''', 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 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)
convert("output_model", "model.ckpt")
shutil.rmtree('instance_images')
shutil.make_archive("diffusers_model", 'zip', "output_model")
shutil.rmtree("output_model")
torch.cuda.empty_cache()
return [gr.update(visible=True, value=["diffusers_model.zip", "model.ckpt"]), 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
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! 🤑
''')
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=swap_values_files, inputs=file_collection, outputs=[steps])
type_of_thing.change(fn=swap_text, inputs=[type_of_thing], outputs=[thing_description, thing_image_example, things_naming, perc_txt_encoder], queue=False)
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 (zip file are diffusers weights, *.ckpt are CompVis/AUTOMATIC1111 weights)", visible=True)
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])
generate_button.click(fn=generate, inputs=prompt, outputs=result)
push_button.click(fn=push, inputs=model_repo_tag, outputs=[])
demo.launch()