''', 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
for i, input in enumerate(inputs):
if(i < maximum_concepts-1):
if(input):
files = inputs[i+(maximum_concepts*2)]
for j, tile_temp in enumerate(files):
file_counter+= 1
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 {file_counter} files for {Training_Steps} steps. This should take around {round(Training_Steps/1.5, 2)} seconds, or {round((Training_Steps/1.5)/3600, 2)}. The T4 GPU costs US$0.60 for 1h, so the estimated costs for this training run should be {round(((Training_Steps/1.5)/3600)*0.6, 2)}"))
def train(*inputs):
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]
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 == "person"):
class_data_dir = "mix"
Train_text_encoder_for=100
args_txt_encoder = argparse.Namespace(
image_captions_filename = True,
train_text_encoder = True,
pretrained_model_name_or_path=model_to_load,
instance_data_dir="instance_images",
class_data_dir=class_data_dir,
output_dir="output_model",
with_prior_preservation=True,
prior_loss_weight=1.0,
instance_prompt="",
seed=42,
resolution=512,
mixed_precision="fp16",
train_batch_size=1,
gradient_accumulation_steps=1,
gradient_checkpointing=True,
use_8bit_adam=True,
learning_rate=2e-6,
lr_scheduler="polynomial",
lr_warmup_steps=0,
max_train_steps=Training_Steps,
num_class_images=200
)
args_unet = argparse.Namespace(
image_captions_filename = True,
train_only_unet=True,
Session_dir="output_model",
save_starting_step=0,
save_n_steps=0,
pretrained_model_name_or_path=model_to_load,
instance_data_dir="instance_images",
output_dir="output_model",
instance_prompt="",
seed=42,
resolution=512,
mixed_precision="fp16",
train_batch_size=1,
gradient_accumulation_steps=1,
gradient_checkpointing=False,
use_8bit_adam=True,
learning_rate=2e-6,
lr_scheduler="polynomial",
lr_warmup_steps=0,
max_train_steps=Training_Steps
)
run_training(args_txt_encoder)
run_training(args_unet)
elif(type_of_thing == "object" or type_of_thing == "style"):
if(type_of_thing == "object"):
Train_text_encoder_for=30
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")
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(path):
pass
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. It will cost you < US$1 to train a model on default settings! 🤑
Now you can attribute a T4 GPU to it (by going to the Settings tab) and run the training below. The GPU will be automatically unassigned after training is over. So you will be billed by the minute between when you activate the GPU and when it finishes training.
''')
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)} 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)} 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]])
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]])
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]])
counter_delete += 1
with gr.Accordion("Advanced Settings", open=False):
swap_auto_calculated = gr.Checkbox(label="Use these advanced setting")
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`.")
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)
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])
type_of_thing.change(fn=swap_text, inputs=[type_of_thing], outputs=[thing_description, thing_image_example, things_naming, perc_txt_encoder], queue=False)
training_summary = gr.Textbox("", visible=False, label="Training Summary")
train_btn = gr.Button("Start Training")
with gr.Box(visible=False) as try_your_model:
gr.Markdown("Try your model")
with gr.Row():
prompt = gr.Textbox(label="Type your prompt")
result = 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_repo_tag = gr.Textbox(label="Model name or URL", placeholder="username/model_name")
push_button = gr.Button("Push to the Hub")
result = gr.File(label="Download the uploaded models in the diffusers format", visible=True)
convert_button = gr.Button("Convert to CKPT", visible=False)
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])
generate_button.click(fn=generate, inputs=prompt, outputs=result)
push_button.click(fn=push, inputs=model_repo_tag, outputs=[])
convert_button.click(fn=convert_to_ckpt, inputs=[], outputs=result)
demo.launch()