flux_train_ui.py

import os
from huggingface_hub import whoami    
os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
import sys
import spaces
# Add the current working directory to the Python path
sys.path.insert(0, os.getcwd())

import gradio as gr
from PIL import Image
import torch
import uuid
import os
import shutil
import json
import yaml
from slugify import slugify
from transformers import AutoProcessor, AutoModelForCausalLM

sys.path.insert(0, "ai-toolkit")
from toolkit.job import get_job

MAX_IMAGES = 150

def load_captioning(uploaded_files, concept_sentence):
    uploaded_images = [file for file in uploaded_files if not file.endswith('.txt')]
    txt_files = [file for file in uploaded_files if file.endswith('.txt')]
    txt_files_dict = {os.path.splitext(os.path.basename(txt_file))[0]: txt_file for txt_file in txt_files}
    updates = []
    if len(uploaded_images) <= 1:
        raise gr.Error(
            "Please upload at least 2 images to train your model (the ideal number with default settings is between 4-30)"
        )
    elif len(uploaded_images) > MAX_IMAGES:
        raise gr.Error(f"For now, only {MAX_IMAGES} or less images are allowed for training")
    # Update for the captioning_area
    # for _ in range(3):
    updates.append(gr.update(visible=True))
    # Update visibility and image for each captioning row and image
    for i in range(1, MAX_IMAGES + 1):
        # Determine if the current row and image should be visible
        visible = i <= len(uploaded_images)
        
        # Update visibility of the captioning row
        updates.append(gr.update(visible=visible))

        # Update for image component - display image if available, otherwise hide
        image_value = uploaded_images[i - 1] if visible else None
        updates.append(gr.update(value=image_value, visible=visible))
        
        corresponding_caption = False
        if(image_value):
            base_name = os.path.splitext(os.path.basename(image_value))[0]
            print(base_name)
            print(image_value)
            if base_name in txt_files_dict:
                print("entrou")
                with open(txt_files_dict[base_name], 'r') as file:
                    corresponding_caption = file.read()
                    
        # Update value of captioning area
        text_value = corresponding_caption if visible and corresponding_caption else "[trigger]" if visible and concept_sentence else None
        updates.append(gr.update(value=text_value, visible=visible))

    # Update for the sample caption area
    updates.append(gr.update(visible=True))
    # Update prompt samples
    updates.append(gr.update(placeholder=f'A portrait of person in a bustling cafe {concept_sentence}', value=f'A person in a bustling cafe {concept_sentence}'))
    updates.append(gr.update(placeholder=f"A mountainous landscape in the style of {concept_sentence}"))
    updates.append(gr.update(placeholder=f"A {concept_sentence} in a mall"))
    updates.append(gr.update(visible=True))
    return updates

def hide_captioning():
    return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False) 

def create_dataset(*inputs):
    print("Creating dataset")
    images = inputs[0]
    destination_folder = str(f"datasets")
    if not os.path.exists(destination_folder):
        os.makedirs(destination_folder)

    jsonl_file_path = os.path.join(destination_folder, "metadata.jsonl")
    with open(jsonl_file_path, "a") as jsonl_file:
        for index, image in enumerate(images):
            new_image_path = shutil.copy(image, destination_folder)

            original_caption = inputs[index + 1]
            file_name = os.path.basename(new_image_path)

            data = {"file_name": file_name, "prompt": original_caption}

            jsonl_file.write(json.dumps(data) + "\n")

    return destination_folder


def run_captioning(images, concept_sentence, *captions):
    #Load internally to not consume resources for training
    device = "cuda" if torch.cuda.is_available() else "cpu"
    torch_dtype = torch.float16
    model = AutoModelForCausalLM.from_pretrained(
        "multimodalart/Florence-2-large-no-flash-attn", torch_dtype=torch_dtype, trust_remote_code=True
    ).to(device)
    processor = AutoProcessor.from_pretrained("multimodalart/Florence-2-large-no-flash-attn", trust_remote_code=True)

    captions = list(captions)
    for i, image_path in enumerate(images):
        print(captions[i])
        if isinstance(image_path, str):  # If image is a file path
            image = Image.open(image_path).convert("RGB")

        prompt = "<DETAILED_CAPTION>"
        inputs = processor(text=prompt, images=image, return_tensors="pt").to(device, torch_dtype)

        generated_ids = model.generate(
            input_ids=inputs["input_ids"], pixel_values=inputs["pixel_values"], max_new_tokens=1024, num_beams=3
        )

        generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
        parsed_answer = processor.post_process_generation(
            generated_text, task=prompt, image_size=(image.width, image.height)
        )
        caption_text = parsed_answer["<DETAILED_CAPTION>"].replace("The image shows ", "")
        if concept_sentence:
            caption_text = f"{caption_text} [trigger]"
        captions[i] = caption_text

        yield captions
    model.to("cpu")
    del model
    del processor

def recursive_update(d, u):
    for k, v in u.items():
        if isinstance(v, dict) and v:
            d[k] = recursive_update(d.get(k, {}), v)
        else:
            d[k] = v
    return d


def get_duration(  lora_name,
    concept_sentence,
    steps,
    lr,
    rank,
    model_to_train,
    low_vram,
    dataset_folder,
    sample_1,
    sample_2,
    sample_3,
    use_more_advanced_options,
    more_advanced_options,):
    return total_second_length * 60

@spaces.GPU(duration=get_duration)
def start_training(
    lora_name,
    concept_sentence,
    steps,
    lr,
    rank,
    model_to_train,
    low_vram,
    dataset_folder,
    sample_1,
    sample_2,
    sample_3,
    use_more_advanced_options,
    more_advanced_options,
):
    push_to_hub = True
    if not lora_name:
        raise gr.Error("You forgot to insert your LoRA name! This name has to be unique.")
    try:
        if whoami()["auth"]["accessToken"]["role"] == "write" or "repo.write" in whoami()["auth"]["accessToken"]["fineGrained"]["scoped"][0]["permissions"]:
            gr.Info(f"Starting training locally {whoami()['name']}. Your LoRA will be available locally and in Hugging Face after it finishes.")
        else:
            push_to_hub = False
            gr.Warning("Started training locally. Your LoRa will only be available locally because you didn't login with a `write` token to Hugging Face")
    except:
        push_to_hub = False
        gr.Warning("Started training locally. Your LoRa will only be available locally because you didn't login with a `write` token to Hugging Face")
            
    print("Started training")
    slugged_lora_name = slugify(lora_name)

    # Load the default config
    with open("config/examples/train_lora_flux_24gb.yaml", "r") as f:
        config = yaml.safe_load(f)

    # Update the config with user inputs
    config["config"]["name"] = slugged_lora_name
    config["config"]["process"][0]["model"]["low_vram"] = low_vram
    config["config"]["process"][0]["train"]["skip_first_sample"] = True
    config["config"]["process"][0]["train"]["steps"] = int(steps)
    config["config"]["process"][0]["train"]["lr"] = float(lr)
    config["config"]["process"][0]["network"]["linear"] = int(rank)
    config["config"]["process"][0]["network"]["linear_alpha"] = int(rank)
    config["config"]["process"][0]["datasets"][0]["folder_path"] = dataset_folder
    config["config"]["process"][0]["save"]["push_to_hub"] = push_to_hub
    if(push_to_hub):
        try:
            username = whoami()["name"]
        except:
            raise gr.Error("Error trying to retrieve your username. Are you sure you are logged in with Hugging Face?")
        config["config"]["process"][0]["save"]["hf_repo_id"] = f"{username}/{slugged_lora_name}"
        config["config"]["process"][0]["save"]["hf_private"] = True
    if concept_sentence:
        config["config"]["process"][0]["trigger_word"] = concept_sentence
    
    if sample_1 or sample_2 or sample_3:
        config["config"]["process"][0]["train"]["disable_sampling"] = False
        config["config"]["process"][0]["sample"]["sample_every"] = steps
        config["config"]["process"][0]["sample"]["sample_steps"] = 28
        config["config"]["process"][0]["sample"]["prompts"] = []
        if sample_1:
            config["config"]["process"][0]["sample"]["prompts"].append(sample_1)
        if sample_2:
            config["config"]["process"][0]["sample"]["prompts"].append(sample_2)
        if sample_3:
            config["config"]["process"][0]["sample"]["prompts"].append(sample_3)
    else:
        config["config"]["process"][0]["train"]["disable_sampling"] = True
    if(model_to_train == "schnell"):
        config["config"]["process"][0]["model"]["name_or_path"] = "black-forest-labs/FLUX.1-schnell"
        config["config"]["process"][0]["model"]["assistant_lora_path"] = "ostris/FLUX.1-schnell-training-adapter"
        config["config"]["process"][0]["sample"]["sample_steps"] = 4
    if(use_more_advanced_options):
        more_advanced_options_dict = yaml.safe_load(more_advanced_options)
        config["config"]["process"][0] = recursive_update(config["config"]["process"][0], more_advanced_options_dict)
        print(config)
    
    # Save the updated config
    # generate a random name for the config
    random_config_name = str(uuid.uuid4())
    os.makedirs("tmp", exist_ok=True)
    config_path = f"tmp/{random_config_name}-{slugged_lora_name}.yaml"
    with open(config_path, "w") as f:
        yaml.dump(config, f)
    
    # run the job locally
    job = get_job(config_path)
    job.run()
    job.cleanup()

    return f"Training completed successfully. Model saved as {slugged_lora_name}"

config_yaml = '''
device: cuda:0
model:
  is_flux: true
  quantize: true
network:
  linear: 16 #it will overcome the 'rank' parameter
  linear_alpha: 16 #you can have an alpha different than the ranking if you'd like
  type: lora
sample:
  guidance_scale: 3.5
  height: 1024
  neg: '' #doesn't work for FLUX
  sample_every: 1000
  sample_steps: 28
  sampler: flowmatch
  seed: 42
  walk_seed: true
  width: 1024
save:
  dtype: float16
  hf_private: true
  max_step_saves_to_keep: 4
  push_to_hub: true
  save_every: 10000
train:
  batch_size: 1
  dtype: bf16
  ema_config:
    ema_decay: 0.99
    use_ema: true
  gradient_accumulation_steps: 1
  gradient_checkpointing: true
  noise_scheduler: flowmatch 
  optimizer: adamw8bit #options: prodigy, dadaptation, adamw, adamw8bit, lion, lion8bit
  train_text_encoder: false #probably doesn't work for flux
  train_unet: true
'''

theme = gr.themes.Monochrome(
    text_size=gr.themes.Size(lg="18px", md="15px", sm="13px", xl="22px", xs="12px", xxl="24px", xxs="9px"),
    font=[gr.themes.GoogleFont("Source Sans Pro"), "ui-sans-serif", "system-ui", "sans-serif"],
)
css = """
h1{font-size: 2em}
h3{margin-top: 0}
#component-1{text-align:center}
.main_ui_logged_out{opacity: 0.3; pointer-events: none}
.tabitem{border: 0px}
.group_padding{padding: .55em}
"""
with gr.Blocks(theme=theme, css=css) as demo:
    gr.Markdown(
        """# LoRA Ease for FLUX 🧞‍♂️
### Train a high quality FLUX LoRA in a breeze ༄ using [Ostris' AI Toolkit](https://github.com/ostris/ai-toolkit)"""
    )
    with gr.Column() as main_ui:
        with gr.Row():
            lora_name = gr.Textbox(
                label="The name of your LoRA",
                info="This has to be a unique name",
                placeholder="e.g.: Persian Miniature Painting style, Cat Toy",
            )
            concept_sentence = gr.Textbox(
                label="Trigger word/sentence",
                info="Trigger word or sentence to be used",
                placeholder="uncommon word like p3rs0n or trtcrd, or sentence like 'in the style of CNSTLL'",
                interactive=True,
            )
        with gr.Group(visible=True) as image_upload:
            with gr.Row():
                images = gr.File(
                    file_types=["image", ".txt"],
                    label="Upload your images",
                    file_count="multiple",
                    interactive=True,
                    visible=True,
                    scale=1,
                )
                with gr.Column(scale=3, visible=False) as captioning_area:
                    with gr.Column():
                        gr.Markdown(
                            """# Custom captioning
<p style="margin-top:0">You can optionally add a custom caption for each image (or use an AI model for this). [trigger] will represent your concept sentence/trigger word.</p>
""", elem_classes="group_padding")
                        do_captioning = gr.Button("Add AI captions with Florence-2")
                        output_components = [captioning_area]
                        caption_list = []
                        for i in range(1, MAX_IMAGES + 1):
                            locals()[f"captioning_row_{i}"] = gr.Row(visible=False)
                            with locals()[f"captioning_row_{i}"]:
                                locals()[f"image_{i}"] = gr.Image(
                                    type="filepath",
                                    width=111,
                                    height=111,
                                    min_width=111,
                                    interactive=False,
                                    scale=2,
                                    show_label=False,
                                    show_share_button=False,
                                    show_download_button=False,
                                )
                                locals()[f"caption_{i}"] = gr.Textbox(
                                    label=f"Caption {i}", scale=15, interactive=True
                                )

                            output_components.append(locals()[f"captioning_row_{i}"])
                            output_components.append(locals()[f"image_{i}"])
                            output_components.append(locals()[f"caption_{i}"])
                            caption_list.append(locals()[f"caption_{i}"])

        with gr.Accordion("Advanced options", open=False):
            steps = gr.Number(label="Steps", value=1000, minimum=1, maximum=10000, step=1)
            lr = gr.Number(label="Learning Rate", value=4e-4, minimum=1e-6, maximum=1e-3, step=1e-6)
            rank = gr.Number(label="LoRA Rank", value=16, minimum=4, maximum=128, step=4)
            model_to_train = gr.Radio(["dev", "schnell"], value="dev", label="Model to train")
            low_vram = gr.Checkbox(label="Low VRAM", value=True)
            with gr.Accordion("Even more advanced options", open=False):
                use_more_advanced_options = gr.Checkbox(label="Use more advanced options", value=False)
                more_advanced_options = gr.Code(config_yaml, language="yaml")

        with gr.Accordion("Sample prompts (optional)", visible=False) as sample:
            gr.Markdown(
                "Include sample prompts to test out your trained model. Don't forget to include your trigger word/sentence (optional)"
            )
            sample_1 = gr.Textbox(label="Test prompt 1")
            sample_2 = gr.Textbox(label="Test prompt 2")
            sample_3 = gr.Textbox(label="Test prompt 3")
        
        output_components.append(sample)
        output_components.append(sample_1)
        output_components.append(sample_2)
        output_components.append(sample_3)
        start = gr.Button("Start training", visible=False)
        output_components.append(start)
        progress_area = gr.Markdown("")

    dataset_folder = gr.State()

    images.upload(
        load_captioning,
        inputs=[images, concept_sentence],
        outputs=output_components
    )
    
    images.delete(
        load_captioning,
        inputs=[images, concept_sentence],
        outputs=output_components
    )

    images.clear(
        hide_captioning,
        outputs=[captioning_area, sample, start]
    )
    
    start.click(fn=create_dataset, inputs=[images] + caption_list, outputs=dataset_folder).then(
        fn=start_training,
        inputs=[
            lora_name,
            concept_sentence,
            steps,
            lr,
            rank,
            model_to_train,
            low_vram,
            dataset_folder,
            sample_1,
            sample_2,
            sample_3,
            use_more_advanced_options,
            more_advanced_options
        ],
        outputs=progress_area,
    )

    do_captioning.click(fn=run_captioning, inputs=[images, concept_sentence] + caption_list, outputs=caption_list)

if __name__ == "__main__":
    demo.launch(share=True, show_error=True)