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
from diffusers import StableDiffusionPipeline
css = '''
.instruction{position: absolute; top: 0;right: 0;margin-top: 0px !important}
.arrow{position: absolute;top: 0;right: -8px;margin-top: -8px !important}
#component-4, #component-3, #component-10{min-height: 0}
'''
model_to_load = "multimodalart/sd-fine-tunable"
maximum_concepts = 3
#Pre download the files even if we don't use it here
StableDiffusionPipeline.from_pretrained(model_to_load)
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, 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."))
def train(*inputs):
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")
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)
width, height = file.size
side_length = min(width, height)
left = (width - side_length)/2
top = (height - side_length)/2
right = (width + side_length)/2
bottom = (height + side_length)/2
image = file.crop((left, top, right, bottom))
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]
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,
)
run_training(args_general)
torch.cuda.empty_cache()
#convert("output_model", "model.ckpt")
#shutil.rmtree('instance_images')
#shutil.make_archive("diffusers_model", 'zip', "output_model")
with zipfile.ZipFile('diffusers_model.zip', 'w', zipfile.ZIP_DEFLATED) as zipf:
zipdir('output_model/', zipf)
torch.cuda.empty_cache()
return [gr.update(visible=True, value=["diffusers_model.zip"]), gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)]
def generate(prompt):
from diffusers import StableDiffusionPipeline
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):
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)
if(where_to_upload == "My personal profile"):
api = HfApi()
your_username = api.whoami(token=hf_token)["name"]
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}
{image_string}![{instance_prompt} {i}](https://huggingface.co/{model_name_slug}/resolve/main/sample_images/{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)
Sample pictures of this concept:
{image_string}
'''
#Save the readme to a file
readme_file = open("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()
create_repo(model_id,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="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
)
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"])
with gr.Blocks(css=css) as demo:
with gr.Box():
if "IS_SHARED_UI" in os.environ:
gr.HTML('''
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. As each T4 costs US$0,60/h, it should cost you < US$1 to train a model with less than 100 images on default settings! 🤑
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 you turn it off.