import gradio as gr import os from pathlib import Path import argparse import shutil from train_dreambooth import run_training from convertosd import convert from PIL import Image from slugify import slugify import requests import torch import zipfile import tarfile import urllib.parse import gc from diffusers import StableDiffusionPipeline from huggingface_hub import snapshot_download is_spaces = False if "SPACE_ID" in os.environ else False # local is_shared_ui = False if "IS_SHARED_UI" in os.environ else False # local is_gpu_associated = torch.cuda.is_available() css = ''' .instruction{position: absolute; top: 0;right: 0;margin-top: 0px !important} .arrow{position: absolute;top: 0;right: -110px;margin-top: -8px !important} #component-4, #component-3, #component-10{min-height: 0} .duplicate-button img{margin: 0} ''' maximum_concepts = 3 #Pre download the files if(is_gpu_associated): model_v1 = snapshot_download(repo_id="multimodalart/sd-fine-tunable") model_v2 = snapshot_download(repo_id="stabilityai/stable-diffusion-2") model_v2_512 = snapshot_download(repo_id="stabilityai/stable-diffusion-2-base") safety_checker = snapshot_download(repo_id="multimodalart/sd-sc") model_to_load = model_v1 with zipfile.ZipFile("mix.zip", 'r') as zip_ref: zip_ref.extractall(".") def swap_text(option, base): resize_width = 768 if base == "v2-768" else 512 mandatory_liability = "You must have the right to do so and you are liable for the images you use, example:" if(option == "object"): instance_prompt_example = "cttoy" freeze_for = 30 return [f"You are going to train `object`(s), upload 5-10 images of each object you are planning on training on from different angles/perspectives. You can use services like birme for smart cropping. {mandatory_liability}:", '''''', 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, gr.update(visible=False)] elif(option == "person"): instance_prompt_example = "julcto" freeze_for = 70 #show_prior_preservation = True if base != "v2-768" else False show_prior_preservation=False if(show_prior_preservation): prior_preservation_box_update = gr.update(visible=show_prior_preservation) 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 birme for smart cropping. {mandatory_liability}:", '''''', 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"): instance_prompt_example = "trsldamrl" freeze_for = 10 return [f"You are going to train a `style`, upload 10-20 images of the style you are planning on training on. You can use services like birme for smart cropping. Name the files with the words you would like {mandatory_liability}:", '''''', 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, gr.update(visible=False)] def swap_base_model(selected_model): if(is_gpu_associated): global model_to_load if(selected_model == "v1-5"): model_to_load = model_v1 elif(selected_model == "v2-768"): model_to_load = model_v2 else: model_to_load = model_v2_512 def count_files(*inputs): file_counter = 0 concept_counter = 0 for i, input in enumerate(inputs): if(i < maximum_concepts-1): files = inputs[i] 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] 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 if(experimental_faces): its = 1 elif(selected_model == "v2-512"): its = 0.8 if(experimental_faces): 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. The setup, compression and uploading the model can take up to 20 minutes.
As the T4-Small GPU costs US$0.60 for 1h, the estimated cost for this training is below US${round((((Training_Steps/its)/3600)+0.3+0.1)*0.60, 2)}.

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.

''' else: summary_sentence = f'''You are going to train {concept_counter} {type_of_thing}(s), with {file_counter} images for {Training_Steps} steps.

''' return([gr.update(visible=True), gr.update(visible=True, value=summary_sentence)]) def update_steps(*files_list): file_counter = 0 for i, files in enumerate(files_list): if(files): file_counter+=len(files) return(gr.update(value=file_counter*200)) def pad_image(image): w, h = image.size if w == h: return image elif w > h: new_image = Image.new(image.mode, (w, w), (0, 0, 0)) new_image.paste(image, (0, (w - h) // 2)) return new_image else: new_image = Image.new(image.mode, (h, h), (0, 0, 0)) new_image.paste(image, ((h - w) // 2, 0)) return new_image def train(*inputs): if is_shared_ui: raise gr.Error("This Space only works in duplicated instances") if not is_gpu_associated: raise gr.Error("Please associate a T4 GPU for this Space") torch.cuda.empty_cache() if 'pipe' in globals(): global pipe, pipe_is_set 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") if os.path.exists("model.ckpt"): os.remove("model.ckpt") if os.path.exists("hastrained.success"): os.remove("hastrained.success") file_counter = 0 which_model = inputs[-10] resolution = 512 if which_model != "v2-768" else 768 for i, input in enumerate(inputs): if(i < maximum_concepts-1): if(input): os.makedirs('instance_images',exist_ok=True) files = inputs[i+(maximum_concepts*2)] prompt = inputs[i+maximum_concepts] if(prompt == "" or prompt == None): raise gr.Error("You forgot to define your concept prompt") for j, file_temp in enumerate(files): file = Image.open(file_temp.name) image = pad_image(file) image = image.resize((resolution, resolution)) extension = file_temp.name.split(".")[1] 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] 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 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 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) gc.collect() torch.cuda.empty_cache() if(which_model == "v1-5"): print("Adding Safety Checker to the model...") 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: tar.add("output_model", arcname=os.path.basename("output_model")) if os.path.exists("intraining.lock"): os.remove("intraining.lock") trained_file = open("hastrained.success", "w") trained_file.close() print("Training completed!") return [ gr.update(visible=True, value=["diffusers_model.tar"]), #result gr.update(visible=True), #try_your_model gr.update(visible=True), #push_to_hub gr.update(visible=True), #convert_button gr.update(visible=False), #training_ongoing gr.update(visible=True) #completed_training ] else: hf_token = inputs[-5] model_name = inputs[-7] where_to_upload = inputs[-8] push(model_name, where_to_upload, hf_token, which_model, True) hardware_url = f"https://huggingface.co/spaces/{os.environ['SPACE_ID']}/hardware" headers = { "authorization" : f"Bearer {hf_token}"} body = {'flavor': 'cpu-basic'} requests.post(hardware_url, json = body, headers=headers) pipe_is_set = False def generate(prompt, steps): torch.cuda.empty_cache() from diffusers import StableDiffusionPipeline global pipe_is_set if(not pipe_is_set): global pipe 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] 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") from huggingface_hub import HfApi, HfFolder, CommitOperationAdd from huggingface_hub import create_repo model_name_slug = slugify(model_name) api = HfApi() your_username = api.whoami(token=hf_token)["name"] 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 = [] previous_instance_prompt = '' for i, image in enumerate(images_upload): instance_prompt = image.split("_")[0] if(instance_prompt != previous_instance_prompt): title_instance_prompt_string = instance_prompt instance_prompt_list.append(instance_prompt) 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}![{instance_prompt} {i}](https://huggingface.co/{model_id}/resolve/main/concept_images/{urllib.parse.quote(image)})''' readme_text = f'''--- license: creativeml-openrail-m tags: - text-to-image widget: - text: {instance_prompt_list[0]} --- ### {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! Sample pictures of: {image_string} ''' #Save the readme to a file readme_file = open("model.README.md", "w") readme_file.write(readme_text) readme_file.close() #Save the token identifier to a file text_file = open("token_identifier.txt", "w") text_file.write(', '.join(instance_prompt_list)) text_file.close() try: create_repo(model_id,private=True, token=hf_token) except: import time epoch_time = str(int(time.time())) create_repo(f"{model_id}-{epoch_time}", private=True,token=hf_token) operations = [ CommitOperationAdd(path_in_repo="token_identifier.txt", path_or_fileobj="token_identifier.txt"), CommitOperationAdd(path_in_repo="README.md", path_or_fileobj="model.README.md"), CommitOperationAdd(path_in_repo=f"model.ckpt",path_or_fileobj="model.ckpt") ] api.create_commit( repo_id=model_id, operations=operations, commit_message=f"Upload the model {model_name}", token=hf_token ) api.upload_folder( folder_path="output_model", repo_id=model_id, token=hf_token ) api.upload_folder( folder_path="instance_images", path_in_repo="concept_images", repo_id=model_id, token=hf_token ) if is_spaces: if(not comes_from_automated): extra_message = "Don't forget to remove the GPU attribution after you play with it." else: extra_message = "The GPU has been removed automatically as requested, and you can try the model via the model page" api.create_discussion(repo_id=os.environ['SPACE_ID'], title=f"Your model {model_name} has finished trained from the Dreambooth Train Spaces!", description=f"Your model has been successfully uploaded to: https://huggingface.co/{model_id}. {extra_message}",repo_type="space", token=hf_token) return [gr.update(visible=True, value=f"Successfully uploaded your model. Access it [here](https://huggingface.co/{model_id})"), gr.update(visible=True, value=["diffusers_model.tar", "model.ckpt"])] def convert_to_ckpt(): if 'pipe' in globals(): global pipe, pipe_is_set del pipe pipe_is_set = False gc.collect() convert("output_model", "model.ckpt") return gr.update(visible=True, value=["diffusers_model.tar", "model.ckpt"]) def check_status(top_description): if os.path.exists("hastrained.success"): if is_spaces: update_top_tag = gr.update(value=f'''

Your model has finished training โœ…

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 settings page and downgrade your Space to a CPU Basic

''') else: update_top_tag = gr.update(value=f'''

Your model has finished training โœ…

Yay, congratulations on training your model. Scroll down to play with with it, save it (either downloading it or on the Hugging Face Hub).

''') show_outputs = True elif os.path.exists("intraining.lock"): update_top_tag = gr.update(value='''

Don't worry, your model is still training! โŒ›

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

''') show_outputs = False else: update_top_tag = gr.update(value=top_description) show_outputs = False if os.path.exists("diffusers_model.tar"): update_files_tag = gr.update(visible=show_outputs, value=["diffusers_model.tar"]) else: update_files_tag = gr.update(visible=show_outputs) return [ update_top_tag, #top_description gr.update(visible=show_outputs), #try_your_model gr.update(visible=show_outputs), #push_to_hub update_files_tag, #result gr.update(visible=show_outputs), #convert_button ] def checkbox_swap(checkbox): return [gr.update(visible=checkbox), gr.update(visible=checkbox), gr.update(visible=checkbox), gr.update(visible=checkbox)] with gr.Blocks(css=css) as demo: with gr.Box(): if is_shared_ui: top_description = gr.HTML(f'''

Attention - This Space doesn't work in this shared UI

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!  Duplicate Space

''') elif(is_spaces): if(is_gpu_associated): top_description = gr.HTML(f'''

You have successfully associated a GPU to the Dreambooth Training Space ๐ŸŽ‰

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.

''') else: top_description = gr.HTML(f'''

You have successfully duplicated the Dreambooth Training Space ๐ŸŽ‰

There's only one step left before you can train your model: attribute a T4 GPU to it (via the Settings tab) 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.

''') else: top_description = gr.HTML(f'''

You have successfully cloned the Dreambooth Training Space locally ๐ŸŽ‰

Do a pip install requirements-local.txt

''') gr.Markdown("# Dreambooth Training UI ๐Ÿ’ญ") gr.Markdown("Customize Stable Diffusion v1 or v2 (โฟแต‰สท!) by giving it a few examples of a concept. Based on the [๐Ÿงจ 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 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('''''') 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(): file_collection = [] concept_collection = [] buttons_collection = [] delete_collection = [] is_visible = [] row = [None] * maximum_concepts for x in range(maximum_concepts): ordinal = lambda n: "%d%s" % (n, "tsnrhtdd"[(n // 10 % 10 != 1) * (n % 10 < 4) * n % 10::4]) if(x == 0): visible = True is_visible.append(gr.State(value=True)) else: visible = False is_visible.append(gr.State(value=False)) 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''')) 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, [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 = 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) elif(not is_gpu_associated): 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 โŒ›... 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('''# โœ… 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") prompt = gr.Textbox(label="Type your prompt") 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_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 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) #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) #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 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, share=True)