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import gradio as gr |
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import os |
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from pathlib import Path |
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import argparse |
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import shutil |
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from train_dreambooth import run_training |
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from convertosd import convert |
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from PIL import Image |
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from slugify import slugify |
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import requests |
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import torch |
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css = ''' |
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.instruction{position: absolute; top: 0;right: 0;margin-top: 0px !important} |
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.arrow{position: absolute;top: 0;right: -8px;margin-top: -8px !important} |
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#component-4, #component-3, #component-10{min-height: 0} |
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''' |
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model_to_load = "multimodalart/sd-fine-tunable" |
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maximum_concepts = 3 |
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|
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def swap_text(option): |
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mandatory_liability = "You must have the right to do so and you are liable for the images you use" |
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if(option == "object"): |
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instance_prompt_example = "cttoy" |
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freeze_for = 50 |
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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}:", '''<img src="file/cat-toy.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 512x512.", freeze_for] |
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elif(option == "person"): |
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instance_prompt_example = "julcto" |
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freeze_for = 100 |
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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}:", '''<img src="file/person.png" />''', 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] |
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elif(option == "style"): |
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instance_prompt_example = "trsldamrl" |
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freeze_for = 10 |
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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}:", '''<img src="file/trsl_style.png" />''', 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] |
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|
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def count_files(*inputs): |
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file_counter = 0 |
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concept_counter = 0 |
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for i, input in enumerate(inputs): |
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if(i < maximum_concepts-1): |
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files = inputs[i] |
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if(files): |
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concept_counter+=1 |
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file_counter+=len(files) |
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uses_custom = inputs[-1] |
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type_of_thing = inputs[-4] |
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if(uses_custom): |
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Training_Steps = int(inputs[-3]) |
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else: |
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if(type_of_thing == "person"): |
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Training_Steps = file_counter*200*2 |
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else: |
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Training_Steps = file_counter*200 |
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return(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. This should take around {round(Training_Steps/1.5, 2)} seconds, or {round((Training_Steps/1.5)/3600, 2)} hours. As a reminder, the T4 GPU costs US$0.60 for 1h. Once training is over, don't forget to swap the hardware back to CPU.")) |
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def train(*inputs): |
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if "IS_SHARED_UI" in os.environ: |
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raise gr.Error("This Space only works in duplicated instances") |
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if os.path.exists("output_model"): shutil.rmtree('output_model') |
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if os.path.exists("instance_images"): shutil.rmtree('instance_images') |
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if os.path.exists("diffusers_model.zip"): os.remove("diffusers_model.zip") |
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if os.path.exists("model.ckpt"): os.remove("model.ckpt") |
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file_counter = 0 |
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for i, input in enumerate(inputs): |
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if(i < maximum_concepts-1): |
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if(input): |
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os.makedirs('instance_images',exist_ok=True) |
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files = inputs[i+(maximum_concepts*2)] |
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prompt = inputs[i+maximum_concepts] |
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if(prompt == "" or prompt == None): |
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raise gr.Error("You forgot to define your concept prompt") |
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for j, file_temp in enumerate(files): |
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file = Image.open(file_temp.name) |
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width, height = file.size |
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side_length = min(width, height) |
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left = (width - side_length)/2 |
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top = (height - side_length)/2 |
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right = (width + side_length)/2 |
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bottom = (height + side_length)/2 |
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image = file.crop((left, top, right, bottom)) |
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image = image.resize((512, 512)) |
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extension = file_temp.name.split(".")[1] |
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image = image.convert('RGB') |
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image.save(f'instance_images/{prompt}_({j+1}).jpg', format="JPEG", quality = 100) |
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file_counter += 1 |
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os.makedirs('output_model',exist_ok=True) |
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uses_custom = inputs[-1] |
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type_of_thing = inputs[-4] |
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if(uses_custom): |
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Training_Steps = int(inputs[-3]) |
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Train_text_encoder_for = int(inputs[-2]) |
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else: |
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Training_Steps = file_counter*200 |
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if(type_of_thing == "object"): |
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Train_text_encoder_for=30 |
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elif(type_of_thing == "person"): |
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Train_text_encoder_for=60 |
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elif(type_of_thing == "style"): |
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Train_text_encoder_for=15 |
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class_data_dir = None |
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stptxt = int((Training_Steps*Train_text_encoder_for)/100) |
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args_general = argparse.Namespace( |
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image_captions_filename = True, |
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train_text_encoder = True, |
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stop_text_encoder_training = stptxt, |
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save_n_steps = 0, |
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pretrained_model_name_or_path = model_to_load, |
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instance_data_dir="instance_images", |
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class_data_dir=class_data_dir, |
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output_dir="output_model", |
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instance_prompt="", |
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seed=42, |
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resolution=512, |
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mixed_precision="fp16", |
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train_batch_size=1, |
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gradient_accumulation_steps=1, |
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use_8bit_adam=True, |
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learning_rate=2e-6, |
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lr_scheduler="polynomial", |
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lr_warmup_steps = 0, |
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max_train_steps=Training_Steps, |
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) |
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run_training(args_general) |
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torch.cuda.empty_cache() |
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shutil.make_archive("diffusers_model", 'zip', "output_model") |
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torch.cuda.empty_cache() |
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return [gr.update(visible=True, value=["diffusers_model.zip"]), gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)] |
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def generate(prompt): |
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from diffusers import StableDiffusionPipeline |
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pipe = StableDiffusionPipeline.from_pretrained("./output_model", torch_dtype=torch.float16) |
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pipe = pipe.to("cuda") |
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image = pipe(prompt).images[0] |
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return(image) |
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def push(model_name, where_to_upload, hf_token): |
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if(not os.path.exists("model.ckpt")): |
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convert("output_model", "model.ckpt") |
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from huggingface_hub import HfApi, HfFolder, CommitOperationAdd |
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from huggingface_hub import create_repo |
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model_name_slug = slugify(model_name) |
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if(where_to_upload == "My personal profile"): |
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api = HfApi() |
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your_username = api.whoami(token=hf_token)["name"] |
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model_id = f"{your_username}/{model_name_slug}" |
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else: |
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model_id = f"sd-dreambooth-library/{model_name_slug}" |
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headers = {"Authorization" : f"Bearer: {hf_token}", "Content-Type": "application/json"} |
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response = requests.post("https://example.com/get-my-account-detail", headers=headers) |
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images_upload = os.listdir("instance_images") |
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image_string = "" |
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instance_prompt_list = [] |
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previous_instance_prompt = '' |
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for i, image in enumerate(images_upload): |
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instance_prompt = image.split("_")[0] |
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if(instance_prompt != previous_instance_prompt): |
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title_instance_prompt_string = instance_prompt |
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instance_prompt_list.append(instance_prompt) |
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else: |
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title_instance_prompt_string = '' |
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previous_instance_prompt = instance_prompt |
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image_string = f''' |
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{title_instance_prompt_string} |
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{image_string} |
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''' |
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readme_text = f'''--- |
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license: creativeml-openrail-m |
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tags: |
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- text-to-image |
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--- |
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### {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) |
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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) |
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Sample pictures of this concept: |
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{image_string} |
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''' |
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readme_file = open("README.md", "w") |
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readme_file.write(readme_text) |
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readme_file.close() |
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text_file = open("token_identifier.txt", "w") |
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text_file.write(', '.join(instance_prompt_list)) |
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text_file.close() |
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operations = [ |
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CommitOperationAdd(path_in_repo="token_identifier.txt", path_or_fileobj="token_identifier.txt"), |
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CommitOperationAdd(path_in_repo="README.md", path_or_fileobj="README.md"), |
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CommitOperationAdd(path_in_repo=f"model.ckpt",path_or_fileobj="model.ckpt") |
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] |
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api.create_commit( |
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repo_id=model_id, |
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operations=operations, |
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commit_message=f"Upload the model {model_name}", |
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token=hf_token |
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) |
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api.upload_folder( |
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folder_path="output_model", |
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repo_id=model_id, |
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token=hf_token |
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) |
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api.upload_folder( |
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folder_path="instance_images", |
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path_in_repo="concept_images", |
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repo_id=model_id, |
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token=hf_token |
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) |
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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"])] |
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def convert_to_ckpt(): |
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convert("output_model", "model.ckpt") |
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return gr.update(visible=True, value=["diffusers_model.zip", "model.ckpt"]) |
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with gr.Blocks(css=css) as demo: |
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with gr.Box(): |
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if "IS_SHARED_UI" in os.environ: |
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gr.HTML(''' |
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<div class="gr-prose" style="max-width: 80%"> |
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<h2>Attention - This Space doesn't work in this shared UI</h2> |
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<p>For it to work, you have to duplicate the Space and run it on your own profile where a (paid) private GPU will be attributed to it during runtime. It will cost you < US$1 to train a model on default settings! 🤑</p> |
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<img class="instruction" src="file/duplicate.png"> |
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<img class="arrow" src="file/arrow.png" /> |
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</div> |
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''') |
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else: |
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gr.HTML(''' |
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<div class="gr-prose" style="max-width: 80%"> |
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<h2>You have successfully cloned the Dreambooth Training Space</h2> |
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<p>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 between when you activate the GPU until when you turn it off.</p> |
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</div> |
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''') |
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gr.Markdown("# Dreambooth training") |
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gr.Markdown("Customize Stable Diffusion by giving it with few-shot examples") |
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with gr.Row(): |
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type_of_thing = gr.Dropdown(label="What would you like to train?", choices=["object", "person", "style"], value="object", interactive=True) |
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with gr.Row(): |
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with gr.Column(): |
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thing_description = gr.Markdown("You are going to train an `object`, 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") |
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thing_image_example = gr.HTML('''<img src="file/cat-toy.png" />''') |
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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.") |
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with gr.Column(): |
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file_collection = [] |
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concept_collection = [] |
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buttons_collection = [] |
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delete_collection = [] |
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is_visible = [] |
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row = [None] * maximum_concepts |
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for x in range(maximum_concepts): |
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ordinal = lambda n: "%d%s" % (n, "tsnrhtdd"[(n // 10 % 10 != 1) * (n % 10 < 4) * n % 10::4]) |
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if(x == 0): |
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visible = True |
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is_visible.append(gr.State(value=True)) |
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else: |
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visible = False |
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is_visible.append(gr.State(value=False)) |
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file_collection.append(gr.File(label=f"Upload the images for your {ordinal(x+1)} concept", file_count="multiple", interactive=True, visible=visible)) |
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with gr.Column(visible=visible) as row[x]: |
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concept_collection.append(gr.Textbox(label=f"{ordinal(x+1)} concept prompt - use a unique, made up word to avoid collisions")) |
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with gr.Row(): |
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if(x < maximum_concepts-1): |
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buttons_collection.append(gr.Button(value="Add +1 concept", visible=visible)) |
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if(x > 0): |
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delete_collection.append(gr.Button(value=f"Delete {ordinal(x+1)} concept")) |
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counter_add = 1 |
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for button in buttons_collection: |
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if(counter_add < len(buttons_collection)): |
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button.click(lambda: |
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[gr.update(visible=True),gr.update(visible=True), gr.update(visible=False), gr.update(visible=True), True, None], |
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None, |
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[row[counter_add], file_collection[counter_add], buttons_collection[counter_add-1], buttons_collection[counter_add], is_visible[counter_add], file_collection[counter_add]]) |
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else: |
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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]]) |
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counter_add += 1 |
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counter_delete = 1 |
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for delete_button in delete_collection: |
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if(counter_delete < len(delete_collection)+1): |
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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]]) |
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counter_delete += 1 |
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with gr.Accordion("Custom Settings", open=False): |
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swap_auto_calculated = gr.Checkbox(label="Use custom settings") |
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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.") |
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steps = gr.Number(label="How many steps", value=800) |
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perc_txt_encoder = gr.Number(label="Percentage of the training steps the text-encoder should be trained as well", value=30) |
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type_of_thing.change(fn=swap_text, inputs=[type_of_thing], outputs=[thing_description, thing_image_example, things_naming, perc_txt_encoder], queue=False) |
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training_summary = gr.Textbox("", visible=False, label="Training Summary") |
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steps.change(fn=count_files, inputs=file_collection+[type_of_thing]+[steps]+[perc_txt_encoder]+[swap_auto_calculated], outputs=[training_summary]) |
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perc_txt_encoder.change(fn=count_files, inputs=file_collection+[type_of_thing]+[steps]+[perc_txt_encoder]+[swap_auto_calculated], outputs=[training_summary]) |
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for file in file_collection: |
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file.change(fn=count_files, inputs=file_collection+[type_of_thing]+[steps]+[perc_txt_encoder]+[swap_auto_calculated], outputs=[training_summary]) |
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train_btn = gr.Button("Start Training") |
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with gr.Box(visible=False) as try_your_model: |
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gr.Markdown("## Try your model") |
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with gr.Row(): |
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prompt = gr.Textbox(label="Type your prompt") |
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result_image = gr.Image() |
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generate_button = gr.Button("Generate Image") |
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with gr.Box(visible=False) as push_to_hub: |
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gr.Markdown("## Push to Hugging Face Hub") |
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model_name = gr.Textbox(label="Name of your model", placeholder="Tarsila do Amaral Style") |
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where_to_upload = gr.Dropdown(["My personal profile", "Public Library"], label="Upload to") |
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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.") |
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hf_token = gr.Textbox(label="Hugging Face Write Token") |
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push_button = gr.Button("Push to the Hub") |
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result = gr.File(label="Download the uploaded models in the diffusers format", visible=True) |
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success_message_upload = gr.Markdown(visible=False) |
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convert_button = gr.Button("Convert to CKPT", visible=False) |
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train_btn.click(fn=train, inputs=is_visible+concept_collection+file_collection+[type_of_thing]+[steps]+[perc_txt_encoder]+[swap_auto_calculated], outputs=[result, try_your_model, push_to_hub, convert_button]) |
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generate_button.click(fn=generate, inputs=prompt, outputs=result_image) |
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push_button.click(fn=push, inputs=[model_name, where_to_upload, hf_token], outputs=[success_message_upload, result]) |
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convert_button.click(fn=convert_to_ckpt, inputs=[], outputs=result) |
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demo.launch() |
