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 urllib.parse import gc from diffusers import StableDiffusionPipeline from huggingface_hub import snapshot_download 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 even if we don't use it here model_to_load = snapshot_download(repo_id="multimodalart/sd-fine-tunable") safety_checker = snapshot_download(repo_id="multimodalart/sd-sc") def zipdir(path, ziph): # ziph is zipfile handle for root, dirs, files in os.walk(path): for file in files: ziph.write(os.path.join(root, file), os.path.relpath(os.path.join(root, file), os.path.join(path, '..'))) def swap_text(option): 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 = 50 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. {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 512x512.", freeze_for] elif(option == "person"): instance_prompt_example = "julcto" freeze_for = 100 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. {mandatory_liability}:", '''''', f"You should name the files with a unique word that represent your concept (e.g.: `{instance_prompt_example}` here). Images will be automatically cropped to 512x512.", freeze_for] 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. Name the files with the words you would like {mandatory_liability}:", '''''', f"You should name your files with a unique word that represent your concept (e.g.: `{instance_prompt_example}` here). Images will be automatically cropped to 512x512.", freeze_for] 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] if(uses_custom): Training_Steps = int(inputs[-3]) else: if(type_of_thing == "person"): Training_Steps = file_counter*200*2 else: Training_Steps = file_counter*200 return([gr.update(visible=True), gr.update(visible=True, value=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/1.1, 2)} seconds, or {round((Training_Steps/1.1)/60, 2)} minutes. The setup, compression and uploading the model can take up to 20 minutes.
As the T4-Small GPU costs US$0.60 for 1h, the estimated cost for this training is US${round((((Training_Steps/1.1)/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.

''')]) 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): torch.cuda.empty_cache() if 'pipe' in globals(): del pipe gc.collect() if "IS_SHARED_UI" in os.environ: raise gr.Error("This Space only works in duplicated instances") 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.zip"): os.remove("diffusers_model.zip") if os.path.exists("model.ckpt"): os.remove("model.ckpt") if os.path.exists("hastrained.success"): os.remove("hastrained.success") file_counter = 0 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((512, 512)) 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] if(uses_custom): Training_Steps = int(inputs[-3]) Train_text_encoder_for = int(inputs[-2]) else: Training_Steps = file_counter*200 if(type_of_thing == "object"): Train_text_encoder_for=30 elif(type_of_thing == "person"): Train_text_encoder_for=60 elif(type_of_thing == "style"): Train_text_encoder_for=15 class_data_dir = None stptxt = int((Training_Steps*Train_text_encoder_for)/100) args_general = argparse.Namespace( image_captions_filename = True, train_text_encoder = True, 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=class_data_dir, output_dir="output_model", instance_prompt="", seed=42, resolution=512, 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, ) print("Starting training...") lock_file = open("intraining.lock", "w") lock_file.close() run_training(args_general) gc.collect() torch.cuda.empty_cache() 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") print("Zipping model file...") with zipfile.ZipFile('diffusers_model.zip', 'w', zipfile.ZIP_DEFLATED) as zipf: zipdir('output_model/', zipf) print("Training completed!") if os.path.exists("intraining.lock"): os.remove("intraining.lock") trained_file = open("hastrained.success", "w") trained_file.close() if(remove_attribution_after): hf_token = inputs[-5] model_name = inputs[-7] where_to_upload = inputs[-8] push(model_name, where_to_upload, hf_token, 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) return [ gr.update(visible=True, value=["diffusers_model.zip"]), #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 ] def generate(prompt): torch.cuda.empty_cache() from diffusers import StableDiffusionPipeline global pipe pipe = StableDiffusionPipeline.from_pretrained("./output_model", torch_dtype=torch.float16) pipe = pipe.to("cuda") image = pipe(prompt).images[0] return(image) def push(model_name, where_to_upload, hf_token, 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 --- ### {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) 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(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.zip", "model.ckpt"])] def convert_to_ckpt(): convert("output_model", "model.ckpt") return gr.update(visible=True, value=["diffusers_model.zip", "model.ckpt"]) def check_status(top_description): if os.path.exists("hastrained.success"): 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

''') 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.zip"): update_files_tag = gr.update(visible=show_outputs, value=["diffusers_model.zip"]) 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" in os.environ: top_description = gr.HTML(f'''

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

For it to work, you have to 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 a model with less than 100 images using default settings!  Duplicate Space

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

You have successfully duplicated the Dreambooth Training Space ๐ŸŽ‰

If you haven't already, attribute a T4 GPU to it (via the Settings tab) and run the training below. You will be billed by the minute from when you activate the GPU until when it is turned it off.

''') gr.Markdown("# Dreambooth training") gr.Markdown("Customize Stable Diffusion by training it on a few examples of concepts, up to 3 concepts on the same model. This Space is based on TheLastBen's [fast-DreamBooth Colab](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) with [๐Ÿงจ diffusers](https://github.com/huggingface/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) #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_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 number of steps and % of frozen encoder will be tuned automatically according to the amount of images you upload and whether you are training an `object`, `person` or `style` as follows: The number of steps is calculated by number of images uploaded multiplied by 20. The text-encoder is frozen after 10% of the steps for a style, 30% of the steps for an object and is fully trained for persons.") steps = gr.Number(label="How many steps", value=800) 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=False, label="Training Summary") training_summary_checkbox = gr.Checkbox(label="Automatically remove paid GPU attribution and upload model to the Hugging Face Hub after training", value=False) training_summary_model_name = gr.Textbox(label="Name of your model", visible=False) training_summary_where_to_upload = gr.Dropdown(["My personal profile", "Public Library"], label="Upload to", visible=False) 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=False) training_summary_token = gr.Textbox(label="Hugging Face Write Token", type="password", visible=False) train_btn = gr.Button("Start Training") 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() 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], outputs=[thing_description, thing_image_example, things_naming, perc_txt_encoder], queue=False, show_progress=False) #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=count_files, inputs=file_collection+[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+[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+[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 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+[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, 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], 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)