has finished training ✅</h2>
-                <p>Yay, congratulations on training your model. Scroll down to play with with it, save it (either downloading it or on the Hugging Face Hub). Once you are done, your model is safe, and you don't want to train a new one, go to the <a href="https://huggingface.co/spaces/{os.environ['SPACE_ID']}" target="_blank">settings page</a> and downgrade your Space to a CPU Basic</p> 
+                <p>Yay, congratulations on training your model. Scroll down to play with with it, save it (either downloading it or on the Hugging Face Hub). Once you are done, your model is safe, and you don't want to train a new one, go to the <a href="https://huggingface.co/spaces/{os.environ['SPACE_ID']}" target="_blank">settings page</a> and downgrade your Space to a CPU Basic</p>
             </div>
             ''')
         else:
             update_top_tag = gr.update(value=f'''
             <div class="gr-prose" style="max-width: 80%">
                 <h2>Your model has finished training ✅</h2>
-                <p>Yay, congratulations on training your model. Scroll down to play with with it, save it (either downloading it or on the Hugging Face Hub).</p> 
+                <p>Yay, congratulations on training your model. Scroll down to play with with it, save it (either downloading it or on the Hugging Face Hub).</p>
             </div>
             ''')
         show_outputs = True
@@ -417,7 +374,7 @@ def check_status(top_description):
         update_top_tag = gr.update(value='''
         <div class="gr-prose" style="max-width: 80%">
             <h2>Don't worry, your model is still training! ⌛</h2>
-            <p>You closed the tab while your model was training, but it's all good! It is still training right now. You can click the "Open logs" button above here to check the training status. Once training is done, reload this tab to interact with your model</p> 
+            <p>You closed the tab while your model was training, but it's all good! It is still training right now. You can click the "Open logs" button above here to check the training status. Once training is done, reload this tab to interact with your model</p>
         </div>
         ''')
         show_outputs = False
@@ -446,7 +403,7 @@ with gr.Blocks(css=css) as demo:
                 <div class="gr-prose" style="max-width: 80%">
                 <h2>Attention - This Space doesn't work in this shared UI</h2>
                 <p>For it to work, you can either run locally or duplicate the Space and run it on your own profile using a (paid) private T4 GPU for training. As each T4 costs US$0.60/h, it should cost < US$1 to train most models using default settings!&nbsp;&nbsp;<a class="duplicate-button" style="display:inline-block" target="_blank" href="https://huggingface.co/spaces/{os.environ['SPACE_ID']}?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></p>
-                <img class="instruction" src="file/duplicate.png"> 
+                <img class="instruction" src="file/duplicate.png">
                 <img class="arrow" src="file/arrow.png" />
                 </div>
             ''')
@@ -455,39 +412,48 @@ with gr.Blocks(css=css) as demo:
                 top_description = gr.HTML(f'''
                         <div class="gr-prose" style="max-width: 80%">
                         <h2>You have successfully associated a GPU to the Dreambooth Training Space 🎉</h2>
-                        <p>Certify that you got a T4. You can now train your model! You will be billed by the minute from when you activated the GPU until when it is turned it off.</p> 
+                        <p>Certify that you got a T4. You can now train your model! You will be billed by the minute from when you activated the GPU until when it is turned it off.</p>
                         </div>
                 ''')
             else:
                 top_description = gr.HTML(f'''
                         <div class="gr-prose" style="max-width: 80%">
                         <h2>You have successfully duplicated the Dreambooth Training Space 🎉</h2>
-                        <p>There's only one step left before you can train your model: <a href="https://huggingface.co/spaces/{os.environ['SPACE_ID']}/settings" style="text-decoration: underline" target="_blank">attribute a <b>T4 GPU</b> to it (via the Settings tab)</a> and run the training below. Other GPUs are not compatible for now. You will be billed by the minute from when you activate the GPU until when it is turned it off.</p> 
+                        <p>There's only one step left before you can train your model: <a href="https://huggingface.co/spaces/{os.environ['SPACE_ID']}/settings" style="text-decoration: underline" target="_blank">attribute a <b>T4 GPU</b> to it (via the Settings tab)</a> and run the training below. Other GPUs are not compatible for now. You will be billed by the minute from when you activate the GPU until when it is turned it off.</p>
                         </div>
                 ''')
         else:
             top_description = gr.HTML(f'''
-                    <div class="gr-prose" style="max-width: 80%">
-                    <h2>You have successfully cloned the Dreambooth Training Space locally 🎉</h2>
-                    <p>Do a <code>pip install requirements-local.txt</code></p> 
+                    <div style="text-align: center; max-width: 650px; margin: 0 auto;">
+                    <div>
+                        <img class="logo" src="file/mirage.png" alt="Mirage Logo"
+                            style="margin: auto; max-width: 7rem;">
+                        <br />
+                        <h1 style="font-weight: 900; font-size: 2.5rem;">
+                        Dreambooth Training UI
+                        </h1>
+                    </div>
+                    <br />
+                    <br />
+                    <p style="margin-bottom: 10px; font-size: 94%">
+                    Customize Stable Diffusion v1 or v2 by giving it a few examples of a concept.
+                    Based on the <a href="https://github.com/huggingface/diffusers">diffusers</a> implementation, additional techniques from <a href="https://github.com/TheLastBen/diffusers">TheLastBen</a> and <a href="https://github.com/ShivamShrirao/diffusers">ShivamShrirao</a>"
+                    </p>
                     </div>
                 ''')
-    gr.Markdown("# Dreambooth Training UI 💭")
-    gr.Markdown("Customize Stable Diffusion v1 or v2 (ⁿᵉʷ!) by giving it a few examples of a concept. Based on the [🧨 diffusers](https://github.com/huggingface/diffusers) implementation, additional techniques from [TheLastBen](https://github.com/TheLastBen/diffusers) and [ShivamShrirao](https://github.com/ShivamShrirao/diffusers)")
-    
-    with gr.Row() as what_are_you_training:
-        type_of_thing = gr.Dropdown(label="What would you like to train?", choices=["object", "person", "style"], value="object", interactive=True)
-        base_model_to_use = gr.Dropdown(label="Which base model would you like to use?", choices=["v1-5", "v2-512", "v2-768"], value="v1-5", interactive=True)
-    
-    #Very hacky approach to emulate dynamically created Gradio components   
+
+    #Very hacky approach to emulate dynamically created Gradio components
     with gr.Row() as upload_your_concept:
         with gr.Column():
             thing_description = gr.Markdown("You are going to train an `object`, please upload 5-10 images of the object you are planning on training on from different angles/perspectives. You must have the right to do so and you are liable for the images you use, example")
             thing_experimental = gr.Checkbox(label="Improve faces (prior preservation) - can take longer training but can improve faces", visible=False, value=False)
             thing_image_example = gr.HTML('''<img src="file/cat-toy.png" />''')
             things_naming = gr.Markdown("You should name your concept with a unique made up word that has low chance of the model already knowing it (e.g.: `cttoy` here). Images will be automatically cropped to 512x512.")
-            
+
         with gr.Column():
+            with gr.Row() as what_are_you_training:
+                base_model_to_use = gr.Dropdown(label="Which base model would you like to use?", choices=["v1-5", "v2-512", "v2-768"], value="v1-5", interactive=True)
+
             file_collection = []
             concept_collection = []
             buttons_collection = []
@@ -506,45 +472,45 @@ with gr.Blocks(css=css) as demo:
 
                 file_collection.append(gr.File(label=f'''Upload the images for your {ordinal(x+1) if (x>0) else ""} concept''', file_count="multiple", interactive=True, visible=visible))
                 with gr.Column(visible=visible) as row[x]:
-                    concept_collection.append(gr.Textbox(label=f'''{ordinal(x+1) if (x>0) else ""} concept prompt - use a unique, made up word to avoid collisions'''))  
+                    concept_collection.append(gr.Textbox(label=f'''{ordinal(x+1) if (x>0) else ""} concept prompt - use a unique, made up word to avoid collisions'''))
                     with gr.Row():
                         if(x < maximum_concepts-1):
                             buttons_collection.append(gr.Button(value="Add +1 concept", visible=visible))
                         if(x > 0):
                             delete_collection.append(gr.Button(value=f"Delete {ordinal(x+1)} concept"))
-            
+
             counter_add = 1
             for button in buttons_collection:
                 if(counter_add < len(buttons_collection)):
                     button.click(lambda:
                     [gr.update(visible=True),gr.update(visible=True), gr.update(visible=False), gr.update(visible=True), True, None],
-                    None, 
+                    None,
                     [row[counter_add], file_collection[counter_add], buttons_collection[counter_add-1], buttons_collection[counter_add], is_visible[counter_add], file_collection[counter_add]], queue=False)
                 else:
                     button.click(lambda:[gr.update(visible=True),gr.update(visible=True), gr.update(visible=False), True], None, [row[counter_add], file_collection[counter_add], buttons_collection[counter_add-1], is_visible[counter_add]], queue=False)
                 counter_add += 1
-            
+
             counter_delete = 1
             for delete_button in delete_collection:
                 if(counter_delete < len(delete_collection)+1):
                     delete_button.click(lambda:[gr.update(visible=False),gr.update(visible=False), gr.update(visible=True), False], None, [file_collection[counter_delete], row[counter_delete], buttons_collection[counter_delete-1], is_visible[counter_delete]], queue=False)
                 counter_delete += 1
-                  
+
     with gr.Accordion("Custom Settings", open=False):
         swap_auto_calculated = gr.Checkbox(label="Use custom settings")
         gr.Markdown("If not checked, the % of frozen encoder will be tuned automatically to whether you are training an `object`, `person` or `style`. The text-encoder is frozen after 10% of the steps for a style, 30% of the steps for an object and 75% trained for persons. The number of steps varies between 1400 and 2400 depending on how many images uploaded. If you see too many artifacts in your output, it means it may have overfit and you need less steps. If your results aren't really what you wanted, it may be underfitting and you need more steps.")
         steps = gr.Number(label="How many steps", value=2400)
         perc_txt_encoder = gr.Number(label="Percentage of the training steps the text-encoder should be trained as well", value=30)
-        
+
     with gr.Box(visible=False) as training_summary:
         training_summary_text = gr.HTML("", visible=True, label="Training Summary")
         is_advanced_visible = True if is_spaces else False
         training_summary_checkbox = gr.Checkbox(label="Automatically remove paid GPU attribution and upload model to the Hugging Face Hub after training", value=True, visible=is_advanced_visible)
         training_summary_model_name = gr.Textbox(label="Name of your model", visible=True)
         training_summary_where_to_upload = gr.Dropdown(["My personal profile", "Public Library"], value="My personal profile", label="Upload to", visible=True)
-        training_summary_token_message = gr.Markdown("[A Hugging Face write access token](https://huggingface.co/settings/tokens), go to \"New token\" -> Role : Write. A regular read token won't work here.", visible=True)            
+        training_summary_token_message = gr.Markdown("[A Hugging Face write access token](https://huggingface.co/settings/tokens), go to \"New token\" -> Role : Write. A regular read token won't work here.", visible=True)
         training_summary_token = gr.Textbox(label="Hugging Face Write Token", type="password", visible=True)
-        
+
     train_btn = gr.Button("Start Training")
     if(is_shared_ui):
         training_ongoing = gr.Markdown("## This Space only works in duplicated instances. Please duplicate it and try again!", visible=False)
@@ -552,11 +518,11 @@ with gr.Blocks(css=css) as demo:
         training_ongoing = gr.Markdown("## Oops, you haven't associated your T4 GPU to this Space. Visit the Settings tab, associate and try again.", visible=False)
     else:
         training_ongoing = gr.Markdown("## Training is ongoing ⌛... You can close this tab if you like or just wait. If you did not check the `Remove GPU After training`, you can come back here to try your model and upload it after training. Don't forget to remove the GPU attribution after you are done. ", visible=False)
-    
+
     #Post-training UI
-    completed_training = gr.Markdown('''# ✅ Training completed. 
+    completed_training = gr.Markdown('''# ✅ Training completed.
     ### Don't forget to remove the GPU attribution after you are done trying and uploading your model''', visible=False)
-    
+
     with gr.Row():
         with gr.Box(visible=False) as try_your_model:
             gr.Markdown("## Try your model")
@@ -564,54 +530,53 @@ with gr.Blocks(css=css) as demo:
             result_image = gr.Image()
             inference_steps = gr.Slider(minimum=1, maximum=150, value=50, step=1)
             generate_button = gr.Button("Generate Image")
-        
+
         with gr.Box(visible=False) as push_to_hub:
             gr.Markdown("## Push to Hugging Face Hub")
             model_name = gr.Textbox(label="Name of your model", placeholder="Tarsila do Amaral Style")
             where_to_upload = gr.Dropdown(["My personal profile", "Public Library"], label="Upload to")
             gr.Markdown("[A Hugging Face write access token](https://huggingface.co/settings/tokens), go to \"New token\" -> Role : Write. A regular read token won't work here.")
             hf_token = gr.Textbox(label="Hugging Face Write Token", type="password")
-            
+
             push_button = gr.Button("Push to the Hub")
-    
+
     result = gr.File(label="Download the uploaded models in the diffusers format", visible=True)
     success_message_upload = gr.Markdown(visible=False)
     convert_button = gr.Button("Convert to CKPT", visible=False)
-    
+
     #Swap the examples and the % of text encoder trained depending if it is an object, person or style
-    type_of_thing.change(fn=swap_text, inputs=[type_of_thing, base_model_to_use], outputs=[thing_description, thing_image_example, things_naming, perc_txt_encoder, thing_experimental], queue=False, show_progress=False)
-    
+
     #Swap the base model
-    base_model_to_use.change(fn=swap_text, inputs=[type_of_thing, base_model_to_use], outputs=[thing_description, thing_image_example, things_naming, perc_txt_encoder, thing_experimental], queue=False, show_progress=False)
+    base_model_to_use.change(fn=swap_text, inputs=[base_model_to_use], outputs=[thing_description, thing_image_example, things_naming, perc_txt_encoder, thing_experimental], queue=False, show_progress=False)
     base_model_to_use.change(fn=swap_base_model, inputs=base_model_to_use, outputs=[])
 
-    #Update the summary box below the UI according to how many images are uploaded and whether users are using custom settings or not 
+    #Update the summary box below the UI according to how many images are uploaded and whether users are using custom settings or not
     for file in file_collection:
         #file.change(fn=update_steps,inputs=file_collection, outputs=steps)
-        file.change(fn=count_files, inputs=file_collection+[thing_experimental]+[base_model_to_use]+[type_of_thing]+[steps]+[perc_txt_encoder]+[swap_auto_calculated], outputs=[training_summary, training_summary_text], queue=False)
-    
-    thing_experimental.change(fn=count_files, inputs=file_collection+[thing_experimental]+[base_model_to_use]+[type_of_thing]+[steps]+[perc_txt_encoder]+[swap_auto_calculated], outputs=[training_summary, training_summary_text], queue=False)
-    base_model_to_use.change(fn=count_files, inputs=file_collection+[thing_experimental]+[base_model_to_use]+[type_of_thing]+[steps]+[perc_txt_encoder]+[swap_auto_calculated], outputs=[training_summary, training_summary_text], queue=False)
-    steps.change(fn=count_files, inputs=file_collection+[thing_experimental]+[base_model_to_use]+[type_of_thing]+[steps]+[perc_txt_encoder]+[swap_auto_calculated], outputs=[training_summary, training_summary_text], queue=False)
-    perc_txt_encoder.change(fn=count_files, inputs=file_collection+[thing_experimental]+[base_model_to_use]+[type_of_thing]+[steps]+[perc_txt_encoder]+[swap_auto_calculated], outputs=[training_summary, training_summary_text], queue=False)
-    
+        file.change(fn=count_files, inputs=file_collection+[thing_experimental]+[base_model_to_use]+[steps]+[perc_txt_encoder]+[swap_auto_calculated], outputs=[training_summary, training_summary_text], queue=False)
+
+    thing_experimental.change(fn=count_files, inputs=file_collection+[thing_experimental]+[base_model_to_use]+[steps]+[perc_txt_encoder]+[swap_auto_calculated], outputs=[training_summary, training_summary_text], queue=False)
+    base_model_to_use.change(fn=count_files, inputs=file_collection+[thing_experimental]+[base_model_to_use]+[steps]+[perc_txt_encoder]+[swap_auto_calculated], outputs=[training_summary, training_summary_text], queue=False)
+    steps.change(fn=count_files, inputs=file_collection+[thing_experimental]+[base_model_to_use]+[steps]+[perc_txt_encoder]+[swap_auto_calculated], outputs=[training_summary, training_summary_text], queue=False)
+    perc_txt_encoder.change(fn=count_files, inputs=file_collection+[thing_experimental]+[base_model_to_use]+[steps]+[perc_txt_encoder]+[swap_auto_calculated], outputs=[training_summary, training_summary_text], queue=False)
+
     #Give more options if the user wants to finish everything after training
     if(is_spaces):
         training_summary_checkbox.change(fn=checkbox_swap, inputs=training_summary_checkbox, outputs=[training_summary_token_message, training_summary_token, training_summary_model_name, training_summary_where_to_upload],queue=False, show_progress=False)
     #Add a message for while it is in training
     train_btn.click(lambda:gr.update(visible=True), inputs=None, outputs=training_ongoing)
-    
+
     #The main train function
-    train_btn.click(fn=train, inputs=is_visible+concept_collection+file_collection+[base_model_to_use]+[thing_experimental]+[training_summary_where_to_upload]+[training_summary_model_name]+[training_summary_checkbox]+[training_summary_token]+[type_of_thing]+[steps]+[perc_txt_encoder]+[swap_auto_calculated], outputs=[result, try_your_model, push_to_hub, convert_button, training_ongoing, completed_training], queue=False)
-    
+    train_btn.click(fn=train, inputs=is_visible+concept_collection+file_collection+[base_model_to_use]+[thing_experimental]+[training_summary_where_to_upload]+[training_summary_model_name]+[training_summary_checkbox]+[training_summary_token]+[steps]+[perc_txt_encoder]+[swap_auto_calculated], outputs=[result, try_your_model, push_to_hub, convert_button, training_ongoing, completed_training], queue=False)
+
     #Button to generate an image from your trained model after training
     generate_button.click(fn=generate, inputs=[prompt, inference_steps], outputs=result_image, queue=False)
     #Button to push the model to the Hugging Face Hub
     push_button.click(fn=push, inputs=[model_name, where_to_upload, hf_token, base_model_to_use], outputs=[success_message_upload, result], queue=False)
-    #Button to convert the model to ckpt format 
+    #Button to convert the model to ckpt format
     convert_button.click(fn=convert_to_ckpt, inputs=[], outputs=result, queue=False)
-    
+
     #Checks if the training is running
     demo.load(fn=check_status, inputs=top_description, outputs=[top_description, try_your_model, push_to_hub, result, convert_button], queue=False, show_progress=False)
 
-demo.queue(default_enabled=False).launch(debug=True)
+demo.queue(default_enabled=False).launch(debug=True, share=True)